This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 14-Dec-2023 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman Institute. The dataset forms part of the outputs from a project titled ‘Fostering cultures of open qualitative research’ which ran from January 2023 to June 2023, and 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-2023. 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 from reuse. It has been deposited under a CC-BY-NC license. Overall, this dataset comprises: 1 x Workshop transcript - in .docx file format which can be opened with Microsoft Word, Google Doc, or an open-source equivalent. The workshop took place on 18-Jul-2023 at the Wave Building, University of Sheffield. All five attendees have read and approved a portion of transcripts containing their own discussion. All workshop attendees have had an opportunity to retract details should they wish to do so. All workshop attendees have chosen whether to be pseudonymised or named directly. The pseudonym or real name can be used to identify individual participant responses in the qualitative coding held within accompanying dataset from the same project - Survey Responses: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 1 – Survey Responses. The University of Sheffield. DOI: 10.15131/shef.data.23567250.v1. Interviews: Hanchard M and San Roman Pineda I (2023) Fostering cultures of open qualitative research: Dataset 2 – Interview Transcripts. The University of Sheffield. DOI: 10.15131/shef.data.23567223.v2. As a limitation, the audio recording of the workshop session that this transcript is based upon is missing a section (due to a recording error) and may contain errors/inaccuracies (due to poor audio conditions within the workshop room). Every effort has been taken to correct these, including participants themselves reviewing their discussion/quotes, but the transcript may still contain minor inaccuracies, typos, and/or other errors in the text - as is noted on the transcript itself. The project was undertaken by two staff: Co-investigator: Dr. Itzel San Roman Pineda (Postdoctoral Research Assistant) ORCiD ID: 0000-0002-3785-8057 i.sanromanpineda@sheffield.ac.uk Labelled as ‘Researcher 1’ throughout all project datasets. Principal Investigator (corresponding dataset author): Dr. Matthew Hanchard (Research Associate) ORCiD ID: 0000-0003-2460-8638 m.s.hanchard@sheffield.ac.uk iHuman Institute, Social Research Institutes, Faculty of Social Science Labelled as ‘Researcher 2’ throughout all project datasets.

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 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

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)

The data comprises of two datasets. The first consists of text files of anonymised transcripts from focus group discussions (FGDs) on livelihood activities, ecosystem services and the prevalent human and animal health problems in irrigated and non-irrigated areas in northeastern Kenya. The second comprises of scores from proportional piling exercises which showed the distribution of wealth categories and livestock species kept. The study was conducted between August and October, 2013 and the data were collected as open-ended meeting notes and audio clips captured using digital recorders. Written/thumb print consent was always obtained from each individual in the group. All the discussions were also recorded, with the participant's permission. Thirteen FGDs were held in the irrigated areas in Bura and Hola, Tana River County involving farmers who grew a variety of crops for subsistence and commercial purposes. The others were held in Ijara and Sangailu, Garissa County inhabited by transhumance pastoralists. Each group comprised of 10 to 12 people and the discussions were guided by a check list. The transcribed documents were formatted in Microsoft Word (2013) and saved as text files in preparation for analysis. The aim of the study was to collate perceptions of land use change and their effects on ecosystem services. The data were collected by enumerators trained by experienced researchers from the University of Nairobi and the International Livestock Research Institute (Kenya). This dataset is part of a wider research project, the Dynamic Drivers of Disease in Africa Consortium (DDDAC). The research was funded by NERC project NE-J001570-1 with support from the Ecosystem Services for Poverty Alleviation Programme (ESPA). Additional funding was provided by the CGIAR Research Program Agriculture for Nutrition and Health. Full details about this dataset can be found at https://doi.org/10.5285/4f569d73-30c5-4b12-bca7-8901fb567594

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 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.

A multicentre qualitative ethnographic study of medicines safety processes and systems in English paediatric in-patient units. Three sites in the North of England were studied. 72 participant observation sessions (~230 hours) and 19 semi-structured interviews were conducted.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

The study is mixed methods research.Quantitative Data: Datasets are of sociodemographic data of women accessing cervical cancer screening at a woman's clinic. The datasets and do files can be opened in analytic software, STATA . Qualitative data: Qualitative data consists of preliminary analysis tables and reflective notes from in-depth interviews with female patients and healthcare providers. .

These are the data sets in machine readable files from a quantitative dye tracer test conducted at Langle Spring November 13-December 2, 2017 as part of the USGS training class, GW2227 Advanced Field Methods in Karst Terrains, held at the Savoy Experimental Watershed, Savoy Arkansas. Langle Spring is NWIS site 71948218, latitude 36.11896886, longitude -94.34548871. One pound of RhodamineWT dye was injected into a sinking stream at latitude 36.116772 longitude -94.341883 NAD83 on November 13, 2017 at 22:50. The data sets include original fluorimeter data logger files from Langle and Copperhead Springs, Laboratory Sectra-fluorometer files from standards and grab samples, and processed input and output files from the breakthrough curve analysis program Qtracer2 (Field, USEPA, 2002 EPA/600/R-02/001).

Qualitative dataset for the study: Safety-Seeking Behaviors and Psychological Distress in Older Victims of Community-Crime: A Cross-Sectional Study Using a Novel Person-Reported MeasureThis dataset is for the qualitative component of the Person-Reported Safety-Seeking Behavior Measure (PRSBM). Older victims of community crime were asked whether they engaged in six types of behaviors since the crime: (checking, reassurance-seeking, rumination, avoidance, rituals, hypervigilance). If so, they were asked to describe their behaviors. Older victims were also asked to rate how frequently they engaged in each behavior and how much of change it was since the crime; the data for this is available in the corresponding quantitative dataset.

Data represent information extracted from published literature meeting filtering criteria regarding quantification of among-individual variation in spatial behaviors. Information includes manuscript identifiers, descriptions of study design, as well as information directly input into a statistical meta-analysis regression framework.

269 transcribed excerpts coded from 22 interviews to self-researchers for the study "Shared motivations, goals and values in the practice of personal science - A community perspective on self-tracking for empirical knowledge". Interviews with participants were conducted via video conferencing and were based on a list of open-ended questions, separated into key sections around participation and collaboration in personal science. Participants who agreed to be interviewed, gave informed consent in like with the ethics approval by the Inserm Institutional Review Board (IRB) for this study, and regarding this data set, previous agreement in compliance with privacy and anonymity requirements. Academic article based on this dataset: Senabre Hidalgo, E., Ball, M. P., Opoix, M., & Greshake Tzovaras, B. (2022). Shared motivations, goals and values in the practice of personal science: a community perspective on self-tracking for empirical knowledge. Humanities and Social Sciences Communications, 9(1), 1-12. https://doi.org/10.1057/s41599-022-01199-0

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.

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 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.

Qualitative Datasets of Gendered Aquaculture Value Chain Analysis in Northwestern Bangladesh. This data presents a value chain study with an integrated gender lens of the aquaculture sector in Rajshahi and Rangpur in northwestern Bangladesh. The study forms part of the contextual knowledge foundation for the IDEA project, which works in all 16 districts of the Rangpur and Rajshahi divisions. Its ultimate goal is to reach 1 million households for its aquaculture production outcomes and 2 million households for its nutrition outcomes. The aim of the value chain study was to generate a knowledge base for designing project interventions. These focus specifically on inclusive aquaculture value chains that are both more productive and contribute to poverty reduction, and in which women and youths can be equitably included and benefit in safe and dignified manners. A market study and an empowerment study (using WEAI) were also conducted but never analyzed for the report.

The quantitative data contains 1,228 conditions (rows) and 23 variables (columns). As described in the main article, some conditions are split into sub-conditions; each sub-condition is a separate line in the dataset. Detailed variable definitions are listed in the next section. Key variables of our analysis are policy areas (variable Policy) and ideological models (variable Model). The qualitative data is an Atlas.ti file. The qualitative analysis has been conducted in Atlas.ti version 7.5.18. The hermeneutic-unit (working space) has been bundled into the file IMF agriculture qualitative analysis-submission version.atlcb. See Read me file for further details.

Teaching undergraduates with quantitative data in the social sciences at University of California Santa Barbara

https://doi.org/10.25349/D9402J

Description of the data and file structure

This deposit includes a deid-transcripts.zip folder containing 10 pdf files with de-identified transcripts of semi-structured interviews. It also includes a copy of the recruitment email sent to participants, the interview guide, and the codebook with key themes.