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Supplementary material 5 from: Augustinus BA, Lommen STE, Fogliatto S, Vidotto F, Smith T, Horvath D, Bonini M, Gentili RF, Citterio S, Müller-Schärer H, Schaffner U (2020) In-season leaf damage by a biocontrol agent explains reproductive output of an invasive plant species. NeoBiota 55: 117-146. https://doi.org/10.3897/neobiota.55.46874
Figure S2
Supplementary material 2 from: Augustinus BA, Lommen STE, Fogliatto S, Vidotto F, Smith T, Horvath D, Bonini M, Gentili RF, Citterio S, Müller-Schärer H, Schaffner U (2020) In-season leaf damage by a biocontrol agent explains reproductive output of an invasive plant species. NeoBiota 55: 117-146. https://doi.org/10.3897/neobiota.55.46874
Census dates
Supplementary material 1 from: Augustinus BA, Lommen STE, Fogliatto S, Vidotto F, Smith T, Horvath D, Bonini M, Gentili RF, Citterio S, Müller-Schärer H, Schaffner U (2020) In-season leaf damage by a biocontrol agent explains reproductive output of an invasive plant species. NeoBiota 55: 117-146. https://doi.org/10.3897/neobiota.55.46874
Study sites
Supplementary material 6 from: Augustinus BA, Lommen STE, Fogliatto S, Vidotto F, Smith T, Horvath D, Bonini M, Gentili RF, Citterio S, Müller-Schärer H, Schaffner U (2020) In-season leaf damage by a biocontrol agent explains reproductive output of an invasive plant species. NeoBiota 55: 117-146. https://doi.org/10.3897/neobiota.55.46874
Successful raceme formation
Model Outputs for Lobster Farm location Statistics
<p>Results of the Delft3D model for summer and winter scenarios to look at how the current velocities vary between the different areas of the lobster farm, results compared using ANOVA and post hoc statistical tests.</p>
CNRM-CM6-conv5 AMIP simulation monthly output (pr, ts, areacella)
<p>The present dataset consists in monthly output of an AMIP simulation performed with the CNRM-CM6-conv5 model, covering the period 1979-2008.</p> <p>CNRM-CM6-conv5 is the same model as CNRM-CM6-1 (<a href="https://doi.org/10.1029/2019MS001683">Voldoire et al. 2019</a>, <a href="http://www.umr-cnrm.fr/cmip6/references">http://www.umr-cnrm.fr/cmip6/references</a>) except that the convection scheme was replaced by the one previously used in CNRM-CM5.1 (<a href="https://doi.org/10.1007/s00382-011-1259-y">Voldoire et al. 2013</a>), namely the convection scheme developed by <a href="https://doi.org/10.1175/1520-0493(1985)113<2108:ASPOTL>2.0.CO;2">Bougeault (1985)</a>. The details of the atmospheric component of CNRM-CM6-1 (ARPEGE-Climat 6.3) and those of the AMIP simulation are discussed in <a href="https://doi.org/10.1029/2020MS002075">Roehrig et al. (2020)</a>.</p> <p>3 netCDF files are provided in this dataset, corresponding to surface precipitation (pr), surface temperature (ts) and model grid cell area (areacella). The netCDF file format follow the CF-1.7 and CMIP-6.2 conventions.</p>
Figure S1 - Time-series and trends of VIs, DIN-output and S-input for all catchments
<p>Figure S1: Trends (dotted lines) in long-term standard scores for measured sulfur deposition (S-input) and stream water total dissolved inorganic nitrogen export (DIN-output) during the observation period 1994-2016 and trends in vegetation indices extracted from Landsat satellite data for the observation period 1984-2016 for all catchments</p> <p> </p> <p>Table S1: Linear regression coefficients of trend lines in Figure S1</p>
Output from oil drift simulations for NORSE19 experiment
<p>Output from oil drift simulation simulations for the NORSE2019 oil-on-water experiment conducted in the North Sea June 2019. The data and experiment is described in Brekke et al., Integration of multi-sensor datasets and oil drift simulations - a free floating oil experiment in open ocean, to be submitted to JGR Oceans June 2020.</p> <p> </p> <p>The dataset consists of 4 netCDF files, corresponding to 4 simulations using different forcing/input data:</p> <p>- drifter_sailbuoy_wdf2.nc<br> Current from CODE drifter, 2% wind from sailbuoy, Stokes drift from forecast model<br> This is considered the reference simulation, with near perfect agreement with observed oil drift.</p> <p>- drifter_sailbuoy_wdf3.nc<br> Current from CODE drifter, 3% wind from sailbuoy, no Stokes drift- none_sailbuoy_wdf3.nc<br> No current or Stokes drift, only 3% of wind from sailbuoy</p> <p>- model_model_wdf2.nc<br> Current, wind (2%) and Stokes drift from forecast models.</p> <p> </p> <p>The data format is described on <a href="https://opendrift.github.io/">https://opendrift.github.io/</a></p>
Supporting model output for article "Relative Impact of Sea Ice and Temperature Changes on Arctic Marine Production"
<p>The following is a summary of processed model output data from a series of HiLAT model runs. A description of the model runs and visualization of model output and analysis can be found in the accompanying manuscript.</p> <p>Gibson, G.A., Weijer, W., Jeffery, N. and Wang, S (2020) Relative impact of sea ice and temperature changes on Arctic marine production. Journal of Geophysical Research: Biogeosciences. DOI: 10.1029/2019JG005343</p> <p>The model outputs and accompanying research was supported by the National Science Foundation 173886 and by DOE | SC |Biological and Environmental Research (BER).</p> <p>There are 4 versions of most output file types, corresponding to the 4 experiments that were conducted.</p> <p>These experiments are:</p> <p>i) the baseline model simulation (Base);</p> <p>ii) a moderate ice reduction experiment (S1);</p> <p>iii) a dramatic ice reduction experiment (S2); and</p> <p>iv) an atmospheric warming scenario (RCP45).</p> <p>Each of the 4 experiments is described in detail in Gibson et al (2020).</p> <p>Many variables in the output files are regional averages where model regions are indicated by a number *note - for aesthetics, the numbering used in the model output files differs slightly from the numbering used in the accompanying manuscript. </p> <p>Bering Sea =1</p> <p>Chukchi Sea =2 </p> <p>East Siberian Sea =3</p> <p>Laptev Sea =4 </p> <p>Beaufort Sea =5</p> <p>Barents Sea =6</p> <p>Canadian =7 </p> <p>Eurasian Basins =8 </p> <p>Nordic Seas =9</p> <p>Labrador Sea=10</p> <p>Kara Sea =11</p> <p> </p> <p><strong>Grid </strong></p> <p>gx1v6_polar_mask.10.12.18.nc This file is the original grid file used for all model runs with the addition of Arctic_Region and Antarctic_Polar_Front variables which indicates which grid cells are in the model regions described above.</p> <p> </p> <p><strong>Forcing</strong></p> <p>ncep_RCP45 ncep.t_10.T62.*.nc where * is 1948-2009 These files contain the modified atmospheric forcing used for the RCP45 model experiment. The unmodified NCEP forcing is freely publically available elsewhere to not duplicated here</p> <p> </p> <p><strong>Ice </strong></p> <p>Ice-Regional-Monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the monthly averaged ice variables (ice area, ice algae production) for each model region for the last ten years of the model run.</p> <p> </p> <p>cice-monthly-177-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file are the monthly averaged output filed from the cice model. Variables are averaged over each model analysis region.</p> <p> </p> <p>Ice_snow_depth_region_Avg-137-186-*.nc where * indicates model run - Base, S1, S2 or RCP45. These file contain the regionally averaged ice and snow depth for each analysis region.</p> <p> </p> <p>IceVol_Base-137-186.nc Contains ice variable file for the baseline model run. Model contains ice variables hi and aice for every grid cell of the model domain. Ice volume was computed as hi * Tarea (from grid file).</p> <p> </p> <p>Annual-SummerAvg-IceArea-177-186_*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average ice area averaged over Summer months (JuneAugust) for last 10 years of model run (200-2009) for each grid cell in the HiLAT model</p> <p> </p> <p><strong>Ocean variables </strong></p> <p>TempNitrate TEMP_NO-177-186.nc This file contains model estimates of temperature and nitrate for the baseline model run for model years 177-186 (2000-2009, the last en years of the model run) for the upper five model layers (500, 1500, 2500, 3500, 4500 cm).</p> <p> </p> <p><strong>CO2flux </strong></p> <p>AnnAvgAllCflux*.nc</p> <p>Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (200-2009) for each grid cell of the HiLAT model.</p> <p> </p> <p>CO2flux_region_avg-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. Each of these files contains the annual average CO2 Flux for last 10 years of model run (2000-2009) for each region of the HiLAT model.</p> <p> </p> <p><strong>Ocean Production </strong></p> <p>OceanProd_Year-147-156-*.nc OceanProd_Year-157-166-*.nc OceanProd_Year-167-176-*.nc OceanProd_Year-177-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each grid cell. Each file contains 10 model years is the sum of diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>Region_production_by_phyto_group-diat_ps-137-186-*.nc Where * indicates model run - Base, S1, S2 or RCP45. These file contain the annual average vertically integrated ocean phytoplankton production for each region. Each file contains group specific production for diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p>OceanProd_Daily-177-186-BASE.nc This file contains the daily average vertically integrated ocean phytoplankton production for each grid cell. The file contains 10 model years. Production is the sum of all four phytoplankton groups - diatoms, diazotrophs, phaeocystis, and smaller phytoplankton.</p> <p> </p> <p><strong>PAR </strong></p> <p>PAR-MonthyAvg-177-168-*.nc</p> <p>Photosynthetically Active Radiation in the ocean. Where * indicates model run - Base, S1, S2 or RCP45.</p>
LTP of inhibition at PV interneuron output synapses requires developmental BMP signaling
<p>This dataset represents the raw data that gave rise to the study by Vickers et al., 2020 in Scientific Reports (<a href="https://doi.org/10.1038/s41598-020-66862-5">DOI: 10.1038/s41598-020-66862-5</a>). Please refer to the original publication regarding experimental design and methodological details of data acquisition and analysis. Below we supply information on the provided metadata files (which, in turn, refer to individual raw data files), and essential details on the specific data formats:</p> <ol> <li>raw data is organized in the datasets related to Figs. 1, 2, 3, 4-E, 4F-H of the paper (Vickers et al., 2020);</li> <li>the metadata for each individual dataset, listing individual data filenames from the respective dataset, are stored in separate “.csv” files, one per each dataset. Field separator: comma;</li> <li>individual metadata files for each dataset are described in the master metadata file “metadata_master.csv”;</li> <li>patch clamp recordings (Figs. 1-3, 4F-H) are provided as “.dat” files, unmodified from the original version created by the acquisition software PatchMaster (HEKA Elektronik, Germany). Besides original PatchMaster software, these files can be imported using one of the following methods: I) via Igor Pro extension bpc_ReadHeka.xop (for 32-bit Igor Pro versions 5.xx - 6.37) by Holger Taschenberger (<a href="https://www.wavemetrics.com/project/bpc_ReadHeka">https://www.wavemetrics.com/project/bpc_ReadHeka</a>); II) via Python script by Luke Campagnola (<a href="https://github.com/campagnola/heka_reader">https://github.com/campagnola/heka_reader</a>); III) via Matlab script HEKA PatchMaster Importer by Christian Keine (<a href="https://github.com/ChristianKeine/HEKA_Patchmaster_Importer">https://github.com/ChristianKeine/HEKA_Patchmaster_Importer</a>);</li> <li>exact names of patch-clamp traces used for each Figure are provided in the corresponding “.csv” metadata files. Traces names are given in the format: ExperimentName_1_SeriesNumber_SweepNumber_ChannelNumber. When “*” is used, it means that every sweep of corresponding series was used for analysis (e.g. Nov0317_1_13_*_2 all sweeps from series number 13 were used). Description in the metadata files also specifies which series correspond to which experimental epoch (baseline or plasticity testing), and to pharmacological manipulations, if applicable;</li> <li>widefield fluorescent images of coronal brain slices, cropped images of subregions in the primary auditory cortex used for quantification, and confocal Z-stacks zooming onto single nuclei of neurons stained for FISH experiments (Fig. 4A-E) were converted into composite TIFF format from the proprietary formats of Axioscan Z1 slide scanning microscope and LSM700 confocal microscope (Carl Zeiss). TIFF format is readable by FIJI/ImageJ (<a href="https://fiji.sc/">https://fiji.sc/</a> or <a href="https://imagej.net/Fiji/Downloads">https://imagej.net/Fiji/Downloads</a>). Pixel resolution and voxel sizes are embedded in these TIFF files. Assignment of the color channels for each image is described in the metadata file.</li> </ol>
Numerical model output data for publication "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low"
<p>Contains MITgcm model output data to reproduce the analyses of the paper "Control of the oceanic heat content of the Getz-Dotson Trough, Antarctica, by the Amundsen Sea Low", by Dotto and co-authors, published in Journal of Geophysical Research-Oceans (doi: 10.1029/2020JC016113). The model simulation is presented and described in Kimura et al. (2017; doi: 10.1002/2017JC012926) and in Dotto et al. (2019; doi: 10.1175/JPO-D-19-0064.s1). See the 'ReadMe.txt' file for a description of the data. </p> <p> </p>
Sea salt optical modeling - output data
<p>Model data for optical calculations of dried sea salt aerosol</p> <p>This repository provides the raw output data used for plots in a manuscript submitted to the Journal of Geophysical Research: Atmospheres.</p> <p>The folders contain optical data from calculations performed with the discrete dipole approximation (DDA) code ADDA (version 1.3b4) and the T-matrix code Tsym (version 6.6α) for dried sea salt aerosol particles. The output of each code was saved in the folder with the respective name.</p> <p>For an interpretation of the raw output, please see documentations of the respective codes [1,2].</p> <p>The subdirectory adda contains results for four main types of geometries (cubes, Gaussian random cubes, convex polyhedral, and non-cubical superellipsoids). In addition, the results for these geometries an additional subfolder for calculations performed to gauge the uncertainties stemming from dipole spacing and orientation averaging. Uncertainty estimated were performed for two superellipsoids with a roundness parameter of n=e=0.0 (i.e., a cube) and a roundness parameter of n=e=0.2 (i.e., a rounded cube) respectively. The subdirectory Tsym contains results for superellipsoids. For definitions of the shapes, please see the manuscript.</p> <p>The naming of the folders within the subdirectories reflects the changes of the parameters investigated.</p> <p>Version 2 of the data set contains the files contained in Version 1, as well as, the results from additionally performed calculations and additional README files. The additionally performed calculations include further calculations related to impact of dipole spacing and number of orientations considered (in case of ADDA calculations) and an additional refractive index (in case of Tsym calculations).</p> <p> </p> <p> </p> <p>[1] ADDA (version 1.3b4): <a href="https://github.com/adda-team/adda/releases/tag/v1.3b4">https://github.com/adda-team/adda/releases/tag/v1.3b4</a></p> <p>[2] Tsym (version 5.2): <a href="https://github.com/michaelkahnert/Tsym-5.2">https://github.com/michaelkahnert/Tsym-5.2</a></p> <p>(The Tsym output files do not differ between versions 5.2 and 6.6α)</p>
Rapid Radiative Transfer Model Output Evaluating Cryospheric Surface Emissivities
<p>Rapid Radiative Transfer Model (RRTM) (Mlawer, et al.,1997) output use to evaluate three cryospheric surface emissivities spectrally across three atmospheric profiles. README file attached provides information regarding output format. </p>
Input and output model data for Horvath et al. 2020 (https://doi.org/10.5194/bg-2020-149)
<p>Please see "readme" file. </p>
Demeter - Input and Output Data
<p>This dataset includes input and output data for 20 combinations of GCM/RCP scenarios for the Demeter model. The GCM and RCP selected for the study of Argentina Energy-Water-Land Systems are MIROC-ESM-CHEM and RCP 6.0.</p>
Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators
<p>This upload contains the data and code used in the following paper:</p> <p>J. T. Kavulich, B. P. Van Deren, and M. Schlosshauer, “Searching for evidence of algorithmic randomness and incomputability in the output of quantum random number generators,” <em>Phys. Lett. A</em> 388, 127032 (2021), <a href="https://doi.org/10.1016/j.physleta.2020.127032">doi.org/10.1016/j.physleta.2020.127032</a></p> <p>The contents of the data set are as follows:</p> <p>1) Random strings for two QRNGs and four PRNGs. For each RNG, a zip archive provides 100 strings containing 25 x 2<sup>26</sup> = 1,677,721,600 bits each.</p> <p>2) C++ code for the Chaitin–Schwartz–Solovay–Strassen (CSSS) and Borel-normality tests (code.zip, 13 KB).</p> <p>The bulk of this code is not ours, but was written and made publicly available at <a href="https://www.cs.auckland.ac.nz/research/groups/CDMTCS/export/80_random_seqs">this link</a> by the authors of the following paper:</p> <p>A. A. Abbott, C. S. Calude, M. J. Dinneen, and N. Huang, <em>Phys. Scri.</em> 94 (2019) 045103, <a href="https://doi.org/10.1088/1402-4896/aaf36a">doi:10.1088/1402-4896/aaf36a</a></p> <p>We have made just a few small modifications to their original code:</p> <ul> <li>For the CSSS tests, a text file containing the Carmichael numbers (for tests 1–3) and odd composites up to 100 (for test 4) is read in and used to perform the tests. (Note: The set of Carmichael numbers used in the tests was generously provided to us by R. G. E. Pinch. Reference: R. G. E. Pinch, The Carmichael numbers up to 10<sup>21</sup>, in: A.-M. Ernvall-Hytönen (Ed.), Proceedings of Conference on Algorithmic Number Theory, Vol. 46, Turku Centre for Computer Science, Turku, Finland, 2007, pp. 129–131.)</li> <li>We combined the first and second CSSS tests into a single program.</li> <li>We reformatted the display of the output, and included a VERBOSE flag for additional status output.</li> </ul> <p>3) Results from CSSS and Borel-normality tests in Python format (results.zip, 22 KB). This archive also contains a Python script (analyze.py) that reads the result files, carries out the statistical analysis, and displays the plots.</p>
Data from: Piping Plover population increase after Hurricane Sandy mediated by immigration and reproductive output
<p>Evaluating population-level responses to conservation action following large-scale disturbance can improve the efficacy of future habitat conservation measures. In October 2012, Hurricane Sandy storm surges cleared vegetation and opened inlets through the barrier islands, Fire Island and Westhampton Island, New York, creating Piping Plover (Charadrius melodus) habitat. Storm effects prompted an island-wide stabilization project, which had the potential to negatively affect novel Piping Plover habitat. Certain sections of Fire Island were designed to create and/or improve habitat (hereafter, restoration areas) to mitigate possible habitat loss or degradation. Since Piping Plovers in New York appear to be habitat-limited, we anticipated positive population growth following habitat creation. From 2013–2018, we captured and banded 152 adults and 353 chicks, and monitored 279 nests and 160 broods. We developed an integrated population model to assess demographic processes in response to hurricane created-habitat (2013–2018) and the creation of restoration areas (2015–2018). We observed positive population growth in three of five years, and overall growth through the period (λ =1.13). Immigration and reproductive output were correlated with population growth (r = 0.92 [95% CI = 0.22–0.98], and 0.84 [95% CI = -0.47–0.95], respectively). Compared to the rest of the study area, restoration areas had higher chick survival, and lower nest survival and after second year site fidelity. The result was population growth in restoration areas (λ =1.14) similar to the whole study area. In the short term, restoration areas seemed to mimic natural Piping Plover habitat. Vegetation removal, an important process in renewing natural Piping Plover habitat, likely will be necessary to maintain habitat suitability. Efforts to increase immigration of new breeding adults into the system, and to improve reproductive output, primarily by habitat creation or maintenance, are likely to have the greatest local effect on population growth.</p>
Data from: Flexibility in the duration of parental care: female leopards prioritise cub survival over reproductive output
1.Deciding when to terminate care of offspring is a key consideration for parents. Prolonging care may increase fitness of current offspring, but it can also reduce opportunities for future reproduction. Despite its evolutionary importance, few studies have explored the optimal duration of parental care, particularly among large carnivores. 2.We used a 40-year dataset to assess the trade-offs associated with the length of maternal care in leopards in the Sabi Sand Game Reserve, South Africa. We compared the costs imposed by care on the survival and residual reproductive value of leopard mothers against the benefits derived from maternal care in terms of increased offspring survival, recruitment and reproduction. We also examined the demographic and ecological factors affecting the duration of care in light of five explanatory hypotheses: litter-size, sex-allocation, resource-limitation, timing-of-independence, and terminal-investment. 3.Duration of care exhibited by female leopards varied markedly, from 9–35 months. Mothers did not appear to suffer any short- or long-term survival costs from caring for cubs, but extending care reduced the number of litters that mothers could produce during their lifetimes. Interestingly, the duration of care did not appear to affect the post-independence survival or reproductive success of offspring (although it may have indirectly affected offspring survival by influencing dispersal distance). However, results from generalised linear mixed models showed that mothers prolonged care during periods of prey scarcity, supporting the resource-limitation hypothesis. Female leopards also cared for sons longer than daughters, in line with the sex-allocation hypothesis. 4.Cub survival is an important determinant of the lifetime reproductive success in leopards. By buffering offspring against environmental perturbation without jeopardizing their own survivorship, female leopards apparently 'hedge their bets' with current offspring rather than gamble on future offspring which have a small probability of surviving. 5.In many species, parents put their own needs before that of their offspring. Leopard mothers appear sensitive to their offspring's demands, and adjust levels of care accordingly.
Data from: Scientific output scales with resources. A comparison of US and European Universities
By using a comprehensive dataset of US and European universities, we demonstrate super-linear scaling between university revenues and their volume of publications and (field-normalized) citations. We show that this relationship holds both in the US and in Europe. In terms of resources, our data show that three characteristics differentiate the US system: (1) a significantly higher level of resources for the entire system, (2) a clearer distinction between education-oriented institutions and doctoral universities and (3) a higher concentration of resources among doctoral universities. Accordingly, a group of US universities receive a much larger amount of resources and have a far higher number of publications and citations when compared to their European counterparts. These results demonstrate empirically that international rankings are by and large richness measures and, therefore, can be interpreted only by introducing a measure of resources. Implications for public policies and institutional evaluation are finally discussed.
Data from: Male breeding experience, not mate familiarity, affects reproductive output in black brant geese
Individuals in species with long-term social monogamy often incur fitness costs after mate change. We evaluated the relative contributions of mate familiarity and male breeding experience to the costs of mate change of female black brant geese (Branta bernicla nigricans). We assessed the effects of mate change on relative nest initiation date, clutch size, brood size at hatch, growth rates of goslings, and prefledging survival of offspring. We modeled variation in relative nest initiation dates, clutch size, initial brood size, and gosling growth rates using generalized linear mixed models in Program R. We estimated prefledging survival with Cormack–Jolly–Seber models in Program MARK using reencounters of webtagged goslings. Mate change had a small effect on relative nest initiation date, clutch size, and initial brood size, but no effect on growth rates of brant goslings. Females breeding with an unfamiliar, but experienced male had goslings with higher prefledging survival rates (ϕ = 0.44±0.11 [95% confidence interval]) than those breeding with a familiar, experienced mate (ϕ = 0.30±0.04). However, females that changed mates and re-paired with an inexperienced male had goslings with substantially lower prefledging survival rates (ϕ = 0.20±0.04) than any other group of females. Our findings indicate that reproductive output by female brant is affected by male experience rather than mate familiarity. We believe that female brant typically benefit from mate retention because most will not acquire an experienced partner after mate change.
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.