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78 results for “Research Identifiers”
Dataset to "Persistent Identifiers for File Formats: enabling preservation and re-use of research data"
<p>This fileset includes a "preprint" and the main dataset <em>fileformatRecognizer</em> (as .xlsx and .csv) to the paper "Persistent identifiers for file formats: enabling preservation and re-use of research data" submitted to iPRES 2019, but subsequently rejected after peer review. For the sake of transparency, permission to make available here the anonymous reviews motivating the rejection (<em>ReviewsPIDs4fileFormats.odt</em>) was asked, but was left without response. Some images (screendumps) and text result files from file identification tools tested are included. Further, a simple xquery command file (BaseX) for <em>fetch:content-type</em>()<em>, </em>used for getting MIME-types for files, is also provided.</p>
Identified Charcoal Hearths from "Slope Analysis of 'Digital Elevation Model for Blue Mountain Charcoal Research Project'"
<p>This is a GeoJSON file that lists all of the potential charcoal hearths along the Blue Mountain of eastern Pennsylvania. For a detailed description of how this data was produced, please see:</p> <p>Carter, Benjamin. (2018, May 29). Description of Methods for Identifying Charcoal Hearths along the Blue Mountain of Pennsylvania. (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1255101</p> <p>These hearths were identified using this data:</p> <p>Carter, Benjamin. (2018). Slope Analysis of "Digital Elevation Model for Blue Mountain Charcoal Research Project" (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252977</p> <p>The above is derived from:</p> <p>Carter, Benjamin P. (2018). Digital Elevation Model for Blue Mountain Charcoal Research Project (Version 0.1.0). Zenodo. http://doi.org/10.5281/zenodo.1252441</p> <p> </p>
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania
<p>This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.</p> <p>The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.</p>
Identifying Interdisciplinary Research in Research Projects
<p>The dataset is based on the paper: </p> <p>Hoang-Son Pham, Bram Vancraeynest, Hanne Poelmans, Sadia Vancauwenbergh and Amr Ali-Eldin ‘’Identifying Interdisciplinary Research in Research Projects’’, Scientometrics (Accepted).</p> <p>The dataset includes: </p> <p>1. FRIS_project_2019: a list of project metadata extracted from FRIS portal</p> <p>2. VODS_disciplines: a list of VODS disciplines</p> <p>3. Distance matrix: Distance matrix</p> <p> </p> <p>* Kindly refer to our paper for more details on the dataset.</p>
Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes
<p>The lack of high-quality information on data-poor species can hinder efforts to inform conservation actions via spatial distribution modeling. This is particularly true for tropical birds of conservation concern, for which ecological studies and assessments of their conservation status have received limited funding. Here we use a cost- and time-efficient protocol for assessing the distribution of range-restricted taxa and to identify priority areas for their conservation based on a sequential application of Environmental Niche Models (ENMs) and Occupancy-Detection Models. This approach first uses available geographical information and niche-theory to prioritize potential study sites, which can later be surveyed to obtain high-quality presence-absence data to accurately model distributional ranges with limited resources. We apply this protocol to identify priority areas for two Neotropical birds of conservation concern endemic to the Colombian Andes: Yellow-headed Brush-finch (<i>Atlapetes flaviceps</i>) and Tolima Dove (<i>Leptotila conoveri</i>). We first fitted ENMs using spatially-filtered datasets containing all available records up to 2018. We then conducted field surveys across climatically suitable areas identified for both species, carrying out a total of 1750 counts to generate input data for the occupancy models. Overall, our results suggested more extended and more continuous distribution ranges for both species than previously reported, but also identified population strongholds that are not currently represented within the national protected areas system. Both species occupied a narrow elevational belt (~1300–2600) of the Central Andes of Colombia primarily on the slopes of the Magdalena River valley, with isolated populations in the Western and Eastern Andes; these areas have undergone some of the most marked landscape transformations in Colombia. This straightforward protocol maximizes available information and minimizes costs, while allowing for estimation of occurrence probabilities for range-restricted, data-poor taxa.</p>
Research Data Management - Persistent identifier (Video)
<p>Video of our latest presentation for the training seminars in Research Data Management of the FoDaKo-project: <a href="https://fodako.de/">https://fodako.de</a></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>
Supplementary Material to "Identifying major research themes in Sport Science" (Interactive Science Maps)
<p><strong>Links to interactive maps</strong></p> <p>(1) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=4">World Map</a></strong> (<a href="https://tinyurl.com/2zufzslg">https://tinyurl.com/2zufzslg</a>)</p> <p>(2) Link: <strong><a href="https://app.vosviewer.com/?map=https://drive.google.com/uc?id=182n1HEZygAoS_9Caxj83hi27y41N6SWm&item_size=5&item_size_variation=0.4&scale=0.9">Sport Science Map</a></strong> (<a href="https://tinyurl.com/2fztyjhf">https://tinyurl.com/2fztyjhf</a>)</p>
Supplementary Material to "Identifying major research themes in sport sciences" (Dataset)
<p>The Excel file is organized into the following sheets:</p> <ul> <li><strong>SM_Sheet1</strong> - global bibliometric indicators in the sport sciences</li> <li><strong>SM_Sheet2</strong> - main indicators at country for two different period</li> <li><strong>SM_Sheet3</strong> - list of 4159 research topics for world science research</li> <li><strong>SM_Sheet4</strong> - a detailed description of main research topics in sport sciences</li> <li><strong>SM_Sheet5</strong> - the number of journals included in the Journal Citation Reports (JCR) in the sport science category and the topic distribution according to the Gini Index are as follows</li> </ul>
Survey to help identify whether Octopus might help researchers produce high quality, open research
<h2>Survey to help identify whether Octopus might help researchers produce high quality, open research</h2><p>In order to complement in-depth, but necessarily small sample size, interviews, an online survey allowed us to reach a broader population to gain some quantitative data on the current research culture and barriers to best practice, as well as whether the Octopus platform might help overcome them.</p><p>The finalised survey was implemented on EUSurvey. It is a fully open source online survey platform developed and administered by the European Commission, adhering to relevant privacy regulations (e.g. the General Data Protection Regulation (GDPR)).</p><p>The survey was open from 17 January to 5 February 2023.</p><p>Details on the implementation of this survey is published on Octopus.</p>
Data from: Sequential use of niche and occupancy models identifies conservation and research priority areas for two data-poor endemic birds from the Colombian Andes
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MiRoR1 - P2 - Key stakeholders' perspectives and experiences with defining, identifying and displaying gaps in health research: a qualitative study protocol
<p>Supporting data items for protocol on key stakeholders’ perspectives and experiences with defining, identifying and displaying gaps in health research: a qualitative study protocol</p>
Research data supporting article "Recurrent horizontal transfer identifies mitochondrial positive selection in a transmissible cancer"
<p>Gene expression data supporting analyses described in the article "Recurrent horizontal transfer identifies mitochondrial positive selection in a transmissible cancer" (Strakova et al., 2020).</p>
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.
Identifying hotspots and priority areas for xenarthran research and conservation
<p><span><b>Aim: </b>Limited funds for conservation and research require the development of prioritization schemes. Traditionally, biodiversity metrics were used to delineate priority areas; however, a growing realization emphasizes that logistic factors should be also considered. Here, we combine species richness, past collection efforts, degree of habitat loss and accessibility to define priority areas and spatially orient fieldwork in a cost-effective manner for xenarthrans, one of the four main mammalian radiation.</span></p> <p><span><b>Location: </b>Neotropics.</span></p> <p><span><b>Methods: </b>We assessed spatial patterns of species richness in Xenarthra and identify diversity hotspots based on species distribution models. Spatial patterns and biases in the Xenarthra past collection efforts were analyzed using a comprehensive database including 33,464 individual records. Finally, we produced priority area indices relating species richness and collection efforts with levels of habitat loss and accessibility (roads and rivers network) to highlight important but neglected areas. </span></p> <p><span><b>Results:</b> Collection efforts were concentrated to a small portion of the Neotropics (central-southern Brazil, eastern Bolivia and north-western Argentina) and were biased toward access routes. Major xenarthran diversity hotspots include the Amazonian lowlands of Bolivia and the dry Chaco of Paraguay and Argentina. Priority areas for research varied depending on the metric analyzed. Amazon holds a high diversity that remains poorly explored. Central Argentina and northeastern Brazil are priority areas for research and conservation given the low sampling efforts, high diversity and endemic species, high levels of habitat loss and a dense road network.</span></p> <p><span><b>Main conclusions: </b>Most areas of the Neotropics lack a proper assessment of the xenarthran assemblage, reflecting extensive knowledge shortfalls. Overall, sites close to roads tend to be better sampled, but many areas with a dense road network are under-sampled, being good candidates for low-cost studies. There is an alarming spatial overlap between Xenarthra diversity hotspots and areas facing intense human modification. Some of the priority areas for research should also be viewed as priority areas for conservation.</span></p>
Supplementary materials of the paper Identifying benchmark units for research management and evaluation
<p>Supplementary materials of the paper Identifying benchmark units for research management and evaluation</p> <p>Supplementary1 Rsearch topics of Hero-m and SciLifeLab</p> <p>Supplementary2 Topics of benchmarks</p> <p>Supplementary3 Measures of connectivity</p>
Supplementary material 3 from: Hardisty AR, Addink W, Glöckler F, Güntsch A, Islam S, Weiland C (2021) A choice of persistent identifier schemes for the Distributed System of Scientific Collections (DiSSCo). Research Ideas and Outcomes 7: e67379. https://doi.org/10.3897/rio.7.e67379
Comparison of main roles and responsibilities in the two main categories of administration of a Handle-based PID scheme
Research on Identifying and Treatment Prognosis of Chronic Rhinosinusitis Based on Image and Sequencing Data
ClinicalTrials.gov study NCT05983003. IPD Sharing: NO. Countries: 1. Publications: 2.
Identifying hotspots and priority areas for xenarthran research and conservation
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Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Open the record for dataset details and reuse information.
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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.