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606 results for “data exploration”
Outputs of the Jupyter Notebook - Exploring Land Cover Data (Impact Observatory)
<p>The dataset contains the outputs of the notebook "Exploring Land Cover Data (Impact Observatory)" published in The Environmental Data Science Book.</p>
The Netherlands - China Low Frequency Explorer signal chain pre launch test-data. (version 1.0)
<p>The Netherland Chinese Low Frequency Explorer Pre launch system ground test results with analog and digital signal chain. The tests include the instrument in various experiment settings that are avalable as pre-set functions during the observation phase.</p> <p>The data set is processed to L1B data, a script is provided together with the data to process this and plot the power spectra for all the monopole antennas.</p> <p> </p>
Dataset for: Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manusript:<br> Perrier L, Blondal E, MacDonald H. Exploring the experiences of academic libraries with research data management: a meta-ethnographic analysis of qualitative studies. 2018; 40(3-4): 173-183. doi: 10.1016/j.lisr.2018.08.002</p> <p>Full-text available at: <a href="https://doi.org/10.1016/j.lisr.2018.08.002">https://doi.org/10.1016/j.lisr.2018.08.002</a> </p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset:</p> <ol> <li>Data Dictionary: RDMMetaEthnography_DataDictionary_v1.pdf</li> <li>Data Abstraction Sheet: RDMMetaEthnography_StudyCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_ParticipantCharacteristics.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_Outcomes.csv</li> <li>Data Abstraction Sheet: RDMMetaEthnography_COREQ,csv</li> </ol> <p>Contact: Laure Perrier: <a href="https://orcid.org/0000-0001-9941-7129">orcid.org/0000-0001-9941-7129</a></p>
Extended data of the project "A survey exploring biomedical editors' perceptions of editorial interventions to improve adherence to reporting guidelines"
<p>Figure S1: Survey questionnaire</p> <p>Table S2: Barriers, facilitators and possible improvements of the interventions included in the survey</p>
Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values - Dataset
<p>This dataset accompanies the report <em>"Monitoring and evaluation of UKRI's Open Access Policy: Exploring the use of open data sources to inform baseline values"</em>, which is available via Zenodo.<br><br>It provides record-level data of UKRI-funded and UK-affiliated research output (limited to journal articles with Crossref DOIs) published between 2012 and 2022 - including bibliographic metadata as well as data on open access availability, publisher, national and international collaborations, citations, views and downloads, altmetrics and subjects (fields). All variables are documented in the data dictionary included in this Zenodo record.</p> <p>The code used to generate the dataset from open data sources is available on GitHub. </p> <p>The following data sources were used:</p> <ul> <li> <p>Gateway to Research (records downloaded between 2023-11-05 and 2023-11-13)</p> </li> <li> <p>Crossref (Metadata Plus snaphot 2023-10-31, Crossref member route API 2024-01-23)</p> </li> <li> <p>OpenAlex (data snapshot 2023-10-18)</p> </li> <li> <p>Unpaywall (data snapshot 2023-11-27)</p> </li> <li> <p>IRUS UK (2024-04-03)</p> </li> <li> <p>Crossref Event Data (2023-04-01)</p> </li> </ul> <p><strong></strong><br><br>The project made use of Curtin Open Knowledge Initiative (COKI) infrastructure, which is documented on GitHub: <a href="https://github.com/The-Academic-Observatory">https://github.com/The-Academic-Observatory</a>. </p>
Source Data for Manuscript: Identifying genomic data use with the Data Citation Explorer
<p>This page contains the source data for the manuscript describing the Data Citation Explorer, currently in review for publication. The preprint version can be found on this page.</p> <p>Files:</p> <p><strong>DCE_manual_eval_sample.xlsx:</strong></p> <p>This file was used to manually evaluate hits generated by the Data Citation Explorer. There are two separate sheets: one with publications returned by searches in PubMed and PubMed Central and another with publications returned by searches in Dimensions. Column descriptions can be found in the file itself. Each row in each evaluation sheet refers to a pair between a JAMO record and a linked publication.</p> <p><strong>DCE_citation_report.csv</strong></p> <p>Contains JAMO record IDs and PubMed IDs from the initial 2020 DCE trial run. There are 238,994 unique JAMO IDs and 30,641 unique PubMed IDs. 78,104 JAMO records are linked with publications.</p> <p>Columns:</p> <ul> <li>jamo_id - unique JAMO record ID</li> <li>sample_group - Sample strata from which manually evaluated records were pulled</li> <li>citation_count - Number of citations associated with each record</li> <li>citations - comma-delimited PubMed IDs for linked publications</li> <li>sampled - True/False, denoting which records were included in the initial evaluation sample</li> <li>notes - descriptions for why certain sampled records were excluded from manual evaluation</li> <li>unprocessed - True/False. These 7,890 records contained anomalous fields that caused them to be rejected for processing. They are represented as zero-length files in the archive.</li> </ul> <p><strong>DCE_source_files.zip:</strong></p> <p>This folder contains 3 files for each JAMO record in DCE_citation_report.tsv. For each JAMO record listed in the citation report, three files are provided:</p> <ol> <li>JAMO_ID_source.yaml - The fields extracted from the JAMO record that were relevant to the citation search, including any previously known PMIDs (manually curated).</li> <li>JAMO_ID_expand.yaml - The source record augmented with additional metadata discovered in other resources, including the citations that were discovered based on querying PubMed Central for the values in those metadata fields.</li> <li>JAMO_ID_audit.json - The audit path as a directed acyclic graph, in JSON.</li> </ol>
Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles
<p>Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe<sup>3+</sup> containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe<sup>2+</sup><sub>(1−3x)</sub>Fe<sup>3+</sup><sub>(2x)</sub>S<sup>2-</sup><sub>(1-y)</sub>(S<sub>n</sub><sup>2-</sup>)<sub>y</sub>. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe<sup>2+</sup>Fe<sup>3+</sup><sub>2</sub>S<sup>2-</sup><sub>4</sub>) along a metal oxidation path, and Fe<sup>2+</sup>S<sup>2-</sup> and pyrite (Fe<sup>2+</sup>S<sub>2</sub><sup>2-</sup>) along a ligand oxidation path, respectively.</p>
Longitudinal data to explore changes in flood risk awareness and preparedness
<p>This upload includes two different longitudinal datasets. The Panel datasets includes two rounds of surveys where the same individuals were interviewed. The Repeated Cross-Sectional includes two rounds of surveys where different individuals were interviewed in each round. The first survey round is the same in the two datasets. Data were collected in the municipality of Negrar (VR), in North-eastern Italy in February 2019 and in February 2020, following a flash flood which occurred in September 2018. </p>
Supplementary data files for exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins
<p>This dataset includes 20 supplementary data files for manuscript <em>Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins </em>by Jakub W. Wojciechowski, Emirhan Tekoglu, Marlena Gąsior-Głogowska, Virginie Coustou, Natalia Szulc, Monika Szefczyk, Marta Kopaczyńska, Sven J. Saupe, and Witold Dyrka (under revision). </p> <ul> <li>SF2. Profile HMMs of NLR effector domains. The file includes previously unpublished models.</li> <li>SF3. Multiple sequence alignments of N-termini clusters. </li> <li>SF4. Tabularized results of N-termini annotation.</li> <li>SF5. Structure prediction of HeLo-/Goodbye-/MLKL-like domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF6. Structure prediction of previously unannotated domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF7. PCFGs for BASS. The file includes previously unpublished grammars and a sample scanning configuration.</li> <li>SF8. Candidate short NLR N-termini with ASMs. The FASTA file includes sequences from clusters with high content of ASM-like sequences, according to the BASS PCFGs (SF7).</li> <li>SF9. Profile HMMs of ASMs found in short NLR N-termini.</li> <li>SF10. Profile HMM of HeLo-related HRAMs.</li> <li>SF11. Genomic neighbors of candidate short N-termini NLRs with ASMs The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF8).</li> <li>SF12. Short C-termini of 200–400 aa long proteins genomically neighboring candidate short NLR N-termini with ASMs. The FASTA file concerns target proteins listed in SF11.</li> <li>SF13. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically neighboring proteins. The table is based on SF8–9 and SF11–12. </li> <li>SF14. Lists of HMMER domain hits of effector domain profiles. The lists were obtained through iterative searches in NCBI “nr” starting from Pfam profiles of known NLR effector domains.</li> <li>SF15. Short C-termini of effector proteins. The FASTA file concerns target proteins listed in SF14.</li> <li>SF16. Short N-termini of Pfam NACHT and NB-ARC proteins. The FASTA file concerns proteins from NCBI “nr” associated with the two families in the Pfam database.</li> <li>SF17. Profile HMMs of ASMs found both in effector C-termini and NLR N-termini of genomically neighboring proteins.</li> <li>SF18. Genomic neighbors of candidate short N-termini Pfam NACHT and NB-ARC proteins. The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF16).</li> <li>SF19. Pairwise hits of the same ASMs in N-termini of NACHT/NB-ARC NLRs and C-termini of genomically neighboring effector proteins. The table is based on SF15–18. </li> <li>SF20. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically co-occurring effector proteins. The table is based on SF8–9 and SF15. </li> <li>SF21. BaMLKL homologs identified with hmmsearch in Basidiomycota. A FASTA file.</li> </ul>
Data used in the paper: Heatwaves, droughts, and fires: Exploring compound and cascading dry T hazards at the pan-European scale
<p>These datasets were used to analyze European compound and cascading dry hazards. The scripts are publicly available on GitHub: https://github.com/sjsutanto/Dryhazards.git.</p> <p>File Daily_SM_drought_WB_Converted.nc is for soil moisture drought, fwi_1990_2016_binary_95th_lowThreshold.nc is for wildfires, and datacube_2mtpp_19902016_HW.nc is for heatwaves.</p> <p> </p>
Raw data files associated with the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees"
<p>These are the raw data CSV files associated with the results described in the paper "Beyond generalists: the Brassicaceae pollen specialist Osmia brevicornis as a prospective model organism when exploring pesticide risk to bees".</p> <p>By Sara Hellström, Verena Strobl, Lars Straub, Wilhelm H. A. Osterman, Robert J. Paxton, Julia Osterman</p>
Data from: CoAct Citizen Science chatbot explores social support networks in mental health based on lived experiences
<p>A data set on lived experiences in the context of social support in mental health, created within a Citizen Social Science project. </p> <p><br> Societies around the world increasingly encounter wicked and complex problems, such as those related to mental health, environmental justice, and youth employment. <strong>CoAct as a EU-funded global effort</strong> addresses these problems by deploying Citizen Social Science. </p> <p> </p> <p><strong>Citizen Social Science</strong> is understood here as participatory research co-designed and directly driven by citizen groups sharing a social concern. This methodology wants to give citizen groups an equal ‘seat at the table’ through <strong>active participation in research</strong>, from the design to the interpretation of the results and their transformation into concrete actions. Citizens thus act as <strong>co-researchers</strong> and are recognised as in-the-field competent experts. </p> <p> </p> <p>In Barcelona, a group of <strong>32 co-researchers</strong> work together with the OpenSystems group, Universitat de Barcelona, the Catalan Federation of Mental Health (Federació Salut Mental Catalunya), and with the help of many others on a better understanding of informal <strong>social support networks in mental health</strong> in the project <em>CoActuem per la Salut Mental</em> (lit. “We act together for mental health”). The co-researchers, who are either persons with a personal history of mental health problems or are family members of the latter, contributed their <strong>personal experiences related to social support</strong> in the form of <strong>222 micro-stories</strong>, each shorter than 400 characters, and most accompanied by an illustration by Pau Badia.</p> <p> </p> <p>Those micro-stories form the heart of the first co-created Citizen Science chatbot, the code of which is open on <a href="https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git">https://github.com/Chaotique/CoActuem_per_la_Salut_Mental_Chatbot.git</a> . The <strong>Telegram chatbot</strong> sends them to participants <strong>on a daily basis over the course of a year</strong> and asks them either, whether they and/ or their close surrounding lived this experience, too (stories of type C), or, how they would or would have reacted in the presented situation (stories of type T). The answers of each participant can be contrasted with the individual participants’ answer to a 32-questions <strong>socio-demographic survey</strong>. Further, the timing of the messages is included to allow for a broader analysis. </p> <p> </p> <p>The chatbot is still running, hence this data set will still be updated. For further information on the project <strong>CoAct</strong>, see <a href="https://coactproject.eu/">https://coactproject.eu/</a>. For further details on the co-creation process and purpose of the chatbot <strong>CoActuem per la Salut Mental</strong>, take a look on <a href="https://coactuem.ub.edu/">https://coactuem.ub.edu/</a>. Please direct your questions regarding the data set to <strong>coactuem[at]ub.edu</strong>.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>The CoAct project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 873048. We especially thank the co-researchers for the passion and time invested.</p>
Deep Sea Explorers open classification data
<p>This is a reduced, anonymised dataset containing the classifications made in the Deep Sea Explorers demonstrator project available on Zooniverse during the implementation period - from the 19th of October, 2021, to the 23rd of October, 2023 - as part of the REINFORCE project.</p>
Perturbed Parameters for ICEPACK-DART Study Titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation"
<p>The file contains the values of the two perturbed CICE parameters that were used in the study titled "Exploring Bounded Non-parametric Ensemble Filter Impacts on Sea Ice Data Assimilation." The tw perturbed parameters are the standard deviation of the dry snow grain radius (Rsnow), and the thermal conductivity of snow (Ksnow). There are 80 values since the ensemble used in the study had 80 members.</p>
Code and data: Exploring congruent diversification histories with flexibility and parsimony
<p>This repository contains the code and data for the article "Exploring congruent diversification histories with flexibility and parsimony" (abstract bellow).</p> <p>Data :</p> <ul> <li><strong>4705sp_mammal-time.tree</strong>: Species-level calibrated mammalian phylogeny from <em>Alvarez-Carretero et al. </em>(<a href="https://doi.org/10.6084/m9.figshare.14885691">https://doi.org/10.6084/m9.figshare.14885691</a>)</li> <li><strong>mammals_samplingfraction.csv</strong> : Clade-specific sampling fractions from <em>Quintero et al.</em>(<a href="https://www.biorxiv.org/content/10.1101/2022.08.09.503355v1.full">https://www.biorxiv.org/content/10.1101/2022.08.09.503355v1.full</a>).</li> </ul> <p>Code :</p> <ul> <li><strong>CRABS-v1.1.0.9004.zip</strong>: Archived version of the CRABS package with our extension.</li> <li><strong>Mammalian_rates_EBD_HSMRF.rev</strong>: <em>Rev</em> script for the mammalian diversification analysis in RevBayes with regularized priors on diversification rates.</li> <li><strong>Mammalian_rates_EBD_independent.rev</strong>: <em>Rev</em> script for the mammalian diversification analysis in RevBayes with independent diversification rates at each interval.</li> <li><strong>Mammals_proccess_RevBayes_outputs.Rmd</strong>: R notebook for processing the outputs from the RevBayes mammalian diversification analysis, plotting the rates through time, and saving the median trajectories used for further analyses.</li> <li><strong>Exploring_congruent_diversification_histories_with_flexibility_and_parsimony.Rmd</strong>: R notebook for comparing the initial CRABS features and our new extensions. It enables replicating the figures in the article.</li> </ul> <p>Outputs :</p> <ul> <li><strong>output_inferredIntervals_fixedRhp_HSMRF.zip</strong> & <strong>output_inferredIntervals_fixedRhp_independent.zip</strong>: The raw traces from the RevBayes analysis, and the resulting median rate trajectories that are used to construct the congruence class illustrated in the article.</li> </ul> <p><br> Abstract</p> <ol> <li>Using phylogenies of present-day species to estimate diversification rate trajectories -- speciation and extinction rates over time -- is a challenging task due to non-identifiability issues. Given a phylogeny, there exists an infinite set of trajectories that result in the same likelihood; this set has been coined a congruence class. Previous work has developed approaches for sampling trajectories within a given congruence class, with the aim to assess the extent to which congruent scenarios can vary from one another. Based on this sampling approach, it has been suggested that rapid changes in speciation or extinction rates are conserved across the class. Reaching such conclusions requires to sample the broadest possible set of distinct trajectories.</li> <li>We introduce a new method for exploring congruence classes, that we implement in the R package CRABS. Whereas existing methods constrain either the speciation rate or the extinction rate trajectory, ours provides more flexibility by sampling congruent speciation and extinction rate trajectories simultaneously. This allows covering a more representative set of distinct diversification rate trajectories. We also implement a filtering step that allows selecting the most parsimonious trajectories within a class.</li> <li>We demonstrate the utility of our new sampling strategy using a simulated scenario. Next, we apply our approach to the study of mammalian diversification history. We show that rapid changes in speciation and extinction rates need not be conserved across a congruence class, but that selecting the most parsimonious trajectories shrinks the class to concordant scenarios.</li> <li>Our approach opens new avenues both to truly explore the myriad of potential diversification histories consistent with a given phylogeny, embracing the uncertainty inherent to phylogenetic diversification models, and to select among these different histories. This should help refining our inference of diversification trajectories from extant data.</li> </ol>
Data from: Exploring rainforest diversification using demographic model testing in the African foam-nest treefrog (Chiromantis rufescens)
Aim: Species with wide distributions spanning the African Guinean and Congolian rainforests are often composed of genetically distinct populations or cryptic species with geographic distributions that mirror the locations of the remaining forest habitats. We used phylogeographic inference and demographic model testing to evaluate diversification models in a widespread rainforest species, the African Foam-nest Treefrog (Chiromantis rufescens). Location: Guinean and Congolian rainforests, West and Central Africa. Taxon: Chiromantis rufescens. Methods: We collected mitochondrial DNA (mtDNA) and single nucleotide polymorphism (SNP) data for 130 samples of Chiromantis rufescens. After estimating population structure and inferring species trees using coalescent methods, we tested demographic models to evaluate alternative population divergence histories that varied with respect to gene flow, population size change, and periods of isolation and secondary contact. Species distribution models were used to identify regions of climatic stability that could have served as forest refugia since the Last Interglacial. Results: Population structure within Chiromantis rufescens resembles the major biogeographic regions of the Guinean and Congolian forests. Coalescent-based phylogenetic analyses provide strong support for an early divergence between the western Upper Guinean forest and the remaining populations. Demographic inferences support diversification models with gene flow and population size changes even in cases where contemporary populations are currently allopatric, which provides support for forest refugia and barrier models. Species distribution models suggest that forest refugia were available for each of the populations throughout the Pleistocene. Main conclusions: Considering historical demography is essential for understanding population diversification, especially in complex landscapes such as those found in the Guineo-Congolian forest. Population demographic inferences help connect patterns of genetic variation to diversification model predictions. The diversification history of Chiromantis rufescens was shaped by a variety of processes, including vicariance from river barriers, forest fragmentation, and adaptive evolution along environmental gradients.
Data from: Maximum cardiac performance of Antarctic fishes that lack haemoglobin and myoglobin: exploring the effect of warming on nature's natural knockouts
Comparisons among related species provide valuable insight into the functional consequences of natural genetic mutations. We assessed cardiac function at ambient and elevated temperatures in Antarctic notothenioids with contrasting levels of the oxygen binding proteins, haemoglobin (Hb) and myoglobin (Mb), to elucidate changes in cardiac performance that may compensate for impaired O2 transport. Notothenia coriiceps (Hb+Mb+) at 1oC had the highest maximum cardiac work rate (WC) and pressure generating capacity, but lowest relative ventricular mass and maximum cardiac output (Q̇) when compared with two icefish species, Chionodraco rastrospinosus (Hb-Mb+) and Chaenocephalus aceratus (Hb-Mb-). Cardiomegaly associated with absence of Hb generated an exceptionally large maximum stroke volume (VS) and Q̇, but a lower WC. However, C. rastrospinosus had a larger ventricle, a higher intrinsic heart rate (fH), and greater maximum VS and Q̇ than C. aceratus, suggesting that cardiac Mb has functional relevance. Warming to 4oC increased fH, but only increased maximum Q̇ in icefishes, while maximum WC and pressure development increased in N. coriiceps (both ~2.5x that of C. aceratus). The Hb+Mb+ myocardium generated considerable Q̇ against raised afterload, unlike icefish hearts. The presence of Hb and Mb enhances cardiac performance, and likely resilience to near-future ocean warming.
Data from: Exploring hymenopteran parasitoid communities and their hosts: A comparative study of farmland and semi-natural ecotones with focus on pentatomoid bugs and their antagonists
<p>Here, we provide abundance data, the respective R-script and R-readable data files from a small-scale study on hymenopteran parasitoid communities and their hosts, with special focus on pentatomoid bugs and their egg parasitoids. This comparative study was conducted in farmland and semi-natural ecotones from June to September 2020 in South Tyrol, Italy. The fauna was sampled during four sampling events with sweep netting, beat netting, yellow pan traps, Malaise traps and visual inspections. Arthropods were identified to order level, hymenopteran parasitoids to family level, pentatomoid bugs to species level and egg parasitoids of pentatomoids as far as possible to species level. If identification was not possible, pentatomoid parasitoids were left at genus level. The abundance data collected with each survey method were pooled per survey events and survey site.</p>
Input geophysical and geological data for "Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees"
<p>This is a companion dataset to the manuscript: <br><br>Geologically constrained geometry inversion and null-space navigation to explore alternative geological scenarios: a case study in the Western Pyrenees,</p><p>by: Jeremie Giraud , Mary Ford, Guillaume Caumon, Lachlan Grose, Vitaliy Ogarko, Roland Martin, and Paul Cupillard.<br><br>This dataset contains the input data used in the inversion, in terms of the gravity data and the geological data used in the inversion.<br><br>The *.txt file contains the gravity data as inverted in the manuscript: X, Y, Z, Value.<br>The *.csv file contains the geological data: location of the contacts and orientation data.</p>
Supplementary material for "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences"
<p>This material supplements the following conference publication:</p> <p>Winkler, Rosenthal, Strecker (2024). "Exploring Conceptual Data Modeling Processes: Insights from Clustering and Visualizing Modeling Sequences". Modellierung 2024.</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.