{"type": "FeatureCollection", "facets": {"type": {"type": "terms", "property": "type", "buckets": [{"value": "Journal Article", "count": 6}, {"value": "Dataset", "count": 1}, {"value": "Report", "count": 1}]}, "soil_chemical_properties": {"type": "terms", "property": "soil_chemical_properties", "buckets": [{"value": "soil organic carbon", "count": 3}, {"value": "carbon", "count": 2}, {"value": "soil organic matter", "count": 2}]}, "soil_biological_properties": {"type": "terms", "property": "soil_biological_properties", "buckets": []}, "soil_physical_properties": {"type": "terms", "property": "soil_physical_properties", "buckets": []}, "soil_classification": {"type": "terms", "property": "soil_classification", "buckets": []}, "soil_functions": {"type": "terms", "property": "soil_functions", "buckets": []}, "soil_threats": {"type": "terms", "property": "soil_threats", "buckets": [{"value": "environmental degradation", "count": 8}, {"value": "soil degradation", "count": 1}, {"value": "land degradation", "count": 1}]}, "soil_processes": {"type": "terms", "property": "soil_processes", "buckets": []}, "soil_management": {"type": "terms", "property": "soil_management", "buckets": []}, "ecosystem_services": {"type": "terms", "property": "ecosystem_services", "buckets": []}}, "features": [{"id": "10.1016/j.ese.2020.100013", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:16Z", "type": "Journal Article", "created": "2020-01-13", "title": "Microbial electrochemistry for bioremediation", "description": "Lack of suitable electron donors or acceptors is in many cases the key reason for pollutants to persist in the environment. Externally supplementation of electron donors or acceptors is often difficult to control and/or involves chemical additions with limited lifespan, residue formation or other adverse side effects. Microbial electrochemistry has evolved very fast in the past years - this field relates to the study of electrochemical interactions between microorganisms and solid-state electron donors or acceptors. Current can be supplied in such so-called bioelectrochemical systems (BESs) at low voltage to provide or extract electrons in a very precise manner. A plethora of metabolisms can be linked to electrical current now, from metals reductions to denitrification and dechlorination. In this perspective, we provide an overview of the emerging applications of BES and derived technologies towards the bioremediation field and outline how this approach can be game changing.", "keywords": ["0301 basic medicine", "AUTOTROPHIC DENITRIFICATION", "elecetrobioremediation", "Bioremediaci\u00f3", "FUEL-CELLS", "Environmental technology. Sanitary engineering", "Microbial biotechnology", "01 natural sciences", "POLYCYCLIC AROMATIC-HYDROCARBONS", "03 medical and health sciences", "WASTE-WATER", "DECHLORINATION", "TD Environmental technology. Sanitary engineering", "Electrochemistry", "POLLUTANTS", "GE1-350", "TD1-1066", "0105 earth and related environmental sciences", "NITRATE-CONTAMINATED GROUNDWATER", "ENVIRONMENTAL REMEDIATION", "Q Science (General)", "QR Microbiology", "NITROGEN REMOVAL", "6. Clean water", "Environmental sciences", "Electroqu\u00edmica", "ORGANIC", "BIOELECTROCHEMICAL SYSTEMS", "13. Climate action", "Earth and Environmental Sciences", "Perspective", "Biotecnologia microbiana", "Bioremediation"]}, "links": [{"href": "https://iris.cnr.it/bitstream/20.500.14243/540323/1/1-s2.0-S2666498420300053-main.pdf"}, {"href": "https://doi.org/10.1016/j.ese.2020.100013"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Environmental%20Science%20and%20Ecotechnology", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.ese.2020.100013", "name": "item", "description": "10.1016/j.ese.2020.100013", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.ese.2020.100013"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.1016/j.jaridenv.2004.03.002", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:16:39Z", "type": "Journal Article", "created": "2004-04-22", "title": "Changes In Soil Organic Carbon And Other Physical Soil Properties Along Adjacent Mediterranean Forest, Grassland, And Cropland Ecosystems In Turkey", "description": "Abstract   Cultivation, overgrazing, and overharvesting are seriously degrading forest and grassland ecosystems in the Taurus Mountains of the southern Mediterranean region of Turkey. This study investigated the effects of changes on soil organic carbon (SOC) content and other physical soil properties over a 12-year period in three adjacent ecosystems in a Mediterranean plateau. The ecosystems were cropland (converted from grasslands in 1990), open forest, and grassland. Soil samples from two depths, 0\u201310 and 10\u201320\u00a0cm, were collected for chemical and physical analyses at each of cropland, open forest, and grassland ecosystems. SOC pools at the 0\u201320\u00a0cm depth of cropland, forest, and grassland ecosystems were estimated at 32,636, 56,480, and 57,317\u00a0kg\u00a0ha\u22121, respectively. Conversion of grassland into cropland during the 12-year period increased the bulk density by 10.5% and soil erodibility by 46.2%; it decreased SOM by 48.8%, SOC content by 43%, available water capacity (AWC) by 30.5%, and total porosity by 9.1% for the 0\u201320\u00a0cm soil depth (p", "keywords": ["2. Zero hunger", "Land cover", "Mediterranean plateau", "Soil organic carbon", "13. Climate action", "Land use", "0401 agriculture", " forestry", " and fisheries", "04 agricultural and veterinary sciences", "15. Life on land", "Environmental degradation"]}, "links": [{"href": "https://doi.org/10.1016/j.jaridenv.2004.03.002"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Journal%20of%20Arid%20Environments", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.jaridenv.2004.03.002", "name": "item", "description": "10.1016/j.jaridenv.2004.03.002", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.jaridenv.2004.03.002"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2004-12-01T00:00:00Z"}}, {"id": "10.1016/j.soilbio.2015.03.018", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:17:12Z", "type": "Journal Article", "created": "2015-04-06", "title": "Community Structure Of Arbuscular Mycorrhizal Fungi Associated With Robinia Pseudoacacia In Uncontaminated And Heavy Metal Contaminated Soils", "description": "The significance of arbuscular mycorrhizal fungi (AMF) in soil remediation has been widely recognized because of their ability to promote plant growth and increase phytoremediation efficiency in heavy metal (HM) polluted soils by improving plant nutrient absorption and by influencing the fate of the metals in the plant and soil. However, the symbiotic functions of AMF in remediation of polluted soils depend on plant\u2013fungus\u2013soil combinations and are greatly influenced by environmental conditions. To better understand the adaptation of plants and the related mycorrhizae to extreme environmental conditions, AMF colonization, spore density and community structure were analyzed in roots or rhizosphere soils of Robinia pseudoacacia. Mycorrhization was compared between uncontaminated soil and heavy metal contaminated soil from a lead\u2013zinc mining region of northwest China. Samples were analyzed by restriction fragment length polymorphism (RFLP) screening with AMF-specific primers (NS31 and AM1), and sequencing of rRNA small subunit (SSU). The phylogenetic analysis revealed 28 AMF group types, including six AMF families: Glomeraceae, Claroideoglomeraceae, Diversisporaceae, Acaulosporaceae, Pacisporaceae, and Gigasporaceae. Of all AMF group types, six (21%) were detected based on spore samples alone, four (14%) based on root samples alone, and five (18%) based on samples from root, soil and spore. Glo9 (Rhizophagus intraradices), Glo17 (Funneliformis mosseae) and Acau3 (Acaulospora sp.) were the three most abundant AMF group types in the current study. Soil Pb and Zn concentrations, pH, organic matter content, and phosphorus levels all showed significant correlations with the AMF species compositions in root and soil samples. Overall, the uncontaminated sites had higher species diversity than sites with heavy metal contamination. The study highlights the effects of different soil chemical parameters on AMF colonization, spore density and community structure in contaminated and uncontaminated sites. The tolerant AMF species isolated and identified from this study have potential for application in phytoremediation of heavy metal contaminated areas.", "keywords": ["2. Zero hunger", "Agricultural and Veterinary Sciences", "Pollution and Contamination", "Arbuscular mycorrhizal fungi", "Environmental interactions", "Soil Science", "Agronomy & Agriculture", "04 agricultural and veterinary sciences", "Biological Sciences", "15. Life on land", "16. Peace & justice", "Heavy metal pollution", "Microbiology", "Phytoremediation", "Soil sciences", "Robinia pseudoacacia", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Environmental Sciences"]}, "links": [{"href": "https://doi.org/10.1016/j.soilbio.2015.03.018"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Soil%20Biology%20and%20Biochemistry", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1016/j.soilbio.2015.03.018", "name": "item", "description": "10.1016/j.soilbio.2015.03.018", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1016/j.soilbio.2015.03.018"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2015-07-01T00:00:00Z"}}, {"id": "10.3929/ethz-b-000460471", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:21:41Z", "type": "Journal Article", "created": "2020-10-26", "title": "Continental-scale controls on soil organic carbon across sub-Saharan Africa", "description": "<p>Abstract. Earlier studies have demonstrated that soil texture and geochemistry strongly affect soil organic carbon (SOC) content. However, those findings primarily rely on data from temperate regions with soil mineralogy, weathering status and climatic conditions that generally differ from tropical and sub-tropical regions. We investigated soil properties and climate variables influencing SOC concentrations across sub-Saharan Africa. A total of 1,601 samples were analyzed, collected from two depths (0\uffe2\uff80\uff9320\uffe2\uff80\uff89cm and 20\uffe2\uff80\uff9350\uffe2\uff80\uff89cm) at 45 sentinel sites from 17 countries as part of the Africa Soil Information Service (AfSIS) project. The dataset spans climatic conditions from arid to humid and includes soils with a wide range of pHH20 values, weathering status, soil texture, exchangeable cations, extractable metals and a variety of important land cover types. The most important SOC predictors were identified by linear mixed effects models, regression trees and random forest models. Our results indicate that SOC is primarily controlled by aridity index (PET/MAP), exchangeable calcium (Caex) and oxalate-extractable aluminum (Alox); this was found across both depth intervals. Oxalate-extractable iron (Feox) emerged as the most important predictor for both depth intervals in the regression tree and random forest analyses. However, its influence on SOC concentrations was strong only below Feox concentrations of 0.25\uffe2\uff80\uff89wt\uffe2\uff80\uff89%. This suggests that Feox can act as a pedogenic threshold \uffe2\uff80\uff93 even on a continental scale. Across model-ling approaches, clay and fine silt content (                         </p>", "keywords": ["2. Zero hunger", "QE1-996.5", "Soil organic matter", "Biogeochemistry; Land-use; Soil organic matter; Clay mineralogy; Pedogenic threshold", "ddc:550", "carbon", "environmental degradation", "Geology", "04 agricultural and veterinary sciences", "Biogeochemistry", "15. Life on land", "Clay mineralogy", "soil organic carbon", "Environmental sciences", "13. Climate action", "Pedogenic threshold", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "soil analysis", "Land-use"]}, "links": [{"href": "https://soil.copernicus.org/articles/7/305/2021/soil-7-305-2021.pdf"}, {"href": "https://doi.org/10.3929/ethz-b-000460471"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/SOIL", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3929/ethz-b-000460471", "name": "item", "description": "10.3929/ethz-b-000460471", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3929/ethz-b-000460471"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-26T00:00:00Z"}}, {"id": "10.5061/dryad.5dv41nsd1", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:21:53Z", "type": "Dataset", "created": "2024-01-22", "title": "Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations", "description": "unspecified# Data and R code used in: Plant geographic distribution influences  chemical defenses in native and introduced Plantago lanceolata populations  ## Description of the data  and file structure * **00_ReadMe_DescriptonVariables.csv**: A list with  the description of variables from each file used. *  **00_Metadata_Coordinates.csv :** A dataset that includes the coordinates  of each Plantago lanceolata population used. * **00_Metadata_Climate.csv  :** A dataset that includes coordinates, bioclimatic parameters, and the  results of PCA. The dataset was created based on the script  '1_Environmental variables.qmd' *  **00_Metadata_Individuals.csv:** A dataset that includes general  information about each plant individual. Information about root traits and  chemistry is missing in four samples since we lost the samples. *  **01_Datset_PlantTraits.csv:** Size-related and resource allocation traits  measured of *Plantago lanceolata* and herbivore damage. *  **02_Dataset_TargetedCompounds.csv:** Phytohormones, Iridoid glycosides,  Verbascoside and Flavonoids quantification of the leaves and roots of  *Plantago lanceolata*. Data generated from HPLC *  **03_Dataset_Volatiles_Area.csv:** Area of identified volatile compounds.  Data generated from GC-FID * **03_Dataset_Volatiles_Compounds.csv:**  Information on identified volatile compounds. Data generated from GC-MS. *  **04_Dataset_Metabolome_Negative_Metadata.txt:** Metadata for files in  negative mode * **04_Dataset_Metabolome_Negative_Intensity.xlsx :** File  with the intensity of the metabolite features in negative mode. The file  was generated from Metaboscape and adapted as required for the Notame  package. * **04_Dataset_Metabolome_Negative_Intensity_filtered.xlsx:**  File generated after preprocessing of features in negative mode. During  the notadame pacakged preprossesing 0 were converted to na *  **04_Dataset_Metabolome_Negative.msmsonly.csv:** File with a intensity of  the the metabolite features in negative mode with ms/ms data. File  generated from Metaboscape. *  **04_Results_Metabolome_Negative_canopus_compound_summary.tsv:** Feature  classification. Results generated from Sirius software. *  **04_Results_Metabolome_Negative_compound_identifications.tsv:** Feature  identification. Results generated from Sirius software. *  **05_Dataset_Metabolome_Positive_Metadata.txt:** Metadata for files in  positive mode * **05_DatasetMetabolome_Positive_Intensity.xlsx :** File  with a intensity of the the metabolite features in positive mode. File  generated from Metaboscape and adapted as required for the Notame package.  * **05_Dataset_Metabolome_Positive_Intensity_filtered:** File generated  after preprocessing of features in positive mode.During the notadame  pacakged preprossesing 0 were converted to na ## ## Code/Software *  **1_Environmental vairables.qmd:** Rscript to Retrieve bioclimatic  variables from based on the coordinates  of each population and then perform a principal components analysis to  reduce the axes variation and included the first principal component as an  explanatory variable in our model to estimate trait differences between  native and introduced populations. Figure 1b and 1d *  **2_PlantTraits_and_Herbivory:** Rscript for statistical anaylsis of  size-related traits, resource allocation traits and herbivore damage.  Figure 2. It needs to source: Model_1_Fucntion.R, Model_2_Fucntion.R,  Plot_Function.R * **3_Metabolome:** Rscript for statistical anaylsis of  *Plantago lanceolata* metabolome. Figure 3. It needs to source:  Metabolome_preprocessing_R, Model_1_Fucntion.R, Model_2_Fucntion.R,  Plot_Function.R. * **4_TargetedCompounds:** Rscript for statistical  anaylsis of *Plantago lanceolata* targeted compounds. Figure 4. It needs  to source: Model_1_Fucntion.R, Model_2_Fucntion.R, Plot_Function.R *  **5_Volatilome:** Rscript for statistical anaylsis of *Plantago  lanceolata* metabolome. Figure 5. It needs to source: Model_1_Fucntion.R,  Model_2_Fucntion.R, Plot_Function.R * **Model_1_Function.R** : Function to  run statistical models * **Model_2_Function.R** : Function to run  statistical models * **Plots_Function.R** : Function to run plot graphs *  **Metabolome_prepocessing.R:** Script to preprocess features", "keywords": ["environmental gradient", "FOS: Biological sciences", "Metabolomics", "Herbivory", "Volatile organic compounds", "iridoid glycosides", "verbascoside", "plant invasion"], "contacts": [{"organization": "Medina-van Berkum, Pamela, Schm\u00f6ckel, Eric, Bischoff, Armin, Carrasco-Farias, Natalia, Catford, Jane, Feldmann, Reinart, Groten, Karin, Henry, Hugh, Bucharova, Anna, H\u00e4nniger, Sabine, Luong, Justin, Meis, Julia, Oetama, Vincensius S. P., P\u00e4rtel, Meelis, Power, Sally, Villellas, Jesus, Welk, Erik, Wingler, Astrid, Rothe, Beate, Gershenzon, Jonathan, Reichelt, Michael, Roscher, Christiane, Unsicker, Sybille B.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5061/dryad.5dv41nsd1"}, {"rel": "self", "type": "application/geo+json", "title": "10.5061/dryad.5dv41nsd1", "name": "item", "description": "10.5061/dryad.5dv41nsd1", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5061/dryad.5dv41nsd1"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2024-02-08T00:00:00Z"}}, {"id": "10568/114212", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:19Z", "type": "Journal Article", "created": "2020-10-26", "title": "Continental-scale controls on soil organic carbon across sub-Saharan Africa", "description": "<?xml version='1.0' encoding='UTF-8'?><article><p>Abstract. Earlier studies have demonstrated that soil texture and geochemistry strongly affect soil organic carbon (SOC) content. However, those findings primarily rely on data from temperate regions with soil mineralogy, weathering status and climatic conditions that generally differ from tropical and sub-tropical regions. We investigated soil properties and climate variables influencing SOC concentrations across sub-Saharan Africa. A total of 1,601 samples were analyzed, collected from two depths (0\u201320\u2009cm and 20\u201350\u2009cm) at 45 sentinel sites from 17 countries as part of the Africa Soil Information Service (AfSIS) project. The dataset spans climatic conditions from arid to humid and includes soils with a wide range of pHH20 values, weathering status, soil texture, exchangeable cations, extractable metals and a variety of important land cover types. The most important SOC predictors were identified by linear mixed effects models, regression trees and random forest models. Our results indicate that SOC is primarily controlled by aridity index (PET/MAP), exchangeable calcium (Caex) and oxalate-extractable aluminum (Alox); this was found across both depth intervals. Oxalate-extractable iron (Feox) emerged as the most important predictor for both depth intervals in the regression tree and random forest analyses. However, its influence on SOC concentrations was strong only below Feox concentrations of 0.25\u2009wt\u2009%. This suggests that Feox can act as a pedogenic threshold \u2013 even on a continental scale. Across model-ling approaches, clay and fine silt content (</p></article>", "keywords": ["2. Zero hunger", "QE1-996.5", "Soil organic matter", "Biogeochemistry; Land-use; Soil organic matter; Clay mineralogy; Pedogenic threshold", "ddc:550", "carbon", "environmental degradation", "Geology", "04 agricultural and veterinary sciences", "Biogeochemistry", "15. Life on land", "Clay mineralogy", "soil organic carbon", "Environmental sciences", "13. Climate action", "Pedogenic threshold", "0401 agriculture", " forestry", " and fisheries", "GE1-350", "soil analysis", "Land-use"]}, "links": [{"href": "https://soil.copernicus.org/articles/7/305/2021/soil-7-305-2021.pdf"}, {"href": "https://doi.org/10568/114212"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/SOIL", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10568/114212", "name": "item", "description": "10568/114212", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10568/114212"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-26T00:00:00Z"}}, {"id": "11590/484290", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:25:33Z", "type": "Journal Article", "created": "2024-05-08", "title": "A unifying modelling of multiple land degradation pathways in Europe", "description": "Abstract<p>Land degradation is a complex socio-environmental threat, which generally occurs as multiple concurrent pathways that remain largely unexplored in Europe. Here we present an unprecedented analysis of land multi-degradation in 40 continental countries, using twelve dataset-based processes that were modelled as land degradation convergence and combination pathways in Europe\uffe2\uff80\uff99s agricultural (and arable) environments. Using a Land Multi-degradation Index, we find that up to 27%, 35% and 22% of continental agricultural (~2 million km2) and arable (~1.1 million km2) lands are currently threatened by one, two, and three drivers of degradation, while 10\uffe2\uff80\uff9311% of pan-European agricultural/arable landscapes are cumulatively affected by four and at least five concurrent processes. We also explore the complex pattern of spatially interacting processes, emphasizing the major combinations of land degradation pathways across continental and national boundaries. Our results will enable policymakers to develop knowledge-based strategies for land degradation mitigation and other critical European sustainable development goals.</p", "keywords": ["Degradation (telecommunications)", "Soil Degradation", "Science", "Soil Science", "01 natural sciences", "Environmental protection", "Article", "Environmental science", "12. Responsible consumption", "Agricultural and Biological Sciences", "Agricultural land", "Sustainable development", "11. Sustainability", "Arable land", "Environmental resource management", "Biology", "0105 earth and related environmental sciences", "2. Zero hunger", "Global and Planetary Change", "Global Analysis of Ecosystem Services and Land Use", "Geography", "Ecology", "Q", "1. No poverty", "Life Sciences", "Agriculture", "04 agricultural and veterinary sciences", "15. Life on land", "Computer science", "Soil Erosion and Agricultural Sustainability", "Land Tenure and Property Rights in Agriculture", "Threatened species", "Environmental degradation", "Habitat", "Archaeology", "Land Fragmentation", "13. 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