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77 results for “interview study”
Characteristics of households that were interviewed in a study that investigated factors driving tree species in cocoa farms in Cote d'Ivoire
<p>1. Intensive cocoa production in Côte d'Ivoire, the World's leading cocoa producer, has grown at the expense of forest cover. To reverse this trend, the country has adopted a 'zero deforestation' agricultural policy and committed to rehabilitate its forest cover through the planting of high-value tree species in cocoa landscapes using a participatory approach. However, less is known on the factors influencing farmers' introduction of high-value tree species in cocoa landscapes.</p> <p>2. We tested the hypothesis that ten factors previously reported to influence agroforestry systems adoption predict the number and choice of tree species that farmers introduce in cocoa farms. We interviewed 683 households in the cocoa-producing zone of Côte d'Ivoire and counted tree species in their cocoa farms.</p> <p>3. On average two tree species were recorded per surveyed farm. Generalized Poisson regression models revealed that cocoa production area, experience in tree planting and expected benefits influence tree species introduction through planting or 'retention' when clearing land for cocoa establishment. Age of farmer also influenced (P = 0.017) farmers' tree species planting in cocoa farms. Few tree species were introduced in current intensive cocoa-production areas than in 'old cocoa-loop' and forested areas. The number of tree species introduced in cocoa farms increased with expected benefits and experience in tree planting. The number of planted tree species also increased with farmers' age. Tree species were mostly selected for provision of shade to cocoa, production of useful tree products (38%) and income from the sale of these products (7%). Fruit tree species were the most planted while timber tree species were mostly spared when clearing land for cocoa production.</p> <p>Synthesis and applications. Agroforestry is gaining momentum in Côte d'Ivoire, as public and private institutions including the World Agroforestry Centre are developing and deploying multi-strata cocoa-based agroforestry systems. The results of the present study can guide the design and implementation of biodiverse cocoa-based agroforestry in the cocoa-producing zones of the country</p>
Finnish National Election Study 2011: Telephone Interviews among Finnishspeaking Voters (example assignm.)
<p>xx</p>
Interview Corpora for the Study of Multilingual Repertoires in South-South Migration Dynamics I: Haitians in Chapecó (SC, Brazil)
<p>Corpus of 19 multilingual interviews with Haitian migrants in Chapecó (Santa Catarina, Brazil), conducted in various languages: in order from the most to the least documented in the interviews, Portuguese, French, Spanish, and Haitian Creole. This corpus is part of broader research on the evolution of multilingual repertoires in South-South migration dynamics. The interviews were conducted in March 2023 by the author in collaboration with Leonie Ette (University of Augsburg) and the two coordinators of the research group <em>Atlas das Línguas em Contato na Fronteira</em>, Professors Cristiane Horst and Marcelo Krug (UFFS, Campus Chapecó).</p>
The public part of the interview extractions as thematic codings: used for the consumer behavior study
<p>Extractions from interview transcripts created by the thematic analysis, this data is part of the interview study done on consumer energy behavior during price hikes,</p>
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
Interview and Observation Result for Post-metaverse Class Implementation "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects""
<p>This data file is open for viewing as proof of Author; Brandon Lie - BINUS University for conffrence paper<strong> "Innovations in Sustainable Education; Case Study on Metaverse Implementation in BINUS University Language Departments and Future Prospects" <br><br>Data can be use an reffrence with author permission*</strong></p>
Supplementary Material - All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential
<p>This dataset is the supplementary material for the paper "All Eyes on Traceability: An Interview Study on Industry Practices and Eye Tracking Potential" accepted at RE '23.</p> <p>The PDF titled "Interview-Questions" presents the interview questions used for the semi-structured interview of the paper.<br> The PDF titled "Interview-Questions_ReplicatorVersion" presents the interview questions enriched with comments on the qualitative and quantitative extent of the expected interviewee's responses and the category codes assigned to them during the analysis process.<br> The PDF "Eye-Tracking-Traceability-Explanatory-Slide" contains the Slide used in part 4 of the interview.<br> The spreadsheet "ArtifactsLinked.xlsx" was used to determine the number of interviewees who mentioned particular pairings of artifact types as currently being linked or ideally linked. This is the raw data for Fig. 5 in the paper.</p>
Qualitative Interview Study Regarding Dentists' Repair Behaviour
ClinicalTrials.gov study NCT03279874. IPD Sharing: NO. Countries: 1. Publications: 4.
Motivational Interviewing for Getting Healthy TodaY Study
ClinicalTrials.gov study NCT03410225. IPD Sharing: NO. Countries: 1. Publications: 8.
The Pain & Stress Interview Study for People With Chronic Pain
ClinicalTrials.gov study NCT04498663. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Does Geography and Place of Residence Affect Cancer Care: An Interview Study
ClinicalTrials.gov study NCT03836794. IPD Sharing: NO. Countries: 1. Publications: 9.
Fetal Surgery Interview Study: Parental Perceptions of Fetal Surgery
ClinicalTrials.gov study NCT03788122. IPD Sharing: UNDECIDED. Countries: 2. Publications: 8.
Motivational Interviewing to Support LDL-C Therapeutic Goals and Lipid-Lowering Therapy Compliance in Patients With Acute Coronary Syndromes: a Prospective Randomized Clinical Study
ClinicalTrials.gov study NCT02927808. IPD Sharing: NO. Countries: 1. Publications: 2.
DYNAMIC Study: (Diabetes Nurse Case Management And Motivational Interviewing for Change)
ClinicalTrials.gov study NCT00308386. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Pilot RCT and Interview Study on an HIV Chatbot in Nigeria
ClinicalTrials.gov study NCT06814041. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Evaluating the Validity and Acceptability of a Fully-automated Interview to Diagnose Insomnia Disorder: a Pilot Study
ClinicalTrials.gov study NCT05805527. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Characteristics of households that were interviewed in a study that investigated factors driving tree species in cocoa farms in Cote d'Ivoire
Open the record for dataset details and reuse information.
LTS Study - Composer Interviews
<p>This document contains a set of interviews that were conducted in textual form with composers and musicians who participated in the research project "Machine-Learning Assisted Music Composition". The participants were asked to use the machine-learning-based software "Latent Timbre Synthesis" to create a short musical composition. The interviews were conducted after the compositions had been created and served to purpose of evaluating how the software influences the compositional process. The project has been funded through the Scientific Exchanges program of the Swiss National Science Foundation (Grant number: IZSEZ0_190757).</p>
Data from: The personal and social experiences of community-dwelling younger adults after stroke in Australia: a qualitative interview study
Objectives: To examine the personal and social experiences of younger adults after stroke. Design: Qualitative study design involving in-depth semi-structured interviews and rigorous qualitative descriptive analysis informed by social constructionism. Participants: Nineteen young stroke survivors aged 18 to 55 at the time of their first-ever stroke. Setting: Participants were recruited from urban and rural settings across Australia. Interviews took place in a clinic room of the Florey Institute of Neuroscience and Mental Health (Melbourne, Australia), over an online conference platform or by telephone. Results: Four main themes emerged from the discourses: (1) psycho-emotional experiences after young stroke; (2) losing pre-stroke life construct and relationships; (3) recovering and adapting after young stroke; and (4) invalidated by the old-age, physical concept of stroke. While these themes ran through the narratives of all participants, data analysis also drew out interesting variation between individual experiences. Conclusions: For many younger adults, stroke is an unexpected and devastating life event that profoundly diverts their biography and presents complex and continued challenges to fulfilling age-normative roles. While adaptation, resilience and post-traumatic growth are common, this study suggests that more bespoke support is needed for younger adults after stroke. Increasing public awareness of young stroke is also important, as is increased research attention to this problem.
DevOps Education: An Interview Study of Challenges and Recommendations
<p>Over the last years, the software industry has adopted several DevOps technologies related to practices such as continuous integration and continuous delivery. The high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies. This work presents an interview study with 14 DevOps educators from different universities and countries, aiming to identify the main challenges and recommendations for DevOps teaching. Our study identified 83 challenges, 185 recommendations, and several association links and conflicts between them. Our findings can help educators plan, execute and evaluate DevOps courses. They also highlight several opportunities for researchers to propose new methods and tools for teaching DevOps.</p>
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Allen Brain Atlas
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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.