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4,276 results for “Estimation”
INTELLIMAN_WP2_Application Requirements and Integration_T2.4_Fresh food handling use case analysis, integration and validation_Apple 6D pose estimation dataset_v0
<p>The dataset contains the data generated for the training of the 6D pose estimation neural network<br>DOPE related to the publication:<br>M. Costanzo, M. De Simone, S. Federico, C. Natale and S. Pirozzi, "Enhanced 6D Pose Estimation for<br>Robotic Fruit Picking," 2023 9th International Conference on Control, Decision and Information<br>Technologies (CoDIT), Rome, Italy, 2023, pp. 901-906, doi: 10.1109/CoDIT58514.2023.10284072.</p>
Estimation of Unfactorizable Systematic Uncertainties
<p>Dataset for the "Efficient Estimation of Unfactorizable Systematic Uncertainties" paper. </p> <p>The dataset consists of 30,000 simulated three-jet events. </p> <p>Keys and datasets:</p> <ul> <li>j1_threeM: Dataset of shape (30000, 3) containing the three-momenta of the hardest jets in the events.</li> <li>j2_threeM: Dataset of shape (30000, 3) containing the three-momenta of the second hardest jets in the events.</li> <li>j3_threeM: Dataset of shape (30000, 3) containing the three-momenta of the softest jets in the events.</li> </ul>
Estimating Usage Of Open Source Projects - Flutter Telemetry Case Study - MSR' 24
<p>This dataset (CSV) was assembled to support analysis within a case study that will be published in the proceedings of the <a href="https://conf.researchr.org/home/msr-2024">Mining Software Repositories</a> conference (MSR ‘24) April 14-15 2024: <a title="Estimating Usage Of Open Source Projects" href="https://doi.org/10.1145/3643991.3645066">Estimating Usage Of Open Source Projects.</a></p> <p>This case study explored whether publicly available metrics could serve as proxies to estimate usage of an open source project. Using the <a href="https://flutter.dev/">Flutter</a> project as our case study, we collected monthly proxy metrics from GitHub, StackOverflow and Slack to compare with Flutter’s monthly active user count over the same time period: January 2018 through February 2021.</p> <p>All metrics correspond to the last day of the month and/or represent aggregate activity in that month. Data from GitHub shows aggregate activity counts across the entire <a href="https://github.com/flutter">Flutter GitHub organization</a> (up to 33 repositories). </p> <p>Our specific metrics include:</p> <div> <table> <tbody> <tr> <td> <p>Source</p> </td> <td> <p>Metric</p> </td> <td> <p>Aggregation method</p> </td> <td> <p>Details</p> </td> </tr> <tr> <td> <p>Flutter</p> </td> <td> <p>Monthly Active Users (MAU)</p> </td> <td> <p>Google internal tooling</p> </td> <td> <p>Flutter users active in the last 30 days, collected on the last day of each month</p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>PullRequest Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> <p>As Google governs changes to this code base, we excluded known Google employees in code change related metrics </p> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issues Created in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Issue Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork events in month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>GitHub</p> </td> <td> <p>Fork cumulative count at the end of month</p> </td> <td> <p><a href="http://gharchive.org">gharchive.org</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Question Authors in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>StackOverflow</p> </td> <td> <p>Questions in month</p> </td> <td> <p><a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a>, hosted by <a href="http://bitergia.com">bitergia.com</a></p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Claimed (cumulative) members at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> <tr> <td> <p>Slack</p> </td> <td> <p>Cumulative messages at the end of the month</p> </td> <td> <p>fluttercommunity.slack</p> </td> <td> </td> </tr> </tbody> </table> </div> <p>Tools: </p> <ul> <li> <p>We used an instance of <a href="https://chaoss.github.io/grimoirelab/">GrimoireLab</a> hosted by <a href="https://bitergia.com/">Bitergia</a> to aggregate GitHub PullRequest Authors, GitHub Issue Authors, GitHub Issues, across all repositories under the Flutter Organization, and StackOverflow Question Authors, and StackOverflow Questions for questions that mention Flutter. We used the <a href="http://github.com/chaoss/grimoirelab-sortinghat">Sorting Hat</a> of feature GrimoireLab to identify Google employees in this sample.</p> </li> </ul> <ul> <li> <p><a href="http://gharchive.org">GHArchive</a> via <a href="https://cloud.google.com/blog/topics/public-datasets/github-on-bigquery-analyze-all-the-open-source-code">BigQuery</a> was used to count GitHub Star and Fork events across all repositories under the Flutter Organization</p> </li> <li> <p>We pulled Slack activity directly from <a href="http://fluttercommunity.slack.com">Flutter's slack channel </a>dashboard</p> </li> </ul>
Total dataset of Estimating moss harvest quantity and biomass
Open the record for dataset details and reuse information.
Tsunami source model of the earthquake beneath Hyuganada Sea on 8 August 2024 estimated using ocean-bottom pressure gauge records of N-net and DONET
<p>This dataset contains the results obtained by the tsunami waveform inversion analysis of the offshore tsunami data recorded by the ocean-bottom pressure gauges of N-net and DONET. See README.txt for the detail of this dataset.</p>
Data for manuscript: An extrapolation algorithm for estimating river bed grain size distributions across basins
<p>Pebble counts collected and used for the analysis presented in the manuscript: An extrapolation agorithm for estimating river bed grain size distributions across drainage basins.</p>
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</p>
APPENDIX 1 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>APPENDIX 1.— Supplementary material including the fossil specimens from the studied sites and the morphological character scores for CLAMP analysis</p><table><thead><tr><th><b>Lower Río Turbio Fm</b></th><th><b>LMA</b></th><th><b>LAA</b></th><th><b>Source</b></th></tr></thead><tbody><tr><th><i>Acrodiclidium flavianum</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Allophylus graciliformis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Anacardites pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Annona</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Casearia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cissus pichileufensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cinnamomum neogaea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania grosse-serrata</i></th><td>*</td><td>-</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania latifolioides</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Cupania santacrucensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Drimys</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Eucalyptus</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Escallonia</i> sp.</th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrcia</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myricaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Myrica hunzikerii</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nectandra prolifica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus subferruginea</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus variabilis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus serrulata</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Nothofagus elongata</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967</td></tr><tr><th><i>Ocotea menendezi</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Ocotea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Persea</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Psidium liociardensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Qualea patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Schinopsis patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax</i> sp.</th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Styrax glandulifera</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Tetracera</i> cf. <i>patagonica</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Zyziphus chubutensis</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Malvaceae</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Urticaceae</i></th><td>*</td><td><b>–</b></td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th>cf. <i>Pouterlabatia clark</i></th><td>*</td><td>*</td><td>Hünicken 1967; Vento & Prámparo 2018</td></tr><tr><th><i>Paullinia</i> sp.</th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr><tr><th><i>Banisteriophyllum</i></th><td>*</td><td><b>–</b></td><td>Panti 2018</td></tr></tbody></table>
TABLE 4 in Paleoclimate estimates for the Paleogene-Neogene in southern South America using fossil leaves as proxies
<p>TABLE 4 — Estimated values for the climate variables in the lower Río Turbio Formation member from CLAMP analysis using the CLAMP3B SA and CLAMPSH90 data set.</p><table><thead><tr><th><b>CLAMP Climate parameters</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Dataset</b></th><th><b>Estimates</b></th><th><b>Error</b></th><th><b>Data set</b></th></tr></thead><tbody><tr><th>Mean annual temperature (°C)</th><td>12.7</td><td>2.1</td><td>CLAMP3BSA</td><td>12.2</td><td>4.83</td><td>CLAMPSH90</td></tr><tr><th>Warm month mean temperature (°C)</th><td>23.2</td><td>3.3</td><td>CLAMP3BSA</td><td>21.5</td><td>7.5</td><td>CLAMPSH90</td></tr><tr><th>Cold month mean temperature (°C)</th><td>3.4</td><td>3.8</td><td>CLAMP3BSA</td><td>4</td><td>4.6</td><td>CLAMPSH90</td></tr><tr><th>Mean Growing Season Precipitation (mm)</th><td>1741</td><td>42.6</td><td>CLAMP3BSA</td><td>1394</td><td>68</td><td>CLAMPSH90</td></tr><tr><th>3-Wettest Months Precipitation (mm)</th><td>784</td><td>19.8</td><td>CLAMP3BSA</td><td>460</td><td>35</td><td>CLAMPSH90</td></tr><tr><th>3-Driest Months Precipitation (mm)</th><td>334</td><td>15.3</td><td>CLAMP3BSA</td><td>160</td><td>22.8</td><td>CLAMPSH90</td></tr><tr><th>Length Growing Season</th><td>7.7</td><td>1.2</td><td>CLAMP3BSA</td><td>7.4</td><td>0.8</td><td>CLAMPSH90</td></tr></tbody></table>
CNN-Driven DBMS Estimation from Aeromagnetic Data and Applications in Southern Peninsular India
<p>Datasets for the manuscript titled </p> <pre><span>CNN-trained </span><span>centroid</span><span> method for estimating Depth to the Bottom of the Magnetic Sources (DBMS) and its application to the south Indian shield</span></pre>
Ancestral allele estimates for cattle using est-sfs software with the K2 model
<h1>Overview</h1> <p>The assignment of bovine ancestral alleles was based on a model comparison of alleles from cattle with alleles from outgroup species: Water Buffalo, Sheep, and White-Tailed Deer. </p> <p>The frequency of cattle alleles are determined using 79 representative individuals from 1000 Bull Genomics Project. We utilized multiple sequence alignments of 110 species (78 ruminants and 32 mammalian outgroup species), available from http://animal.omics.pro/code/index.php/RGD/loadByGet?address[]=RGD/Download/comSynDownload.php, to determine the alleles in Water Buffalo, Sheep, and White-Tailed Deer at each locus.</p> <p>We employed the est-sfs software with the K2 model to infer the probability (Pancs) of the major allele in cattle being ancestral. Alleles were determined to be ancestral if they were the major allele at a site with Pancs > 0.8 or the minor allele at a site with Pancs < 0.2.</p> <p>Please email bft990914@163.com for any queries.</p> <p>The columns of this dataframe are</p> <p>chrome: chromosome index.</p> <p>pos: physical location of SNV.</p> <p>cattle_ref: reference allele of cattle.</p> <p>cattle_alt: alternative allele of cattle.</p> <p>cattle_maj: major allele of cattle.</p> <p>water_buffalo: the sequence of water_buffalo.</p> <p>sheep: the sequence of sheep.</p> <p>white_tailed_deer: the sequence of white_tailed_deer.</p> <p>p_maj_anc: the probability of the major allele of cattle being ancestral.</p> <p>ancestral_allele: the inferred ancestral allele.</p>
Supporting data: Divergent Estimates of China's Forest Carbon Sink Can Not Be Well Coordinated based on a Collective Analysis
<p>Differences in the carbon sink results obtained from the inventory method, DGVMs, ATM, and EC were compensated using relevant data under a harmonized definition of the NetFCsink. All the carbon flux results used have been stored in an xlsx file, categorized according to the corresponding method.</p>
Data from: N-mixture models estimate abundance reliably: a field test on Marsh Tit using time-for-space substitution
<p>Imperfect detection in field studies on animal abundance, including birds, is common and can be corrected for in various ways. The binomial N-mixture (hereafter binmix) model developed for this task is widely used in ecological studies owing to its simplicity: it requires replicated count results as the input. However, it may overestimate abundance and be sensitive to even small violations of its assumptions. We used a 33-year dataset on the Marsh Tit, Poecile palustris, a sedentary forest passerine, from Białowieża Forest, Poland to validate inference from binmix models by comparing model-estimated abundances to the true number of breeding pairs within the plots, determined by exhaustive population study. The abundance estimates, derived from six springtime (April-May) counts of males on each plot in each year, were highly reliable: 116 out of 132 year-plot estimates (88%) included the true number of pairs within the 95% confidence intervals. Over- and underestimations were thus rare and similarly frequent (9 and 12 cases, respectively), with a tendency to overestimate at low densities and underestimate at high densities. Marsh Tits sing rarely but the frequency of countersinging increases with abundance, leading to non-independence in detections. When accounted for in a submodel for detection, the per-survey number of countersinging events positively affected detection probability but only weakly affected abundance estimates. Simulations further demonstrate that this property, overestimation at low densities and underestimation at high densities, may be a systematic bias of binmix model even if density-dependent detection is absent. While the behaviour of binmix models in specific situations requires more study, we conclude that these models are a valid tool to estimate abundance reliably when intensive population monitoring is not feasible.</p>
POMET for the meter estimation task
<p>The availability of appropriate research corpora is a fundamental concern in music information retrieval (MIR) research. Design, development, and evaluation of a poetic corpus, POMET for the meter estimation task is addressed in this paper. Poems, which communicate through rhythm and apparent meaning, have a vital role in many literary traditions. Metrical rhythm generally involves periodic arrangements of sequences of stressed and unstressed syllables in each line of poems. The aesthetic and emotional perception of poetry can be studied well using poetic meter analysis. A corpus with eight meters is designed and recorded in a studio environment for Malayalam, one of the prominent languages in South India. A pilot evaluation is performed with musical texture features (MTF) and spectrograms using deep neural-network architectures. We hope that the corpus can be used as a benchmark dataset for poetic meter estimation, rhythmic analysis, and corpus-based prosody analysis</p>
Replication package for: Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis
<p>This zip file contains all of the programs and data necessary to replicate the results, tables, and figures <br> in the following paper:</p> <p>Shapiro, Adam H., and Daniel J. Wilson (2021). "Taking the Fed at its Word: A New Approach to Estimating Central Bank Objectives using Text Analysis," forthcoming at Review of Economic Studies.</p>
Estimating wolf abundance from cameras
<p>Detection histories for wolves derived from cameras in 3 study areas in Idaho, USA, 2016-2018. </p>
Residual strains estimations in the annulus fibrosus through digital image correlation
<p>This upload correspond to the experimental data and all the python and LMGC software pipeline to process the data.</p> <p>The raw data correspond to images of the stress/strain relaxation process within the annulus fibrosus following a radial cut.</p> <p>The corresponding scientific article has been published in the following Diamond Open Access Journal : Journal of Theoretical, Computational and Applied Mechanics.</p>
Evaluating Augmented Reality Light Probe Estimation Techniques using a Single Camera (Supplemental Material)
<p>This repository contains the dataset for the paper "Evaluating Single Camera Augmented Reality Light Probe Estimation Techniques".</p> <p>The data archive includes:</p> <ul> <li>The complete image set (3 scenes with 6 techniques and ground truth in 90 time points)</li> <li>SSIM data of the panoramas as a CSV file</li> <li>SSIM data of the rendered images as a CSV file</li> <li>Various graphs to visualize the data</li> <li>R files used to plot the graphs</li> </ul> <p>The data archive includes additional plots that are not included in the paper.</p>
Input data and code supporting the cod_v2 population estimates
<p>The <strong><em>model.zip</em></strong> file contains input data and code supporting the cod_v2 population estimates. The file<strong> <em>modelData.RData</em></strong> provides the input data to the JAGS model and the file<em> <strong>modelCode.R</strong></em> contains the source code for the model in the JAGS language. The files can be used to run the model for further assessments and as a starting point for further model development.</p> <p>The data and the model were developed using the statistical software <strong>R version 4.0.2</strong> (https://cran.r-project.org/bin/windows/base/old/4.0.2) and <strong>JAGS 4.3.0</strong> (https://mcmc-jags.sourceforge.io), a program for analysis of Bayesian graphical models using Gibbs sampling, through the R package <strong>runjags 2.2.0</strong> (https://cran.r-project.org/web/packages/runjags).</p>
Data for: Theoretical and practical considerations when using retroelement insertions to estimate species trees in the anomaly zone
<p>A potential shortcoming of concatenation methods for species tree estimation is their failure to account for incomplete lineage sorting. Coalescent methods address this problem but make various assumptions that, if violated, can result in worse performance than concatenation. Given the challenges of analyzing DNA sequences with both concatenation and coalescent methods, retroelement insertions (RIs) have emerged as powerful phylogenomic markers for species tree estimation. Here, we show that two recently proposed quartet-based methods, SDPquartets and ASTRAL_BP, are statistically consistent estimators of the unrooted species tree topology under the coalescent when RIs follow a neutral infinite-sites model of mutation and the expected number of new RIs per generation is constant across the species tree. The accuracy of these (and other) methods for inferring species trees from RIs has yet to be assessed on simulated data sets, where the true species tree topology is known. Therefore, we evaluated eight methods given RIs simulated from four model species trees, all of which have short branches and at least three of which are in the anomaly zone. In our simulation study, ASTRAL_BP and SDPquartets always recovered the correct species tree topology when given a sufficiently large number of RIs, as predicted. A distance-based method (ASTRID_BP) and Dollo parsimony also performed well in recovering the species tree topology. In contrast, unordered, polymorphism, and Camin-Sokal parsimony (as well as an approach based on MDC) typically fail to recover the correct species tree topology in anomaly zone situations with more than four ingroup taxa. Of the methods studied, only ASTRAL_BP automatically estimates internal branch lengths (in coalescent units) and support values (i.e., local posterior probabilities). We examined the accuracy of branch length estimation, finding that estimated lengths were accurate for short branches but upwardly biased otherwise. This led us to derive the maximum likelihood (branch length) estimate for when RIs are given as input instead of binary gene trees; this corrected formula produced accurate estimates of branch lengths in our simulation study, provided that a sufficiently large number of RIs were given as input. Lastly, we evaluated the impact of data quantity on species tree estimation by repeating the above experiments with input sizes varying from 100 to 100,000 parsimony-informative RIs. We found that, when given just 1,000 parsimony-informative RIs as input, ASTRAL_BP successfully reconstructed major clades (i.e clades separated by branches >0.3 CUs) with high support and identified rapid radiations (i.e., shorter connected branches), although not their precise branching order. The local posterior probability was effective for controlling false positive branches in these scenarios.</p>
ScienceDex guides
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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.