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Dataset results
31 results for “research data quality”
Dataset Dental research data availability and quality according to FAIR principles
<p>This dataset contains open access publications in EPMC dental journals from 2016 to 2021 and 500 non-open access dental publications. We evaluated the level of compliance with the FAIR principles. The original dataset and codebook are attached. </p>
Data quality assurance at research data repositories: Survey data
<p>This dataset documents findings form a survey on the status quo of data quality assurance practices at research data repositories.</p> <p>The personalized online survey was conducted among repositories indexed in re3data in 2021. It covered the scope of the repository, types of data quality assessment, quality criteria, responsibilities, details of the review process, and data quality information, and yielded 332 complete responses.</p> <p>The dataset comprises a documentation file, the data file, a codebook, and the survey instrument.</p> <p>The <strong>documentation file</strong> (documentation.pdf) outlines details of the survey design and administration, survey response, and data processing. The <strong>data file</strong> (01_survey_data.csv) contains all 332 complete responses to 19 survey questions, fully anonymized. The <strong>codebook</strong> (02_codebook.csv) describes the variables, and the <strong>survey instrument</strong> (03_survey_instrument.pdf) comprises the questionnaire that was distributed to survey participants.</p>
Multi-stakeholder research data management training as a tool to improve the quality, integrity, reliability and reproducibility of research: Quantitative data of the post-course surveys
<p>Data contains doctoral students' and postdoc researchers' (n=168) self-ratings of their RDM competencies before and after the 3 ECTS credits "Basics of Research Data Management" (BRDM) trainings held 2019-2021 in the University of Turku and Åbo Akademi University, Finland. Moreover, data contains respondents' self-reported further learning needs.</p>
Data relating to Clyne et al. Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study
<p>Data relating to the study reported in the paper "Quality, scope and reporting standards of randomised controlled trials in Irish Health Research: an observational study".</p>
Assessing Quality Variations in Early Career Researchers' Data Management Plans: Quantitative Data of the Content Analysis
<p>The data includes the numerical results of the ranking of the data management plans created during the Basics of Research Data Management (BRDM) courses worth 3 ECTS credits in the years 2020 - 2022. The ranking was made using the Finnish DMP Evaluation Guidance (https://doi.org/10.5281/zenodo.4729831). Additionally, the data contains the results of the analysis of the best RDM practices included in the DMPs.</p> <p>Note 1: The comma-separated coded CSV version 1 (5.2.2024) may not open correctly on MacOS. You can use the comma-delimited CSV file version 2 or 3 (31.5.2024).</p> <p>Note 2: Versions 1 (Quality_variations_in_ECRs_DMPs_data) and 3 (Quality_variations_in_ECRs_DMPs_data_ver_3) contain evaluations of DMPs, best practices for data management, as well as methods for data sharing, storage, and preservation. In version 2 (Quality_variations_in_ECRs_DMPs_data_ver_2), the methods for data sharing, storage, and preservation are missing.</p> <p>Data is related to the research article https://doi.org/10.2218/ijdc.v18i1.873.</p>
Does size matter? Quality assessment of the size property in research data repositories
<p>Code and data for master's thesis on quality assessment of the size property in research data repositories. Research questions:</p> <ul> <li> <p> For what semantic concepts is the size property of repositories being used?</p> </li> <li> <p>What kind of quality factors can be detected when assessing the size property in a registry for research data repositories?</p> </li> <li> <p>Which automated and intellectual measures can improve the quality of the size property?</p> </li> </ul> <p>Method 1: Data analysis of size and related properties over all re3data records</p> <ul> <li> <p>Property selection</p> </li> <li> <p>Data extraction from API</p> </li> <li> <p>Data normalization</p> </li> <li> <p>Typing of patterns: mainly units of size</p> </li> <li> <p>Analysis: ~quantitative, mainly univariate, but also some multivariate / time</p> </li> </ul> <p>[Included in the publication:</p> <p>Method 2: Case Study of size in individual repositories</p> <ul> <li> <p>Repository selection: purposive sampling</p> </li> <li> <p>Data capture from GUI / API</p> </li> <li> <p>Analysis: ~qualitative]</p> </li> </ul>
Data from an experiment on the effects of using an experimental framework on the quality of research designs
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Data and Code for "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility"
<p>This snapshot contains code and data for the preprint "Value dissonance in research(er) assessment: Individual and institutional priorities in review, promotion and tenure criteria related to research quality, quantity, openness and responsibility".</p> <p>Instructions on re-using the data and running the code can be found in the README.md.</p> <p>Changes:</p> <ul> <li>Added survey instrument and informed consent.</li> </ul>
Research Data Quality Assurance – an intellectual property perspective
<p>The presentation summarizes the intellectual property issues, regarding the second Demonstrator in the FAIR Data Spaces project, Research Data Quality Assurance. It will especially outline the specific copyright law paragraphs, which may be and are important for this demonstrator and the participants in the project.</p>
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
<p><strong>Background</strong></p> <p>Understanding the use of invasive procedures (IPs) at the end-of-life (EoL) is important to avoid under- and overtreatment, but epidemiologic analysis is hampered by limited methods to define treatment intent and EoL phase. This study applied novel methods to report IPs at the EoL using a colorectal cancer (CRC) case study.</p> <p><strong>Methods</strong></p> <p>An English population-based cohort of adult patients diagnosed between 2013 and 2015 was used with follow-up to 2018. Procedure intent (curative, non-curative, diagnostic) by cancer site and stage at diagnosis was classified by two surgeons independently. Joinpoint regression modelled weekly rates of IPs for 36 sub-cohorts of patients with incremental survival of 0-36 months. EoL phase was defined by a significant IP rate change before death. Zero-inflated Poisson regression explored associations between IP rates and clinical/sociodemographic variables.</p> <p><strong>Results</strong></p> <p>Of 87,731 patients included, 41,972 (48%) died. 9,492 procedures were classified by intent (interrater agreement 99.8%). Patients received 502,895 IPs (1.39 and 3.36 per person year for survivors and decedents). Joinpoint regression identified significant increases in IPs four weeks before death in those living 3-6 months, and eight weeks before death in those living 7–36 months from diagnosis. 7,908 (18.8%) patients underwent IPs at the EoL, with stoma formation the most common major procedure. Younger age, early-stage disease, men, lower comorbidity, those receiving chemotherapy and living longer from diagnosis were associated with IPs.</p> <p><strong>Conclusions</strong></p> <p>Methods to identify and classify IPs at the EoL were developed and tested within a CRC population. This approach can be now extended and validated to identify potential under- and overtreatment. </p>
Data from: Novel methods to define invasive procedures at the end-of-life were developed to improve quality of end of life care research: A population-based cohort study in colorectal cancer
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Data from: Reporting guidelines for community-based participatory research (CBPR) did not improve the reporting quality of published studies: a systematic review of studies on smoking cessation
<p>Although a guideline for reporting the results of community-based participatory research (CBPR) was published in 2010, the impact on the quality of reporting a CBPR on smoking cessation is unknown. Here we provide the raw data of a systematic review that assessed the impact of a 2010 community-based participatory research reporting guideline. on the quality of reporting a CBPR on smoking cessation. Specifically, we searched the MEDLINE, Embase, the Cochrane Central Register for Controlled Trials (CENTRAL), PsycINFO, and CINAHL databases and included articles published up to October 2018 (PROSPERO: CRD42019111668). We assessed reporting quality using a 13-item checklist.</p>
Data from: Improper data practices erode the quality of global ecological databases and impede the progress of ecological research
<p>The scientific community has entered an era of big data. However, with big data comes big responsibilities, and best practices for how data are contributed to databases have not kept pace with the collection, aggregation, and analysis of big data. Here, we rigorously assess the quantity of data for specific leaf area (SLA) available within the largest and most frequently used global plant trait database, the TRY Plant Trait Database, exploring how much of the data were <em>applicable </em>(i.e., original, representative, logical, and comparable) and <em>traceable </em>(i.e., published, cited, and consistent). Over three-quarters of the SLA data in TRY either lacked applicability or traceability, leaving only 22.9% of the original data usable compared to the 64.9% typically deemed usable by standard data cleaning protocols. The remaining usable data differed markedly from the original for many species, which led to altered interpretation of ecological analyses. Though the data we consider here make up only 4.5% of SLA data within TRY, similar issues of <em>applicability </em>and <em>traceability </em>likely apply to SLA data for other species as well as other commonly measured, uploaded, and downloaded plant traits. We end with suggested steps forward for global ecological databases, including suggestions for both uploaders to and curators of databases with the hope that, through addressing the issues raised here, we can increase data quality and integrity within the ecological community.</p>
An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters: Data and R code
<p>These are the data and R code that accompany the Forest Ecology and Management publication titled "An evidence map of research assessing the effects of timber harvesting on water quality, biotic and biodiversity indicators in running waters". </p>
Integrating Data Quality in Industrial Big Data Architectures: an Action Design Research Study
<p>This dataset includes the results of the ATAM analysis and the summary of the ADR process. </p>
Translanguaging experimental research: list of eligible studies and quality assessment data
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Data from: Reporting guidelines for community-based participatory research (CBPR) did not improve the reporting quality of published studies: a systematic review of studies on smoking cessation
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Data from: Improper data practices erode the quality of global ecological databases and impede the progress of ecological research
Open the record for dataset details and reuse information.
Data from: A call for more transparent reporting of error rates: the quality of AFLP data in ecological and evolutionary research
Despite much discussion of the importance of quantifying and reporting genotyping error in molecular studies, it is still not standard practice in the literature. This is particularly a concern for amplified fragment length polymorphism (AFLP) studies, where differences in laboratory, peak-calling and locus-selection protocols can generate data sets varying widely in genotyping error rate, the number of loci used and potentially estimates of genetic diversity or differentiation. In our experience, papers rarely provide adequate information on AFLP reproducibility, making meaningful comparisons among studies difficult. To quantify the extent of this problem, we reviewed the current molecular ecology literature (470 recent AFLP articles) to determine the proportion of studies that report an error rate and follow established guidelines for assessing error. Fifty-four per cent of recent articles do not report any assessment of data set reproducibility. Of those studies that do claim to have assessed reproducibility, the majority (~90%) either do not report a specific error rate or do not provide sufficient details to allow the reader to judge whether error was assessed correctly. Even of the papers that do report an error rate and provide details, many (≥23%) do not follow recommended standards for quantifying error. These issues also exist for other marker types such as microsatellites, and next-generation sequencing techniques, particularly those which use restriction enzymes for fragment generation. Therefore, we urge all researchers conducting genotyping studies to estimate and more transparently report genotyping error using existing guidelines and encourage journals to enforce stricter standards for the publication of genotyping studies.
Figure 9 from: Klein A (2016) Crowdsourcing voice editing and quality assessment of data collected from the largest mobile phone-based research study of Parkinson disease. Research Ideas and Outcomes 2: e8848. https://doi.org/10.3897/rio.2.e8848
Figure 9 - Timeline. We will prepare and update the Web app (Aim 2) as we develop it for use in annotating gold standard audio data (Aim 1) and as we get feedback on its use in connection with Amazon's Mechanical Turk (Aim 2). Year 2 will consist primarily of testing the aggregation of annotated audio data for further analysis (Aim 2), to train an automated approach (Exploratory Aim), and to publish and present our findings.
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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.