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2,565 results for “overview”
Overview of the time series in the PALMOD 130k marine palaeoclimate data synthesis
<p>Palaeoclimate time series in the PALMOD 130k marine palaeoclimate data synthesis v1.0.1. This table lists the site names and location, parameters including additional information as well as the source of the data and the original publications where the data were presented.</p>
S80 | PFASGLUEGE | Overview of PFAS Uses
<p>This is the collection associated with list S80 PFASGLUEGE Overview of PFAS Uses on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>An overview of the uses of per- and polyfluoroalkyl substances (PFAS) from Glüge <em>et al</em> (2020) DOI: <a href="https://doi.org/10.1039/D0EM00291G">10.1039/D0EM00291G</a>.</p> <p>This data collection contains:</p> <ul> <li>Original ESI files (ESI-2.xlsm, ESI-3.xlsm) CC-BY-NC</li> <li>ESI-3 on a single sheet with structure mappings (as XLSX and CSV)</li> <li>ESI-2 on a single sheet with structure mappings (as XLSX and CSV) [added Jan 2022]</li> <li>PFASGLUEGE substances (unique structural information, as XLSX and CSV) [including ESI2 Jan 2022]</li> <li>InChIKey and DTXSID mapping files</li> <li>Additional mapping files to value add ESI-2 and ESI-3 content as a zip file (also on GitLab).</li> </ul> <p>Huge thanks to Parviel Chirsir for the FAIRification of this set.</p> <p>Update 21-22 March 2022: adjusted use information for the <a href="https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101">classification tree</a>.</p>
Transcribing audio data: overview and transcripts of several automatic transcription tools
<p>Throughout institutions, audio recordings are being made regularly. To be able to further process these recordings, the audio often needs to be transcribed. In order to avoid having to transcribe the audio manually, there is a wealth of tools available for doing so automatically. In this record, we present an overview of several often-used tools to automatically transcribe pre-recorded audio data, including their features, costs, and security.</p> <p>To check the quality of the tool, we also recorded an audio fragment in Dutch that we ran through all tools in this overview in March of 2022. This original audio fragment (Test_interview_20220203.mp3), the cleaned-up transcription (Test_interview_cleaned_transcript.odt) and each tool’s raw transcript of the audio fragment (Test_interview_[name-tool]_raw_[date-run]) are included in this record as well. The raw transcripts were downloaded as .docx or .txt files and the .docx files saved as .odt. No edits to the transcripts were made before saving them, except an incidental removal of a personal email address or hyperlink.</p> <p>The overview contains information and transcripts of following transcription tools:</p> <ul> <li>Amberscript</li> <li>HappyScribe</li> <li>Kaldi</li> <li>NVIVO transcription</li> <li>Sonix</li> <li>SpokenOnline</li> <li>Transcribe</li> <li>Trint</li> <li>Microsoft Word 365 Online</li> </ul> <p><strong>About</strong></p> <p>This overview was created through a collaboration between Utrecht University’s Research Data Management (RDM) Support and the <a href="https://datahub.sites.uu.nl/">DataHub SSH</a> programme situated at the faculty of Humanities.</p> <p>The details in the overview have last been updated April 19, 2022. Please note that at the time you are downloading these files, the quality of the (Dutch) speech-to-text conversion may have been improved by the respective supplier.</p>
Attributes: A Curriculum Analytics System for measuring learning outcomes - Overview
<p><span><strong>Link to video </strong><a href="https://vimeo.com/1015456231?share=copy#t=0"><strong>https://vimeo.com/1015456231?share=copy - t=0</strong></a><br><br>The Curriculum Analytics System at the Instituto Tecnológico de Costa Rica, integrated into TEC Digital, supports faculty, coordinators, and students in assessing engineering learning outcomes during accreditation processes. The system offers two key user modules: one for coordinators to map and manage learning outcomes, and another for instructors to conduct assessments through the course portal. Coordinators oversee course and attribute mapping using visual representations of study plans, control points, and outcome visualizations. Instructors configure assignments and evaluate student submissions with standardized rating scales. The system tracks progress in real-time and generates</span> <span>comprehensive reports with performance metrics, facilitating continuous improvement in academic programs.</span></p> <p><strong><span>Key words: </span></strong><span>attributes, learning outcomes, TEC Digital, curriculum analytics, continuous improvement. </span></p>
RDA Overview for the Social Sciences - October 2022
<p>This upload is the fourth version of a dataset on the RDA groups and outputs which was generated in October 2022. It is connected to a report that described a project that assessed the RDA Groups and Outcomes and their relevance for social science researchers. This datasets gives an overview of the results of this analysis, providing a list of all RDA Groups as well as an indication of the relevance for Social Sciences researchers. The project was executed and updated by Data Archiving and Networked Services (DANS).</p> <p>This dataset contains:</p> <p>- an excel overview of the RDA Working and Interest groups</p> <p>- additional csv copies of the separate sheets of the excel overview</p>
Spike-ACE2 interaction overview
<p>UnityMol 3D model export related to FAIR sharing of molecular visualization experiences illustrated with COVID-19-related data. See our paper (to come)</p>
Build-in-Wood Regulation Analysis – Overview
<p>This dataset contains an overview of selected EU Member State building regulations relevant for construction of multi-storey wood buildings. The data has been collected as part of the Build-in-Wood project (<a href="https://www.build-in-wood.eu/)">https://www.build-in-wood.eu/)</a> which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 862820.</p> <p>Disclaimer: The presented data might be outdated, flawed, or otherwise incomplete. Users are responsible for checking the correctness of the presented data.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - overview videos
<p>We reconstruct the 3D shape of the micro-vasculature in human bone marrow from serial sections.</p> <p>This upload shows three kinds of overview data.</p> <p>- R[n]_overview_... shows an overview of the mesh from ROI n as video<br> - R[n]_input_animated_... shows a thumb cinema overlay of initial input data<br> - R[n]_both.png shows a still with a frame from overview video (blue) and diameter measurement video (red).</p>
Future Blood Testing Network+ Overview and Recap - Dr Weizi (Vicky) Li
<p>This video is the first talk from our Future of Healthcare: Remote Blood Testing, Monitoring & AI Meeting that took place on 07-08/11/2023. </p><p>Future Blood Testing Network+ Overview and Recap - Dr Weizi (Vicky) Li (Henley Business School, University of Reading). </p><p>Bio: Weizi (Vicky) Li is a Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is a Fellow of Charted Institute of IT (British Computer Society). She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is currently the Principal Investigator and Director of EPSRC Future Blood Testing for Inclusive Monitoring and Personalised Analytics NetworkPlus; and EPSRC AI for Health project: Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud-based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 and 4*/3* impact case study in REF 2021 for her research impact on healthcare quality improvement. She is the academic lead of a machine learning-based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received the Research Engagement and Impact award in 2020, shortlisted for 2022 impact award and Health Service Journal (HSJ) patient safety award. </p><p>Further details on this event can be found at: https://www.futurebloodtesting.org/fbtn2023 </p><p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p><p>YouTube Link: https://youtu.be/Fqqekmhg79Q?si=l74rXxTFMx42mEsV</p>
CCG Programme overview: information flows
<p>Schematic representation of the CCG Programme and related information flows across Output Areas (OA) and Work Streams (WS), with expected outcomes and interactions with external partner countries and international partner organisations. </p>
An epidemiological overview of the equine influenza epidemic in Great Britain during 2019: Dataset
<p>This repository contains datasets and code used for the manuscript as titled. All details regarding the data source and considerations that should be noted are discussed in the manuscript.</p> <p><strong>Referencing this dataset</strong></p> <p>Fleur Whitlock, John Grewar & J. Richard Newton (2022) An epidemiological overview of the equine influenza epidemic in Great Britain during 2019 [Dataset]. University of Cambridge. <a href="https://doi.org/10.5281/zenodo.5886153">https://doi.org/10.5281/zenodo.7010228</a></p> <p> </p>
Overview of OMOP2OBO
<p>This figure provides an overview of the OMOP2OBO algorithm. As shown in the figure, the algorithm consists of five steps as indicated by the red-labeled grey boxes: (1) <strong>Query OMOP CDM. </strong>This step processes a table of OMOP concepts including identifiers, source codes, labels, synonyms, and concept ancestors. (2) <strong>Process OBO Foundry Ontologies.</strong> During this step, one or more OBO Foundry ontologies are downloaded and current classes, database cross-references, labels, and synonyms are extracted. (3) <strong>Map OMOP Standard Vocabulary Concepts to OBO Foundry Ontology Concepts.</strong> This step obtains three types of mappings (as indicated by the white boxes) and relies on publicly available resources including OHDSI Ananke and the UMLS Metathesaurus. First, exact-string matches between OMOP and OBO concept labels, definitions, and synonyms are obtained. Exact matches between OMOP standard concept codes and source codes and OBO ontology database cross-references are also obtained. Then, a scoring metric, like cosine similarity, is applied to embeddings learned from labels and synonyms for all OMOP and OBO concepts and concept ancestors. Finally, for all concepts not automatically mapped, this step also accepts manual mappings. (4) <strong>Synthesize and Process Mapping Results.</strong> This steps processes and synthesizes the mappings from the prior steps providing a mapping category (i.e., string constructed by combining: (i) the approach used to create it (i.e., “automatic”, “manual”, “cosine similarity”, or “unmapped”), (ii) cardinality (i.e., one-to-one or one-to-many), and (iii) level (i.e., concept or ancestor)) and mapping evidence (i.e., pipe-delimited free-text phrases that explain what fields were used to construct the mapping) for each mapping. (5) <strong>Output Mappings. </strong>The final step outputs the mappings as a flat-file, SQL database dump, or RDF/XML file.</p> <p>Acronyms: OBO (Open Biological and Biomedical Ontology); OHDSI (Observational Health Data Sciences and Informatics); OMOP (Observational Medical Outcomes Partnership); UMLS (Unified Medical Language System).</p>
Overview of the PheKnowLator Ecosystem
<p>This figure provides an overview of the (<strong>Phe</strong>notype <strong>Know</strong>ledge Trans<strong>Lator</strong>) Ecosystem. The Ecosystem consists of three components as indicated by the three gray boxes: (1) <strong>Knowledge Graph Construction Resources.</strong> This component consists of resources to download and process a wide variety of heterogeneous data sources and an algorithm to customize the construction of large-scale biomedical knowledge graphs; (2) <strong>Knowledge Graph Benchmarks.</strong> This component consists of benchmarks that can be used to assess knowledge graph construction and data source decisions, compare against existing knowledge graphs, and evaluate downstream analyses, workflows, and learning algorithms; (3) <strong>Knowledge Graph Tools.</strong> This component consists of tools to use knowledge graphs, which include Jupyter Notebook-based use cases and tutorials, cloud-based storage, APIs, and triplestores. Acronyms: NT (N-Triples file format); OWL (Web Ontology Language); PKL (Python pickle file format); SPARQL (SPARQL Protocol and RDF Query Language).</p>
A recent overview of the state-of-the-art elements of text classification - dataset
<p>The two available datasets were used to conduct the quantitative analysis of the text classification area. The set, such as:</p> <ol> <li>biblio.bib contains all articles that are grouped in categories</li> <li>biblio.csv contains processed records from biblio.bib, based on it were built the statistics presented in the article</li> </ol>
Edward FitzGerald Life and Letters – Overview of all letters
<p>These files form part of an archive of research material relating to the Life and Letters of Edward FitzGerald. The data have been compiled by independent researchers W H (Bill) Martin and Sandra Mason; their contact details are below. The files comprise a number of searchable listings of information contained in FitzGerald’s letters. The information formed input to a book on Edward FitzGerald which is referenced below.</p> <p>This section of the archive contains a large database, <em>efgdb</em>, giving an overview of the letters, their dating, the people to whom FitzGerald wrote, his location at the time of writing, and a broad classification of the content of each letter. The letters are those contained in the collection published by A M Terhune and A B Terhune in 1980 – see reference below. An explanatory README text file contains further information on the database, including a table showing the fields included in the database and giving definitions of them and of the codings used where relevant. </p>
PhasAGE Training School 1 -Overview of bioinformatics tools for the life sciences & Classification and evolution of non-globular proteins- LECTUREs
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Overview: Simulation Module + Optimisation Algorithm + LCA Support Tool
<p>Overview of the Model2Bio elements: Simulation Module + Optimisation Algorithm + LCA Support Tool</p> <p>Platforms showing all the processes of the Model2Bio project. From the beginning of the Simulation Module, the variables for the production line models (agriculture and food production industries) to bio-products created from the residues.</p>
PhasAGE Training School 1 - Computational prediction and databases of protein phase separation Overview- LECTURE
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
DECISO Digital Ecosystem overview
<p>The Digital Ecosystem is part of the overall exosystemic approach used within the DECISO project, which implies the stakeholders' engagement at different levels and different scales in the face2face, virtual and hybrid activities of the project, but also after the end of the project for exploiting the results and share experiences and the knowledge produced by the consortium. The DECISO Digital Ecosystem will facilitate organizing events and sharing content using a participatory approach, i.e., each partner can directly upload and manage content related to events. The Digital Ecosystem is directly accessible from the DECISO Digital Ecosystem section of the DECISO website and has been designed by CNR starting from the experience of the MARINA and BIOVOICES platforms.</p>
Dataset: an overview of knowledge graphs in NFDI
<p>This dataset contains a list of knowledge graphs (KGs), KG software, KG publications and KG use cases in context of NFDI (German National Research Data Infrastructure). The related manuscript is submitted to a poster session at the <em>1st Conference on Research Data Infrastructure - Connecting Communities, </em>12. – 14. September 2023, Karlsruhe, Germany: 'Who is using Knowledge Graphs in NFDI? An overview by the Working Group "Knowledge Graphs"'.</p>
ScienceDex guides
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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.