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1,425 results for “Agriculture”
Physical and technical characterization of main agricultural plastics articles used for protected cultivation systems
<p>The folder "Thermal and spectroscopic data on the characterization of mulch films.rar" includes differential scanning calorimetry (DSC), thermogravimetry (TG), Fourier-transform infrared spectroscopy (FTIR), and water contact angle (WCA) raw data and images relevant to the characterization of conventional and biodegradable mulch films. 7 mulching films consisting of 3 different conventional linear low-density polyethylene (LLDPE, coded as PE) films and 4 different biodegradable (PBAT-based, coded as BIO) films are analysed.In particular, data regarding M-BIOIT-15-black-0, M-PEIT-15-black-0, M-BIOEL-15-black-0, M-PEEL-15-black-0, M-PEEL-50-transparent-0, M-BIODE-30-black-0 and M-BIOFI-15-black-0 are included.</p>
Eddy Covariance dataset for BR-IAB: Instituto Arruda Botelho - Wooded Cerrado and Mixed Agriculture
<p>The micrometeorological variables were measured using slow (rainfall, temperature, relative humidity, net radiation, soil heat flux, and soil moisture at 1 Hz) and fast (wind speed and direction, water and carbon dioxide molar fractions at 20 Hz) instrumentation. The instruments were fixed on a 24-meter-high metal tower, which was equipped with a data acquisition and storage system. Despite the tower height, the main pieces of equipment were positioned below the maximum tower height due to footprint restrictions and lightning protection (16 m). The flux tower was operated between 2018 and 2021. The tower is listed with the site ID BR-IAB on the Ameriflux platform (https://ameriflux.lbl.gov/sites/siteinfo/BR-IAB). The site is located in the city of Itirapina, within the state of São Paulo, Brazil (22° 10.2517' S, 47° 52.2567' W).</p>
juan-duenas/NHESS: Soil conditioner mixtures as an agricultural management alternative to mitigate drought impacts: a proof-of-concept.
<p>The dataset and the R script have been enhanced and corrected, respectively. The main figures of the associated publication have been added in two different qualities.</p> <p>This data is associated to a paper that will appear in an special issue of the journal Natural Hazards and Earth System Sciences. https://nhess.copernicus.org/articles/special_issue1295.html</p>
Agricultural Residue Burning Emissions 2019 v1
<p>The data provides the agricultural residue burning emission estimates over 11 agroclimatic zones of Madhya Pradesh, India, for the 2019 rabi season. It also includes supplementary information used in the emission estimation for the reader's reference. </p> <p>References:</p> <p>1. Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual (Volume 3)https://www.ipcc-nggip.iges.or.jp/public/gl/invs6c.html </p> <p>2. Gupta, P. K.; Sahai, S.; Singh, N.; Dixit, C. K.; Singh, D. P.; Sharma, C.; Tiwari, M. K.; Gupta, R. K.; Garg, S. C. Residue Burning in Rice-Wheat Cropping System: Causes and Implications. Curr. Sci. 2004, 87 (12).</p> <p>3. Jain, N.; Bhatia, A.; Pathak, H. Emission of Air Pollutants from Crop Residue Burning in India. Aerosol Air Qual. Res. 2014, 14 (1).<a href="https://doi.org/10.4209/aaqr.2013.01.0031"> https://doi.org/10.4209/aaqr.2013.01.0031</a>.</p> <p>4. Venkatramanan, V.; Shah, S.; Rai, A. K.; Prasad, R. Nexus Between Crop Residue Burning, Bioeconomy and Sustainable Development Goals Over North-Western India. Front. Energy Res. 2021, 8.<a href="https://doi.org/10.3389/fenrg.2020.614212"> https://doi.org/10.3389/fenrg.2020.614212</a>.</p> <p>5. Van Der Werf, G. R.; Randerson, J. T.; Giglio, L.; Collatz, G. J.; Mu, M.; Kasibhatla, P. S.; Morton, D. C.; Defries, R. S.; Jin, Y.; Van Leeuwen, T. T. Global Fire Emissions and the Contribution of Deforestation, Savanna, Forest, Agricultural, and Peat Fires (1997-2009). Atmos. Chem. Phys. 2010, 10 (23). <a href="https://doi.org/10.5194/acp-10-11707-2010">https://doi.org/10.5194/acp-10-11707-2010</a>.</p>
Fig. 1 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 1. − Study area including the Pama reserve and neighbouring PAs of the western WAPO complex. The Pama, Tindangou and Madjoari areas are enclaves where agriculture is allowed. The small country map in the lower right shows the position of the study area within Burkina Faso.
Fig. 3 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 3. − Maps of mean maximum plant size (calculated as average of maximum plant size of all species predicted as present within a grid cell). A. Grasses (Poaceae) (30-360 cm); B. Woody species (3-25 m). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
Fig. 2 in The impact of land use on species composition and habitat structure in Sudanian savannas - A modelling study in protected areas and agricultural lands of southeastern Burkina Faso
Fig. 2. − Maps of species richness. A. All plant species (2-211 spp.); B. Graminoids (0-50 spp.); C. Forbs (0-86 spp.); D. Woody species (0-52 spp.); E. Weedy species (0-48 spp.); F. Non-weedy species (0-140 spp.). The color coding stretches from light yellow for the lowest values via orange and red to violet for the highest values.
Formalizing Objectives and Criteria for Urban Agriculture Sustainability with a Participatory Approach
<p>The last few years have seen an exponential development of urban agriculture projects within global North countries, especially professional intra-urban farms which are professional forms of agriculture located within densely settled areas of city. Such projects aim to cope with the challenge of sustainable urban development and today the sustainability of the projects is questioned. To date, no set of criteria has been designed to specifically assess the environmental, social and economic sustainability of these farms at the farm scale. Our study aims to identify sustainability objectives and criteria applicable to professional intra-urban farms. It relies on a participatory approach involving various stakeholders of the French urban agriculture sector comprising an initial focus group, online surveys and interviews. We obtained a set of six objectives related to environmental impacts, link to the city, economic and ethical meaning, food and environmental education, consumer/producer connection and socio-territorial services. In addition, 21 criteria split between agro-environmental, socio-territorial and economic dimensions were identified to reach these objectives. Overall, agro-environmental and socio-territorial criteria were assessed as more important than economic criteria, whereas food production was not mentioned. Differences were identified between urban farmers and decision makers, highlighting that decision makers were more focused on projects' external sustainability. They also pay attention to the urban farmer agricultural background, suggesting that they rely on urban farmers to ensure the internal sustainability of the farm. Based on our results, indicators could be designed to measure the sustainability criteria identified, and to allow the sustainability assessment of intra-urban farms.</p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Riparian buffers maintain aquatic trophic structure in agricultural landscapes
<p>Supporting data and R code for the publication entitled "Riparian buffers maintain aquatic trophic structure in agricultural landscapes".</p> <p> </p>
Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions (v3.0)
<p><strong>Agricultural land resources – a global suitability evaluation (v3.0)</strong></p> <p>Local climate, soil and topography determine the conditions under which agricultural crops are suitable for growth or not. The methodology uses a fuzzy logic approach that is described in Zabel et al. (2014). The approach is based on Liebig's law of the minimum. Accordingly, plant suitability is determined not by total available resources, but by the scarcest resource. The limiting factor depends on the local environmental conditions and the crop-specific requirements, that are taken from literature. </p> <p><strong>Determining Agricultural Suitability</strong></p> <p>Agricultural suitability is calculated for each of 5 climate models (GFDL, HadGEM2, IPSL, MIROC and NorESM1) from the AR5 ISIMIP fast track protocol. Daily climate model data for temperature, precipitation and solar radiation are statistically downscaled to 30 arc seconds spatial resolution. A monthly bias-correction is applied using WorldClim data. The provided suitability data refers to the model median over the 5 climate simulations. Soil data is taken from the Harmonized World Soil Database (HWSD) v1.21. Considered soil properties are texture, proportion of coarse fragments and gypsum, base saturation, pH content, organic carbon content, salinity, sodicity. Soil depth is taken into account according to Pelletier et al. (2015). Topography data is applied from the Shuttle Radar Topography Mission (SRTM). Irrigation has strong impact on the suitability of crops and is considered in this approach.</p> <p><strong>Agricultural Suitability</strong></p> <p>The agricultural suitability data is provided at a spatial resolution of 30 arc seconds (approximately 1 km<sup>2</sup> at the equator). The dataset contains four time periods (1980-2009, 2010-2039, 2040-2069, 2070-2099) and two climate change scenarios (RCP2.6 and RCP 8.5). Agricultural suitability is provided for rainfed conditions and for irrigated conditions seperately. Additionally, we provide a dataset in which the current irrigation areas according to Maier et al. (2018) are applied. The suitability is provided for 23 food, feed, fibre, and 1st and 2nd generation bio-energy crops. An 'overall suitability' is provided for all crops that considers the most suitable crop on each pixel. Additionally, we provide a dataset excluding 2nd generation bioenergy crops (18-23) from the overall aggregation of crops.</p> <table> <caption><strong>Food, feed, fiber and first-generation bioenergy crops</strong></caption> <tbody> <tr> <td>Barley</td> <td>Potato</td> <td>Sugarbeet</td> </tr> <tr> <td>Cassava</td> <td>Rapeseed</td> <td>Sugarcane</td> </tr> <tr> <td>Groundnut</td> <td>Rice</td> <td>Sunflower</td> </tr> <tr> <td>Maize</td> <td>Rye</td> <td>Summer wheat</td> </tr> <tr> <td>Millet</td> <td>Sorghum</td> <td>Winter wheat</td> </tr> <tr> <td>Oilpalm</td> <td>Soybean</td> <td> </td> </tr> </tbody> </table> <table> <caption> <p><strong>Second-generation bioenergy crops</strong></p> </caption> <tbody> <tr> <td>Jatropha</td> <td>Reed canary grass</td> </tr> <tr> <td>Miscanthus</td> <td>Eucalyptus</td> </tr> <tr> <td>Switchgrass</td> <td>Willow</td> </tr> </tbody> </table> <p><strong>Growing Season Adaptation</strong></p> <p>The agricultural suitability considers the adaptation of the growing season. For each pixel and crop, the growing season is optimized throughout the year, taking the annual course of precipitation, temperature, and solar radiation as well as their interplay, into account.</p> <p><strong>Most Suitable Crop</strong></p> <p>The most suitable crop for each pixel is provided in the data. Please note that a value of 126 means that no crop suitable and 127 means that multiple crops have the same suitability.</p> <p><strong>Further information</strong></p> <p>Detailled information are available in the following publications:</p> <p>Zabel F, Putzenlechner B, Mauser W (2014) Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLOS ONE 9(9): e107522. doi: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0107522">10.1371/journal.pone.0107522</a></p> <p>Cronin, J., Zabel, F., Dessens, O., Anandarajah, G. (2020): Land suitability for energy crops under scenarios of climate change and land-use. GCB Bioenergy, 12(8). doi: <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/gcbb.12697">10.1111/gcbb.12697</a></p> <p>Schneider. J.M., Zabel, F., Mauser, W. (2022): Global inventory of suitable, cultivable and available cropland under different scenarios and policies. Scientific Data 9, 527. doi: <a href="https://doi.org/10.1038/s41597-022-01632-8">10.1038/s41597-022-01632-8</a></p> <p>Meier, J., Zabel, F., Mauser, W. (2018): A global approach to estimate irrigated areas – a comparison between different data and statistics. Hydrol. Earth Syst. Sci., 22, 1119–1133, 2018. doi: <a href="https://hess.copernicus.org/articles/22/1119/2018/">10.5194/hess-22-1119-201</a></p> <p>Pelletier, J. D., Broxton, P. D., Hazenberg, P., Zeng, X., Troch, P. A., Niu, G.-Y., Williams, Z., Brunke, M. A., and Gochis, D. (2016), A gridded global data set of soil, immobile regolith, and sedimentary deposit thicknesses for regional and global land surface modeling, <em>J. Adv. Model. Earth Syst.</em>, 8, 41– 65, doi: <a href="https://doi.org/10.1002/2015MS000526">10.1002/2015MS000526</a>.</p> <p><strong>Improvements in v3.0</strong></p> <p>Compared to the previous version (<a href="https://zenodo.org/record/3748350">v2.0</a>), this version (v3.0) <em>uses updated input data for soil (HWSD v1.21) and high resolution irrigated areas (Maier et al. 2018), and additionally considers soil depth (Pelletier et al. 2016). Moreover, the suitability is calculated for an ensemble of 5 climate models, and is available for more crops, including a number of second generation bioenergy crops.</em></p> <p><strong>Contact</strong></p> <p>Please contact: Dr. Florian Zabel, <a href="mailto:f.zabel@lmu.de">f.zabel@lmu.de</a>, Department of Geography, LMU München (<a href="http://www.geografie.uni-muenchen.de">www.geografie.uni-muenchen.de</a>)</p>
Dataset created in the context of the project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field"
<p>The project "Stable methodologies to evaluate and measure quality, interoperability, blockchain and reuse of open data in the agricultural field", whose website is https://datause.es/, is a project funded by the Ministry of Science and Innovation - State Research Agency, with reference PID2019-105708RB-C22.</p> <p>Within the framework of the project, a bibliographic search is carried out in all thematic categories of the Web of Science (WoS) related to agriculture and related areas. The search equation included the following categories:</p> <p><strong>WC </strong>= (FOOD SCIENCE TECHNOLOGY OR PLANT SCIENCES OR FORESTRY OR AGRICULTURAL ENGINEERING OR AGRONOMY OR HORTICULTURE OR AGRICULTURE DAIRY ANIMAL SCIENCE OR AGRICULTURE MULTIDISCIPLINARY OR AGRICULTURAL ECONOMICS POLICY) </p> <p>This data set shows the distribution of journals and the quartile they occupy in each of the thematic categories in 2019, with the aim of serving researchers in this area and for future data mining.</p>
Tool for the selection of indicators for agriculture sustainability assessment
<p>This is a preliminary version of the Excel-based tool that is being developed within the Working Group of the Crop diversification cluster on Multi-criteria Assessment and Sustainability Indicators (MCA & SI cluster WG). The tool has the aim to assist different end-users in the selection of sustainability indicators tailored to assess the impacts and effects of crop diversification. Currently this preliminary version contains only the indicators developed by the DiverIMPACTS and DiverFARMING projects (2 out 5 projects of Crop diversification cluster).</p> <p>The tool is composed by three sheets: 1) the “Selection” sheet for querying the database and obtaining the results; 2) the “Database” sheet where all indicators of the cluster projects are reported; 3) the “SAFA_HierachicalClassification” sheet with the description of the SAFA framework, and the items identified for the indicators of the cluster projects reported in the database.</p>
Development of thrips barcode database and multiplex real-time PCR assay for quarantine and agriculture pest species
<p>Thrips (Order Thysanoptera) species are agriculturally important as plant sap sucking pests and vectors of several plant diseases. They are very small insects and commonly associated with imported commodities at New Zealand border in all life stages. Morphological identification of thrips is mainly performed on adults, but the available identification keys for immature stages do not include many species and are inadequate, thus DNA barcode was regularly used for thrips identification, here, we have generated DNA barcode data for over 29 thrips species from over 100 individuals. At New Zealand border,<em> Frankliniella occidentalis </em>is the dominant species intercepted, followed by <em>F. panamensis</em>, <em>Thrips palmi</em> and <em>T. tabaci </em>and several other thrips species. Hence, we have also developed a multiplex real time PCR assay, targeting the four thrips species to facilitate the identification of quarantine interceptions with more accurate and faster diagnostic method for any developmental stages. The DNA barcode database further assists in thrip identification. The assay showed high specificity for all the four target species and could detect 10 copies/ µL of the target DNA. Linear responses and high correlation coefficients between the amount of DNA and <em>C</em><sub>q</sub> values for each species were also achieved. The method was tested on single egg, larva and adult and proved to be applicable for all life stages of the four species. This study has demonstrated the assay is a useful biosecurity tool for rapid and reliable identification of the target thrips species. </p>
Data from: Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity
<p> </p> <p><strong>Short description</strong></p> <p>This repository contains the relevant data and code used for the analyses of the scientific publication: "<em>Riparian reforestation on the landscape scale – Navigating trade-offs among agricultural production, ecosystem functioning and biodiversity</em>", published in the Journal of Applied Ecology.</p> <p>For further details please see the original article and its supplementary materials.</p> <p> </p> <p><strong>Organization of the data</strong></p> <p>The repository contains two main folders:</p> <p> 1. Target indicators & spatial analysis</p> <p><em>‘target indicators.csv’</em>: Measured variables that have been quantified at the CROSSLINK field sampling campaign in the Zwalm catchment (EPT taxa richness, diatoms functional evenness, cotton-strip assay).</p> <p><em>‘bio-suitability segments.csv’</em>: Biophysical suitability for food production of the arable land for each riparian segment of the Zwalm.</p> <p><em>‘spatial analysis.xlsx’</em>: Results of the Zwalm spatial analyses addressing land-use and physiographic properties of the (1) local riparian corridors; (2) full riparian corridors within in the upstream catchments and (3) total upstream catchment areas for each sampling site.</p> <p><em>‘Summary model development Zwalm.pptx’</em>: Additional information on the models that have been used in the CoMOLA optimization framework.</p> <p> 2. CoMOLA input & parameterisation</p> <p>The files in this folder can be used for the parameterisation of the Python tool CoMOLA (Strauch et al., 2019). Source for CoMOLA, including user manual: https://github.com/michstrauch/CoMOLA</p> <p><em>‘config.ini’</em>: Basic configuration file of CoMOLA (needs to be adjusted to local settings)</p> <p><em>‘input’ folder</em>: Includes the CoMOLA input files that have been used in our study. See CoMOLA manual for more details on each file.</p> <p><em>‘models’ folder</em>: Includes the Python code of the models that are used for the calculation of all target indicators within the optimization framework (‘Zwalm_4_Models_v1_utf8.py’). The sub-folders ‘GIS_temp_files’ and ‘Input’ contain all files that are needed and have been used to run the Python code.</p> <p> </p>
Fig. 5 in Factors Affecting Avifaunal Diversity In Selected Agro-Ecosystems Of Himachal Pradesh Agricultural University, Palampur, Himachal Pradesh, India
Fig. 5. The heatmap transformed data shows the contributions of different feeding guilds of bird species for clustering of habitats. Blue colour represents negative contribution while red represents positive contribution.
Limited effect of thermal pruning on wild blueberry crop and its root-associated microbiota - Agricultural dataset
<p>These datasets contain all the agricultural data (soil chemistry, blueberry performance, weeds and diseases...) used in our study.</p>
UrbanOccupationsOETR_Temettuat_Agricultural_Production_Dataset_Gemlik_KumlaSagir_Kursunlu_070922
<p>This dataset is constructed first by manual data entry from the Ottoman temettuat registers (available at the Turkish Presidency State Archives of the Republic of Turkey – Department of Ottoman Archives, (ML. VRD. TMT. d.) collection) into our customized relational database and then by making a selection available in spreadsheet format.</p> <p><br> Our team located, curated, and extracted agricultural production data for the town of Gemlik and two villages, Kumla-i Sagir and Kurşunlu from the Bursa region for 1844/45. We coded both agricultural production areas as well as product type using the Corine Land Cover nomenclature (CLC2018, <a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html">https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html</a>).</p> <p> </p> <p>Please cite the below paper in your publications if you use the dataset:</p> <p>Akın Sefer, Efe Erünal, and M. Erdem Kabadayı, “Mid-Nineteenth-Century Gemlik and Its Environs: A Survey of a West Anatolian Region and Its Long-Term Economic and Demographic Development,” <em>Journal of Balkan and Near Eastern Studies</em>, (July 14, 2023): 1–20, <a href="https://doi.org/10.1080/19448953.2023.2233354">https://doi.org/10.1080/19448953.2023.2233354</a>.</p>
UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset
<p><strong>‘UrbanOccupationsOETR_1844/45 Bursa Region Temettuat Agricultural Production Dataset’</strong> is constructed first by manual data entry from the Ottoman temettuat registers (available at the Turkish Presidency State Archives of the Republic of Turkey – Department of Ottoman Archives, (ML. VRD. TMT. d.) collection) into our customized relational database and then by making a selection available in spreadsheet format.</p> <p><br> Our team located, curated, and extracted agricultural production data for a total of 81 geosampled villages from the Bursa region for 1844/45. We coded both agricultural production areas as well as product type using the Corine Land Cover nomenclature (CLC2018, <a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html">https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html</a>) and made available only the category, ‘2.1.1 Non-irrigated arable land’ (<a href="https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/index-clc-211.html">https://land.copernicus.eu/user-corner/technical-library/corine-land-cover-nomenclature-guidelines/html/index-clc-211.html</a>), as a proxy for grain production.</p> <p><a href="https://drive.google.com/file/d/1-ikBlHshXqyDBiKYuhMkLW3LSWyBgAFj/view?usp=sharing"><strong>02122020-A5_Total</strong></a> count of agricultural entries CLC 2.1.1 coded, qualified (dönüm and convertible) and quantified (numerical value)<br> provides data on the cultivated area in square meters of 4,657 units, belonging to 3,019 producers, of 2,892 households, in 81 villages.</p> <p><strong><a href="https://drive.google.com/file/d/1XeJnAvPhH0TJpb3ZwAk_sVgPqBzGMjj4/view?usp=sharing">02122020_T6_Total</a></strong> count of tithe tax entries CLC 2.1.1 coded, qualified (kile and convertible), and quantified (numerical value)<br> provides data on production volumes in kgs of 4,516 units, belonging to 1,924 producers, of 1,873 households, in 70 villages.</p> <p><br> Please cite the below paper in your publications if you use the dataset:</p> <p> </p> <blockquote> <p>Ustaoglu, Eda, M. Erdem Kabadayı, and Petrus Johannes Gerrits, ‘The Estimation of Non-Irrigated Crop Area and Production Using the Regression Analysis Approach: A Case Study of Bursa Region (Turkey) in the Mid-Nineteenth Century’, PLOS ONE 16, no. 4 (30 April 2021): e0251091, <a href="https://doi.org/10.1371/journal.pone.0251091">https://doi.org/10.1371/journal.pone.0251091</a>.</p> </blockquote>
Agricultural Parcel_CO2flux_France-Toulouse_0.30_15102018_15102019
<p>Set of agricultural parcels, with their geometry, crop type, value of CO2 flux (due to the crop vegetation cycle during an agricultural campaign year) and a few other attributes (intermediary results, quality information).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.