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725 results for “recommendation”
Simple Dataset for Proof Method Recommendation in Isabelle/HOL
<p>Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers.</p> <p>However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Isabelle/HOL, from applying their tools and expertise to theorem proving.</p> <p>In this data description, we present a simple dataset that contains data on over 400k proof method applications in the Archive of Formal Proofs along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic.</p> <p>Our simple data format allows machine learning practitioners to try machine learning tools to predict proof methods in Isabelle/HOL, even if they are unfamiliar with theorem proving.</p>
Data from: Recommendations for assessing earthworm populations in Brazilian ecosystems
<p><strong>Earthworms are often related to fertile soils and frequently used as environmental quality indicators. However, to optimize their use as bioindicators, their populations must be evaluated together with environmental and anthropogenic variables regulating earthworm communities. In this review we identify the earthworm, soil chemical, physical, environmental and management-related variables evaluated in 124 published studies that quantified earthworm abundance (>7300 samples) in 765 sites with different types of climate, soils, land use and management systems in Brazil. Most soil chemical and physical attributes (except pH) were less reported (<50% of studies) than other environmental variables such as sampling date, altitude, temperature, precipitation, climate and soil type and land use (all >50% of studies). Earthworms were rarely identified (24%) and few studies (31%) measured their biomass, although most provided adequate information on sampling protocol. Based on the importance in regulating earthworm populations, we propose a set of variables that should be evaluated when studying earthworm communities </strong>and other macrofauna groups<strong>. This should help guide future studies on earthworms in Brazil and other countries, optimize data collection and replicability, allow comparisons between different studies and promote the use of earthworms as soil quality bioindicators.</strong></p>
SURF: Replication Package for: "What Would Users Change in My App? Summarizing App Reviews for Recommending Software Changes"
<p>Description of the content of folder "SURF_replication_package": 1) "Experiment I" contains: a) the folder "summaries" which contains all the html summaries generated through SURF and browsed by study participants involved in the Experiment I. b) the folder "XMLreviews" which contains, for each of the apps involved in the Experiment I, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input files for the SURF tool for generating the summaries contained in the "summaries" folder c) "Experiment_I_results.xlsx" which contains all the answers to our survey collected from the Experiment I participants.</p> <p>2) "Experiment II" contains: a) the folder "summaries" which contains the two html summaries generated through SURF and browsed by study participants in the Experiment II. b) the folder "XMLreviews" which contains, for each of the two apps involved in the Experiment II, the corresponding XML file containing all the collected reviews for that app. These xml files have been used as input of the SURF tool for generating the summaries contained in the "summaries" folder. c) "Experiment_II_results.xlsx" which contains all the user feedbacks extracted/validated by survey participants in the two sub-experiments. d) "Experiment_II_survey_answers.xlsx" which contains all the answers to our survey collected in the Experiment II participants.</p> <p>3) "Survey.pdf" which contains the pdf version of the survey performed by the participants</p> <p>4) "SURF_tool.zip" contains: a) "SURF.jar", which contains the class files of a prototypical implementation of SURF b) "README.txt" which contains the instructions to run the SURF tool c) the "lib" folder, which contains all the java libraries needed for running SURF.</p>
A list with recommended journals by Russian VAK
<p>Processed list of recommended journals in social science and computer science by VAK (source of raw data: http://vak.ed.gov.ru/87, date: 19.04.2017).</p>
Dataset for Application of Recommender Systems on Police Photo Lineup Assembling task
<p>For more information about the Recommender Systems for Police Photo Lineup project, please visit: http://www.ksi.mff.cuni.cz/~peska/lineup/paper.pdf</p> <p>The dataset consists of two parts:</p> <p><strong>The raw dataset </strong>contains visual and attribute-based desriptors of the list of candidate persons:</p> <p>- personsCB_IDs.csv: ordered list of persons IDs<br> - personsData.csv: raw attribute features of the persons<br> - personsCBVectors.csv: derived binary attribute-features with TF-IDF applied. The ordering of records is the same as in personsCB_IDs.csv<br> - personsVectors.csv: derived visual descriptors of persons' images. Probability layer of VGG-Face CNN was applied for this task. The ordering of records is the same as in personsCB_IDs.csv.</p> <p><strong>The implicit feedback dataset</strong> (feedbackDatasetCB-RSFeatures.csv) contains information received from the user-study on lineup assembling task performed by seven domain experts. This table has following structure:</p> <p>- evaluatorID;<br> - lineupID (id of the suspect);<br> - candidateID (person recommended to this particular suspect);<br> - calculated content-based similarity<br> - selection (1 if evaluator selected this candidate, 0 otherwise);<br> - substraction of CB features of the suspect and the candidate (this can be simply modified by accessing raw dataset to contain visual descriptors or any other combination of features)</p> <p> </p>
Updated list of QPS-recommended microorganisms for safety risk assessments carried out by EFSA
<p>The European Food Safety Authority (EFSA) asked the Panel on Biological Hazards (BIOHAZ) to deliver a scientific Opinion on the maintenance of the list of qualified presumption of safety (QPS) biological agents. The QPS approach was developed by the EFSA Scientific Committee to provide a harmonised generic pre-evaluation to support safety risk assessments of biological agents intentionally introduced into the food and feed chain, in support of the concerned scientific Panels and Units in the frame of market authorisations.</p> <p>The taxonomic identity, body of knowledge, the safety concerns in relation to pathogenicity and virulence, and the safety for the environment of those microbiological agents are assessed. Safety concerns identified for a respective taxonomic unit (TU) are, where possible and reasonable in number, reflected as ‘qualifications’ that are assessed at the strain level by the EFSA’s scientific Panels.</p> <p>The<strong> “list of microorganisms with QPS status”</strong> first established in 2007, has been revised and updated annually until 2014 via <strong>QPS</strong> <strong>Opinions</strong>; since 2014 the updates are carried out and published every 3 years. If new information is retrieved from extended literature searches (ELS) that would change the QPS status of a TU or its qualifications, this is also published in the Panel Statement covering the previous 6-months period. The <strong>ELS </strong><strong>protocol</strong> can be found at <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Fdoi%2F10.5281%2Fzenodo.3607188&data=05%7C02%7C%7Ca719c81b95134433fddc08dc112814e1%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C638404111040390688%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=FaY3tbLnn%2BB%2BDu3PrtEzJR0zPa14z%2FpoKWUBBOaHk34%3D&reserved=0">https://zenodo.org/doi/10.5281/zenodo.3607188</a> and the <strong>Search</strong><strong> strategies</strong> are available at: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.3607193&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527347066&sdata=tCzKMQLR2I%2BZBr%2F0sOXPDoPitdjQyqKdlVqDqwigA%2Bo%3D&reserved=0">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.3607192">10.5281/zenodo.3607192</a>.</p> <p> </p> <p>The <strong>QPS Panel Statement</strong> also includes the evaluation of microbiological agents notified to EFSA within the 6-month period for an assessment for feed additives, food enzymes, food additives and flavourings, and novel foods or plant protection products for a possible QPS status. The new QPS status recommendations are incorporated into the 2022 updated<strong> “list of microorganisms with QPS status</strong>” is available in this upload. The list of “<strong>Microbi</strong><strong>ological agents </strong><strong>as notified to EFSA</strong><strong>”</strong> from 2007, in the context of technical dossiers to EFSA Units, for intentional use in feed and/or food or as sources of food and feed additives, enzymes and plant protection products (PPPs) for safety assessment can be found at <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.5281%2Fzenodo.3607184&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527357024&sdata=%2Fe7qiRXiuKQi%2FBvK5S%2F7undYHv7ibxBlLEHPcSlRz24%3D&reserved=0">https://doi.org/10.5281/zenodo.</a><a href="https://doi.org/10.5281/zenodo.3607183">3607183</a>.</p> <p> </p> <p><strong>Useful links on EFSA QPS</strong></p> <p>EFSA topic on QPS: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.efsa.europa.eu%2Fen%2Ftopics%2Ftopic%2Fqualified-presumption-safety-qps&data=02%7C01%7C%7Ca3e4d3a851c84defc2a008d7aa44f9e5%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637165085527366985&sdata=TlcglouTWhBY24K5ApAoawNpADKgyl%2FefxVUEbxjQo0%3D&reserved=0">https://www.efsa.europa.eu/en/topics/topic/qualified-presumption-safety-qps</a></p> <p>Link to the virtual issue on QPS on Wiley Online Library: <a href="https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fefsa.onlinelibrary.wiley.com%2Fdoi%2Ftoc%2F10.1002%2F(ISSN)1831-4732.QPS&data=02%7C01%7C%7C7f88eba744034d83233b08d7a8cd3c1c%7C406a174be31548bdaa0acdaddc44250b%7C1%7C0%7C637163471731831002&sdata=AiprIUek%2B%2But46VabBGk0IMIgLR98CDmxoINYPkKBbs%3D&reserved=0">https://efsa.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)1831-4732.QPS</a></p> <p><strong>Versions history:</strong></p> <p>Versions 1 and 2 were substituted by version 3 – all are associated with the QPS Panel statement EFSA 7: suitability of taxonomic units notified to EFSA until September 2017: <a href="https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131">https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/j.efsa.2018.5131</a> </p> <p>Version 4 is associated with the QPS Panel statement EFSA 8: suitability of taxonomic units notified to EFSA until March 2018: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2018.5315</a> </p> <p>Versions 5 and 6 were substituted by version 7 – all are associated with the QPS Panel statement EFSA 9: suitability of taxonomic units notified to EFSA until September 2018: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5555</a> </p> <p>Version 8 is associated with the QPS Panel statement EFSA 10: suitability of taxonomic units notified to EFSA until March 2019: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2019.5753</a> </p> <p>Version 9 is associated with the QPS Panel statement EFSA 11: suitability of taxonomic units notified to EFSA until September 2019: <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5965</a> and with the Scientific Opinion on the update of the list of QPS-recommended biological agents intentionally added to food or feed as notified to EFSA (2017-2019): <a href="https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966">https://efsa.onlinelibrary.wiley.com/doi/10.2903/j.efsa.2020.5966</a> </p> <p>Version 10: updates version 9 following the update of the qualification of <em>Bacillus velezensis</em> as: ‘absence of toxigenic potential and absence of aminoglycoside production ability’ - applied in the QPS Statement part 11 (ON-5965) and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 11: updates version 10 following the addition of the <em>Bacillus circulans</em> ‘for production purposes only’ - applied in the QPS Statement part 13 (ON-6377) and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 12 updates version 11 following the addition of the <em>Bacillus paralicheniformis</em><strong> </strong>with the qualifications 'absence of toxigenic activity’ and ‘absence of genetic information to synthesize bacitracin';<strong> </strong>and adding <em>Schizochytrium limacinum</em>, which is a synomym for <em>Aurantiochytrium</em> <em>limacinum,</em> - applied in the QPS Statement part 14 (ON-6689)<strong> </strong>and in the 2019 Scientific Opinion (ON-5966).</p> <p>Version 13 updates version 12 following the addition of <em>Haematococcus lacustris</em> synonym <em>Haematococcus pluvialis</em>, recommended for QPS status with the qualification ‘for production purposes only’. Some other changes related to taxonomy and qualifications are described in the QPS Statement part 15 (ON-7045)<strong>.</strong></p> <p>Version 14, update of version 13, is related to QPS Statement part 16 (ON-7408)<strong>.</strong></p> <p>Version 15 is related to QPS Statement part 17 (ON-7746)<strong>.</strong></p> <p>Version 16 is related to QPS Statement part 18 (ON-8092)<strong>.</strong></p> <p>Version 17 is related to QPS Statement part 19 (ON-8517)<strong>.</strong></p> <p>Version 18 is also related to QPS Statement part 19 (ON-8517) with a small correction<strong>.</strong></p> <p>Version 19 and 20 are related to QPS Statement part 20 (ON-8882).</p> <p>Version 21 is related to QPS Statement part 21 (ON-9169)</p> <p>Version 22 is related to QPS Statement part 22 (ON-9510)</p> <p> </p> <p> </p> <p><strong>Note: </strong>As of January 2022 the updated list is provided only as excel file format.</p>
Analyzing the Human Recommendation Community 'ifyoulikeblank' on Reddit — Auxiliary materials
<p>This repository contains auxiliary materials for the iConference 2024 paper "“If I like BLANK, what else will I like?”: Analyzing<br>a Human Recommendation Community on Reddit" by Thi Binh Minh Cao and Toine Bogers (= corresponding author)</p> <p>Published in: <em>Proceedings of the 2024 iConference</em>, April 15--26, 2024, Changchun, China.</p> <p>The paper presents the results of an analysis of /r/ifyoulikeblank, a Reddit community dedicated to requesting and providing for recommendations. This repository contains the following auxiliary materials:</p> <ul> <li>The annotated sample of threads from the /r/ifyoulikeblank subreddit (<strong>annotated-dataset.xlsx</strong>). The second sheet in the Excel file explains the contents of the file.</li> <li>The R code for performing the analysis described in the paper (<strong>annotation-analysis.R</strong>)</li> <li>CSV file containing the genres attributed to the artists as crawled from the Spotify API (<strong>artist-spotify-genres.csv</strong>)</li> <li>CSV file containing the popularity scores crawled from the Spotify API for the seed items and Spotify recommendations (<strong>recommendation-popularity.reddit-vs-spotify.csv</strong>)</li> <li>CSV file containing the popularity scores crawled from the Spotify API for the Reddit (<strong>recommendation-popularity.reddit.csv</strong>)</li> <li>The stopwords file used in the textual analysis (<strong>stopwords.csv</strong>)</li> <li>Excel file containing the activity data for the /r/ifyoulikeblank subreddit (<strong>subreddit-stats.xlsx</strong>)</li> </ul>
Empowering Coffee Farming Using Counterfactual Recommendation based RNN-IoT Integrated Soil Fertility Control System
Open the record for dataset details and reuse information.
Case Base for Fertilizer Recommender System
<p>Dataset that contains recommendations for NPK fertilizer rates for different cases of climatic and soil conditions in coffee crops in the Cauca region in Colombia.</p> <p>The variables of this dataset are described below:</p> <p>Density: Represents the planting density of plants in a coffee crop, that is, the number of plants per hectare.</p> <p>Shadow coverage: Indicates the shade per hectare of the coffee crop, measured on a scale from 0 to 1.</p> <p>Season: Refers to the climatic season to which the year in which fertilization is to be carried out was classified. 'Seca' represents the dry season, 'Lluviosa' represents the rainy season and 'Normal' represents the normal season.</p> <p>Humidity: Represents the percentage of moisture in the soil, measured on scales from 0 to 1.</p> <p>N: It is the level of N in the soil, measured in mg/Kg.</p> <p>P: It is the level of P in the soil, measured in mg/Kg.</p> <p>K: It is the level of K in the soil, measured in mg/Kg.</p> <p>pH: It is the pH level in the soil.</p> <p>Cond_N: It is the rate of nitrogenous fertilizer to recommend.</p> <p>Cond_P: It is the rate of phosphorus fertilizer to recommend.</p> <p>Cond_K: It is the rate of potassium fertilizer to recommend.</p> <p>Note: Fertilizer rates are represented as follows: 1 is low rate, 2 is normal rate, 3 is high rate and 4 is very high rate. The fertilizer rate depends on the sowing density, this is indicated in the corresponding research article.</p> <p>Plus_A and Plus_B are additional recommendations, regarding irrigation and soil and crop care.</p>
FIG. 1 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 1. — Number of species published by semester from the end of the expedition, with first semester: July-December 2015, and last semester: January-July 2021. The expedition was accomplished in August 2015.
FIG. 4 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 4. —Density map of the shared data from the GBIF portal (https://www.gbif.org) for insects at the scale of the Guiana Shield. Only data with a high occurrence resolution (<1 km) are included.
FIG. 2 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 2. — Number of new (non-marine) animal species of French Guiana's fauna described since 2000. New species collected during the Mitaraka survey are indicated in colour.
FIG. 5 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 5. — Number of holotypes collected according to the different techniques used. Abbreviations: BS, beating sheet; HC, hand collecting; PVB, cross flight intercept trap with a blue LED; PVP, idem, with pink LED; PGL, idem, with GemLight; BPT, blue pan trap; SLAM, sea and land air malaise trap; SW & NS, sweeping & net sweeping; WPT, white pan trap; YPT, yellow pan trap (for a more detailed description of the techniques, see Touroult et al. 2018).
FIG. 6 in The "Our Planet Reviewed" Mitaraka 2015 expedition: a full account of its research outputs after six years and recommendations for future surveys
FIG. 6. — Level of compliance with recommendations for citation of expedition, ABS authorization and deposit of specimens according to two discriminating variables (n = 90 articles).
Question Bank for Open Syllabus UNESCO Recommendation on Open Science
<p>Question bank of multiple choice, multiple response, and essay format questions to accompany the Open Syllabus: UNESCO Recommendation on Open Science. These questions are intended to be used for low-stakes comprehension checks and discussion. The file format is compatible with the Respondus tool for importing questions into learning management systems.</p>
Recommender Systems for Science: A basic Taxonomy
<p>This dataset is accompanying the "<strong>Recommender system for science: A basic taxonomy</strong>" paper published at IRCDL 2022 conference. </p> <p>This study had a Systematic Mapping Approach on the Recommender system for science. In particular, the study aims at responding to four questions on recommender systems in science cases: users and their interests representation, item typologies and their representation, recommendation algorithms, and evaluation, and then providing a taxonomy. </p> <p>This dataset contains <strong>209 papers </strong>of interest that have been published between 2015 and 2022.</p> <p>The dataset has <strong>11</strong> columns which organised as follows: </p> <p>Column <strong>Title: </strong>This column contains the title of the papers.</p> <p>Column <strong>DOI: </strong>This column contains the DOI of the papers.</p> <p>Column <strong>Publication_year</strong>: This column contains the year that the paper is published.</p> <p>Column <strong>DB: </strong>This column contains the repository that the paper is retrieved.</p> <p>Column <strong>Keywords</strong>: This column contains the keywords provided for the paper.</p> <p>Column <strong>Content_type: </strong>This column contains the paper type which can be: <strong>Article,</strong> <strong>Conference</strong> or <strong>Review.</strong></p> <p>Column <strong>Citing_paper_count: </strong>This column contains the citation number of the paper.</p> <p>Column <strong>Recommended_artefact: </strong>This column contains the scientific product that is recommended to users which can be <strong>paper</strong>, <strong>workflow</strong>, <strong>collaborator</strong>, <strong>dataset</strong> or <strong>others</strong>.</p> <p>Column <strong>User_type: </strong>This column contains the type of user who receives the recommendation, which can be an<strong> Individual </strong>user or a<strong> Group</strong> of users.</p> <p>Column <strong>Algorithm</strong><strong>: </strong>This column contains the recommendation algorithm that the paper proposed, which can be: <strong>HB </strong>(Hybrid-based), <strong>CB</strong> (Content-based), <strong>CFB</strong> (Collaborative-filtering-based), or <strong>GB</strong> (Graph-based).</p> <p>Column <strong>Evaluation_method</strong><strong>: </strong>This column contains the method of the algorithm evaluation which can be <strong>OFFLINE</strong>, <strong>ONLINE, BOTH, </strong>or<strong> NO_EVALUATION.</strong></p>
Interview data on history-oriented theologians' recommendations for visualizations in libraries
<p>Results of an interview study with the target group "history-oriented theologians" on their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p> [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.</p>
Interview data on experts' recommendations for visualizations in libraries
<p>Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.</p> <p>The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p> [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.</p>
Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?
<p>Replication package for "To What Extent do Deep Learning-based Code Recommenders Generate Predictions by Cloning Code from the Training Set?"</p>
minimum dan recommendation spesification Watch Dogs Legion
<p>if you can read more, visit this link <a href="https://www.tentangberbagi.eu.org/2022/04/spesifikasi-watch-dogs-legion.html">minimum dan recommendation spesification Watch Dogs Legion</a> and visit my link too for games article <a href="https://www.tentangberbagi.eu.org/">TentangBerbagi</a> </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.