Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,047
datasets available to search
ShareScore release 0.9.0
Dataset results
1,047 results for “constraint”
Topoedaphic constraints on woody plant cover in a semi-arid grassland
<p>Provided is an excel spreadsheet which contains data used to estimate maximum potential shrub cover across a semi-arid grassland in Southern Arizona. Data was obtained using a classified shrub cover (mesquite) map of Las Cienegas National Conservation Area in Southeastern Arizona which was derived using 2017 NAIP imagery which is free available on EarthExplorer. Classified shrub cover map was created using an unsupervised ISO classification technique within ArcGIS. This shrub cover map was upscaled to 100m and a number of topoedaphic spatial layers were overlaid onto this shrub cover layer and their layers extracted per pixel. This data was then analyized within R using a segmented quantile regression approach to identify maximum shrub cover by topoedaphic characteristics at the 95th percent quantile. For sample of quantile code please contact the corresponding author.</p> <p>Topoedaphic variables analyzed in this data set are: <br> Shrub Cover (%)<br> Elevation (m)<br> Slope Inclination (°)<br> Slope Aspect (Cardinal Direction)<br> Value 2 = North<br> Value 3 = East<br> Value 4 = South<br> Value 5 = West<br> Percent Clay between 0 to 5cm (%)<br> Depth to bedrock (cm)<br> Topographic Wetness index (TWI) (unitless with higher values representing more run-on/wetter conditions)</p> <p>Shrub cover was analyzed at the study site level and at the ecological site level. </p>
2746-node Polish Energy System Data of Transmission and Voltage Constraints Contribution to the Formation of LMP
<p>This is the dataset that is used for the original article: "Contribution of Transmission and Voltage Constraints in the Formation of Locational Marginal Prices"</p> <p> </p> <p>MATPOWER is required. The dataset obtained by MATPOWER ver6.0 tool [1].</p> <p>Run run_matpower_acopf.m in MATLAB to start AC OPF. Offers should be saved in the MATLAB search path. Results of AC OPF are saved in bus, branch and gen files. Column names correspond to MATPOWER case file.</p> <p>Price-bonding factors are saved in lambda_P_PBF and lambda_Q_PBF. Ones in MP, MQ columns correspond to marginal nodes for real and reactive power respectively. Ones in CV, CD columns correspond to controlled voltage magnitude and phase respectively. </p> <p>Acronyms in column names:</p> <ul> <li>C(N) - bidding price of a marginal generator at node N </li> <li>Fmax(N) - maximum allowable real power throught line N, where N - index number of a line in branch.csv with binding constraint</li> <li>LAMP_P - LMP for real power</li> <li>LAM_Q - LMP for reactive power</li> <li>PBF - price-bonding factor</li> <li>TC - transmission constraint</li> <li>VC - voltage constraint</li> <li>V(N) - maximum (minimum) allowed voltages at node N</li> </ul>
FaaS Characteristics and Constraints Knowledge Base
<p>YAML-formatted and timestamped description of Function-as-a-Service (FaaS) service characteristics and constraints such as maximum execution time and pricing. This dataset allows for adaptive software and workflow generation in dynamically evolving Serverless Computing environments. We envision the inclusion of the dataset into code generators, code transformers, workflow schedulers and compatibility modes of open source FaaS runtimes.</p> <p>Furthermore, due to evidences of evolving values being given by hyperlinks, the dataset will serve as single source of truth about the technological development in the FaaS space.</p>
Data accompanying the manuscript "Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake", published in Limnology and Oceanography (doi: 10.1002/lno.12687)
<p>CTD and geochemical data accompanying the publication: Biogeochemical cycling of trace elements and nutrients in ferruginous waters – constraints from a deep oligotrophic ancient lake in Limnology & Oceanography (doi: 10.1002/lno.12687).</p>
Data for "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa" by Haynes et al.
<p>Accompanying data products for publication entitled "Constraints on the Observability of Energetic Neutral Atoms from the Magnetosphere-Atmosphere Interactions at Callisto and Europa". The manuscript was submitted to JGR Space Physics shortly after upload.</p> <p>Data includes all simulation outputs that are depicted in this work, both for the AIKEF hybrid model (i.e., Figure 4) and the model used to produce synthetic ENA images (Figures 3, 6, 8, 9, 11, A1, and B1). All other figures in the work are used for illustrative purposes and were not generated with simulation output. </p> <p>Information regarding the organization and file structure can be found in H24_data_readme.txt , as well as which dataset corresponds to which figure. Any inquiries, questions, or comments may be addressed through the email associated with this data publication.</p>
Astrophysical constraints on neutron star f -modes with a nonparametric equation of state representation
<p>Data release for Mohanty et al. "<em>Astrophysical constraints on neutron star f-modes with a nonparametric equation of state representation"</em></p> <p>The data release consists of three files: </p> <ol> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR.h5</a> </li> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR+GW.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR+GW.h5</a> </li> <li><a href="https://zenodo.org/api/records/13952437/draft/files/EoS_posterior_samples_PSR+GW+NICER.h5/content" target="_blank" rel="noopener noreferrer">EoS_posterior_samples_PSR+GW+NICER.h5</a> </li> </ol> <p>Each file contains 9,835 samples of EOS draws. The equation of state id's matches those of Legred et. al. 2022</p> <p>The data structure follows Legred, I. (2022) “<em>Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter: Neutron star equation of state posterior samples</em>”. Zenodo. doi: 10.5281/zenodo.6502467.</p> <p>Samples were generated using stanspy, a general relativistic neutron star code written by Sailesh Ranjan Mohanty. </p> <p>Please see the readme (adapted from Legred et. al. 2022 Zenodo. doi: 10.5281/zenodo.6502467) </p>
Supplementary Files for "Geodetic constraints on cratonic microplates and broad strain during rifting of thick Southern African lithosphere"
<p>This repository contains the supplementary files and tables for the manuscript:</p> <p><strong>Geodetic constraints on cratonic microplates and broad strain during rifting of thick Southern African lithosphere</strong></p> <p><strong>L. N. J. Wedmore<sup>1</sup>, Biggs, J.<sup>1</sup>, Floyd, M.<sup>2</sup>, Fagereng, Å.<sup>3</sup>, Mdala, H.<sup>4</sup>, Chindandali, P.<sup>5</sup>, Williams, J.<sup>3</sup>, Mphepo, F.<sup>4</sup></strong></p> <p><sup>1</sup>School of Earth Sciences, University of Bristol, Bristol, UK</p> <p><sup>2</sup>Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA</p> <p><sup>3</sup>School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK</p> <p><sup>4</sup>Geological Survey Department, Mzuzu Regional Office, Mzuzu, Malawi</p> <p><sup>5</sup>Geological Survey Department, Zomba, Malawi</p> <p>This manuscriptis published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2021GL093785">https://doi.org/10.1029/2021GL093785</a></p> <p>Please contact the author (luke.wedmore@bristol.ac.uk) for more information.</p> <p> </p> <p>File Information</p> <p>File S1 – Table of GNSS station velocities for the combined southern Malawi/GeoPRISMS/Saria et al. (2014) solution in the ITRF14 reference frame.</p> <p>File S2 – Table of GNSS station and the references for the data used in this paper.</p> <p>File S3 – Details of the sites used for the two-plate test and the results of this inversion.</p> <p>File S4 – Details of the sites used for the three-plate test and the results of this inversion.</p> <p>File S5 – A sig_neu command file with details of the random noise added to outlier sites within GLOBK.</p>
Data for paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints"
<p>This is the data set (models, sources and results) for the paper "Parametric schedulability analysis of a launcher flight control system under reactivity constraints" published in Informatica Fundamentae in 2021.</p>
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter : Weighted Monte Carlo samples for neutron star observables
<p>This data release contains weighted Monte Carlo samples associated with</p> <p>Legred, Chatziioannou, Essick, Han, and Landry, 2021</p> <p>"Impact of PSR J0740+6620 radius constraint on the properties of high-density matter"</p> <p>Phys. Rev. D 104, 063003;</p> <p>doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p> </p> <p> </p> <p> </p>
Constraints on the emplacement of Martian nakhlite igneous rocks and their source volcano from advanced micro-petrofabric analysis
<p>Martian nakhlite meteorite electron backscatter data and magma body unit thickness calculation code.</p>
Medical Temporal Constraint Extraction Dataset
<p>This dataset contains 836 drug usage guidelines labeled with medical temporal constraints. These constraints conform to a context-free grammar, allowing their semantics to be computationally represented.</p>
Supplementary Data: Collider constraints on electroweakinos in the presence of a light gravitino
<p>This record contains the full dataset generated for the study "Collider constraints on electroweakinos in the presence of a light gravitino" by the GAMBIT Collaboration. The record also includes a GAMBIT configuration file and example scripts for plotting. See the README.md file for further details.</p>
Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions
<p>This archive includes the scripts and related input data to produce results for the paper entitled - "Structural constraints in current stomatal conductance models preclude accurate estimation of evapotranspiration and its partitions". Following is the description of files/folders:</p> <p>1. Input_Data: This folder contains all the required input data including FluxNet data, soil properties, quality controlled training-validation data, and metadata & other supporting information of the sites. </p> <p>2. Model_EMP: This folder contains all the scripts for empirical model of stomatal conductance. (Note: Scripts have been written in MATLAB"). No need to change anything except the MATLAB executive path in two files "run_all_tasks_to_optimize_params.sh" and "prediction.sh". Read "ReadMe.txt" file in the folder "Model_EMP" for more instructions on running the model. </p> <p>3. Model_ML: This folder contains all the scripts for pure machine learning model of stomatal conductance. It contains four sub-folders: 1. Model_Config_1 (Model with configuration-1); 2. Model_Config_2_TEA (Model with Configuration-2 & TEA-based T estimates); 3. Model_Config_2_uWUE (Model with Configuration-2 & uWUE-based T estimates); 4. Model_Config_2_Yu22 (Model with Configuration-2 & Yu22-based T estimates). Further instructions have been given in each jupyter notebooks. Briefly, in folder "Model_Config_1", the notebook "train_ML_config_1.ipynb" trains the model parameters and notebook "Predictions_ML_config_1" is used to do predictions. Similar instructions apply for other subfolders. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>4. Model_PH_exp: This folder contains all the scripts for plant hydraulics model with explicit representation. All the scripts are self explanatory and further instructions are provided in the scripts as needed. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.</p> <p>5. Model_PN_imp: This folder contains all the scripts for plant hydraulics model with implicit representation. Instructions given for "Model_ML" are applicable here. (Note: Scripts have been written in Python Language"). All the scripts are fully functional as long as all the required modules are installed.<br> </p> <p>Versions: Tensorflow 2.11.0, MATLAB_R2022a, Python 3.10.9</p>
Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments [software and dataset]
<p>Simulation software and results for "<strong>Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments</strong>"</p>
Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment
<p><strong>[EN] Subset of 'MLSUM: The Multilingual Summarization Corpus' for constraints annotation experiment.</strong></p> <ul> <li><strong>Description</strong>: MLSUM is a dataset of newspappers articles aimed at training summaring model. We use it for a constraints annotation experiment on newspapper titles according to their topic classification.</li> <li><strong>Content</strong>: For constraints annotation experiment based on data similarity, this dataset have been subsetted (randomly pick 75 articles in the following 14 most used topics: 'economie', 'politique', 'sport', 'planete' (renamed in 'ecologie'), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'educations', 'climat' (renamed in 'meteo'), 'immobilier') and filtered (keep articles that have an obvious topics regarding their titles, without their bodies). Two reviewers have working on this task in order to limit the subjectivity of the filtering. This subsetted dataset is used (1) to estimate needed time to annotate titles similarity with constraints (MUST-LINK, CANNOT-LINK) and (2) to test interactive clustering methodology (constraints annotation and constrained clustering).</li> <li><strong>Origin</strong>: The dataset is bassed on the original 'MLSUM: The Multilingual Summarization Corpus' dataset (https://doi.org/10.48550/arXiv.2004.14900).</li> </ul> <p><br> <strong>[FR] Echantillon de 'MLSUM: The Multilingual Summarization Corpus' pour une expérience d'annotation de contraintes.</strong></p> <ul> <li><strong>Description </strong>: MLSUM est un ensemble de données d'articles de journaux destinés à l'entraînement d'un modèle de résumé automatique. Nous l'utilisons pour une expérience d'annotation de contraintes sur des titres de journaux en fonction de leur classification thématique.</li> <li><strong>Contenu </strong>: Pour une expérience d'annotation de contraintes basée sur la similarité des données, cet ensemble de données a été échantillonné (sélectionner au hasard de 75 articles dans les 14 sujets les plus utilisés : 'économie', 'politique', 'sport', 'planète' (renommé en « écologie »). ), 'sciences', 'police-justice', 'disparitions', 'emploi', 'sante', 'musiques', 'arts', 'éducations', 'climat' (renommé en 'meteo'), 'immobilier' ) et filtré (conserver les articles qui ont un sujet évident par rapport à leur titre, sans leur corps). Deux relecteurs ont travaillé sur cette tâche afin de limiter la subjectivité du filtrage. Ce sous-ensemble de données est utilisé (1) pour estimer le temps nécessaire pour annoter la similarité des titres avec des contraintes (MUST-LINK, CANNOT-LINK) et (2) pour tester la méthodologie de clustering interactif (annotation de contraintes et clustering contraint).</li> <li><strong>Origine </strong>: L'ensemble de données est basé sur l'ensemble de données original 'MLSUM : The Multilingual Summarization Corpus' (https://doi.org/10.48550/arXiv.2004.1490).</li> </ul>
Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints
<p>This dataset consists of a collection of self-consistent radial seismic Earth models. The models are derived by inverting a large set of normal-mode centre-frequencies, quality factors, and geodetic data, including mass, moment of inertia, and tidal response.</p><p>The dataset is accompanied by a research paper titled "Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints" (DOI: <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a>). The paper presents the methodology and findings related to the development of the models.</p><p>This version (V0.2) of the dataset replaces the previous version (V0.1). </p><p><strong>Dataset Details</strong></p><p>The dataset includes confidence intervals (CIs) for these parameters at 25%, 50%, and 75% levels that are representative of the uncertainty of the sampled model parameters. For example, the files <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-high.dat?versionId=b3b349c2-0a0b-48cc-b1f3-90137b8d1dcb">screm-25p-high.dat</a> and <a href="https://zenodo.org/api/files/fa9f19f0-bf6a-41a8-8ca7-489a5b1b9c49/screm-25p-low.dat?versionId=81db34f2-29f4-48aa-95c4-bb73b050a47f">screm-25p-low.dat</a> contain the upper and lower bound of the 25% CI sampled model range. A python plotting script is available, which plots the CIs.</p><p>The dataset files are provided in comma-separated values (CSV) format, containing the following columns:</p><ol><li><strong>Radius (km)</strong>: Radial distance from the center of the Earth.</li><li><strong>Depths (km)</strong>: Depth from the surface of the Earth.</li><li><strong>Density (g/cm³)</strong>: Radial density structure.</li><li><strong>P-wave velocity (vp) (km/s)</strong>: Radial compressional (P) wave velocity structure</li><li><strong>S-wave velocity (vs) (km/s)</strong>: Radial shear (S) wave velocity structure.</li><li><strong>Qkappa</strong>: Radial bulk attenuation structure.</li><li><strong>Qmu</strong>: Radial shear wave attenuation structure.</li><li><strong>Bulk Modulus (K) (GPa)</strong>: Radial bulk modulus structure.</li><li><strong>Shear Modulus (Mu) (GPa)</strong>: Radial shear modulus structure.</li><li><strong>Pressure (GPa)</strong>: Radial pressure profile.</li><li><strong>Temperature (K)</strong>: Radial geothermal profile.</li></ol><p><strong>Citation</strong></p><p>If you use this dataset in your research or refer to the models, please cite the following paper:</p><p><strong>Title:</strong> Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints<br><strong>Authors:</strong> J. Kemper, A. Khan, G. Helffrich, M. van Driel, D. Giardini<br><strong>Journal:</strong> Geophysical Journal International<br><strong>Year: </strong>2023<br><strong>DOI:</strong> <a href="https://doi.org/10.1093/gji/ggad254">10.1093/gji/ggad254</a></p><p>Please also acknowledge the dataset by providing a link to the Zenodo repository and its DOI.</p><p>Bibtex:<br>@article{Kemper_etal23,<br>author = {Kemper, J and Khan, A and Helffrich, G and van Driel, M and Giardini, D},<br>title = "{Self-consistent models of Earth's mantle and core from long-period seismic and tidal constraints}",<br>journal = {Geophysical Journal International},<br>pages = {ggad254},<br>year = {2023},<br>month = {06},<br>issn = {0956-540X},<br>doi = {10.1093/gji/ggad254},<br>url = {https://doi.org/10.1093/gji/ggad254},<br>} </p><p> </p>
Hydraulic Constraints on Two Life History Stages of Larrea tridentata in a Chihuahuan Desert Creosote Shrubland at the Sevilleta National Wildlife Refuge, New Mexico (2002-2003)
Maintaining high rates of water loss during times of high resource availability could allow establishing woody desert perennials to grow quickly by allowing them to take advantage of the fleeting but abundant monsoonal moisture typical of warm deserts like the Chihuahuan. However, a plant cannot endlessly increase water loss in order to grow faster --there are hydraulic constraints on rates of water loss. The hydraulic properties of each particular plant xylem and soil microsite, as well as the AR:AL absorbing root area to transpiring leaf area ratio) interact to set limits on rates of water loss. If transpiration rates become too high, cavitation may limit the ability of the xylem to supply water to the leaves. The main objective of this study was to test two hypotheses on a population of Larrea tridentata at the Sevilleta LTER in central New Mexico (1) do small plants grow faster and use water less conservatively than large, and (2) are there differences in the hydraulic constraints on small and large plants. Measurements were made every six weeks in the spring, summer and fall from April 2002 - August 2003. Field measurements of shoot growth, gas exchange and plant and soil water potentials were made to determine growth rates and water use. Measurements of leaf specific conductance determined the ability of the xylem to supply water to the leaves. Excavation findings were used to estimate (AR:AL). Xylem vulnerability curves and soil texture analysis were used to determine the hydraulic properties of the plant xylem and soil. A model determined where the limiting conductance occurred in the plant-soil continuum.
Measuring coselectional constraint in learner corpora: A graph-based approach
<p>All data from the thesis, plots, scripts, rough draft of annotation guidelines (more is included in thesis). not all svgs are included yet, but can be computed from scripts + data. more data will be added in next version (after defense).</p>
Constraints used for realization of SKUA implicit model of comet 67P Churyumov-Gerasimenko
<p>#The following files are related to the paper:</p> <p>#"Global-scale brittle plastic rheology at the cometesimal merging of comet 67P/Churyumov-Gerasimenko"</p> <p>#by Marco Franceschi, Luca Penasa, Matteo Massironi, Giampiero Naletto, Sabrina Ferrari, Michele Fondriest,<br> #Dennis Bodewits, Carsten Güttler, Alice Lucchetti, Stefano Mottola, Maurizio Pajola, Imre Toth,<br> #Jacob Deller, Holger Sierks, Cecilia Tubiana</p> <p>#accepted for publication on the Proceedings of the National Academy of Sciences of the United States of America</p> <p>#doi:10.1073/pnas.1914552117</p> <p>#Data are surfaces and points used as constraints in the modelling described in the paper.</p> <p>#The folder contains the following files:</p> <p>BL_center.dxf - center of the ellipsoidal model of the Big Lobe (Penasa et al., MNRAS 2017)<br> BL_normals.dxf - normals to terraces visible on the surface of the Big Lobe<br> BL_outer_ellipsoid.dxf - shell of the Big Lobe ellipsoidal model (Penasa et al., MNRAS 2017) used as top constraint for the implicit modelling of the Big Lobe in SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)<br> constraint surfs big lobe.dxf - constraint surfaces used for implicit modelling of the Big Lobe realized using SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)<br> constraint surfs small lobe.dxf - constraint surfaces used for implicit modelling of the Small Lobe realized using SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)<br> SL_center.dxf- center of the ellipsoidal model of the Small Lobe (Penasa et al., MNRAS 2017)<br> SL_normals.dxf - normals to terraces visible on the surface of the Small Lobe<br> SL_outer_ellipsoid.dxf- shell of the Small Lobe ellipsoidal model (Penasa et al., MNRAS 2017) used as top constraint for the implicit modelling of the Big Lobe in SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)<br> SL_center.dxf- center of the ellipsoidal model of the Small Lobe (Penasa et al., MNRAS 2017)<br> BL_EMav.dxf - average shell of the Big Lobe ellipsoidal model (Penasa et al., MNRAS 2017)<br> SL_EMav.dxf - average shell of the Small Lobe ellipsoidal model (Penasa et al., MNRAS 2017)<br> BL_ILMav.dxf - average shell of the Big Lobe implicit model realized in SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)<br> SL_ILMav.dxf - average shell of the Small Lobe implicit model realized in SKUA/Gocad Structural Lab Plugin (Caumon et al., IEE Transactions on Geoscience and Remote Sensing 2013)</p> <p><br> #April 2020</p>
Nutritional constraints on brain evolution: sodium and nitrogen limit brain size
Nutrition has been hypothesized as an important constraint on brain evolution. However, it is unclear whether the availability of specific nutrients or the difficulty of locating high quality diets limits brain evolution, especially over long periods of time. We show that dietary nutrient content predicted brain size across 42 species of butterflies. Brain size, relative to body size, was associated with the sodium and nitrogen content of a species' diet. There was no evidence that host plant apparency (measured by plant height) was related to brain evolution. The timing of diet shifts varied from 3.5 to 90 million years ago, but nutritional constraints did not lessen over time as species adapted to a diet. While nutrition was linked to overall brain volume, there was no evidence that nutrition was related to the relative size of individual brain regions. Lab rearing experiments confirmed the underlying assumption of most comparative studies that the majority of interspecific trait variation stems from species differences rather than an individual's current developmental environment. This study highlights a novel role of sodium and nitrogen in brain evolution, which is additionally interesting given current anthropogenic change in the availability of these nutrients.
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.