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95 results for “Digital collections”
Berlin State Library (2024). Metadata of the Digitized Collections of the Berlin State Library (SBB)
<p>The motivation for creating this dataset was to enable research on the basis of metadata which are available in a cultural heritage institution on a large scale. Libraries such as the Staatsbibliothek zu Berlin – Berlin State Library (SBB) typically provide three kinds of data: Images (scans of books, illustrations contained in the scanned material, or else), texts (OCR'd from digitized books or manuscripts), and metadata. However, metadata form an underresearched resource, which is lamentable: These metadata are of a high quality since they have been established by trained librarians, archivists, or other cultural heritage practitioners. The publication of a set of metadata of more than 200.000 works aims therefore at providing an underresearched high-quality type of data. The basic interest of the funder in this data publication is the stimulation of innovation.</p> <p>The dataset consists of a single table containing the metadata of all 219.419 works which were available in the Digitized Collections of the Berlin State Library (SBB) on July 29th, 2024. The size of the .parquet file is about 46 MB.</p>
Graphic Illustration of our Digital Collections Data and Tracking Disease Workshop Session: Discussion and Synthesis
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Discussion section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Case Studies
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Case Studies section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Graphic Illustration of Talks in our Digital Collections Data and Tracking Disease Workshop Section: Museum Perspectives
<p>Karina Branson of <a href="https://www.conversketch.com/" target="_blank" rel="noopener">ConverSketch</a>, graphically recorded and helped to facilitate this Museum Perspectives section of our NSF-supported Workshop: Digital Collections Data and Tracking Disease.</p>
Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology
<p>What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators.</p> <p>Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories.</p> <p>Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography.</p> <p><strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong></p>
Extracted Illustrations of the Berlin State Library's Digitized Collections (part 1 of 4)
<p>The dataset consists of various illustrations extracted from 26,233 historical books and other media offered in the Berlin State Library's Digitized Collections. The media objects are older than 1920.</p> <p>Version 1.0 contains of 594,890 extracted illustrations in total.</p> <p>The extraction of illustrations is driven by the coordinates given by the ABBYY FineReader OCR engine (in ALTO XML) . The extracted illustrations have not been resized but compressed and saved in JPEG format.</p> <p>Pre-trained models in order to separate color scales, hand-written signatures, library stamps or the like from interesting content are available under: <a href="https://github.com/elektrobohemian/imi-unicorns">https://github.com/elektrobohemian/imi-unicorns</a>.</p> <p>The extracts for each media object are stored in separated sub-folders and tar files named after the PPN (a unique ID used in the library) to facilitate further processing. Additional metadata can be obtained with help of the PPN as described here: <a href="https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md">https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md</a> .</p> <p>The dataset is published as a set of ZIP files, each fitting on a Blu Ray disc. <strong>After decompression, the contents will consume ca. 166 GB.</strong></p> <p><em>Change Log for Version</em></p> <ol> <li>original dataset</li> <li>added color histograms (RGB, separated by channel) in JSON and Python pickle format as extracted by the <a href="https://pillow.readthedocs.io/en/stable/">Pillow</a> package (see https://github.com/elektrobohemian/StabiHacks/tree/master/image-tools)</li> </ol> <p><strong>This is part 1 of 4. The following datasets contain the other ZIP files (8 files in total):</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.2598145">https://doi.org/10.5281/zenodo.2598145</a></li> <li><a href="https://doi.org/10.5281/zenodo.2598261">https://doi.org/10.5281/zenodo.2598261</a></li> <li><a href="https://doi.org/10.5281/zenodo.2598270">https://doi.org/10.5281/zenodo.2598270</a></li> </ul> <p> </p>
OCR fulltexts of the Digital Collections of the Berlin State Library (DC-SBB)
<p>The digital collections of the SBB contain 153,942 digitized works from the time period of 1470 to 1945.</p> <p>At the time of publication, 28,909 works have been OCR-processed resulting in 4,988,099 full-text pages.<br> For each page with OCR text, the language has been determined by <em>langid </em>(Lui/Baldwin 2012).</p> <p>corpus-entropy.pkl entropy rate per document page</p> <p>corpus-language.pkl language per document page</p> <p>corpus.zip fulltext corpus (extracts to .txt format)</p> <p>de_corpus.zip German sub-corpus (extracts to .txt format)</p> <p>selection_de.pkl Selection list of German documents</p> <p>xml2csv_alto.csv fulltext corpus per document page (incl.OCR word confidences)</p> <p> </p> <p><em>Sources</em></p> <p>Marco Lui and Timothy Baldwin. 2012. Langid.py:</p> <p>An off-the-shelf language identification tool. In Proceedings of the ACL 2012 System Demonstrations,</p> <p>ACL ’12, pages 25–30, Stroudsburg, PA, USA. Association for Computational Linguistics</p>
Graphic materials from the Prus Plus Digital Collection
<p>A digital collection of visual materials (drawings) from 19th-century periodicals held in the resources of the <a href="http://ibl.waw.pl/pl/o-instytucie/biblioteka">IBL PAN Library</a>, prepared for the Prus Plus digital monography (in <a href="https://nplp.pl/kolekcja/prus-plus/">Polish </a>and <a href="http://nplp.pl/en/kolekcja/prus-plus/">English</a>) by the New Panorama of the Polish Literature team.</p> <p>Note: Authors in this case means: people processing, remixing, selecting and analyzing the collection of visual materials (New Panorama of Polish Literature) and the institution curating these resources (IBL PAN Library).</p> <p>Kolekcja materiałów wizualnych (rysunków) z XIX-wiecznych czasopism, znajdujących się w zbiorach <a href="https://ibl.waw.pl/pl/o-instytucie/biblioteka">Biblioteki IBL PAN</a>, przygotowana na potrzeby cyfrowej monografii Prus Plus (w <a href="https://nplp.pl/kolekcja/prus-plus/">języku polskim</a> i <a href="http://nplp.pl/en/kolekcja/prus-plus/">angielskim</a>) przez zespół Nowej Panoramy Literatury Polskiej.</p> <p>Uwaga! Autorzy oznacza w tym przypadku: osoby przetwarzające, remiksujące, selekcjonujące i analizujące kolekcję materiałów wizualnych (Nowa Panorama Literatury Polskiej) oraz instytucję przechowującą zasoby (Biblioteka IBL PAN). </p> <p> </p> <p> </p>
The Collection Management System Collection - Crowd-sourcing a list of digital repository options
<p><strong>The Collection Management System Collection - Crowd-sourcing a list of digital repository options</strong></p> <p>This dataset contains a list of digital repository options for collection management systems. It has been started and complited by Ashley Blewer.<br> The data set contains:</p> <ul> <li>a PDF capture of the blog describing motivation and background, columns of the spreadsheet and further resources; originally published at https://bits.ashleyblewer.com/blog/2017/08/09/collection-management-system-collection/</li> <li>The dataset / spreadsheet of The Collection Management System Collection, originally published at https://docs.google.com/spreadsheets/d/1cXOug3qM0pNNeD_wssiVEv9c0W1Y5I1VDTnSPTk7fb4/<br> The data was exported from the google spreadsheet on November 14th 2020 into the following formats: <ul> <li>PDF</li> <li>XLSX</li> <li>CSV</li> <li>TSV</li> </ul> </li> </ul> <p>The list contains basic information, administration considerations, interface considerations, technical considerations and social considerations for 70 different repository systems.</p>
A survey of access to the digital collections of 195 UK GLAMs across internal and external platforms - Appendix 1 for A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK
<p>Created for the<a href="https://doi.org/10.5281/zenodo.6242611"> 'A Culture of Copyright: A scoping study on open access to digital cultural heritage collections in the UK' </a>report, this sample replicates and expands the Open galleries, libraries, archives and museums (GLAMs) Survey data extraction and methodology to include a range of GLAMs across the UK and new data points. The initial sample of 350 organisations included Independent Research Organisations (IROs) and Research Centre Institutes (RCIs), GLAMs associated with <a href="https://www.nationalcollection.org.uk/">Towards a National Collection</a> Foundation and Discovery projects, UK GLAMs in the Open GLAM Survey, and other UK GLAMs and related organisations. An initial review was performed to identify and remove organisations outside the scope of inquiry (<em>e.g.</em>, no permanent collections). The final sample included 195 organisations.</p> <p>From the final sample, 24 are IROs (all RCIs were removed). Another 32 are Universities (including GLAMs within universities). This brings the total number of organisations eligible for AHRC funding to 56 (or 28.6%). The remaining 140 include public and private GLAMs at national, regional and local levels (<em>e.g., </em>councils, historic buildings) and research initiatives or data aggregators (<em>e.g.,</em> Portable Antiquities Scheme, Culture Grid, Archaeology Data Service). Organisations are distributed across the UK as follows: Channel Islands (1 total); England (154 total); Isle of Man (1 total); Northern Ireland (5 total); Scotland (28 total); Wales (6 total).</p> <p>A survey of the copyright and open access policies of 63 GLAMs from the UK GLAM Sample is available on Zenodo at: <a href="https://doi.org/10.5281/zenodo.6242559">https://doi.org/10.5281/zenodo.6242559</a></p>
LadiesDebating-KG: A Knowlege Graph for representing the "Edinburgh Ladies' Debating Society Digital Collection" (1865 - 1880)
<p>This Knowlege Graph represents the information of the "Edinburgh Ladies’ Debating Society<strong>"</strong> (years: 1865 - 1880) collection in RDF (ttl format). This collection consists of the complete runs of two Edinburgh journals, <strong>‘The Attempt’ (10 volumes, 1865-74)</strong> and its successor ‘<strong>The Ladies’ Edinburgh Magazine’ (6 volumes, 1875-80)</strong>. These publications were produced by a leading Edinburgh women’s club, known during the period as the Edinburgh Essay Society or the Ladies’ Edinburgh Essay Society, but subsequently as the Ladies’ Edinburgh Debating Society. The Society existed from 1865 to 1935. The raw dataset is provided by the NLS in this <a href="https://data.nls.uk/data/digitised-collections/edinburgh-ladies-debating-society/">link</a>. As other NLS data collections, they are originally provided using two XMLs schemas: METS for descriptive, structural, technical and administrative metadata (Title, Author, Publisher, etc); and ALTO for encoding the OCR text of a page.</p> <p>In this work, we have extracted the information from METS and ALTO XMLS using <a href="https://github.com/francesNLP/defoe">defoe</a> tool and developed a <a href="https://github.com/francesNLP/defoe/blob/master/defoe/nls/queries/write_metadata_pages_yml.py">new information extraction defoe query</a> , and created a new Knowlege Graph called LadiesDebating-KG. The LadiesDebating-KG uses the <a href="https://francesnlp.github.io/NLS-ontology/doc/index-en.html">NLS Ontology </a>to represent the information extracted. Furthermore, during the information extraction phase, we have employed several techniques to mitigate two common OCR errors: long-S and the line-break hyphenation.</p> <p>The LadiesDebating-KG contains 38,279 RDF triples. It has information from 2 series and 16 volumes: <strong>'The attempt' </strong>serie has 10 volumes and <strong>'The Ladies' </strong>serie<strong> </strong>has 6 volumes . Each serie has an Editor, mmsid, Shelf-Locator, publication year, etc. A Volume has several Pages, with text in them. The data model of the LadiesDebating-KG can be found <a href="https://francesnlp.github.io/NLS-ontology/doc/dataModel.png">here</a>.</p> <pre> </pre>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (Decision-Making Model)
<p>When considering the complex challenges faced by cultural heritage organisations in collecting, preserving and sharing born digital and hybrid objects, it becomes clear that the process of defining solutions as a community of practice is in its early probing phase: characterised as tentative, exploratory, questioning, experimental. The workshops within this Preserving and sharing born-digital and hybrid objects from and across the National Collection project, which examined the case studies from multiple angles, yielded a richly discursive examination of the main considerations.</p> <p>This Decision Model represents an attempt to create a structured representation of those main considerations and the discourse from the workshops, to codify the main decision-making processes that an organisation may go through when assessing an acquisition of such an object, categorised into high level areas. It attempts to create a traversable system that could be used by collections professionals in their work - policy makers, managers, collections management or digital preservation practitioners, conservators.</p>
Preserving and sharing born-digital and hybrid objects from and across the National Collection (January 2022)
<p>This report is one of a set of outputs from the Arts and Humanities Research Council funded project ‘Preserving and sharing born-digital and hybrid objects from and across the National Collection’. It has been designed to provide an extensive account of the project research activities and findings, to be useful to museum, heritage, and preservation professionals, as well as to scholars interested in born-digital materials.</p>
Extracted Illustrations of the Berlin State Library's Digitized Collections (part 4 of 4)
<p><strong>This is part 4 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre> <p>The dataset consists of various illustrations extracted from 26,233 historical books and other media offered in the Berlin State Library's Digitized Collections. The media objects are older than 1920.</p> <p>Version 1.0 contains of 594,890 extracted illustrations in total.</p> <p>The extraction of illustrations is driven by the coordinates given by the ABBYY FineReader OCR engine (in ALTO XML) . The extracted illustrations have not been resized but compressed and saved in JPEG format.</p> <p>Pre-trained models in order to separate color scales, hand-written signatures, library stamps or the like from interesting content are available under: <a href="https://github.com/elektrobohemian/imi-unicorns">https://github.com/elektrobohemian/imi-unicorns</a>.</p> <p>The extracts for each media object are stored in separated sub-folders and tar files named after the PPN (a unique ID used in the library) to facilitate further processing. Additional metadata can be obtained with help of the PPN as described here: <a href="https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md">https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md</a> .</p> <p>The dataset is published as a set of ZIP files, each fitting on a Blu Ray disc. <strong>After decompression, the contents will consume ca. 166 GB.</strong></p> <p><strong>This is part 4 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre>
Extracted Illustrations of the Berlin State Library's Digitized Collections (part 3 of 4)
<p><strong>This is part 3 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre> <p>The dataset consists of various illustrations extracted from 26,233 historical books and other media offered in the Berlin State Library's Digitized Collections. The media objects are older than 1920.</p> <p>Version 1.0 contains of 594,890 extracted illustrations in total.</p> <p>The extraction of illustrations is driven by the coordinates given by the ABBYY FineReader OCR engine (in ALTO XML) . The extracted illustrations have not been resized but compressed and saved in JPEG format.</p> <p>Pre-trained models in order to separate color scales, hand-written signatures, library stamps or the like from interesting content are available under: <a href="https://github.com/elektrobohemian/imi-unicorns">https://github.com/elektrobohemian/imi-unicorns</a>.</p> <p>The extracts for each media object are stored in separated sub-folders and tar files named after the PPN (a unique ID used in the library) to facilitate further processing. Additional metadata can be obtained with help of the PPN as described here: <a href="https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md">https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md</a> .</p> <p>The dataset is published as a set of ZIP files, each fitting on a Blu Ray disc. <strong>After decompression, the contents will consume ca. 166 GB.</strong></p> <p><strong>This is part 3 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre>
Extracted Illustrations of the Berlin State Library's Digitized Collections (part 2 of 4)
<p><strong>This is part 2 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre> <p>The dataset consists of various illustrations extracted from 26,233 historical books and other media offered in the Berlin State Library's Digitized Collections. The media objects are older than 1920.</p> <p>Version 1.0 contains of 594,890 extracted illustrations in total.</p> <p>The extraction of illustrations is driven by the coordinates given by the ABBYY FineReader OCR engine (in ALTO XML) . The extracted illustrations have not been resized but compressed and saved in JPEG format.</p> <p>Pre-trained models in order to separate color scales, hand-written signatures, library stamps or the like from interesting content are available under: <a href="https://github.com/elektrobohemian/imi-unicorns">https://github.com/elektrobohemian/imi-unicorns</a>.</p> <p>The extracts for each media object are stored in separated sub-folders and tar files named after the PPN (a unique ID used in the library) to facilitate further processing. Additional metadata can be obtained with help of the PPN as described here: <a href="https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md">https://github.com/elektrobohemian/StabiHacks/blob/master/ppn-howto.md</a> .</p> <p>The dataset is published as a set of ZIP files, each fitting on a Blu Ray disc. <strong>After decompression, the contents will consume ca. 166 GB.</strong></p> <p><strong>This is part 2 of 4. The following dataset contains a reference the other ZIP files: </strong></p> <pre>https://doi.org/10.5281/zenodo.2598101</pre>
Tweet IDs for the #brexit tweet dataset collected for the Helsinki Digital Humanities Hackathon 2019
<p>This dataset contains lists of tweet ids for the tweets used as material by the "Brexit in Transna­tional So­cial Me­dia" group in the <a href="http://heldig.fi/dhh19/">Helsinki Digital Humanities Hackathon 2019</a>.</p> <p>Due to restrictions in Twitter's terms of service, the full tweet dataset cannot be made public. However, Twitter allows the publication of tweet ids, from which the dataset can be reconstituted, <em>with the exception of deleted tweets</em>.</p> <p>The dataset was gathered as follows: Between 2019-01-22T09:19Z and 2019-04-15T14:42Z, a <a href="https://github.com/DocNow/twarc">Twarc</a> version 1.6.1 script was called hourly to retrieve and archive tweets from Twitter matching the #brexit hashtag using the Twitter search API. All 5,547,585 tweet IDs returned by this run are listed in the file <code>original_ids.txt.gz</code>.</p> <p>However, upon further inspection, problems were identified in the archiving. For an unidentified reason, gathering did not occur between 2019-02-13T06:17Z and 2019-02-26T09:24Z. In addition, the Twarc script had been run without the <code>--extended</code> argument, so the tweet data contained only truncated contents for many tweets.</p> <p>Due to this, a decision was taken to rehydrate a new dataset of tweets falling between 2019-02-26T09:24Z and 2019-04-15T14:42Z. Of the original 4,197,059 tweets gathered for this time period (listed in <code>continuous_ids.txt</code>), 3,941,653 could be rehydrated (i.e., they had not been deleted in the interim). The ids of tweets in this dataset are listed in the file <code>continuous_rehydrated_ids.txt</code>.</p> <p>Finally, out of these rehydrated tweets, a subset was derived that filtered out all tweets that were pure retweets. This subset consisted of 1,104,514 tweets, whose ids are listed in the file <code>continuous_rehydrated_no_retweets_ids.txt</code>.</p>
Linked collectors and determiners for: Naturalis Biodiversity Center (NL) - Museum collection digitized at storage unit level.
Natural history specimen data linked to collectors and determiners held within, "Naturalis Biodiversity Center (NL) - Museum collection digitized at storage unit level". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/62d82928-dc6f-40dc-85b3-f2be47e7b49a">https://bionomia.net/dataset/62d82928-dc6f-40dc-85b3-f2be47e7b49a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/62d82928-dc6f-40dc-85b3-f2be47e7b49a">https://gbif.org/dataset/62d82928-dc6f-40dc-85b3-f2be47e7b49a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Historic Butterfly Specimens in the UVM Zadock Thompson Natural History Collection Digitized for the Vermont Butterfly Atlas.
Natural history specimen data linked to collectors and determiners held within, "Historic Butterfly Specimens in the UVM Zadock Thompson Natural History Collection Digitized for the Vermont Butterfly Atlas". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a5d361b0-410b-44fd-9666-0a938c75abd1">https://bionomia.net/dataset/a5d361b0-410b-44fd-9666-0a938c75abd1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a5d361b0-410b-44fd-9666-0a938c75abd1">https://gbif.org/dataset/a5d361b0-410b-44fd-9666-0a938c75abd1</a>. Formatted as a Frictionless Data package.
Digital Collections Data and Tracking Disease Workshop 2: Introduction and Museum Perspectives Talks
<p>Recordings of talks by speakers in the Museum Perspectives section of our Digital Collections Data and Tracking Disease Workshop 2 at the American Society of Mammalogists Meeting in Boulder, Colorado in June of 2024.</p> <ul> <li><strong>Topic -- A Museum Perspective</strong> <ul> <li><strong>Museums and Emerging Pathogens in the Americas</strong>, Joe Cook, University of New Mexico<strong><br></strong></li> <li><strong>Museums and pathogens: Challenges, potential, and management</strong>, <a href="https://orcid.org/0000-0002-6931-6420" target="_blank" rel="noopener">Adam Ferguson</a>, Field Museum of Natural History</li> <li><strong>The UX Side of Extended Specimen Databases</strong>, <a href="https://orcid.org/0000-0002-8775-7254" target="_blank" rel="noopener">Kelly Speer</a></li> <li><strong>Preventing the next viral pandemic via elucidation of mammalian reservoirs</strong>, Rick White, University of North Carolina-Charlotte</li> <li><strong>Scientific collections and public health surveillance: institutional weaknesses and perspectives from Uruguay,</strong> <a href="https://orcid.org/0000-0002-4055-9277" target="_blank" rel="noopener">Germán Botto Nuñez</a>, Universidad de la República <ul> <li>(note talk sound did not work for this presentation)</li> </ul> </li> <li><strong>Building integrated archives for parasite and pathogen discovery</strong><br><a href="https://orcid.org/0000-0002-8065-0833" target="_blank" rel="noopener">Kurt Galbreath</a>, Northern Michigan University</li> <li><strong>The Dark Side of the Light: Fireflies, Snails, and Viromes</strong>, <a href="https://orcid.org/0000-0001-5067-3316" target="_blank" rel="noopener">Oliver Keller</a>, University of Michigan</li> </ul> </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.