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Code and Datasets for "A simple mechanism for collective decision-making in the absence of payoff information"
<p>This repository contains R code and datasets used for analyses in the paper:</p> <p>"A simple mechanism for collective decision-making in the absence of payoff information"</p> <p>Published in <em>Proceedings of the National Academy of Sciences of the United States of America</em> (PNAS) in 2023</p> <p>by Daniele Carlesso, Justin M. McNab, Christopher J. Lustri, Simon Garnier, and Chris. R. Reid.</p> <pre><code>✓ Ensure that all files are in the same folder for the code to run properly. ✓ the repository also includes a summary HTML file for easier visualisation. </code></pre> <p>Datasets:</p> <pre><code>• chain_growth.csv • cumsum_df.csv • growth_data.csv • GrowthMP.csv • JL_chaingrowth.csv • joining_distance.csv • leave_10.csv • leaving_positions.csv • timespent.csv • traffic_mean.csv </code></pre> <p>Code:</p> <pre><code>• Code.Rmd </code></pre> <p>Markdown summary:</p> <pre><code>• Code.html </code></pre>
Data from: To eat, or not to eat: A phantom decoy affects information-gathering behavior by a free-ranging mammalian herbivore
<p><span>When foraging, making appropriate food choices is crucial to an animal's fitness. Classic foraging ecology theories assume animals choose food of greatest benefit based on their absolute value across multiple dimensions. Consequently, poorer options are considered irrelevant alternatives that should not influence decision-making among better options. But heuristic studies demonstrate that irrelevant alternatives (termed decoys) can influence decisions of some animals, indicating they use a relative rather than absolute evaluation system. Our aim was to test whether a decoy influenced the decision-making process – i.e. information-gathering and food choice – of a free-ranging mammalian herbivore. We tested swamp wallabies, <em>Wallabia bicolor</em>, comparing their behavior towards, and choice of, two available food options over time in the absence or presence of the decoy. We used a phantom decoy—unavailable option—and ran two trials in different locations and seasons. Binary preferences (decoy absent) for the two available food options differed between trials. Irrespective of this difference, across both trials the presence of the decoy resulted in animals more likely to overtly investigate available food options. But the decoy only shifted food choice, weakly, in one trial. Our results indicate that the decoy influenced the information-gathering behavior during decision-making, providing first evidence that decoys can affect decision-making process of free-ranging mammalian herbivores in an ecologically realistic context. I</span><span>t is premature to say these findings confirm the use of relative evaluation systems. </span><span>Whether the foraging outcome is more strongly affected by other decoys, food dimensions, or ecological contexts, is yet to be determined.</span></p>
The impact of information disclosure on consumer acceptance of genome-edited foods in Japan
<p>Genome editing is promising to accelerate the rate of genetic progress by enabling the replacement of the germ cell lineage of commercial breeding animals with cells derived from genetically elite lines. This is an efficient way to produce foods and reduce food waste. However, foods using genome editing are not very familiar in Japan. The purpose of this study is to investigate consumer acceptance of genome-edited foods and the impact of information disclosure on it. We constructed an acceptance model for genome-edited foods and investigate the amount of willingness to pay for the level of information disclosure. Genome-edited food acceptance model illustrated that information credibility and perceived usefulness were significantly related to the consumer acceptance of genome-edited foods. WTP of functional information label is 76 JPY (approx. 0.69USD), and that of genome-edited food label is 11 JPY (approx. 0.1USD). For genome editing technology description, it was 38 JPY (approx. 0.345USD). The most important factor is functional information. Since this study is an early study in the genome-edited food acceptance model, we believe that there is room for various discussions in the future.</p>
Rotation Period Predictions for Low-mass Stars with Kinematic Information from Gaia DR3
<pre><strong>DR3_kine_prot_pred.csv:</strong> 17.6 million predicted periods for all stars with Gaia DR3 RVs. This data should only be used to vet periods and not directly as period measurements. Works best for stars with GBP-GRP > 1.5 and rotation period > 20 days. Do not work for fast-rotating low-mass stars. Column descriptions see below </pre> <p><strong>source_id</strong>: Gaia DR3 source_id</p> <p><strong>ra</strong>: Gaia DR3 ra measurements</p> <p><strong>dec</strong>: Gaia DR3 dec measurements</p> <p><strong>parallax</strong>: Gaia DR3 parallax measurements</p> <p><strong>bp_rp</strong>: Gaia DR3 G<sub>BP</sub>-G<sub>RP</sub> measurements</p> <p><strong>phot_g_mean_mag</strong>: Gaia DR3 G mag</p> <p><strong>abs_G</strong>: absolute Gaia G magnitude derived from Gaia DR3 G mag and parallax</p> <p><strong>ruwe</strong>: Gaia DR3 ruwe measurements </p> <p><strong>Prot_pred</strong>: Predicted periods using Gaia DR3 parameters and kinematics</p> <p> </p> <p><strong>vet_ztf_lowmass.csv</strong>: 65k vetted ZTF period measurements for stars with G<sub>BP</sub>-G<sub>RP</sub> > 1.5. Column descriptions see below</p> <p><strong>source_id</strong>: Gaia DR3 source_id</p> <p><strong>ra</strong>: Gaia DR3 ra measurements</p> <p><strong>dec</strong>: Gaia DR3 dec measurements</p> <p><strong>parallax</strong>: Gaia DR3 parallax measurements</p> <p><strong>bp_rp</strong>: Gaia DR3 G<sub>BP</sub>-G<sub>RP</sub> measurements</p> <p><strong>gmag</strong>: Gaia DR3 G mag</p> <p><strong>abs_G</strong>: absolute Gaia G magnitude derived from Gaia DR3 G mag and parallax</p> <p><strong>rv</strong>: Gaia DR3 rv measurements</p> <p><strong>Prot</strong>: measured and vetted periods from ZTF </p>
Data for: Population structure of a grassland songbird (Dolichonyx oryzivorus) to inform conservation units
<p>Understanding the patterns and processes driving population structure is crucial for conservation planning. Spatial conservation units (CUs) for one or more species have been defined using similarities in communities, habitats, and resource management concerns. Additionally, CU boundaries can be informed by population genetic structure. Here, we determined the population structure of the Bobolink (<em>Dolichonyx</em> <em>oryzivorus</em>), a migratory grassland bird. Its breeding range spans longitudinally across most of North America in the northern U.S. and southern Canada, and its population has declined by 59% from 1971 to 2014. We sampled blood from Bobolinks at seven breeding sites and used a ddRAD sequencing approach to identify 3236 SNPs for population genetic analyses. Using the Bayesian clustering approach, STRUCTURE, we found low levels of genetic differentiation across the breeding range. F st values ranged from 0.002 to 0.036 among all population pairs, and genetic structure followed an isolation-by-distance model. Despite low levels of genetic differentiation, we found evidence for four genetic groupings— breeding populations in Oregon and British Columbia were distinct from each other as well as from Central and eastern North American breeding populations. Investigating the demographic history of the populations using approximate Bayesian computation, we found evidence that western Bobolink populations are not relict populations but instead were founded during a recent westward range expansion, possibly enabled by agricultural expansion. We identify four genetics-based CUs that may serve as a complementary spatial framework, broader in scale compared to the commonly-used Bird Conservation Regions, for defining and achieving population objectives.</p>
Supplementary information for the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly'
<p>This is the supplementary information of the manuscript 'GFViz: A tutorial on creating interactive visualization of genomic features using R Tidyverse and plotly' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p>
Supplementary information for the manuscript 'Mutation in SHAGGY-like kinase AsGSK2.1 causes a short kernel phenotype in oat (Avena sativa)'
<p>This is the supplementary information of the manuscript 'Mutation in SHAGGY-like kinase AsGSK2.1 causes a short kernel phenotype in oat (<em>Avena sativa</em>)' published as a part of the thesis 'Annotating and making use of the <em>Avena sativa</em> cv. Sang reference genome' by Nikos Tsardakas Renhuldt.</p> <p>SI.pdf contains supplementary figures 1-3.</p> <p>Supplementary figure 4.html contains supplementary figure 4.</p> <p>Supplementary tables.xlsx contains supplementary tables 1-5.</p>
Supplementary Information for "Analytical electron microscopy study of the composition of BaHfO₃ nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition"
<p>Supplementary information containing image and spectroscopy data, and data-processing notebooks for the article "<strong>Analytical electron microscopy study of the composition of BaHfO<sub>3</sub> nanoparticles in REBCO films: The influence of rare-earth ionic radii and REBCO composition</strong>".</p> <p>Article (open access): <a href="https://doi.org/10.1039/D3MA00447C">https://doi.org/10.1039/D3MA00447C</a></p> <p>The data is sorted in folders for each figure, and the <em>Notes.pdf</em> file contains additional information about the different file formats and processing steps.</p> <p>If you have questions, you can contact Lukas Grünewald at the E-Mail listed on the ORCID page: <a href="https://orcid.org/0000-0002-5898-0713">https://orcid.org/0000-0002-5898-0713</a></p>
Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales
<p>This is a repository contains </p> <p>1)training data (x_data, y_data, snow_max) </p> <p>2) Developed Deep Learning model (snow_model.py)</p> <p>3) Training weights (*.hdf files)</p> <p>for publication " Preferential information extraction from space-based passive microwave measurements enables accurate characterization of snow depth variability at continental scales"</p> <p> </p>
Dataset for: Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE
<p><strong>Based on the Github respostitory: <a href="https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main">https://github.com/Khadrawi/Physics-Informed-Neural-Networks-for-Estimation-of-Hydraulic-Conductivity/tree/main</a></strong></p> <p>This repository contains the data used for the paper "Novel Physics Informed-Neural Networks for Estimation of Hydraulic Conductivity of Green Infrastructure as a Performance Metric by Solving Richards-Richardson PDE"<br> You'll find the csv files for the three simulated (Hydrus 1D) scenarios explained in the paper. These files were processed from the 'Nod_Inf.out' files to csv format.</p> <p><strong>Acknowledgments</strong><br> The publicly available data used for this study (scenario 1 & 2) as well as the code for the second PINN architecture (based on Dr. Maziar Raissi PINN code) and the code used to transform “Nod_inf.out” files from Hydrus 1D to csv files created by Dr. Toshiyuki Bandai and Dr. Teamrat A. Ghezzehei were helpfulfor this study.</p>
Read coverage information for analysis missing plasmid sequences
<p>This file contains two directories: read_coverage, and read_coverage_contigs. This directories should be decompressed and located within ecoli-binary-classifier/2021_11_missing_sequences_analysis/results/ in order to reproduce results described the in the mansucript.</p>
Appendix - Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition
<p>Appendix tables for the paper "Informative Speech Features based on Emotion Classes and Gender in Explainable Speech Emotion Recognition".</p> <p>Feature informativeness information was gathered using SHAP values.</p> <p>TABLE VIII: Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE IX: (continue)Table of Statistics and Individual t-Test Results Between Each Emotion vs Neutral</p> <p>TABLE X: Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XI: (continue) Table of Statistics and Individual t-Test Results Between Genders</p> <p>TABLE XII: Table of 5 the most informative feature for each model, according to SHAP values</p> <p>TABLE XIII: (continue)Table of 5 the most informative feature for each model, according to SHAP values</p>
Supporting information from Integrating a Chemicals Perspective into the Global Plastic Treaty
<p>Supporting Information (open data) Integrating a Chemicals Perspective into the Global Plastic Treaty</p>
Supporting Information from Per- and Polyfluoroalkyl Substances in North American School Uniforms
<p>Supporting data (open data) from the publication Per- and Polyfluoroalkyl Substances in North American School Uniforms</p>
Supporting information from Prospective environmental risk screening of seven advanced materials based on production volumes and aquatic ecotoxicity
<p>Supporting information (Opne data) for the publication Prospective environmental risk screening of seven advanced materials based on production volumes and aquatic ecotoxicity</p>
Supporting information for: Integrating morphological, molecular, and cytogenetic data for F2 sea turtle hybrids diagnosis revealed balanced chromosomal sets
<p><span>Hybridization could be considered part of the evolutionary history of many species. The hybridization among sea turtle species on the Brazilian coast is atypical and occurs where nesting areas and reproductive seasons overlap. Integrated analysis of morphology and genetics is still scarce, and there is no evidence of the parental chromosome set distribution in sea turtle interspecific hybrids. In this study, chromosome markers previously established for pure sea turtle species were combined with morphological and molecular analyses aiming to recognize genetic composition and chromosome sets in possible interspecific hybrids initially identified by mixed morphology. The data showed that one hybrid could be an F<sub>2</sub> individual among <em>Caretta caretta </em>× <em>Eretmochelys</em> <em>imbricata</em> × <em>Chelonia</em> <em>mydas</em>, and another is resulting from backcross between <em>C. caretta </em>× <em>Lepidochelys</em> <em>olivacea</em>. Native alleles of different parental lineages were reported in the hybrids, and, despite this, it was verified that the hybrid chromosome sets were still balanced. Thus, how sea turtle hybridism can affect genetic features in the long term is a concern, as the implications of the crossing-over in hybrid chromosomal sets and the effects on genetic function are still unpredictable. </span></p>
Information regarding the particulate methane monooxygenase enzyme of known methanotrophs
<p>A collection of information with regard to the particulate methane monooxygenase (pmmo) enzyme of known methanotrophs which was extracted from NCBI is published in this database. </p>
MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction
<p><strong>MedProcNER</strong> stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the <strong>detection, normalization and indexing of clinical procedures in medical documents</strong> in <strong>Spanish</strong>. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection.</p><p><strong>Please cite if you use this dataset:</strong></p><p>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</p><p>@article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} }</p><p>This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible <strong>SNOMED CT codes</strong> for the normalization and indexing tasks is also part of the bundle as a lexical resource. </p><p>In addition, a cross-mapping file of all SNOMED CT codes to <strong>MeSH</strong> is also included.</p><p>Finally, we release an experimental <strong>multilingual Silver Standard</strong> version derived from the Spanish Gold Standard in <strong>9 languages</strong>: <i><strong>English</strong></i>, <i><strong>Catalan</strong></i>, <i><strong>Italian</strong></i>, <i><strong>French</strong></i>, <i><strong>Portuguese</strong></i>, <i><strong>Romanian</strong></i>, <i><strong>Czech</strong></i>, <i><strong>Dutch</strong></i> and <i><strong>Swedish</strong></i>. </p><p>These documents have been generated using an automatic annotation transfer process that works as follows:</p><ol><li>The text files were translated with a neural machine translation system.</li><li>The annotations were translated with the same neural machine translation system.</li><li>The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries.</li></ol><p>MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: <a href="https://temu.bsc.es/medprocner">https://temu.bsc.es/medprocner</a>.</p><p> </p><p><strong>Resources:</strong></p><ul><li><a href="https://temu.bsc.es/medprocner"><strong>Web</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Citation</strong></a><strong>: </strong>Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023.</li><li><a href="https://doi.org/10.5281/zenodo.7817666"><strong>Annotation guidelines</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/"><strong>Proceedings and participant papers</strong></a></li><li><a href="https://ceur-ws.org/Vol-3497/paper-002.pdf"><strong>Overview paper</strong></a></li><li><a href="https://www.slideshare.net/MartinKrallinger/medprocnerproctemist-shared-task-on-clinical-procedure-detection-and-normalization-task-overview-presentation-at-bioasq-workshop-of-clef-2023-conference"><strong>Overview talk slides at BioASQ/CLEF</strong></a></li></ul><p> </p><p><strong>Additional resources and corpora</strong></p><p>If you are interested in MedProcNER, you might want to check out these corpora and resources:</p><ul><li><a href="https://zenodo.org/records/7614764">DisTEMIST</a> (Corpus of disease mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/8413866">SympTEMIST </a>(Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection)</li><li><a href="https://zenodo.org/records/4270158">PharmaCoNER</a> (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection)</li><li><a href="https://zenodo.org/records/7116201">MEDDOPROF</a> (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/8403498">MEDDOPLACE</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/4279323">MEDDOCAN</a> (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, modified synthetic verions of the document collection)</li><li><a href="https://zenodo.org/records/3978041">CANTEMIST</a> (Corpus of cancer tumor morphology mentions and normalization, different document collection)</li><li><a href="https://zenodo.org/records/3837305">CodiESp</a> (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection)</li><li><a href="https://zenodo.org/records/7684093">LivingNER</a> (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents)</li><li><a href="https://zenodo.org/records/2560344">SPACCC-POS</a> (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-TOKEN</a> (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection)</li><li><a href="https://zenodo.org/records/2560338">SPACCC-SPLIT</a> (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection)</li><li><a href="https://zenodo.org/records/5602914">MESINESP-2</a> (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection)</li></ul><p><strong>License</strong></p><p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p><p><strong>Contact</strong></p><p>If you have any questions or suggestions, please contact us at:</p><p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
Supporting Information for On-Surface Synthesis of a Doubly Anti-Aromatic Carbon Allotrope
<p>This dataset contains computational outputs associated with the publication On-Surface Synthesis of a Doubly Anti-Aromatic Carbon Allotrope. </p>
Supplementary information for: Dental microwear texture analysis reveals a likely dietary shift within Late Cretaceous ornithopod dinosaurs.
<p>This supplementary information includes 19 datasets and 95 sur files. Dataset 1 to 11 and 13 to 19 are in one excel file (“1. Supplementary Dataset 1-11 13-17_MS.xlsx “) and each dataset is in a separate excel sheet. Dataset 12 is a nexus file that contains a phylogenetic tree of ornithischian dinosaurs used in the analysis of this study (“2. DatasetS12 tree.nex”). Other 95 sur format files are original 3D surface files that are obtained by scanning tooth surface of ornithischian tooth fossils using a laser microscope VK-9700. Sur file can be opened by a surface roughness software MountainsMap. Surface roughness parameters obtained from these Sur files are in Supplementary dataset 1.</p> <p>Datasets 13 to 19 are results of statistical analyses that excluded data from <em>Thescelosaurs</em>.</p> <p> </p> <p>Below is an explanation for each dataset.</p> <p>Supplementary Dataset 1. Normalized dental microwear texture parameters.</p> <p>Supplementary Dataset 2. Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 3. Results of the statistical analysis that include body size as an explanatory variable.</p> <p>Supplementary Dataset 4. Eigen values of principal components obtained by the PCA of dental microwear texture parameters.</p> <p>Supplementary Dataset 5. Loading matrix of the PCA.</p> <p>Supplementary Dataset 6. Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 7. Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 8. Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 9. Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 10. Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 11. Bayes factors for the evolutionary model fitting of Vvv.</p> <p>Supplementary Dataset 12. Phylogenetic trees used for the model fitting.</p> <p>Supplementary Dataset 13. Without <em>Thescelosaurus</em>: Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 14. Without <em>Thescelosaurus</em>: Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 15. Without Thescelosaurus: Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 16. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 17. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 18. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 19. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Vvv.</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.