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ShareScore release 0.9.0
Dataset results
88 results for “digital research”
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>
Open dataset for the research of "Assessing accuracy improvement of integrating digital footprints into gridded population mapping: spatiotemporal variations and data bias"
<p>Result datasets for "Assessing accuracy improvement of integrating digital footprints into gridded populationmapping:spatiotemporal variations and data bias":</p> <ol> <li> S1 is the results for gridded population mapping using different methods.</li> <li> S2 is the aggregate results of population mapping at county level.</li> <li> S3 is the results for intraday variation of population disaggregation accuracy,</li> <li> S4 is the data bias of different digital footprints.</li> </ol>
The dataset of "Evaluation of Digital Supply Chain Technology's Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain"
<p>This dataset involves the data from the questionnaire, which come from the project "Evaluation of Digital Supply Chain Technology’s Impact on Sustainability Under the Moderate Effect of Supply Chain Dynamism: An Empirical Research in the Chinese Energy Supply Chain". It comprises three dimensions questions, technology, sustainability and supply chain dynamism. The datas come from two Chinese energy firms, <span>China Resources Power Zhejiang Company and Hunan HuaDian Changsha Electric Co., Ltd.</span></p>
Digital publishing of Indic manuscripts and inscriptions using the READ Workbench corpus development, research and publishing framework
<p>Paper presented on Friday 11 June 2021 at the Digital Medievalist Global Symposium <em>The past, present, and future of Digital Medieval Studies</em> for the Asia & Oceania Panel, in the session Reading Indic and Japanese scripts.</p> <p> </p>
Digital research data from: Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar
<p>This is digital research data corresponding to a published manuscript, <em>Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar</em>. Biochar 5:64. https://doi.org/10.1007/s42773-023-00263-5. </p> <p>Biochars with a high affinity for phosphorus (P) are promising soil amendments for reducing P in agricultural runoff. Poultry litter (PL) is an abundant biochar feedstock. However, PL-derived biochars are typically high in soluble P and therefore require chemical modification to become effective P sorbents. This study investigated the effect of magnesium (Mg) activation on extractable P (EP) and P sorption capacities of PL-derived biochars. Biochar was produced at 500–900 °C from PL activated with 0–1 M Mg. Three differentially aged PL feedstocks were evaluated (1-, 3–5-, and 7–9-year-old). </p>
DARTER: Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers
<p>The DARTER dataset contains a sample (for now) of driving data (camera, LiDAR, IMU, steering wheel angle, etc.) without labels gathered at AstaZero's test track using the Chalmers ReVeRe lab's SnowFox test vehicle. DARTER stands for Digital twins for Accessible Real Testing grounds for automotive Engineers and Researchers. This research was possible thanks to the funds of a SAFER pre-study grant.</p> <p> </p> <p><strong>About DARTER:</strong></p> <p>Verification and validation (V&V) of Intelligent Transport Solutions and their components in real traffic is difficult (costs, passers-by, etc.) and running tests under all possible conditions (weather, traffic, etc.) is impossible. For these reasons, controlled proving grounds and simulations are being used to safely increase the coverage of V&V. </p> <p>The DARTER project, a SAFER pre-study, addresses two issues connected to these alternatives. On the one hand, the limited access to real proving grounds and their corresponding high-fidelity simulations of academic researchers, key in evaluating the benefits and social and environmental harms that technological advances can pose. On the other hand, the fidelity gap between the virtual and the real world, that prevents the usage of simulations at vehicle integration test level and needs to be understood and measured. </p> <p>SAFER pre-studies https://www.saferresearch.com/content/safer-pre-studies </p>
Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar
Open the record for dataset details and reuse information.
Data from: Digitizing extant bat diversity: an open-access repository of 3D μCT-scanned skulls for research and education
Open the record for dataset details and reuse information.
Digital research data from: Magnesium activation affects the properties and phosphate sorption capacity of poultry litter biochar
Open the record for dataset details and reuse information.
"What Influences Citizens' Expectations towards Digital Government?" - Research Data
<p>This excel sheet contains the data from the survey entitled "What Influences Citizens' Expectations towards Digital Government?".</p> <p> </p>
Original research data for the paper "Additive Manufacturing of Polymeric Gradient Index Optics via Grayscale Digital Light Processing Vat Photopolymerization Technology"
<p>[CMOS camera data]</p> <p>[conversion maps]</p> <p>[cure kinetics]</p> <p>[GRIN profiles arbitrary]</p> <p>[MonoPrinter grayscale power density]</p> <p>[MonoPrinter print files]</p> <p>[pictures of arbitrary GRINs]</p> <p>[predicted printing param matrices]</p> <p>[refractive index vs conversion]</p> <p>[working curve]</p>
Digital research materials of spectral data for the article: [3+2]-Cycloaddition of azomethine ylides to 5-methylidene-3-aryl-2-сhalcogen-imidazolones: Access to dispiro indolinone-pyrrolidine-imidazolones
<p>Digital research materials of NMR spectral data for the article "[3+2]-Cycloaddition of azomethine ylides to 5-methylidene-3-aryl-2-сhalcogen-imidazolones: access to dispiro indolinone-pyrrolidine-imidazolones" submitted to the <em>Royal Society Open Science. </em>NMR spectra were recorded on Bruker Avance 400 and Agilent MR-400 spectrometers in DMSO-d<sub>6</sub> and C<sub>6</sub>D<sub>6</sub>. Chemical shifts were measured relative to residual solvent signals and referenced in parts per million to TMS. Original fid files are presented, which can be opened, for example, using the ASD Labs software package.</p>
African Digital Research Repositories: Mapping the Landscape
<p>This data set accompanies the text at doi <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. // Correspondence: JH: <a href="mailto:info@africarxiv.org">info@africarxiv.org</a>, SK: <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a></p> <p><strong>Visual Map: </strong><a href="https://kumu.io/access2perspectives/african-digital-research-repositories"><strong>https://kumu.io/access2perspectives/african-digital-research-repositories</strong></a><strong> <br> Dataset: </strong><a href="https://tinyurl.com/African-Research-Repositories"><strong>https://tinyurl.com/African-Research-Repositories</strong></a><br> <strong>Archived at </strong><a href="https://info.africarxiv.org/african-digital-research-repositories/"><strong>https://info.africarxiv.org/african-digital-research-repositories/</strong></a><strong> <br> Submission form: </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a></p> <p> </p> <p><strong>Licensing</strong>: Text and Visual Map – CC-BY-SA 4.0 // Dataset – CC0 (Public Domain) // The licensing of each database is determined by the database itself</p> <p>Preprint doi: <a href="https://doi.org/10.5281/zenodo.3732273">10.5281/zenodo.3732273</a>. <br> Data set doi: <a href="http://doi.org/10.5281/zenodo.3732172">10.5281/zenodo.3732172</a> // available in different formats (pdf, xls, ods, csv)</p> <p> </p> <p><strong><a href="https://info.africarxiv.org">AfricarXiv</a> in collaboration with the <a href="https://www.internationalafricaninstitute.org">International African Institute </a>(IAI) presents an interactive map of African digital research literature repositories. This drew from IAI’s earlier work from 2016 onwards to identify and list Africa-based institutional repositories that focused on identifying repositories based in African university libraries. Our earlier resources are available at </strong><a href="https://www.internationalafricaninstitute.org/repositories"><strong>https://www.internationalafricaninstitute.org/repositories</strong></a><strong>.</strong></p> <p><strong>The interactive map extends the work of the IAI to include organizational, governmental, and international repositories. It also maps the interactions between research repositories. In this dataset, we focus on institutional repositories for scholarly works, as defined by Wikipedia contributors (March 2020).</strong><br> </p> <p><strong>Objective</strong></p> <p>The map of African digital repositories was created as a resource to be used in activities addressing the following aims:</p> <ol> <li> <p>Improving the discoverability of African research and publications </p> </li> <li> <p>Enhance the interoperability of existing and emerging African repositories</p> </li> <li> <p>Identify ways through which digital scholarly search engines can enhance the discoverability of African research</p> </li> </ol> <p>We promote the dissemination of research-based knowledge from African repositories as part of a bigger landscape that also includes online journals, research data repositories, and scholarly publishers to enhance the interconnectivity and accessibility of such repositories across and beyond the African continent and to contribute to a more granular understanding of the continent’s scholarly resources. </p> <p> </p> <p><strong>Data archiving and maintenance</strong></p> <p>The map and corresponding dataset are hosted on the AfricArXiv website under ‘Resources’ at <a href="https://info.africarxiv.org/african-digital-research-repositories/">https://info.africarxiv.org/african-digital-research-repositories/</a>. The listing is not exhaustive and therefore we encourage any repositories relevant for the African continent not listed here to the <strong>submission form at </strong><a href="https://forms.gle/CnyGPmBxN59nWVB38"><strong>https://forms.gle/CnyGPmBxN59nWVB38</strong></a>, or to notify the International African Institute (email <a href="mailto:sk111@soas.ac.uk">sk111@soas.ac.uk</a>). Both AfricArXiv and IAI will continue to maintain the list of repositories as a resource for African researchers and other stakeholders including international African studies communities.</p>
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy. in Deep learning brings speed, accuracy to the life sciences.
WebMicroscope's Deep Learning AI platform automates image analyses with an approach that is faster and able to understand tissue context, which reduces steps needed for accurate results. Researchers can gain access to digitized samples, such as this image of breast-cancer tissue (left), and analyze results through the cloud platform anywhere, anytime. This is a whole slide image of a tissue section of an adrenal gland (right). Fimmic's WebMicroscope cloud platform allows researchers to manage, share, and view digital gigapixel images with any modern browser. Researchers can rapidly pan, zoom, and analyze a digital sample. Photographs: Courtesy of Fimmic Oy.
Digital rock dataset for use in machine learning research of permeability prediction
<p>Datasets and codes for the paper "Hierarchical homogenization with deep-learning-based surrogate model for rapid estimation of effective permeability from digital rocks"</p>
Podcast: Digitization and Research Collaborations. Tobias Hodel, Universität Bern
<p>Wie definiert man die Methodik in den Digital Humanities? Ein Gespräch über interdisziplinären Austausch und digitale Kooperationen im Forschungsbereich.</p> <p>How to define methodology in Digital Humanities? A conversation about interdisciplinary exchange and digital collaborations in the research field.</p> <p>Come si definisce la metodologia nelle Digital Humanities? Una conversazione sullo scambio interdisciplinare e sulle collaborazioni digitali nel campo della ricerca.</p> <p>Comment définir la méthodologie dans les Digital Humanities? Une discussion sur l'échange interdisciplinaire et la coopération numérique dans le domaine de la recherche.</p>
Monday 4 May: Web archives as a critical method for the future of digital research, Matthew Weber
<p>Monday 4 May: Web archives as a critical method for the future of digital research, Matthew Weber</p> <p>Matthew S. Weber, Associate Professor in the Hubbard School of Journalism and Mass Communication at the University of Minnesota</p>
The SUNRISE Project is Dedicated to Enhancing Primary Cancer Prevention Among Adolescents in Europe by Leveraging Innovative Digital Tools and Methodologies. SmartCoach, Developed by Swiss Research In
ClinicalTrials.gov study NCT06922201. IPD Sharing: NO. Countries: 8. Publications: 12.
Unhide® Project: A Digital Health Platform to Collect Lifestyle Data for Brain Inflammation Research
ClinicalTrials.gov study NCT04806620. IPD Sharing: YES. Countries: 1. Publications: 1.
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.