This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 23-Jun-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute.

The dataset forms part of three outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023:

· Fostering cultures of open qualitative research: Dataset 1 – Survey Responses · Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts · Fostering cultures of open qualitative research: Dataset 3 – Coding Book

The project was funded with £13,913.85 Research England monies held internally by the University of Sheffield - as part of their ‘Enhancing Research Cultures’ scheme 2022-2023.

The dataset aligns with ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee (ref: 051118) on 23-Jan-2021.This includes due concern for participant anonymity and data management.

ORDA has full permission to store this dataset and to make it open access for public re-use on the basis that no commercial gain will be made form reuse. It has been deposited under a CC-BY-NC license.

This dataset comprises one spreadsheet with N=91 anonymised survey responses .xslx format. It includes all responses to the project survey which used Google Forms between 06-Feb-2023 and 30-May-2023. The spreadsheet can be opened with Microsoft Excel, Google Sheet, or open-source equivalents.

The survey responses include a random sample of researchers worldwide undertaking qualitative, mixed-methods, or multi-modal research.

The recruitment of respondents was initially purposive, aiming to gather responses from qualitative researchers at research-intensive (targetted Russell Group) Universities. This involved speculative emails and a call for participant on the University of Sheffield ‘Qualitative Open Research Network’ mailing list. As result, the responses include a snowball sample of scholars from elsewhere.

The spreadsheet has two tabs/sheets: one labelled ‘SurveyResponses’ contains the anonymised and tidied set of survey responses; the other, labelled ‘VariableMapping’, sets out each field/column in the ‘SurveyResponses’ tab/sheet against the original survey questions and responses it relates to.

The survey responses tab/sheet includes a field/column labelled ‘RespondentID’ (using randomly generated 16-digit alphanumeric keys) which can be used to connect survey responses to interview participants in the accompanying ‘Fostering cultures of open qualitative research: Dataset 2 – Interview transcripts’ files.

A set of survey questions gathering eligibility criteria detail and consent are not listed with in this dataset, as below. All responses provide in the dataset gained a ‘Yes’ response to all the below questions (with the exception of one question, marked with an asterisk (*) below):

· I am aged 18 or over · I have read the information and consent statement and above. · I understand how to ask questions and/or raise a query or concern about the survey. · I agree to take part in the research and for my responses to be part of an open access dataset. These will be anonymised unless I specifically ask to be named. · I understand that my participation does not create a legally binding agreement or employment relationship with the University of Sheffield · I understand that I can withdraw from the research at any time. · I assign the copyright I hold in materials generated as part of this project to The University of Sheffield. · * I am happy to be contacted after the survey to take part in an interview.

The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk

Postdoctoral Research Assistant Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk Research Associate iHuman Institute, Social Research Institutes, Faculty of Social Science

Objective(s): Momentum for open access to research is growing. Funding agencies and publishers are increasingly requiring researchers make their data and research outputs open and publicly available. However, clinical researchers struggle to find real-world examples of Open Data sharing. The aim of this 1 hr virtual workshop is to provide real-world examples of Open Data sharing for both qualitative and quantitative data. Specifically, participants will learn: 1. Primary challenges and successes when sharing quantitative and qualitative clinical research data. 2. Platforms available for open data sharing. 3. Ways to troubleshoot data sharing and publish from open data. Workshop Agenda: 1. “Data sharing during the COVID-19 pandemic” - Speaker: Srinivas Murthy, Clinical Associate Professor, Department of Pediatrics, Faculty of Medicine, University of British Columbia. Investigator, BC Children's Hospital 2. “Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project.” - Speaker: Maggie Woo Kinshella, Global Health Research Coordinator, Department of Obstetrics and Gynaecology, BC Children’s and Women’s Hospital and University of British Columbia This workshop draws on work supported by the Digital Research Alliance of Canada. Data Description: Presentation slides, Workshop Video, and Workshop Communication Srinivas Murthy: Data sharing during the COVID-19 pandemic presentation and accompanying PowerPoint slides. Maggie Woo Kinshella: Our experience with Open Data for the 'Integrating a neonatal healthcare package for Malawi' project presentation and accompanying Powerpoint slides. This workshop was developed as part of Dr. Ansermino's Data Champions Pilot Project supported by the Digital Research Alliance of Canada. NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator on this page under "collaborate with the pediatric sepsis colab."

This data set contains the replication data and supplements for the article "Knowing, Doing, and Feeling: A three-year, mixed-methods study of undergraduates’ information literacy development." The survey data is from two samples: - cross-sectional sample (different students at the same point in time) - longitudinal sample (the same students and different points in time)Surveys were distributed via Qualtrics during the students' first and sixth semesters. Quantitative and qualitative data were collected and used to describe students' IL development over 3 years. Statistics from the quantitative data were analyzed in SPSS. The qualitative data was coded and analyzed thematically in NVivo. The qualitative, textual data is from semi-structured interviews with sixth-semester students in psychology at UiT, both focus groups and individual interviews. All data were collected as part of the contact author's PhD research on information literacy (IL) at UiT. The following files are included in this data set: 1. A README file which explains the quantitative data files. (2 file formats: .txt, .pdf)2. The consent form for participants (in Norwegian). (2 file formats: .txt, .pdf)3. Six data files with survey results from UiT psychology undergraduate students for the cross-sectional (n=209) and longitudinal (n=56) samples, in 3 formats (.dat, .csv, .sav). The data was collected in Qualtrics from fall 2019 to fall 2022. 4. Interview guide for 3 focus group interviews. File format: .txt5. Interview guides for 7 individual interviews - first round (n=4) and second round (n=3). File format: .txt 6. The 21-item IL test (Tromsø Information Literacy Test = TILT), in English and Norwegian. TILT is used for assessing students' knowledge of three aspects of IL: evaluating sources, using sources, and seeking information. The test is multiple choice, with four alternative answers for each item. This test is a "KNOW-measure," intended to measure what students know about information literacy. (2 file formats: .txt, .pdf)7. Survey questions related to interest - specifically students' interest in being or becoming information literate - in 3 parts (all in English and Norwegian): a) information and questions about the 4 phases of interest; b) interest questionnaire with 26 items in 7 subscales (Tromsø Interest Questionnaire - TRIQ); c) Survey questions about IL and interest, need, and intent. (2 file formats: .txt, .pdf)8. Information about the assignment-based measures used to measure what students do in practice when evaluating and using sources. Students were evaluated with these measures in their first and sixth semesters. (2 file formats: .txt, .pdf)9. The Norwegain Centre for Research Data's (NSD) 2019 assessment of the notification form for personal data for the PhD research project. In Norwegian. (Format: .pdf)

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: Handling and Sharing Qualitative Data Responsibly and Effectively.

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: 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?

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.

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.

Interviews were conducted over Zoom and audio recorded with participants' consent. The interview included four main questions, which were consistent across all interviews:

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.

Q2. In what ways do you believe content creators/digital influencers shape consumer behavior? Could you share any examples?

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?

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?

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.

Project Overview Trends toward open science practices, along with advances in technology, have promoted increased data archiving in recent years, thus bringing new attention to the reuse of archived qualitative data. Qualitative data reuse can increase efficiency and reduce the burden on research subjects, since new studies can be conducted without collecting new data. Qualitative data reuse also supports larger-scale, longitudinal research by combining datasets to analyze more participants. At the same time, qualitative research data can increasingly be collected from online sources. Social scientists can access and analyze personal narratives and social interactions through social media such as blogs, vlogs, online forums, and posts and interactions from social networking sites like Facebook and Twitter. These big social data have been celebrated as an unprecedented source of data analytics, able to produce insights about human behavior on a massive scale. However, both types of research also present key epistemological, ethical, and legal issues. This study explores the issues of context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership, with a focus on data curation strategies. The research suggests that connecting qualitative researchers, big social researchers, and curators can enhance responsible practices for qualitative data reuse and big social research. This study addressed the following research questions: RQ1: How is big social data curation similar to and different from qualitative data curation? RQ1a: How are epistemological, ethical, and legal issues different or similar for qualitative data reuse and big social research? RQ1b: How can data curation practices such as metadata and archiving support and resolve some of these epistemological and ethical issues? RQ2: What are the implications of these similarities and differences for big social data curation and qualitative data curation, and what can we learn from combining these two conversations? Data Description and Collection Overview The data in this study was collected using semi-structured interviews that centered around specific incidents of qualitative data archiving or reuse, big social research, or data curation. The participants for the interviews were therefore drawn from three categories: researchers who have used big social data, qualitative researchers who have published or reused qualitative data, and data curators who have worked with one or both types of data. Six key issues were identified in a literature review, and were then used to structure three interview guides for the semi-structured interviews. The six issues are context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. Participants were limited to those working in the United States. Ten participants from each of the three target populations—big social researchers, qualitative researchers who had published or reused data, and data curators were interviewed. The interviews were conducted between March 11 and October 6, 2021. When scheduling the interviews, participants received an email asking them to identify a critical incident prior to the interview. The “incident” in critical incident interviewing technique is a specific example that focuses a participant’s answers to the interview questions. The participants were asked their permission to have the interviews recorded, which was completed using the built-in recording technology of Zoom videoconferencing software. The author also took notes during the interviews. Otter.ai speech-to-text software was used to create initial transcriptions of the interview recordings. A hired undergraduate student hand-edited the transcripts for accuracy. The transcripts were manually de-identified. The author analyzed the interview transcripts using a qualitative content analysis approach. This involved using a combination of inductive and deductive coding approaches. After reviewing the research questions, the author used NVivo software to identify chunks of text in the interview transcripts that represented key themes of the research. Because the interviews were structured around each of the six key issues that had been identified in the literature review, the author deductively created a parent code for each of the six key issues. These parent codes were context, data quality and trustworthiness, data comparability, informed consent, privacy and confidentiality, and intellectual property and data ownership. The author then used inductive coding to create sub-codes beneath each of the parent codes for these key issues. Selection and Organization of Shared Data The data files consist of 28 of the interview transcripts themselves – transcripts from Big Science Researchers (BSR), Data Curators (DC), and Qualitative Researchers (QR)...

Summary Over the past decade, many scholarly journals have adopted policies on data sharing, with an increasing number of journals requiring that authors share the data underlying their published work. Frequently, qualitative data are excluded from those policies explicitly or implicitly. A few journals, however, intentionally do not make such a distinction. This project focuses on articles published in eight of the open-access journals maintained by Public Library of Science (PLOS). All PLOS journals introduced strict data sharing guidelines in 2014, applying to all empirical data on the basis of which articles are published. We collected a database of more than 2,300 articles containing a qualitative data component published between January 1, 2015 and August 23, 2023 and analyzed the data availability statements (DAS) researchers made regarding the availability, or lack thereof, of their data. We describe the degree to which and manner in which data are reportedly available (for example, in repositories, via institutional gate-keepers, or on request from author) versus those that are declared to be unavailable We also outline several dimensions of patterned variation in the data availability statements, including describe temporal patterns and variation by data type. Based on the results, we also provide recommendations to both researchers on how to make their data availability statements clearer, more transparent and more informative, and to journal editors and reviewers, on how to interpret and evaluate statements to ensure they accurately reflect a given data availability scenario. Finally, we suggest a workflow which can link interactions with repositories most productively as part of a typical editorial process. Data Overview This data deposit includes data and code to assemble the dataset, generate all figures and values used in the paper and appendix, and generate the codebook. It also includes the codebook and the figures. The analysis.R script and the data in data/analysis are sufficient to reproduce all findings in the paper. The additional scripts and the data files in data/raw are included for full transparency and to facilitate the detection of any errors in the data processing pipeline. Their structure is due to the development of the project over time.

In 2022, ************** were the most used traditional qualitative methodologies in the market research industry worldwide. During the survey, ** percent of respondents stated that they regularly used this method. Second in the list was data visualization/dashboards, where ** percent of respondents gave this as their answer.

Excerpts from an interview with a participant from the Bureau of Health (a9).Example of qualitative content analysis.

Framing has been termed a "fractured paradigm" by Robert Entman. Frames as media-text features are prime examples of coding complexity since frames may be regarded as factual media content or a loose extracted collection of data snippets docking at a specific theme or event. The potential of the concept for analyzing power relations within political communication is enormous and would benefit from further guiding information when working with CAQDAS. This paper seeks to provide an integral empirical perspective, and it includes suggestions for code families, coding rules, and query examples within ATLAS.ti. Furthermore, it discusses issues like frame types, frame setting, and frame sending. At its core, the paper joins text-based analysis with probing for the relevant actors' view via guideline interviews. By doing so, it connects actor and process-oriented aspects of frame analysis, following one prevailing approach on framing in communication science. It also advises a flexible theoretical docking, but opts for a concise network perspective on actor-document relations. The result of the paper is not quite an empirical blueprint but a collection of helpful yet optional procedures for frame analysis.

Instances of overexcitability identified in Dabrowski's works.

Qualitative data analysis coding categories, criteria, and example responses.

To address Toronto's 2012 budget gap of $774 million, City Council launched a review of all of its services and implemented a multi-year financial planning process. This data set contains the responses to the open-ended questions on the Services Review Public Consultation Feedback Form from members of the public. Approximately 13,000 responses were received (full and partial). The consultation was held between May 11 and June 17, 2011. As a public consultation, respondents chose to participate, and chose which questions to answer. This produced a self-selected sample of respondents. The majority of the responses were from City of Toronto residents. There were some responses from GTA residents. City staff reviewed the data and removed personal information and input violating city policies (for example, contravenes the City's current anti-discrimination policy or confidentiality policy).

Descriptions of overexcitability extracted from Piechowski's work using QDA Miner.

This data collection includes a User Guide and the anonymised transcripts of 30 semi-structured interviews of #60-90 minutes each, with 31 high household energy consumers, about their homes, appliances, infrastructures, vehicles, and everyday life and travel practices that generate household (domestic and travel-related) energy demand. It also includes the anonymised transcripts of 4 3-hour deliberatively workshops of ~8 public participants and 2 facilitators, one recruited from the interviewees, and the others recruited to represent different levels of domestic and travel-related energy consumption, held to discuss the validity, fairness, effectively and acceptability of four broad policy approaches to reduce (particularly high levels of) household energy consumption: Rationing; Economic (Dis)Incentives; Structural Change; and Behaviour Change.

Increased electrification of heating and transport may result in localised strain on the electricity grid. How can these potential costly upgrades be avoided or the costs of these infrastructure investments be fairly distributed? Tackling ‘over consumption’ is a potentially efficient and equitable approach to reducing energy demand. Achieving this will rely on understanding the reasons for high energy use and the structural, social, cultural and economic influences on these behaviours. This project uses novel datasets of domestic and mobility-related consumption data (for example, see www.motproject.net) plus primary quantitative and qualitative data, to develop and test a methodology for identifying, characterising and assessing locations that have disproportionately high levels of energy consumption (i.e. gas, electricity and car based mobility). The findings are considered in the context of political theory and theories of consumption to structure definitions of high use consumers and to develop and assess approaches to equitable radical reductions. This understanding is informing another project to model electricity networks at a local level.

What we are asking How can we meaningfully identify, assess and characterise households or locations with disproportionately high levels of energy consumption? What is energy demand being used for in the highest consuming households? To what extent is high energy demand for domestic use correlated with high energy demand from mobility? When is income a principal determinant of excess demand, and when is it not? What is the relationship between energy poverty and excess demand? To what extent do those who consume most energy also have the greatest social and economic capital to reduce consumption?

• Transcripts of eight semi-structured interviews with researchers from life and natural sciences about their wishes and requirements for metadata for research data
• Interview guide (Interviewleitfaden) that was used to conduct all 8 Interviews
• Needs assessment in order to develop new needs-based Research Data Management services
• This dataset forms the basis of a Master's Thesis "Metadaten für Forschungsdaten: Bedürfnisse und Anforderungen in den Naturwissenschaften" (= Metadata for Research Data: A Needs Assessment in The Sciences) in Library and Information Science at the Humboldt University of Berlin (2019)
• The study was conducted as part of the work package "Metadata" of the Austrian project e-Infrastructures Austria Plus (2017-2019)
• Transcripts of the Interviews C, E, F are in English; Transcript of the interviews A, B, D, G, H are in German
• Abstract (DE): Bei der Entwicklung von bedarfsgerechten Services im Bereich eScience sind Untersuchungen der Bedürfnisse und Anforderungen der Zielgruppen von besonderer Relevanz. Angelehnt an die Grounded Theory wurden im Rahmen des österreichweiten Projektes e-Infrastructures Austria Plus von April bis Mai 2019 acht Leitfadeninterviews mit Naturwissenschaftlern von der Universität Wien und der Medizinischen Universität Wien durchgeführt. Die Ergebnisse zeigen, dass ein Verständnis des Begriffes „Metadaten“ unter Forschenden noch nicht flächendeckend vorhanden ist. Es konnten aber mehrere Beispiele der Beschreibung von Daten in der Praxis identifiziert, sowie sechs Bereiche an Wünschen der Forschenden herausgearbeitet werden. Diese beschäftigen sich u. a. mit der Notwendigkeit, Services auf Deutsch und Englisch zu konzipieren, mit den Anforderungen an technische Infrastruktur oder dem Wunsch nach dem Einsatz von Data Stewards, die Fachkenntnisse aus einer relevanten Disziplin mitbringen.
• Abstract (EN): When developing needs-based services for eScience, studies of the needs and requirements of the target groups are of particular relevance. Based on the Grounded Theory, eight guided interviews with scientists from the University of Vienna and the Medical University of Vienna were conducted from April to May 2019 within the framework of the Austria-wide project e-Infrastructures Austria Plus. The results show that an understanding of the term "metadata" among researchers is not yet widely available. However, the study identifies several examples of describing data as well as six areas of researchers' needs in this area. These cover, among other things, the need to design services in German and English, the requirements for technical infrastructure or the desire to use data stewards who bring expertise from a relevant discipline.

Background Data sharing is commonly seen as beneficial for science, but is not yet common practice. Research funding agencies are known to play a key role in promoting data sharing, but German funders’ data sharing policies appear to lag behind in international comparison. This study aims to answer the question of how German data sharing experts inside and outside funding agencies perceive and evaluate German funders’ data sharing policies and overall efforts to promote data sharing. Methods This study is based on sixteen guideline-structured interviews with representatives of German funding agencies and German research data experts from other organisations, who shared their perceptions of German’ funders efforts to promote data sharing. By applying the method of qualitative content analysis to our interview data, we categorise and describe noteworthy aspects of the German data sharing policy landscape and illustrate our findings with interview passages. Research data This dataset contains summaries from interviews with data sharing and funding policy experts from German funding agencies and what we call "stakeholder organisations" (e.g., universities, research data infrastructure providers, etc.). We asked the interviewees about their perspectives on German funders' data sharing policies, for example regarding the actual status quo, their expectations about the potential role that funders can play in promoting data sharing, as well as general developments in this area. Supplement_1_Interview_guideline_funders.pdf and Supplement_2_Interview_guideline_stakeholders.pdf provide supplemental information in the form of the (german) interview guidelines used in this study. Supplement_3_Transcription_and_coding_guideline.pdf lays out the rules we followed in our transcription and coding process. Supplement_4_Category_system.pdf describes the underlying category system of the qualitative content analysis we conducted.

Digital technology has made it easier for researchers to conduct and produce multimodal data. In terms of a social semiotic understanding, multimodal means that data are produced from different sign resources, such as field protocols combined with visual recordings or document analysis consisting of audiovisual material. The increase in multimodal data brings the challenge of developing analytical tools not only to collect data but also to examine them. In this article, I introduce a research approach for how to integrate multimodal data within the framework of grounded theory by extending the coding process with a social semiotic understanding of data as a combination of different sign modes. This approach makes it possible not only to analyze data based on different modes separately but also to analyze their combination, for example, the interweaving of text and image.

Examples of conversations where the patients misunderstand health information.

Examples from the qualitative content analysis; from code to theme.

This dataset compiles examples of use of the following terms: covid-19, coronavirus, confinamiento, SARS-CoV-2, pandemia and virus. This are selected in a double quantitative and qualitative methodology from the linguistic corpora in Spanish of scientific dissemination texts from The Conversation.