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407 results for “greenhouses”
Figure 1 in New species of Megastylus (Hymenoptera: Ichneumonidae: Orthocentrinae) reared from larvae of Keroplatidae fungus gnats (Diptera) in a Dutch orchid greenhouse
Figure 1. Megastylus woelkei sp. nov.ι holotype female: (a) whole insectι scale bar 0.5 mm; (b) head (anterior view)ι scale bar 0.2 mm; (c) head and mesosoma (latero-ventral view)ι scale bar 0.5 mm; (d) head (dorsal view)ι scale bar 0.2 mm; (e) propodeum and tergites 1–5 of metasoma (dorsal view)ι scale bar 0.5 mm; (f) wingsι scale bar 0.5 mm.
Global-scale greenhouse cultivation areas (Version 1)
<p>This data set provides spatially explicit estimates of the area at 3 meters resolution directly used for greenhouse cultivation on a global scale. It was derived using PlanetScope data and Sentinel-2 data for the year 2019. This data set does not cover all existing greenhouses across the globe. The data can be viewed here: <a title="Follow link" href="https://rs-cph.projects.earthengine.app/view/greenhouse" target="_blank" rel="nofollow noopener">https://rs-cph.projects.earthengine.app/view/greenhouse. </a><a target="_blank">Please contact xito@ign.ku.dk for data and inform us if you identify areas that require an update.</a></p> <p>Any usage must be solely for Noncommercial education or scientific research purposes, and publication in academic or scientific research journals. Licensee agrees that all such publications must include an attribution that clearly and conspicuously identifies Planet Labs PBC.</p>
Dataset for "Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision"
<h2>Overall</h2> <p>A strawberry dataset for the paper "Qi Yang, Licheng Liu, Junxiong Zhou, Mary Rogers, Zhenong Jin, 2024. Predicting the growth trajectory and yield of greenhouse strawberries based on knowledge-guided computer vision, Computers and Electronics in Agriculture, 220, 108911. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.compag.2024.108911" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.compag.2024.108911</a>"</p> <h2>Plant traits measurements</h2> <p>The folder "measurement.zip" includes treatment-level and fruit-level ground truth data. </p> <h3>Treatment-level</h3> <pre><code>data_dryMatter_2022.csv data_dryMatter_2023.csv data_freshMatter_2022.csv data_freshMatter_2023.csv data_fruitNumber_2022.csv data_fruitNumber_2023.csv data_plantBiomass_2022.csv data_plantBiomass_2023.csv</code></pre> <h3>Fruit-level</h3> <p>Fruit conditions with five classes, 1-5 represent Normal, Wizened, Malformed, Wizened & Malformed, and Overripe, respectively.</p> <pre><code>data_size_freshWeight_condition_2022_0N.csv data_size_freshWeight_condition_2022_50N.csv data_size_freshWeight_condition_2022_100N.csv data_size_freshWeight_condition_2022_150N.csv</code></pre> <p>Fruit size for tagged fruits</p> <pre><code>data_taggedFruit_diameter_2022.csv data_taggedFruit_diameter_2023.csv data_taggedFruit_length_2022.csv data_taggedFruit_length_2023.csv</code></pre> <p>Fresh yield and lifespan for tagged fruits (only available in experiment 2023)</p> <pre><code>data_taggedFruit_freshMatter_2023.csv data_taggedFruit_lifespan_2023.csv</code></pre> <h3>Weather data</h3> <pre><code>weather_daily_2022.csv weather_daily_2023.csv</code></pre> <h2>Image data with label</h2> <h3>Object and phenology detection</h3> <p>The folder "strawberry_img_random.zip" contains images and the corresponding JSON labels for object and phenological stages detection.</p> <h3>Fruit size and decimal phenological stage</h3> <p>The folder "strawberry_img_tagged.zip" contains images and the corresponding JSON labels for fruit size and decimal phenological stages detection.</p> <pre><code>For example, "label": "small g, 8.84, 7.62, 0.4", This label means the fruit has an 8.84mm diameter and 7.62mm length, with the main stage being small green and the decimal stage being DS-4 </code></pre> <h3>Merge and split Data</h3> <p>A Python script, "datasetProcessing.py", can be used to merge and split the image data into training and testing set.</p> <h3>Pre-trained models</h3> <p>models.zip</p> <p> </p> <p><em>Data collector: Dr. Qi Yang, University of Minnesota, USA. Email: qiyang577@gmail.com</em></p> <p><em>All the files belong to Prof. Zhenong Jin, University of Minnesota, USA. Email: jinzn@umn.edu</em></p>
Dataset on laboratory soil greenhouse gas fluxes and soil microbial parameters as affected by soil management strategies in SOMMIT long term experiments
<p>We present a database containing over 50 soil-microbial parameters from eight long-term European experiments where specific soil management strategies are compared. Intact topsoil cores (7 cm diameter, 7 cm height) were collected from LTE participating in the tasks WP3.2 and WP3.3. from the SOMMIT experiment, and incubated under standard conditions in the laboratory. The soil management strategies considered at the eight LTEs ("ACBB Estrées-Mons, France", "Rutzendorf_17, Austria", , "Foggia, Italy", "ULBF-Ljubljana, Slovenia", "Toholampi, Finland", "Senés, Spain", "La Poveda, Spain" and "Grabow 1, Poland") included crop residue management, addition of different types of compost and sludge and biochar addition. All LTEs are referenced according to the LTE Index from https://doi.org/10.5281/zenodo.7598122 . The soil cores were subjected to i) a pre-equilibration phase of five days ("pre.inc"), followed by ii) a drying phase ("DR") of ten days and finally a iii) rewetting (five days, "RW"). A subset of the cores were kept under iv) constantly moist conditions for comparison purposes ("moist"). By the end of the "pre.inc" phase, soil microbial biomass, soil microbial community (phospholipidic fatty acids), enzymatic activities and soil nutrient data were collected. After the "pre.inc" phase, soil fluxes of N2O and CO2 were monitored with an automated system at subdaily temporal resolution. In addition, soil nutrients and microbial biomass data were estimated during at the end of the "DR", "RW" and "moist" phases. This dataset contributes to a better understanding of the linkages between soil conditions, soil microbes and soil greenhouse gas fluxes as affected by different soil management strategies across European agro-ecosystems. This product is part of the EJP SOIL internal project SOMMIT, and serves as deliverable WP3.4</p>
Field data of soil greenhouse gas fluxes from SOMMIT long-term experiments
<p>This is a database of field data of soil greenhouse gas fluxes and ancillary data from long-term experiments (LTEs) that participated in the task 1 of the work package 3 from SOMMIT. As of November 2024 (V 1.0) six LTEs have contributed with data: "ACBB Estrées-Mons, France", "Rutzendorf_17, Austria", "Maintainance of organic orchards, Italy", "Fagna, Italy", "ULBF-Ljubljana, Slovenia" and "Grabow 2, Poland". All LTEs are referenced according to the LTE Index from <a href="https://doi.org/10.5281/zenodo.7598122" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7598122</a>. The data includes soil greenhouse gas fluxes (N2O, CO2 and CH4) under different management practices, as well as ancillary data that might be useful to explain the observed fluxes. The database includes metadata on how soil greenhouse flux data and ancillary data were collected. Individual soil gas flux estimates are also expressed in CO2 equivalents [mg CO2-eq m-2 h-1], thereby providing a dataset on the contribution of soil CO2, N2O and CH4 fluxes to the soil global warming potential. This product is part of the EJP SOIL internal project SOMMIT, and serves as deliverables WP3.2 and WP3.3.</p>
Reduction of iron-organic carbon associations shifts net greenhouse gas release after initial permafrost thaw
<p>This dataset contains data associated with the manuscript "Reduction of iron-organic carbon associations shifts net greenhouse gas release after initial permafrost thaw". doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.soilbio.2025.109735" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.soilbio.2025.109735</span></span></a></p>
Beaufort Gyre Liquid Freshwater Content Change under Greenhouse Warming from an Eddy-resolving Climate Simulation
<p>This archive contains data and MATLAB code used to generate figures in the manuscript "Beaufort Gyre Liquid Freshwater Content Change under Greenhouse Warming from an Eddy-resolving Climate Simulation". </p>
AgRobTomato Dataset: Greenhouse tomatoes with different ripeness stages
<p><strong>Dataset of greenhouse tomatoes for object detection in Pascal VOC.</strong></p> <p>This dataset was collected on two different days (August 6 and 8, 2020) at a greenhouse in Barroselas, Viana do Castelo, Portugal.</p> <p>Mobile robot AgRob v16, controlled by a human operator, was guided through the greenhouse inter-rows and captured RGB images of the tomato plants using a ZED camera, recording them as a video in a single ROSBag file. The video was converted into images by sampling a frame every 3 seconds to reduce the correlation between images but ensuring an overlapping ratio of about 60%. The images collected on the two days were merged, resulting in a dataset of 449 images with a resolution of 1280x720 px each.</p> <p>The images captured on August 8, 2020 were already publicly available at INESC TEC Research Data Repository (see: https://rdm.inesctec.pt/dataset/ii-2021-001).</p> <p>Manually labelled using the CVAT annotation tool, considering 4 ripeness classes:</p> <ul> <li>Unriped;</li> <li>Breaking Stage;</li> <li>Reddish;</li> <li>Riped;</li> </ul> <p><strong>Framed within the activities of the ROBOCARE project, P2020 developed by INESC TEC (https://www.inesctec.pt/pt/projetos/robocare)</strong></p>
An improved carbon greenhouse gas simulation in GEOS-Chem version 12.1.1
<p>The files represent all the modelled data used for the work "An improved carbon greenhouse gas simulation in GEOS-Chem version 12.1.1" by Beata Bukosa, Jenny A. Fisher, Nicholas M. Deutscher and Dylan B. A. Jones, submitted to Geoscientific Model Development (GMD), 2021.</p> <p><br> The files include:</p> <p> </p> <ul> <li>GEOS-Chem v12.1.1 source code with the new coupled simulation implemented</li> <li>The run directories for all the simulations with the specific input files defining all the setup, restart files and ATom input files</li> <li>Specific GEOS-Chem input files that are shown in the paper (i.e., OH used in all simulations)</li> <li>The output fields from all the GEOS-Chem simulations used in the analysis presented in the paper</li> <li>All the codes used for the analysis in the paper and for creating the plots</li> <li>A README file that has detailed information about all the uploaded files and directory structure</li> </ul>
Data for "Changing spatial distribution of water flow charts major change in Mars' greenhouse effect"
<p>Data for "Changing spatial distribution of water flow charts major change in Mars’ greenhouse effect". The script "summaryplot_v2.m" generates the summary figures used in the paper. The script "GCM_data_comparison_v5.m" generates additional figures used in the paper. The full underlying temperature output from the GCM runs listed in Table S1 is given in the allTsurf.txt file within the corresponding numbered subdirectory.</p>
Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite
<p>Scripts and results for our "Remotely Sensing River Greenhouse Gas Exchange Velocity Using the SWOT Satellite" manuscript.</p> <p>Consult the README file for a more detailed description of the data, results, and scripts.</p>
Greenhouse plant-soil feedback experiment at Cedar Creek Ecosystem Science Reserve
<p>We conducted a reciprocal greenhouse experiment to examine how the growth of prairie grass species depended on the soil communities conditioned by conspecific or heterospecific plant species in the field. The source soil came from monocultures in a long-term competition experiment (LTCE, Cedar Creek Ecosystem Science Reserve, MN, USA). Within the LTCE, six species of perennial prairie grasses were grown in monocultures or in eight pairwise competition plots for 12 years under conditions of low and high soil nitrogen availability. In six cases, one species clearly excluded the other; in two cases, the pair appeared to coexist. In year 12, we gathered soil from all 12 soil types (monocultures of six species by two nitrogen levels) and grew seedlings of all six species in each soil type for seven weeks.</p>
CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
cGENIE model experiment results for 'Sensitivity of ocean circulation to warming during the Early Eocene greenhouse'
Open the record for dataset details and reuse information.
Greenhouse gas (GHG) during the Atmospheric Research Expedition to Abu Dhabi (AREAD)
Open the record for dataset details and reuse information.
plant trial at greenhouse laboratory
<p>Plants under testing at the green house of ICARDA in Morocco, April 2024</p>
Roles of the Labrador Current in the Atlantic Meridional Overturning Circulation responses to greenhouse warming
<p>This archive contains data and MATLAB code used to generate figures in the paper "Roles of the Labrador Current in the Atlantic Meridional Overturning Circulation responses to greenhouse warming". </p>
Ambitious hydropower plans will accelerate greenhouse gases emissions from the Hindu-Kush Himalaya region
<p>01-<em>07: Database of GHG emisisons from existing and future hydropower plants in the Hindu-Kush Himalaya region. 08</em> : Source codes for simulating reservoir flooded area and GHG fluxes.</p>
(DATA) Exploring the potential of boron-nitride nanobelts in environmental applications: greenhouse gases capture
<ul> <li>Input structures.</li> <li>Scrip to run all the calculations.</li> <li>Output files from several calculations.</li> </ul>
Optimizing Cucumber Growth: Integrating Smart Irrigation with various Fertilization Strategies in Greenhouse Conditions
<div> <div>Supplemental materials for:</div> <div>"Optimizing Cucumber Growth: Integrating Smart Irrigation with various Fertilization Strategies in Greenhouse Conditions", under submission.</div> <br> <div>The 'datas.xlsx' file contains tables of raw data collected from the experiment site.</div> <div> </div> <div>Features:</div> <div> <ul> <li>Leaf Area</li> <li>Iwp</li> <li>Photosynthesis</li> <li>Yield</li> <li>Fruit Quality</li> <li>Summary</li> </ul> </div> </div>
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.