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.

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.

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)

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."

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.

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 prevalence of teenage pregnancy is worrisome in Rwanda, and little is known about the consequences faced by teen mothers aged 15–19. Therefore, the present study aims to explore the consequences of adolescent childbearing among teen mothers in Gatsibo district, Rwanda.

Qualitative Data Analysis Software Market Outlook

In 2023, the global qualitative data analysis software market size was valued at approximately USD 1.2 billion. With an impressive compound annual growth rate (CAGR) of 15%, the market is projected to reach USD 3.3 billion by 2032. This growth is driven by an increasing demand for data-driven decision-making processes across various industries, as well as advancements in artificial intelligence and machine learning technologies that are enhancing the capabilities of qualitative data analysis tools. Organizations are increasingly recognizing the value of qualitative insights, which complement quantitative data by providing deeper, context-rich understanding of phenomena, which is a significant growth factor in this market.

The demand for qualitative data analysis software is expanding due to the growing need for holistic research methods that incorporate diverse data types. In academic research, qualitative data analysis plays a critical role in understanding complex social phenomena by analyzing text, audio, video, and images. The rise of interdisciplinary studies that demand robust qualitative analysis solutions is propelling software adoption. Additionally, the business and enterprise sector has increasingly leveraged these tools to extract consumer insights from unstructured data sources like social media, reviews, and customer feedback. These insights are crucial for developing marketing strategies and enhancing customer engagement, thus driving market growth.

Healthcare is another sector significantly contributing to the market's expansion. Qualitative data analysis is crucial for understanding patient narratives and improving patient-centered care models. With the shift towards personalized medicine, healthcare providers are utilizing qualitative insights to better comprehend patient experiences and treatment outcomes. Moreover, the integration of qualitative data analysis tools with other healthcare systems is enhancing clinical research and operational efficiency. The continuous development in healthcare analytics and the increasing volume of healthcare data are expected to further boost demand in this sector.

Government and public sector organizations are also adopting qualitative data analysis software to improve policy formulation and public services. By analyzing feedback from citizens and stakeholders, governments can make informed decisions that address public needs more effectively. The growing emphasis on transparency and accountability in governance is driving the adoption of these tools. Additionally, the ongoing digital transformation across public sectors globally is facilitating the integration of advanced data analysis tools in government operations, thus contributing to the market's growth.

Regionally, North America dominates the market due to its advanced technological infrastructure and high adoption rate of data-driven decision-making processes across various sectors. Europe follows, with a strong presence of academic research institutions and enterprises investing in qualitative data analysis tools. The Asia Pacific region is expected to witness the fastest growth, driven by rapid digitalization and increasing research activities in countries like China, India, and Japan. Latin America and the Middle East & Africa regions are also beginning to explore the potential of qualitative data analysis, although they currently constitute a smaller portion of the market.

Component Analysis

The qualitative data analysis software market is segmented by component into software and services. The software segment is the backbone of the market, offering a variety of tools that allow users to code, categorize, and analyze qualitative data. The demand for sophisticated software solutions is rising as organizations seek tools that offer enhanced features such as data visualization, collaboration capabilities, and integration with other data sources. The push towards comprehensive data analysis platforms that can manage large datasets and provide intuitive interfaces is driving innovation in software development. Furthermore, the integration of artificial intelligence into these software solutions is significantly enhancing their capabilities, making them more efficient and reducing the time required for data analysis.

In contrast, the services segment encompasses a range of offerings including consulting, implementation, training, and support services. As organizations increasingly adopt sophisticated qualitative data analysis tools, there is a growing need for professional services to ensure

Social scientists working in rule-bound and evidence-based traditions need to show how they know what they know. The less visible the process that produced a conclusion, the less one can see of the conclusion. A sufficiently diminished view of that process undermines the claim.

What an author needs to do to fulfill this transparency obligation differs depending on the nature of the work, the data that were used, and the analyses that were undertaken. For a scholar arriving at a conclusion using a statistical software package to analyze a quantitative dataset, making the claim transparent would include providing the dataset and software commands.

Research transparency is a much newer proposition for qualitative social science, especially where “granular” data are generated from individual sources, and the data are analyzed individually or in small groups.
Because the data are not used holistically as a dataset, however, new ways have to be developed to associate the claims with the granular data and their analysis.

The Qualitative Data Repository has been working on annotation for transparent inference (ATI) for some time, and has made considerable progress, particularly in specifying what information needs to be surfaced for readers to be able to understand and evaluate published claims. With these requirements in mind, this paper will develop a list of functional specifications and a set of criteria for choosing an annotation standard to use as the basis for ATI.

This data is associated with the publication of the manuscript "Nightlife as Counterspace: Potentials of Nightlife for Social Wellbeing" in Annals of Leisure Research. It contains a data set on the (german) standardized survey that is directly cited in the manuscript, the Cluster analysis, as well as the german original transcripted records of the cited group discussions.

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).

This dataset has an access level Restricted, which means it is not available via direct download but must be requested. Our accessing research data guidance outlines the reasons access may be limited and the request process. In order to request access to this data please complete the data request form.*

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.

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).

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.

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.

This dataset contains data collected during a study ("Towards High-Value Datasets determination for data-driven development: a systematic literature review") conducted by Anastasija Nikiforova (University of Tartu), Nina Rizun, Magdalena Ciesielska (Gdańsk University of Technology), Charalampos Alexopoulos (University of the Aegean) and Andrea Miletič (University of Zagreb) It being made public both to act as supplementary data for "Towards High-Value Datasets determination for data-driven development: a systematic literature review" paper (pre-print is available in Open Access here -> https://arxiv.org/abs/2305.10234) and in order for other researchers to use these data in their own work.

The protocol is intended for the Systematic Literature review on the topic of High-value Datasets with the aim to gather information on how the topic of High-value datasets (HVD) and their determination has been reflected in the literature over the years and what has been found by these studies to date, incl. the indicators used in them, involved stakeholders, data-related aspects, and frameworks. The data in this dataset were collected in the result of the SLR over Scopus, Web of Science, and Digital Government Research library (DGRL) in 2023.

Methodology

To understand how HVD determination has been reflected in the literature over the years and what has been found by these studies to date, all relevant literature covering this topic has been studied. To this end, the SLR was carried out to by searching digital libraries covered by Scopus, Web of Science (WoS), Digital Government Research library (DGRL).

These databases were queried for keywords ("open data" OR "open government data") AND ("high-value data*" OR "high value data*"), which were applied to the article title, keywords, and abstract to limit the number of papers to those, where these objects were primary research objects rather than mentioned in the body, e.g., as a future work. After deduplication, 11 articles were found unique and were further checked for relevance. As a result, a total of 9 articles were further examined. Each study was independently examined by at least two authors.

To attain the objective of our study, we developed the protocol, where the information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information.

Test procedure Each study was independently examined by at least two authors, where after the in-depth examination of the full-text of the article, the structured protocol has been filled for each study. The structure of the survey is available in the supplementary file available (see Protocol_HVD_SLR.odt, Protocol_HVD_SLR.docx) The data collected for each study by two researchers were then synthesized in one final version by the third researcher.

Description of the data in this data set

Protocol_HVD_SLR provides the structure of the protocol Spreadsheets #1 provides the filled protocol for relevant studies. Spreadsheet#2 provides the list of results after the search over three indexing databases, i.e. before filtering out irrelevant studies

The information on each selected study was collected in four categories: (1) descriptive information, (2) approach- and research design- related information, (3) quality-related information, (4) HVD determination-related information

Descriptive information
1) Article number - a study number, corresponding to the study number assigned in an Excel worksheet 2) Complete reference - the complete source information to refer to the study 3) Year of publication - the year in which the study was published 4) Journal article / conference paper / book chapter - the type of the paper -{journal article, conference paper, book chapter} 5) DOI / Website- a link to the website where the study can be found 6) Number of citations - the number of citations of the article in Google Scholar, Scopus, Web of Science 7) Availability in OA - availability of an article in the Open Access 8) Keywords - keywords of the paper as indicated by the authors 9) Relevance for this study - what is the relevance level of the article for this study? {high / medium / low}

Approach- and research design-related information 10) Objective / RQ - the research objective / aim, established research questions 11) Research method (including unit of analysis) - the methods used to collect data, including the unit of analy-sis (country, organisation, specific unit that has been ana-lysed, e.g., the number of use-cases, scope of the SLR etc.) 12) Contributions - the contributions of the study 13) Method - whether the study uses a qualitative, quantitative, or mixed methods approach? 14) Availability of the underlying research data- whether there is a reference to the publicly available underly-ing research data e.g., transcriptions of interviews, collected data, or explanation why these data are not shared? 15) Period under investigation - period (or moment) in which the study was conducted 16) Use of theory / theoretical concepts / approaches - does the study mention any theory / theoretical concepts / approaches? If any theory is mentioned, how is theory used in the study?

Quality- and relevance- related information
17) Quality concerns - whether there are any quality concerns (e.g., limited infor-mation about the research methods used)? 18) Primary research object - is the HVD a primary research object in the study? (primary - the paper is focused around the HVD determination, sec-ondary - mentioned but not studied (e.g., as part of discus-sion, future work etc.))

HVD determination-related information
19) HVD definition and type of value - how is the HVD defined in the article and / or any other equivalent term? 20) HVD indicators - what are the indicators to identify HVD? How were they identified? (components & relationships, “input -> output") 21) A framework for HVD determination - is there a framework presented for HVD identification? What components does it consist of and what are the rela-tionships between these components? (detailed description) 22) Stakeholders and their roles - what stakeholders or actors does HVD determination in-volve? What are their roles? 23) Data - what data do HVD cover? 24) Level (if relevant) - what is the level of the HVD determination covered in the article? (e.g., city, regional, national, international)

Format of the file .xls, .csv (for the first spreadsheet only), .odt, .docx

Licenses or restrictions CC-BY

For more info, see README.txt

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.

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