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33 results for “Conservation agriculture”
3C dataverse: Community capitals, cover crops, & conservation agriculture in the U.S. corn-soybean belt, version 2.2
<p><strong>What? </strong></p> <p>A dataset containing 315 total variables from 33 secondary sources. There are 262 unique variables, and 53 variables that have the same measurement but are reported for a different year; e.g. average farm size in 2017 (CapitalID: N27a) and 2022 (N27b). Variables were grouped by the community capital framework's seven capitals—Natural (96 total variables), Cultural (38), Human (39), Social (40), Political (18), Financial (67), & Built (15)—and temporally and thematically ordered. The geographic boundary is NOAA NCEI's corn and soybean belt (figure below), which stretches across 18 states and includes N=860 counties/observations. Cover crop data for the 80 Crop Reporting Districts in the boundary are also included for 2015-2021.</p> <p><strong>Why? </strong></p> <p>Comprehensively assessing how community capital clustered variables, for both farmers and nonfarmers, impact conservation practices (and perennial groundcover) over time helps to examine county-level farm conservation agriculture practices in the context of community development. We contribute to the robust U.S. cover crop literature a better understanding of how overarching cultural, social, and human factors influence conservation agriculture practices to encourage better farm management practices. Analyses of this Dataverse will be presented as recomendations for farmers, nonfarmers, ag-adjacent stakeholders, and community leaders.</p> <p><strong>How? </strong></p> <p>Variables used in this dataset range 20 years, from 2004-2023, though primary analyses focus on data collected between 2017-2024, primarily 2017 and 2022 (NASS Ag Census years). First, JAM-K requested, accessed, and downloaded data, most of which was already publically available. Next, JAM-K cleaned the data and aggregated into one dataset, and made it publically available on Google Drive and Zenodo. </p> <p><strong>What is 'new' or corrected in version 2.2? </strong></p> <p><em>Edited/amended</em>: Carroll, KY is now spelled correctly (two 'l's, not one); variable names, full and abbreviated, were updated to include the data year; Pike County's (IL) FIPS has been corrected from its wrong 17153 (same as Pulaski County) to 17149 (correct fips), and all Pike County (IL) data has been correctly amended; Farming dependent (ERS) updated for all variables; Data for built capital variables irrCorn17, irrSoy17, irrHcrp17, tractor17, and combine17 were incorrect for v.1, but were corrected for v.2; Several variable labels aggregated by Wisconsin University's Population Health Institute's County Health Rankings and Roadmaps were corrected to have the data's original source and years included, rather than citing CHR&R as the source (except for CHR&R's originally-produced values such as quartiles or rank scores); variables were reorganized by hypothesized community capital clusters (Natural -> Built), and temporally within each cluster. </p> <p><em>Added</em>: 55 variables, mostly from the 2022 Ag Census, and v 2.2 added a .pdf file with descriptives of data sources and years, and a .sav file. </p> <p><em>Omitted</em>: Four variables deemed irrelevant to the study; V1 codebook's "years internally available" column. Variable herbac22 for 55079, Milwaukee, WI, incorrectly had the value 2,049.612. That value was correctly changed to missing, with no data in the cell.</p> <p><strong>CRediT</strong>: conceptualization, CBF, JAM-K; methodology, JAM-K; data aggregation and curation, JAM-K; formal analysis, JAM-K; visualization, JAM-K; supervision, CBF; funding acquisition, CBF; project administration, CBF; resources, CBF, JAM-K</p> <p><strong>Acknowledgements</strong>: This research was funded by the Agriculture and Food Research Initiative Competitive Grant No. 2021-68012-35923 from the United States Department of Agriculture National Institute for Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this presentation are those of the authors and do not necessarily reflect the view of the U.S. Department of Agriculture. Much thanks to Corteva for granting data access of OpTIS 2.0 (2005-2019), and Austin Landini for STATA code and visualization assistance. </p>
Proactive conservation to prevent habitat losses to agricultural expansion
<p>The projected loss of millions of square kilometres of natural ecosystems to meet future demand for food, animal feed, fibre, and bioenergy crops is likely to massively escalate threats to biodiversity. Reducing these threats requires a detailed knowledge of how and where they are likely to be most severe. We developed a geographically explicit model of future agricultural land clearance based on observed historic changes and combine the outputs with species-specific habitat preferences for 19,859 species of terrestrial vertebrates. We project that 87.7% of these species will lose habitat to agricultural expansion by 2050, with 1,280 species projected to lose ≥25% of their habitat. Proactive policies targeting how, where, and what food is produced could reduce these threats, with a combination of approaches potentially preventing almost all these losses while contributing to healthier human diets. As international biodiversity targets are set to be updated in 2021, these results highlight the importance of proactive efforts to safeguard biodiversity by reducing demand for agricultural land.</p>
The CWR richness dataset of manuscript "ENHANCING IN SITU CONSERVATION OF CROP WILD RELATIVES FOR FOOD AND AGRICULTURE IN LITHUANIA"
<p>The CWR National Inventory database has been created by combining data from the Database of EU Habitat Mapping in Lithuania (BIGIS), the Herbarium Database of the Nature Research Centre (BILAS), the Lithuanian Vegetation Database (EU-LT-001), and the Global Biodiversity Information Facility (GBIF). It was used to calculate CWR richness in 4 by 4 kilometres grid cell. The dataset contains three GIS files (.shp) - Boundaries of Lithuania, CWR richness in grid cells and 45 potential genetic reserve sites.</p>
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River in Bombus distinguendus Morawitz, 1869 (Hymenoptera: Apidae) in Arkhangelsk Oblast, Russia: Distribution, ecology and conservation
Рис. 2. Основные места концентрации фуражирующих особей Bombus distinguendus в АрхангеΛьской обΛасти: 1 — Разнотравно-зΛаковый Λуг с Trifolium pratense и Trifolium repens в окрестностях гороΑа Мезень; 2 — Разнотравно-зΛаковый Λуг по обочине Αороги с Centaurea scabiosa в окрестностях сеΛа ХоΛмогоры; 3 – Агроценоз со Stachys palustris в ΑеΛьте реки Северная Δвина; 4 — РуΑераΛьное сообщество с Chamaenerion angustifolium в ΑеΛьте реки Северная Δвина Fig. 2. Typical foraging habitats of Bombus distinguendus in Arkhangelsk Oblast: 1 — Meadow with Trifolium pratense and Trifolium repens near the town of Mezen; 2 — Roadside meadow with Centaurea scabiosa near the village of Kholmogory; 3 — Agricultural habitat with Stachys palustris in the delta of the Northern Dvina River; 4 — Ruderal community with Chamaenerion angustifolium in the delta of the Northern Dvina River
Saffron-cowled Blackbirds' reduced nest success in Argentina's agricultural land highlights the importance of non-agricultural habitat for its conservation
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Proactive conservation to prevent habitat losses to agricultural expansion
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Data from: Farmer preferences for conservation incentives that promote voluntary phosphorus abatement in agricultural watersheds
Financial incentives are commonly used to promote voluntary adoption of agricultural best management practices (BMPs), but little is known about farmer preferences among alternative incentives. Using experimental procurement auctions, we evaluate how different conservation incentives affect farmer willingness to adopt BMPs that reduce phosphorus (P) runoff, a major driver of harmful algal blooms in Lake Erie. We rank incentives (e.g., payment, BMP insurance, tax credit, and certification price premium) by the cost per pound of P runoff reduction. Payments and tax credits that target high impact areas of the watershed are more cost-effective than untargeted price premiums for product certification. Farmers demand higher payments for contracts offering BMP insurance (i.e., protection against yield loss from BMP use) due to uncertainty about how the program will be implemented and the reliability of indemnities, as well as anticipated transaction costs associated with the program. Understanding farmer preferences for different types of conservation incentives is critical to design agri-environmental programs that engage more farmers and cost-effectively enhance ecosystem services.
The soil surface food web in conservation agriculture as the foundation for conservation biocontrol
<p>Data on Collembola (per 5 cm diameter sample), spiders (per 0.25 m<sup>2</sup>), carabid beetles (per 0.25 m<sup>2</sup>), aphids (per straw), and simulated max density of aphids from conservation agriculture fields (CA) and conventionally tilled fields (CT).</p>
Supplementary Information for "Mapping potential conflicts between global agriculture and terrestrial conservation"
<p>This archive contains supplementary figures from mapping potential conflicts between global agriculture and terrestrial conservation in 2010 and 2070. Please see the captions of these figures below.</p> <p><strong>Supplementary Figure 3.</strong> Distribution of regional land use by agricultural commodity and conservation priority (CP) index intervals (48 x 3 sub-figures).<br> <strong>Supplementary Figure 4.</strong> Distribution of regional land use (left) and global land use (right) of 48 agricultural commodities (48 x 3 sub-figures). The x-axis is the land use as a proportion of the total global production area.<br> <strong>Supplementary Figure 5.</strong> Comparison of the distribution of regional land use for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 7 x 2 sub-figures).<br> <strong>Supplementary Figure 6.</strong> Comparison of land use and conservation conflict between national and global levels. The y-axis refers to the land use as a proportion of the total global production area. There are 48 agricultural commodities for 197 countries (197 x 48 x 3 sub-figures).<br> <strong>Supplementary Figure 7.</strong> Comparison of the land use distribution of top producers for 48 agricultural commodities between 2010 and 2070 scenarios (48 x 10 x 2 sub-figures).<br> <strong>Supplementary Figure 8.</strong> Spatial distribution maps of production areas for 48 agricultural commodities (48 x 3 maps).<br> <strong>Supplementary Figure 9.</strong> Conflict between conservation priority sites and (non-)domestic land use for 42 agricultural commodities associated with consumption in 197 countries (197 x 42 x 3 sub-figures).<br> <strong>Supplementary Figure 10.</strong> Land use maps of 42 agricultural commodities linked to consumption in 197 countries (197 x 42 x 3 maps).<br> <strong>Supplementary Figure 11.</strong> Conservation priority index and export rate of selected agricultural commodity for primary production cells (land use of each agricultural commodity > 10% of cell area). <strong>a</strong>) Current status in 2010. <strong>b</strong>) Shifts of conservation priority in 2070 (RCP 8.5). Triangles point-up and point-down to indicate increased and decreased CP, respectively. Density plots on top and right show cell densities corresponding to export rate and CP index, respectively.<br> <strong>Supplementary Figure 12.</strong> Maps of global conservation priority index in 2010 and 2070.<br> <strong>Supplementary Figure 13. a</strong>, Performance curves for prioritization per scenario 2010, 2070-RCP2.6, and 2070-RCP8.5). Curves show the average fraction of species' range covered in each scenario weighted by each species weight, divided by the total weight of species (y-axis), by a given fraction of the landscape (x-axis). <strong>b</strong>, Histograms and boxplots of all CP map pixels. Each jittered point in boxplots represents a map pixel.</p>
Field margins as substitute habitat for the conservation of birds in agricultural wetlands; Supplementary information
<p>Supplementary material, dataset and script links to the research paper submit for recommendation by PCI Ecology.</p>
Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
<p>Sowing flower strips along field edges is a widely adopted method for conserving pollinating insects in agricultural landscapes. To maximize the effect of flower strips given limited resources, we need spatially explicit tools that can prioritize their placement, and for identifying plant species to include in seed mixtures.</p> <p>We sampled bees and plant species as well as their interactions in a semi-controlled field experiment with roadside/field edge pairs with/without a sown flower strip at 31 sites in Norway and used a regional spatial model of solitary bee species richness to test if the effect of flower strips on bee species richness was predictable from the modelled solitary bee species richness.</p> <p>We found that sites with flower strips were more bee species rich compared to sites without flower strips and that this effect was greatest in areas that the regional solitary bee species richness model had identified to be particularly important for bees. Spatial models revealed that even within small landscapes there were pronounced differences between field edges in the predicted effect of sowing flower strips.</p> <p>Of the plant species that attracted the most bee species, the majority mainly attracted bumblebees and only few species also attracted solitary bees. Considering both the taxonomic diversity of bees and the species richness of bees attracted by plants we suggest that seed mixes containing <em>Hieracium </em>spp. such as <em>Hieracium umbellatum </em>and <em>Pilosella officinarum</em>; <em>Taraxacum</em> spp; <em>Trifolium repens</em>;<em> Lotus corniculatus</em>; S<em>tellaria graminea</em>; and <em>Achillea millefolium</em> would provide resources for diverse bee communities in our region.</p> <p>Spatial prediction models of bee diversity can be used to identify locations where flower strips are likely to have the largest effect and can thereby provide managers with an important tool for prioritizing how funding for agri-environmental schemes such as flower strips should be allocated. Such flower strips should contain plant species that are attractive to both solitary and bumblebees, and do not need to be particularly plant species rich as long as the selected plants complement each other.</p>
Data from: Soil carbon change in intensive agriculture after 25 years of conservation management
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Data from: The contributions of flower strips to wild bee conservation in agricultural landscapes can be predicted using pollinator habitat suitability models
Open the record for dataset details and reuse information.
Data from: Farmer preferences for conservation incentives that promote voluntary phosphorus abatement in agricultural watersheds
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Data for: Critical habitat thresholds for effective pollinator conservation in agricultural landscapes
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Data and code for Chapter 1: An expanded scope of biodiversity in urban agriculture, with implications for conservation.
<p>Data and code for Chapter 1: An expanded scope of biodiversity in urban agriculture, with implications for conservation, in <em>Urban Agroecology: Interdisciplinary Research and Future Directions</em> (Monika Egerer and Hamutahl Cohen eds). CRC Press, Taylor & Francis, Abingdon, UK</p> <p> </p> <p> </p>
Data from: Response of non-grassland avian guilds to adjacent herbaceous field buffers: testing hypotheses about configuration of targeted conservation practices in agricultural landscapes
1. A substantial part of the world's land base is dominated by agriculture, and forest habitat often consists of discrete patches of forest and linear woody corridors. These natural components provide habitat for some forest birds, but make conservation of these species difficult. In-field practices applied outside forest patches, such as specific juxtapositions of herbaceous field buffers adjacent to forest habitat, could increase avian diversity contributions of existing forest without creation of additional forest habitat. Our prediction was that herbaceous field buffers would increase bird richness in adjacent forest, and we evaluated four potential mechanisms. 2. We used bird count data from a conservation buffer monitoring program and hierarchical community models to estimate species richness of forest generalist, forest interior, and shrubland (edge) species near forest edges with and without adjacent herbaceous field buffers. We accounted for heterogeneity in detection probabilities and forest cover in surrounding landscapes when estimating species- and guild-level responses. 3. Consistent with the drift fence hypothesis, adjacent herbaceous buffers were associated with a modest increase in richness of forest interior birds in woody corridors, but not in forest blocks. Consistent with resource complementation, adjacent herbaceous buffers were associated with modest increases in richness of shrubland (edge) birds in both woody corridors and forest blocks. 4. Across all species and guilds, adjacent buffers generally associated with greater abundance (e.g. 28 of 39 species), but these increases were also relatively small and highly variable (i.e. overlapping 95% credible intervals). Corroborating existing research, effects of adjacent herbaceous buffers are likely real, but neither pervasive nor strong. 5. Synthesis and applications. Conservation practices targeted to grassland species often produce measurable conservation benefits for target species. However, biodiversity return for investment would be further increased if targeted practices could be deployed in ways that also produce benefits for non-target species in adjacent habitats. Our results suggest that additional benefits for non-target species using adjacent forest habitat are likely to be modest, so conservation planning should focus on species targeted by the conservation practices and avoidance of potential negative impacts on those species when positive benefits to adjacent habitat are weak or lacking.
Data from: Dispersal constraints for the conservation of the grassland herb Thymus pulegioides L. in a highly fragmented agricultural landscape
Species-rich grassland communities are one of the most important habitats for biodiversity and of high conservation priority in Europe. Restoration actions are mainly focused on the improvement of abiotic conditions, such as nutrient depletion techniques, and are generally based on the assumption that the target community will re-establish at the restored site when the target species exist in the neighborhood. Information on the contemporary seed-dispersal range is therefore crucial to develop effective conservation measures. Here, we investigated the contemporary long-distance seed dispersal and genetic structure of the grassland herb Thymus pulegioides in an intensively managed agricultural landscape in Flanders (Northern Belgium). Assignment tests based on amplified fragment length polymorphisms revealed very low levels of effective seed dispersal between populations although seed availability and seed viability was not a limiting factor. The process of fragmentation has resulted in a high population differentiation and without further incoming gene flow the remnant populations are prone to further genetic erosion and perhaps extinction. Our findings illustrate that restoring suitable abiotic habitat conditions in the neighborhood of existing populations does likely not guarantee colonization for this grassland specialist. For the survival of the species, existing populations should be functionally connected and seed addition may be necessary for successful conservation to overcome dispersal-limitation.
R code and supplementary data for : "A framework for mapping conservation agricultural fields using time-series optical and radar imagery"
<p>Source code and cover crop maps for the paper "A framework for mapping conservation cropland using optical and radar time series imagery." (Zhou et al., 2025)</p> <p>https://doi.org/10.1016/j.rse.2025.114858</p> <p> </p> <p>The entire workflow consists of these steps:</p> <p>1. Obtain satellite data from Google Earth Engine platform. script path: (<a href="https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI">https://code.earthengine.google.com/?scriptPath=users%2Fyuez9466%2FCApractice%3ANDVI</a>). You need to obtain the NDVI, NBR2, Sentinel-1 Radar dataset and Precipitation data for your research area and seltected time interval. Download .csv data from Google Cloud, then convert the format of the data for following calculations.(see 1_import_transfer_data.R)</p> <p>2. Obtain the annual crop types in your study area, either through agricultural census data or remote sensing predictions (not mentioned in this paper), calculate organic carbon input based on the crop types. Extracting seasons based on time-series NDVI values using phenofit package. (see 2_NDVI_Smooth_Divide_seasons.R)</p> <p>3. Calculating the length of the cover crop growing season and periods of bare soil, also get the nessasary covariates for tillage model meanwhile. (see 3_CC_BS_length_add_Tillage.R)</p> <p>4. Build a tillage model. (see 4_Build_Tillage_model)</p> <p>Build your own conservation agriculture fields model.</p>
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008). in Muridae
Nesokia is sister to Bandicota and are nested in Rattus phylogenetically, making Rat- tus paraphyletic. Tarsomys, Limnomys, and Diplothrix are also phylogenetically in Rat- tus, and the clade is in need of focused re- vision at the generic level. Nesokia bunnui was originally described as a separate ge-nus, Erythronesokia, because it is morphologically very distinctive from N. indica. Type specimen was destroyed during the Iraq War, and a neotype was recently designated to replace it. Monotypic. Distribution. Tigris and Euphrates river valleys, SE Iraq. Descriptive notes. Head—body 230-260 mm, tail 205-270 mm, ear 18-21 mm, hindfoot 49-58 mm; weight 519 g. The Long-tailed Bandicoot Rat is larger than the Short-tailed Bandicoot Rat (N. indica). Pelage is soft and woolly, interspersed with harsher coarse hair and long black hairs near mid-back. Dorsum is fawn to ocherous red, washed with purple or chestnuton darker individuals. Hairs are basally slate-gray and distally rufous, occasionally with whitish or black tips. Muzzle is drab. Sides arefawn, with gray edge toward venter. Venteris whitish, extending onto cheeks where the same pattern from gray to fawn to dorsal pelage occurs. Feet are large and robust, being light brown and well-furred dorsally. Claws are amber on forefeet and dull brown on hindfeet; pollux is extremely small. Ears are moderately long and brownish, with no hair internally. Tail is ¢.82-104% of head-body length and deep brownish drab, interspersed with visible white hair. Skull is large and robust, similarly to the Short-tailed Bandicoot Rat. Habitat. Marsh and swamp land. Food and Feeding. No information. Breeding. No information. Activity patterns. The Long-tailed Bandicoot Rat is terrestrial, although it isfound in swampy and marshy areas and is probably amphibious. Movements, Home range and Social organization. No information. Status and Conservation. Classified as Endangered on The IUCN Red List. The Longtailed Bandicoot Rat is apparently rare and is known from very few specimens. Marsh and swamp habitats in which it is found were completely destroyed during the Iraq War by draining, war damage, and agricultural expansion. In recent years, flooding from Tigris and Euphrates rivers and high snow fall and melt haveresulted in partial restoration ofits native habitat, although restoration is not a complete. Populations are now probably highly fragmented. Bibliography. Al-Ansari et al. (2012), Al-Robaae & Felten (1990), Khajuria (1981), Krystufek et al. (2017), Musser & Carleton (2005), Richardson & Hussain (2006), Stuart (2008).
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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.