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2,373 results for “Platform”
Dataset for a systematic technology review of Function-as-a-Service platforms
<p>Description</p> <p>This dataset comprises the results of analysis of 3 commercial and 7 open source FaaS platforms. The included Excel sheet lists the results grouped into multiple classification categories and dimensions. The data for each platform reside in a separate tab, and for every collected fact there is a corresponding link to the documentation or software repository.</p>
Dataset for the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems"
<p>This dataset contains additional material related to the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems". The article is available at <a href="https://doi.org/10.1063/1.5144298">https://doi.org/10.1063/1.5144298</a> (open access).<br> <br> Note that the current version of the dataset is not complete. The complete dataset will be uploaded as soon as possible.</p>
Personal movie reviews from IMDB platform
<p>The dataset contains basic information about movies and reviews published on IMDB platform. The dataset contains the following information.</p> <ul> <li>Basic information about movies.</li> <li>Genres from the previous movies.</li> <li>A list of user reviews containing 1000 reviews per movie.</li> <li>Metadata about the extraction process.</li> </ul> <p>The dataset consists of a series of csv files. The semicolon is used as a separator.</p> <p>The exact data is restricted to three specific movies.</p> <ol> <li>The Godfather</li> <li>The Godfather: Part II.</li> <li>The Godfather. Part III.</li> </ol> <p>The data is owned by IDMB and it is published under the CC BY-NC-SA 4.0 license. You may not share or modify this date without giving credit to the original owners. The data cannot be used under any commercial purposes. This dataset can only be used with academic purposes.</p>
easyMF: A Web Platform for Matrix Factorization-based Biological Discovery from Large-scale Transcriptome Data
<p>With the development of high-throughput experimental technologies, large-scale RNA sequencing (RNA-Seq) data have been and continue to be produced, but have led to challenges in extracting relevant biological knowledge hidden in the produced high-dimensional gene expression matrices. Here, we present easyMF, a user-friendly web platform that aims to facilitate biological discovery from large-scale transcriptome data through matrix factorization (MF). The easyMF platform enables users with little bioinformatics experience to streamline transcriptome analysis from raw reads to gene expression and to decompose expression matrix from thousands of genes to a handful of metagenes. easyMF also offers a series of functional modules for metagene-based exploratory analysis with an emphasis on functional gene discovery. As a modular, containerized and open-source platform, easyMF can be customized to satisfy users’ specific demands and deployed as a web server for broad applications. easyMF is freely available at https://github.com/cma2015/easyMF. We demonstrated the application of easyMF with four case studies using 940 RNA sequencing datasets from maize (<em>Zea mays </em>L<em>.</em>).</p>
Data from the ENTSO-E Transparency Platform - SQLite files
<p>Data extracted from the ENTSO-E Transparency Platform (transparency.entsoe.eu/).</p> <p> </p> <p>## AggregatedGenerationPerType (UPDATED 5/1/2021)</p> <p>Data in SQLite format. Timestamps (fields Datetime and Submission) are stored as seconds since 1970-1-1.</p> <p>CREATE TABLE gen_data<br> (Datetime INTEGER,<br> Submission INTEGER,<br> Resolution TEXT,<br> Area TEXT,<br> Type TEXT,<br> Generation REAL,<br> Consumption REAL,<br> PRIMARY KEY (Datetime, Area, Type))<br> </p>
Data from: Conodont size, trophic level and the evolution of platform elements.
Conodonts are among the first vertebrates to have evolved mineralized tooth-like structures. Among these, the so-called P1 elements are known to have been used to break down food, and display a wide variety of morphologies. In particular, the repeated independent evolution of platform-like P1 elements have been suggested to correspond to similar functional constraints linked to diet. To test this hypothesis of convergence, we measured size (as element length) for various conodont taxa, and used it as a proxy for trophic level. We then tested the correlation between size and platform presence/absence, both on raw data and in a phylogenetic context. Retaining or excluding the platform traits from the character matrix has limited impact on the resulting phylogeny. Contrary to platform presence/absence, size shows no phylogenetic signal. Using the raw data, size and platform presence appear positively correlated. That correlation however is no longer significant if one corrects for the phylogeny. We conclude that platform presence cannot be explained by an enlargement of the conodont element, be it via a trophic level change or developmental constraints. This suggests that conodonts as a whole, and in particular platform-bearing conodonts, were an ecologically diverse group and that the various known platform types are likely to reflect different, rather than convergent, ecological niches.
Exploring dynamical phase transitions with a Cavity-QED platform
<p>Atom-light interactions in optical cavities provide a platform for investigating many-body quantum physics in controlled environments. In particular, they have been proposed for the realization of collective quantum spin models with tunable long-range interactions. Besides the investigation of the rich steady-state phases that can arise due to the interplay between atom-light interactions and dissipation from the cavity, one opportunity offered by these systems is the study of out-of-equilibrium dynamical phases of matter precluded from existence at equilibrium . Moreover, these phases can display intriguing universal behaviors akin to standard equilibrium phase transitions. Here, we report the observation of distinct dynamical phases of matter in a nearly unitary implementation of the collective XY spin model with transverse and longitudinal fields simulated via an ensemble of ∼10<sup>6</sup> <sup>88</sup>Sr atoms. The unique properties of our cavity-QED platform allow us to probe thedependence of the associated dynamical phase transitions on parameter space, system size and initial state. In the spirit of quantum simulation our observations can be linked to similar dynamical phases featured in a range of related scystems, including the Josephson effect in superfluid helium , coupled atomic and solid-state polariton condensates, with complementary types of control including the magnitude and sign of Hamiltonian parameters. Moreover, our system offers potential for the generation of metrologically useful entangled states in optical transitions, which can enable real metrological gains via quantum enhancement in state-of-the-art atomic clocks.</p>
Stone Guardian Lion and Carved Coffin Platform
Lion (lion at right mirrored to create lion at left): http://https://www.metmuseum.org/art/collection/search/49380 Front of a coffin platform: description from Met placard included in annotation (could not be found in the online catalogue). Both these artifacts are in the collection of the Metropolitan Museum of Art, and are currently on display in the Chinese Art Gallery (second floor). Source: Objaverse 1.0 / Sketchfab
WHO International Clinical Trials Registry Platform (ICTRP), April 2015
<p>The World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP) dataset, retrieved in April 2015.</p> <p>At the time of writing, the WHO ICTRP does not have any way for the general public to retrieve their complete dataset. They provided special access to us (ClinicalTrials.gov) to retrieve the data, and gave permission for us to make the data publicly available.</p> <p>We distinguish records (a trial as registered in a specific registry) and variants (different versions of a record within that registry). Only the EU Clinical Trials Register (EUCTR) has multiple variants for some records, one for each member state where the trial was registered.</p> <p>The dataset consists of 320,790 variants of 285,177 unique records from sixteen registries. Variants were retrieved one-at-a-time over the course of the first week of April, as requests took ~ 2 seconds each to resolve. A list of non-ClinicalTrials.gov records was provided by the WHO, and a list of ClinicalTrials.gov records was retrieved from ClinicalTrials.gov.</p>
The morphologically glossed Rigveda - The Zurich annotation corpus revised and extended. Hosted by VedaWeb - Online Research Platform for Old Indic Texts.
<p>This file contains morphological and lexicographic annotations for the Rigveda. It was created in the DFG-funded research project Vedaweb and used as source data for the linguistic research platform <a href="https://vedaweb.uni-koeln.de">vedaweb.uni-koeln.de</a>.</p> <p>Prof. Dr. Paul Widmer and Dr. Salvatore Scarlata from the "Institut für Vergleichende Sprachwissenschaft" (Universität Zürich) provided the VedaWeb project a Filemaker file that was later transformed in Cologne into an Excel file. This data contained a version of the Rigveda by Prof. Dr. A. Lubotsky ("Indo-European Linguistics", Leiden University) that had been morphosytactically annotated over the course of more than 10 years at the University of Zurich. It also contained for each token, if available, a reference to an entry in Grassmann's dictionary for the Rigveda.</p> <p> </p> <p><strong>Modifications made by Jakob Halfmann and Natalie Korobzow to the data in 2020:</strong></p> <p>Disambiguation of the relevant categories, if unspecified in Zurich data, according to the Grassmann dictionary (updates from 6th edition partially included up to page 274):</p> <ul> <li>case, gender and number for nouns, pronouns (columns G–I)</li> <li>number, person, mood, tense and voice for verbs (columns I–M) up to line 109216</li> <li>case, gender, number, tense and voice for participles (columns G–I, L–M) up to line 109216</li> <li>absolutives are marked as Abs. in columns N and V</li> <li>Inconsistencies between the original file from Zurich and the Grassmann dictionary as well as internal inconsistencies in Grassmann are noted in column AE, whenever they were noticed.</li> <li>Zurich data was overwritten by conflicting Grassmann data in columns G–M but retained elsewhere.</li> <li>Verb classes according to Whitney (1885) and Jamison (1983) for class 10 in column Y, differences in root spelling between Whitney and Grassmann are noted in column Z. All potential verb classes provided by Whitney are given for every occurrence of the root.</li> <li>Local particles and verbal forms containing them are marked as LP in column AF.</li> <li>Comparatives and superlatives are marked as such in column X and desideratives as Des. in column Y.</li> </ul> <p> </p> <p><strong>Modifications made by Anna Fischer (data transformation, technical realisation) to the data:</strong></p> <p>New structure of data table for linguistic annotations with new column titles:</p> <ul> <li>A - "VERS_NR": renamed column (from "belege::stelleMMSSSRR")</li> <li>B - "PADA_NR": renamed column (from "belege::pada")</li> <li>C - "PADA_TEXT_LUBOTSKY": renamed column (from "belege::lubotskypada")</li> <li>D - "TOKEN_NR_VERS": renamed column (from "belege::wortnummer rc")</li> <li>E - "TOKEN_NR_PADA": renamed column (from "belege::wortnummer pada")</li> <li>F - "FORM": renamed column (from "belege::form")</li> <li>G - "KASUS": renamed column (from "belege::kasus")</li> <li>H - "GENUS": renamed column (from "belege::genus")</li> <li>I - "NUMERUS": renamed column (from "belege::numerus")</li> <li>J - "PERSON": renamed column (from "belege::person")</li> <li>K - "TEMPUS": moved and renamed column (from L "belege::tempus")</li> <li>L - "PRAESENSKLASSE": created new column for present stem class for each form</li> <li>M - "LEMMA_PRAESENSKLASSEN": created column for present stem classes of respective lemma: Moved and renamed column (from Y "formen::zusätzliche merkmale verb"), moved values "Abs." and "Inf." to column P "INFINIT", moved values "Prek." and "si-Ipv." to column N "MOOD", moved value "Des." to column Q "ABGELEITETE_KONJUGATION": moved value "se-Form" to column W "WEITERE_WERTE"</li> <li>N - "MODUS": moved and renamed column (from K "belege::modus")</li> <li>O - "DIATHESE": moved and renamed column (from M "belege::diathese")</li> <li>P - "INFINIT": created new column for infinite forms "Abs.", "Inf.", "Ptz.", "ta-Ptz.", "na-Ptz."</li> <li>Q - "ABGELEITETE_KONJUGATION": created new column for secondary conjugation "Des.", "Int.", "Kaus."</li> <li>R - "GRADUS": created new column for degree: "Comp.", "Sup."</li> <li>S - "LOKALPARTIKEL": moved and renamed column (from AF "LP")</li> <li>T - "LEMMA_ZÜRICH": moved and renamed column (from AA "lemmata klassisch::lemma")</li> <li>U - "LEMMA_ZÜRICH_LEMMATYP": moved and renamed column (from AB "lemmata klassisch::lemmatyp")</li> <li>V - "LEMMA_ZÜRICH_BEDEUTUNG": moved and renamed column (from AC "lemmata klassisch::bedeutung")</li> <li>W - "WEITERE_WERTE": created new column for all miscellaneous values: e.g. "Hyperchar.", "n-haltig", "se-Form"</li> <li>X - "KOMMENTAR": created new column merging former columns Z "formen::HELPformbestimmung", AD "lemmata klassisch::HELPbedeutung" and AE "anmerkungen abweichungen"</li> </ul> <p>Columns that were removed due to redundant information:</p> <ul> <li>"formen::zusätzliche merkmale nomen": values "superlative" And "comparative" were renamed "sup." and "comp." and moved to new column R "GRADUS", all other values were moved to new column for miscellaneous W "WEITERE_WERTE"</li> <li>"belege::belegbestimmung summe simpel": values "Ptz.", "ta-Ptz." and "na-Ptz." were moved to new new column P "INFINIT"</li> <li>"belege::kasus bestof"</li> <li>"belege::genus bestof"</li> <li>"belege::numerus bestof"</li> <li>"belege::person bestof"</li> <li>"belege::modus bestof"</li> <li>"belege::tempus bestof"</li> <li>"belege::diathese bestof"</li> <li>"belege::belegbestimmung bestof summe sophistiziert"</li> </ul> <p> </p> <p><strong>Revisions and additions made by Antje Casaretto to the data in 2023:</strong></p> <ul> <li>F-T: - revision and correction (wherever necessary) of all annotations (books 1-7)</li> <li>G,H,I - disambiguation of case forms, reg. pronouns and nominal forms, if unspecified in Zurich data (books 1-7)</li> <li>L - disambiguation of present stem classes (book 7 and book 1 up to line 21050 vers 01.125.01)</li> <li>M - disambiguation of denominal verbs from primary verbs of the 10th class (books 1-10)</li> <li>N - disambiguation of precative and optative forms wherever possible (books 1-7)</li> <li>Q - new annotations for "Int." (intensives) and "Kaus." (causatives) (books 1-7)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions and additions made by Antje Casaretto to the data in 2024 with support in data modeling and automation by Anna Fischer:</strong></p> <ul> <li>F-T: revision and correction (wherever necessary) of all annotations (books 8-10)</li> <li>G,H,I: disambiguation of case and gender forms in nominal and pronominal forms, if unspecified in Zurich data (books 8-10)</li> <li>L, M: disambiguation of present stem classes (books 1-10)</li> <li>N: disambiguation of precative and optative forms wherever possible (books 8-10)</li> <li>P: new annotations for "Gdv." (gerundives)</li> <li>Q: new annotations for "Den." (denominatives) (books 1-10) and further annotations of “Kaus.” (causatives) and “Int.” (intensives) (books 8-10)</li> <li>T: revision of lemmatization (books 1-10)</li> <li>V: update of meanings according to revised lemmatization; minimal revision</li> <li>W: revised annotation of ending -se (“se-Form”) (books 1-10); no systematic revision</li> <li>X: no systematic revision</li> <li>A-U: general revision of formal inconsistencies and typing errors (book 1-10)</li> </ul> <p> </p> <p dir="ltr"><strong>Revisions made by Natalie Korobzov and Pascal Coenen to the data in 2024 with computational support by Anna Fischer:</strong></p> <ul> <li>Y - "LEMMA_GRASSMANN_ID": new column for references to Grassmann dictionary (books 1-10) and revision of Grassmann references</li> </ul>
Dataset of the Paper "Empirical Insight of Quantum Software Programming on Q&A Platform"
<p>This dataset comprises all questions from Stack Exchange, including Stack Overflow, Quantum Computing, Code Review, and Software Engineering. It is designed for conducting an empirical study on quantum software programming within Q&A platforms.</p> <p> </p> <p>• <strong>so.csv</strong>: Questions from Stack Overflow, including the following fields:</p> <p>Id, AcceptedAnswerId, CreationDate, Score, ViewCount, Body, Title, Tags, AnswerCount, CommentCount, FavoriteCount, ClosedDate</p> <p>• <strong>se.csv</strong>: Questions from Software Engineering, including the following fields:</p> <p>Id, AcceptedAnswerId, CreationDate, Score, ViewCount, Body, Title, Tags, AnswerCount, CommentCount, FavoriteCount, ClosedDate</p> <p>• <strong>qc.csv</strong>: Questions from Quantum Computing Stack Exchange, including the following fields:</p> <p>Id, AcceptedAnswerId, CreationDate, Score, ViewCount, Body, Title, Tags, AnswerCount, CommentCount, FavoriteCount, ClosedDate</p> <p>• <strong>cr.csv</strong>: Questions from Code Review Stack Exchange, including the following fields:</p> <p>Id, AcceptedAnswerId, CreationDate, Score, ViewCount, Body, Title, Tags, AnswerCount, CommentCount, FavoriteCount, ClosedDate</p> <p>• <strong>code.ipynb</strong>: Source code that provides data preprocessing, merging, and LDA modeling for topic distribution. It also includes visualizations for topic trends, topic distribution, and evaluations of popularity and difficulty, along with their corresponding visualizations. Also, estimation of quantum programming-related topics, and coding process.</p>
NAUTILOS - Temperature profiles 2012-2020 - Fishing Vessels profiles by AdriFOOS platform - CNR IRBIM
<p>Dataset of depth and temperature profiles obtained from 2012 to 2020 using commercial fishing vessels of the AdriFOOS fleet in the Adriatic Sea</p> <p><span lang="EN-GB">Full metadata: </span><a title="https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html" href="https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html" target="_blank" rel="noopener"><span lang="EN-GB">https://data-nautilos-h2020.eu/erddap/info/AdriFOOS_profiles_2012-2020/index.html</span></a></p> <p><span lang="EN-GB">This dataset is part of the NAUTILOS project outcomes. NAUTILOS is funded from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101000825.</span></p>
Dataset: A continuous open source data collection platform for architectural technical debt assessment
<p>The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".</p> <p> </p> <p>Abstract</p> <p>Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p> </p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.</p> <p>In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p> </p> <p>To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).</p> <p>Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.</p>
Dataset for paper 'McAN: a novel computational algorithm and platform for constructing and visualizing haplotype networks'
<p>The .zip file includes four datasets for testing the performance of McAN (doi: https://doi.org/10.1093/bib/bbad174).</p>
Most popular songs on 11/11/2023 from the SoundCloud platform: Top 50
<p>This dataset contains the top 50 songs of all genres from the SoundCloud website and stores them in a CSV. This project corresponds to a practice assignment for 'Tipologia i Cicle de Vida de les Dades' (UOC)</p>
Replication Data for "Inverse-designed low-index-contrast structures on silicon photonics platform for vector-matrix multiplication"
<p>COMSOL files and Python post-processing code.</p>
Platform-Specific APIs
<p>This dataset was utilized in the research paper titled "Availability and Usage of Platform-Specific APIs: A First Empirical Study" for publication in Mining Software Repositories (MSR). Herein, we present a concise abstract of the study.</p> <p>A platform-specific API is an API implemented for a particular platform (e.g., operating system), therefore, it may not work on other platforms than the target one. In this paper, we propose a first empirical study to assess the availability and usage of platform-specific APIs. We analyze the platform-specific APIs provided by the Python Standard Library and mine their usage in 100 popular systems. We find that 21% of the Python Standard Library APIs are platform-specific and that 15% of the modules contain at least one. The platforms with the most availability restrictions are WASI (43.69%), Emscripten (43.64%), Unix (6,76%), and Windows (2.12%). Moreover, we find that platform-specific APIs are largely used in Python. We detect over 19K API usages in all 100 projects, in both production (52.6%) and test code (47.4%). We conclude by discussing practical implications for practitioners and researchers.</p> <p> </p> <p>For more information: <a href="https://dl.acm.org/doi/10.1145/3643991.3644925">https://dl.acm.org/doi/10.1145/3643991.3644925</a></p>
Dataset of five citizen science platforms with reagards endemic bird species of the Atlantic Forest
<p>Data on the contribution of observers of five different citizen science platforms in Brazil (eBird, WikiAves, Biofaces, iNaturalist, and Táxeus) related to endemic bird species in the Atlantic Forest.</p>
ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries – Data and Models
<p>This repository contains data and models used in the following paper.</p> <p> </p> <p>Swanson, K., Walther, P., Leitz, J., Mukherjee, S., Wu, J. C., Shivnaraine, R. V., & Zou, J. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. In review.</p> <p> </p> <p>The data and models are meant to be used with the <a href="https://github.com/swansonk14/admet_ai">ADMET-AI</a> code, which runs the ADMET-AI web server at <a href="https://admet.ai.greenstonebio.com/">admet.ai.greenstonebio.com</a>.</p> <p> </p> <p>The data.zip file has the following structure.</p> <p>data</p> <p> drugbank: Contains files with drugs from the <a href="https://go.drugbank.com/">DrugBank</a> that have received regulatory approval. drugbank_approved.csv contains the full set of approved drugs along with ADMET-AI predictions, while the other files contain subsets of these molecules used for testing the speed of ADMET prediction tools.</p> <p> tdc_admet_all: Contains the data (.csv files) and RDKit features (.npz files) for all 41 single-task ADMET datasets from the <a href="https://tdcommons.ai/">Therapeutics Data Commons</a> (TDC).</p> <p> tdc_admet_multitask: Contains the data (.csv files) and RDKit features (.npz files) for the two multi-task datasets (one regression and one classification) constructed by combining the tdc_admet_all datasets.</p> <p> tdc_admet_all.csv: A CSV file containing all 41 ADMET datasets from tdc_admet_all. This can be used to easily look up all ADMET properties for a given molecule in the TDC.</p> <p> tdc_admet_group: Contains the data (.csv files) and RDKit features (.npz files) for the 22 TDC ADMET Benchmark Group datasets with five splits per dataset.</p> <p> tdc_admet_group_raw: Contains the raw data (.csv files) used to construct the five splits per dataset in tdc_admet_group.</p> <p> </p> <p>The models.zip file has the following structure. Note that the ADMET-AI website and Python package use the multi-task Chemprop-RDKit models below.</p> <p>models</p> <p> tdc_admet_all: Contains Chemprop and Chemprop-RDKit models trained on all 41 single-task TDC ADMET datasets.</p> <p> tdc_admet_all_multitask: Contains Chemprop and Chemprop-RDKit models trained on the two multi-task TDC ADMET datasets (one regression and one classification).</p> <p> tdc_admet_group: Contains Chemprop and Chemprop-RDKit models trained on the 22 TDC ADMET Benchmark Group datasets.</p>
Shine Dalgarno strength data for manuscript "Occlusion of the anti-Shine-Dalgarno on the 30S platform reduces the efficiency of prfB programmed frameshifting in Flavobacterium johnsoniae"
<p>This data set contains Shine Dalgarno strengths for every gene in 4362 bacterial genomes that were analyzed in the manuscript titled "Analysis of programmed frameshifting during translation of prfB in Flavobacterium johnsoniae". There is one comma separated file per genome. They are split between six zip archives. Each line in these files represents one gene. The columns are the genome ID, the name of the gene, the SD strength in kcal/mol as determined by free_scan, the mock SD strength in kcal/mol as determined by free_scan, the anti Shine Dalgarno sequence identified from the 16S ribosomal RNAs of the organism, the sequence of the SD region that was scanned, and the sequence of the mock SD region that was scanned. The mock SD region is further upstream of the gene and should not contain a true SD sequence and is provided as a control.</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.