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Fig. 2 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 2. Ordination plots of the correspondence analysis (first two axes) based on fishermen answers about fishing methods and baits of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 8 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 8. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about reproductive (spawning) season of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
Fig. 5 in Fishermen's local ecological knowledge on Southeastern Brazilian coastal fishes: contributions to research, conservation, and management
Fig. 5. Ordination plot of the correspondence analysis (first two axes) based on fishermen's answers about migratory behavior of the nine studied fish species in the southeastern Brazilian coast: Absa = Abudefduf saxatilis; Boru = Bodianus rufus; Cala = Caranx latus; Epma = Epinephelus marginatus; Haau = Haemulon aurolineatum; Heba = Hemiramphus balao; Kysp = Kyphosus spp.; Mifu = Micropogonias furnieri; Sesp = Seriola spp.
PheKnowLator Human Disease Knowledge Graph Benchmarks Archive
<h2><strong>PKT Human Disease KG Benchmark Builds</strong></h2> <p>The PheKnowLator (PKT) Human Disease KG (PKT-KG) was built to model mechanisms of human disease, which includes the Central Dogma and represents multiple biological scales of organization including molecular, cellular, tissue, and organ. The knowledge representation was designed in collaboration with a PhD-level molecular biologist (<a href="https://user-images.githubusercontent.com/8030363/195469903-86598760-40b7-4126-857c-3d6368305a86.png">Figure</a>). </p> <p>The <strong>PKT Human Disease KG</strong> was constructed using 12 OBO Foundry ontologies, 31 Linked Open Data sets, and results from two large-scale experiments (<a href="https://doi.org/10.48550/arXiv.2307.05727">Supplementary Material</a>). The 12 OBO Foundry ontologies were selected to represent chemicals and vaccines (i.e., ChEBI and Vaccine Ontology), cells and cell lines (i.e., Cell Ontology, Cell Line Ontology), gene/gene product attributes (i.e., Gene Ontology), phenotypes and diseases (i.e., Human Phenotype Ontology, Mondo Disease Ontology), proteins, including complexes and isoforms (i.e., Protein Ontology), pathways (i.e., Pathway Ontology), types and attributes of biological sequences (i.e., Sequence Ontology), and anatomical entities (Uberon ontology). The RO is used to provide relationships between the core OBO Foundry ontologies and database entities.</p> <p>The <strong>PKT Human Disease KG</strong> contained 18 node types and 33 edge types. Note that the number of nodes and edge types reflects those that are explicitly added to the core set of OBO Foundry ontologies and does not take into account the node and edge types provided by the ontologies. These nodes and edge types were used to construct 12 different PKT Human Disease benchmark KGs by altering the Knowledge Model (i.e., class- vs. instance-based), Relation Strategy (i.e., standard vs. inverse relations), and Semantic Abstraction (i.e., OWL-NETS (yes/no) with and without Knowledge Model harmonization [OWL-NETS Only vs. OWL-NETS + Harmonization]) parameters. Benchmarks within the PheKnowLator ecosystem are different versions of a KG that can be built under alternative knowledge models, relation strategies, and with or without semantic abstraction. They provide users with the ability to evaluate different modeling decisions (based on the prior mentioned parameters) and to examine the impact of these decisions on different downstream tasks.</p> <p>The Figures and Tables explaining attributes in the builds can be found <a href="https://github.com/callahantiff/PheKnowLator/wiki/Archived-Builds">here</a>.</p> <p> </p> <h3><strong>Build Data Access</strong></h3> <h4><strong>Important Build Information</strong></h4> <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 this Zenodo-based archive for the builds. While the original GCP resources contained all of the resources needed to generate the builds, 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 logs associated with each build.</p> <p>🗂 For additional information on the KG file types please see the following <a href="https://github.com/callahantiff/PheKnowLator/wiki/KG-Construction#table-knowledge-graph-build-output">Wiki page</a>, which is also available as a download from this repository (PheKnowLator_HumanDiseaseKG_Output_FileInformation.xlsx). </p> <h4><strong>v1.0.0</strong></h4> <ul> <li>KGs: <a href="../doi/10.5281/zenodo.7030200">https://zenodo.org/doi/10.5281/zenodo.7030200</a></li> <li>Embeddings: <a href="../doi/10.5281/zenodo.7030188">https://zenodo.org/doi/10.5281/zenodo.7030188</a></li> </ul> <h4><strong>All Other Build Versions</strong></h4> <p><strong>Class-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029957">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180239">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180539">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180774">MAY2021</a>;<a href="../doi/10.5281/zenodo.8180825"> JUN2021</a>; <a href="../doi/10.5281/zenodo.8180972">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183987">AUG2021</a>;<a href="../doi/10.5281/zenodo.8184090"> SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184131">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184205">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029953">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180255">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180545">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180772">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180827">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180974">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183989">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184088">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184133">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184208">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029893">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180269">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180550">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180766">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180829">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180976">JUL2021</a>;<a href="../doi/10.5281/zenodo.8183991"> AUG2021</a>; <a href="../doi/10.5281/zenodo.8184086">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184135">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184210">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029921">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180279">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180555">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180768">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180833">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180982">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183993">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184084">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184137">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184212">NOV2021</a></li> </ul> </li> </ul> <p><strong>Instance-based Builds</strong></p> <p><em>Standard Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029941">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180333">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180558"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180764">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180835">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180984">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183995">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184082">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184139">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184216">NOV2021 </a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029939">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180335">JAN2021</a>;<a href="../doi/10.5281/zenodo.8180564"> FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180762">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180837">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180986">JUL2021</a>; <a href="../doi/10.5281/zenodo.8183997">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184080">SEP2021</a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184141">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184218">NOV2021</a></li> </ul> </li> </ul> <p><em>Inverse Relations</em></p> <ul> <li>OWL Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029945">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180338">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180588">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180758">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180878">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180992">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184001">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184078">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184143">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184220">NOV2021</a></li> </ul> </li> <li>OWL-NETS Build <ul> <li>v2.0.0: <a href="../doi/10.5281/zenodo.7029919">MAY2020</a><a href="../record/8178783">; </a><a href="../doi/10.5281/zenodo.8180340">JAN2021</a>; <a href="../doi/10.5281/zenodo.8180584">FEB2021</a></li> <li>v2.1.0: <a href="../doi/10.5281/zenodo.8180756">MAY2021</a>; <a href="../doi/10.5281/zenodo.8180823">JUN2021</a>; <a href="../doi/10.5281/zenodo.8180996">JUL2021</a>; <a href="../doi/10.5281/zenodo.8184003">AUG2021</a>; <a href="../doi/10.5281/zenodo.8184076">SEP2021 </a></li> <li>v3.0.2: <a href="../doi/10.5281/zenodo.8184145">OCT2021</a>; <a href="../doi/10.5281/zenodo.8184222">NOV2021</a></li> </ul> </li> </ul>
European Olfactory Knowledge Graph
<p>The European Olfactory Knowledge Graph (EOKG) includes information about smell from (digital) text and image collections from the European history (1600-1920), extracted in the context of the <a title="Odeuropa" href="https://odeuropa.eu/" target="_blank" rel="noopener">Odeuropa project</a> in a cultural heritage preservation perspective.</p> <p>It contains over 2,500,000 olfactory reference coming from over 43,000 images and 2,400,000 texts in six languages, organised according to the <a href="https://data.odeuropa.eu/ontology" target="_blank" rel="noopener">Odeuropa Ontology</a> and leveraging machine learning to recognise and categorise olfactory elements.</p> <h3>Additional Links</h3> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>EOKG Vocabularies: <a href="https://vocab.odeuropa.eu/" target="_blank" rel="noopener noreferrer">https://vocab.odeuropa.eu/</a> (vocabulary browser)<br>Odeuropa Ontology: <a href="https://data.odeuropa.eu/ontology/" target="_blank" rel="noopener noreferrer">https://data.odeuropa.eu/ontology/</a> (data model)<br>EOKG API: <a href="https://grlc.eurecom.fr/api/Odeuropa/kg-api/" target="_blank" rel="noopener noreferrer">https://grlc.eurecom.fr/api/Odeuropa/kg-api/</a> (API)<br>EOKG technical report: <a href="https://odeuropa.eu/wp-content/uploads/2024/10/D4_3_European_Olfactory_Knowledge_Graph_v2_final.pdf">https://odeuropa.eu/wp-content/uploads/2024/10/D4_3_European_Olfactory_Knowledge_Graph_v2_final.pdf</a> (documentation)<br>Odeuropa Smell Explorer: <a href="https://explorer.odeuropa.eu/" target="_blank" rel="noopener noreferrer">https://explorer.odeuropa.eu/</a> (demonstrator)</div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div>
Figure 1 in Contribution to the knowledge of Parichoronyssus bakeri Morales-Malacara and Guerrero, 2007 (Mesostigmata: Macronyssidae): new locality and host-association records with additional molecular data
Figure 1 Light Microscopy images of the female Parichoronyssus bakeri. A – General view of the ventral idiosoma; B – General view of the dorsal idiosome; C – Close up of sternal shield; D – Close up of genital and anal shields; E – Gnathosoma and coxa of the Leg I, with the black arrow pointed out the spine-like projection; F – Close up of the dorsal shield. Scales: A and B 50µm, C-F 20µm.
Datasets for Paper "MetagenomicKG: a knowledge graph for metagenomic applications"
<p>This repository contains some required data that is used for building MetagenomicKG. Please see more details in <a href="https://github.com/KoslickiLab/MetagenomicKG">https://github.com/KoslickiLab/MetagenomicKG</a>.</p>
(Processed data) How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study: a descriptive study
<p>Processed data used in the article 'How do Spanish educational researchers use X's platform to promote the dissemination of scientific knowledge: a descriptive study', from the project Comscienciaeduspain (FCT-20-15761), executed with the collaboration of the Spanish Foundation for Science and Technology – Ministry of Science and Innovation.</p>
Fig. 2 in To the knowledge of genus Pachyrhynchus Germar, 1824 (Coleoptera: Curculionidae: Pachyrhynchini) species from SMNH (Stockholm, Sweden), with description of a new species from the Sibuyan Island (Philippines)
Fig. 2. Male genitalia and female terminalia of P. sibuyanensis sp. nov., holotype, male: A – C; paratype, female: D – F; A – aedeagus in lateral view; B – sternite IX in dorsal view; C – tegmen in dorsal view; D – srternite VIII in ventral view; E – spermatheca; F – apex of ovipositor in dorsal view. Scale: 1.00 mm.
Clustering of Librarian Knowledge in Sidrap Regency
<p>Using K-means, this research attempts to determine the best grouping and number of librarians on Sidenreng Rappang for some training.</p>
Fig.5 in Contribution to the knowledge of the genus Doliops Waterhouse, 1841 (Coleoptera: Cerambycidae)
Fig.5. Mimicry: A - Doliops savenkovi sp. n.; B - D. sklodowskii sp. n.; C - D. metallica Breuning; D - Pachyrrhynchus orbifer Waterhouse; E - D. stradinsi sp. n.; F - P. argus Pascoe; G - D. anichtchenkoi sp. n.; H – P. erichsoni Waterhouse; I - D. vivesi sp. n.; J – P. modestior Behrens; K - D. valainisi sp. n.; L - Pachyrrhynchus sp.
Fig. 4 in Contribution to the knowledge of the genus Doliops Waterhouse, 1841 (Coleoptera: Cerambycidae)
Fig. 4. Aedeagus: A - D. anichtchenkoi sp. n.; B - D. sklodowskii sp. n.; C - D. curculionoides Waterhouse; D - D. metallica Breuning; E - D. duodecimpunctata Heller; F – D. savenkovi sp. n.; G - D. shavrini sp. n.; H - D. stradinsi sp. n.; I - D. valainisi sp. n.; J - D. dupaxi Vives.
Fig. 3. A, I in Contribution to the knowledge of the genus Doliops Waterhouse, 1841 (Coleoptera: Cerambycidae)
Fig. 3. A, I - Doliops helleri Vives; B, J - D. savenkovi sp. n.; C - D. metallica Breuning; D, K - D. sklodowskii sp. n.; E - D. dupaxi Vives; F, L - D. stradinsi sp. n.; G, M - D. valainisi sp. n.; H, N - D. vivesi sp. n.
Fig. 2. A, B, I in Contribution to the knowledge of the genus Doliops Waterhouse, 1841 (Coleoptera: Cerambycidae)
Fig. 2. A, B, I - Doliops animula Kriesche; C, J - D. anichtchenkoi sp. n.; D, K - D. curculionoides Waterhouse; E, L - D. gutowskii sp. n., F, M - D. duodecimpunctata Heller; G, N - D. shavrini sp. n.; H, O – D. emmanueli Vives. A - H – dorsal view; I - O – lateral view.
ODLEP Knowledge Repository
<p>The database contains three files:</p> <p>1) ALL_INSTRUCTORS_PF_FINAL.csv: it is a file with 275 answers to a survey that was developed in the context of the ODLEP project (<strong>Project reference No: 2022-1-EL01-KA220-HED-000089152</strong>) and </p> <p>The questionnaire includes one hundred questions, as mentioned earlier and it is divided into four discrete parts:</p> <ul> <li> <p>Participants’ Profile</p> </li> <li> <p>Distance Education Experience</p> </li> <li> <p>Learning and Teaching Styles</p> </li> <li> <p>Personality Characteristics</p> </li> </ul> <p>2) ALL_STUDENTS_FINAL_PF_FINAL.csv: <strong> Students from six universities participated in the research and 646 responses were collected. </strong></p> <p>The survey that the students answered consists of the following discrete parts:</p> <ul> <li> <p>Introductory note on the Specific Research Survey</p> </li> <li> <p>Students’ Attributes</p> </li> <li> <p>Students’ Distance Education Experience</p> </li> <li> <p>Students’ Preferred Learning Styles</p> </li> <li> <p>Fellow Students’ Preferred Learning Styles</p> </li> <li> <p>Preferred teaching style of Instructors as considered by students</p> </li> <li> <p>Students’ Personality (big five personality traits and facets)</p> </li> </ul> <p> </p> <p>3)ODLEP_Knowledge_repository_v2.xlsx: An excel file with two tabs. In the first tab 42 distance HE programs are summarized. The tab contains text that answers the following elements: Country, Program Name, Period that it was active, Characteristics that made it successful, Limiting Factors, Additional Information.</p> <p>In addition, the second tab contains information on 97 scientific papers on the issue of Distance/online education and the parameters that made it successful. The column names are: Title, Authors, Year of Publication, Journal Name, Objective of the paper, Most important Conclusions</p> <p> </p>
Predictors of medical staff's knowledge, attitudes, and behavior of dysphagia assessment: A cross-sectional study
<p>This study aimed to develop training resources and standardize the assessment of dysphagia in patients with stroke. This study was a cross-sectional study. A total of 430 nurses and doctors from four provinces(Guangdong Province, Hunan Province, Guangxi Province, and Shaanxi Province) who were selected by convenience sampling were invited to complete the questionnaire through WeChat, DingTalk, and Tencent QQ from May 23 to 31, 2022. A self-reported questionnaire was used to assess participants' Knowledge, Attitude, and Behavior regarding dysphagia. Participants' sociodemographic, training, and nursing experience were measured using the general information sheet and assessed as potential predictors of medical staff's Knowledge, Attitudes, and Behavior of dysphagia assessment. A multiple linear regression model was used to identify the factors predicting medical staff's Knowledge, Attitudes, and Behavior regarding dysphagia assessment. The mean scores for Knowledge, Attitudes, and Behavior of dysphagia assessments were 92.654(SD 17.519). Multiple linear regression results indicated that experience in dysphagia patients' nursing, related training for dysphagia, working years in the field of dysphagia-related diseases, specialized training in geriatric, swallowing & rehabilitation, and department related to neurology, rehabilitation & elderly were significant predictors, accounting for 35.1% of the variance in scores of medical staff's Knowledge, Attitudes and Behavior of dysphagia assessment. Our findings imply that nursing experience, training, and work for patients with swallowing disorders could have positive effects on the Knowledge, Attitudes, and Behavior of medical staff regarding dysphagia assessment. Hospital administrators should provide relevant resources, such as videos of dysphagia assessment, training centers for the assessment of dysphagia, and swallowing specialist nurses. It is important that health policies fully recognize the role of training and support systems in caring for people with dysphagia.</p>
Fig. 12 in Contribution to the Knowledge of the Genus Atholus (Coleoptera: Histeridae: Histerinae: Histerini) from the Indonesian Archipelago
Fig. 12. Map showing the occurrence of some species of Atholus from the Indonesian archipelago and its extra-limital distribution in the Oriental Region.
Fig. 11 in Contribution to the Knowledge of the Genus Atholus (Coleoptera: Histeridae: Histerinae: Histerini) from the Indonesian Archipelago
Fig. 11. Map showing the occurrence of some species of Atholus from the Indonesian archipelago and its extra-limital distribution in the Oriental Region. A, Atholus coelestis; B, A. philippinesis; C, A. torquatus; D, A. bifrons.
Fig. 10 in Contribution to the Knowledge of the Genus Atholus (Coleoptera: Histeridae: Histerinae: Histerini) from the Indonesian Archipelago
Fig. 10. Atholus singalanus (Marseul, 1880), SEM micrographs, IC-22-in07. A, Elytra, dorsal view; B, ditto, oblique view; C, prosternal process; D, meso- and metaventrite; E, propygidium and pygidium; F, propygidium (punctation); G, protibia, dorsal view; H, ditto, ventral view.
Fig. 9 in Contribution to the Knowledge of the Genus Atholus (Coleoptera: Histeridae: Histerinae: Histerini) from the Indonesian Archipelago
Fig. 9. Atholus singalanus (Marseul, 1880), SEM micrographs, IC-22-in07. A, Habitus, dorsal view; B, ditto, ventral view; C, ditto, oblique view; D, head, dorsal view; E, pronotum; F, mouthparts, ventral view.
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.