What Emotions Bring to Managing, Caring for, and Sharing Qualitative Data

Authors

DOI:

https://doi.org/10.47989/ir31154039

Abstract

Introduction. Motivated by increasing policies for data management, this paper presents the findings of a collaborative autoethnography designed to deeply explore how qualitative researchers relate to and care for their data.

Method. We (8 researchers at a European university) collectively researched our data practices, using the established method of autoethnography. This included writing field notes, collective written reflections, a workshop, and semi-structured interviews.

Analysis. Inductive coding and thematic analysis were performed. Identified themes informed the theoretical framing used in our analysis: the relational nature of data and data care.

Results. The emotions experienced when working with data emerged as being integral to our (responsible) research practices. These, often negative, emotions are intertwined with three ways in which we care for data: i) as a means of caring for research participants; ii) caring for data maintenance and infrastructure; and iii) caring for data’s quality and usefulness. The emotions and caring relations we identify are often in tension with common expectations for data sharing.

Conclusion. We conclude by reflecting along three lines about the implications of our findings for how data management and sharing might be carried out in ways which acknowledge the affective nature of research data practices.

References

Albornoz, D., Huang, M., Martin, I. M., Mateus, M., Touré, A. Y., & Chan, L. (2018). Framing power: tracing key discourses in open science policies. In L. Chan & P. Mounier (Eds.), ELPUB 2018: Vol. Connecting the Knowledge Commons: From Projects to Sustainable Infrastructure. ElPub. https://doi.org/10.4000/proceedings.elpub.2018.23

Anderson, L. (2006). Analytic autoethnography. Journal of Contemporary Ethnography, 35(4), 373–395. https://doi.org/10.1177/0891241605280449

Anderson, T. D., & Fourie, I. (2015). Collaborative autoethnography as a way of seeing the experience of caregiving as an information practice. Information Research, 20(1), 170–182. http://InformationR.net/ir/20-1/isic2/isic33.html

Baker, K. S., & Karasti, H. (2018). Data care and its politics: Designing for local collective data management as a neglected thing. Proceedings of the 15th Participatory Design Conference: Full Papers - Volume 1, 1–12. https://doi.org/10.1145/3210586.3210587

Bishop, L. (2009). Ethical sharing and reuse of qualitative data. Australian Journal of Social Issues, 44(3). https://doi.org/10.1002/j.1839-4655.2009.tb00145.x

Bishop, L., & Kuula-Luumi, A. (2017). Revisiting qualitative data reuse: A decade on. Sage Open, 7(1), 2158244016685136. https://doi.org/10.1177/2158244016685136

Borgman, C. L. (2015). Big data, little data, no data: Scholarship in the networked world. MIT Press.

Borgstrom, E., Driessen, A., Krawczyk, M., Kirby, E., MacArtney, J., & Almack, K. (2024). Grieving academic grant rejections: Examining funding failure and experiences of loss. The Sociological Review, 72(5), 998–1017. https://doi.org/10.1177/00380261231207196

Buckley, R. (2015). Autoethnography helps analyse emotions. Frontiers in Psychology, 6, 209. https://doi.org/10.3389/fpsyg.2015.00209

CESSDA. (2022). CESSDA Data management expert guide. Zenodo. https://doi.org/10.5281/zenodo.3820473

Choroszewicz, M. (2022). Emotional labour in the collaborative data practices of repurposing healthcare data and building data technologies. Big Data & Society, 9(1), 205395172210984. https://doi.org/10.1177/20539517221098413

Cox, A. M., & Pinfield, S. (2014). Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science, 46(4), 299–316. https://doi.org/10.1177/0961000613492542

Davies, C. A. (1998). Reflexive ethnography: A guide to researching selves and others. Routledge. https://doi.org/10.4324/9780203069370

Davies, S., Pham, B.-C., Dessewffy, E., Schikowitz, A., & Gámez, F. M. (2022). Pinboarding the pandemic: Experiments in representing autoethnography. Catalyst: Feminism, Theory, Technoscience, 8(2), Article 2. https://doi.org/10.28968/cftt.v8i2.38868

D’Ignazio, C., & Klein, L. F. (2020). Data feminism. MIT Press.

Edwards, J. (2021). Ethical autoethnography: Is it possible? International Journal of Qualitative Methods, 20, 1609406921995306. https://doi.org/10.1177/1609406921995306

European Commission. (2018). Cost-benefit analysis for FAIR research data: Cost of not having FAIR research data. Publications Office of the European Union. https://doi.org/10.2777/02999

Feldman, S., & Shaw, L. (2019). The epistemological and ethical challenges of archiving and sharing qualitative data. American Behavioral Scientist, 63(6), 699–721. https://doi.org/10.1177/0002764218796084

Gregory, K., & Koesten, L. (2022). Human-centered data discovery. Springer International Publishing. https://doi.org/10.1007/978-3-031-18223-5

Gregory, K., Ninkov, A., Ripp, C., Roblin, E., Peters, I., & Haustein, S. (2023). Tracing data: A survey investigating disciplinary differences in data citation. Quantitative Science Studies, 4(3), 622–649. https://doi.org/10.1162/qss_a_00264

Haraway, D. (1988). Situated knowledges: The science question in feminism and the privilege of partial perspective. Feminist Studies, 14(3), 575–599. https://doi.org/10.2307/3178066

Horst, H., & Sinanan, J. (2021). Digital housekeeping: Living with data. New Media & Society, 23(4), 834–852. https://doi.org/10.1177/1461444820953535

Huvila, I., & Sinnamon, L. S. (2024). When data sharing is an answer and when (often) it is not: Acknowledging data-driven, non-data, and data-decentered cultures. Journal of the Association for Information Science and Technology, 75(13), 1515–1530. https://doi.org/10.1002/asi.24957

Kennedy, H., & Hill, R. L. (2018). The feeling of numbers: Emotions in everyday engagements with data and their visualisation. Sociology, 52(4), 830–848. https://doi.org/10.1177/0038038516674675

Khan, N., Thelwall, M., & Kousha, K. (2023). Data sharing and reuse practices: Disciplinary differences and improvements needed. Online Information Review. Advance online publication. https://doi.org/10.1108/OIR-08-2021-0423

Kim, Y., & Adler, M. (2015). Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositories. International Journal of Information Management, 35(4), 408–418. https://doi.org/10.1016/j.ijinfomgt.2015.04.007

Kim, Y., & Yoon, A. (2017). Scientists’ data reuse behaviors: A multilevel analysis. Journal of the Association for Information Science and Technology, 68, 2709–2719. https://doi.org/10.1002/asi.23892

Leonelli, S. (2015). What counts as scientific data? A relational framework. Philosophy of Science, 82(5), 810–821. https://doi.org/10.1086/684083

Leonelli, S. (2016). Data-centric biology: A philosophical study. University of Chicago Press.

Leonelli, S. (Ed.). (2020). Learning from data journeys. In Data journeys in the sciences (pp. 1–24). Springer International Publishing. https://doi.org/10.1007/978-3-030-37177-7

Leonelli, S. (2023). Philosophy of open science. Cambridge University Press. https://doi.org/10.1017/9781009416368

Leonelli, S., & Ankeny, R. A. (2015). Repertoires: How to transform a project into a research community. BioScience, 65(7), 701–708. https://doi.org/10.1093/biosci/biv061

Lindén, L., & Lydahl, D. (2021). Editorial: Care in STS. Nordic Journal of Science and Technology Studies, 3–12. https://doi.org/10.5324/njsts.v9i1.4000

Lupton, D. (2017). Feeling your data: Touch and making sense of personal digital data. New Media & Society, 19(10), 1599–1614. https://doi.org/10.1177/1461444817717515

Lupton, D. (2018). How do data come to matter? Living and becoming with personal data. Big Data & Society, 5(2), 2053951718786314. https://doi.org/10.1177/2053951718786314

Mattern, J. B., Kohlburn, J., & Moulaison-Sandy, H. (2024). Why academics under-share research data: A social relational theory. Journal of the Association for Information Science and Technology, 75(9), 988–1001. https://doi.org/10.1002/asi.24938

Mauthner, N. (2012). Are research data a “common” resource? Feminists@law, 2(2). https://doi.org/10.22024/UNIKENT/03/FAL.60

Mol, A., Moser, I., & Pols, J. (Eds.). (2010). Care in practice: On tinkering in clinics, homes and farms. Transcript Verlag. https://doi.org/10.1515/transcript.9783839414477

Moore, N. (2006). The contexts of context: Broadening perspectives in the (re)use of qualitative data. Methodological Innovations Online, 1(2), 21–32. https://doi.org/10.4256/mio.2006.0009

Mosconi, G., de Carvalho, A. F. P., Syed, H. A., Randall, D., Karasti, H., & Pipek, V. (2023). Fostering research data management in collaborative research contexts: Lessons learnt from an ‘embedded’ evaluation of ‘data story.’ Computer Supported Cooperative Work (CSCW). https://doi.org/10.1007/s10606-023-09467-6

Mosconi, G., Randall, D., Karasti, H., Aljuneidi, S., Yu, T., Tolmie, P., & Pipek, V. (2022). Designing a data story: A storytelling approach to curation, sharing and data reuse in support of ethnographically-driven research. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 289:1–289:23. https://doi.org/10.1145/3555180

Narayan, B., Zijlema, A., Reyes, V., & Kennan, M. A. (2024). An information behaviour exploration of personal and family information and curation of our life histories. Information Research, 29(2), Article 2. https://doi.org/10.47989/ir292839

Ninkov, A., Gregory, K., Peters, I., & Haustein, S. (2021a). Datasets on DataCite—An initial bibliometric investigation. Zenodo. https://doi.org/10.5281/zenodo.4730857

Oliver, G., Cranefield, J., Lilley, S., & Lewellen, M. (2023). Data cultures: A scoping literature review. Information Research, 28(1), Article 1. https://doi.org/10.47989/irpaper950

Peck, E. M., Ayuso, S. E., & El-Etr, O. (2019). Data is personal: Attitudes and perceptions of data visualization in rural Pennsylvania. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–12. https://doi.org/10.1145/3290605.3300474

Pinel, C., Prainsack, B., & McKevitt, C. (2020). Caring for data: Value creation in a data-intensive research laboratory. Social Studies of Science, 50(2), 175–197. https://doi.org/10.1177/0306312720906567

Pinel, C., & Svendsen, M. N. (2023). Domesticating data: Traveling and value-making in the data economy. Social Studies of Science. https://doi.org/10.1177/03063127231212506

Poirier, L., Fortun, K., Costelloe-Kuehn, B., & Fortun, M. (2020). Metadata, digital infrastructure, and the data ideologies of cultural anthropology. In J. W. Crowder, M. Fortun, R. Besara, & L. Poirier (Eds.), Anthropological data in the digital age: New possibilities – new challenges (pp. 209–237). Springer International Publishing. https://doi.org/10.1007/978-3-030-24925-0_10

Poirier, L., DiFranzo, D., & Gloria, M. J. K. (2014). Light structure in the platform for experimental collaborative ethnography. Web Science 2014 Workshop: Interdisciplinary Coups to Calamities.

Puig de la Bellacasa, M. (2011). Matters of care in technoscience: Assembling neglected things. Social Studies of Science, 41(1), 85–106. https://doi.org/10.1177/0306312710380301

Reyes, N. A. S., Carales, V. D., & Sansone, V. A. (2021). Homegrown scholars: A collaborative autoethnography on entering the professoriate, giving back, and coming home. Journal of Diversity in Higher Education, 14(4), 480–492. https://doi.org/10.1037/dhe0000165

Robinson-Garcia, N., Jimenez-Contreras, E., & Torres-Salinas, D. (2016). Analyzing data citation practices using the data citation index. Journal of the Association for Information Science and Technology, 67(12), 2964–2975. https://doi.org/10.1002/asi.23529

Schikowitz, A., Dessewffy, E., Davies, S., Pham, B.-C., Gregory, K., Goldberg, E., Avkiran, A. S., & Mora-Gámez, F. (2025). Writing choreographies: (STS) knowledge production in post-digital academia. Tecnoscienza: Italian Journal of Science & Technology Studies,16(1), 65-85. https://doi.org/10.6092/issn.2038-3460/18169

Smale, N. A., Unsworth, K., Denyer, G., Magatova, E., & Barr, D. (2020). A review of the history, advocacy and efficacy of data management plans. International Journal of Digital Curation, 15(1), Article 1. https://doi.org/10.2218/ijdc.v15i1.525

Smith, G. J. (2018). Data doxa: The affective consequences of data practices. Big Data & Society, 5(1), 2053951717751551. https://doi.org/10.1177/2053951717751551

Sparkes, A. C. (2024). Autoethnography as an ethically contested terrain: Some thinking points for consideration. Qualitative Research in Psychology, 21(1), 107–139. https://doi.org/10.1080/14780887.2023.2293073

Stahlman, G. R. (2022). From nostalgia to knowledge: Considering the personal dimensions of data lifecycles. Journal of the Association for Information Science and Technology, 73(12), 1692–1705. https://doi.org/10.1002/asi.24687

Supper, A. (2014). Sublime frequencies: The construction of sublime listening experiences in the sonification of scientific data. Social Studies of Science, 44(1), 34–58. https://doi.org/10.1177/0306312713496875

Tenopir, C., Rice, N. M., Allard, S., Baird, L., Borycz, J., Christian, L., Grant, B., Olendorf, R., & Sandusky, R. J. (2020). Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwide. PLOS ONE, 15(3), e0229003. https://doi.org/10.1371/journal.pone.0229003

Thomer, A. K., Akmon, D., York, J. J., Tyler, A. R. B., Polasek, F., Lafia, S., Hemphill, L., & Yakel, E. (2022). The craft and coordination of data curation: Complicating workflow views of data science. Proceedings of the ACM on Human-Computer Interaction, 6, 1–29. https://doi.org/10.1145/3555139

Thomer, A. K., & Rayburn, A. J. (2023). “A patchwork of data systems”: Quilting as an analytic lens and stabilizing practice for knowledge infrastructures. Science, Technology, & Human Values. https://doi.org/10.1177/01622439231175535

Tsai, A. C., Kohrt, B. A., Matthews, L. T., Betancourt, T. S., Lee, J. K., Papachristos, A. V., Weiser, S. D., & Dworkin, S. L. (2016). Promises and pitfalls of data sharing in qualitative research. Social Science & Medicine, 169, 191–198. https://doi.org/10.1016/j.socscimed.2016.08.004

Verburg, M., Braukmann, R., & Mahabier, W. (2023). Making qualitative data reusable—A short guidebook for researchers and data stewards working with qualitative data. Zenodo. https://doi.org/10.5281/zenodo.7777519

Weller, S. (2022). Fostering habits of care: Reframing qualitative data sharing policies and practices. Qualitative Research. Advance online publication. https://doi.org/10.1177/14687941211061054

Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., Bouwman, J., Brookes, A. J., Clark, T., Crosas, M., Dillo, I., Dumon, O., Edmunds, S., Evelo, C. T., Finkers, R., … Mons, B. (2016). The FAIR guiding principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18

Willson, R., & Given, L. M. (2020). “I'm in sheer survival mode”: Information behaviour and affective experiences of early career academics. Library & Information Science Research, 42(2), 101014. https://doi.org/10.1016/j.lisr.2020.101014

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Published

2026-01-15

How to Cite

Gregory, K., Schikowitz, A., Goldberg, E., & Davies, S. R. (2026). What Emotions Bring to Managing, Caring for, and Sharing Qualitative Data. Information Research an International Electronic Journal, 31(1), 247–267. https://doi.org/10.47989/ir31154039

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Peer-reviewed papers