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CETAF-DiSSCo/COVID19-TAF biodiversity-related knowledge hub working group: indexed biotic interactions and review summary
<p>This data publication originated as part of developing a biodiversity-related knowledge hub on COVID-19 via COVID19-TAF - Communities Taking Action (https://cetaf.org/covid19-taf-communities-taking-action), a community-rooted initiative raised jointly by the Consortium of European Taxonomic Facilitaties (CETAF, https://cetaf.org) and Distributed Systems of Scientific Collections (DiSSCo, https://www.dissco.eu/).</p> <p>This archive contains the biodiversity datasets of interest identified in period 14 April-6 October 2020 through COVID19-TAF activities and subsequently indexed by Global Biotic Interactions (GloBI, https://globalbioticinteractions.org). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, virus-host, parasite-host) by combining existing open datasets using open source software.</p> <p>These identified datasets (see references and reviews below) add to a growing collection of open species interaction datasets already indexed by GloBI. So, this data publication only includes a small subset of indexed datasets and include only datasets that were added as a direct consequence of COVID19-TAF activities of the biodiversity-related knowledge hub working group.</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.</p> <p>Funding:<br> The creation of this archive was made possible in part by reporting software developed as part of the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926 . Also, this material is based upon work supported by the National Science Foundation under Grant No. DGE-1545433 .</p> <p>References:<br> Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005.</p> <p>GloBI Data Review Report</p> <p>Datasets under review:<br> - Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org accessed via https://github.com/globalbioticinteractions/batbase/archive/9c65cfeee1a054f9db8cd8bf6892017fd1b3c840.zip on 2020-10-04T22:53:45.576Z<br> - Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org accessed via https://github.com/globalbioticinteractions/batplant/archive/a2e1b57052244d5251d17e96ea61f58bea88975e.zip on 2020-10-04T22:54:28.727Z<br> - Daniel Becker, Gregory F Albery, Anna R Sjodin, Timothee Poisot, Tad Dallas, Evan A. Eskew, Maxwell J. Farrell, Sarah Guth, Barbara A Han, Nancy B Simmons, Colin J Carlson. 2020. Predicting wildlife hosts of betacoronaviruses for SARS-CoV-2 sampling prioritization. bioRxiv 2020.05.22.111344; doi: https://doi.org/10.1101/2020.05.22.111344 accessed via https://github.com/globalbioticinteractions/becker2020/archive/47c6ad28e1c5058f3c13ca69a59fdf21229e8d7f.zip on 2020-10-04T22:54:46.723Z<br> - Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 accessed via https://github.com/globalbioticinteractions/dbatvir/archive/a906d76e362484d3ca1edbe9683f672838ab70b0.zip on 2020-10-04T22:56:13.913Z<br> - Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. accessed via https://github.com/globalbioticinteractions/drodvir/archive/0346c0e8d4d66c6400e9965bd6a6aeed24cd7586.zip on 2020-10-04T23:06:04.368Z<br> - Agosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . accessed via https://github.com/globalbioticinteractions/linnaeus1758/archive/a818060080fa04a88dac6df1ae5b897304ae8877.zip on 2020-10-05T00:46:04.852Z<br> - Mollentze, Nardus, & Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 accessed via https://github.com/globalbioticinteractions/mollentze2019/archive/ad12dc74d03c3d992618f16c37cafb7f7ffd9d01.zip on 2020-10-05T00:50:55.878Z<br> - Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . accessed via https://github.com/globalbioticinteractions/ncbi-virus/archive/531a8d743d7adcf1153a19087e5d3c5b76750e3e.zip on 2020-10-05T00:53:53.646Z<br> - Olival, K. J., Hosseini, P. R., Zambrana-Torrelio, C., Ross, N., Bogich, T. L., & Daszak, P. (2017). Host and viral traits predict zoonotic spillover from mammals. Nature, 546(7660), 646–650. doi:10.1038/nature22975 accessed via https://github.com/globalbioticinteractions/olival2017/archive/f61070a5339d0e6c6e76d7eb4e2102decb52317d.zip on 2020-10-05T00:56:43.356Z<br> - Pensoft Darwin Core Archives with associateTaxa columns accessed via https://github.com/globalbioticinteractions/pensoft-dwca/archive/ee8831a2a391203f4fa8c05a0ddd927202b234bf.zip on 2020-10-05T00:56:51.868Z<br> - Pensoft Darwin Core Archives available via Integrated Publication Toolkit accessed via https://github.com/globalbioticinteractions/pensoft-ipt/archive/4ad4b47978324681289e36f8c2b247b1bcc97b1a.zip on 2020-10-05T00:58:01.912Z<br> - De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 accessed via https://github.com/globalbioticinteractions/pensoft-table/archive/3488e0397ca4e083d5eca6949951e426a75713e3.zip on 2020-10-05T00:58:03.647Z<br> - Marcus Guidoti, Tatiana Ruschel, Donat Agosti. 2020. Corona virus related biotic associations manually extracted from literature. Plazi. accessed via https://github.com/globalbioticinteractions/plazi-covid19/archive/326578b0d9f974760dcd2e962d86636a6487a6c0.zip on 2020-10-05T00:58:08.025Z<br> - Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463 accessed via https://github.com/globalbioticinteractions/shaw2020/archive/bb9ab857b7fdbb4e931752d01b43d37b3ada77cf.zip on 2020-10-05T01:05:23.554Z<br> - OpenBiodiv. 2020. Annotated biotic interaction tables from Pensoft publications. accessed via https://github.com/pensoft/pensoft-interaction-tables/archive/bb7d1dc9f2eba220a61502e06e6114053fd30788.zip on 2020-10-05T03:03:23.372Z<br> - Quentin J. Groom. 2020. Bat interation data manually extracted from literature. accessed via https://github.com/qgroom/batinterations/archive/70108945f9014aa0ac1db920191867f7e151c793.zip on 2020-10-05T03:04:11.533Z</p> <p>Generated on:<br> 2020-10-06</p> <p>by:<br> GloBI's Elton 0.10.2<br> (see https://github.com/globalbioticinteractions/elton).</p> <p> </p> <p>Note that all files ending with .tsv are files formatted<br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv:<br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar:<br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p><br> datasets.zip:<br> source datasets collected by elton in process of executing the generate_report.sh script.</p> <p>generate_report.sh:<br> program used to generate the report</p> <p>generate_report.log:<br> log file generated as part of running the generate_report.sh script</p>
Introduction to Ancient Metagenomics Textbook (Edition 2025): Introduction to Git(Hub)
<p>Data and conda software environment file for the chapter 'Introduction to Git(Hub)' of the SPAAM Community's textbook: Introduction to Ancient Metagenomics (https://www.spaam-community.org/intro-to-ancient-metagenomics-book).</p>
UB2030 | The Future of Research Libraries as Knowledge Hubs | Interviews
<p>UB2030 is a podcast about the innovation of the university library through technological changes and shifts in research and education. In this podcast, David Oldenhof and Maurice Vanderfeesten discuss different subjects. They will be accompanied by guests who bring in an outside perspective.</p> <p>This release contains 11 episodes.</p> <p>Follow for more at <a href="https://ubvu.github.io/ub2030/">https://ubvu.github.io/ub2030/</a></p> <p>📖 <a href="https://doi.org/10.5281/zenodo.14615659"><strong>CLICK TO READ THE REPORT</strong></a></p> <p>🎧<strong>Listen on</strong> <a href="https://soundcloud.com/vu-library-live/sets/ub2030-the-future-of-research-libraries"><strong>SoundCloud</strong></a>, <a href="https://open.spotify.com/show/7dgTKn69lE3cnvs7CKv59v"><strong>Spotify</strong></a>, or your favorite <a href="https://antennapod.org/">(open)</a> podcast app.</p> <ul> <li>Authors: Maurice Vanderfeesten, David Oldenhof</li> <li>Client: Joeri Both</li> <li>Organization: <a href="https://vu.nl/nl/over-de-vu/diensten/universiteitsbibliotheek">University Library, Vrije Universiteit Amsterdam</a></li> <li>Date: 2023-05-19</li> </ul> <p><a href="https://doi.org/10.5281/zenodo.14615659" target="_blank" rel="noopener">DOI:10.5281/zenodo.14615659</a> (Rapport)</p> <p><a href="https://doi.org/10.5281/zenodo.10666049" target="_blank" rel="noopener">DOI:10.5281/zenodo.10666049</a> (Data)</p> <p><a href="https://ubvu.github.io/ub2030/">Project Page</a> | <a href="https://soundcloud.com/vu-library-live/sets/ub2030-the-future-of-research-libraries">Listen on SoundCloud</a> | <a href="https://feeds.soundcloud.com/users/soundcloud:users:527805591/sounds.rss">Podcast RSS</a> | <a href="https://forms.office.com/e/KX08BEenpu">Listener Feedback</a></p> <h2>Reason (Why Now)</h2> <p>The world is rapidly changing technologically, around AI, blockchain (NFTs), and linked data. As a library, you want to remain relevant for state-of-the-art research and education. We need to not only implement existing projects but also explore the horizon of opportunities and threats that await us. The client for this project is Joeri Both.</p> <h2>Project Goal (Why and How)</h2> <ul> <li>This project provides input for the next multi-year plan, creating a roadmap with a horizon up to 2030.</li> <li>With this project, we aim to increase the knowledge level of the UB by identifying key innovative/disruptive developments, to become a full-fledged partner for providers of (new) technological applications.</li> <li>From there, we translate innovative developments/trends into UB practice in broad terms, offering suggestions for workable/realistic pilot projects that contribute to the UB ambitions for researchers.</li> <li>The approach is to deliver an innovation sub-report each month, including a podcast episode, with the aim of making innovation an actively discussed topic within the UB.</li> <li>This gives the management team insight into the wide range of possibilities and developments, allowing them to make strategic choices for starting pilots and better embedding the innovation process in the organization.</li> </ul> <h2>Project Scope (What is and isn't included)</h2> <p>Through interviews, we gather information and ideas from each department and external experts. This information is linked to the ambition themes in the multi-year plan, focusing mainly on technological developments and their impact on our work processes and product/service offerings. The collected information is available in the form of an audio recording/podcast and interview report. For the Research Support department, these ideas are further developed into pilot proposals on three implementation levels: short, medium, and long term. The interviews are scheduled per department, divided into the themes of the ambitions in the multi-year plan.</p> <h2>Episodes</h2> <ul> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-01-introductie/"><strong>Episode 01 -- Introduction</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-02-rdm/"><strong>Episode 02 -- Future of Research Data Management and Research Software Management</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-03-research_intelligence/"><strong>Episode 03 -- Future of Research Intelligence</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-04-open_science/"><strong>Episode 04 -- Future of Open Science</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-05-research_support/"><strong>Episode 05 -- Future of Research Support</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-06-digital_services_and_infrastructures/"><strong>Episode 06 -- Future of Digital Services and Infrastructures</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-07-education_support/"><strong>Episode 07 -- Future of Education Support</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-08-special_collections/"><strong>Episode 08 -- Future of Special Collections</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-09-information_services/"><strong>Episode 09 -- Future of Information Services (not public)</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-10-library_desk_services/"><strong>Episode 10 -- Future of Library Desk Services</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-11-aquisition_and_metadata/"><strong>Episode 11 -- Future of Acquisition and Metadata (canceled)</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-12-society/"><strong>Episode 12 -- Future of the Library in Society</strong></a></li> <li><a href="https://github.com/ubvu/ub2030/blob/main/ub2030-bonus-01-desci/"><strong>Episode 13 -- BONUS DeSci: Future of Open Science Ecosystems</strong></a></li> </ul> <p><strong>Full Changelog</strong>: <a href="https://github.com/ubvu/ub2030/compare/v1.1...v1.6">https://github.com/ubvu/ub2030/compare/v1.1...v1.6</a></p>
PWAS Hub: exploring gene-based associations of complex diseases with sex dependency - backing data
<p>The contents of the PWAS database is presented on <a title="The PWAS hub" href="https://pwas.huji.ac.il/?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il</a>. The frontend and backend were build on top of a dynamical databse system. Please consult the direct API for PWAS if you wish to query the database directly: <a title="The PWAS API" href="https://pwas.huji.ac.il/API?ver=2" target="_blank" rel="noopener">pwas.huji.ac.il/API</a></p> <p>This is a PostgreSQL dump file that was created using <code>pg_dump</code>, the backup/restore procedure for PostgreSQL. To restore this into PostgreSQL do</p> <p>[a] create a database</p> <p><code>createdb DATABASE</code></p> <p>[b] on the terminal run</p> <p><code>pg_restore -vcC -h HOST -p PORT -d DATABASE < pwas_dump.20220628.psql</code></p> <p>The HOST and PORT are determined by your installation and DATABASE is given by you in step [a] abobe.</p> <p> </p> <p>To access the PWAS tables, look for table names that begin with <code>pwasAPI_</code></p> <p>A possible query to the database may look like this:</p> <p><code>SELECT * FROM "pwasAPI_genediseasestatpwas" WHERE uniprot_id = 'P09914' AND disease = 'C44';</code></p> <p>This query lists the data that associate uniprot id <strong>P09914</strong> (gene symbol IFIT1) and disease ICD-10 <strong>C44</strong> (Other malignant neoplasms of skin)</p>
The Elements of the ENVRI-Hub
<p>The illustration visualises the elements of the ENVRI-Hub, the open-access platform of the environmental sciences community in Europe.</p> <p>The ENVRI-Hub and its elements are accessible through <a href="https://envri-hub.envri.eu/">https://envri-hub.envri.eu/</a>.</p>
Additional Data: Poised PABP-RNA hubs implement signal-dependent mRNA decay in development
<p>This repository contains processed data resulting from iCLIP experiments that were analysed in the following paper:"<strong>Poised PABP-RNA hubs implement signal-dependent mRNA decay in development</strong>"<br>The paper is published at Nature Structural and Molecular BIology.</p> <h2><br>Archived data</h2> <p>Data archived in this repository include:</p> <ol> <li>Data derived from iCLIP experiments targeting LIN28A, PABPC1, and PABPC4, that were analysed in the manuscript (see iCLIP.zip). Raw data is available from ENA, with the accession code PRJEB60519. <ol> <li>Sample descriptions are given in iCLIP-SampleAnnotation.csv</li> <li>Crosslink files in BED6 format (individual replicates and merged replicates)</li> <li>Peak files generated with the Clippy peak caller in BED6 format</li> <li>K-mer enrichment around high-confidence crosslink sites in the 3'-UTRs, calculated by the PEKA software</li> </ol> </li> <li>Expression values (salmon quantfiles) for 3'-seq experiments, specified in "QuantseqExperimentsAnnotation.tsv", are available in "SalmonQuantfiles.zip". Raw data is available from ENA, with the accession code PRJEB60519.</li> <li>Source code of the nextflow pipeline, which was used on the iMaps webserver to analyse iCLIP data and produce the files archived here (see imaps-nf-0.30.zip).</li> <li>A list of naive genes, that were analysed in the manuscript (see NaiveGeneIds.csv).</li> </ol> <h2>Details on iCLIP data generation</h2> <p>iCLIP data for LIN28A-WT (in 2iL and FGF2 treated cells), LIN28A-S200A (in FGF2 treated cells) as well as for PABPC1 and PABPC4 (in LIN28A KO cells with and without LIN28A overexpression), were analysed on iMaps Goodwright server (<a href="https://imaps.goodwright.com/">https://imaps.goodwright.com/</a>). The LIN28A iCLIPs were analysed on 18th of July, 2022; the PABPC iCLIPs were analysed on 26th of December, 2022. The code and settings used in the pipeline (release v0.30) can be viewed at <a href="https://github.com/goodwright/imaps-nf">https://github.com/goodwright/imaps-nf </a>, and is also archived here - (imaps-nf-0.30.zip)<br> </p> <ul> <li>First, reads were demultiplexed using Ultraplex and barcodes were trimmed from the reads. The default Ultraplex settings were applied, as denoted below:</li> </ul> <blockquote> <p>adapter='AGATCGGAAGAGCGGTTCAG'<br>adapter2='AGATCGGAAGAGCGTCGTG'<br>barcodes='barcode.csv',<br>final_min_length=20<br>fiveprimemismatches=1<br>ignore_no_match=False<br>ignore_space_warning=False<br>inputfastq='MOD4878A1-merged.fastq.gz',<br>keep_barcode=False,<br>min_trim=3,<br>outputprefix='demux',<br>phredquality=30,<br>phredquality_5_prime=0,<br>sbatchcompression=False,<br>threads=10,<br>threeprimemismatches=0,<br>ultra=False</p> </blockquote> <p> </p> <ul> <li>TrimGalore was used to run FASTQC and quality trim the reads and remove reads with length less than 10 nt:</li> </ul> <blockquote> <p>trim_galore --fastqc --length 10 -q 20 --cores 8 --gzip file.fastq.gz</p> </blockquote> <p> </p> <ul> <li>Reads were then premapped to rRNA, tRNA sequences referred to as small RNA, smRNA, using mouse genome build (GRCm39 GENCODE M28 annotation) with Bowtie v1.3.0 (Langmead et al., 2009)</li> </ul> <blockquote> <p>bowtie --threads 12 --sam -x $INDEX -q --un file.unmapped.fastq -v 2 -m 100 --norc --best --strata file.fq.gz 2</p> </blockquote> <p> </p> <ul> <li>Reads that did not map with Bowtie were then aligned with STAR v2.7.9a (Dobin et al., 2013) to mouse genome build (GRCm39 GENCODE M28 annotation).</li> </ul> <blockquote> <p>STAR \<br>--genomeDir star \<br>--readFilesIn file.unmapped.fastq.gz \<br>--runThreadN 12 \<br>--outFileNamePrefix 1_R1. \<br>\<br>--sjdbGTFfile Homo_sapiens_filtered.gtf \<br>--outSAMattrRGline 'ID:1_R1' 'SM:1_R1' \<br> --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outSAMattributes All --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 1000000 --outFilterScoreMin 10 --alignEndsType Extend5pOfRead1 --twopassMode Basic</p> </blockquote> <p> </p> <ul> <li>PCR-duplicates were removed using UMI-tools (Smith, Heger and Sudbery, 2017)</li> </ul> <blockquote> <p>java -jar /UMICollapse/umicollapse.jar \<br> bam \<br> -i file.Aligned.sortedByCoord.out.bam \<br> -o file.dedup.bam \<br> --umi-sep rbc:</p> </blockquote> <p> </p> <ul> <li>The nucleotide preceding each sequencing read was assigned as the crosslink event.</li> </ul> <p> </p> <ul> <li>Peaks of crosslinking signal were identified with Clippy v1.4.1, using the default settings.</li> </ul> <p> </p> <ul> <li>Obtained peaks and crosslink sites were used to run PEKA v1.0.0 (Kuret et al., 2022), using the default settings.</li> </ul> <p> </p> <ul> <li>For Clippy and PEKA, the GENCODE primary assembly annotation M28 was filtered to retain only entries with transcript support level 1 or 2, in genes where such transcripts were available, and used to produce a segmentation file with the <em>get_segments</em> function from the iCount tool (Curk, 2019).</li> </ul> <p> </p> <ul> <li>All files generated during data processing are available from the iMaps Goodwright webserver for analysis of CLIP data (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively).</li> </ul> <h2>Source data</h2> <p>Raw sequencing reads, from which the data enclosed here were derived, are accessible at ENA (PRJEB60519).<br>The raw sequencing reads and all data produced by the analysis pipeline is also available at the iMaps webserver (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively); and on the updated Flow webserver (see <a href="https://app.flow.bio/projects/882635250203/">https://app.flow.bio/projects/882635250203/</a> and <a href="https://app.flow.bio/projects/340215254997/">https://app.flow.bio/projects/340215254997/ </a>for LIN28A and PABPC1/4 iCLIPs, respectively).</p> <h2>Downstream computational analysis of enclosed data</h2> <p>The code, used to analyse the data enclosed here and train the CNN to predict transcript stability in naive-to-primed transition based on 3'UTR nucleotide sequence, is available at GitHub (<a href="https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics">https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics</a>) and archived on Zenodo (<a href="../doi/10.5281/zenodo.10054297">https://zenodo.org/doi/10.5281/zenodo.10054297</a><strong>).</strong></p>
Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub
<p>In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.</p> <p>In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.</p>
Einführung in den Legume Hub
<p> </p> <p>Was genau ist der Legume Hub? Für wen ist er gedacht? Wie können Sie sich beteiligen? Die Antworten finden Sie in diesem kurzen Video, welches einen Einblick in den Legume Hub bietet. Besuchen Sie den Legume Hub: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqbDdod2VuNTBnZ1laWk9sd0FDOVZPM0RIM0lBQXxBQ3Jtc0trdlpCSklwYjFZTWhPQmhIQ2xoakFBRVVtZkFIWHdFa3E0alRjQ2ZuekxpXzBPQ0NBQW52bjJPRUd3aERqU3B0blZxTXVNQVJCc2FKTGo1dXV2cEtSenljQXRHUWhaWkJqRkoyTlczdzM4OWx3VWxObw&q=https%3A%2F%2Fwww.legumehub.eu%2F&v=qT8iWZl0irs">https://www.legumehub.eu</a></p>
Introduction to the Legume Hub
<p>What is the Legume Hub? Who does it serve? How can you get involved? Get the answers in this short video, introducing the Legume Hub. Visit the Legume Hub: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqblU0aGlnc0lheUhpbExxQ3djVWhwSXBkZzFMZ3xBQ3Jtc0trOGduVGNhTENnS1o0RjFIVmJhbWN2Z2xrQ1BBQ00zcGpQa3AxRHp6ZTlOZmdSa240eXRoMkEwLUVIRk5QdlJvdTZEekN5VERCU05NWkdfRlU5bzhzbGJSVGtVZUNrLUxkQzE4Sll2cl9JOGlMWXBTWQ&q=https%3A%2F%2Fwww.legumehub.eu%2F&v=L5EHlUucKyU">https://www.legumehub.eu</a></p>
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</p>
Dataset assoziated with the paper "Optimisation of mobility hub locations for a sustainable mobility system"
<p>This is supplementary data for the paper 'Optimisation of mobility hub locations for a sustainable mobility system'. The Excel file 'InputParameters' contains the parameters used as input for the bilevel optimization model. Note that it contains two sheets: one for the calibrated parameters in the utility function, and one for the mode-specific input parameters. The external cost data are based on the study by Bieler, C. & Sutter, D. (2019), whereas the cost parameters were derived from the websites of the local service providers.</p> <p>The result folder contains the result files of all the experiments discussed in the paper. Each subfolder corresponds to one test instance. The subfolders contain the information on the built mobility hubs (build_mobilityhubs.csv), the modal split information (wegcount.rating.csv for both absolute and proportional data), the number of transfers for each mode at each station (transfercount.csv), and also the full list of modes that each user group used in their travels (user_paths.csv). Note that the stations are given by ID, and the ID is taken from the GTFS data for Aachen.</p> <p>The additional experiments from Section 5.5 on the modal split for a higher number of bike- and car-sharing stations are contained in the "Further Maximization of Sharing Modes Test.zip." Each subfolder contains specific data for the test instances, while the Excel sheet modal_split_Percent.xlsx summarizes and visualizes the modal split data.</p> <p>Further result data can be provided upon request.</p> <p><a name="_CTVL00166b62df5ea8545b3990eea974b27cf8a"></a>Bieler, C., Sutter, D., 2019. Externe Kosten des Verkehrs in Deutschland: Straßen-, Schienen-, Luft- und Binnenschiffverkehr 2017.</p>
Herbarium specimen image of Gypsophila tuberculosa Hub.-Mor., part of the collection of National Museum of Natural History, Paris
Part of a training dataset of scanned herbarium specimens. The data paper and a summary landing page will be published on Zenodo as it gets published.<br><br>Content of this deposition:<br><br>- A JSON-LD datafile listing the label data associated with this herbarium specimen. The Darwin and Dublin Core data standards are used for most values.<br>- A JPEG image file of the scanned herbarium sheet.
Promiscuity cliffs (PCs), promiscuity cliff pathways (PCPs), and promiscuity hubs (PHs) formed by inhibitors of human kinases
<p>The PC, PCP, and PH data structures have been introduced for the analysis of compound promiscuity [1-3]. A comprehensive collection of PCs, PCPs, and PHs formed by kinase inhibitors covering more than 80% of the human kinome is made available. See readme.txt for more information regarding the provided files.</p> <p>References:</p> <ol> <li>Dimova, D.; Gilberg, E.; Bajorath, J. Identification and Analysis of Promiscuity Cliffs Formed by Bioactive Compounds and Experimental Implications. RSC Adv. 2017, 7, 58–66.</li> <li>Miljković, F.; Bajorath, J. Computational Analysis of Kinase Inhibitors Identifies Promiscuity Cliffs across the Human Kinome. ACS Omega 2018, 3, 17295–17308.</li> <li>Miljković, F; Vogt, M; Bajorath, J. Systematic Computational Identification of Promiscuity Cliff Pathways Formed by Inhibitors of the Human Kinome. J. Comput. Aided Mol. Des. 2019, in press, doi: doi.org/10.1007/s10822-019-00198-9</li> </ol>
Compartment and Hub Definitions Tune Metabolic Networks for Metabolomic Interpretations
<p>This archive contains data for a report by the same title.<br> Data relate to software projects MetaboNet and DyMetaboNet.<br> MetaboNet: https://github.com/tcameronwaller/metabonet<br> DyMetaboNet: https://github.com/tcameronwaller/dymetabonet</p> <p>File descriptions</p> <p>dymetabonet_2019-08-29.mp4 ... raw screen capture video of DyMetaboNet<br> dock_metabonet_2019-08-18.zip ... complete MetaboNet export<br> model_* ... curation of human metabolic model by MetaboNet<br> model_dymetabonet.zip ... format for DyMetaboNet<br> model_compartments* ... compartments<br> model_processes* ... processes<br> model_reactions* ... reactions<br> model_metabolites* ... metabolites<br> measurement_* ... curation of metabolomic measurements by MetaboNet<br> measurement_study_*_report.tsv ... summary of match measurements to metabolites<br> measurement_study_*.tsv ... metabolites' fold changes and probabilities between groups<br> measurement_study_*_metaboanalyst.txt ... format for MetaboAnalyst<br> measurement_study_*_metaboanalyst_pair.txt ... format for MetaboAnalyst with sample pairs<br> network_* ... multiple definitions of metabolic networks<br> network_compartments-true_hubs-true.zip ... compartmental network with hubs<br> network_compartments-true_hubs-false.zip ... compartmental network without hubs<br> network_compartments-false_hubs-true.zip ... noncompartmental network with hubs<br> network_compartments-false_hubs-false.zip ... noncompartmental network without hubs<br> network_compartments-*_hubs_*/network_cytoscape.json ... format for Cytoscape<br> network_compartments-*_hubs_*/network_networkx.pickle ... format for NetworkX<br> network_compartments-*_hubs_*/nodes_reactions.pickle ... network's nodes for reactions<br> network_compartments-*_hubs_*/nodes_metabolites.pickle ... network's nodes for metabolites<br> network_compartments-*_hubs_*/links.pickle ... network's links<br> network_compartments-*_hubs_*/analysis/nodes_reactions.tsv ... nodes' metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/nodes_metabolites.tsv ... nodes' metrics relative to metabolites<br> network_compartments-*_hubs_*/analysis/network_reactions.tsv ... network's metrics relative to reactions<br> network_compartments-*_hubs_*/analysis/network_metabolites.tsv ... network's metrics relative to metabolites<br> network_compartments-*_hubs_*/measurement/metabolites.tsv ... measurements on nodes for metabolites</p>
Land-Cover Classification of Mesoamerica's Cimate Hubs
<p>This project presents land use classification maps obtained through satellite image analyses for 10 study areas in Mesoamerica. A classification algorithm was adapted specifically for the tropics and developed using the latest satellite images available. We applied Object Based Image Analysis method & Random Forest algorithm on 10m Sentinel 1 (S1) & Sentinel 2 (S2) image collections of years 2022/2023. The study area encompassed 10 landscapes spanning 8 countries across Mesoamerica. These areas cover a total of 256 971 km² and were previously defined by Osa Conservation as potential climate adaptation hubs.</p>
Dataset for Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs
<p>The purpose of this dataset is to enable the replication of the research results presented in the article: Kłodawski Michał, Jachimowski Roland, & Chamier-Gliszczyński Norbert, 2024. „Analysis of the Overhead Crane Energy Consumption Using Different Container Loading Strategies in Urban Logistics Hubs”. Energies 17: 1–24. https://doi.org/10.3390/en17050985 - published online: 2024-02-20, which discusses the application of simulation in solving the problem of the overhead crane energy consumption using different container loading strategies in Urban Logistics Hubs.</p> <p>Dataset contains:</p> <ul> <li>Readme.txt: description of the dataset.</li> <li>Data_Crane.xlsx: Contains the input data used in the model for estimating crane energy consumption.</li> <li>Results_01.csv: Contains output data - Simulation results of energy consumption, and total average energy recovery for each scenario.</li> <li>Results_02.csv: Contains output data - Simulation results - mean values from the results of all scenario replications.</li> </ul> <p>The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).<br>Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.<br> E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).<br>- The Chinese part of the project is funded by National Natural Science Foundation of China.<br>- The Swedish part of the project is funded by Swedish Energy Agency.<br>- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).</p>
Peat-Hub-Ireland Database Version 1.0
<p>The <strong>Peat Hub Ireland Database</strong> of Research on Irish Peatlands is a freely accessible resource that makes research data on Irish peatlands available to researchers and other stakeholders. The database provides a searchable, updatable, and comprehensive collection of research findings, covering studies from January 2000 to October 2023. This database supported a review that used a comprehensive evidence synthesis approach, combining expert and stakeholder input with peer-reviewed studies and grey literature. The goal of the review was to collate evidence to encourage researchers, civic societies, and policymakers to adopt an unbiased, evidence-based approach toward the future sustainable management and improved planning of Irish peatlands.</p> <p>The PHI database aims to raise awareness amongst researchers and the wider stakeholder community engaging in peatlands research in Ireland and further afield. Alongside the ‘living’ glossary of peatland terminology, the database will also help researchers gain a greater understanding of work from other disciplines to overcome academic silos and facilitate incorporation of insights from a range of disciplines. It will also help researchers avoid duplication of effort, raise awareness of previous research and established methodologies, build upon existing data to identify research gaps and new projects, and encourage collaborative work within the peatland scientific community.</p> <p>The database contains a total of 900 records organised into the following thematic areas: Biodiversity, Soil, Climate Change, Water, Archaeology and Palaeo-environment, Technology and Mapping, Society and Culture, Management, Growing Media/Substrate, Policy and Law. Each record in the database is given a unique ID number and is searchable by Author, Publication year, Theme, Sub-Topic, Title, DOI/URL and the Source of Citation. See Metatdata for details. </p> <p>You can find the replicable Methodology and other outputs of the Peat Hub Ireland project on the website; www.ucd.ie/peat-hub-ireland and in the EPA final report : https://www.epa.ie/publications/</p> <p>Recommended citation: </p> <p>Renou-Wilson, F., D. Wilson, and K. Flood. 2024. Peat-Hub-Ireland Database Version 1.0. DOI:10.5281/zenodo.13911873</p>
Phylogenetic analyses of hub genes accompanying the study "Environmental gradients reveal stress hubs predating plant terrestrialization"
<p>135 ML phylogenies of hub genes identified in the study "Environmental gradients reveal stress hubs predating plant terrestrialization"</p>
Heatwaves characterization derived from observations and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the observations the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/insitu-gridded-observations-europe?tab=overview">e-OBS</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_eobs_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_eobs_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_eobs_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
Heatwaves characterization derived from reanalysis and climate projections to assess thermal behavior of 7 European city-hubs: Milano, Athens, Logroño, Cork, Gdynia, Lillestrøm and Amsterdam (1981-2100)
<p>This dataset includes the processing results used to create the interactive climate service <a href="https://thermal-assessment.urban.tecnalia.dev/">Thermal Assessment Tool</a>. It provides frequency and severity of heatwaves under past, current and future climate conditions which allows to estimate the thermal behavior of regions and cities in Europe during episodes of extreme heat.</p> <p>A heatwave is typically defined as a “prolonged” period of “extremely high” temperature for a particular region or location. In REACHOUT, “prolonged” is defined by a period of two or more days and “extremely high” is determined per region when daily maximal temperature exceeds its threshold (95th percentile) and the daily minimum temperature exceeds its threshold (90th percentile). The percentiles were obtained considering the values of maximum and minimum temperatures of the region during the summer season of the baseline period of 1981 to 2010.</p> <p>To provide homogeneous data for the whole EU, the input variables used to generate this dataset come from the public, independent and authoritative <a href="https://climate.copernicus.eu/">Copernicus Climate Change Service</a> (C3S). For the reanalysis the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview">ERA5-Land</a> dataset is used and for the future projections the <a href="https://cds.climate.copernicus.eu/cdsapp#!/dataset/projections-cordex-domains-single-levels?tab=overview">EURO-CORDEX</a> dataset. The intermediate (<strong>RCP4.5</strong>) and very high (<strong>RCP8.5</strong>) emissions scenarios were considered. All the data was downloaded from the <a href="https://cds.climate.copernicus.eu/">Copernicus Climate Data Store</a> (CDS).</p> <p>The database is organized in three datasets:</p> <p>Regional_era5land_thresholds_Reachout.csv: contains the thresholds that were used to detect the heatwaves for each region. They were calculated considering the values of maximum and minimum temperatures during the summer season of the baseline period (1981-2010). The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>tmax</strong>: daily maximum temperature threshold.</li> <li><strong>tmin</strong>: daily minimum temperature threshold.</li> </ul> <p>Historical_era5land_heatwaves_Reachout.csv: heatwaves of the historical period (1981-2021) for each region. The columns are:</p> <ul> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>start</strong>: first date of the heatwave.</li> <li><strong>tmax</strong>: maximum temperature reached during the heatwave.</li> <li><strong>intensity</strong>: the sum of the degrees of the maximum and minimum temperatures over their corresponding thresholds.</li> <li><strong>duration</strong>: duration of the heatwave.</li> </ul> <p>Future_and_baseline_era5land_heatwaves_Reachout.csv: ensemble future projections of heatwaves. The columns are:</p> <ul> <li><strong>hazard_level</strong>: it can be a warning, an alert or an alarm.</li> <li><strong>region</strong>: unique identifier of the corresponding EUROSTAT NUTS_ID or GISCO_ID.</li> <li><strong>experiment</strong>: emission scenario. It can be baseline, rcp-4-5 or rcp-8-5.</li> <li><strong>period</strong>: it can be 1981-2010 for the baseline or 2011-2040, 2021-2050, 2031-2060, 2041-2070, 2051-2080, 2061-2090 or 2071-2100 for the future.</li> <li><strong>decade_frequency</strong>: decade mean frequency. In the case of the future this is the ensemble of the models.</li> <li><strong>decade_frequency_best</strong>: only applicable to the future. It determines the best projection among the models.</li> <li><strong>decade_frequency_worst</strong>: only applicable to the future. It determines the worst projection among the models.</li> <li><strong>year_days</strong>: average annual days.</li> <li><strong>year_tmax_intensity</strong>: the average annual degrees of the maximum temperature over its corresponding threshold.</li> <li><strong>year_tmin_intensity</strong>: the average annual degrees of the minimum temperature over its corresponding threshold.</li> </ul>
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.