{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.15077367", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:43Z", "type": "Report", "title": "SYNTHESIS OF Ti3C2Tx AND ITS POTENTIAL USE IN WATER PURIFICATION PROCESSES", "description": "Abstract  A group of drugs such as \u03b2-blockers are among the most commonly prescribed drugs worldwide. Their excessive use leads to a shortage of drinkable water resources. In addition to stability in water, the problem is also their conjugates, which often return to the original compounds, so it is necessary to remove \u03b2-blockers from water. Photocatalytic degradation compared to conventional methods of water purification has been shown to be an effective method for the removal of drugs from the aqueous medium [1].  \u00a0  MXenes are carbides, nitrides and carbonitrides of transition metals, and represent a rapidly developing group of 2D materials, where Ti3C2Tx is the most studied material [2]. Ti3C2Tx could be suitable for photocatalytic water purification because of the large and aboundant surface area covered by the large number of hydrophilic terminal groups (\u2013F, \u2013O and \u2013OH) that enable drug adsorption [3]. In this work, the synthesis of singlephase Ti3C2Tx from Ti3AlC2 was performed using HF obtained in situ in a mixture of LiF and HCl. The synthesized material was characterized by X-ray diffraction (XRD), Raman spectroscopy and BET analysis. In our research, the photolysis and photocatalysis of the \u03b2-blocker PIN were carried out in Ti3AlC2 and Ti3C2Tx aqueous suspensions under the influence of UV radiation. Also, under the same experimental conditions, without UV radiation, the adsorption of PIN on the Ti3C2Tx surface was examined. The photolysis kinetics of photocatalysis and adsorption were monitored by HPLC analysis. The results show that PIN is stable during photolysis and that pure Ti3C2Tx is not photocatalytically active due to the challenge of band gap tailoring.", "keywords": ["Ti3C2Tx", "synthesis", "Single phase", "\u03b2-blockers", "water purification", "MXenes"], "contacts": [{"organization": "Peri\u0107, Milinko, Lazi\u0107, Andrea, Toth, Elvira, Paska\u0161, Jovana, Srdi\u0107, Vladimir V., Armakovi\u0107, Sanja J., Kanas, Nikola,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15077367"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15077367", "name": "item", "description": "10.5281/zenodo.15077367", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15077367"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "20.500.11850/706699", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:44Z", "type": "Journal Article", "created": "2024-11-11", "title": "Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. New (a)biotic conditions resulting from climate change are expected to change disturbance dynamics, such as windthrow, forest fires, droughts, and insect outbreaks, and their interactions. These unprecedented natural disturbance dynamics might alter the capability of forest ecosystems to buffer atmospheric CO2 increases, potentially leading forests to transform from sinks into sources of CO2. This study aims to enhance the ORCHIDEE land surface model to study the impacts of climate change on the dynamics of the bark beetle, Ips typographus, and subsequent effects on forest functioning. The Ips typographus outbreak model is inspired by previous work from Temperli et al.\u00a0(2013) for the LandClim landscape model. The new implementation of this model in ORCHIDEE r8627 accounts for key differences between ORCHIDEE and LandClim: (1)\u00a0the coarser spatial resolution of ORCHIDEE; (2)\u00a0the higher temporal resolution of ORCHIDEE; and (3)\u00a0the pre-existing process representation of windthrow, drought, and forest structure in ORCHIDEE. Simulation experiments demonstrated the capability of ORCHIDEE to simulate a variety of post-disturbance forest dynamics observed in empirical studies. Through an array of simulation experiments across various climatic conditions and windthrow intensities, the model was tested for its sensitivity to climate, initial disturbance, and selected parameter values. The results of these tests indicated that with a single set of parameters, ORCHIDEE outputs spanned the range of observed dynamics. Additional tests highlighted the substantial impact of incorporating Ips typographus outbreaks on carbon dynamics. Notably, the study revealed that modeling abrupt mortality events as opposed to a continuous mortality framework provides new insights into the short-term carbon sequestration potential of forests under disturbance regimes by showing that the continuous mortality framework tends to overestimate the carbon sink capacity of forests in the 20- to 50-year range in ecosystems under high disturbance pressure compared to scenarios with abrupt mortality events. This model enhancement underscores the critical need to include disturbance dynamics in land surface models to refine predictions of forest carbon dynamics in a changing climate.</p></article>", "keywords": ["cycle du carbone", "[SDE] Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_24242", "P40 - M\u00e9t\u00e9orologie et climatologie", "mod\u00e8le de simulation", "Ips typographus", "http://aims.fao.org/aos/agrovoc/c_16411", "http://aims.fao.org/aos/agrovoc/c_2391", "http://aims.fao.org/aos/agrovoc/c_1666", "K70 - D\u00e9g\u00e2ts caus\u00e9s aux for\u00eats et leur protection", "http://aims.fao.org/aos/agrovoc/c_6111", "http://aims.fao.org/aos/agrovoc/c_4549f84e", "perturbation de l'\u00e9cosyst\u00e8me", "surveillance \u00e9pid\u00e9miologique", "mod\u00e9lisation", "s\u00e9cheresse", "changement climatique", "QE1-996.5", "http://aims.fao.org/aos/agrovoc/c_230ab86c", "U10 - Informatique", " math\u00e9matiques et statistiques", "Geology", "H10 - Ravageurs des plantes", "http://aims.fao.org/aos/agrovoc/c_331583", "s\u00e9questration du carbone", "dynamique des populations", "[SDE]Environmental Sciences", "http://aims.fao.org/aos/agrovoc/c_30153", "http://aims.fao.org/aos/agrovoc/c_17299"]}, "links": [{"href": "https://doi.org/20.500.11850/706699"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Model%20Development", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/706699", "name": "item", "description": "20.500.11850/706699", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/706699"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-11-11T00:00:00Z"}}, {"id": "20.500.11850/731117", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:45Z", "type": "Journal Article", "created": "2025-04-08", "title": "Combining national forest inventories reveals distinct role of climate on tree recruitment in European forests", "description": "Open AccessISSN:0167-8892", "keywords": ["Forest recruitment modelling", "ingrowth", "growth", "Ingrowth", "drought", "mortality", "Forest regeneration", "Forest regeneration; Forest recruitment modelling; Ingrowth; Forest dynamic models", "zero-inflated models", "nitrogen", "fagus-sylvatica", "change mitigation", "seed production", "temperate forest", "Forest dynamic models"]}, "links": [{"href": "https://doi.org/20.500.11850/731117"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Ecological%20Modelling", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11850/731117", "name": "item", "description": "20.500.11850/731117", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11850/731117"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-01T00:00:00Z"}}, {"id": "10.5281/zenodo.15166358", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops).  The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4].  The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5].  Please see the license terms for underlying datasets below:       Data source Data licensing terms   Austria     INSPIRE public access license & CC-BY-AT 4.0     Denmark         INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal       France             Custom open license         Germany                 Custom open licenses: NRW, Brandenberg, LS           Netherlands                     INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license             Portugal     CC BY 4.0     Spain     Custom open license", "keywords": ["crop classification", "earth observation", "reference data"], "contacts": [{"organization": "Holden, Piers, Davis, Timothy, Holmes, Christopher, Senaras, Caglar, Wania, Annett,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166358"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166358", "name": "item", "description": "10.5281/zenodo.15166358", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166358"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "10.5281/zenodo.15166359", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:24:45Z", "type": "Dataset", "title": "RapidCrops: A pan-European label dataset for large-scale crop classification", "description": "Under the AI4SoilHealth project we have created a dataset (\u201cRapidCrops\u201d) to support the automatic mapping of crop types across Europe. Crop type information is essential for monitoring soil health as it provides\u00a0 systematic insights into crop rotations over time and supports efforts to detect other cropping practices that affect soil health (e.g. tillage & cover crops).  The RapidCrops dataset provides approximately 99M agricultural parcel boundaries with harmonised crop type information across a wide spatio-temporal extent; with coverage across seven EU countries for 5-7 years. Based on parcel boundaries and crop type information reported under the EU IACS programme, our methodology seeks to improve the usability of the parcel boundaries without diluting their integrity. Additional attributes are provided to support the use of the data in ML workflows; especially for those leveraging EO data. The dataset builds on top of the EuroCrops [1,2] crop type harmonisation initiative and the fiboa [3] open data standard for parcel boundaries. For enhanced access to the data, the dataset is also made freely available on Source Cooperative [4].  The dataset was also utilised under the Horizon Europe project Open-Earth-Monitor to perform pan-European crop identification across 51M parcels from the year 2022; classifying each parcel into one of 29 crop types [5].  Please see the license terms for underlying datasets below:       Data source Data licensing terms   Austria     INSPIRE public access license & CC-BY-AT 4.0     Denmark         INSPIRE public access license & INSPIRE no conditions & CC0 1.0 Universal       France             Custom open license         Germany                 Custom open licenses: NRW, Brandenberg, LS           Netherlands                     INSPIRE public access license & INSPIRE no conditions & Dutch creative commons license             Portugal     CC BY 4.0     Spain     Custom open license", "keywords": ["crop classification", "earth observation", "reference data"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15166359"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15166359", "name": "item", "description": "10.5281/zenodo.15166359", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15166359"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-04-08T00:00:00Z"}}, {"id": "20.500.12079/70987", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:45Z", "type": "Journal Article", "created": "2022-04-08", "title": "Effects of Multi-Species Microbial Inoculants on Early Wheat Growth and Litterbag Microbial Activity", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The use of microbial consortia (MC) with complementing features is considered to be a promising method of sustainable crop intensification, potentially trumping the limited performance of single-strain applications. We assessed the effect of two novel MC on early wheat growth and litterbag microbial activity in heated and unheated soil. Pot experiments were carried out in duplicate in a greenhouse over 63 days using a completely randomized design with six replications. A range of parameters of plant growth and nutrient uptake were regularly assessed and statistically analyzed by ANOVA. The litterbag-NIRS method was used to trace the microbial activity. Averaged over both trials, soil heating resulted in a significant increase in shoot biomass (+53%) and subsequent nitrogen uptake (+307 mg N pot\u22121) but strongly reduced root development (\u221246%) compared with unheated soil. The application of MC had no effect on wheat growth in the heated soil. By contrast, in the unheated soil, shoot (+12%) and root (+15%) biomass and shoot nitrogen uptake (+11%) were significantly increased after double inoculation with MC compared with autoclaved MC. The litterbag-NIRS method confirmed clear effects of soil heating on microbial activity. Differences between MC application and the control were noted, indicating a buffering effect of MC.</p></article>", "keywords": ["2. Zero hunger", "Greenhouse", "S", "Litterbag-NIRS method", "microbial consortia inoculants", "plant-microbe interactions", "Agriculture", "04 agricultural and veterinary sciences", "Microbial consortia inoculants", "Plant-growth-promoting microorganisms", "plant-growth-promoting microorganisms; microbial consortia inoculants; microbial fertilizer; plant-microbe interactions; pot experiments; greenhouse; litterbag-NIRS method", "microbial fertilizer", "Pot experiments", "plant-growth-promoting microorganisms", "greenhouse", "0401 agriculture", " forestry", " and fisheries", "pot experiments", "Plant-microbe interactions", "Microbial fertilizer"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/12/4/899/pdf"}, {"href": "https://iris.enea.it/bitstream/20.500.12079/70987/1/Effects%20of%20Multi-Species%20Microbial%20Inoculants%20on%20Early%20Wheat%20Growth%20and%20Litterbag%20Microbial%20Activity.pdf"}, {"href": "https://www.mdpi.com/2073-4395/12/4/899/pdf"}, {"href": "https://doi.org/20.500.12079/70987"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.12079/70987", "name": "item", "description": "20.500.12079/70987", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.12079/70987"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-08T00:00:00Z"}}, {"id": "1adc50de-e179-475c-a664-27f43843239c", "type": "Feature", "geometry": null, "properties": {"updated": "2025-09-02T09:51:47", "type": "Dataset", "language": "de", "title": "INSPIRE-WMS Geology / Reflexionsseismische Horizonte 2D BB: Fl\u00e4chen", "description": "Der interoperable INSPIRE-WMS ist ein Darstellungsdienst, der Daten im Annex-Schema Geology (abgeleitet aus dem origin\u00e4ren Datensatz: Reflexionssesimische Horizonte 2D BB) bereitstellt. Die Horizonte entsprechen einer Ableitung aus dem 3D-Untergrundmodell Brandenburgs (B3D) in Form eines 2D-Datensatzes. Das 3D-Modell B3D stellt den Untergrund Brandenburgs in Form ausgew\u00e4hlter reflexionsseismischer Horizonte bis in eine Tiefe von ca. 7000 m dar.      Informationen zur Darstellung der linienhaften Ableitungen (St\u00f6rungszonen/Ausbissgrenzen und Konturlinien) finden Sie unter https://inspire.brandenburg.de/services/ge-core_seismikhorizonte_l_wms?request=GetCapabilities&service=WMS.     Gem\u00e4\u00df der INSPIRE-Datenspezifikation Geology (D2.8.II.4) liegen die Inhalte INSPIRE-konform vor.     Der WMS beinhaltet die folgenden Layer: GE.TransgressionSurfaceCenozoic (T1 - horizon), GE.TransgressionAreaMiddleAlbToCenomanian (B2-T2 - horizon), GE.TransgressionAreaUnder-AlbToWealden (T3-T4 - horizon), GE.Intra-OxfordToKimmeridge (E1-E2 - horizon), GE.Intra-Toarc (L1 - horizon), GE.TopUpperGypsumKeuper (K2 - horizon), GE.Intra-MainShellLimestone (M1 - horizon), GE.TopSalinaRed (S1 - horizon), GE.TopZechsteinSalinar (X1 - horizon), GE.SurfaceBasalAnhydriteOfTheSta\u00dffurtFormationInTheZechstein (Z1 - horizon), GE.BasisWerraAnhydrit (Z3 - horizon), GE.BaseUpperRedII (R6 - horizon).     ---     The compliant INSPIRE WMS is a view service that provides data in the annex schema Geology (derived from the original dataset: Reflexionsseismische Horizonte 2D BB). The horizons correspond to a derivation from the 3D subsurface model of Brandenburg (B3D) in the form of a 2D data set. The 3D model B3D represents the subsurface of Brandenburg in the form of selected seismic reflection horizons down to a depth of approx. 7000 m.      Information on the visualisation of the fault zones / outcrop limits and contour lines can be found at https://inspire.brandenburg.de/services/ge-core_seismikhorizonte_l_wms?request=GetCapabilities&service=WMS.     The content is compliant to the INSPIRE data specification for the annex theme Geology (D2.8.II.4_v3.3.0).     The WMS contains the following Layers: GE.TransgressionSurfaceCenozoic (T1 - horizon), GE.TransgressionAreaMiddleAlbToCenomanian (B2-T2 - horizon), GE.TransgressionAreaUnder-AlbToWealden (T3-T4 - horizon), GE.Intra-OxfordToKimmeridge (E1-E2 - horizon), GE.Intra-Toarc (L1 - horizon), GE.TopUpperGypsumKeuper (K2 - horizon), GE.Intra-MainShellLimestone (M1 - horizon), GE.TopSalinaRed (S1 - horizon), GE.TopZechsteinSalinar (X1 - horizon), GE.SurfaceBasalAnhydriteOfTheSta\u00dffurtFormationInTheZechstein (Z1 - horizon), GE.BasisWerraAnhydrit (Z3 - horizon), GE.BaseUpperRedII (R6 - horizon).", "formats": [{"name": "HTML"}], "keywords": ["3d-untergrundmodell", "High value dataset", "b2d", "b3d", "bboxbebb", "boden", "brandenburg", "de", "erdbeobachtung-und-umwelt", "geologiccollection", "geologicevent", "geologicunit", "geologie", "geologycore", "horizont", "infomapaccessservice", "inspireidentifiziert", "interoperabel", "interoperability", "interoperable-daten", "opendata", "reflexionsseismische-horizontkarte", "regional", "sheardisplacementstructure", "wms"], "contacts": [{"organization": "Landesamt f\u00fcr Bergbau, Geologie und Rohstoffe Brandenburg (LBGR)", "roles": ["creator"]}]}, "links": [{"href": "https://geoportal.brandenburg.de/detailansichtdienst/render?view=gdibb&url=https%3A%2F%2Fgeoportal.brandenburg.de%2Fgs-json%2Fxml%3Ffileid%3D1adc50de-e179-475c-a664-27f43843239c"}, {"href": "https://inspire.brandenburg.de/services/ge-core_seismikhorizonte_f_wms?REQUEST=GetCapabilities&SERVICE=WMS"}, {"href": "http://data.europa.eu/88u/dataset/1adc50de-e179-475c-a664-27f43843239c"}, {"rel": "self", "type": "application/geo+json", "title": "1adc50de-e179-475c-a664-27f43843239c", "name": "item", "description": "1adc50de-e179-475c-a664-27f43843239c", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/1adc50de-e179-475c-a664-27f43843239c"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"null": "date"}}, {"id": "10.5281/zenodo.15398850", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:24:51Z", "type": "Dataset", "title": "Wetland sediment soil organic carbon stock and sequestration rates in undisturbed and rewetted Canadian wetlands", "description": "This workbook shows the ID, the geographical location, the year of sampling, and sediment core information in samples collected from undisturbed and rewetted wetlands situated across four provinces of Canada (Alberta, Saskatchewan, Manitoba, and Ontario) from 2016 to 2019.", "keywords": ["restoration", "carbon", "sediments", "sequestration", "wetlands", "soil"], "contacts": [{"organization": "Creed, Irena", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15398850"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15398850", "name": "item", "description": "10.5281/zenodo.15398850", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15398850"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-13T00:00:00Z"}}, {"id": "10.5281/zenodo.8090398", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-12-16", "title": "Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R2 = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model.</p></article>", "keywords": ["2. Zero hunger", "soil salinity; remote sensing; machine learning; predictive mapping", "soil salinity", "remote sensing", "machine learning", "13. Climate action", "Science", "Q", "0401 agriculture", " forestry", " and fisheries", "predictive mapping", "04 agricultural and veterinary sciences", "15. Life on land", "6. Clean water"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/12/24/4118/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8090398"}, {"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.8090398", "name": "item", "description": "10.5281/zenodo.8090398", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090398"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-16T00:00:00Z"}}, {"id": "10.5281/zenodo.15349739", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D5.5 - Map of potential for PM(T) substitution in one high-stake sector", "description": "This report (Deliverable D5.5), produced under the H2020 PROMISCES project, addresses the substitution of Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances, which pose significant environmental and human health risks. Utilizing a comprehensive dataset encompassing over 120,000 substances, the report employs three distinct analytical methodologies: sectoral analysis, functional use analysis, and a case study on benzotriazoles. The findings reveal the extensive distribution of PMT substances across various sectors, highlight substantial data deficiencies, and underscore the complexities involved in identifying safer alternatives. The study emphasizes the necessity for robust data and systematic assessments to facilitate effective substitution strategies and inform regulatory decision-making.", "keywords": ["Functional use analysis", "Zero Pollution", "PROMISCES Decision Support Framework", "Substitution", "PMT substances"], "contacts": [{"organization": "BOUCARD, Pierre, Sardi, Adriana E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15349739"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15349739", "name": "item", "description": "10.5281/zenodo.15349739", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15349739"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-06T00:00:00Z"}}, {"id": "10.5281/zenodo.15349740", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Deliverable D5.5 - Map of potential for PM(T) substitution in one high-stake sector", "description": "This report (Deliverable D5.5), produced under the H2020 PROMISCES project, addresses the substitution of Persistent, Mobile, and Toxic (PMT) and very Persistent and very Mobile (vPvM) substances, which pose significant environmental and human health risks. Utilizing a comprehensive dataset encompassing over 120,000 substances, the report employs three distinct analytical methodologies: sectoral analysis, functional use analysis, and a case study on benzotriazoles. The findings reveal the extensive distribution of PMT substances across various sectors, highlight substantial data deficiencies, and underscore the complexities involved in identifying safer alternatives. The study emphasizes the necessity for robust data and systematic assessments to facilitate effective substitution strategies and inform regulatory decision-making.", "keywords": ["Functional use analysis", "Zero Pollution", "PROMISCES Decision Support Framework", "Substitution", "PMT substances"], "contacts": [{"organization": "BOUCARD, Pierre, Sardi, Adriana E.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15349740"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15349740", "name": "item", "description": "10.5281/zenodo.15349740", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15349740"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-06T00:00:00Z"}}, {"id": "10.5281/zenodo.15393410", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Carbon Farming Mitigation Potential: Evaluating the mitigation potential (and uncertainties) of carbon farming practices", "description": "unspecifiedThis is the updated version of the old one (version 1.0)", "keywords": ["Carbon sequestration", "Environmental benefits", "Agricultural Systems", "Greenhouse gas emissions", "Mitigation potential", "Agricultural systems", "Carbon farming"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15393410"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15393410", "name": "item", "description": "10.5281/zenodo.15393410", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15393410"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15393411", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:50Z", "type": "Report", "title": "Carbon Farming Mitigation Potential: Evaluating the mitigation potential (and uncertainties) of carbon farming practices", "description": "Open AccessThis is the updated version of the old one (version 1.0)", "keywords": ["Carbon sequestration", "Environmental benefits", "Agricultural Systems", "Greenhouse gas emissions", "Mitigation potential", "Agricultural systems", "Carbon farming"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15393411"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15393411", "name": "item", "description": "10.5281/zenodo.15393411", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15393411"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15484766", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:24:55Z", "type": "Dataset", "title": "Wetland sediment soil organic carbon sequestration rates in undisturbed Canadian wetlands and data to support predictive modeling", "description": "Table in RF Model Data worksheet in workbook shows (1) ID, \u00a0geographical location, \u00a0year of sampling, and measured organic carbon sequestration rates (OCSR) \u00a0in samples collected from undisturbed wetlands situated across Canada, and (2) direct controls on OCSR extracted from geospatial data. These data were used to support random forest (RF) modeling to develop modeled estimates of OCSR.\u00a0 Table data is also provided as a .csv file and supporting readme document.", "keywords": ["carbon", "sediments", "sequestration", "wetlands", "soil"], "contacts": [{"organization": "Creed, Irena", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15484766"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15484766", "name": "item", "description": "10.5281/zenodo.15484766", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15484766"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-21T00:00:00Z"}}, {"id": "10.5281/zenodo.8090608", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2020-01-13", "title": "Construction of ecological security pattern based on the importance of ecosystem service functions and ecological sensitivity assessment: a case study in Fengxian County of Jiangsu Province, China", "description": "Abstract<p>The construction of ecological security pattern is one of the important ways to alleviate the contradiction between economic development and ecological protection, as well as the important contents of ecological civilization construction. How to scientifically construct the ecological security pattern of small-scale counties, and achieve sustainable economic development based on ecological environment protection, it has become an important proposition in regulating the ecological process effectively. Taking Fengxian County of China as an example, this paper selected the importance of ecosystem service functions and ecological sensitivity to evaluate the ecological importance and identify ecological sources. Furthermore, we constructed the ecological resistance surface by various landscape assignments and nighttime lighting modifications. Through a minimum cumulative resistance model, we obtained ecological corridors and finally constructed the ecological security pattern comprehensively combining with ecological resistance surface construction. Accordingly, we further clarified the specific control measures for ecological security barriers and regional functional zoning. This case study shows that the ecological security pattern is composed of ecological sources and corridors, where the former plays an important security role, and the latter ensures the continuity of ecological functions. In terms of the spatial layout, the ecological security barriers built based on ecological security pattern and regional zoning functions are away from the urban core development area. As for the spatial distribution, ecological sources of Fengxian County are mainly located in the central and southwestern areas, which is highly coincident with the main rivers and underground drinking water source area. Moreover, key corridors and main corridors with length of approximately 115.71\uffc2\uffa0km and 26.22\uffc2\uffa0km, respectively, formed ecological corridors of Fengxian County. They are concentrated in the western and southwestern regions of the county which is far away from the built-up areas with strong human disturbance. The results will provide scientific evidence for important ecological land protection and ecological space control at a small scale in underdeveloped and plain counties. In addition, it will enrich the theoretical framework and methodological system of ecological security pattern construction. To some extent, it also makes a reference for improving the regional ecological environment carrying capacities and optimizing the ecological spatial structure in such kinds of underdeveloped small-scale counties.</p", "keywords": ["Ecological corridors", "Ecological sensitivity", "Fengxian County of Jiangsu Province China", "Ecological sources", "15. Life on land", "01 natural sciences", "Ecological importance", "6. Clean water", "12. Responsible consumption", "Ecological security pattern", "13. Climate action", "8. Economic growth", "11. Sustainability", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.8090608"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environment%2C%20Development%20and%20Sustainability", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8090608", "name": "item", "description": "10.5281/zenodo.8090608", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8090608"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-13T00:00:00Z"}}, {"id": "10.5281/zenodo.8091189", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:42Z", "type": "Journal Article", "created": "2021-06-24", "title": "Soil Health Evaluation of Farmland Based on Functional Soil Management\u2014A Case Study of Yixing City, Jiangsu Province, China", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Given that farmland serves as a strategic resource to ensure national food security, blind emphasis on the improvement of food production capacity can lead to soil overutilization and impair other soil functions. Hence, the evaluation of soil health (SH) should comprehensively take soil productivity and ecological environmental effects into account. In this study, five functions from the perspective of functional soil management were summarized, including primary productivity, provision and cycling of nutrients, the provision of functional and intrinsic biodiversity, water purification and regulation, and carbon sequestration and regulation. For each soil function, in view of the natural and ameliorable conditions affecting SH, basic indicators were selected from the two aspects of inherent and dynamic properties, and restrictive indicators were chosen considering the external properties or environmental elements, with the minimum limiting factor method coupled with weighted linear model. The new evaluation system was tested and verified in Yixing City, China. The healthy and optimally functional soils were concentrated in the northeast and mid-west of Yixing City, whereas unhealthy soils were predominant in the south and around Taihu Lake. The main limitations to SH improvement included cation exchange capacity, nutrient elements, and soluble carbon. The SH evaluation method was verified using the crop performance validation method, and a positive correlation was noted between food production stability index and soil health index, indicating that the evaluation system is reasonable.</p></article>", "keywords": ["2. Zero hunger", "soil obstacles", "soil health", "Agriculture (General)", "0401 agriculture", " forestry", " and fisheries", "sustainable soil management", "04 agricultural and veterinary sciences", "15. Life on land", "soil multifunctionality", "6. Clean water", "S1-972", "soil ecosystem services", "12. Responsible consumption"]}, "links": [{"href": "http://www.mdpi.com/2077-0472/11/7/583/pdf"}, {"href": "https://doi.org/10.5281/zenodo.8091189"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agriculture", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.8091189", "name": "item", "description": "10.5281/zenodo.8091189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.8091189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-24T00:00:00Z"}}, {"id": "10.5281/zenodo.15538379", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:57Z", "type": "Dataset", "title": "Divergent mycorrhizal pathways in soil nutrient cycling and carbon storage under afforestation: insights from large-scale afforestation in China", "description": "Divergent mycorrhizal pathways in soil nutrient cycling and carbon storage under afforestation: insights from large-scale afforestation in China  \u00a0   Location: China.  Time Period: 1980\u20132024.  Major Taxa Studied: Forest.  Methods: We conducted a meta-analysis of 169 studies comprising 1459 paired observations from afforestation sites across diverse climatic zones (mean annual precipitation 400\u20131,200 mm). We quantified changes in soil organic carbon (SOC) and macronutrients (nitrogen, phosphorus, potassium) following cropland-to-forest conversion. Structural equation modelling was employed to disentangle the mechanistic pathways linking mycorrhizal dominance, climate factors, and afforestation duration to soil nutrient responses.", "keywords": ["Forest ecosystem; Land-use change; Meta-analysis; Soil organic carbon; Soil nutrient cycling."], "contacts": [{"organization": "Li, Yuan, Xiang, Yangzhou,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15538379"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15538379", "name": "item", "description": "10.5281/zenodo.15538379", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15538379"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-28T00:00:00Z"}}, {"id": "20.500.12079/80427", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:45Z", "type": "Journal Article", "created": "2024-12-12", "title": "Genome Insights into Beneficial Microbial Strains Composing SIMBA Microbial Consortia Applied as Biofertilizers for Maize, Wheat and Tomato", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>For the safe use of microbiome-based solutions in agriculture, the genome sequencing of strains composing the inoculum is mandatory to avoid the spread of virulence and multidrug resistance genes carried by them through horizontal gene transfer to other bacteria in the environment. Moreover, the annotated genomes can enable the design of specific primers to trace the inoculum into the soil and provide insights into the molecular and genetic mechanisms of plant growth promotion and biocontrol activity. In the present work, the genome sequences of some members of beneficial microbial consortia that have previously been tested in greenhouse and field trials as promising biofertilizers for maize, tomato and wheat crops have been determined. Strains belong to well-known plant-growth-promoting bacterial genera such as Bacillus, Burkholderia, Pseudomonas and Rahnella. The genome size of strains ranged from 4.5 to 7.5 Mbp, carrying many genes spanning from 4402 to 6697, and a GC content of 0.04% to 3.3%. The annotation of the genomes revealed the presence of genes that are implicated in functions related to antagonism, pathogenesis and other secondary metabolites possibly involved in plant growth promotion and gene clusters for protection against oxidative damage, confirming the plant-growth-promoting (PGP) activity of selected strains. All the target genomes were found to possess at least 3000 different PGP traits, belonging to the categories of nitrogen acquisition, colonization for plant-derived substrate usage, quorum sensing response for biofilm formation and, to a lesser extent, bacterial fitness and root colonization. No genes putatively involved in pathogenesis were identified. Overall, our study suggests the safe application of selected strains as \u201cplant probiotics\u201d for sustainable agriculture.</p></article>", "keywords": ["biofertilizers", "0301 basic medicine", "0303 health sciences", "03 medical and health sciences", "traceability", "PGP bacteria", "whole-genome sequencing", "QH301-705.5", "microbial consortia", "risk assessment", "Biology (General)", "Article"]}, "links": [{"href": "https://iris.enea.it/bitstream/20.500.12079/80427/1/Genome%20Insights%20into%20Beneficial%20Microbial%20Strains%20Composing%20SIMBA%20Microbial%20Consortia%20Applied%20as%20Biofertilizers%20for%20Maize%2c%20Wheat%20and%20Tomato.pdf"}, {"href": "https://www.mdpi.com/2076-2607/12/12/2562/pdf"}, {"href": "https://doi.org/20.500.12079/80427"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Microorganisms", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.12079/80427", "name": "item", "description": "20.500.12079/80427", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.12079/80427"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-12-12T00:00:00Z"}}, {"id": "20.500.12079/84487", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:45Z", "type": "Journal Article", "created": "2024-10-24", "title": "Culturomics- and metagenomics-based insights into the soil microbiome preservation and application for sustainable agriculture", "description": "<p>Soil health is crucial for global food production in the context of an ever-growing global population. Microbiomes, a combination of microorganisms and their activities, play a pivotal role by biodegrading contaminants, maintaining soil structure, controlling nutrients\uffe2\uff80\uff99 cycles, and regulating the plant responses to biotic and abiotic stresses. Microbiome-based solutions along the soil-plant continuum, and their scaling up from laboratory experiments to field applications, hold promise for enhancing agricultural sustainability by harnessing the power of microbial consortia. Synthetic microbial communities, i.e., selected microbial consortia, are designed to perform specific functions. In contrast, natural communities leverage indigenous microbial populations that are adapted to local soil conditions, promoting ecosystem resilience, and reducing reliance on external inputs. The identification of microbial indicators requires a holistic approach. It is fundamental for current understanding the soil health status and for providing a comprehensive assessment of sustainable land management practices and conservation efforts. Recent advancements in molecular technologies, such as high-throughput sequencing, revealed the incredible diversity of soil microbiomes. On one hand, metagenomic sequencing allows the characterization of the entire genetic composition of soil microbiomes, and the examination of their functional potential and ecological roles; on the other hand, culturomics-based approaches and metabolic fingerprinting offer complementary information by providing snapshots of microbial diversity and metabolic activities both in and ex-situ. Long-term storage and cryopreservation of mixed culture and whole microbiome are crucial to maintain the originality of the sample in microbiome biobanking and for the development and application of microbiome-based innovation. This review aims to elucidate the available approaches to characterize diversity, function, and resilience of soil microbial communities and to develop microbiome-based solutions that can pave the way for harnessing nature\uffe2\uff80\uff99s untapped resources to cultivate crops in healthy soils, to enhance plant resilience to abiotic and biotic stresses, and to shape thriving ecosystems unlocking the potential of soil microbiomes is key to sustainable agriculture. Improving management practices by incorporating beneficial microbial consortia, and promoting resilience to climate change by facilitating adaptive strategies with respect to environmental conditions are the global challenges of the future to address the issues of climate change, land degradation and food security.</p", "keywords": ["sustainable agriculture", "microbiome-based solutions; soil health; microbiome preservation; SynComs; NatComs; omics approaches; microbiome application; sustainable agriculture", "microbiome-based solutions", "omics approaches", "soil health", "microbiome preservation", "microbiome application", "NatComs", "Microbiology", "SynComs", "QR1-502"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/1116082/2/fmicb-15-1473666.pdf"}, {"href": "https://doi.org/20.500.12079/84487"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Microbiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.12079/84487", "name": "item", "description": "20.500.12079/84487", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.12079/84487"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-24T00:00:00Z"}}, {"id": "10.5281/zenodo.156142", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:58Z", "type": "Journal Article", "created": "2016-09-08", "title": "Review of the genus Onchopelma Hesse, with descriptions of new species (Diptera: Mythicomyiidae)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>The genus Onchopelma Hesse is reviewed and a key to species is given. Four new species are described and illustrated: Onchopelma brevifasciatum, sp.n., O. irwini, sp.n., O. majus, sp.n., and O. nitidum, sp.n. Seven species are currently known in the genus, which occurs only in Namibia and South Africa.</p></article>", "keywords": ["0106 biological sciences", "Insecta", "Arthropoda", "Diptera", "Animalia", "Biodiversity", "Bombyliidae", "01 natural sciences", "Taxonomy"], "contacts": [{"organization": "Evenhuis, Neal L.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.156142"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Zootaxa", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.156142", "name": "item", "description": "10.5281/zenodo.156142", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.156142"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2002-08-22T00:00:00Z"}}, {"id": "10072/426049", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:41Z", "type": "Journal Article", "created": "2023-09-09", "title": "Micro- and nanoplastics in soil: Linking sources to damage on soil ecosystem services in life cycle assessment", "description": "Soil ecosystems are crucial for providing vital ecosystem services (ES), and are increasingly pressured by the intensification and expansion of human activities, leading to potentially harmful consequences for their related ES provision. Micro- and nanoplastics (MNPs), associated with releases from various human activities, have become prevalent in various soil ecosystems and pose a global threat. Life Cycle Assessment (LCA), a tool for evaluating environmental performance of product and technology life cycles, has yet to adequately include MNPs-related damage to soil ES, owing to factors like uncertainties in MNPs environmental fate and ecotoxicological effects, and characterizing related damage on soil species loss, functional diversity, and ES. This study aims to address this gap by providing as a first step an overview of the current understanding of MNPs in soil ecosystems and proposing a conceptual approach to link MNPs impacts to soil ES damage. We find that MNPs pervade soil ecosystems worldwide, introduced through various pathways, including wastewater discharge, urban runoff, atmospheric deposition, and degradation of larger plastic debris. MNPs can inflict a range of ecotoxicity effects on soil species, including physical harm, chemical toxicity, and pollutants bioaccumulation. Methods to translate these impacts into damage on ES are under development and typically focus on discrete, yet not fully integrated aspects along the impact-to-damage pathway. We propose a conceptual framework for linking different MNPs effects on soil organisms to damage on soil species loss, functional diversity loss and loss of ES, and elaborate on each link. Proposed underlying approaches include the Threshold Indicator Taxa Analysis (TITAN) for translating ecotoxicological effects associated with MNPs into quantitative measures of soil species diversity damage; trait-based approaches for linking soil species loss to functional diversity loss; and ecological networks and Bayesian Belief Networks for linking functional diversity loss to soil ES damage. With the proposed conceptual framework, our study constitutes a starting point for including the characterization of MNPs-related damage on soil ES in LCA.", "keywords": ["2. Zero hunger", "Damage modeling", "Life Cycle Stages", "Terrestrial ecology", "Soil organisms", "Pollution and contamination", "Microplastics", "Bayes Theorem", "15. Life on land", "/dk/atira/pure/sustainabledevelopmentgoals/responsible_consumption_and_production; name=SDG 12 - Responsible Consumption and Production", "6. Clean water", "Soil sciences", "Soil", "/dk/atira/pure/sustainabledevelopmentgoals/affordable_and_clean_energy; name=SDG 7 - Affordable and Clean Energy", "13. Climate action", "Soil health", "11. Sustainability", "Biodiversity loss", "Humans", "Animals", "Life cycle impact assessment", "Soil ecosystem", "Ecosystem"]}, "links": [{"href": "https://doi.org/10072/426049"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Science%20of%20The%20Total%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10072/426049", "name": "item", "description": "10072/426049", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10072/426049"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-01T00:00:00Z"}}, {"id": "10.5281/zenodo.1566066", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:24:59Z", "type": "Dataset", "title": "N2O and CH4 fluxes/concentrations reported in Krauss et al. 2017", "description": "Open Access{'references': ['Krauss, M., Ruser, R., M u00fcller, T., Hansen, S., M u00e4der, P., Gattinger, A. (2017) Impact of reduced tillage on greenhouse gas emissions and soil carbon stocks in an organic grass-clover ley - winter wheat cropping sequence. Agriculture, Ecosystems &amp; Environment, 239, p. 324-333']}", "keywords": ["2. Zero hunger", "nitrous oxide", " methane", " greenhouse gas emissions", " conservation tillage", " organic farming", "13. Climate action", "11. Sustainability", "12. Responsible consumption"], "contacts": [{"organization": "Krauss, Maike", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.1566066"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.1566066", "name": "item", "description": "10.5281/zenodo.1566066", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.1566066"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-11-27T00:00:00Z"}}, {"id": "10.5281/zenodo.17092587", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:06Z", "type": "Dataset", "title": "Peatland Mid-Infrared Database (1.0.0)", "description": "README  2025-09-10     Introduction  The peatland mid-infrared database (pmird) stores data from peat, vegetation, litter, and dissolved organic matter samples, in particular mid-infrared spectra and other variables, from previously published and unpublished data sources. The majority of samples in the database are peat samples from northern bogs. Currently, the database contains entries from 26 studies, 11216 samples, and 3877 mid infrared spectra. The aim is to provide a harmonized data source that can be useful to re-analyse existing data, analyze peat chemistry, develop and test spectral prediction models, and provide data on various peat properties.     Usage notes    Download and Setup  The peatland mid-infrared database can be downloaded from https://doi.org/10.5281/zenodo.17092587. The publication contains the following files and folders:      pmird-backup-2025-09-10.sql: A mysqldump backup of the pmird database.     pmird_prepared_data: A folder that contains:    Folders like c00001-2020-08-17-Hodgkins with the raw spectra for samples from each dataset in the pmird database (see below for how to import the spectra).  Files like pmird_prepare_data_c00001-2020-08-17-Hodgkins.Rmd that contain the R code used to process and import the data from each dataset into the database. Corresponding html files contain the compiled scripts.  pmird_prepare_data.Rmd: An Rmarkdown script that was used to run the scripts that created the database (the top level script).      mysql_scripts: A folder that contains:    pmird_mysql_initialization.sql: MariaDB script to initialize the database.  001-db-initialize.Rmd: Rmarkdown script that executes pmird_mysql_initialization.sql and populated dataset-independent tables.  add-citations.Rmd: Rmarkdown script that adds information on references to the database.  add-licenses.Rmd: Rmarkdown script that adds information on licenses to the database.  add-mir-metadata-quality.Rmd Rmarkdown script that adds information on the quality of the infrared spectra to the database.      Dockerfile: A Dockerfile that defines the computing environment used to create the database.     renv.lock A renv.lock file that lists the R packages used to create the database.    The database can be set up as follows: The downloaded database needs to be imported in a running MariaDB instance. In a linux terminal, the downloaded sql file can be imported like so:  mysql -u<user> -p pmird < pmird-backup-2025-09-10.sql  Here, <user> is the database user name.  The database itself does not contain the infrared spectra. These data are in folder pmird_prepared_data which needs to be stored at any place in the file system.      R interface  The R package \u2018pmird\u2019 (Teickner 2025) provides an R interface to the database, based on the packages \u2018RMariaDB\u2019 (M\u00fcller et al. 2021) and \u2018dm\u2019 (Schieferdecker, M\u00fcller, and Bergant 2022). This interface can also be used to import the mid-infrared spectra that belong to extracted data records (please see the documentation of the \u2018pmird\u2019 R package for details, https://henningte.github.io/pmird/).     Citation  If you use data from the Peat Decomposition Database, cite the database and each of the original data sources you use. Bibliographic information on each data source are stored in table datasets (column reference_publication).  The database can be cited as:    Teickner, H., Agethen, S., Berger, S., Boelsen, R. I., Borken, W., Bragazza, L., Broder, T., De La Cruz, F. B., Diaconu, A.-C., Dise, N. B., Drollinger, S., Estop-Aragon\u00e9s, C., Ga\u0142ka, M., Mart\u00ed, M., Glatzel, S., Gro\u00df, J., Harris, L., Heffernan, L., Hodgkins, S. B., \u2026 Knorr, K.-H. (2025). Peatland mid-infrared database [Dataset]. https://doi.org/10.5281/zenodo.17092587      Data sources  Data in the database were derived from the following sources: De la Cruz, Osborne, and Barlaz (2016), Hodgkins et al. (2018), Knierzinger et al. (2020), Knierzinger (2020), M\u00fcnchberger (2019), M\u00fcnchberger et al. (2019), Schuster et al. (2022), Drollinger, Kuzyakov, and Glatzel (2019), Drollinger et al. (2020), Agethen and Knorr (2018), Kendall (2020), L. I. Harris et al. (2023), L. Harris and Olefeldt (2023), Pelletier et al. (2017), Teickner, Gao, and Knorr (2021), Teickner, Gao, and Knorr (2022), Heffernan (2019), Heffernan et al. (2020), Broder et al. (2012), Anzenhofer (2014), Mathijssen et al. (2019), Wagner (2013), H\u00f6mberg (2014), Berger et al. (2017), Berger et al. (2018), Moore et al. (2019), Diaconu et al. (2020), Ga\u0142ka, H\u00f6lzer, et al. (2022), Ga\u0142ka, Diaconu, et al. (2022), Harris et al. (2018), Harris et al. (2019), Boothroyd et al. (2021), Worrall (2021), Reuter et al. (2019b), Reuter et al. (2019a), Reuter et al. (2020), Liu and Lennartz (2019), Moore et al. (2005), Turunen et al. (2004).     Acknowledgements  Development of this database was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) grant no. KN 929/23-1 to Klaus-Holger Knorr and grant no. PE 1632/18-1 to Edzer Pebesma.     References      Agethen, Svenja, and Klaus-Holger Knorr. 2018. \u201cJuncus Effusus Mono-Stands in Restored Cutover Peat Bogs \u2013 Analysis of Litter Quality, Controls of Anaerobic Decomposition, and the Risk of Secondary Carbon Loss.\u201d Soil Biology and Biochemistry 117: 139\u201352. https://doi.org/10.1016/j.soilbio.2017.11.020.     Anzenhofer, Regina. 2014. \u201cBiogeochemical Characterization of Peat Profiles Along a Vegetation Gradient in an Ombrotrophic Bog, Patagonia.\u201d Master\u2019s thesis.     Berger, Sina, Gerhard Gebauer, Christian Blodau, and Klaus-Holger Knorr. 2017. \u201cPeatlands in a Eutrophic World \u2013 Assessing the State of a Poor Fen-Bog Transition in Southern Ontario, Canada, After Long Term Nutrient Input and Altered Hydrological Conditions.\u201d Soil Biology and Biochemistry 114 (November): 131\u201344. https://doi.org/10.1016/j.soilbio.2017.07.011.     Berger, Sina, Leandra S. E. Praetzel, Marie Goebel, Christian Blodau, and Klaus-Holger Knorr. 2018. \u201cDifferential Response of Carbon Cycling to Long-Term Nutrient Input and Altered Hydrological Conditions in a Continental Canadian Peatland.\u201d Biogeosciences 15 (3): 885\u2013903. https://doi.org/10.5194/bg-15-885-2018.     Boothroyd, I. M., F. Worrall, C. S. Moody, G. D. Clay, G. D. Abbott, and R. Rose. 2021. \u201cSulfur Constraints on the Carbon Cycle of a Blanket Bog Peatland.\u201d Journal of Geophysical Research: Biogeosciences 126 (8). https://doi.org/10.1029/2021JG006435.     Broder, T., C. Blodau, H. Biester, and K. H. Knorr. 2012. \u201cPeat Decomposition Records in Three Pristine Ombrotrophic Bogs in Southern Patagonia.\u201d Biogeosciences 9 (4): 1479\u201391. https://doi.org/10.5194/bg-9-1479-2012.     De la Cruz, Florentino B., Jason Osborne, and Morton A. Barlaz. 2016. \u201cDetermination of Sources of Organic Matter in Solid Waste by Analysis of Phenolic Copper Oxide Oxidation Products of Lignin.\u201d Journal of Environmental Engineering 142 (2): 04015076. https://doi.org/10.1061/(ASCE)EE.1943-7870.0001038.     Diaconu, Andrei-Cosmin, Ioan Tan\u0163\u0103u, Klaus-Holger Knorr, Werner Borken, Angelica Feurdean, Andrei Panait, and Mariusz Ga\u0142ka. 2020. \u201cA Multi-Proxy Analysis of Hydroclimate Trends in an Ombrotrophic Bog over the Last Millennium in the Eastern Carpathians of Romania.\u201d Palaeogeography, Palaeoclimatology, Palaeoecology 538 (January): 109390. https://doi.org/10.1016/j.palaeo.2019.109390.     Drollinger, Simon, Klaus-Holger Knorr, Wolfgang Knierzinger, and Stephan Glatzel. 2020. \u201cPeat Decomposition Proxies of Alpine Bogs Along a Degradation Gradient.\u201d Geoderma 369 (June): 114331. https://doi.org/10.1016/j.geoderma.2020.114331.     Drollinger, Simon, Yakov Kuzyakov, and Stephan Glatzel. 2019. \u201cEffects of Peat Decomposition on \u03b413C and \u03b415N Depth Profiles of Alpine Bogs.\u201d CATENA 178 (July): 1\u201310. https://doi.org/10.1016/j.catena.2019.02.027.        Ga\u0142ka, Mariusz, Andrei-Cosmin Diaconu, Angelica Feurdean, Julie Loisel, Henning Teickner, Tanja Broder, and Klaus-Holger Knorr. 2022. \u201cRelations of Fire, Palaeohydrology, Vegetation Succession, and Carbon Accumulation, as Reconstructed from a Mountain Bog in the Harz Mountains (Germany) During the Last 6200 Years.\u201d Geoderma 424 (October): 115991. https://doi.org/10.1016/j.geoderma.2022.115991.     Ga\u0142ka, Mariusz, Adam H\u00f6lzer, Angelica Feurdean, Julie Loisel, Henning Teickner, Andrei-Cosmin Diaconu, Marta Szal, Tanja Broder, and Klaus-Holger Knorr. 2022. \u201cInsight into the Factors of Mountain Bog and Forest Development in the Schwarzwald Mts.: Implications for Ecological Restoration.\u201d Ecological Indicators 140 (July): 109039. https://doi.org/10.1016/j.ecolind.2022.109039.     Harris, Lorna I., Tim R. Moore, Nigel T. Roulet, and Andrew J. Pinsonneault. 2019. \u201cData from: Lichens: A Limit to Peat Growth?\u201d Data. https://doi.org/10.5061/dryad.s136dc8.     \u2014\u2014\u2014. 2018. \u201cLichens: A Limit to Peat Growth?\u201d Edited by John Lee. Journal of Ecology 106 (6): 2301\u201319. https://doi.org/10.1111/1365-2745.12975.     Harris, Lorna I., David Olefeldt, Nicolas Pelletier, Christian Blodau, Klaus-Holger Knorr, Julie Talbot, Liam Heffernan, and Merritt Turetsky. 2023. \u201cPermafrost Thaw Causes Large Carbon Loss in Boreal Peatlands While Changes to Peat Quality Are Limited.\u201d Global Change Biology, August, gcb.16894. https://doi.org/10.1111/gcb.16894.     Harris, Lorna, and David Olefeldt. 2023. \u201cPermafrost Thaw Causes Large Carbon Loss in Boreal Peatlands While Changes to Peat Quality Are Limited.\u201d Dryad. https://doi.org/10.5061/DRYAD.47D7WM3KK.     Heffernan, Liam. 2019. \u201cPeat Carbon, \u03b414C, Macrofossil, and Humification Data from a Thawing Permafrost Peatland in Western Canada.\u201d UAL Dataverse. https://doi.org/10.7939/DVN/MKM0ZE.           Heffernan, Liam, Cristian Estop-Aragon\u00e9s, Klaus-Holger Knorr, Julie Talbot, and David Olefeldt. 2020. \u201cLong-Term Impacts of Permafrost Thaw on Carbon Storage in Peatlands: Deep Losses Offset by Surficial Accumulation.\u201d Journal of Geophysical Research: Biogeosciences 125 (3). https://doi.org/10.1029/2019JG005501.     Hodgkins, Suzanne B., Curtis J. Richardson, Ren\u00e9 Dommain, Hongjun Wang, Paul H. Glaser, Brittany Verbeke, B. Rose Winkler, et al. 2018. \u201cTropical Peatland Carbon Storage Linked to Global Latitudinal Trends in Peat Recalcitrance.\u201d Nature Communications 9 (1): 3640. https://doi.org/10.1038/s41467-018-06050-2.     H\u00f6mberg, Annkathrin. 2014. \u201cGeochemische Charakterisierung von Mooren der Changbai Mountains.\u201d Bachelor thesis, M\u00fcnster: M\u00fcnster.     Kendall, Rachel Anne. 2020. \u201cMicrobial and Substrate Decomposition Factors in Commercially Extracted Peatlands in Canada.\u201d Master\u2019s thesis, Montr\u00e9al: McGill University.     Knierzinger, Wolfgang. 2020. \u201c(Bio)Geochemical Data P\u00fcrgschachen Moor.\u201d Pangaea.     Knierzinger, Wolfgang, Ruth Drescher-Schneider, Klaus-Holger Knorr, Simon Drollinger, Andreas Limbeck, Lukas Brunnbauer, Felix Horak, Daniela Festi, and Michael Wagreich. 2020. \u201cAnthropogenic and Climate Signals in Late-Holocene Peat Layers of an Ombrotrophic Bog in the Styrian Enns Valley (Austrian Alps).\u201d E&G Quaternary Science Journal 69 (2): 121\u201337. https://doi.org/10.5194/egqsj-69-121-2020.     Liu, Haojie, and Bernd Lennartz. 2019. \u201cHydraulic Properties of Peat Soils Along a Bulk Density Gradient-A Meta Study.\u201d Hydrological Processes 33 (1): 101\u201314. https://doi.org/10.1002/hyp.13314.     Mathijssen, Paul J. H., Mariusz Ga\u0142ka, Werner Borken, and Klaus-Holger Knorr. 2019. \u201cPlant Communities Control Long Term Carbon Accumulation and Biogeochemical Gradients in a Patagonian Bog.\u201d Science of the Total Environment 684 (September): 670\u201381. https://doi.org/10.1016/j.scitotenv.2019.05.310.     Moore, Tim, Christian Blodau, Jukka Turunen, Nigel T. Roulet, and Pierre J. H. Richard. 2005. \u201cPatterns of Nitrogen and Sulfur Accumulation and Retention in Ombrotrophic Bogs, Eastern Canada.\u201d Global Change Biology 11 (2): 356\u201367. https://doi.org/10.1111/j.1365-2486.2004.00882.x.     Moore, Tim R., Klaus-Holger Knorr, Lauren Thompson, Cameron Roy, and Jill L. Bubier. 2019. \u201cThe Effect of Long-Term Fertilization on Peat in an Ombrotrophic Bog.\u201d Geoderma 343 (June): 176\u201386. https://doi.org/10.1016/j.geoderma.2019.02.034.     M\u00fcller, Kirill, Jeroen Ooms, David James, Saikat DebRoy, Hadley Wickham, and Jeffrey Horner. 2021. \u201cRMariaDB: Database Interface and \u2019MariaDB\u2019 Driver.\u201d     M\u00fcnchberger, Wiebke. 2019. \u201cPast and Present Carbon Dynamics in Contrasting South Patagonian Bog Ecosystems.\u201d PhD thesis, M\u00fcnster: University M\u00fcnster.     M\u00fcnchberger, Wiebke, Klaus-Holger Knorr, Christian Blodau, Ver\u00f3nica A. Pancotto, and Till Kleinebecker. 2019. \u201cZero to Moderate Methane Emissions in a Densely Rooted, Pristine Patagonian Bog \u2013 Biogeochemical Controls as Revealed from Isotopic Evidence.\u201d Biogeosciences 16 (2): 541\u201359. https://doi.org/10.5194/bg-16-541-2019.     Pelletier, Nicolas, Julie Talbot, David Olefeldt, Merritt Turetsky, Christian Blodau, Oliver Sonnentag, and William L Quinton. 2017. \u201cInfluence of Holocene Permafrost Aggradation and Thaw on the Paleoecology and Carbon Storage of a Peatland Complex in Northwestern Canada.\u201d The Holocene 27 (9): 1391\u20131405. https://doi.org/10.1177/0959683617693899.     Reuter, Hendrik, Julia Gensel, Marcus Elvert, and Dominik Zak. 2019a. \u201cCuO Lignin, and Bulk Decomposition Data of a 75-Day Anoxic Phragmites Australis Litter Decomposition Experiment in Soil Substrates from Three Northeast German Wetlands.\u201d PANGAEA - Data Publisher for Earth & Environmental Science. https://doi.org/10.1594/PANGAEA.902176.     \u2014\u2014\u2014. 2019b. \u201cInfrared Spectra (FTIR) of Phragmites Australis Litter, Initial and After Anoxic Decomposition in Three Wetland Substrates.\u201d PANGAEA - Data Publisher for Earth & Environmental Science. https://doi.org/10.1594/PANGAEA.902069.     \u2014\u2014\u2014. 2020. \u201cEvidence for Preferential Protein Depolymerization in Wetland Soils in Response to External Nitrogen Availability Provided by a Novel FTIR Routine.\u201d Biogeosciences 17 (2): 499\u2013514. https://doi.org/10.5194/bg-17-499-2020.     Schieferdecker, Tobias, Kirill M\u00fcller, and Darko Bergant. 2022. \u201cdm: Relational Data Models.\u201d     Schuster, Wiebke, Klaus-Holger Knorr, Christian Blodau, Mariusz Ga\u0142ka, Werner Borken, Ver\u00f3nica A. Pancotto, and Till Kleinebecker. 2022. \u201cControl of Carbon and Nitrogen Accumulation by Vegetation in Pristine Bogs of Southern Patagonia.\u201d Science of the Total Environment 810 (March): 151293. https://doi.org/10.1016/j.scitotenv.2021.151293.     Teickner, Henning. 2025. \u201cpmird: R Interface to the Peatland Mid Infrared Spectra Database.\u201d     Teickner, Henning, Chuanyu Gao, and Klaus-Holger Knorr. 2021. \u201cReproducible Research Compendium with R Code and Data for: \u2019Electrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition\u2019.\u201d Zenodo. https://doi.org/10.5281/zenodo.5792970.     \u2014\u2014\u2014. 2022. \u201cElectrochemical Properties of Peat Particulate Organic Matter on a Global Scale: Relation to Peat Chemistry and Degree of Decomposition.\u201d Global Biogeochemical Cycles 36 (2): e2021GB007160. https://doi.org/10.1029/2021GB007160.     Turunen, Jukka, Nigel T. Roulet, Tim R. Moore, and Pierre J. H. Richard. 2004. \u201cNitrogen Deposition and Increased Carbon Accumulation in Ombrotrophic Peatlands in Eastern Canada: N Deposition and Peat Accumulation.\u201d Global Biogeochemical Cycles 18 (3). https://doi.org/10.1029/2003GB002154.     Wagner, Sindy. 2013. \u201cAnalysis of Peat Decomposition, Element Distribution Patterns and Element Output of Two Peat Bogs in the Thuringian Forest.\u201d Master\u2019s thesis, University Bayreuth.     Worrall, Fred. 2021. \u201cSulphur Constraints on the Carbon Cycle of a Blanket Bog Peatland [Dataset].\u201d Durham University. https://doi.org/10.15128/R2PK02C9794.", "keywords": ["Sphagnum", "FTIR", "mid infrared spectra", "peat", "peatland", "pmird", "database", "ATR-FTIR"], "contacts": [{"organization": "Teickner, Henning, Agethen, Svenja, Berger, Sina, Boelsen, Rieke Inga, Borken, Werner, Bragazza, Luca, Broder, Tanja, De la Cruz, Florentino, Diaconu, Andrei-Cosmin, Dise, Nancy, Drollinger, Simon, Estop-Aragon\u00e9s, Cristian, Galka, Mariusz, Mart\u00ed Gener\u00f3, Magal\u00ed, Glatzel, Stephan, Gro\u00df, Jessica, Harris, Lorna, Heffernan, Liam, Hodgkins, Suzanne, H\u00f6mberg-Grandjean, Annkathrin, Hoppe, Helga, Kleinebecker, Till, Knierzinger, Wolfgang, Liu, Haojie, Mathijssen, Paul, Mollmann, Christopher, Schuster, Wiebke, N\u00e4rtker, Lisa, Olefeldt, David, Pancotto, Veronica A., Pelletier, Nicolas, Reuter, Hendrik, Robroek, Bjorn, Svensson, Bosse, Talbot, Julie, Thompson, Lauren M., Worrall, Fred, Yu, Zhi-Guo, Knorr, Klaus-Holger,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17092587"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17092587", "name": "item", "description": "10.5281/zenodo.17092587", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17092587"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-09-10T00:00:00Z"}}, {"id": "10.5281/zenodo.15730426", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Report", "title": "PREPSOIL workshop report - Earth observation for soil health monitoring; obstacles and  proposals in overcoming them", "description": "Description of a workshop on 'Earth observation for soil health monitoring; obstacles and \u00a0proposals in overcoming them' held on 7 November 2024. The report is an addition to PREPSOIL 5.2, which contains a review of scientific knowledge (bibliography, expert opinions, current EU projects), an inventory of the technological resources mobilised (vectors, sensors, current and planned products, services), and the identification of obstacles to greater use of Earth observations for soil monitoring and measurement needs to reduce/minimise these difficulties.", "keywords": ["Earth observation", "Soil", "soil health", "soil sensing", "soil monitoring"], "contacts": [{"organization": "van Egmond, Fenny", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15730426"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15730426", "name": "item", "description": "10.5281/zenodo.15730426", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15730426"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-25T00:00:00Z"}}, {"id": "10.5281/zenodo.15750778", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Dataset", "title": "Supporting data: Stabilization of PFAS-contaminated soil with sewage sludge- and wood-based biochar sorbents", "keywords": ["soil stabilization", "PFAS", "column tests", "Waste-based biochar", "Sorbents"], "contacts": [{"organization": "Cornelissen, Gerard", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15750778"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15750778", "name": "item", "description": "10.5281/zenodo.15750778", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15750778"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15750777", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Dataset", "title": "Supporting data: Stabilization of PFAS-contaminated soil with sewage sludge- and wood-based biochar sorbents", "keywords": ["soil stabilization", "PFAS", "column tests", "Waste-based biochar", "Sorbents"], "contacts": [{"organization": "Cornelissen, Gerard", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15750777"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15750777", "name": "item", "description": "10.5281/zenodo.15750777", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15750777"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15753537", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Other", "created": "2025-06-20", "title": "Possible contribution of remote sensing to soil monitoring", "description": "A slideshow for a lecture given for the online training activity \u201cSupporting Capacity Building in Soil Monitoring in Europe\u201d organised for Task T5.4 of PREPSOIL project (CSA, Horizon EUrope).  It is based on the results of Task 5.2 and shows that:    Although necessary, soil monitoring methods based on in situ observations or soil sampling are costly, give estimates with low spatial & temporal resolutions, and provide no information on uncertainties outside the characterized sites;\u00a0  RS provides access to certain soil information or makes soil property covariates available with a spatial resolution and revisit frequency that can be very high;  By using both soil data and other data including RS data, digital soil mapping makes it possible to obtain precise property maps, as well as uncertainty maps.", "keywords": ["Monitoring", "Healthy Soils", "Traditional methods", "Remote sensing", "Digital Soil Mapping", "PREPSOIL"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15753537"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15753537", "name": "item", "description": "10.5281/zenodo.15753537", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15753537"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-27T00:00:00Z"}}, {"id": "10.5281/zenodo.15763496", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Report", "title": "Technical feasibility in using CLMS satellite-based EO to estimate soil health indicators", "description": "The slideshow contains a summary of the main results issued from PREPSOIL Task T5.2.", "keywords": ["2. Zero hunger", "Earth observation", "Technology", "Monitoring", "Scientific knowledge", "Communication", "Skills", "Sustainable soil management", "Success factors", "Healthy Soils", "15. Life on land", "PREPSOIL", "Remote Sensing", "Gaps"], "contacts": [{"organization": "Renault, Pierre, Xie, Guanyao, Weiss, Marie,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15763496"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15763496", "name": "item", "description": "10.5281/zenodo.15763496", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15763496"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-06-28T00:00:00Z"}}, {"id": "10.5281/zenodo.15754958", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:00Z", "type": "Software", "title": "Workshop framework: Earth observation for soil health monitoring; obstacles and proposal in overcoming them.", "description": "Addition to Deliverable D5.2 containing the method proposed to organize national workshops aiming to:    Identify bottlenecks (scientific, technological, technical, skills \u2026) to greater use of satellite Earth Observations (EO) and CLMS and/or Galileo/ EGNOS products;  Propose measures to reduce/minimize these difficulties, ranking them successively according to their supposed impact on the bottlenecks, and their ease - or even cost - of implementation.   The Presentation includes instructions, a slideshow to adapt to local context and language, and an informed consent form for participants", "keywords": ["Remote Sensing", "Obstacles", "Monitoring", "Healthy Soils", "PREPSOIL"], "contacts": [{"organization": "Renault, Pierre", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15754958"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15754958", "name": "item", "description": "10.5281/zenodo.15754958", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15754958"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-27T00:00:00Z"}}, {"id": "10072/428410", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:26:41Z", "type": "Journal Article", "created": "2023-10-18", "title": "Community composition and physiological plasticity control microbial carbon storage across natural and experimental soil fertility gradients", "description": "Abstract                <p>Many microorganisms synthesise carbon (C)-rich compounds under resource deprivation. Such compounds likely serve as intracellular C-storage pools that sustain the activities of microorganisms growing on stoichiometrically imbalanced substrates, making them potentially vital to the function of ecosystems on infertile soils. We examined the dynamics and drivers of three putative C-storage compounds (neutral lipid fatty acids [NLFAs], polyhydroxybutyrate [PHB], and trehalose) across a natural gradient of soil fertility in eastern Australia. Together, NLFAs, PHB, and trehalose corresponded to 8.5\uffe2\uff80\uff9340% of microbial C and 0.06\uffe2\uff80\uff930.6% of soil organic C. When scaled to \uffe2\uff80\uff9cstructural\uffe2\uff80\uff9d microbial biomass (indexed by polar lipid fatty acids; PLFAs), NLFA and PHB allocation was 2\uffe2\uff80\uff933-times greater in infertile soils derived from ironstone and sandstone than in comparatively fertile basalt- and shale-derived soils. PHB allocation was positively correlated with belowground biological phosphorus (P)-demand, while NLFA allocation was positively correlated with fungal PLFA : bacterial PLFA ratios. A complementary incubation revealed positive responses of respiration, storage, and fungal PLFAs to glucose, while bacterial PLFAs responded positively to PO43-. By comparing these results to a model of microbial C-allocation, we reason that NLFA primarily served the \uffe2\uff80\uff9creserve\uffe2\uff80\uff9d storage mode for C-limited taxa (i.e., fungi), while the variable portion of PHB likely served as \uffe2\uff80\uff9csurplus\uffe2\uff80\uff9d C-storage for P-limited bacteria. Thus, our findings reveal a convergence of community-level processes (i.e., changes in taxonomic composition that underpin reserve-mode storage dynamics) and intracellular mechanisms (e.g., physiological plasticity of surplus-mode storage) that drives strong, predictable community-level microbial C-storage dynamics across gradients of soil fertility and substrate stoichiometry.</p", "keywords": ["2. Zero hunger", "Science & Technology", "Ecology", "Fatty Acids", "Fungi", "Soil Science", "Trehalose", "Environmental Sciences & Ecology", "15. Life on land", "Markvetenskap", "Microbiology", "Article", "Carbon", "Environmental sciences", "Biological sciences", "Soil", "Biomass", "Life Sciences & Biomedicine", "Ecosystem", "Soil Microbiology", "Phospholipids"]}, "links": [{"href": "https://doi.org/10072/428410"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/The%20ISME%20Journal", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10072/428410", "name": "item", "description": "10072/428410", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10072/428410"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-10-18T00:00:00Z"}}, {"id": "11567/1235396", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-03T16:27:12Z", "type": "Report", "title": "AI-based solutions for autonomous underwater observing systems and science discovery", "keywords": ["Artificial Intelligence", " Intelligent Observing Systems", " Edge Computing", " Science Discovery", " Biodiversity"], "contacts": [{"organization": "Simone Marini, Daniele Lagomarsino Oneto, Mattia Cavaiola, Jacopo Aguzzi, Daniele D\u2019Agostino,", "roles": ["creator"]}]}, "links": [{"href": "https://iris.unige.it/bitstream/11567/1235396/1/DAgostino-Biochange.pdf"}, {"href": "https://doi.org/11567/1235396"}, {"rel": "self", "type": "application/geo+json", "title": "11567/1235396", "name": "item", "description": "11567/1235396", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11567/1235396"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-01T00:00:00Z"}}, {"id": "3185943994", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:28:43Z", "type": "Journal Article", "created": "2022-03-17", "title": "Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003\u20132017)", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Quantifying the dust optical depth (DOD) and its uncertainty across spatiotemporal scales is key to understanding and constraining the dust cycle and its interactions with the Earth System. This study quantifies the DOD along with its monthly and year-to-year variability between 2003 and 2017 at global and regional levels based on the MIDAS (ModIs Dust AeroSol) dataset, which combines Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua retrievals and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis products. We also describe the annual and seasonal geographical distributions of DOD across the main dust source regions and transport pathways. MIDAS provides columnar mid-visible (550\u2009nm) DOD at fine spatial resolution (0.1\u2218\u00d70.1\u2218), expanding the current observational capabilities for monitoring the highly variable spatiotemporal features of the dust burden. We obtain a global DOD of 0.032\u00b10.003 \u2013 approximately a quarter (23.4\u2009%\u00b12.4\u2009%) of the global aerosol optical depth (AOD) \u2013 with about 1\u00a0order of magnitude more DOD in the Northern Hemisphere (0.056\u00b10.004; 31.8\u2009%\u00b12.7\u2009%) than in the Southern Hemisphere (0.008\u00b10.001; 8.2\u2009%\u00b11.1\u2009%) and about 3.5 times more DOD over land (0.070\u00b10.005) than over ocean (0.019\u00b10.002). The Northern Hemisphere monthly DOD is highly correlated with the corresponding monthly AOD (R2=0.94) and contributes 20\u2009% to 48\u2009% of it, both indicating a dominant dust contribution. In contrast, the contribution of dust to the monthly AOD does not exceed 17\u2009% in the Southern Hemisphere, although the uncertainty in this region is larger. Among the major dust sources of the planet, the maximum DODs (\u223c1.2) are recorded in the Bod\u00e9l\u00e9 Depression of the northern Lake Chad Basin, whereas moderate-to-high intensities are encountered in the Western Sahara (boreal summer), along the eastern parts of the Middle East (boreal summer) and in the Taklamakan Desert (spring). Over oceans, major long-range dust transport is observed primarily along the tropical Atlantic (intensified during boreal summer) and secondarily in the North Pacific (intensified during boreal spring). Our calculated global and regional averages and associated uncertainties are consistent with some but not all recent observation-based studies. Our work provides a simple yet flexible method to estimate consistent uncertainties across spatiotemporal scales, which will enhance the use of the MIDAS dataset in a variety of future studies.                     </p></article>", "keywords": ["Mineral dusts", ":Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia [\u00c0rees tem\u00e0tiques de la UPC]", "Physics", "QC1-999", "MIDAS global dataset", "16. Peace & justice", "01 natural sciences", "Atmospheric Sciences", "Climate Action", "Chemistry", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria agroaliment\u00e0ria::Ci\u00e8ncies de la terra i de la vida::Climatologia i meteorologia", "13. Climate action", "Mineral dust particles", "Simulaci\u00f3 per ordinador", "Pols", "Meteorology & Atmospheric Sciences", "Datasets", "Dust optical depth (DOD)", "Earth System", "QD1-999", "Astronomical and Space Sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/22/3553/2022/acp-22-3553-2022.pdf"}, {"href": "https://escholarship.org/content/qt9v38c6qs/qt9v38c6qs.pdf"}, {"href": "https://doi.org/3185943994"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "3185943994", "name": "item", "description": "3185943994", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/3185943994"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-19T00:00:00Z"}}, {"id": "10.5281/zenodo.15849753", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:02Z", "type": "Software", "title": "LandRisk: A GUI python based software to assess contaminated land risk using Source - Pathway - Receptor setting", "description": "Python-Based Risk Screening System (RSS) for Environmental Risk Assessment  a Python-based Risk Screening System (RSS) designed to perform qualitative environmental risk assessments by evaluating potential contaminant linkages. The system assesses risk based on three fundamental components:      Hazard (Source)\u00a0\u2013 The origin of potential contamination (e.g., chemical spills, industrial waste).     Exposure Pathway\u00a0\u2013 The route through which the hazard propagates to reach a receptor (e.g., soil leaching, groundwater migration, airborne dispersion).     Receptor\u00a0\u2013 The entity that may be adversely affected (e.g., human populations, aquatic ecosystems, wildlife).    Key Applications  Pre-Survey Screening:, serves as an initial qualitative assessment before a walkover survey, helping prioritise areas of concern.\u00a0It evaluates risk pathways, including:    On-site to on-site\u00a0(internal contamination spread)  On-site to off-site\u00a0(migration beyond property boundaries)  Off-site to on-site\u00a0(external sources impacting the site)   Supports evaluation across different environmental pathways:    Soil (e.g., toxic metals, hydrocarbons)  Groundwater\u00a0(e.g., leaching contaminants)  Surface water (e.g., runoff contamination)  Air\u00a0(e.g., volatile organic compounds)  Sediment (e.g., accumulated contaminants in water bodies)   Risk Scoring Methodology  The RSS calculates risk using a\u00a0Likelihood \u00d7 Impact\u00a0framework, where:      Likelihood\u00a0depends on hazard magnitude and pathway completeness.     Impact\u00a0considers receptor sensitivity and population exposure.    This systematic approach enables\u00a0rapid risk prioritization, guiding further investigation and remediation planning.", "keywords": ["source - pathway - receptor", "risk assessment", "contaminated land"], "contacts": [{"organization": "Tyrologou, Pavlos, Couto, Nazar\u00e9, Koukouzas, Nikolaos, Makris, George, Kaija, Juha,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15849753"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15849753", "name": "item", "description": "10.5281/zenodo.15849753", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15849753"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-09T00:00:00Z"}}, {"id": "10.5281/zenodo.17618737", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-03T16:25:09Z", "type": "Dataset", "title": "The Namibian Soil Profile Database (NSPD2025): An updated, expanded and harmonised compilation of national soil data", "description": "Introduction  The Namibian Soil Profile Database (NSPD2025) is a national compilation of 5,099 soil point observations and 13,425 horizons or layers, comprising site, profile, horizon, and analytical data from diverse legacy datasets. Data were verified, cleaned, and standardised to ensure compatibility with international systems.\u00a0  The spatial distribution of points shows denser sampling in agricultural and research areas, and sparser data in remote rural areas and the Namib Desert. The level of detail varies considerably, from basic site observations to full profile descriptions with analytical data.  The dataset is provided as a Microsoft Excel file with four worksheets \u2013\u00a0Reg&Site, Hor_lab, Hor_field, and Metadata \u2013 and a QGIS project with the profile locations and basic geographical information.   NSPD2025 is intended for use in soil classification, conventional and digital soil mapping, environmental modelling and land management in Namibia and beyond.   Data Sources  NSPD2025 integrates data from multiple legacy sources:  National Soil Survey (1998\u20132000) and MAWRD-AEZ Campaigns (2001\u20132009)  Systematic soil data collection in post-independence Namibia began with the National Soil Survey Phase I (NSS), conducted by the Ministry of Agriculture, Water and Rural Development[1] (MAWRD) in collaboration with the Cartographic Institute of Catalonia (ICC) from 1998 to 2000. It produced four soil maps and associated MS Access relational databases: NSD1000, NSD250, NSDkava, and NSDowa (ICC & MAWRD, 2000). Soil profiles were described according to the FAO Guidelines for Soil Profile Description (FAO, 1990).   The data were reformatted into the Namibian Soil and Terrain Digital Database, NAMSOTER (Coetzee, 2001), based on the methodology of Van Engelen and Wen (1995), for inclusion in the Southern African SOTERSAF (FAO/ISRIC, 2003).  The Agro-Ecological Zoning (AEZ) Programme of MAWRD/MAWF continued soil survey campaigns in under-sampled regions between 2000 and 2009. These datasets were consolidated and maintained within the Namibian Agricultural Resources Information System (NARIS). By 2009, NSS NARIS contained 2,155 soil records of varying completeness.  All laboratory analyses for these profiles were performed by the ministerial Agriculture Laboratory (AgricLab) in Windhoek, which follows ISO-based quality management through standard operating procedures, internal standards, replicate analyses, and participation in the Agri-Laboratory Association of Southern Africa (AgriLASA) inter-laboratory proficiency testing.  During the present update, additional analytical and field data were recovered from original NSS records and integrated into the NSPD2025.  African Soil Microbial Genomics Project (AMP, AMP-NA, AMP-NDLS)  The African Soil Microbial Genomics Project (Wild, 2016; Cowan et al., 2022) contributed 179 topsoil samples (0\u201320 cm) across three datasets. Eleven physicochemical soil properties were analysed by the University of Pretoria using AgriLASA (2004) protocols.  BIOTA Project  The Biodiversity Monitoring Transect Analysis in Africa (BIOTA) Programme established 11 long-term observatories across Namibia (Petersen, 2008; https://biota-africa.org/). It contributed 638 samples from 257 profiles (0\u201310 cm, 10\u201330 cm, 30\u201360 cm), analysed for texture, pH (CaCl\u2082), electrical conductivity, total nitrogen, and organic carbon by the University of Hamburg\u2019s Institute of Soil Science.  GIZ Bush-SOC Project  The Assessment of the Impact of Bush Encroachment and Bush Control on Soil Organic Carbon in Namibia project (Strohbach et al., 2024) contributed 162 topsoil samples (0\u201330 cm) analysed by the AgricLab for texture, pH (H\u2082O), bulk density, electrical conductivity, extractable cations, plant-available phosphorus, and organic carbon.  LandPKS Project  The Land Potential Knowledge System (LandPKS) (Herrick et al., 2016) developed a mobile app integrating user inputs with cloud-based geospatial data. Piloted in Namibia and Kenya, it provided 918 georeferenced observations, recording site, land use, vegetation cover, effective depth, stoniness, and field-estimated texture at seven standard depths (0\u20131 cm, 1\u201310 cm, 10\u201320 cm, 20\u201350 cm, 50\u201370 cm, 70\u2013100 cm, 100\u2013150 cm.). No laboratory data are included, and data quality varies due to contributions by non-specialists.  Land Degradation Neutrality (LDN, LDN-Omusati)  The Land Degradation Neutrality (LDN) Project (Nijbroek et al., 2018) contributed 319 topsoil (0\u201330 cm) and 277 subsoil (30\u2013100 cm) samples, analysed by the AgricLab for organic carbon, bulk density, and particle size.  SASSCAL  The Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) Phase I subproject Monitoring agricultural ecosystems with regards to climate change effects (De Bl\u00e9court et al., 2018) added 181 samples from 30 profiles across four sites. These were analysed for texture, bulk density, pH (H\u2082O and CaCl\u2082), electrical conductivity, total nitrogen, organic carbon, cation exchange capacity, and porosity by the University of Hamburg\u2019s Institute of Soil Science. Landscape and profile photographs are included.  Soils4Africa (S4A)  The EU Horizon 2020 Soils4Africa Project (https://www.soils4africa-h2020.eu/) contributed 139 site and descriptions, and laboratory data for 64 topsoil samples. Analyses were conducted by the South African Agricultural Research Council\u2019s Institute for Soil, Climate and Water (ARC-ISCW) (Paterson, 2021). Bulk density measurements were carried out by the AgricLab.  Future Okavango (TFO)  The Future Okavango Project (Pr\u00f6pper et al., 2015; https://www.future-okavango.org/) focused on knowledge-based land-use management in the Okavango catchment and contributed 484 samples from 141 profiles. These were analysed for texture, pH (H\u2082O and CaCl\u2082), bulk density, electrical conductivity, cation exchange capacity, total nitrogen, and organic carbon by the University of Hamburg and the University of Botswana\u2019s Okavango Research Institute. Landscape and profile photographs are included.  Additional Contributions  Smaller datasets were obtained from Simmonds & Forbes (1995), Buch (1990), Trippner (1998), Kempf (2000), and Coetzee (in Strohbach et al., 2019).  Methods and Data Processing  All datasets were collated in Microsoft Excel, using both automated procedures and visual inspection \u2013 based on subject knowledge \u2013 for quality-control of data. Records lacking georeferencing or containing irreparable errors were removed.   Key steps included:    Verification and correction of NSS and MAWF data against original field forms, and addition of previously undigitised information. Digital records could not be verified against the following missing field forms: AO, DOR, MAR, NDONGA, OMAH_A, OMAH_B, ONGO, OVI, SPERR, TSUM.  Standardisation of classes, codes, and measurement units following FAO Guidelines for Soil Profile Description (FAO, 2006) and SOTER/ISRIC standards (Van Engelen & Dijkshoorn, 2013).  Verification of profile identifications and horizon designations; assigning layer identifiers to non-pedogenic layers (e.g. LandPKS fixed depth intervals).  Conversion of coordinates to decimal degrees and addition of the coordinate reference system.  Confirmation of attribute definitions (e.g. organic matter vs organic carbon; minimum diameter of sand fraction 50 or 63 \u00b5m) and value ranges, some in combination with other attributes (e.g. the sum of particle size fractions being 100\u00b11%; horizon thickness corresponding to the difference between upper and lower horizon limits).  Verification of conspicuous outliers.  Addition of registration and environmental information.  Where available, diagnostic elements (horizons, materials and properties) were used for classification according to the WRB (IUSS, 2015)   Limitations: variable data completeness, differences in analytical methods, and unverified records for some missing field forms. However, the harmonisation process ensures national consistency and compatibility with global frameworks.  Database Design  The NSPD2025 dataset is provided as an MS Excel workbook, NSPD2025.xlsx, with four worksheets (see all fields below):     Reg&Site contains profile registration information and site descriptions.  Hor_field contains physical and morphological characteristics of the profiles and horizons.   \u00a0Hor_lab contains laboratory measurements.  Metadata   The primary identifier of each point observation is the PRID (profile identification), with secondary identifiers for horizons/layers in the HONU field.  The design embeds some redundancy: repetition of data in different formats (e.g. individual values, class ranges, class codes and descriptive text) and measuring units. For example, dates of profile description are recorded in YYYY-MM-DD, DD-Mon-YYYY, DD/MM/YYYY formats as well as in separate Year, Month and Day fields, while Organic Carbon is recorded in both % and g/kg units. This was done to facilitate integration with international soil information systems, and to accommodate users who are unfamiliar with the FAO profile description codes. The same rationale is behind the use of descriptive field names rather than codes.     \u00a0  Fields of the Reg&Site table:       PRID    Degree Square    Grazer Type    Surface Sealing Hardness Desc      Dataset    Quarter Degree Square    Human Influence Code    Surface Cracks Width cm      PRID Let    Farm No    Human Influence Desc    Surface Cracks Width Code      PRID Dig    Farm Name    Rock Outcrops Abundance/Cover Code    Distance Between Surface Cracks (m)      ALT PRID    Location    Rock Outcrops Cover %    Salt (Surface Salinity)      Date (YYYY-MM-DD)    Diagnostic Horizon / Property / Material    Rock Outcrops Cover Desc    Bleached Sand Code      Date (DD-Mon-YY)    Present Weather Code    Rock Outcrops - m between    Bleached Sand %      Date (DD/MM/YYYY)    Present Weather Desc    Rock Outcrops Type    Bleached Sand Desc      Year    Past Weather Code    Coarse Surface Fragments Cover Code    Flooded >2 weeks/a      Month    Past Weather Desc    Coarse Surface Fragments Cover %    Major Landform Code (2nd level)      Day    Soil Temperature Regime Code    Coarse Surface Fragments Cover Desc    Major Landform Desc      Coordinate Reference System    Soil Temperature Regime Desc    Coarse Surface Fragments Size Code    Minor Landform desc      Latitude    Soil Moisture Regime Code    Coarse Surface Fragments Size cm    Position code      Longitude    Soil Moisture Regime Desc    Coarse Surface Fragments Size Desc    Position Desc      Elevation m GPS    Slope Gradient Class    Surface Coarse Surface Fragments Type    Vegetation      Elevation m SRTM    Slope Form Vert    Erosion Type Code    Land Cover      Country ID    Slope Form Hor    Erosion Type    Parent Material      Data Origin    Slope Gradient Measured %    Erosion Area Affected Code    Lithology      Surveyor(s)    Slope Gradient Desc    Erosion Area Affected %    Drainage Desc      Status    Gradient % (for landform) from SRTM    Erosion Degree Code    Drainage Code      Profile Classification Original    Slope % SRTM    Erosion Degree Desc    Geology      Profile Classification Original Code    Relief m/km    Erosion Status Code    Effective Depth      Classification System    Drainage density    Erosion Status Desc    Depth Class      WRB2015 Reclassification RSG    Present Land Use Code    Surface Sealing Thickness Code    Biological activity      WRB2015 Reclassification Principal Qualifiers    Present land Use Desc    Surface Sealing Thickness mm    Notes      WRB2015 Reclassification Supplementary Qualifiers    Past Land Use Code    Surface Sealing Thickness Desc  \u00a0     WRB Classification by S Nambambi    Past Land Use Desc    Surface Sealing Hardness Code  \u00a0     \u00a0Fields of the Hor_lab table:       PRID    Electrical Conductivity \u00b5S/cm [2 soil:5 water suspension] (EL25)    Gypsum %    coSa %      HONU    Electrical Conductivity dS/m [2 soil:5 water suspension] (EL25)    Gypsum g/kg (GYPS)    meSa %      Dataset    Electrical Conductivity \u00b5S/cm [Saturated Paste Extract] (ELCO)    Total Nitrogen mg/kg (TOTN)    fiSa %      Latitude    Electrical Conductivity dS/m [Saturated Paste Extract] (ELCO)    Total Nitrogen g/kg (TOTN) Kjeldahl    vfiSa %      Longitude    Ca % [Exchangeable & Soluble; Mehlich #3, ICP-AES]    Total Nitrogen %    coSi %      Laboratory    Mg % [Exchangeable & Soluble; Mehlich #3, ICP-AES]    Total Phosphorus mg/kg [Mehlich #3, ICP-AES]    fiSi %      Soil Lab ID    K % [Exchangeable & Soluble; Mehlich #3, ICP-AES]    Total P unknown method & unit    EC (1:5) mS/m      Representative Profile    Na % [Exchangeable & Soluble; Mehlich #3, ICP-AES]    Phosphorus [Olsen] mg/kg    CaCO3 g/kg      Hor thickness cm [contains >]    Ca mg/kg [Extractable; 1M NH4acetate, AAS] (SOCA)    P2O5 mg/kg [Olsen; P x 2.291]    inorgC g/kg      Upper Depth cm [contains >]    Mg mg/kg [Extractable; 1M NH4acetate, AAS] (SOMG)    Organic Carbon % LOI    totC g/kg      Lower Depth cm [contains >]    K mg/kg [Extractable; 1M NH4acetate, AAS] (SOLK)    Organic Carbon % WB    orgC g/kg CNS analyser      Hor thickness cm    Na mg/kg [Extractable; 1M NH4acetate, AAS] (SONA)    Organic Carbon g/kg WB    exchH cmol(-)/kg      Upper Depth cm    Cl mg/kg [Extractable] (SOCL)    Organic Matter % [OC*1.74]    exchAl cmol(-)/kg      Lower Depth cm    Chloride % [Extractable]    Mn mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]    BaseSat % ?      Horizon Code Short    SSO4 ppm    Fe mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]    exchAcid cmol(+)/kg      Dominant Sand Grade Code    Sulfate %    Al mg/kg [Exchangeable & Soluble; Mehlich #3, ICP-AES]    totMo EPA 6010C?:2007      Dominant Sand Grade Desc    Soluble carbonate meq/l (SCO3)    Porosity Measured    totCd mg/kg EPA 6010C:2007      Sand % < 53 \u00b5m (SDTO)    Total Carbonate Equivalent g/kg (TCEQ)    Fe_ox mg/g acid-oxalate extr    totPb mg/kg EPA 6010C:2007      Silt % [2-53\u00b5m] (STPC)    Carbonate Estimate %    Al_ox mg/g acid-oxalate extr    totV mg/kg EPA 6020A:2007      Clay % [< 2 \u00b5m] (CLPC)    Exchangeable Calcium cmol(+)/kg (EXCA)    Al mg/kg?    totHg mg/kg EPA 6020A:2007      Silt + Clay %    Exchangeable Magnesium cmol(+)/kg (EXMG)    B mg/kg?    totCr mg/kg EPA 6010C:2007      Texture Code (PSCL)    Exchangeable Potassium cmol(+)/kg (EXCK)    Cu mg/kg?    totCo mg/kg EPA 6010C:2007      Texture Desc    Exchangeable Sodium cmol(+)/kg (EXNA)    Fe mg/kg?    totNi mg/kg EPA 6010C:2007      Bulk Density kg/dm3    CEC Effective cmol(+)/kg [Sum of Exchangeable Bases]    Mn mg/kg?    totCu mg/kg EPA 6010C:2007      pH CaCl2 (PHCA) [2 soil:5 CaCl2]    CEC Measured cmol(+)/kg    P mg/kg?    totZn mg/kg EPA 6010C:2007      pH water (PHAQ)    Base Saturation Estimate % [from pHwater]    S mg/kg?    totAs mg/kg EPA 6020A:2007      pH water other lab    Base Saturation % [CECeffective/CECmeasured]    Zn mg/kg?    totSb mg/kg EPA 6020A:2007      pH KCl (PHKC)    \u00a0    \u00a0    \u00a0      \u00a0  Fields of the Hor_field table:       PRID    Coarse Fragments Abundance Desc    Vertic Properties    Coatings Form Desc      HONU    Coarse Fragments Abundance %    properties for classification    Coatings Location Code      Dataset    Coarse Fragments Size Code    CLAF    Coatings Location Desc      Latitude    Coarse Fragments Size Desc    LOWER LEVEL UNITS    Cementation /\u00a0 Compaction Nature Code      Longitude    Coarse Fragments Size mm    REFERENCE GROUP    Cementation / Compaction Nature Desc      Hor thickness cm    Course Fragments Shape Code    CLAV    Cementation /\u00a0 Compaction Continuity Code      Upper Depth cm    Coarse Fragments Shape Desc    ALT PRID    Cementation / Compaction Continuity Desc      Lower Depth cm    Coarse Fragments Degree of Weathering    Consistence Dry Code    Cementation /\u00a0 Compaction Fabric Code      Diagnostic Horizon / Property / Material Code    Coarse Fragments Type    Consistence Dry Desc    Cementation / Compaction Fabric Desc      Diagnostic Horizon    Drainage Code    Consistence Moist Code    Mineral Concentrations Nature Code      Diagnostic Property    Drainage Desc    Consistence Moist Desc    Mineral Concentrations Nature Desc      Diagnostic Material    Field Texture Code    Consistence Wet - Stickiness Code    Mineral Concentrations Abundance Code      Qualifier    Field Texture Desc    Consistence Wet - Stickiness\u00a0 Desc    Mineral Concentrations Abundance Desc      Horizon Code Short    Carbonate Estimate\u00a0 %.1    Consistence Wet - Plasticity Code    Mineral Concentrations Abundance %      Horizon Code Original    Carbonate Reaction Code    Consistence Wet - Plasticity Desc    Mineral Concentrations Size Code      Subordinate Horizon Code    Carbonate Reaction Desc    Porosity Class Code    Mineral Concentrations Size Desc      Lithology of R/C    Secondary Carbonates Code    Porosity Class Desc    Mineral Concentrations Shape Code      Horizon Transition Distinctness Code    Secondary Carbonates Desc    Porosity Class %    Mineral Concentrations Shape Desc      Horizon Transition Distinctness Desc    Munsell_col_moist    Voids Type Code    Mineral Concentrations Hardness Code      Horizon Transition Distinctness cm    Hue_moist    Voids Type Desc    Mineral Concentrations Hardness Desc      Horizon Transition Topography Code    Value_moist    Voids Size Class    Roots Abundance Code      Horizon Transition Topography Desc    Chroma_moist    Voids Size Desc    Roots Abundance Desc      Structure Type Code    Colour Moist Desc [Munsell]    Voids Size mm    Roots Abundance No      Structure Type Desc    Munsell_col_dry    Voids Abundance Class    Roots Diameter Code      Structure Strength Code    Hue_dry    Voids Abundance Desc    Roots Diameter Desc      Structure Strength Desc    Value_dry    Voids Abundance Number    Roots Diameter mm      Structure Size Code    Chroma_dry    Coatings Nature Code    Biological Features Code      Structure Size Desc    Colour Dry Desc [Munsell]    Coatings Nature Desc    Biological Features Desc      Coarse Fragments Abundance Code    Mottling Desc    Coatings Form Code    Notes      Supporting Files and Documents  GIS_zipped: QGIS project file (Namibia_Soil_Profiles.qgz) & shape files (Namibia_Soil_Points, National boundary, Regional boundaries, Trunk Roads, Main Roads, District Roads, All settlements, Larger settlements, Rivers, Etosha National Park)  Lab & Field Methods: AgricLab soil analysis methods; Soils4Africa lab & field observation guidelines; Africa Micobiome Project methods; FAO Guidelines for Soil Profile Description  Images: Distribution map of soil point data; Datasets in the database   Usage Notes  The NSPD2025 dataset is intended for:    \u00a0 \u00a0 \u00a0 \u00a0Conventional soil mapping and classification  \u00a0 \u00a0 \u00a0 \u00a0Digital soil mapping (DSM) and predictive modelling  \u00a0 \u00a0 \u00a0 \u00a0Land evaluation and agro-ecological zoning  \u00a0 \u00a0 \u00a0 \u00a0Environmental and hydrological modelling  \u00a0 \u00a0 \u00a0 \u00a0Educational and research purposes   Users are requested to cite the dataset when using the data. Derived products should acknowledge the original sources.   Acknowledgements  The author gratefully acknowledges the Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR), formerly MAWRD/MAWF/MAWLR, for providing data and documentation from the Agro-Ecological Zoning Programme. Appreciation is extended to Ms Eva Corral-Pazos-de-Provens (Universidad de Huelva, Spain) for designing and populating a prototype relational database, and to the numerous surveyors, laboratory analysts, and GIS specialists who contributed to the original fieldwork and data compilation efforts (see Metadata).  References  AgriLASA. (2004). AgriLASA soil handbook. Pretoria: Agri Laboratory Association of Southern Africa.  Buch, M. W. (1990). Soils, soil erosion and vegetation in the Etosha National Park / Northern Namibia - Field & laboratory results of the investigations of the year 1989. Part I \u2013 field results; Part II \u2013 laboratory results. University of Regensburg (unpublished).  Coetzee, M.E. (2001). NAMSOTER \u2013 A SOTER database for Namibia. Agro-Ecological Zoning Programme, MAWRD. Windhoek.  Cowan, D., Lebre, P., Amon, C., Becker, R.W., Boga, H.I., Boulang\u00e9, A., Chiyaka, T.L., Coetzee, T., De Jager, P.C., Dikinya, O., Eckardt, F., Greve, M., Harris, M.A., Hopkins, D.W., Houngnandan, H.B., Houngnandan, P., Jordaan, K., Kaimoyo, E., Kambura, A.K., Kamgan-Nkuekam, G., Makhalanyane, T.P., Maggs-K\u00f6lling, G., Marais, E., Mondlane, H., Nghalipo, E., Olivier, B.W., Ortiz, M., Pertierra, L.R., Ramond, J.-B., Seely, M., Sithole-Niang, I., Valverde, A., Varliero, G., Vikram, S., Wall D.H., & Zeze, A. (2022). Biogeographical survey of soil microbiomes across sub-Saharan Africa: structure, drivers, and predicted climate-driven changes. Microbiome 10, 131. https://doi.org/10.1186/s40168-022-01297-w  De Bl\u00e9court, M., R\u00f6der, A., Gr\u00f6ngr\u00f6ft, A., Baumann, S., Frantz, D. & Eschenbach, A. (2018) Deforestation for agricultural expansion in SW Zambia and NE Namibia and the impacts on soil fertility, soil organic carbon- and nutrient levels In: Climate change and adaptive land management in southern Africa \u2013 assessments, changes, challenges, and solutions (ed. by Revermann, R., Krewenka, K.M., Schmiedel, U., Olwoch, J.M., Helmschrot, J. & J\u00fcrgens, N.), pp. 242-250, Biodiversity & Ecology, 6, Klaus Hess Publishers, G\u00f6ttingen & Windhoek. https://doi.org/10.7809/b-e.00330  Dijkshoorn, J.A. (2003). SOTER database for Southern Africa (SOTERSAF ver. 1.0). Technical Report. Wageningen, the Netherlands: ISRIC (International Soil Reference and Information Centre).  FAO. (1990). Guidelines for soil description (3rd ed.). Soil Resources, Management and Conservation Service, Land and Water Development Division, Food and Agriculture Organization of the United Nations.  FAO/ISRIC. (2003). Soil and Terrain Database for Southern Africa. Land and Water Digital Media Series # 26. FAO, Rome.  Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., Elias, E., Hengl, T., Karl, J. W., Liniger, H., Matuszak, J., Neff, J. C., Ndungu, L. W., Obersteiner, M., Shepherd, K. D., Urama, K. C., Bosch, R., & Webb, N. P. (2016). The land\u2010potential knowledge system (LandPKS): mobile apps and collaboration for optimizing climate change investments. Ecosystem Health and Sustainability, 2(3).\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 https://doi.org/10.1002/ehs2.1209  ICC, MAWRD. (2000). Project to support the Agro-Ecological Zoning Programme (AEZ) in Namibia. Main report. Institut Cartogr\u00e0fic de Catalunya (ICC), & Ministry of Agriculture Water and Rural Development (MAWRD). Windhoek.  IUSS (International Union of Soil Scientists) Working Group World Reference Base (WRB) (2015) World reference base for soil resources 2014. International soil classification system for naming soils and creating legends for soil maps. Update 2015. World Soil Resources Reports No 106. FAO, Rome.  Kempf, J. (2000). Klimageomorphologische Studien in Zentral-Namibia: Ein Beitrag zur Morpho-, Pedo- und \u00d6kogenese. Dissertation, Univ. W\u00fcrzburg  Nijbroek, R., Piikki, K., S\u00f6derstr\u00f6m, M., Kempen, B., Turner, K.G., Hengari, S., & Mutua, J. (2018). Soil organic carbon baselines for land degradation neutrality: Map accuracy and cost tradeoffs with respect to complexity in Otjozondjupa, Namibia. Sustainability, 2018(10), 1610. https://doi.org/10.3390/su10051610.   Paterson, G. (2021). Guidance for the laboratory analysis - Soils4Africa Project.   https://www.soils4africa-h2020.eu/serverspecific/soils4africa/images/Documents/GuidanceonLaboratoryAnalysis.pdf  Petersen, A. (2008). Pedodiversity of southern African drylands. PhD dissertation. University of Hamburg, Germany.  Pr\u00f6pper, M., Gr\u00f6ngr\u00f6ft, A., Finckh, M., Stirn, S., De Cauwer, V., Lages, F., Masamba, W., Murray-Hudson, M., Schmidt, L., Strohbach, B., J\u00fcrgens, N. (2015). The Future Okavango \u2013 Findings, Scenarios and Recommendations for Action. Research Project Final Synthesis  Simmonds, S.E.B., Forbes Irving, T.J.M. (1995). Soils Assessment and Land Evaluation. Report prepared by Interconsult Namibia (Pty) Ltd for Africare, for Rural Water Supply Maintenance Project in Southern Kunene Region, Namibia.  Strohbach, B.J., Adank, W.F., Coetzee, M.E., Jankowitz, W.J. (2019). A baseline description of the soils and vegetation of farm Klein Boesman, Khomas Region, Namibia. Namibian Journal of Environment, 3:37-55  Strohbach, B., Strydom, E., Nesongano, C., Zimmermann, I., De Cauwer, V., Coetzee, M. (2023). Final Report: Assessment of the Impact of Bush Encroachment and Bush Control on Climate Change Mitigation and Adaptation in Namibia. AHT GROUP GmbH & Perivoli Rangeland Institute, for Deutsche Gesellschaft f\u00fcr Internationale Zusammenarbeit. Windhoek, Namibia.  Trippner, Christian. (1998). Semi-detailed soil survey and landscape ecological risk evaluation in the south-western and central-western parts of the Etosha National Park/N-Namibia. Part 2. Project report: DFG/GTZ Research Cooperation Project \u2018Environmental Change in the Etosha National Park/Northern Namibia\u2019 (Az. Bu 659/4-1+4-2).  Van Engelen, V.W.P., & Dijkshoorn, J.A. (2013) Global and national soils and terrain databases (SOTER). Procedures manual, version 2.0. (eds.) Wageningen: ISRIC \u2013 World Soil Information.  Van Engelen, V.W.P., & Wen, T.T. (eds.) (1995). Global and national soils and terrain digital databases \u2013 SOTER. Procedures manual. World Soil Resources Report 74. Rome: UNEP, ISSS, ISRIC, FAO. https://edepot.wur.nl/493802  Wild, S. (2016). Quest to map Africa\u2019s soil microbiome begins. Nature 539: 152. https://doi.org/10.1038/539152a  \u00a0  Corresponding author:  Marina E. Coetzee (mcoetzee@nust.na; marina.e.coetzee@gmail.com)  Affiliations:\u00a0    Faculty of Engineering and the Built Environment, Namibia University of Science and Technology, Private Bag 13388, Windhoek, Namibia.  Doctoral School of Environmental Sciences, Magyar Agr\u00e1r- \u00e9s \u00c9lettudom\u00e1nyi Egyetem (MATE; Hungarian University of Agriculture and Life Sciences), P\u00e1ter K\u00e1roly u. 1. H-2100 G\u00f6d\u00f6ll\u0151, Hungary.       [1] Ministry's name changed over time: Ministry of Agriculture, Water and Rural Development (MAWRD; 1991-2000); Ministry of Agriculture, Water and Forestry (MAWF; 2000-2020); Ministry of Agriculture, Water and Land Reform (MAWLR; 2020-2025); Ministry of Agriculture, Water, Fisheries and Land Reform (MAWFLR, since 2025).", "keywords": ["Database", "Profile Description", "Soil Horizon", "Namibia", "Soil Profile", "Laboratory Data"], "contacts": [{"organization": "Coetzee, Marina", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17618737"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17618737", "name": "item", "description": "10.5281/zenodo.17618737", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17618737"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-11-15T00:00:00Z"}}, {"id": "10.5281/zenodo.15850279", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:02Z", "type": "Dataset", "title": "Dataset of \"Effects of biochar, hydrochar and nitrogen fertilization on greenhouse gas fluxes, soil organic carbon pools, and biomass yield of a boreal legume grassland\"", "description": "These data are available to public for their broader use. Any concerns or questions or missing information about the data could be answered or made available upon contacting the corresponding author or data creator.", "keywords": ["Biochar", "Greenhouse gas emissions", "Denitrification", "Sustainable agriculture", "Grassland ecosystem", "Nitrogen cycle", "Nitrification", "Soil Microbiology", "Boreal soil"], "contacts": [{"organization": "Bhattarai, Hem Raj", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.15850279"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15850279", "name": "item", "description": "10.5281/zenodo.15850279", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15850279"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-09T00:00:00Z"}}, {"id": "10.5683/sp3/bmslxj", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:52Z", "type": "Dataset", "created": "2003-09-02", "title": "Enqu\u00eate sociale g\u00e9n\u00e9rale, Cycle 16, 2002 [Canada] : Le vieillissement et le soutien social, Fichier d'aide fournie par le r\u00e9pondant (65 ans et plus)", "description": "Open AccessLe cycle 16 de l'ESG est le deuxi\u00e8me (apr\u00e8s le cycle 11) \u00e0 recueillir de l'information sur le soutien social des personnes vieillissantes du Canada, tout en introduisant des modules sur la planification et l'exp\u00e9rience de la retraite. L'ESG est une enqu\u00eate t\u00e9l\u00e9phonique annuelle portant sur la population non institutionnelle des 10 provinces. Les r\u00e9pondants ont \u00e9t\u00e9 choisis au hasard \u00e0 partir d'une liste de personnes \u00e2g\u00e9es de 45 ans et plus qui avaient d\u00e9j\u00e0 particip\u00e9 \u00e0 une autre enqu\u00eate de Statistique Canada. Les donn\u00e9es ont \u00e9t\u00e9 recueillies sur une p\u00e9riode de 11 mois, soit de f\u00e9vrier \u00e0 d\u00e9cembre 2002. L'\u00e9chantillon repr\u00e9sentatif comptait environ 25 000 r\u00e9pondants. Le taux de r\u00e9ponse \u00e9tait \u00e0 un peu moins de 84 %. Alors que l'objectif principal de l'ESG de 2002 \u00e9tait de fournir des donn\u00e9es sur la population vieillissante, l'enqu\u00eate permettra d'analyser en d\u00e9tail les caract\u00e9ristiques de la famille et des amis qui offrent de l'aide aux a\u00een\u00e9s, les caract\u00e9ristiques des a\u00een\u00e9s qui re\u00e7oivent de l'aide \u00e0 la maison et dans la collectivit\u00e9, les facteurs d\u00e9terminants \u00e0 propos de la sant\u00e9 (tels que le revenu, la scolarit\u00e9 et les relations sociales) ainsi que la planification et l'exp\u00e9rience de la retraite. Le programme de l\u2019Enqu\u00eate sociale g\u00e9n\u00e9rale (ESG), qui a d\u00e9but\u00e9 en 1985, consiste \u00e0 mener des enqu\u00eates t\u00e9l\u00e9phoniques dans les 10 provinces. L\u2019ESG est reconnue comme un outil qui assure la collecte continue de donn\u00e9es transversales, ce qui permet en retour d\u2019analyser les tendances, et qui permet l\u2019\u00e9laboration et la mise \u00e0 l\u2019essai de nouveaux concepts qui tiennent compte des nouvelles questions d\u2019int\u00e9r\u00eat. L\u2019Enqu\u00eate sociale g\u00e9n\u00e9rale a pour objectifs principaux de : rassembler des donn\u00e9es sur les tendances sociales, de mani\u00e8re \u00e0 suivre l\u2019\u00e9volution des modes de vie et du bien-\u00eatre des Canadiens; et fournir des renseignements imm\u00e9diats sur des questions de politique sociale pr\u00e9cises qui suscitent d\u00e9j\u00e0 ou qui susciteront de l\u2019int\u00e9r\u00eat.", "keywords": ["Age", "Soins de sant\u00e9", "Finances", "Soutien social", "Social Sciences", "Enqu\u00eates sociales", "Revenu", "Fournisseurs de soins", "Soins", "Emploi", "Retraite"], "contacts": [{"organization": "Division de la statistique sociale, du logement et des familles", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5683/sp3/bmslxj"}, {"rel": "self", "type": "application/geo+json", "title": "10.5683/sp3/bmslxj", "name": "item", "description": "10.5683/sp3/bmslxj", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5683/sp3/bmslxj"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.16040138", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:03Z", "type": "Dataset", "created": "2024-11-29", "title": "Full soil particle size fingerprints determined by laser diffraction of 48 selected test samples from Flanders (Belgium)", "description": "A set of 48 soil test samples was selected for a study (1) comparing two common methods for granulometric analysis: pipette-sieving method versus laser diffractometry (LD),\u00a0 (2) comparing the results among various LD instruments and (3) analysing the effect size of various settings on LD instruments (e.g. effect of diffraction models & optical parameters).\u00a0   All 48 samples originated from soils of Flanders (North of Belgium) and were taken in 4 landuses (croplands (n=8), grasslands (n=18), gardens (n=13) and natural habitats (n=9)) on the most common soil types and textures. The samples widely varied in organic carbon content and pH.   The datasets are the output of the full soil textural \u2018fingerprint\u2019 (particle size range from 0,4 - 2000 \u00b5m) of the test samples analysed by a Beckman COULTER LS13320 laser diffraction instrument. Pretreatment of the soil samples was conducted according to ISO 11464 and the removal of organic matter and carbonates was done according to ISO 11272 as for the Sieving and Sedimentation method. Background information is given in the presentation (PDF): \u201cShould laser diffraction become the new standard for soil particle size analysis ?\u201d  The equivalent LD diameters applied to convert the pipette based standard limits for clay fraction (2 \u00b5m) were set to 6 \u00b5m when using LD particle sizes\u00a0 and for silt (50 \u00b5m) to 63 \u00b5m (LD size).\u00a0   For each test sample the full textural fingerprint is given according to size bins showing for each bin the mean fraction (in vol%) of 5 replicated measurements along with the standard deviation (sd) under repeatability conditions. Based on the full fingerprint various fractions can be derived and statistically compared. The full fingerprint is also useful for calibrating\u00a0pedotransfer functions for estimating other soil physical properties and for in silico\u00a0modeling purposes.", "keywords": ["Granulometry", "Soil texture", "Textural fingerprint", "Laser diffraction", "Equivalent diameter"], "contacts": [{"organization": "De Vos, Bruno", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16040138"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16040138", "name": "item", "description": "10.5281/zenodo.16040138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16040138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-17T00:00:00Z"}}, {"id": "20.500.12556/RUNG-8752", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:46Z", "type": "Journal Article", "created": "2023-12-22", "title": "Variability in sediment particle size, mineralogy, and Fe mode of occurrence across dust-source inland drainage basins: the case of the lower Dr\u00e2a Valley, Morocco", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. The effects of desert dust upon climate and ecosystems depend strongly on its particle size and size-resolved mineralogical composition. However, there is very limited quantitative knowledge on the particle size and composition of the parent sediments along with their variability within dust-source regions, particularly in dust emission hotspots. The lower Dr\u00e2a Valley, an inland drainage basin and dust hotspot region located in the Moroccan Sahara, was chosen for a comprehensive analysis of sediment particle size and mineralogy. Different sediment type samples (n=\u200942) were collected, including paleo-sediments, paved surfaces, crusts, and dunes, and analysed for particle-size distribution (minimally and fully dispersed samples) and mineralogy. Furthermore, Fe sequential wet extraction was carried out to characterise the modes of occurrence of Fe, including Fe in Fe (oxyhydr)oxides, mainly from goethite and hematite, which are key to dust radiative effects; the poorly crystalline pool of Fe (readily exchangeable ionic Fe and Fe in nano-Fe oxides), relevant to dust impacts upon ocean biogeochemistry; and structural Fe. Results yield a conceptual model where both particle size and mineralogy are segregated by transport and deposition of sediments during runoff of water across the basin and by the precipitation of salts, which causes a sedimentary fractionation. The proportion of coarser particles enriched in quartz is higher in the highlands, while that of finer particles rich in clay, carbonates, and Fe oxides is higher in the lowland dust emission hotspots. There, when water ponds and evaporates, secondary carbonates and salts precipitate, and the clays are enriched in readily exchangeable ionic Fe, due to sorption of dissolved Fe by illite. The results differ from currently available mineralogical atlases and highlight the need for observationally constrained global high-resolution mineralogical data for mineral-speciated dust modelling. The dataset obtained represents an important resource for future evaluation of surface mineralogy retrievals from spaceborne spectroscopy.</p></article>", "keywords": ["Mineral dusts", "geology", "550", "QC1-999", "Climate", "01 natural sciences", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Enginyeria ambiental", "Pols minerals", "QD1-999", "Sahara", "0105 earth and related environmental sciences", "mineral dust", "S\u00e0hara", "info:eu-repo/classification/ddc/550", "ddc:550", "Physics", "Aire--Contaminaci\u00f3", "15. Life on land", "info:eu-repo/classification/udc/502.3/.7", "6. Clean water", "Earth sciences", "Chemistry", "13. Climate action", "Air--Pollution", "Desert dust", "aerosols"]}, "links": [{"href": "https://acp.copernicus.org/articles/23/15815/2023/acp-23-15815-2023.pdf"}, {"href": "https://doi.org/20.500.12556/RUNG-8752"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Atmospheric%20Chemistry%20and%20Physics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.12556/RUNG-8752", "name": "item", "description": "20.500.12556/RUNG-8752", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.12556/RUNG-8752"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.16420122", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:04Z", "type": "Dataset", "title": "Thermal PV Panel Detection and Fault Detection Dataset for UAV-Based Inspection", "description": "This dataset focuses on automated photovoltaic (PV) panel detection and fault detection using thermal imagery captured by UAV and includes annotated thermal images of PV panels.  The raw thermal images were captured using the DJI Mavic 3T UAV at a photovoltaic farm in Sindos, Thessaloniki. These images were processed to generate a non-overlapping subset of 351 images (640\u00d7512) resolution, each containing fully visible PV panel arrays.  The PV panel count in each subset can be seen in the following table:     Set Images Annotated PV Panels   Training 235 18487   Validation 83 5828   Test 35 2363   Total 353 26678     This dataset is intended for training and evaluating deep learning-based object detection models and supports research for renewable energy systems.", "keywords": ["photovoltaics", "solar energy", "dataset", "object detection", "pv", "solar panels"], "contacts": [{"organization": "Christakakis, Panagiotis, Pechlivani, Eleftheria-Maria, Dimou, Periklis,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16420122"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16420122", "name": "item", "description": "10.5281/zenodo.16420122", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16420122"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-07-25T00:00:00Z"}}, {"id": "10.5281/zenodo.16889567", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:05Z", "type": "Dataset", "title": "Siora Open Soil Nutrient Dataset \u2013 Ukraine (2000, 2010, 2020)", "description": "This dataset contains direct satellite-derived estimates of soil nutrient properties for Ukraine at ~250 m resolution, covering the years 2000, 2010, and 2020. Variables include Nitrogen (N), Phosphorus (P), Potassium (K), Organic Carbon (OC), and pH. \u00a0  The data are derived from multispectral reflectance spectroscopy, corrected with topographic, climatic, and geological auxiliary datasets to isolate nutrient signatures. Each variable is provided as a single-band GeoTIFF (zipped), with accompanying PNG heatmaps, thumbnails, README, metadata JSON, and CC-BY 4.0 license. \u00a0  Comparable laboratory references: \u00a0- pH \u2013 ISO 10390:2005 (Glass electrode) \u00a0- Nitrogen \u2013 ISO 11261:1995 (Modified Kjeldahl) \u00a0- Phosphorus \u2013 ISO 11263:1994 (Spectrometric determination) \u00a0- Potassium \u2013 USDA\u2013NRCS (Atomic absorption after NH\u2084OAc extraction) \u00a0- Organic Carbon \u2013 ISO 10694:1995 (Dry combustion) \u00a0  Suggested uses: nutrient trend analysis, soil health monitoring, precision agriculture benchmarking, model training and validation.", "keywords": ["soil", " nitrogen", " phosphorus", " potassium", " organic carbon", " pH", " Ukraine", " agriculture", " remote sensing", " geotiff", " spectroscopy", " open data"], "contacts": [{"organization": "Siora, Radev, Velin,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16889567"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16889567", "name": "item", "description": "10.5281/zenodo.16889567", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16889567"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16725281", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:04Z", "type": "Dataset", "title": "PTF4Med: two pseudo-continuous neural network pedotransfer functions for the water retention curve in the Mediterranean Region", "description": "This dataset was compiled to develop and validate two pseudo-continuous pedotransfer functions (PTFs), HYDRO-GRAV and HYDRO-VOL, for estimating gravimetric and volumetric soil water content across multiple matric potentials in Mediterranean region. The file, provided in Excel format, contains two sheets: HYDRO-GRAV and HYDRO-VOL. In the HYDRO-GRAV sheet, the field U reports gravimetric soil water content, while in the HYDRO-VOL sheet, it reports volumetric soil water content. Both sheets include the same set of predictors and metadata: SAND (sand content, %), CLAY (clay content, %), OC (organic carbon, %), Pot (soil matric potential, kPa), Dataset (source dataset name), ID (unique sample identifier), Location (site or region), Date (sampling date, when available), Latitude and Longitude (geographic coordinates), Layer description (description of the soil layer), Layer number (sequential number of the soil layer), and Upper limit and Lower limit (depth limits of the soil layer, cm). The dataset harmonizes legacy soil data from multiple Mediterranean sources, providing measurements of soil water content at different matric potentials. These data enabled pseudo-continuous modeling through artificial neural networks and were used to train and evaluate the HYDRO-GRAV and HYDRO-VOL models.", "keywords": ["Mediterranean Region", "water retention curve", "pedotransfer functions", "pseudo-continuous PTFs", "soil legacy data"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16725281"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16725281", "name": "item", "description": "10.5281/zenodo.16725281", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16725281"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-02T00:00:00Z"}}, {"id": "2078.1/284215", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:51Z", "type": "Journal Article", "created": "2023-11-17", "title": "Comparison of nitrogen fertilisation recommendations of West European Countries", "description": "Abstract                   <p>                     Nitrogen (N) budgets at farm level are influenced by N fertilisation recommendations. In this study, we reviewed and analysed the underlying principles and methods of N fertilisation recommendations in 10 West European countries, to identify similarities and differences, and develop suggestions for reconsideration and improvement. An analysis of national official documents on N fertilisation recommendations revealed that there were three main categories of calculation methods: (i) \uffe2\uff80\uff98N mass balances\uffe2\uff80\uff99 (France, Italy, Spain), (ii) \uffe2\uff80\uff98Corrected standards\uffe2\uff80\uff99 (Germany, Netherlands, Switzerland, Luxembourg), and (iii) \uffe2\uff80\uff98Pre\uffe2\uff80\uff90parameterised calculations\uffe2\uff80\uff99, which rely on a soil N supply typology (United Kingdom, Ireland, Belgium). In total 16 variables were identified in the calculation methods. The more complex methods use 10 (Italy, France), while the simplest only rely on 3 (Luxembourg). The most common variables include the availability of N in manure, the N uptake by a crop, and the N released by crop residues. Few countries explicitly consider N losses to ground and surface waters or to the atmosphere in the calculation methods. In some countries, the N fertilisation recommendation has a voluntary status, and in other countries, a legal one (caps on maximum allowable N rates). We compared the N fertiliser recommendations for a wheat crop grown on a farm with livestock, and for a farm with a diverse arable crop rotation without livestock. Across the 10 countries, large differences in the N fertilisation calculation methods and resulting N recommendations existed for the two management scenarios, ranging from almost no fertilisation to 135\uffe2\uff80\uff89kg\uffe2\uff80\uff89N\uffe2\uff80\uff89ha                     \uffe2\uff88\uff921                     , and from 111 to 210\uffe2\uff80\uff89kg\uffe2\uff80\uff89N\uffe2\uff80\uff89ha                     \uffe2\uff88\uff921                     , respectively. The differences were not accounted for by the complexity of the equations used, but rather resulted from contrasting reference values for N availability in manure, N uptake by crop and N leaching. However, the study concluded that standardisation of the method to calculate N fertilisation recommendations is likely to be counterproductive as there are no objective reasons to favour one method more than the others. Nonetheless, improvements in N use efficiency are necessary. Farm scale mass balance, combined with parameters such as minimum residual soil mineral N test at harvest, was suggested as being an important consideration.                   </p", "keywords": ["2. Zero hunger", "advice; fertiliser guide; harmonisation; innovative approaches; mass balance; nitrate; regulation", "harmonisation", "Soil Science", "regulation", "04 agricultural and veterinary sciences", "15. Life on land", "01 natural sciences", "6. Clean water", "innovative approaches", "advice", "nitrate", "fertiliser guide", "0401 agriculture", " forestry", " and fisheries", "mass balance", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/1032329/2/2023_EuropeanJSoilScience-2023-JordanMeille-ComparisonofnitrogenfertilisationrecommendationsofWestEuropean_acceptedversion.pdf"}, {"href": "https://doi.org/2078.1/284215"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/European%20Journal%20of%20Soil%20Science", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2078.1/284215", "name": "item", "description": "2078.1/284215", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2078.1/284215"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-11-01T00:00:00Z"}}, {"id": "10.5281/zenodo.16842801", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:05Z", "type": "Journal Article", "created": "2022-06-17", "title": "Natural Time Series Parameters Forecasting: Validation of the Pattern-Sequence-Based Forecasting (PSF) Algorithm; A New Python Package", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Climate change has contributed substantially to the weather and land characteristic phenomena. Accurate time series forecasting for climate and land parameters is highly essential in the modern era for climatologists. This paper provides a brief introduction to the algorithm and its implementation in Python. The pattern-sequence-based forecasting (PSF) algorithm aims to forecast future values of a univariate time series. The algorithm is divided into two major processes: the clustering of data and prediction. The clustering part includes the selection of an optimum value for the number of clusters and labeling the time series data. The prediction part consists of the selection of a window size and the prediction of future values with reference to past patterns. The package aims to ease the use and implementation of PSF for python users. It provides results similar to the PSF package available in R. Finally, the results of the proposed Python package are compared with results of the PSF and ARIMA methods in R. One of the issues with PSF is that the performance of forecasting result degrades if the time series has positive or negative trends. To overcome this problem difference pattern-sequence-based forecasting (DPSF) was proposed. The Python package also implements the DPSF method. In this method, the time series data are first differenced. Then, the PSF algorithm is applied to this differenced time series. Finally, the original and predicted values are restored by applying the reverse method of the differencing process. The proposed methodology is tested on several complex climate and land processes and its potential is evidenced.</p></article>", "keywords": ["Technology", "330", "QH301-705.5", "univariate", "T", "Physics", "QC1-999", "forecasting", "02 engineering and technology", "Engineering (General). Civil engineering (General)", "forecasting; univariate; time series; Python; PSF", "Chemistry", "0203 mechanical engineering", "13. Climate action", "0202 electrical engineering", " electronic engineering", " information engineering", "time series", "TA1-2040", "Biology (General)", "QD1-999", "PSF", "Python"]}, "links": [{"href": "http://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://www.mdpi.com/2076-3417/12/12/6194/pdf"}, {"href": "https://research.usq.edu.au/download/a41f7e6afaf72d3aab08e4fbf5850ce9baed364db9cd274b284e7956b4aa1a6e/1339682/applsci-12-06194-v3.pdf"}, {"href": "https://doi.org/10.5281/zenodo.16842801"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Applied%20Sciences", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16842801", "name": "item", "description": "10.5281/zenodo.16842801", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16842801"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16889685", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:05Z", "type": "Dataset", "title": "Siora Open Soil Nutrient Dataset \u2013 Murcia, Spain (2000, 2010, 2020)", "description": "This dataset provides regional-scale direct estimates of soil nutrient properties across Spain\u2019s Region of Murcia at ~30 m spatial resolution. It includes three time slices (2000, 2010, and 2020) with coverage of five key soil parameters: Nitrogen (N), Phosphorus (P), Potassium (K), Organic Carbon (OC), and pH. \u00a0  Values were derived using calibrated reflectance spectroscopy applied to multispectral satellite observations. Terrain, climatic, and geological datasets were incorporated to minimize noise and strengthen the nutrient-specific signal. Each parameter is distributed as a single-band GeoTIFF (compressed into ZIP archives), with additional preview images for quick inspection. \u00a0  Reference laboratory methods for comparability include: \u00a0- Nitrogen \u2013 ISO 11261:1995 (Modified Kjeldahl) \u00a0- Phosphorus \u2013 ISO 11263:1994 (Spectrometric) \u00a0- Potassium \u2013 USDA\u2013NRCS (NH\u2084OAc extraction + AAS) \u00a0- Organic Carbon \u2013 ISO 10694:1995 (Dry combustion) \u00a0- pH \u2013 ISO 10390:2005 (Glass electrode) \u00a0  The Murcia release forms part of a broader open dataset series curated by Siora, covering multiple countries and regions. Intended applications include monitoring of nutrient dynamics, supporting precision agriculture workflows, environmental research, and as training data for geospatial or ML models.", "keywords": ["soil", " Murcia", " Spain", " nitrogen", " phosphorus", " potassium", " organic carbon", " pH", " geotiff", " 30 m resolution", " agriculture", " remote sensing", " spectroscopy"], "contacts": [{"organization": "Siora.ai", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16889685"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16889685", "name": "item", "description": "10.5281/zenodo.16889685", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16889685"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-17T00:00:00Z"}}, {"id": "10.5281/zenodo.16895135", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:05Z", "type": "Journal Article", "created": "2023-08-31", "title": "Comparing the environmental impact of poultry manure and chemical fertilizers", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>One of the challenges in livestock production is the significant volume of manure generated, which must be appropriately managed to mitigate its environmental impacts. Untreated manure poses a potential hazard to soil, surface water, groundwater, and human and animal health. Based on the life cycle assessment (LCA) method, the research aims to evaluate the ecological load of composted-pelletized poultry litter (CPPL) in maize and winter wheat production. Furthermore, the environmental loads of CPPL applications are compared with those of other N, P, and K fertilizers. The research study utilized the openLCA software with the Agribalyse 3.1 database to calculate eleven impact categories. In the case of maize, only ozone depletion has higher emissions. For winter wheat production, scenarios where the P fertilizer was MAP had lower impacts for NPK combinations. While for the CPPL, fuel was the main contributor to loads, for the NPK fertilizer scenarios, energy use for fertilizer production contributed more. The results can be relevant to the burdens of using different nutrient replacement products and creating diverse feed mixtures. The application of CPPL promises to reduce the burden of crop production and, consequently, feed production. Additionally, it allows for the recovery of manure not useable by the livestock industry.</p></article>", "keywords": ["2. Zero hunger", "0211 other engineering and technologies", "environmental impacts", "02 engineering and technology", "15. Life on land", "maize", "Engineering (General). Civil engineering (General)", "7. Clean energy", "winter wheat", "12. Responsible consumption", "life cycle assessment", "HT165.5-169.9", "13. Climate action", "composted-pelletized poultry litter", "0202 electrical engineering", " electronic engineering", " information engineering", "TA1-2040", "City planning", "chemical fertilizers"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.16895135"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Frontiers%20in%20Built%20Environment", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.16895135", "name": "item", "description": "10.5281/zenodo.16895135", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.16895135"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-08-31T00:00:00Z"}}, {"id": "11573/1419330", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:13Z", "type": "Journal Article", "created": "2020-06-05", "title": "Variability in pulmonary diffusing capacity in heart failure", "description": "As pulmonary diffusing capacity is related to mortality risk and prognosis in patients with heart failure (HF), it is measured frequently. As such, it would be essential to know the week-to-week variability (reproducibility) of pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO). This variability would let clinicians understand what a clinically measurable change in DLCO and DLNO would be in these patients.On three different days spanning over ten weeks, 40\u2009H\u2009F patients underwent testing for DLCO and DLNO. DLCO was determined after a 4\u2009s and 10\u2009s breath-hold maneuver, while DLNO was determined after a 4\u2009s breath-hold maneuver.Forty heart failure patients (66\u2009\u00b1\u200910 years; BMI\u2009=\u200928.4\u2009\u00b1\u20094.6\u2009kg\u2219m-2; 28 males), that were referred to our clinic were able to complete the protocol. DLCO (4\u2009s breath-hold) and DLNO (4\u2009s breath-hold) were 79\u2009\u00b1\u200919 % and 59\u2009\u00b1\u200914 % predicted, respectively. Fifty percent of patients (n\u2009=\u200920) were below the lower limit of normal (LLN, below the 5th percentile) for predicted DLCO (4\u2009s), while 78 % of patients (n\u2009=\u200931) were below the LLN for predicted DLNO. All 16 patients that were below the LLN for DLCO were also below the LLN for DLNO. Over a ten week period, the reproducibility of DLNO (4\u2009s) DLCO (4\u2009s) and DLCO (10\u2009s) was 18.9, 8.2, and 5.9\u2009mL\u2009min\u2009mmHg-1, respectively.The week-to-week fluctuation in DLNO (4\u2009s), as a percentage, is less than DLCO (4\u2009s) in patients with HF. The reproducibility of DLNO in patients with HF is like that of healthy subjects.", "keywords": ["Male", "DLCO; DLNO; lung function; heart failure; reproducibility", "Physiology (science-metrix)", "Heart Disease (rcdc)", "Pulmonary Diffusing Capacity (mesh)", "3208 Medical physiology (for-2020)", "Heart failure", "Nitric Oxide", "Lung (rcdc)", "DLCO", "DLCO; DLNO; Heart failure; Lung function; Reproducibility;", "Clinical Research (rcdc)", "03 medical and health sciences", "0302 clinical medicine", "1102 Cardiorespiratory Medicine and Haematology (for)", "Middle Aged (mesh)", "Reproducibility of Results (mesh)", "Humans", "32 Biomedical and Clinical Sciences (for-2020)", "Male (mesh)", "3202 Clinical Sciences (for-2020)", "Carbon Monoxide (mesh)", "Aged", "DLNO", "Heart Failure", "Humans (mesh)", "Carbon Monoxide", "Cardiovascular (hrcs-hc)", "Aged (mesh)", "3201 Cardiovascular medicine and haematology (for-2020)", "Reproducibility of Results", "Heart Failure (mesh)", "Middle Aged", "1109 Neurosciences (for)", "Lung function", "Reproducibility", "3. Good health", "1116 Medical Physiology (for)", "4.2 Evaluation of markers and technologies (hrcs-rac)", "Female (mesh)", "Nitric Oxide (mesh)", "Pulmonary Diffusing Capacity", "Cardiovascular (rcdc)", "Female"]}, "links": [{"href": "https://air.unimi.it/bitstream/2434/743296/2/agostoni%203.pdf"}, {"href": "https://doi.org/11573/1419330"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Respiratory%20Physiology%20%26amp%3B%20Neurobiology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11573/1419330", "name": "item", "description": "11573/1419330", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11573/1419330"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-09-01T00:00:00Z"}}, {"id": "10.5281/zenodo.17000055", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:06Z", "type": "Dataset", "title": "Soil parameters measured in European Mole (Talpa europaea) mounds and nearby control areas on a meadow near Csom\u00e1d, Hungary", "description": "Soil parameters measured by the near-infrared device of Agrocares Ltd (the Netherlands): pH(H2O), soil organic matter (%), P (M3) (mg/kg), total nitrogen (g/kg), exchangeable K, Mg and Ca (mmol/kg), organic carbon (g/kg), potentially mineralizable nitrogen (g/kg), cation exchange capacity (mmol/kg), total Al (g/kg), total Fe (g/kg), clay (%) and soil moisture (%).", "keywords": ["comparison", "Landscape ecology", "nature conservation", "land use", "European mole", "", "effects", "landscape"], "contacts": [{"organization": "Centeri, Csaba", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17000055"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17000055", "name": "item", "description": "10.5281/zenodo.17000055", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17000055"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-10-23T00:00:00Z"}}, {"id": "10.5281/zenodo.17296374", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:07Z", "type": "Dataset", "title": "NUTS2-level predictions of Nature's Contributions to People under different levels of organic farming and climate scenarios across Europe", "description": "This dataset contains NUTS2-level predictions for a suite of Nature\u2019s Contributions to People (NCP). We modelled the delivery of five functions that are linked to NCPs: soil organic carbon (SOC) stock (climate regulation), hydraulic conductivity (regulation of freshwater quantity), saturated water content (regulation of freshwater quantity), crop yield (food and feed production), and bacterial diversity.  The predictions are made for a set of scenarios which vary the proportion of organic farming and climate projection. The proportions of organic farming are as follows: the current country-specific proportion of Utilised Agricultural Area (UAA), a proportion of 25% in each country, and a proportion of 50% in each country. The climate scenarios are the current climate and the climate projection under the Representative Concentration Pathway RCP4.5 and Shared Socio-economic Pathway (SSP2) for the period 2041-2026.  The dataset also contains a point-level prediction on which the NUTS2-level predictions are based.  The development of this dataset was part of the Soilguard project, which was funded by the European Union Horizon 2020 Research & Innovation programme under the Grant Agreement no. 101000371. The aim of the project was to understand how soil management can contribute to environmental, economic, and social wellbeing. The supporting documentation document entails information on how the predictions in this dataset were obtained. For more detail on the underlying assumptions, the development of the models, and the data generation, we refer to two deliverables of the Soilguard project, more specifically Deliverable 5.2 \u201cReport compiling the region-specific set of evidence chains\u201d (Jones et al., 2025) and Deliverable 5.3 \u201cReport on the quantification of environmental, economic and social consequences of soil management and climate change\u201d (Dhiedt et al., 2025). The methodology described in these deliverables followed the Soil Biodiversity and Well-being Framework described by Llad\u00f3 et al. (2025, https://doi.org/10.1016/j.oneear.2025.101391).", "keywords": ["evidence chains", "Nature's Contributions to People", "Ecosystem services", "Soil biodiversity", "data-driven models"], "contacts": [{"organization": "Dhiedt, Els, Jones, Briony, Owen, Danial, Patton, Justine, Robinson, David, van Soest, Maud, Shaikh, Aseem, Jones, Laurence,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17296374"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17296374", "name": "item", "description": "10.5281/zenodo.17296374", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17296374"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-08T00:00:00Z"}}, {"id": "10.5281/zenodo.17326891", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:25:07Z", "type": "Report", "title": "Cyrtolabulus emirufus Selis, 2025, sp. nov.", "description": "unspecifiedPublished as part of Selis, Marco, 2025, The solitary vespid wasps of Madagascar (Hymenoptera: Vespidae: Eumeninae, Raphiglossinae and Zethinae), pp. 1-171 in Zootaxa 5705 (1) on pages 53-56, DOI: 10.11646/zootaxa.5705.1.1, http://zenodo.org/record/17326767", "keywords": ["Cyrtolabulus", "Insecta", "Eumenidae", "Cyrtolabulus emirufus", "Arthropoda", "Animalia", "Biodiversity", "Hymenoptera", "Taxonomy"], "contacts": [{"organization": "Selis, Marco", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.17326891"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.17326891", "name": "item", "description": "10.5281/zenodo.17326891", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.17326891"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-08T00:00:00Z"}}, {"id": "11577/3398065", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-03T16:27:14Z", "type": "Journal Article", "created": "2021-07-25", "title": "Can Long-Term Experiments Predict Real Field N and P Balance and System Sustainability? Results from Maize, Winter Wheat, and Soybean Trials Using Mineral and Organic Fertilisers", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Agri-environmental indicators such as nutrient balance may play a key role in soil and water quality monitoring, although short-term experiments might be unable to capture the sustainability of cropping systems. Therefore, the objectives of this study are: (i) to evaluate the reliability of long-term experimental N and P balance estimates to predict real field (RF) (i.e., short-term transitory) conditions; and (ii) to compare the sustainability of short- and long-term experiments. The LTE-based predictions showed that crops are generally over-fertilised in RF conditions, particularly maize. Nutrient balance predictions based on the LTE data tended to be more optimistic than those observed under RF conditions, which are often characterised by lower outputs; in particular, 13, 44, and 47% lower yields were observed for winter wheat, maize, and soybean, respectively, under organic management. The graphical evaluation of N and P use efficiency demonstrated the benefit of adopting crop rotation practices and the risk of nutrient loss when liquid organic fertiliser was applied on a long-term basis. In conclusion, LTE predictions may depend upon specific RF conditions, representing potential N and P use efficiencies that, in RF, may be reduced by crop yield-limiting factors and the specific implemented crop sequence.</p></article>", "keywords": ["2. Zero hunger", "0106 biological sciences", "S", "phosphorus use efficiency", "phosphorus balance", "Agriculture", "04 agricultural and veterinary sciences", "nitrogen balance", "15. Life on land", "01 natural sciences", "nitrogen use efficiency", "6. Clean water", "12. Responsible consumption", "Long-term experiment; Nitrogen balance; Nitrogen use efficiency; Phosphorus balance; Phosphorus use efficiency; Real field condition", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "real field condition", "long-term experiment"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/11/8/1472/pdf"}, {"href": "https://www.research.unipd.it/bitstream/11577/3398065/1/Piccoli%20et%20al%20_2021_agronomy-11-01472-v2.pdf"}, {"href": "https://www.mdpi.com/2073-4395/11/8/1472/pdf"}, {"href": "https://doi.org/11577/3398065"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Agronomy", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11577/3398065", "name": "item", "description": "11577/3398065", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11577/3398065"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-07-24T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=se&offset=3850&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=se&offset=3850&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=se&offset=3800", "hreflang": "en-US"}, {"rel": "next", "type": "application/geo+json", "title": "items (next)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=se&offset=3900", "hreflang": "en-US"}], "numberMatched": 10456, "numberReturned": 50, "distributedFeatures": [], "timeStamp": "2026-04-04T13:00:11.273042Z"}