{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.3685753", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production and soil organic carbon", "description": "This data set contains a data-mining performed to assess the impact of intercropping, tillage and fertilizer type on soil organic carbon and crop yield in arable crops from four selected European pedoclimatic regions and typical cropping systems in the Atlantic, Boreal, Mediterranean North, and Mediterranean South regions. A further meta-analysis was performed with these data. These data correspond to the open-access articles: - Diversified Arable Cropping Systems and Management Schemes in Selected European Regions Have Positive Effects on Soil Organic Carbon Content. Agriculture 2019, 9, 261. https://www.mdpi.com/2077-0472/9/12/261?type=check_update&version=2 - Diversification and Management Practices in Selected European Regions. A Data-analysis of Arable Crops Production. Agronomy 2020, 10, 297; doi:10.3390/agronomy10020297. https://www.mdpi.com/2073-4395/10/2/297 - Deficit Drip Irrigation in Processing Tomato Production in the Mediterranean Basin: A Data Analysis for Italy. Agriculture 2019, 9, 79; doi:10.3390/agriculture9040079. https://www.mdpi.com/2077-0472/9/4/79?type=check_update&version=2 The research and publications have been funded by he European Commission Horizon 2020 project Diverfarming [grant agreement 728003].", "keywords": ["2. Zero hunger", "multiple cropping", "rotations", "soil organic carbon", "crop diversification", "13. Climate action", "tillage", "cropping systems", "15. Life on land", "crop yield", "fertilizer", "intercropping", "agriculture"], "contacts": [{"organization": "Bene, Claudia Di, Francaviglia, Rosa, \u00c1lvaro-Fuentes, Jorge, Lingtong Gai, Regina, Kristiina, Turtola, Eila,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3685753"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3685753", "name": "item", "description": "10.5281/zenodo.3685753", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3685753"}, {"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.3678393", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "Dataset of annual and monthly nitrous oxide flux from global forests", "description": "The database of N2O fluxes was constructed from published literature that was searched for using the keywords \u201cnitrous oxide flux\u201d and \u201cforests\u201d in the database of ISI Web of Science. The site-level N2O flux data were compiled from articles published until 2018. The sites consist of natural or semi-natural forests, and samplings from economic forests were rejected to avoid the influence of human activity, and laboratory studies were not included. In addition, the chamber method was only selected in this database to avoid the uncertainties caused by different measurement techniques. Ultimately, a total of 191 records of annual N2O fluxes from 99 published literatures were collected to form our database. Moreover, 112 sites in the database have monthly N2O flux data, totaling 2,053 records. The species in our database were classified into different biotic forest groups (leaf traits (i.e., broad and coniferous, LT) and leaf habits (evergreen and deciduous, LH)), according to the information which selected from corresponding articles (e.g. dominant species). Geographic, climatic, vegetation, including latitude, longitude, soil type, vegetation type, climate variables (i.e., mean annual temperature and mean annual precipitation), and edaphic factors (e.g., soil dissolved organic carbon (DOC), ammonium concentration (NH4+), nitrate concentration (NO3-), water filled pore space (WFPS) and soil temperature), were also collected from corresponding articles. For each site, we calculated the means of annual N2O fluxes during the observation period, and the monthly values of N2O fluxes were calculated based on the average of two or three daily fluxes obtained from corresponding articles. The daily N2O flux dataset was extracted from the published figures and tables using GetData Graph Digitizer version 2.24. More information please contact Kerou Zhang (zhangkerou1991@nwafu.edu.cn).", "keywords": ["13. Climate action", "15. Life on land", "Nitrous oxide fluxes", "Forests"], "contacts": [{"organization": "Zhang, Kerou", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3678393"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3678393", "name": "item", "description": "10.5281/zenodo.3678393", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3678393"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-02-21T00:00:00Z"}}, {"id": "10.5281/zenodo.4486577", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Other", "title": "GREENER Project official video: InteGRated systems for Effective ENvironmEntal Remediation", "description": "Find out more about the GREENER's goals and technologies applied, aiming to remediate a range of organic and inorganic pollutants of high concern, while producing useful end-products, such as bioelectricity and harmless metabolites.", "keywords": ["13. Climate action", "remediation", " soil", " water", " sediments", "6. Clean water"], "contacts": [{"organization": "Axia Innovation", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4486577"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4486577", "name": "item", "description": "10.5281/zenodo.4486577", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4486577"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.5281/zenodo.3749508", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "Natural potential for future cropland expansion", "description": "Open AccessNatural potentials for future cropland expansion The potential for the expansion of cropland is restricted by the availability of land resources and given local natural conditions. As a result, area that is highly suitable for agriculture according to the prevailing local biophysical conditions but is not under cultivation today has a high natural potential for expansion. Policy regulations can further restrict the availability of land for expansion by designating protected areas, although they may be suitable for agriculture. Conversely, by applying e.g. irrigation practices, land can be brought under cultivation, although it may naturally not be suitable. Here, we investigate the potentials for agricultural expansion for near future climate scenario conditions to identify the suitability of non-cropland areas for expansion according to their local natural conditions. We determine the available energy, water and nutrient supply for agricultural suitability from climate, soil and topography data, by using a fuzzy logic approach according to Zabel et al. (2014). It considers the 16 globally most important staple and energy crops. These are: barley, cassava, groundnut, maize, millet, oil palm, potato, rapeseed, rice, rye, sorghum, soy, sugarcane, sunflower, summer wheat, winter wheat. The parameterization of the membership functions that describe each of the crops\u2019 specific natural requirements is taken from Sys et al. (1993). The considered natural conditions are: climate (temperature, precipitation, solar radiation), soil properties (texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity), and topography (elevation, slope). As a result of the fuzzy logic approach, values in a range between 0 and 1 describe the suitability of a crop for each of the prevailing natural conditions at a certain location. The smallest suitability value over all parameters finally determines the suitability of a crop. The daily climate data is provided by simulation results from the global climate model ECHAM5 (Jungclaus et al. 2006) for near future (2011-2040) SRES A1B climate scenario conditions. Soil data is taken from the Harmonized World Soil Database (HWSD) (FAO et al. 2012), and topography data is applied from the Shuttle Radar Topography Mission (SRTM) (Farr et al. 2007). In order to gather a general crop suitability, which does not refer to one specific crop, the most suitable crop with the highest suitability value is chosen at each pixel. In addition the natural biophysical conditions, we consider today\u2019s irrigated areas according to (Siebert et al. 2013). We assume that irrigated areas globally remain constant until 2040, since adequate data on the development of irrigated areas do not exist, although it is likely that freshwater availability for irrigation could be limited in some regions, while in other regions surplus water supply could be used to expand irrigation practices (Elliott et al. 2014). However, it is difficult to project where irrigation practices will evolve, since it is driven by economic investment costs that are required to establish irrigation infrastructure. In principle, all agriculturally suitable land that is not used as cropland today has the natural potential to be converted into cropland. We assume that only urban and built-up areas are not available for conversion, although more than 80% of global urban areas are agriculturally suitable (Avellan et al. 2012). However, it seems unlikely that urban areas will be cleared at the large scale due to high investment costs, growing cities and growing demand for settlements. Concepts of urban and vertical farming usually are discussed under the aspects of cultivating fresh vegetables and salads for urban population. They are not designed to extensively grow staple crops such as wheat or maize for feeding the world in the near future. Urban farming would require one third of the total global urban area to meet only the global vegetable consumption of urban dwellers (Martellozzo et al. 2015). Thus, urban agriculture cannot substantially contribute to global agricultural production of staple crops. Protected areas or dense forested areas are not excluded from the calculation, in order not to lose any information in the further combination with the biodiversity patterns (see chapter 2.3). We use data on current cropland distribution by Ramankutty et al. (2008) and urban and built-up area according to the ESA-CCI land use/cover dataset (ESA 2014). From this data, we calculate the \u2018natural expansion potential index\u2019 (Iexp) that expresses the natural potential for an area to be converted into cropland as follows: Iexp = S * Aav The index is determined by the quality of agricultural suitability (S) (values between 0 and 1) multiplied with the amount of available area (Aav) for conversion (in percentage of pixel area). The available area includes all suitable area that is not cultivated today, and not classified as urban or artificial area. The index ranges between 0 and 100 and indicates where the conditions for cropland expansion are more or less favorable, when taking only natural conditions into account, disregarding socio-economic factors, policies and regulations that drive or inhibit cropland expansion. The index is a helpful indicator for identifying areas where cropland expansion could take place in the near future. Further information Detailled information are available in the following publication: Delzeit, R., F. Zabel, C. Meyer and T. V\u00e1clav\u00edk (2017). Addressing future trade-offs between biodiversity and cropland expansion to improve food security. Regional Environmental Change 17(5): 1429-1441. DOI: 10.1007/s10113-016-0927-1 Contact Please contact: Dr. Florian Zabel, f.zabel@lmu.de, Department f\u00fcr Geographie, LMU M\u00fcnchen (www.geografie.uni-muenchen.de)", "keywords": ["2. Zero hunger", "13. Climate action", "Climate Change", "11. Sustainability", "Cropland expansion", "15. Life on land", "Potential", "Land use change", "6. Clean water"], "contacts": [{"organization": "Zabel, Florian", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3749508"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3749508", "name": "item", "description": "10.5281/zenodo.3749508", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3749508"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-02-04T00:00:00Z"}}, {"id": "10.5281/zenodo.3790156", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "Supplementary Data for \"Organic matter preservation in ancient soils of Earth and Mars\"", "description": "Open AccessGlobal compilation of organic carbon content (TOC) of paleosols (ancient soils) throughout the geological record on Earth. Pleistocene (1 Ma) to the Archean (3.7 Ga)!", "keywords": ["martian paleosols", "13. Climate action", "astropedology", "precambrian paleosols", "15. Life on land", "biosignature detection"], "contacts": [{"organization": "Broz, Adrian", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3790156"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3790156", "name": "item", "description": "10.5281/zenodo.3790156", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3790156"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-06T00:00:00Z"}}, {"id": "10.5281/zenodo.3884383", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "The vertical distribution of soil microbial biomass carbon: A global dataset", "description": "Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data are collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data were also collectd including climate, ecosystem types, and edaphic factors. More specifically, we develop this dataset by compiling data from 59 papers published in the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data included in this dataset meet two creteria: 1) there are at least three soil layers along a soil profile, and 2) soil MBC is measured using the fumigation extraction method. The data were obtained from tables and texts from literature directly, and the data in figures were extracted using GetData Graph digitizer software version 2.25. When climate and soil properties are not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables are converted to the standard international units or commonly used ones and the values are converted correspondingly. For example, the value of soil organic matter (SOM) is converted to SOC using the equation (SOC = SOM \u00d7 0.58). Soil depth is calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer. This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil C models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.", "keywords": ["2. Zero hunger", "soil organic carbon", "13. Climate action", "deep soils", "soil clay content", "soil profile", "soil C/N ratio", "15. Life on land", "micorbial quotient"], "contacts": [{"organization": "Sun, Tingting, Wang, Yugang, Hui, Dafeng, Jing, Xin, Feng, Wenting,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3884383"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3884383", "name": "item", "description": "10.5281/zenodo.3884383", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3884383"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-21T00:00:00Z"}}, {"id": "10.5281/zenodo.3895003", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:17Z", "type": "Report", "title": "Bioremediation of contaminated waters via microbial electrochemical reactions", "description": "Industrial applications have contaminated environmental waters with complex mixtures of anthropogenic toxic compounds, including chlorinated hydrocarbons and heavy metals. Microbial populations at the contaminated sites can often naturally convert toxic contaminants into harmless end- products, but the efficiency of the remediation can be limited by lack of suitable electron donors or acceptors. Certain micro-organisms can exchange electrons directly or via a mediator with solid matter, such as an electrode in an electrochemical system. This enables the conversion of the chemical energy stored in the contaminants to electrical energy. Alternatively, electrical energy can be used to drive the microbial degradation of the contaminants. Bio-electrochemical systems were inoculated with sediment samples collected from environmental sites contaminated with metals or organic pollutants to enrich electroactive microbial cultures capable of efficient degradation of the contaminants. The enrichment was done in two-chamber bio-electrochemical systems with either bioanodes (for oxidisable contaminants) and/or biocathodes (for reducible contaminants). The required electrical energy input or obtainable output was measured using linear sweep voltammetry. The success of the enrichment was confirmed by the increasing electrical current output. The microbial communities in the original sediment samples and in the enriched biofilms were analysed in order to assess their metabolic potential and the possibilities to improve the remediation efficiency through the modification of the composition of the microbial communities using e.g. selective pressures. This research was done as a part of GREENER -project (InteGRated systems for Effective ENvironmEntal Remediation), developing green and sustainable low-cost bioremediation technologies. The project has received funding from the European Union\u2019s Horizon 2020 Research and Innovation Programme under grant agreement No. 826312.", "keywords": ["13. Climate action", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Sulonen, Mira, Avignone Rossa, Claudio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3895003"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3895003", "name": "item", "description": "10.5281/zenodo.3895003", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3895003"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.3964082", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Lysimeters data from Windmoser and Michel 2020", "description": "Open AccessThe authors thanks the von Humboldt Stiftung for financial support of the MaNiP project through the Max Planck research prize 2013 to Markus Reichstein", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Windmoser, Peter, Perez-Priego, Oscar, El-Madany, Tarek S., Carrara, Arnaud, Kolle, Olaf, Hertel, Martin, L\u00f3pez-Jimenez, Ramon, Reichstein, Markus, Migliavacca, Mirco,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3964082"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3964082", "name": "item", "description": "10.5281/zenodo.3964082", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3964082"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-28T00:00:00Z"}}, {"id": "10.5281/zenodo.4293454", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:18Z", "type": "Dataset", "title": "Soil organic carbon distribution for 0-3 m soils at 1 km2 scale of the frozen ground in the Third Pole Regions", "description": "Soil organic carbon (SOC) is very important in the vulnerable ecological environment of the Third Pole; however, data regarding the spatial distribution of SOC are still scarce and uncertain. Based on multiple environmental variables and soil profile data from 458 pits (depth of 0\u20131 m) and 114 cores (depth of 0\u20133 m), this study uses a machine-learning approach to evaluate the SOC storage and spatial distribution at different soil depths (0\u201330 cm, 0\u201350 cm, 0\u2013100 cm, 0\u2013200 cm, and 0\u2013300 cm) in the frozen ground area of the Third Pole region. Our results provide information on the storage, patterns, and environmental controls of SOCSs at a 1 km2 scale for areas of frozen ground in the Third Pole region, thus providing a scientific basis for future studies pertaining to Earth system models. Soil organic carbon data is stored in grids format, and the file name is 'TP-SOC-d.tif', where d represents soil depth, for example, 'TP-SOC-30.tif' represents the spatial distribution of soil organic carbon stocks in the Third Pole regions of the upper 30 cm depth interval.", "keywords": ["13. Climate action", "15. Life on land"], "contacts": [{"organization": "Wang, Dong, Tonghua Wu, Xiaodong Wu,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4293454"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4293454", "name": "item", "description": "10.5281/zenodo.4293454", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4293454"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-27T00:00:00Z"}}, {"id": "10.5281/zenodo.3832031", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:16Z", "type": "Dataset", "title": "Radiocarbon content of carbon dioxide, methane, dissolved organic carbon and particulate organic carbon from the northern permafrost region and other studies", "description": "The dataset includes 14C measurements of CO2, CH4, DOC and POC mostly from the northern permafrost region. Some other studies are included from sites not underlained by permafrost. The dataset focuses on 14C measurements of gaseous soil emissions and waterborne ecosystem C fluxes but the database also included C forms belowground, such as soil gases and pore water DOC.", "keywords": ["13. Climate action", "15. Life on land", "radiocarbon", " permafrost", " carbon dioxide", " methane", " dissolved organic carbon", " particulate organic carbon", " DOC", " POC", " thermokarst", " thaw"], "contacts": [{"organization": "Estop-Aragon\u00e9s, Cristian", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3832031"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3832031", "name": "item", "description": "10.5281/zenodo.3832031", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3832031"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-18T00:00:00Z"}}, {"id": "10.5281/zenodo.3895002", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:16Z", "type": "Report", "title": "Bioremediation of contaminated waters via microbial electrochemical reactions", "description": "Industrial applications have contaminated environmental waters with complex mixtures of anthropogenic toxic compounds, including chlorinated hydrocarbons and heavy metals. Microbial populations at the contaminated sites can often naturally convert toxic contaminants into harmless end- products, but the efficiency of the remediation can be limited by lack of suitable electron donors or acceptors. Certain micro-organisms can exchange electrons directly or via a mediator with solid matter, such as an electrode in an electrochemical system. This enables the conversion of the chemical energy stored in the contaminants to electrical energy. Alternatively, electrical energy can be used to drive the microbial degradation of the contaminants. Bio-electrochemical systems were inoculated with sediment samples collected from environmental sites contaminated with metals or organic pollutants to enrich electroactive microbial cultures capable of efficient degradation of the contaminants. The enrichment was done in two-chamber bio-electrochemical systems with either bioanodes (for oxidisable contaminants) and/or biocathodes (for reducible contaminants). The required electrical energy input or obtainable output was measured using linear sweep voltammetry. The success of the enrichment was confirmed by the increasing electrical current output. The microbial communities in the original sediment samples and in the enriched biofilms were analysed in order to assess their metabolic potential and the possibilities to improve the remediation efficiency through the modification of the composition of the microbial communities using e.g. selective pressures. This research was done as a part of GREENER -project (InteGRated systems for Effective ENvironmEntal Remediation), developing green and sustainable low-cost bioremediation technologies. The project has received funding from the European Union\u2019s Horizon 2020 Research and Innovation Programme under grant agreement No. 826312.", "keywords": ["13. Climate action", "6. Clean water", "12. Responsible consumption"], "contacts": [{"organization": "Sulonen, Mira, Avignone Rossa, Claudio,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3895002"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3895002", "name": "item", "description": "10.5281/zenodo.3895002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3895002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.3971022", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "VDMBC_vertical_distribution_soil_microbial_biomass_carbon", "description": "Soil microbial biomass carbon (SMBC) is important in regulating soil organic carbon (SOC) dynamics along soil profiles by mediating the decomposition and formation of SOC. The dataset (VDMBC) is about the vertical distributions of SOC, SMBC, and soil microbial quotient (SMQ = SMBC/SOC) and their relations to environmental factors across five continents. Data were collected from literature, with a total of 289 soil profiles and 1040 observations in different soil layers compiled. The associated environment data collectd include climate, ecosystem types, and edaphic factors. We developed this dataset by searching the the Web of Sciene and the China National Knowledge Infrastructure from the year of 1970 to 2019. All the data in this dataset met two creteria: 1) there were at least three mineral soil layers along a soil profile, and 2) SMBC was measured using the fumigation extraction method. The data in tables and texts were obtained from literature directly, and the data in figures were extracted by using the GetData Graph digitizer software version 2.25. When climate and soil properties were not available from publications, we obtainted the data from the World Weather Information Service (https://worldweather.wmo.int/en/home.html) and SoilGrids at a spatial resolution of 250 meters (version 0.5.3, https://soilgrids.org). The units of all the variables were converted to the standard international units or commonly used ones and the values were converted correspondingly. For example, the value of soil organic matter (SOM) was converted to SOC using the equation (SOC = SOM \u00d7 0.58). Soil depth was calculated as the arithmetic mean value of the upper and lower boundaries for a given soil layer. This dataset can be used in predicting global SOC change along soil profiles using the multi-layer soil carbon models. It can also be used to analyse how soil microbial biomass changes with plant roots as well as the composition, structure, and functions of soil microbial communities along soil profiles at large spatial scales. This dataset offers opportunities to improve our prediction of SOC dynamics under global changes and to advance our understanding of the environmental controls.", "keywords": ["2. Zero hunger", "soil organic carbon", "13. Climate action", "deep soils", "soil clay content", "soil C/N ratio", "soil profile", "15. Life on land", "micorbial quotient"], "contacts": [{"organization": "Sun, Tingting, Wang, Yugang, Hui, Dafeng, Jing, Xin, Feng, Wenting,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3971022"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3971022", "name": "item", "description": "10.5281/zenodo.3971022", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3971022"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-08-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4069473", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Supplemental information for McClelland et al. (2020). Management of cover crops in temperate climates influences soil organic carbon stocks \u2013 A meta-analysis", "description": "All supplemental information for McClelland et al. (2020). Management of cover crops in temperate climates influences soil organic carbon stocks \u2013 A meta-analysis.", "keywords": ["2. Zero hunger", "13. Climate action", "Soil organic carbon", "Cover crop", "15. Life on land"], "contacts": [{"organization": "McClelland, Shelby C, Paustian, Keith, Schipanski, Meagan E,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4069473"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4069473", "name": "item", "description": "10.5281/zenodo.4069473", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4069473"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-06T00:00:00Z"}}, {"id": "10.5281/zenodo.4090927", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "iSDAsoil: soil organic carbon for Africa predicted at 30 m resolution at 0-20 and 20-50 cm depths", "description": "Open AccessiSDA is a social enterprise with the mission to improve smallholder farmer profitability across Africa. iSDA builds on the legacy of the African Soils information service (AfSIS) project. We are grateful for the outputs generated by all former AfSIS project partners: Columbia University, Rothamsted Research, World Agroforestry (ICRAF), Quantitative Engineering Design (QED), ISRIC \u2014 World Soil Information, International Institute of Tropical Agriculture (IITA), Ethiopia Soil Information Service (EthioSIS), Ghana Soil Information Service (GhaSIS), Nigeria Soil Information Service (NiSIS) and Tanzania Soil Information Service (TanSIS). More details on AfSIS partners and data contributors can be found at https://isda-africa.com/isdasoil", "keywords": ["2. Zero hunger", "iSDA", "13. Climate action", "organic carbon", "Africa", "15. Life on land", "soil"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4090927"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4090927", "name": "item", "description": "10.5281/zenodo.4090927", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4090927"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4091029", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Mapping the irrecoverable carbon in Earth's ecosystems", "description": "These datasets provide global maps of carbon density (aboveground, belowground biomass carbon and soil organic carbon stocks) for the year 2010 and 2018 at ~300-m spatial resolution in Mg ha-1 (Coordinate System: WGS 1984, float format). Input maps were collected from published literature, and where necessary, updated to cover the focal time period. These updates were applied to the manageable carbon, vulnerable carbon and irrecoverable carbon maps. Manageable carbon is carbon in terrestrial and coastal ecosystems that could experience an anthropogenic land-use conversion event . Vulnerable carbon is the carbon that would be that would be released in a typical land-use conversion. Irrecoverable carbon is the carbon that, if lost, would not recover by mid-century. Datasets are disaggregated for carbon density in biomass or soils. To view these datasets, go to: https://irrecoverable.resilienceatlas.org/map.", "keywords": ["13. Climate action", "14. Life underwater", "15. Life on land", "carbon density", " manageable carbon", " vulnerable carbon", " irrecoverable carbon"], "contacts": [{"organization": "Noon, Monica, Goldstein, Allie, Ledezma, Juan Carlos, Roehrdanz, Patrick, Cook-Patton, Susan C., Spawn-Lee, Seth A., Wright, Timothy Maxwell, Gonzalez-Roglich, Mariano, Hole, David G., Rockstr\u00f6m, Johan, Turner, Will R.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4091029"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4091029", "name": "item", "description": "10.5281/zenodo.4091029", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4091029"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-09-13T00:00:00Z"}}, {"id": "10.5281/zenodo.4139861", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Dataset associated to Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications", "description": "This file contains the dataset associated to the published research article 'Tailored glycosylated anode surfaces: Addressing the exoelectrogen bacterial community via functional layers for microbial fuel cell applications'. The dataset contains Atomic Force Microscopy, electrochemistry, Microbial Fuel Cells power output and water contact angle raw data from their relative instruments. This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sk\u0142odowska-Curie grant agreement No. 799175 (HiBriCarbon). The results of this publication reflect only the authors' view and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No. 13/CDA/2213. The authors also thank the France-Ireland PHC ULYSSES programme for support, project 36028UB. JAB acknowledges support from the Irish Research Council under Grant No. GOIPG/2014/399.", "keywords": ["microbial fuel cells", "exoelectrgenic biofilm", "13. Climate action", "Nanoscience & Materials", "BIOFILM FORMATION", "Fuel Cells", "7. Clean energy"], "contacts": [{"organization": "Iannaci, Alessandro, Myles, Adam, Flinois, Thomas, Behan A., James, Barri\u00e8re, Fr\u00e9d\u00e9ric, Scanlan M., Eoin, Colavita E., Paula,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4139861"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4139861", "name": "item", "description": "10.5281/zenodo.4139861", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4139861"}, {"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.4161694", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Dataset for \"Changes in Global Terrestrial Live Biomass over the 21st Century\"", "description": "Live woody vegetation is the largest reservoir of biomass carbon with its restoration considered one of the most effective natural climate solutions. However, carbon fluxes associated with terrestrial ecosystems still remain the largest source of uncertainty of the global carbon balance. Here, we develop spatially explicit estimates of global carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show live biomass has removed 4.9-5.5 PgC yr-1 from the atmosphere in this century, offsetting 4.6\u00b10.1 PgC yr-1 of gross emissions from land-use and environmental disturbances and adding substantially (0.23-0.88 PgC yr-1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.", "keywords": ["inventory", "remote sensing", "biomass", "13. Climate action", "vegetation", "carbon", "15. Life on land"], "contacts": [{"organization": "Xu, Liang, Saatchi, Sassan S., Yang, Yan, Yu, Yifan, Pongratz, Julia, Bloom, A. Anthony, Bowman, Kevin, Worden, John, Liu, Junjie, Yin, Yi, Domke, Grant, McRoberts, Ronald E., Woodall, Christopher, Nabuurs, Gert-Jan, de-Miguel, Sergio, Keller, Michael, Nancy, Harris, Maxwell, Sean, Schimel, David,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4161694"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4161694", "name": "item", "description": "10.5281/zenodo.4161694", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4161694"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4247969", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Soil Moisture Active/Passive (SMAP) Level 4 Carbon (L4C) Nature Run version 7.2", "description": "Open AccessThe Soil Moisture Active/ Passive (SMAP) Level 4 Carbon (L4C) product is a daily, global, terrestrial carbon budget driven, in part, by soil moisture estimates from the Level 4 Soil Moisture (L4SM) product and, in turn, on brightness temperature observations from the SMAP satellite [1,2]. The SMAP L4C operational product's record begins on March 31, 2015, shortly after the launch of SMAP, and continues to the present, with an average latency of 9 days [3]. SMAP L4C data are posted to a global, 9-km equal-area EASE-Grid 2.0 [4]. In order to improve the longitudinal coverage of the SMAP L4C record, a model-only 'Nature Run' was devised, with daily carbon budget estimates beginning January 1, 2000. The Nature Run differs from the SMAP L4C operational product in the following ways: - The SMAP L4C Nature Run uses the MERRA-2 re-analysis dataset for meteorological driver data, instead of the GEOS-5 FP driver data used in the operational product.
- The SMAP L4C Nature Run uses soil moisture and soil temperature estimates from the L4SM Nature Run, which is a model-only version of the operational L4SM product that does not assimilate SMAP brightness temperature data. This repository contains the full README for the data. The data can be downloaded from: http://files.ntsg.umt.edu/data/SMAP_L4C_NatureRun/NRv7.2/", "keywords": ["carbon flux", "soil organic carbon", "primary productivity", "13. Climate action", "net ecosystem exchange", "15. Life on land", "earth system", "respiration"], "contacts": [{"organization": "Endsley, K. Arthur, Jones, Lucas, Kimball, John,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4247969"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4247969", "name": "item", "description": "10.5281/zenodo.4247969", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4247969"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "10.5281/zenodo.4125709", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:17Z", "type": "Dataset", "title": "Dataset for: Short-term temperature history affects mineralization of fresh litter and extant soil organic matter, irrespective of agricultural management", "description": "Open AccessDataset for the article: Mason-Jones, K., Vrehen, P, Koper, K., Wang, J., van der Putten, W.H., Veen, G.F. 2020. Short-term temperature history affects mineralization of fresh litter and extant soil organic matter, irrespective of agricultural management. Soil Biology and Biochemistry, 150, 107985. Article DOI: 10.1016/j.soilbio.2020.107985", "keywords": ["2. Zero hunger", "13. Climate action", "Analysed data", "Life Science", "Mineralization dynamics", " Temperature sensitivity", " Soil carbon", " Priming effect", "15. Life on land", "Geanalyseerde data"], "contacts": [{"organization": "Mason-Jones, Kyle, Vrehen, Pim, Koper, Kevin, Wang, Jin, van der Putten, Wim H., Veen, G.F. (Ciska),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4125709"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4125709", "name": "item", "description": "10.5281/zenodo.4125709", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4125709"}, {"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.4268490", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:18Z", "type": "Dataset", "title": "Dataset to Manuscript: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Marcus Schiedung et al. (Biogeosciences)", "description": "Dataset to manuscript: Schiedung, M., Bell\ufffd\ufffd, S.-L., Sigmund, G., Kalbitz, K., and Abiven, S.: Vertical mobility of pyrogenic organic matter in soils: A column experiment, Biogeosciences, https://doi.org/10.5194/bg-17-6457-2020, 2020. All parameters and variables are described in 'Var_names' files.", "keywords": ["13C labelling", "13. Climate action", "soil column experiment", "pyrogenic carbon", "soil"], "contacts": [{"organization": "Schiedung, Marcus, Abiven, Samuel,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4268490"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4268490", "name": "item", "description": "10.5281/zenodo.4268490", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4268490"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-11T00:00:00Z"}}, {"id": "10.5281/zenodo.4277166", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:18Z", "type": "Dataset", "title": "Data from: Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon", "description": "In the tundra, woody plants are dispersing towards higher latitudes and altitudes due to increasingly favourable climatic conditions. The coverage and height of woody plants are increasing, which may influence the soils of the tundra ecosystem. Here, we use structural equation modelling to analyse 171 study plots and to examine if the coverage and height of woody plants affect the growing-season topsoil moisture and temperature (< 10 cm) as well as soil organic carbon stocks (< 80 cm). In our study setting, we consider the hierarchy of the ecosystem by controlling for other factors, such as topography, wintertime snow depth and the overall plant coverage that potentially influence woody plants and soil properties in this dwarf-shrub dominated landscape in northern Fennoscandia. We found strong links from topography to both vegetation and soil. Further, we found that woody plants influence multiple soil properties: the dominance of woody plants inversely correlated with soil moisture, soil temperature, and soil organic carbon stocks (standardised regression coefficients = -0.39; -0.22; -0.34, respectively), even when controlling for other landscape features. Our results indicate that the dominance of dwarf shrubs may lead to soils that are drier, colder, and contain less organic carbon. Thus, there are multiple mechanisms through which woody plants may influence tundra soils. Kemppinen, Niittynen, Virkkala, Happonen, Riihim\u00e4ki, Aalto & Luoto (2021). Dwarf shrubs impact tundra soils: drier, colder, and less organic carbon. Ecosystems. These are the data from Kemppinen et al. (2021).", "keywords": ["tundra", "Arctic", "13. Climate action", "carbon cycle", "structural equation model", "15. Life on land", "snow", "shrubification", "microclimate", "dwarf shrubs"], "contacts": [{"organization": "Kemppinen, Julia, Niittynen, Pekka, Virkkala, Anna-Maria, Happonen, Konsta, Riihim\u00e4ki, Henri, Aalto, Juha, Luoto, Miska,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4277166"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4277166", "name": "item", "description": "10.5281/zenodo.4277166", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4277166"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-17T00:00:00Z"}}, {"id": "10.5281/zenodo.4472669", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:18Z", "type": "Report", "title": "EU Citizen Observatories Landscape Report II: Addressing the Challenges of Awareness, Acceptability, and Sustainability", "description": "Citizen Observatories (COs) are community-based environmental monitoring initiatives that invite the public to contribute observations, data and information in complement to authoritative, traditional insitu and remote sensing Earth Observation data. COs can play an important role in crucial areas such as climate change, sustainable development, air monitoring, flood and drought monitoring, land cover or land-use change. They can also provide new data sources for policy-making, and can result in increased citizen participation in environmental management and governance at a large scale. With the increasing prevalence of COs globally, there have been calls for a more integrated approach to handling their complexities, and to sharing knowledge for the design and management of stable, reliable and scalable CO programmes. Answering this challenge in the European context, the Horizon 2020-funded project WeObserve aims to improve coordination between existing COs and related European activities, while tackling three key challenges that inhibit the mainstreaming of citizen science, namely: Awareness, Acceptability, and Sustainability. This D2.4 Landscape Report frames the second part of a dynamic exercise to examine the three core challenges faced by these COs, and to consolidate the experience of a range of stakeholders into a set of recommendations for strengthening the ecosystem around COs in Europe.", "keywords": ["0301 basic medicine", "citizen science", " citizen observatories", "0303 health sciences", "03 medical and health sciences", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4472669"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4472669", "name": "item", "description": "10.5281/zenodo.4472669", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4472669"}, {"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.4476816", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Report", "title": "Protocols for sampling general, general soil characterization and soil biodiversity analysis", "description": "This handbook presents the methods to carry out all the analyses that will allow establishing the characteristics of the different pedoclimatic regions. The protocols considered in this handbook are:
- Soil sampling (oriented to determine dry bulk density, biodiversity or physicochemical characteristics).
- Earthworm sampling.
- Physical characterization of the soil including determination of dry bulk density, coarse fragments, humidity, particle size distribution and aggregates.
- Soil chemical characterization including determination of pH, content of organic matter, organic and inorganic carbon, total and inorganic nitrogen, available phosphorus, potassium, calcium, magnesium, effective exchange capacity, available micronutrients (iron, manganese, copper and zinc), and pesticides.
- Soil biological analyses that include biodiversity measurements for earthworms, nematodes and microorganisms (fungi and prokaryotes). This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "analysis", "15. Life on land", "handbook", "soil"], "contacts": [{"organization": "Fern\ufffd\ufffdndez Calvi\ufffd\ufffdo, David, Soto G\ufffd\ufffdmez, Diego, Koefoed Brandt, Kristian, Waeyenberge, Lieven,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4476816"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4476816", "name": "item", "description": "10.5281/zenodo.4476816", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4476816"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4472670", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:19Z", "type": "Report", "title": "EU Citizen Observatories Landscape Report II: Addressing the Challenges of Awareness, Acceptability, and Sustainability", "description": "Citizen Observatories (COs) are community-based environmental monitoring initiatives that invite the public to contribute observations, data and information in complement to authoritative, traditional insitu and remote sensing Earth Observation data. COs can play an important role in crucial areas such as climate change, sustainable development, air monitoring, flood and drought monitoring, land cover or land-use change. They can also provide new data sources for policy-making, and can result in increased citizen participation in environmental management and governance at a large scale. With the increasing prevalence of COs globally, there have been calls for a more integrated approach to handling their complexities, and to sharing knowledge for the design and management of stable, reliable and scalable CO programmes. Answering this challenge in the European context, the Horizon 2020-funded project WeObserve aims to improve coordination between existing COs and related European activities, while tackling three key challenges that inhibit the mainstreaming of citizen science, namely: Awareness, Acceptability, and Sustainability. This D2.4 Landscape Report frames the second part of a dynamic exercise to examine the three core challenges faced by these COs, and to consolidate the experience of a range of stakeholders into a set of recommendations for strengthening the ecosystem around COs in Europe.", "keywords": ["0301 basic medicine", "citizen science", " citizen observatories", "0303 health sciences", "03 medical and health sciences", "13. Climate action", "11. Sustainability", "15. Life on land", "12. Responsible consumption"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4472670"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4472670", "name": "item", "description": "10.5281/zenodo.4472670", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4472670"}, {"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.4486578", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Other", "title": "GREENER Project official video: InteGRated systems for Effective ENvironmEntal Remediation", "description": "Find out more about the GREENER's goals and technologies applied, aiming to remediate a range of organic and inorganic pollutants of high concern, while producing useful end-products, such as bioelectricity and harmless metabolites.", "keywords": ["13. Climate action", "remediation", " soil", " water", " sediments", "6. Clean water"], "contacts": [{"organization": "Axia Innovation", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4486578"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4486578", "name": "item", "description": "10.5281/zenodo.4486578", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4486578"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4486741", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Report", "title": "GREENER Project Newsletter Issue 2", "description": "GREENER Project Newsletter Issue 2 on InteGRated systems for Effective ENvironmEntal Remediation", "keywords": ["13. Climate action"], "contacts": [{"organization": "Axia Innovation", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4486741"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4486741", "name": "item", "description": "10.5281/zenodo.4486741", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4486741"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4486740", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Report", "title": "GREENER Project Newsletter Issue 2", "description": "GREENER Project Newsletter Issue 2 on InteGRated systems for Effective ENvironmEntal Remediation", "keywords": ["13. Climate action"], "contacts": [{"organization": "Axia Innovation", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4486740"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4486740", "name": "item", "description": "10.5281/zenodo.4486740", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4486740"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-02-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4487144", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:19Z", "type": "Dataset", "title": "Eddy Covariance data from ICOS-associated station IT-NIV \u2013 August-November 2019", "description": "RestrictedData stored here refer to Eddy Covariance (EC) data measured in 2019 between August and November at the Alpine CZO (Critical Zone Observatory, hereafter CZO@Nivolet) which was established at the Nivolet Plain (Piani del Nivolet) in the Gran Paradiso National Park (GPNP), located in the western Italian Alps. The EC site (IT-NIV) is an ICOS-associated station. CZO@Nivolet is aimed at investigating the cross-scale interactions between climatic shifts and ecosystem functions multiple scales, involving multidisciplinary studies. The main research questions that we aim to answer are concerning: (a) the effect of bedrock lithology, soil physics and chemisty, topographic hetereogenity, biotic components and meteo-climatic parameters in modulating CO2 flux in alpine grassland; and (b) what are the controlling factors of organic C and weathering under geologic substrates and different topographic positions. The investigations started in 2017. In 2019, the EC tower was added to deeply study CO2, H20, latent and sensible heat exchanges between soil, vegetation, and atmosphere. Carbon dioxide fluxes and environmental variables are recorded during the snow-free season to estimate carbon storage and explore CO2 fluxes drivers in high-altitude grasslands. Further developments will regard the integration of different techniques (Eddy Covariance, Remote Sensing, Flux chambers) to improve both spatial and temporal extent of carbon fluxes estimates to finally assess grasslands' productivity.", "keywords": ["13. Climate action", "alpine grassland", "15. Life on land", "Gran Paradiso National Park", "Mountain", "EO_Data", "Eddy Covariance", "Net Ecosystem Exchange", "ecosystem-atmosphere carbon exchange"], "contacts": [{"organization": "Vivaldo, Gianna, Raco, Brunella, Baneschi, Ilaria, Giamberini, Maria Silvia,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4487144"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4487144", "name": "item", "description": "10.5281/zenodo.4487144", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4487144"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.4519230", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:19Z", "type": "Report", "title": "Protocols for sampling general, general soil characterization and soil biodiversity analysis", "description": "This handbook presents the methods to carry out all the analyses that will allow establishing the characteristics of the different pedoclimatic regions. The protocols considered in this handbook are:
- Soil sampling (oriented to determine dry bulk density, biodiversity or physicochemical characteristics).
- Earthworm sampling.
- Physical characterization of the soil including determination of dry bulk density, coarse fragments, humidity, particle size distribution and aggregates.
- Soil chemical characterization including determination of pH, content of organic matter, organic and inorganic carbon, total and inorganic nitrogen, available phosphorus, potassium, calcium, magnesium, effective exchange capacity, available micronutrients (iron, manganese, copper and zinc), and pesticides.
- Soil biological analyses that include biodiversity measurements for earthworms, nematodes and microorganisms (fungi and prokaryotes). This work was funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "analysis", "15. Life on land", "handbook", "soil"], "contacts": [{"organization": "Fern\ufffd\ufffdndez Calvi\ufffd\ufffdo, David, Soto G\ufffd\ufffdmez, Diego, Koefoed Brandt, Kristian, Waeyenberge, Lieven,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4519230"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4519230", "name": "item", "description": "10.5281/zenodo.4519230", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4519230"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-07-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4581699", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:25:20Z", "type": "Journal Article", "title": "Accretional soil formation in northern hemisphere loess regions - evidence from OSL- dating of the Pleistocene-Holocene climatic transition from China, Europe and North America", "description": "Loess deposits intercalated by paleosols are detailed terrestrial archives of Quaternary climate variability providing information on the global dust cycle and landscape dynamics. Their paleoclimate significance is most often explored by quantifying the
mineral magnetic properties due to their sensitivity to local/regional hydroclimate variability. Detailed chronological assessment of such regional proxy records around the climatic transitions allow a better understanding of how regional records react to
major global climatic transitions such as the Pleistocene-Holocene climatic transition.
First, logs of high-resolution magnetic susceptibility and its frequency dependence were used as paleoclimatic proxies to define the environmental transition from the last glacial loess to the current interglacial soil as reflected in nine loess-paleosol
sequences across the northern hemisphere, from the Chinese Loess Plateau, the southeastern European loess belt and the central Great Plains, Nebraska, USA. Second, the onset of increase in their values above typical loess values was used to assess the onset of, and developments during, the Pleistocene-Holocene climatic transition. High-resolution luminescence dating is applied on multiple grain-sizes (4-11 \u03bcm, 63-90\u03bcm, 90-125 \u03bcm) of quartz extracts from the same sample in order to investigate the
timing of Pleistocene-Holocene climatic transition in the investigated sites. The magnetic susceptibility signal shows a smooth and gradual increase for the majority of the sites from the typical low loess values to the interglacial ones. The initiation of this increase, interpreted as recording the initiation of the Pleistocene-Holocene climatic transition at each site, was dated to 14-17.5 ka or even earlier. Our results highlight the need of combining paleoclimatic proxies (magnetic susceptibility) with absolute dating when investigating the Pleistocene-Holocene climatic transition as reflected by the evolution of this proxy in order to avoid chronostratigraphic misinterpretations in loess-paleosol records caused by simple pattern correlation. The detailed luminescence chronologies evidence the continuity of eolian mineral dust accumulation regardless of glacial or interglacial global climatic regimes. Coupled with magnetic susceptibility records it indicates that dust sedimentation and pedogenesis act simultaneously and result in the formation of accretional Holocene soils in loess regions across the Northern Hemisphere. The luminescence ages allowed the
modelling of accumulation rates for the Holocene soil which are similar for European, Chinese and U.S. loess sites investigated and vary from 2 cm ka-1 to 9 cm ka-1. While accretional pedogenesis has often been implicitly or explicitly assumed in paleoclimatic
interpretation of loess-paleosol sequences, especially in the Chinese Loess Plateau, our luminescence data add direct evidence for ongoing sedimentation as soils formed.", "keywords": ["13. Climate action", "15. Life on land"], "contacts": [{"organization": "Daniela, Constantin, Joseph, Mason, Veres Daniel, Hambach Ulrich, Panaiotu Cristian, Zeeden Christian, Liping, Zhou, Markovi\u0107 Slobodan, Gerasimenko Natalia, Anca, Avram, Tecsa Viorica, Sacaciu-Groza Madalina, Laura, Del Valle Villalonga, Begy Robert, Timar-Gabor Alida,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4581699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Earth%20Science%20Reviews", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4581699", "name": "item", "description": "10.5281/zenodo.4581699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4581699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4574684", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:19Z", "type": "Dataset", "title": "Data for \"Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: implications for forest ecosystem carbon cycling\"", "description": "One csv file named \u201cData for Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: implications for forest ecosystem carbon cycling\u201d contains data of soil total respiration (Rs), soil heterotrophic respiration (Rh), soil autotrophic respiration (Ra), soil temperature for total soil respiration at 5cm (T_for_ Rs), soil temperature for heterotrophic respiration in trenched point at 5cm (T_for_TE), soil moisture for total soil respiration at 5 cm (W_for_ Rs), soil water content for heterotrophic respiration in trenched point at 5cm (W_for_TE), soil microbial biomass carbon (MBC), soil pH value (pH), fine roots biomass (FR), soil ammonium-nitrogen (NH4), soil organic carbon (SOC), soil nitrate-nitrogen (NO3) in control and burned plots were collected from 2017/05/15 to 2018/4/15. Another csv file named of \u201cData Dictionary\u201d contains the description of information the exact row header name of csv file named \u201cData for Effects of fire on soil respiration and its components in a Dahurian larch (Larix gmelinii) forest in northeast China: implications for forest ecosystem carbon cycling\u201d.", "keywords": ["2. Zero hunger", "13. Climate action", "Fire ecology", "Forestry", "15. Life on land"], "contacts": [{"organization": "Tongxin, Hu, Long, Sun,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4574684"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4574684", "name": "item", "description": "10.5281/zenodo.4574684", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4574684"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4643557", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:20Z", "type": "Dataset", "title": "Cd K-edge XAS spectra of Cd reference compounds relevant to the soil/plant system", "description": "Here we present a library of Cd reference compounds Cd K-edge XAS spectra relevant to soil and plant samples. We reported mu (eV) .txt files for each reference as well as a .pdf file that gives details on the preparation of the references and on the acquisition of the spectra. Additional information can be found in Pons et al., 2021, Environmental Pollution. https://doi.org/10.1016/j.envpol.2021.116897", "keywords": ["XAS spectroscopy", "reference compounds", "13. Climate action", "X-ray absorption spectroscopy", "Cadmium"], "contacts": [{"organization": "Pons, Marie-Laure", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4643557"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4643557", "name": "item", "description": "10.5281/zenodo.4643557", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4643557"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.5281/zenodo.4643558", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:20Z", "type": "Dataset", "title": "Cd K-edge XAS spectra of Cd reference compounds relevant to the soil/plant system", "description": "Here we present a library of Cd reference compounds Cd K-edge XAS spectra relevant to soil and plant samples. We reported mu (eV) .txt files for each reference as well as a .pdf file that gives details on the preparation of the references and on the acquisition of the spectra. Additional information can be found in Pons et al., 2021, Environmental Pollution. https://doi.org/10.1016/j.envpol.2021.116897", "keywords": ["XAS spectroscopy", "reference compounds", "13. Climate action", "X-ray absorption spectroscopy", "Cadmium"], "contacts": [{"organization": "Pons, Marie-Laure", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4643558"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4643558", "name": "item", "description": "10.5281/zenodo.4643558", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4643558"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-29T00:00:00Z"}}, {"id": "10.5281/zenodo.4650290", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:20Z", "type": "Journal Article", "title": "Circular economy in the dairy industry: Processing wastes to P-rich bio-based fertilisers", "description": "Sergio Pons\u00e1 talks about the valorization of the wastes generated by the dairy industry to recover phosphorous, a relevant macronutrient for plant growth that currently depends on a critical raw material. REFLOW, an interdisciplinary cross-sectoral H2020 European Training Network project, will develop and provide a solid economic and environmental alternative to inorganic fertilizers by delivering cost-effective, nutritive and safe standardised Bio Based Fertilizers (BBF) products. In accordance with the circular economy framework, new fertilizer products will increase, or at least maintain, actual production yields while avoiding environmental impacts in the dairy processing industry.", "keywords": ["2. Zero hunger", "Bio Based Fertlizers", "Waste Management", "13. Climate action", "Phosphorus", "Circular Economy", "Dairy Industry", "7. Clean energy", "12. Responsible consumption"], "contacts": [{"organization": "Salas, Sergio Pons\u00e1", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4650290"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Open%20Access%20Government", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4650290", "name": "item", "description": "10.5281/zenodo.4650290", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4650290"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4647078", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:20Z", "type": "Dataset", "title": "Carbon dioxide fluxes and carbon balance of an agricultural grassland in southern Finland", "description": "The data set contains CO2 and H2O eddy covariance flux data and meteorological measurements from agricultural grassland site at Qvidja in southern Finland (60.29550\u00b0N, 22.39281\u00b0E, elevation 5 m) measured in 2018 - 2020. Additionally, the data set contains leaf area index data and measurements of soil organic carbon content. Fluxes_Heimsch_et_al_2020.csv contains the CO2 and H2O flux data. Meteorology_Heimsch_et_al_2020.csv and Precipitation_Heimsch_et_al_2020.csv contain the meteorological measurements. LAI_Heimsch_et_al_2020.csv contains the leaf area index data obtained from the Sentinel-2 satellite. SOC_Heimsch_et_al_2020.xlsx contains the measurements of soil organic carbon from five soil core samples.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Heimsch, Laura, Lohila, Annalea, Tuovinen, Juha-Pekka, Vekuri, Henriikka, Heinonsalo, Jussi, Nevalainen, Olli, Korkiakoski, Mika, Liski, Jari, Laurila, Tuomas, Kulmala, Liisa,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4647078"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4647078", "name": "item", "description": "10.5281/zenodo.4647078", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4647078"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-30T00:00:00Z"}}, {"id": "10.5281/zenodo.4745479", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:20Z", "type": "Dataset", "title": "Soil carbon loss in warmed subarctic grasslands is rapid and restricted to topsoil", "description": "This file includes the variable description of the data files published on Zenodo for the manuscript submitted to Biogeosciences titled 'Soil carbon loss in warmed subarctic grasslands is rapid and restricted to topsoil' 'stocks_data.csv'; includes the C stock data
grassland: medium-term warmed (MTW) or long-term warmed (LTW) grassland
sampling_year: year in which the soil samples were taken (2013 or 2018)
SOC_stock_corr: Mass-corrected soil organic carbon stock
SOC_stock: Uncorrected soil organic carbon stock
C_perc: soil C % of the soil sample
warming: degrees of soil warming (\u00b0C)
ID: plot soil ID
soil_layer: layer of soil sampled (0-10cm or 10-30cm)
BD: bulk density (g cm-3)
YOW: Years of warming; years the soil was warmed before sampling
'C_input_data.csv'; includes the C input data
ID: plot soil ID
grassland: medium-term warmed (MTW) or long-term warmed (LTW) grassland
AMF_Cnew: C sequestered by AMF (ton C ha-1); data from Zhang et al., 2020
fine_root_C: fine root C in soil (ton C ha-1)
AGB_C: aboveground biomass C on top of soil (ton C ha-1)
warming: degrees of soil warming (\u00b0C) 'agg_data.csv'; includes the aggregate fractionation data
ID: plot soil ID
grassland: medium-term warmed (MTW) or long-term warmed (LTW) grassland
warming: degrees of soil warming (\u00b0C)
rel_mass: Relative aggregate fraction (%)
C_perc: C amount in the aggregate fraction (%)
abs_C_mass: absolute amount of C per fraction (g C 100 g-1 dw soil) 'DOC_data.csv'; includes the dissolved organic carbon leachate data (data from Edlinger, 2016)
year: sampling year of the leachates
grassland: medium-term warmed (MTW) or long-term warmed (LTW) grassland
ID: plot soil ID
warming: degrees of soil warming (\u00b0C)
DOC: dissolved organic carbon (ppm) 'T_profile_LTW.csv'; includes temperature profile data from the long-term warmed grassland
grassland: long-term warmed (LTW) grassland
ID: plot soil ID
depth: depth (cm) at which the the temperature was measured
T_diff_from_10cm: temperature difference compared to 10cm depth (\u00b0C) 'fine_root_LTW.csv'; includes fine root density data from the long-term warmed grassland
warming: degrees of soil warming (\u00b0C)
fine_root_density: fine root density (mg roots cm-3)
soil_layer: layer of soil sampled (0-10cm or 10-30cm)
grassland: long-term warmed (LTW) grassland
ID: plot soil ID", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Verbrigghe, Niel, Leblans, Niki I. W., Sigurdsson, Bjarni D., Vicca, Sara, Fang, Chao, Fuchslueger, Lucia, Soong, Jennifer L., Weedon, James T., Poeplau, Christopher, Ariza-Carricondo, Cristina, Bahn, Michael, Guenet, Bertrand, Gundersen, Per, Gunnarsd\ufffd\ufffdttir, Gunnhildur E., K\ufffd\ufffdtterer, Thomas, Liu, Zhanfeng, Maljanen, Marja, Mara\ufffd\ufffd\ufffd\ufffdn-Jim\ufffd\ufffdnez, Sara, Meeran, Kathiravan, Oddsd\ufffd\ufffdttir, Edda S., Ostonen, Ivika, Pe\ufffd\ufffdelas, Josep, Richter, Andreas, Sardans, Jordi, Sigur\ufffd\ufffdsson, P\ufffd\ufffdll, Van Bodegom, Peter M., Verbruggen, Erik, Walker, Tom W. N., Wallander, H\ufffd\ufffdkan, Janssens, Ivan A.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4745479"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4745479", "name": "item", "description": "10.5281/zenodo.4745479", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4745479"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-10T00:00:00Z"}}, {"id": "10.5281/zenodo.4884672", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "Soil microbial, fungal and bacterial abundance impacted by crop diversification, tillage and fertilizer type", "description": "This data set contains a data-mining performed to assess the impact of crop diversification, tillage and fertilizer type on soil microbial, fungal and bacterial abundance under arable crops worldwide by a further meta-analysis of the data. These data correspond to the open-access article ' The impact of crop diversification, tillage and fertilization type on soil total microbial, fungal and bacterial abundance: A worldwide meta-analysis of agricultural sites ' published in Agriculture Ecosystems and Environment. (doi:10.1016/j.agee.2022.107867), funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Morug\u00e1n-Coronado, Alicia, P\u00e9rez-Rodr\u00edguez, Paula, Insolia, Eliana, Soto-G\u00f3mez, Diego, Fern\u00e1ndez-Calvi\u00f1o, David, Zornoza, Ra\u00fal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4884672"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4884672", "name": "item", "description": "10.5281/zenodo.4884672", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4884672"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-31T00:00:00Z"}}, {"id": "10.5281/zenodo.5348287", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:22Z", "type": "Dataset", "title": "Annual maps of cropland abandonment, land cover, and other derived data for time-series analysis of cropland abandonment", "description": "Open AccessThis archive contains raw annual land cover maps, cropland abandonment maps, and accompanying derived data products to support: Crawford C.L., Yin, H., Radeloff, V.C., and Wilcove, D.S. 2022. Rural land abandonment is too ephemeral to provide major benefits for biodiversity and climate. Science Advances doi.org/10.1126/sciadv.abm8999. An archive of the analysis scripts developed for this project can be found at: https://github.com/chriscra/abandonment_trajectories (https://doi.org/10.5281/zenodo.6383127). Note that the label '_2022_02_07' in many file names refers to the date of the primary analysis. 'dts\u201d or \u201cdt\u201d refer to \u201cdata.tables,' large .csv files that were manipulated using the data.table package in R (Dowle and Srinivasan 2021, http://r-datatable.com/). \u201cRasters\u201d refer to \u201c.tif\u201d files that were processed using the raster and terra packages in R (Hijmans, 2022; https://rspatial.org/terra/; https://rspatial.org/raster). Data files fall into one of four categories of data derived during our analysis of abandonment: observed, potential, maximum, or recultivation. Derived datasets also follow the same naming convention, though are aggregated across sites. These four categories are as follows (using \u201cage_dts\u201d for our site in Shaanxi Province, China as an example): observed abandonment identified through our primary analysis, with a threshold of five years. These files do not have a specific label beyond the description of the file and the date of analysis (e.g., shaanxi_age_2022_02_07.csv); potential abandonment for a scenario without any recultivation, in which abandoned croplands are left abandoned from the year of initial abandonment through the end of the time series, with the label \u201c_potential\u201d (e.g., shaanxi_potential_age_2022_02_07.csv); maximum age of abandonment over the course of the time series, with the label \u201c_max\u201d (e.g., shaanxi_max_age_2022_02_07.csv); recultivation periods, corresponding to the lengths of recultivation periods following abandonment, given the label \u201c_recult\u201d (e.g., shaanxi_recult_age_2022_02_07.csv). This archive includes multiple .zip files, the contents of which are described below: age_dts.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for, as of that year, also referred to as length, duration, etc.), for each year between 1987-2017 for all 11 sites. These maps are stored as .csv files, where each row is a pixel, the first two columns refer to the x and y coordinates (in terms of longitude and latitude), and subsequent columns contain the abandonment age values for an individual year (where years are labeled with 'y' followed by the year, e.g., 'y1987'). Maps are given with a latitude and longitude coordinate reference system. Folder contains observed age, potential age (\u201c_potential\u201d), maximum age (\u201c_max\u201d), and recultivation lengths (\u201c_recult\u201d) for all sites. Maximum age .csv files include only three columns: x, y, and the maximum length (i.e., \u201cmax age\u201d, in years) for each pixel throughout the entire time series (1987-2017). Files were produced using the custom functions 'cc_filter_abn_dt(),' \u201ccc_calc_max_age(),' \u201ccc_calc_potential_age(),\u201d and \u201ccc_calc_recult_age();\u201d see '_util/_util_functions.R.' age_rasters.zip - Maps of abandonment age (i.e., how long each pixel has been abandoned for), for each year between 1987-2017 for all 11 sites. Maps are stored as .tif files, where each band corresponds to one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Folder contains observed age, potential age (\u201c_potential\u201d), and maximum age (\u201c_max\u201d) rasters for all sites. Maximum age rasters include just one band (\u201clayer\u201d). These rasters match the corresponding .csv files contained in 'age_dts.zip.\u201d derived_data.zip - summary datasets created throughout this analysis, listed below. diff.zip - .csv files for each of our eleven sites containing the year-to-year lagged differences in abandonment age (i.e., length of time abandoned) for each pixel. The rows correspond to a single pixel of land, and the columns refer to the year the difference is in reference to. These rows do not have longitude or latitude values associated with them; however, rows correspond to the same rows in the .csv files in 'input_data.tables.zip' and 'age_dts.zip.' These files were produced using the custom function 'cc_diff_dt()' (much like the base R function 'diff()'), contained within the custom function 'cc_filter_abn_dt()' (see '_util/_util_functions.R'). Folder contains diff files for observed abandonment, potential abandonment (\u201c_potential\u201d), and recultivation lengths (\u201c_recult\u201d) for all sites. input_dts.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment (https://doi.org/10.1016/j.rse.2020.111873). Like \u201cage_dts,\u201d these maps are stored as .csv files, where each row is a pixel and the first two columns refer to x and y coordinates (in terms of longitude and latitude). Subsequent columns contain the land cover class for an individual year (e.g., 'y1987'). Note that these maps were recoded from Yin et al. 2020 so that land cover classification was consistent across sites (see below). This contains two files for each site: the raw land cover maps from Yin et al. 2020 (after recoding), and a \u201cclean\u201d version produced by applying 5- and 8-year temporal filters to the raw input (see custom function \u201ccc_temporal_filter_lc(),\u201d in \u201c_util/_util_functions.R\u201d and \u201c1_prep_r_to_dt.R\u201d). These files correspond to those in 'input_rasters.zip,' and serve as the primary inputs for the analysis. input_rasters.zip - annual land cover maps for eleven sites with four land cover classes (see below), adapted from Yin et al. 2020 Remote Sensing of Environment. Maps are stored as '.tif' files, where each band corresponds one of the 31 years in our analysis (1987-2017), in ascending order (i.e., the first layer is 1987 and the 31st layer is 2017). Maps are given with a latitude and longitude coordinate reference system. Note that these maps were recoded so that land cover classes matched across sites (see below). Contains two files for each site: the raw land cover maps (after recoding), and a \u201cclean\u201d version that has been processed with 5- and 8-year temporal filters (see above). These files match those in 'input_dts.zip.' length.zip - .csv files containing the length (i.e., age or duration, in years) of each distinct individual period of abandonment at each site. This folder contains length files for observed and potential abandonment, as well as recultivation lengths. Produced using the custom function 'cc_filter_abn_dt()' and \u201ccc_extract_length();\u201d see '_util/_util_functions.R.' derived_data.zip contains the following files: 'site_df.csv' - a simple .csv containing descriptive information for each of our eleven sites, along with the original land cover codes used by Yin et al. 2020 (updated so that all eleven sites in how land cover classes were coded; see below). Primary derived datasets for both observed abandonment (\u201carea_dat\u201d) and potential abandonment (\u201cpotential_area_dat\u201d). area_dat - Shows the area (in ha) in each land cover class at each site in each year (1987-2017), along with the area of cropland abandoned in each year following a five-year abandonment threshold (abandoned for >=5 years) or no threshold (abandoned for >=1 years). Produced using custom functions 'cc_calc_area_per_lc_abn()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' persistence_dat - A .csv containing the area of cropland abandoned (ha) for a given 'cohort' of abandoned cropland (i.e., a group of cropland abandoned in the same year, also called 'year_abn') in a specific year. This area is also given as a proportion of the initial area abandoned in each cohort, or the area of each cohort when it was first classified as abandoned at year 5 ('initial_area_abn'). The 'age' is given as the number of years since a given cohort of abandoned cropland was last actively cultivated, and 'time' is marked relative to the 5th year, when our five-year definition first classifies that land as abandoned (and where the proportion of abandoned land remaining abandoned is 1). Produced using custom functions 'cc_calc_persistence()' via 'cc_summarize_abn_dts()'. See scripts 'cluster/2_analyze_abn.R' and '_util/_util_functions.R.' This serves as the main input for our linear models of recultivation (\u201cdecay\u201d) trajectories. turnover_dat - A .csv showing the annual gross gain, annual gross loss, and annual net change in the area (in ha) of abandoned cropland at each site in each year of the time series. Produced using custom functions 'cc_calc_abn_diff()' via 'cc_summarize_abn_dts()' (see '_util/_util_functions.R'), implemented in 'cluster/2_analyze_abn.R.' This file is only produced for observed abandonment. Area summary files (for observed abandonment only) area_summary_df - Contains a range of summary values relating to the area of cropland abandonment for each of our eleven sites. All area values are given in hectares (ha) unless stated otherwise. It contains 16 variables as columns, including 1) 'site,' 2) 'total_site_area_ha_2017' - the total site area (ha) in 2017, 3) 'cropland_area_1987' - the area in cropland in 1987 (ha), 4) 'area_abn_ha_2017' - the area of cropland abandoned as of 2017 (ha), 5) 'area_ever_abn_ha' - the total area of those pixels that were abandoned at least once during the time series (corresponding to the area of potential abandonment, as of 2017), 6) 'total_crop_extent_ha' - the total area of those pixels that were classified as cropland at least once during the time series, 7) 'total_area_abn_remaining_2017' - duplicate of 'area_abn_ha_2017,' the area abandoned as of 2017 (ha), taken from 'area_recult_threshold,' 8) 'total_initial_area_abn' - the sum of the initial area of each cohort of abandonment when it is first classified as 'abandoned,' i.e., at the 5 year mark (note that this is cumulative, and because it counts those pixels that were abandoned more than once, it is therefore larger than 'area_ever_abn_ha'), taken from 'area_recult_threshold' 9) 'total_area_abn_recultivated_2017' - the area of abandoned land that was recultivated as of 2017 (cumulatively, i.e., 'total_initial_area_abn' - 'area_abn_ha_2017'), taken from 'area_recult_threshold,' 10) 'proportion_recultivated' - the proportion of all abandoned cropland (including multiple periods per pixel) that was recultivated by 2017, taken from 'area_recult_threshold,' 11) 'area_2017_as_prop_site' - area abandoned as of 2017 as a proportion of the total site area, 12) 'area_2017_as_prop_total_crop' - area abandoned as of 2017 as a proportion of the total crop extent, 13) 'area_2017_as_prop_crop87' - area abandoned as of 2017 as a proportion of cropland area in 1987, 14) 'area_ever_abn_as_prop_site' - area ever abandoned as a proportion of the total site area, 15) 'area_ever_abn_as_prop_total_crop' - area ever abandoned as a proportion of the total crop extent, 16) 'area_ever_abn_as_prop_crop87' - area ever abandoned as a proportion of cropland area in 1987. See script '1_summary_stats.Rmd.' area_recult_threshold - Contains data on the proportion of observed abandoned cropland area that is recultivated by the end of our time series. This includes the area of abandoned cropland as of 2017 ('total_area_abn_remaining_2017') and the sum of the initial area of each cohort of abandonment when it is first classified as abandoned (at year 5; 'total_initial_area_abn'). This 'total_initial_area_abn' is cumulative, and allows for pixels that were abandoned multiple times during the time series to be counted multiple times. The difference between these two columns yields the 'total_area_abn_recultivated_2017,' which in turn is used to calculate the 'proportion_recultivated,' and the (ascending) 'order' of sites based on this proportion. This file includes recultivation stats for each site for three abandonment definitions: 5, 7, and 10 years. See script '1_summary_stats.Rmd.' abn_lc_area_2017 - Contains the number of pixels and corresponding area (in ha) of abandoned cropland in the year 2017 at each site, according to the land cover class (either woody vegetation [2], or herbaceous vegetation [4]) and the age in 2017 (5 to 30 years). See script 'cluster/6_lc_of_abn.R.' abn_prop_lc_2017 - Contains the number of pixels and corresponding area (ha) of cropland abandoned in the year 2017 in each land cover type (woody vegetation [2], or herbaceous vegetation [4]). It also shows this area as a proportion of the total area abandoned at each site (i.e., in either land cover class: 2 or 4). See script 'cluster/6_lc_of_abn.R.' Carbon carbon_df \u2013 contains the observed and potential carbon accumulation in abandoned croplands in each site in each year (in Mg C), for two abandonment thresholds: 5 years (our default abandonment definition) and 1 year (i.e., no threshold). Each data point corresponds to one of two scenarios (\u201ctype\u201d column), either \u201cobserved\u201d or \u201cpotential.\u201d Carbon accumulation figures are for both the sum of forest and soil carbon at each site in a given year. Carbon accumulation is listed in three columns: 1) \u201cC_up_to_20\u201d contains the total carbon accumulated in those abandoned croplands with abandonment durations between 5 and 20 years. 2) \u201cC_21_30\u201d contains the total carbon accumulation in croplands with durations between 21 and 30 years, which are differentiated in order to account for non-linear carbon accumulation rates in soils over time, and 3) \u201ctotal_C_Mg\u201d contains the sum of the previous two columns, representing the total carbon accumulated across all abandoned croplands in each year. soc_mean \u2013 contains mean soil organic carbon accumulation rates for years 1-20 and years 21-80, derived from Sanderman et al. 2020 (in Mg C; https://doi.org/10.7910/DVN/HA17D3). These values correspond to accumulation rates in croplands upon abandonment and regeneration to natural vegetation (Sanderman et al. 2020\u2019s \u201crewilding\u201d scenario). These mean values are calculated across those pixels identified as cropland by Sanderman et al. 2020 at each site. Mean values in year 20 and 80 are contained in columns \u201cmean_soc_20\u201d and \u201cmean_soc_80\u201d respectively, and the annualized rate over the first 20 years and the subsequent years 21 through 80 are contained in columns \u201cmean_annual_soc_1_20\u201d and \u201cmean_annual_soc_21_80\u201d respectively. Decay model data \u2013 two R data files containing data products for our linear models of abandonment recultivation trajectories. decay_endpoints_files \u2013 an R data file (.rds) containing seven data products produced as part of our common endpoint analysis, which calculated mean trajectories for each site across a range of common endpoints, ensuring that means were based on coefficient estimates derived from a consistent number of observations for each cohort. These files are: common_endpoint_dat \u2013 a .csv containing subsets of \u201cpersistence_dat\u201d for each \u201cendpoint\u201d (7 through 29). endpoint_n \u2013 a .csv describing, for each endpoint, the corresponding number of observations per cohort (\u201cn_obs\u201d), the number of cohorts (\u201cn_cohorts\u201d), the total number of observations across cohorts included (\u201ctotal_obs\u201d), and the cohorts that meet the endpoint threshold (\u201ccohorts\u201d). coef_l3_endpoints \u2013 corresponding model coefficients for our primary model (\u201cl3\u201d) parameterized by the range of subsets across endpoints. augment_endpoints \u2013 fitted values (i.e., model predictions) for linear models produced across the full range of endpoint subsets. fitted_endpoints \u2013 a simplified .csv containing the mean linear and log coefficients for each site at each endpoint, and the corresponding predicted proportion remaining abandoned through time (based on the \u201cage,\u201d or duration, of abandonment). time_to_endpoints \u2013 a .csv containing, for mean trajectories for each endpoint at each site, the estimated time required for a given amount of abandoned cropland in a cohort to be recultivated (deciles, 10% through 100%). endpoint_half_lives \u2013 a .csv containing the half-lives calculated for the mean trajectories for each endpoint at each site. decay_mod_archive - an R data file (.rds) containing eleven data products derived from linear models of abandonment recultivation ('decay'): lm_mega_lin_log_lin_l \u2013 the primary linear model produced in our analysis. This model is referred to as \u201clin_log_lin\u201d (or \u201cl3\u201d) because the model predicts linear persistence (\u201clin\u201d) as a function of a log term of time (\u201clog\u201d) and a linear term of time (\u201clin\u201d). \u201cmega\u201d refers to the fact that this model is run for the full dataset, pooled acro", "keywords": ["2. Zero hunger", "Carbon sequestration", "Cropland abandonment", "13. Climate action", "Agricultural abandonment", "Agriculture", "15. Life on land", "Land-cover mapping", "Farmland abandonment", "Biodiversity conservation", "Secondary succession"], "contacts": [{"organization": "Crawford, Christopher L., Yin, He, Radeloff, Volker C., Wilcove, David S.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5348287"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5348287", "name": "item", "description": "10.5281/zenodo.5348287", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5348287"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-03-26T00:00:00Z"}}, {"id": "10.5281/zenodo.4787631", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale", "description": "Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale produced by GEOZS, Slovenia. European datasets harvested from national WFS for geologic units or national geological units datasets, based on OneGeology and INSPIRE Lithology and Geochronologic Era URI codelists. Layers include: EGDI_GE_GeologicUnit_EN_1M_Surface_LithologyPolygon_v2_250m_epsg.3035.tif = original EGDI surface lithology map; dtm_surface.lithology_egdi.1m_c_250m_s_20000101_20221231_eu_epsg.3035_v20240530.tif = gap filled surface lithology map; Missing values in the original EGDI lithology map have been imputed by training a random forest classifier model based on parameters derived from DTM and soil regions map from Die Bundesanstalt f\u00fcr Geowissenschaften und Rohstoffe (BGR). By generating 1 million random points, geographically balanced over the whole pan-EU land area, each class in the map was covered properly. Classes whose number of samples is less than 10 were discarded from the model training. The hyperparameter tuning of the model was carried out via a Bayesian approach with a criteria to maximize accuracy of 5k-fold cross validation. The tuned random forest model achieved an accuracy of 47% (Kappa=0.43) for the testing data, 20% of the generated sample points. The lithology of Turkey, on the other hand, was digitised from the available geology map produced by the General Directorate of Mineral Research and Exploration (MTA). The available raster map was post-processed and classified as 20 lithology classes using the k-means algorithm. These classes were harmonized with the classes in the EGDI lithology map. Acknowledgment: GEOZS, Continental Shelf Department at the Ministry for Transport and Infrastructure.", "keywords": ["EGDI", "13. Climate action", "surface geology", "15. Life on land", "Geo-harmonizer"], "contacts": [{"organization": "Isik, Serkan, Minarik, Robert, Hengl, T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4787631"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4787631", "name": "item", "description": "10.5281/zenodo.4787631", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4787631"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-30T00:00:00Z"}}, {"id": "10.5281/zenodo.4884673", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "Soil microbial, fungal and bacterial abundance impacted by crop diversification, tillage and fertilizer type", "description": "This data set contains a data-mining performed to assess the impact of crop diversification, tillage and fertilizer type on soil microbial, fungal and bacterial abundance under arable crops worldwide by a further meta-analysis of the data. These data correspond to the open-access article ' The impact of crop diversification, tillage and fertilization type on soil total microbial, fungal and bacterial abundance: A worldwide meta-analysis of agricultural sites ' published in Agriculture Ecosystems and Environment. (doi:10.1016/j.agee.2022.107867), funded by the European Commission Horizon 2020 project SoildiverAgro [grant agreement 817819].", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Morug\u00e1n-Coronado, Alicia, P\u00e9rez-Rodr\u00edguez, Paula, Insolia, Eliana, Soto-G\u00f3mez, Diego, Fern\u00e1ndez-Calvi\u00f1o, David, Zornoza, Ra\u00fal,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4884673"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4884673", "name": "item", "description": "10.5281/zenodo.4884673", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4884673"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-31T00:00:00Z"}}, {"id": "10.5281/zenodo.4954979", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "Dataset: Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil", "description": "Open Access
The files stored in this repository contain data and additional information for the study 'Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil' by Tino Peplau, Julia Schroeder, Edward Gregorich and Christopher Poeplau. climate-data-takhini.txt: Contains a dataset with climate data used for Figure 1a and b. The data was downloaded from https://climatedata.ca/download/ as single variables and later on put into this single file. degree_days.xlsx: Contains soil temperature data with according calculation of cumulative degree days. temperature.xlsx: Contains raw data of soil temperature teabags_HS.xlsx: Contains information about all 24 buried teabags. The table contains 6 columns: 1)'sample' gives the individual name of the sample. 2) 'rep' is the replication at each plot 3) 'plot' is the plot, according to the soil warming intensity 4) 'depth' is the depth at which the teabag was buried 5) 'weight_start' is the weight of tea before at start of the experiment 6) 'weight_end' ist the weight of the tea after one year of burial HS_data_final.xlsx: Contains all data of the soil samples. It is divided into two sheets: 'sample_data': Provides information about every single soil sample, including chemical data, bulk density, organic and inorganic carbon, nitrogen and fractions. 'plot_data': Provides a summary of the data for every soil core (repetition) and plot, including mass corrected SOC and N stocks of the whole profile, topsoil and subsoil.
", "keywords": ["2. Zero hunger", "Soil organic matter", "Canada", "Whole-profile", "13. Climate action", "Soil warming", "Teabags", "Fractionation", "15. Life on land", "Takhini hot springs", "6. Clean water", "Thermosequence"], "contacts": [{"organization": "Peplau, Tino, Schroeder, Julia, Gregorich, Edward, Poeplau, Christopher,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4954979"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4954979", "name": "item", "description": "10.5281/zenodo.4954979", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4954979"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4896835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Report", "title": "Virtual loads predictions of wake-affected wind turbines: Gaussian process regression and deep neural networks", "description": "Load analysis of wind turbines may be performed either via physics-based models or via direct measurement. On the first case, loads are calculated with aero-elastic models, based on significant assumptions on the mechanical and aeroelastic properties of the structure and the acting forces (wind, wave and control). Otherwise, loads can be directly measured based on a sensor network, which entails increased costs due to installation, maintenance and calibration of sensors and IT infrastructure. These costs can be manageable for a single wind turbine but become substantial on densely instrumented wind farms. In turn, the increased costs negatively affect the levelized cost of energy. This is, in fact, the main reason why stakeholders shy away from applying monitoring technologies in wind farms. To overcome the above challenges, we need an alternative way of estimating the loads which would involve a reduced number of sensors while replicating the actual load measurement scenario. To this end, we propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. We have already successfully demonstrated this concept in the past on simulated wind turbine Damage Equivalent Loads (DELs) based on Gaussian Process regression [1,2], and on real wind turbine data [3]. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework of the Research Alpha Ventus (RAVE) project. Two Senvion turbines are selected for this study. One of the wind turbines is used to train and validate a regression model to predict the tower base DELs based on SCADA, wind inflow and other environmental variables. Afterwards, the trained model is used to predict the loads in the second wind turbine. Load prediction is attained with two machine learning methods, the first one based on Gaussian Process Regression (GPR) and the second one based on Artificial Neural Networks (ANN). For the first one, a decision tree is used to separate the different operating modes of the wind turbine (idling, operating and transitioning). The decision tree is built on simple heuristics on a subset of SCADA variables (mean and standard deviation of the rotor RPM and blade pitch angle). Subsequently, a GPR is built for each one of the operating modes. In the second method, the SCADA variables are fed to the ANN after undergoing an initial transformation for data compression and collinearity reduction.", "keywords": ["machine learning", "structural health monitoring", "13. Climate action", "wind turbines", "virtual sensing", "7. Clean energy"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Abdallah, Imad, Venu, Anish, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896835", "name": "item", "description": "10.5281/zenodo.4896835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5171830", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "Cryoturbation leads to iron-organic carbon associations along a permafrost soil chronosequence in northern Alaska", "description": "In permafrost soils, substantial amounts of organic carbon (OC) are potentially protected from microbial degradation and transformation into greenhouse gases by association with reactive iron (Fe) minerals. As permafrost environments respond to climate change, increased drainage of thaw lakes in permafrost regions is predicted. Soils will subsequently develop on these drained thaw lakes, but the role of Fe-OC associations in future OC stabilization during this predicted soil development is unknown. To fill this knowledge gap, we have examined Fe-OC associations in organic, cryoturbated and mineral horizons along a 5500-year chronosequence of drained thaw lake basins in Utqia\u0121vik, Alaska. By applying chemical extractions, we found that ~17 % of the total OC content in cryoturbated horizons is associated with reactive Fe minerals, compared to ~10 % in organic or mineral horizons. As soil development advances, the total stocks of Fe-associated OC more than double within the first 50 years after thaw lake drainage, because of increased storage of Fe-associated OC in cryoturbated horizons (from 8 to 75 % of the total Fe-associated OC stock). Spatially-resolved nanoscale secondary ion mass spectrometry showed that OC is primarily associated with Fe(III) (oxyhydr)oxides which were identified by 57Fe M\u00f6ssbauer spectroscopy as ferrihydrite. High OC:Fe mass ratios (>0.22) indicate that Fe-OC associations are formed via co-precipitation, chelation and aggregation. These results demonstrate that, given the proposed enhanced drainage of thaw lakes under climate change, OC is increasingly incorporated and stabilized by the association with reactive Fe minerals as a result of soil formation and increased cryoturbation.", "keywords": ["carbon", " iron", " thermokarst", " cryoturbation", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Joss, Hanna, Patzner, Monique S., Maisch, Markus, Mueller, Carsten W., Kappler, Andreas, Bryce, Casey,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5171830"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5171830", "name": "item", "description": "10.5281/zenodo.5171830", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5171830"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-09T00:00:00Z"}}, {"id": "10.5281/zenodo.5205401", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:22Z", "type": "Dataset", "title": "SOILCARE_WP_7_D7.2_Adoption factors and policy actions", "description": "Dataset accompanying D7.2: \u201cReport on the selection of good policy alternatives at EU and study site level\u201d. The file provides data collected from stakeholders at the European level as well as at national, regional and local level within the 16 SoilCare study site countries. The data set provides stakeholder views on factors enabling and hampering the uptake of Soil Improving Cropping Systems as well as actions to facilitate their adoption. The methodology for collecting the data is detailed in D7.2 available at https://www.soilcare-project.eu/resources/deliverables.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. 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Heinen, R. Rietra, and R. Hessel. 2017. A review of soil-improving cropping systems (full report). SoilCare Scientific Report 07, Deliverable D2.1, SoilCare Project, Wageningen Environmental Research, the Netherlands. Available at: https://soilcare-project.eu/downloads/soilcare-reports-and-deliverables']}", "keywords": ["2. Zero hunger", "Soil improving cropping systems", "Literature review of published meta-analysis studies", "13. Climate action", "15. Life on land", "6. 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Global process-based characterization factors of soil carbon depletion for life cycle impact assessment.", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "12. Responsible consumption"], "contacts": [{"organization": "Teixeira, R.F.M., Morais, T.G., Domingos, T.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5148787"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5148787", "name": "item", "description": "10.5281/zenodo.5148787", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5148787"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-04T00:00:00Z"}}, {"id": "10.5281/zenodo.5205400", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:21Z", "type": "Dataset", "title": "SOILCARE_WP_7_D7.2_Adoption factors and policy actions", "description": "Dataset accompanying D7.2: \u201cReport on the selection of good policy alternatives at EU and study site level\u201d. The file provides data collected from stakeholders at the European level as well as at national, regional and local level within the 16 SoilCare study site countries. The data set provides stakeholder views on factors enabling and hampering the uptake of Soil Improving Cropping Systems as well as actions to facilitate their adoption. The methodology for collecting the data is detailed in D7.2 available at https://www.soilcare-project.eu/resources/deliverables.", "keywords": ["2. Zero hunger", "13. Climate action", "11. Sustainability", "15. Life on land", "sustainable agricultural practices", "sustainable soil management", " adoption factors", " adoption barriers", " policy", " stakeholders"], "contacts": [{"organization": "McNeill, Alicia, Muro, Melanie, Tugran, Tugce, Lucakova, Zuzana,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5205400"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5205400", "name": "item", "description": "10.5281/zenodo.5205400", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5205400"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5226665", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:22Z", "type": "Dataset", "title": "SOILCARE_database1_WP2_SICS_aspects", "description": "Open Access{'references': ['Oenema, O., M. Heinen, R. Rietra, and R. Hessel. 2017. A review of soil-improving cropping systems (full report). SoilCare Scientific Report 07, Deliverable D2.1, SoilCare Project, Wageningen Environmental Research, the Netherlands. Available at: https://soilcare-project.eu/downloads/soilcare-reports-and-deliverables']}", "keywords": ["2. Zero hunger", "Soil improving cropping systems", "Literature review of published meta-analysis studies", "13. Climate action", "15. Life on land", "6. 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This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth\u2019s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

", "keywords": ["land use and land cover analysis", "vegetation response", "Sentinel-2; drought; soil moisture; evapotranspiration; vegetation response; surface water and wetland analysis; land use and land cover analysis", "Science", "Q", "evapotranspiration", "0207 environmental engineering", "drought", "02 engineering and technology", "15. Life on land", "01 natural sciences", "6. Clean water", "surface water and wetland analysis", "13. Climate action", "Sentinel-2; drought", "Sentinel-2", "soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/13/17/3355/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5509889"}, {"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": "10.5281/zenodo.5509889", "name": "item", "description": "10.5281/zenodo.5509889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-24T00:00:00Z"}}, {"id": "10.5281/zenodo.5511764", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:22Z", "type": "Report", "title": "Application of organic fertilizers alter the physical and biogeochemical properties of agricultural topsoil and subsoil", "description": "Open AccessvEGU21: Gather Online | 19\u201330 April 2021", "keywords": ["2. Zero hunger", "13. Climate action", "15. Life on land", "7. Clean energy", "6. Clean water", "12. Responsible consumption", "Organic amendments", " Organic carbon stocks", " subsoil ", " Vis-NIR"], "contacts": [{"organization": "Neumeier, Anke, Guigue, Julien, Ostovari, Yaser, Muskolus, Andreas, Holmer, Anna, Martens, Henk, Me\u0161inovi\u0107, Emina, K\u00f6gel-Knabner, Ingrid, Creamer, Rachel, Van Groenigen, Jan Willem, Vidal, Alix,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5511764"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5511764", "name": "item", "description": "10.5281/zenodo.5511764", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5511764"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-20T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Climate&offset=6050&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Climate&offset=6050&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "prev", "title": "items (prev)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Climate&offset=6000", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=+Climate&offset=6100", "hreflang": "en-US"}], "numberMatched": 7604, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-04-04T11:13:05.641065Z"}