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.
7,503
datasets available to search
ShareScore release 0.7.1
Dataset results
7,503 results for “methods”
Dataset related to the Journal Article 'A deep learning method for the prediction of ship fuel consumption in real operational conditions'
<p>This dataset contains the data used to plot the graphs and create tables corresponding to the figure/table number in the published version of the paper.<br>Paper DOI:https://doi.org/10.1016/j.engappai.2023.107425</p> <p> </p>
Dataset for Method for Delivery Planning in Urban Areas with Environmental Aspects
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Michał Lasota, Aleksandra Zabielska, Marianna Jacyna, Piotr Gołębiowski, Renata Żochowska, Mariusz Wasiak. Method for Delivery Planning in Urban Areas with Environmental Aspects. Sustainability 2024, 16(4), 1571. https://doi.org/10.3390/su16041571 - published online: 2024-02-13, which a method of large-criteria decision-making support was developed in the field of urban supply planning, taking into account the minimization of harmful compound emissions.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset</li> <li>InputData.xlsx: Contains the input data in the model. The data is presented in three tables.</li> <li>OutputOptimization.xlsx: Contains the output optimization data. The data is prsented in four tables.</li> <li>OutputSummary.xlsx: Contains contains the final results of the aggregated variable.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Fast calculation methods for the magnetic field of particle lattices: Datasets and scripts
<div>*********************************************** README.txt **************************************************</div> <div> </div> <div>Title: Fast calculation methods for the magnetic field of particle lattices: </div> <div>Datasets and scripts</div> <div>Version: 1.0</div> <div>Date of Release: 2024/10/11</div> <div>Identifier: doi:10.5281/zenodo.13930969</div> <div>Permalink: http://dx.doi.org/10.5281/zenodo.13930969</div> <div> </div> <div>*************************************************************************************************************</div> <div> </div> <div>Associated publication: I. Royo-Silvestre, D. Gandia, J. J. Beato-López, E. Garaio, C. Gómez-Polo </div> <div>"Fast calculation methods for the magnetic field of particle lattices" </div> <div>(paper yet to be published)</div> <div> </div> <div>Link to publication: (paper yet to be published)</div> <div> </div> <div>Suggested citation: Please reference the associated publication above when using any datasets or</div> <div> materials described in this README file.</div> <div> </div> <div>Contact information: Isaac Royo Silvestre, </div> <div>Universidad Pública de Navarra, </div> <div>Pamplona, Spain, </div> <div>isaac.royo@unavarra.es</div> <div> </div> <div>License: CC BY 4.0</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div>This directory contains the following datasets and supplementary materials:</div> <div> </div> <div> ------------------------------</div> <div> SCRIPTS</div> <div> ------------------------------</div> <div> </div> <div> - scripts.zip Matlab scripts (compressed zip file) used to calculate the magnetic field of </div> <div>lattices of magnetic particles by analytical and semianalytical methods (more information in the associated paper) </div> <div> </div> <div> --------------------------------</div> <div> DATASETS</div> <div> --------------------------------</div> <div> </div> <div> - data.zip: Tabular data required to plot curves (compressed zip file) in csv format,</div> <div>also data used to obtain average values</div> <div> </div> <div> </div> <div>Specific documentation of each file is described in readme files.</div> <div> </div> <div>Refer to the original manuscript (see above) for additional information regarding the collection and generation of these data.</div> <div> </div> <div>------------------------------------------------------------------------------------------------------------</div> <div> </div> <div> ---------------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'scripts.zip'</div> <div> ---------------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>and multiple .m files. m files are Matlab scripts, text files that can be read using any text editor. However it has to be executed via Matlab, scripts contain documentation as comments.</div> <div> </div> <div> ---------------------------------------------------------------</div> <div> DOCUMENTATION FOR 'data.zip'</div> <div> ---------------------------------------------------------------</div> <div> </div> <div> The zip file contains another readme.txt file (that explains the content of the zip file in detail), </div> <div>multiple .dat files with data used to obtain averaged valus (see format in the readme.txt </div> <div>contained in the zip), and a folder "curves".</div> <div>The curves folder contains tabular data in .csv files, these files can be used to plot the curves</div> <div>in the manuscript.</div> <p> </p>
Formal Methods in Railways: a Systematic Mapping Study - List of Primary Studies and Data Extraction
<p>This Excel file includes the list of papers analyzed in the systematic mapping study titled "Formal Methods in Railways: a Systematic Mapping Study". The study has been submitted for publication, and its preprint is also included in this repository. </p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2nd Web-Delphi process to HTA stakeholders, organized in a single panel
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 2<sup>nd</sup> Web-Delphi process to HTA stakeholders, organized in a single panel (all stakeholder groups in a single panel, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1st Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels
<p>IMPACT HTA, WP7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2 (Multi-criteria evaluation framework), Results of the 1<sup>st</sup> Web-Delphi process to HTA stakeholders, organized into 6 separate parallel panels (one panel per stakeholder group, 2 rounds), about the views of stakeholders regarding “This aspect should be considered in the evaluation of new medicines on a common basis” (2019)</p> <p>For details on the Web-Delphi process, see: IMPACT HTA, Work Package 7 (Methodological tools using multi-criteria value methods for HTA decision-making), Task 2, Deliverable 7.2 (Multi-criteria evaluation framework), Advancing knowledge and MCDA tools to assist HTA agencies in evaluating medicines on a common basis (2021) Oliveira, M.D. (IST), Panos Kanavos (LSE), Bana e Costa, C. (IST)</p>
DATASETS and OUTCOMES - Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the DRASTIC method: the case of South America
<p>A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.</p> <p>This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.</p> <p>Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).</p>
Improving Methods to Measure Comparable Mortality by Cause - Gold Standard Verbal Autopsy Data 2011-2014
<p>These data were collected and compiled as part of the Improving Methods to Measure Comparable Mortality by Cause (IMMCMC) project, funded by Australia's National Health and Medical Research Council (NHMRC). Verbal autopsies (VAs) were conducted between 2011 and 2014 in three sites: Bohol, Philippines; Chandpur and Comila Districts, Bangladesh; and Central and Eastern Highlands Provinces, Papua New Guinea. Diagnostic criteria and cause lists similar to those employed in the Population Health Metrics Research Consortium (PHMRC) study were used to identify gold standard (GS) deaths. This study added 3512 deaths (2491 adults, 320 children, and 701 neonates) to the GS VA database created from the PHMRC study. This dataset contains the combined PHMRC and IMMCMC data for an updated GS VA database.</p>
Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.
<p>## Abstract</p> <p>from [1]:</p> <p>Mechanochemistry is a fast-developing field of interdisciplinary research with a growing number of applications. Therefore, many theoretical methods have been developed to quickly predict the outcome of mechanically induced reactions. Constrained geometries simulate External Force (CoGEF) is one of the earlier methods in this field. It is easily implemented and can be conducted with most DFT codes. However, recently, we observed totally different predictions for model systems of epoxy resins in different conformations and with different density functionals. To better understand the conformational and functional dependence in typical CoGEF calculations we present a systematic evaluation of the CoGEF method for different model systems covering homolytic and heterolytic bond cleavage reactions, electrocyclic ring opening reactions and scission of non-covalent interactions in hydrogen-bond complexes. From our calculations we observe that many mechanochemical descriptors strongly depend on the functional used, however, a systematic trend exists for the relative maximum Force. In general, we observe that the CoGEF procedure is forcing the system to high energetic regions on the molecular potential energy profiles, which can lead to unexpected and uncorrelated predictions of mechanochemical reactions. This is questioning the true predictive character of the method.</p> <p> </p> <p>## Contact</p> <p>Christian R. Wick</p> <p>Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Faculty of Science, Department of Physics, PULS Group, Interdisciplinary Center for Nanostructured Films (IZNF), Cauerstrasse 3, 91058, Germany</p> <p> </p> <p>## License</p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p>## Context</p> <p>Dataset to paper [1]</p> <p> </p> <p>## Contents</p> <ul> <li>All COGEF trajectories in xyz format.</li> <li>All CoGEF distances and DFT Energies in csv format.</li> </ul> <p>The following DFT levels of theory were investigated:</p> <ul> <li>B3LYP/6-31G(d)</li> <li>B3LYP-D3BJ/def2-SVP</li> <li>BP86-D3/def2-SVP</li> <li>PBE1PBE/def2-SVP</li> <li>M06-D3/def2-SVP</li> </ul> <p> </p> <p>## Folder structure</p> <ul> <li>- compound_X : data set for compound number X (numbering corresponds to the numbering scheme in [1]) <ul> <li>the xyz trajectories follow the following naming convention: "DFT_method"_"unrestricted/restricted".xyz</li> <li>the csv files follow the naming convention: "DFT_method"_"unrestricted/restricted".xyz.csv</li> </ul> </li> </ul> <p>## Software</p> <p>### COGEFF calculations: COGEF.py v1.8.0</p> <p>Zenodo release:</p> <p>https://doi.org/10.5281/zenodo.7079733</p> <p>### DFT calculations:</p> <p>Gaussian 16 Rev B [2]</p> <p> </p> <p>## Funding</p> <p>This research was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 377472739/GRK 2423/1-2019 FRASCAL.</p> <p><br> ## References</p> <p>[1] C. R. Wick, E. Topraksal, D. M. Smith, A.-S. Smith, "Evaluating the predictive character of the method of Constrained Geometries Simulate External Force with Density Functional Theory.", Forces in Mechanics, 9, 100143; doi:10.1016/j.finmec.2022.100143</p> <p>[2] Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Petersson, G. A.; Nakatsuji, H.; et al. Gaussian 16 Rev. B.01, 2016.</p>
Dataset from: Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications
<p>This record includes the data associated with the study "Reporting of patient involvement: A mixed-methods analysis of current practice in health research publications". In this study, we evaluated the extent and quality of patient involvement reporting in examples of current practice in health research. We used a targeted search strategy to identify publications that report on patient involvement using the following three samples:</p> <ul> <li>Publications published in 2019 in <em>The BMJ, </em>which requires reporting on patient involvement in research articles</li> <li>Publications listed in the PCORI database. We filtered for topic: example of engagement in health research; stakeholder involvement: patients; year: 2019</li> <li>Publications citing the <a href="https://www.bmj.com/content/358/bmj.j3453">GRIPP2 reporting checklist</a> or a <a href="https://onlinelibrary.wiley.com/doi/full/10.1111/j.1369-7625.2010.00607.x">critical appraisal guideline to assess the quality and impact of user involvement in research</a></li> </ul> <p>After applying our inclusion and exclusion criteria, the final sample consisted of 87 publications that reported on patient involvement. Included publications were coded according to three coding schemes.</p> <p>This deposit includes the following:</p> <ul> <li>Overview of publications that did not meet our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Overview of publications and additional documents (if applicable) that met our inclusion criteria across all 3 samples (BMJ, PCORI, and citation)</li> <li>Coded segments and analysis for the coding scheme "Phase of involvement" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table. </li> <li>Coded segments and analysis for the coding scheme "GRIPP2-SF reporting guidelines according to Staniszewska et al., 2017" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> <li>Coded segments and analysis for the coding scheme "Critical appraisal tool according to Wright et al., (2010)" across all 3 samples (BMJ, PCORI, and citation). This includes the count of each sub-code across publications and the final results table.</li> </ul>
Post-remediation evaluation of contaminated site using geophysical methods: photos
<p>Photos of the research area.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods” </p>
Post-remediation evaluation of contaminated site using geophysical methods: ERT
<p>The ERT measurements (7 profiles: M1-M7) were performed using the LUND electrical imaging system with a SAS 4000 Terrameter produced by ABEM Malå (Guideline Geo) with 0.5 m electrode separation and the Wenner-Schlumberger configuration. </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Post-remediation evaluation of contaminated site using geophysical methods: Multispectral UAV data Olkusz (Poland) 20220629
<p>In order to analyze the vegetation condition, photos were taken in the infrared (NIR, 750 - 2500 nm) and infrared (Red Edge, 690-720 nm) range. The DJI Matrice 600 platform was used for the raid. The photos were taken from the ceiling of 150 m with the MicaSense Red Edge M camera with a focal length of 6 mm.</p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Post-remediation evaluation of contaminated site using geophysical methods: Digital Elevation Model Olkusz (Poland) 20220629
<p>The Digital Elevation Model is based on 449 aerial photos taken by a Mavic PRO Unmanned Aerial Vehicle (UAV) fitted with an FC220 camera (focal<br> length: 35 mm; charge-coupled device: 5472 × 3078 pixels, DJI, Shenzhen, China) on 29 June 2022. The final product is a DEM with a 51.1 cm/pix raster field resolution. These products were mapped in the ellipsoid WGS 84 (EPSG:4326). </p> <p>This research was funded by National Science Centre, Poland MINIATURA-5 2021/05/X/ST10/00673 “Post-remediation evaluation of contaminated site using geophysical methods”</p>
Data for paper "An adaptive nonlinear iterative method for predicting seafloor topography from altimetry-derived gravity data"
<p>LM is the linear inversion seafloor topography model</p> <p>NLM is the nonlinear inversion seafloor topography model</p> <p>PM is the prior seafloor topography model</p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images
<p>Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images. A README file with the contents of the dataset is included. </p>
A modified decontamination and storage method for sputum from patients with tuberculosis
<p>Sputum sampling is a cheap and non-invasive method for diagnosis Tuberculosis. A modified method is devised to enhance handling capacity and minimize risk of contamination in culture.</p> <p>"22_samples_TTP_GU_method_comparison_dataset.csv" is a dataset for comparing standard method and modified method of sputum handling and storing procedures before being cultured in MGIT. MGIT is used in BD BACTEC 960 MGIT system which generates "Time to positive" hours for a culture to growth and "Growth Unit" for estimating the amount of growth.</p> <p>"348_samples_TTP_GU_modified method_dataset.csv " is a dataset for applying modified method on selected 348 sputum samples for culture in MGIT. The dataset contains "Time to positive", "Growth Unit", "ZN smear grade", and "Duration of frozen:.</p>
GEOLAB - Transnational Access project QC-CEM - Mapping quick clay with geophysical methods
<p>Quick clay is characterised by complete collapse and liquid-like mobility when overloaded. Quick clay is found primarily in Norway and Sweden, but also exists in Finland, Russia, Canada and Alaska. Quick clay landslides, with their retrogression characteristics and extreme mobility, pose significant risk to human lives, infrastructure, property and surrounding ecosystems. Hence, the proper characterization of quick clay sites is essential for ensuring the safety and resilience of infrastructure in Norway and elsewhere in Europe.<br> The current practice for mapping quick clay in Norway relies heavily on borehole data with either rotary sounding or total sounding and core samples tested in the laboratory. The only method for identifying quick clay with certainty is physical testing in the laboratory, but it is time-consuming, expensive and gives limited information, i.e., only at the depths and locations where the samples are taken. In Norway, rotary sounding and total soundings are frequently used in mapping of quick clay. There is increasing interest in using geophysical methods such as Electrical Resistivity Tomography (ERT) to supplement the results from soundings, particularly in early stage of ground investigation for mapping of quick clay. ERT is a near surface geophysical method that uses direct current to measure the earth's electrical resistivity. The current is injected into the subsurface through steel electrodes installed 10-20 cm into the ground, and the apparent resistivity distribution along a profile or area is measured. Using data processing and inverse modelling a 2D or 3D resistivity model of the subsurface can be derived.<br> Geophysical methods such as ERT show capability to identify not quick clay such as sand, silt, dry crust, moraine and bed rock reasonably accurate, but the identification of quick clay is still generally limited. The detection of leached clay (thus potentially quick clay) is however possible.<br> Transnational Access project QC-CEM is funded through the 1st call for proposal for the GEOLAB project. This project aims at testing various geophysical methods for their capability for soil characterisation, particularly for detecting quick clay.</p> <p>The objectives of the QC-CEM project are:<br> (i) to test different configurations of Electrical Resistivity Tomography survey for detection of quick clay<br> (ii) to test innovative and efficient electromagnetic based methods for mapping of quick clay. Results from this investigation is not available to share at this stage.<br> (iii) to investigation the effectiveness of cross-interpretation using different geophysical methods for soil characterisation. The results from this activity will be published in open publication after they are processed.</p>
Finding the right XAI Method --- Dataset
<p>This dataset provides the complementary preprocessed data for the training of the neural networks used in Bommer et. al. and according source code (<a href="https://github.com/philine-bommer/Climate_X_Quantus">https://github.com/philine-bommer/Climate_X_Quantus</a>). In the publication , we introduce XAI evaluation in the context of climate research and assess different desired explanation properties, namely, robustness, faithfulness, randomization, complexity, and localization. To this end we build upon previous work (Labe and Barnes et. al. 2021) and<br> train a multi-layer perceptron (MLP) and a convolutional neural network (CNN) to predict the decade based on annual-mean temperature maps.</p> <p>Following Labe and Barnes et. al. 2021, we use data simulated by the general climate model, CESM1 (Hurrell et. al. 2013). We use the global 2-m air temperature (T2m) temperature maps from 1920 to 2080. The data consist of 40 ensemble members and each member is generated by varying the atmospheric initial conditions with fixed external forcing, i.e. historical forcings are imposed from 1920 to 2005 and Representative Concentration Pathways 8.5 for the following years (Kay et. al. 2015).<br> Following Labe and Barnes et. al. 2021, we compute annual averages and apply a bilinear interpolation. This results in T=161 temperature maps for each member, with v=144 longitude grid cells and h=95 latitude grid cells, given the 1.9° sampling in latitude and 2.5° sampling in longitude. The temperature maps are finally standardized by removing the multi-year (1920 to 2080) mean and subsequently dividing by the corresponding standard deviation.</p> <p>Unlike the flattened input used for the MLP (temperature maps are flattened into a vector), the CNN maintains the longitude-latitude grid of the temperature maps. Similar to Labe and Barnes et. al. 2021, for training, validation and testing we use the model data discussed above. For both MLP and CNN we consider 20% of the data as test set and the remaining 80% is split into a training (64%) and validation (16%) set. We train both networks to solve a fuzzy classification problem which combines classification and regression. In the classification setting, the network assigns each map to one of the 20 different classes, where each class corresponds to one decade between 1900 and 2100 (necessary class devision for later regression, as done by Labe and Barnes et. al. 2021). The network output thus, is a probability vector containing a probability for each class.</p> <p>To assess the network performance we use the monthly 2m air temperature of the 20th century Reanalysis data (V3) (Slivinski et. al. 2019) from 1920 to 2015.</p> <p>The dataset includes two compressed .npz-files and a Readme.md. A full description of the data contained in this dataset and instructions on the data usage are provided in the Readme-file. </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.