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1,037 results for “quality improvement”
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>
Experimental data for the study: "Naturalistic visualization of reaching movements using head-mounted displays improves movement quality and proves high usability compared to conventional computer screens"
<p>The datasets contains the motor performance metrics and the questionnaire responses for two experiments involving a motor task with a VR controller (experiment 1, healthy old participants) or a rehabilitation assistive device (experiment 2, brain-injured patients) and three visualization technologies: an immersive virtual reality (IVR) head-mounted display (HMD), an augmented reality (AR) HMD, and a computer screen (2D screen). The study was performed in the Motor Learning and Neurorehabilitation Laboratory at the University of Bern. All data are stored in “csv” files. The variables inside the files are explained in “DataFrameDescription.rtf”. For questions, please contact L.MarchalCrespo@tudelft.nl.</p>
Improvement of editorial quality of journals indexed in DOAJ
<p>In 2013, Directory of Open Access Journals (DOAJ) completely changed the inclusion criteria and journal evaluation process, starting to remove journals that not comply with these criteria. The present dataset contains 12.577 journals included in DOAJ since the launch of the Directory in 2002 until May 15th, 2016 that was examined and enriched with other data, in order to examinatethe results of the new process and its capability to improve the quality of the directory and the reliability of the contained information.</p>
Weekly county-level pollution data for China from Zhang, Carleton, Lin, and Zhou (accepted, Nature Sustainability), "Estimating the role of air quality improvements in the decline of suicide rates in China"
<p>This dataset contains weekly, county-level air pollution data for 2,839 counties from 2013 to early 2018. These data are used and described in Zhang, Carleton, Lin, and Zhou (accepted, <em>Nature Sustainability</em>), "Estimating the role of air quality improvements in the decline of suicide rates in China". When the paper is published a link to the manuscript will be added here. </p> <p>The manuscript Methods section details data construction. In summary, these county-level observations are obtained from monitoring stations maintained by the China National Environmental Monitoring Center (CNEMC), which is affiliated with the Ministry of Ecology and Environment of China. CNEMC began publishing hourly air pollution data in 2013, including the Air Quality Index, PM2.5, PM10, ozone, sulfur dioxide, nitrogen dioxide, and carbon monoxide. We average hourly data to the station-day level and use inverse-distance weighting with a radius of 200km to convert data from station to the county level. We average across days to generate county-level weekly values. Any missing station-hour observations in the raw data are omitted in this spatial and temporal aggregation. Our main analysis relies on PM2.5, but all pollutants are released here.</p>
Project "Public services management system to improve the quality and accessibility of services" (01.2.2-LMT-K-718-03-0019) interviews
<p>The dataset of depersonalized qualitative semi-structured interviews with the representatives of public sector organisations providing public services in Lithuania. The data were collected in December-November, 2022 as a part of the project "Public services management system to improve the quality and accessibility of services" ("Viešųjų paslaugų vadybos sistema paslaugų kokybei ir prieinamumui gerinti"), grant no. 01.2.2-LMT-K-718-03-0019, funded by the Lithuanian research council. The interviews are in the Lithuanian language.</p>
Project "Public services management system to improve the quality and accessibility of services" (01.2.2-LMT-K-718-03-0019) literature review screening results
<p>The results of the keyword query in Scopus search with the abstracts were screened using <i>abstractr </i>platform at <a href="http://abstrackr.cebm.brown.edu">http://abstrackr.cebm.brown.edu</a>. Four reviewers reviewed intersecting subsets of the overall list of publications in separate reviews, therefore duplicate records in the file are possible. The results from four reviews were combined into one file using functionality of <i>abstractr </i>platform. The reviews were finalized in February, 2022. Majority of the publications from the Scopus query results were automatically assigned low relevance scores thanks to the active learning algorithm used by <i>abstractr</i> and therefore were not reviewed manually.</p><p>Notes on the columns of the dataset:</p><ul><li>(internal) id - internal id added by <i>abstractr.</i></li><li>(source) id - Scopus document id followed by underscore and '1' (if publication has DOI) or 'n' (if publication has no DOI).</li><li>keywords - authors' keywords and Scopus keywords concatenated from the Scopus query results.</li><li>abstract - an abstract of a publication from the Scopus query results.</li><li>title - title of a publication from the Scopus query results.</li><li>journal - journal of a publication from the Scopus query results.</li><li>authors - authors of a publlication from the Scopus query results.</li><li>consensus - for publications reviewed by multiple reviewers the consensus decision is signified by '1', no consensus - by 'x' and unable to asses consensus by 'o'. These codes are generated by <i>abstrackr.</i></li><li>eg - the first reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>dj - the second reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>rp - the third reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>mp - the fourth reviewer id. Code '1' means the publication was selected based on title and abstract, '0' - unsure, '-1' rejected.</li><li>count (+1) - integer, the number of reviewers selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (0) - integer, the number of reviewers not sure of selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>count (-1) - integer, the number of reviewers not selecting the publication for fulltext reading. Calculated from 'eg ','dj ','rp' and 'mp' columns.</li><li>at leat once selected - binary integer, representing the final decision rule to select publications for fulltext reading and further analysis.</li></ul><p>The data were collected as a part of the project "Public services management system to improve the quality and accessibility of services" ("Viešųjų paslaugų vadybos sistema paslaugų kokybei ir prieinamumui gerinti"), grant no. 01.2.2-LMT-K-718-03-0019, funded by the Lithuanian research council.</p>
Soilcare data agricultural soil quality improvement
<p>Dataset that contains survey data on Spanish and UK respondents on agricultural soil quality protection and improvement</p>
Fig. 3 in Performance improvement through quality evaluations of sterile cactus moths, Cactoblastis cactorum (Lepidoptera: Pyralidae), mass-reared at two insectaries
Fig. 3. The mean percentage increase in mating of Cactoblastis cactorum females in mating cage bioassays as influenced by the insectary (DPI or TIF) and the trial conducted before (trial 1) and afer (trial 2) quality improvements were made to the rearing and handling protocols at the DPI insectary. Vertical bars denote 0.95 confidence intervals.
Fig. 6 in Performance improvement through quality evaluations of sterile cactus moths, Cactoblastis cactorum (Lepidoptera: Pyralidae), mass-reared at two insectaries
Fig. 6. The mean distance (m) from the field release site that Cactoblastis cactorum males were recaptured was significantly influenced by the insectary (DPI or TIF), the day that released males were recaptured, and the trial conducted before (trial 1) and afer (trial 2) quality improvements were made to the rearing and handling protocols at the DPI insectary. Vertical bars denote 0.95 confidence intervals.
Fig. 2 in Performance improvement through quality evaluations of sterile cactus moths, Cactoblastis cactorum (Lepidoptera: Pyralidae), mass-reared at two insectaries
Fig. 2. The mean percentage of Cactoblastis cactorum females that were mated at time of collection from the insectaries (DPI or TIF) and the trial conducted before (trial 1) and afer (trial 2) quality improvements were made to the rearing and handling protocols at the DPI insectary. Vertical bars denote 0.95 confidence intervals.
Fig. 5 in Performance improvement through quality evaluations of sterile cactus moths, Cactoblastis cactorum (Lepidoptera: Pyralidae), mass-reared at two insectaries
Fig. 5. The relationship between the mean percentage recapture of Cactoblastis cactorum males released in the field as influenced by the day that released males were recaptured. Males recaptured include individuals from both DPI and TIF insectaries. Vertical bars denote 0.95 confidence intervals.
Fig. 4 in Performance improvement through quality evaluations of sterile cactus moths, Cactoblastis cactorum (Lepidoptera: Pyralidae), mass-reared at two insectaries
Fig. 4. The mean percentage recapture of Cactoblastis cactorum males released in the field as influenced by the insectary (DPI or TIF) and the trial conducted before (trial 1) and afer (trial 2) quality improvements were made to the rearing and handling protocols at the DPI insectary. Vertical bars denote 0.95 confidence intervals.
Improve air quality in Cities - Simulation of Sentinel-5p and Breeze Technologies
<p>The number of measuring stations in cities areinsufficient to get a realistic picture about the Air quality (AQ). The existing technique is too expensive and wastes too much limited urban space due to their dimensions. Therefore, the Breeze Technology helps to overcome this data gap by offering their own compact low-cost AQ sensors as a supplement to the existing station. To improve the spatial coverage of the measurement, Sentinel-5P data was simulated with the sensor based measured data. The accuracy will be further increased by integrating satellite data to predict pollutuíon level e.g. in areas without sensors to overcome measurement gaps.</p>
Program data - quality improvement for CBFP
<p>This data is de-identified program data from a quality improvement project on community-based family planning in two districts in Uganda.</p>
FIG. 6 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 6. — Temporal distribution of Annonaceae specimens collected in the Herbonautes dataset. The Histogram and left hand axis represent specimens collected per 5-year intervals. The right-hand axis and continuous line represent the cumulative specimens collected in total over the entire time period. The earliest Annonaceae collected and transcribed within the dataset is from 1740, a specimen of Annona squamosa L. collected in China by Pierre Nicolas le Chéron d'Incarville. The newest transcribed specimens are from 2015.
FIG. 5 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 5. — Spatial distribution of species richness in datasets for Madagascar at 0.5 × 0.5° grid resolution: A, curated expert dataset; B, herbonautes transcribed data; C, GBIF data. Equirectangular (EPSG 4326) projection.
FIG. 4 in High-quality herbarium-label transcription by citizen scientists improves taxonomic and spatial representation of the tropical plant family Annonaceae
FIG. 4. — Global spatial coverage of datasets, showing regions covered by both Herbonautes (P) and GBIF data, just by GBIF and just by Herbonautes. Grid resolution 1 × 1° (c. 110 × 110 km at the equator), equirectangular (EPSG 4326) projection.
Dataset used in "Uncertainty-Aware Learning for Improvements in Image Quality of the Canada-France-Hawaii Telescope" (https://arxiv.org/abs/2107.00048)
<p>'x_train.p', 'y_train.p': pickle files for training split containing 50,757 samples</p> <p>'x_val.p', 'y_val.p': pickle file for validation split containing 5,640 samples</p> <p>'x_test.p', 'y_test.p': pickle file for test split containing 6,267 samples</p> <p>'feature_names.p': pickle file containing names of all 119 features</p>
The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics (Dataset)
<p>This dataset contains the data used to completed the article under peer review:</p> <p>References:<br> Cabezas, A.; Milanés, Y.;Alba, R.; Delgado, A.M. (2023). The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics. (Article under peer review)</p> <p>Institutions: Spain (Universidad Internacional de La Rioja, Universidad Pablo de Olavide, Hospital Universitario Virgen de las Nieves)</p>
Feasibility Study of Personalized Trials to Improve Sleep Quality
ClinicalTrials.gov study NCT05349188. 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
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.