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773 results for “data science”
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
Supporting Data for "Synthesis and electrokinetics of cationic spherical nanoparticles in salt-free non-polar media" (Chemical Science, doi:10.1039/c7sc03334f)
<p>TEM micrographs of diblock copolymer micelles (magnification given in file name).</p> <p>Small-angle X-ray (SAXS) and small-angle neutron scattering (SANS) data (Q [1/Å], I(Q) [SAXS - arbitrary, SANS - 1/cm], error I(Q) [same units]).</p>
[DATA_SCIENCE] Interviews Oceanography, March 2015 - May 2017
<p>This is a collection of transcripts from interviews conducted by Gregor Halfmann as part of the ERC project "The Epistemology of Data-intensive Science". The interviews served as the empirical basis for Halfmann's PhD thesis "Seafarers, Silk, and Science: Oceanographic Data in the Making", submitted at the University of Exeter in July 2018. The interviews relate to the research practices, in particular the production and processing of research samples and scientific data, by marine ecologists and biological oceanographers working at the Marine Biological Association of the UK and for the Continuous Plankton Recorder Survey. Researchers have consented to have these transcripts made available as Open Data.</p>
Illustrative Darwin core archive to input data on a citizen science platform from a collection management system
<p>Illustrative DwC archive to send data from a collection management system to a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, multi-determined case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Multi-determined herbarium sheet case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p01978557</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, simple case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, multi-imaged case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Multi-imaged vertebrate specimen case : http://coldb.mnhn.fr/catalognumber/mnhn/zo/2013-152</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, simple case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Single molecule data for unphosphorylated Aurora-A (Gilburt et al, Chemical Science 2019)
<p>Raw and partially processed single molecule intensity histogram and dwell time histogram data for the following publication:</p> <p>James A H Gilburt, Paul Girvan, Julian Blagg, Liming Ying, Charlotte A Dodson (2019) Ligand discrimination between active and inactive activation loop conformations of Aurora-A kinase is unmodified by phosphorylation. Chemical Science. DOI: 10.1039/c8sc03669a</p> <p><strong><em>Please cite our publication in any use of this data.</em></strong></p> <p> </p>
Data for "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"
<p>This dataset contains all the annotations done by either citizen scientists or experts of the publication: "On the impact of Citizen Science-derived data quality on deep learning based classification in marine images"</p> <p><strong>CSP.csv</strong> -> CS annotations of the Citizen Science Primer-experiment</p> <p><strong>CSPExpert.csv</strong> -> Expert annotations of the Citizen Science Primer-experiment</p> <p><strong>CSS.csv</strong> -> CS annotations of the Citizen Science Study</p> <p><strong>CSSExpert.csv</strong> -> Expert annotations of the Citizen Science Study</p> <p>Visual exploration of the image data is possible in the BIIGLE 2.0 image annotation system at <a href="https://biigle.de/projects/139">https://biigle.de/projects/159</a> using the login <em><a href="mailto:cs@example.com">cs@example.com</a></em> and the password <em>plosonecs</em>.</p>
Data for Medical Data Science Shortcourse
<p>This is a .csv version of the World Bank Data on Health Nutrition and Population, cf. https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics and derived data sets for training purposes.</p> <p> </p>
The ecosystem of technologies for social science research, data
<p>This is the list of 417 software tools, packages, apps and platforms we have reviewed as part of SAGE Ocean. The dataset contains a number of features: name, pitch, type of tool, country, year, some papers, funders, founders, founding team etc. The latest version of this list will be available on github along with the metadata: <a href="https://github.com/danielagduca/SAGE_tools_social_science/tree/master/data">https://github.com/danielagduca/SAGE_tools_social_science/tree/master/data</a></p> <p>The supporting white paper describing the tools is:</p> <p>Duca, D., & Metzler, K. (2019). *The ecosystem of technologies for social science research* (White paper). London, UK:<br> Sage. doi: 10.4135/wp191101</p>
Community science data for Melaleuca L. (Myrtaceae) taxa in South Africa
<p>A dataset comprising cleaned and filtered iNaturalist records of <em>Melaleuca</em> L. (Myrtaceae; here including the genus <em>Callistemon</em>) taxa in South Africa. The corresponding study explores the utility of iNaturalist in informing management practices for the widely cultivated and naturalised genus <em>Melaleuca</em> in South Africa.</p>
MULTIPLIERS_WP5_Science learning project on Forest use vs. forest protection_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Forest use vs. forest protection</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with OSC members, and student groups (</span><span>Pseudo-/Anonymised)</span></li> </ul>
MULTIPLIERS_WP5_Science learning project on Biodiversity and agriculture_UBO_Public data_20241014_v1
<p><span>This dataset contains the following data related to</span><span> the science learning project on <em>Biodiversity and agriculture</em></span><span>:</span></p> <ul> <li><span>S</span><span>ummary of transcripts from interviews with teachers, and OSC members (</span><span>Pseudo-/Anonymised)</span></li> </ul>
Data sets - The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities
<p>Data sets </p> <p>The attitude of computer science teachers to inclusive education, Motivation to teach, Perception of the possible impact of computer science on students with mental disabilities. <br>In the period from February to October 2024, a survey of 112 computer science teachers in Kazakhstan (Pavlodar region) was conducted to determine attitudes to inclusive education, motivation to teach, and perception of the possible impact of computer science on students with mental disabilities.</p> <p>Questionnaire <br>https://docs.google.com/document/d/1LzukKSqW_mHMZXbMtN0ecmmU4cKJiwgf0laTWBHQSng/edit?usp=sharing</p> <p><strong>This research has been funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP14872400).</strong></p>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Public and Stakeholder Engagement
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Data Matrix Theme-Specific Analysis of the Recommendation on Science and Scientific Researchers (RSSR): Ethics and Ethical Governance
<p>This Table sets out findings from the mapping exercise conducted as part of the objectives of subtask 6.1 of the RRING project.</p> <p>Aim: Alignment of RRI to advance the UN SDGs.</p> <p>Objectives:</p> <ul> <li>Mapping the RSSR to the SDGs </li> </ul> <p>Mapping the RSSR to the SDGs is aimed at providing new perspectives, ideas and approaches that can help to improve the operationalization and implementation of each SDG, <em>by facilitating the integration of RRI (or RRI-like) practices in the SDGs, to make them more achievable.</em> The impact of the new perspectives, ideas and approaches in SDG operationalization and implementation will be aimed at the level of <em>national and international policy (making); future research and innovation projects (in industry and academia); as well as education and training of researchers, policy makers and other stakeholders.</em></p> <p>Two documents were used for this task:</p> <ul> <li>2017 Recommendation on Science and Scientific Researchers ([RSSR], UNESCO), and</li> <li>the United Nations 2030 Agenda for Sustainable Development with the 17 Sustainable Development Goals (SDGs).</li> </ul>
Data for From principles to practices: Open Science at European Universities. 2020-2021 EUA Open Science Survey Results
<p>This database refers to the data collected by the European University Association (EUA) for its 2020-2021 EUA Open Science Survey, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://www.eua.eu/resources/publications/976:from-principles-to-practices-open-science-at-europe%E2%80%99s-universities-2020-2021-eua-open-science-survey-results.html">https://www.eua.eu/resources/publications/976:from-principles-to-practices-open-science-at-europe%E2%80%99s-universities-2020-2021-eua-open-science-survey-results.html</a> (<a href="http://doi.org/10.5281/zenodo.5062982">http://doi.org/10.5281/zenodo.5062982</a>).</p> <p>All information that could lead to the identification of individual universities and higher education institutions was removed from the database. The following files are available:</p> <ul> <li>2020-2021 EUA Open Science Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel) and .sav (IBM SPSS)</li> <li>Survey Codebook: includes information on all the variables and their coding (.xlsx)</li> <li>Data Management Plan.</li> </ul>
The LSST Dark Energy Science Collaboration (DESC) Science Requirements Document v1 Released Data Products
<p>This tarball includes software and data products associated with the DESC Science Requirements Document (SRD) v1. See the "Executive Summary and User Guide" in the enclosed PDF of the DESC SRD for instructions on how to use and cite those products. The DESC SRD is described on <a href="https://arxiv.org/abs/1809.01669">arXiv</a> as follows:</p> <p>The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. The Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment.</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.