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1,944 results for “Class I”
PSC01 Exploring effects on stream ecosystem properties by two size classes of prairie stream cyprinids
Losses in freshwater fish diversity might produce a loss in important ecological services provided by fishes in particular habitats. An important gap in our understanding of ecosystem services by fishes is the influence of individuals from different size classes, which is predicted based on known ontogenetic shifts in habitat and diet. I used twenty experimental stream mesocosms located on Konza Prairie Biological Station (KPBS), KS, USA to assess the influence of fish size on ecosystem properties. Mesocosms included two macrohabitats: one riffle upstream from one pool filled with consistent pebble and gravel substrate. There were four experimental and one control treatment, each replicated four times (N = 20). I used two size classes of Central Stonerollers (Campostoma anomalum) and Southern Redbelly Dace (Chrosomus erythrogaster). Five ecosystem properties were assessed: algal filament length (cm), benthic chlorophyll a (µg/cm2), benthic organic matter (g/m2), macroinvertebrate biomass (g/m2), and stream metabolism (g O2/m2/day1). Size structure of fish populations affected some, but not all, ecosystem properties and these effects were dependent upon species identity. Size structure of both species had effects on algal filament lengths where stonerollers of both size classes reduced algal filaments, but only small redbelly dace kept filaments short. A better understanding of the relationship between these prairie stream minnows and their small stream habitats could be useful to both predict changes in stream properties if species are lost or size structure shifts, and to redbelly dace populations, a Species In Need of Conservation.
Cover class data for Saddle grid, 1993 - 1994.
During the summer of 1992 10-m transects were established at each of the Niwot Ridge Saddle grid points, except grid points 1, 5, 13, 15, 25, 30A, 35, 37, 41, 42, 45, 46, 55, 63, 65, and 71. No transects were set up at locations 1, 13, 30A, 41, 42, 63, and 71 because they were essentially barren of vegetation or, in the case of grid point 37, were covered with a willow shrub thicket. Transect locations were chosen so that the vegetation along the transect would be consistent with that in the 1x1-m plot used to characterize the species composition at the corresponding Saddle grid point. These transects were established as part of a study designed to quantify any relationship between spectral emissivity and net primary productivity. Spectral measurements were made, using a PS-II (Analytical Spectral Devices) spectrometer, of approximately 50- x 20-cm plots along a transect immediately prior to it being clip harvested for aboveground biomass determinations. Photographs were taken at the same time that the spectral measurements were made in 1994, but at different times in 1993. The photographs were subsequently used to quantify various cover classes for the harvested portion of the transect.
Parramore Island of the Virginia Coast Reserve Permanent Plot Resurvey: Landcover Class Aggregation data 1996
First 3-5 year resurvey of permanent monitoring plots using essentially the same protocol as the intial survey of 1992-1993 except that: 1.) standing biomass of the herbaceous groundcover was added (including for new lower salt marsh plots) using clip plots at the subplot locations; and 2.) an estimate of landcover/habitat class aggregation was conducted surrounding each plot center out to 60m in the four cardinal directions. Extends baseline data useful for estimating landscape-scale vegetative productivity, mortality, and turnover; for establishing pre-disturbance conditions in the case of later stand- or island-wide disturbance; and for assisting in the ground-truthing of landcover and habitat classification using aerial or satellite remote-sensing imagry.
Databases for exploratory mode of RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery
<p>RREFinder is a bioinformatic tool for the detection of RiPP Recognition Elements (RREs). See "RRE-Finder: A Genome-Mining Tool for Class-Independent RiPP Discovery".</p> <p>This database contains the required databases to run exploratory mode of the tool.</p>
Fig. 3 in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 3. Area codes as used in the atlas, from Fauna Europaea guidelines (Jong et al. 2014, supplementary material 1). Reproduced with permission. Note that MN (Montenegro) and SB (Serbia) in the above list and throughout this paper are shown as YU (Yugoslavia) on this map.
Fig. 1. A in Atlas of European millipedes 2: Order Julida (Class Diplopoda)
Fig. 1. A selection of European species of Julida. A. Boreoiulus tenuis (Bigler, 1913) (Blaniulidae) (J. Spelda phot.). B. Trichoblaniulus hirsutus (Brölemann, 1899) (Trichoblaniulidae) (D. Cheung phot.). C–J. Julidae. C. Cylindroiulus boleti (C.L. Koch, 1847) (J. Spelda phot.). D. Leptoiulus belgicus (Latzel, 1884) (J. Spelda phot.). E. Ommatoiulus hoffmani Akkari & Enghoff, 2012 (K. Mohr phot.). F. Ommatoiulus sabulosus (Linnaeus, 1758), colour variety from the Italian Riviera (D. Cheung phot.). G. Pteridoiulus aspidiorum Verhoeff, 1913 (J. Spelda phot.). H. Pachyiulus cattarensis (Latzel, 1884) (D. Antić phot.). I. Serboiulus deelemanni Strasser, 1971 (D. Antić phot.). J. Unciger foetidus (C.L. Koch, 1838) (J. Spelda phot.). Not to scale.
Monocyte class switch and hyperinflammation characterise severe COVID-19 in type 2 diabetes
<p>raw and source data for manuscript EMM-2020-13038 under revision and preprint doi: https://doi.org/10.1101/2020.06.02.20119909</p>
Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study
<p>Software Product Line Traceability and Product Configuration in Class and Sequence Diagrams: an Empirical Study</p>
Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams
<p>Software Product Line Configuration and Traceability: an Empirical Study on SMarty Class and Component Diagrams</p>
Dataset for class comment analysis
<p>A list of different projects selected to analyze class comments (available in the source code) of various languages such as Java, Python, and Pharo. The projects vary in terms of size, contributors, and domain.</p> <p> </p> <pre><code class="language-markdown">## Structure ``` Projects/ Java_projects/ eclipse.zip guava.zip guice.zip hadoop.zip spark.zip vaadin.zip Pharo_projects/ images/ GToolkit.zip Moose.zip PetitParser.zip Pillar.zip PolyMath.zip Roassal2.zip Seaside.zip vm/ 70-x64/Pharo Scripts/ ClassCommentExtraction.st SampleSelectionScript.st Python_projects/ django.zip ipython.zip Mailpile.zip pandas.zip pipenv.zip pytorch.zip requests.zip ``` ## Contents of the Replication Package --- **Projects/** contains the raw projects of each language that are used to analyze class comments. - **Java_projects/** - `eclipse.zip` - Eclipse project downloaded from the GitHub. More detail about the project is available on GitHub [Eclipse](https://github.com/eclipse). - `guava.zip` - Guava project downloaded from the GitHub. More detail about the project is available on GitHub [Guava](https://github.com/google/guava). - `guice.zip` - Guice project downloaded from the GitHub. More detail about the project is available on GitHub [Guice](https://github.com/google/guice) - `hadoop.zip` - Apache Hadoop project downloaded from the GitHub. More detail about the project is available on GitHub [Apache Hadoop](https://github.com/apache/hadoop) - `spark.zip` - Apache Spark project downloaded from the GitHub. More detail about the project is available on GitHub [Apache Spark](https://github.com/apache/spark) - `vaadin.zip` - Vaadin project downloaded from the GitHub. More detail about the project is available on GitHub [Vaadin](https://github.com/vaadin/framework) - **Pharo_projects/** - **images/** - - `GToolkit.zip` - Gtoolkit project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `Moose.zip` - Moose project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `PetitParser.zip` - Petit Parser project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `Pillar.zip` - Pillar project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `PolyMath.zip` - PolyMath project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `Roassal2.zip` - Roassal2 project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - `Seaside.zip` - Seaside project is imported into the Pharo image. We can run this image with the virtual machine given in the `vm/` folder. The script to extract the comments is already provided in the image. - **vm/** - - **70-x64/Pharo** - Pharo7 (version 7 of Pharo) virtual machine to instantiate the Pharo images given in the `images/` folder. The user can run the vm on macOS and select any of the Pharo image. - **Scripts/** - It contains the sample Smalltalk scripts to extract class comments from various projects. - `ClassCommentExtraction.st` - A Smalltalk script to show how class comments are extracted from various Pharo projects. This script is already provided in the respective project image. - `SampleSelectionScript.st` - A Smalltalk script to show sample class comments of Pharo projects are selected. This script can be run in any of the Pharo images given in the images/ folder. - **Python_projects/** - `django.zip` - Django project downloaded from the GitHub. More detail about the project is available on GitHub [Django](https://github.com/django) - `ipython.zip` - IPython project downloaded from the GitHub. More detail about the project is available on GitHub on [IPython](https://github.com/ipython/ipython) - `Mailpile.zip` - Mailpile project downloaded from the GitHub. More detail about the project is available on GitHub on [Mailpile](https://github.com/mailpile/Mailpile) - `pandas.zip` - pandas project downloaded from the GitHub. More detail about the project is available on GitHub on [pandas](https://github.com/pandas-dev/pandas) - `pipenv.zip` - Pipenv project downloaded from the GitHub. More detail about the project is available on GitHub on [Pipenv](https://github.com/pypa/pipenv) - `pytorch.zip` - PyTorch project downloaded from the GitHub. More detail about the project is available on GitHub on [PyTorch](https://github.com/pytorch/pytorch) - `requests.zip` - Requests project downloaded from the GitHub. More detail about the project is available on GitHub on [Requests](https://github.com/psf/requests/) </code></pre> <p> </p> <p> </p>
Secretin-like class B G protein-coupled receptor (GPCR) mutation data set
<p>Curated set of 2463 quantitative mutation data points covering 13 secretin-like class B G protein-coupled receptors.</p> <p>Annotated according to GPCRdb standards (http://gpcrdb.org/), complemented by GPCRdb Ballesteros-Weinstein numbers assigned using GPCRdb KNIME nodes (https://github.com/3D-e-Chem/knime-gpcrdb).</p> <p>The data set is build from data sets published in:</p> <p>- Siu et al. Nature 2013, 499: 444-449. doi:10.1038/nature12393</p> <p>- Hollenstein, de Graaf et al. Tr Pharmacol Sci 2014, 35: 12-22. doi: 10.1016/j.tips.2013.11.001</p> <p>- Yang, de Graaf et al. J Biol Chem 2016, 291: 12991-3004. doi: 10.1074/jbc.M116.721977</p>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.1.0 - May 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.1.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>May 01, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/May-01%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Inverse Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Standard Relations-OWL (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-Standard Relations-OWL</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
PheKnowLator Human Disease KG Benchmarks: Class-Inverse Relations-OWLNETS (v2.0.0 - February 2021)
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds (v2.0.0)</strong></p><p><strong>Build Type: </strong><i>Class-InverseRelations-OWLNETS</i></p><p><strong>Build Date: </strong>February 11, 2021</p><p> </p><h3><strong>Important Build Information</strong></h3><p>The benchmarks were originally built and stored using Google Cloud Platform (GCP) resources. For details and a complete description of this process, can be found on GitHub (<a href="https://github.com/callahantiff/PheKnowLator/tree/master/builds#readme">here</a>). Note that we have developed an archive for the builds on Zenodo. While the original GCP resources contained all associated files, due to the file size upload limits associated with each archive, we have limited the uploaded files to the KGs, associated metadata, and log files. The list of resources, including their URLs, and date of download, can all be found in the associated logs.</p><p>Details on each of the files generated by the build process can be found in the file associated with this directory (<a href="https://zenodo.org/records/10065431/files/PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx?download=1">PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx</a>).</p><p> </p><p>🚨 <strong>AVAILABLE FILES </strong>🚨 </p><ul><li>Available KG benchmark files are zipped and listed below.</li><li>For additional details on what each file contains, please see the associated Wiki page 👉 <a href="https://github.com/callahantiff/PheKnowLator/wiki/February-11%2C-2021">here</a>.</li></ul>
NDC-classes
<h3><strong>Overview</strong></h3><p>This dataset consists of the pharmaceutical classes used to classify drugs in the National Drug Code Directory maintained by the Food and Drug Administration between 1946 and 2017. The original data was downloaded from the <a href="https://www.fda.gov/Drugs/InformationOnDrugs/ucm142438.htm">FDA</a>.</p><p>This is a temporal hypergraph dataset, which here means a sequence of timestamped hyperedges where each hyperedge is a set of nodes. Timestamps are in ISO8601 format. In the original dataset, the same drug substance can have more than one NDC code. For example, different dosages of the same drug may result in multiple NDC codes.<br> </p><h4><strong>Statistics</strong></h4><p>Some basic statistics of this dataset are:</p><ul><li>Number of nodes: 1,161</li><li>Number of timestamped hyperedges: 49,726</li><li>distribution of the connected components:</li></ul><p>Component Size, Number</p><ul><li>628, 1</li><li>60, 1</li><li>26, 1</li><li>18, 1</li><li>8, 2</li><li>7, 3</li><li>6, 2</li><li>5, 2</li><li>4, 11</li><li>3, 20</li><li>2, 127</li><li>1, 12</li></ul><h4><strong>Source of original data</strong></h4><p>Sources:</p><ul><li><a href="https://www.cs.cornell.edu/~arb/data/NDC-classes/">NDC-classes dataset</a></li><li><a href="https://www.fda.gov/Drugs/InformationOnDrugs/ucm142438.htm">FDA</a></li></ul><h4><strong>References</strong></h4><p>If you use this dataset, please cite these references:</p><ul><li><a href="https://doi.org/10.1073/pnas.1800683115">Simplicial closure and higher-order link prediction</a>, Austin R. Benson, Rediet Abebe, Michael T. Schaub, Ali Jadbabaie, and Jon Kleinberg. Proceedings of the National Academy of Sciences (PNAS), 2018.</li></ul>
A Peatland Sub-Class Map for the Canadian Boreal Forest
<p><strong>Authors: </strong><br>Pontone, N., Millard, K., Thompson, D. K., Guindon, L., Beaudoin A. (2024)</p> <p><br><strong>Contact:</strong><br>NicholasPontone@cmail.carleton.ca</p> <p> </p> <p><strong>Description:</strong><br>A map of peatland sub-classes (bog, poor fen, rich fen and permafrost peat complex) for the Canadian Boreal Forest circa 2020 created using a three-stage hierarchical classification framework. Training and validation data consisted of peatland locations derived from various sources (field data, aerial photo interpretation, measurements documented in literature). A combination of multispectral data, L-band SAR and C-Band interferometric SAR coherence, forest structure, and ancillary variables were used as model predictors. Ancillary data were used to mask agricultural areas and urban regions, and account for regions that may exhibit permafrost</p> <p><br><strong>Pixel Values:</strong></p> <p>1: Bog<br>2: Rich Fen<br>3: Poor Fen<br>4: Peatland Permafrost Complex<br>5: Mineral Wetlands<br>6: Water<br>7: Upands<br>8: Agriculture<br>9: Urban</p> <p><br><strong>Recommended Colours</strong></p> <p>1: 4C0073<br>2: FFFF00<br>3: E64C00<br>4: 727272<br>5: F4C2C2<br>6: 0070FF<br>7: 4C7300<br>8: 623131<br>9: 000000</p> <p> </p> <p><strong>Please cite as:</strong></p> <p>Pontone, N., Millard, K., Thompson, D.K., Guindon, L. and Beaudoin, A. (2024), A hierarchical, multi-sensor framework for peatland sub-class and vegetation mapping throughout the Canadian boreal forest. Remote Sens Ecol Conserv. https://doi.org/10.1002/rse2.384</p> <p> </p> <p>This data was released in combination with PALSAR-2 L-band dual-polarized radar backscatter summer composites (circa 2020). </p> <p>Beaudoin, A., Villemaire, P., Gignac, C., Tolszczuk, S., Guindon, L., Pontone, N., Millard, C. (2024). Canada’s PALSAR-2 dual-polarized L-band radar summer backscatter composite, circa 2020. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, Quebec, Canada. <a href="https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209" target="_blank" rel="nofollow noopener">https://doi.org/10.23687/8ec4ee78-9240-4bd0-9c97-d3a27829e209</a></p> <p>The peatland map is also available as a Google Earth Engine asset (projects/ee-peatlandthesis/assets/PeatlandMap8b_2023_07_17). </p> <p> </p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Dataset for: DECaPS and SMA discovery of a highly-inclined class I YSO with an outflow: IRAS 08235-4316
<p>This repository provides the dataset for the paper "DECaPS and SMA discovery of a highly-inclined class I YSO with an outflow: IRAS 08235-4316" by Joshua Bennett Lovell et al. It is associated with manuscript number AJ AAS57883.</p> <p>All data and code for the project stored here.</p> <p>A jupyter notebook file is included which provides the script to reproduce all presented figures in the paper.</p> <p>A set of .fits extension files are provided, which include the SMA 12CO cube, associated moment map images, the SMA continuum image, and the DECaPS RGB cube.</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.