{"type": "FeatureCollection", "features": [{"id": "PMC6668394", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:24:41Z", "type": "Journal Article", "created": "2019-07-31", "title": "A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series", "description": "Abstract<p>Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human\uffe2\uff80\uff93environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970\uffe2\uff80\uff932050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16\uffe2\uff80\uff89W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.</p", "keywords": ["Statistics and Probability", "Data Descriptor", "13. Climate action", "Library and Information Sciences", "Statistics", " Probability and Uncertainty", "01 natural sciences", "7. Clean energy", "Computer Science Applications", "Education", "Information Systems", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41597-019-0143-1.pdf"}, {"href": "https://doi.org/PMC6668394"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC6668394", "name": "item", "description": "PMC6668394", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC6668394"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-31T00:00:00Z"}}, {"id": "10.1038/s41597-019-0143-1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2019-07-31", "title": "A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series", "description": "Abstract<p>Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human\uffe2\uff80\uff93environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970\uffe2\uff80\uff932050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16\uffe2\uff80\uff89W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.</p", "keywords": ["Statistics and Probability", "Data Descriptor", "13. Climate action", "Library and Information Sciences", "Statistics", " Probability and Uncertainty", "01 natural sciences", "7. Clean energy", "Computer Science Applications", "Education", "Information Systems", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41597-019-0143-1.pdf"}, {"href": "https://doi.org/10.1038/s41597-019-0143-1"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-019-0143-1", "name": "item", "description": "10.1038/s41597-019-0143-1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-019-0143-1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-31T00:00:00Z"}}, {"id": "10.1038/s41597-023-02751-6", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2024-01-02", "title": "A global dataset on phosphorus in agricultural soils", "description": "Abstract<p>Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha\uffe2\uff88\uff921 for cropland and 91 kgP ha\uffe2\uff88\uff921 for grassland in 2018 for the top 0\uffe2\uff80\uff930.3\uffe2\uff80\uff89m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.</p", "keywords": ["0301 basic medicine", "2. Zero hunger", "[SDU.OCEAN]Sciences of the Universe [physics]/Ocean", "Data Descriptor", "550", "Atmosphere", "[SDU.OCEAN] Sciences of the Universe [physics]/Ocean", " Atmosphere", "Science", "Q", "ANZSRC::410603 Soil biology", "15. Life on land", "01 natural sciences", "[SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces", " environment", "ANZSRC::300801 Field organic and low chemical input horticulture", "03 medical and health sciences", "ANZSRC::410605 Soil physics", "Life Science", "ANZSRC::410604 Soil chemistry and soil carbon sequestration (excl. carbon sequestration science)", "[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces", "environment", "ANZSRC::300101 Agricultural biotechnology diagnostics (incl. biosensors)", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41597-023-02751-6.pdf"}, {"href": "https://doi.org/10.1038/s41597-023-02751-6"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-023-02751-6", "name": "item", "description": "10.1038/s41597-023-02751-6", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-023-02751-6"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-01-02T00:00:00Z"}}, {"id": "10.1038/s41597-025-04437-7", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2025-01-14", "title": "Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia", "description": "Abstract  This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis. Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. This resulted in a valuable dataset of HR multi-spectral orthomosaics across ecological zones, alongside land cover classification with extensive metrics and training data for each site. This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.", "keywords": ["Data Descriptor", "Science", "Q"]}, "links": [{"href": "https://doi.org/10.1038/s41597-025-04437-7"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-025-04437-7", "name": "item", "description": "10.1038/s41597-025-04437-7", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-025-04437-7"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-14T00:00:00Z"}}, {"id": "10.1038/s41597-025-04443-9", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2025-01-18", "title": "The first geospatial dataset of irrigated fields (2020\u20132024) in Vojvodina (Serbia)", "description": "Abstract           <p>Irrigation is a cornerstone of global food security, enabling sustainable agricultural production and helping to ensure that food is available for people around the world, now and in the future. Mapping irrigated fields provides valuable information for sustainable water management, agricultural development, and environmental conservation efforts. However, the collection of high-quality training data, which is necessary for accurate irrigation mapping remains costly and labour-intensive. To address this, we created a georeferenced regional dataset consisting of location, crop type, and occurrence of the irrigation equipment which are essential information for mapping irrigated fields. Four main irrigated crops were considered: maize, soybean, sugar beet, and wheat. The dataset, consisting of a total of 1256 parcels, is created for Vojvodina, the main agricultural area in Serbia, spanning the period of five years (2020\uffe2\uff80\uff932024). This study\uffe2\uff80\uff99s goal is to give accessibility to our dataset which further can be explored and used for building or fine-tuning machine learning and deep learning models for the automatic detection of irrigated fields using satellite imagery.</p", "keywords": ["Data Descriptor", "Science", "Q", "Life Science"]}, "links": [{"href": "https://www.nature.com/articles/s41597-025-04443-9.pdf"}, {"href": "https://doi.org/10.1038/s41597-025-04443-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-025-04443-9", "name": "item", "description": "10.1038/s41597-025-04443-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-025-04443-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-18T00:00:00Z"}}, {"id": "10.1038/s41597-025-05074-w", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2025-05-07", "title": "A hybrid in situ and on-screen survey to monitor gully erosion across the European Union", "description": "Abstract           <p>After the successful mapping of gully erosion channels in the 2018 Eurostat Land Use/Cover Area Frame (topsoil) statistical survey (LUCAS, n\uffe2\uff80\uff89=\uffe2\uff80\uff8924,759 locations), the methodology was further expanded across the full LUCAS 2022 survey (n\uffe2\uff80\uff89=\uffe2\uff80\uff89399,591 locations). This expert-based assessment identifies the presence or absence of gully erosion forms at each LUCAS location. Its goal is to improve understanding of gully erosion geography in the EU and develop forecasting methods to support soil health indicators proposed by the new Directive on Soil Monitoring and Resilience (COM(2023)416) and Common Agricultural Policy monitoring. Here, we present the findings of our analysis which led to the development and validation of the LUCAS Gully Erosion Model (GE-LUCAS v1.1), a pan-European inventory of gully erosion channels comprising 3,116 locations (~0.8% of all monitored locations) affected by gully erosion throughout the European Union. We further present gully erosion patterns and provide insights on how GE-LUCAS v1.1 inventory can be used to estimate the probability of gully occurrence in areas beyond the monitored locations.</p", "keywords": ["Data Descriptor", "Science", "Q"]}, "links": [{"href": "https://doi.org/10.1038/s41597-025-05074-w"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-025-05074-w", "name": "item", "description": "10.1038/s41597-025-05074-w", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-025-05074-w"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-07T00:00:00Z"}}, {"id": "10.1038/s41597-025-05238-8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2025-06-02", "title": "A global dataset of experimental agricultural management on soil carbon accrual, its synergies and trade-offs", "description": "Maintaining and enhancing soil organic carbon (SOC) in agricultural soils is proposed as a key practice to mitigate climate change. While there is agreement on the co-benefits of SOC accrual on other agroecosystem services, its potential trade-offs in terms of greenhouse gas emissions and nutrient losses are still under debate. We present a global dataset compiling the results of 232 articles that experimentally compare the effects of agricultural management practices with a potential to preserve or enhance SOC against conventional practices. The dataset reports 570 experimental effects of practices to minimise soil disturbance, diversify cropping systems, or increase organic inputs in 254 experiments across 38 countries. The dataset further reports the qualitative (positive, neutral or negative) effects of these management practices on SOC accrual, crop yield, and other response variables related to soil structure, soil biota, CO(2) and N(2)O emissions, and nitrogen and phosphorus losses. This dataset helps understanding the synergies and trade-offs of SOC accrual practices with other ecosystem services, detect current knowledge gaps, and guide future agricultural policies.", "keywords": ["Data Descriptor", "Science", "Q", "Life Science", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study"]}, "links": [{"href": "https://doi.org/10.1038/s41597-025-05238-8"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-025-05238-8", "name": "item", "description": "10.1038/s41597-025-05238-8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-025-05238-8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-06-02T00:00:00Z"}}, {"id": "10.1038/s41597-025-05976-9", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:16:48Z", "type": "Journal Article", "created": "2025-10-27", "title": "A global database on land use and management change effects on soil KMnO4-oxidisable organic carbon (POXC)", "description": "Abstract                   <p>                     Soil carbon transformation is vital for ecosystem functions like food production and climate regulation. While soil organic carbon is a key soil health indicator, its sensitivity to management changes is debated. Alternative indicators, such as permanganate-oxidisable carbon (POXC), are being explored. This database compiles 10,068 comparisons of soil POXC content from 284 peer-reviewed studies published up to 2023, covering 45 countries and 63 land use types, including arable land, grassland, agroforestry, and forests. Most studies focused on arable land (                     n                     \uffe2\uff80\uff89=\uffe2\uff80\uff897,809), examining input changes (                     n                     \uffe2\uff80\uff89&gt;\uffe2\uff80\uff89500) and tillage intensity (                     n                     \uffe2\uff80\uff89&gt;\uffe2\uff80\uff89200). The most studied land-use changes were grassland conversion to arable land (n\uffe2\uff80\uff89=\uffe2\uff80\uff89324) and vice versa (n\uffe2\uff80\uff89=\uffe2\uff80\uff89261). The dataset includes rich metadata on geographical context, soil types, key properties (pH, clay content), POXC protocols, and data quality scores. This resource supports scientific and policy discussions on POXC\uffe2\uff80\uff99s potential as a practical indicator for improving land use and soil health management.                   </p", "keywords": ["agroforesterie", "cycle du carbone", "changement climatique", "Data Descriptor", "http://aims.fao.org/aos/agrovoc/c_195", "http://aims.fao.org/aos/agrovoc/c_7170", "http://aims.fao.org/aos/agrovoc/c_24866", "gestion des ressources naturelles", "http://aims.fao.org/aos/agrovoc/c_1348040570280", "utilisation des terres", "http://aims.fao.org/aos/agrovoc/c_207", "services \u00e9cosyst\u00e9miques", "fertilit\u00e9 du sol", "politique fonci\u00e8re", "gestion fonci\u00e8re", "http://aims.fao.org/aos/agrovoc/c_1070", "http://aims.fao.org/aos/agrovoc/c_9000115", "http://aims.fao.org/aos/agrovoc/c_4182", "http://aims.fao.org/aos/agrovoc/c_1666", "http://aims.fao.org/aos/agrovoc/c_17299"], "contacts": [{"organization": "C\u00e9cile Ch\u00e9ron-Bessou, Damien Beillouin, Alexis Thoumazeau, Lydie Chapuis-Lardy, Tiphaine Chevallier, Julien Demenois, Paul N. Nelson,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.1038/s41597-025-05976-9"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1038/s41597-025-05976-9", "name": "item", "description": "10.1038/s41597-025-05976-9", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1038/s41597-025-05976-9"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-10-27T00:00:00Z"}}, {"id": "10.17169/refubium-31202", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:18:32Z", "type": "Journal Article", "created": "2021-05-21", "title": "Global data on earthworm abundance, biomass, diversity and corresponding environmental properties", "description": "Abstract<p>Earthworms are an important soil taxon as ecosystem engineers, providing a variety of crucial ecosystem functions and services. Little is known about their diversity and distribution at large spatial scales, despite the availability of considerable amounts of local-scale data. Earthworm diversity data, obtained from the primary literature or provided directly by authors, were collated with information on site locations, including coordinates, habitat cover, and soil properties. Datasets were required, at a minimum, to include abundance or biomass of earthworms at a site. Where possible, site-level species lists were included, as well as the abundance and biomass of individual species and ecological groups. This global dataset contains 10,840 sites, with 184 species, from 60 countries and all continents except Antarctica. The data were obtained from 182 published articles, published between 1973 and 2017, and 17 unpublished datasets. Amalgamating data into a single global database will assist researchers in investigating and answering a wide variety of pressing questions, for example, jointly assessing aboveground and belowground biodiversity distributions and drivers of biodiversity change.</p>", "keywords": ["2401.17 Invertebrados", "0301 basic medicine", "592", "Data Descriptor", "Ecology and Evolutionary Biology", "earthworms", "Data Descriptor ; Biodiversity ; Biogeography ; Community ecology", "Plan_S-Compliant-OA", "https://purl.org/becyt/ford/1.6", "[SDV.EE.ECO] Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "Diversity data", "Biomass", "S Agriculture (General)", "Ekologia ja evoluutiobiologia", "[SDV.SA.SDS] Life Sciences [q-bio]/Agricultural sciences/Soil study", "biodiversity", "2. Zero hunger", "maaper\u00e4", "abundance", "Data", "Diversity", "0303 health sciences", "Ecology", "Q", "eli\u00f6yhteis\u00f6t", "Biodiversity", "maaper\u00e4eli\u00f6st\u00f6", "ddc:", "Computer Science Applications", "Biogeography", "2401.06 Ecolog\u00eda animal", "international", "Statistics", " Probability and Uncertainty", "environment/Ecosystems", "Information Systems", "Statistics and Probability", "Ecolog\u00eda (Biolog\u00eda)", "570", "lierot", "Science", "Invertebrados", "577", "Global database", "[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study", "Library and Information Sciences", "574", "333", "soil", "eli\u00f6maantiede", "Education", "diversity", "03 medical and health sciences", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", " environment/Ecosystems", "BIODIVERSITY CHANGE", "Life Science", "Earthworms", "Datasets", "Animals", "Community ecology", "Oligochaeta", "https://purl.org/becyt/ford/1", "eartworm", "biogeography", "Ecosystem", "LAND-USE", "biomass", "500", "Biology and Life Sciences", "PLATFORM", "Global dataset", "Oligochaeta/classification", "500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie", "Ecolog\u00eda", "15. Life on land", "biodiversiteetti", "Environmental sciences", "[SDE.BE] Environmental Sciences/Biodiversity and Ecology", "maaper\u00e4el\u00e4imist\u00f6", "Ecology", " evolutionary biology", "13. Climate action", "Earthworm", "[SDV.EE.ECO]Life Sciences [q-bio]/Ecology", "570 Life sciences; biology", "[SDE.BE]Environmental Sciences/Biodiversity and Ecology", "eartworm ; abundance ; biomass ; diversity", "COMMUNITIES", "community ecology"]}, "links": [{"href": "https://www.nature.com/articles/s41597-021-00912-z.pdf"}, {"href": "https://pub.epsilon.slu.se/25868/1/phillips_h_r_p_et_al_211019.pdf"}, {"href": "https://boris.unibe.ch/165726/1/48.__Global_data_on_earthworm_abundance__biomass__diversity_and_corresponding_environmental_properties.pdf"}, {"href": "https://www.iris.unict.it/bitstream/20.500.11769/509583/1/SCIENTIFIC%20DATA%20%282021%29%20GLOBAL%20DATA%20ON%20EARTHWORMS.pdf"}, {"href": "https://rau.repository.guildhe.ac.uk/id/eprint/16454/1/Phillips_et_al-2021-Scientific_Data.pdf"}, {"href": "https://doi.org/10.17169/refubium-31202"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.17169/refubium-31202", "name": "item", "description": "10.17169/refubium-31202", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.17169/refubium-31202"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-05-21T00:00:00Z"}}, {"id": "10.3929/ethz-c-000783069", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:19:38Z", "type": "Journal Article", "created": "2025-08-23", "title": "Data covering soil management practices and farm characteristics on Swiss arable farms", "description": "Abstract           <p>Soil health is the cornerstone of sustainable agriculture, but studies have shown that agricultural soils are degrading due to inadequate soil management practice adoption. To understand the status quo, we surveyed 2,728 Swiss arable farms in 2024. The dataset captures the soil management practices used alongside the decision-making surrounding their implementation in the 2022/2023 production season. Four core components are covered: (1) farm and farmer characteristics, including gender, age, experience, labour, farm size and Agri-environmental scheme participation; (2) detailed records of twelve arable soil management practices, including uptake extent, number of years used, perceived knowledge and peer adoption; (3) farmers\uffe2\uff80\uff99 priorities for soil health and their assessment of key agricultural challenges; and (4) production data for a subset of farms cultivating milling wheat, including wheat area, wheat yield and input application rates. We enriched the dataset with linked secondary plot-level census data. This combined dataset provides a comprehensive resource that enables the analysis of current farming practices, knowledge gaps and challenges to maintain and improve the health of arable soils.</p", "keywords": ["Data Descriptor"]}, "links": [{"href": "https://doi.org/10.3929/ethz-c-000783069"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-c-000783069", "name": "item", "description": "10.3929/ethz-c-000783069", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-c-000783069"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-08-23T00:00:00Z"}}, {"id": "10.5281/zenodo.15044078", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:20:45Z", "type": "Journal Article", "created": "2025-01-14", "title": "Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia", "description": "Abstract  This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis. Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. This resulted in a valuable dataset of HR multi-spectral orthomosaics across ecological zones, alongside land cover classification with extensive metrics and training data for each site. This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.", "keywords": ["Data Descriptor", "Science", "Q"]}, "links": [{"href": "https://www.nature.com/articles/s41597-025-04437-7.pdf"}, {"href": "https://doi.org/10.5281/zenodo.15044078"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.15044078", "name": "item", "description": "10.5281/zenodo.15044078", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.15044078"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-01-14T00:00:00Z"}}, {"id": "20.500.11755/30733e2b-dea3-4cb4-8f63-50a9b23ba039", "type": "Feature", "geometry": null, "properties": {"updated": "2026-09-21T16:22:34Z", "type": "Journal Article", "created": "2025-09-26", "title": "A global database of soil microbial phospholipid fatty acids and enzyme activities", "description": "Abstract           <p>Soil microbes drive ecosystem function and play a critical role in how ecosystems respond to global change. Research surrounding soil microbial communities has rapidly increased in recent decades, and substantial data relating to phospholipid fatty acids (PLFAs) and potential enzyme activity have been collected and analysed. However, studies have mostly been restricted to local and regional scales, and their accuracy and usefulness are limited by the extent of accessible data. Here we aim to improve data availability by collating a global database of soil PLFA and potential enzyme activity measurements from 12,258 georeferenced samples located across all continents, 5.1% of which have not previously been published. The database contains data relating to 113 PLFAs and 26 enzyme activities, and includes metadata such as sampling date, sample depth, and soil pH, total carbon, and total nitrogen. This database will help researchers in conducting both global- and local-scale studies to better understand soil microbial biomass and function.</p", "keywords": ["Ekologi", "ddc:500", "ddc:610", "Data Descriptor", "Ecology", "microbial communities", "Microbial communities", "570 Biologie", "microbial ecology", "microbiology techniques", "Climate Science", "Microbial ecology", "500 Naturwissenschaften und Mathematik", "Biowissenschaften; Biologie", "ddc:570", "610 Medizin und Gesundheit", "Microbiology techniques", "Klimatvetenskap"], "contacts": [{"organization": "van Galen, L.G., Smith, G.R., Margenot, A.J., Waldrop, M.P., Crowther, T.W., Peay, K.G., Jackson, R.B., Yu, K., Abrah\u00e3o, A., Ahmed, T.A., Alatalo, J.M., Anslan, S., Anthony, M.A., Araujo, A.S.F., Ascher-Jenull, J., Bach, E.M., Bahram, M., Baker, C.C.M., Baldrian, P., Bardgett, R.D., Barrios-Garcia, M.N., Bastida, F., Beggi, F., Benning, L.G., Bragazza, L., Broadbent, A.A.D., Cano-D\u00edaz, C., Cates, A.M., Cerri, C.E.P., Cesarz, S., Chen, B., Classen, A.T., Dahl, M.B., Delgado-Baquerizo, M., Eisenhauer, N., Evgrafova, S.Y., Fanin, N., Fornasier, F., Francisco, R., Franco, A.L.C., Frey, S.D., Fritze, H., Garc\u00eda, C., Garc\u00eda-Palacios, P., G\u00f3mez-Brand\u00f3n, M., Gonzalez-Polo, M., Gozalo, B., Griffiths, R., Guerra, C., Hallama, M., Hiiesalu, I., Hossain, M.Z., Hu, Y., Insam, H., Jassey, V.E.J., Jiang, L., Kandeler, E., Kohout, P., K\u00f5ljalg, U., Krashevska, V., Li, X., Lu, J.-Z., Lu, X., Luo, S., Lutz, S., Mackie-Haas, K.A., Maestre, F.T., Malmivaara-L\u00e4ms\u00e4, M., Mangelsdorf, K., Manjarrez, M., Marhan, S., Martin, A., Mason, K.E., Mayor, J., McCulley, R.L., Moora, M., Morais, P.V., Mu\u00f1oz-Rojas, M., Murugan, R., Nottingham, A.T., Ochoa, V., Ochoa-Hueso, R., Oja, J., Olsson, P.A., \u00d6pik, M., Ostle, N., Peltoniemi, K., Pennanen, T., Pescador, D.S., Png, G.K., Poll, C., P\u00f5lme, S., Potapov, A.M., Priem\u00e9, A., Pritchard, W., Puissant, J., Rocha, S.M.B., Rosinger, C., Ruess, L., Sayer, E.J., Scheu, S., Sinsabaugh, R.L., Slaughter, L.C., Soudzilovskaia, N.A., Sousa, J.P., Stanish, L., Sugiyama, S.-I., Tedersoo, L., Trivedi, P., Vahter, T., Voriskova, J., Wagner, D., Wang, C., Wardle, D.A., Whitaker, J., Yang, Y., Zhong, Z., Zhu, K., Ziolkowski, L.A., Zobel, M., van den Hoogen, J.,", "roles": ["creator"]}]}, "links": [{"href": "https://eprints.lancs.ac.uk/id/eprint/232560/1/41597_2025_Article_5759.pdf"}, {"href": "https://eprints.lancs.ac.uk/id/eprint/232560/2/41597_2025_5759_MOESM1_ESM.pdf"}, {"href": "https://doi.org/20.500.11755/30733e2b-dea3-4cb4-8f63-50a9b23ba039"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "20.500.11755/30733e2b-dea3-4cb4-8f63-50a9b23ba039", "name": "item", "description": "20.500.11755/30733e2b-dea3-4cb4-8f63-50a9b23ba039", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/20.500.11755/30733e2b-dea3-4cb4-8f63-50a9b23ba039"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-09-26T00:00:00Z"}}, {"id": "2960758411", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:22:58Z", "type": "Journal Article", "created": "2019-07-31", "title": "A new global gridded anthropogenic heat flux dataset with high spatial resolution and long-term time series", "description": "Abstract<p>Exploring global anthropogenic heat and its effects on climate change is necessary and meaningful to gain a better understanding of human\uffe2\uff80\uff93environment interactions caused by growing energy consumption. However, the variation in regional energy consumption and limited data availability make estimating long-term global anthropogenic heat flux (AHF) challenging. Thus, using high-resolution population density data (30 arc-second) and a top-down inventory-based approach, this study developed a new global gridded AHF dataset covering 1970\uffe2\uff80\uff932050 based historically on energy consumption data from the British Petroleum (BP); future projections were built on estimated future energy demands. The globally averaged terrestrial AHFs were estimated at 0.05, 0.13, and 0.16\uffe2\uff80\uff89W/m2 in 1970, 2015, and 2050, respectively, but varied greatly among countries and regions. Multiple validation results indicate that the past and future global gridded AHF (PF-AHF) dataset has reasonable accuracy in reflecting AHF at various scales. The PF-AHF dataset has longer time series and finer spatial resolution than previous data and provides powerful support for studying long-term climate change at various scales.</p", "keywords": ["Statistics and Probability", "Data Descriptor", "13. Climate action", "Library and Information Sciences", "Statistics", " Probability and Uncertainty", "7. Clean energy", "01 natural sciences", "Computer Science Applications", "Education", "Information Systems", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.nature.com/articles/s41597-019-0143-1.pdf"}, {"href": "https://doi.org/2960758411"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2960758411", "name": "item", "description": "2960758411", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2960758411"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-07-31T00:00:00Z"}}, {"id": "PMC12058966", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-09-21T16:24:40Z", "type": "Journal Article", "created": "2025-05-07", "title": "A hybrid in situ and on-screen survey to monitor gully erosion across the European Union", "description": "Abstract           <p>After the successful mapping of gully erosion channels in the 2018 Eurostat Land Use/Cover Area Frame (topsoil) statistical survey (LUCAS, n\uffe2\uff80\uff89=\uffe2\uff80\uff8924,759 locations), the methodology was further expanded across the full LUCAS 2022 survey (n\uffe2\uff80\uff89=\uffe2\uff80\uff89399,591 locations). This expert-based assessment identifies the presence or absence of gully erosion forms at each LUCAS location. Its goal is to improve understanding of gully erosion geography in the EU and develop forecasting methods to support soil health indicators proposed by the new Directive on Soil Monitoring and Resilience (COM(2023)416) and Common Agricultural Policy monitoring. Here, we present the findings of our analysis which led to the development and validation of the LUCAS Gully Erosion Model (GE-LUCAS v1.1), a pan-European inventory of gully erosion channels comprising 3,116 locations (~0.8% of all monitored locations) affected by gully erosion throughout the European Union. We further present gully erosion patterns and provide insights on how GE-LUCAS v1.1 inventory can be used to estimate the probability of gully occurrence in areas beyond the monitored locations.</p", "keywords": ["Multidisciplinary Sciences", "SOIL", "Data Descriptor", "Science & Technology", "Science", "Q", "Science & Technology - Other Topics"]}, "links": [{"href": "https://www.nature.com/articles/s41597-025-05074-w.pdf"}, {"href": "https://doi.org/PMC12058966"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Scientific%20Data", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "PMC12058966", "name": "item", "description": "PMC12058966", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/PMC12058966"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2025-05-07T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+Descriptor&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=Data+Descriptor&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": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+Descriptor&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=Data+Descriptor&offset=14", "hreflang": "en-US"}], "numberMatched": 14, "numberReturned": 14, "distributedFeatures": [], "timeStamp": "2026-09-22T05:53:46.311730Z"}