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34 results for “Quality by Design”
Sensory-Informed Architectural Design Qualities in Autism
<p><span>This dataset provides a collection of design qualities for autism-friendly designs. The collected data relies on the current literature, including various guidelines and research papers.</span></p>
Dataset for "Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"
<p>This package provides all published resources used and produced in the context of the research study leading to the article <em>"Designing Microservice Systems Using Patterns: An Empirical Study on Quality Trade-Offs"</em>, presented in ICSA 2022's technical track.</p>
Data from an experiment on the effects of using an experimental framework on the quality of research designs
Open the record for dataset details and reuse information.
Supporting data for the manuscript "Protein quality assessment with graph convolution guided by a loss function designed for high quality decoys"
<p>This dataset provides the predictions of the protein quality<br> assessment method Q_epsilon with respect to CASP13 and CASP14, as well<br> as a snapshot of the github repository providing the code for the<br> method.<br> The format of the file is as follows: The first column represents the<br> target and decoy names in the format <target name>_<decoy name>. The<br> second column is the true GDTTS, and the third column represents the<br> predicted GDTTS by Q_epsilon.</p>
GIS and Pollution Data: Designating Regional Airsheds for Air Quality Management in India
<p>Full journal article published here<br><strong>Designating Airsheds in India for Urban and Regional Air Quality Management<br></strong><a href="https://doi.org/10.3390/air2030015" target="_blank" rel="noopener">https://doi.org/10.3390/air2030015</a><strong><br></strong></p> <p>[Summary presentation <a href="https://urbanemissions.info/wp-content/uploads/docs/UEinfo-Designating-Airsheds-in-India.pptx">download</a>]</p> <p>Datasets used for proposing India's 15 regional airsheds for air quality management are the following</p> <p>PM2.5 Datasets<br>Raw data source: <a href="https://sites.wustl.edu/acag/datasets/surface-pm2-5">https://sites.wustl.edu/acag/datasets/surface-pm2-5</a></p> <ul> <li>Gridded 0.1 degree resolution source apportionment results from WUSTL's global model simulations<br>File: india_data_pm25_wustl_source_cont_0p1deg.xlsx<br>Aggregated Source definitions used in this presentation <ul> <li>1. DUST = Anthropogenic dust = AFCID</li> <li>2. WINDUST = Wind erosion (dust storms) = WDUST</li> <li>3. WASTE = Waste burning = WST</li> <li>4. RESI = All commercial and residential cooking, lighting, and heating = RCOC + RCOO + RCORbiofuel + RCORcoal + RCORother</li> <li>5. TRANS = All transport (excluding aviation) = ROAD + NRTR + SHP</li> <li>6. POWER = Energy generation = ENEcoal + ENEother</li> <li>7. INDUS = All industries and product use = INDcoal + INDother + SLV</li> <li>8. BIOB = Biomass burning, including forest fires and agricultural waste burning = GFEDoburn + GFEDagburn</li> <li>9. AGR = Agricultural activities (excluding agricultural waste burning) = AGR</li> <li>10. OTHER = All others = OTHER</li> </ul> </li> <li>Gridded 0.1 degree resolution, reanalysis data from WUSTL's global model simulations<br>File: india_data_pm25_wustl_reanalysis_0p1deg.xlsx<br>Time period: 1998 to 2022, annual averages</li> <li>Gridded 0.1 degree achive for monthly averages from WUSTL's global model simulations<br>File: <a href="https://www.urbanemissions.info/wp-content/uploads/misc/IndiaSubcontinent-Gridded-Monthly-WUSTL-v4.rar">Download-44MB</a></li> </ul> <p>Population Datasets<br>Raw data source: <a href="https://landscan.ornl.gov">https://landscan.ornl.gov</a></p> <ul> <li>Gridded 0.1 degree resolution population density data<br>File: india_data_population_2021_0p1deg.xlsx</li> </ul> <p>GIS databases used in this study</p> <ul> <li>ESRI shapefile of 0.1 x 0.1 degree mesh file for the Indian Subcontinent covering longitudes from 67E to 99E and latitudes from 7N to 39N<br>File: india_gis_grids-0.1x0.1deg.rar</li> <li>ESRI shapefile of India administrative level 2 data - 28 states and 8 union territories (as of December 2023)<br>File: india_gis_states28+8_2023.rar</li> <li>ESRI shapefile of India administrative level 3 data - 755 districts (as of December 2023): district23 and states23 codes are re-designed for emissions and pollution mapping and data tracking purposes<br>File: India_gis_districts755_2023.rar (original source: <a href="https://projects.datameet.org/maps">https://projects.datameet.org/maps</a>)</li> <li>ESRI shapefile of India's Agro-Climatic zones<br>File: india_gis_agroclimatic_zones.rar (original source: <a href="https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions">https://karnataka.data.gov.in/resource/boundaries-agro-climatic-regions</a> </li> <li>ESRI shapefile of India's meteorological sub-divisions<br>File: india_gis_meteo_subdivisions.rar (original source: <a href="https://mausam.imd.gov.in/">https://mausam.imd.gov.in</a>)</li> </ul>
Which RESTful API Design Rules are Important and How Do They Improve Software Quality? A Delphi Study with Industry Experts
<p>The dataset of a Delphi study with 8 industry experts who reached consensus on the perceived importance and positive software quality impact of 82 RESTful API design rules from the catalogue by Mark Massé ("REST API Design Rulebook", O’Reilly Media, 2011). The replication package contains:</p> <ul> <li><strong>rules.csv:</strong> the final consensus results for the 82 rules in CSV format</li> <li><strong>rule-importance.xlsx</strong>: the detailed results and analysis for rule importance as an Excel spreadsheet</li> <li><strong>rule-sw-quality-impact.xlsx</strong>: the detailed results and analysis for rule impact on software quality as an Excel spreadsheet</li> </ul> <p>In this version, we updated the final numbers for the software quality mapping with the results from the synchronous meeting.</p>
Trial on Oral Appliance Design for Improving Upper Airway Function and Sleep Quality
ClinicalTrials.gov study NCT03219034. IPD Sharing: NO. Countries: 1. Publications: 1.
'Thriving With Bipolar Disorder': Co-design and Evaluation of a Peer-Led Education Program Focused on Quality of Life
ClinicalTrials.gov study NCT06878937. IPD Sharing: YES. Countries: 1. Publications: 13.
PyonAir: An open design, open source, air quality monitor for community driven particulate matter sensing.
<p>This dataset present some of the data recorded by two low-cost PM sensors, a Plantower PMS5003 and a Sensirion SPS030 located at Southampton AURN reference station on the 2nd December 2019 between 17:00 and 22:00 during a fire that occurred in the city. It also present the data from the Fidas 200, averaged every 15min also located at the AURN station.</p> <p>The file SPS_PMS_fire.csv contain the following rows:</p> <ul> <li>date</li> <li>pm25 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> <li>sensor - Serial number of the sensor</li> <li>site - name of the location of the sensor</li> </ul> <p>The file fidas_15min.csv contains the following rows:</p> <ul> <li>date</li> <li>PM2.5 - PM<sub>2.5</sub> mass concentration in ug/m<sup>3</sup></li> </ul> <p> </p>
Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality
<p>Presentation of the paper titled "Building DEVS Models from the Functional Design of Software Architecture Components to Estimate Quality".</p>
The Impact of Website Design, E-Service Quality, Satisfaction, Trust to Intention to Purchase Skin Care Products in the E-Marketplace
<p><strong><span>The study aims to identify factors influencing pricing, understand how pricing affects purchase intention and customer satisfaction, and provide valuable insights for skincare businesses to develop effective pricing strategies in Indonesia’s skincare market. The study used a purposive sampling approach, collecting data from 123 respondents who were internet users in Indonesia in January 2024. These respondents have engaged with online retailers to purchase skin care products. The results found that website design affects e-service quality, affecting customer satisfaction and trust. Customer satisfaction and trust have a significant impact on repurchase intentions. This study recommends that skincare businesses focus on website design, provide high-quality e-services, and implement customer satisfaction and loyalty programs. Future research can explore additional factors and conduct similar studies in different locations or product categories.<span> </span></span></strong></p> <p><span>Keywords—Website design, skin care, e-marketplace, purchase intention, customer satisfaction, customer trust, e-service quality.</span></p>
Supplementary material 1 from: Behei N, Tryhubchak O, Pryymak B (2022) Development of amlodipine and enalapril combined tablets based on quality by design and artificial neural network for confirming of qualitative composition. Pharmacia 69(3): 779-789. https://doi.org/10.3897/pharmacia.69.e86876
The results of the study of pharmaco-technological parameters of intermediates and amlodipine tablets with enalapril, data of the functions of desirability
Part design aiming at dataset generation for training a vision based quality monitoring system of a circular economy cell
<p>Part design aiming at dataset generation</p> <p> </p> <p>Related to the publication: Stavropoulos, P., Papacharalampopoulos, A., Athanasopoulou, L., Kampouris, K., & Lagios, P. (2022). Designing a digitalized cell for remanufacturing of automotive frames. <em>Procedia CIRP</em>, <em>109</em>, 513-519.</p> <p> </p> <p>Contents of the ZIP file: Different configurations as CATPart files</p> <p> </p> <p>It can open with the help of CATIA software</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>
Dataset The Importance of Interface Design and Security Awareness in Improving Service Quality and Its Impact on Digital Customer Loyalty of Smart Mobile Banking
Open the record for dataset details and reuse information.
Supplementary Material for: 'An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring'
<p>This supplementary material accompanies:</p> <p>Charlton P.H. <em>et al.</em>, "<a href="https://doi.org/10.1016/j.bspc.2020.102339">An impedance pneumography signal quality index for respiratory rate monitoring: design, assessment and application</a>", <em>Biomedical Signal Processing and Control</em>, 65, 102339, 2021.</p> <p>The Impedance Pneumography Signal Quality Index (SQI) dataset and accompanying scripts (in Matlab format) are provided to facilitate reproduction of the analyses using data from the MIMIC III dataset in this publication.</p> <p><strong>Summary of Publication</strong></p> <p>In this article we developed and assessed the performance of a signal quality index (SQI) for the impedance pneumography signal.<br> The SQI was developed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a>, and assessed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a> and MIMIC III datasets.<br> The SQI was found to accurately classify segments of impedance pneumography signal as either high or low quality. Furthermore, when it was coupled with a high performance RR algorithm, highly accurate and precise RRs were estimated from those segments deemed to be high quality. In this study performance was assessed in the critical care environment - further work is required to deteremine whether the SQI is suitable for use with wearable sensors. Both the dataset and code used to perform this study are publicly available.</p> <p><strong>Reproducing this Publication</strong></p> <p>The work relating to the MIMIC dataset in this publication can be reproduced as follows:</p> <p><strong> - Reproducing the analysis</strong><br> These steps can be used to quickly reproduce the analysis using the curated and annotated dataset.</p> <p>* Download the curated and annotated dataset from <a href="https://doi.org/10.5281/zenodo.3973770">Zenodo</a> using this <a href="https://zenodo.org/record/3973771/files/mimic_imp_sqi_data.mat?download=1">direct download link</a>.<br> * Run the analysis using the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.</p> <p><strong> - Full reproduction</strong><br> These steps include downloading the raw data files, extracting data from these files, collating the dataset, manually annotating the data, and performing the analysis.</p> <p>* Use the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script to download raw MIMIC data files from PhysioNet, and collate them into a single Matlab file.<br> * Prepare the dataset for manual annotation by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.<br> * Manually annotate the signals by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_mimic_imp_annotation.m</em></a> script - the annotations are stored in separate files (the original annotation files are available <a href="https://zenodo.org/record/3974113/files/2019_annotations.zip?download=1">here</a>).<br> * Import the manual annotations into the collated data file by re-running the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script.<br> * Run <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> to perform the analysis described in the publication.</p> <p><strong> - Submitted manuscript</strong></p> <p>The submitted manuscript is available <a href="https://zenodo.org/record/5211463/files/Impedance%20SQI%20manuscript%20-%20Oct%202020%20revision.docx?download=1">here</a>.</p> <p>The scripts are also stored (alongside details of how to use them) are available in the <a href="http://peterhcharlton.github.io/RRest/">RRest GitHub repository</a> at: <a href="https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI">https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI</a></p> <p>License: The dataset (<a href="https://zenodo.org/record/3974113/files/mimic_imp_sqi_data.mat?download=1"><em>mimic_imp_sqi_data.mat</em></a>) is distributed under the terms specified in the accompanying LICENSE file. The scripts are distributed under the GNU General Public Licence (as specified towards the start of each file).</p> <p>Version 1.0: This version includes the submitted manuscript.</p> <p> </p>
Developers Talking About Code Quality: study design and dataset
<p><strong>Electronic supplement for the article <em>Developers Talking About Code Quality</em></strong></p> <p>Please refer to the following paper when you cite/use this data:</p> <p>J. Börstler, K.E. Bennin, S. Hooshangi, J. Jeuring, H. Keuning, C. Kleiner, B. MacKellar, R. Duran, H. Störrle, D. Toll, J van Assema. Developers Talking About Code Quality. <em>Empirical Software Engineering</em>, 2023.<br> https://link.springer.com/article/10.1007/s10664-023-10381-0</p> <p><strong>Overview of the electronic supplement</strong> (sorted according to file names)</p> <ol> <li>The data <ol> <li>data_all-except-Q4.xlsx<br> The original cleaned data for Questions Q1-Q3 and Q5-Q11.</li> <li>data_Q4 assertions.xlsx<br> The "assertions" which are based on the transcripts of Q4.</li> </ol> </li> <li>The information provided to study participants <ol> <li>participants_consent-form_EN.pdf<br> The consent form the participants signed (English version, interviewees received a copy on request).</li> <li>participants_interview-info.pdf<br> The information sheet that was handed out for recruiting interview participants (they could keep this form).</li> </ol> </li> <li>The study design and guidelines <ol> <li>study-design_coding-guide_birds-eye-summaries.pdf<br> The guidelines for coding the transcripts of Q4 into "assertions" (which we call "birds-eye summaries" in these guidelines).</li> <li>study-design_main.pdf<br> The main study guide comprising (among others) a <strong>detailed interview script</strong>.</li> <li>study-design_transcription.pdf<br> The guidelines used for transcribing Q4.</li> </ol> </li> </ol>
The Effect of the Free-toroidal Design Lenses on the Subjective Visual Quality in the Myopic Population
ClinicalTrials.gov study NCT05684302. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluation of an Intervention Designed to Enhance the Quality of Decision Support Provided to Patients Making Decisions About End-of-life Place of Care
ClinicalTrials.gov study NCT00614003. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effects of Different Designs of Orthokeratology Lens on Myopia Control and Visual Quality
ClinicalTrials.gov study NCT05192824. IPD Sharing: NO. Countries: 1. Publications: 5.
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.