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48 results for “soil erosion”
Reduced erosion augments soil carbon storage under cover crops
This dataset comprises field measurements of soil organic carbon erosion and soil organic carbon stock from 152 paired control and cover crop treatments, collected from 57 published studies worldwide. It also provides related information on the collected study sites, including climate (mean annual temperature and mean annual precipitation), geography (slope and altitude), soil properties (silt+clay and SOC concentration), and agricultural management (cover crop species, tillage intensity and experimental duration). Furthermore, it includes the estimated effect sizes of soil organic carbon erosion reduction induced by cover crops in agricultural lands at the global scale.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation A - increased Phase II soil organic matter
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with increased Phase II soil organic matter compared to the base simulation. Data is presented for day 250 of each year.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation C - increased Phase I and Phase II soil organic matter
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with increased Phase I and Phase II soil organic matter compared to the base simulation. Data is presented for day 250 of each year.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation D - reduced Phase I and Phase II soil organic matter
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with reduced Phase I and Phase II soil organic matter compared to the base simulation. Data is presented for day 250 of each year. .
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation B - increased Phase I soil organic matter
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with increased Phase I soil organic matter compared to the base simulation. Data is presented for day 250 of each year.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra regrowth after a thermal erosion event: Simulation E - reduced Phase I soil organic matter
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 100 years of tussock tundra recovery after a thermal erosion event. This simulation is of TEF recovery with decreasing Phase I soil organic matter compared to the base simulation. Data is presented for day 250 of each year.
SERENA EJPSOIL SK SOIL Erosion ErosionControl
<div> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <div> <p><span><span>The present data was prepared according to the </span><span>methodology</span><span> of SERENA soil erosion control cookbook</span><span> for the territory of </span><span>Slovakia</span><span>. </span><span>The map of soil loss by water erosion (soil threat</span><span>) </span><span>was based on the </span><span>RUSLE model.</span> <span>or the soil erosion control, the difference between the erosion map without vegetation (C-factor = 1) and the erosion map with vegetation was calculated.</span></span><span> </span></p> </div> </div> <div> <p>The objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. </p> </div> <div> <p>To create the soil loss map we used theese data: </p> </div> <div> <p> R factor - we used data from 100 automatic rain stations on minute rainfall for about 10-year period (national dataset) </p> </div> <div> <p>K factor – we used the source proposed in the cookbook from ESDAC dataset: Soil Erodibility (K- Factor) High Resolution dataset for Europe </p> </div> <div> <p>LS factor – we used the source proposed in the cookbook from ESDAC dataset: LS-factor (Slope Length and Steepness factor) for Slovakia </p> </div> <div> <p>C factor – we used LPIS database-this has information about crops on agricultural soil. We have values of C factor for all crops. </p> </div> <div> <p>P factor – we used the source proposed in the cookbook from ESDAC dataset: P factor for Slovakia. This map has values about 0.99 for Slovakia, so P-factor does not have much effect on the resulting erosion. </p> </div> <div> <p>The delivered map was prepared in GeoTIFF format in the resolution of 500 * 500 m. </p> </div>
SERENA EJPSOIL PL EROSION CONTROL SOIL MASS NOT ERODED
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. </p>
SERENA EJPSOIL PL EROSION SOIL LOSS
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. All attached GeoTIFFs were described below:</p> <p>SERENA_EJPSOIL_PL_EROSION__K_factor_2018.tif</p> <p>The result of modelling K factor - soil-erodibility factor </p> <p>SERENA_EJPSOIL_PL_EROSION_C_factor_2018.tif</p> <p>The result of modelling C factor - land cover and management factor</p> <p>SERENA_EJPSOIL_PL_EROSION_LS_factor_2018.tif</p> <p>The result of modelling LS factor - slope length and steepness factor (Source: https://esdac.jrc.ec.europa.eu/themes/slope-length-and-steepness-factor-ls-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_P_factor_2018.tif</p> <p>The result of modelling P factor - support practices factor (Source: https://esdac.jrc.ec.europa.eu/themes/support-practices-factor)</p> <p>SERENA_EJPSOIL_PL_EROSION_R_factor_2018.tif</p> <p>The result of modelling R factor -erosivity factor (rainfall event's ability to cause soil water erosion)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_2018.tif</p> <p>Total soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p> <p>SERENA_EJPSOIL_PL_EROSION_SOIL_LOSS_MAX_EROSION_2018.tif</p> <p>Total maximum soil erosion loss by water modelled for agricultural soils in Poland for 2018 (Excluding C factor) (Map unit: <span>Mg ha<sup>−1</sup> yr<sup>−1</sup></span>)</p>
Flume Erosion Testing Data of Root-Permeated and Organic Matter Amended Soil Samples Using Three Streambank Boundary Conditions.
The data published here is expected to accompany one publicly available dissertation (Chapter 6 of dissertation) and one separate journal publication. Once published and available online, the metadata will be updated with the relevant article information. The journal article/dissertation will have additional information regarding the published datasets and the methods used to collect the data. All data collected from these studies, and the accompanying Acoustic Doppler Profiler MATLAB files, are presented here. Journal Article title: Artificial Roots and Soil Microorganisms Increase Soil Resistance to Fluvial Erosion
Erosion Rates, Soil Core Descriptions and Organic Matter on the Virginia Coast
These data include stratigraphic, organic matter, and organic carbon analyses of sediment cores, as well as values used to calculate the time-averaged carbon erosion rate for the central 10 islands of the Virginia Barrier Island chain.
SERENA EJPSoil Soil loss by water erosion of Tuscany (Italy)
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>One of the objective of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the methodology of the SERENA Soil erosion and soil erosion control cookbook. Soil loss was used as an indicator for soil erosion (ST). The map of soil loss by water erosion (soil threat) was based on the RUSLE model. For Italy, the cookbook was applied in the Tuscany region. <br> <br>To create the soil loss map we used:</p> <ul> <li>for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990–2022, Lamma Consortium) and a local linear equation between R and mean annual precipitation (P);</li> <li>for C -factor, Regional Land use map 1:10.000 (2018, freely available at: https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html) and ESDAC method (https://doi.org/10.1016/j.landusepol.2015.05.021) ; </li> <li>for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO’s methodology in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;</li> <li>for LS-factor, DEM 10 m of Tuscany, (freely available at https://www502.regione.toscana.it/geoscopio/cartoteca.html99) and Desmet & Govers (1996) SAGA tool (applied at 10 m and upscaled);</li> <li>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor of 0.5 was considered, based on expert evaluation)</li> </ul> <p>Maps was delivered in the GeoTIFF format in the resolution of 100m. <br>Delivered data will be validated by stakeholders from Italy (scientist) in October, 2024.</p>
SERENA EJPSoil Soil erosion control in Tuscany (Italy)
<div> <p><span><span>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</span></span></p> <p><span><span>One of the </span><span>objective</span><span> of SERENA project was to develop methods to calculate and map soil-based ecosystem services and soil threats. The present data was prepared according to the </span><span>methodology</span><span> of the SERENA Soil erosion and soil erosion control cookbook</span><span>. </span><span>Soil loss was used as an indicator for soil erosion (ST). T</span><span>he map of soil mass not eroded was based on the RUSLE model. </span><span>Soil erosion control was calculated as the difference between potential and actual soil erosion (SES, ecosystem service of soil erosion protection, i.e. soil eroded mass </span><span>retained</span><span> by vegetation, Mg/ha/y)</span><span>. For Italy, the cookbook was applied in the Tuscany region. </span></span><span> </span></p> </div> <div> <p><span><span>To create the soil loss map we used:</span></span><span> </span></p> </div> <div> <ul> <li><span><span>for R-factor, not freely available database of meteorological parameters spatialized at 250 m (minimum and maximum daily air temperature; cumulate daily precipitation) over Tuscany region (period 1990–2022, Lamma </span><span>Consortium) and</span><span> a local linear equation between R and mean annual precipitation (P);</span></span> </li> <li><span>for C -factor, Regional Land use map 1:10.000 (2018, freely available at: </span><span>https://www502.regione.toscana.it/geoscopio/usocoperturasuolo.html</span><span>) and ESDAC method (</span><span>https://doi.org/10.1016/j.landusepol.2015.05.021</span><span>) ;</span> <span> </span></li> <li><span>for K-factor, sand, silt, clay, and O.C. (%) maps (built from 4.000 soil profiles, following FAO’s </span><span>methodology</span><span> in GSP-GSOC map, Lamma Consortium), and Torri et al. (1997) function;</span><span> </span></li> <li><span>for LS-factor, DEM 10 m of Tuscany, (freely available at </span><span>https://www502.regione.toscana.it/geoscopio/cartoteca.html99</span><span>) and Desmet & </span><span>Govers</span><span> (1996) SAGA tool (applied at 10 m and upscaled);</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> <li><span>for P-factor, not freely available database 1:10.00 of terraced areas (Lamma Consortium, 2020) (for terraced areas a multiplication factor </span><span>of 0.5</span><span> was considered, based on expert evaluation)</span><span> </span></li> </ul> </div> <div> <p><span><span>Maps was delivered in the </span><span>GeoTIFF</span><span> format in the resolution of 100m. </span></span><span> </span></p> </div> <div> <p><span><span>Delivered data will be </span><span>validated</span><span> by stakeholders from Italy (scientist) in </span><span>October,</span><span> 2024.</span></span><span> </span></p> </div>
Flume Erosion Testing of Unamended and Organic Matter Amended Soil Samples Using an Acoustic Doppler Profiler, 2021
This data accompanies a publication titled "Soil Amended with Organic Matter Increases Fluvial Erosion Resistance of Cohesive Streambank Soil". Briefly, fluvial erosion testing was conducted on soil samples using an indoor flume channel. Soil samples were previously collected from the riparian zone of a river near Virginia Tech's campus in Blacksburg, VA, USA. The soil was subsequently air-dried and stored until use. Prior to erosion testing, soil samples were amended with varying amounts of organic matter (0%, 1%, and 4% OM by mass), compacted to a bulk density of 0.95 KilogramsPerCubicCentiMeters in growth containers, and allowed to mature in a greenhouse setting for 50 days prior to flume erosion testing. An Acoustic Doppler Profiler (ADP) was used to measure soil erosion and collect three-dimensional velocity data during erosion tests; raw velocity and soil depth data for each sample tested were stored in MATLAB files. Follow testing, the soil remaining from each sample was collected, stored, and analyzed for aggregate stability, soil organic matter (SOM), and extracellular polymeric substances (EPS). Additionally, soil temperature, water temperature, and volumetric water content were also measured prior to or during erosion testing. Data collected from this study, and the accompanying ADP MATLAB files, are presented here.
Modelling impacts of tramlines on soil erosion processes at the catchment scale.
<p>The data refers to the following article:</p> <p>Saggau, P., M. Kuhwald, W. B. Hamer, R. Duttmann (2021): Are compacted tramlines underestimated features in soil erosion modelling? A catchment‐scale analysis using a process‐based soil erosion model. In: Land Degradtaion & Development. doi: 10.1002/ldr.4161</p> <p>The data only contains distributable raw data and R-codes.</p>
Data from "Into the unknown: The role of post-fire soil erosion in the carbon cycle"
<p>Wildfires directly emit 2.1 Pg carbon (C) to the atmosphere annually. The net effect of wildfires on the C cycle, however, involves many interacting source and sink processes beyond these emissions from combustion. Among those, the role of post-fire enhanced soil organic carbon (SOC) erosion as a C sink mechanism remains essentially unquantified. Wildfires can greatly enhance soil erosion due to the loss of protective vegetation cover and changes to soil structure and wettability. Post-fire SOC erosion acts as a C sink when off-site burial and stabilization of C eroded after a fire, together with the on-site recovery of SOC content, exceed the C losses during its post-fire transport. Here we synthesize published data on post-fire SOC erosion and evaluate its overall potential to act as longer-term C sink. To explore its quantitative importance, we also model its magnitude at continental scale using the 2017 wildfire season in Europe. Our estimations show that the C sink ability of SOC water erosion during the first post-fire year could account for around 13% of the C emissions produced by wildland fires. This indicates that post-fire SOC erosion is a quantitatively important process in the overall C balance of fires, and highlights the need for more field data to further validate this initial assessment.</p> <p>Here we provide the post-fire SOC erosion dataset ("Post-fire SOC erosion rates" file) used for calculating the SOC ratio of eroded sediments implemented in the RUSLE modelling; as well as the list of data sources ("List of data sources" file).</p>
Experimental erosion of microbial diversity decreases soil CH4 consumption rates
<p>Biodiversity-ecosystem functioning (BEF) experiments have predominantly focused on communities of higher organisms, in particular plants, with comparably little known to date about the relevance of biodiversity for microbially-driven biogeochemical processes. Methanotrophic bacteria play a key role in Earth’s methane (CH<sub>4</sub>) cycle by removing atmospheric CH<sub>4</sub> and reducing emissions from methanogenesis in wetlands and landfills. Here, we used a dilution-to-extinction approach to simulate diversity loss in a methanotrophic landfill cover soil community. Replicate samples were diluted 10<sup>1</sup> to 10<sup>7</sup>-fold, and pre-incubated under a high CH<sub>4</sub> atmosphere for the microbial communities to recover to approximately equal size. Then, the samples were incubated for 86 days at constant or diurnally-cycling temperature. Our hypotheses were that (1) CH<sub>4</sub> consumption would decrease as methanotrophic diversity was lost, and that (2) this effect would be more pronounced under variable environmental conditions (here: variable temperature). We followed net CH<sub>4</sub> consumption by gas chromatography. Microbial community composition was determined four times by DNA extraction and sequencing of amplicons specific to methanotrophs and bacteria (pmoA and 16S gene fragments). We found that the richness of operational taxonomic units (OTU) of methanotrophic and non-methanotrophic bacteria decreased approximately linearly with <em>log</em>-dilution. CH<sub>4</sub> consumption decreased with the number of taxonomic units lost. This effect was independent of community size, which we determined by quantitative PCR, and consistent over the study period. The temperature treatment (constant vs. cycling temperature) did not affect any of these results. The diversity effects we found occurred in relatively diverse communities, challenging the notion of high functional redundancy mediating high resistance to diversity erosion in natural microbial systems. The effects we report resemble the ones for higher organisms, suggesting that BEF-relationships are universal across taxa and spatial scales.</p>
Data to support the publication "The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment" https://doi.org/10.3390/land10090964
<p>Underlying data of soil measurements and analysis by TUC team for the publication “The Impact of Soil-Improving Cropping Practices on Erosion Rates: A Stakeholder-Oriented Field Experiment Assessment” <a href="https://doi.org/10.3390/land10090964">https://doi.org/10.3390/land10090964</a> from the SoilCare project study sites in Crete. </p> <p>Abstract:</p> <p>The risk of erosion is particularly high in Mediterranean areas, especially in areas that are subject to a not so effective agricultural management–or with some omissions–, land abandonment or wildfires. Soils on Crete are under imminent threat of desertification, characterized by loss of vegetation, water erosion, and subsequently, loss of soil. Several large-scale studies have estimated average soil erosion on the island between 6 and 8 Mg/ha/year, but more localized investigations assess soil losses one order of magnitude higher. An experiment initiated in 2017, under the framework of the SoilCare H2020 EU project, aimed to evaluate the effect of different management practices on the soil erosion. The experiment was set up in control versus treatment experimental design including different sets of treatments, targeting the most important cultivations on Crete (olive orchards, vineyards, fruit orchards). The minimum-to-no tillage practice was adopted as an erosion mitigation practice for the olive orchard study site, while for the vineyard site, the cover crop practice was used. For the fruit orchard field, the crop-type change procedure (orange to avocado) was used. The experiment demonstrated that soil-improving cropping techniques have an important impact on soil erosion, and as a result, on soil water conservation that is of primary importance, especially for the Mediterranean dry regions. The demonstration of the findings is of practical use to most stakeholders, especially those that live and work with the local land.</p>
SERENA EJPSOIL IE EROSION SOIL LOSS
<p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>The dataset corresponds to a map of potential soil loss (Mg/ha/yr) due to erosion risk. The map is the result of applying the Erosion cookbook developed in SERENA/EJP-Soil. The map is created by calculating the erosion factor by the RUSLE model. </p> <p>Further research into the P-factor and R-factor values is necessary to produce a more reliable erosion map for Ireland. This would enhance the accuracy of soil erosion assessments and support more effective soil conservation strategies.</p>
Soil erosion by water in the 1980s-2020s in the steppe region of the southeast of the East European Plain (Volgograd region, Russia)
<p>The dataset contains rasters with a 30m resolution of the distribution of soil erosion by water. Raster "Soil Losses 1980s" has shown the average soil losses by water erosion in the 1980-1990s, and raster "Soil Losses 2020s" has shown average soil losses by water erosion in the 2010-2020s.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.