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606 results for “data exploration”
Data from: Exploring polymorphism in a palatable prey: Predation risk and frequency-dependence in relation to distinct levels of conspicuousness
<p>Camouflage and warning signals are different anti-predator strategies, which offer an excellent opportunity to study the evolutionary forces acting on prey appearance. Edible prey often escape detection via camouflage, which usually leads to apostatic selection favoring rare morphs. By contrast, defended prey often display conspicuous coloration acting as warning signals to predators, which usually leads to positive frequency-dependence and signal uniformity. However, when two morphs of the same species vary greatly in conspicuousness, the maintenance of both cryptic and conspicuous forms in profitable prey populations remains enigmatic. Using the white and melanic morphs of the invasive box tree moth (<em>Cydalima perspectalis</em>) presented at three different frequencies, we investigate a) the palatability of caterpillars and adult moths to birds, b) predation rates on the less conspicuous melanic morph, and c) the role of frequency-dependence in balancing morph frequencies. Our results show that caterpillars are distasteful for birds but not adult moths, which are fully palatable. We found that the less conspicuous, melanic morph, benefits from reduced predation due to its lower detectability. The more conspicuous, white morph, instead, is more predated and is best off when common, suggesting positive-frequency dependence. These results offer new insights into the evolution of color polymorphism and prey defenses in a polymorphic moth species. Further investigation is required to understand the role of different predation regimes on the maintenance of the polymorphism in this species, and test if additional selection pressures operate in natural populations.</p>
Raw data for publication: Bioinspired Living Coating System for Wood Protection: Exploring Fungal Species on Wood Surfaces Coated with Biofinish during its Service Life
<p>Weather Data.xlsx</p> <p>This file contains hourly local weather conditions in Izola, Slovenia from October 2021- August 2022</p> <p>Number of colonies.xlsx</p> <p>This file contains the number of fungal colonies isolated from the InnoRenew CoE facade</p> <p>FUNGAL STRAINS_DNA sequence analysis.xlsx</p> <p>This file contains the Genomic DNA of the fungal strains detected on the InnoRenew CoE facade</p>
Data and code from: SIDERITE: Unveiling Hidden Siderophore Diversity in the Chemical Space Through Digital Exploration
<h1>TMAP of COCONUT database</h1> <p>The script and data used in SIDERITE paper to generate TMAP picture (Figure S3 in supplementart material).</p> <p>Requirement: tmap</p> <p>You can install tmap by conda.</p> <blockquote> <p>conda create -n tmap python=3.7</p> <p>conda activate tmap</p> <p>conda install -c tmap tmap</p> <p>pip install faerun</p> <p>pip install matplotlib</p> <p>conda install scipy</p> <p>conda install -c rdkit rdkit</p> <p>conda install -c conda-forge mhfp</p> </blockquote> <p> </p> <p>Usage: python plot_COCONUT.py</p> <p>Then it will use COCONUT.csv to generate index.html and index.js. Open index.html to see result.</p> <h1>Other large input files</h1> <p>Tanimoto_COCONUT_SIDERITE.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/Clustering_coconut.m">SIDERITE/predicted_new/Clustering_coconut.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>Sid_structure_output3.xlsx is used in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/statistics/Figure1.m" target="_blank" rel="noopener">SIDERITE/statistics/Figure1.m at main · RuolinHe/SIDERITE</a>, <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a> and <a href="https://github.com/RuolinHe/SIDERITE/blob/main/siderophore_process/Sid_process_code.m" target="_blank" rel="noopener">SIDERITE/siderophore_process/Sid_process_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT4MetFrag_Canonical.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>.</p> <p> </p> <p>COCONUT_r.txt is the output file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/tmap_code.m" target="_blank" rel="noopener">SIDERITE/TAMP/tmap_code.m at main · RuolinHe/SIDERITE</a>. and the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/predicted_new/isSiderophore1.py" target="_blank" rel="noopener">SIDERITE/predicted_new/isSiderophore1.py at main · RuolinHe/SIDERITE.</a></p> <p> </p> <p>COCONUT4MetFrag.xlsx is the input file in <a href="https://github.com/RuolinHe/SIDERITE/blob/main/TAMP/CheckSMILES2.py" target="_blank" rel="noopener">SIDERITE/TAMP/CheckSMILES2.py at main · RuolinHe/SIDERITE</a>.</p> <p> </p>
Figure 4. Results from the phylogenetic analysis using discrete data only. A in Exploring phylogenetic relationships of Pteraspidiformes heterostracans (stem-gnathostomes) using continuous and discrete characters
Figure 4. Results from the phylogenetic analysis using discrete data only. A, strict consensus of 275 most parsimonious trees with equal character weights; length 276 steps, consistency index (CI) = 0.35, retention index (RI) = 0.59, and rescaled consistency index (RC) = 0.22. B, strict consensus of four most parsimonious trees with implied character weighting (k = 3) (tree length 23.11). Psammosteidae taxa in bold.
The data used for "Exploring how differences in dust particle size distribution and complex refractive indices affect dust direct radiative fluxes using the CAS-FGOALS-SPRINTARS global climate model"
<p>These data are used for " Exploring how differences in dust particle size distribution (PSD) and complex refractive indices (CRI) affect direct radiative effect (DRE) using the CAS-FGOALS-SPRINTARS global climate model ". </p> <p>(1) AS83+OPAC: The control experiment, dust PSD is the original AS83, and the generic CRI is from OPAC. </p> <p>(2) BFT22+OPAC: Same as the control experiment, but the PSD is updated to use BFT22.</p> <p>(3) BFT22+DB: Same as the experiment BFT22+OPAC, but the generic OPAC CRI is replaced by nine regionally dependent DB CRIs.</p> <p>(4) BFT22+DB strong abs: Same as the experiment BFT22+DB, but the generic CRI consists of 10% percentile real and 90% percentile imaginary parts and no regional dependencies.</p> <p>(5) BFT22+DB weak abs: Same as the experiment BFT22+DB, but the generic CRI consists of 90% percentile real and 10% percentile imaginary parts and no regional dependencies.</p> <p>All experiments mentioned above are run for 5 years (2010-2014). The annual average simulation results are stored here.</p> <p><strong>Note:</strong> AS83 represents the dust PSD scheme from d'Almeida and Schütz. (1983). BFT22 represents the new dust PSD developed by Meng et al. (2022) based on the improved brittle fragmentation theory. OPAC: the Optical Properties for Aerosols and Clouds dataset, DB: the CRIs from Di Biagio et al. (2017, 2019).</p> <p><strong>References</strong></p> <p>d'Almeida, G. A., & Schütz, L. (1983). Number, Mass and Volume Distributions of Mineral Aerosol and Soils of the Sahara. <em>Journal of Applied Meteorology and Climatology</em>,<em> 22</em>(2), 233-243. https://doi.org/https://doi.org/10.1175/1520-0450(1983)022<0233:NMAVDO>2.0.CO;2</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2019). Complex refractive indices and single-scattering albedo of global dust aerosols in the shortwave spectrum and relationship to size and iron content. <em>Atmospheric Chemistry and Physics</em>,<em> 19</em>(24), 15503-15531. https://doi.org/10.5194/acp-19-15503-2019</p> <p>Di Biagio, C., Formenti, P., Balkanski, Y., Caponi, L., Cazaunau, M., Pangui, E., et al. (2017). Global scale variability of the mineral dust long-wave refractive index: a new dataset of in situ measurements for climate modeling and remote sensing. <em>Atmospheric Chemistry and Physics</em>,<em> 17</em>(3), 1901-1929. https://doi.org/10.5194/acp-17-1901-2017</p> <p>Meng, J., Huang, Y., Leung, D. M., Li, L., Adebiyi, A. A., Ryder, C. L., et al. (2022). Improved Parameterization for the Size Distribution of Emitted Dust Aerosols Reduces Model Underestimation of Super Coarse Dust. Geophysical Research Letters, 49(8), e2021GL097287, https://doi.org/https://doi.org/10.1029/2021GL097287</p>
Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using LLMs
<p>This dataset provides the input data and intermediate results of the paper titled "Scholarly Wikidata: Population and Exploration of Conference Data in Wikidata using Large Language Models and Semantic Web Techniques". It contains the following resources.</p> <ul> <li>conference proceedings front matter links - these links can be used to download the pdf files of the conference proceeding front matters that include information about the number of submitted and accepted papers that can be used to calculate acceptance rates, names of all conference organization committee members, list of programme committee and senior programme member names for each track with other interesting facts such as the main topics of the submitted papers and emerging topics according to the editors, etc.</li> <li>web crawl of conference websites - this contains a set of crawled content from each conference website in both HTML and text formats. Each file contains web pages from a specific conference along with the page URL, page title, and page content. Information such as important dates (deadlines) and other announcements can be extracted from the content of the web sites. </li> <li>papers and paper-authors list for each conference in a given conference series - this contains the paper list along with their corresponding authors for each conference series extracted from DBLP. </li> <li>OpenRefine projects - this contains examples of open refile projects that were used to perform entity linking and reconciliation as well as the schemas that was used to map the tabular data columns to Wikidata properties, and qualifiers and cell values to Wikidata entities.</li> <li>evaluation benchmark - this contains the outputs of LLM generations for the tasks (a) extracting the number of submitted and accepted papers per each track at a given conference, (b) extraction of organizers with their roles for each conference, (c) extraction of programme committee members with track and their role (member, SPC member), and (d) extraction of important dates or deadlines for each activity (submission, notification, etc.) in each track. </li> </ul> <p>The corresponding source code is available at the <a href="https://github.com/scholarly-wikidata/scholarly-wikidata/">scholary-data repo</a>.</p>
LC-MS/MS, PSM and BLG properties data from "Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"
<p>This dataset contains the data analyzed in:</p> <p>Rodriguez Palomo I, Nair B, Chang Y, Dartigues B, Dekker K, Mackie M, Evans M, Macleod R, Olsen JV, Collins MJ. (2023) "<em>Benchmarking the identification of a single degraded protein to explore optimal search strategies for ancient proteins"</em></p> <p>It contains the following data:</p> <ul> <li>raw_files.zip Thermo RAW files for the 0, 4 and 128 days samples</li> <li>benchmark_results.zip PSMs data from the analysis of the RAW files <ul> <li>Data from runs in Mascot, Fragpipe, pFind, Metamorpheus, MaxQuant and DeNovoGUI</li> <li>Parameters and workflow files for MaxQuant and Fragpipe</li> </ul> </li> <li>bovin_blg_prop.zip BLG properties files: amyloid formation, 3D structure and solvent accessibility</li> <li>benchmark_table.csv Table with runs settings for benchmarking</li> <li>parameters_table.xlsx Spreadsheet with software parameters, derived from files used to run each software</li> </ul> <p> </p>
Training data for the "Biodiversity data exploration" Galaxy-E tutorial
<p>Dataset sample from Reef life survey initiative https://reeflifesurvey.com/ to serve as a training set for "Biodiversity data exploration" tutorial for Galaxy and notably Galaxy for ecology initiative</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The code to generate and analyze the data is available on GitHub in the scripts at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p>
Data in support of "An exploration of linkage fine-mapping on sequences from case-control studies"
<p>These data were simulated for an exploration of linkage fine-mapping on sequences from case-control studies. The scripts to generate and analyze the data are available at <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>. Queries may be directed to Payman Nickchi at <a href="mailto:pnickchi@sfu.ca">pnickchi@sfu.ca</a> or Charith (Bhagya) Karunarathna at <a href="mailto:ch757276@dal.ca">ch757276@dal.ca</a>.</p> <p><strong>README file for All_data directory</strong></p> <p><strong>Directory structure</strong></p> <p>The All_data directory consists of this README file and 500 sub-directories named DatasetX, for X=1 to 500. Within each DatasetX sub-directory are further sub-directories named alt and null containing files named pop_data.RData and sample_data.RData.</p> <p><strong>alt <em>versus</em> null directories</strong></p> <p>The files in the alt and null directories contain the same variant data but different phenotype data. In particular, under the null hypothesis, disease status is simulated at random according to a 5% prevalence in the population, whereas under the alternative hypothesis disease status is simulated according to a penetrance model that depends on causal SNVs. The R script to simulate data<br> under the alternative hypothesis is in the file 1_SimulateData.R in the Github repository <a href="https://github.com/SFUStatgen/PBJ0">https://github.com/SFUStatgen/PBJ0</a>.</p> <p><strong>pop_data.RData and sample_data.RData files</strong></p> <p>The data structures contained in the pop_data.RData and sample_data.RData files are described below. The structure is the same under both the null and alternative hypothesis.</p> <p><strong>pop_data.RData</strong></p> <p>From R, load("pop_data.RData") loads a list named pop_data whose elements describe the population’s haplotype and phenotype data. The list elements are as follows.</p> <ul> <li>Variants: a matrix of variants for the population of 6200 haplotypes <ul> <li>rows are SNVs,</li> <li>columns are sequences</li> </ul> </li> <li>Positions: a data frame of SNV positions <ul> <li>rows are SNVs,</li> <li>column 1 is the SNV name and column 2 is the SNV position in base pairs</li> </ul> </li> <li>Population.Mapping: a data frame telling us how the sequences are paired into individuals <ul> <li>rows are individuals</li> <li>First column 1 is an individual ID from 1,…,3100; columns 2 and 3 are the sequence IDs of the first and second sequence for that individual where the sequence IDs are the column names of the Variants matrix.</li> </ul> </li> <li>Genotype.Matrix: a matrix of genotypes (i.e. variant counts) for the 3100 individuals <ul> <li>rows are SNVs</li> <li>columns are the individuals</li> </ul> </li> <li>causal_region: a vector containing the lower- and upper-limit of the causal region in base pairs.</li> <li>cSNV: a vector containing the IDs of the causal SNVs, where the SNV IDs are the row names of the Variants matrix.</li> <li>DISCRETE: a list with the following elements. <ul> <li>CaseIndividuals: vector of IDs of the affected individuals in the population.</li> <li>ControlIndividuals: vector of IDs of the unaffected in the population.</li> <li>BinaryTrait: a vector of trait status (0=unaffected, 1=affected) for each individual.</li> </ul> </li> </ul> <p><strong>Note:</strong> Within the same DatasetX directory, the only difference between the pop_data data structures under the null and alternative hypothesis is the phenotype information contained in their respective DISCRETE list elements. Both the null and alternative pop_data data structure share list elements: Variants, Positions, Population.Mapping, Genotype.Matrix, causal_region and cSNV.</p> <p><strong>sample_data.RData</strong></p> <p>From R, load("sample_data.RData") loads a list whose elements describe the sequences and phenotypes of the sample of 50 affected individuals (cases) and 50 unaffected individuals (controls) from the population.</p> <ul> <li>Haps: a list with two elements. <ul> <li>sample_haps: a matrix of 200 sequences for the 50 cases and 50 controls. Rows are SNVs and columns are sequences, with the sequences of sampled cases appearing first (i.e. first 100 columns), followed by the sequences of sampled controls (i.e. last 100 columns). Sequences include only those SNVs that are polymorphic in the sample.</li> <li>ccStatus: a vector indicating the case/control status of the individual to which the sequence belongs, with case=1 and control=0.</li> </ul> </li> <li>Genos: a list with two elements. <ul> <li>sample_genos: a matrix of 100 genotypes for the 50 cases and 50 controls. Rows are SNVs and columns are genotypes, with genotypes of cases appearing first, followed by genotypes of controls.</li> <li>ccStatus: a vector indicating the case/control status of each individual, with case=1 and control=0.</li> </ul> </li> <li>Posn: a data frame of SNV positions for each SNV that is polymorphic in the sample. The first column is the SNV name and the second is the SNV position in base pairs. Posn is a subset of pop_data$Positions.</li> <li>poly_cSNV: a vector of IDs for causal SNVs that are polymorphic in the sample.</li> <li>CaseIND: a vector of individual IDs for the case individuals (see pop_data$Population.Mapping).</li> <li>ControlIND: a vector of individual IDs for the control individuals (see pop_data$Population.Mapping).</li> <li>CaseHapID: a vector of IDs for the sequences that belong to cases (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> <li>ControlHapID: a vector of IDs for the sequences that belong to controls (see the sequence IDs in the column names of the matrix pop_data$Variants).</li> </ul> <p> </p>
Secondary Data: Measuring Person-centred Care in German Nursing Homes – Exploring Construct Validity of the Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis
<p>This is the secondary data set and R-Code of R statistical software (version 4.0.4) to explore construct validity of the German Dementia Policy Questionnaire using Adjusted Multiple Correspondence Analysis.</p>
IODAQ Exploration Data Set
<p>This repository contains the data underlying the results concerning the exploration of reactions starting from iodine and water presented in Türtscher, P. L.; Reiher, M. <strong>2022 </strong><em>arXiv:2209.04039 [physics.chem-ph].</em></p>
EXPLORE Expert Data Challenges 2022 - Craters dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Fairweather et al. (2022</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
EXPLORE Expert Data Challenges 2022 - Boulders dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Watkins, Ryan (2019)</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
Research data for "Exploring the configurational space of amorphous graphene with machine-learned atomic energies"
<p>This dataset supports the paper: "Exploring the configurational space of amorphous graphene with machine-learned atomic energies" (<a href="https://doi.org/10.1039/D2SC04326B">https://doi.org/10.1039/D2SC04326B</a>).</p> <p>Trajectory data for the 200-atom structures (Fig. 3) and the final configurations for the 612-atom structures as well as the GAP-17-optimised 610-atom structure from Toh et al are provided (Fig. 4). Additionally, the structures used for data analysis in Fig. 5 are given.</p> <p>The files are in extended xyz (.xyz) format and contain the raw data for coordinates, forces, and atomic energies (labelled 'c_1'). The files also contain the atomic energies relative to pristine graphene, labelled "Energy_per_atom", and the locally averaged energy relative to pristine graphene, labelled "NN_Energy_per_atom". Topological information is included at the end of the .xyz file for the 612-atom structures ('fig_4'/) and for the structures in 'fig_5/'.</p> <p>All raw atomic energies were computed using LAMMPS default settings and were output with six significant figures, with the exception of the Toh et al. structure (for which ASE was used, outputting a higher number of significant figures). </p> <p>The data can be read using, for example, the Atomic Simulation Environment (ASE), or visualised using Ovito.</p> <p> </p>
Data for: Do ectomycorrhizal exploration types reflect mycelial foraging strategies?
<p class="MsoListParagraphCxSpFirst"><span>·<span> </span></span><span>Ectomycorrhizal exploration types are commonly assumed to denote spatial foraging patterns and resource-related niches of extraradical mycelia. However, empirical evidence of the consistency of foraging strategies within exploration types is lacking.</span></p> <p class="MsoListParagraphCxSpMiddle"><span>·<span> </span></span><span>Here, we analysed ectomycorrhizal foraging patterns by incubating root-excluding ingrowth mesh bags filled with six different substrates in mature <em>Picea abies </em>forests. High-throughput sequencing was used to characterize ectomycorrhizal fungal communities in the mesh bags and on adjacent fine roots after one growing season.</span></p> <p class="MsoListParagraphCxSpMiddle"><span>·<span> </span></span><span>Contrary to expectations, many ectomycorrhizal genera of exploration types thought to produce little extraradical mycelium colonised ingrowth bags extensively, whereas genera commonly associated with ample mycelial production occurred sparsely in ingrowth bags relative to their abundance on roots. </span></p> <p class="MsoListParagraphCxSpLast"><span>·<span> </span></span><span>Previous assumptions about soil foraging patterns of exploration types do not seem to hold. Instead, we propose that variation in the proliferation of extraradical mycelium is related to intergeneric differences in mycelial longevity and mobility of targeted resources. </span></p>
Data from: Open access levels: a quantitative exploration using Web of Science and oaDOI data
<p>This is the raw data behind the publication (on PeerJ Preprints):</p> <p><strong>Open access levels: a quantitative exploration using Web of Science and oaDOI data</strong></p> <p>Across the world there is growing interest in open access publishing among researchers, institutions, funders and publishers alike. It is assumed that open access levels are growing, but hitherto the exact levels and patterns of open access have been hard to determine and detailed quantitative studies are scarce. Using newly available open access status data from oaDOI in Web of Science we are now able to explore year-on-year open access levels across research fields, languages, countries, institutions, funders and topics, and try to relate the resulting patterns to disciplinary, national and institutional contexts. With data from the oaDOI API we also look at the detailed breakdown of open access by types of gold open access (pure gold, hybrid and bronze), using universities in the Netherlands as an example. There is huge diversity in open access levels on all dimensions, with unexpected levels for e.g. Portuguese as language, Astronomy & Astrophysics as research field, countries like Tanzania, Peru and Latvia, and Zika as topic. We explore methodological issues and offer suggestions to improve conditions for tracking open access status of research output. Finally, we suggest potential future applications for research and policy development. We have shared all data and code openly.</p>
DATA: Chatbots in Airport Customer Service – Exploring Use Cases and Technology Acceptance
<p>This dataset (n=191) investigates use cases and technology acceptance of chatbots in airport customer service.</p>
Data from the MA Thesis "Does She Talk Differently?: Exploring Implications of Gender in US Presidential and Vice Presidential Debates" and Coded Transcriptions of the 7 Analyzed Debates
<p>The raw data collected for the master's thesis "Does She Talk Differently?: Exploring Implications of Gender in US Presidential and Vice Presidential Debates", the tables and graphs created based on the data as well as the transcriptions for the seven debates analyzed for the research paper can be found in the files.</p>
Unveiling Mexico City's Airbnb Market Trends: A Data Analysis Exploration.
<p>The present document focuses on analyzing, exploring and visualizing data from the Airbnb market in Mexico City using tools such as Python. The data used for this work was cleaned and prepared to extract relevant insights and relations between popular locations, occupancy rate, and reviews (classified into positive or negative using BERT) of accommodations and market trends. The results obtained provide an in-depth insight into the Airbnb market in Mexico City and may be valuable for property owners to make an informed decision in their dynamic pricing strategy.</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.