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31 results for “digital database”
Written and spoken digits database for multimodal learning
<p><strong>Database description:</strong></p> <p>The written and spoken digits database is not a new database but a constructed database from existing ones, in order to provide a ready-to-use database for multimodal fusion [1].</p> <p>The written digits database is the original MNIST handwritten digits database [2] with no additional processing. It consists of 70000 images (60000 for training and 10000 for test) of 28 x 28 = 784 dimensions.</p> <p>The spoken digits database was extracted from Google Speech Commands [3], an audio dataset of spoken words that was proposed to train and evaluate keyword spotting systems. It consists of 105829 utterances of 35 words, amongst which 38908 utterances of the ten digits (34801 for training and 4107 for test). A pre-processing was done via the extraction of the Mel Frequency Cepstral Coefficients (MFCC) with a framing window size of 50 ms and frame shift size of 25 ms. Since the speech samples are approximately 1 s long, we end up with 39 time slots. For each one, we extract 12 MFCC coefficients with an additional energy coefficient. Thus, we have a final vector of 39 x 13 = 507 dimensions. Standardization and normalization were applied on the MFCC features.</p> <p>To construct the multimodal digits dataset, we associated written and spoken digits of the same class respecting the initial partitioning in [2] and [3] for the training and test subsets. Since we have less samples for the spoken digits, we duplicated some random samples to match the number of written digits and have a multimodal digits database of 70000 samples (60000 for training and 10000 for test).</p> <p>The dataset is provided in six files as described below. Therefore, if a shuffle is performed on the training or test subsets, it must be performed in unison with the same order for the written digits, spoken digits and labels.</p> <p> </p> <p><strong>Files:</strong></p> <ul> <li>data_wr_train.npy: 60000 samples of 784-dimentional written digits for training;</li> <li>data_sp_train.npy: 60000 samples of 507-dimentional spoken digits for training;</li> <li>labels_train.npy: 60000 labels for the training subset;</li> <li>data_wr_test.npy: 10000 samples of 784-dimentional written digits for test;</li> <li>data_sp_test.npy: 10000 samples of 507-dimentional spoken digits for test;</li> <li>labels_test.npy: 10000 labels for the test subset.</li> </ul> <p> </p> <p><strong>References:</strong></p> <ol> <li>Khacef, L. et al. (2020), "Brain-Inspired Self-Organization with Cellular Neuromorphic Computing for Multimodal Unsupervised Learning".</li> <li>LeCun, Y. & Cortes, C. (1998), “MNIST handwritten digit database”.</li> <li>Warden, P. (2018), “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition”.</li> </ol>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - PART I
<p>The database concerns kettle-holes of glacial flood origin. They are located at various outwash levels of Skeiðarársandur in S Iceland. The database contains 87 digital elevation models (DEM) with a minimum resolution of 0.05 m and additional files, e.g. field measurements data, frames selected from the video, errors calculation, point cloud, 3D view. These data document the process of obtaining the material using the photogrammetric ‘Structure from Motion’ method from fieldwork conducted in June 2022 through the processing stages in free, mainly open-source software. The data is prepared in the local Cartesian system and includes relative heights, where 0 m is the lowest point of the kettle-hole. The simple technique used, based on filming the landforms with a digital camera, enables mapping of depressions up to 1250 m<sup>2</sup> in the area and a maximum depth of up to 8 m with the assumed high accuracy.</p>
Database of Digital Technology for Co-creation (2DTC)
<p>This CSV file contains a list of 50 technologies commonly used in the co-creation process. The database is organised around a taxonomy for digital technology used in co-creation, developed by the Health CASCADE consortium. It can be used to select the most adapted digital technology for specific co-creation processes based on detailed functional and non-functional requirements.</p> <p>Futur development will allow taxonomy development, increase the number of classified technologies, and update the available ones.</p>
Alteromonas Digital Organism Databases
<p>This is the home of the database for the <i>Alteromonas</i> Digital Organism, one of C-CoMP's collaborative efforts. This database was created in anvi'o (anvio-dev) primarily by Michelle DeMers (Massachusetts Institute of Technology) and Rogier Braakman (Massachusetts Institute of Technology), with significant help from members of the Meren Lab (A. Murat Eren, Iva Veseli, and Matthew Schechter) and Moran Lab (Zac Cooper and Mary Ann Moran). This version upload consists of:</p><p>Alteromonas_Pangenome_v2.1.1.md: A reproducible workflow that details the additions made to this version of the pangenome since v2.1.0.</p><p>Alteromonas2.1.1dbs.tar.gz: Collection of all updated contigs databases and genomes storage database.</p><p>external-genomes-v2.txt: Text file consisting of a list of the genomes used in this digital organism with ID, strain, and source information.</p><p>Alteromonas2.1.1pangenome.db.tar.gz: Compressed file containing the <i>Alteromonas</i> pangenome (digital organism).</p><p>Alteromonas2.1.1pangenomefiles.tar.gz: Compressed directory containing any information that anvi'o created when forming the pangenome.</p><p>Alteromonas2.1.1ANI.tar.gz: Compressed directory containing all output files from assessing genome similarity.</p><p>bayesian_2_1_concatenated_proteins*: Concatenated core protein files made from bayesian core gene sets.</p><p>RAxML_*boots: New RAxML tree artifacts produced for this version of the pangenome, with bootstrap values. Includes midpoint rooted tree.</p><p>layer_orders.txt: Tab-delimited file used to import newick tree into the pangenome.</p><p>view.txt: Tab-delimited file containing isolate and strain names.</p>
Ruegeria pomeroyi digital microbe databases
<p>These databases consolidate a variety of datasets related to the model organism Ruegeria pomeroyi DSS-3. The data were primarily generated by members of the Moran Lab at the University of Georgia, and put together in this format using anvi'o v7.1-dev through the collaborative efforts of Zac Cooper, Sam Miller, and Iva Veseli (special thanks to Christa Smith and Lidimarie Trujillo Rodriguez for their help with gene annotations). The data includes:</p> <p>- (R_POM_DSS3-contigs.db) the complete genome and megaplasmid sequence of R. pomeroyi, along with highly-curated gene annotations established by the Moran Lab and automatically-generated annotations from NCBI COGs, KEGG KOfam/BRITE, Pfams, and anvi'o single-copy core gene sets. It also contains annotations for the Moran Lab's TnSeq mutant library (<a href="https://doi.org/10.1101/2022.09.11.507510">https://doi.org/10.1101/2022.09.11.507510</a>; <a href="https://doi.org/10.1038/s43705-023-00244-6">https://doi.org/10.1038/s43705-023-00244-6</a>).</p> <p>- (PROFILE-VER_01.db) read-mapping data from multiple transcriptome and metatranscriptome samples generated by the Moran lab to the R. pomeroyi genome. Some coverage data is stored in the AUXILIARY-DATA.db file. This data can be visualized using anvi-interactive. Publicly-available samples are labeled with their SRA accession number.</p> <p>- (DEFAULT-EVERYTHING.db) gene-level coverage data from the transcriptome and meta-transcriptomes samples stored in the profile database, as well as per-gene normalized spectral abundance counts from proteomes matched to a subset of the transcriptomes and gene mutant fitness data from <a href="https://doi.org/10.1073/pnas.2217200120">https://doi.org/10.1073/pnas.2217200120</a>. This data can also be visualized using anvi-interactive (see instructions below). The proteome data layers are labeled according to their matching transcriptome samples.</p> <p>- (R_pom_reproducible_workflow.md) a reproducible workflow describing how the databases were generated.</p> <p><em>Please note that using these databases requires the development version of anvi'o `v8-dev`, or a later version of anvi'o if available. They are not usable with anvi'o `v8` or earlier.</em></p> <p>Instructions for <strong>visualizing the genes database</strong> in the anvi'o interactive interface: Anvi'o expects genes databases to be located in a folder called `GENES`, so in order to use the specific database included in this datapack, you must move it to the expected location by running the following commands in your terminal:</p> <blockquote> <p>mkdir GENES<br>mv DEFAULT-EVERYTHING.db GENES/<br> </p> </blockquote> <p>Once that is done, you can use the following command to visualize the gene-level information:</p> <blockquote> <p>anvi-interactive -c R_POM_DSS3-contigs.db -p PROFILE-VER_01.db -C DEFAULT -b EVERYTHING --gene-mode</p> </blockquote> <p>To view <strong>only the proteomic data</strong> and its matched transcriptomes, you can add the flag `--state-autoload proteomes` to the above command.</p> <p>To view all transcriptomes and the proteomes <strong>organized by study of origin</strong>, you can add the flag `--state-autoload figure` to the above command.</p>
World Bank Digitalization Database ODDEA Project
<p><span>Data collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (<em>Project ID: HORIZON MSCA-SE 101086381). It consists of </em>8 ICT indicators for 266 countries and country groups for 2012 – 2022 period. Excel files with metadata compacted in zip file. </span></p>
DATABASE OF THE DIGITAL ELEVATION MODELS OF THE SKEIÐARÁRSANDUR KETTLE-HOLES (S ICELAND), JUNE 2022 - Part II. VIDEO
<p>The database contains 83 video files in .MOV format, shot by a digital camera at 23.98 frames per second. The average length of videos is 100–600 seconds. They are documentation of fieldwork carried out in June 2022, aimed at preparing the footage to generate high-resolution digital elevation models (Part I) using the 'Structure from Motion' technique. The study covered kettle-holes of the glacial flood origin located at Skeiðarársandur in S Iceland.</p>
Database for article: "Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates"
<p>This database is an addendum to the article "<strong>Privacy Perceptions in Digital Games: A Study with Information Technology (IT) Undergraduates</strong>" to provide information regarding the anonymously collected data.</p><p><strong>Abstract of the article</strong></p><p>This study explores the perceptions and practices of undergraduates in Information Technology (IT) regarding privacy issues in digital games. This topic becomes relevant in the current scenario where artificial intelligence (AI) is increasingly integrated into digital games, providing an enhanced experience for players. However, this integration poses security and privacy challenges, the understanding of which is crucial for both players and developers.<br>The primary objective of this research is to comprehend the participants' perceptions and understandings of privacy in digital games. We employed a qualitative and quantitative methodology to address our research inquiries. Through an online form of data collection, we obtained 61 responses. Among the obtained information, we observed that 40\% of the students are interested in pursuing a career in game development, and 49.18% would consider this possibility. Noteworthy among the identified issues is the necessity for companies to devise more effective means of communicating their privacy policies to players/users, adapting the language to their target audience. Participants reported attacks related to online multiplayer games and expressed concerns about the security of personal data.</p>
Database of digital media publications on maternal (family) capital in Russia in 2006-2019
<p><strong>Abstract</strong></p> <p>The database contains data from publications of digital Russian-language media of the Russian Federation on the topic of maternity capital in the period from May 10, 2006 to June 30, 2019. The database includes uploading general data on publications on maternity capital in .xls formats (UTF-8 encoding) . Full texts of publications are presented in .xml format.</p> <p>A specialized request was generated for the aggregator of publications of Russian-language digital mass media <a href="https://vk.com/away.php?to=http%3A%2F%2Fpublic.ru&cc_key=">public.ru</a> . In total, the database consists of 457888 publications of 7665 publishing houses from 1251 settlements in Russia on the territory of 85 regions. The database includes information about the date, type, authors, publisher and place of publication (municipality, region of the publisher), as well as full texts of publications.</p> <p> </p> <p>Keywords: database, digital media, maternal (family) capital, central and municipal media, Russia</p> <p>JEL codes: J10, J13, Z18.</p> <p> </p> <p><strong>Data format and access:</strong></p> <p>The database consists of full-text publications of digital media on the topic of maternity capital. Materials in Russian have been published in federal, regional and local digital media. Publication period: May 10, 2006 to June 30, 2019.</p> <p>The database consists of 457888 publications of 7665 publishing houses from 1251 settlements in Russia on the territory of 85 regions. Presentation format .csv, .xml (full texts). The file "Matkap_SMI_17_11_2021.csv" contains processed information from the extended full-text sample by years (contained in the "XML.rar" archive). </p> <p>pubData - date of publication (format "YYYY-MM-DD")</p> <p>text - text from "description" in xml-files after lemmatization (removal of punctuation, lowercase and remove punctuation, spaces, numbers)</p> <p>source - name of the electronic edition</p> <p>place - town or city in Russia</p> <p>type - type of electronic edition (newspaper, magazine, TV program, internet resource)</p> <p>period - frequency of publication</p> <p>positive - the number of unique positive words from the RuSentiLex2017 dictionary</p> <p>negative - the number of unique negative words from the RuSentiLex2017 dictionary</p> <p>neutral - the number of unique neutral words from the RuSentiLex2017 dictionary</p> <p><strong>Data collection methodology: </strong></p> <p>The aggregator of publications of Russian-language digital mass media <a href="https://vk.com/away.php?to=http%3A%2F%2Fpublic.ru&cc_key=">public.ru was used</a>. The selection of publications was limited to the time period from May 10, 2006 (Russian President Vladimir Vladimirovich Putin first announced the maternity capital programme in his message to the Federal Assembly, as one of the mechanisms to stimulate fertility and overcome the demographic crisis) to June 30, 2019 (until this date, the programme allowed full uploading of media publications without losses during the period of uploading publications from 01.08.2021 to 15.08.2021).</p> <p>Key words used to select articles on maternity capital: “maternal capital”, “maternity capital”, “family capital”, “paternal capital”. The publication had to repeat at least two phrases from the request, while the distance between the phrases had to be no more than 4 sentences. This excluded publications in which the topic of maternity capital was mentioned incidentally, indirectly.</p> <p>Duplicate articles were removed from the database. Duplicates related to publications that included a full repetition of the text of the publication itself, the publishing house and the municipality (location) of the publishing house. Duplications (reprints) of articles in other publishing houses or in other regions were not excluded. </p> <p>After lemmatization of the text (as well as after reducing the text to lower case, removing unnecessary spaces, numbers and punctuation), according to the RuSentiLex2017 dictionary (Loukachevitch N., Levchik A. Creating a General Russian Sentiment Lexicon. In Proceedings of Language Resources and Evaluation Conference LREC-2016, 2016.) unique positive, negative and neutral words and phrases (variables) were counted. Repetitions of tonal words (stances) are <strong>not counted.</strong></p>
Fig. 3 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 3. The ten-year mean values (1997–2006) of roe deer antler weights for each game management unit in Hungary. Source: National Game Management Database of Hungary
Fig. 2 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 2. The spatial distribution of main soil types, based on the soil types and subtypes from the Agrotopography Map of Hungary. (* For abbreviations see Table 1). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricultural Chemistry
Fig. 5 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 5. The means and standard deviations of soil-evaluation-numbers (SEN; soil fertility index representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) and percentages of land-use types (agriculture and forest) for each main soil types* in Hungary.
Fig. 1 in Possible Use Of Nationwide Digital Soil Database On Predicting Roe Deer Antler Weight
Fig. 1. The values of soil-evaluation-number (representing the natural fertility of different soils in the percentage of the fertility of the most fertile soil) from the Agrotopography Map of Hungary. (* 1, <10%; 2, 10–20%; 3, 20–30%; 4, 30–40%; 5, 40–50%; 6, 50–60%; 7, 60–70%; 8, 70–80%; 9, 80–90%; 10, 90–100%). Source: Hungarian Academy of Sciences, Research Institute of Soil Science and Agricul-
Linked collectors and determiners for: Database and digitization of bees in Thailand.
Natural history specimen data linked to collectors and determiners held within, "Database and digitization of bees in Thailand". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/cb0ab16c-7589-4a44-80d0-30bae8c952ef">https://bionomia.net/dataset/cb0ab16c-7589-4a44-80d0-30bae8c952ef</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/cb0ab16c-7589-4a44-80d0-30bae8c952ef">https://gbif.org/dataset/cb0ab16c-7589-4a44-80d0-30bae8c952ef</a>. Formatted as a Frictionless Data package.
The Svalbox Digital Model Database (DMDb)
<p><strong>The Svalbox Digital Model Database</strong><br> The Svalbox Digital Model Database (DMDb) is a regional DOM database geographically constrained to the Norwegian High Arctic Archipelago of Svalbard at 74-81 °N and 10-35 °E. Svalbard offers exceptional quality, vegetation-free outcrops with a wide range of lithologies and tectono-magmatic styles including extension, compression and magmatism. Data and metadata of the systematically digitized outcrops across Svalbard are shared according to FAIR principles through the Svalbox DMDb. Fully open-access and downloadable DOMs include not just the DOM itself but also the input data, processing reports and projects, and other data products such as footprints and orthomosaics. Rich metadata for each DOM comprises both the technical and geological parameters (metadata), enabling visualisation and integration with regional geoscientific data on the Svalbox portal.<br> <br> <strong>Release information</strong><br> The v2023.3 release of the Svalbox DMDb, documented in this contribution, covers 135 DOMs cumulatively covering 113.946 km2 and offering Proterozoic to Cenozoic stratigraphy.<br> <br> <strong>Release documentation</strong><br> The Svalbox DMDb v2023.3 is shared in the following formats:</p> <ul> <li>GeoJSON (.geojson)</li> <li>Geopackage (.gpkg)</li> </ul> <p>Documentation of the types and units associated with the data columns is documented through the SvalboxDMDb parameters files, available in:</p> <ul> <li>Comma-separated values (.csv)</li> <li>JSON (.json)</li> </ul> <p>Updated metadata is continuously updated to the Svalbox server and made available through:</p> <ul> <li><a href="https://www.svalbox.no/map/">https://www.svalbox.no/map/</a></li> <li>Svalbox ArcGIS REST access point (<a href="https://svalbox.unis.no/arcgis/rest/services/">https://svalbox.unis.no/arcgis/rest/services/</a>)</li> </ul> <p><strong>Citation</strong><br> In addition to model-specific DOIs, please cite the following papers when using the Svalbox DMDb and its models:</p> <ul> <li>Betlem, et al. "The Svalbox Digital Model Database: a geoscientific window to the High Arctic." Geosphere (in review).</li> <li>Senger, et al. "Using digital outcrops to make the high Arctic more accessible through the Svalbox database." Journal of Geoscience Education 69.2 (2021): 123-137.</li> </ul>
WARLUX nodegoat database, on recruits of Schifflange/Luxembourg, Luxembourg Centre for Contemporary and Digital History/University of Luxembourg
<p> </p> <p><a href="https://www.c2dh.uni.lu/projects/warlux-soldiers-and-their-communities-wwii-impact-and-legacy-war-experiences-luxembourg">Project WARLUX - Soldiers and their communities in WWII: The impact and legacy of war experiences in Luxembourg</a> is a research project based at the <a>Luxembourg Centre for Contemporary and Digital History (C²DH)</a> (University of Luxembourg). The projects focuses on the war experiences of male Luxembourgers born between 1920 and 1927 who were recruited and conscripted into Nazi German services (<em>Reichsarbeitsdienst</em> (<em>RAD</em>) and <em>Wehrmacht</em>) under the Nazi occupation in Luxembourg during the Second World War. </p> <p><strong>Data Sample</strong></p> <p>While over 12,000 men and women were affected by the conscription, Project WARLUX focuses on a case study of 304 recruits from <em>Schifflange</em> and their families. In total, the data sample includes around 1200 persons, recruits and their family members. </p> <p><strong>Origin of the data </strong></p> <p>The dataset primarily consists of compiled archival documentation, including organizational and official documents, statistics, and standardized fiches and cards. These sources are primarily sourced from the Luxembourgish National Archives and other relevant repositories.</p> <p>In addition to basic information such as name, birth date, and residence, the (internal) dataset also incorporates military records sourced from German archives. Furthermore, supplementary information related to captivity, repatriation, and compensation was collected in the post-war period. The surveys and statistics conducted by the Luxembourgish state provide valuable insights into the experiences and trajectories of the war-affected generation.</p> <p>It is important to note that the dataset is a composite of multiple heterogeneous sources, reflecting its diverse origins. </p> <p><strong>Database</strong></p> <p>The researchers involved in the WARLUX project opted for the utilization of a relational database, <em><a href="https://nodegoat.net/">nodegoat.</a></em></p> <p>The WARLUX project adheres to an object-oriented approach, which is reflected in the core functionalities provided by <em>nodegoat</em>. Given the project's specific focus on the war experiences of recruited Luxembourgers within Nazi services such as the <em>Wehrmacht</em> and <em>RAD</em>, the included data model (warlux data model file) represents only a partial depiction of the comprehensive <em>nodegoat </em>environment employed in the WARLUX project. Within this data model, the interconnected objects and their respective sub-objects are presented, with particular emphasis placed on the individual profiles of recruits and their involvement in military service.</p> <p>As the data can not be published due to restriction, the team provides a pseudonymized dataset as an example of the data structure. </p> <p>The provided dataset shows the male recruits (and conscripts) of the Case Study <em>Schifflange</em> (born between 1920 and 1927). It includes</p> <ul> <li><em>nodegoat</em> ID</li> <li>their birthdate</li> <li>information on death if it occurred during the war</li> <li>whereabout after the war (unknown, missing, KIA, returned etc.)</li> </ul> <p>The dataset also includes references to their recruitment into </p> <ul> <li>the <em>Wehrmacht</em> and/or the <em>RAD</em> as well as their subsequent activities such as </li> <li>being captured as a Prisoner of War (POW)</li> <li>serving for the Allied Forces</li> <li>desertion, or </li> <li>draft evasion (<em>réfractaire</em>).</li> </ul> <p>The access to the WARLUX nodegoat database, on recruits of <em>Schifflange</em>/Luxembourg is restricted due to sensitive data. For further questions please contact <a href="mailto:warlux@uni.lu">warlux@uni.lu</a></p> <p>The project is funded by the Fond National de la Recherche Luxembourg <a href="https://www.fnr.lu/">(FNR)</a>.</p>
Province of Cremona - Environment and Territory Department - Online digital library database
<p>The province of Cremona lies in Northern Italy, along the left bank of the River Po floodplain. This is the database of the online digital library of the Province Administration Environment and Territory Department. At present the library comprises 610 contributions concerning the environment, ecology, geography, history and landscape of the province, of the Po basin at large and of other areas as well. Most contributions have a short or extended abstract in English. Each publication in the library is freely downloadable as a PDF file from the library website http://bibliotecadigitale.provincia.cremona.it.</p> <p>The database fields are as follows: Publication code, Publication series, Publication title in Italian, Publication title in English, Publication type (magazine or monograph), ISSN number (if assigned), Issue number, Issue year, Contribution type (article, report or short note, monograph etc.), Title in Italian<em>,</em> Title in English, Topic, Subtopic 1, Subtopic 2, Pages, Author 1, Author 2, …, Author 16, Link to the file of each publication issue on the digital library website, Notes.</p> <p>Further information on the digital library and the database can be found in the open access article (in English) in V. 2 N. 1 (2022) of BORNH (Bullettin of Regional Natural History of the Società dei Naturalisti in Napoli): <a href="https://serena.sharepress.it/index.php/bornh/issue/view/653">https://serena.sharepress.it/index.php/bornh/issue/view/653</a></p> <p>Please note that upon opening the file with LibreOffice (and perhaps other softwares) it might be necessary to select Windows 1252 as the character type and the comma as the separator.</p>
Databases and information systems for research output: digital humanities outlook (DARIAH Bibliographical Data Working Group, September, 30th 2022)
<p>The video recording of the workshop "Databases and information systems for research output: digital humanities outlook" organized by DARIAH Bibliographical Data Working Group.</p>
Biographische Informationssysteme (DPBs, Digital Knowledge Databases, Virtual Research Environments)
<p>The table is an overview of database and online systems to manage/publish prosopographical and biographical data. </p>
Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts
<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Tag1: Curator tags (optional)</p> <p> Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv: Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong> </strong>tag: Tag name</p> <p> definition: Definition of the tag</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.