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707 results for “qualitative”
Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis
<p>Vignettes and COREQ checklist for Applied Partnership Award project: Resource Allocation, Priority-Setting and Consensus in Dementia Care. See Keogh F, Pierse T, O'Shea E <em>et al.</em> Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis [version 1; peer review: awaiting peer review]. <em>HRB Open Res</em> 2020, <strong>3</strong>:69 (<a href="https://doi.org/10.12688/hrbopenres.13147.1">https://doi.org/10.12688/hrbopenres.13147.1</a>)</p>
Data set for "Quantitative and Qualitative bibliometric scope toward the Synthesis of Rose Oxide as a Natural Product in perfumery"
<p>This is the bibliometric data for "Quantitative and Qualitative bibliometric scope toward the Synthesis of Rose Oxide as a Natural Product in perfumery" study which were derived from SCOPUS database, on 23<sup>rd</sup> September 2019, based on title search.</p>
Supplementary Material on "Processes, Methods, and Tools in Model-based Engineering --- A Qualitative Multiple-Case Study"
<p>This dataset provides the supplementary material that we applied for all interviews conducted in the context of our qualitative study resulting in the JSS article mentioned in the title:</p> <ul> <li>The semi-structured interview guide,</li> <li>the codebook,</li> <li>the blank consent form that our interviewees signed,</li> <li>and the blank invitation mail that we used to ask our interviewees to participate in our study.</li> </ul>
When Do Authoritarian Regimes Use Digital Technologies for Covert Repression? A Qualitative Comparative Analysis (QCA) of Politico-Economic Conditions
<p>This is a replication dataset together with the QCA script used for the analysis of the article "When Do Authoritarian Regimes Use Digital Technologies for Covert Repression? A Qualitative Comparative Analysis (QCA) of Politico-Economic Conditions" submitted to the Swiss Political Science Review in 2024, for the special issue "Re-Authoritarianisation with Digital Means? Recent Developments in Digital Politics in East Central Europe and Central Asia". The package contains the following elements:</p> <p>1) The original QCA dataset with references.</p> <p>2) The supplementary dataset with the calculations related to autocratic linkages.</p> <p>3) The R script used for the QCA.</p> <p> </p>
Toy Qualitative Data Project (Interview Transcripts)
<p><strong>Please be advised that this project is intended solely for instructional purposes and should not be used for actual research. This dataset is intended to complement the instructional material and provide a hands-on learning experience for the workshop: <a href="https://rcurty.github.io/qualdata-training">Handling and Sharing Qualitative Data Responsibly and Effectively</a>.</strong></p> <p>This hypothetical research project is designed to demonstrate key concepts related to human subject qualitative data management and thematic analysis coding. It includes interview transcripts generated with ChatGPT 4.0 Mini for a fictional graduate student in Communication named Sarah, whose main research question is: <em><strong>How do content creators/digital influencers view their role in shaping their followers' consumer behavior, and what ethical dilemmas do they face when promoting products?</strong></em></p> <div> <p>Given the novelty of this research topic and the limited academic literature available, Sarah hopes that the insights gained from this small-scale qualitative exploratory study will help identify key variables for a larger survey study with a representative sample of content creators/digital influencers across the U.S.</p> <p>Sarah has previous experience with quantitative methods but is very new to qualitative research and could use our help for better handling the data. Having already conducted six short structured interviews with subjects from top revenue niches (i.e., Home Decor and DYI, Travel & Adventure, Fashion & Style, Health & Wellness, Finance & Investment, Beauty & Skincare) and planning to conduct a dozen more, Sarah is eager to begin engaging with the data she has collected so far and deciding how to best organize and interpret it. We’ll be walking her through this process, providing the necessary guidance and support for effective and responsible data management.</p> <p>Interviews were conducted over Zoom and audio recorded with participants' consent. The interview included four main questions, which were consistent across all interviews:</p> <p><em>Q1. Please tell me a little about your work as a content creator/digital influencer how it started, and how you have established yourself in your current niche.</em></p> <p><em>Q2. In what ways do you believe content creators/digital influencers shape consumer behavior? Could you share any examples?</em></p> <p><em>Q3. What strategies would you say content creators/digital influencers typically use to increase sales of sponsored products and services? Which ones have you used? What worked and what did not work for you? Why?</em></p> <p><em>Q4. In your view, what are the essential ethical responsibilities that content creators and digital influencers should uphold? Can you share any personal experiences that illustrate these responsibilities in action?</em></p> <p>Each interview generated approximately 15 minutes of audio recording, which Sarah manually transcribed. Sarah decided to keep the transcription true to the recordings and seek assistance to mitigate any risk of identification. </p> </div>
Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using 1H NMR
<p>Dataset to accompany the manuscript "Automated Qualitative and Quantitative Analysis of Complex Forensic Drug Samples using <sup>1</sup>H NMR"</p>
Tara Pacific Qualitative Photo Annotations
<p>This data is the result of photographic annotations done manually through Matlab for the photographs captured during the Tara Pacific Expedition (2016-2018). More details can be found in the readme file.</p>
Towards Collaborative Immersive Qualitative Analysis
<p>Figures for a published conference article in the 15th International Conference on Computer Supported Collaborative Learning, June 2022.</p> <ul> <li>Figure 1 - Screenshot of a CAVA360VR session R1 (left) and R2 (right)</li> <li>Figure 2 - Aerial view of participants and camera posi</li> <li>Figure 3 - Student group collaboration represented by a comic transcript</li> <li>Figure 4 - Bringing up an additional camera view to accomplish an alternative viewing</li> <li>Figure 5 - Drawing as a way of highlighting and creating shared awareness</li> </ul>
Mobilitätsberichterstattung Erhebungsdaten der qualitative Methoden Community Mapping und Teilnehmende Beobachtungen
<p>Als Forschungsdaten der qualitativen Methoden in der Mobilitätsberichterstattung werden die Interviewmitschnitte der Proband*innen aus den neun Community Mappings und 18 Teilnehmende Beobachtungen zur Verfügung gestellt. Aus Datenschutzgründen wurden die Interviews anonymisiert. Die Dateinamen zeigen die Personenmerkmale, aufgrunddessen die Proband*innen für die Erhebungen ausgewählt wurden. Dies umfasste eine gemischte Gruppen aus den drei Stadtraumtypen in Pankow: Innenstadt, Innenstadtrand und suburbanes Gebiet. Darüber hinaus wurden in allen drei Gebieten Schulkinder beteiligt: Primarstufe in der Innenstadt (PS), Sekundarstufe I im Innenstadtrand (ISS) und Sekundarstufe II (OSZ) im suburbanen Gebiet. Zusätzlich wurden Personengruppen mit Mobilitätseinschränkungen und besonderen Mobilitätsbedarfen beteiligt: Menschen mit Gehbehinderungen in der Innenstadt, Senior*innen im Innenstadtrand und zur Arbeit Pendelnde im suburbanen Gebiet. Über die Dateiendung "TB" wird angezeigt, dass das Interview nach einer Teilnehmenden Beobachtung mit den Proband*innen auf ihren Alltagswegen durchgeführt wurde. Diese wurden meistens auf Hinwegen am Morgen und Rückwegen am Nachmittag durchgeführt, wodurch meistens zwei Audiodateien pro Person entstanden sind, die in der Dateibezeichnung nummeriert wurden. Alle anderen Interviews wurden mithilfe des Community Mappings durchgeführt.</p> <p>Genauere Informationen zur Methodik können dem Leitfaden Mobilitätsberichterstattung und auf der Webseite <a href="http://mobilbericht.de">mobilbericht.de</a> entnommen werden. Die Ergebnisse wurden in der Erstellung des 1. Pankower Mobilitätsberichts aufgearbeitet.</p>
The input data set includes 729 objects (patients) and 39 variables (clinical qualitative and quantitative descriptors).
<p>For reliable data treatment and interpretation qualitative descriptors were omitted and only numerical clinical indicators were included in the data matrix. Finally, the data set dimension was [729 x 18].</p> <p> The data were treated by hierarchical cluster analysis and factor analysis. The major goal of the data mining was to reach statistically significant partitioning of the objects and variables into similarity patterns (clusters) which helps to better understand the data structure, to assess the meaning of the partitioning achieved, thus promoting the evaluation of the health status of the patients and the role of specific descriptors for the formation of the partitioning patterns.</p> <p>3D classification Python tool.</p>
Extracted and Anonymised Qualitative Data on Students' Acceptance of an Early Warning System
<p>The data published in this record was adopted in the following study: </p> <p><em><strong>Exploring Higher Education students' experience with AI-powered educational tools: The case of an Early Warning System </strong></em></p> <p>The study analyses the students' experience of an early warning system developed at a fully online university. The study is based on 21 semi-structured interviews that yielded a corpus of 21,761 words, for which a mixed inductive and deductive codification approach was applied after thematic analysis. We focused on 11 themes, 52 subthemes, and 396 coded segments to perform content analysis. Our findings revealed that the students, primarily senior workers with a high-level academic self-efficacy, had little experience with this type of system and low expectations about it. However, a usage experience triggered interest and meaningful reflections on the mentioned tool. Nevertheless, a comparative analysis between disciplines related to Computer Science and Economics showed higher confidence and expectation about the system and artificial intelligence overall by the first group. These results highlight the relevance of supporting students' further experiences and understanding of artificial intelligence systems in education to accept them and mainly to participate in iterative development processes of such tools to achieve quality, relevance, and fairness.</p> <p>The three records attached as part of the dataset include:</p> <p>1- The General CodeTree with exemplar coding excerpts in Spanish<br> 2- Extract of transcriptions in English<br> 3- Full Report in Spanish as extracted from NVIVO, including the extracted codes for the synthesis (1,2) in blue, and the comments made by the two researchers engaged in the interrater agreement.<br> 4- General Content Analysis (Spreadsheet ODS)</p> <p> </p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Data set - What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study
<p><strong>Data set from- What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study</strong></p> <p><strong>Abstract of the study: </strong>The treatment of cancer can have a significant impact on quality of life in older patients and this needs to be taken into account in decision making. However, quality of life can consist of many different components with varying importance between individuals. We set out to assess how older patients with cancer define quality of life and the components that are most significant to them. This was a single-centre, qualitative interview study. Patients aged 70 years or older with cancer were asked to answer open-ended questions: What makes life worthwhile? What does quality of life mean to you? What could affect your quality of life? Subsequently, they were asked to choose the five most important determinants of quality of life from a predefined list: cognition, contact with family or with community, independence, staying in your own home, helping others, having enough energy, emotional well-being, life satisfaction, religion and leisure activities. Afterwards, answers to the open-ended questions were independently categorized by two authors. The proportion of patients mentioning each category in the open-ended questions were compared to the predefined questions. Overall, 63 patients (median age 76 years) were included. When asked, “What makes life worthwhile?”, patients identified social functioning (86%) most frequently. Moreover, to define quality of life, patients most frequently mentioned categories in the domains of physical functioning (70%) and physical health (48%). Maintaining cognition was mentioned in 17% of the open-ended questions and it was the most commonly chosen option from the list of determinants (72% of respondents). In conclusion, physical functioning, social functioning, physical health and cognition are important components in quality of life. When discussing treatment options, the impact of treatment on these aspects should be taken into consideration.</p> <p><strong>Reference of research paper: </strong>Seghers PAL, Kregting JA, van Huis-Tanja LH, Soubeyran P, O'Hanlon S, Rostoft S, Hamaker ME, Portielje JEA. What Defines Quality of Life for Older Patients Diagnosed with Cancer? A Qualitative Study. <em>Cancers</em>. 2022; 14(5):1123. https://doi.org/10.3390/cancers14051123</p> <p><strong>Content of the data set: </strong>The first Tab describes what questions were asked, the second tab shows all individual anonymised answers to the open questions, the fourth shows the definitions that were used to classify all answers. Q1-Q4 show how the answers were categorised. </p>
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest. in Assessment Of Phytogeographic Reference Regions For Cenozoic Vegetation: A Case Study On The Miocene Flora Of Wiesa (Germany)
Text-fig. 5. Vegetation zones in P. R. China (Editorial Committee of Vegetation Map of China, The Chinese Academy of Sciences 2007), and assumed location of extant reference vegetation type of Wiesa fossil assemblage (rectangle), as revealed from qualitative floristic analysis. Extant reference vegetation type present in southern belt of zone of subtropical evergreen broadleaved forest, with minor overlap into zone of tropical forest.
Research Management Systems: Systematic Mapping of Literature (2007-2017) - Number of articles included during the search and qualitative evaluation process of the study
<p>This image is uploaded as an integrated part of systematic mapping of literature "Research Management Systems: Systematic Mapping of Literature (2007-2017)". This image will be cited across all future publications related to this project as Attribution-NonCommercial-NoDerivatives 4.0 International image.</p>
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye. in Bivalve mollusks of the intertidal zone of the Far Eastern seas of Russia
Рис. 2. Распредение биомассы Mytilus trossulus septentrionalis на литорали дальневоcточных морей России. Здесь и далее на гистограммах по оси абцисс после географических пунктов в скобках укаЗана выборка (число иЗученных проб), по оси ординат – максимальные ЗначениЯ биомассы вида. Под Значением биомассы 0.1 г/м² подраЗумеваютсЯ качественные пробы. СокраЩениЯ (бмп) и (топ) оЗначают соответственно беринговоморское и тихоокеанское побережьЯ Восточной Камчатки. Побережье Зал. Петра Великого от устьЯ р. Туманной к северу до м. Поворотного условно отноcитсЯ к южному Приморью; побережье к северу от м. Поворотного (пос. Преображение, б. СоколовскаЯ) до б. Ольга, включительно, условно относитсЯ к среднему Приморью; побережье к северу от б. Ольга до м. Белкина и материковое побережье Татарского пролива относим к северному Приморью. Fig. 2. The distribution of biomass of Mytilus trossulus septentrionalis in the intertidal zone of the Far Eastern seas of Russia. Here and throughout on histograms, on the abcissa is the number of studied samples (numbers in parentheses following the names geographic localities), on the ordinate is the maximum biomass of species. The number 0.1 g wet wt m-2 means the qualitative samples. Abbreviations (bmp) and (top) mean the Bering Sea coast and the Pacific coast of eastern Kamchatka. The coast of Peter the Great Bay from the mouth of the Tumannaya River to Cape Povorotny is conditionally referred to as southern Primorye; the area north of Cape Povorotny (Preobrazhenie Settlement, Sokolovskaya Bay) to Olga Bay inclusive is conditionally referred to as middle Primorye; north of Olga Bay to Cape Belkin and the mainland coast of the Tatar Strait to as northern Primorye.
Dataset: Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance
<p>This dataset (packaged as the zip file history_share.zip) accompanies the article titled "Location- and feature-based selection histories make independent, qualitatively distinct contributions to urgent visuomotor performance" by EE Oor, E Salinas, and TR Stanford which is available as a preprint in bioRxiv. The experimental results in the article are based on behavioral data collected from 2 monkey subjects during performance of a visuomotor task (the compelled oddball task), as described in the text. This dataset contains the trial-by-trial behavioral results collected for each subject and upon which all subsequent analyses were based.</p> <p>In addition to the trial-wise data arrays (stored in the files dataC.csv, dataN.csv, and dataCN.csv), the package includes Matlab functions and scripts (*.m files) used to analyze the data and recreate the results and figures in the article. Instructions and specifics are detailed in the README file. </p>
Fig. 2 in Qualitative and quantitative methods for estimating Spirorchiidiasis burden in sea turtles
Fig. 2. Scatter plot with trend line showing the correlation between eggs counts using the two quantitative methods (MH and CH) in the spleen.
Fig. 1 in Qualitative and quantitative methods for estimating Spirorchiidiasis burden in sea turtles
Fig. 1. Eggs of H. mistroides in the McMaster chamber for quantification of splenic egg burden (Scale bar: 200 μm).
Figure 1. (a) Classical set and (b) Fuzzy set 2.3.-An Efficient Expert System Generator for Qualitative Feed-Back Loop Analysis
<p>A membership function is a curve that represents the degree of points which belong to the<br> specific fuzzy variable. Selecting the appropriate membership function plays an essential rule in<br> design of a fuzzy logic controller. The shape of membership function could be defined based on the<br> simplicity, convenience, speed and efficiency. Many different membership functions are introduced<br> in the literatures such as triangular, trapezoidal and Gaussian. The membership function which<br> represented in figure 1(b) is a trapezoidal type.</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.