{"type": "FeatureCollection", "features": [{"id": "10.5281/zenodo.3589102", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:47Z", "type": "Dataset", "title": "OSMO soil health dataset 2015-2018", "description": "Open AccessThis is soil health data collected from 24 Finnish fields between 2015-2018. The fields were chosen to represent fields with poor and good productivity on a given farm. The dataset is coded based on farm code (two letters) and field code: 0 poor field, no interventions; 1 poor field, improved soil management; K good field. Fields were monitored for soil nutrients, soil physical quality and some biological parameters (aggregate stability, earthworms, arthropods, soil carbon dioxide burst respiration). The overview dataset contains the parameters used for monitoring development in soil health in the fields, the additional dataset present plant tissue nutrient concentrations and soil structure evaluation as well as a comparison of organic matter analysis from three laboratories and a comparison of two aggregate stability assessment methods. More information on the study can be found in the two reports (in Finnish, w. English abstracts): http://hdl.handle.net/10138/229450 http://hdl.handle.net/10138/309062", "keywords": ["2. Zero hunger", "soil compaction", "agroecology", "soil health", "plant nutrients", "agricultural management", "15. Life on land"], "contacts": [{"organization": "Mattila, Tuomas, Rajala, Jukka,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3589102"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3589102", "name": "item", "description": "10.5281/zenodo.3589102", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3589102"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-08T00:00:00Z"}}, {"id": "10.5281/zenodo.4291855", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:49Z", "type": "Dataset", "title": "EstSoil-EH: A high-resolution eco-hydrological modelling parameters dataset for Estonia (dataset)", "description": "Open AccessThis research has been supported by the Marie Sk\u0142odowska-Curie Actions individual fellowships under the Horizon 2020 Programme grant agreement number 795625, the Mobilitas Pluss postdoctoral researcher grant number MOBJD233 and grant numbers PRG352, PRG609, and PRG874 of the Estonian Research Council (ETAG), the European Regional Development Fund (Centre of Excellence EcolChange), the NUTIKAS programme of the Archimedes foundation, and by the Estonian Environmental Investment Centre.", "keywords": ["https://www.eionet.europa.eu/gemet/en/theme/35", "13. Climate action", "https://www.eionet.europa.eu/gemet/en/concept/4855", "soil", " texture", " FAO", " WRB", " available water capacity", " AWC", " estonia", " soilmap", " hydraulic properties", " soil organic carbon", " SOC", " bulk density", " saturated hydraulic conductivity", " ecosystem services", "https://www.eionet.europa.eu/gemet/en/concept/15138", "15. Life on land", "https://www.eionet.europa.eu/gemet/en/group/4856"], "contacts": [{"organization": "Kmoch, Alexander, Kanal, Arno, Astover, Alar, Kull, Ain, Virro, Holger, Helm, Aveliina, P\u00e4rtel, Meelis, Ostonen, Ivika, Uuemaa, Evelyn,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4291855"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4291855", "name": "item", "description": "10.5281/zenodo.4291855", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4291855"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-26T00:00:00Z"}}, {"id": "10.5281/zenodo.3698071", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-04-04T16:23:48Z", "type": "Report", "title": "Mesoporous Silica-based Nanocarriers for pH-Responsive Delivery of Anticancer Metal Complexes", "description": "Novel cancer-targeting nanotherapeutics are developed based on mesoporous silica nanoparticles (MSN) loaded with metal complexes, for potent activity against cancer. MSN was synthesized by a surfactant-templated method. The nanomaterials are designed, through post-synthetic surface-functionalization with the capability to release Ruthenium- and Iridium-based drugs from MSN nanomaterials by lowering pH value. This effect would lead to preferential cancer-targeting due to the more acidic tumor tissue in comparison to the healthy tissue. Novel Ru(II) and Ir(III) coordination compounds were also synthesized, containing the same ligands which are contained on the surface of MSN, and their anticancer activity was evaluated with and without their covalent attachment to MSN. The constructed series of novel nanomaterials and coordination compounds were characterized by scanning electron microscopy, thermogravimetric analysis, infrared spectroscopy, X-ray crystallography, and elemental analysis. The characteristics of prepared metal complexes and novel nanomaterials demonstrate high potential for their effective application in targeted cancer treatment.", "keywords": ["3. Good health"], "contacts": [{"organization": "Neboj\u0161a, Ili\u0107, Minja, Mladenovi\u0107, Goran, Kalu\u0111erovi\u0107, Nikola, Kne\u017eevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3698071"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3698071", "name": "item", "description": "10.5281/zenodo.3698071", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3698071"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-12T00:00:00Z"}}, {"id": "10.5281/zenodo.3746437", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:48Z", "type": "Report", "created": "2020-01-24", "title": "Microbial competition reduces interaction distances to the low \u00b5m-range", "description": "Abstract

Metabolic interactions between cells affect microbial community compositions and hence their function in ecosystems. It is well-known that under competition for the exchanged metabolite, concentration gradients constrain the distances over which interactions can occur. However, interaction distances are typically quantified in two-dimensional systems or without accounting for competition or other metabolite-removal, conditions which may not very often match natural ecosystems. We here analyze the impact of cell-to-cell distance on unidirectional cross-feeding in a three-dimensional system with competition for the exchanged metabolite. Effective interaction distances were computed with a reaction-diffusion model and experimentally verified by growing a synthetic consortium of 1 \uffc2\uffb5m-sized metabolite producer, receiver and competitor cells in different spatial structures. We show that receivers cannot interact with producers \uffe2\uff88\uffbc15 \uffc2\uffb5m away from them, as product concentration gradients flatten close to producer cells. We developed an aggregation protocol and created variants of the receiver cells\uffe2\uff80\uff99 import system, to show that within producer-receiver aggregates even low affinity receiver cells could interact with producers. These results show that competition or other metabolite-removal of a public good in a three-dimensional system reduces the interaction distance to the low micrometer-range, highlighting the importance of concentration gradients as physical constraint for cellular interactions.The applicability of the single-aliquot regenerative-dose (SAR) protocol, by using the optically stimulated luminescence (OSL) signal of quartz as well as the post-infrared\uffe2\uff80\uff93infrared (pIRIR) signals of polymineral fine grains, namely pIRIR<sub>225</sub> and pIRIR<sub>290</sub>, was assessed for dating loess in New Zealand South Island. OSL signals of quartz grains displayed low sensitivity. However, the application of repeated irradiation/bleaching cycles did not result in an increase in sensitivity; annealing in the 300\uffe2\uff80\uff93500\uffc2\uffb0C temperature range generated the sensitisation of both the 110\uffc2\uffb0C thermoluminescence (TL) peak as well as the OSL signal, likely by activation of yet unidentified luminescence centres. After heating, the quartz signal is comparable to that of ideal samples, but the annealing is precluding successful dating. On the other hand, feldspar infrared-stimulated signals displayed satisfactory properties, allowing estimation of ages ranging from 14 \uffc2\uffb1 1\uffe2\uff80\uff9329 \uffc2\uffb1 3 ka for the investigated deposit. It was shown that pIRIR<sub>225</sub> and pIRIR<sub>290</sub> methods have potential for dating loess in the South Island of New Zealand, based on the following observations: (i) Dose recovery tests were successful with recovered-to-given dose ratios with a <10% deviation from unity, (ii) constant residual values of about 4 Gy and about 10 Gy were obtained after exposures for 48 h in the case of pIRIR<sub>225</sub> signals and 96 h in the case of pIRIR<sub>290</sub> signals, respectively, (iii) while a slight dose-dependence of the residual was reported, and for a dose as large as 1600 Gy the residual values are \uffe2\uff89\uff859 Gy and \uffe2\uff89\uff8519 Gy for pIRIR<sub>225</sub> and pIRIR<sub>290</sub> signals, respectively.

", "keywords": ["Mass-wasting -- New Zealand", "quartz; feldspar; luminescence; sensitivity; loess; New Zealand", "Submarine topography -- New Zealand", "Landslide hazard analysis -- New Zealand", "Submarine geology -- New Zealand", "15. Life on land", "Marine sediments -- New Zealand", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://oceanrep.geomar.de/id/eprint/52652/1/10.2478_geochr-2021-0005.pdf"}, {"href": "https://oceanrep.geomar.de/id/eprint/52652/2/geochr-2021-0005_sm.pdf"}, {"href": "https://doi.org/10.5281/zenodo.4536871"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geochronometria", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4536871", "name": "item", "description": "10.5281/zenodo.4536871", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4536871"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-04-30T00:00:00Z"}}, {"id": "10.5281/zenodo.3953560", "type": "Feature", "geometry": null, "properties": {"license": "Closed Access", "updated": "2026-04-04T16:23:48Z", "type": "Report", "title": "Mesoporous Silica-based Nanocarriers for pH-Responsive Delivery of Anticancer Metal Complexes", "description": "Novel cancer-targeting nanotherapeutics are developed based on mesoporous silica nanoparticles (MSN) loaded with metal complexes, for potent activity against cancer. MSN was synthesized by a surfactant-templated method. The nanomaterials are designed, through post-synthetic surface-functionalization with the capability to release Ruthenium- and Iridium-based drugs from MSN nanomaterials by lowering pH value. This effect would lead to preferential cancer-targeting due to the more acidic tumor tissue in comparison to the healthy tissue. Novel Ru(II) and Ir(III) coordination compounds were also synthesized, containing the same ligands which are contained on the surface of MSN, and their anticancer activity was evaluated with and without their covalent attachment to MSN. The constructed series of novel nanomaterials and coordination compounds were characterized by scanning electron microscopy, thermogravimetric analysis, infrared spectroscopy, X-ray crystallography, and elemental analysis. The characteristics of prepared metal complexes and novel nanomaterials demonstrate high potential for their effective application in targeted cancer treatment.", "keywords": ["3. Good health"], "contacts": [{"organization": "Neboj\u0161a, Ili\u0107, Minja, Mladenovi\u0107, Goran, Kalu\u0111erovi\u0107, Nikola, Kne\u017eevi\u0107,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3953560"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3953560", "name": "item", "description": "10.5281/zenodo.3953560", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3953560"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-06-12T00:00:00Z"}}, {"id": "10.5281/zenodo.3832031", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:48Z", "type": "Dataset", "title": "Radiocarbon content of carbon dioxide, methane, dissolved organic carbon and particulate organic carbon from the northern permafrost region and other studies", "description": "The dataset includes 14C measurements of CO2, CH4, DOC and POC mostly from the northern permafrost region. Some other studies are included from sites not underlained by permafrost. The dataset focuses on 14C measurements of gaseous soil emissions and waterborne ecosystem C fluxes but the database also included C forms belowground, such as soil gases and pore water DOC.", "keywords": ["13. Climate action", "15. Life on land", "radiocarbon", " permafrost", " carbon dioxide", " methane", " dissolved organic carbon", " particulate organic carbon", " DOC", " POC", " thermokarst", " thaw"], "contacts": [{"organization": "Estop-Aragon\u00e9s, Cristian", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3832031"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3832031", "name": "item", "description": "10.5281/zenodo.3832031", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3832031"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-05-18T00:00:00Z"}}, {"id": "10.5281/zenodo.3924255", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:48Z", "type": "Software", "title": "rpmodel v1.0.1", "description": "R package at initial submission to GMD Discussions.", "keywords": ["Photosynthesis", " Gross primary productivity modelling"], "contacts": [{"organization": "Stocker, Benjamin", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.3924255"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3924255", "name": "item", "description": "10.5281/zenodo.3924255", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3924255"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-08-04T00:00:00Z"}}, {"id": "10.5281/zenodo.3957267", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:48Z", "type": "Journal Article", "created": "2020-06-04", "title": "MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior.", "description": "

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

", "keywords": ["Technology and Engineering", "Markov random field", "LORAKS", "Chemical technology", "TP1-1185", "02 engineering and technology", "image reconstruction", "Article", "NETWORKS", "magnetic resonance imaging; Markov random field; image reconstruction", "03 medical and health sciences", "0302 clinical medicine", "0202 electrical engineering", " electronic engineering", " information engineering", "magnetic resonance imaging", "MAGE-RECONSTRUCTION"], "contacts": [{"organization": "Pani\u0107, M., Jakoveti\u0107, Du\u0161an, Vukobratovi\u0107, Dejan, Crnojevi\u0107, Vladimir, Pi\u017eurica, Aleksandra,", "roles": ["creator"]}]}, "links": [{"href": "http://www.mdpi.com/1424-8220/20/11/3185/pdf"}, {"href": "https://doi.org/10.5281/zenodo.3957267"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Sensors", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.3957267", "name": "item", "description": "10.5281/zenodo.3957267", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.3957267"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-06-03T00:00:00Z"}}, {"id": "10.5281/zenodo.4104138", "type": "Feature", "geometry": null, "properties": {"license": "Open Access", "updated": "2026-04-04T16:23:49Z", "type": "Journal Article", "created": "2020-10-17", "title": "Initial quartz OSL and dust mass accumulation rate investigation of the Kisiljevo loess sequence in north-eastern Serbia", "description": "The thick and apparently continuous loess-palaeosol sequences in the Vojvodina region of northern Serbia are recognized and well understood as some of the oldest and most complete terrestrial European palaeoclimatic archives. By contrast, there are few published records for loess profiles from other regions in Serbia. Here we address this knowledge gap by investigating an 8 m thick loess sequence exposed near the village of Kisiljevo in north eastern Serbia, describing the pedostratigraphy and environmental magnetic signatures in detail and placing these within a chronologic framework using quartz optically stimulated luminescence (OSL) on the 4-11 and 63-90 \u03bcm size fractions. Our results show dust accumulation over the last c. 32 ka, with substantial primary loess accumulation during the Early Holocene prior to the formation of the modern soil. We applied two age-depth modelling approaches to estimate dust mass accumulation rates: the Bacon.35 r software and ADmin model. Both yield high accumulation rates, especially during MIS 2, averaging 550- 600 g m\u22122 a\u22121 which exceed estimates for other investigated loess sequences in the region.", "keywords": ["15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4104138"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Quaternary%20International", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4104138", "name": "item", "description": "10.5281/zenodo.4104138", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4104138"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-01T00:00:00Z"}}, {"id": "10.5281/zenodo.4173186", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:49Z", "type": "Dataset", "title": "Detailed global modelling of soil organic carbon in cropland, grassland and forest soils", "description": "Supporting information of the paper: Morais, T.G., Teixeira, R.F.M., Domingos, T. 2019. Detailed global modelling of soil organic carbon in cropland, grassland and forest soils. PloS One. Version 2 includes raster files (.tif) for each land use class (including: Attainable SOC stock, mineralization rate, and fator K).", "keywords": ["Attainable SOC", "Soil Organic Carbon", "Land use", "SOC mineralization", "15. Life on land", "RothC", "Ecological modelling"], "contacts": [{"organization": "Morais, T.G., Teixeira, R.F.M., Domingos, Tigao,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4173186"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4173186", "name": "item", "description": "10.5281/zenodo.4173186", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4173186"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-09-05T00:00:00Z"}}, {"id": "10.5281/zenodo.4247969", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:49Z", "type": "Dataset", "title": "Soil Moisture Active/Passive (SMAP) Level 4 Carbon (L4C) Nature Run version 7.2", "description": "Open AccessThe Soil Moisture Active/ Passive (SMAP) Level 4 Carbon (L4C) product is a daily, global, terrestrial carbon budget driven, in part, by soil moisture estimates from the Level 4 Soil Moisture (L4SM) product and, in turn, on brightness temperature observations from the SMAP satellite [1,2]. The SMAP L4C operational product's record begins on March 31, 2015, shortly after the launch of SMAP, and continues to the present, with an average latency of 9 days [3]. SMAP L4C data are posted to a global, 9-km equal-area EASE-Grid 2.0 [4]. In order to improve the longitudinal coverage of the SMAP L4C record, a model-only 'Nature Run' was devised, with daily carbon budget estimates beginning January 1, 2000. The Nature Run differs from the SMAP L4C operational product in the following ways: - The SMAP L4C Nature Run uses the MERRA-2 re-analysis dataset for meteorological driver data, instead of the GEOS-5 FP driver data used in the operational product.
- The SMAP L4C Nature Run uses soil moisture and soil temperature estimates from the L4SM Nature Run, which is a model-only version of the operational L4SM product that does not assimilate SMAP brightness temperature data. This repository contains the full README for the data. The data can be downloaded from: http://files.ntsg.umt.edu/data/SMAP_L4C_NatureRun/NRv7.2/", "keywords": ["carbon flux", "soil organic carbon", "primary productivity", "13. Climate action", "net ecosystem exchange", "15. Life on land", "earth system", "respiration"], "contacts": [{"organization": "Endsley, K. Arthur, Jones, Lucas, Kimball, John,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4247969"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4247969", "name": "item", "description": "10.5281/zenodo.4247969", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4247969"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-11-05T00:00:00Z"}}, {"id": "10.5281/zenodo.4384105", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:50Z", "type": "Software", "title": "A Colab-Python script code to identify palaeo-landscape features", "description": "Open Access{'references': ['1. Python Software Foundation. Python Language Reference. 2020. Available: http://www.python.org', '2. Wu Q. geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software. 2020;5: 2305', '3. Bisong E. Google Colaboratory. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: u00a0 u00a0 u00a0A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. pp. 59 u201364', '4. Project Jupyter. Jupyter Notebook. 2020. Available: https://jupyter.org/', '5. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2019. u00a0 u00a0 u00a0Available: https://www.qgis.org/en/site/index.html', '6. Gillies S et al. Rasterio: geospatial raster I/O for Python programmers. Mapbox; 2013. Available: https://github.com/mapbox/rasterio', '7. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9: 90 u201395.']}", "keywords": ["Remote Sensing", "Multispectral analysis", "Landscape Archaeology", "Spectral decomposition", "15. Life on land", "Sentinel-2", "Riverscape", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Python"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4384105"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4384105", "name": "item", "description": "10.5281/zenodo.4384105", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4384105"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-22T00:00:00Z"}}, {"id": "10.5281/zenodo.4333554", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:50Z", "type": "Dataset", "title": "Best4Soil_databases_datamining_V1", "description": "This document contains data on the host plant status of field crops, vegetable crops and green manure crops for soilborne plant parasitic nematodes and soilborne fungal pathogens. The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n\u00b0817696). The databases are accessible through the website https://ww.best4soil.eu/database ; they are hosted on https://nematodes.soilhealthtool.eu/", "keywords": ["2. Zero hunger", "field crop", "Best4Soil", " database", " hostplant", " soilborne pathogen", " nematode", " field crop", " vegetable", " green manure", " cover crop", "green manure", "hostplant", "nematode", "Best4Soil", "vegetable", "cover crop", "15. 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The data was used for the construction of two databases in context of the Best4Soil Thematic Network, a Horizon 2020 project (Grant Agreement n\u00b0817696). The databases are accessible through the website https://ww.best4soil.eu/database ; they are hosted on https://nematodes.soilhealthtool.eu/", "keywords": ["2. Zero hunger", "field crop", "Best4Soil", " database", " hostplant", " soilborne pathogen", " nematode", " field crop", " vegetable", " green manure", " cover crop", "green manure", "hostplant", "nematode", "Best4Soil", "vegetable", "cover crop", "15. 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The soil fungal community plays an important role in determining plant growth and health. In this study, we investigated the fungal diversity and community composition in the roots and soil of 21 potato (Solanum tuberosum L.) cultivars using high-throughput sequencing at three different time points across the growing season. In soil and roots, the fungal richness and relative abundance of pathogens and saprotrophs were mainly affected by sampling time. While sampling time affected fungal composition in soil, root fungal communities were also significantly affected by cultivar. The cultivar had the strongest effect on diversity of pathogens and abundance of particular pathogen species. Our results demonstrate changes in soil and root fungal communities of potato over the growing season, as well as highlighting the importance of potato cultivar on root fungal communities and abundance of pathogens.

", "keywords": ["2. Zero hunger", "0301 basic medicine", "0303 health sciences", "agroecosystems", "S", "high-throughput sequencing", "fungal guild", "Solanum tuberosum", "Agriculture", "15. Life on land", "fungal diversity", "03 medical and health sciences", "potato cultivars", "host specificity", "Solanum tuberosum"]}, "links": [{"href": "http://www.mdpi.com/2073-4395/10/10/1535/pdf"}, {"href": "https://www.mdpi.com/2073-4395/10/10/1535/pdf"}, {"href": "https://doi.org/10.5281/zenodo.4476976"}, {"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": "10.5281/zenodo.4476976", "name": "item", "description": "10.5281/zenodo.4476976", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4476976"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-10-09T00:00:00Z"}}, {"id": "10.5281/zenodo.4462143", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:50Z", "type": "Dataset", "title": "ECOBREED WP3 entomopathogenic fungi-wireworm data related to Razinger et al. 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(10328826), Patama, Marjo (10328829), Zanetti, Alessandro (10328832), Pihlatie, Mari (6502226),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4609835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4609835", "name": "item", "description": "10.5281/zenodo.4609835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4609835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-17T00:00:00Z"}}, {"id": "10.5281/zenodo.4609836", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Dataset", "title": "An automated system for trace gas flux measurements from plantfoliage and other plant compartments", "description": "Data processing code and raw measurement data for Kohl et al., Atmospheric Measurement Techniquest Discussion (under review).", "keywords": ["Environmental Sciences not elsewhere classified", "Static chamber", "Information Systems not elsewhere classified", "Plant Biology", "Pinus sylvestris", "Trace Gas", "Dynamic chamberb", "Methane", "Plant shoot"], "contacts": [{"organization": "Kohl Lukas (10328817), Koskinen, Markku (6502211), Polvinen, Tatu (10328820), Tenhovirta, Salla (10328823), Rissanen, Kaisa A. (10328826), Patama, Marjo (10328829), Zanetti, Alessandro (10328832), Pihlatie, Mari (6502226),", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4609836"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4609836", "name": "item", "description": "10.5281/zenodo.4609836", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4609836"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-17T00:00:00Z"}}, {"id": "10.5281/zenodo.4655380", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Journal Article", "created": "2021-03-29", "title": "pH-Responsive Release of Ruthenium Metallotherapeutics from Mesoporous Silica-Based Nanocarriers.", "description": "

Ruthenium complexes are attracting interest in cancer treatment due to their potent cytotoxic activity. However, as their high toxicity may also affect healthy tissues, efficient and selective drug delivery systems to tumour tissues are needed. Our study focuses on the construction of such drug delivery systems for the delivery of cytotoxic Ru(II) complexes upon exposure to a weakly acidic environment of tumours. As nanocarriers, mesoporous silica nanoparticles (MSN) are utilized, whose surface is functionalized with two types of ligands, (2-thienylmethyl)hydrazine hydrochloride (H1) and (5,6-dimethylthieno[2,3-d]pyrimidin-4-yl)hydrazine (H2), which were attached to MSN through a pH-responsive hydrazone linkage. Further coordination to ruthenium(II) center yielded two types of nanomaterials MSN-H1[Ru] and MSN-H2[Ru]. Spectrophotometric measurements of the drug release kinetics at different pH (5.0, 6.0 and 7.4) confirm the enhanced release of Ru(II) complexes at lower pH values, which is further supported by inductively coupled plasma optical emission spectrometry (ICP-OES) measurements. Furthermore, the cytotoxicity effect of the released metallotherapeutics is evaluated in vitro on metastatic B16F1 melanoma cells and enhanced cancer cell-killing efficacy is demonstrated upon exposure of the nanomaterials to weakly acidic conditions. The obtained results showcase the promising capabilities of the designed MSN nanocarriers for the pH-responsive delivery of metallotherapeutics and targeted treatment of cancer.

", "keywords": ["Ruthenium-based anti-cancer drugs", "ruthenium-based anticancer drugs", "PH-responsive drug delivery", "Mesoporous silica nanoparticles", "pH-responsive drug delivery", "02 engineering and technology", "controlled drug delivery", "01 natural sciences", "Article", "cancer treatment", "ddc:", "3. Good health", "0104 chemical sciences", "RS1-441", "Pharmacy and materia medica", "Cancer treatment", "mesoporous silica nanoparticles", "0210 nano-technology", "Controlled drug delivery"]}, "links": [{"href": "http://www.mdpi.com/1999-4923/13/4/460/pdf"}, {"href": "https://www.mdpi.com/1999-4923/13/4/460/pdf"}, {"href": "https://doi.org/10.5281/zenodo.4655380"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Pharmaceutics", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4655380", "name": "item", "description": "10.5281/zenodo.4655380", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4655380"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-03-28T00:00:00Z"}}, {"id": "10.5281/zenodo.4748444", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:51Z", "type": "Dataset", "title": "The extent of woody plant invasion in selected sites of the communally managed Molopo District, North West Province.", "description": "EmbargoWoody plant invasion (bush encroachment) is a problem in the semi arid communal areas of the North West Province which had been affecting the Molopo Area as early as 1960. It affects the livelihoods of the communal farmer because it reduces carrying capacity and is a form of veld degradation. The extent of woody plant encroachment was quantified at selected sites and reference sites in the Molopo District. There was a study site and reference site selected in a commercially managed area. Soil samples from these selected sites were also analysed for chemical and physical properties as well as nutrient content that could have an influence on the proliferation of the woody plants. Social surveys were also conducted to investigate the perceptions and influence of the affected communities towards woody plant invasion. The prominent species identified in the area included Acacia mellifera, Dichrostachys cenerea, Prosopis velutina and Terminalia sericea. All of the study sites, except the benchmark sites, had woody plant densities of more than 2 000 TE/ha that according to Moore & Odendaal (1987), almost totally suppress grass growth. It was clear from the data that the nutrient status of soils of encroached areas was higher than the benchmark sites although some of the differences were statistically insignificant. Organic carbon was higher at most of the encroached sites (71 % of the sites) where 80 % of the enriched sites had significantly higher organic carbon than that of the benchmark sites. There is a need to develop small scale farming practices that are appropriate in terms of sustainable development in the local context.", "keywords": ["2. Zero hunger", "woody plants", "Molopo district", "Prosopis velutina", "Dichrostachys cenerea", "Masters", "15. Life on land", "Acacia mellifera", "North West Province", "Terminalia sericea", "Molopo Area"], "contacts": [{"organization": "Mogodi, Phemelo", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4748444"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4748444", "name": "item", "description": "10.5281/zenodo.4748444", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4748444"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2010-01-01T00:00:00Z"}}, {"id": "10.5445/ir/1000160199", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:24:17Z", "type": "Journal Article", "created": "2023-06-14", "title": "A new process-based and scale-aware desert dust emission scheme for global climate models \u2013 Part I: Description and evaluation against inverse modeling emissions", "description": "

Abstract. Desert dust accounts for most of the atmosphere's aerosol burden by mass and produces numerous important impacts on the Earth system. However, current global climate models (GCMs) and land-surface models (LSMs) struggle to accurately represent key dust emission processes, in part because of inadequate representations of soil particle sizes that affect the dust emission threshold, surface roughness elements that absorb wind momentum, and boundary-layer characteristics that control wind fluctuations. Furthermore, because dust emission is driven by small-scale (\u223c\u20091\u2009km or smaller) processes, simulating the global cycle of desert dust in GCMs with coarse horizontal resolutions (\u223c\u2009100\u2009km) presents a fundamental challenge. This representation problem is exacerbated by dust emission fluxes scaling nonlinearly with wind speed above a threshold wind speed that is sensitive to land-surface characteristics. Here, we address these fundamental problems underlying the simulation of dust emissions in GCMs and LSMs by developing improved descriptions of (1)\u00a0the effect of soil texture on the dust emission threshold, (2)\u00a0the effects of nonerodible roughness elements (both rocks and green vegetation) on the surface wind stress, and (3)\u00a0the effects of boundary-layer turbulence on driving intermittent dust emissions. We then use the resulting revised dust emission parameterization to simulate global dust emissions in a standalone model forced by reanalysis meteorology and land-surface fields. We further propose (4)\u00a0a simple methodology to rescale lower-resolution dust emission simulations to match the spatial variability of higher-resolution emission simulations in GCMs. The resulting dust emission simulation shows substantially improved agreement against regional dust emissions observationally constrained by inverse modeling. We thus find that our revised dust emission parameterization can substantially improve dust emission simulations in GCMs and\u00a0LSMs.

", "keywords": ["Atmospheric sciences", "info:eu-repo/classification/ddc/550", "550", "Climate change science", "ddc:550", "Physics", "QC1-999", "15. Life on land", "Atmospheric Sciences", "Climate Action", "[SDU] Sciences of the Universe [physics]", "Earth sciences", "Chemistry", "13. Climate action", "[SDU]Sciences of the Universe [physics]", "Earth Sciences", "Meteorology & Atmospheric Sciences", "QD1-999", "Astronomical and Space Sciences"]}, "links": [{"href": "https://acp.copernicus.org/articles/23/6487/2023/acp-23-6487-2023.pdf"}, {"href": "https://escholarship.org/content/qt2fk4w0h1/qt2fk4w0h1.pdf"}, {"href": "https://doi.org/10.5445/ir/1000160199"}, {"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": "10.5445/ir/1000160199", "name": "item", "description": "10.5445/ir/1000160199", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5445/ir/1000160199"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-06-14T00:00:00Z"}}, {"id": "10.5281/zenodo.4954979", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Dataset: Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil", "description": "Open Access
The files stored in this repository contain data and additional information for the study 'Long-term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil' by Tino Peplau, Julia Schroeder, Edward Gregorich and Christopher Poeplau. climate-data-takhini.txt: Contains a dataset with climate data used for Figure 1a and b. The data was downloaded from https://climatedata.ca/download/ as single variables and later on put into this single file. degree_days.xlsx: Contains soil temperature data with according calculation of cumulative degree days. temperature.xlsx: Contains raw data of soil temperature teabags_HS.xlsx: Contains information about all 24 buried teabags. The table contains 6 columns: 1)'sample' gives the individual name of the sample. 2) 'rep' is the replication at each plot 3) 'plot' is the plot, according to the soil warming intensity 4) 'depth' is the depth at which the teabag was buried 5) 'weight_start' is the weight of tea before at start of the experiment 6) 'weight_end' ist the weight of the tea after one year of burial HS_data_final.xlsx: Contains all data of the soil samples. It is divided into two sheets: 'sample_data': Provides information about every single soil sample, including chemical data, bulk density, organic and inorganic carbon, nitrogen and fractions. 'plot_data': Provides a summary of the data for every soil core (repetition) and plot, including mass corrected SOC and N stocks of the whole profile, topsoil and subsoil.
", "keywords": ["2. Zero hunger", "Soil organic matter", "Canada", "Whole-profile", "13. Climate action", "Soil warming", "Teabags", "Fractionation", "15. Life on land", "Takhini hot springs", "6. Clean water", "Thermosequence"], "contacts": [{"organization": "Peplau, Tino, Schroeder, Julia, Gregorich, Edward, Poeplau, Christopher,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4954979"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4954979", "name": "item", "description": "10.5281/zenodo.4954979", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4954979"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-06-15T00:00:00Z"}}, {"id": "10.5281/zenodo.4896835", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Report", "title": "Virtual loads predictions of wake-affected wind turbines: Gaussian process regression and deep neural networks", "description": "Load analysis of wind turbines may be performed either via physics-based models or via direct measurement. On the first case, loads are calculated with aero-elastic models, based on significant assumptions on the mechanical and aeroelastic properties of the structure and the acting forces (wind, wave and control). Otherwise, loads can be directly measured based on a sensor network, which entails increased costs due to installation, maintenance and calibration of sensors and IT infrastructure. These costs can be manageable for a single wind turbine but become substantial on densely instrumented wind farms. In turn, the increased costs negatively affect the levelized cost of energy. This is, in fact, the main reason why stakeholders shy away from applying monitoring technologies in wind farms. To overcome the above challenges, we need an alternative way of estimating the loads which would involve a reduced number of sensors while replicating the actual load measurement scenario. To this end, we propose data-driven models to predict the loads acting on different components of a wind turbine. These models use SCADA, wind inflow and other variables to predict loads in components of interest of a wind turbine. We have already successfully demonstrated this concept in the past on simulated wind turbine Damage Equivalent Loads (DELs) based on Gaussian Process regression [1,2], and on real wind turbine data [3]. In this work, we validate this approach on actual wind turbine data from the Alpha Ventus Wind Farm obtained within the framework of the Research Alpha Ventus (RAVE) project. Two Senvion turbines are selected for this study. One of the wind turbines is used to train and validate a regression model to predict the tower base DELs based on SCADA, wind inflow and other environmental variables. Afterwards, the trained model is used to predict the loads in the second wind turbine. Load prediction is attained with two machine learning methods, the first one based on Gaussian Process Regression (GPR) and the second one based on Artificial Neural Networks (ANN). For the first one, a decision tree is used to separate the different operating modes of the wind turbine (idling, operating and transitioning). The decision tree is built on simple heuristics on a subset of SCADA variables (mean and standard deviation of the rotor RPM and blade pitch angle). Subsequently, a GPR is built for each one of the operating modes. In the second method, the SCADA variables are fed to the ANN after undergoing an initial transformation for data compression and collinearity reduction.", "keywords": ["machine learning", "structural health monitoring", "13. Climate action", "wind turbines", "virtual sensing", "7. Clean energy"], "contacts": [{"organization": "Avenda\u00f1o-Valencia, Luis David, Abdallah, Imad, Venu, Anish, Chatzi, Eleni,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.4896835"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.4896835", "name": "item", "description": "10.5281/zenodo.4896835", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.4896835"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5171830", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:52Z", "type": "Dataset", "title": "Cryoturbation leads to iron-organic carbon associations along a permafrost soil chronosequence in northern Alaska", "description": "In permafrost soils, substantial amounts of organic carbon (OC) are potentially protected from microbial degradation and transformation into greenhouse gases by association with reactive iron (Fe) minerals. As permafrost environments respond to climate change, increased drainage of thaw lakes in permafrost regions is predicted. Soils will subsequently develop on these drained thaw lakes, but the role of Fe-OC associations in future OC stabilization during this predicted soil development is unknown. To fill this knowledge gap, we have examined Fe-OC associations in organic, cryoturbated and mineral horizons along a 5500-year chronosequence of drained thaw lake basins in Utqia\u0121vik, Alaska. By applying chemical extractions, we found that ~17 % of the total OC content in cryoturbated horizons is associated with reactive Fe minerals, compared to ~10 % in organic or mineral horizons. As soil development advances, the total stocks of Fe-associated OC more than double within the first 50 years after thaw lake drainage, because of increased storage of Fe-associated OC in cryoturbated horizons (from 8 to 75 % of the total Fe-associated OC stock). Spatially-resolved nanoscale secondary ion mass spectrometry showed that OC is primarily associated with Fe(III) (oxyhydr)oxides which were identified by 57Fe M\u00f6ssbauer spectroscopy as ferrihydrite. High OC:Fe mass ratios (>0.22) indicate that Fe-OC associations are formed via co-precipitation, chelation and aggregation. These results demonstrate that, given the proposed enhanced drainage of thaw lakes under climate change, OC is increasingly incorporated and stabilized by the association with reactive Fe minerals as a result of soil formation and increased cryoturbation.", "keywords": ["carbon", " iron", " thermokarst", " cryoturbation", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Joss, Hanna, Patzner, Monique S., Maisch, Markus, Mueller, Carsten W., Kappler, Andreas, Bryce, Casey,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5171830"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5171830", "name": "item", "description": "10.5281/zenodo.5171830", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5171830"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-09T00:00:00Z"}}, {"id": "10.5281/zenodo.5235030", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Software", "title": "A Colab-Python script code to identify palaeo-landscape features", "description": "Open Access{'references': ['1. Python Software Foundation. Python Language Reference. 2020. Available: http://www.python.org', '2. Wu Q. geemap: A Python package for interactive mapping with Google Earth Engine. Journal of Open Source Software. 2020;5: 2305', '3. Bisong E. Google Colaboratory. In: Bisong E, editor. Building Machine Learning and Deep Learning Models on Google Cloud Platform: u00a0 u00a0 u00a0A Comprehensive Guide for Beginners. Berkeley, CA: Apress; 2019. pp. 59 u201364', '4. Project Jupyter. Jupyter Notebook. 2020. Available: https://jupyter.org/', '5. QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. 2019. u00a0 u00a0 u00a0Available: https://www.qgis.org/en/site/index.html', '6. Gillies S et al. Rasterio: geospatial raster I/O for Python programmers. Mapbox; 2013. Available: https://github.com/mapbox/rasterio', '7. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9: 90 u201395.']}", "keywords": ["Remote Sensing", "Multispectral analysis", "Landscape Archaeology", "Spectral decomposition", "15. Life on land", "Sentinel-2", "Riverscape", "Fluvial and Alluvial Archaeology", "12. Responsible consumption", "Python"]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5235030"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5235030", "name": "item", "description": "10.5281/zenodo.5235030", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5235030"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-22T00:00:00Z"}}, {"id": "10754/685569", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:20Z", "type": "Journal Article", "created": "2022-11-03", "title": "Environmental micro\u2010niche filtering shapes bacterial pioneer communities during primary colonization of a Himalayas' glacier forefield", "description": "Abstract

The pedogenesis from the mineral substrate released upon glacier melting has been explained with the succession of consortia of pioneer microorganisms, whose structure and functionality are determined by the environmental conditions developing in the moraine. However, the microbiome variability that can be expected in the environmentally heterogeneous niches occurring in a moraine at a given successional stage is poorly investigated. In a 50\uffe2\uff80\uff89m2 area in the forefield of the Lobuche glacier (Himalayas, 5050\uffe2\uff80\uff89m above sea level), we studied six sites of primary colonization presenting different topographical features (orientation, elevation and slope) and harbouring greyish/dark biological soil crusts (BSCs). The spatial vicinity of the sites opposed to their topographical differences, allowed us to examine the effect of environmental conditions independently from the time of deglaciation. The bacterial microbiome diversity and their co\uffe2\uff80\uff90occurrence network, the bacterial metabolisms predicted from 16S rRNA gene high\uffe2\uff80\uff90throughput sequencing, and the microbiome intact polar lipids were investigated in the BSCs and the underlying sediment deep layers (DLs). Different bacterial microbiomes inhabited the BSCs and the DLs, and their composition varied among sites, indicating a niche\uffe2\uff80\uff90specific role of the micro\uffe2\uff80\uff90environmental conditions in the bacterial communities' assembly. In the heterogeneous sediments of glacier moraines, physico\uffe2\uff80\uff90chemical and micro\uffe2\uff80\uff90climatic variations at the site\uffe2\uff80\uff90spatial scale are crucial in shaping the microbiome microvariability and structuring the pioneer bacterial communities during pedogenesis.

This paper systematically reviews the potential of the Sentinel-2 (A and B) in assessing drought. Research findings, including the IPCC reports, highlighted the increasing trend in drought over the decades and the need for a better understanding and assessment of this phenomenon. Continuous monitoring of the Earth\u2019s surface is an efficient method for predicting and identifying the early warnings of drought, which enables us to prepare and plan the mitigation procedures. Considering the spatial, temporal, and spectral characteristics, the freely available Sentinel-2 data products are a promising option in this area of research, compared to Landsat and MODIS. This paper evaluates the recent developments in this field induced by the launch of Sentinel-2, as well as the comparison with other existing data products. The objective of this paper is to evaluate the potential of Sentinel-2 in assessing drought through vegetation characteristics, soil moisture, evapotranspiration, surface water including wetland, and land use and land cover analysis. Furthermore, this review also addresses and compares various data fusion methods and downscaling methods applied to Sentinel-2 for retrieving the major bio-geophysical variables used in the analysis of drought. Additionally, the limitations of Sentinel-2 in its direct applicability to drought studies are also evaluated.

", "keywords": ["land use and land cover analysis", "vegetation response", "Sentinel-2; drought; soil moisture; evapotranspiration; vegetation response; surface water and wetland analysis; land use and land cover analysis", "Science", "Q", "evapotranspiration", "0207 environmental engineering", "drought", "02 engineering and technology", "15. Life on land", "01 natural sciences", "6. Clean water", "surface water and wetland analysis", "13. Climate action", "Sentinel-2; drought", "Sentinel-2", "soil moisture", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://www.mdpi.com/2072-4292/13/17/3355/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5509889"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5509889", "name": "item", "description": "10.5281/zenodo.5509889", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5509889"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-24T00:00:00Z"}}, {"id": "10.5281/zenodo.5615357", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:53Z", "type": "Dataset", "title": "Supplementary Table for Earth observation data-driven cropland soil monitoring: A review", "description": "Table including 46 manuscripts written in English referring to topsoil monitoring related to Earth observation data-driven cropland soil monitoring: A review paper.", "keywords": ["soil organic carbon", "hyperspectral", "spectral signatures", "carbon farming", "deep learning", "earth observation", "food security", "15. Life on land", "common agricultural policy"], "contacts": [{"organization": "Tziolas, Nikolaos, Tsakiridis, Nikolaos, Chabrillat, Sabine, Dematt\u00ea, Jos\u00e9 A.M., Ben-Dor, Eyal, Gholizadeh, Asa, Zalidis, George, Van Wesemael, Bas,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5615357"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5615357", "name": "item", "description": "10.5281/zenodo.5615357", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5615357"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-10-22T00:00:00Z"}}, {"id": "10.5281/zenodo.5653246", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Data from: Diversity and functionality of soil fauna", "description": "The goal of this study was to assess the recovery of soil micro-arthropods in different periods, in heathlands.
Study area and sampling strategy
All studied plots were located in the Veluwe area, a large nature area central in the Netherlands, consisting of extended forests and heathlands. The area has a humid Atlantic climate with an average temperature of 3.1 \ufffd\ufffdC in January and 17.9 \ufffd\ufffdC in July and an annual precipitation of 950 mm more or less spread evenly over the year (data from a 30-year period). For our research we needed detailed management information, which was obtained from two nature management organizations: National Park De Hoge Veluwe (52\ufffd\ufffd05\ufffd\ufffd\ufffdN, 5\ufffd\ufffd50\ufffd\ufffd\ufffdE) and the municipality of Nunspeet (52\ufffd\ufffd23\ufffd\ufffd\ufffdN, 5\ufffd\ufffd47\ufffd\ufffd\ufffdE). Both organizations keep detailed records on heathland management making it possible to select plots that have been sod-cut only once in a given year, without additional management ever since.
At the Hoge Veluwe area we selected 5 plots (uncut and cut 40, 7and 2 years before sampling) and at the Nunspeet area we selected 10 plots (uncut and cut 30, 29, 28, 27, 26, 25, 18, 16, 12 years before sampling. Soil type on all plots is a spodic dystrudept (Soil Survey Staff, 1999), developed on the gentle slope of push moraine ridges with some cover sand on top. The parent material is coarse sandy with 13-18% of loam in the deeper soil layers. Topsoil has been extensively leached, creating a more spodic tophorizon, resembling the one of an orthod. Humus form is originally moderlike, but contains also larger proportions of amorphous organic compounds leaching into the B-horizon as seen in orthods. Organic matter in the topsoil (0-10 cm.) is 5.5 \ufffd\ufffd 2.5%, pH[NaCl] is 3.6 \ufffd\ufffd 0.7 on average over all plots. Soil micro-arthropod and soil chemical sampling
All plots were sampled on 8 March 2019, taking four cores per plot. Cores were 5 cm \ufffd\ufffd and 5 cm deep mineral soil plus upper litter. Cores were taken in the middle of the plots, 1 m apart of each other. Cores were extracted on a Tullgren funnel for 7 days. During that period temperature was increased from 35 to 45 \ufffd\ufffdC. Ethanol 70% was used as preservative and micro-arthropods obtained were put into lactic acid 40% for clarification and identification (Siepel and van de Bund 1988). All micro-arthropods from the Tullgren funnel were identified at the species level using appropriate keys. Nomenclature and identification for the main groups is according to Weigmann (2006) for Oribatida, Karg (1993) for Gamasina and Karg (1989) for Uropodina.
The soil core samples were taken to measure environmental variables after extraction of the soil micro-arthropods. These air-dried soil samples were sieved to 1 mm. Soil chemistry variables used in our analysis included soil organic matter, soil total nitrogen, moisture, pH [NaCl], available phosphorus (P-Olsen). The pH of the solution was measured immediately using a combined pH electrode after mixing fresh soil with NaCl solution. The soil phosphorus (P-Olsen) was determined using extraction with sodium bicarbonate (Olsen et al., 1954).
We have two data files:
sod cutting environment factor.csv
sod cutting microarthropods.csv Explanation of the variables in the datasets:
Area: Nunspeet, Hoge Veluwe
year: sod cutting happened in which year
Feeding: B, FB, FG, HG, OHF, H, HB, HG,O, P B: browsers
FB:Fungivorous browsers
FG:Fungivorous grazers
HG:Herbofungivorous grazers
OHF: Opportunistic Herbofungivorous
H: herbofungivores
HB: Herbivorous browsers
HG: Herbivorous grazers
O: Omnivores
P: Predators pH: The pH of the solution was measured immediately using a combined pH electrode after mixing fresh soil with NaCl solution.
moisture %: percentage moisture of the soil sample
org matter %: percentage organic matter of the soil sample
N (mg/g): total nitrogen concentration of the soil sample
C (mg/g): total carbon concentration of the soil sample
mol P per kgram dry soil: The soil phosphorus (P-Olsen) was determined using extraction with sodium bicarbonate (Olsen et al., 1954).
C/N ratio: ratio of total carbon to total nitrogen in the soil sample", "keywords": ["sod cutting; soil micro-arthropods; recovery; life history strategy; feeding guilds; GLMM", "recovery", "life history strategy", "15. Life on land", "sod cutting", "soil micro-arthropods", "sod cuttingsoil micro-arthropodsrecoverylife history strategyfeeding guildsglmm", "GLMM", "feeding guilds"], "contacts": [{"organization": "Guo, Y., Guo, Y., Jongejans, E., Siepel, H.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5653246"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5653246", "name": "item", "description": "10.5281/zenodo.5653246", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5653246"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.5652048", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Dataset", "title": "Transport of Particulate Organic Carbon in The Huanghe: Insights from Lateral And Vertical Heterogeneity in a River Cross-section", "description": "The Huanghe (Yellow River), one of the largest turbid river systems in the world, has long been recognized as a major contributor of suspended particulate matter (SPM) to the ocean. However, over the last few decades, the SPM export flux of the Huanghe has decreased over 90% due to the high management, impacting the global export of particulate organic carbon (POC). To better constrain sources and modes of transport of POC beyond the previously investigated transportation of POC near the channel surface, SPM samples were for the first time collected over a whole channel cross-section in the lower Huanghe. Riverine SPM samples were analyzed for particle size and major element contents, as well as for POC content and dual carbon isotopes (13C and 14C). The results show clear vertical and lateral heterogeneity of SPM physical and chemical characteristics within the river cross section, with for example finer SPM carrying more POC with higher 14C activity near the surface and the right bank. Notably, we discuss how bank erosion in the alluvial plain is likely to generate lateral heterogeneity in POC composition. The Huanghe POC is millennial-aged (4,020 \u00b1 500 radiocarbon years), dominated by organic carbon (OC) from the biosphere, while the lithospheric fraction reaches up to ca. 33%. The mobilization of aged and refractory OC from deeper soil horizons of the loess-paleosol sequence through erosion in the Chinese Loess Plateau is an important mechanism contributing to fluvial POC in the Huanghe drainage basin. The involvement of this OC fraction has significance for the regional and global carbon cycles, especially regarding its final fate in the estuary. Altogether, this study sheds light on the mechanism of fluvial transfer of POC and corresponding impacts on the carbon cycle in large river systems strongly perturbed by anthropogenic activities.", "keywords": ["particulate organic carbon", "13. Climate action", "Huanghe", "bank erosion", "radiocarbon", "depth profile sampling", "14. Life underwater", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Yutian, Ke, Calmels Damien, Bouchez Julien, Massault Marc, Chetelat Benjamin, Noret Aur\u00e9lie, Hongming, Cai, Jiubin, Chen, Gaillardet J\u00e9r\u00f4me, Quantin C\u00e9cile,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.5652048"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.5652048", "name": "item", "description": "10.5281/zenodo.5652048", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5652048"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-06T00:00:00Z"}}, {"id": "10.5281/zenodo.5770286", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Journal Article", "created": "2021-08-18", "title": "UAV-Based Land Cover Classification for Hoverfly (Diptera: Syrphidae) Habitat Condition Assessment: A Case Study on Mt. Stara Planina (Serbia)", "description": "

Habitat degradation, mostly caused by human impact, is one of the key drivers of biodiversity loss. This is a global problem, causing a decline in the number of pollinators, such as hoverflies. In the process of digitalizing ecological studies in Serbia, remote-sensing-based land cover classification has become a key component for both current and future research. Object-based land cover classification, using machine learning algorithms of very high resolution (VHR) imagery acquired by an unmanned aerial vehicle (UAV) was carried out in three different study sites on Mt. Stara Planina, Eastern Serbia. UAV land cover classified maps with seven land cover classes (trees, shrubs, meadows, road, water, agricultural land, and forest patches) were studied. Moreover, three different classification algorithms\u2014support vector machine (SVM), random forest (RF), and k-NN (k-nearest neighbors)\u2014were compared. This study shows that the random forest classifier performs better with respect to the other classifiers in all three study sites, with overall accuracy values ranging from 0.87 to 0.96. The overall results are robust to changes in labeling ground truth subsets. The obtained UAV land cover classified maps were compared with the Map of the Natural Vegetation of Europe (EPNV) and used to quantify habitat degradation and assess hoverfly species richness. It was concluded that the percentage of habitat degradation is primarily caused by anthropogenic pressure, thus affecting the richness of hoverfly species in the study sites. In order to enable research reproducibility, the datasets used in this study are made available in a public repository.

", "keywords": ["Map of the Natural Vegetation of Europe", "Orfeo ToolBox", "unmanned aerial vehicle; object-based image analysis; Orfeo ToolBox; QGIS; random forest; hoverfly; Map of the Natural Vegetation of Europe", "Science", "Q", "0211 other engineering and technologies", "Unmanned aerial vehicle", "02 engineering and technology", "15. Life on land", "01 natural sciences", "Object-based image analysis", "Map of the Natural Vegetation of Europe", "13. Climate action", "unmanned aerial vehicle", "object-based image analysis", "Hoverfly", "QGIS", "random forest", "Random forest", "hoverfly", "0105 earth and related environmental sciences"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/13/16/3272/pdf"}, {"href": "https://doi.org/10.5281/zenodo.5770286"}, {"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.5770286", "name": "item", "description": "10.5281/zenodo.5770286", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.5770286"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2021-08-18T00:00:00Z"}}, {"id": "10.5281/zenodo.580814", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:54Z", "type": "Journal Article", "title": "Mapping the abstractions of forest landscape patterns", "description": "The evaluation of landscape patterns is necessary to explain the relationships between ecological processes and spatial patterns. For decades, landscape metrics have been used for measuring and abstracting landscape patterns. Since the emergence of FRAGTATS in 1993 the measures and methods incorporated in this software are very widely used and they have become a de facto standard tool for calculating landscape metrics. There are no special metrics for forest landscapes. The selection of metrics rather depends on the purpose of the study than on the land use type. However, there are some metrics that are more used for forest habitats. Forest landscape patterns are changing fast due to natural and human disturbances. Remote sensing offers rapid method of acquiring up-to-date information over a large geographical area and is therefore widely used as a source of data needed for pattern assessment. However, in order to obtain meaningful results from landscape metrics calculation, the correct preparation of the data is essential. In this chapter we will give an overview of the various metrics used to measure forest landscapes for different purposes. The chapter will deal with five main issues from the perspective of forest landscape patterns: (1) data preparation for metrics calculation (vector vs raster, scale, classification etc); (2) landscape configuration and composition measured by metrics; (3) interpretation of the results; (4) possible usages of the outcomes; (5) future perspectives (3D landscape metrics).", "keywords": ["0106 biological sciences", "pattern analysis", " configuration", " composition", " landscape metrics", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"], "contacts": [{"organization": "Uuemaa, Evelyn; Oja, T\u00f5nu", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.580814"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Mapping%20Forest%20Landscape%20Patterns", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.580814", "name": "item", "description": "10.5281/zenodo.580814", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.580814"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2017-05-18T00:00:00Z"}}, {"id": "11583/2959536", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:30Z", "type": "Journal Article", "created": "2021-09-13", "title": "EC-Earth3-AerChem: a global climate model with interactive aerosols and atmospheric chemistry participating in CMIP6", "description": "

Abstract. This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). EC-Earth3-AerChem has interactive aerosols and atmospheric chemistry and contributes to the Aerosols and Chemistry Model Intercomparison Project (AerChemMIP). In this paper, we give an overview of the model, describe in detail how it differs from the other EC-Earth3 configurations, and outline the new features compared with the previously documented version of the model (EC-Earth 2.4). We explain how the model was tuned and spun up under preindustrial conditions and characterize the model's general performance on the basis of a selection of coupled simulations conducted for CMIP6. The net energy imbalance at the top of the atmosphere in the preindustrial control simulation is on average \u22120.09\u2009W\u2009m\u22122 with a standard deviation due to interannual variability of 0.25\u2009W\u2009m\u22122, showing no significant drift. The global surface air temperature in the simulation is on average 14.08\u2009\u2218C with an interannual standard deviation of 0.17\u2009\u2218C, exhibiting a small drift of 0.015\u2009\u00b1\u20090.005\u2009\u2218C per century. The model's effective equilibrium climate sensitivity is estimated at 3.9\u2009\u2218C, and its transient climate response is estimated at 2.1\u2009\u2218C. The CMIP6 historical simulation displays spurious interdecadal variability in Northern Hemisphere temperatures, resulting in a large spread across ensemble members and a tendency to underestimate observed annual surface temperature anomalies from the early 20th century onwards. The observed warming of the Southern Hemisphere is well reproduced by the model. Compared with the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis version 5 (ERA5), the surface air temperature climatology for 1995\u20132014 has an average bias of \u22120.86\u2009\u00b1\u20090.05\u2009\u2218C with a standard deviation across ensemble members of 0.35\u2009\u2218C in the Northern Hemisphere and 1.29\u2009\u00b1\u20090.02\u2009\u2218C with a corresponding standard deviation of 0.05\u2009\u2218C in the Southern Hemisphere. The Southern Hemisphere warm bias is largely caused by errors in shortwave cloud radiative effects over the Southern Ocean, a deficiency of many climate models. Changes in the emissions of near-term climate forcers (NTCFs) have significant effects on the global climate from the second half of the 20th century onwards. For the SSP3-7.0 Shared Socioeconomic Pathway, the model gives a global warming at the end of the 21st century (2091\u20132100) of 4.9\u2009\u2218C above the preindustrial mean. A 0.5\u2009\u2218C stronger warming is obtained for the AerChemMIP scenario with reduced emissions of NTCFs. With concurrent reductions of future methane concentrations, the warming is projected to be reduced by 0.5\u2009\u2218C.

", "keywords": ["Atmospheric chemistry", ":Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental [\u00c0rees tem\u00e0tiques de la UPC]", "EARTH SYSTEM MODELS", "MINERAL-COMPOSITION", "MODIFIED BAND APPROACH", "7. Clean energy", ":Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica [\u00c0rees tem\u00e0tiques de la UPC]", "SULFURIC-ACID", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria qu\u00edmica::Qu\u00edmica del medi ambient::Qu\u00edmica atmosf\u00e8rica", "EC-EARTH", "ORGANIC AEROSOL", "\u00c0rees tem\u00e0tiques de la UPC::Desenvolupament hum\u00e0 i sostenible::Degradaci\u00f3 ambiental", "Aerosols", "QE1-996.5", "Escalfament global", "Global warming", "Geology", "Climatic changes", "16. Peace & justice", "Climate Science", "COMPUTATIONAL PERFORMANCE", "DUST AEROSOLS", "Qu\u00edmica atmosf\u00e8rica", "13. Climate action", "GREENHOUSE-GAS CONCENTRATIONS", "BIOMASS BURNING EMISSIONS", "Geosciences", "Klimatvetenskap", "Canvis clim\u00e0tics"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2959536/1/vannoije2021_gmd.pdf"}, {"href": "https://gmd.copernicus.org/articles/14/5637/2021/gmd-14-5637-2021.pdf"}, {"href": "https://doi.org/11583/2959536"}, {"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": "11583/2959536", "name": "item", "description": "11583/2959536", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11583/2959536"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-12-21T00:00:00Z"}}, {"id": "10773/25427", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:20Z", "type": "Journal Article", "created": "2018-01-07", "title": "Toxicokinetics of Zn and Cd in the earthworm Eisenia andrei exposed to metal-contaminated soils under different combinations of air temperature and soil moisture content", "description": "This study evaluated how different combinations of air temperature (20\u202f\u00b0C and 25\u202f\u00b0C) and soil moisture content (50% and 30% of the soil water holding capacity, WHC), reflecting realistic climate change scenarios, affect the bioaccumulation kinetics of Zn and Cd in the earthworm Eisenia andrei. Earthworms were exposed for 21\u202fd to two metal-contaminated soils (uptake phase), followed by 21\u202fd incubation in non-contaminated soil (elimination phase). Body Zn and Cd concentrations were checked in time and metal uptake (k1) and elimination (k2) rate constants determined; metal bioaccumulation factor (BAF) was calculated as k1/k2. Earthworms showed extremely fast uptake and elimination of Zn, regardless of the exposure level. Climate conditions had no major impacts on the bioaccumulation kinetics of Zn, although a tendency towards lower k1 and k2 values was observed at 25\u00a0\u00b0C\u00a0+\u00a030% WHC. Earthworm Cd concentrations gradually increased with time upon exposure to metal-contaminated soils, especially at 50% WHC, and remained constant or slowly decreased following transfer to non-contaminated soil. Different combinations of air temperature and soil moisture content changed the bioaccumulation kinetics of Cd, leading to higher k1 and k2 values for earthworms incubated at 25\u00a0\u00b0C\u00a0+\u00a050% WHC and slower Cd kinetics at 25\u00a0\u00b0C\u00a0+\u00a030% WHC. This resulted in greater BAFs for Cd at warmer and drier environments which could imply higher toxicity risks but also of transfer of Cd within the food chain under the current global warming perspective.", "keywords": ["Soil invertebrates", "Bioavailability", "Climate Change", "0211 other engineering and technologies", "02 engineering and technology", "Global Warming", "01 natural sciences", "Soil", "Metals", " Heavy", "SDG 13 - Climate Action", "Climate change", "Animals", "Soil Pollutants", "Oligochaeta", "0105 earth and related environmental sciences", "2. Zero hunger", "Triazines", "Temperature", "Water", "Bioaccumulation", "Mining wastes", "Toxicokinetics", "Zinc", "Heavy metals", "Metals", "13. Climate action", "Environmental Pollution", "Cadmium"]}, "links": [{"href": "https://doi.org/10773/25427"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Chemosphere", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10773/25427", "name": "item", "description": "10773/25427", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10773/25427"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2018-04-01T00:00:00Z"}}, {"id": "10.5281/zenodo.6411321", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:56Z", "type": "Dataset", "title": "Litter decomposition is moderated by scale-dependent microenvironmental variation in tundra ecosystems", "description": "QHI_crop.tiff = We carried out topographic surveys using unoccupied aerial vehicles photogrammetry in August 2017. We used three UAV platforms to collect RGB multispectral data at a fine (3 cm) spatial resolution: DJI Phantom 4 Pro and Advanced (multicopter), and Phantom FX-61 (fixed wing), and used used structure from motion with multiview steriopsis to obtain a fine-grain 10 cm spatial resolution digital surface model and orthomosaic as described in Cunliffe et al. (2019a, 2019b). thermsum.tif = We used the microclima package in R (Kearney et al., 2020; Maclean et al., 2019) to model surface air temperature at a 1-m spatial grain. Using our fine resolution DSM, we modelled mean surface temperatures at the study site for each day spanning the teabag burial period of 13th July to 9th August 2017. The microclima model incorporates local daily climate, radiation, cloud cover and coastal exposure data from gridded global datasets derived from RCNEP (Kemp et al., 2012). We summed the 28 TIF files produced through this modelling technique to produce a 28-day thermal sum variable - a metric which captures the overall heating of the ground surface over the course of the experiment. Cited Works: Cunliffe, A., I. Myers-Smith. J. Kerby and W. Palmer (2019a). Orthomosaic of permafrost landscape on Qikiqtaruk \u2013 Herschel Island, Yukon, Canada: August 2017. NERC Polar Data Centre. DOI:10.5285/29bf1c9f-a39a-452c-b9f9-de35d9fb9179. Cunliffe, A., G. Tanski, B. Radosavljevic, W. Palmer, T. Sachs, H. Lantuit, J. Kerby, and I. Myers-Smith (2019b) Rapid retreat of permafrost coastline observed with aerial drone photogrammetry. The Cryosphere 13(5):1513-1528. DOI: 10.5194/tc-13-1513-2019. Maclean, I. M. (2020). Predicting future climate at high spatial and temporal resolution. Global Change Biology, 26(2), 1003\u20131011. Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P., & Maclean, I. M. (2020). A method for computing hourly, historical, terrain\u2010corrected microclimate anywhere on Earth. Methods in Ecology and Evolution, 11(1), 38-43. Kemp, M. U., Van Loon, E. E., Shamoun-Baranes, J., & Bouten, W. (2012). RNCEP: global weather and climate data at your fingertips. Methods in Ecology & Evolution, 3(1), 65-70. Paper Abstract: The Arctic tundra is one of the world\u2019s largest organic carbon stores, yet this carbon is vulnerable to accelerated decomposition as climate warming progresses. We currently know very little about landscape-scale controls of litter decomposition in tundra ecosystems, which hinders our understanding of the global carbon cycle. Here, we examined how local-scale topography, surface air temperature, soil moisture and permafrost conditions influenced litter decomposition rates across a heterogeneous tundra landscape on Qikiqtaruk - Herschel Island (Yukon, Canada). We used the Tea Bag Index protocol to derive decomposition metrics which we then compared across environmental gradients, including thermal sum surface temperature data derived from fine-resolution microclimate data modelled from drone derived topographic data. We found greater green tea litter mass loss and faster decomposition rates in wetter and warmer areas within the landscape, and to a lesser extent in areas with deeper permafrost active layer thickness. Spatially heterogeneous belowground conditions (soil moisture and active layer depth) explained variation in decomposition metrics at the landscape-scale (> 10 m) better than surface temperature. Surprisingly, there was no strong control of elevation or slope of litter decomposition. We also found higher decomposition rates on North-facing relative to South-facing aspects at microsites that were wetter rather than warmer.", "keywords": ["dsm", "decomposition", "13. Climate action", "microclima", "15. Life on land", "thermal sum", "microclimate"], "contacts": [{"organization": "Gallois, Elise, Myers-Smith, Isla, Daskalova, Gergana, Kerby, Jeffrey, Thomas, Haydn, Cunliffe, Andrew,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6411321"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6411321", "name": "item", "description": "10.5281/zenodo.6411321", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6411321"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-14T00:00:00Z"}}, {"id": "10.5281/zenodo.6413955", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:56Z", "type": "Dataset", "title": "Realistic soil carbon sequestration considering food security and climate change", "description": "This dataset contains soil organic carbon stocks as described in Keel et al. Global Change Biology (submitted) Annual soil organic carbon (SOC) stocks (t C ha-1, 0-30 cm depth) of Swiss agricultural soils simulated with the model RothC for the years 2020-2100. Simulations were performed for 240 strata (regions with similar agricultural production types, climatic conditions and clay content). The SOC stocks are weighted averages across strata for the national scale.
Each column contains SOC stocks for a specific combination of a climate model chains (nine in total) and an emission scenario (three in total: RCP 26, RCP 45, RCP 85) (specified in column header). The results include simulated SOC stocks for a baseline scenario and five soil carbon sequestration (SCS) scenarios (cover crops, biochar amendment at two rates, biochar amendment based on biomass from two agroforestry scenarios).
The SCS scenarios were only performed on cropland, therefore there is only a single file for grassland (the baseline scenario).
All simulations (i.e. baseline as well as the five scenarios) account for changes in crop shares and organic matter additions associated with growing food demand as well as climate change. The scenarios are described in Keel et al. Global Change Biology (submitted) CL_baseline: Baseline scenario for cropland (CL)
GL_baseline: Baseline scenario for permanent grassland (GL)
CL_cover_crops: Cover crop scenario for cropland
CL_biochar_I: Biochar I scenario for cropland
CL_biochar_II: Biochar II scenario for cropland
CL_agroforestry_I: Agroforestry I scenario for cropland
CL_agroforestry_II: Agroforestry II scenario for cropland", "keywords": ["2. Zero hunger", "13. Climate action", "soil organic carbon", " negative emission technology", " carbon dioxide removal", " 4p1000", " climate change", " population growth", " food security", " soil carbon modelling", " biomass availability", " RothC", " biochar", " cover crops", " agroforestry", "15. Life on land", "7. Clean energy"], "contacts": [{"organization": "Keel, Sonja G.", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6413955"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6413955", "name": "item", "description": "10.5281/zenodo.6413955", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6413955"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-05T00:00:00Z"}}, {"id": "10.5281/zenodo.6500189", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:56Z", "type": "Dataset", "title": "Methane fluxes from four elevation zones in a St. Lawrence Estuary salt marsh", "description": "Dataset used in Spartina alterniflora has the highest methane emissions in a St. Lawrence estuary salt marsh - IOPscience. The dataset contains methane fluxes calculated from gas measurements taken over a 40 or 60 minute period using a dark static chamber method. Methane fluxes were measured at six locations in four elevation zones of a northern salt marsh on the St. Lawrence River estuary at La Pocati\u00e8re, Quebec (47\u00b022'24.7'N 70\u00b003'26.3'W). Additional environmental data was collected including carbon dioxide fluxes, extractable soil nitrate, extractable soil ammonium, extractable soil dissolved organic carbon, extractable soil total dissolved nitrogen, salinity, temperature, water table depth, soil total organic carbon, soil total nitrogen, soil organic carbon to nitrogen ratio and bulk density. Soil cores were collected from 0-15 cm and used for extractable nutrient analysis, bulk density and soil organic carbon and nitrogen analysis. The work was carried out with funding from the European Union\u2019s Horizon 2020 Research and Innovation Programme under the Marie Sklodowska-Curie Grant Agreement 838296, a NSERC Discovery Grant and a Natural Environment Research Council grant number (NE/T012323/1). This dataset is used in a publication entitled Spartina alterniflora has the highest methane emissions in a St. Lawrence Estuary salt marsh in Environmental Research: Ecology (https://doi.org/10.1088/2752- 664X/ac706a), which also contains more details on fieldsite and methodology. Gas samples were collected from dark, static chambers (18L, 26 cm diameter), which were placed onto pre-inserted collars in the vegetated zones (inserted to 2.5 cm, 3 days prior to sampling) or placed directly onto the mudflat. The chambers were insulated and fitted with fans and venting tubes. Gas samples were collected on the 23rd August 2020 from all sites, soil cores were collected between the 24-25th August 2020 and the 19-20th September 2020. Soil samples were collected at 0-15 cm using a 2.5 cm diameter dutch gouge corer. Soil temperature was measured at 10 cm depth using a soil thermometer, (\u00b0C, DeltaTrak 11050, Pleasanton, USA), salinity was measured in the laboratory using a portable ATC refractometer. Water table depth was measured using a PVC piezometer, a plastic pipe with tubing was inserted into the piezometer and blown into to determine water table depth through bubbling sound (cm). Soil cores were dried at 60 \u00b0C to constant weight and the dry weight over core volume used to calculate bulk density (g cm-3), soil was finely ground and analysed for total organic carbon and total nitrogen (%) using an Elemental Analyser (ThermoFinnigan Flash EA 1112 CN analyser, Carlo Erba, Milan, Italy) with an accuracy of \u00b15 % for N and \u00b11 % for C, and a limit of 171 detection of 0.05 % for both N and C. Extractable nitrate+nitrite (assumed to be nitrate) were analysed in soil extractant (2M KCl, 5:1 of extractant to soil) using a microplate reader and methods in Sims et al., 1995 (https://doi.org/10.1080/00103629509369298) with a limit of detection of 0.1 ppm and accuracy of \u00b15%. Extractable dissolved organic carbon and total dissolved nitrogen were analysed in soil extractant (ultrapure water 18.2 M\u03a9, 5:1 of extractant to soil) on a TOC/TDN analyser (TOC VCSn + TMN-1, Shimadzu, Kyoto, Japan), with a 50 mg C l -1 standard resulting in an accuracy and precision of 3.0 and \u00b14.4 mg l-1, respectively. CH4 and CO2 concentrations were measured in the gas samples using a gas chromatograph (GC-14, Shimadzu, Kyoto, Japan) fitted with a flame ionisation detector, CO2 was methanised to CH4 before analysis. Standards of CH4 (5.1 ppm) and CO2 (5000 ppm) resulted in an accuracy and precision of 6.6\u00b11.5 and 0.4 ppm, and 5324\u00b1324 and 78 ppm, respectively, for CH4 and CO2. Changes in gas concentration over time were converted to fluxes using a linear regression of the linear portion fo the flux and if fluxes were below the minimum detectable concentration difference (see https://doi.org/10.1002/2017JG003783), they were set to zero. Results from the experiments were entered into an Excel spreadsheet for ingestion into the Zenodo data repository.", "keywords": ["13. Climate action", "15. Life on land", "6. Clean water", "methane", " CH4", " salt marsh", " saltmarsh", " greenhouse gas fluxes", " carbon sequestration", " elevation zones"], "contacts": [{"organization": "Comer-Warner, Sophie, Ullah, Sami, Ampuero Reyes, Wendy, Krause, Stefan, Chmura, Gail,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6500189"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6500189", "name": "item", "description": "10.5281/zenodo.6500189", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6500189"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-04-28T00:00:00Z"}}, {"id": "10.5281/zenodo.6719945", "type": "Feature", "geometry": null, "properties": {"license": "unspecified", "updated": "2026-04-04T16:23:58Z", "type": "Report", "title": "Validation of the method for the assessment of food impact on breath VOCs using portable membrane inlet mass spectrometer", "description": "Introduction Nutritional balance is among the major concerns of modern people, as it is known that diet directly impacts the overall state of the human body. To choose the optimal diet for everyone, a personalized approach is needed. The purpose of this study was to validate a sensor for food impact assessment by analyzing the levels of volatile organic compounds (VOCs) in human breath. It aims to establish a prototype for future auxiliary technique for nutritional status clarification. Breath research has been very popular in the last 10 years, mainly due to its non-invasive diagnostic potential. As the proposed sensor does not require laboratory conditions, its application could be widespread due to fast analysis, portability, and affordability. \u00a0 Methods This pilot study recruited various groups of participants from several European countries, with informed consents and ethical approvals. The groups included overweight participants, those with obesity, and those who have poor dietary habits. Food impact assessment was conducted by monitoring levels of several VOCs related to macronutrients metabolism in exhaled breath samples using the new portable sensor based on membrane inlet mass spectrometry (MIMS). Each participant provided a sample before standardized meal (after 12h food restraint) and 120 min after the meal. Also, information about participants\u2019 lifestyle was collected via a short questionnaire. Samples were collected in 1L single-use Tedlar\u00a0\u00ae\u00a0bags and analyzed using the MIMS system with PDMS sheet membrane probe. \u00a0 Preliminary Data Functionality of the new portable breath VOC sensor was verified by examining several analytical parameters in our preliminary research study on a group of healthy participants (not published yet). That pre-pilot study showed that the examined MIMS system could be used successfully for breath VOCs analysis, and for monitoring the changes in VOCs levels upon food consumption. A large-scale pilot study employing the new method on more population groups was the natural continuation of the pre-pilot study. Concentration levels for selected breath VOCs (acetone, ethanol, isoprene and n-pentane) were determined in samples before the meal (BM) and after the meal (AM). Additionally, qualitative and quantitative aspects of food impact assessment were determined and evaluated. The qualitative assessment provided information about whether a specific breath VOC increased or decreased as a consequence of food ingestion. Furthermore, quantification of the food impact was established via comparison factor. This factor represents the ratio of VOC levels after and before the meal, i.e., AM/BM for the same participant. Comparison factor served to express the intensity of the change in VOCs signal. More than 10% of signal increment or decrement was considered as food impact. Additionally, the experimentally obtained results were examined against the data collected via the questionnaire in order to discover possible correlations between VOCs changes and specific population group or their lifestyle habits. The results obtained in this pilot study support the fact that proposed VOC sensor can be utilized in breath research and contribute to the enrichment of the breath research area. \u00a0 Novel Aspect Portable MIMS sensor for detection of VOCs showed potential to become fast screening methodology for nutritional status diagnostics.", "keywords": ["2. Zero hunger", "16. Peace & justice", "3. Good health"], "contacts": [{"organization": "Jak\u0161i\u0107, Milena, Mihajlovi\u0107, Andrea, Vuji\u0107, Djordje, Brki\u0107, Boris,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6719945"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6719945", "name": "item", "description": "10.5281/zenodo.6719945", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6719945"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-06-06T00:00:00Z"}}, {"id": "11583/2984964", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:25:30Z", "type": "Journal Article", "created": "2023-12-26", "title": "Identification of Plastics in Mixtures and Blends through Pyrolysis-Gas Chromatography/Mass Spectrometry", "description": "

In this paper, the possibility of detecting polymers in plastic mixtures and extruded blends has been investigated. Pyrolysis\u2013gas chromatography/mass spectrometry (py-GC/MS) allows researchers to identify multicomponent mixtures and low amounts of polymers without high spatial resolution, background noise and constituents mix interfering, as with molecular spectrometry techniques normally used for this purpose, such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy and differential scanning calorimetry (DSC). In total, 15 solid mixtures of low-density polyethylene (LDPE), polypropylene (PP), polystyrene (PS), polyamide (PA) and polycarbonate (PC) in various combinations have been qualitatively analyzed after choosing their characteristic pyrolysis products and each polymer has been detected in every mix; thus, in extruded blends of high-density polyethylene (HDPE), PP and PS had varying weight percentages of the individual constituents ranging from 10 up to 90. Moreover, quantitative analysis of these polymers has been achieved in every blend with a trend that can be considered linear with coefficients of determination higher than 0.9, even though the limits of quantification are lower with respect to the ones reported in the literature, probably due to the extrusion process.

", "keywords": ["blends; calibration curves; polymers; py-GC/MS", "01 natural sciences", "Article", "0104 chemical sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://iris.polito.it/bitstream/11583/2984964/1/Identification_of_Plastics_in_Mixtures_and_Blends_.pdf"}, {"href": "https://doi.org/11583/2984964"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Polymers", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "11583/2984964", "name": "item", "description": "11583/2984964", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/11583/2984964"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2023-12-26T00:00:00Z"}}, {"id": "10.5281/zenodo.6566752", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:57Z", "type": "Dataset", "title": "Soil carbon stock, litter decomposition, and weather data from Ethiopian forests", "description": "Open AccessIntroduction 100 sampling units (SU) were selected from the total of 631 SUs of the Forest Reference Level submission 2017 (FRL 2017). The sampling was designed unbiased for total growing stock per SU, altitude,and mean litter depth per SU. The actual field sampling succeeded on 98 of the pre-selected SUs due to accessibility restrictions. Soil profile sampling Soil sampling was performed from November 2017 till mid-January 2018. Samples were taken from undisturbed soil from depths of 0-10 cm, 10-20 cm, and 20-30 cm below the organic layer. Volumetric samples of 107.5 cm3 were taken vertically, using a 10 cm long conically shaped corer with a cutting lower edge diameter of 37 mm and upper diameter of 40 mm. Composite samples were formed by combining the volumetric samples taken from different depths of two parallel soil profiles. The samples were transported to EEFRI Soil Laboratory in Addis Ababa after 1-4 weeks of sampling at distant locations. Soil physical characteristics The soil samples were air-dried, homogenized, and subjected to oven-drying at 105\u00b0C until constant mass. Total bulk density was determined using the total dry mass and volume of the composite samples. Organic carbon content (C % by wet oxidation method), and soil physical characteristics: moisture content, bulk density of the total sample, and bulk density of fine fraction (particles passing the 2 mm sieve). The mass of the coarse fraction was weighed. The soil fine fraction was also subjected to laser diffraction for more accurate particle size analysis for proportions of clay, silt, and sand. In addition to this 28 samples were also analyzed for C content in the laboratory of Natural Resources Institute Finland to determine C content by LECO CHN analyzer. This was done to calibrate the bulk of wet digestion-based estimates (Fig. 1). Before analysis, the soils were tested for the presence of inorganic C. For Figure 1. See Soil_C_Ethiopia.pdf Figure 1. Comparison of results from wet oxidation (Walkley-Black) and dry oxidation (CHN analyzer). The dotted line shows the theoretical 1:1 match between the axis, the solid line shows linear regression (intercept = 0) between the methods. The estimated slope value of 1.165 was used in adjusting the wet digestion results to match those obtained by dry oxidation: OCadj = 1.165 * OCwet. Based on a linear regression between the wet and dry oxidation analysis results, a correction factor of 1.165 was applied to adjust the organic C% obtained by wet digestion. The adjusted data are shown in the file \u201cSOC_Ethiopia_2017-2018.csv\u201d. SOC stocks were calculated by multiplying the proportion of organic C with BD of fine earth, after which the result was corrected for stoniness, a visually estimated proportion of large stones (S, value from 0 to 1) in the soil profile that could not be included in the volumetric soil samples (FAO VS-FAST). (SOCstock = C_{org} * BD_{fe} * (1-S) ) Soil organic carbon stock data Files: \u201cSOC_Ethiopia_2017-2018.csv\u201d and \u201cSOC_Ethiopia_2017-2018.xlsx\u201d The file includes soil characteristics from layers of 0-10 cm, 10-20 cm, and 20-30 cm below the loose organic layer on top of the soil. The data are used for SOC stock estimation in the respective layers as described above. In the .csv file individual columns are for LAT is the latitude of the sampling site corresponding to FieldCode and SU_nr LON is the longitude of the sampling site corresponding to FieldCode and SU_nr The coordinates are expressed as decimal degrees of the WGS84 system FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) SU_nr is the Sampling Unit number of the Ethiopian NFI Region is the name of the administrative region where the sample was taken Biome is the name of the forest biome type where the sample was collected BiomeSimplified is the name of a biome with some close types combined DepthRange is the upper and lower limit of the soil sample in the field, cm StoninessVFAST is a percentage of stones (VS-FAST by FAO) in the ca. 40 cm deep soil profile exposed during the sampling FreshMassInField is the mass of the total composite soil sample of the given layer, g, primarily indicative of checking the correct number of subsamples in composite NrComposites is the number of subsamples included in the composite for each soil layer CorerVolume is a constant of 107.5 cm3 because only one type of corer was used for undisturbed, volumetric sampling CompositeVolume is the volume of the composite sample for each soil depth layer CoarseFractionMass is the dry mass, g of soil particles > 2mm that did not pass the sieve, but were included in the sample volume FE_DryMass is oven-dry mass, g of the fine fraction that passed the 2 mm sieve. BDtot is total bulk density, g m-3, calculated for the composite sample BDfe is the bulk density of the fine earth fraction, g m-3 OC_adj is organic carbon (OC) content (%) in the composite sample, adjusted according to the comparison between dry and wet oxidation methods (Fig. 1) SOCfe is SOC stock calculated for soil fine earth fraction, t ha-1 in the 10 cm deep soil layer SOCfe_stoniness is SOC stock of the fine earth fraction, t ha-1 in the 10 cm deep soil layer, adjusted for stoniness. The correction assumes that the volume occupied by larger stones would be void of OC. Litter stock data File: \u201cLitter_Ethiopia_2017-2018.csv\u201d The file includes measurements of litter layer on Ethiopian NFI Sampling Unit (SU) sites where sampling for SOC stock determination was done. The depth of the litter layer was measured in the SU\u2019s of the NFI, and this data contains in addition to depth also a volumetric sample of the litter layer. The dry bulk density was used to calculate the carbon stocks in the litter pool. The depth of the litter layer was measured in the field. Litter from the respective spot was sampled quantitatively from a frame of 0.01m2 of area for litter dry mass estimate. The organic C stock in a litter (L) was calculated as, (L = {M over z} * {C_{om} over A}, ) where M = Dry mass of the litter sample, g z = Depth of the litter layer in the field, m Com = Conversion factor from dry organic matter to carbon (C), 0.5 A = area of quantitative collection of litter (0.01 m2) In the .csv file individual columns are for LAT, LON is the GPS coordinates (decimal degrees of WGS84) for the Sampling Units (SU_ID) SU_ID is the Sampling Unit identification number of the Ethiopian NFI FieldCode refers to the Region and Sampling Unit number of the Ethiopian NFI (see below) Region is the name of the administrative region where the sample was taken Litter_dry is the dry mass, g of the litter sample Area_m2 is the area, m2 of litter sampling MeanLitterDepth is the mean depth of the litter layer at the sampling area CDensityLitter is the dry bulk density of the litter, g m-2 multiplied by the assumed organic C proportion of the oven-dry litter materials (0.50) LitterCStock_tha is the litter stock, t ha-1 calculated from the C density of the litter layer Litter bag data (decomposition and quality) The leaves and twigs were sampled from 2 species (Juniperus and Podocarpus) and 3 locations of the elevation gradient in the Chilimo forest (Table 1). The forest was considered an old-growth with Juniperus procera and Podocarpus falcatusbeing the main species forming the tree canopy. The sites form an elevation gradient (Table 1). Table 1. Geographical locations of the study sites in the Chilimo forest. id Latitude (deg.) Longitude (deg.) Elevation (m a.s.l) 1 9.0672 38.1443 2500 2 9.0712 38.1556 2670 3 9.0869 38.1684 2800 The dying and dead leaves were sampled directly from the trees later referred to as \u201cfresh\u201d and from the branches found on the ground, referred to as \u201cold\u201d. The old leaves were assumed to be dead for around 3 months. The diameter of the branches/twigs was less than 1 cm in diameter. The samples were first sorted and air-dried in an elevated temperature of the greenhouse and thereafter oven-dried in the oven overnight at 45 \u00b0C. The samples were analyzed for acid, water, ethanol dissolved,and undissolved fractions (AWEN) (Table 2) and for the decomposition rates of the litter installed into the litter bags corresponding to each of the Chilimo sites. Table 2. Acid, water, ethanol (A, W, E, respectively) dissolved and undissolved fractions (N) from the litter components of the dominant tree species in the Chilimo forest. Litter type Species A W E N leaves fresh Juniperus 0.45 0.13 0.1 0.33 leaves fresh Podocarpus 0.42 0.28 0.05 0.25 leaves old Juniperus 0.44 0.07 0.08 0.41 leaves old Podocarpus 0.44 0.09 0.05 0.42 twigs Juniperus 0.61 0.04 0.02 0.32 twigs Podocarpus 0.56 0.15 0.02 0.27 A sufficient amount of litter was placed into the litter bags (polyurethane mesh 1 mm) and the mesh bags were installed on top of the soil surface under the forest canopy (later referred to as \u201ccanopy\u201d) and in the forest gap caused by harvesting (later referred as \u201copen\u201d). The installation of the litter bags (for each species 3 replicates of each litter type for each site and canopy type for the 3 periods, in total 12 litter bags for leaves and 6 bags for twigs) was done on 22.9.2017. The mesh bags were left on the ground, protected from grazing by the fence, and retrieved subsequently on 12.10.2017, 31.10.2017, and 12.12.2017. Despite the efforts took few samples were lost. The retrieved samples were oven-dried and initial mass and mass loss data for each period and litter type with a detailed description of the variables can be found in the file \u201clitter.chilimo_07.02.22.xlsx\u201d. Soil temperature data During the period from 22.9.2017 to 12.12.2017, we monitored the soil temperature at 5 cm depth under the canopy and in the open canopy on all Chilimo sites continuously every 4 hours intervals with the Maxim iButton temperature loggers. However, some sensors were lost. Daily means and their standard deviation of the continuous temperatures can be found in the file \u201csoil.temp.chilimo_07.02.22.xlsx\u201d. Processed weather data The air temperature and precipitation data for 98 sampling units corresponding to soil carbon data originated from 73 weather stations located across Ethiopia and were obtained from Ethiopian Meteorological Agency (http://www.ethiomet.gov.et/). Sampling units were joined with weather data by the closest proximity to their corresponding weather stations. Precipitation was unaltered. The air temperature required correction by elevation is described in more detail in Lehtonen et al. (2020). The monthly values of air temperature and precipitation with an accompanied readme description of the variables can be found for 98 sampling units in the file \u201csampling.units98_meteo_07.02.22.xlsx\u201d and the Chilimo study sites in the file \u201cmonthly.weather.chilimo_07.02.22.xlsx\u201d. The monthly values in the file 'sampling.units98_meteo_07.02.22.xlsx' correspond to long-term average over the period from 1986 to 2017. References: Lehtonen, A., \u0164upek, B., Nieminen, T.M., Bal\u00e1zs, A., Anjulo, A., Teshome, M., Tiruneh, Y. and Alm, J., 2020. Soil carbon stocks in Ethiopian forests and estimations of their future development under different forest use scenarios. Land Degradation & Development, 31(18), pp.2763-2774. FRL 2017. https://redd.unfccc.int/files/ethiopia_frel_3.2_final_modified_submission.pdf", "keywords": ["2. Zero hunger", "REDD", " soil carbon stock", " litter bag studies", " Ethiopia", "15. Life on land", "6. Clean water"], "contacts": [{"organization": "Alm, Jukka, \u0164upek, Boris, Anjulo, Agena, Teshome, Mindaye, Tiruneh, Yibeltal, Abay, Abebe, Alebachew, Mehari, Tervahauta, Arja, Lehtonen, Aleksi,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6566752"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6566752", "name": "item", "description": "10.5281/zenodo.6566752", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6566752"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-05-20T00:00:00Z"}}, {"id": "10.5281/zenodo.6611475", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:57Z", "type": "Dataset", "title": "Large dataset of soil organic carbon and topographic derivatives", "description": "EmbargoAbstract: The dataset compiles 840 georeferenced SOC measurements over a 26-ha agricultural field located in southern Ontario, Canada with a sampling density of ~32 points per ha. As SOC is influenced by site topography (i.e., slope and landscape position), each point of the database was associated with a wide range of topographic derivatives. The columns include sample ID, SOC measurement, latitude, Longitude, NDVI values, as well as a set of 54 topographic derivatives (i.e., primary and secondary - see metadat.pdf attached file) with a spatial resolution of a 5 m.", "keywords": ["2. Zero hunger", "Keywords: Soil Organic Carbon dataset", " LiDAR", " topographic derivatives", " southern Ontario", " Canada", " digital soil mapping", "13. Climate action", "15. Life on land"], "contacts": [{"organization": "Laamrani Ahmed, Voroney Paul, Saurette Daniel, D., Berg Aaron, Blackburn Line, Gillespie Adam, Martin Ralph, C.,", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6611475"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6611475", "name": "item", "description": "10.5281/zenodo.6611475", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6611475"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2022-01-01T00:00:00Z"}}, {"id": "10.5281/zenodo.6622619", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:23:57Z", "type": "Report", "title": "STUDIES IN CHALKONES. PART I. CHALKONES DERIVED FROM RESACETOPHENONE AND ITS DIMETHYL ETHER.", "description": "Resacetophenone dimethyl ether has been condensed with o-vanillin, isovanillin, \u00adand 6-methoxylsalicylaldehyde, and resacetophenone with o-vanillin to yield correspond\u00ading hydroxymethoxychalkones. Attempt has been made to determine the optimum condition for these condensations.", "keywords": ["solution", "sand-bath", "3. Good health"], "contacts": [{"organization": "JAGRAJ BEHARI LAL", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/10.5281/zenodo.6622619"}, {"rel": "self", "type": "application/geo+json", "title": "10.5281/zenodo.6622619", "name": "item", "description": "10.5281/zenodo.6622619", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5281/zenodo.6622619"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "1939-12-31T00:00:00Z"}}, {"id": "21.11116/0000-0005-8A29-2", "type": "Feature", "geometry": null, "properties": {"updated": "2026-04-04T16:26:03Z", "type": "Journal Article", "created": "2019-04-09", "title": "Comparison With Global Soil Radiocarbon Observations Indicates Needed Carbon Cycle Improvements in the E3SM Land Model", "description": "Abstract

We evaluated global soil organic carbon (SOC) stocks and turnover time predictions from a global land model (ELMv1\uffe2\uff80\uff90ECA) integrated in an Earth System Model (E3SM) by comparing them with observed soil bulk and \uffce\uff9414C values around the world. We analyzed observed and simulated SOC stocks and \uffce\uff9414C values using machine learning methods at the Earth System Model grid cell scale (~200\uffc2\uffa0km). In grid cells with sufficient observations, the model provided reasonable estimates of soil carbon stocks across soil depth and \uffce\uff9414C values near the surface but underestimated \uffce\uff9414C at depth. Among many explanatory variables, soil albedo index, soil order, plant function type, air temperature, and SOC content were major factors affecting predicted SOC \uffce\uff9414C values. The influences of soil albedo index, soil order, and air temperature were primarily important in the shallow subsurface (\uffe2\uff89\uffa430\uffc2\uffa0cm). We also performed sensitivity studies using different vertical root distributions and decomposition turnover times and compared to observed SOC stock and \uffce\uff9414C profiles. The analyses support the role of vegetation in affecting soil carbon turnover, particularly in deep soil, possibly through supplying fresh carbon and degrading physical\uffe2\uff80\uff90chemical protection of SOC via root activities. Allowing for grid cell\uffe2\uff80\uff90specific rooting and decomposition rates substantially reduced discrepancies between observed and predicted \uffce\uff9414C values and SOC content. Our results highlight the need for more explicit representation of roots, microbes, and soil physical protection in land models.