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4,694 results for “Analysis data”
Drainage reorganisation and species evolution: model sensitivity analysis data
<p>Data description:</p> <ul> <li><strong>‘trial_factor_values.csv’:</strong> The factor values for experiment trials were generated using a quasi-random Sobol sequence (Sobol, 1967). The table field, ‘initial_landscape_id’ is the identifier for unique combinations of the following factor values that controlled the landscape elevation in the initial conditions phase of the model: initial elevation seed, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, and <span class="math-tex">\(k_d\)</span>. The factors, <span class="math-tex">\(U\)</span>, <span class="math-tex">\(K\)</span>, <span class="math-tex">\(k_d\)</span>, <span class="math-tex">\(P_m\)</span>, and allopatric wait time varied logarithmically. The values of these factors in the file are the exponent of base 10.</li> <li><strong>‘trial_response_values_initial_conditions_phase.csv’:</strong> Topographic relief at steady state along with the model time to initial steady state are the trial model responses included in the file. Values are listed for each initial landscape ID rather than trial because many trials had the same combinations of the factors that controlled the topography of the initial landscape. </li> <li><strong>‘trial_response_values_perturb_phase_base_level_fall_scenario.csv’ and ‘trial_response_values_perturb_phase_fault_throw_scenario.csv’:</strong> Model responses of the perturb phase for base level fall and fault throw scenario along with the initial landscape ID, species count values, and the model time back to steady state.</li> <li><strong>The files beginning with `sobol`</strong>: the sensitivity analysis results output by the software, ‘SALib’ (Herman and Usher, 2017). ‘S1’, ‘S2’, and ‘ST’ in the file name indicates if the file contains data of the Sobol first, second, or total order effect, respectively.</li> </ul>
An updated mass-radius analysis of the 2017-2018 NICER data set of PSR J0030+0451
<p>Summarised posterior sample files associated with the preprint "An updated mass-radius analysis of the 2017-2018 NICER data set of PSR J0030+0451" by Vinciguerra et al. (2023; <a href="https://doi.org/10.48550/arXiv.2308.09469">arXiv</a>; accepted for publication in ApJ).</p><p>Also included are examples of model modules in the Python language using the X-PSI framework; and Jupyter analysis notebooks.</p><p>Please refer to the READme for detailed information.</p>
DNA Origami Raw AFM Data - NanoLocz: Image analysis platform for AFM, high-speed AFM and localization AFM
<p>The data file is in the original ARIS data format as captured on a Cypher VRS1250 AFM (Oxford Instruments)<br><br><br></p>
Raw data for the submitted manuscript entitled "Prospective Scenarios for Addressing the Agricultural Plastic Waste Issue: Results of a Territorial Analysis"
<p><span>Agricultural activities have been positively affected by the use of plastic products, but this has resulted in the production of plastic waste and led to an increase in environmental pollution. </span><span>This file concerns plastic waste indices to different crop types and plastic products allowed quantifying and georeferencing actual plastic waste production. Two improved scenarios were considered, the first consisted of extending the lifespan of some plastics, and the second entailed the introduction of some biodegradable alternatives. </span></p>
Code and data set for data analysis published as manuscript "Bacttle: a microbiology educational board game for lay public and schools"
<p>Code that processed raw data and plots the figures of the manuscript "Bacttle: a microbiology educational board game for lay public and schools"</p> <p>Below is a table with the original survey questions. The ID corresponds to the column displayed on the data set. When letters are followed by a number (1 or 2), it means that the question was answered before playing the game (1) and after playing the game (2).</p> <table> <tbody> <tr> <td> <p><em>ID<sup>1</sup></em></p> </td> <td> <p><em>Question text</em></p> </td> <td> <p><em>Possible answers<sup>2</sup></em></p> </td> </tr> <tr> <td> <p><em>A</em></p> </td> <td> <p>How old are you?</p> </td> <td> <p> </p> </td> </tr> <tr> <td> <p><em>B</em></p> </td> <td> <p>Do you know what a bacterium is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>C</em></p> </td> <td> <p>Do you know what a bacterial capsule is?</p> </td> <td> <p>y/n</p> </td> </tr> <tr> <td> <p><em>D</em></p> </td> <td> <p>Do bacteria have tools to harm each other?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>E</em></p> </td> <td> <p>Do bacteria reproduce at the same pace?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>F</em></p> </td> <td> <p>What is sporulation?</p> </td> <td> <p>A resistant state that some bacteria can achieve under unfavorable conditions.</p> </td> </tr> <tr> <td> <p>The release of toxins by bacteria.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>G</em></p> </td> <td> <p>What are flagella used for?</p> </td> <td> <p>Sticking to surfaces.</p> </td> </tr> <tr> <td> <p>Motility in liquid environments.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>H</em></p> </td> <td> <p>What does it mean to be lithotrophic?</p> </td> <td> <p>A bacterium can get energy from minerals.</p> </td> </tr> <tr> <td> <p>A bacterium can get energy from the sunlight.</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>I</em></p> </td> <td> <p>Can bacteria be infected by viruses?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>J</em></p> </td> <td> <p>Are all bacteria harmful for humans?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>K</em></p> </td> <td> <p>How many bacteria are in a coffee spoon of yoghurt?</p> </td> <td> <p>Millions</p> </td> </tr> <tr> <td> <p>Hundreds</p> </td> </tr> <tr> <td> <p>idk</p> </td> </tr> <tr> <td> <p><em>L</em></p> </td> <td> <p>How easy did you find the gameplay?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>M</em></p> </td> <td> <p>Did you find the card content easy to understand?</p> </td> <td> <p>VE/E/A/D/VD</p> </td> </tr> <tr> <td> <p><em>N</em></p> </td> <td> <p>Did you like the setup of the game?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>O</em></p> </td> <td> <p>Would you like to play this game again?</p> </td> <td> <p>y/n/idk</p> </td> </tr> <tr> <td> <p><em>P</em></p> </td> <td> <p>What can we improve?</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <p>1) Question A categorizes the player’s age; B and C assess the initial level of knowledge in microbiology (none -both questions are answered negatively-, basic -player knows what a bacterium is but not a bacterial capsule-, or advanced -both answers are positive-); questions D-I score knowledge acquisition; J and K are control questions; L-O evaluate the appreciation of the game; and P is an optional free text-entry answer for additional feedback. <br>2) y= yes, n=no, idk=I don’t know, VE=very easy, E=easy, A=adequate, D=difficult, VD=very difficult.</p>
Iceland as stepping stone for intercontinental spread of highly pathogenic avian influenza H5N1 virus between Europe and North America: data set on phylogeographic analysis
<p>Highly pathogenic avian influenza viruses (HPAIV) subtype H5 clade 2.3.4.4b have widely spread within the northern hemisphere since 2020 and threaten wild bird populations as well as poultry production. For the very first time, HPAIV were detected in wild birds and, subsequently, in poultry holdings in Iceland.</p> <p>Here, we present phylogeographic evidence that Iceland has been used as a stepping stone for HPAIV translocation from Northern Europe to North America in 2021 and describe two independent incursions of HPAI H5N1 clade 2.3.4.4b viruses of two different genotypes to Iceland in 2021 and 2022.</p>
Integrated analysis of anatomical and electrophysiological human intracranial data
<p>The exquisite spatiotemporal precision of human intracranial EEG recordings (iEEG) permits characterizing neural processing with a level of detail that is inaccessible to scalp-EEG, MEG, or fMRI. However, the same qualities that make iEEG an exceptionally powerful tool also present unique challenges. Until now, the fusion of anatomical data (MRI and CT images) with the electrophysiological data and its subsequent analysis has relied on technologically and conceptually challenging combinations of software. Here, we describe a comprehensive protocol that addresses the complexities associated with human iEEG, providing complete transparency and flexibility in the evolution of raw data into illustrative representations. The protocol is directly integrated with an open source toolbox for electrophysiological data analysis (FieldTrip). This allows iEEG researchers to build on a continuously growing body of scriptable and reproducible analysis methods that, over the past decade, have been developed and employed by a large research community. We demonstrate the protocol for an example complex iEEG data set to provide an intuitive and rapid approach to dealing with both neuroanatomical information and large electrophysiological data sets. We explain how the protocol can be largely automated and readily adjusted to iEEG data sets with other characteristics. The protocol can be implemented by a graduate student or post-doctoral fellow with minimal MATLAB experience and takes approximately an hour, excluding the automated cortical surface extraction.</p> <p>This collection contains the data described in the protocol and that can be used to replicate all results.</p>
CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: EACEA subset analysis
<p>This dataset was created within the research project Constructing AcTive CitizensHip with European Youth: Policies, Practices, Challenges and Solutions (CATCH-EyoU) funded by European Union, Horizon 2020 Programme, Grant Agreement No 649538. Work Package 4 of this project (Exploiting European data and testing the integrated theory of youth active EU citizenship) is focused on the re-analysis of existing European data. This dataset contains a subset of data originally collected within the project “<em>EACEA 2010/03: Youth Participation in Democratic Life</em>”, coordinated by the London School of Economic and Political Science. Specifically, an online questionnaire survey in seven European countries was conducted among young people age 15-30 in 2011. This dataset contains a subset of 22 variables that were employed for the reanalysis within the CATCH-EyoU project.</p>
Replication Data for: "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis"
<p>This data package contains all the data relevant to reproduce the results presented in the publication "Copularity of French and Dutch (semi-)copular constructions: a behavioral profile analysis".</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Data on a citation context analysis focusing on natural sciences and social sciences and humanities
<p>This dataset contains data on citation context analysis between natural sciences (NS) and social sciences and humanities (SSH). In particular, the data were created through manual coding of each citation between papers related to SDG7 (renewable energy) and SDG13 (climate change) and papers cited by them. This dataset consists of 9 files, associated with the article: Nishikawa, K. How and why are citations between disciplines made? A citation context analysis focusing on natural sciences and social sciences and humanities. Scientometrics (2023). <a href="https://doi.org/10.1007/s11192-023-04664-y">https://doi.org/10.1007/s11192-023-04664-y</a></p> <p> </p> <p>The files are numbered as follows:</p> <ul> <li>00 – README</li> <li>01 – Data by citation pair for SDG7 (original)</li> <li>02 – Data by citation pair for SDG13 (original)</li> <li>03 – Data by mention location for SDG7 (original)</li> <li>04 – Data by mention location for SDG13 (original)</li> <li>05 – Data by citation pair for SDG7 (additional)</li> <li>06 – Data by citation pair for SDG13 (additional)</li> <li>07 – Data by mention location for SDG7 (additional)</li> <li>08 – Data by mention location for SDG13 (additional)</li> </ul> <p>See README for more information.</p>
Data for: Techno-economic analysis of a novel laccase production process utilizing perennial biomass and the aqueous phase of bio-oil, Iowa, USA 2023-2025
This dataset contains the experimental design, measurements, and derived variables used to parameterize a techno‑economic model of laccase production via two‑stage solid‑state fermentation of prairie biomass with bio‑oil aqueous phase induction. It includes nutrient screening data for Pleurotus ostreatus growth on prairie biomass with alternative nitrogen sources and a corn‑steep solids dose series; factorial/response‑surface experiments varying substrate bed depth, substrate‑to‑inoculum (S:I) ratio, and pre‑induction growth time; and time‑resolved induction measurements. For each run and replicate, the data record the full set of spectrophotometric absorbances at 0–210 s, fitted slopes and r-square values, dilution and volume factors, and laccase activities normalized per mL and per gram of biomass, alongside the exact culture timings and environmental conditions used in the ABTS assay at 420 nm. Results tables provide the fitted central‑composite design model terms (coefficients, F‑statistics, and p‑values) used directly as inputs to the minimum laccase selling price (MLSP) calculations, together with the underlying per‑condition raw results.
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
Data release for paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models"
<p>This data release for the paper "Towards the routine use of subdominant harmonics in gravitational-wave inference: re-analysis of GW190412 with generation X waveform models" [<a href="https://arxiv.org/abs/2010.05830">arXiv:2010.2010.05830</a>] contains posterior samples for the GW190412 binary black hole merger event obtained from public GWOSC data with the parallel bilby Bayesian inference package, dynesty nested sampler and a set of waveforms from the "generation X" of phenomenological waveform models: IMRPhenomXAS, IMRPhenomXHM, IMRPhenomXP, IMRPhenomXPHM, IMRPhenomT and IMRPhenomTHM. The provided file is a "meta file" that can be read with the <a href="https://lscsoft.docs.ligo.org/pesummary/">PESummary</a> python package. The posterior samples included correspond to runs [2,6,10,12,14,26] in Table III of the paper (standard settings for each waveform, standar priors and sampler settings of Nlive=2048 and Nact=10 or 50). If you make use of these samples, please cite both this data release and the paper.</p>
IPBES Data Management Tutorials - Session 5.4: Processing and analysis
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on <em>processing and analysis </em>reviews common scripting languages for data analysis and processing, such as python and R. </p>
Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis
<p>Contents</p> <ol> <li><strong>moccasin_paper_env.yaml</strong>: conda environment file with R and Python packages and modules needed to reproduce analyses.</li> <li><strong>FigureReproduction.zip</strong>: data and code to reproduce main and supplemental figures.</li> <li><strong>MOCCASIN_ExampleDataset.zip</strong>: A small subset of the simulated data with example code to run MOCCASIN.</li> <li><strong>encode_corrected.zip</strong>: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol> <p> </p> <p> </p> <p>(1) <strong>moccasin_paper_env.yaml</strong></p> <p>Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced.</p> <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> <p><br> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.</p> <p> </p> <p> </p> <p>(2) <strong>FigureReproduction.zip</strong></p> <p>Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.</p> <p> </p> <p> </p> <p>(3) <strong>MOCCASIN_ExampleDataset.zip</strong></p> <p>Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper. </p> <p> </p> <p> </p> <p>(4) <strong>encode_corrected.zip</strong></p> <p>Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment. </p>
Data From: Exploring Gelatin-A and Mouse Proline-Rich Protein 5 as Probes for Wine Polyphenols analysis by Quartz Crystal Microbalance with Dissipation Monitoring
<p>Polyphenols are essential in winemaking, affecting the wine's quality, color, astringency, bitterness, and chemical stability. Conventional methods for assessing polyphenolic content are both expensive and time-intensive, underscoring the need for new, efficient techniques.</p> <p>The Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) sensor is recognized for its speed and reliability as a label-free detection tool. This study applies QCM-D to evaluate Gelatin Type A (Gel-A) from porcine skin and Mouse Proline-Rich Protein 5 (MP5) for polyphenol analysis in red wines without pre-treatment. MP5 notably exhibited a linear dissipation signal response with both total polyphenol and hydroxybenzoic acid concentrations. These findings highlight the potential for creating a stand-alone sensor platform for real-time polyphenol monitoring in winemaking.</p>
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis
<p>All the data for 'mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography–Mass Spectrometry Based Non-targeted Metabolomics Data Analysis'</p> <p>sim.zip is stimulated data for intensity cutoff 0.05. simxcms.csv is peak intensity profiles for their simulated peaks.</p> <p>sim3.zip are simulated data for normal/leading/tailing peaks with tailing factor of 1, 0.8, and 1.5, respectively.</p> <p>All the csv files begin with sim3 are extracted peaks list from the sim3.zip with corresponding data analysis software.</p> <p>csv.zip recorded the m/z, retention time, intensity, and compounds name for simulated compound for each condition (sim.zip and sim3.zip).</p> <p>sep1.mzML: simulation for 8 isomers with similar m/z while different retention times. 7 peaks are non baseline separation peaks. Peaks profile is saved in spe1.csv file.</p> <p>xcms.csv, mzmine.csv, openms.csv: peaks found in sep1.mzML by xcms, mzmine 4.5 and openms, respectively.</p> <p>R code: <a href="https://github.com/yufree/democode/blob/master/meta/simfin.R">https://github.com/yufree/democode/blob/master/meta/simfin.R</a></p> <p>Website of mzrtsim package: https://yufree.github.io/mzrtsim/</p>
Hail Event on 2022-06-28 in Locarno-Monti (TI), Switzerland: Drone Photogrammetry Imagery, Mask R-CNN Model and Analysis Data of Hailstones
<p>This hail data collection belongs to a drone hail survey performed on 2022-06-28 in Locarno-Monti (TI, Switzerland). The supercell reached the location around 07:50 UTC in the morning. Only one photogrammetry flight could be performed and thus no estimation of the hail melting process is available. The orthophoto is masked to ignore parts where detection of hail is unwanted.</p> <p> </p> <p>Expert 1 (lai, mlainer), Expert 2 (jtm), Expert 3 (por, jportmann)</p>
A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature
<p>A characterization of cloud data center logs, analyzing its workload, energy and thermal characteristics. For more details of the dataset, please read the following paper: <a href="http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf">http://hpc.ec.tuwien.ac.at/files/UCC_23_data_center_analysis.pdf.</a></p><p> </p><p>If you use the dataset, please cite the following work:</p><p>Shashikant Ilager, Adel N. Toosi, Mayank Raj Jha, Ivona Brandic, Rajkumar Buyya, "A Data-driven Analysis of a Cloud Data Center: Statistical Characterization of Workload, Energy and Temperature", In Proceedings of the 16th IEEE/ACM International Conference on Utility and Cloud Computing (UCC2023), Messina, Italy, December 4-7, 2023.</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.