In designing PECE, we have presumed that sharing ethnographic data – so that it can be interpreted and reinterpreted from multiple perspectives (and not just that of a solo anthropologist) – deepens and enriches the contextualization of data. Acknowledging that cultural analysts can only ever offer partial, situated perspectives on cultural phenomena – that their individual analyses will always marginalize certain communities and narratives – we have aimed to design PECE to invite collaborative analysis. PECE has been designed to advance “explanatory pluralism” – to illicit multiple interpretations of data – not in an effort to converge on one right interpretation of data, but instead in an effort to perpetually thicken the description of data. As Evelyn Fox Keller (2003, 303) reminds us, in investigating complex phenomena, such explanatory pluralism “represent[s] our best chance of coming to terms with the world around us.”
This marks a considerable departure from predominant norms in ethnography. Ethnographers, for a long time, have tended to work in isolation. Typically, an ethnographer will go to a field site, collect their field notes, conduct interviews, analyze the data, and then formalize the analysis into a publication without ever sharing their data with other ethnographers. There are many reasons for this. First, because anthropologists tend to collect their data from human subjects, they remain greatly concerned about releasing sensitive information about vulnerable populations to the public. Further, anthropologists tend to get much more credit (towards tenure or other promotions) for solo publications than they do for co-authored publications.
These have become go-to responses for why building a discourse for data sharing in anthropology has been so slow-moving. However, I’ve come to understand there to be much deeper concerns about advancing data sharing in anthropology (see Figure 1), and they have to do with the information architecture needed to make data sharing in anthropology possible. Ethnographers often do not see their work as producing data – at least not in the way that scientists produce data. Ethnographic analysis is typically developed through extended fieldwork and constant iterations of research questions. Ethnographic analysis emerges from interpreting observations in relation to each other; anthropologists continuously toggle between figure and ground – between individual field notes and bigger picture cultural landscapes – in order to eventually make cultural claims. Anthropologists also understand their observations to be inevitably biased according to the assumptions and commitments that they bring to their work; these biases shape the lens through which the anthropologist interacts with and observes a community. Thus, typically the first time that ethnographic observations are shared is after they’ve been formalized into a publication – once the ethnographer has had a chance to curate and contextualize observations collected over extended periods of time. In order to challenge an authorial voice that has historically positioned culture as simply “out there,” ethnographers often experiment with forms of writing that weave themselves into the narrative. They do so in order to draw attention to how their own positioning within the research implicates how they go about observing, recording, and writing up culture.
The information architecture supporting ethnography, conceived this way, is like a piece of knitting; observations are woven together to produce the final product, and if you were to remove one stitch, the whole thing could unravel. With this information architecture, no one observation can be interpreted apart from the entire narrative. However, “sharing data” presumes that ethnographers will disaggregate their extended observations into discrete units so that others can access them. Anthropologists are concerned that, in slicing up ethnographic data for inclusion in community databases, ethnographic research will become overly reductionist. As Alexander Galloway (2014) argues, the information architecture for this would look more like a crystalline structure – from which individual atoms can be extracted without jeopardizing the integrity of the structure.
Designing an information architecture to investigate complexity, then, is a double bind – because understanding complexity must be a collaborative endeavor (with diverse perspectives represented), but to enable collaboration, we need to structure a space that enables collaborators to respond to something common, and that very structuring has the potential to reduce complexity. This is a semiotic challenge just as much as it is a practice-based challenge: we do not want to lose the richness of meaning of data collected in particular contexts, as part of particular collections, and according to particular assumptions and commitments. But we also want that meaning to have the possibility of taking on new meaning as it is examined collaboratively. This is one of the limits we have had to learn to pursue in designing the information architecture of PECE.
One of the ways we have approached this is by changing the architectural metaphor; PECE’s information architecture is not like a piece of knitting, nor is it like a crystalline structure. Instead, it’s like a kaleidoscope (Fortun 2012). While data gets uploaded to the system in discrete units – as a single interview, image, field note, or document – it is almost always accessed from pages that situate it somewhere within a larger collection. We have designed PECE to support the “juxtaposition” of data; multiple features in the platform have been specifically designed to place ethnographic data side-by-side, encouraging analysis that considers not only data, but also the broader contexts in which it needs to be understood. At times, the way that data in the platform gets juxtaposed is random, aiming to subvert habitual modes of curation and to surprise researchers with connections they may have never before considered. At other times, in order to advance explanatory pluralism, we’ve enabled diverse researchers to curate each other’s data into collages, demonstrating how multiple, diverse narratives can emerge from the same data points.
We have also, over time, developed a considerable commitment to following best practices in research data management. We’ve learned from the natural sciences that researchers do not necessarily have to wait until data analysis and write-up to do the reflexive work of describing the history and context of data production. Metadata frameworks enable researchers of all types to couple their data with descriptions of the conditions that produced the data. Scientists, who too were concerned about their data being interpreted out of context, helped develop and implement these frameworks so that they could, to the extent possible, represent the meaning of their data to interdisciplinary communities interested in leveraging it for diverse types of analysis. Notably, doing the work of describing the history and context of data production at the data level (rather than as part of a final write-up) is quite time-consuming. And it is difficult to encourage anthropologists to invest time in doing this work when the academic worlds in which they are embedded tend to give more credit to solo-authored research than to collaborative research. However, as Kim Fortun and colleagues (2014) note, the etymology of collaboration [Latin, con- (‘with’) + laboro [DON’T HAVE THE RIGHT KEYBOARD CHARACTERS HERE](‘work’)] suggests that it is going to be laborious.
Notably, as anthropologists begin using PECE to organize and advance their ethnographic projects – as their ethnographic data gets ordered according to the information architectures the PECE design team has structured – we inevitably also become collaborators in their ethnographic projects. Our own assumptions and commitments about ethnography, formalized into the design of the system, also play a part in shaping the cultural claims ethnographers using the system end up making. Thus, we’ve come to see our design logics as metadata for PECE, describing the assumptions and commitments that inform it. To render visible the assumptions and commitments that have guided the design of PECE’s architecture, we have created a page (that comes pre-packaged with every download of the platform) that lists our design logics. We have made it so that new users cannot edit these design logics. We need users to understand the genealogies of thinking that are interwoven in the design of our system and that structure how their data will be ordered. Yet, in designing it in this way, we have undermined the very design logics that we seek to advance, hard-coding authoritative paradigms for how the system should be interpreted, rather than leaving it open to explanatory pluralism. This is a limit of collaborative experimentalism. We need collaborative insight to broaden partial perspectives, and to make collaborative analysis work, ethnographers need to acknowledge and render visible the genealogies of thinking that have shaped the data they share. But they can never fully do this from a partial perspective. As I will show in the next section, our commitment to experimentalism further complicates this bind.
 Further, taking seriously the efforts to dismantle colonial tendencies in anthropology, we believe that ethnographers should not have any special ownership rights to data they collect based on observations and analysis of other communities and that data produced as part of research projects funded with public grant money should be considered a public resource.
 Alexander Galloway (2014) has argued that new capitalist-oriented digital humanities work (championed by Google, Facebook, and others) tends to “atomize” cultural data – to cut it into distinct units – which can eclipse the complexity of cultural phenomena. This work assumes (in line with a computational logic) that data can be interpreted in discrete chunks and that culture can be understood as a sum of its parts.
 And, of course, just because ethnographic analysis has been crafted into a publication does not necessarily ensure that others will interpret the narrative according to how the ethnographer intended.
This is a section from the second chapter of my dissertation that discusses the cultural and architectural challenges to advancing the "collaborative" components of experimental ethnography
second chapter of my dissertation