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Data to reproduce the results presented in Sehgal et al. 2022. Water Resources Research, https://doi.org/10.1029/2021WR030624 ("Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer")
<p>This repository consists data to reproduce results as presented in: "Inferring suspended sediment carbon content and particle size at high-frequency from the optical response of a submerged spectrometer", Water Resorces Research. Kindly refer to the readme.text file to navigate through the dataset.</p> <p> </p> <p> </p>
TweetyNet results
<p>This dataset accompanies the eLife publication "Automated annotation of birdsong with a neural network that segments spectrograms". In the article, we describe and benchmark a neural network architecture, TweetyNet, that automates the annotation of birdsong as we describe in the text. Here we provide checkpoint files that contain the weights of trained TweetyNet models. The checkpoints we provide correspond to the models that obtained the lowest error rates on the benchmark datasets used (as reported in the Results section titled "TweetyNet annotates with low error rates across individuals and species"). We share these checkpoints to enable other researchers to replicate our key result, and to allow users of our software to leverage them, for example to improve performance on their data by adapting pre-trained models with transfer learning methods.</p> <p> </p>
Public BI benchmark - part 2 & results
<p>Originally published on: https://github.com/cwida/public_bi_benchmark </p> <p>Originally compressed with bzip2. Compressed here with gzip.</p> <p>"User generated benchmark derived from the DBTest'18 paper [1] by Tableau. It contains real data and queries from 46 public workbooks in Tableau Public [2].</p> <p>We downloaded 46 of the biggest workbooks and converted the data to <em>.csv</em> files and collected the SQL queries that appear in the Tableau log when the workbooks are visualized. We processed the <em>.csv</em> files and queries with the purpose of making them load and run on different database systems.</p> <p>Each directory is associated with a workbook and contains:</p> <pre><code>samples: a sample of each .csv file (first 20 rows) tables: .sql files containing the schema of each .csv file queries: .sql files containing the queries data-urls.txt: links for downloading the full .csv.bz2 compressed files </code></pre> <p>There are 46 workbooks containing 206 tables (.csv files) with the total size of 41 GB compressed and 386 GB uncompressed.</p> <p>Multiple .csv files may overlap but are not identical. This is because Tableau extracts the same workbook in multiple different ways for different queries."</p>
Performance results of different scheduling algorithms used in the simulation of a modern game engine
<p><strong>Performance results of different scheduling algorithms used in the simulation of a modern game engine</strong></p> <p>These results are a companion to the paper entitled "<em>Exploring scheduling algorithms for parallel task graphs: a modern game engine case study</em>" by M. Regragui et al.</p> <p><strong>General information</strong></p> <p>This dataset contains raw outputs and scripts to visualize and analyze the scheduling results from our game engine simulator.<br> The result analysis can be directly reproduced using the script run_analysis.sh. A series of Jupyter Notebook files are also available to help visualize the results.</p> <p><strong>File information</strong></p> <p>- All Scenario*.ipynb files contain python scripts to visualize and analyze the simulation results.<br> - The Scenario*.py files contain python scripts that can be run directly with Jupyter Notebook.<br> - The requirements.txt file contains the names and versions of python packages necessary to reproduce the analysis.<br> - The run_analysis.sh file contains a bash script to install the required python packages and run the Scenario*.py scripts.</p> <p>The results are organized in five folders:</p> <p>1. Result_1 contains the results for Scenario 1 generated using file input_scenario_1.txt.<br> 2. Result_2 contains the results for Scenario 2 generated using file input_scenario_2.txt.<br> 3. Result_3 contains the results for Scenario 3 generated using file input_scenario_3.txt.<br> 4. Result_CP_1 contains the results for the critical path of Scenarios 1 and 2 generated using file input_CP_scenario_1.txt.<br> 5. Result_CP_3 contains the results for the critical path of Scenario 3 generated using file input_CP_scenario_3.txt.</p> <p>Each result file (e.g., HLF_NonSorted_Random_1_200_10.txt) contains 200 lines representing information of the 200 frames that were simulated. Each line contains four values: the frame number, the duration of the frame (in microseconds), a critical path estimation for the previous frame (in microseconds), and the load parameter (value between 0 and 1).</p> <p>The outputs of this analysis include some PDF files representing the figures in the paper (in order) and some CSV files representing the values shown in tables. The standard output shows the p-values computed in parts of the statistical analysis.</p> <p><strong>Software and hardware information</strong></p> <p>The simulation results were generated on an Intel Core i7-1185G7 processor, with 32 GB of LPDDR4 RAM (3200 MHz). The machine ran on Ubuntu 20.04.3 LTS (5.14.0-1034-oem), and g++ 9.4.0 was used for the simulator's compilation (-O3 flag).</p> <p>The results were analyzed using Python 3.8.10, pip 20.0.2 and jupyter-notebook 6.0.3. The following packages and their respective versions were used:</p> <p>- pandas 1.3.2<br> - numpy 1.21.2<br> - matplotlib 3.4.3<br> - seaborn 0.11.2<br> - scipy 1.7.1<br> - pytz 2019.3<br> - python-dateutil 2.7.3<br> - kiwisolver 1.3.2 <br> - pyparsing 2.4.7 <br> - cycler 0.10.0 <br> - Pillow 7.0.0<br> - six 1.14.0 </p> <p><strong>Simulation information</strong></p> <p>Simulation results were generated from 4 to 20 resources. Each configuration was run with 50 different RNG seeds (1 up to 50).</p> <p>Each simulation is composed of 200 frames. The load parameter (lag) starts at zero and increases by 0.01 with each frame up to a value equal to 100% in frame 101. After that, the load parameter starts to decrease in the same rhythm down to 0.01 in frame 200.</p> <p><strong>Algorithms abbreviation in presentation order</strong></p> <p>FIFO serves as the baseline for comparisons.</p> <p>1. FIFO: First In First Out.<br> 2. LPT: Longest Processing Time First.<br> 3. SPT: Shortest Processing Time First.<br> 4. SLPT: LPT at a subtask level.<br> 5. SSPT: SPT at a subtask level.<br> 6. HRRN: Highest Response Ratio Next. <br> 7. WT: Longest Waiting Time First.<br> 8. HLF: Hu's Level First with unitary processing time of each task.<br> 9. HLFET: HLF with estimated times.<br> 10. CG: Coffman-Graham's Algorithm.<br> 11. DCP: Dynamic Critical Path Priority.</p> <p><strong>Metrics</strong></p> <p>* SF: slowest frame (maximum frame execution time)<br> * DF: number of delayed frames (with 16.667 ms as the due date)<br> * CS: cumulative slowdown (with 16.667 ms as the due date)<br> </p>
Test Result for IIA Zone protection method.
<p>Test results related to the paper:</p> <p>"Application of IIA Method and Virtual Bus Theory for Backup Protection of a Zone Using PMU Data in a WAMPAC System", Energies Magazine, 2022. </p> <p>Link Energies: https://www.mdpi.com/1996-1073/15/9/3470</p> <p>DOI: <a href="https://doi.org/10.3390/en15093470">https://doi.org/10.3390/en15093470</a></p> <p>Authors of paper: Aníbal Antonio Prada Hurtado, Eduardo Martinez Carrasco, Maria Teresa Villén Martínez and Jose Saldaña.</p> <p> </p>
Model results based on COSMOS climate model (old version MPI-ESM1)
<p>Nino 3.4 SST of individual models (COSMOS-Nordemg and COSMOS-Tiedtke) and supermodel</p>
When is a Ring Road a 'Ring Road'? - results of the survey
<p>Images and results from the survey described in the paper "When is a Ring Road a 'Ring Road'? A Brief Perceptual Study".</p>
Performance results of ARMOSA model parallelized with COMPSs
<p>Scripts and data results to build the performance graph of the LandSupport ARMOSA application parallelized with COMPSs. This results have been published in D3.6 - REPORT ON THE FINAL MODELS IMPLEMENTATION AND OPTIMIZATION.</p> <p>The archive contains the python script to generate the graphs, and the dataset of the times resulting from the execution of the application in the LandSupport platform. The execution times can only be generated on such platform.</p>
Survey questionnaire and results on Structural Barriers to Investment in Energy Efficiency Policies in the Private Rented Sector
<p>The online survey was designed and conducted in the framework of the EU H2020 project ENPOR ("Actions to Mitigate Energy Poverty in the Private Rented Sector). The aim of the survey was to receive statistically sound insights on structural factors that affect the implementation of energy efficiency policies for the alleviation of energy poverty in the European Private Rented Sector. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “EUSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s, landlords and tenant associations, etc.</p>
Survey questionnaire and results on Pay-for-Performance (P4P) schemes for energy efficiency measures
<p>The online survey was designed and conducted in the framework of the EU H2020 SENSEI project. The aim of the survey was to identify stakeholders’ perceptions on how P4P programmes could be integrated into the existing EU regulatory and market framework. We developed a semi-quantitative, self-completion online questionnaire, using the online tool “Alchemer”. The online survey collects input from different experts from the field of academia, consultancies, policymaking, and the energy industry. </p> <p>The questionnaire is used by <em>Tzani</em> <em>et al </em>(2022) to investigate how policy developments and adjustments in the EU can facilitate the design of performance-based energy efficiency programmes. The study combines a Strengths, Weaknesses, Opportunities, and Threats framework with an Analytical Hierarchy Process method and a Threats, Opportunities, Weaknesses, and Strengths matrix for the analysis of different stakeholder perceptions and the formulation of policy strategies.</p> <p>If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Tzani, D., Exintaveloni, D.S., Stavrakas, V., Flamos, A. </em><em>Devising policy strategies for the deployment of energy efficiency Pay-for-Performance programmes in the European Union</em><em>. </em></p>
Challenges of the New Accreditation in Digital Competences for Citizens (ACTIC): Update, Validation and Results
<p>The aim of this study is to explain the process used to update and validate the Catalan Accreditation of Information and Communications Technology Skills (ACTIC), conducted by the Open University of Catalonia and the Blanquerna Foundation of Ramon Llull University, at the initiative of the Catalan Department for Digital Policy and Public Administration, Government of Catalonia.</p> <p>Using a mixed method, the study is divided into 3 phases and presents the definitions of the 5 competence areas, 19 competences, 3 achievement levels and a total of 233 indicators. The definitions were validated by a group of experts. As a result of the study, three key dimensions have emerged that improve digital awareness among citizens while empowering them and providing them with a way of having their skills accredited and acknowledged in the workplace: (i) identity, centred around participatory, social, ethical and civic issues; (ii) skills learning among citizens; and (iii) technological development and digital autonomy of citizens.</p>
Input data and Supplementary Results for "Turnover in life-strategies recapitulates marine microbial succession colonizing model particles"
<p><strong>README</strong></p> <p>This page contains processed input data used for downstream analysis and some Supplementary Results for the paper:</p> <p>Pascual-García, A., Schwartzman, J., Enke, T.N., Iffland-Stettner, A., Cordero, O.X., Bonhoeffer, S., Turnover in life-strategies recapitulates marine microbial succession colonizing model particles (2022).</p> <p> </p> <p><strong>Input data</strong></p> <p> </p> <ul> <li> <p>File <em>“count_table.ESV.biom”</em>: Table containing the abundance of each Exact Sequence Variant (ESV) in the different samples (biom format).</p> </li> <li> <p>File <em>“count-table_</em><em>metagenomes</em><em>_KEGGs.L3.spf”</em>. Table containing the abundances of genes found in the shotgun metagenomics experiments annotated in KEGG and then aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_</em><em>PICRUST2</em><em>_KEGGs.L3.spf</em>”. Table containing the abundances of genes predicted with PICRUSt v2. These genes were annotated in KEGG and aggregated into classes according to the finest hierarchy in KEGG (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“count-table_Isolates_KEGGs.L3.spf</em>”. Table containing the abundances of genes found in the isolates genomes that were annotated in KEGG and aggregated into classes according to the finest classification in KEGG's hierarchy (level 3). This is a tsv-formatted file that can be directly used in STAMP to perform statistical analysis (spf format).</p> </li> <li> <p>File <em>“samples_metadata.tsv”</em>. Metadata table describing the samples.</p> </li> <li> <p>File <em>“isolates_metadata.tsv”.</em> Metadata table describing the isolates, it includes shallow phylogenetic levels and a categorical identifier describing the environmental preference estimated for the ESV having a 100% sequence identity with a ZINB-GLM.</p> </li> <li> <p>File <em>“sequences.ESV.</em><em>fasta</em><em>”.</em> File containing the Exact Sequence Variants fasta.</p> </li> </ul> <p><strong>Supplementary Materials</strong></p> <p> </p> <ul> <li> <p>File <em>“qiime2_visualizations.zip”</em>. A file containing visualizations compatible with the qiime2 viewer (simply drag and drop the file in <a href="https://view.qiime2.org/">https://view.qiime2.org/</a>) for each sample or combination of samples, labelled as `$substrate.$medium.$replicate`, where `$medium = {Beads, Seawater}` and `$replicate = {A,B,C}`. If the label is not present for one field, it means that all samples are aggregated for that field e.g.:</p> <ul> <li> <p>“<em>count_table.ESV.Chitosan.Beads.A.bar-plots.</em><em>qzv”</em> Contains the replicate experiment A for communities on the synthetic beads in chitosan.</p> </li> <li> <p>“<em>count_table.ESV.Chitosan.bar-plots.</em><em>qzv”</em> Contains all samples in chitosan (both seawater communities and the three replicates of communities on the beads).</p> </li> </ul> </li> <li> <p>File <em>“README.odt”</em>. This readme in libreoffice format.</p> </li> <li> <p>File <em>“</em><em>Genome_deposition_information.xlsx”. </em> NCBI identifiers for the isolates’ genomes.</p> </li> <li> <p>File "barcodes_to_samples_MGRAST.xlsx". Contains the barcodes of each sample and its metadata as it was deposited in MG-RAST. In a second tab, there is a subset of samples with a low number of reads that MG-RAST analyzed together, generating a single entry (termed "mixed").</p> </li> <li> <p>Access to raw an processed metagenomes and analysis are provided through MG-RAST following [this link](<a href="https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635">https://www.mg-rast.org/mgmain.html?mgpage=project&project=mgp85635</a>).</p> <ul> <li> <p>As of May 30th, 2022, there are two issues with the dataset in MG-RAST which are out of our scope to solve. We will report any update here. The first problem is related to the entry TCCTGAGC-GTAAGGAG-s_2_, which does not load in MG-RAST. These are very low samples and were discarded in most analyses. In addition, you will find in the metadata 17 metagenomes that do not belong to our project.</p> </li> </ul> </li> </ul>
Results from surveys to trainees participating into enriched sessions with practitioners
<p><strong>BACKGROUND:</strong></p> <p>The presented data was obtained during the implementation of the European project, DISCOVERY LEARNING. DISCOVERY LEARNING aims at experimenting towards a participatory, empowered and evolutionary work-based learning in PhD programmes for effectively training <strong>transferable skills</strong> related to Open Science and Innovation, achieving replicable and sustainable results over time. The ultimate goal was to establish a set of<strong> knowledge performance indicators (KPIs) about what works in doctoral education for training a set of transferable skills. </strong>If you want to get more information on the project, please click here:<strong> <a href="https://discoverylearning.eu/">https://discoverylearning.eu/</a> </strong></p> <p>At the level of <strong>pedagogical model</strong>, the chosen paradigm was the so-called <strong>Personalized Learning, </strong>which adjusts not only to the characteristics and needs of each student (adapting the pace and methodology), but also takes into account their particular preferences and interests (so that the student influences and actively participates in the definition of its objectives and in the selection of contents, as well as in the ways of demonstrating learning) (Looi et al., 2009).</p> <p>Within this double focus into transferable skills (related to open science and innovation) and the implementation of personalized learning mechanisms, the main experimental areas during the project were the following:</p> <ol> <li>Involving practitioners</li> <li>Making use of gamification</li> <li>Implementing real work-based learning</li> </ol> <p>On the basis of the above and as the result of a joint reflection process amongst partners, DISCOVERY LEARNING has formulated the following hypothesis:</p> <p><em>-If specific / punctual enriched activities by practitioners are inserted within the training programme, then:</em></p> <ul> <li><em>The level of engagement and performance improves significantly </em></li> <li><em> Institutions can train more students in transferable skills related to open science and innovation. </em></li> </ul> <p>In order to test and verify the above hypothesis, it has been decided to carry out a series of experimentation protocols (online and off-line trainings on a number of science & technology trends related to transferable skills) with early-stage researchers that can measure their applicability and reproducibility of our hypothesis over time. If you want to know more about the project results, please click here: <a href="https://discoverylearning.eu/results/traininglearning-options/dl-resources">https://discoverylearning.eu/results/traininglearning-options/dl-resources</a></p> <p><strong>METHODS:</strong></p> <p>We have designed an ad-hoc survey to gather relevant information from the participants of these experimentation protocols. These surveys will happen before and after the trainings.</p> <p>The following section show the surveys being used presently within the project to ask trainees around the aforementioned hypothesis. This survey has been refined in relation to the lessons learnt during stage 1 of the project’ experimentation strategy. For launching them, a professional software that allows optimising the design and User Experience, as well as managing the data gathered, was being used (<a href="https://app.surveyanyplace.com/">https://app.surveyanyplace.com/</a>)</p> <p> </p> <p><strong>• STATISTICAL INFORMATION GATHERED (general information):</strong></p> <p><strong>-Title: </strong>Surveys to trainees participating into enriched sessions with practitioners</p> <p><strong>-Method:</strong> Survey questions will incorporate Likert scale questions, multiple choice and free text answers.</p> <p><strong>-General information</strong>: Gender (optional), country, age range, discipline, internet connectivity and type and ownership of the device being used, language, institution.</p> <p><strong>-Information about the training in which the person is to participate / has participated:</strong></p> <p>Title and date. Unless desired different by the participant, the survey is anonymous.</p> <p><strong>-Period of surveying</strong>: 22.10.2021-19.03.2022</p> <p><strong>-Responsible person</strong>: Eva García Muntión (RTDI)</p> <p><strong><em>Terms of use:</em></strong> These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</p> <p><strong><em>Funding:</em></strong><em> This project has received funding from the Research and Innovation framework Horizon 2020 of the European Union under grant agreement nº 101006452.</em></p> <p><br> </p>
Mitochondrial DNA sequencing results control and MELAS fibroblasts. Povea-Cabello, S. et al 2022.
<p>Mitochondrial DNA sequencing results from control and MELAS patients-derived dermal fibroblasts. Povea-Cabello, S. et al 2022. </p>
Convergence in phosphorus constraints to photosynthesis dataset - modelling results
<p>The archive contains the extract of model results shown in the publication 'Convergence in phosphorus constraints to photosynthesis' by David Ellsworth et al. in Nature Communication.</p> <p>README:</p> <p># Simulation results from ORCHIDEE LSM v1.2 (https://doi.org/10.14768/20200407002.1)<br> # time period: 1992-2021<br> # author: Daniel Goll (dsgoll123@gmail.com)</p> <p># the following variables are found in these files:<br> # GPP [g/m2/yr] -> gpp.nc<br> # POTENTIAL GPP (assuming maximum P content) [g/m2/yr] -> gpp_pot_max.nc<br> # POTENTIAL GPP (assuming average P content) [g/m2/yr] -> gpp_pot_avg.nc<br> # LEAF N:P RATIO [g/g] -> leafNP.nc</p>
Integral projection model results of the planktonic foraminifer Trilobatus sacculifer
<p>Developmental plasticity, where traits change state in response to environmental cues, is well-studied in modern populations. It is also suspected to play a role in macroevolutionary dynamics, but due to a lack of long-term records the frequency of plasticity-led evolution in deep time remains unknown. Populations are dynamic entities, yet their representation in the fossil record is a static snapshot of often isolated individuals. Here, we apply for the first time contemporary integral projection models (IPMs) to fossil data to link individual development with expected population variation. IPMs describe the effects of individual growth in discrete steps on long-term population dynamics. We parameterize the models using modern and fossil data of the planktonic foraminifer <em>Trilobatus sacculifer</em>. Foraminifera grow by adding chambers in discrete stages and die at reproduction, making them excellent case studies for IPMs. Our results predict that somatic growth rates have almost twice as much influence on population dynamics than survival and more than eight times more influence than reproduction, suggesting that selection would primarily target somatic growth as the major determinant of fitness. As numerous palaeobiological systems record growth rate increments in single genetic individuals, and imaging technologies are increasingly available, our results open up the possibility of evidence-based inference of developmental plasticity spanning macroevolutionary dynamics. Given the centrality of ecology in palaeobiological thinking, our model is one approach to help bridge eco-evolutionary scales while directing attention towards the most relevant life-history traits to measure.</p>
TADA QQ Experiment Results -T2DV2
<p>The results of semantic labelling of numeric columns in the T2Dv2 dataset applying quantile-quantile plot.</p>
Results of the geochemical and magnetic studies on cryodust from glacial cores of the Southern Spitsbergen (Svalbard, Norway)
<p>Results of the geochemical and magnetic studies on natural mineral aerosol deposited and trapped in glaciers (cryodust). Samples were collected from glacial cores taken from five glaciers of Southern Spitsbergen (Svalbard, Norway). </p> <p>The samples were collected by means of a hand-operated Kovacs Enterprise® Mark II coring system. Samples (90 mm in diameter) were packed into polyethylene bags, secured, and transported to the Polish Polar Station Hornsund. The core samples were rinsed using deionized water (Polwater DL100; Norm PN-EN ISO 117 3696:1999; conductivity <0.06 μS/cm) and melted at room temperature in the closed new polyethylene bags. After melting samples were filtered through pre-rinsed sterile Millipore Mixed Cellulose Esters filters (white gridded and 0.45 𝜇𝜇m pore size). After filtration, the filters with residuum were dryer at the temperature of 60<sup>o</sup>C.</p> <p>Solid particulates of cryodust were subjected to analysis by Electron MicroProbe (EMP) with special attention paid to their internal structure. A scanning electron microscope (SEM) fitted with a backscattered electron (BSE) detector was used to trace grains topography and composition. Special attention was given to monazite chemical dating. Magnetic methods comprised analyses of magnetic susceptibility <em>κ</em> vs temperature <em>T</em> variations and determination of magnetic hysteresis parameters.</p> <p>More about the methodology, analyses and results can be found here: <a href="https://doi.org/10.3390/atmos11121325">https://doi.org/10.3390/atmos11121325</a></p>
Dataset for the manuscript "Crowding results from optimal integration of visual targets with contextual information"
<p>There are seven experimental datasets, two program with which data are collected, two supplemetary programs needed to run the main code and one program to analyse data. Two .txt files are included, where we describe how to use the stimulation and analysis programs.</p>
Compiled executables and their resulting Binary Ninja database files
<p>2,972 amd64 executables compiled with gcc and clang and their resulting Binary Ninja Database Files (.bndb). This dataset was made available for the conference paper "What Makefile? Detecting Compiler Information Using The Code Property Graph Without Source". Analysis of the 2,972 executables took 23 hours, 59 minutes, and 1 second on Binary Ninja build 3.1.3561-dev on macOS with an Intel Xeon W-3235. Analysis was done serially but without limiting the amount of threads working on analysis at a single time. CPU utilization averaged 290.2%.</p> <p>Executables compiled by Davide Pizzolotto and Katsuro Inoue of Osaka University and originally available on <a href="https://doi.org/10.5281/zenodo.4659370">Zenodo</a>. Please cite them if you use this work.</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.