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Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Access to Opioid Agonist Treatment during COVID-19 Project
<p>Data on methadone clinics and travel times used for research on opioid treatment access during COVID-19 public transport disruptions. For data on 2020 neighbourhood-level ambulance calls in Toronto, please contact "edau@toronto.ca".</p>
Raw dataset of Gerrit-based projects
<p>This archive contains data from Gerrit-based projects mined by <a href="https://github.com/JetBrains-Research/MR-loader/tree/main/core">MR-loader</a>. The data is presented as is, without any processing. A processed version of the dataset can be found <a href="https://zenodo.org/record/7817010#.ZEfDei8RrT8">here</a>.</p>
Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change (Data)
<p>This repository contains the data necessary to reproduce the study developed in Legrand et al. (2023) "Bayesian spatiotemporal modelling of wildfire occurrences and sizes for projections under climate change"</p>
Project Logo
<p>Designed logo for the project</p>
RISEUP project logo
<p>project logo</p>
Replication Package for ASE 2023 Paper "Personalized First Issue Recommender for Newcomers in Open Source Projects"
<p>This replication package contains a replication package for ASE 2023 paper titled "Personalized First Issue Recommender for Newcomers in Open Source Projects." This package includes a dataset of 68,858 issues from 100 GitHub projects, records of 123 manually labeled issue samples, and Python scripts for analyzing the data and evaluating models. The package is also stored in the GitHub repository <a href="https://github.com/mcxwx123/PFIRec">https://github.com/mcxwx123/PFIRec</a>.</p> <p>Required Environment</p> <p>We recommend setting up the required environment on a commodity Linux machine with at least 1 CPU Core, 8GB Memory, and 100GB empty storage space. Our experiments were conducted on an Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>Files and Replicating Results</p> <p>We used the GFI-bot database and the GitHub GraphQL API to collect features of 68,858 candidate issues and restore historical states of resolvers of 11,615 FIs (first issues).</p> <p>The followings are the files and replicating results:</p> <p>Dataset:</p> <p>The raw data of newcomer-issue pairs' features are stored in <code>ReplicationPackage/data/dataset_{bertmodel}_{num}.pkl</code>, where {bertmodel} is one of the four BERT-based language models: SIMCSE, RoBERTa, CodeBERT, and BERTOverflow, corresponding to the dataset whose textual features are extracted by one of the four language models. And {num} is 0 to 19, corresponding to the 20 chronological folds. The training sets of the GFI-Bot approach are contained in <code>ReplicationPackage/data/training_set_recgfi_simcse_{num}.pkl</code>. <code>ReplicationPackage/data/newcomerdata.json</code> contains first issues' title and description and their resolvers' total commit number and number of commits in the latest month, and <code>ReplicationPackage/data/processeddata.pkl</code> contains the 37 developers' features for the empirical study. <code>ReplicationPackage/data/isstexts.json</code> contains issues titles and descriptions for Stanik et al.'s approach.</p> <p>Python scripts:</p> <p><code>ReplicationPackage/empirical.py</code> is the script for reproducing all the results in Section III of the paper. <code>ReplicationPackage/model.py</code> is the script for reproducing all the results in Section IV of the paper.</p> <p>Records:</p> <p><code>ReplicationPackage/PFIs.csv</code> records the manually labeled issues for the empirical study.</p> <p>Figures:</p> <p>By running <code>ReplicationPackage/empirical.py</code> and <code>ReplicationPackage/model.py</code>, you can get all the figures in the fold <code>ReplicationPackage/figures/</code>. Besides the figures in the paper, <code>ReplicationPackage/figures/</code> also contains <code>typedis_{num}.png</code>, and <code>domaindis_{num}.png</code>, {num} is 1 to 4, representing additional results of newcomer features for Figure 4 in the paper.</p>
Experimentally analysed data for Quantitative Microbiology with Microscopy: Effects of Projection and Diffraction
<p>Experimentally analysed data for Quantitative Microbiology with Microscopy: Effects of Projection and Diffraction</p> <p> </p>
Represent project UC2 Land Cover Dataset
<p>The dataset is composed of Sentinel-2 data, while the ground truths come from Copernicus Land Monitoring Service – Hot Spot Mapping (HSM), that provides high resolution land cover maps over several Natural Protected Areas mainly in Africa. The land cover legend is based on the FAO Land Cover Classification System (LCCS). These labels have been produced and validated mainly through photointerpretation of HR data.<br> The mapped area of interest (AOI) represents a key landscape for conservation area (KLC). The KLC has a total size of almost 14,000,000 140,000 km2 and is covering a vast area in southern Tanzania all the way to northern Mozambique.<br> In Tanzania the AOI is covering the entire Selous game reserve, an area of around 50,000 km2, representing 6% of Tanzania’s land surface. This world heritage site is not only the oldest, but also the largest single protected area in Africa. It is characterised by an extensive area of natural miombo woodlands representing also one of the most extensive forest areas under protection. the reserve contains some of the most important populations of elephants, buffalos, antelopes, lions, wild dogs and other predators in Africa.<br> Located in northern Mozambique, the Niassa reserve is with 42,400 km2 the largest conservation area of the country, containing also the greatest concentration of wildlife of the country. the two reserved are connected by the Selous – Niassa wildlife corridor, an area of approximately 9000 km2 located entirely on the Tanzanian side which represents an important biological link between the two reserves and consequently for the miombo woodland eco-system. the Selous - Niassa ecosystem is one of the largest trans-boundary natural dry forest eco-regions in Africa. It constitutes one of the largest elephant ranges in the world and contains half of the world remaining wild dog population. it enables migration of wildlife and gene flow and contributing to the conservation of biodiversity.</p> <p>The dataset is organised in tiles, thus there is one folder for each of the Sentinel-2 tiles used. For each of these folders, there are the labels (both in vector and raster format) and the acquisitions organised in different acquisitions times.<br> Specifically, the dataset has the following folder structure:</p> <ul> <li>LandCover_Train_v2: directory for the training dataset of UC2 <ul> <li>00XXX (e.g.: 37MCN): folders with Sentinel-2 tile name. <ul> <li>labels: folder with raster and vector labels. The name of the label files in both format is the name of the Sentinel-2 tile to which they refer to (e.g.: 37MCN.tif/37MCN.shp) <ul> <li>raster: labels in raster format (.tif)</li> <li>vector: labels in vector format (.shp)</li> </ul> </li> <li>AAAAMMDD (e.g.: 20170630): folders with datetime of the Sentinel-2 acquisition. It contains all the 13 bands of the acquisition plus the TCI (True Color Image) in jp2 format.</li> </ul> </li> </ul> </li> <li>LandCover_Test: directory for the test dataset of UC2 <ul> <li>00XXX (e.g.: 37MCN): folders with Sentinel-2 tile name. <ul> <li>labels: folder with raster and vector labels. The name of the label files in both format is the name of the Sentinel-2 tile to which they refer to (e.g.: 37MCN.tif/37MCN.shp) <ul> <li>raster: labels in raster format (.tif)</li> <li>vector: labels in vector format (.shp)</li> </ul> </li> <li>AAAAMMDD (e.g.: 20170630): folders with datetime of the Sentinel-2 acquisition. It contains all the 13 bands of the acquisition plus the TCI (True Color Image) in jp2 format.</li> </ul> </li> </ul> </li> <li>GT_legend.xlsx: excel file with the association between the classes and the assigned number value in the raster version of the labels</li> </ul>
Table S1: Projective cover of dwarf shrub, grass, moss and lichens layers at the studied bog massif (%)
<p>Projective cover of dwarf shrub, grass, moss layers and lichens at the ombrotrophic Ilasskoe bog, Arkhangelsk regoin russia</p>
Data and code: High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
<p>Data and code supporting the research article:High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method - <br> Fasil M. Rettie, Sebastian Gayler, Tobias KD Weber, Kindie Tesfaye, Thilo Streck. Please, find detail description of the codes and datasets in readme file.</p>
Database: European project ERASMUS + ParaLimits
<p>Database of the European ERASMUS + ParaLimits project (reference number 622213-EPP-1-2020-1-ES-SPO-SCP)</p>
An annotated list of horizon scanned technologies with potential for application in alien species citizen science projects
<p><strong>Context</strong></p> <p>The contribution of volunteers in recording invasive alien species (IAS) has been fostered by technological developments such as social media, apps, low-cost sensors, search engines and predictive analytics. These technology developments, an increased attention to citizen science and a cultural change towards collaboration and openness in research within the policy agenda should increase the contribution of volunteer recording. Within the framework of the COST Action CA17122 <a href="https://www.ceh.ac.uk/our-science/projects/alien-csi"><em>Increasing Understanding of Alien Species through Citizen Science</em></a> (<a href="https://doi.org/10.3897/rio.4.e31412">Roy et al. 2018</a>) a group of researchers explored the value of emerging technologies for citizen science in the context of alien species, recognizing the contribution of volunteers and reviewing their potential to engage broad audiences, motivate volunteers, improve data collection, increase data quality etc.</p> <p><strong>Survey</strong></p> <p>The following criteria were then used to evaluate the potential of these technologies for alien species citizen science through a dedicated <a href="https://forms.gle/9GQJctnAbPLKyxDE7">survey</a>:</p> <p>● <em><strong>Audience</strong></em>: the technology can attract new target audiences for IAS citizen science and/or support more inclusivity in IAS citizen science (can overcome inequalities in participation, attract under-privileged audiences/those underrepresented in the scientific enterprise, allow participation of sensory/cognitive/otherwise impaired...)<br> ● <em><strong>Engagement </strong></em>with others: the technology supports better connections with other participants, helpful in building a community<br> ● <em><strong>Engagement via feedback</strong></em>: the technology increases the quality, amount or rate of feedback (including supporting learning) to participants<br> ● <strong><em>Application</em></strong>: the technology can be embedded in everyday life and therefore has the potential for wide, generic application</p> <p>● <em><strong>New data</strong></em>: the technology yields new types of data that would not be available without the technology (improved the detectability of IAS, new types of data, species interactions, new information sources)<br> ● <strong><em>Extends data</em></strong>: the technology expands the scope of data collection or analysis (e.g. better coverage spatially, temporally)<br> ● <strong><em>Improves data quality</em></strong>: the technology improves species ID, reduces uncertainty, improves validation<br> ● <strong><em>Improves the flow of data</em></strong>: the technology increases the speed of record transmission (e.g. for early warning)<br> ● <strong><em>Improves the curation of data</em></strong>: the technology itself allows for improved data curation (better metadata, sustainability and long term preservation data, open data, tracked provenance of data, FAIR data management, enable to better credit citizen scientists for their data contributions)</p> <p><strong>Dataset description</strong></p> <p>This dataset represents the list of technologies (in the broadest sense, including approaches) that were identified collectively by the experts as being relevant technologies in the framework of (alien species) citizen science. The dataset includes the following fields:</p> <ul> <li><em>Name</em>: name of the approach/technology</li> <li><em>Category</em>: broad categorisation of the approach/technology (Hardware and infrastructure, data collection and analysis tools, tools to improve user experience). If some approaches are combinations this is mentioned in description.</li> <li><em>Description</em>: a definition and/or description of the approach/technology</li> <li><em>Reference</em>: a reference on the approach/technology (e.g. paper, online reference), mostly with a doi</li> <li><em>Example</em>: an example of the approach/technology, mostly with reference to an (alien species) citizen science project that applied it</li> <li><em>Notes: </em>any further remarks</li> </ul>
Moored ADCP Cape Canaveral from Sep 2013 to Sep 2016, part of BOEM's project "Ecological Function and Recovery of Biological Communities within Dredged Ridge-Swale Habitats in the South-Atlantic Bight"
<p>Bottom-mounted ADCP data (mooring data) for "Ecological Function and Recovery of Biological Communities within Dredged Ridge-Swale Habitats in the South-Atlantic Bight"</p> <p>All ADCP mooring deployments are detailed in the spreadsheet "Canaveral_Mooring_Deployment_details_BOEM_project" appearing with the zipped folders. These zipped folders have been separated by deployment number, as indicated by their names.The great majority of ADCP records were obtained with Teledyne instrumentation. A few records were secured with Aquadopp and AWAC (Nortek) ADPs.</p> <p>Most data have been transformed to Matlab format. Only some downward pointing ADCPs, those not in Matlab format, have been left (and uploaded) in their original format (binary from Teledyne), typically with extension '000' or '001'. These files are Gigabytes big and can be visualized with the 'WinADCP' program that can be downloaded for free from the Teledyne website.</p> <p>Missing files, still appearing in the spreadsheet, are a consequence of instrument malfunction, platform destruction, or instrument lost.</p> <p> </p>
Selecting Projects with <TOOL>: What do Brazilian Instructors say when they Think Aloud?
<p>Dataset for the paper: Selecting Projects with <TOOL>: What do Brazilian Instructors say when they Think Aloud?</p>
Selecting Projects with <TOOL>: What do Instructors say when they Think Aloud?
<p>Dataset for the paper: Selecting Projects with <TOOL>: What do Instructors say when they Think Aloud?</p>
Projected changes in forest biomass to 2100 by county and species for 20 future scenarios
<p>Climate change and atmospheric deposition of nitrogen (N) and sulfur (S) are important drivers of forest demography. Here we apply previously-derived growth and survival responses for 94 tree species, representing >90% of the contiguous U.S. forest basal area, to project how changes in mean annual temperature, precipitation, and N and S deposition from 20 different future scenarios may affect forest composition to 2100. We find that under the low climate change scenario (RCP 4.5), reductions in aboveground tree biomass from higher temperatures are roughly offset by increases in aboveground tree biomass from reductions in N and S deposition. However, under the higher climate change scenario (RCP 8.5) the decreases from climate change overwhelm increases from reductions in N and S deposition. These broad trends underlie wide variation among species. We found that averaged across temperature scenarios, the relative abundance of 60 species was projected to decrease by more than 5%, 20 species were projected to increase by more than 5%, and reductions of N and S deposition led to a decrease for 13 species and an increase for 40 species. This suggests large shifts in the composition of U.S. forests in the future. Negative climate effects were mostly from elevated temperature and were not offset by scenarios with wetter conditions. We found that by 2100 an estimated 1 billion trees under the RCP 4.5 scenario and 20 billion trees under the RCP 8.5 scenario may be pushed outside the temperature record upon which these relationships were derived. These results may not fully capture future changes in forest composition as several other factors were not included. Overall efforts to reduce atmospheric deposition of N and S will likely be insufficient to overcome climate change impacts on forest demography across much of the United States unless we adhere to the low climate change scenario.</p>
Replication Package for ESEC/FSE 2023 Paper "How Early Participation Determines Long-Term Sustained Activity in GitHub Projects?"
<p>This replication package can be used for replicating results in the paper. It contains 1) a dataset of 290,255 repositories; and 2) Python scripts for training and interpreting models. The GitHub repository of the paper is available at <a href="https://github.com/mcxwx123/Sustainable_projects">https://github.com/mcxwx123/Sustainable_projects</a>.</p> <p>We recommend manually setup the required environment in a commodity Linux machine with at least 1 CPU Core, 8GB Memory and 100GB empty storage space. We conduct development and execute all our experiments on a Ubuntu 20.04 server with two Intel Xeon Gold CPUs, 320GB memory, and 36TB RAID 5 Storage.</p> <p>We use GHTorrent to restore historical states of 290,255 repositories with more than 57 commits, 4 PRs, 1 issue, 1 fork and 2 stars. The raw data of repositories (collected in their first 1,3,5 months(s)) are stored in `Replication Package/data/prodata_1.pkl`, `Replication Package/data/prodata_3.pkl`, and `Replication Package/data/prodata_5.pkl`. The contribution of features resulting from LIME model is stored in `Replication Package/data/limeres_m3_t2_k1.pkl`.<br> `Replication Package/data/X_test_m3_t2_k1.pkl` and `Replication Package/data/y_test_m3_t2_k1.pkl` store the test dataset for the LIME model. You can run `Replication Package/fitdata.py` to get the results in Table 3 and 4, run `Replication Package/draw_compare_variable.py` to get Figure 2 and run `Replication Package/allvari_statistics.py` to get Table 5. In `Replication Package/Variable_comparison_with_different_parameter.pdf`, we show the LIME results under different parameters. In `Replication Package/sample_pros.csv`, we also provide the list of randomly selected repositories in Section 3.1.<br> </p> <p>The explanations for collecting the variables, the examples of variable effects on project sustainability, and the hyperparameter setting of the machine learning models are provided in the README.md file.</p>
Data from field campaign project TACTIC EO-Africa
<p>Field ground data from the EO-Africa project Tactic (2023) and the previous field campaigns of 2016 and 2017. Leaf area index measurements with licor2200, spectral information measurements with ASD LabSpec spectrometer. Protocols of measurement. GPS points. Vegetation characteristics.</p>
Materials - audio stimuli for COLOR project
<p>These recordings are auditory stimuli associated to an experimental data about English-like word learning with color cues</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.