This is a hands-on workshop on the management of qualitative social science data, with a focus on data sharing and transparency. While the workshop addresses data management throughout the lifecycle – from data management plan to data sharing – its focus is on the particular challenges in sharing qualitative data and in making qualitative research transparent. One set of challenges concerns the ethical and legal concerns in sharing qualitative data. We will consider obtaining permissions for sharing qualitative data from human participants, strategies for (and limits of) de-identifying qualitative data, and options for restricting access to sensitive qualitative data. We will also briefly look at copyright and licensing and how they can inhibit the public sharing of qualitative data.

A second set of challenges concerns the lack of standardized guidelines for making qualitative research processes transparent. Following on some of the themes touched on in the talk, we will jointly explore some cutting edge approaches for making qualitative research transparent and discuss their potentials as well as shortcomings for different forms of research.

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

Data management is a critical aspect of empirical research. Unfortunately, principles of good data management are rarely taught to social scientists in a systematic way as part of their methods training. As a result, researchers often do things in an ad hoc fashion and have to learn from their mistakes.

The Qualitative Data Repository (QDR, www.qdr.org) presented a webinar on social science data management, with a special focus on keeping qualitative data safe and secure. The webinar will emphasize best practices with the aim of helping participants to save time and minimize frustration in their future research endeavors. We will cover the following topics:

1) The value of planning and Data Management Plans (DMPs)

2) Transparency and data documentation

3) Ethical, legal, and logistical challenges to sharing qualitative data and best practices to address them

4) Keeping data safe and secure.

Attribution: Parts of this presentation are based on slides used in a course co-taught by personnel from QDR and the UK Data Service. All materials provided under a CC-BY license.

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)...

Qualitative data for It projects

Market Research Intellect presents the Qualitative Data Analysis Software Market Report-estimated at USD 450 million in 2024 and predicted to grow to USD 1.1 billion by 2033, with a CAGR of 10.5% over the forecast period. Gain clarity on regional performance, future innovations, and major players worldwide.

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.

Archive for storing and sharing digital data (and accompanying documentation) generated or collected through qualitative and multi method research in social sciences. QDR provides data management consulting services and actively curates all data projects, maintaining value and usefulness of data over time, and ensuring their availability and findability for re-use.

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."

Data collected from interviews and group discussions, called qualitative data, can help researchers understand people's experiences, values, and cultures. But large amounts of qualitative data can be hard to show in a way that's easy for people to understand. In this study, the research team created charts called ethnoarrays. These charts use color coding to show individual stories and overall patterns in qualitative data. The team wanted to learn whether ethnoarrays were useful and easy to understand.

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 of 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. Overall, this dataset comprises:

· 15 x Interview transcripts - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent.

All participants have read and approved their transcripts and have had an opportunity to retract details should they wish to do so.

Participants chose whether to be pseudonymised or named directly. The pseudonym can be used to identify individual participant responses in the qualitative coding held within the ‘Fostering cultures of open qualitative research: Dataset 3 – Coding Book’ files.

For recruitment, 14 x participants we selected based on their responses to the project survey., whilst one participant was recruited based on specific expertise.

· 1 x Participant sheet – in .csv format which may by opened with Microsoft Excel, Google Sheet, or an open-source equivalent.

The provides socio-demographic detail on each participant alongside their main field of research and career stage. It includes a RespondentID field/column which can be used to connect interview participants with their responses to the survey questions in the accompanying ‘Fostering cultures of open qualitative research: Dataset 1 – Survey Responses’ files.

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 Labelled as ‘Researcher 1’ throughout the dataset

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 Labelled as ‘Researcher 2’ throughout the dataset

Workshop at the Ohio State University, July 23rd 2018 A hands-on workshop on the dos and don’ts of data management with a focus on qualitative social science data. The data management plan is at the core of this workshop and participants will either develop their DMP or learn how to develop existing DMPs throughout the workshop. We will cover topics throughout the data lifecycle, from planning data management to sharing the data, with a focus on practical, hands-on advice.

This webinar explores the value of qualitative methods, which include a number of data collection strategies suitable for many purposes: ethnography, cognitive psychology, case study, action research, oral history, and policy research. In qualitative inquiry, every story's important, and every voice is an important version of the truth; it gives a voice to people who often go unheard. Presenters: Cassandra Firman, Training and Technical Assistance Coordinator, FRIENDS; Susan Janko Summers, author and consultant to FRIENDS View Webinar (WMV - 76 MB) Metadata-only record linking to the original dataset. Open original dataset below.

Interview transcripts. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: It is stored on the O drive- PRIV -IRBData - MaxwellDWDM. Format: IRB human subjects research data. This dataset is associated with the following publication: Matsler, A.M., K. Maxwell, and S. Henson. ‘Discarding well’ after disasters? Examination of disaster waste and debris management in the United States. Human Organization. Society for Applied Anthropology, Oklahoma City, OK, USA, 4(2): 133-144, (2025).

This dataset includes a list of text segments from focus group participants, related to the manuscript's research. The text segments are sorted into categories (i.e., uses, weaknesses, and recommendations) and sub-categories. The data is de-indentified. All personably-identifiable information was redacted or removed.

The qualitative data analysis software market is experiencing robust growth, driven by the increasing need for in-depth understanding of consumer behavior, market trends, and social dynamics. The market's expansion is fueled by the rising adoption of qualitative research methods across various industries, including market research, healthcare, social sciences, and academia. Technological advancements, such as improved AI-powered functionalities for data coding, thematic analysis, and automated transcription, are enhancing the efficiency and accuracy of qualitative analysis, further boosting market growth. The availability of user-friendly interfaces and cloud-based solutions is making these powerful tools accessible to a broader range of researchers and analysts. While the market is competitive, with established players like NVivo and MAXQDA alongside emerging solutions, the overall trend indicates significant future growth potential. This growth is expected to continue as organizations increasingly recognize the value of rich, nuanced insights derived from qualitative data for informed decision-making. A key factor influencing market segmentation is the type of qualitative data being analyzed (e.g., interviews, focus groups, social media data). Different software solutions cater to specific data types and analytical needs. The pricing models of these solutions also vary, with options ranging from subscription-based services to one-time purchases. Geographic distribution of the market shows strong growth in North America and Europe, driven by high research and development spending and a strong adoption of advanced analytical techniques. However, growth in Asia-Pacific and other emerging markets is also expected to contribute significantly to the overall market expansion in the coming years, as these regions experience increasing investment in research and data analysis capabilities. Competitive pressures will likely drive innovation, with continuous improvements in features, accessibility, and integration with other analytical tools. We anticipate sustained market expansion throughout the forecast period, driven by these factors.

We propose a framework for meta-analysis of qualitative causal inferences. We integrate qualitative counterfactual inquiry with an approach from the quantitative causal inference literature called extreme value bounds. Qualitative counterfactual analysis uses the observed outcome and auxiliary information to infer what would have happened had the treatment been set to a different level. Imputing missing potential outcomes is hard and when it fails, we can fill them in under best- and worst-case scenarios. We apply our approach to 63 cases that could have experienced transitional truth commissions upon democratization, 8 of which did. Prior to any analysis, the extreme value bounds around the average treatment effect on authoritarian resumption are 100 percentage points wide; imputation shrinks the width of these bounds to 51 points. We further demonstrate our method by aggregating specialists' beliefs about causal effects gathered through an expert survey, shrinking the width of the bounds to 44 points.

The factor significance review comprised 94 studies. This brought additional studies to those used earlier in the quantitative review (limited to 75 studies documenting mass load reductions) and queried the findings differently. Sediment, nitrate, dissolved and total P were well-studied (≥35 each), total N and runoff volume moderately studied (≥19 studies each) and pesticides, ammonium and coliforms least studied (<19 studies each), with only one study dealing with colloidal P. Evidence comes primarily from warm temperate climate zones, less from snow zone climates and most limited from arid and tropical regions. Studies described effects dominantly in simulated (ie a plot or soil monolith isolated from its landscape context) and created RBZ (a land change to a buffer in a realistic environmental context), with fewer studies in semi-natural riparian zones. There was a mixture of simulated or natural rainfall; only sediment retention evidence draws heavily on studies using artificial plots and/or simulated rain applications. A mixture of temporal scales was used; artificial plot studies tended to be of shorter duration with few isolated rain events, whereas studies of buffers in their landscape context tended to exceed one year. Study spatial scale was dominated more by plot scale investigations with fewer at reach to catchment scales.

Project Summary As part of a qualitative study of abortion reporting in the United States, the research team conducted cognitive interviews to iteratively assess new question wording and introductions designed to improve the accuracy of abortion reporting in surveys (to be shared on the Qualitative Data Repository in a separate submission). As expectations to share the data that underlie research increase, understanding how participants, particularly those taking part in qualitative research, respond to requests for data sharing is necessary. We assessed research participants’ willingness to, understanding of, and motivations for data sharing. Data Overview The data consist of excerpts from cognitive interviews with 64 cisgender women in two states in January and February of 2020 in which researchers asked for respondents for consent to share de-identified data. Eligibility criteria included: assigned female at birth, currently identified as a woman between the ages of 18-49, English-speaking, and reported ever having penile-vaginal sex. Respondents were screened for abortion history as well to ensure that at least half the sample reported a prior abortion. At the end of interviews, participants were asked to reflect on their motivations for agreeing or declining to share their data. The data included here are coded excerpts of their answers. Most respondents consented to data sharing, citing helping others as a primary motivation for agreeing to share their data. However, a substantial number of participants demonstrated limited understanding of “data sharing.” Data available here include the following materials: overview of methods, cognitive interview consent form (with language for data sharing consent), and data sharing analysis coding scheme.

About EBN courses and TBL with nursing postgraduate students at Guangzhou University of Chinese Medicine（2019）