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Results complementing the European Union summary report on surveillance for the presence of transmissible spongiform encephalopathies (TSE) - Austria
<p>This dataset contains TSE surveillance results in cattle, sheep, goats, cervids and other species, and genotyping in sheep, pursuant to Regulation (EC) 999/2001.</p> <p><strong>Reporting authorities contributing to each data collection</strong>:</p> <ul> <li>TSE_2023_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2022_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2021_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2020_AT: Austrian Agency for Health and Food Safety (AGES)</li> <li>TSE_2019_AT: Austrian Agency for Health and Food Safety (AGES)</li> </ul>
Supplementary data to Dating the timbers from the 'Sparrow-Hawk', a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports 103374
<p>This record gives access to all supplementary data that forms the background to the paper: Daly, A., Hocker, F. & Mires, C., 2022. Dating the timbers from the ‘Sparrow-Hawk’, a shipwreck from Cape Cod, USA. Journal of Archaeological Science: Reports https://doi.org/10.1016/j.jasrep.2022.103374</p> <p>In 1626, a vessel making its way to Virginia was forced off course and damaged in a storm, which drove the ship onto the eastern shore of the Cape Cod peninsula, Massachusetts. Onboard were two English merchants and some servants and farmers, many of whom were Irish. In 1863, a storm exposed the weathered remains of a vessel at Old Ship Harbor. At the time, it was hailed as the same ship that had brought the Virginia-bound passengers to Plymouth in 1626. Recent wiggle-match C14 dating and dendrochronology suggests that this is indeed a ship from the early seventeenth century.</p>
IPBES Data Management Tutorials - Session 3.5: Data management report details: Sensitive data, anonymization, and ethical considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data and knowledge management policy. They cover topics ranging from data and knowledge management policy, reports, active research data, tools, and examples.</p> <p>The <em>IPBES data management reports </em>chapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session on <em>data management report details: Sensitive data, anonymization, and ethical considerations </em>captures specific considerations and processes for IPBES experts regarding sensitive data and Indigenous and local knowledge within data management reports. </p>
Summary statistics accompanying the article "Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency" in Scientific Reports (2022)
<p>Summary statistics for genome-wide association studies reported in:</p> <p>Bell, S., Tozer, D.J., & Markus H.S. (2022). Genome-wide association study of the human brain functional connectome reveals strong vascular component underlying global network efficiency. <em>Scientific Reports</em>, DOI: <a href="https://dx.doi.org/10.1038/s41598-022-19106-7">10.1038/s41598-022-19106-7</a>. </p> <p><strong>Abstract</strong></p> <p>Complex brain networks play a central role in integrating activity across the human brain, and such networks can be identified in the absence of any external stimulus. We performed 10 genome-wide association studies of resting state network measures of intrinsic brain activity in up to 36,150 participants of European ancestry in the UK Biobank. We found that the heritability of global network efficiency was largely explained by blood oxygen level-dependent (BOLD) resting state fluctuation amplitudes (RSFA), which are thought to reflect the vascular component of the BOLD signal. RSFA itself had a significant genetic component and we identified 24 genomic loci associated with RSFA, 157 genes whose predicted expression correlated with it, and 3 proteins in the dorsolateral prefrontal cortex and 4 in plasma. We observed correlations with cardiovascular traits, and single-cell RNA specificity analyses revealed enrichment of vascular related cells. Our analyses also revealed a potential role of lipid transport, store-operated calcium channel activity, and inositol 1,4,5-trisphosphate binding in resting-state BOLD fluctuations. We conclude that that the heritability of global network efficiency is largely explained by the vascular component of the BOLD response as ascertained by RSFA, which itself has a significant genetic component.</p> <p> </p> <p>Further information on the files uploaded here can be found in the README. Users interested in bulk downloading these summary statistics may find <a href="https://github.com/dvolgyes/zenodo_get">zenodo_get</a> helpful.</p>
Project Tycho Level 2 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1888-2014
Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point.<p></p><p>Project Tycho level 2 version 1.1.0 data include data counts that have been filtered from the raw data to render standardized data that can be used immediately for analysis. All level 2 data were originally reported in a consistent format and have not been transformed into a standard format by Project Tycho staff, except for smallpox records that included repeated counts for the same location and week, but sometimes with different numbers. These duplicate smallpox records have been averaged into one count for each location and week. Level 2 data include counts for a wide variety of diseases and locations for varying time periods. Because we removed data in an inconsistent format from level 2 data, counts may be missing for certain diseases, locations, or years. For the most complete collection of standardized data, we encourage users to use Project Tycho version 2.0 datasets.</p><p>More detailed methods and additional information about the origin of Projec Tycho level 2 version 1.1.0 data can be found in our original publication in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a></p><p>Level 2 version 1.1.0 data is represented in a CSV file with 11 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>country: a two digit country abbreviation, only including "US" in version 1.1.0</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported, in all capitals</li><li>event: an indicator representing the disease outcome reported, including "CASES" or "DEATHS"</li><li>number: the reported number of cases or deaths</li><li>from_date: the start date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>to_date: the end date of the time interval for which a count was reported, as yyyy-mm-dd</li><li>url: the URL of the source document from which the count was obtained</li></ul><p></p>
Project Tycho Level 1 data: Counts of multiple diseases reported in UNITED STATES OF AMERICA, 1916-2011
<p>Project Tycho data include counts of infectious disease cases or deaths per time interval. A count is equivalent to a data point. Project Tycho level 1 data include data counts that have been standardized for a specific, published, analysis. Standardization of level 1 data included representing various types of data counts into a common format and excluding data counts that are not required for the intended analysis. In addition, external data such as population data may have been integrated with disease data to derive rates or for other applications.</p><p>Version 1.0.0 of level 1 data includes counts at the state level for smallpox, polio, measles, mumps, rubella, hepatitis A, and whooping cough and at the city level for diphtheria. The time period of data varies per disease somewhere between 1916 and 2011. This version includes cases as well as incidence rates per 100,000 population based on historical population estimates. These data have been used by investigators at the University of Pittsburgh to estimate the impact of vaccination programs in the United States, published in the New England Journal of Medicine: <a href="http://www.nejm.org/doi/full/10.1056/NEJMms1215400">http://www.nejm.org/doi/full/10.1056/NEJMms1215400</a>. See this paper for additional methods and detail about the origin of level 1 version 1.0.0 data.</p><p>Level 1 version 1.0.0 data is represented in a CSV file with 7 columns:</p><ul><li>epi_week: a six digit number that represents the year and epidemiological week for which disease cases or deaths were reported (yyyyww)</li><li>state: the two digit postal code state abbreviation that represents the state for which a count has been reported</li><li>loc: the name of a state or city for which a count has been reported, capitalized</li><li>loc_type: the type of location (STATE or CITY) for which a count has been reported</li><li>disease: the disease for which a count has been reported: HEPATITIS A, MEASLES, MUMPS, PERTUSSIS, POLIO, RUBELLA, SMALLPOX, or DIPHTHERIA</li><li>cases: the number of cases reported for the specified disease, epidemiological week, and location</li><li>incidence_per_100000: the number of cases per 100,000 people, computed using historical population counts for cities and states as reported by the US Census Bureau</li></ul><p></p>
Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."
<p>Dataset of report "A.2.2.6: Validation of the fitness of purpose of the performance assessment protocol developed in A2.1.4 by demonstrating its applicability for 2 terpenes using TD-GC/MS/FID and the static standards produced in A1.1.2."</p>
Supplementary Report for the paper "A Preliminary Analysis on the Effect of Randomness in a CEGAR Framework"
<p> A supplementary report for the paper "A Preliminary Analysis on the Effect of Randomness in a CEGAR Framework" by Ákos Hajdu and Zoltán Micskei, presented at the 25th PhD Mini-Symposium (2018), organized by the Department of Measurement and Information Systems at the Budapest University of Technology and Economics.</p>
Types, open citations, closed citations, publishers, and participation reports of Crossref entities
<p>This publication contains several datasets that have been used in the paper "Crowdsourcing open citations with CROCI – An analysis of the current status of open citations, and a proposal" submitted to the <a href="https://www.issi2019.org/">17th International Conference on Scientometrics and Bibliometrics (ISSI 2019)</a>, available at <a href="https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/">https://opencitations.wordpress.com/2019/02/07/crowdsourcing-open-citations-with-croci/</a>.</p> <p>Additional information about the analyses described in the paper, including the code and the data we have used to compute all the figures, is available as a Jupyter notebook at <a href="https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb">https://github.com/sosgang/pushing-open-citations-issi2019/blob/master/script/croci_nb.ipynb</a>. The datasets contain the following information.</p> <p><strong>non_open.zip:</strong> it is a zipped (~5 GB unzipped) CSV file containing the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, dated October 2018. All the entity types retrieved from Crossref were aligned to one of following five categories: journal, book, proceedings, dataset, other. The open CC0 citation data we used came from the CSV dump of <a href="https://doi.org/10.6084/m9.figshare.6741422.v3">most recent release of COCI dated 12 November 2018</a>. The number of closed citations was calculated by subtracting the number of open citations to each entity available within COCI from the value “is-referenced-by-count” available in the Crossref metadata for that particular cited entity, which reports all the DOI-to-DOI citation links that point to the cited entity from within the whole Crossref database (including those present in the Crossref ‘closed’ dataset).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>doi:</em> the DOI of the publication in Crossref;</li> <li><em>type:</em> the type of the publication as indicated in Crossref;</li> <li><em>cited_by:</em> the number of open citations received by the publication according to COCI;</li> <li><em>non_open:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>croci_types.csv:</strong> it is a CSV file that contains the numbers of open citations and closed citations received by the entities in the Crossref dump used in our computation, as collected in the previous CSV file, alligned in five classes depening on the entity types retrieved from Crossref: <em>journal</em> (Crossref types: journal-article, journal-issue, journal-volume, journal), <em>book</em> (Crossref types: book, book-chapter, book-section, monograph, book track, book-part, book-set, reference-book, dissertation, book series, edited book), <em>proceedings</em> (Crossref types: proceedings-article, proceedings, proceedings-series), <em>dataset</em> (Crossref types: dataset), <em>other</em> (Crossref types: other, report, peer review, reference-entry, component, report-series, standard, posted-content, standard-series).</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>type:</em> the type publication between "journal", "book", "proceedings", "dataset", "other";</li> <li><em>label:</em> the label assigned to the type for visualisation purposes;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publication type according to COCI;</li> <li><em>crossref_close_cit:</em> the number of closed citations received by the publication according to Crossref + COCI.</li> </ul> <p><strong>publishers_cits.csv:</strong> it is a CSV file that contains the top twenty publishers that received the greatest number of open citations. The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher</em>: the name of the publisher;</li> <li><em>doi_prefix</em>: the list of DOI prefixes used assigned by the publisher;</li> <li><em>coci_open_cit</em>: the number of open citations received by the publications of the publisher according to COCI;</li> <li><em>crossref_close_cit</em>: the number of closed citations received by the publications of the publishers according to Crossref + COCI;</li> <li><em>total_cit</em>: the total number of citations received by the publications of the publisher (= <em>coci_open_cit</em> + <em>crossref_close_cit</em>).</li> </ul> <p><strong>20publishers_cr.csv: </strong>it is a CSV file that contains the numbers of the contributions to open citations made by the twenty publishers introduced in the previous CSV file as of 24 January 2018, according to the data available through the Crossref API. The counts listed in this file refers to the number of publications for which each publisher has submitted metadata to Crossref that include the publication’s reference list. The categories 'closed', 'limited' and 'open' refer to publications for which the reference lists are not visible to anyone outside the Crossref Cited-by membership, are visible only to them and to Crossref Metadata Plus members, or are visible to all, respectively. In addition, the file also record the total number of publications for which the publisher has submitted metadata to Crossref, whether or not those metadata include the reference lists of those publications.</p> <p>The columns of the CSV file are the following ones:</p> <ul> <li><em>publisher: </em>the name of the publisher;</li> <li><em>open: </em>the number of publications in Crossref with an 'open' visibility for their reference lists;</li> <li><em>limited: </em>the number of publications in Crossref with an 'limited' visibility for their reference lists;</li> <li><em>closed: </em>the number of publications in Crossref with an 'closed' visibility for their reference lists;</li> <li><em>overall_deposited:</em> the overall number of publications for which the publisher has submitted metadata to Crossref.</li> </ul>
MiRoR11 - P2 - Annotated corpus for the relation between reported outcomes and their significance levels
<p>Corpus of relations between outcomes and significance levels</p> <p>This dataset contains annotations of the relations between reported outcomes and their significance levels.<br> Tab-separated format is used. The file contains the following comumns:<br> filename, sentence text, outcome, primary outcome start position, primary outcome end position, reported outcome, reported outcome start position, reported outcome end position, label</p> <p>The folder out_sig_rel contains the dataset splits for 10-fold cross-validation.</p>
BioDeep/metabolomics-report-standards: BioDeep LC-MS Metabolite Identification Demo Report
<p><em>A Metabolomics unknown feature identification report industry standards from <a href="http://www.bionovogene.com/">BioNovoGene</a> corporation.</em></p> <p>2019.08.16# at Suzhou, China</p> <p>There is a general consensus that supports the need for standardized reporting of metadata or information describing large-scale metabolomics data sets. Reporting of standard metadata provides a biological and empirical context for the data, enables the reinterrogation and comparison of data by others, which is also could let us interpret the result in a more clearly way.</p> <p>This article is mainly address at the unknown metabolite identification in LC-MS experiment, and proposes the reporting standards related to the chemical analysis aspects of metabolomics experiments its metabolite identification.</p> <p>Some terms in this article that address to:</p> <ul> <li>feature, the term feature in this article is refer to a parent ion in LC-MS experiment result raw data. Where a parent ion feature is a peak in chromatography data, which is consist of mass to charge ratio in ms1 level and its retention time (with a range of lower bound and upper bound) in chromatography experiment result.</li> <li>annotation, the term annotation in this article is refer to the multidimensional information about the metabolite that assigned to a unknown feature, which such multidimensional information consist with the metabolite its cross reference id in different database, common name, basic chemical data like mass and formula composition and its molecule structure information, etc.</li> <li>alignment, the term alignment means a kind of operation that use to compare the similarity of the mass spectrum data between user sample and the reference standard library. Such similarity comparison result is the most important evidence that use for unknown feature its identification.</li> <li>score, the term score is a kind of numeric value that produced by the alignment comparison calculation. Literally, the higher score the alignment it produce, the better the result it is.</li> </ul> <p>Our metabolite identification report consist with two parts of data which present to our user:</p> <ol> <li>Report excel table that contains the raw sample information and the meta annotation information of the metabolite.</li> <li>Data visual plot for the mass spectrum alignment details.</li> </ol>
Data for manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.
<p>Dataset for the manuscript Marmet, Studer, Lemoine, Grazioli, Bertholet & Gmel (2019). Reconsidering the associations between self-reported alcohol use disorder and mental health problems in the light of co-occurring addictions in young Swiss men. Plos One. DOI: 10.1371/journal.pone.0222806.</p> <p>The dataset contains all data needed to reproduce the results in the above cited manuscript. Variable description and labels can be found in the codebook. For further information on the instruments used please refer to the manuscript.</p> <p>The data was collected between April 2016 and March 2018 in Switzerland by the C-SURF study (<a href="http://www.c-surf.ch">www.c-surf.ch</a>). Participants were on average 25 years old when they answered the questionnaires. The final sample size used in the manuscript is 5516. Please note that the dataset contains 25 datasets created with multiple imputation, therefore there are no missing values in the dataset.</p> <p>The research protocol for this study was approved by the Human Research Ethics Committee of the Canton Vaud (Protocol No. 15/07). Data collection was funded by the Swiss National Science Foundation (FN 33CSC0-122679, FN 33CS30_139467, FN 33CS30_148493)</p>
STAR4BBS D3.2 Report on additional indicators of monitoring system_Appendix 6.2 System Level Matrix dataset
<p>This dataset contains the final set of indicators selected for the system level of the new monitoring system. The data is part of the D3.2 "Report on additional indicators<br>of monitoring system". The indicators are organised by category, principles, criteria, requirements and references. </p> <p> </p>
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C1
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the environmental pillar. These 56 indicators are the result of a search limited to highly relevant sources in the field (specified in D3.1) and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C3
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the social pillar. These 571 indicators are the result of the analysis of 43 relevant articles and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C2
<p><span>This appendix presents the full set of LCA indicators identified in D3.1 for the economic pillar. These 22 indicators are the result of the consultation of various books and scientific articles aligned with the LCC and TEA methodology (specified in D3.1) and were used for the final selection according to pre-established criteria.</span></p>
STAR4BBS D3.1 Report on sustainability indicators for the monitoring system based on LCA_Appendix C4
<p><span>This appendix includes some recent research reports that have applied LCA methodology and circularity analysis to different sectors of the bioeconomy. A total of 30 research articles covering the environmental, social and economic pillars of sustainability, as well as circularity assessments, were analyzed</span>.</p>
STAR4BBS D1.4 Report on existing monitoring schemes_Annex A2
<p><span>This dataset contains the full list of identified monitoring tools from the grey literature reveiw that is part of the<span> </span>STAR4BBS deliverable D1.4 (Annex A2). The review of the identified 23 different assessment systems represents the findings, of which 19 monitoring tools were used for the further review of their characteristics, methods and results intepretation. It forms the basis of the further in-depth analysis and is included for transparency.</span></p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </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.