Digitisation and the extraction of data are assembling a powerful imaginary around the nature of data as the only workable form capable of telling what matters. This panel examines "data worldings" and the kinds of meetings that data and "the digital" hope to facilitate and strive to curtail.
Collections of records, specimens, and artifacts continue to be digitised at unprecedented rates. Concurrently, extraction and oftentimes sly elicitation of "data" by devices and novel socio-technical arrangements is justified by and in turn generates imperatives concerning the absolute value and necessity of (ever more and better) "data". Having worked in the context of the biological and ecological sciences, we observe that "nature" and its attendant configurations like biodiversity, ecology or species increasingly become apprehended exclusively through "data", whether genetic, environmental, or financial. These projects are assembling a powerful imaginary around the nature of data, particularly concerning their supposedly infinite relationalities.
STS has crafted critical handles for grappling with data and data infrastructures, examining standardisation and commensuration of very different things and the persistence of what Richards calls "the fictive thought of imperial control" in the pursuit of positive and comprehensive data for all and everything. What is particularly interesting about data practices is how they enact tensions between holism and partiality, composition and decomposition, collectivity and individuation, connection and separation. While nurturing aspirations of universalism and omniscience, they also trouble divisions between data and object/specimen, information and flesh, insides and outsides.
In this panel we are interested in examining the kinds of meetings that data and "the digital" hope to facilitate and strive to curtail. When data become the only universal, acceptable and workable form capable of telling what matters, what is the scope and efficacy of assembly? Who can come together and how? What can congregate and where?
This panel is closed to new paper proposals.
Bets, bots and bodies: erasing erroneous environments
I analyse ethnographically how humans & non-humans are reconfigured in a corporate carbon accounting machinery to perform smooth environments, removing erroneous environments. The company bets on specific human bots to sustain the machinery. Dream&datascapes are configured for capitalist purposes.
Capitalist promises of reconciling nature, technology and markets rely profoundly on the imaginary of datafied environments that are readily readable for decision-makers, figured as centralised elites, managers or decentralised consumers. Corporate carbon accounting purports to render legible a company's impacts on the environment. Using ethnographic engagement with a multinational Fortune 50 company's Corporate Responsibility Team, I experiment with a four-fold story. First, I consider the corporate carbon machinery, its heterogeneous configuration, including the presence of human bodies and their ontic practices. Second, I show how doing carbon within this machinery effects, what I call, erroneous environments. Third, I complicate the politics of bodies, showing how they are both used in bets to repair breakdowns of the machinery and how betting on making some bodies absent promises a sustainable machinery. Fourth, I suggest that the ideal human body in the imaginary of a sustainable machinery of carbon accounting is figured like a bot. My figure of the bot employs data do achieve social and political effects. I offer this four-fold story to question the imaginary that "nature" increasingly becomes apprehended exclusively through "data". The story I tell foregrounds a "through 'data'" in which the focus is on the "through": in the company, it matters to datafy environments such that these environments can be overseen; and by overseeing, some environments are not only performed as managed or governed but they are also overlooked, ignored. The company configures environments so that erroneous environments disappear. What matters is a clean and green dreamscape.
Remaking the world for reproducibility
This paper examines the data imaginaries of the "reproducibility crisis," and the connection between these imaginaries and laboratory practice. Using ethnographic data, it examines how scientists enact particular kinds of stability and commensurability far upstream in their research process.
This paper examines the data imaginaries of the "reproducibility crisis," and the connection between these imaginaries and laboratory practice. The reproducibility crisis, a recent phenomenon where scientists have found many findings to be difficult to replicate on subsequent investigation, is grounded in particular assumptions about the stability and universality of biomedical data. Scientists have come to expect that findings might vary between sexes, for example, but are much more disturbed to find that they vary between technicians or laboratories. This variation across time and geographical space calls into question initiatives to extract, recombine, and extrapolate from these bodies of research.
Using ethnographic data, I will examine the ways in which scientists are presently attempting to enact particular kinds of stability and commensurability in their laboratory work. In particular, I will focus on how scientists enact these assumptions far upstream in their research process, not only through data cleaning or experimental design, but through the way that animals are housed before they even reach an experiment. This examination draws attention to the deep ways scientists remake the world with particular data imaginaries in mind. Drawing from STS work on ontology, I argue that we need to pay more attention to "ontological systems"; that is, how science shapes the topography of our reality through the management of numerous, interrelated objects from which data are extracted.
Matters of scale, or how Satellite Remote Sensing, grasslands and vegetation models meet in data
This paper explores an interdisciplinary research project linking Satellite Remote Sensing, environmental modelling and others to monitor and predict the mowing of grasslands. It argues that the project's way of meeting in data is in incongruent scales, or dealing with matters of scale.
This paper explores the meetings of heterogeneously scaled data between Satellite Remote Sensing (SRS) and other sciences dealing with biodiversity and agriculture. It is based on an ethnographic inquiry in the making - part of my PhD-research - into an interdisciplinary research project in its first year. Its aim is the SRS-based monitoring and modelling of grasslands in Germany in order to better grasp different "mowing regimes" and predict them both for economic purposes and in relation to issues of biodiversity.
The task of making different data meet is a core concern for the project's success. With six different research organisations and at least three major approaches to doing science assembled - SRS, modelling and field experiments - the research questions easily break up into a series of diverging data that need to be balanced carefully. Knowledge of varying species compositions in grasslands is confronted with interests in model validation or spectral information.
These approaches and data come with different spatial scales, including 1m² plots, models with 1km² resolutions or SRS data with pixels of 20m². I argue that it is precisely in such meetings that the research project becomes vivid, as apparent in the search for research locations that meet criteria of visibility from outer space, sufficiently equipped technology for in situ measurements and access to farmers willing to collaborate: How is this BigData-approach collaboratively evolving or failing to address the variety of concerns it is meant to handle, e.g. monitoring the whole of Germany and site-specific consultation?
'Data basing' Danish educational governance - Relations, 'gaps' - and ontological experimentation
The Data Warehouse (TDW) is an important infrastructural component in Danish educational governance. We examine how TDW connects, but also produces inter-organisational 'gaps'. Educational governance appears as a series of ontological experiments rather than a system with one overarching logic.
If you visit the Danish Ministry of Education's webpage The Data Warehouse (TDW) dashboards reporting various indicators of the performance of schools will populate your screen. In Denmark, TDW has become an important infrastructural component in educational governance, as schools, ministries and municipalities are required to produce and use the data it provides.
This reflects an international tendency. Studies of educational governance systems in the UK propose that data is becoming a determining factor in educational governance. Selwyn (2015) e.g. suggests that databased infrastructures lead to a 'recursive state where data analysis begins to produce educational settings, as much as educational settings producing data'. (Selwyn 2015, 72, see also Williamson 2016). Such studies suggest that governance materialize as self-reinforcing, cybernetic feedback-loops where data structures practices, centralizes power and defines inter-organizational relations.
We propose that addressing databased governance from different positions in the network challenge the idea of an overall logic and system. Our case is based on studies of concerns and practices and inter-organizational relations emerging at three sites: the ministerial agency responsible for the development and maintenance of TDW, a municipality, and a school. TDW appears in this case to afford relations that are incomplete or 'partial' (Strathern). TDW connects, but also produces 'gaps', which is managed variously at the different sites, changing also the status of the TDW. Databasing Danish educational governance can therefore be considered a series of ontological experiments (Jensen & Morita), rather than as the implementation of a governance system with one overarching logic.
Biodiversity databases - wishful meetings, differing ontologies
I address Finnish attempts at collecting biodiversity data for the benefit of science, conservation, and society. Bulk of the data comes from volunteer citizen scientists, but harmonious meeting in data cannot be taken for granted, as the parties perceive data partly ontologically differently.
Finland is a leading country in knowing biodiversity, largely thanks to an established citizen science tradition. European and Finnish biodiversity policies rely on strong biodiversity science and efficient knowledge management. Information technology and databases have become important underpinnings to policymaking. Internationally, the Global Biodiversity Information Facility (GBIF) is an effort to bring together and release data on the world's species in order to facilitate research and conservation. The corresponding Finnish project is the Finnish Biodiversity Information Facility (FinBIF) which brings together actors working with biodiversity data and aims at gathering data on all Finnish flora and fauna in one place.
From STS perspective, technological development tends to focus too narrowly on the technology at stake and lose sight of the social, political and cultural worlds built into it. There are good intentions and attempts for inclusion, but the neoliberalist perspective sees the citizen scientist as a data producer. The data managers may fail to see that the different groups and individuals connected to the biodiversity data network perceive the technological socio-natural arrangements as ontologically different. This brings about questions of exchange, ownership, reliability, validation and use of the data and legitimacy of the project. Roughly two ontologies can be identified: enabling and restricting. This paper presents an analysis of the Finnish biodiversity information "scene" with various actors, histories and platforms, and discusses the usefulness of the ontological approach in negotiating the sovereignty of nature.
European Natural History Museums as part of a global biodiversity-research infrastructure
An ethnographic study of curators and scientists has been done to understand how processes of digitalization, virtualization and DNA sequencing affect the role of natural history museums as part of a global biodiversity research infrastructure.
Biodiversity change can be analyzed bringing together a variety of data sources into a meaningful infrastructure bringing together structured and standardized data, funding and skills.
Natural history museums are institutions holding vast, centuries-spanning, standardized specimen collections and species-data, being ideal vaults for long-term biodiversity-research. Natural history museums have had doubts about their role in biodiversity science but have now regained self-esteem as collection-workers , even considering themselves as key to scientific research on biodiversity assessment. Do natural history museums see themselves as just data vaults or do they take the lead in defining the biodiversity research agenda?
Recent EU-funded programs like EDIT, ViBRANT and Synthesys reflect the urge of natural history institutions to cooperate on a European scale, the question is how collaboration at the museum work floor takes form. There are indications that curators and scientists in natural history museums engage in ongoing collaborative practices with peers from other institutions and how they help to transform traditional views on natural history museums into a biodiversity research agenda, based on digitization and virtualization of traditional collections combined with DNA sequencing, thus creating an infrastructure for biodiversity assessment.
To make sense of these changes we are going to investigate how practices of curators and scientists in natural history museums are affected by ideas of collections as being part of a wider scientific biodiversity infrastructure. Through ethnographic research on curators and scientists in natural history museums we want to understand the relationship between traditional natural history collections and contemporary biodiversity research.
To share or not to share: following the journeys of the data collected by citizen scientists
This paper investigates how citizen scientists decided to share their data or knowledge based on a practice-based investigation into the data flow between online and offline data practices, meetings and alignments of multiple socio-technical and socio-political configurations.
Most of the discourse about knowledge economy has viewed knowledge exchange and data sharing positively. However, as more research suggests that the increasingly personalised and data intensive economy is built on the exploitation of datafied bodies and digital labour, it is a concern if people should continue to sh\are their data or knowledge at all. Indeed, as the recent incident about the fitness tracking app Strava giving away location of secret US army bases reveals, data about exercise routes shared online could even lead to identification of the users (soldiers in this case) and further to breaching national security. This paper questions the culture of sharing the data collected by citizen scientists from a practice-based perspective. The 'data journey' methodology (Bates et al. 2016) guides the exploration of citizen scientists' data practices, datafication process of their everyday mundane routines, and see how the culture of sharing is challenged when different social worlds, cultures, and values systems collapse and intersect.
This panel is closed to new paper proposals.