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.

1,548

datasets available to search

ShareScore release 0.9.0

Dataset results

1,548 results for “Trajectory”

Learn how ShareScore rates datasets ↗

zenodo44/100

Beta-lactamase Trajectory Data

Processed Beta-lactamase trajectories from Gromacs. PBC corrected, alpha carbon only. Matrix produced from JEDi software. Used in related work.

opencc-by-4.0Jan 2021View details →

zenodo44/100

Data-driven physics-based modeling of pedestrian dynamics - dataset: Pedestrian trajectories at Eindhoven train station

Pedestrian trajectories measured at train station Eindhoven Centraal (the Netherlands) on platform 2 with acces to tracks 3 and 4. The dataset is partitioned in files containing 10 consecutive days each, recording 4 data fields: <ul> <li>time_ms: Passed time since start of the measurements. Unit: milliseconds.</li> <li>object_identifier: unique id identifying an object.</li> <li>x_position_mm: coordinates of the object along the x-axis at the given time. Unit: millimeters.</li> <li>y_position_mm: coordinates of the object along the y-axis at the given time. Unit: millimeters.</li> </ul> Each object resembles a pedestrian on the train platform recorded with 10 frames per second. We deliberately removed exact date and time information for privacy reasons (see additional note). The data set consists of 60 consecutive days starting at an unkown time between 00:00 AM and 01:00 AM of a random date between April 1st and May 1st 2022. An overhead image of the platform is included showing train track 3 in the bottom and train track 4 in the top of the image. The data set is supplemented to the paper <a title="Data-driven physics-based modeling of pedestrian dynamics" href="https://doi.org/10.48550/arXiv.2407.20794" target="_blank" rel="noopener">Data-driven physics-based modeling of pedestrian dynamics</a> and can be processed by the associated <a title="Software: Data-driven physics-based modeling of pedestrian dynamics" href="https://github.com/c-pouw/physics-based-pedestrian-modeling" target="_blank" rel="noopener">Python implementation</a> to create pedestrian models.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Dataset for article - Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations

Data supporting findings in "Dislocation dynamics in Ni: Parameterising dislocation trajectories from atomistic simulations". In this work, 8 identical Molecular Dynamics (MD) simulations of edge dislocations in pure Nickel were run and analysed, where dislocation positions were extracted and each dislocation is tracked for further analysis. Each simulation only differs in the random seed used for setting the initial atom velocities. Files needed to reproduce a specific simulation can be found in the directories provided in data.zip: <ul> <li>fr_18430</li> <li>fr_27743</li> <li>fr_44345</li> <li>fr_46165</li> <li>fr_54382</li> <li>fr_54992</li> <li>fr_97181</li> <li>fr_98232</li> </ul> The random seed needed to set the initial velocities of the atoms are set in the LAMMPS script. The log files generated by LAMMPS are provided for the different stages of a given simulation in the LAMMPS_logs sub-directories. The corresponding output files from the OVITO DXA are also included in the sub-directory Ni_disloc_const. The files  disloc_data_*.ca contain the raw output from the OVITO DXA and can be opened directly in OVITO. The disloc_data_*.txt files contain information extracted from the OVITO DXA in a format that can be read in and processed by the dislocation tracking code (<a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a>). The wildcard character "*" is replaced with the simulation timestep. In each directory, the dislocation position vs. time data is included as a text file named perfect_pos_<seed>.txt. The Mishin 2004 EAM interatomic potential file NiAl_Mishin_2004.eam.alloy used to generate the data is also provided. Additional simulations were carried out at a range of applied shear stresses (20 MPa - 50 MPa). Files needed to reproduce theses simulations are provided in data_stress.zip and follow the same format described above. The data provided here was generated with the <code>LAMMPS/29Sep2021-kokkos</code> module on the SULIS Tier 2 HPC platform, which has been built with the OpenMP backend of the <code>kokkos</code> package and uses the <code>foss-2021b</code> toolchain (<a href="https://docs.easybuild.io/common-toolchains/">https://docs.easybuild.io/common-toolchains/</a>). For further information, please refer to <a href="https://sulis-hpc.github.io/appnotes/lammps.html">https://sulis-hpc.github.io/appnotes/lammps.html</a>. For more details on the calculations, please refer to the publication (in preparation). Please refer to the GitHub repository at <a href="https://github.com/geraldineanis/DislocCode/tree/v1.0.0">https://github.com/geraldineanis/DislocCode/tree/v1.0.0</a> for the analysis tools developed and for detailed instructuctions for their use.

opencc-by-4.0Nov 2024View details →

zenodo44/100

Data for The Disparities and Development Trajectories of Nations in Achieving the Sustainable Development Goals

This dataset provides the source data for Tables and Figures in the main text and the supplementary information, and the code for the main figure of the article.

opencc-by-4.0Oct 2024View details →

zenodo44/100

Data and code for "Autonomous demon exploiting heat and information at the trajectory level"

Code and numerically generated data for the article "Autonomous demon exploiting heat and information at the trajectory level" <a href="https://arxiv.org/abs/2409.05823">arXiv:2409.05823</a>, see README.md for details. Changes: The performance quantifiers X_TUR have been rescaled by a constant factor (see Eq. (20) in the article) compared to the first version.

opencc-by-4.0Sep 2024View details →

zenodo44/100

Dry trajectories of SARS-CoV-2 RBD from accelerated molecular dynamics simulation

These are supplementary files to the preprint/paper "SARS-CoV-2 spike protein unlikely to bind to integrins via the Arg-Gly-Asp (RGD) motif of the Receptor Binding Domain: evidence from structural analysis and microscale accelerated molecular dynamics" (http://dx.doi.org/10.1101/2021.05.24.445335). The attached code in Jupyter notebook can be run after installing the virtual environment using the `environment.yml ` The file `data.zip` needs to be extracted to the same path where the notebook is run from

opencc-by-4.0Dec 2021View details →

zenodo44/100

A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories

Containes input data   for MD simulations of 3 HSP90- small compound complexes from the paper A workflow for exploring ligand dissociation from a macromolecule: Efficient random acceleration molecular dynamics simulation and interaction fingerprint analysis of ligand trajectories" from Daria B. Kokh, Bernd Doser , Stefan Richter , Fabian Ormersbach , Xingyi Cheng, Rebecca C. Wade, publishe in J. Chem. Phys. 153, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a> <ul> <li>ref.pdb - structure of the complex in PDB format</li> <li>ref.prmtop - topology file in AMBER</li> <li>ref-equal-NTP.pdb  - structure  after NTP equilibration </li> <li>ref-equal-NTP.rst7  - coordinates  after NTP equilibration</li> <li>ref-equal-NTP.crd  - coordinates  after NTP equilibration </li> <li>gromacs.gro - coordinates in Gromacs format (after NTP equalibration)</li> <li>gromacs.top - Gromacs topology </li> </ul>

opencc-by-4.0Jan 2022View details →

zenodo44/100

Greek Text to Trajectories Sign Language Dataset

Entails the 2D human pose trajectories of Greek Elementary Sign Language Dataset and Greek News Sign Language Dataset (31681 examples).

opencc-by-4.0Dec 2021View details →

zenodo44/100

Model Lagrangian trajectories and deformation data analyzed in the Sea Ice Rheology Experiment - Part I

Model Lagrangian trajectories and deformation estimates for sea-ice models participating in the Sea Ice Rheology Experiment (SIREx) - Part I. Model Lagrangian trajectories are integrated offline, starting on January 1st with all available raw RGPS cells positions (interpolated to January 1st 00:00:00 UTC). The trajectories are advected at an hourly time step with the models daily velocity output until March 31st. The trajectories are then sampled at a 3-day interval to match the RGPS composite time stamps, and the velocity derivatives (deformation) are calculated using the line integral approximations on the cells' contour. All model trajectories and Lagrangian deformation data therefore have nominal temporal and spatial scales of 3-days and 10-km (same as the RGPS composite), regardless of the original resolution of the model output. The model Lagrangian deformation estimates form the basis quantity for the statistical and spatio-temporal scaling analysis presented in Bouchat et al., Sea Ice Rheology Experiment (SIREx), Part I: Scaling and statistical properties of sea-ice deformation fields, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the model trajectory integration and deformation calculation. There is one netCDF file per model, per year (1997 and/or 2008). Data are organized in matrices where the (i,j) indices are the Lagrangian cells identifier. This allows us to keep track of neighbouring cells for the scaling analysis. See below for more information on what variables are included in the files, their structure, and how to cite.    1. File naming convention "< Model simulation label >" + _ + "deformation" + _ +  "< year >"    2. Variables included <ul> <li>(x1,y1), (x1,y2), (x3,y3), (x4,y4): Position of the cells' corners (Lagrangian trajectories) - (meters);</li> <li>A: Cells' area - (meters squared);</li> <li>dudx, dudy, dvdx, dvdy: Cell's velocity derivatives (strain rates/deformation) - (1/seconds);</li> <li>d_dudx, d_dudy, d_dvdx, d_dvdy: Trajectory error on cells' velocity derivatives - (1/seconds);</li> <li>time: Day of year.</li> </ul> *Note: the model trajectories are terminated if they move within 100 km from land. Before computing deformation statistics to compare with RGPS composite data, one should mask both deformation sets to only keep cells available in both the model and RGPS data sets.   3. Variable structure All variables (except time) are matrices with axes (it, i, j ), where it is the time stamp/iteration and i,j are the cells identifiers. See below for how the cells are defined:   |--------------------------------------------------------------> j-axis    |    |   (x1_ij,y1_ij) o ------------------- o (x2_ij,y2_ij)    |                        |                                    |    |                        |     A_ij  or dudx_ij     |    |                        |                                    |    |   (x4_ij,y4_ij) o ------------------- o (x3_ij,y3_ij)    |    | Vi-axis     Hence, coordinates are repeated between neighbouring cells, for example: (x2_ij,y2_ij) = (x1_ij+1,y1_ij+1) and (x4_ij,y4_ij) = (x1_i+1j,y1_i+1j)   4. Recommended citation usage If all simulations included in the current archive are used in a future study, we ask to cite this archive and the SIREx paper (Bouchat et al., 2022).  If only selected simulations are used, we ask to cite both this archive and the reference paper(s) applying to the selected simulation(s) (as stated indicated in Table 1 of the SIREx papers).

opencc-by-4.0Feb 2022View details →

zenodo44/100

CH3CH2OCH3 molecule 200 ps MD trajectory with energies and forces

Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble -------------------------------------------------- MD params: temp = 300.0  K / 500.0 K time = 200.0  ps dump time = 10.0   fs step =  0.4  fs   SOAP params: species=["H", "C", "O"], periodic=False, rcut=5.0, sigma=0.5, nmax=5, lmax=5, average="outer" / "inner", crossover=True, dtype="float64", ------------------------------------------------ SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)   Energies and forces are in eV and eV/Angstrom Filenames are intended to be self-explanatory Dataset is intended to be used for machine learning algorithms tests.

opencc-by-4.0Mar 2022View details →

zenodo44/100

Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022

This data set contains four parts: 1) compressed folder with input parameters and results for the hydrodynamic model 2) compressed folder with input parameters and results for the particle tracking 3) compressed folder with satellite data  4) code used in the article for hydrodynamic model, particle tracking and image processing Each folder contains a readme file,

opencc-by-4.0May 2022View details →

zenodo44/100

Ab-initio molecular dynamics trajectories of fully hydrated TiO2 surfaces

This data set contains trajectories of ab-initio molecular dynamics simulations of TiO2 surfaces in water described in the paper: L.Agosta, E.G.Brandt and A.P.Lyubartsev "Diffusion and reaction pathways of water near fully hydrated TiO2 surfaces from ab initio molecular dynamics", J.Chem.Phys., 147, 024704 (2107) doi: http://dx.doi.org/10.1063/1.4991381 Trajectories of 6 fully hydrated TiO2 surfaces are stored under respective names. Each trajectory file contains 50 ps of simulation with frames saved every 0.0005 ps. Format: PDB, gzipped.

opencc-by-4.0Sep 2017View details →

zenodo44/100

VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic grids for interpolation

VEL-Ar trajectory prediction model linear, co-seismic, and post-seismic interpolation grids in ASCII format. The generation of these grids is described in http://doi.org/10.1007/s00190-015-0871-8

opencc-by-4.0Dec 2015View details →

zenodo44/100

Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% DOPS with Na+ counterions using ff99 Ions

System: Symmetric bilayer of anionic DOPS (1,2-Dioleoyl-sn-glycero-3-phosphoserine 100 mol-%) lipids with sodium (Na+) counter ions. Number of DOPS: 128. Number of Na+-ions: 128. Number of waters: 4480. Lipid model: Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)]. Ion models:  Amber ff99 [J Åqvist J. Phys. Chem. 94 8021 (1990)]. Water model: TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein J. Chem. Phys. 79 926 (1983)]. Simulation engine: Amber16 [DA Case et al. AMBER 2017 UCSF (2017)]. Number of independent repeats per setup: 2. Trajectory lengths per repeat: 400 ns + 100 ns. Previously equilibrated for: 100 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Langevin' at T = 303 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984); J. Chem. Phys. 103 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Theory Comput. 9 3878 (2013)]. Van der Waals: Turned off between 1.0 nm and 1.5 nm. Constraints: Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [J. Comput. Phys. 23 327 (1977)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).

opencc-by-4.0Jan 2018View details →

zenodo44/100

Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% POPS with Na+ counterions using Joung-Cheatham Ions

System: Symmetric bilayer of anionic POPS (palmitoyl-oleoyl-phosphatidylserine 100 mol-%) lipids with sodium (Na+) counter ions. Number of POPS: 128. Number of Na+-ions: 128. Number of waters: 4480. Lipid model: Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)]. Ion model: Joung–Cheatham [IS Joung, TE Cheatham III J. Phys. Chem. B 112 9020 (2008)]. Water model: TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein J. Chem. Phys. 79 926 (1983)]. Simulation engine: Amber16 [DA Case et al. AMBER 2017 UCSF (2017)]. Number of independent repeats per setup: 2. Trajectory lengths per repeat: 400 ns + 100 ns. Previously equilibrated for: 100 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Langevin' at T = 298 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984); J. Chem. Phys. 103 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Theory Comput. 9 3878 (2013)]. Van der Waals: Turned off between 1.0 nm and 1.5 nm. Constraints: Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [J. Comput. Phys. 23 327 (1977)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).

opencc-by-4.0Jan 2018View details →

zenodo44/100

Fishing activities and trajectories for 2 fishing vessels

This data set provides the pseudo-positions in space and time of two fishing vessels and the associated activities (fishing, cruising, stopped, recorded by an on board observer). It supports the analyses provided in a paper published in Methods in Ecology and Evolution and the methods of the R package m2b (https://cran.r-project.org/package=m2b). For privacy concerns, original latitude, longitude, time and vessels id were modified. Spatial data were scaled and centred to a fictional position (R'lyeh position, Lovecraft 1928) keeping the relative geometry unchanged (acceleration, time between two positions....). Time and vessel id were modified in the same manner, keeping the relative properties of the tracks unchanged (time succession, different vessel id...). Vessel id and time are purely fictional and follow the historical context proposed by Lovecraft (1928) in the R'lyeh surroundings. Again, if the absolute spatial and temporal description of the fishing track were changed, their relative mathematical properties are conserved and can support behaviour detection based on relative movement analysis.   For the data_vessel.csv file (csv file with header), the variables are x : pseudo longitude y : pseudo latitude t : pseudo time in year-month-day hour:minutes:second format b: fishing activity, namely "fishing", "cruising", "stopped" id: unique id by vessels (fictional names). Reference H. P. Lovecraft, "The Call of Cthulhu" (1928)

opencc-by-4.0Feb 2018View details →

zenodo44/100

Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% POPS with Na+ counterions using ff99 ions

System: Symmetric bilayer of anionic POPS (palmitoyl-oleoyl-phosphatidylserine 100 mol-%) lipids with sodium (Na+) counter ions. Number of POPS: 128. Number of Na+-ions: 128. Number of waters: 4480. Lipid model: Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)]. Ion model: Amber ff99 [J Åqvist J. Phys. Chem. 94 8021 (1990)]. Water model: TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein J. Chem. Phys. 79 926 (1983)]. Simulation engine: Amber16 [DA Case et al. AMBER 2017 UCSF (2017)]. Number of independent repeats per setup: 2. Trajectory lengths per repeat: 400 ns + 100 ns. Previously equilibrated for: 100 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Langevin' at T = 298 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984); J. Chem. Phys. 103 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Theory Comput. 9 3878 (2013)]. Van der Waals: Turned off between 1.0 nm and 1.5 nm. Constraints: Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [J. Comput. Phys. 23 327 (1977)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).

opencc-by-4.0Jan 2018View details →

zenodo44/100

Molecular dynamics simulation trajectory of an anionic lipid bilayer: 100 mol% DOPS with Na+ counterions using Joung-Cheetham Ions

System: Symmetric bilayer of anionic DOPS (1,2-Dioleoyl-sn-glycero-3-phosphoserine 100 mol-%) lipids with sodium (Na+) counter ions. Number of DOPS: 128. Number of Na+-ions: 128. Number of waters: 4480. Lipid model: Amber Lipid 17 [IR Gould, AA Skjevik, CJ Dickson, BD Madej, RC Walker: "Lipid17: A Comprehensive AMBER Force Field for the Simulation of Zwitterionic and Anionic Lipids" in prep. (2018)]. Ion models: Joung–Cheatham [IS Joung, TE Cheatham III J. Phys. Chem. B 112 9020 (2008)]. Water model: TIP3P [WL Jorgensen, J Chandrasekhar, JD Madura, RW Impey, ML Klein J. Chem. Phys. 79 926 (1983)]. Simulation engine: Amber16 [DA Case et al. AMBER 2017 UCSF (2017)]. Number of independent repeats per setup: 2. Trajectory lengths per repeat: 400 ns + 100 ns. Previously equilibrated for: 100 ns. Sampling rate: every 10 ps. Time integration step: 2 fs. Thermodynamic ensemble: NpT.  Temperature coupling: 'Langevin' at T = 303 K. Pressure coupling: 'Berendsen' [J. Chem. Phys. 81 3684 (1984); J. Chem. Phys. 103 10252 (1995)] with xy and z coupled separately at p = 1.0 bar with no surface tension. Electrostatics: PME [J. Chem. Phys. 98 10089 (1993); J. Chem. Theory Comput. 9 3878 (2013)]. Van der Waals: Turned off between 1.0 nm and 1.5 nm. Constraints: Lengths of covalent bonds involving Hydrogens in lipids using SHAKE [J. Comput. Phys. 23 327 (1977)], in water using SETTLE [J. Comput. Chem. 13 952 (1992)]. Used in publications: OHS Ollila et al. "NMRlipids IV: Headgroup & glycerol backbone structures, and cation binding in bilayers with PS lipids" in prep (2018).

opencc-by-4.0Jan 2018View details →

zenodo44/100

Map of 1992-2015 landscape change trajectories

Map of 1992-2015 landscape change trajectories. Landscapes are colored depending on their change trajectories and a percentage of changed area; small < 10%, medium (10% to 30%), and large (> 30%). To access and visualize the map use: <a href="https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c">https://landgis.opengeohub.org/#/?base=OpenTopoMap&opacity=80&layer=ldg_landscape.degradation_sil.9km_c</a> Creation of this map is explained in details at <a href="https://www.sciencedirect.com/science/article/pii/S0303243418305841">https://www.sciencedirect.com/science/article/pii/S0303243418305841</a> (preprint at <a href="https://eartharxiv.org/k3rmn/">https://eartharxiv.org/k3rmn/</a>).

opencc-by-4.0Dec 2018View details →

zenodo44/100

Unbalanced species losses and gains lead to non-linear trajectories as grasslands become forests

Datasets for the article "Unbalanced species losses and gains lead to non-linear trajectories as grasslands become forests" in Journal of Vegetation Science. Plot data contains information on grassland sites in the archipelago, including how long they have been abandoned for and the surrounding landscape composition. Species occurrence matrix contains data on the plant communities found in vegetation sampling plots at respective sites.

opencc-by-4.0Sep 2019View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record