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.
Introduction
Researchers are increasingly expected to responsibly manage and share their data with the scientific community and broader publics. The rationales for doing so are multiple, ranging from encouraging the reuse of data to ensuring responsible and ethical conduct (European Commission, 2018; Oliver et al., 2023). While well-intended ideals, such as transparency, openness, or serving the greater good, are frequently emphasized in such viewpoints (Albornoz et al., 2018), researchers do not always take up opportunities to share data, or may even be resistant to the idea of doing so (Mattern et al., 2024).
These hesitancies are particularly apparent in the context of qualitative research data (Huvila and Sinnamon, 2024), which may be difficult to anonymise or be structured in very different ways to the quantitative data in which data sharing policies are historically grounded (Smale et al., 2020; Wilkinson et al., 2016). While the different nature and challenges of qualitative data sharing are often acknowledged (Verburg et al., 2023), these differences tend to be discussed as a management problem: something that requires, for instance, additional effort to make data share-able through de-identification or by specifying access restrictions (Tsai et al., 2016). For some researchers, however, the challenges are more fundamental, as the very nature of qualitative empirical material can make data sharing epistemologically difficult (Feldman and Shaw, 2019).
In this paper, we reflect on these challenges from a slightly different angle. We draw on the findings of an empirical collaborative autoethnography (Anderson, 2006; Edwards, 2021) designed to explore the central question of how qualitative researchers relate to and care for their data. We analyse our rich collection of autoethnographic material through a theoretical lens combining relational views of data (Leonelli, 2015, 2023) as well as literature about data care (Pinel et al., 2020; Weller, 2022). Through our analysis, we find that emotions emerge as an inextricable aspect of data management throughout phases of our academic work. As we care for and relate to our data, those data become interwoven into our identities as scholars, which in turn shapes our approaches (and hesitancies) to sharing qualitative data. We argue that better understanding the role of emotions in data production, management, and sharing is therefore a possible direction for better understanding why data sharing often does not happen as expected.
In what follows, we develop this argument in a number of steps. In the next section, we discuss literature on sharing qualitative data, as well as what is known with regard to the role of emotions in data practices. We then outline the theories and concepts we draw on in our analysis, followed by a section outlining our methodological approach of empirical autoethnography and the material we analyse. In the findings, we show how emotion and researcher identity emerge as central themes in how we manage and care for data. In closing, we reflect on the implications of this analysis for qualitative data sharing and consider possible directions which could account for the affective nature of data practices.
Background and theoretical framing
Sharing data in data repositories is increasing but remains more common in certain disciplines than others (Ninkov et al., 2021b; Tenopir et al., 2020). Both surveys (Khan et al., 2023; Tenopir et al., 2020) and bibliometric analyses (Ninkov et al., 2021a; Robinson-Garcia et al., 2016) show that data are shared and reused more often by researchers in the natural sciences than by social scientists and humanists or by researchers using qualitative methodologies (Gregory et al., 2023).
An increase in data production and sharing (across disciplines) is partially a result of data management initiatives and policies, ranging from well-known guidelines such as the FAIR principles (Wilkinson et al., 2016) to more bespoke institutional policies. Many data management policies, tools and research take a data-as-object perspective, where data are seen as relatively static things which can be described, shared, searched for, or otherwise acted upon (Gregory and Koesten, 2022). Although policies often mention that sharing needs to be done in contextualised and responsible ways, details of how to do so are often not well developed. This stands in contrast to relational views of data, which foreground the dynamic, changing nature of data as they travel through different contexts and relations (Leonelli, 2016).
Managing and sharing qualitative data
One reason that data sharing is less common in qualitative research could be attributed to the fact that data management policies tend to have their roots in more quantitatively oriented fields, such as the natural or life sciences (Smale et al., 2020). Qualitative data are notably diverse and of varying structure, ranging from interview transcripts to audio/visual recordings, from diary entries to unstructured observations (CESSDA, 2022). They also often contain personal and sensitive information, which contributes to the complexity of publishing qualitative data (Verburg et al., 2023).
Qualitative researchers have long expressed concerns about data sharing. Such concerns include protecting the confidentiality of research participants; obtaining informed consent for the reuse of data; potential harm to the researcher-participant relationship; and being able to adequately describe situated research contexts (Bishop, 2009; Moore, 2006). For researchers using interpretivist methodologies, in particular, there is a sense that the co-constitutive character of empirical material may make data sharing in a repository epistemologically difficult (Feldman and Shaw, 2019; Poirier et al., 2020). Many of these concerns highlight the question of whether data are seen as a common resource or not (Mauthner, 2012), and whether data that are made available can in practice be reused in context-aware and ethical ways (Choroszewicz, 2022).
Despite these concerns, the sharing of qualitative data has increased over the last decades (Bishop and Kuula-Luumi, 2017). Researchers have critically engaged with what it means to care for and share qualitative data and have explored how to do so in creative ways. From a conceptual standpoint, Susie Weller (2022) proposes that caring for qualitative data involves four aspects: being attentive to researchers’ needs (caring about); a willingness to assume responsibility for data (caring for); competence and practical organisational skills (caregiving); and responsiveness, or thinking about and tracing how data are used (care receiving). Data management and curation are seen as crafts that need to be learnt and developed through these processes of care (Thomer et al., 2022; Thomer and Rayburn, 2023). Mosconi and colleagues have similarly designed and evaluated a concept for the sharing and curation of ethnographic data using a storytelling approach (Mosconi et al., 2022, 2023). Framing data curation as a narrative story, they created a process and tool for ethnographic researchers to showcase small snippets of heterogeneous, anonymised data. This approach provides both context to shared data and serves as an example of how to think about data sharing and data care as a process.
Emotions in (research) data practices
Although affect has been explored in both archival studies and information behavior research (e.g. Wilson and Given, 2020), very few studies explicitly explore the role of emotions in managing research data. Some surveys investigate the impact of individual motivations and career ambitions on data management practices (Kim and Adler, 2015; Kim and Yoon, 2017), although these do not focus on affective factors explicitly. In an interview study with astronomers across career stages, Gretchen Stahlman (2022) identified six affective dimensions which align with stages in managing scholarly data, including emotions related to painstakingness, altruism, legacy and nostalgia. Career stages and paths influence researchers’ feelings toward their data; early career researchers in the study tended to feel more possessive of their data, in part because of time pressures during this career stage. Later career astronomers did not feel as possessive and were more open to data sharing. Stahlman argues that these emotions shape data practices and should therefore be accounted for in data management planning.
Other work has examined the role of emotions in understanding and making sense of data (and data visualisations) as they are encountered in daily life (D’Ignazio and Klein, 2020; Kennedy and Hill, 2018), rather than in scholarly settings. Individuals react to data through emotions about numbers, accompanying text, and aesthetic choices in how data are presented (Kennedy and Hill, 2018). They also relate to data through personal experiences and strong feelings about known places (Peck et al., 2019). Other sensory modalities, such as making data perceived and relatable through sonification (Supper, 2014) or data materialisations (Lupton, 2017) have also been used to further emotional connections with data. This line of research typically looks at affective relationships in personal data practices, where people interact with the digital data that they generate through daily life. Data are viewed as lively personal companions which accompany individuals and shape their daily experiences (Lupton, 2017, 2018). In some cases, individuals may develop doxic relationships with data, in which data are seen as normal, necessary, and enabling; these doxic relationships can obscure unequal power dynamics and increase possibilities for exploitation (Smith, 2018).
Literature on personal data practices (and their associated emotions) has also focused on organising and managing digital data. Horst and Sinanan (2021) propose the idea of digital housekeeping, where personal data are organised through processes of tidying, spring cleaning, and moving house. These routines are intertwined with affective dimensions such as neatness and control, which enable daily life to become easier. Investing in such digital housekeeping demands emotional investment and varying levels of emotional attachment to the data themselves (Horst and Sinanan, 2021).
We contribute to the gap in understanding the role of emotions in working with data in scholarly contexts, demonstrating that emotions not only shape how data are understood, but also how data are produced, managed, and shared.
Theoretical framing
We analyse our empirical material through a theoretical lens combining relational views of data (Leonelli, 2015; Leonelli, 2023) and the notion of data care (Weller, 2022; Pinel et al., 2020), which we introduce in more detail below.
Data as relational
What counts as data or evidence is always relative (Borgman, 2015). Data are defined in relation to particular research questions, methods, timepoints in research processes, and research repertoires (Borgman, 2015; Leonelli, 2015; Leonelli and Ankeny, 2015). Sabina Leonelli, for instance, proposes that scholarly data are best thought of as a relational category of research outputs that are used as evidential sources. Their
behavior and scientific significance depend on the context in which they are used. In this view, data do not have truth-value in and of themselves, nor can they be seen as straightforward representations of given phenomena. Rather, data are essentially fungible objects, which are defined by their portability and their prospective usefulness as evidence. (Leonelli, 2015, p.2)
The idea that data are made mobile through collaborative work between researchers and data curation experts is central to this framework (Leonelli, 2016). Data do not travel easily to new contexts; when they do, they are transformed as they are interpreted and used in new situations (Leonelli, 2020).
However, not all data will travel in the same way. In more recent work, Leonelli (2023) proposes that open science is not simply a matter of making data freely accessible, or data management processes transparent. Rather, making data open should begin by foregrounding considerations of how to make science inclusive and diverse; openness is then realised by forging judicious connections between human actors to produce well-curated, quality data, some of which make their way into data and knowledge infrastructures. In this view, open science is not about sharing all data produced during research; rather, it involves careful considerations about what to share, with whom, for which purposes and to which effects. Such choices are made as researchers (and others) care for and manage data.
Caring for and managing data
Discussions of data care tend to be situated in literature within Science and Technology Studies (STS) related to the analytical concept of care more generally. This literature advocates for attending to matters of care as a way of highlighting the attachments, responsibilities, commitments and affective practices involved in scientific work (Puig de la Bellacasa, 2011). Data management discourse, on the other hand, tends to be rooted in Library and Information Science, and often has a focus on practical skills, tools and systems needed to effectively organise and share data (Cox and Pinfield, 2014).
Although rooted in different literatures, data management and data care are not in opposition, but are rather constituent parts of each other. Caring for data consists of configurations of social and emotional labour alongside practical data management skills (Baker and Karasti, 2018; Pinel et al., 2020). This is seen in work by Pinel and Svendsen (2023), which finds that, similar to the idea of digital housekeeping (Horst and Sinanan, 2021), scientists create homes for their data, as data are forced into various rituals and routines, such as formatting, storytelling and standardisation. Through these routines, data become domesticated, and scientists build long-term relationships with their data through mundane, daily practices of care (Pinel et al., 2020). Applying data care as part of our analytical lens therefore means paying attention to the mundane activities which constitute daily routines of working with and managing data, as well as to how (emotional) relations are built with data in situated contexts (Lindén and Lydahl, 2021; Mol et al., 2010).
We bring this theoretical framing to analyse our central research question: How do we (as qualitative researchers) relate to and care for our data? In order to collect material to answer this question, we employed a method tailored to draw out mundane, authentic, daily practices: collaborative autoethnography.
Methods and dataAutoethnography as a method
Autoethnography (research in which a researcher takes their own experiences as an object of study) has been widely used in qualitative sociological research (Edwards, 2021). Here we employ a version similar to Anderson’s (2006) analytical autoethnography, in which the researcher is a member of the area under study and embraces analytic reflexivity (Davies, 1998), by continually reflecting on their connection to a situation and their effects upon it. Our autoethnography is a collaborative one; we collectively researched our shared experiences, an approach which has also been used to investigate shared information practices (Anderson and Fourie, 2015; Narayan et al., 2024) and the experiences of academic researchers (Reyes et al., 2021). It has also been suggested that autoethnographies offer the opportunity to study emotions on a more honest level, particularly for emotions that are difficult to describe, or are not often discussed (Buckley, 2015), such as in a study of grieving funding rejections in academia (Borgstrom et al., 2024). Autoethnographies have been criticised in terms of self-indulgence, confidentiality issues, and a lack of generalisability (Sparkes, 2024). As with other ethnographic approaches, however, this method does not aim for representativeness, but facilitates deep, contextual understanding and knowledge that may be relevant more widely.
Data collection and analysis
Data were collected from a group of eight researchers (four of whom comprise the authorship team) who have worked together at different times over the last four years in a research group at the University of Vienna. Our group consists of research Masters students, PhD scholars, senior postdoctoral researchers (on different lengths of contracts) and a tenured professor. In our writing, we use the first-person plural as a signal that we are telling the collective story of our research, although our research is not homogeneous. We work with and produce diverse forms of data, yet all of us carry out ethnographically-oriented research, such as semi-structured interviews, document analysis, film-making, or participant observation. In the context of this article, we therefore decided to focus on our experiences of the production and sharing of such qualitative data. Motivated by the importance of data at every stage of our work and by recent developments in research data management, we decided to incorporate exploration of our data practices into the most recent iteration of a four-year autoethnographic project, which has examined our knowledge production during the COVID-19 pandemic (Davies et al., 2022) and our academic writing practices (Schikowitz et al., 2025).
We used a variety of methods to do this. We began by writing field notes in response to a set of prompts (Appendix A), which were informed in part by the concept of data care (Weller, 2022). We then shared these field notes with each other. Each of us closely read all of the field notes and posted reflections about them in our shared Slack space. In these reflections, we identified initial themes across field notes, including the presence of emotions, and responded to things that we found particularly interesting, or could relate to in our own practices. We followed this analysis with a workshop in which field notes and Slack reflections were divided among team members who again closely read and annotated the material. This was followed by a group session to discuss emerging patterns. We then broke into small groups and conducted semi-structured interviews, in which we interviewed each other, guided by a set of questions (Appendix B). The interview questions were informed by themes identified in our initial round of analysis. Field notes were written in April-May 2023; Slack reflections were created and posted during June-July 2023, and the workshop took place in July, 2023.
After the workshop, the first and second author inductively coded the field notes and Slack reflections and reviewed the interview transcripts. These authors refined codes during joint discussions and identified overarching themes, many of which were related to the emotions involved in our data practices and the relationality of data. This inductive analysis, coupled with the structure of the field note prompts, informed the choice of the theoretical framing which we mobilise here: the relational nature of data and data care. These methods support a deep, contextual account of caring for qualitative data. Our aim is therefore not to provide a universal account, but rather one that is rooted in our experiences at a specific time and place, whilst also pointing to more general patterns and implications.
Findings
In this section we outline central findings that emerged from this autoethnographic exploration of data practices. We do this in three sections. First, and in line with the literature described above, we find that managing qualitative data is an affective practice, one that is often marked by anxiety or unease. Second, we zoom in on ways in which we care for our data, describing how we build caring relations through our data practices. A variety of (mostly negative) emotions are present throughout these descriptions of data care. Finally, we argue that emotions and care are central to our relations with research data because other entities, such as our identities as researchers, are co-constituted along with them. We argue that data practices are often experienced through anxiety, or a sense of failure, because what is at stake is not just the status of particular datasets, but the other relations and entities that co-become alongside them.
Managing data, managing emotions
Emotions are interwoven with many of the data management practices described in the material. In particular, feelings of guilt, shame, vulnerability, excitement, fear, pride and frustration are present. Notably, the majority of emotions described are negative. For example, we feel frustrated with the tedious, but necessary, work of formatting and reformatting data, or guilty that our data management practices are not as systematic and organised as we want them to be.
The field notes also show feelings of vulnerability with regard to how visible we are in our data. While our presence is often implicit, for instance in decisions about how we have collected and recorded data, we also worry about moments when we are more visible, such as in interview transcripts or ethnographic field notes. We worry what other researchers might think if we were to share such data openly.
In these transcripts, I am very visible in the data, not just as an interviewer, but also through comments about my own experiences, my preferences, and my ways of communicating [..] I have to wonder - how much of myself do I want to make visible to a wider public? What would my peers think if they were to see how much I deviate from the interview protocol? Sharing this type of data, where I am so visible, would make me feel very vulnerable.
Such anxieties are related to concerns about how our actions and decisions, which are visible in the data, might be interpreted. Similar feelings of insecurity are also present in descriptions of how we order our data. The following quote reflects many others emphasising feelings of shame and discomfort.
I have by now created a quite elaborate folder system on my desktop. I somehow hate it. It includes sub-folders, sub-sub folders and sub-sub-sub-folders with Word documents, PDFs, audiofiles, photographs, and video recordings. It is a mess. It makes me feel uncomfortable. I do not want to look at it for too long. I just want to quickly access whatever I am working on at the moment.
Such expressions of shame, guilt, discomfort, and perceived deficiencies appear over and over again in our material. This was the case across all career stages, ages, and other social characteristics. Assuming that these feelings of deficiency are not rooted in bad practice, we consider them to stem from a mismatch between expectations of what good data practices are, and how we experience data practices in our daily work and institutional environments. The above quote also demonstrates a common point of friction in the field notes. We feel that we are expected to archive and handle data in perfectly ordered and systematic ways. When we struggle to do so, or do not stick to our organisational systems, we feel guilty and accuse ourselves of being messy or too lazy to stick to systematic ways of working.
Strikingly, there were very few descriptions of joy or playfulness in our material. Rather, data are something that we take seriously, feel personally responsible for, and ‘guard like treasure’. More positive emotions in the material, such as feelings of intimacy and responsibility, further described below, are often rooted in anxieties about protecting and safekeeping our data. While we accuse ourselves of being lazy and careless, we devote thought, concern, and time to caring for our data in different ways.
Care and responsibility for data
Our materials show three main ways in which we care for data, ways which are intertwined with our emotions. First, we care for our data because they embody our relationships to our research participants; caring for data means caring and taking responsibility for our participants and their values and aims. Second, we care for data maintenance and infrastructure; for the integrity and security of our data, as well as for their future availability. Third, we care for the quality and usefulness of our data for epistemic purposes, curating them in a way that allows for knowledge production.
Caring for data as caring for research participants
The relationships of trust we build with our research participants are key motivations to engage in careful data handling. The field notes document that we feel obliged to protect the anonymity of the people we work with; we therefore adopt formal practices, such as informed consent forms and anonymisation, as required by ethical review committees. Caring for participants and data relating to them goes beyond these formal measures, however. We also express a strong sense of responsibility to protect participants themselves, with whom we have often developed deep and intimate relationships during data collection. These relationships are further strengthened as we engage with data during analysis. Working closely with data over a long period of time creates a curiously one-sided intimacy that goes beyond direct contact during the fieldwork and makes us empathise with our research partners.
Analysis is an emotional practice, and when analysing and writing I always have these people in mind. I wonder what they would think of my interpretation of their accounts. I want to do them justice, but at the same time, I don’t want to romanticise them. I still want to conduct a critical analysis.
Caring for research participants, through caring for data, is an ongoing process. It involves not only the non-disclosure of personal details, but reflection on what participants would (not) want to reveal about their issues of concern. However, as the above quote also suggests, there is a sense of tension between caring for the participants and their needs and caring for conducting a good and critical analysis. In handling data carefully and balancing (assumed) needs, values, and vulnerabilities of participants with our own stance as researchers, we also use our emotions as checks, to gauge whether an interpretation or quote is appropriate.
When using a quote or writing up an argument, I always check that against the personal relation I have to the materials. I use my emotions towards it as a test of the ‘validity’ and the morality of that claim – if it feels appropriate or not.
Analysis, as well as data management itself, is an affective experience in which emotions are used to double-check interpretations. The idea that our emotions and relationships to participants can work to safeguard the quality and integrity of data is seen to stand in opposition to implicit (objective) ideals about data management. Emotional attachments and feelings of care towards our data also complicate the possibility of sharing data in a repository for some of us. Publishing data would remove them from the care that we provide and the personal responsibility that we feel toward them.
If I would put that data in a repository, even if it would be thoroughly anonymised, I would not have any control over who would use them in what ways, and whether that would respect the concerns of my interlocutors. Sharing data with collaborators and co-authors is something else. But even here, I would not just share the raw versions of the transcripts as a whole, but parts of them which are relevant for the common writing project in a pseudonymised form. And as a co-author I take care in which ways these accounts are used.
In this case, again, data are understood as relational. They embody a particular set of associations built in the field, where caring for data is caring for research participants. If data were to be shared in a repository for others to use, the relations that are necessary to protect participants are seen as being no longer in place. Detachment of data from a researcher thus (in this view) makes caring for participants difficult.
Caring for data maintenance and infrastructures
As well as care for data representing our relationship to research participants, the material also shows expressions of care for the data in and of themselves and for the infrastructures that allow us to maintain, access, and use data. We care that data persist and can be used over time and that they remain secure.
I guard the hard drive on which the data and the project are stored like a treasure and take it with me everywhere in case I get to work on it while I’m on the road. This means that I have also developed a very intimate relationship with my research data, because so much work went into producing them. For me, there is half a year’s work on this hard drive.
Both the value and the fragility of research data are clear in this quote. Data are something on which our research relies, and which are produced with significant effort. The possibility of losing hard-earned data is a constant anxiety. Caring for data involves protecting them from unwarranted access, for instance by password protecting data, or storing them on secure servers. One central way in which we care for data is therefore by paying attention to the infrastructures used for data storage. For instance, the field notes show that while we make use of university-provided infrastructures and tools to manage and analyse our data, on the grounds that these are more secure than commercial options, we also use parallel infrastructures and tools as back-ups, or because they better suit our personal preferences or methods. The result is often a scattered collection of systems needed to work with our data.
I have some documents saved on a Google Drive folder, some on the local computer at work, and some on the [university] server, which I can access directly from my own folder directory on my private laptop. Time and time again I’ll implement a new folder structure, but I do not manage to maintain a system [..] I can think of enough excuses, but the fact remains that my ‘data’ – the corpus – isn’t saved in one single place. It is scattered across multiple platforms and accounts. It is duplicated and incomplete.
This is in part because of our own preferences, e.g. for working with printed material or with software that we are accustomed to. Despite this, it is not always easy to integrate various software programs, institutional systems, and work locations. ‘When I started coding [in the pandemic when access to the office was restricted]’, writes one of us in their field notes, ‘I only had MaxQDA installed on the iMac in the office and couldn’t figure out how to obtain a license for my private laptop …. I ended up printing out the main documents and simply highlighted and annotated them on paper’. Such workarounds are necessary to accomplish our work, but still result in feelings of shame. ‘I didn’t manage to live up to what I had expected of myself’, writes the same person, ‘to work methodically, diligently, and responsibly with data’.
Importantly, such infrastructural hacks that merge different systems, tools, or data storage methods are connected to our experiences of academic employment. In a system in which changing institutions is an almost inescapable aspect of academic careers, many of us use commercial or cloud servers for data storage so that we can continue to have access to our material should we move to a different organisation or employment situation. Using these systems then becomes deeply integrated into our practices, accumulating as data grow and as these systems become a part of other practices. We tend to not be content with this situation.
I have always stored my ‘data’ – photos, audio files, interview transcripts – on a normal commercial cloud server, in part because this seemed necessary when I was moving institutions every two years and in part because these are usually easier to use than university drives. I am trying not to do this anymore … but this way of storing data is something that is very bedded down in terms of my practices.
Over time, and particularly when maintaining a research career across different forms of (un)employment, we therefore become locked into particular ways of working, building up complex, personalised, and idiosyncratic habits of file storage and data management.
This section shows that caring for data and their maintenance involves a number of tensions: between restricting unauthorised access and allowing our own long-term use of data; between institutional infrastructures and short-term employment; and between security and usability or convenience. Data are dependent on and grow into the infrastructures around them. This includes how and where they are stored, and which tools are used for processing, curation, and analysis. We as researchers are further part of these relations, in that we not only choose certain tools and infrastructures, but we also develop them alongside the data. Our knowledge of particular infrastructures, embodied skills of handling them, and personal preferences all mean that data are entangled both with us and with the infrastructures. Once again, detaching data from these infrastructures and the researchers, and publicly sharing them via repositories, or in other ways, is understood as, at best, challenging.
Caring for the epistemic value and usefulness of data
Implicit in the preceding sections is the sense that data are valuable and useful. We care for data because we want to get something out of them, and we do not want to waste or lose them. In this section we discuss this care for the epistemic value and usefulness of data in knowledge production more explicitly.
One aspect of this is a concern that our data remain accessible and usable for our collaborators, as well as for ourselves, over time. This involves working to overcome the idiosyncrasies and ad-hoc personal systems built up around data described above. The field notes document struggles when a team member leaves, for example, and data become difficult to access for those who did not develop and routinely work with the data ordering system. ‘I was looking for the original Word files for the interview transcripts’, writes one of us, ‘in the end I emailed [a former team member] to ask if he had them and could send them to me’. In this case, separating the data system from the researcher who developed and curated it was unsuccessful; to access the tacit knowledge relating to a file system, the researcher needed to be included again.
Care therefore involves working to be transparent and explicit about how data are managed and made available, particularly given the temporary nature of many academic contracts and the fluidity of research teams. Metadata and descriptions are added as one way of doing this. As in the following quote, such descriptions can be motivated by relationships which we have with our collaborators, or with other people who may use the data:
I try to label data entries as informatively as possible. For example, field notes include information on date, place, and names of attendees. Sharing data informs the way I file and archive data. There is definitely an element of care in that … I care for my work, my data, and the person accessing the data, so I try to make an effort.
While we seek to label and describe data systematically to make them understandable to (future) collaborators, we also observe that our own personal and often messy ways of working with data can lead to opening up new questions and research directions. This speaks to a central tension that recurs in our accounts of caring for data in ways that ensure its epistemic value and usefulness. On the one hand, we seek to be systematic and orderly in handling and analysing data, feeling that this is the most responsible path. On the other, we experience intuition and mess as allowing for new insights within analysis. When we try to be systematic and detached, it can be easy to lose connection with more intuitive modes of working. Caring for data quality therefore involves navigating this tension.
Our data, our selves
A central tension apparent thus far is the strong feeling that data sharing that does not involve the researcher possibly risks losing important aspects that make up data and their value. In this section we follow up on this idea by exploring how caring for data, and the data themselves, are constituent parts of our identities as researchers. Our sense of ourselves as scholars co-becomes with our data, as we gather, store, analyse, and write about data, our changing relationships embed data in our ways of thinking and working.
I notice how I build up a relationship with my data that changes over the course of the research project: first there is pride at having collected and stored so much data, then overwhelm at having to sift through and process it all, frustration when something goes wrong, which is usually the case, and always concerns about the rights of the data.Finally, when you work with them every day, they become part of your thinking and working.
Data become part of us as we make choices about which data to use, how to arrange them, and how to store or preserve them. The way that we feel about our data can signal where we are in the course of a project and our sense of how the research is progressing. Similarly, we build deeply intimate relations with our data throughout our research. Data become integrated into our ways of thinking and conducting research until they are almost inseparable from our own selves and our thinking.
I knew these 15 interviews of 2 hours each almost by heart. I had read, reflected, and re-arranged them over and over again. I did not need the table which translated between the pseudonyms and the actual names of the interviewees, because I could hear the voice and see the person in front of me when reading a single quote. I knew exactly at which points they would laugh, or stumble, or get emotional.
Our memories, feelings, and tacit knowledge transform data from things that are merely material to symbols that are imbued with meaning. In our field notes, data are not solely viewed as objects that circulate or that can be used to represent a particular research experience. Rather, data enable relationships between ourselves, our participants, and our memories of research encounters:
They [data] act as an aide memoire or as representations that capture some aspects of a situation, but to me they are avatars for particular experiences. I think ‘data’ are the sum of the impressions, memories, texts, records, hunches, and feelings one takes from empirical encounters, both individually and within a project or corpus as a whole. They are exactly not the texts and records in and of themselves, but what they take you to.
Data are thus tied to our objects of research, the progress of a particular project, and our research encounters. Our embodied memories, emotions, and thinking make data meaningful and epistemically productive. As shown in the quote above, data act as points of association between research encounters in the field and our analyses and arguments. This relation is, however, always unfinished and incomplete. No matter how many rounds of data cleaning and improvement we undertake, our data will never be ‘so accurate, neatly organised or so well put’ that they would speak for themselves and fully represent our research experiences with participants and the personal connections which were made.
As a whole, our material suggests that data are not separate from the researchers who collect, manage, and analyse them. Data only become data through a relation with a researcher, while (our) researcher identities co-become with data. As one of us wrote, ‘my data are really MY data, not because I produced or possess them, but because they become part of me, part of my thinking and part of my feeling’. Again, this tight relation between data and researcher identity is linked to hesitancies relating to data sharing. The same person continues:
…sharing this kind of data in the sense of putting it in a repository for others to access would not be an option for me. [..] My personal relation to my data secures ethical conduct, not only in terms of storing it safely, but in terms of using, interpreting and communicating it in responsible ways.
Discussion
We have drawn on the findings of a collaborative autoethnography to propose that data management is an inherently affective practice. The emotions which we experience when working with data are integral to our (responsible) research practices and are visible as we care for our research participants, for data maintenance and infrastructure, and for the quality and usefulness of data. We further find that our identities as researchers co-become with our data, which strengthens the attachments we have with them. We suggest that data practices are often experienced through anxiety, or a sense of failure, because what is at stake is not just the fate of our data, but also that of the other relations and entities that co-become alongside them, including our sense of ourselves as scholars.
We have also suggested that these same emotions and relations can result in tensions with common expectations for data sharing. For instance, our analysis demonstrates tensions concerning vulnerability and opening our data practices to critique, protecting the anonymity and concerns of participants (Bishop, 2009), and concerns that our data become meaningful only in combination with our memories of their collection (Poirier et al, 2020). The largest tension which emerges through this analysis is a concern that publicly sharing data in a repository risks separation of the researcher from the data; sharing data is seen to some extent to be the end of a relationship with data, rather than a continuance of it. In closing, we consider the implications of these findings by reflecting on how data management and sharing might be carried out in ways that acknowledge the affective nature of research data practices.
First, it is vital to recognise the complexities of data management and sharing raised in our analysis. The tensions we present tend to be rooted in conflicting views about ideal best practices and the messy (and emotional) realities of creating and handling data. Our analysis shows that the negative emotions which we experience often emerge from normative pressures rooted in ideals of data practices as objective, systematic, and orderly. Even as ethnographically-oriented researchers, we appear to have been socialised into such ideals; dominant views of data management and sharing tend to reinvoke these deeply ingrained beliefs (Leonelli, 2023). This mismatch between implicit expectations and lived experiences often leaves us with feelings of unease and guilt when it comes to data management and sharing. At the same time, it is clear that our relations to our data are care-ful. Our failure to live up to ideals of objectivity and structure does not mean that we treat our data lightly; rather, quite the opposite is the case. The question therefore becomes: how can views and models of data management/sharing foreground and respect embedded, affective notions of data care rather than ideals of objectivity and detachment?
Second, we suggest that approaches that acknowledge the relationality of data, and which foster continued relationships between researchers and their data, may be one way of doing this. While our materials document an overall hesitancy to share qualitative data in repositories, there is also a somewhat implicit view of data sharing as being all or nothing in our material, which does not necessarily account for more nuanced possibilities. Data could, for example, be published in a repository under restricted licenses which do not allow for reuse, but which only expose metadata about our projects (Verburg et al., 2023). Alternately, licenses could be arranged which necessitate the involvement of the original researcher, or participants in future reuse, facilitating continued relations. While current data sharing policies and practices acknowledge such a nuanced spectrum of sharing, there is room for these options to be better articulated.
Our field notes also document numerous examples of considerations about how to share data more closely with our collaborators and project teams. This suggests a need for data systems that are designed to support collaborative data work for qualitative researchers (and research participants), alongside data repositories that are primarily geared toward making data more widely available. Here, systems through which ethnographic projects can be created and data shared with different types of specified teams, similar to projects such as the Platform for Collaborative Ethnography (http://worldpece.ss.uci.edu/; Poirier et al., 2014) could offer a means for more careful data sharing. Similarly, Mosconi et al. (2022) have proposed sharing data stories to document and collaboratively work with snippets of deidentified ethnographic data within teams. While such local tools and approaches can help to support data sharing with collaborators and teams, more of these are needed, as is support for their local implementation. Overall, our view is that flexibility and diversity in data sharing possibilities should be central: a relational view of data highlights that many approaches to data sharing are possible, and that careful consideration of which data to share with whom and why is key (Leonelli, 2023). One size fits all approaches should thus not be mandated by funders or other policy makers.
Third, it is similarly important to acknowledge the place of emotions in data production and handling, in qualitative research, but also in natural and physical sciences (Stahlman, 2022), and to take this into account in policies and systems for data management. Critically, this role should be viewed as a feature, not a bug. Emotions and subjectivity are, in the scholarly traditions in which we work, aspects of research practice that ensure that we handle data responsibly, that are integral to forming identities as researchers, and that allow robust forms of analysis (Haraway, 1988). Perhaps we might move from requesting data management plans to implementing data care plans, narrative accounts that incorporate details of the multiple ways that data will be cared for. As Weller (2022) highlights, data care, particularly for qualitative data, involves being attentive to needs (caring about); a willingness to assume responsibility (caring for); competence and skill (care giving); and responsiveness, or thinking about and tracing how data are used (care receiving). Accounts of best practice in data management and sharing should thus incorporate not only the possibility for difference and diversity in data handling practices (as suggested above), but find languages for acknowledging, and celebrating the role of emotion in research-oriented data work.
Copyright
Authors contributing to Information Research agree to publish their articles under a Creative Commons CC BY-NC 4.0 license, which gives third parties the right to copy and redistribute the material in any medium or format. It also gives third parties the right to remix, transform and build upon the material for any purpose, except commercial, on the condition that clear acknowledgment is given to the author(s) of the work, that a link to the license is provided and that it is made clear if changes have been made to the work. This must be done in a reasonable manner, and must not imply that the licensor endorses the use of the work by third parties. The author(s) retain copyright to the work. You can also read more at: https://publicera.kb.se/ir/openaccess
Acknowledgements
The first and second author contributed equally to the study and the writing of this article. In addition to the authors, the researchers who took part in this study are Esther Dessewffy, Bau-Chau Pham, Ariadne Sevgi Avikran, and Rafaela Cavalcanti de Alcantara. Fredy Mara Gàmez and Costantin Holmer were also active research group members at the time, although they did not participate in this part of the autoethnography. We thank them for their participation and insights.
Field notes for prompts
Over the next weeks
Take 3 (+/-) photos that reflect your data practices - e.g. how you produce, work with, care for, document, store, share, (re)use data within your daily work
Write field notes or text fragments (1000 words, +/-) that respond to these images. Consider (for instance):
What do you consider to be ‘your data?’ Is this always the case?
How do your data change during the research process, what causes these changes, and in which situations?
How are you visible/invisible in your data?
How do you (not) attend to and care for your data? What responsibilities do you feel to your data and how do you enact these responsibilities? How do you protect your data? (When) are you ‘irresponsible’?
How and where do you ‘share’ data, with whom, and to which ends? How do you feel when you do so/don’t? Does data sharing make sense for your data? For STS?
Interview Guide
The interviews were semi-structured. These questions served as initial prompts, but follow-up questions and discussions varied per group.
Building on theme of emotions identified in first round of analysis
What are you proud of in your data practices? What brings you joy? Why?
What causes you concern? What do you wish you would do differently? Why and how?
…
Building on theme of responsibility and norms for qualitative data identified in first round of analysis
What constitute norms of ‘good’ qualitative data practice/analysis? For you? Your community? Your institution or more broadly?
What do you see as being valued in terms of your (and others) ‘data work’? How is it valued and by whom?
…
Extending out and imagining
(How) could data sharing be otherwise to address your concerns/to match your practice?
Do you see a value (to you or more broadly) in data sharing? For which qualitative data would this make sense?
…
ReferencesAlbornozD.HuangM.MartinI. M.MateusM.TouréA. Y.ChanL.2018Framing power: tracing key discourses in open science policiesChanL.MounierP.ELPUB 2018: Vol. Connecting the Knowledge Commons: From Projects to Sustainable InfrastructureElPubhttps://doi.org/10.4000/proceedings.elpub.2018.23AndersonL.2006Analytic autoethnographyJournal of Contemporary Ethnography354373395https://doi.org/10.1177/0891241605280449AndersonT. D.FourieI.2015Collaborative autoethnography as a way of seeing the experience of caregiving as an information practiceInformation Research201170182http://InformationR.net/ir/20-1/isic2/isic33.htmlBakerK. S.KarastiH.2018Data care and its politics: Designing for local collective data management as a neglected thingProceedings of the 15th Participatory Design Conference: Full Papers - Volume 11–https://doi.org/10.1145/3210586.3210587BishopL.2009Ethical sharing and reuse of qualitative dataAustralian Journal of Social Issues443https://doi.org/10.1002/j.1839-4655.2009.tb00145.xBishopL.Kuula-LuumiA.2017Revisiting qualitative data reuse: A decade onSage Open712158244016685136https://doi.org/10.1177/2158244016685136BorgmanC. L.2015Big data, little data, no data: Scholarship in the networked worldMIT PressBorgstromE.DriessenA.KrawczykM.KirbyE.MacArtneyJ.AlmackK.2024Grieving academic grant rejections: Examining funding failure and experiences of lossThe Sociological Review7259981017https://doi.org/10.1177/00380261231207196BuckleyR.2015Autoethnography helps analyse emotionsFrontiers in Psychology6209https://doi.org/10.3389/fpsyg.2015.00209CESSDA2022CESSDA Data management expert guideZenodohttps://doi.org/10.5281/zenodo.3820473ChoroszewiczM.2022Emotional labour in the collaborative data practices of repurposing healthcare data and building data technologiesBig Data & Society91205395172210984https://doi.org/10.1177/20539517221098413CoxA. M.PinfieldS.2014Research data management and libraries: Current activities and future prioritiesJournal of Librarianship and Information Science464299316https://doi.org/10.1177/0961000613492542DaviesC. A.1998Reflexive ethnography: A guide to researching selves and othersRoutledgehttps://doi.org/10.4324/9780203069370DaviesS.PhamB.-C.DessewffyE.SchikowitzA.GámezF. M.2022Pinboarding the pandemic: Experiments in representing autoethnographyCatalyst: Feminism, Theory, Technoscience82Article 2https://doi.org/10.28968/cftt.v8i2.38868D’IgnazioC.KleinL. F.2020Data feminismMIT PressEdwardsJ.2021Ethical autoethnography: Is it possibleInternational Journal of Qualitative Methods201609406921995306https://doi.org/10.1177/1609406921995306European Commission2018Cost-benefit analysis for FAIR research data: Cost of not having FAIR research dataPublications Office of the European Unionhttps://doi.org/10.2777/02999FeldmanS.ShawL.2019The epistemological and ethical challenges of archiving and sharing qualitative dataAmerican Behavioral Scientist636699721https://doi.org/10.1177/0002764218796084GregoryK.KoestenL.2022Human-centered data discoverySpringer International Publishinghttps://doi.org/10.1007/978-3-031-18223-5GregoryK.NinkovA.RippC.RoblinE.PetersI.HausteinS.2023Tracing data: A survey investigating disciplinary differences in data citationQuantitative Science Studies43622649https://doi.org/10.1162/qss_a_00264HarawayD.1988Situated knowledges: The science question in feminism and the privilege of partial perspectiveFeminist Studies143575599https://doi.org/10.2307/3178066HorstH.SinananJ.2021Digital housekeeping: Living with dataNew Media & Society234834852https://doi.org/10.1177/1461444820953535HuvilaI.SinnamonL. S.2024When data sharing is an answer and when (often) it is not: Acknowledging data-driven, non-data, and data-decentered culturesJournal of the Association for Information Science and Technology751315151530https://doi.org/10.1002/asi.24957KennedyH.HillR. L.2018The feeling of numbers: Emotions in everyday engagements with data and their visualisationSociology524830848https://doi.org/10.1177/0038038516674675KhanN.ThelwallM.KoushaK.2023Data sharing and reuse practices: Disciplinary differences and improvements neededOnline Information ReviewAdvance online publicationhttps://doi.org/10.1108/OIR-08-2021-0423KimY.AdlerM.2015Social scientists’ data sharing behaviors: Investigating the roles of individual motivations, institutional pressures, and data repositoriesInternational Journal of Information Management354408418https://doi.org/10.1016/j.ijinfomgt.2015.04.007KimY.YoonA.2017Scientists’ data reuse behaviors: A multilevel analysisJournal of the Association for Information Science and Technology6827092719https://doi.org/10.1002/asi.23892LeonelliS.2015What counts as scientific data? A relational frameworkPhilosophy of Science825810821https://doi.org/10.1086/684083LeonelliS.2016Data-centric biology: A philosophical studyUniversity of Chicago PressLeonelliS.2020Learning from data journeys.Data journeys in the sciences124Springer International Publishinghttps://doi.org/10.1007/978-3-030-37177-7LeonelliS.2023Philosophy of open scienceCambridge University Presshttps://doi.org/10.1017/9781009416368LeonelliS.AnkenyR. A.2015Repertoires: How to transform a project into a research communityBioScience657701708https://doi.org/10.1093/biosci/biv061LindénL.LydahlD.2021Editorial: Care in STSNordic Journal of Science and Technology Studies312https://doi.org/10.5324/njsts.v9i1.4000LuptonD.2017Feeling your data: Touch and making sense of personal digital dataNew Media & Society191015991614https://doi.org/10.1177/1461444817717515LuptonD.2018How do data come to matter? Living and becoming with personal dataBig Data & Society522053951718786314https://doi.org/10.1177/2053951718786314MatternJ. B.KohlburnJ.Moulaison-SandyH.2024Why academics under-share research data: A social relational theoryJournal of the Association for Information Science and Technology7599881001https://doi.org/10.1002/asi.24938MauthnerN.2012Are research data a “common” resourceFeminists@law22https://doi.org/10.22024/UNIKENT/03/FAL.60MolA.MoserI.PolsJ.2010Care in practice: On tinkering in clinics, homes and farmsTranscript Verlaghttps://doi.org/10.1515/transcript.9783839414477MooreN.2006The contexts of context: Broadening perspectives in the (re)use of qualitative dataMethodological Innovations Online122132https://doi.org/10.4256/mio.2006.0009MosconiG.de CarvalhoA. F. P.SyedH. A.RandallD.KarastiH.PipekV.2023Fostering 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/s10606-023-09467-6MosconiG.RandallD.KarastiH.AljuneidiS.YuT.TolmieP.PipekV.2022Designing a data story: A storytelling approach to curation, sharing and data reuse in support of ethnographically-driven researchProceedings of the ACM on Human-Computer Interaction, 6(CSCW2)289128923https://doi.org/10.1145/3555180NarayanB.ZijlemaA.ReyesV.KennanM. A.2024An information behaviour exploration of personal and family information and curation of our life historiesInformation Research292Article 2https://doi.org/10.47989/ir292839NinkovA.GregoryK.PetersI.HausteinS.2021aDatasets on DataCite—An initial bibliometric investigationZenodohttps://doi.org/10.5281/zenodo.4730857OliverG.CranefieldJ.LilleyS.LewellenM.2023Data cultures: A scoping literature reviewInformation Research281Article 1https://doi.org/10.47989/irpaper950PeckE. M.AyusoS. E.El-EtrO.2019Data is personal: Attitudes and perceptions of data visualization in rural PennsylvaniaProceedings of the 2019 CHI Conference on Human Factors in Computing Systems1–https://doi.org/10.1145/3290605.3300474PinelC.PrainsackB.McKevittC.2020Caring for data: Value creation in a data-intensive research laboratorySocial Studies of Science502175197https://doi.org/10.1177/0306312720906567PinelC.SvendsenM. N.2023Domesticating data: Traveling and value-making in the data economySocial Studies of Sciencehttps://doi.org/10.1177/03063127231212506PoirierL.FortunK.Costelloe-KuehnB.FortunM.2020Metadata, digital infrastructure, and the data ideologies of cultural anthropologyCrowderJ. W.FortunM.BesaraR.PoirierL.Anthropological data in the digital age: New possibilities – new challenges209237Springer International Publishinghttps://doi.org/10.1007/978-3-030-24925-0_10PoirierL.DiFranzoD.GloriaM. J. K.2014Light structure in the platform for experimental collaborative ethnographyWeb Science 2014 Workshop: Interdisciplinary Coups to CalamitiesPuig de la BellacasaM.2011Matters of care in technoscience: Assembling neglected thingsSocial Studies of Science41185106https://doi.org/10.1177/0306312710380301ReyesN. A. S.CaralesV. D.SansoneV. A.2021Homegrown scholars: A collaborative autoethnography on entering the professoriate, giving back, and coming homeJournal of Diversity in Higher Education144480492https://doi.org/10.1037/dhe0000165Robinson-GarciaN.Jimenez-ContrerasE.Torres-SalinasD.2016Analyzing data citation practices using the data citation indexJournal of the Association for Information Science and Technology671229642975https://doi.org/10.1002/asi.23529SchikowitzA.DessewffyE.DaviesS.PhamB.-C.GregoryK.GoldbergE.AvkiranA. S.Mora-GámezF.2025Writing choreographies: (STS) knowledge production in post-digital academiaTecnoscienza: Italian Journal of Science & Technology Studies1616585https://doi.org/10.6092/issn.2038-3460/18169SmaleN. A.UnsworthK.DenyerG.MagatovaE.BarrD.2020A review of the history, advocacy and efficacy of data management plansInternational Journal of Digital Curation151Article 1https://doi.org/10.2218/ijdc.v15i1.525SmithG. J.2018Data doxa: The affective consequences of data practicesBig Data & Society512053951717751551https://doi.org/10.1177/2053951717751551SparkesA. C.2024Autoethnography as an ethically contested terrain: Some thinking points for considerationQualitative Research in Psychology211107139https://doi.org/10.1080/14780887.2023.2293073StahlmanG. R.2022From nostalgia to knowledge: Considering the personal dimensions of data lifecyclesJournal of the Association for Information Science and Technology731216921705https://doi.org/10.1002/asi.24687SupperA.2014Sublime frequencies: The construction of sublime listening experiences in the sonification of scientific dataSocial Studies of Science4413458https://doi.org/10.1177/0306312713496875TenopirC.RiceN. M.AllardS.BairdL.BoryczJ.ChristianL.GrantB.OlendorfR.SanduskyR. J.2020Data sharing, management, use, and reuse: Practices and perceptions of scientists worldwidePLOS ONE153e0229003https://doi.org/10.1371/journal.pone.0229003ThomerA. K.AkmonD.YorkJ. J.TylerA. R. B.PolasekF.LafiaS.HemphillL.YakelE.2022The craft and coordination of data curation: Complicating workflow views of data scienceProceedings of the ACM on Human-Computer Interaction6129https://doi.org/10.1145/3555139ThomerA. K.RayburnA. J.2023“A patchwork of data systems”: Quilting as an analytic lens and stabilizing practice for knowledge infrastructuresScience, Technology, & Human Valueshttps://doi.org/10.1177/01622439231175535TsaiA. C.KohrtB. A.MatthewsL. T.BetancourtT. S.LeeJ. K.PapachristosA. V.WeiserS. D.DworkinS. L.2016Promises and pitfalls of data sharing in qualitative researchSocial Science & Medicine169191198https://doi.org/10.1016/j.socscimed.2016.08.004VerburgM.BraukmannR.MahabierW.2023Making qualitative data reusable—A short guidebook for researchers and data stewards working with qualitative dataZenodohttps://doi.org/10.5281/zenodo.7777519WellerS.2022Fostering habits of care: Reframing qualitative data sharing policies and practicesQualitative ResearchAdvance online publicationhttps://doi.org/10.1177/14687941211061054WilkinsonM. D.DumontierM.AalbersbergI. J. J.AppletonG.AxtonM.BaakA.BlombergN.BoitenJ.-W.da Silva SantosL. B.BourneP. E.BouwmanJ.BrookesA. J.ClarkT.CrosasM.DilloI.DumonO.EdmundsS.EveloC. T.FinkersR.MonsB.2016The FAIR guiding principles for scientific data management and stewardshipScientific Data3160018https://doi.org/10.1038/sdata.2016.18WillsonR.GivenL. M.2020“I’m in sheer survival mode”: Information behaviour and affective experiences of early career academicsLibrary & Information Science Research422101014https://doi.org/10.1016/j.lisr.2020.101014