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Cowpea drought stress application at early vegetative stage 2022 - screen 01 - day 02 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 01 and day 2 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 02 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 00 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 0 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 12 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 12 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 10 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 10 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 14 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 14 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 02 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 02 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 02 - day 04 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 02 and day 04 after drought stress application. </p>
NDVI-based monitoring of long-term vegetation dynamics and responses to multi-time scales droughts in Inner Mongolia
<p>The characteristics of vegetation and drought for different seasons between 1982 and 2015 in Inner Mongolia were studied based on the Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The response of vegetation to drought over various time scales for different seasons and vegetation types was investigated using the maximum Pearson correlation, allowing a discussion about the possible causes of any changes. The results indicate that the vegetation NDVI in Inner Mongolia showed an increasing trend in different seasons, with spring vegetation NDVI (April to May) having the largest significant increasing rate, followed by the growing season (April to October), autumn (September to October), and summer (June to August). Accordingly, the proportion of stations with decreasing SPEI was, in descending order, summer, growing season, autumn, and spring. Additionally, the magnitude of the SPEI decrease was greater in eastern Inner Mongolia. NDVI and SPEI were positively correlated in most regions of Inner Mongolia, indicating that changes in vegetation in most parts of this region were affected by the spatial and temporal characteristics of drought, the correlation being them being strongest in the growing season, followed by summer, then spring and autumn. Considering the different types of vegetation, forests were less affected by drought, with broadleaf forests more affected than coniferous forests. The meadow steppes and typical steppes were more affected by 12-month droughts in the growing season and summer, 6-month droughts in spring, and 3-month droughts in autumn, with desert steppes mainly affected by 3-month droughts. The shrubs, sandy vegetation, and cropland were mostly affected by droughts in summer, and show a greater response to 3-month droughts in autumn. Finally, the water balance was found to be the most important factor affecting the response of vegetation to drought in Inner Mongolia.</p>
Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 02 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 03 and day 02 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 00 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 03 and day 00 after drought stress application. </p>
Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 06 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 03 and day 06 after drought stress application.</p>
Cowpea drought stress application at early vegetative stage 2022 - screen 03 - day 04 - raw image data
<p>Cowpea (<em>Vigna unguiculata</em>) miniCore accessions were screened for responses to drought stress at early vegetative stress. The cowpea seedlings were exposed to drought stress at 17 days after germination using the weight of the pot and AAWEsmo device, developed in Julkowska Lab, Boyce Thompson Institute. The seedlings were kept at 60 and 10% of soil water holding capacity for 2 weeks and the data on cowpea shoot size, evapotranspiration and photosystem II efficiency was collected. </p> <p>This dataset represents the images collected for Screen number 03 and day 04 after drought stress application.</p>
Similar vegetation-geomorphic disturbance feedbacks shape unstable glacier forelands across mountain regions
<p>Glacier forelands are among the most rapidly changing landscapes on Earth. Stable ground is rare as geomorphic processes move sediments across large areas of glacier forelands for decades to centuries following glacier retreat. Yet, most ecological studies sample exclusively on stable terrain to fulfil chronosequence criteria, thus missing potential feedbacks between geomorphic disturbances and vegetation colonization. By influencing vegetation and soil development, such vegetation-geomorphic disturbance feedbacks could be crucial to understand glacier foreland ecosystem development in a changing climate. We surveyed vegetation and environmental properties, including geomorphic disturbance intensities, in 105 plots located on both stable and unstable moraine terrain in two geomorphologically active glacier forelands in New Zealand and Switzerland. Our plot data showed that geomorphic disturbance intensities changed permanently from high/moderate to low/stable when vegetation reached cover values around 40%. Around this cover value, species with response and effect traits adapted to geomorphic disturbances dominated. This suggests that such species can act as 'biogeomorphic' ecosystem engineers that stabilize ground through positive feedback loops. Across floristic regions, biogeomorphic ecosystem engineer traits creating ground stabilization, such as mat growth and association with mycorrhiza, are remarkably similar. Non-metric multidimensional scaling revealed a linked sequence of decreasing geomorphic disturbance intensities and changing species composition from pioneer to late successional species. We interpret this linked geomorphic disturbance-vegetation succession sequence as 'biogeomorphic succession', a common successional pathway in unstable river and coastal ecosystems across the world. Soil and vegetation development were related to this sequence, and only advanced once biogeomorphic ecosystem engineer species covered 40–45% of a plot, indicating a crucial role of biogeomorphic ecosystem engineer stabilization. Different topoclimatic conditions could explain variance in biogeomorphic succession timescales and ecosystem engineer root traits between the glacier forelands. As glacier foreland ground is widely unstable, we propose to consider glacier forelands as 'biogeomorphic ecosystems' in which ecosystem structure and function are shaped by geomorphic disturbances and their feedbacks with adapted plant species, similar to rivers and coasts.</p>
Functional stability of vegetation following biocontrol of an invasive riparian shrub
<p>Understanding plant community response to environmental change is a crucial aspect of biological conservation and restoration, but species-based approaches are limited in that they do not reveal the underlying mechanisms driving vegetation dynamics. An understanding of trait-environment relationships is particularly important in the case of invasive species which may alter abiotic conditions and available resources. This study is the first to measure the functional response of riparian plant communities to biocontrol of an invasive species. We focused on an invasive shrub <em>Tamarix</em> (saltcedar) that is defoliated by a beetle that was released by the US Department of Agriculture along the Upper Colorado River (southwestern United States). We calculated community weighted means and functional dispersion of individual traits, multivariate functional dispersion and species diversity. We used linear mixed effect models (LME) to compare these metrics at paired vegetation patches dominated and not dominated by <em>Tamarix</em> during cycles of defoliation and refoliation over eight years. We found that community-weighted average trait values, species diversity and functional dispersion changed little in response to defoliation, and instead seemed to be responding to fluctuations in yearly precipitation. Average height and seed weight were greater in <em>Tamarix</em>-dominated patches relative to control patches. Functional dispersion followed a similar trajectory to species diversity, but was a more sensitive indicator of plant community change. We showed that riparian vegetation can be resilient to <em>Tamarix</em> biocontrol, and that defoliation might not necessarily always lead to substantial changes in ecosystem function.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2019 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2019 (001–177). Please <em><a href="https://zenodo.org/record/7500321#.Y7S1dNVBw2y"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7499330#.Y7OTKdVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2019 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2019 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (185–361)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2018 (185–361). <em>Please <a href="https://zenodo.org/record/7502675#.Y7WAotVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (001–177)</strong> and <em><a href="https://zenodo.org/record/7498056#.Y7OSfdVBw2x"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2019 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
Cloud forests of the Orinoco River Basin (Colombia): Variation in vegetation and soil macrofauna composition along the hydrometeorological gradient
<p>We present vegetation, soil macrofauna, soil, hydrometeorological and topographical data collected from Tropical Montane Cloud Forests in the Orinoco River basin. Specifically, from the municipality of Chámeza, department of Casanare, Colombia. These data sets were used to evaluate how vegetation and soil macrofauna diversity vary along the 1700–2200 m a.s.l. elevation range. Within this elevation range, we have previously described a hydrometeorological gradient largely driven by a fog incidence increase with elevation. Vegetation data were collected for all individuals with a diameter at breast height (DBH) > 5 cm in four vegetation plots (5 x 50 m; total: 0.1 ha) every 100 m in altitude between 1700–2200 m a.s.l. From each plot, we obtained three soil monoliths from the organic layer and three from the mineral horizon, and manually extracted their soil macrofauna, and soil samples for determining pH, organic matter content, and soil texture, among others in a soil laboratory. Topographical data was inferred from Digital Elevation Models. Hydrometeorological data was collected in a previous study, but it was interpolated to the sampling plots. Here we present the interpolated hydrometeorological data.</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2018 (001–177)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2018 (001–177). <em>Please <a href="https://zenodo.org/record/7503627#.Y7ZZEn1ByUk"><strong><em>click</em> here</strong></a> to download the MUSES FVC product <strong>in 2017 (185–361)</strong></em>, and <em><a href="https://zenodo.org/record/7500321#.Y7TEIdVBw2x"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2018 (185–361</strong></em><em><strong>)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2018 (001–177)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 250m SIN Grid in 2017 (185–361)
<p>The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (<a href="https://muses.bnu.edu.cn/">https://muses.bnu.edu.cn/</a>).</p> <p>This dataset is the MUSES global FVC product at 250m spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 250m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous.</p> <p>This dataset is the MUSES FVC product in 2017 (185–361). <em>Please <a href="https://zenodo.org/record/7505201#.Y7durn1ByUk"><strong>click here</strong></a> to download the MUSES FVC product <strong>in 2017 (001–177)</strong> and <em><a href="https://zenodo.org/record/7502675#.Y7WENNVBw2x"><strong>click here</strong></a> to download the MUSES FVC produ</em>ct <strong>in 2018 (001–177)</strong></em>.</p> <p><strong>Dataset Characteristics:</strong></p> <ul> <li>Spatial Coverage: Global</li> <li>Temporal Coverage: 2017 (185–361)</li> <li>Spatial Resolution: 250m</li> <li>Temporal Resolution: 8 days</li> <li>Projection: Sinusoidal</li> <li>Data Format: HDF</li> <li>Scale: 0.01</li> <li>Valid Range: 0 – 100</li> </ul> <p><strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong></p> <ol> <li>Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337.</li> </ol> <p>If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).</p>
ScienceDex guides
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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.