Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
914
datasets available to search
ShareScore release 0.9.0
Dataset results
914 results for “filter”
High quality protein residues: Top2018 mainchain-filtered residues - mmCIF
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University. This version contains files in mmCIF format.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on mainchain atoms.</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue-level filtering, and the Top2018 best-parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. As long as the question concerns mainchain protein atoms, these files should be usable as is. There is a separate version that has been filtered on all atoms that is suitable for sidechains.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_mcfilter_60pct_complete.txt and found in top2018_cifs_mc_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_mc_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_mc_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_mcfilter_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_mc_filted.csv and as _top2018.percent_passrate in the .cif file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p>Sidechain atoms beyond CB have not been considered in the filtering. However, all sidechains have been included for residues that passed the mainchain filters. DO NOT use this set of files for serious questions involving sidechains. See our all-atom filtered dataset instead.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>Mainchain atoms are defined as N, CA, C, O, CB.<br> Note that CB is included, since its ideal position is defined by the other mainchain atoms.</p> <p>All mainchain atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p><br> Filtering doumentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned residues and included segments in a cif data block named data_top2018_dataset. This block can be found at the end of the file.</p> <p>In the _top2018_deleted_residue loop, causes of pruning are documented. If a residues was removed due to failing the B-factor filter, a "b" will appear in the appropriate column. Otherwise, a "." will appear. Other filtering criteria are treated similarly with the following codes:<br> b - B-factor<br> c - RSCC<br> m - map value<br> g - geometry outliers<br> o - steric overlaps<br> a - alternate conformations</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9<br> Initial upload to establish DOI</p> <p>Version 1.0<br> Initial complete upload</p>
High quality protein residues: Top2018 all-atom-filtered residues - mmCIF
<p>Introduction<br> --------------------------------------------------------------------------------<br> This directory contains files from the Top2018 dataset by the Richardson Lab at Duke University.</p> <p>These are high-quality residues from high-quality, low redundancy protein chains in the PDB.</p> <p>This dataset is quality-filtered on all atoms in the residue.</p> <p>The accompanying publication is:<br> Williams, C. J., Richardson, D. C., & Richardson, J. S. (2021). The importance of residue-level filtering, and the Top2018 best-parts dataset of high‐quality protein residues. Protein Science. http://doi.org/10.1002/pro.4239</p> <p>Usage recommendations<br> --------------------------------------------------------------------------------<br> Protein residues that fail the filtering criteria described below have been removed from the files. As a result, these files can be considered pre-filtered and will return only results for residues of good model quality with supporting experimental data. All protein atoms have been considered in filtering; these files should be usable for any protein question. If your work is strictly limited to mainchain atoms (plus CB), there is a separate version that has been filtered on only mainchain atoms.</p> <p>The Top2018 contains several different levels of homology clustering (30%, 50%, 70%, 90%) to ensure nonredundant datasets. The 70% homology level is a reliable default. These chains are listed in top2018_chains_hom70_fullfiltered_60pct_complete.txt and found in top2018_pdbs_full_filtered_hom70.tar.gz</p> <p>Files are organized in subdirectories based on the first two letters of their PDB ids. The included python script sample_file_loop.py may aid in accessing the directory structure.</p> <p>Files already contain hydrogens added by Reduce. NQH flips have been performed to ensure that these are the best versions of these structures.</p> <p>top2018_metadata_full_filtered.csv contains information on release date, resolution, and validation scores for each file.</p> <p>top2018_passrates_full_filtered.csv contains information on how many protein residues from the original chain passed the quality filters.</p> <p><br> Homology sets:<br> --------------------------------------------------------------------------------<br> Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the best chain was selected for inclusion in the dataset. This ensures minimal sequence/structural redundancy.</p> <p>The Top2018 is available at several different levels of homology clustering, which may be appropriate to different uses. Lists of the included chains at each homology level are included in this distribution.</p> <p>Lower homology numbers mean less redundancy, but fewer total chains in the dataset.</p> <p>For general use, ***we recommend the 70% homology set*** as a good balance between inclusivity and variety. This list is given in the file top2018_chains_hom70_fullfiltered_60pct_complete.txt</p> <p><br> Usage caveats:<br> --------------------------------------------------------------------------------<br> These files are incomplete. They are single chains from structures that may have had multiple chains. Residues that fail the filtering criteria have been removed. Programs with strong requirements for completeness or uninterrupted chains should be used with care. Chain completeness and fragmentation statistics are available in top2018_passrates_full_filtered.csv and as _top2018.percent_passrate in the .cif file.</p> <p>All ligands and waters associated with the chain have been preserved without filtering. Robust ligand filtering is beyond the scope of this dataset. Trust the ligands at your own discretion.</p> <p><br> Filtering criteria: Chain level<br> --------------------------------------------------------------------------------<br> Chain is protein<br> Released on or before Dec 31, 2018<br> Resolution < 2.0<br> MolProbity Score < 2.0<br> <3% residues have cbeta deviations<br> <2% residues have covalent bond length outliers<br> <2% residues have covalent bond geometry outliers</p> <p>Using sequence homology clusters provided by the RCSB PDB, for each homology cluster, the chain with the best (lowest) average of Resolution and MolProbity Score was selected.</p> <p><br> Filtering criteria: Residue level<br> --------------------------------------------------------------------------------<br> Even excellent structures usually contain some poorly-resolved regions. Residue-level filtering helps avoid including these regions in otherwise high-quality data</p> <p>All atoms in a residue:<br> Bfactor <= 40<br> Real-space correlation coefficient (rscc) >= 0.7<br> 2Fo-Fc map value >= 1.2</p> <p>Additionally, residues are not allowed to have:<br> Covalent geometry outliers<br> Steric overlaps or "clashes", as per Probe<br> Alternate conformations</p> <p><br> Chain Completeness criteria<br> --------------------------------------------------------------------------------<br> Chains which lost >40% of their residues during filtering were dropped from this dataset. All chains present here are at least 60% complete.</p> <p>Filtering documentation<br> --------------------------------------------------------------------------------<br> Each file documents its pruned residues and included segments in a cif data block named data_top2018_dataset. This block can be found at the end of the file.</p> <p>In the _top2018_deleted_residue loop, causes of pruning are documented. If a residues was removed due to failing the B-factor filter, a "b" will appear in the appropriate column. Otherwise, a "." will appear. Other filtering criteria are treated similarly with the following codes:<br> b - B-factor<br> c - RSCC<br> m - map value<br> g - geometry outliers<br> o - steric overlaps<br> a - alternate conformations</p> <p>Version history<br> --------------------------------------------------------------------------------<br> Version 0.9<br> Initial upload to establish DOI</p> <p>Version 1.0<br> Initial full upload</p>
Filtered-CoPhy
<p><a href="https://filteredcophy.github.io/">FilteredCoPhy</a></p>
NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model"
<p>NetCDF data used in analysis presented in "Assessment of the z~ time-filtered Arbitrary Lagrangian-Eulerian coordinate in a global eddy-permitting ocean model", submitted to Journal of Advances in Modelling the Earth System.</p> <p>The data are produced from an ensemble of six experiments based on the GO8p0 configuration of NEMO v4.0.1 on a global 1/4° grid, as described in the paper. The ensemble is intended to test the z~ vertical coordinate, and includes a control with the default "z-star" fixed coordinate, and five experiments with the z-tilde vertical coordinate, using a selection of values for the two z-tilde timescale parameters. The data includes time series of global mean ocean and ice fields; large-scale transports; and fields from diapycnal mixing analysis.</p> <p>The first part of each filename refers to the experiment from the ensemble ("zstar", "ztilde_5_30", "ztilde_10_30", "ztilde_20_30", ztilde_20_60" and "ztilde_40_60"); the following five-character string identifies the respective suite on the Met Office Rose system and the MASS archive system; and the rest of the name specifies the type of data contained in the file.</p>
ProtNAff: Protein-bound Nucleic Acid filters and fragment libraries
<p>This archive contains data obtained by running the <strong>ProtNAff</strong> pipeline (<a href="https://github.com/isaureCdB/ProtNAff">https://github.com/isaureCdB/ProtNAff</a>) and used to perform the analyses described in the original ProtNAff paper. The input list of PDB IDs was obtained in October 2021 by searching all PDB structures that contain protein chains and RNA chains but no DNA, and where the resolution is less than 3 A or where the method is NMR. The ribosomes are removed from this database due to their size.</p> <p>The files provided are:</p> <p>- structures.json : the database containing <strong>metadata and parsing data for all the protein-RNA structures</strong> from the input list</p> <p>- fragments_clust.json : the list and description of all the trinucleotide fragments extracted from those structures</p> <p>- trinucl_clust1A_allatom: the <strong>all-atom coordinates of the trinucleotide fragment library</strong> , i.e. the centers of 1A clusters such that each initial fragment has a RMSD of less than 1A from at least one of those centers.</p> <p>- trinucl_clust1A_ATTRACT: the coordinates of the trinucleotide fragment library , but reduced into ATTRACT <strong>coarse-grained representation</strong> (Setny and Zacharias, NAR 2011).</p>
Functional traits and metacommunity theory reveal that habitat filtering and competition maintain bird diversity in a human shared landscape
<p>Human shared landscapes cover much of Earth, yet their conservation value is contested. This controversy may persist because previous studies have examined species diversity, rather than the processes through which such diversity is maintained. For example, a site exhibiting high diversity may not actually bolster populations if the diversity is only maintained through net immigration. Recent research has begun to isolate the processes that maintain metacommunities and develop functional trait methods to identify these processes. However, the processes underlying bird communities remain obscure. Here, we leverage metacommunity theory, functional trait partitioning, and a Bayesian multispecies abundance model to assess whether a shared landscape – woody perennial polyculture farms – bolsters bird diversity. Such farms grow multiple species of food-producing woody perennials together with vegetative groundcover. We surveyed birds and their <em> in situ </em> functional traits across the US Midwest in traditional agriculture, woody perennial polyculture, prairie, and woods. We found that woody perennial polycultures exhibited the highest bird diversity and were the most preferred by many species (including threatened ones). Moreover, our functional trait analysis suggests that this diversity is maintained through habitat filtering and competition, rather than merely immigration. Thus, shared landscapes can likely conserve birds by providing a distinct habitat. These results suggest that woody perennial polyculture farms offer substantial potential to support bird populations in the US Midwest. Our study demonstrates the utility of <em> in situ </em> functional trait partitioning within a Bayesian framework to unmask ecological processes and help assess the conservation value of landscapes.</p>
Data and code for Freshwater corridors in the conterminous US: a coarse-filter approach based on lake-stream networks
<p>This repository contains various datasets used to map and analyze freshwater connectivity (i.e., corridors) in the conterminous US based on networks of lakes, streams and rivers. We considered lake-stream networks as analogous to habitat corridors. Hub lakes are individual lakes that are disproportionately important for maintaining intact networks. We also analyzed the protection status of freshwater connectivity using the US Protected Areas Database v. 2.0. R analysis scripts can also be found in this repository. Much of the data we used came from published or soon-to-be published sources, which are referenced below.</p>
Experimental Datasets and Processing Codes for the Semantic PHD Filter
<p>The water bottle detection dataset and measurement model dataset for the paper titled "The Semantic PHD Filter for Multi-class Target Tracking: From Theory to Practice" by Jun Chen, Zhanteng Xie and Philip Dames, and the paper titled "Experimental Datasets and Processing Codes for the Semantic PHD Filter" by Zhanteng Xie, Jun Chen and Philip Dames</p> <p><strong>1. Detection dataset: </strong></p> <p>Size: <br> Total: 4870 images<br> Training: 4000 images<br> Validation: 870 images</p> <p>Bottle Classes: Aquafina, Deer, Kirkland, Nestle</p> <p>Format: PASCAL VOC, Darknet</p> <p>Folder Structure:<br> - Annotations: containing the xml label files in PASCAL VOC format<br> - ImageSets: containing the training index files <br> - JPEGImages: containing the image data in jpg format<br> - Labels: containing the txt label files in Darknet format</p> <p><strong>2. Measurement model dataset:</strong></p> <p>Format: ROSBAG</p> <p>Duration: 19:59s (1199s)</p> <p>Topics:<br> /darknet_ros/detection_image 3543 msgs : sensor_msgs/Image<br> /map 1 msg : nav_msgs/OccupancyGrid<br> /sphd_measurements 3585 msgs : sphd_msgs/SPHDMeasurements<br> /tf 142727 msgs : tf2_msgs/TFMessage<br> /tf_static 1 msg : tf2_msgs/TFMessage</p> <p>Message Types:<br> nav_msgs/OccupancyGrid<br> sensor_msgs/Image<br> sphd_msgs/SPHDMeasurements<br> tf2_msgs/TFMessage</p> <p> </p> <p><strong>3. Processing codes:</strong></p> <p>Detection processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066045<br> GitHub: https://github.com/TempleRAIL/yolov3_bottle_detector<br> <br> Measurement model processing:<br> Zenodo: https://doi.org/10.5281/zenodo.7066050<br> GitHub: https://github.com/TempleRAIL/sphd_sensor_models</p>
Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood: Additional data
<p>Contains data used in the following publication:</p> <p>Felix Neff, Jonas Hagge, Rafael Achury, Didem Ambarlı, Christian Ammer, Peter Schall, Sebastian Seibold, Michael Staab, Wolfgang W. Weisser, Martin M. Gossner (2022). <em>Hierarchical trait filtering at different spatial scales determines beetle assemblages in deadwood. </em>Functional Ecology. <a href="https://doi.org/10.1111/1365-2435.14186">https://doi.org/10.1111/1365-2435.14186</a></p> <p>These are complementary data, which are needed to reproduce the analyses. Most data are archived in the Biodiversity Exploratories Information System (<a href="https://doi.org/10.17616/R32P9Q">https://doi.org/10.17616/R32P9Q</a>).</p> <p>The following data are included:</p> <ul> <li><strong>BELongDead_Subplots_Normal.csv</strong>: List of <em>normal</em> subplots within the BELongDead projects (only these were included in the analyses)</li> <li><strong>Body_length.csv</strong>: Body length data for study species assembled from Freude et al. (1965-1998)</li> <li><strong>Lightness_completion.csv</strong>: Colour lightness recorded from measured individuals and photos from coleonet.de (Lompe, 2002)</li> <li><strong>Name_standardisation.csv</strong>: Dataset used to standardise taxonomic names from different sources</li> <li><strong>Saproxylic_species_sub.csv</strong>: List of saproxylic species (according to Schmidl & Bussler (2004)), which were recorded in the project</li> <li><strong>Similar_Species.csv</strong>: List of similar species for species with missing traits. Based on these, missing traits were estimated</li> </ul> <p><strong>References</strong></p> <p>Freude, H., Harde, K. W., & Lohse, G. A. (1965–1998). <em>Die Käfer Mitteleuropas Band 1-15</em>. Goecke und Evers.</p> <p>Lompe, A. (2002). <em>Käfer Europas</em>. <a href="http://coleonet.de/">http://coleonet.de/</a></p> <p>Schmidl, J., & Bussler, H. (2004). Ökologische Gilden xylobionter Käfer Deutschlands. <em>Naturschutz und Landschaftsplanung</em>, <em>36</em>(7), 202–218.</p>
Ground tilt data at Campi Flegrei filtered in the fortnightly (Mf=13.66d) and [12h-90d] bands.
<p>Ground tilt (NS and EW components) at Campi Flegrei from April 1st 2015 to April 30th 2022 recorded at three borehole instruments (CMP, ECO and HDM) of the INGV network. </p> <p>1) Time series filtered in the fortnightly (Mf =13.66 days) tidal band [13 - 14.083 days]. Sampling rate=1min. </p> <p>2) Time series filtered in the [12 hours - 90 days] band. Sampling rate=1hour. </p>
Figure 5. Low-pass filter-EKG Through Sound-Card
<p>The low-pass filter, presented in figure 5, allows signals with frequencies up to 34 Hz (cutoff<br> frequency) to pass unaltered while it strongly attenuates those with frequencies exceeding the<br> cut-off frequency. It is an active filter, of the butterworth sallen-key type which contains one of the<br> four operational amplifiers of the TL084 integrated circuit.</p>
Figure 5. Mel-spaced filter bank-Development an Automatic Speech to Facial Animation Conversion for Improve Deaf Lives
<p>Physiologic changes show that human comprehension of frequency content of sound does<br> not obey a linear space. Therefore for each individual it is computed and measure with a real<br> frequency of sound peak at Mel seal. Using the below equation one can change the frequency at<br> Hertz scale to Mel Scale.</p>
Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements
<p>All data presented in the figures of "Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements".</p>
Data and scripts for: Idiosyncratic responses to biotic and environmental filters in wood-inhabiting fungal communities
<p>These files include the data, the scripts, and the pipeline for bioinformatic analyses for reproducing the results presented in the manuscript "<em>Idiosyncratic responses to biotic and environmental filters in wood-inhabiting fungal communities</em>".</p> <p>Description of the files can be found from the README.docx file.</p>
Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters"
<p>Data and codes for "Decay-protected superconducting qubit with fast control enabled by integrated on-chip filters".</p>
Figure 1. Diagnostic characteristics identifying NMV P252567 in Suction feeding preceded filtering in baleen whale evolution
Figure 1. Diagnostic characteristics identifying NMV P252567 as an aetiocetid. A, explanatory line drawing of the skull; B, photograph of the skull (left) and mandible (right), both in dorsal view.
Figure 3 in Suction feeding preceded filtering in baleen whale evolution
Figure 3. Additional teeth of NMV P252567. A, left upper incisor; B, right upper incisor; C, double-rooted postcanine 4. All teeth are shown in lingual (left) and labial (right) views. The lack or comparatively small degree of wear on the incisors suggests they may have been largely (left upper incisor) or partially (right upper incisor) enclosed within the gingiva, protecting them from the abrasive wear that affected the other teeth. A fifth double-rooted postcanine closely resembles postcanines 2 and 4 in terms of its wear, but is still partially encased in sediment and hence not shown here.
Figure 5 in Suction feeding preceded filtering in baleen whale evolution
Figure 5. Suction feeding precedes baleen filtering in mysticete evolution. A, consensus tree of aetiocetid evolutionary relationships, based on all cladistic studies published to date (e.g. Deméré and Berta, 2008; Deméré et al., 2008; Fitzgerald, 2010; Geisler and Sanders, 2003; Marx and Fordyce, 2015; Steeman, 2007), showing major feeding-related synapomorphies; B, life reconstructions (top) and skulls (in lateral view) of a representative archaeocete (Dorudon atrox), aetiocetid (NMV P252567), eomysticetid (Yamatocetus canaliculatus) and extant suction feeding mysticete (grey whale, Eschrichtius robustus); C, inferred behaviours and feeding strategies. Life reconstructions by Carl Buell.
Figure 2 in Suction feeding preceded filtering in baleen whale evolution
Figure 2. Wear patterns on representative teeth of NMV P252567, suggesting suction feeding in an aetiocetid. A, left upper canine or first premolar; B, double-rooted postcanine 1; C,?lower double-rooted postcanine 2; D,?lower double-rooted postcanine 3; E, micro-computed tomography cross section of postcanine 1, showing the depth and rounded edges of the horizontal striations (marked by large black arrows). A–C are shown in lingual, labial and anterior/posterior view, D in lingual view only.
Figure 4 in Suction feeding preceded filtering in baleen whale evolution
Figure 4. Cross section of the rostrum and lower jaws of A, a balaenid, B, a balaenopterid, and C, an aetiocetid, illustrating the relative movement of the mandible during jaw closure (red arrows). All drawings show the mouth slightly open. In right whales (A) and rorquals (B), the laterally bowed mandibles and/or tall lower lips rotate inwards on to the labial surface of the baleen plates, thereby leaving the rack intact. In aetiocetids (C), the movement of the mandible is mostly vertical and the upper and lower jaws need to approach each other enough to allow the teeth to occlude, thereby risking interference with any baleen present. A and B are adapted from Pivorunas (1979: fig. 3).
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.