{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.8057232", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:57Z", "type": "Dataset", "title": "Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning", "description": "Data Description: To improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively. Files in collection (32): Collection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts gNATSGO TIF files: \u251c\u2500\u2500 available_water_storage_30arc_30cm_us.tif [30 cm depth soil available water storage]
\u251c\u2500\u2500 available_water_storage_30arc_100cm_us.tif [100 cm depth soil available water storage]
\u251c\u2500\u2500 caco3_30arc_30cm_us.tif [30 cm depth soil CaCO3 content]
\u251c\u2500\u2500 caco3_30arc_100cm_us.tif [100 cm depth soil CaCO3 content]
\u251c\u2500\u2500 cec_30arc_30cm_us.tif [30 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 cec_30arc_100cm_us.tif [100 cm depth soil cation exchange capacity]
\u251c\u2500\u2500 clay_30arc_30cm_us.tif [30 cm depth soil clay content]
\u251c\u2500\u2500 clay_30arc_100cm_us.tif [100 cm depth soil clay content]
\u251c\u2500\u2500 depthWT_30arc_us.tif [depth to water table]
\u251c\u2500\u2500 kfactor_30arc_30cm_us.tif [30 cm depth soil erosion factor]
\u251c\u2500\u2500 kfactor_30arc_100cm_us.tif [100 cm depth soil erosion factor]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [100 cm depth soil pH]
\u251c\u2500\u2500 ph_30arc_100cm_us.tif [30 cm depth soil pH]
\u251c\u2500\u2500 pondingFre_30arc_us.tif [ponding frequency]
\u251c\u2500\u2500 sand_30arc_30cm_us.tif [30 cm depth soil sand content]
\u251c\u2500\u2500 sand_30arc_100cm_us.tif [100 cm depth soil sand content]
\u251c\u2500\u2500 silt_30arc_30cm_us.tif [30 cm depth soil silt content]
\u251c\u2500\u2500 silt_30arc_100cm_us.tif [100 cm depth soil silt content]
\u251c\u2500\u2500 water_content_30arc_30cm_us.tif [30 cm depth soil water content]
\u2514\u2500\u2500 water_content_30arc_100cm_us.tif [100 cm depth soil water content] SOC TIF files: \u251c\u2500\u250030cm SOC mean.tif [30 cm depth soil SOC]
\u251c\u2500\u2500100cm SOC mean.tif [100 cm depth soil SOC]
\u251c\u2500\u250030cm SOC CV.tif [30 cm depth soil SOC coefficient of variation]
\u2514\u2500\u2500100cm SOC CV.tif [100 cm depth soil SOC coefficient of variation] site observations csv files: ISCN_rmNRCS_addNCSS_30cm.csv 30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data ISCN_rmNRCS_addNCSS_100cm.csv 100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data
Data format: Geospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution. Geospatial projection: 
GEOGCS['GCS_WGS_1984', DATUM['D_WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['Degree',0.017453292519943295]] (base) [jbk@theseus ltar_regionalization]$ g.proj -w GEOGCS['wgs84', DATUM['WGS_1984', SPHEROID['WGS_1984',6378137,298.257223563]], PRIMEM['Greenwich',0], UNIT['degree',0.0174532925199433]]
", "keywords": ["gNATSGO", "the United States SOC", "US soil properties", "15. Life on land", "Gridded National Soil Survey Geographic Database", "International Soil Carbon Network (ISCN)"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8057232"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8057232", "name": "item", "description": "10.5281/zenodo.8057232", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8057232"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-25T00:00:00Z"}}, {"id": "10.7910/DVN/T8CMAT", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:39Z", "type": "Dataset", "created": "2016-02-28", "title": "GMCSD-2. Global Mangrove Carbon, 2000 to 2012, 1 Arc-second, 1 m soil.", "description": "Open AccessGlobal Mangrove Carbon, 2000 to 2012, 1 Arc-Second, 1 m Soil, mid, EQ5.

Annual stocks.

Each of these 13 years is 3TB when extracted. So that is 39 TB as a tif.

We needed to use file geodatabase format to compress enough to post on the Dataverse. Hence no TIffs.", "keywords": ["Earth and Environmental Sciences", "Raster", "ArcGIS file Geodatabase rasters", "Global Mangrove Carbon"], "contacts": [{"organization": "Hamilton, Stuart", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/T8CMAT"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/T8CMAT", "name": "item", "description": "10.7910/DVN/T8CMAT", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/T8CMAT"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10.1073/pnas.1116364109", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:17:52Z", "type": "Journal Article", "created": "2012-01-10", "title": "High-Yield Maize With Large Net Energy Yield And Small Global Warming Intensity", "description": "

Addressing concerns about future food supply and climate change requires management practices that maximize productivity per unit of arable land while reducing negative environmental impact. On-farm data were evaluated to assess energy balance and greenhouse gas (GHG) emissions of irrigated maize in Nebraska that received large nitrogen (N) fertilizer (183 kg of N\uffe2\uff8b\uff85ha \uffe2\uff88\uff921 ) and irrigation water inputs (272 mm or 2,720 m 3 ha \uffe2\uff88\uff921 ). Although energy inputs (30 GJ\uffe2\uff8b\uff85ha \uffe2\uff88\uff921 ) were larger than those reported for US maize systems in previous studies, irrigated maize in central Nebraska achieved higher grain and net energy yields (13.2 Mg\uffe2\uff8b\uff85ha \uffe2\uff88\uff921 and 159 GJ\uffe2\uff8b\uff85ha \uffe2\uff88\uff921 , respectively) and lower GHG-emission intensity (231 kg of CO 2 e\uffe2\uff8b\uff85Mg \uffe2\uff88\uff921 of grain). Greater input-use efficiencies, especially for N fertilizer, were responsible for better performance of these irrigated systems, compared with much lower-yielding, mostly rainfed maize systems in previous studies. Large variation in energy inputs and GHG emissions across irrigated fields in the present study resulted from differences in applied irrigation water amount and imbalances between applied N inputs and crop N demand, indicating potential to further improve environmental performance through better management of these inputs. Observed variation in N-use efficiency, at any level of applied N inputs, suggests that an N-balance approach may be more appropriate for estimating soil N 2 O emissions than the Intergovernmental Panel on Climate Change approach based on a fixed proportion of applied N. Negative correlation between GHG-emission intensity and net energy yield supports the proposition that achieving high yields, large positive energy balance, and low GHG emissions in intensive cropping systems are not conflicting goals.

", "keywords": ["land use change", "Greenhouse Effect", "2. Zero hunger", "Agricultural Irrigation", "330", "Databases", " Factual", "Plant Sciences", "Nitrous Oxide", "Agriculture", "Nebraska", "food security", "04 agricultural and veterinary sciences", "crop intensification", "15. Life on land", "Zea mays", "6. Clean water", "Soil", "13. Climate action", "Air Pollution", "11. Sustainability", "0401 agriculture", " forestry", " and fisheries", "agro-ecosystem", "Fertilizers", "environmental footprint"], "contacts": [{"organization": "Grassini, Patricio, Cassman, Kenneth,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1073/pnas.1116364109"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%20National%20Academy%20of%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1073/pnas.1116364109", "name": "item", "description": "10.1073/pnas.1116364109", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1073/pnas.1116364109"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-09T00:00:00Z"}}, {"id": "10.1007/s00122-021-03815-0", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:14:22Z", "type": "Journal Article", "created": "2021-03-25", "title": "Genomic prediction models trained with historical records enable populating the German ex situ genebank bio-digital resource center of barley (Hordeum\u00a0sp.) with information on resistances to soilborne barley mosaic viruses", "description": "Abstract Key message

Genomic prediction with special weight of major genes is a valuable tool to populate bio-digital resource centers.

Abstract

Phenotypic information of crop genetic resources is a prerequisite for an informed selection that aims to broaden the genetic base of the elite breeding pools. We investigated the potential of genomic prediction based on historical screening data of plant responses against the Barley yellow mosaic viruses for populating the bio-digital resource center of barley. Our study includes dense marker data for 3838 accessions of winter barley, and historical screening data of 1751 accessions for Barley yellow mosaic virus (BaYMV) and of 1771 accessions for Barley mild mosaic virus (BaMMV). Linear mixed models were fitted by considering combinations for the effects of genotypes, years, and locations. The best linear unbiased estimations displayed a broad spectrum of plant responses against BaYMV and BaMMV. Prediction abilities, computed as correlations between predictions and observed phenotypes of accessions, were low for the marker-assisted selection approach amounting to 0.42. In contrast, prediction abilities of genomic best linear unbiased predictions were high, with values of 0.62 for BaYMV and 0.64 for BaMMV. Prediction abilities of genomic prediction were improved by up to\uffe2\uff80\uff89~\uffe2\uff80\uff895% using W-BLUP, in which more weight is given to markers with significant major effects found by association mapping. Our results outline the utility of historical screening data and W-BLUP model to predict the performance of the non-phenotyped individuals in genebank collections. The presented strategy can be considered as part of the different approaches used in genebank genomics to valorize genetic resources for their usage in disease resistance breeding and research.

", "keywords": ["Genetic Markers", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Genotype", "Chromosome Mapping", "Genetic Variation", "Hordeum", "Genomics", "Potyviridae", "Linkage Disequilibrium", "Plant Breeding", "03 medical and health sciences", "Phenotype", "Databases", " Genetic", "Original Article", "Genetic Association Studies", "Disease Resistance", "Plant Diseases"]}, "links": [{"href": "https://link.springer.com/content/pdf/10.1007/s00122-021-03815-0.pdf"}, {"href": "https://doi.org/10.1007/s00122-021-03815-0"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Theoretical%20and%20Applied%20Genetics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1007/s00122-021-03815-0", "name": "item", "description": "10.1007/s00122-021-03815-0", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1007/s00122-021-03815-0"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-25T00:00:00Z"}}, {"id": "10.1038/s41467-020-16438-8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:17:32Z", "type": "Journal Article", "created": "2020-05-25", "title": "Large-scale genome-wide analysis links lactic acid bacteria from food with the gut microbiome", "description": "Abstract

Lactic acid bacteria (LAB) are fundamental in the production of fermented foods and several strains are regarded as probiotics. Large quantities of live LAB are consumed within fermented foods, but it is not yet known to what extent the LAB we ingest become members of the gut microbiome. By analysis of 9445 metagenomes from human samples, we demonstrate that the prevalence and abundance of LAB species in stool samples is generally low and linked to age, lifestyle, and geography, with Streptococcus thermophilus and Lactococcus lactis being most prevalent. Moreover, we identify genome-based differences between food and gut microbes by considering 666 metagenome-assembled genomes (MAGs) newly reconstructed from fermented food microbiomes along with 154,723 human MAGs and 193,078 reference genomes. Our large-scale genome-wide analysis demonstrates that closely related LAB strains occur in both food and gut environments and provides unprecedented evidence that fermented foods can be indeed regarded as a possible source of LAB for the gut microbiome.

", "keywords": ["Primates", "0301 basic medicine", "2. Zero hunger", "0303 health sciences", "Science", "Probiotics", "Q", "gut microbiome", "Article", "Gastrointestinal Microbiome", "lactic acid bacteria", "Lactococcus lactis", "03 medical and health sciences", "Lactobacillales", "Databases", " Genetic", "Food Microbiology", "Animals", "Humans", "Metagenome", "Streptococcus thermophilus", "Fermented Foods", "[PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph]", "Life Style", "genome analysis"]}, "links": [{"href": "https://iris.unitn.it/bitstream/11572/269813/1/s41467-020-16438-8.pdf"}, {"href": "https://www.iris.unina.it/bitstream/11588/811717/2/NatComm%2c2020_LABfoodgut.pdf"}, {"href": "https://www.nature.com/articles/s41467-020-16438-8.pdf"}, {"href": "https://doi.org/10.1038/s41467-020-16438-8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Nature%20Communications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41467-020-16438-8", "name": "item", "description": "10.1038/s41467-020-16438-8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41467-020-16438-8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-25T00:00:00Z"}}, {"id": "10.1111/ejss.13398", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:24Z", "type": "Journal Article", "created": "2023-07-12", "title": "National soil data in EU countries, where do we stand?", "description": "Abstract

At the European scale, soil characteristics are needed to evaluate soil quality, soil health and soil\uffe2\uff80\uff90based ecosystem services in the context of the European Green Deal. While some soil databases exist at the European scale, a much larger wealth of data is present in individual European countries, allowing a more detailed soil assessment. There is thus an urgent and crucial need to combine these data at the European scale. In the frame of a large European Joint Programme on agricultural soils launched by the European Commission, a survey was conducted in the spring of 2020, in the 24 European participating countries to assess the existing soil data sources, focusing on agricultural soils. The survey will become a contribution to the European Soil Observatory, launched in December 2020, which aims to collect metadata of soil databases related to all kind of land uses, including forest and urban soils. Based upon a comprehensive questionnaire, 170 soil databases were identified at local, regional and national scales. Soil parameters were divided into five groups: (1) main soil parameters according to the Global Soil Map specifications; (2) other soil chemical parameters; (3) other physical parameters; (4) other pedological parameters; and (5) soil biological features. A classification based on the environmental zones of Europe was used to distinguish the climatic zones. This survey shows that while most of the main pedological and chemical parameters are included in more than 70% of the country soil databases, water content, contamination with organic pollutants, and biological parameters are the least frequently reported parameters. Such differences will have consequences when developing an EU policy on soil health as proposed under the EU soil strategy for 2023 and using the data to derive soil health indicators. Many differences in the methods used in collecting, preparing, and analysing the soils were found, thus requiring harmonization procedures and more cooperation among countries and with the EU to use the data at the European scale. In addition, choosing harmonized and useful interpretation and threshold values for EU soil indicators may be challenging due to the different methods used and the wide variety of soil land\uffe2\uff80\uff90use and climate combinations influencing possible thresholds. The temporal scale of the soil databases reported is also extremely wide, starting from the '20s of the 20th century.Terrestrial ecosystems sequester 2.1\uffe2\uff80\uff83Pg of atmospheric carbon annually. A large amount of the terrestrial sink is realized by forests. However, considerable uncertainties remain regarding the fate of this carbon over both short and long timescales. Relevant data to address these uncertainties are being collected at many sites around the world, but syntheses of these data are still sparse. To facilitate future synthesis activities, we have assembled a comprehensive global database for forest ecosystems, which includes carbon budget variables (fluxes and stocks), ecosystem traits (e.g. leaf area index, age), as well as ancillary site information such as management regime, climate, and soil characteristics. This publicly available database can be used to quantify global, regional or biome\uffe2\uff80\uff90specific carbon budgets; to re\uffe2\uff80\uff90examine established relationships; to test emerging hypotheses about ecosystem functioning [e.g. a constant net ecosystem production (NEP) to gross primary production (GPP) ratio]; and as benchmarks for model evaluations. In this paper, we present the first analysis of this database. We discuss the climatic influences on GPP, net primary production (NPP) and NEP and present the CO2 balances for boreal, temperate, and tropical forest biomes based on micrometeorological, ecophysiological, and biometric flux and inventory estimates. Globally, GPP of forests benefited from higher temperatures and precipitation whereas NPP saturated above either a threshold of 1500\uffe2\uff80\uff83mm precipitation or a mean annual temperature of 10 \uffc2\uffb0C. The global pattern in NEP was insensitive to climate and is hypothesized to be mainly determined by nonclimatic conditions such as successional stage, management, site history, and site disturbance. In all biomes, closing the CO2 balance required the introduction of substantial biome\uffe2\uff80\uff90specific closure terms. Nonclosure was taken as an indication that respiratory processes, advection, and non\uffe2\uff80\uff90CO2 carbon fluxes are not presently being adequately accounted for.

", "keywords": ["0106 biological sciences", "environment/Bioclimatology", "550", "[SDV]Life Sciences [q-bio]", "01 natural sciences", "630", "SDG 17 - Partnerships for the Goals", "carbon cycle", "SDG 13 - Climate Action", "carbon cycle; forest ecosystems; global database; gross primary productivity; net ecosystem productivity; net primary productivity", "net primary productivity", "global database", "0105 earth and related environmental sciences", "Ecology", "net ecosystem productivity", "forest ecosystems", "Biological Sciences", "15. Life on land", "Climate Action", "[SDV] Life Sciences [q-bio]", "[SDV.EE.BIO] Life Sciences [q-bio]/Ecology", " environment/Bioclimatology", "13. Climate action", "[SDV.EE.BIO]Life Sciences [q-bio]/Ecology", "CO2", "gross primary productivity", "Environmental Sciences"]}, "links": [{"href": "https://escholarship.org/content/qt57t1t77c/qt57t1t77c.pdf"}, {"href": "https://doi.org/10.1111/j.1365-2486.2007.01439.x"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Global%20Change%20Biology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1111/j.1365-2486.2007.01439.x", "name": "item", "description": "10.1111/j.1365-2486.2007.01439.x", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1111/j.1365-2486.2007.01439.x"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2007-08-21T00:00:00Z"}}, {"id": "10.1128/aem.02209-19", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:18:52Z", "type": "Journal Article", "created": "2019-12-04", "title": "Casimicrobium huifangae gen. nov., sp. nov., a Ubiquitous \u201cMost-Wanted\u201d Core Bacterial Taxon from Municipal Wastewater Treatment Plants", "description": "

The activated sludge process is the most widely applied biotechnology and is one of the best ecosystems to address microbial ecological principles. Yet, the cultivation of core bacteria and the exploration of their physiology and ecology are limited. In this study, the core and novel bacterial taxon C. huifangae was cultivated and characterized. This study revealed that C. huifangae functioned as an important module hub in the activated sludge microbiome, and it potentially plays an important role in municipal wastewater treatment plants.

", "keywords": ["0301 basic medicine", "activated sludge microbiome", "DATABASE", "DIVERSITY", "nitrogen and phosphorus removal", "GENOME ANNOTATION", "POLYPHOSPHATE-ACCUMULATING ORGANISMS", "12. Responsible consumption", "ACTIVATED-SLUDGE", "03 medical and health sciences", "SEARCH", "RNA", " Ribosomal", " 16S", "11. Sustainability", "microbial network", "Phylogeny", "WWTP", "0303 health sciences", "IDENTIFICATION", "Sewage", "Microbiota", "Betaproteobacteria", "core taxa", "15. Life on land", "6. Clean water", "COMMUNITY", "RNA", " Bacterial", "Casimicrobium huifangae", "13. Climate action", "Earth and Environmental Sciences", "BIOLOGICAL PHOSPHORUS REMOVAL", "municipal wastewater treatment plant", "CARBON SOURCE"]}, "links": [{"href": "https://journals.asm.org/doi/pdf/10.1128/AEM.02209-19"}, {"href": "https://doi.org/10.1128/aem.02209-19"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20and%20Environmental%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1128/aem.02209-19", "name": "item", "description": "10.1128/aem.02209-19", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1128/aem.02209-19"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-03T00:00:00Z"}}, {"id": "10.1177/0309133319873309", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:19:00Z", "type": "Journal Article", "created": "2019-09-09", "title": "The landscape of soil carbon data: Emerging questions, synergies and databases", "description": "

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model\uffe2\uff80\uff93data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

", "keywords": ["long-term ecological research", "2. Zero hunger", "soil chronosequence", "model\u2013data integration", "soil carbon stabilization", "Soil carbon data", "15. Life on land", "01 natural sciences", "wetland carbon", "6. Clean water", "root traits", "soil database", "soil radiocarbon", "13. Climate action", "11. Sustainability", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://journals.sagepub.com/doi/pdf/10.1177/0309133319873309"}, {"href": "https://doi.org/10.1177/0309133319873309"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Progress%20in%20Physical%20Geography%3A%20Earth%20and%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1177/0309133319873309", "name": "item", "description": "10.1177/0309133319873309", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1177/0309133319873309"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-08T00:00:00Z"}}, {"id": "10.17169/refubium-31202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:19:26Z", "type": "Journal Article", "created": "2021-05-21", "title": "Global data on earthworm abundance, biomass, diversity and corresponding environmental properties", "description": "Abstract

Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.

", "keywords": ["2401.17 Invertebrados", "0301 basic medicine", "592", "Data Descriptor", "Ecology and Evolutionary Biology", "earthworms", "Data Descriptor ; Biodiversity ; Biogeography ; Community ecology", "Plan_S-Compliant-OA", "https://purl.org/becyt/ford/1.6", "[SDV.EE.ECO] Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "Diversity data", "Biomass", "S Agriculture (General)", "Ekologia ja evoluutiobiologia", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "biodiversity", "2. Zero hunger", "maaper\u00e4", "abundance", "Data", "Diversity", "0303 health sciences", "Ecology", "Q", "eli\u00f6yhteis\u00f6t", "Biodiversity", "maaper\u00e4eli\u00f6st\u00f6", "ddc:", "Computer Science Applications", "Biogeography", "2401.06 Ecolog\u00eda animal", "international", "Statistics", " Probability and Uncertainty", "environment/Ecosystems", "Information Systems", "Statistics and Probability", "Ecolog\u00eda (Biolog\u00eda)", "570", "lierot", "Science", "Invertebrados", "577", "Global database", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "Library and Information Sciences", "574", "333", "soil", "eli\u00f6maantiede", "Education", "diversity", "03 medical and health sciences", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "BIODIVERSITY CHANGE", "Life Science", "Earthworms", "Datasets", "Animals", "Community ecology", "Oligochaeta", "https://purl.org/becyt/ford/1", "eartworm", "biogeography", "Ecosystem", "LAND-USE", "biomass", "500", "Biology and Life Sciences", "PLATFORM", "Global dataset", "Oligochaeta/classification", "500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie", "Ecolog\u00eda", "15. Life on land", "biodiversiteetti", "Environmental sciences", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "maaper\u00e4el\u00e4imist\u00f6", "Ecology", " evolutionary biology", "13. Climate action", "Earthworm", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", "570 Life sciences; biology", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology", "eartworm ; abundance ; biomass ; diversity", "COMMUNITIES", "community ecology"]}, "links": [{"href": "https://www.nature.com/articles/s41597-021-00912-z.pdf"}, {"href": "https://pub.epsilon.slu.se/25868/1/phillips_h_r_p_et_al_211019.pdf"}, {"href": "https://boris.unibe.ch/165726/1/48.__Global_data_on_earthworm_abundance__biomass__diversity_and_corresponding_environmental_properties.pdf"}, {"href": "https://www.iris.unict.it/bitstream/20.500.11769/509583/1/SCIENTIFIC%20DATA%20%282021%29%20GLOBAL%20DATA%20ON%20EARTHWORMS.pdf"}, {"href": "https://rau.repository.guildhe.ac.uk/id/eprint/16454/1/Phillips_et_al-2021-Scientific_Data.pdf"}, {"href": "https://doi.org/10.17169/refubium-31202"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17169/refubium-31202", "name": "item", "description": "10.17169/refubium-31202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17169/refubium-31202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-21T00:00:00Z"}}, {"id": "10.21203/rs.3.rs-3607847/v1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:19:37Z", "type": "Journal Article", "created": "2023-11-15", "title": "Advancements in Biotransformation Pathway Prediction: Enhancements, Datasets, and Novel Functionalities in enviPath", "description": "Abstract

enviPath is a widely used database and prediction system for microbial biotransformation pathways of primarily xenobiotic compounds. Data and prediction system are freely available both via a web interface and a public REST API. Since its initial release in 2016, we extended the data available in enviPath and improved the performance of the prediction system and usability of the overall system. We now provide three diverse data sets, covering microbial biotransformation in different environments and under different experimental conditions. This also enabled developing a pathway prediction model that is applicable to a more diverse set of chemicals. In the prediction engine, we implemented a new evaluation tailored towards pathway prediction, which returns a more honest and holistic view on the performance. We also implemented a novel applicability domain algorithm, which allows the user to estimate how well the model will perform on their data. Finally, we improved the implementation to speed up the overall system and provide new functionality via a plugin system. Overall, enviPath has developed into a reliable database and prediction system with a unique use case in research in microbial biotransformations.

", "keywords": ["10120 Department of Chemistry", "0301 basic medicine", "0303 health sciences", "Biodegradation database", "Information technology", "T58.5-58.64", "1704 Computer Graphics and Computer-Aided Design", "3. Good health", "Database", "Chemistry", "03 medical and health sciences", "Metabolic pathways", "540 Chemistry", "Machine learning", "1706 Computer Science Applications", "Biodegradation pathway prediction", "3309 Library and Information Sciences", "1606 Physical and Theoretical Chemistry", "QD1-999"]}, "links": [{"href": "https://doi.org/10.21203/rs.3.rs-3607847/v1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Cheminformatics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.21203/rs.3.rs-3607847/v1", "name": "item", "description": "10.21203/rs.3.rs-3607847/v1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.21203/rs.3.rs-3607847/v1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-15T00:00:00Z"}}, {"id": "10.18419/opus-2935", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:19:30Z", "type": "Report", "title": "Datenmanagementpatterns in multi-skalaren Simulationsworkflows", "description": "In den vergangenen Jahren haben sich im unternehmerischen Umfeld Workflows zur Beschreibung und Ausf\u00fchrung von (Gesch\u00e4fts-)Prozessen durchgesetzt. Seit kurzem wird diese Technologie auch in der Wissenschaft eingesetzt. Z.B. werden Simulationsabl\u00e4ufe als Workflows modelliert. Charakteristisch f\u00fcr solche Simulationen bzw. Simulationsabl\u00e4ufe sind komplexe mathematische Berechnungen sowie verschiedene Aufgaben im Bereich der Datenverwaltung und Datenbereitstellung. Oftmals m\u00fcssen gro\u00dfe Datenmengen, die in propriet\u00e4ren Formaten vorliegen, aus verschiedenen Quellen verarbeitet werden. Damit diese Daten durch einen Simulationsworkflow und den von ihm eingebundenen Programmen und Diensten verarbeitet werden k\u00f6nnen, m\u00fcssen sie in passende Eingabeformate transformiert werden. Gerade bei umfangreichen Simulationen, die eine Vielzahl an Datenquellen ben\u00f6tigen, f\u00fchrt dies aufgrund der enormen Komplexit\u00e4t zu Problemen. Um diese Probleme zu l\u00f6sen, wurde das SIMPL-Rahmenwerk (SimTech - Information Management, Processes and Languages) entwickelt. Das SIMPL-Rahmenwerk ist in ein Scientifc Workflow Management System eingebettet und schafft eine Abstraktionsebene f\u00fcr die Defnition des Datenmanagements. SIMPL bietet einheitliche Zugriffsmethoden, um, aus einem Simulationsworkflow heraus, auf beliebige Datenquellen zuzugreifen. Ein weiterer Bestandteil des SIMPL-Rahmenwerks sind Datenmanagementpatterns. Dabei handelt es sich um vorgefertigte Datenmanagement-Operationen, die nur noch parametrisiert werden m\u00fcssen. Auf diese Weise wird eine neue Abstraktionsebene geschaffen. In einer vorherigen Arbeit wurden bereits erste Datenmanagementpatterns erarbeitet. So k\u00f6nnen z.B. Daten zwischen zwei Datenressourcen ausgetauscht werden. Des Weiteren wurde ein Konzept erarbeitet, um Datenmanagementpatterns auf ausf\u00fchrbare Workflow-Fragmente abzubilden. Dieses Konzept nutzt Transformationsregeln sowie gespeicherte Metadaten \u00fcber beteiligte Ressourcen als Basis. Im Rahmen dieser Diplomarbeit wird das bereits entwickelte Konzept erweitert und wenn n\u00f6tig angepasst, um auf multi-skalare Simulationen angewendet werden zu k\u00f6nnen. Dar\u00fcber hinaus wird die prototypische Umsetzung des SIMPL-Rahmenwerks um Datenmanagementpatterns erweitert.", "keywords": ["000", "Heterogeneous Databases (CR H.2.5)", "Datenmanagementpatterns", "Software Engineering Software Architectures (CR D.2.11)", "wissenschaftliche Workflows", "Office Automation (CR H.4.1)", "Datenmanagement", "Simulationsworkflows", "Simulation Support Systems (CR I.6.7)", "Datenbereitstellung", "004"], "contacts": [{"organization": "Pietranek, Henrik Andreas", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.18419/opus-2935"}, {"rel": "self", "type": "application/geo+json", "title": "10.18419/opus-2935", "name": "item", "description": "10.18419/opus-2935", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.18419/opus-2935"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2012-01-01T00:00:00Z"}}, {"id": "10.3390/rs10111720", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:32Z", "type": "Journal Article", "created": "2018-10-31", "title": "Towards Estimating Land Evaporation at Field Scales Using GLEAM", "description": "

The evaporation of water from land into the atmosphere is a key component of the hydrological cycle. Accurate estimates of this flux are essential for proper water management and irrigation scheduling. However, continuous and qualitative information on land evaporation is currently not available at the required spatio-temporal scales for agricultural applications and regional-scale water management. Here, we apply the Global Land Evaporation Amsterdam Model (GLEAM) at 100 m spatial resolution and daily time steps to provide estimates of land evaporation over The Netherlands, Flanders, and western Germany for the period 2013\uffe2\uff80\uff932017. By making extensive use of microwave-based geophysical observations, we are able to provide data under all weather conditions. The soil moisture estimates from GLEAM at high resolution compare well with in situ measurements of surface soil moisture, resulting in a median temporal correlation coefficient of 0.76 across 29 sites. Estimates of terrestrial evaporation are also evaluated using in situ eddy-covariance measurements from five sites, and compared to estimates from the coarse-scale GLEAM v3.2b, land evaporation from the Satellite Application Facility on Land Surface Analysis (LSA-SAF), and reference grass evaporation based on Makkink\uffe2\uff80\uff99s equation. All datasets compare similarly with in situ measurements and differences in the temporal statistics are small, with correlation coefficients against in situ data ranging from 0.65 to 0.95, depending on the site. Evaporation estimates from GLEAM-HR are typically bounded by the high values of the Makkink evaporation and the low values from LSA-SAF. While GLEAM-HR and LSA-SAF show the highest spatial detail, their geographical patterns diverge strongly due to differences in model assumptions, model parameterizations, and forcing data. The separate consideration of rainfall interception loss by tall vegetation in GLEAM-HR is a key cause of this divergence: while LSA-SAF reports maximum annual evaporation volumes in the Green Heart of The Netherlands, an area dominated by shrubs and grasses, GLEAM-HR shows its maximum in the national parks of the Veluwe and Heuvelrug, both densely-forested regions where rainfall interception loss is a dominant process. The pioneering dataset presented here is unique in that it provides observational-based estimates at high resolution under all weather conditions, and represents a viable alternative to traditional visible and infrared models to retrieve evaporation at field scales.

", "keywords": ["microwave remote sensing", "EVAPOTRANSPIRATION", "WACMOS-ET PROJECT", "Science", "FLUXNET", "Q", "LSA-SAF", "15. Life on land", "01 natural sciences", "6. Clean water", "MODEL", "CARBON", "VARIABILITY", "terrestrial evaporation", "root-zone soil moisture", "13. Climate action", "Earth and Environmental Sciences", "SURFACE EVAPORATION", "GLOBAL DATABASE", "WATER", "SOIL-MOISTURE RETRIEVALS", "terrestrial evaporation; root-zone soil moisture; microwave remote sensing; GLEAM; LSA-SAF", "GLEAM", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/10/11/1720/pdf"}, {"href": "https://doi.org/10.3390/rs10111720"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs10111720", "name": "item", "description": "10.3390/rs10111720", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs10111720"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-10-31T00:00:00Z"}}, {"id": "10.3390/proceedings2019030079", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:32Z", "type": "Journal Article", "created": "2020-06-01", "title": "Monitoring Cropping Systems: From Data Collection to Cloud Database Storage Using Open Source Software", "description": "Agricultural cropping systems and experiments include complex interactions of processes and various management practices and/or treatments under a wide range of environmental and climatic conditions. The use of standardized formats to monitor and document these systems and experiments can help researchers and stakeholders to efficiently exchange data, promote interdisciplinary collaborations, and simplify modelling and analysis procedures. In the scope of the SoilCare Horizon 2020 project monitoring and assessment work package, an integrated scheme to collect, validate, store, and access cropping system information and experimental data from 16 study sites, was created. The aim of the scheme is to make the data readily available in a way that the information is useful, easy to access and download, and safe, relying only on open source software. The database design considers data and metadata required to properly and easily monitor, process, and analyse cropping systems and/or agricultural experiments. The scheme allows for the storage of data and metadata regarding the experimental set-up, associated people and institutions, information about field management operations and experimental procedures which are clearly separated for making analysis procedures faster, links between system components, and information about the environmental and climatic conditions. Raw data are entered by the users into a structured spreadsheet. The quality is checked before storing the data into the database. Providing raw data allows processing and analysing as each other user needs. A desktop import application has been created to upload the information from spreadsheet to database, which includes automated error checks of relationship tables, data types, data constraints, etc. The final component of the scheme is the database web application interface, which enables users to access and query the database across the study sites without the knowledge of query languages and to download the required data. For this system design, PostgreSQL is used for storing the data, pgAdmin 4 for database management administration, MongoDB for user management and authentication, Python for the development of the import application, Angular and Node.js/Express for the web application and spreadsheets compatible with LibreOffice Calc. The system is currently tested with data provided by the SoilCare study sites. Preliminary testing indicated that extended quality control of the spreadsheets was required from the system\u2019s administrator to meet the standards and restrictions of the import application. Initial comments from the users indicate that the database scheme, even if it initially seems complicated, includes all the variables and details required for a complete monitoring and modelling of an agricultural cropping system.", "keywords": ["2. Zero hunger", "cropping systems", "02 engineering and technology", "15. Life on land", "01 natural sciences", "General Works", "0104 chemical sciences", "monitoring", "13. Climate action", "A", "0202 electrical engineering", " electronic engineering", " information engineering", "SoilCare", "database", "open-source"], "contacts": [{"organization": "Ioanna Panagea, Dangol Anuja, Marc Olijslagers, Jan Diels, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.3390/proceedings2019030079"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/TERRAenVISION%202019", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/proceedings2019030079", "name": "item", "description": "10.3390/proceedings2019030079", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/proceedings2019030079"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-01T00:00:00Z"}}, {"id": "10.2478/jofnem-2023-0006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:06Z", "type": "Journal Article", "created": "2023-04-22", "title": "18S-NemaBase: Curated 18S rRNA Database of Nematode Sequences", "description": "Abstract

Nematodes are the most abundant and diverse animals on the planet but lack representation in biodiversity research. This presents a problem for studying nematode diversity, particularly when molecular tools (i.e., barcoding and metabarcoding) rely on well-populated and curated reference databases, which are absent for nematodes. To improve molecular identification and the assessment of nematode diversity, we created and curated an 18S rRNA database specific to nematodes (18S-NemaBase) using sequences sourced from the most recent publicly available 18S rRNA SILVA v138 database. As part of the curation process, taxonomic strings were standardized to contain a fixed number of taxonomic ranks relevant to nematology and updated for the most recent accepted nematode classifications. In addition, apparent erroneous sequences were removed. To test the efficacy and accuracy of 18S-NemaBase, we compared it to an older but also curated SILVA v111 and the newest SILVA v138 by assigning taxonomies and analyzing the diversity of a nematode dataset from the Western Nebraska Sandhills. We showed that 18S-NemaBase provided more accurate taxonomic assignments and diversity assessments than either version of SILVA, with a much easier workflow and no need for manual corrections. Additionally, observed diversity further improved when 18S-NemaBase was supplemented with reference sequences from nematodes present in the study site. Although the 18S-NemaBase is a step in the right direction, a concerted effort to increase the number of high-quality, accessible, full-length nematode reference sequences is more important now than ever.

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, new ETL (Extract, Load, Transform) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving, and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the \u201cWoSIS 2023-snapshot\u201d. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository, and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures, and standardisation of the units of measurement. The \u201c2023 snapshot\u201d considers the following soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil/solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for \u201cfitness-for-intended-use\u201d are provided to facilitate informed data use: a) positional uncertainty of the profile\u2019s site location, b) possible uncertainty associated with the operationally defined analytical procedures, and c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various webservices. To permit consistent referencing and citation we also provide a static snapshot (in casu, December 2023). This snapshot comprises quality-assessed and standardised data for 228 k geo-referenced profiles. The data come from 174 countries and represent more than 900 k soil layers (or horizons) and over 6 million records. The number of measurements for each soil property vary (greatly) between pro\ufb01les and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of \u201cpublic\u201d soil data by prospective data contributors. The WoSIS 2023-snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

", "keywords": ["Environmental sciences", "QE1-996.5", "13. Climate action", "GE1-350", "Geology", "FAIR data principles", "15. Life on land", "global", "database", "6. Clean water", "soil"]}, "links": [{"href": "https://doi.org/10.5194/essd-16-4735-2024"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-16-4735-2024", "name": "item", "description": "10.5194/essd-16-4735-2024", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-16-4735-2024"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-27T00:00:00Z"}}, {"id": "10.3389/fmicb.2021.634325", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:16Z", "type": "Journal Article", "created": "2021-06-17", "title": "How Tillage and Crop Rotation Change the Distribution Pattern of Fungi", "description": "

Massive sequencing of fungal communities showed that climatic factors, followed by edaphic and spatial variables, are feasible predictors of fungal richness and community composition. This study, based on a long-term field experiment with tillage and no-tillage management since 1995 and with a crop rotation introduced in 2009, confirmed that tillage practices shape soil properties and impact soil fungal communities. Results highlighted higher biodiversity of saprotrophic fungi in soil sites with low disturbance and an inverse correlation between the biodiversity of ectomycorrhizal and saprotrophic fungi. We speculated how their mutual exclusion could be due to a substrate-mediated niche partitioning or by space segregation. Moreover, where the soil was ploughed, the species were evenly distributed. There was higher spatial variability in the absence of ploughing, with fungal taxa distributed according to a small-scale pattern, corresponding to micro-niches that probably remained undisturbed and heterogeneously distributed. Many differentially represented OTUs in all the conditions investigated were unidentified species or OTUs matching at high taxa level (i.e., phylum, class, order). Among the fungi with key roles in all the investigated conditions, there were several yeast species known to have pronounced endemism in soil and are also largely unidentified. In addition to yeasts, other fungal species emerged as either indicator of a kind of management or as strongly associated with a specific condition. Plant residues played a substantial role in defining the assortment of species.

", "keywords": ["2. Zero hunger", "0301 basic medicine", "0303 health sciences", "Agriculture", "indicator value", "15. Life on land", "soil yeasts", "rotation", "Microbiology", "QR1-502", "soil", "3. Good health", "FUNGuild database", "03 medical and health sciences", "tillage", "mycorrhizae", "agriculture"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/453431/1/fmicb-12-634325.pdf"}, {"href": "https://doi.org/10.3389/fmicb.2021.634325"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3389/fmicb.2021.634325", "name": "item", "description": "10.3389/fmicb.2021.634325", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3389/fmicb.2021.634325"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-17T00:00:00Z"}}, {"id": "10.34894/u9hspv", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:40Z", "type": "Dataset", "title": "ARCADE: The pan-ARctic CAtchment DatabasE", "description": "Earth\u2019s rapidly changing climate is particularly evident in the Arctic. Outside of the Arctic, the emergence of large-sample catchment databases has transformed science from an emphasis on local case-studies towards more systematic insights into drivers of watershed functioning. Here we present an integrated pan-ARctic CAtchments summary DatabasE (ARCADE) of >40,000 catchments, including small and medium-sized watersheds, draining into the Arctic Ocean. These watersheds, delineated at a high-resolution (90 m), are provided with 103 geospatial, environmental, climatic, and physiographic catchment properties. ARCADE is the first aggregated database of pan-Arctic river catchments that includes small watersheds at a high resolution. These small catchments are experiencing the greatest climatic warming while also storing large quantities of soil carbon in landscapes that are especially prone to degradation of permafrost (i.e., ice wedge polygon terrain) and associated hydrological regime shifts. The publication of this database is a necessary step toward more integrated monitoring of the pan-Arctic watershed.", "keywords": ["Earth and Environmental Science", "Pan-Arctic", "Climate", "Permafrost", "Climate change in the Arctic environment", "15. Life on land", "Biogeochemistry", "Pan Arctic", "Catchment", "Hydroclimatology", "Biospheric Sciences", "Database", "Arctic", "13. Climate action", "Earth and Environmental Sciences", "Climate change", "14. Life underwater", "Watersheds", "Hydrology", "Environmental Research", "Natural Sciences", "Geosciences"], "contacts": [{"organization": "Speetjens, N. J., Hugelius, G., Gumbricht, T., Lantuit, H., Berghuijs, W.R., Pika, P.A., Poste, A., Vonk, J.E.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.34894/u9hspv"}, {"rel": "self", "type": "application/geo+json", "title": "10.34894/u9hspv", "name": "item", "description": "10.34894/u9hspv", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.34894/u9hspv"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5194/essd-2024-14", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:16Z", "type": "Journal Article", "created": "2024-03-27", "title": "Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023)", "description": "

Abstract. Snapshots derived from the World Soil Information Service (WoSIS) are served freely to the international community. These static datasets provide quality-assessed and standardised soil profile data that can be used to support digital soil mapping and environmental applications at broad scale levels. Since the release of the preceding snapshot in 2019, new ETL (Extract, Load, Transform) procedures for screening, ingesting and standardising disparate source data have been developed. In conjunction with this, the WoSIS data model was overhauled making it compatible with the ISO 28258 and Observations and Measurements (O&M) domain models. Additional procedures for querying, serving, and downloading the publicly available standardised data have been implemented using open software (e.g. GraphQL API). Following up on a short discussion of these methodological developments we discuss the structure and content of the \u201cWoSIS 2023-snapshot\u201d. A range of new soil datasets was shared with us, registered in the ISRIC World Data Centre for Soils (WDC-Soils) data repository, and subsequently processed in accordance with the licences specified by the data providers. An important effort has been the processing of forest soil data collated in the framework of the EU-HoliSoils project. We paid special attention to the standardisation of soil property definitions, description of the soil analytical procedures, and standardisation of the units of measurement. The \u201c2023 snapshot\u201d considers the following soil chemical properties (total carbon, organic carbon, inorganic carbon (total carbonate equivalent), total nitrogen, phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures that are operationally comparable. Method options are defined for each analytical procedure (e.g. pH measured in water, KCl or CaCl2 solution, molarity of the solution, and soil/solution ratio). For each profile we also provide the original soil classification (i.e. FAO, WRB and USDA system with their version) and pedological horizon designations as far as these have been specified in the source databases. Three measures for \u201cfitness-for-intended-use\u201d are provided to facilitate informed data use: a) positional uncertainty of the profile\u2019s site location, b) possible uncertainty associated with the operationally defined analytical procedures, and c) date of sampling. The most recent (i.e. dynamic) dataset, called wosis_latest, is freely accessible via various webservices. To permit consistent referencing and citation we also provide a static snapshot (in casu, December 2023). This snapshot comprises quality-assessed and standardised data for 228 k geo-referenced profiles. The data come from 174 countries and represent more than 900 k soil layers (or horizons) and over 6 million records. The number of measurements for each soil property vary (greatly) between pro\ufb01les and with depth, this generally depending on the objectives of the initial soil sampling programmes. In the coming years, we aim to gradually fill gaps in the geographic distribution of the profiles, as well as in the soil observations themselves, this subject to the sharing of a wider selection of \u201cpublic\u201d soil data by prospective data contributors. The WoSIS 2023-snapshot is archived and freely available at https://doi.org/10.17027/isric-wdcsoils-20231130 (Calisto et al., 2023).

", "keywords": ["Environmental sciences", "QE1-996.5", "13. Climate action", "GE1-350", "Geology", "FAIR data principles", "15. Life on land", "global", "database", "6. Clean water", "soil"]}, "links": [{"href": "https://doi.org/10.5194/essd-2024-14"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20System%20Science%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/essd-2024-14", "name": "item", "description": "10.5194/essd-2024-14", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/essd-2024-14"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-03-27T00:00:00Z"}}, {"id": "10.48786/edbt.2023.45", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:20:51Z", "type": "Dataset", "title": "WedgeBlock: An Off-Chain Secure Logging Platform for Blockchain Applications", "keywords": ["Database Technology"]}, "links": [{"href": "https://doi.org/10.48786/edbt.2023.45"}, {"rel": "self", "type": "application/geo+json", "title": "10.48786/edbt.2023.45", "name": "item", "description": "10.48786/edbt.2023.45", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.48786/edbt.2023.45"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5061/dryad.rn8pk0pm8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:01Z", "type": "Dataset", "created": "2024-06-28", "title": "Uncertainties in greenhouse gas emission factors: A comprehensive analysis of switchgrass-based biofuel production", "description": "unspecifiedThis study investigates uncertainties in greenhouse gas (GHG) emission factors related to switchgrass-based biofuel production in Michigan. Using three life cycle assessment (LCA) databases\u2014 US lifecycle inventory database (USLCI), GREET, and Ecoinvent\u2014each with multiple versions, we recalculated the global warming intensity (GWI) and GHG mitigation potential in a static calculation. Employing Monte Carlo simulations along with local and global sensitivity analyses, we assess uncertainties and pinpoint key parameters influencing GWI. The convergence of results across our previous study, static calculations, and Monte Carlo simulations enhances the credibility of estimated GWI values. Static calculations, validated by Monte Carlo simulations, offer reasonable central tendencies, providing a robust foundation for policy considerations. However, the wider range observed in Monte Carlo simulations underscores the importance of potential variations and uncertainties in real-world applications. Sensitivity analyses identify biofuel yield, GHG emissions of electricity, and soil organic carbon (SOC) change as pivotal parameters influencing GWI. Decreasing uncertainties in GWI may be achieved by making greater efforts to acquire more precise data on these parameters. Our study emphasizes the significance of considering diverse GHG factors and databases in GWI assessments and stresses the need for accurate electricity fuel mixes, crucial information for refining GWI assessments and informing strategies for sustainable biofuel production.", "keywords": ["Sensitivity Analysis", "Switchgrass", "FOS: Environmental engineering", "Cellulosic biofuel", "Global warming intensity", "Greenhouse gas emission factor", "LCA database", "uncertainty analysis"], "contacts": [{"organization": "Kim, Seungdo, Dale, Bruce, Basso, Bruno,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.rn8pk0pm8"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.rn8pk0pm8", "name": "item", "description": "10.5061/dryad.rn8pk0pm8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.rn8pk0pm8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-07-16T00:00:00Z"}}, {"id": "10.5281/zenodo.4333554", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:38Z", "type": "Dataset", "title": "Best4Soil_databases_datamining_V1", "description": "This document contains data on the host plant status of field crops, vegetable crops and green manure crops for soilborne plant parasitic nematodes and soilborne fungal pathogens. The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n\u00b0817696). The databases are accessible through the website https://ww.best4soil.eu/database ; they are hosted on https://nematodes.soilhealthtool.eu/", "keywords": ["2. Zero hunger", "field crop", "Best4Soil", " database", " hostplant", " soilborne pathogen", " nematode", " field crop", " vegetable", " green manure", " cover crop", "green manure", "hostplant", "nematode", "Best4Soil", "vegetable", "cover crop", "15. Life on land", "soilborne pathogen", "database"], "contacts": [{"organization": "Molendijk, Leendert, van Asperen, Paulien, Michel, Vincent, H\u00e4ller, Bruno, Gaffney, Michael, de Cara, Miguel, Damsgaard Thorsted, Marian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4333554"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4333554", "name": "item", "description": "10.5281/zenodo.4333554", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4333554"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4333555", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:38Z", "type": "Dataset", "title": "Best4Soil_databases_datamining_V1", "description": "This document contains data on the host plant status of field crops, vegetable crops and green manure crops for soilborne plant parasitic nematodes and soilborne fungal pathogens. The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n\u00b0817696). The databases are accessible through the website https://ww.best4soil.eu/database ; they are hosted on https://nematodes.soilhealthtool.eu/", "keywords": ["2. Zero hunger", "field crop", "Best4Soil", " database", " hostplant", " soilborne pathogen", " nematode", " field crop", " vegetable", " green manure", " cover crop", "green manure", "hostplant", "nematode", "Best4Soil", "vegetable", "cover crop", "15. Life on land", "soilborne pathogen", "database"], "contacts": [{"organization": "Molendijk, Leendert, van Asperen, Paulien, Michel, Vincent, H\u00e4ller, Bruno, Gaffney, Michael, de Cara, Miguel, Damsgaard Thorsted, Marian,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4333555"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4333555", "name": "item", "description": "10.5281/zenodo.4333555", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4333555"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.5194/bg-19-3505-2022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:08Z", "type": "Journal Article", "created": "2022-07-28", "title": "Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential", "description": "

Abstract. In the age of big data, soil data are more available and richer than ever, but \u2013 outside of a few large soil survey resources \u2013 they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

", "keywords": ["FOS: Computer and information sciences", "0301 basic medicine", "Data Sharing", "Information Systems and Management", "literature review", "1904 Earth-Surface Processes", "Social Sciences", "data set", "01 natural sciences", "Decision Sciences", "Data science", "Life", "QH501-531", "910 Geography & travel", "soil analysis", "database", "QH540-549.5", "2. Zero hunger", "QE1-996.5", "000", "Ecology", "communication", "Physics", "Earth", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "World Wide Web", "10122 Institute of Geography", "soil survey", "Physical Sciences", "Data Reuse", "environment", "Information Systems", "Evolution", "future prospect", "Data management", "Data Sharing and Stewardship in Science", "Database", "Big data", "03 medical and health sciences", "Behavior and Systematics", "Data mining", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Management and Reproducibility of Scientific Workflows", "Metadata", "Data curation", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Acoustics", "15. Life on land", "Computer science", "1105 Ecology", " Evolution", " Behavior and Systematics", "Surface Processes", "Harmonization", "FOS: Biological sciences", "Computer Science", "Environmental Science", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil management", "Research Data", "Environmental DNA in Biodiversity Monitoring"]}, "links": [{"href": "https://doi.org/10.5194/bg-19-3505-2022"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-19-3505-2022", "name": "item", "description": "10.5194/bg-19-3505-2022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-19-3505-2022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-28T00:00:00Z"}}, {"id": "10.5194/bg-2021-323", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:09Z", "type": "Report", "created": "2021-12-15", "title": "Reviews and syntheses: The promise of big soil data, moving current practices towards future potential", "description": "

Abstract. In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources- remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

", "keywords": ["FOS: Computer and information sciences", "Data Sharing", "Biomedical Ontologies and Text Mining", "Data management", "Leverage (statistics)", "01 natural sciences", "Data science", "Data Sharing and Stewardship in Science", "Database", "Big data", "Biochemistry", " Genetics and Molecular Biology", "Machine learning", "Molecular Biology", "Data mining", "0105 earth and related environmental sciences", "2. Zero hunger", "Metadata", "Ecology", "Data curation", "Physics", "Life Sciences", "Acoustics", "15. Life on land", "Computer science", "World Wide Web", "Harmonization", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Physical Sciences", "Environmental Science", "Data Reuse", "Environmental DNA in Biodiversity Monitoring", "Information Systems"]}, "links": [{"href": "https://doi.org/10.5194/bg-2021-323"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-2021-323", "name": "item", "description": "10.5194/bg-2021-323", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-2021-323"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-15T00:00:00Z"}}, {"id": "10.5194/bg-3-571-2006", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:09Z", "type": "Journal Article", "created": "2010-04-29", "description": "

Abstract. Eddy covariance technique to measure CO2, water and energy fluxes between biosphere and atmosphere is widely spread and used in various regional networks. Currently more than 250 eddy covariance sites are active around the world measuring carbon exchange at high temporal resolution for different biomes and climatic conditions. In this paper a new standardized set of corrections is introduced and the uncertainties associated with these corrections are assessed for eight different forest sites in Europe with a total of 12 yearly datasets. The uncertainties introduced on the two components GPP (Gross Primary Production) and TER (Terrestrial Ecosystem Respiration) are also discussed and a quantitative analysis presented. Through a factorial analysis we find that generally, uncertainties by different corrections are additive without interactions and that the heuristic u*-correction introduces the largest uncertainty. The results show that a standardized data processing is needed for an effective comparison across biomes and for underpinning inter-annual variability. The methodology presented in this paper has also been integrated in the European database of the eddy covariance measurements.

", "keywords": ["european database of the eddy covariance measurements", "550", "net ecosystem exchange", "Molecular Biology/Biochemistry [q-bio.BM]", "[SDU.STU]Sciences of the Universe [physics]/Earth Sciences", "[SDU.ASTR] Sciences of the Universe [physics]/Astrophysics [astro-ph]", "[PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]", "Life", "QH501-531", "[SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry", " Molecular Biology/Biochemistry [q-bio.BM]", "QH540-549.5", "eddy covariance technique", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "QE1-996.5", "algorithm", "[SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph]", "Ecology", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "500", "Geology", "15. Life on land", "terrestrial ecosystem respiration", "gross primary production", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "[SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry", "[PHYS.ASTR.CO] Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO]", "13. Climate action", "[SDU.STU] Sciences of the Universe [physics]/Earth Sciences", "co2", "measurement", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment"]}, "links": [{"href": "https://doi.org/10.5194/bg-3-571-2006"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/bg-3-571-2006", "name": "item", "description": "10.5194/bg-3-571-2006", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/bg-3-571-2006"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2006-11-27T00:00:00Z"}}, {"id": "10.5194/hess-25-5749-2021", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:19Z", "type": "Journal Article", "created": "2021-11-09", "title": "The International Soil Moisture Network: serving Earth system science for over a decade", "description": "

Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "Earth and Planetary Sciences", "Physical Sciences", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "name=Earth and Planetary Sciences (miscellaneous)", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "Soil Moisture International Network", "0207 environmental engineering", "Epistemology", "Environmental science", "G", "Database", "Soil Moisture; network", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Consecutive dry days", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "Globe", "Computer science", "Environmental sciences", "QE Geology", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "soil moisture", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://research.unipg.it/bitstream/11391/1498417/2/2021_The%20international%20soil_OA.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/10.5194/hess-25-5749-2021"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/hess-25-5749-2021", "name": "item", "description": "10.5194/hess-25-5749-2021", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/hess-25-5749-2021"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "10.5281/zenodo.10812366", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:29Z", "type": "Other", "title": "SoilCare database 3: schema (empty database) and Report 34 (D5.1): Database with monitoring data", "description": "The Deliverable 5.1 reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested\u00a0cropping systems and/or\u00a0field agricultural experiments in the 16 Study sites.\u00a0\u00a0 To properly monitor a cropping system and/or a field agricultural experiment a lot of information is required to capture all the possible interactions. The SoilCare WP5 devised a common data management system for all the study-sites. One important objective is to collect complete and comparable data for an analysis across study sites and data that allows any user to get all the required information when analysing a cropping system.The data model structure created based on the entity-relationship diagram and designed\u00a0to capture all the possible dependencies and complex interactions in a cropping system. All information is grouped in different pools: i. (experiments\u2019) Basic information such as institution and person metadata ii. (experimental) Field information like climate, inherent soil properties and spatial arrangement iii. The experimental setup which includes the details for the different treatments and the factors iv. Management data that includes all the detailed information for each group of management categories v. Results which include the measured data and metadata for the measurements/observations.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "", "15. Life on land"], "contacts": [{"organization": "Panagea Ioanna, Dangol Anuja, Olijslagers Marc, Wyseure Guido,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.10812366"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.10812366", "name": "item", "description": "10.5281/zenodo.10812366", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.10812366"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-05T00:00:00Z"}}, {"id": "10.5281/zenodo.14018253", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:53Z", "type": "Report", "title": "D1.1 SERENA Stakeholder's Database Development", "description": "Procedure for developing the SERENA stakeholder database (D1.1). A compiled list of contacts is organized in a excell format, ensuring accessibility for all project partners and it is protected by General Data Protection Regulation.", "keywords": ["soil based ecosystem services", "multistakeholders database", "stakeholder"], "contacts": [{"organization": "Bondi, Giulia, O'Sullivan, Lilian, Astover, Alar, Asins-Velis, Sabina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14018253"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14018253", "name": "item", "description": "10.5281/zenodo.14018253", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14018253"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.14018254", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:53Z", "type": "Report", "title": "D1.1 SERENA Stakeholder's Database Development", "description": "Procedure for developing the SERENA stakeholder database (D1.1). A compiled list of contacts is organized in a excell format, ensuring accessibility for all project partners and it is protected by General Data Protection Regulation.", "keywords": ["soil based ecosystem services", "multistakeholders database", "stakeholder"], "contacts": [{"organization": "Bondi, Giulia, O'Sullivan, Lilian, Astover, Alar, Asins-Velis, Sabina,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14018254"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14018254", "name": "item", "description": "10.5281/zenodo.14018254", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14018254"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-31T00:00:00Z"}}, {"id": "20.500.11820/8916c5c3-7d9f-49a8-86bc-f621eb79d53a", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:28Z", "type": "Journal Article", "created": "2019-09-09", "title": "The landscape of soil carbon data: Emerging questions, synergies and databases", "description": "

Soil carbon has been measured for over a century in applications ranging from understanding biogeochemical processes in natural ecosystems to quantifying the productivity and health of managed systems. Consolidating diverse soil carbon datasets is increasingly important to maximize their value, particularly with growing anthropogenic and climate change pressures. In this progress report, we describe recent advances in soil carbon data led by the International Soil Carbon Network and other networks. We highlight priority areas of research requiring soil carbon data, including (a) quantifying boreal, arctic and wetland carbon stocks, (b) understanding the timescales of soil carbon persistence using radiocarbon and chronosequence studies, (c) synthesizing long-term and experimental data to inform carbon stock vulnerability to global change, (d) quantifying root influences on soil carbon and (e) identifying gaps in model\u2013data integration. We also describe the landscape of soil datasets currently available, highlighting their strengths, weaknesses and synergies. Now more than ever, integrated soil data are needed to inform climate mitigation, land management and agricultural practices. This report will aid new data users in navigating various soil databases and encourage scientists to make their measurements publicly available and to join forces to find soil-related solutions.

", "keywords": ["long-term ecological research", "2. Zero hunger", "soil chronosequence", "model\u2013data integration", "soil carbon stabilization", "Soil carbon data", "15. Life on land", "01 natural sciences", "wetland carbon", "6. Clean water", "root traits", "soil database", "soil radiocarbon", "13. Climate action", "11. Sustainability", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://journals.sagepub.com/doi/pdf/10.1177/0309133319873309"}, {"href": "https://doi.org/20.500.11820/8916c5c3-7d9f-49a8-86bc-f621eb79d53a"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Progress%20in%20Physical%20Geography%3A%20Earth%20and%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11820/8916c5c3-7d9f-49a8-86bc-f621eb79d53a", "name": "item", "description": "20.500.11820/8916c5c3-7d9f-49a8-86bc-f621eb79d53a", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11820/8916c5c3-7d9f-49a8-86bc-f621eb79d53a"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-08T00:00:00Z"}}, {"id": "10.5281/zenodo.14179949", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:21:57Z", "type": "Dataset", "title": "EOM4SOIL - Physico-chemical characteristics of external organic matters (EOMs) database", "description": "Physico-chemical characteristics of external organic matters (EOMs) database. The database is a non-relationnal database in column format. Established in the EJP Soil EOM4SOIL project, the database considers physico-chemical characteristics from about 120 types of EOMs encompassing urban, industrial and agricultural origins (e.g. urine, sludge, composts, digestates, farmyard manures; from various origins) and about 90 characteristics (major elements, mineral trace elements, emerging contaminants, mineralised C and N). There is an average of about 20 variables collected per type of EOM. The Physico-chemical characteristics EOM database gathered EOM references collected in national databases and surveys from various European countries completed by data published in scientific articles.", "keywords": ["Non-relational databases", "FOS: Agricultural sciences", "Sustainable agriculture", "Agricultural sciences", "organic matter"], "contacts": [{"organization": "Michaud, Aur\u00e9lia, Van Der Smissen, H\u00e9l\u00e8ne, Tampio, Elina, Laakso, Johanna, Levavasseur, Florent, Barcauskaite, Karolina, Lasorella, Valentina, Criscuoli, Irene, Asperen, Paulien, Dehaan, Janjo, Jimenez, Julie, Caradec, Lucille, Houot, Sabine,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14179949"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14179949", "name": "item", "description": "10.5281/zenodo.14179949", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14179949"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.17618737", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:31Z", "type": "Dataset", "title": "The Namibian Soil Profile Database (NSPD2025): An updated, expanded and harmonised compilation of national soil data", "description": "Introduction The Namibian Soil Profile Database (NSPD2025) is a national compilation of 5,099 soil point observations and 13,425 horizons or layers, comprising site, profile, horizon, and analytical data from diverse legacy datasets. Data were verified, cleaned, and standardised to ensure compatibility with international systems.\u00a0 The spatial distribution of points shows denser sampling in agricultural and research areas, and sparser data in remote rural areas and the Namib Desert. The level of detail varies considerably, from basic site observations to full profile descriptions with analytical data. The dataset is provided as a Microsoft Excel file with four worksheets \u2013\u00a0Reg&Site, Hor_lab, Hor_field, and Metadata \u2013 and a QGIS project with the profile locations and basic geographical information. NSPD2025 is intended for use in soil classification, conventional and digital soil mapping, environmental modelling and land management in Namibia and beyond. Data Sources NSPD2025 integrates data from multiple legacy sources: National Soil Survey (1998\u20132000) and MAWRD-AEZ Campaigns (2001\u20132009) Systematic soil data collection in post-independence Namibia began with the National Soil Survey Phase I (NSS), conducted by the Ministry of Agriculture, Water and Rural Development[1] (MAWRD) in collaboration with the Cartographic Institute of Catalonia (ICC) from 1998 to 2000. It produced four soil maps and associated MS Access relational databases: NSD1000, NSD250, NSDkava, and NSDowa (ICC & MAWRD, 2000). Soil profiles were described according to the FAO Guidelines for Soil Profile Description (FAO, 1990). The data were reformatted into the Namibian Soil and Terrain Digital Database, NAMSOTER (Coetzee, 2001), based on the methodology of Van Engelen and Wen (1995), for inclusion in the Southern African SOTERSAF (FAO/ISRIC, 2003). The Agro-Ecological Zoning (AEZ) Programme of MAWRD/MAWF continued soil survey campaigns in under-sampled regions between 2000 and 2009. These datasets were consolidated and maintained within the Namibian Agricultural Resources Information System (NARIS). By 2009, NSS NARIS contained 2,155 soil records of varying completeness. All laboratory analyses for these profiles were performed by the ministerial Agriculture Laboratory (AgricLab) in Windhoek, which follows ISO-based quality management through standard operating procedures, internal standards, replicate analyses, and participation in the Agri-Laboratory Association of Southern Africa (AgriLASA) inter-laboratory proficiency testing. During the present update, additional analytical and field data were recovered from original NSS records and integrated into the NSPD2025. African Soil Microbial Genomics Project (AMP, AMP-NA, AMP-NDLS) The African Soil Microbial Genomics Project (Wild, 2016; Cowan et al., 2022) contributed 179 topsoil samples (0\u201320 cm) across three datasets. Eleven physicochemical soil properties were analysed by the University of Pretoria using AgriLASA (2004) protocols. BIOTA Project The Biodiversity Monitoring Transect Analysis in Africa (BIOTA) Programme established 11 long-term observatories across Namibia (Petersen, 2008; https://biota-africa.org/). It contributed 638 samples from 257 profiles (0\u201310 cm, 10\u201330 cm, 30\u201360 cm), analysed for texture, pH (CaCl\u2082), electrical conductivity, total nitrogen, and organic carbon by the University of Hamburg\u2019s Institute of Soil Science. GIZ Bush-SOC Project The Assessment of the Impact of Bush Encroachment and Bush Control on Soil Organic Carbon in Namibia project (Strohbach et al., 2024) contributed 162 topsoil samples (0\u201330 cm) analysed by the AgricLab for texture, pH (H\u2082O), bulk density, electrical conductivity, extractable cations, plant-available phosphorus, and organic carbon. LandPKS Project The Land Potential Knowledge System (LandPKS) (Herrick et al., 2016) developed a mobile app integrating user inputs with cloud-based geospatial data. Piloted in Namibia and Kenya, it provided 918 georeferenced observations, recording site, land use, vegetation cover, effective depth, stoniness, and field-estimated texture at seven standard depths (0\u20131 cm, 1\u201310 cm, 10\u201320 cm, 20\u201350 cm, 50\u201370 cm, 70\u2013100 cm, 100\u2013150 cm.). No laboratory data are included, and data quality varies due to contributions by non-specialists. Land Degradation Neutrality (LDN, LDN-Omusati) The Land Degradation Neutrality (LDN) Project (Nijbroek et al., 2018) contributed 319 topsoil (0\u201330 cm) and 277 subsoil (30\u2013100 cm) samples, analysed by the AgricLab for organic carbon, bulk density, and particle size. SASSCAL The Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) Phase I subproject Monitoring agricultural ecosystems with regards to climate change effects (De Bl\u00e9court et al., 2018) added 181 samples from 30 profiles across four sites. These were analysed for texture, bulk density, pH (H\u2082O and CaCl\u2082), electrical conductivity, total nitrogen, organic carbon, cation exchange capacity, and porosity by the University of Hamburg\u2019s Institute of Soil Science. Landscape and profile photographs are included. Soils4Africa (S4A) The EU Horizon 2020 Soils4Africa Project (https://www.soils4africa-h2020.eu/) contributed 139 site and descriptions, and laboratory data for 64 topsoil samples. Analyses were conducted by the South African Agricultural Research Council\u2019s Institute for Soil, Climate and Water (ARC-ISCW) (Paterson, 2021). Bulk density measurements were carried out by the AgricLab. Future Okavango (TFO) The Future Okavango Project (Pr\u00f6pper et al., 2015; https://www.future-okavango.org/) focused on knowledge-based land-use management in the Okavango catchment and contributed 484 samples from 141 profiles. These were analysed for texture, pH (H\u2082O and CaCl\u2082), bulk density, electrical conductivity, cation exchange capacity, total nitrogen, and organic carbon by the University of Hamburg and the University of Botswana\u2019s Okavango Research Institute. Landscape and profile photographs are included. Additional Contributions Smaller datasets were obtained from Simmonds & Forbes (1995), Buch (1990), Trippner (1998), Kempf (2000), and Coetzee (in Strohbach et al., 2019). Methods and Data Processing All datasets were collated in Microsoft Excel, using both automated procedures and visual inspection \u2013 based on subject knowledge \u2013 for quality-control of data. Records lacking georeferencing or containing irreparable errors were removed. Key steps included: Verification and correction of NSS and MAWF data against original field forms, and addition of previously undigitised information. Digital records could not be verified against the following missing field forms: AO, DOR, MAR, NDONGA, OMAH_A, OMAH_B, ONGO, OVI, SPERR, TSUM. Standardisation of classes, codes, and measurement units following FAO Guidelines for Soil Profile Description (FAO, 2006) and SOTER/ISRIC standards (Van Engelen & Dijkshoorn, 2013). Verification of profile identifications and horizon designations; assigning layer identifiers to non-pedogenic layers (e.g. LandPKS fixed depth intervals). Conversion of coordinates to decimal degrees and addition of the coordinate reference system. Confirmation of attribute definitions (e.g. organic matter vs organic carbon; minimum diameter of sand fraction 50 or 63 \u00b5m) and value ranges, some in combination with other attributes (e.g. the sum of particle size fractions being 100\u00b11%; horizon thickness corresponding to the difference between upper and lower horizon limits). Verification of conspicuous outliers. Addition of registration and environmental information. Where available, diagnostic elements (horizons, materials and properties) were used for classification according to the WRB (IUSS, 2015) Limitations: variable data completeness, differences in analytical methods, and unverified records for some missing field forms. However, the harmonisation process ensures national consistency and compatibility with global frameworks. Database Design The NSPD2025 dataset is provided as an MS Excel workbook, NSPD2025.xlsx, with four worksheets (see all fields below): Reg&Site contains profile registration information and site descriptions. Hor_field contains physical and morphological characteristics of the profiles and horizons. \u00a0Hor_lab contains laboratory measurements. Metadata The primary identifier of each point observation is the PRID (profile identification), with secondary identifiers for horizons/layers in the HONU field. The design embeds some redundancy: repetition of data in different formats (e.g. individual values, class ranges, class codes and descriptive text) and measuring units. For example, dates of profile description are recorded in YYYY-MM-DD, DD-Mon-YYYY, DD/MM/YYYY formats as well as in separate Year, Month and Day fields, while Organic Carbon is recorded in both % and g/kg units. This was done to facilitate integration with international soil information systems, and to accommodate users who are unfamiliar with the FAO profile description codes. The same rationale is behind the use of descriptive field names rather than codes. \u00a0 Fields of the Reg&Site table: PRID Degree Square Grazer Type Surface Sealing Hardness Desc Dataset Quarter Degree Square Human Influence Code Surface Cracks Width cm PRID Let Farm No Human Influence Desc Surface Cracks Width Code PRID Dig Farm Name Rock Outcrops Abundance/Cover Code Distance Between Surface Cracks (m) ALT PRID Location Rock Outcrops Cover % Salt (Surface Salinity) Date (YYYY-MM-DD) Diagnostic Horizon / Property / Material Rock Outcrops Cover Desc Bleached Sand Code Date (DD-Mon-YY) Present Weather Code Rock Outcrops - m between Bleached Sand % Date (DD/MM/YYYY) Present Weather Desc Rock Outcrops Type Bleached Sand Desc Year Past Weather Code Coarse Surface Fragments Cover Code Flooded >2 weeks/a Month Past Weather Desc Coarse Surface Fragments Cover % Major Landform Code (2nd level) Day Soil Temperature Regime Code Coarse Surface Fragments Cover Desc Major Landform Desc Coordinate Reference System Soil Temperature Regime Desc Coarse Surface Fragments Size Code Minor Landform desc Latitude Soil Moisture Regime Code Coarse Surface Fragments Size cm Position code Longitude Soil Moisture Regime Desc Coarse Surface Fragments Size Desc Position Desc Elevation m GPS Slope Gradient Class Surface Coarse Surface Fragments Type Vegetation Elevation m SRTM Slope Form Vert Erosion Type Code Land Cover Country ID Slope Form Hor Erosion Type Parent Material Data Origin Slope Gradient Measured % Erosion Area Affected Code Lithology Surveyor(s) Slope Gradient Desc Erosion Area Affected % Drainage Desc Status Gradient % (for landform) from SRTM Erosion Degree Code Drainage Code Profile Classification Original Slope % SRTM Erosion Degree Desc Geology Profile Classification Original Code Relief m/km Erosion Status Code Effective Depth Classification System Drainage density Erosion Status Desc Depth Class WRB2015 Reclassification RSG Present Land Use Code Surface Sealing Thickness Code Biological activity WRB2015 Reclassification Principal Qualifiers Present land Use Desc Surface Sealing Thickness mm Notes WRB2015 Reclassification Supplementary Qualifiers Past Land Use Code Surface Sealing Thickness Desc \u00a0 WRB Classification by S Nambambi Past Land Use Desc Surface Sealing Hardness Code \u00a0 \u00a0Fields of the Hor_lab table: PRID Electrical Conductivity \u00b5S/cm [2 soil:5 water suspension] (EL25) Gypsum % coSa % HONU Electrical Conductivity dS/m [2 soil:5 water suspension] (EL25) Gypsum g/kg (GYPS) meSa % Dataset Electrical Conductivity \u00b5S/cm [Saturated Paste Extract] (ELCO) Total Nitrogen mg/kg (TOTN) fiSa % Latitude Electrical Conductivity dS/m [Saturated Paste Extract] (ELCO) Total Nitrogen g/kg (TOTN) Kjeldahl vfiSa % Longitude Ca % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total Nitrogen % coSi % Laboratory Mg % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total Phosphorus mg/kg [Mehlich #3, ICP-AES] fiSi % Soil Lab ID K % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Total P unknown method & unit EC (1:5) mS/m Representative Profile Na % [Exchangeable & Soluble; Mehlich #3, ICP-AES] Phosphorus [Olsen] mg/kg CaCO3 g/kg Hor thickness cm [contains >] Ca mg/kg [Extractable; 1M NH4acetate, AAS] (SOCA) P2O5 mg/kg [Olsen; P x 2.291] inorgC g/kg Upper Depth cm [contains >] Mg mg/kg [Extractable; 1M NH4acetate, AAS] (SOMG) Organic Carbon % LOI totC g/kg Lower Depth cm [contains >] K mg/kg [Extractable; 1M NH4acetate, AAS] (SOLK) Organic Carbon % WB orgC g/kg CNS analyser Hor thickness cm Na mg/kg [Extractable; 1M NH4acetate, AAS] (SONA) Organic Carbon g/kg WB exchH cmol(-)/kg Upper Depth cm Cl mg/kg [Extractable] (SOCL) Organic Matter % [OC*1.74] exchAl cmol(-)/kg Lower Depth cm Chloride % [Extractable] Mn mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] BaseSat % ? Horizon Code Short SSO4 ppm Fe mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] exchAcid cmol(+)/kg Dominant Sand Grade Code Sulfate % Al mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES] totMo EPA 6010C?:2007 Dominant Sand Grade Desc Soluble carbonate meq/l (SCO3) Porosity Measured totCd mg/kg EPA 6010C:2007 Sand % < 53 \u00b5m (SDTO) Total Carbonate Equivalent g/kg (TCEQ) Fe_ox mg/g acid-oxalate extr totPb mg/kg EPA 6010C:2007 Silt % [2-53\u00b5m] (STPC) Carbonate Estimate % Al_ox mg/g acid-oxalate extr totV mg/kg EPA 6020A:2007 Clay % [< 2 \u00b5m] (CLPC) Exchangeable Calcium cmol(+)/kg (EXCA) Al mg/kg? totHg mg/kg EPA 6020A:2007 Silt + Clay % Exchangeable Magnesium cmol(+)/kg (EXMG) B mg/kg? totCr mg/kg EPA 6010C:2007 Texture Code (PSCL) Exchangeable Potassium cmol(+)/kg (EXCK) Cu mg/kg? totCo mg/kg EPA 6010C:2007 Texture Desc Exchangeable Sodium cmol(+)/kg (EXNA) Fe mg/kg? totNi mg/kg EPA 6010C:2007 Bulk Density kg/dm3 CEC Effective cmol(+)/kg [Sum of Exchangeable Bases] Mn mg/kg? totCu mg/kg EPA 6010C:2007 pH CaCl2 (PHCA) [2 soil:5 CaCl2] CEC Measured cmol(+)/kg P mg/kg? totZn mg/kg EPA 6010C:2007 pH water (PHAQ) Base Saturation Estimate % [from pHwater] S mg/kg? totAs mg/kg EPA 6020A:2007 pH water other lab Base Saturation % [CECeffective/CECmeasured] Zn mg/kg? totSb mg/kg EPA 6020A:2007 pH KCl (PHKC) \u00a0 \u00a0 \u00a0 \u00a0 Fields of the Hor_field table: PRID Coarse Fragments Abundance Desc Vertic Properties Coatings Form Desc HONU Coarse Fragments Abundance % properties for classification Coatings Location Code Dataset Coarse Fragments Size Code CLAF Coatings Location Desc Latitude Coarse Fragments Size Desc LOWER LEVEL UNITS Cementation /\u00a0 Compaction Nature Code Longitude Coarse Fragments Size mm REFERENCE GROUP Cementation / Compaction Nature Desc Hor thickness cm Course Fragments Shape Code CLAV Cementation /\u00a0 Compaction Continuity Code Upper Depth cm Coarse Fragments Shape Desc ALT PRID Cementation / Compaction Continuity Desc Lower Depth cm Coarse Fragments Degree of Weathering Consistence Dry Code Cementation /\u00a0 Compaction Fabric Code Diagnostic Horizon / Property / Material Code Coarse Fragments Type Consistence Dry Desc Cementation / Compaction Fabric Desc Diagnostic Horizon Drainage Code Consistence Moist Code Mineral Concentrations Nature Code Diagnostic Property Drainage Desc Consistence Moist Desc Mineral Concentrations Nature Desc Diagnostic Material Field Texture Code Consistence Wet - Stickiness Code Mineral Concentrations Abundance Code Qualifier Field Texture Desc Consistence Wet - Stickiness\u00a0 Desc Mineral Concentrations Abundance Desc Horizon Code Short Carbonate Estimate\u00a0 %.1 Consistence Wet - Plasticity Code Mineral Concentrations Abundance % Horizon Code Original Carbonate Reaction Code Consistence Wet - Plasticity Desc Mineral Concentrations Size Code Subordinate Horizon Code Carbonate Reaction Desc Porosity Class Code Mineral Concentrations Size Desc Lithology of R/C Secondary Carbonates Code Porosity Class Desc Mineral Concentrations Shape Code Horizon Transition Distinctness Code Secondary Carbonates Desc Porosity Class % Mineral Concentrations Shape Desc Horizon Transition Distinctness Desc Munsell_col_moist Voids Type Code Mineral Concentrations Hardness Code Horizon Transition Distinctness cm Hue_moist Voids Type Desc Mineral Concentrations Hardness Desc Horizon Transition Topography Code Value_moist Voids Size Class Roots Abundance Code Horizon Transition Topography Desc Chroma_moist Voids Size Desc Roots Abundance Desc Structure Type Code Colour Moist Desc [Munsell] Voids Size mm Roots Abundance No Structure Type Desc Munsell_col_dry Voids Abundance Class Roots Diameter Code Structure Strength Code Hue_dry Voids Abundance Desc Roots Diameter Desc Structure Strength Desc Value_dry Voids Abundance Number Roots Diameter mm Structure Size Code Chroma_dry Coatings Nature Code Biological Features Code Structure Size Desc Colour Dry Desc [Munsell] Coatings Nature Desc Biological Features Desc Coarse Fragments Abundance Code Mottling Desc Coatings Form Code Notes Supporting Files and Documents GIS_zipped: QGIS project file (Namibia_Soil_Profiles.qgz) & shape files (Namibia_Soil_Points, National boundary, Regional boundaries, Trunk Roads, Main Roads, District Roads, All settlements, Larger settlements, Rivers, Etosha National Park) Lab & Field Methods: AgricLab soil analysis methods; Soils4Africa lab & field observation guidelines; Africa Micobiome Project methods; FAO Guidelines for Soil Profile Description Images: Distribution map of soil point data; Datasets in the database Usage Notes The NSPD2025 dataset is intended for: \u00a0 \u00a0 \u00a0 \u00a0Conventional soil mapping and classification \u00a0 \u00a0 \u00a0 \u00a0Digital soil mapping (DSM) and predictive modelling \u00a0 \u00a0 \u00a0 \u00a0Land evaluation and agro-ecological zoning \u00a0 \u00a0 \u00a0 \u00a0Environmental and hydrological modelling \u00a0 \u00a0 \u00a0 \u00a0Educational and research purposes Users are requested to cite the dataset when using the data. Derived products should acknowledge the original sources. Acknowledgements The author gratefully acknowledges the Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR), formerly MAWRD/MAWF/MAWLR, for providing data and documentation from the Agro-Ecological Zoning Programme. Appreciation is extended to Ms Eva Corral-Pazos-de-Provens (Universidad de Huelva, Spain) for designing and populating a prototype relational database, and to the numerous surveyors, laboratory analysts, and GIS specialists who contributed to the original fieldwork and data compilation efforts (see Metadata). References AgriLASA. (2004). AgriLASA soil handbook. Pretoria: Agri Laboratory Association of Southern Africa. Buch, M. W. (1990). Soils, soil erosion and vegetation in the Etosha National Park / Northern Namibia - Field & laboratory results of the investigations of the year 1989. Part I \u2013 field results; Part II \u2013 laboratory results. University of Regensburg (unpublished). Coetzee, M.E. (2001). NAMSOTER \u2013 A SOTER database for Namibia. Agro-Ecological Zoning Programme, MAWRD. Windhoek. Cowan, D., Lebre, P., Amon, C., Becker, R.W., Boga, H.I., Boulang\u00e9, A., Chiyaka, T.L., Coetzee, T., De Jager, P.C., Dikinya, O., Eckardt, F., Greve, M., Harris, M.A., Hopkins, D.W., Houngnandan, H.B., Houngnandan, P., Jordaan, K., Kaimoyo, E., Kambura, A.K., Kamgan-Nkuekam, G., Makhalanyane, T.P., Maggs-K\u00f6lling, G., Marais, E., Mondlane, H., Nghalipo, E., Olivier, B.W., Ortiz, M., Pertierra, L.R., Ramond, J.-B., Seely, M., Sithole-Niang, I., Valverde, A., Varliero, G., Vikram, S., Wall D.H., & Zeze, A. (2022). Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes. Microbiome 10, 131. https://doi.org/10.1186/s40168-022-01297-w De Bl\u00e9court, M., R\u00f6der, A., Gr\u00f6ngr\u00f6ft, A., Baumann, S., Frantz, D. & Eschenbach, A. (2018) Deforestation for agricultural expansion in SW Zambia and NE Namibia and the impacts on soil fertility, soil organic carbon- and nutrient levels In: Climate change and adaptive land management in southern Africa \u2013 assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & J\u00fcrgens, N.), pp. 242-250, Biodiversity & Ecology, 6, Klaus Hess Publishers, G\u00f6ttingen & Windhoek. https://doi.org/10.7809/b-e.00330 Dijkshoorn, J.A. (2003). SOTER database for Southern Africa (SOTERSAF ver. 1.0). Technical Report. Wageningen, the Netherlands: ISRIC (International Soil Reference and Information Centre). FAO. (1990). Guidelines for soil description (3rd ed.). Soil Resources, Management and Conservation Service, Land and Water Development Division, Food and Agriculture Organization of the United Nations. FAO/ISRIC. (2003). Soil and Terrain Database for Southern Africa. Land and Water Digital Media Series # 26. FAO, Rome. Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., Elias, E., Hengl, T., Karl, J. W., Liniger, H., Matuszak, J., Neff, J. C., Ndungu, L. W., Obersteiner, M., Shepherd, K. D., Urama, K. C., Bosch, R., & Webb, N. P. (2016). The land\u2010potential knowledge system (LandPKS): mobile apps and collaboration for optimizing climate change investments. Ecosystem Health and Sustainability, 2(3).\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 https://doi.org/10.1002/ehs2.1209 ICC, MAWRD. (2000). Project to support the Agro-Ecological Zoning Programme (AEZ) in Namibia. Main report. Institut Cartogr\u00e0fic de Catalunya (ICC), & Ministry of Agriculture Water and Rural Development (MAWRD). Windhoek. IUSS (International Union of Soil Scientists) Working Group World Reference Base (WRB) (2015) World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. Update 2015. World Soil Resources Reports No 106. FAO, Rome. Kempf, J. (2000). Klimageomorphologische Studien in Zentral-Namibia: Ein Beitrag zur Morpho-, Pedo- und \u00d6kogenese. Dissertation, Univ. W\u00fcrzburg Nijbroek, R., Piikki, K., S\u00f6derstr\u00f6m, M., Kempen, B., Turner, K.G., Hengari, S., & Mutua, J. (2018). Soil organic carbon baselines for land degradation neutrality: Map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia. Sustainability, 2018(10), 1610. https://doi.org/10.3390/su10051610. Paterson, G. (2021). Guidance for the laboratory analysis - Soils4Africa Project. https://www.soils4africa-h2020.eu/serverspecific/soils4africa/images/Documents/GuidanceonLaboratoryAnalysis.pdf Petersen, A. (2008). Pedodiversity of southern African drylands. PhD dissertation. University of Hamburg, Germany. Pr\u00f6pper, M., Gr\u00f6ngr\u00f6ft, A., Finckh, M., Stirn, S., De Cauwer, V., Lages, F., Masamba, W., Murray-Hudson, M., Schmidt, L., Strohbach, B., J\u00fcrgens, N. (2015). The Future Okavango \u2013 Findings, Scenarios and Recommendations for Action. Research Project Final Synthesis Simmonds, S.E.B., Forbes Irving, T.J.M. (1995). Soils Assessment and Land Evaluation. Report prepared by Interconsult Namibia (Pty) Ltd for Africare, for Rural Water Supply Maintenance Project in Southern Kunene Region, Namibia. Strohbach, B.J., Adank, W.F., Coetzee, M.E., Jankowitz, W.J. (2019). A baseline description of the soils and vegetation of farm Klein Boesman, Khomas Region, Namibia. Namibian Journal of Environment, 3:37-55 Strohbach, B., Strydom, E., Nesongano, C., Zimmermann, I., De Cauwer, V., Coetzee, M. (2023). Final Report: Assessment of the Impact of Bush Encroachment and Bush Control on Climate Change Mitigation and Adaptation in Namibia. AHT GROUP GmbH & Perivoli Rangeland Institute, for Deutsche Gesellschaft f\u00fcr Internationale Zusammenarbeit. Windhoek, Namibia. Trippner, Christian. (1998). Semi-detailed soil survey and landscape ecological risk evaluation in the south-western and central-western parts of the Etosha National Park/N-Namibia. Part 2. Project report: DFG/GTZ Research Cooperation Project \u2018Environmental Change in the Etosha National Park/Northern Namibia\u2019 (Az. Bu 659/4-1+4-2). Van Engelen, V.W.P., & Dijkshoorn, J.A. (2013) Global and national soils and terrain databases (SOTER). Procedures manual, version 2.0. (eds.) Wageningen: ISRIC \u2013 World Soil Information. Van Engelen, V.W.P., & Wen, T.T. (eds.) (1995). Global and national soils and terrain digital databases \u2013 SOTER. Procedures manual. World Soil Resources Report 74. Rome: UNEP, ISSS, ISRIC, FAO. https://edepot.wur.nl/493802 Wild, S. (2016). Quest to map Africa\u2019s soil microbiome begins. Nature 539: 152. https://doi.org/10.1038/539152a \u00a0 Corresponding author: Marina E. Coetzee (mcoetzee@nust.na; marina.e.coetzee@gmail.com) Affiliations:\u00a0 Faculty of Engineering and the Built Environment, Namibia University of Science and Technology, Private Bag 13388, Windhoek, Namibia. Doctoral School of Environmental Sciences, Magyar Agr\u00e1r- \u00e9s \u00c9lettudom\u00e1nyi Egyetem (MATE; Hungarian University of Agriculture and Life Sciences), P\u00e1ter K\u00e1roly u. 1. H-2100 G\u00f6d\u00f6ll\u0151, Hungary. [1] Ministry's name changed over time: Ministry of Agriculture, Water and Rural Development (MAWRD; 1991-2000); Ministry of Agriculture, Water and Forestry (MAWF; 2000-2020); Ministry of Agriculture, Water and Land Reform (MAWLR; 2020-2025); Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR, since 2025).", "keywords": ["Database", "Profile Description", "Soil Horizon", "Namibia", "Soil Profile", "Laboratory Data"], "contacts": [{"organization": "Coetzee, Marina", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17618737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17618737", "name": "item", "description": "10.5281/zenodo.17618737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17618737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-11-15T00:00:00Z"}}, {"id": "10.5281/zenodo.14917034", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:09Z", "type": "Dataset", "title": "Peatland Decomposition Database (1.1.0)", "description": "1 Introduction The Peatland Decomposition Database (PDD) stores data from published litterbag experiments related to peatlands. Currently, the database focuses on northern peatlands and Sphagnum litter and peat, but it also contains data from some vascular plant litterbag experiments. Currently, the database contains entries from 34 studies, 2,160 litterbag experiments, and 7,297 individual samples with 117,841 measurements for various attributes (e.g.\u00a0relative mass remaining, N content, holocellulose content, mesh size). The aim is to provide a harmonized data source that can be useful to re-analyse existing data and to plan future litterbag experiments. The Peatland Productivity and Decomposition Parameter Database (PPDPD) (Bona et al. 2018) is similar to the Peatland Decomposition Database (PDD) in that both contain data from peatland litterbag experiments. The differences are that both databases partly contain different data, that PPDPD additionally contains information on vegetation productivity, which PDD does not, and that PDD provides more information and metadata on litterbag experiments, and also measurement errors. 2 Updates Compared to version 1.0.0, this version has a new structure for table experimental_design_format, contains additional metadata on the experimental design (these were omitted in version 1.0.0), and contains the scripts that were used to import the data into the database. 3 Methods 3.1 Data collection Data for the database was collected from published litterbag studies, by extracting published data from figures, tables, or other data sources, and by contacting the authors of the studies to obtain raw data. All data processing was done with R (R version 4.2.0 (2022-04-22)) (R Core Team 2022). Studies were identified via a Scopus search with search string (TITLE-ABS-KEY ( peat* AND ( 'litter bag' OR 'decomposition rate' OR 'decay rate' OR 'mass loss')) AND NOT ('tropic*')) (2022-12-17). These studies were further screened to exclude those which do not contain litterbag data or which recycle data from other studies that have already been considered. Additional studies with litterbag experiments in northern peatlands we were aware of, but which were not identified in the literature search were added to the list of publications. For studies not older than 10 years, authors were contacted to obtain raw data, however this was successful only in few cases. To date, the database focuses on Sphagnum litterbag experiments and not from all studies that were identified by the literature search data have been included yet in the database. Data from figures were extracted using the package \u2018metaDigitise\u2019 (1.0.1) (Pick, Nakagawa, and Noble 2018). Data from tables were extracted manually. Data from the following studies are currently included: Farrish and Grigal (1985), Bartsch and Moore (1985), Farrish and Grigal (1988), Vitt (1990), Hogg, Lieffers, and Wein (1992), Sanger, Billett, and Cresser (1994), Hiroki and Watanabe (1996), Szumigalski and Bayley (1996), Prevost, Belleau, and Plamondon (1997), Arp, Cooper, and Stednick (1999), Robbert A. Scheffer and Aerts (2000), R. A. Scheffer, Van Logtestijn, and Verhoeven (2001), Limpens and Berendse (2003), Waddington, Rochefort, and Campeau (2003), Asada, Warner, and Banner (2004), Thormann, Bayley, and Currah (2001), Trinder, Johnson, and Artz (2008), Breeuwer et al. (2008), Trinder, Johnson, and Artz (2009), Bragazza and Iacumin (2009), Hoorens, Stroetenga, and Aerts (2010), Strakov\u00e1 et al. (2010), Strakov\u00e1 et al. (2012), Orwin and Ostle (2012), Lieffers (1988), Manninen et al. (2016), Johnson and Damman (1991), Bengtsson, Rydin, and H\u00e1jek (2018a), Bengtsson, Rydin, and H\u00e1jek (2018b), Asada and Warner (2005), Bengtsson, Granath, and Rydin (2017), Bengtsson, Granath, and Rydin (2016), Hagemann and Moroni (2015), Hagemann and Moroni (2016), B. Piatkowski et al. (2021), B. T. Piatkowski et al. (2021), M\u00e4kil\u00e4 et al. (2018), Golovatskaya and Nikonova (2017), Golovatskaya and Nikonova (2017). 4 Database records The database is a \u2018MariaDB\u2019 database and the database schema was designed to store data and metadata following the Ecological Metadata Language (EML) (Jones et al. 2019). Descriptions of the tables are shown in Tab. 1. The database contains general metadata relevant for litterbag experiments (e.g., geographical, temporal, and taxonomic coverage, mesh sizes, experimental design). However, it does not contain a detailed description of sample handling, sample preprocessing methods, site descriptions, because there currently are no discipline-specific metadata and reporting standards. Table 1: Description of the individual tables in the database. Name Description attributes Defines the attributes of the database and the values in column attribute_name in table data. citations Stores bibtex entries for references and data sources. citations_to_datasets Links entries in table citations with entries in table datasets. custom_units Stores custom units. data Stores measured values for samples, for example remaining masses. datasets Lists the individual datasets. experimental_design_format Stores information on the experimental design of litterbag experiments. measurement_scales, measurement_scales_date_time, measurement_scales_interval, measurement_scales_nominal, measurement_scales_ordinal, measurement_scales_ratio Defines data value types. missing_value_codes Defines how missing values are encoded. samples Stores information on individual samples. samples_to_samples Links samples to other samples, for example litter samples collected in the field to litter samples collected during the incubation of the litterbags. units, unit_types Stores information on measurement units. 5 Attributes Table 2: Definition of attributes in the Peatland Decomposition Database and entries in the column attribute_name in table data. Name Definition Example value Unit Measurement scale Number type Minimum value Maximum value String format 4_hydroxyacetophenone_mass_absolute A numeric value representing the content of 4-hydroxyacetophenone, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxyacetophenone_mass_relative_mass A numeric value representing the content of 4-hydroxyacetophenone, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA 4_hydroxybenzaldehyde_mass_absolute A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxybenzaldehyde_mass_relative_mass A numeric value representing the content of 4-hydroxybenzaldehyde, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA 4_hydroxybenzoic_acid_mass_absolute A numeric value representing the content of 4-hydroxybenzoic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA 4_hydroxybenzoic_acid_mass_relative_mass A numeric value representing the content of 4-hydroxybenzoic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA abbreviation In table custom_units: A string representing an abbreviation for the custom unit. gC NA nominal NA NA NA NA acetone_extractives_mass_absolute A numeric value representing the content of acetone extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA acetone_extractives_mass_relative_mass A numeric value representing the content of acetone extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA acetosyringone_mass_absolute A numeric value representing the content of acetosyringone, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA acetosyringone_mass_relative_mass A numeric value representing the content of acetosyringone, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA acetovanillone_mass_absolute A numeric value representing the content of acetovanillone, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA acetovanillone_mass_relative_mass A numeric value representing the content of acetovanillone, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA arabinose_mass_absolute A numeric value representing the content of arabinose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA arabinose_mass_relative_mass A numeric value representing the content of arabinose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA ash_mass_absolute A numeric value representing the content of ash (after burning at 550\u00b0C). 4 g ratio real 0 Inf NA ash_mass_relative_mass A numeric value representing the content of ash (after burning at 550\u00b0C). 0.05 g/g ratio real 0 Inf NA attribute_definition A free text field with a textual description of the meaning of attributes in the dpeatdecomposition database. NA NA nominal NA NA NA NA attribute_name A string describing the names of the attributes in all tables of the dpeatdecomposition database. attribute_name NA nominal NA NA NA NA bibtex A string representing the bibtex code used for a literature reference throughout the dpeatdecomposition database. Galka.2021 NA nominal NA NA NA NA bounds_maximum A numeric value representing the minimum possible value for a numeric attribute. 0 NA interval real Inf Inf NA bounds_minimum A numeric value representing the maximum possible value for a numeric attribute. INF NA interval real Inf Inf NA bulk_density A numeric value representing the bulk density of the sample [g cm-3]. 0,2 g/cm^3 ratio real 0 Inf NA C_absolute The absolute mass of C in the sample. 1 g ratio real 0 Inf NA C_relative_mass The absolute mass of C in the sample. 1 g/g ratio real 0 Inf NA C_to_N A numeric value representing the C to N ratio of the sample. 35 g/g ratio real 0 Inf NA C_to_P A numeric value representing the C to P ratio of the sample. 35 g/g ratio real 0 Inf NA Ca_absolute The absolute mass of Ca in the sample. 1 g ratio real 0 Inf NA Ca_relative_mass The absolute mass of Ca in the sample. 1 g/g ratio real 0 Inf NA cation_exchange_capacity_absolute A numeric value representing the cation exchange capacity. 10 mol ratio real 0 Inf NA cation_exchange_capacity_relative_mass A numeric value representing the cation exchange capacity relative to sample mass. 200 mol/g ratio real 0 Inf NA cellulose_mass_absolute A numeric value representing the content of cellulose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA cellulose_mass_relative_mass A numeric value representing the content of cellulose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA comments_measurement A string representing comments on a measurement. NA NA nominal NA NA NA NA comments_samples A free text field where you can enter all information related to the sample that is not covered by the remaining fields. For example you could provide information on potential contamination sources, issues with specific parameters, additional information to the sampling site, e.g.\u00a0present vegetation, past vegetation, specific conditions during sampling, \u2026 . \u2026 NA nominal NA NA NA NA description A free text field. In table \u201ccustom_units\u201d: A description of a custom unit. NA NA nominal NA NA NA NA dichloromethane_extractives_mass_absolute A numeric value representing the content of dichlromethane extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA dichloromethane_extractives_mass_relative_mass A numeric value representing the content of dichlromethane extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA dimension A string representing the dimension of the unit. L NA nominal NA NA NA NA error A numeric value representing the error of the measured value. The unit of the error is defined by the corresponding attribute_name. 1.2 NA ratio real 0 Inf NA error_type A character representing the type of the error of a measured value (e.g., sd, 95% interval, etc.). sd NA nominal NA NA NA NA ethanol_extractives_mass_absolute A numeric value representing the content of ethanol extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA ethanol_extractives_mass_relative_mass A numeric value representing the content of ethanol extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA experimental_design A character of the format \u2018x_y_z_\u2026\u2019, where x, y, z, \u2026, are integers differentiating hierarchical groups of an experimental design. These groups are explained in table experimental_design_format \u2026 NA nominal NA NA NA NA experimental_design_description A string describing the variables in the csv file identified by column file in table experimental_design_format for each dataset. \u2026 NA nominal NA NA NA NA explanation In table missing_value_codes: A string explaining what the corresponding missing value code means. \u2026 NA nominal NA NA NA NA Fe_absolute The absolute mass of Fe in the sample. 1 g ratio real 0 Inf NA Fe_relative_mass The absolute mass of Fe in the sample. 1 g/g ratio real 0 Inf NA ferulic_acid_mass_absolute A numeric value representing the content of ferulic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA ferulic_acid_mass_relative_mass A numeric value representing the content of ferulic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA file A string representing a path to a file. For table experimental_design_format: Path to a csv file providing details on the experimental design and manipulations. NA NA nominal NA NA NA NA format_string A string defining the format of a nominal variable. YYYY-MM-DD NA nominal NA NA NA NA galactose_mass_absolute A numeric value representing the content of galactose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA galactose_mass_relative_mass A numeric value representing the content of galactose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA galacturonic_acid_mass_absolute A numeric value representing the content of galacturonic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA galacturonic_acid_mass_relative_mass A numeric value representing the content of galacturonic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA glucose_mass_absolute A numeric value representing the content of glucose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA glucose_mass_relative_mass A numeric value representing the content of glucose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA glucuronic_acid_mass_absolute A numeric value representing the content of glucuronic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA glucuronic_acid_mass_relative_mass A numeric value representing the content of glucuronic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA ground_slope The slope of the sample (land surface) as fraction of the vertical distance covered and the horizontal distance. 0.2 cm/cm ratio real 0 Inf NA holocellulose_mass_absolute A numeric value representing the absolute holocellulose mass in the sample. 0.45 g ratio real 0 Inf NA holocellulose_mass_relative_mass A numeric value representing the holocellulose content of the sample [g/g]. 0.45 g/g ratio real 0 1 NA id_citation An integer value representing an id for each entry in the table \u201ccitations\u201c in the dpeatdecomposition database. 1 NA interval natural 1 Inf NA id_dataset A numeric id for the dataset (starting with 1 and increasing by 1; for one data contribution, this should be 1 for all samples and the appropriate id is assigned when the data are merged into the database). 1 NA interval natural 1 Inf NA id_measurement A numeric id for measurements (starting with 1 and increasing by 1). This means that each measurement gets its own rows and measurements for different attributes are considered independent, i.e.\u00a0multiple measurement ids for the same sample just count replicate measurements for any attribute. For attributes with less measurements than for a different attribute, just fill measurements starting from smaller id_measurement and leave the cells in the remaining rows empty. 1 NA interval natural 1 Inf NA id_measurement_denominator An integer value representing the identifier for the measurement which is used as denominator in computing a relative quantity (e.g.\u00a0the absolute mass of the initial sample when computing the mass fraction relative to the initial sample). 1 NA interval natural 1 Inf NA id_measurement_numerator An integer value representing the identifier for the measurement which is used as numerator in computing a relative quantity (e.g.\u00a0the absolute mass of the sample when computing the mass fraction relative to the initial sample). 1 NA interval natural 1 Inf NA id_measurement_scale An integer value representing an id for each entry in the table \u201cmeasurement_scales\u201c in the dpeatdecomposition database. 1 NA interval natural 1 Inf NA id_missing_value_code An integer value representing an id for each entry in the table \u201cmissing_value_codes\u201c in the dpeatdecomposition database. 1 NA interval natural 1 Inf NA id_sample A numeric id for the sample (starting with 1 and increasing by 1). 1 NA interval natural 1 Inf NA id_sample_child An integer representing an identifier for the child (resulting) sample of the transition (some change to a sample). 1 NA interval natural 1 Inf NA id_sample_incubation_start An integer representing an identifier for the sample which is the sample at the start of the incubation (incubation_duration == 0). 1 NA interval natural 1 Inf NA id_sample_origin An integer representing an identifier for the sample which is the original sample in a line of transitions of a sample (modifications of a sample). 1 NA interval natural 1 Inf NA id_sample_parent An integer representing an identifier for the parent (initial) sample of the transition (some change to a sample). 1 NA interval natural 1 Inf NA id_unit An integer value representing an id for each entry in the table \u201cunits\u201c in the dpeatdecomposition database. 1 NA interval natural 1 Inf NA incubation_duration A numeric value representing the number of days over which a sample was incubated. 45 d ratio real 0 Inf NA incubation_environment A character defining the environment in which a litterbag sample was incubated (e.g.\u00a0\u2018peat\u2019, \u2018container\u2019, \u2026). peat NA nominal NA NA NA NA is_incubated A logical value indicating whether a sample was collected during the decomposition incubation of a litterbag experiment or not. TRUE NA nominal NA NA NA NA K_absolute The absolute mass of K in the sample. 1 g ratio real 0 Inf NA K_relative_mass The absolute mass of K in the sample. 1 g/g ratio real 0 Inf NA Klason_lignin_mass_absolute A numeric value representing the absolute Klason lignin mass in the sample. 0.26 g ratio real 0 Inf NA Klason_lignin_mass_relative_mass A numeric value representing the Klason lignin content of the sample [g/g]. 0.26 g/g ratio real 0 1 NA mannose_mass_absolute A numeric value representing the content of mannose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA mannose_mass_relative_mass A numeric value representing the content of mannose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA mass_absolute The mass of the sample. 1200 mg ratio real 0 Inf NA mass_relative_mass The mass of the sample divided by the mass of a sample (e.g.\u00a0the sample before decomposition). 0.87 g/g ratio real 0 Inf NA measurement_scale A string representing the measurement scale for a value. nominal NA nominal NA NA NA NA mesh_size_absolute The width of the mesh the litterbags are made of. 0.2 um ratio real 0 Inf NA Mg_absolute The absolute mass of Mg in the sample. 1 g ratio real 0 Inf NA Mg_relative_mass The absolute mass of Mg in the sample. 1 g/g ratio real 0 Inf NA Mn_absolute The absolute mass of Mn in the sample. 1 g ratio real 0 Inf NA Mn_relative_mass The absolute mass of Mn in the sample. 1 g/g ratio real 0 Inf NA multiplier_to_si A numeric value representing the value with which a given value with a certain measurement unit has to be multiplied in order to convert it to a related SI unit. 100 dimensionless interval real Inf Inf NA N_absolute The absolute mass of nitrogen in the sample. 1.2 mg ratio real 0 Inf NA N_relative_mass The mass of the nitrogen in the sample divided by the mass of a sample (e.g.\u00a0the sample before decomposition). 0.013 g/g ratio real 0 Inf NA number_type A string representing the number type of a numeric variable. NA NA nominal NA NA NA NA P_absolute The absolute mass of P in the sample. 1 g ratio real 0 Inf NA p_coumaric_acid_mass_absolute A numeric value representing the content of p-coumaric acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA p_coumaric_acid_mass_relative_mass A numeric value representing the content of p-coumaric acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA P_relative_mass The absolute mass of P in the sample. 1 g/g ratio real 0 Inf NA parent_si A string representing the SI unit from which a certain derived unit is derived. m NA nominal NA NA NA NA pH A numeric value representing the pH value of the sample. 5,4 dimensionless interval real Inf Inf NA phenolics_PHBA_equivalents_mass_absolute A numeric value representing the mass content of phenolics (p-hydroxy benzoic acid equivalent). 10 g ratio real 0 Inf NA phenolics_PHBA_equivalents_mass_relative_mass A numeric value representing the mass content of phenolics (p-hydroxy benzoic acid equivalent). 0.04 g/g ratio real 0 1 NA phenolics_tannic_acid_equivalents_mass_absolute A numeric value representing the mass content of phenolics (tannic acid equivalent). 10 g ratio real 0 Inf NA phenolics_tannic_acid_equivalents_mass_relative_mass A numeric value representing the mass content of phenolics (tannic acid equivalent). 0.04 g/g ratio real 0 1 NA power An integer value. The power to which the dimension is raised. 2 dimensionless interval integer Inf Inf NA rhamnose_mass_absolute A numeric value representing the content of rhamnose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA rhamnose_mass_relative_mass A numeric value representing the content of rhamnose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA root_diameter_absolute The diameter of roots in the sample. 2 mm ratio real 0 Inf NA S_absolute The absolute mass of S in the sample. 1 g ratio real 0 Inf NA S_relative_mass The absolute mass of S in the sample. 1 g/g ratio real 0 Inf NA sample_depth_lower A numeric value representing the depth of the lower boundary of a sample relative to the land surface (e.g.\u00a0peat surface) [cm]. 15 cm interval real Inf Inf NA sample_depth_upper A numeric value representing the depth of the upper boundary of a sample relative to the land surface (e.g.\u00a0peat surface) [cm]. 12 cm interval real Inf Inf NA sample_label A string representing a label for each sample. S1 NA nominal NA NA NA NA sample_microhabitat A string describing the microhabitat where the sample was collected. For peat, this should be one of \u2018hummock\u2019, \u2018hollow\u2019, \u2018lawn\u2019, \u2018pond\u2019. In other cases, a custom value can be used. hummock NA nominal NA NA NA NA sample_size An integer representing the number of individual measurements which were used to compute the value in column value. 1 NA interval natural 1 Inf NA sample_treatment A string with an description of an experimental tratment if this was applied. By default, this should be \u2018control\u2019, indicating that there was no manipulation. If there was any experimental manipulation, this can be abbreviated by a label (e.g.\u00a0by a treatment level) that is defined in the textual description of the project (in the file \u2018description.docx\u2019). control NA nominal NA NA NA NA sample_type A string describing the type of the sample. Must be one of \u2018peat\u2019, \u2018dom\u2019, \u2018vegetation\u2019, \u2018litter\u2019. peat NA nominal NA NA NA NA sample_type2 A string describing the type of the sample. Here you can provide individual (own) categories which may provide more details than the column sample_type. shoots NA nominal NA NA NA NA sample_wet_mass_absolute A numeric value representing the mass of the wet sample [g]. 5.6 g ratio real 0 Inf NA sampling_altitude A numeric value representing the altitude of the exact sampling position [m above sea level]. 543 m ratio real Inf Inf NA sampling_day An integer representing the day in which a sample was collected. 1 NA interval natural 1 31 NA sampling_latitude A numeric value representing the latitude coordinates of the exact sampling position (in the EPSG:3857 projection coordinate system \u2014 this is the system used by Google and is based on the WGS 84 reference system) [\u00b0N]. 40447 NA interval real -180 180 NA sampling_longitude A numeric value representing the longitude coordinates of the exact sampling position (in the EPSG:3857 projection coordinate system \u2014 this is the system used by Google and is based on the WGS 84 reference system) [\u00b0W]. 79983 NA interval real -180 180 NA sampling_month An integer representing the month in which a sample was collected. 1 NA interval natural 1 12 NA sampling_year An integer representing the year in which a sample was collected. 1 NA interval natural 1 Inf NA site_name A character representing the name of the site where the sample was collected. Mer Bleue NA nominal NA NA NA NA soluble_Klason_lignin_mass_absolute A numeric value representing the mass content of soluble Klason lignin (following Ehrman 1996). 10 g ratio real 0 Inf NA soluble_Klason_lignin_mass_relative_mass A numeric value representing the mass content of soluble Klason lignin (following Ehrman 1996). 0.04 g/g ratio real 0 1 NA soluble_lignin_mass_absolute A numeric value representing the content of soluble lignin, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA soluble_lignin_mass_relative_mass A numeric value representing the content of soluble lignin, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA sphagnan_mass_absolute A numeric value representing the mass content of sphagnan (Ballance et al., 2007). 10 g ratio real 0 Inf NA sphagnan_mass_relative_mass A numeric value representing the mass content of sphagnan (Ballance et al., 2007). 0.04 g/g ratio real 0 1 NA standard_unit A logical value indicating if the unit is a standard unit of the Ecological Metadata Language or not. TRUE NA nominal NA NA NA NA syringe_aldehyde_mass_absolute A numeric value representing the content of syringe aldehyde, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA syringe_aldehyde_mass_relative_mass A numeric value representing the content of syringe aldehyde, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA syringic_acid_mass_absolute A numeric value representing the content of syringic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA syringic_acid_mass_relative_mass A numeric value representing the content of syringic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA taxon_organ A string describing the organ of a taxon the sample represents (if the sample represents a taxon). For example, if the sample is Carex lasiocarpa, this could be \u2018shoot\u2019, or \u2018root\u2019, or \u2018leaves\u2019. root NA nominal NA NA NA NA taxon_rank_name A string describing the taxon rank the value in column taxon_rank_value represents (if the sample can be assigned to a specific taxon). For exampe, if the value in column taxon_rank_value is a species name, then you should enter \u2018species\u2019 here, or if the value in column taxon_rank_value is a genus name, then you should enter \u2018genus\u2019 here. species NA nominal NA NA NA NA taxon_rank_value A string describing the taxon rank value of the sample (if the sample can be assigned to a taxon). For example, if the sample is a distinct species, enter the scientific species name here, or if the sample can be assigned to a genus, enter the scientific genus name here. Sphagnum magellanicum NA nominal NA NA NA NA temperature A numeric value representing the temperature of the sample [K]. 293.4 K ratio real 0 Inf NA text_domain_definition A string representing the text domain for a string. NA NA nominal NA NA NA NA transition_description A string representing a description of what happened to a parent sample during its transition to the child sample. transplantation NA nominal NA NA NA NA udunits_unit A string representing a measurement unit in the udunits format. m NA nominal NA NA NA NA unit_type A string representing the type of a unit. length NA nominal NA NA NA NA value A numeric value representing the measured value. The unit of the value is defined by the corresponding attribute_name. 1.2 NA ratio real 0 Inf NA value_type A character representing the type of the measured value. One of \u2018point\u2019 (for a single measurement without uncertainty), or \u2018mean\u2019 (average of multiple measurements). point NA nominal NA NA NA NA vanillic_acid_mass_absolute A numeric value representing the content of vanillic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA vanillic_acid_mass_relative_mass A numeric value representing the content of vanillic acid, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA vanillin_mass_absolute A numeric value representing the content of vanillin, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA vanillin_mass_relative_mass A numeric value representing the content of vanillin, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA volume A numeric value representing the volume of the sample [cm3]. 20 cm^3 ratio real 0 Inf NA water_extractives_mass_absolute A numeric value representing the content of water extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA water_extractives_mass_relative_mass A numeric value representing the content of water extractives, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA water_mass_absolute A numeric value representing the water mass content of the sample as mass of water divided by the mass of the wet sample [g] 5.6 g ratio real 0 Inf NA water_mass_relative_mass A numeric value representing the water mass content of the sample as mass of water divided by the mass of the wet sample [g/g] 2.4 g/g ratio real 0 1 NA water_mass_relative_volume A numeric value representing the water mass content of the sample as mass of water divided by the volume of the wet sample [g cm-3]. 0.6 g/cm^3 ratio real 0 1 NA water_table_depth A numeric value representing the depth to the water table level relative to the position of the sample. 23.4 cm ratio real -Inf Inf NA xylose_mass_absolute A numeric value representing the content of xylose, as described in Strakov\u00e1 et al. (2010). 0.26 g ratio real 0 Inf NA xylose_mass_relative_mass A numeric value representing the content of xylose, as described in Strakov\u00e1 et al. (2010). 0.26 g/g ratio real 0 1 NA 6 Usage notes 6.1 Download The Peatland Decomposition Database can be downloaded from https://doi.org/10.5281/zenodo.11276065. There you can also download a folder \u201cderived_data\u201d that contains csv files with the experimental design for each study (see attribute file in Tab. 2), and a folder \u201cscripts\u201d with the R Markdown scripts used to import the data into the database. 6.2 Set up The downloaded database needs to be imported in a running MariaDB instance. In a linux terminal, the downloaded sql file can be imported like so: mysql -u -p dpeatdecomposition < dpeatdecomposition-backup-2025-02-24.sql Here, is the database user name. 6.3 R interface The R package \u2018dpeatdecomposition\u2019 (Teickner and Knorr 2024) provides an R interface to the database, based on the packages \u2018RMariaDB\u2019 (M\u00fcller et al. 2021), and \u2018dm\u2019 (Schieferdecker, M\u00fcller, and Bergant 2022). 7 Citation If you use data from the Peat Decomposition Database, cite the database and each of the original data sources you use. Bibliographic information on each data source are stored in table citations and linked to datasets via table citations_to_datasets. The database can be cited as: Teickner, Henning and Klaus-Holger Knorr. 2024. \u201cThe Peatland Decomposition Database.\u201d Zenodo. https://doi.org/10.5281/zenodo.11276065. Bibtex entries for each dataset can also be obtained using the \u2018dpeatdecomposition\u2019 package: # connect to database con <- RMariaDB::dbConnect( drv = RMariaDB::MariaDB(), dbname = 'dpeatdecomposition', default.file = '~/my.cnf' ) # get database as dm object dm_dpeatdecomposition <- dpeatdecomposition::dp_get_dm(con, learn_keys = TRUE) # extract bibtex entries dm_dpeatdecomposition |> dm::dm_zoom_to(datasets) |> dm::left_join(citations_to_datasets, by = 'id_dataset') |> dm::left_join(citations, by = 'id_citation') |> dm::pull_tbl() |> as.data.frame() # disconnect RMariaDB::dbDisconnect(con) A full list of references for the individual datasets is provided in Tab. 3. Table 3: Sources for each dataset in the Peatland Decomposition Database. id_dataset Source 1 Farrish and Grigal (1985) 2 Bartsch and Moore (1985) 3 Farrish and Grigal (1988) 4 Vitt (1990) 5 Hogg, Lieffers, and Wein (1992) 6 Sanger, Billett, and Cresser (1994) 7 Hiroki and Watanabe (1996) 8 Szumigalski and Bayley (1996) 9 Prevost, Belleau, and Plamondon (1997) 10 Arp, Cooper, and Stednick (1999) 11 Robbert A. Scheffer and Aerts (2000) 12 R. A. Scheffer, Van Logtestijn, and Verhoeven (2001) 13 Limpens and Berendse (2003) 14 Waddington, Rochefort, and Campeau (2003) 15 Asada, Warner, and Banner (2004) 16 Thormann, Bayley, and Currah (2001) 17 Trinder, Johnson, and Artz (2008) 18 Breeuwer et al. (2008) 19 Trinder, Johnson, and Artz (2009) 20 Bragazza and Iacumin (2009) 21 Hoorens, Stroetenga, and Aerts (2010) 22 Strakov\u00e1 et al. (2010) 22 Strakov\u00e1 et al. (2012) 23 Orwin and Ostle (2012) 24 Lieffers (1988) 25 Manninen et al. (2016) 26 Johnson and Damman (1991) 27 Bengtsson, Rydin, and H\u00e1jek (2018a) 27 Bengtsson, Rydin, and H\u00e1jek (2018b) 28 Asada and Warner (2005) 29 Bengtsson, Granath, and Rydin (2017) 29 Bengtsson, Granath, and Rydin (2016) 30 Hagemann and Moroni (2015) 30 Hagemann and Moroni (2016) 31 B. Piatkowski et al. (2021) 31 B. T. Piatkowski et al. (2021) 32 M\u00e4kil\u00e4 et al. (2018) 33 Golovatskaya and Nikonova (2017) 34 Golovatskaya and Nikonova (2017) 8 Acknowledgements Development of this database was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/23-1 to Klaus-Holger Knorr and grant no. PE 1632/18-1 to Edzer Pebesma. References Arp, Christopher D., David J. Cooper, and John D. Stednick. 1999. \u201cThe Effects of Acid Rock Drainage on Carex Aquatilis Leaf Litter Decomposition in Rocky Mountain Fens.\u201d Wetlands 19 (3): 665\u201374. https://doi.org/10.1007/BF03161703. Asada, Taro, and Barry G. Warner. 2005. \u201cSurface Peat Mass and Carbon Balance in a Hypermaritime Peatland.\u201d Soil Science Society of America Journal 69 (2): 549\u201362. https://doi.org/10.2136/sssaj2005.0549. Asada, Taro, Barry G Warner, and Allen Banner. 2004. \u201cSphagnum Invasion After Clear-Cutting and Excavator Mounding in a Hypermaritime Forest of British Columbia.\u201d Canadian Journal of Forest Research 34 (8): 1730\u201346. https://doi.org/10.1139/x04-042. Bartsch, I., and T. R. Moore. 1985. \u201cA Preliminary Investigation of Primary Production and Decomposition in Four Peatlands Near Schefferville, Qu\u00e9bec.\u201d Canadian Journal of Botany 63 (7): 1241\u201348. https://doi.org/10.1139/b85-171. Bengtsson, Fia, Gustaf Granath, and H\u00e5kan Rydin. 2016. \u201cPhotosynthesis, Growth, and Decay Traits in Sphagnum \u2013 a Multispecies Comparison.\u201d Ecology and Evolution 6 (10): 3325\u201341. https://doi.org/10.1002/ece3.2119. \u2014\u2014\u2014. 2017. \u201cData from: Photosynthesis, Growth, and Decay Traits in Sphagnum \u2013 a Multispecies Comparison.\u201d Dryad. https://doi.org/10.5061/DRYAD.62054. Bengtsson, Fia, H\u00e5kan Rydin, and Tom\u00e1\u0161 H\u00e1jek. 2018a. \u201cData from: Biochemical Determinants of Litter Quality in 15 Species of Sphagnum.\u201d Dryad. https://doi.org/10.5061/DRYAD.4F8D2. \u2014\u2014\u2014. 2018b. \u201cBiochemical Determinants of Litter Quality in 15 Species of Sphagnum.\u201d Plant and Soil 425 (1-2): 161\u201376. https://doi.org/10.1007/s11104-018-3579-8. Bona, Kelly Ann, Arlene Hilger, Magdalena Burgess, Nicole Wozney, and Cindy Shaw. 2018. \u201cA Peatland Productivity and Decomposition Parameter Database.\u201d Ecology 99 (10): 2406\u20136. https://doi.org/10.1002/ecy.2462. Bragazza, Luca, and Paola Iacumin. 2009. \u201cSeasonal Variation in Carbon Isotopic Composition of Bog Plant Litter During 3 Years of Field Decomposition.\u201d Biology and Fertility of Soils 46 (1): 73\u201377. https://doi.org/10.1007/s00374-009-0406-7. Breeuwer, Angela, Monique Heijmans, Bjorn J. M. Robroek, Juul Limpens, and Frank Berendse. 2008. \u201cThe Effect of Increased Temperature and Nitrogen Deposition on Decomposition in Bogs.\u201d Oikos 117 (8): 1258\u201368. https://doi.org/10.1111/j.0030-1299.2008.16518.x. Farrish, K. W., and D. F. 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J. 1988. \u201cSphagnum and Cellulose Decomosition in Drained and Natural Areas of an Alberta Peatland.\u201d Canadian Journal of Soil Science 68 (4): 755\u201361. https://doi.org/10.4141/cjss88-073. Limpens, Juul, and Frank Berendse. 2003. \u201cHow Litter Quality Affects Mass Loss and N Loss from Decomposing Sphagnum.\u201d Oikos 103 (3): 537\u201347. https://doi.org/10.1034/j.1600-0706.2003.12707.x. M\u00e4kil\u00e4, M., H. S\u00e4\u00e4vuori, A. Grundstr\u00f6m, and T. Suomi. 2018. \u201cSphagnum Decay Patterns and Bog Microtopography in South-Eastern Finland.\u201d Mires and Peat, no. 21 (July): 1\u201312. https://doi.org/10.19189/MaP.2017.OMB.283. Manninen, S., S. Kivim\u00e4ki, I. D. Leith, S. R. Leeson, and L. J. Sheppard. 2016. \u201cNitrogen Deposition Does Not Enhance Sphagnum Decomposition.\u201d Science of The Total Environment 571 (November): 314\u201322. https://doi.org/10.1016/j.scitotenv.2016.07.152. M\u00fcller, Kirill, Jeroen Ooms, David James, Saikat DebRoy, Hadley Wickham, and Jeffrey Horner. 2021. \u201cRMariaDB: Database Interface and \u2019MariaDB\u2019 Driver.\u201d Orwin, Kate H., and Nicholas J. Ostle. 2012. \u201cMoss Species Effects on Peatland Carbon Cycling After Fire: Moss Species Effects on C Cycling After Fire.\u201d Functional Ecology 26 (4): 829\u201336. https://doi.org/10.1111/j.1365-2435.2012.01991.x. Piatkowski, Bryan T., Joseph B. Yavitt, Merritt R. Turetsky, and A. Jonathan Shaw. 2021. \u201cNatural Selection on a Carbon Cycling Trait Drives Ecosystem Engineering by Sphagnum (Peat Moss).\u201d Proceedings of the Royal Society B: Biological Sciences 288 (1957): 20210609. https://doi.org/10.1098/rspb.2021.0609. Piatkowski, Bryan, Joseph B. Yavitt, Merritt Turetsky, and A. Jonathan Shaw. 2021. \u201cOnline Data for 'Natural Selection on a Carbon Cycling Trait Drives Ecosystem Engineering by Sphagnum (Peat Moss).',\u201d August. https://doi.org/10.6084/m9.figshare.14109725.v2. Pick, Joel L., Shinichi Nakagawa, and Daniel W. A. Noble. 2018. \u201cReproducible, Flexible and High-Throughput Data Extraction from Primary Literature: The metaDigitise R Package.\u201d https://doi.org/10.1101/247775. Prevost, Marcel, Pierre Belleau, and Andr\u00e9 P. Plamondon. 1997. \u201cSubstrate Conditions in a Treed Peatland: Responses to Drainage.\u201d \u00c9coscience 4 (4): 543\u201354. https://doi.org/10.1080/11956860.1997.11682434. R Core Team. 2022. R: A Language and Environment for Statistical Computing. Manual. Vienna, Austria: R Foundation for Statistical Computing. Sanger, L. J., M. F. Billett, and M. S. Cresser. 1994. \u201cThe Effects of Acidity on Carbon Fluxes from Ombrotrophic Peat.\u201d Chemistry and Ecology 8 (4): 249\u201364. https://doi.org/10.1080/02757549408038552. Scheffer, R. A., R. S. P Van Logtestijn, and J. T. A. Verhoeven. 2001. \u201cDecomposition of Carex and Sphagnum Litter in Two Mesotrophic Fens Differing in Dominant Plant Species.\u201d Oikos 92 (1): 44\u201354. https://doi.org/10.1034/j.1600-0706.2001.920106.x. Scheffer, Robbert A., and Rien Aerts. 2000. \u201cRoot Decomposition and Soil Nutrient and Carbon Cycling in Two Temperate Fen Ecosystems.\u201d Oikos 91 (3): 541\u201349. https://doi.org/10.1034/j.1600-0706.2000.910316.x. Schieferdecker, Tobias, Kirill M\u00fcller, and Darko Bergant. 2022. \u201cdm: Relational Data Models.\u201d Strakov\u00e1, Petra, Jani Anttila, Peter Spetz, Veikko Kitunen, Tarja Tapanila, and Raija Laiho. 2010. \u201cLitter Quality and Its Response to Water Level Drawdown in Boreal Peatlands at Plant Species and Community Level.\u201d Plant and Soil 335 (1-2): 501\u201320. https://doi.org/10.1007/s11104-010-0447-6. Strakov\u00e1, Petra, Timo Penttil\u00e4, Jukka Laine, and Raija Laiho. 2012. \u201cDisentangling Direct and Indirect Effects of Water Table Drawdown on Above- and Belowground Plant Litter Decomposition: Consequences for Accumulation of Organic Matter in Boreal Peatlands.\u201d Global Change Biology 18 (1): 322\u201335. https://doi.org/10.1111/j.1365-2486.2011.02503.x. Szumigalski, Anthony R., and Suzanne E. Bayley. 1996. \u201cDecomposition Along a Bog to Rich Fen Gradient in Central Alberta, Canada.\u201d Canadian Journal of Botany 74 (4): 573\u201381. https://doi.org/10.1139/b96-073. Teickner, Henning, and Klaus-Holger Knorr. 2024. \u201cdpeatdecomposition: R Interface to the Peatland Decomposition Database.\u201d Thormann, Markus N, Suzanne E Bayley, and Randolph S Currah. 2001. \u201cComparison of Decomposition of Belowground and Aboveground Plant Litters in Peatlands of Boreal Alberta, Canada.\u201d Canadian Journal of Botany 79 (1): 9\u201322. https://doi.org/10.1139/b00-138. Trinder, Clare J., David Johnson, and Rebekka R. E. Artz. 2008. \u201cInteractions Among Fungal Community Structure, Litter Decomposition and Depth of Water Table in a Cutover Peatland.\u201d FEMS Microbiology Ecology 64 (3): 433\u201348. https://doi.org/10.1111/j.1574-6941.2008.00487.x. \u2014\u2014\u2014. 2009. \u201cLitter Type, but Not Plant Cover, Regulates Initial Litter Decomposition and Fungal Community Structure in a Recolonising Cutover Peatland.\u201d Soil Biology and Biochemistry 41 (3): 651\u201355. https://doi.org/10.1016/j.soilbio.2008.12.006. Vitt, Dale H. 1990. \u201cGrowth and Production Dynamics of Boreal Mosses over Climatic, Chemical and Topographic Gradients.\u201d Botanical Journal of the Linnean Society 104 (1-3): 35\u201359. https://doi.org/10.1111/j.1095-8339.1990.tb02210.x. Waddington, J. M., L. Rochefort, and S. Campeau. 2003. \u201cSphagnum Production and Decomposition in a Restored Cutover Peatland.\u201d Wetlands Ecology and Management 11 (1): 85\u201395. https://doi.org/10.1023/A:1022009621693.", "keywords": ["Databases", "Carex", "Sphagnum", "decomposition", "litterbag", "northern peatland", "peatland"], "contacts": [{"organization": "Teickner, Henning, Knorr, Klaus-Holger,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.14917034"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.14917034", "name": "item", "description": "10.5281/zenodo.14917034", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.14917034"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-02-24T00:00:00Z"}}, {"id": "10.5281/zenodo.17092587", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:29Z", "type": "Dataset", "title": "Peatland Mid-Infrared Database (1.0.0)", "description": "README 2025-09-10 Introduction The peatland mid-infrared database (pmird) stores data from peat, vegetation, litter, and dissolved organic matter samples, in particular mid-infrared spectra and other variables, from previously published and unpublished data sources. The majority of samples in the database are peat samples from northern bogs. Currently, the database contains entries from 26 studies, 11216 samples, and 3877 mid infrared spectra. The aim is to provide a harmonized data source that can be useful to re-analyse existing data, analyze peat chemistry, develop and test spectral prediction models, and provide data on various peat properties. Usage notes Download and Setup The peatland mid-infrared database can be downloaded from https://doi.org/10.5281/zenodo.17092587. The publication contains the following files and folders: pmird-backup-2025-09-10.sql: A mysqldump backup of the pmird database. pmird_prepared_data: A folder that contains: Folders like c00001-2020-08-17-Hodgkins with the raw spectra for samples from each dataset in the pmird database (see below for how to import the spectra). Files like pmird_prepare_data_c00001-2020-08-17-Hodgkins.Rmd that contain the R code used to process and import the data from each dataset into the database. Corresponding html files contain the compiled scripts. pmird_prepare_data.Rmd: An Rmarkdown script that was used to run the scripts that created the database (the top level script). mysql_scripts: A folder that contains: pmird_mysql_initialization.sql: MariaDB script to initialize the database. 001-db-initialize.Rmd: Rmarkdown script that executes pmird_mysql_initialization.sql and populated dataset-independent tables. add-citations.Rmd: Rmarkdown script that adds information on references to the database. add-licenses.Rmd: Rmarkdown script that adds information on licenses to the database. add-mir-metadata-quality.Rmd Rmarkdown script that adds information on the quality of the infrared spectra to the database. Dockerfile: A Dockerfile that defines the computing environment used to create the database. renv.lock A renv.lock file that lists the R packages used to create the database. The database can be set up as follows: The downloaded database needs to be imported in a running MariaDB instance. In a linux terminal, the downloaded sql file can be imported like so: mysql -u -p pmird < pmird-backup-2025-09-10.sql Here, is the database user name. The database itself does not contain the infrared spectra. These data are in folder pmird_prepared_data which needs to be stored at any place in the file system. R interface The R package \u2018pmird\u2019 (Teickner 2025) provides an R interface to the database, based on the packages \u2018RMariaDB\u2019 (M\u00fcller et al. 2021) and \u2018dm\u2019 (Schieferdecker, M\u00fcller, and Bergant 2022). This interface can also be used to import the mid-infrared spectra that belong to extracted data records (please see the documentation of the \u2018pmird\u2019 R package for details, https://henningte.github.io/pmird/). Citation If you use data from the Peat Decomposition Database, cite the database and each of the original data sources you use. Bibliographic information on each data source are stored in table datasets (column reference_publication). The database can be cited as: Teickner, H., Agethen, S., Berger, S., Boelsen, R. I., Borken, W., Bragazza, L., Broder, T., De La Cruz, F. B., Diaconu, A.-C., Dise, N. B., Drollinger, S., Estop-Aragon\u00e9s, C., Ga\u0142ka, M., Mart\u00ed, M., Glatzel, S., Gro\u00df, J., Harris, L., Heffernan, L., Hodgkins, S. B., \u2026 Knorr, K.-H. (2025). Peatland mid-infrared database [Dataset]. https://doi.org/10.5281/zenodo.17092587 Data sources Data in the database were derived from the following sources: De la Cruz, Osborne, and Barlaz (2016), Hodgkins et al. (2018), Knierzinger et al. (2020), Knierzinger (2020), M\u00fcnchberger (2019), M\u00fcnchberger et al. (2019), Schuster et al. (2022), Drollinger, Kuzyakov, and Glatzel (2019), Drollinger et al. (2020), Agethen and Knorr (2018), Kendall (2020), L. I. Harris et al. (2023), L. Harris and Olefeldt (2023), Pelletier et al. (2017), Teickner, Gao, and Knorr (2021), Teickner, Gao, and Knorr (2022), Heffernan (2019), Heffernan et al. (2020), Broder et al. (2012), Anzenhofer (2014), Mathijssen et al. (2019), Wagner (2013), H\u00f6mberg (2014), Berger et al. (2017), Berger et al. (2018), Moore et al. (2019), Diaconu et al. (2020), Ga\u0142ka, H\u00f6lzer, et al. (2022), Ga\u0142ka, Diaconu, et al. (2022), Harris et al. (2018), Harris et al. (2019), Boothroyd et al. (2021), Worrall (2021), Reuter et al. (2019b), Reuter et al. (2019a), Reuter et al. (2020), Liu and Lennartz (2019), Moore et al. (2005), Turunen et al. (2004). Acknowledgements Development of this database was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/23-1 to Klaus-Holger Knorr and grant no. PE 1632/18-1 to Edzer Pebesma. References Agethen, Svenja, and Klaus-Holger Knorr. 2018. \u201cJuncus Effusus Mono-Stands in Restored Cutover Peat Bogs \u2013 Analysis of Litter Quality, Controls of Anaerobic Decomposition, and the Risk of Secondary Carbon Loss.\u201d Soil Biology and Biochemistry 117: 139\u201352. https://doi.org/10.1016/j.soilbio.2017.11.020. Anzenhofer, Regina. 2014. \u201cBiogeochemical Characterization of Peat Profiles Along a Vegetation Gradient in an Ombrotrophic Bog, Patagonia.\u201d Master\u2019s thesis. Berger, Sina, Gerhard Gebauer, Christian Blodau, and Klaus-Holger Knorr. 2017. \u201cPeatlands in a Eutrophic World \u2013 Assessing the State of a Poor Fen-Bog Transition in Southern Ontario, Canada, After Long Term Nutrient Input and Altered Hydrological Conditions.\u201d Soil Biology and Biochemistry 114 (November): 131\u201344. https://doi.org/10.1016/j.soilbio.2017.07.011. Berger, Sina, Leandra S. E. Praetzel, Marie Goebel, Christian Blodau, and Klaus-Holger Knorr. 2018. \u201cDifferential Response of Carbon Cycling to Long-Term Nutrient Input and Altered Hydrological Conditions in a Continental Canadian Peatland.\u201d Biogeosciences 15 (3): 885\u2013903. https://doi.org/10.5194/bg-15-885-2018. Boothroyd, I. M., F. Worrall, C. S. Moody, G. D. Clay, G. D. Abbott, and R. Rose. 2021. \u201cSulfur Constraints on the Carbon Cycle of a Blanket Bog Peatland.\u201d Journal of Geophysical Research: Biogeosciences 126 (8). https://doi.org/10.1029/2021JG006435. Broder, T., C. Blodau, H. Biester, and K. H. Knorr. 2012. \u201cPeat Decomposition Records in Three Pristine Ombrotrophic Bogs in Southern Patagonia.\u201d Biogeosciences 9 (4): 1479\u201391. https://doi.org/10.5194/bg-9-1479-2012. De la Cruz, Florentino B., Jason Osborne, and Morton A. Barlaz. 2016. \u201cDetermination of Sources of Organic Matter in Solid Waste by Analysis of Phenolic Copper Oxide Oxidation Products of Lignin.\u201d Journal of Environmental Engineering 142 (2): 04015076. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001038. Diaconu, Andrei-Cosmin, Ioan Tan\u0163\u0103u, Klaus-Holger Knorr, Werner Borken, Angelica Feurdean, Andrei Panait, and Mariusz Ga\u0142ka. 2020. \u201cA Multi-Proxy Analysis of Hydroclimate Trends in an Ombrotrophic Bog over the Last Millennium in the Eastern Carpathians of Romania.\u201d Palaeogeography, Palaeoclimatology, Palaeoecology 538 (January): 109390. https://doi.org/10.1016/j.palaeo.2019.109390. Drollinger, Simon, Klaus-Holger Knorr, Wolfgang Knierzinger, and Stephan Glatzel. 2020. \u201cPeat Decomposition Proxies of Alpine Bogs Along a Degradation Gradient.\u201d Geoderma 369 (June): 114331. https://doi.org/10.1016/j.geoderma.2020.114331. Drollinger, Simon, Yakov Kuzyakov, and Stephan Glatzel. 2019. \u201cEffects of Peat Decomposition on \u03b413C and \u03b415N Depth Profiles of Alpine Bogs.\u201d CATENA 178 (July): 1\u201310. https://doi.org/10.1016/j.catena.2019.02.027. Ga\u0142ka, Mariusz, Andrei-Cosmin Diaconu, Angelica Feurdean, Julie Loisel, Henning Teickner, Tanja Broder, and Klaus-Holger Knorr. 2022. \u201cRelations of Fire, Palaeohydrology, Vegetation Succession, and Carbon Accumulation, as Reconstructed from a Mountain Bog in the Harz Mountains (Germany) During the Last 6200 Years.\u201d Geoderma 424 (October): 115991. https://doi.org/10.1016/j.geoderma.2022.115991. Ga\u0142ka, Mariusz, Adam H\u00f6lzer, Angelica Feurdean, Julie Loisel, Henning Teickner, Andrei-Cosmin Diaconu, Marta Szal, Tanja Broder, and Klaus-Holger Knorr. 2022. \u201cInsight into the Factors of Mountain Bog and Forest Development in the Schwarzwald Mts.: Implications for Ecological Restoration.\u201d Ecological Indicators 140 (July): 109039. https://doi.org/10.1016/j.ecolind.2022.109039. Harris, Lorna I., Tim R. Moore, Nigel T. Roulet, and Andrew J. Pinsonneault. 2019. \u201cData from: Lichens: A Limit to Peat Growth?\u201d Data. https://doi.org/10.5061/dryad.s136dc8. \u2014\u2014\u2014. 2018. \u201cLichens: A Limit to Peat Growth?\u201d Edited by John Lee. Journal of Ecology 106 (6): 2301\u201319. https://doi.org/10.1111/1365-2745.12975. Harris, Lorna I., David Olefeldt, Nicolas Pelletier, Christian Blodau, Klaus-Holger Knorr, Julie Talbot, Liam Heffernan, and Merritt Turetsky. 2023. \u201cPermafrost Thaw Causes Large Carbon Loss in Boreal Peatlands While Changes to Peat Quality Are Limited.\u201d Global Change Biology, August, gcb.16894. https://doi.org/10.1111/gcb.16894. Harris, Lorna, and David Olefeldt. 2023. \u201cPermafrost Thaw Causes Large Carbon Loss in Boreal Peatlands While Changes to Peat Quality Are Limited.\u201d Dryad. https://doi.org/10.5061/DRYAD.47D7WM3KK. Heffernan, Liam. 2019. \u201cPeat Carbon, \u03b414C, Macrofossil, and Humification Data from a Thawing Permafrost Peatland in Western Canada.\u201d UAL Dataverse. https://doi.org/10.7939/DVN/MKM0ZE. Heffernan, Liam, Cristian Estop-Aragon\u00e9s, Klaus-Holger Knorr, Julie Talbot, and David Olefeldt. 2020. \u201cLong-Term Impacts of Permafrost Thaw on Carbon Storage in Peatlands: Deep Losses Offset by Surficial Accumulation.\u201d Journal of Geophysical Research: Biogeosciences 125 (3). https://doi.org/10.1029/2019JG005501. Hodgkins, Suzanne B., Curtis J. Richardson, Ren\u00e9 Dommain, Hongjun Wang, Paul H. Glaser, Brittany Verbeke, B. Rose Winkler, et al. 2018. \u201cTropical Peatland Carbon Storage Linked to Global Latitudinal Trends in Peat Recalcitrance.\u201d Nature Communications 9 (1): 3640. https://doi.org/10.1038/s41467-018-06050-2. H\u00f6mberg, Annkathrin. 2014. \u201cGeochemische Charakterisierung von Mooren der Changbai Mountains.\u201d Bachelor thesis, M\u00fcnster: M\u00fcnster. Kendall, Rachel Anne. 2020. \u201cMicrobial and Substrate Decomposition Factors in Commercially Extracted Peatlands in Canada.\u201d Master\u2019s thesis, Montr\u00e9al: McGill University. Knierzinger, Wolfgang. 2020. \u201c(Bio)Geochemical Data P\u00fcrgschachen Moor.\u201d Pangaea. Knierzinger, Wolfgang, Ruth Drescher-Schneider, Klaus-Holger Knorr, Simon Drollinger, Andreas Limbeck, Lukas Brunnbauer, Felix Horak, Daniela Festi, and Michael Wagreich. 2020. \u201cAnthropogenic and Climate Signals in Late-Holocene Peat Layers of an Ombrotrophic Bog in the Styrian Enns Valley (Austrian Alps).\u201d E&G Quaternary Science Journal 69 (2): 121\u201337. https://doi.org/10.5194/egqsj-69-121-2020. Liu, Haojie, and Bernd Lennartz. 2019. \u201cHydraulic Properties of Peat Soils Along a Bulk Density Gradient-A Meta Study.\u201d Hydrological Processes 33 (1): 101\u201314. https://doi.org/10.1002/hyp.13314. Mathijssen, Paul J. H., Mariusz Ga\u0142ka, Werner Borken, and Klaus-Holger Knorr. 2019. \u201cPlant Communities Control Long Term Carbon Accumulation and Biogeochemical Gradients in a Patagonian Bog.\u201d Science of the Total Environment 684 (September): 670\u201381. https://doi.org/10.1016/j.scitotenv.2019.05.310. Moore, Tim, Christian Blodau, Jukka Turunen, Nigel T. Roulet, and Pierre J. H. Richard. 2005. \u201cPatterns of Nitrogen and Sulfur Accumulation and Retention in Ombrotrophic Bogs, Eastern Canada.\u201d Global Change Biology 11 (2): 356\u201367. https://doi.org/10.1111/j.1365-2486.2004.00882.x. Moore, Tim R., Klaus-Holger Knorr, Lauren Thompson, Cameron Roy, and Jill L. Bubier. 2019. \u201cThe Effect of Long-Term Fertilization on Peat in an Ombrotrophic Bog.\u201d Geoderma 343 (June): 176\u201386. https://doi.org/10.1016/j.geoderma.2019.02.034. M\u00fcller, Kirill, Jeroen Ooms, David James, Saikat DebRoy, Hadley Wickham, and Jeffrey Horner. 2021. \u201cRMariaDB: Database Interface and \u2019MariaDB\u2019 Driver.\u201d M\u00fcnchberger, Wiebke. 2019. \u201cPast and Present Carbon Dynamics in Contrasting South Patagonian Bog Ecosystems.\u201d PhD thesis, M\u00fcnster: University M\u00fcnster. M\u00fcnchberger, Wiebke, Klaus-Holger Knorr, Christian Blodau, Ver\u00f3nica A. Pancotto, and Till Kleinebecker. 2019. \u201cZero to Moderate Methane Emissions in a Densely Rooted, Pristine Patagonian Bog \u2013 Biogeochemical Controls as Revealed from Isotopic Evidence.\u201d Biogeosciences 16 (2): 541\u201359. https://doi.org/10.5194/bg-16-541-2019. Pelletier, Nicolas, Julie Talbot, David Olefeldt, Merritt Turetsky, Christian Blodau, Oliver Sonnentag, and William L Quinton. 2017. \u201cInfluence of Holocene Permafrost Aggradation and Thaw on the Paleoecology and Carbon Storage of a Peatland Complex in Northwestern Canada.\u201d The Holocene 27 (9): 1391\u20131405. https://doi.org/10.1177/0959683617693899. Reuter, Hendrik, Julia Gensel, Marcus Elvert, and Dominik Zak. 2019a. \u201cCuO Lignin, and Bulk Decomposition Data of a 75-Day Anoxic Phragmites Australis Litter Decomposition Experiment in Soil Substrates from Three Northeast German Wetlands.\u201d PANGAEA - Data Publisher for Earth & Environmental Science. https://doi.org/10.1594/PANGAEA.902176. \u2014\u2014\u2014. 2019b. \u201cInfrared Spectra (FTIR) of Phragmites Australis Litter, Initial and After Anoxic Decomposition in Three Wetland Substrates.\u201d PANGAEA - Data Publisher for Earth & Environmental Science. https://doi.org/10.1594/PANGAEA.902069. \u2014\u2014\u2014. 2020. \u201cEvidence for Preferential Protein Depolymerization in Wetland Soils in Response to External Nitrogen Availability Provided by a Novel FTIR Routine.\u201d Biogeosciences 17 (2): 499\u2013514. https://doi.org/10.5194/bg-17-499-2020. Schieferdecker, Tobias, Kirill M\u00fcller, and Darko Bergant. 2022. \u201cdm: Relational Data Models.\u201d Schuster, Wiebke, Klaus-Holger Knorr, Christian Blodau, Mariusz Ga\u0142ka, Werner Borken, Ver\u00f3nica A. Pancotto, and Till Kleinebecker. 2022. \u201cControl of Carbon and Nitrogen Accumulation by Vegetation in Pristine Bogs of Southern Patagonia.\u201d Science of the Total Environment 810 (March): 151293. https://doi.org/10.1016/j.scitotenv.2021.151293. Teickner, Henning. 2025. \u201cpmird: R Interface to the Peatland Mid Infrared Spectra Database.\u201d Teickner, Henning, Chuanyu Gao, and Klaus-Holger Knorr. 2021. \u201cReproducible Research Compendium with R Code and Data for: \u2019Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition\u2019.\u201d Zenodo. https://doi.org/10.5281/zenodo.5792970. \u2014\u2014\u2014. 2022. \u201cElectrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition.\u201d Global Biogeochemical Cycles 36 (2): e2021GB007160. https://doi.org/10.1029/2021GB007160. Turunen, Jukka, Nigel T. Roulet, Tim R. Moore, and Pierre J. H. Richard. 2004. \u201cNitrogen Deposition and Increased Carbon Accumulation in Ombrotrophic Peatlands in Eastern Canada: N Deposition and Peat Accumulation.\u201d Global Biogeochemical Cycles 18 (3). https://doi.org/10.1029/2003GB002154. Wagner, Sindy. 2013. \u201cAnalysis of Peat Decomposition, Element Distribution Patterns and Element Output of Two Peat Bogs in the Thuringian Forest.\u201d Master\u2019s thesis, University Bayreuth. Worrall, Fred. 2021. \u201cSulphur Constraints on the Carbon Cycle of a Blanket Bog Peatland [Dataset].\u201d Durham University. https://doi.org/10.15128/R2PK02C9794.", "keywords": ["Sphagnum", "FTIR", "mid infrared spectra", "peat", "peatland", "pmird", "database", "ATR-FTIR"], "contacts": [{"organization": "Teickner, Henning, Agethen, Svenja, Berger, Sina, Boelsen, Rieke Inga, Borken, Werner, Bragazza, Luca, Broder, Tanja, De la Cruz, Florentino, Diaconu, Andrei-Cosmin, Dise, Nancy, Drollinger, Simon, Estop-Aragon\u00e9s, Cristian, Galka, Mariusz, Mart\u00ed Gener\u00f3, Magal\u00ed, Glatzel, Stephan, Gro\u00df, Jessica, Harris, Lorna, Heffernan, Liam, Hodgkins, Suzanne, H\u00f6mberg-Grandjean, Annkathrin, Hoppe, Helga, Kleinebecker, Till, Knierzinger, Wolfgang, Liu, Haojie, Mathijssen, Paul, Mollmann, Christopher, Schuster, Wiebke, N\u00e4rtker, Lisa, Olefeldt, David, Pancotto, Veronica A., Pelletier, Nicolas, Reuter, Hendrik, Robroek, Bjorn, Svensson, Bosse, Talbot, Julie, Thompson, Lauren M., Worrall, Fred, Yu, Zhi-Guo, Knorr, Klaus-Holger,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17092587"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17092587", "name": "item", "description": "10.5281/zenodo.17092587", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17092587"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-09-10T00:00:00Z"}}, {"id": "10.5281/zenodo.5541296", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:41Z", "type": "Other", "title": "SoilCare database 3: schema (empty database) and Report 34 (D5.1): Database with monitoring data", "description": "The Deliverable 5.1 reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested\u00a0cropping systems and/or\u00a0field agricultural experiments in the 16 Study sites.\u00a0\u00a0 To properly monitor a cropping system and/or a field agricultural experiment a lot of information is required to capture all the possible interactions. The SoilCare WP5 devised a common data management system for all the study-sites. One important objective is to collect complete and comparable data for an analysis across study sites and data that allows any user to get all the required information when analysing a cropping system.The data model structure created based on the entity-relationship diagram and designed\u00a0to capture all the possible dependencies and complex interactions in a cropping system. All information is grouped in different pools: i. (experiments\u2019) Basic information such as institution and person metadata ii. (experimental) Field information like climate, inherent soil properties and spatial arrangement iii. The experimental setup which includes the details for the different treatments and the factors iv. Management data that includes all the detailed information for each group of management categories v. Results which include the measured data and metadata for the measurements/observations.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "", "15. 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Development of OLU has received funding from the European Union's Competitiveness and innovation framework programme under grant agreement No. 621074 called 'Farm-Oriented Open Data in Europe' (FOODIE).", "keywords": ["data model", "OpenLandUse", "land cover", "land use", "15. Life on land", "OLU", "database"], "contacts": [{"organization": "Kepka, Michal, Ko\u017euch, Dmitrij, H\u00e1jek, Pavel, \u0158ezn\u00edk, Tom\u00e1\u0161, Charv\u00e1t, Karel, Chytr\u00fd, Jan, Mildorf, Tom\u00e1\u0161,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6484842"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6484842", "name": "item", "description": "10.5281/zenodo.6484842", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6484842"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-24T00:00:00Z"}}, {"id": "10.5281/zenodo.6484843", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:44Z", "type": "Software", "title": "OpenLandUse 2.0.0 data model", "description": "Open SourceDevelopment of OLU has received funding from the European Union's Competitiveness and innovation framework programme under grant agreement No. 621129 called 'Uptake of Open Geographic Information Through Innovative Services Based on Linked Data' (SDI4Apps). Development of OLU has received funding from the European Union's Competitiveness and innovation framework programme under grant agreement No. 621074 called 'Farm-Oriented Open Data in Europe' (FOODIE).", "keywords": ["data model", "OpenLandUse", "land cover", "land use", "15. Life on land", "OLU", "database"], "contacts": [{"organization": "Kepka, Michal, Ko\u017euch, Dmitrij, H\u00e1jek, Pavel, \u0158ezn\u00edk, Tom\u00e1\u0161, Charv\u00e1t, Karel, Chytr\u00fd, Jan, Mildorf, Tom\u00e1\u0161,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6484843"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6484843", "name": "item", "description": "10.5281/zenodo.6484843", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6484843"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-24T00:00:00Z"}}, {"id": "10.5281/zenodo.7415163", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:52Z", "type": "Dataset", "title": "SoilCare WP5 database with experimental monitoring data", "description": "Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which\u00a0\u00a0reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in\u00a0 \u00a010.5281/zenodo.5541295.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "15. Life on land"], "contacts": [{"organization": "Ioanna Panagea, Rudi Hessel, Alaoui, Abdallah, Bachmann, Felicitas, Baer, Roger, Fleskens, Luuk, Tits, Mia, Elsen, Annemie, B\u00f8e, Frederik, Skaalsveen, Kamilla, Stolte, Jannes, Seehusen, Till, Toth, Zoltan, Dunai, Attila, De Notaris, Chiara, Rubaek, Gitte Holton, Dalgaard, Tommy, Bussell, Jenny, Stoate, Chris, Mayer-Gruner, Paula, Hallama, Moritz, Pilz, Stefan, Pekrun, Carola, Kandeler, Ellen, Calciu, Irina, Vizitu, Olga, Sartori, Felice, Piccoli, Ilaria, Berti, Antonio, Fr\u0105c, Magdalena, Lipiec, Jerzy, Usowicz, Boguslaw, Boulet, Anne-Karine, Ferreira, Antonio, Tsanis, Ioannis, Vozinaki, Irini, Alexakis, Dimitris, Sarchani, Sofia, Koutroulis, Aristeidis, B\u00f6rjesson, Gunnar, Bolinder, Martin, K\u00e4tterer, Thomas, Kirchmann, Holger, Kus\u00e1, Helena, Cuevas, Juli\u00e1n, Pinillos, Virginia, Chiamolera, Fernando, del Moral, Fernando, Cant\u00f3n, Yolanda, Aznar, Jose \u00c1ngel, Galdeano, Emilio, Guilhou, Robin, Le Campion, Antonin, Mar\u00e9chal, Goulven, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7415163"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7415163", "name": "item", "description": "10.5281/zenodo.7415163", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7415163"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-12-08T00:00:00Z"}}, {"id": "10.5281/zenodo.7415164", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:52Z", "type": "Dataset", "title": "SoilCare WP5 database with experimental monitoring data", "description": "Here the database with the experimental data collected in all SoilCare study sites is included. The SoilCare database: schema (empty database) and Report 34 (D5.1) which\u00a0\u00a0reports and explains the database, which the SoilCare project developed and used for storing the monitoring results from the tested cropping systems and/or field agricultural experiments in the 16 Study sites can be accessed in\u00a0 \u00a010.5281/zenodo.5541295.", "keywords": ["2. Zero hunger", "SoilCare", " database", " monitoring", " soil improving cropping systems", " agricultural experiments", "15. Life on land"], "contacts": [{"organization": "Ioanna Panagea, Rudi Hessel, Alaoui, Abdallah, Bachmann, Felicitas, Baer, Roger, Fleskens, Luuk, Tits, Mia, Elsen, Annemie, B\u00f8e, Frederik, Skaalsveen, Kamilla, Stolte, Jannes, Seehusen, Till, Toth, Zoltan, Dunai, Attila, De Notaris, Chiara, Rubaek, Gitte Holton, Dalgaard, Tommy, Bussell, Jenny, Stoate, Chris, Mayer-Gruner, Paula, Hallama, Moritz, Pilz, Stefan, Pekrun, Carola, Kandeler, Ellen, Calciu, Irina, Vizitu, Olga, Sartori, Felice, Piccoli, Ilaria, Berti, Antonio, Fr\u0105c, Magdalena, Lipiec, Jerzy, Usowicz, Boguslaw, Boulet, Anne-Karine, Ferreira, Antonio, Tsanis, Ioannis, Vozinaki, Irini, Alexakis, Dimitris, Sarchani, Sofia, Koutroulis, Aristeidis, B\u00f6rjesson, Gunnar, Bolinder, Martin, K\u00e4tterer, Thomas, Kirchmann, Holger, Kus\u00e1, Helena, Cuevas, Juli\u00e1n, Pinillos, Virginia, Chiamolera, Fernando, del Moral, Fernando, Cant\u00f3n, Yolanda, Aznar, Jose \u00c1ngel, Galdeano, Emilio, Guilhou, Robin, Le Campion, Antonin, Mar\u00e9chal, Goulven, Guido Wyseure,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7415164"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7415164", "name": "item", "description": "10.5281/zenodo.7415164", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7415164"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.7956363", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:56Z", "type": "Other", "title": "EJP SOIL project, WP6 - Questionnaire for supporting harmonised soil information and reporting", "description": "Open AccessThis stocktaking activity aims at collecting metadata information on the georeferenced soil data available in the EJP-SOIL countries. This stocktaking concerns not just the soil data itself, but also auxiliary information needed for the soil mapping activity, and the mapping experience hold in our institutions. Available doesn\u2019t mean that this information is freely available, but just that it exists, with a specific data owner (which can also be different from your institution) and a specific sharing policy. The first sheet, named \u201cdescription of data sources\u201d, is to insert the list of your data sources. We have put some Italian examples to help you understanding the kind of information to be inserted in this sheet (you can delete them). Basically, it is a list of data sources available, either for basic soil data (point data or mapped data), and for auxiliary data. Among auxiliary data we are also looking for mapped data on soil management. Once the first sheet is compiled, the listed data sources will constitute a drop-down list to be used in the compilation of the following sheets. The second sheet, named \u201csoil property_data (SP)\u201d, is for the compilation of the soil property data available in your basic soil data sources. It is most probable that more the one data source exists in your country, storing soil data properties. Each one of these soil data sources should have been described in the first sheet. Then, the soil properties store in each soil data source should be inserted in the second sheet. For each soil property it is requested to indicate the unit of measure used and the analytical method(s) used (can be more then one). In order to help you in the compilation, we have listed, in the third 'methods' sheet, the most commonly used analytical methods, but you can add more methods if you adopt different ones. If the data source list is a soil map already published, we are asking you to compile the method used for mapping. In the fourth sheet, named \u201csoil management (MG)\u201d, you can list the kind of soil management practices which are available in you data sources. We must stress here, that the data sources for soil management that we are looking for, are georeferenced data sources. The last 2 sheets are the drop-down lists used in the questionnaire and a description of the terms used.", "keywords": ["2. Zero hunger", "15. Life on land", "soil property dataset", " metadatabase"], "contacts": [{"organization": "Fantappie, Maria, Bispo, Antonio, Wetterlind, Johanna, Smreczak, Bozena, van Egmond, Fenny, Bakacsi, Zs\u00f3fia, Farkas-Iv\u00e1nyi, Kinga, Moln\u00e1r, S\u00e1ndor,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7956363"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7956363", "name": "item", "description": "10.5281/zenodo.7956363", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7956363"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-22T00:00:00Z"}}, {"id": "10.5281/zenodo.7956364", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:22:56Z", "type": "Other", "title": "EJP SOIL project, WP6 - Questionnaire for supporting harmonised soil information and reporting", "description": "Open AccessThis stocktaking activity aims at collecting metadata information on the georeferenced soil data available in the EJP-SOIL countries. This stocktaking concerns not just the soil data itself, but also auxiliary information needed for the soil mapping activity, and the mapping experience hold in our institutions. Available doesn\u2019t mean that this information is freely available, but just that it exists, with a specific data owner (which can also be different from your institution) and a specific sharing policy. The first sheet, named \u201cdescription of data sources\u201d, is to insert the list of your data sources. We have put some Italian examples to help you understanding the kind of information to be inserted in this sheet (you can delete them). Basically, it is a list of data sources available, either for basic soil data (point data or mapped data), and for auxiliary data. Among auxiliary data we are also looking for mapped data on soil management. Once the first sheet is compiled, the listed data sources will constitute a drop-down list to be used in the compilation of the following sheets. The second sheet, named \u201csoil property_data (SP)\u201d, is for the compilation of the soil property data available in your basic soil data sources. It is most probable that more the one data source exists in your country, storing soil data properties. Each one of these soil data sources should have been described in the first sheet. Then, the soil properties store in each soil data source should be inserted in the second sheet. For each soil property it is requested to indicate the unit of measure used and the analytical method(s) used (can be more then one). In order to help you in the compilation, we have listed, in the third 'methods' sheet, the most commonly used analytical methods, but you can add more methods if you adopt different ones. If the data source list is a soil map already published, we are asking you to compile the method used for mapping. In the fourth sheet, named \u201csoil management (MG)\u201d, you can list the kind of soil management practices which are available in you data sources. We must stress here, that the data sources for soil management that we are looking for, are georeferenced data sources. The last 2 sheets are the drop-down lists used in the questionnaire and a description of the terms used.", "keywords": ["2. Zero hunger", "15. Life on land", "soil property dataset", " metadatabase"], "contacts": [{"organization": "Fantappie, Maria, Bispo, Antonio, Wetterlind, Johanna, Smreczak, Bozena, van Egmond, Fenny, Bakacsi, Zs\u00f3fia, Farkas-Iv\u00e1nyi, Kinga, Moln\u00e1r, S\u00e1ndor,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.7956364"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.7956364", "name": "item", "description": "10.5281/zenodo.7956364", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.7956364"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-05-22T00:00:00Z"}}, {"id": "10.7910/DVN/SQI3IR", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:39Z", "type": "Dataset", "title": "GMCSD-1. Global Mangrove Carbon, 2000 and 2012, 1 Arc-second, 1 m soil.", "description": "Open AccessGlobal Mangrove Carbon, 2000 and 2012, 1 Arc-Second, 1 m Soil, low, mid, high, EQ1, EQ2, EQ3, EQ4, EQ5.

These are large file and we needed to use file geodatabase format to compress enough to post on the Dataverse. Hence no Tiffs.", "keywords": ["Earth and Environmental Sciences", "Raster", "ArcGIS file Geodatabase rasters", "Global Mangrove Carbon"], "contacts": [{"organization": "Hamilton, Stuart", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.7910/DVN/SQI3IR"}, {"rel": "self", "type": "application/geo+json", "title": "10.7910/DVN/SQI3IR", "name": "item", "description": "10.7910/DVN/SQI3IR", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.7910/DVN/SQI3IR"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-01-01T00:00:00Z"}}, {"id": "10481/84824", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:23:57Z", "type": "Journal Article", "created": "2023-07-12", "title": "National soil data in EU countries, where do we stand?", "description": "Abstract

At the European scale, soil characteristics are needed to evaluate soil quality, soil health and soil\uffe2\uff80\uff90based ecosystem services in the context of the European Green Deal. While some soil databases exist at the European scale, a much larger wealth of data is present in individual European countries, allowing a more detailed soil assessment. There is thus an urgent and crucial need to combine these data at the European scale. In the frame of a large European Joint Programme on agricultural soils launched by the European Commission, a survey was conducted in the spring of 2020, in the 24 European participating countries to assess the existing soil data sources, focusing on agricultural soils. The survey will become a contribution to the European Soil Observatory, launched in December 2020, which aims to collect metadata of soil databases related to all kind of land uses, including forest and urban soils. Based upon a comprehensive questionnaire, 170 soil databases were identified at local, regional and national scales. Soil parameters were divided into five groups: (1) main soil parameters according to the Global Soil Map specifications; (2) other soil chemical parameters; (3) other physical parameters; (4) other pedological parameters; and (5) soil biological features. A classification based on the environmental zones of Europe was used to distinguish the climatic zones. This survey shows that while most of the main pedological and chemical parameters are included in more than 70% of the country soil databases, water content, contamination with organic pollutants, and biological parameters are the least frequently reported parameters. Such differences will have consequences when developing an EU policy on soil health as proposed under the EU soil strategy for 2023 and using the data to derive soil health indicators. Many differences in the methods used in collecting, preparing, and analysing the soils were found, thus requiring harmonization procedures and more cooperation among countries and with the EU to use the data at the European scale. In addition, choosing harmonized and useful interpretation and threshold values for EU soil indicators may be challenging due to the different methods used and the wide variety of soil land\uffe2\uff80\uff90use and climate combinations influencing possible thresholds. The temporal scale of the soil databases reported is also extremely wide, starting from the '20s of the 20th century.

Abstract. In\u00a02009, the International Soil Moisture Network\u00a0(ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et\u00a0al.,\u00a02011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28\u00a0October\u00a02021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000\u00a0active users and over 1000\u00a0scientific publications referencing the data sets provided by the network. As of July\u00a02021, the ISMN now contains the data of 71\u00a0networks and 2842\u00a0stations located all over the globe, with a time period spanning from\u00a01952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70\u2009% of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

", "keywords": ["[SDE] Environmental Sciences", "Technology", "Atmospheric Science", "550", "Soil Moisture", "TA Engineering (General). Civil engineering (General)", "02 engineering and technology", "Soil Moisture; ISMN; IMA_CAN1; swc; STEMS", "SMOS BRIGHTNESS TEMPERATURE", "Spatial variability", "Environmental technology. Sanitary engineering", "01 natural sciences", "Agency (philosophy)", "remote sensing", "Antecedent wetness conditions", "Engineering", "Geography. Anthropology. Recreation", "GE1-350", "Geosciences", " Multidisciplinary", "TD1-1066", "Smos brightness temperature", "Heihe river-basin", "T", "Soil Water Retention", "Geology", "Leaf-area index", "004", "FOS: Philosophy", " ethics and religion", "Programming language", "HEIHE RIVER-BASIN", "Earth and Planetary Sciences", "Physical Sciences", "Water Resources", "name=Water Science and Technology", "/dk/atira/pure/subjectarea/asjc/1900/1901", "Medicine", "0406 Physical Geography and Environmental Geoscience", "name=Earth and Planetary Sciences (miscellaneous)", "3709 Physical geography and environmental geoscience", "Mechanics and Transport in Unsaturated Soils", "Environmental Engineering", "SPATIAL VARIABILITY", "IN-SITU MEASUREMENTS", "0207 environmental engineering", "Epistemology", "0905 Civil Engineering", "Environmental science", "G", "Database", "LAND DATA ASSIMILATION", "Soil Moisture; network", "WIRELESS SENSOR NETWORK", "Arctic Permafrost Dynamics and Climate Change", "Scope (computer science)", "Land data assimilation", "Civil and Structural Engineering", "0105 earth and related environmental sciences", "info:eu-repo/classification/ddc/550", "Science & Technology", "3707 Hydrology", "Consecutive dry days", "LEAF-AREA INDEX", "in situ", "FOS: Environmental engineering", "AMSR-E", "15. Life on land", "Remote Sensing of Soil Moisture", "ANTECEDENT WETNESS CONDITIONS", "Globe", "Computer science", "Environmental sciences", "QE Geology", "0907 Environmental Engineering", "Philosophy", "Ophthalmology", "In-situ measurements", "13. Climate action", "ITC-ISI-JOURNAL-ARTICLE", "global scale", "Environmental Science", "G70.212-70.215 Geographic information system", "4013 Geomatic engineering", "soil moisture", "CONSECUTIVE DRY DAYS", "ITC-GOLD", "/dk/atira/pure/subjectarea/asjc/2300/2312", "Wireless sensor network"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2998914/1/prod_447100-doc_161016.pdf"}, {"href": "https://iris.polito.it/bitstream/11583/2998914/2/prod_447100-doc_178365.pdf"}, {"href": "https://cris.unibo.it/bitstream/11585/910145/1/Dourigo_etal_2021.pdf"}, {"href": "https://doi.org/11585/910145"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Hydrology%20and%20Earth%20System%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11585/910145", "name": "item", "description": "11585/910145", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11585/910145"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-11-09T00:00:00Z"}}, {"id": "1854/LU-01KAB9KFWBCYFKX8433R94XMFK", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:18Z", "type": "Journal Article", "created": "2025-09-26", "title": "The Toxicity of Microplastics Explorer (ToMEx) 2.0", "description": "In 2021 the Toxicity of Microplastics Explorer (ToMEx, https://microplastics.sccwrp.org) was released as an open source, open access database and web application for microplastics toxicity. Since then, it has been utilized by the microplastic research community for the exploration, visualization, and analysis of toxicity data for both hazard characterization and risk assessment. The peer-reviewed literature has continued to grow exponentially, making ToMEx out-of-date. To ensure the continued utility of ToMEx, an international crowd-sourcing approach was utilized to update ToMEx by extracting data from additional studies published since the original release. Through this process, both the aquatic and human health ToMEx databases roughly doubled in size, and modest increases in data diversity (e.g., number of species represented, types of test particles) were observed in the aquatic organisms database. However, most trends (e.g., greater toxicities observed with smaller particle sizes, lack of dose-response data etc.) observed in the first iteration of ToMEx remained constant. A previously developed framework for deriving ecological health-based microplastic thresholds using species sensitivity distributions was reapplied to determine how thresholds and their associated uncertainty intervals would change following the database update. Twelve new studies passed minimum screening criteria and were deemed fit for the purpose of threshold derivation. The addition of new data allowed for the separation of freshwater and marine compartments which had previously been combined due to a lack of applicable toxicity data for freshwater species. When molecular and cellular level endpoints were included, freshwater thresholds were comparable or increased from values calculated using previous data (-5 to 2.5-fold change) whereas marine thresholds dramatically decreased (-5000 to -29-fold change). However, when endpoints were restricted to organism and above, marine and freshwater thresholds were comparable to those calculated previously (-20 to 14-fold change). Confidence intervals for both marine and freshwater thresholds remained wide. The doubling of the database increases the value of ToMEx for researchers, particularly those focused on characterizing hazards associated with microplastics. Its utility remains limited for environmental managers as 89% of studies in ToMEx 2.0 failed to meet minimum screening criteria for threshold derivation, highlighting the need to generate fit-for-purpose toxicity data for threshold development. However, ToMEx continues to be a useful research tool, and future iterations could become even more powerful through novel artificial intelligence applications to streamline data curation and even predict toxicological outcomes.", "keywords": ["Database", "Toxicity", "Aquatic organisms", "Earth and Environmental Sciences", "Research", "Microplastic", "Medicine and Health Sciences", "Human health", "Biology and Life Sciences"], "contacts": [{"organization": "Hampton, Leah M. Thornton, Wyler, Dana Briggs, Almroth, Bethanie Carney, Coffin, Scott, Cowger, Win, Doyle, Darragh, Hataley, Eden K., Hutton, Sara J., Mair, Magdalena M., Miller, Ezra L., Moncl\u00fas, Laura, Sharpe, Emma E., Samreen, Siddiqui, Ahmed, Kazi Towsif, Allamby, Quinn P. V., Vital, Ana L. Antonio, Asnicar, Davide, Bare, Jennifer L., Barrick, Andrew, Berreman, Katherine, Bertrand, Lidwina, Boone, Virginia, Bour, Agathe, Brehm, Julian, Carrasco-Navarro, Victor, Cook, Travis, Covernton, Garth A., Cubanski, Patricia, Da Silva, Pedro M. C., de Souza Leite, Luan, Gene, Sam M., Hermabessiere, Ludovic, Hooge, Asta, Iwasaki, Yuichi, Klasios, Natasha, Knauss, Christine M., Kardgar, Azora K\u00f6nig, Kropf, Philipp, Kudu, Isaac B., Kukkola, Anna, Laforsch, Christian, Kennedy, Stephanie B., Leusch, Frederic D. L., Li, Lucy Wei, Lu, Hsuan-Cheng, Mahan, Judd, Saif, Uddin Md, Mondellini, Simona, Norman, John P., Pandelides, Zacharias, Petersson, Tove, Philibert, Danielle A., Kvist, Elina, Ramsperger, Anja F. R. M., Rigutto, Gabrielle, Ritschar, Sven, Sandgaard, Monica H., Schmitt, Jona, Schott, Matthias, Schwarzer, Michael, Seabrook, Katryna J., Seifried, Teresa M., Sepahi, Rohan, Si\u00f1a, Mariella, Testoff, Alex N., Vercauteren, Maaike, Wardlaw, Colleen M., Yeh, Andrew, Zajac-Fay, Rachel, Mehinto, Alvine C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/1854/LU-01KAB9KFWBCYFKX8433R94XMFK"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microplastics%20and%20Nanoplastics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "1854/LU-01KAB9KFWBCYFKX8433R94XMFK", "name": "item", "description": "1854/LU-01KAB9KFWBCYFKX8433R94XMFK", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1854/LU-01KAB9KFWBCYFKX8433R94XMFK"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-09-26T00:00:00Z"}}, {"id": "20.500.11850/521965", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:29Z", "type": "Report", "created": "2021-12-15", "title": "Reviews and syntheses: The promise of big soil data, moving current practices towards future potential", "description": "

Abstract. In the age of big data, soil data are more available than ever, but -outside of a few large soil survey resources- remain largely unusable for informing soil management and understanding Earth system processes outside of the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for global relevance. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: data discovery, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

", "keywords": ["FOS: Computer and information sciences", "Data Sharing", "Biomedical Ontologies and Text Mining", "Data management", "Leverage (statistics)", "01 natural sciences", "Data science", "Data Sharing and Stewardship in Science", "Database", "Big data", "Biochemistry", " Genetics and Molecular Biology", "Machine learning", "Molecular Biology", "Data mining", "0105 earth and related environmental sciences", "2. Zero hunger", "Metadata", "Ecology", "Data curation", "Physics", "Life Sciences", "Acoustics", "15. Life on land", "Computer science", "World Wide Web", "Harmonization", "13. Climate action", "FOS: Biological sciences", "Computer Science", "Physical Sciences", "Environmental Science", "Data Reuse", "Environmental DNA in Biodiversity Monitoring", "Information Systems"]}, "links": [{"href": "https://doi.org/20.500.11850/521965"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/521965", "name": "item", "description": "20.500.11850/521965", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/521965"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-12-15T00:00:00Z"}}, {"id": "20.500.11850/562259", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:24:29Z", "type": "Journal Article", "created": "2022-07-28", "title": "Reviews and syntheses: The promise of big diverse soil data, moving current practices towards future potential", "description": "

Abstract. In the age of big data, soil data are more available and richer than ever, but \u2013 outside of a few large soil survey resources \u2013 they remain largely unusable for informing soil management and understanding Earth system processes beyond the original study. Data science has promised a fully reusable research pipeline where data from past studies are used to contextualize new findings and reanalyzed for new insight. Yet synthesis projects encounter challenges at all steps of the data reuse pipeline, including unavailable data, labor-intensive transcription of datasets, incomplete metadata, and a lack of communication between collaborators. Here, using insights from a diversity of soil, data, and climate scientists, we summarize current practices in soil data synthesis across all stages of database creation: availability, input, harmonization, curation, and publication. We then suggest new soil-focused semantic tools to improve existing data pipelines, such as ontologies, vocabulary lists, and community practices. Our goal is to provide the soil data community with an overview of current practices in soil data and where we need to go to fully leverage big data to solve soil problems in the next century.

", "keywords": ["FOS: Computer and information sciences", "0301 basic medicine", "Data Sharing", "Information Systems and Management", "literature review", "1904 Earth-Surface Processes", "Social Sciences", "data set", "01 natural sciences", "Decision Sciences", "Data science", "Life", "QH501-531", "910 Geography & travel", "soil analysis", "database", "QH540-549.5", "2. Zero hunger", "QE1-996.5", "000", "Ecology", "communication", "Physics", "Earth", "Geology", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "World Wide Web", "10122 Institute of Geography", "soil survey", "Physical Sciences", "Data Reuse", "environment", "Information Systems", "Evolution", "future prospect", "Data management", "Data Sharing and Stewardship in Science", "Database", "Big data", "03 medical and health sciences", "Behavior and Systematics", "Data mining", "0105 earth and related environmental sciences", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Management and Reproducibility of Scientific Workflows", "Metadata", "Data curation", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Acoustics", "15. Life on land", "Computer science", "1105 Ecology", " Evolution", " Behavior and Systematics", "Surface Processes", "Harmonization", "FOS: Biological sciences", "Computer Science", "Environmental Science", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "soil management", "Research Data", "Environmental DNA in Biodiversity Monitoring"]}, "links": [{"href": "https://doi.org/20.500.11850/562259"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Biogeosciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/562259", "name": "item", "description": "20.500.11850/562259", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/562259"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-07-28T00:00:00Z"}}, {"id": "3031376624", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-24T16:25:05Z", "type": "Journal Article", "created": "2020-06-01", "title": "Monitoring Cropping Systems: From Data Collection to Cloud Database Storage Using Open Source Software", "description": "Agricultural cropping systems and experiments include complex interactions of processes and various management practices and/or treatments under a wide range of environmental and climatic conditions. The use of standardized formats to monitor and document these systems and experiments can help researchers and stakeholders to efficiently exchange data, promote interdisciplinary collaborations, and simplify modelling and analysis procedures. In the scope of the SoilCare Horizon 2020 project monitoring and assessment work package, an integrated scheme to collect, validate, store, and access cropping system information and experimental data from 16 study sites, was created. The aim of the scheme is to make the data readily available in a way that the information is useful, easy to access and download, and safe, relying only on open source software. The database design considers data and metadata required to properly and easily monitor, process, and analyse cropping systems and/or agricultural experiments. The scheme allows for the storage of data and metadata regarding the experimental set-up, associated people and institutions, information about field management operations and experimental procedures which are clearly separated for making analysis procedures faster, links between system components, and information about the environmental and climatic conditions. Raw data are entered by the users into a structured spreadsheet. The quality is checked before storing the data into the database. Providing raw data allows processing and analysing as each other user needs. A desktop import application has been created to upload the information from spreadsheet to database, which includes automated error checks of relationship tables, data types, data constraints, etc. The final component of the scheme is the database web application interface, which enables users to access and query the database across the study sites without the knowledge of query languages and to download the required data. For this system design, PostgreSQL is used for storing the data, pgAdmin 4 for database management administration, MongoDB for user management and authentication, Python for the development of the import application, Angular and Node.js/Express for the web application and spreadsheets compatible with LibreOffice Calc. The system is currently tested with data provided by the SoilCare study sites. Preliminary testing indicated that extended quality control of the spreadsheets was required from the system\u2019s administrator to meet the standards and restrictions of the import application. Initial comments from the users indicate that the database scheme, even if it initially seems complicated, includes all the variables and details required for a complete monitoring and modelling of an agricultural cropping system.", "keywords": ["2. Zero hunger", "cropping systems", "02 engineering and technology", "15. Life on land", "01 natural sciences", "General Works", "0104 chemical sciences", "monitoring", "13. Climate action", "A", "0202 electrical engineering", " electronic engineering", " information engineering", "SoilCare", "database", "open-source"], "contacts": [{"organization": "Dangol Anuja, Guido Wyseure, Jan Diels, Ioanna Panagea, Marc Olijslagers,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/3031376624"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/TERRAenVISION%202019", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3031376624", "name": "item", "description": "3031376624", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3031376624"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-01T00:00:00Z"}}, {"id": "4ec4bbca-7096-4659-803b-dad26fcb8ecf", "type": "Feature", "geometry": {"type": "Polygon", "coordinates": [[[33.94, 6.87], [33.94, 13.12], [44.32, 13.12], [44.32, 6.87], [33.94, 6.87]]]}, "properties": {"rights": "Restrictions applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations or warnings on using the resource or metadata. Reports, articles, papers, scientific and non - scientific works of any form, including tables, maps, or any other kind of output, in printed or electronic form, based in whole or in part on the data supplied, must contain an acknowledgement of the form: \"Data reused from the BonaRes Data Centre www.bonares.de. This data were created as part of the Other's research activities.\" Although every care has been taken in preparing and testing the data, the Other and the BonaRes Data Centre cannot guarantee that the data are correct; neither does the Other and the BonaRes Data Centre accept any liability whatsoever for any error, missing data or omission in the data, or for any loss or damage arising from its use. The Other and BonaRes Data Centre will not be responsible for any direct or indirect use which might be made of the data. The access to this data is restricted during embargo time. 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