Ethnographic data-sharing as community building

The open science movement has fundamentally changed how we do and evaluate research. In theory, almost everyone agrees that open research is a good thing. In reality, most researchers struggle to put theory into practice. None more so than ethnographers. Instead of rejecting data sharing, ethnographers are uniquely positioned to contribute positively to open research by decentering data and recentering context, community, and lived experience.

Data sharing principles are hard to implement

Data sharing following “FAIR Principles” has widely come to be seen as best practice in research. Data should be findable, accessible, interoperable, and reusable. However, implementation of the FAIR Principles has proven challenging. Even in STEM disciplines working with quantitative data, “data sharing is still mostly an ideal, honored more in the breach than in practice.” This is due to the fact that there is a gap between the ideal of data sharing and research workflows characterized by time pressures and the imperative to compete for scarce resources.

Implementation barriers particularly affect early career researchers. As a group, they have low awareness of open research, have no space for the additional time commitments needed to create open data, and often face restrictions from supervisors and more senior colleagues.

Protecting the anonymity of participants in shared data

Despite these implementation challenges, funding bodies are increasingly mandating open data. Such mandates create specific challenges for humanities and social sciences scholars working with qualitative data.

Questions of privacy usually spring to mind first: ethnographers wonder how they can protect the anonymity of their participants under an open data regime. On the face of it, this problem seems to have an easy solution. Putting specific user restrictions for a specific dataset in place is not technically difficult. Most data repositories allow a variety of privacy settings from fully open to highly sensitive.

Yet this seemingly straightforward technical solution clashes with how research participants may feel about their research participation. Everyone knows that data breaches are extremely common. Therefore, even the possibility of data being made available beyond the confines of the research team—regardless of what the privacy settings of the repository might be—will mean that some participants think twice about participation. This is particularly true of vulnerable groups, whose experiences will be further marginalized in the scientific record.

Maintaining the richness of ethnographic data

Furthermore, for ethnographers, “data” are created in interaction between researcher and participant. Researcher positionality is central to humanities and social sciences research.

All research is partial and the product of specific relationships between people. Yet, to be sharable we need to transform our observations, interactions, conversations, and relationships into “data.”

Conversations, for example, become audio-recordings and transcripts, erasing the material context of the conversation: the embodied researcher and their relationship with participants. Some of this will be captured in field notes, which constitute another data point. However, practically, we cannot even begin to imagine how much work polishing our field notes to prepare them for data sharing in a repository would entail. Our field notes are built on our “having been there” and usually consist of half sentences, in a mix of languages, full of abbreviations and cross-references meaningful only to ourselves.

Open data as fodder for generative AI

We like to think of open data as “best practice” enabling scientific rigor, transparency, and ultimately enhancing human knowledge. Yet, in reality, open data has become caught up in commercial data scraping and surveillance capitalism. We can no longer think of digital text only positively as “open” but must also exert caution as they provide the fodder for the large language models underlying generative AI. By putting our data out there in digital formats we may well add fuel to the fire of the textocalypse, “a crisis of never-ending spam, a debilitating amalgamation of human and machine authorship.”

Reimagining data sharing

The ambition of open research – for research to extend its benefits as widely as possible – is close to the heart of most researchers. After all, the privilege of knowledge creation carries with it the responsibility of knowledge sharing.

How can we reap the benefits of data sharing without letting “the logic of open data distort ethnographic perspectives”?

Ethnographic data sharing cannot be depositing text in a repository, for the reasons outlined. To preserve the ontological and epistemological integrity of ethnographic research, data sharing must not only maintain the relationships established in the projects being brought together but extend and enhance them.

For the Life in a New Language project, we combined data from 130 participants across six separate ethnographic studies conducted over a period of 20 years to explore the lived experience of adult language learning and migrant settlement.

All six projects had been conducted within the larger Language on the Move research team and four of them were PhD projects supervised by the lead author. Sharing our data thus took place within an established personal relationship. We managed to bring the person of the researcher along with the data.

Reimagining data sharing as creating a research community of practice enabled us to create new knowledge related to our specific research problem, how adult migrants make a new life through the medium of a language they are still learning. Incidentally, we created a working model for how to combine existing small data sets into a larger longitudinal study of a social phenomenon within an open research framework and a collaborative ethics of care.

_{Featured image by Ester Marie Doysabas via Unsplash.}