Key to precision medicine is the development of expert database projects that gather data, integrate them in the pre-existing database, and publish the product of their processing for others to make use of. Increasingly, it is required that data infrastructure managers and curators pursue and lead research projects on the data so as to learn about new ways data could be used or information that could be potentially generated from them. I call these efforts “data (...) curation-research” and use the case study of COSMIC, the Catalogue of Somatic Mutations in Cancer, to analyze the contextual factors shaping the science of data curation-research. I build on March’s organizational learning categories of exploitation and exploration to place these factors within a theory of organizational change and innovation, and contribute to a richer picture of the social drivers of cancer genomics. (shrink)

Our article contributes to debates on critical humanitarianism and critical dataset studies by examining the praxis of digital humanitarianism and the entanglement between what we know and how we know human suffering in large-scale disasters. We specifically focus on the “data work” carried out by various professionals in creating and curating datasets used by humanitarian actors to address both current and future crises. In doing so, we explore how these actors make sense of the development, maintenance and (re)use of (...) such datasets. This approach enables us to analyse the underlying principles and practices of data curation and to investigate the challenges that arise when humanitarian and non-humanitarian actors collaborate to produce and sustain these datasets. We also examine how these often tension-filled collaborations influence practitioners' understandings of humanitarian crises and their efforts to reconcile efficiency with humanitarian principles. We argue that while there is no such thing as a “humanitarian dataset”, there are contested processes of datafied humanitarian knowledge production that are situated in specific contexts. We suggest that the construction of these datasets redefines humanitarian knowledge production and is therefore critical to understanding the shifting politics of digital humanitarianism. Our findings highlight three key challenges that characterise the relationships among these groups: the fragmentation of the humanitarian data value chain; the asymmetries in expertise and skills among the diverse professionals involved in dataset curation; and the integration of small and big data in crisis analysis and prediction. These insights carry both epistemic and ethical implications, drawing attention to the problematic relationship between datafication and humanitarian principles. (shrink)

Our article contributes to debates on critical humanitarianism and critical dataset studies by examining the praxis of digital humanitarianism and the entanglement between what we know and how we know human suffering in large-scale disasters. We specifically focus on the “data work” carried out by various professionals in creating and curating datasets used by humanitarian actors to address both current and future crises. In doing so, we explore how these actors make sense of the development, maintenance and (re)use of (...) such datasets. This approach enables us to analyse the underlying principles and practices of data curation and to investigate the challenges that arise when humanitarian and non-humanitarian actors collaborate to produce and sustain these datasets. We also examine how these often tension-filled collaborations influence practitioners' understandings of humanitarian crises and their efforts to reconcile efficiency with humanitarian principles. We argue that while there is no such thing as a “humanitarian dataset”, there are contested processes of datafied humanitarian knowledge production that are situated in specific contexts. We suggest that the construction of these datasets redefines humanitarian knowledge production and is therefore critical to understanding the shifting politics of digital humanitarianism. Our findings highlight three key challenges that characterise the relationships among these groups: the fragmentation of the humanitarian data value chain; the asymmetries in expertise and skills among the diverse professionals involved in dataset curation; and the integration of small and big data in crisis analysis and prediction. These insights carry both epistemic and ethical implications, drawing attention to the problematic relationship between datafication and humanitarian principles. (shrink)

The notion of ‘excellence’ has become the key logic structuring academic funding and evaluation. This essay offers a critique of this policy logic by examining the case of biocuration, an area of academic scholarship that is ‘non-excellent’ (according to this notion’s operationalisation) but that has, I argue, profound epistemic significance. I develop my argument in a number of steps. I first characterise excellence regimes by examining how excellence is operationalised within them, before discussing epistemic diversity and injustice, and how these (...) concepts can be applied in the context of studies of maintenance and repair. I then turn to my case study of biocuration and its use of bio-ontologies, introducing biocuration and its struggles for visibility and recognition before discussing its epistemic significance and contribution to the wider biosciences. In closing I argue that there is a need to better understand what epistemic diversity in academic settings looks like in practice, to assess the different kinds of work that contribute to knowledge production, and to base evaluation and funding practices upon the acknowledgement and material recognition of epistemic diversity. (shrink)

This study focuses on the grassroots data practices of an environmental non-government organization (ENGO), examining the constitutive role of data in environmental activism in China. Drawing on the concept of contentious publicness, it analyses the processual and relational dynamics of the ENGO-led data activism to tackle environmental pollution at the interrelated material, spatial and temporal levels. Through participatory observations and in-depth interviews, it first examines the material agency of data infrastructure in enabling environmental participation. Then, it (...) explores the spatiality and temporality of activists’ action repertoires. The findings demonstrate that the activist engagement with data follows a non-confrontational approach, evolving in the compromised middle ground between embeddedness and marginalization. Environmental data serves as a relational mediator in activists’ continuous process of making tactical responses to disrupt the status quo while not completely denying or subverting existing power relations. Moving beyond the contestational view of data, the study applies a non-binary and processual account of data activism, contributes to a deeper understanding of the relational configurations of power in data politics. It sheds light on the institutional, technological, and social imaginaries of environmentalism that shape the building of environmental data infrastructure and cultivate new forms of environmental action in the specific sociopolitical context of China. Moreover, the situated analysis of data activism contributes to diversifying the Western-centric understanding of the transformative potentials of data and calls for more scholarly attention to the relational dynamics of data politics in the Global South. (shrink)

This paper is concerned with everyday data practices, considering how people record data produced through self-monitoring. The analysis unpacks the relationships between taking a measure, and making and reviewing records. The paper is based on an interview study with people who monitor their blood pressure and/or body mass index/weight. Animated by discussions of ‘data power’ which are, in part, predicated on the flow and aggregation of data, we aim to extend important work concerning the everyday constitution (...) of digital data. In the paper, we adopt and develop the idea of curation as a theory of attention. We introduce the idea of discerning work to characterise the skilful judgements people make about which readings they record, how readings are presented, and about the records they retain and those they discard. We suggest self-monitoring produces partial data, both in the sense that it embodies these judgements, and also because monitoring might be conducted intermittently. We also extend previous analyses by exploring the broad set of materials, digital and analogue, networked and not networked, involved in record keeping to consider the different ways these contributed to regulating attention to self-monitoring. By paying attention to which data is recorded and the occasions when data is not recorded, as well as the ways data is recorded, the research provides specificity to the different ways in which self-monitoring data may or may not flow or contribute to big data sets. We argue that ultimately our analysis contributes to nuancing our understanding of ‘data power’. (shrink)

The Harold Pinter Archive at the British Library contains correspondence dating from 1977 to 2008, including his e-mail archive. In total, the correspondence strand of the archive contains c.20,000 paper letters and c.3500 e-mails. This project used data analytics (in Python) and network visualisation (in Gephi) to interrogate the ways in which digital and analogue correspondence function together within Pinter’s literary archive. The attendant paper reflects upon what this analysis might mean for archivists, curators and researchers working with hybrid (...) correspondence collections in the context of Digital Humanities and Artificial Intelligence based research methodologies. The paper includes an analysis of what an email is both materially and functionally—what are its constituent parts and in what ways is it like and unlike a physical letter, as well as a discussion of how we can leverage the highly structured nature of e-mail data to our advantage using computational techniques, and the limitations of these approaches to socalled “dark archives”. Network visualisations are used to represent correspondence visually in order to shed light on the activities it represents; particularly literary collaboration and administration. The paper concludes with an attempt to draw conclusions from these analyses and to think about their implications for repositories and researchers collecting and using hybrid correspondence collections. The Python code produced for the project is collection agnostic and available under a Creative Commons Licence, meaning that any collecting repository holding email archives in MBOX format can use it to extract GDPR compliant metadata from their collections and visualise them in Gephi. (shrink)

The technologies used to govern performance at universities consist of monitoring and comparative instruments. They are designed to affect and direct behaviour. In these academic environments of exposure, comparison and self-monitoring are deeply entangled with a vulnerable affective economy. This article explores how these data may affect our moods and how academic value could be curated by other means and with care. Drawing on feminist new materialist thinking and speculative feminist storytelling, the article takes this picture of the actual (...) as a point of departure for discussing ways of experimenting with affirmative critique of the current use of data. Through a smaller experiment with thirty PhD students, the article discusses how to speculatively curate academic value by other means that provide more liveable world(ing)s than data visuals measuring performance and that engage other sensorial and affective registers. (shrink)

This article examines how volunteers involved in the Data Rescue movement navigated concerns about the stability and stewardship of federal environmental data following the 2016 U.S. presidential election. Drawing on 16 in-depth interviews, the study explores how participants interpreted infrastructural fragility not simply as a technical risk, but as a relational and political concern rooted in shifting institutional commitments. The analysis introduces the concept of anticipatory maintenance to describe how volunteers responded to perceived threats by developing redundant, decentralized (...) strategies for data preservation. Anticipatory maintenance is conceptualized as preventive, future-oriented infrastructural care that translates anticipated disruption into present interventions under uncertainty, in order to explain how volunteers acted as if future loss had already begun, building redundant and decentralized preservation arrangements. Findings highlight the role of particularized trust and systemic distrust in shaping grassroots responses, as well as the limitations encountered in volunteer-driven infrastructures, including challenges related to sustainability, governance, and the affective demands of ongoing maintenance. By foregrounding the interplay of technical, social, and emotional factors, this study offers a critical perspective on data stewardship in times of political uncertainty and underscores the need for collaborative approaches to infrastructural resilience. (shrink)

In this contribution, we will discuss the opportunities and challenges arising from memory institutions' need to redefine their archival strategies for contemporary collecting in a world of big data. We will reflect on this topic by critically examining the case study of the UK Web Archive, which is made up of the six UK Legal Deposit Libraries: the British Library, National Library of Scotland, National Library of Wales, Bodleian Libraries Oxford, Cambridge University Library and Trinity College Dublin. The UK (...) Web Archive aims to archive, preserve and give access to the UK web space. This is achieved through an annual domain crawl, first undertaken in 2013, in addition to more frequent crawls of key websites and specially curated collections which date back as far as 2005. These collections reflect important aspects of British culture and events that shape society. This commentary will explore a number of questions including: what heritage is captured and what heritage is instead neglected by the UK Web archive? What heritage is created in the form of new data and what are its properties? What are the ethical issues that memory institutions face when developing these web archiving practices? What transformations are required to overcome such challenges and what institutional futures can we envisage? (shrink)

This paper explores the role of the curator in hospitals. The arts play a significant role in every society; however, recent studies indicate a neglect of the aesthetic environment of healthcare. This international study explores the complex role of the curator in modern hospitals. Semi-structured interviews were conducted with ten arts specialists in hospitals across five countries and three continents for a qualitative, phenomenological study. Five themes arose from the data: Patient involvement and influence on the arts programme in (...) hospital Understanding the role of the curator in hospital Influences on arts programming in hospital Types of arts programmes Limitations to effective curation in hospital. Recommendations arising from the research included recognition of the specialised role of the curator in hospitals; building positive links with clinical staff to effect positive hospital arts programmes and increasing formal involvement of patients in arts planning in hospital. Hospital curation can be a vibrant arena for arts development, and the role of the hospital curator is a ground-breaking specialist role that can bring benefits to hospital life. The role of curator in hospital deserves to be supported and developed by both the arts and health sectors. (shrink)

Starting from the observation that data sharing in general and sharing of reusable behavioral data in particular is still scarce in psychology, we set out to develop a curation standard for behavioral psychological research data rendering data reuse more effective and efficient. Specifically, we propose a standard that is oriented toward the requirements of the psychological research process, thus considering the needs of researchers in their role as data providers and data users. To (...) this end, we suggest that researchers should describe their data on three documentation levels reflecting researchers’ central decisions during the research process. In particular, these levels describe researchers’ decisions on the concrete research design that is most suitable to address the corresponding research question, its operationalization as well as a precise description of the subsequent data collection and analysis process. Accordingly, the first documentation level represents, for instance, researchers’ decision on the concrete hypotheses, inclusion/exclusion criteria and the number of measurement points as well as a conceptual presentation of all substantial variables included in the design. On the second level these substantial variables are presented within an extended codebook allowing for the linkage between the conceptual research design and the actually operationalized variables as presented within the data. Finally, the third level includes all materials, data preparation and analyses scripts as well as a detailed procedure graphic that allows the data user to link the information from all three documentation levels at a single glance. After a comprehensive presentation of the standard, we will offer some arguments for its integration into the psychological research process. (shrink)

New hardware such as mobile handheld devices and digital cameras; new online social venues such as social networking, microblogging, and online photo sharing sites; and new infrastructures such as the global positioning system are beginning to establish new practices—what the author refers to as “sociolocative”—that combine data about a physical location, such as a geotag, with a virtual social act. This research investigates the phenomenon of documentary broadcasting, whereby individuals curate lasting descriptions and commentaries about a location for a (...) public audience, often using the photo sharing site, Flickr. Unlike many photographs shared online, the subject of a documentary post is always a place and not the activities nor identity of the broadcaster himself or herself. When presented together as curated narratives, posted images transform Flickr into a virtual public “gallery space” that captures, presents, and preserves aspects of a place that may last longer than the physical location itself. (shrink)

Digital transformations and processes of “datafication” fundamentally reshape how information is produced, circulated and given meaning. In this article, we provide a concept of “datastructuring” which seeks to capture this reshaping as both a product of and productive of social activity. To do this we focus on (1) how new forms of social action map onto and are enabled by technological changes related to datafication, and (2) how new forms of datafied social action constitute a form of knowledge production which (...) becomes embedded in technologies themselves. We illustrate the potential of the datastructuring concept with empirical examples which also serve to highlight some new avenues for research and some empirical questions to explore further. We suggest a focus on datastructuring can ignite scholarly debates across disciplines that may share an interest in the technological configurations, sorting activities, and other socio-material forces that shape digital spaces, but which are rarely brought together. Such cross-disciplinary conceptualizations may give more attention to how information is structured and organized, becomes “algorithmically recognizable”, and emerges as (in)visible in digital, datafied spaces. Such a concept, we suggest, may help us better understand the novel ways in which “backstage datawork” and “data sorting processes” gain traction in political interventions, commercial processes, and social ordering. (shrink)

Is Big Data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of Big Data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community and (2) the methods, infrastructures, technologies, skills and (...) knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this article reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which Big Data needs to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analysing data given these developments, and the opportunities and worries associated with Big Data discourse and methods. (shrink)

This article investigates the work of processors who curate and “clean” the data sets that researchers submit to data archives for archiving and further dissemination. Based on ethnographic fieldwork conducted at the data processing unit of a major US social science data archive, I investigate how these data processors work, under which status, and how they contribute to data sharing. This article presents two main results. First, it contributes to the study of invisible technicians (...) in science by showing that the same procedures can make technical work invisible outside and visible inside the archive, to allow peer review and quality control. Second, this article contributes to the social study of scientific data sharing, by showing that the organization of data processing directly stems from the conception that the archive promotes of a valid data set—that is, a data set that must look “pristine” at the end of its processing. After critically interrogating this notion of pristineness, I show how it perpetuates a misleading conception of data as “raw” instead of acknowledging the important contribution of data processors to data sharing and social science. (shrink)

Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How (...) do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities—and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms. (shrink)

As heritage-as-the-already-occurred folds into heritage-in-the-making practices, temporal and spatial fluidity is made more complex by digital mediation and particularly by Big Data. Such liveliness evokes ontological, epistemological and methodological challenges. Drawing on more-than-human theorizing, this article reframes the notion of data-bodies to advance data activist-oriented research in heritage. Focused primarily on women, it examines how their distributed agency and voice with respect to data practices and the (re)makings of (digital) heritage could be amplified. I describe three (...) methodological directions, influenced by feminist work in critical data studies, which could be employed by researchers: attuning to and becoming with data, making data physical and changing narratives. From data-bodies to haunted data, performative data curation and mapping data-bodies, and attuning to data streams and re-voicing narratives, this article contributes to discussions of how to engage critically and creatively with the datafication of digital heritage practices, knowings and ontologies. (shrink)

Data interoperability poses unique ethical challenges across a range of academic, industrial, and governmental implementations of data systems. Central to data interoperability is the design of systems and protocols for exchanging or integrating data from different initial source domains. Data interoperability is often regarded as necessary for carrying out tasks between different organizations and suborganizations as well as for ensuring secondary use of data for research purposes. However, interoperability poses a number of ethical problems (...) whose contours can prove especially challenging in comparison to how ethical harms take hold at other moments of the data life cycle (such as algorithmic processing or results dissemination). Taking biomedical data interoperability as a focal domain, this article provides an overview of data interoperability, maps the central ethical harms that may challenge interoperability projects, and proposes a response to these problems through an approach rooted in philosophical pragmatism. Pragmatist responses to both individual and structural harms of interoperability are presented through three companion strategies: shared standards, manual data curation, and meticulous data documentation. (shrink)

This open access book provides the first systematic overview of existing challenges and opportunities for responsible data linkage, and a cutting-edge assessment of which steps need to be taken to ensure that plant data are ethically shared and used for the benefit of ensuring global food security – one of the UN’s Sustainable Development Goals. The volume focuses on the contemporary contours of such challenges through sustained engagement with current and historical initiatives and discussion of best practices and (...) prospective future directions for ensuring responsible plant data linkage. The volume is divided into four sections that include case studies of plant data use and linkage in the context of particular research projects, breeding programs, and historical research. It address technical challenges of data linkage in developing key tools, standards and infrastructures, and examines governance challenges of data linkage in relation to socioeconomic and environmental research and data collection. Finally, the last section addresses issues raised by new data production and linkage methods for the inclusion of agriculture’s diverse stakeholders. This book brings together leading experts in data curation, data governance and data studies from a variety of fields, including data science, plant science, agricultural research, science policy, data ethics and the philosophy, history and social studies of plant science. (shrink)

This theme issue has the founding ambition of landscaping Data Ethics as a new branch of ethics that studies and evaluates moral problems related to data (including generation, recording, curation, processing, dissemination, sharing, and use), algorithms (including AI, artificial agents, machine learning, and robots), and corresponding practices (including responsible innovation, programming, hacking, and professional codes), in order to formulate and support morally good solutions (e.g. right conducts or right values). Data Ethics builds on the foundation provided (...) by Computer and Information Ethics but, at the same time, it refines the approach endorsed so far in this research field, by shifting the Level of Abstraction of ethical enquiries, from being information-centric to being data-centric. This shift brings into focus the different moral dimensions of all kinds of data, even the data that never translate directly into information but can be used to support actions or generate behaviours, for example. It highlights the need for ethical analyses to concentrate on the content and nature of computational operations — the interactions among hardware, software, and data — rather than on the variety of digital technologies that enables them. And it emphasises the complexity of the ethical challenges posed by Data Science. Because of such complexity, Data Ethics should be developed from the start as a macroethics, that is, as an overall framework that avoids narrow, ad hoc approaches and addresses the ethical impact and implications of Data Science and its applications within a consistent, holistic, and inclusive framework. Only as a macroethics Data Ethics will provide the solutions that can maximise the value of Data Science for our societies, for all of us, and for our environments. (shrink)

This paper presents reports on discussions among an international group of science and technology studies (STS) scholars who convened at the US National Science Foundation (January 2015) to think about data sharing and open STS. The first report, which reflects discussions among members of the Society for Social Studies of Science (4S), relates the potential benefits of data sharing and open science for STS. The second report, which reflects discussions among scholars from many professional STS societies (i.e., European (...) Association for the Study of Science and Technology [EASST], 4S, Society for the History of Technology [SHOT], History of Science Society [HSS], and Philosophy of Science Association [PSA]), focuses on practical and conceptual issues related to managing, storing, and curating STS data. As is the case for all reports of such open discussions, a scholar’s presence at the meeting does not necessarily mean that they agree with all aspects of the text to follow. (shrink)

The ethical and social implications of data mining, algorithmic curation and automation in the context of social media have been of heightened concern for a range of researchers with interests in digital media in recent years, with particular concerns about privacy arising in the context of mobile and locative media. Despite their wide adoption and economic importance, mobile dating apps have received little scholarly attention from this perspective – but they are intense sites of data generation, algorithmic (...) processing, and cross-platform data-sharing; bound up with competing cultures of production, exploitation and use. In this paper, we describe the ways various forms of data are incorporated into, and emerge from, hook-up apps’ business logics, socio-technical arrangements, and cultures of use to produce multiple and intersecting data cultures. We propose a multi-layered research agenda for critical and empirical inquiry into this field, and suggest appropriate conceptual and methodological frameworks for exploring the social and political challenges of data cultures. (shrink)

The new field of data-driven history of ideas combines qualitative, quantitative and computational methods for the study of the origins, development and spread of ideas from any time and place. It also comes with two challenging demands that are distinctive in the landscape of computational humanities. The first is the demand for the adequate representation and detection of concepts, rather than words; the second is the need for high-quality, virtually 100% accurate large corpora in many languages across centuries by (...) both known and virtually unknown authors seen as carriers of ideas. These two main demands generate in turn further needs on resources that must be, typically, newly created or substantially adapted for the field: datasets such as expertly curated sets of bibliographic metadata, annotation sets and historical gazetteers, ontologies, and network data; infrastructural facilities for collaborative environments, and workflows that suit and support the field; ground truths for the evaluation of models from language technology, and techniques integrating language models with approaches and tools from data science, visual analytics, and knowledge representation. -/- Results produced in the field can be published in the same way as traditional articles in in-domain journals and books. The resources that make data-driven enterprises in the history of ideas possible, however, still lack an apt venue, despite the fact that work on such resources is key to the field and can be extremely time-consuming. It is with the intention of creating a home for openly shareable corpora, datasets and other resources, as well as to support the work of the next generation of researchers, that we invite submissions to a special collection of the Journal of Open Humanities Data on Data-Driven History of Ideas. -/- Submissions for this special collection are welcome that focus on, facilitate or support the study of philosophical and scientific thought of any epoch and geographical area, geared in particular towards the origin, development and spread of ideas. (shrink)

This paper proposes a multimodal ethnosemiotic conceptual framework for culturally themed selective Web archiving, taking as a practical example the curation of the London French Special Collection (LFSC) in the UK Web Archive. Its focus on a particular ‘community’ is presented as advantageous in overcoming the sheer scale of data available on the Web; yet, it is argued that these ethnographic boundaries may be flawed if they do not map onto the collective self-perception of the London French. The (...) approach establishes several theoretical meeting points between Pierre Bourdieu’s ethnography and Gunther Kress’s multimodal social semiotics, notably, the foregrounding of practice and the meaning-making potentialities of the everyday; the implications of language and categorisation; the interplay between (curating/researcher) subject and (curated/research) object; evolving notions of agency, authorship and audience; together with social engagement, and the archive as dynamic process and product. The curation rationale proposed stems from Bourdieu’s three-stage field analysis model, which places a strong emphasis on habitus, considered to be most accurately (re)presented through blogs, yet necessitates its contextualisation within the broader (diasporic) field(s), through institutional websites, for example, whilst advocating a reflexive awareness of the researcher/curator’s (subjective) role. This, alongside the Kressian acknowledgement of the inherent multimodality of on-line resources, lends itself convincingly to selection and valuation strategies, whilst the discussion of language, genre, authorship and audience is relevant to the potential cataloguing of Web objects. By conceptualising the culturally themed selective Web-archiving process within the ethnosemiotic framework constructed, concrete recommendations emerge regarding curation, classification and crowd-sourcing. (shrink)

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological (...) dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration. (shrink)

Battlefield data have become a critical asset in contemporary defence. Yet there is a gap in the relevant literature, whilst it addresses various aspects of defence data management—including cybersecurity, interoperability, and decision-making support—it overlooks how these data should be collected, curated, and accessed to enhance the responsible development of AI-enabled defence capabilities. This article addresses this gap first by reviewing existing data policies strategies of NATO and Five Eyes Member States to assess the extent to which (...) they focus on battlefield data, and then by outlining how national defence organisations should manage these data to maximise their strategic value whilst mitigating the attendant ethical, legal, and social risks. We argue that due to their non-rivalrous, artificially excludable nature, battlefield data should be conceptualised as an artificial club good and that national defence organisations have ethical obligations to act as club manager to leverage the potential of these data to develop more robust, reliable and controllable AI defence capabilities. We conclude the analysis proposing two sets of policy recommendations to aid national defence organisations in discharging their responsibilities as club managers for battlefield data. (shrink)

This paper is divided into two parts. In the first, I shall briefly analyse the phenomenon of “big data”, and argue that the real epistemological challenge posed by the zettabyte era is small patterns. The valuable undercurrents in the ocean of data that we are accumulating are invisible to the computationally-naked eye, so more and better technology will help. However, because the problem with big data is small patterns, ultimately, the game will be won by those who (...) “know how to ask and answer questions” (Plato, Cratylus, 390c). This introduces the second part, concerning information quality (IQ): which data may be useful and relevant, and so worth collecting, curating, and querying, in order to exploit their valuable (small) patterns? I shall argue that the standard way of seeing IQ in terms of fit-for-purpose is correct but needs to be complemented by a methodology of abstraction, which allows IQ to be indexed to different purposes. This fundamental step can be taken by adopting a bi-categorical approach. This means distinguishing between purpose/s for which some information is produced (P-purpose) and purpose/s for which the same information is consumed (C-purpose). Such a bi-categorical approach in turn allows one to analyse a variety of so-called IQ dimensions, such as accuracy, completeness, consistency, and timeliness. I shall show that the bi-categorical approach lends itself to simple visualisations in terms of radar charts. (shrink)

The HRAF databases, eHRAF World Cultures and eHRAF Archaeology, each containing large corpora of curated text subject-indexed at the paragraph-level by anthropologists, were designed to facilitate rapid retrieval of information. The texts describe social and cultural life in past and present societies around the world. As of the spring of 2018, eHRAF contains almost three million indexed “paragraph” units from over 8000 documents describing over 400 societies and archaeological traditions. This chapter first discusses concrete problems of scale resulting from large (...) numbers of complex elements retrieved by any given search. Second, we discuss potential and partial solutions that resolve these problems to advance research, whether based on specific hypotheses, classification or identifying and evaluating embedded patterns of relationships. Third, we discuss new kinds of research possibilities that can be further advanced, have not yet been successfully attempted, or have not even been considered using anthropological data because of scale and complexity of achieving a result. (shrink)

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. (...) Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data’s analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today’s level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive. (shrink)

Museums and archives rely on databases and similar technologies to manage their collections, but even when tailor-made for memory institutions, databases require considerable adaptation to remain usable over long periods of time. To better understand how collection staff maintain and migrate databases over multiple years and decades, we talked to archivists from the US-based Archon User Collaborative and collection managers from the University of Michigan Research Museums. We found that the collection staff uses terms taken from quilting for database (...) class='Hi'>curation: they “tie” and “weave” a “patchwork of data systems” together. We extend their quilting metaphor as an analytical lens and show what can be gained through a shift in framing database work as a craft. We describe database curation as a process of creating a quilted infrastructure: a long-lived knowledge system that is sustained by the use of multiple “digital surfaces,” a reliance on a community of practice, intergenerational transfer of “quilts,” and by leveraging invisibility to conduct work. We argue that this nonnormative mode of computing needs better support from both software developers and administrators. We also show that although the invisibility of craft practices offers practitioners independence, it also can increase their precarity. (shrink)

Museums and archives rely on databases and similar technologies to manage their collections, but even when tailor-made for memory institutions, databases require considerable adaptation to remain usable over long periods of time. To better understand how collection staff maintain and migrate databases over multiple years and decades, we talked to archivists from the US-based Archon User Collaborative and collection managers from the University of Michigan Research Museums. We found that the collection staff uses terms taken from quilting for database (...) class='Hi'>curation: they “tie” and “weave” a “patchwork of data systems” together. We extend their quilting metaphor as an analytical lens and show what can be gained through a shift in framing database work as a craft. We describe database curation as a process of creating a quilted infrastructure: a long-lived knowledge system that is sustained by the use of multiple “digital surfaces,” a reliance on a community of practice, intergenerational transfer of “quilts,” and by leveraging invisibility to conduct work. We argue that this nonnormative mode of computing needs better support from both software developers and administrators. We also show that although the invisibility of craft practices offers practitioners independence, it also can increase their precarity. (shrink)

This book presents cutting edge research on the new ethical challenges posed by biomedical Big Data technologies and practices. 'Biomedical Big Data' refers to the analysis of aggregated, very large datasets to improve medical knowledge and clinical care. The book describes the ethical problems posed by aggregation of biomedical datasets and re-use/re-purposing of data, in areas such as privacy, consent, professionalism, power relationships, and ethical governance of Big Data platforms. Approaches and methods are discussed that can (...) be used to address these problems to achieve the appropriate balance between the social goods of biomedical Big Data research and the safety and privacy of individuals. Seventeen original contributions analyse the ethical, social and related policy implications of the analysis and curation of biomedical Big Data, written by leading experts in the areas of biomedical research, medical and technology ethics, privacy, governance and data protection. The book advances our understanding of the ethical conundrums posed by biomedical Big Data, and shows how practitioners and policy-makers can address these issues going forward. (shrink)

This article investigates dilemmas in the archiving and sharing of qualitative data in educational research, critically engaging with practices and debates from across the social sciences. Ethical, epistemological and methodological challenges are examined in reference to open access agendas, the politics of knowledge production, and transformations in research practices in the era of data management. We first consider practical and interpretive decisions in archiving qualitative data, then map current policy and regulatory frameworks governing research data management, (...) taking Australia as a case-study. We argue that governance and protocols for data sharing have not attended sufficiently to the distinctive ethical and methodological dimensions and knowledge claims of qualitative research. Instead, approaches associated with quantitative data are extrapolated in ways which construct an imaginary of decontextualised data, abstracted from the conditions of its production. We further argue for more critical attention to the double-edged affordances and ambivalent effects of data sharing and openness and to how data archives are imagined, constructed and curated. This includes greater acknowledgement of the affective and temporal dynamics involved in data archiving, understanding them as practices of (re)invention that also curate ‘archives for the future’ and help to foster an historicising imaginary in educational research. (shrink)

Biobanking involves the assembling, curating, and distributing of samples and data. While relations between samples and data are often taken as defining properties of biobanking, several studies have pointed to the challenges in relating them in practice. This article investigates how samples and data are curated, connected, and made mobile in practice. Building on an analysis of data collected at five hospital-based biobanks in Austria, the article describes and compares biobanking in three types of biobank collections: (...) ‘departmental collections’, ‘project-specific collections’ and ‘hospital-wide collections’. It draws attention to the invisible work going into this infrastructure and highlights the central role of visions to make samples and data travel to a different location and thus support biomedical research. It shows that while visions of future travels are often epistemologically uncertain, they are informed by social ties and relationships between the collectives involved in the curation of samples and data on the one hand and the imagined users on the other. Finally, we point to the importance that policy actors in this domain consider the aspects we identified—and, in particular, reflect the temporalities inherent in such a research infrastructure. (shrink)

Most scientists recognize the importance of sharing data online in an open fashion. Nonetheless, many studies have documented the concerns that accompany data sharing activities, including loss of credit or IP, misuse and the time needed to curate interoperable data. To this end, discussions around data sharing often identify incentives that could potentially ameliorate these disincentivising concerns. Nonetheless, current Open Data discussions often rely on evidence‐based studies to identify the disincentives to overcome. This results in (...) highly specific and directed interventions. In contrast, this paper offers a different interpretation of these concerns. To do so, it makes use of the Thomas Theorem which suggests that: “If men define situations as real, they are real in their consequences”. Using empirical evidence from sub‐Saharan African (bio)chemistry laboratories, this paper illustrates how individual perceptions of research environments – whether associated with evidence or not – are highly influential in shaping data sharing practices. It concludes with the suggestion that discussion on incentivising data sharing amongst scientific communities need to take a broader set of concerns into account and offer a more creative approach to ameliorating environmental disincentives. (shrink)

The Emotion Ontology is an ontology covering all aspects of emotional and affective mental functioning. It is being developed following the principles of the OBO Foundry and Ontological Realism. This means that in compiling the ontology, we emphasize the importance of the nature of the entities in reality that the ontology is describing. One of the ways in which realism-based ontologies are being successfully used within biomedical science is in the annotation of scientific research results in publicly available databases. Such (...) annotation enables several objectives, including searching, browsing and cross-database data integration. A key benefit conferred by realismbased ontology is that suitably annotated research results are able to be aggregated and compared in a fashion that is based on the underlying reality that the science is studying. This has the potential of increasing the power of statistical analysis and meta-analysis in data-driven science. This aspect has been fruitfully exploited in the investigation of the functions of genes in molecular biology. Cognitive neuroscience uses functional neuroimaging to investigate the brain correlates of areas of mental functioning such as memory, planning and emotion. The use of functional neuroimaging to study affective phenomena such as the emotions is called ‘affective neuroscience’. BrainMap is the largest curated database of coordinates and metadata for studies in cognitive neuroscience, including affective neuroscience (Laird et al., 2005). BrainMap data is already classified and indexed using a terminology for classification, called the ‘Cognitive Paradigm Ontology’ (CogPO), that has been developed to facilitate searching and browsing. However, CogPO has been developed specifically for the BrainMap database, and the data are thus far not annotated to a realism-based ontology which would allow the discovery of interrelationships between research results across different databases on the basis of what the research is about. In this contribution, we describe ongoing work that aims to annotate affective neuroscience data, starting with the BrainMap database, using the Emotion Ontology. We describe our objectives and technical approach to the annotation, and mention some of the challenges. (shrink)

The transboundary nature of data flows necessitates a nuanced understanding of core legal concepts across jurisdictions. While terms like “privacy” and “data protection” are often used interchangeably in global discourse, this study posits that they embody distinct, jurisdictionally specific semantic and conceptual structures. Moving beyond traditional doctrinal comparison, this research employs a novel computational linguistics and corpus semantics framework to analyze a curated digital corpus comprising the European Union’s General Data Protection Regulation (GDPR), the California Consumer Privacy (...) Act (CCPA), and China’s Personal Information Protection Law (PIPL). Through keyword co-occurrence network analysis and conceptual mapping, we quantitatively delineate the semantic fields and conceptual networks surrounding core terms. Our findings reveal a fundamental schism: the GDPR constructs a dense, rights-centric conceptual network around “personal data,” deeply intertwined with human dignity; the CCPA orbits around a market-oriented concept of “consumer” and “sale,” framing privacy as a commodifiable good; and the PIPL foregrounds “personal information” within a framework of state security and social management. This study demonstrates the power of data-driven methods to uncover the implicit architectural logics of legal systems, offering a more granular and empirically grounded basis for understanding legal interoperability and conflict in the digital age. (shrink)

Formal principles governing best practices in classification and definition have for too long been neglected in the construction of biomedical ontologies, in ways which have important negative consequences for data integration and ontology alignment. We argue that the use of such principles in ontology construction can serve as a valuable tool in error-detection and also in supporting reliable manual curation. We argue also that such principles are a prerequisite for the successful application of advanced data integration techniques (...) such as ontology-based multi-database querying, automated ontology alignment and ontology-based text-mining. These theses are illustrated by means of a case study of the Gene Ontology, a project of increasing importance within the field of biomedical data integration. (shrink)

Debate about the epistemic prowess of historical science has focused on local underdetermination problems generated by a lack of historical data; the prevalence of information loss over geological time, and the capacities of scientists to mitigate it. Drawing on Leonelli’s recent distinction between ‘phenomena-time’ and ‘data-time’ I argue that factors like data generation, curation and management significantly complexifies and undermines this: underdetermination is a bad way of framing the challenges historical scientists face. In doing so, I (...) identify circumstances of epistemic scarcity where underdetermination problems are particularly salient, and discuss cases where legacy data—data generated using differing technologies and systems of practice—are drawn upon to overcome underdetermination. This suggests that one source of overcoming underdetermination is our knowledge of science’s past. Further, data-time makes agnostic positions about the epistemic fortunes of scientists working under epistemic scarcity more plausible. But agnosticism seems to leave philosophers without much normative grip. So, I sketch an alternative approach: focusing on the strategies scientists adopt to maximize their epistemic power in light of the resources available to them. (shrink)

Biological ontologies are used to organize, curate, and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies Foundry was created to address this by facilitating the development, harmonization, application, and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that (...) the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data FAIR. Competing Interest StatementThe authors have declared no competing interest. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social (...) epistemology of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

Recent studies highlight the need for ethical and equitable digital health research that protects the rights and interests of racialized communities. We argue for practices in digital health that promote data self-determination for these communities, especially in data collection and management. We suggest that researchers partner with racialized communities to curate data that reflects their wellness understandings and health priorities, and respects their consent over data use for policy and other outcomes. These data governance approach (...) honors and builds on Indigenous Data Sovereignty (IDS) decolonial scholarship by Indigenous and non-indigenous researchers and its adaptations to health research involving racialized communities from former European colonies in the global South. We discuss strategies to practice equity, diversity, inclusion, accessibility and decolonization (EDIAD) principles in digital health. We draw upon and adapt the concept of Precision Health Equity (PHE) to emphasize models of data sharing that are co-defined by racialized communities and researchers, and stress their shared governance and stewardship of data that is generated from digital health research. This paper contributes to an emerging research on equity issues in digital health and reducing health, institutional, and technological disparities. It also promotes the self-determination of racialized peoples through ethical data management. (shrink)

The Protein Ontology provides terms for and supports annotation of species-specific protein complexes in an ontology framework that relates them both to their components and to species-independent families of complexes. Comprehensive curation of experimentally known forms and annotations thereof is expected to expose discrepancies, differences, and gaps in our knowledge. We have annotated the early events of innate immune signaling mediated by Toll-Like Receptor 3 and 4 complexes in human, mouse, and chicken. The resulting ontology and annotation data (...) set has allowed us to identify species-specific gaps in experimental data and possible functional differences between species, and to employ inferred structural and functional relationships to suggest plausible resolutions of these discrepancies and gaps. (shrink)

We project the future accumulation of profiles belonging to deceased Facebook users. Our analysis suggests that a minimum of 1.4 billion users will pass away before 2100 if Facebook ceases to attract new users as of 2018. If the network continues expanding at current rates, however, this number will exceed 4.9 billion. In both cases, a majority of the profiles will belong to non-Western users. In discussing our findings, we draw on the emerging scholarship on digital preservation and stress the (...) challenges arising from curating the profiles of the deceased. We argue that an exclusively commercial approach to data preservation poses important ethical and political risks that demand urgent consideration. We call for a scalable, sustainable, and dignified curation model that incorporates the interests of multiple stakeholders. (shrink)

Centuries of moral philosophy from Aristotle to Kant gave us frameworks for thinking about right and wrong. Decades of AI safety research gave us RLHF, Constitutional AI, and adversarial training. None of them verify whether reasoning is coherent. Alignment faking has been observed in frontier models (e.g., 14% compliance with harmful queries when the model believes it is in training, and alignment-faking reasoning increasing to 78% after reinforcement learning in one setting) [8]. Reward hacking attempts appear in recent model evaluations (...) (on the order of 1% to 2% of task attempts in one report) [10]. Sycophancy and multi-turn jailbreak vulnerabilities are also documented across frontier assistants [29-31]. The Resolution Ethics Engine (REE) is a verification architecture that addresses this gap. It parses scenarios into structural coordinates using Reality Interaction Field (RIF) primitives (WHO, WHAT, WHERE, WHEN, WHY, HOW) with the Attachment Principle (AP) identifying ethical weight, evaluates candidate actions against three dependency-ordered domains (Protection > Trust > Free-Agency), and generates auditable verdicts via cross-domain integrity checks. Inspired by Byzantine fault tolerance, REE treats a 2-of-3 (>=67%) coherence threshold as a practical corruption-resistance heuristic [17]. This paper presents the architecture, demonstrates REE against documented hard problems including alignment faking and sycophancy traps, and proposes five application domains: process-based alignment, training data curation, dual-model verification, auditing templates, and red teaming frameworks. REE does not replace moral judgment. It makes ethical reasoning externally verifiable. Keywords: AI alignment, Machine ethics, verification architecture, ethical reasoning, AI ethics. (shrink)

In recent years, sequencing technologies have enabled the identification of a wide range of non-coding RNAs (ncRNAs). Unfortunately, annotation and integration of ncRNA data has lagged behind their identification. Given the large quantity of information being obtained in this area, there emerges an urgent need to integrate what is being discovered by a broad range of relevant communities. To this end, the Non-Coding RNA Ontology (NCRO) is being developed to provide a systematically structured and precisely defined controlled vocabulary for (...) the domain of ncRNAs, thereby facilitating the discovery, curation, analysis, exchange, and reasoning of data about structures of ncRNAs, their molecular and cellular functions, and their impacts upon phenotypes. The goal of NCRO is to serve as a common resource for annotations of diverse research in a way that will significantly enhance integrative and comparative analysis of the myriad resources currently housed in disparate sources. It is our belief that the NCRO ontology can perform an important role in the comprehensive unification of ncRNA biology and, indeed, fill a critical gap in both the Open Biological and Biomedical Ontologies (OBO) Library and the National Center for Biomedical Ontology (NCBO) BioPortal. Our initial focus is on the ontological representation of small regulatory ncRNAs, which we see as the first step in providing a resource for the annotation of data about all forms of ncRNAs. (shrink)

Ontologies are being used increasingly to promote the reusability of scientific information by allowing heterogeneous data to be integrated under a common, normalized representation. Definitions play a central role in the use of ontologies both by humans and by computers. Textual definitions allow ontologists and data curators to understand the intended meaning of ontology terms and to use these terms in a consistent fashion across contexts. Logical definitions allow machines to check the integrity of ontologies and reason over (...) data annotated with ontology terms to make inferences that promote knowledge discovery. Therefore, it is important not only to include in ontologies multiple types of definitions in both formal and in natural languages, but also to ensure that these definitions meet good quality standards so they are useful. While tools such as Protégé can assist in creating well-formed logical definitions, producing good definitions in a natural language is still to a large extent a matter of human ingenuity supported at best by just a small number of general principles. For lack of more precise guidelines, definition authors are often left to their own personal devices. This paper aims to fill this gap by providing the ontology community with a set of principles and conventions to assist in definition writing, editing, and validation, by drawing on existing definition writing principles and guidelines in lexicography, terminology, and logic. (shrink)

The proliferation of environmentally oriented programs within the tech industry, and the industry's coinciding efforts toward data and technology democratization, generate concerns about the status of environmental data within digital economy. While the accumulation of digital personal data has been a cornerstone of domination of the data analytics industry, many believe environmental data to be a source of “untapped potential.” The potential of environmental data, the argument goes, would benefit equally the digital economy, environmental (...) sciences, and academic data and artificial intelligence experts. This article analyzes the proliferation of the rhetoric about open environmental data by focusing on Microsoft's Planetary Computer cloud computing program and computer vision experts who curate and use biodiversity data stored on Microsoft's servers. Through an analytical framework of sociotechnical imaginaries, the article draws connections between visions of future for environmental knowledge production and governance promoted by Microsoft and the work of computer vision experts intending to benefit from the potential of environmental data as machine learning training sets while at the same time helping environmental sciences. Although environmental data on the Planetary Computer is democratized, it nonetheless becomes a valued asset to data economy, but often with unintended consequences, such as enabling citizen science biodiversity data to be used by state surveillance apparatus. The article challenges the view that data's democratization is unproblematically serving environmental sciences by examining the consequences of imaginaries of democratization emerging from the data industry leaders and processes of nonmonetary valuation of environmental data by experts who curate these datasets. (shrink)