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)

Leonelli (2016) suggested a relational view of data against a representational view by emphasizing data-centric biology rather than the theory-centric tradition in the philosophy of science. This is because the first view allows for data journeys across laboratories using public database resources, whereas the second does not. This paper examines Leonelli’s strategies to defend the relational view of data. Contrary to Leonelli’s intention, it indicates that her strategies led to unnecessary misunderstandings of the (...) relationships among data, representation, and evidential values. It will be argued that evidential values of data are inevitably based on a representational feature and that it is better to reconcile both views of data as complementary rather than contrasting. (shrink)

This paper addresses a relatively new mode of ecological research: data synthesis studies. Data synthesis studies involve reusing data to create a general model as well as a reusable, aggregated dataset. Using a case from movement ecology, I analyse the trade-offs and strategies involved in data synthesis. Like theoretical ecological modelling, I find that synthesis studies involve a modelling trade-off between generality, precision and realism; they deal with this trade-off by adopting a pragmatic kludging strategy. I (...) also identify an additional trade-off, the synthesis trade-off, between making data easy to synthesise for a particular project, on the one hand, and facilitating data reuse for other projects, on the other. In response to this synthesis trade-off, researchers create flexible datasets that are relatively easy to use for particular projects and can be adjusted to suit some other purposes. The flexibility compromise is also found in broader open data efforts, making it a significant element in the future of data-intensive ecology. (shrink)

This paper presents an ethical framework designed to support the development of critical data literacy for research methods courses and data training programmes in higher education. The framework we present draws upon our reviews of literature, course syllabi and existing frameworks on data ethics. For this research we reviewed 250 research methods syllabi from across the disciplines, as well as 80 syllabi from data science programmes to understand how or if data ethics was taught. (...) We also reviewed 12 data ethics frameworks drawn from different sectors. Finally, we reviewed an extensive and diverse body of literature about data practices, research ethics, data ethics and critical data literacy, in order to develop a transversal model that can be adopted across higher education. To promote and support ethical approaches to the collection and use of data, ethics training must go beyond securing informed consent to enable a critical understanding of the techno-centric environment and the intersecting hierarchies of power embedded in technology and data. By fostering ethics as a method, educators can enable research that protects vulnerable groups and empower communities. (shrink)

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish (...) the principled foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems. (shrink)

This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like "basic" vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation that (...) take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition. (shrink)

Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, (...) and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online. (shrink)

Nowadays, protecting trust in social sciences also means engaging in open community dialogue, which helps to safeguard robustness and improve efficiency of research methods. The combination of open data, open review and open dialogue may sound simple but implementation in the real world will not be straightforward. However, in view of Begley and Ellis’s (2012) statement that, “the scientific process demands the highest standards of quality, ethics and rigour,” they are worth implementing. More importantly, they are feasible to work (...) on and likely will help to restore plausibility to social sciences research. Therefore, I feel it likely that the triplet of open data, open review and open dialogue will gradually emerge to become policy requirements regardless of the research funding source. (shrink)

A growing awareness of the lack of reproducibility has undermined society’s trust and esteem in social sciences. In some cases, well-known results have been fabricated or the underlying data have turned out to have weak technical foundations.

Biodiversity science is in a pivotal period when diverse groups of actors—including researchers, businesses, national governments, and Indigenous Peoples—are negotiating wide-ranging norms for governing and managing biodiversity data in digital repositories. These repositories, often called biodiversity data portals, are a type of organization for which governance can address or perpetuate the colonial history of biodiversity science and current inequities. Researchers and Indigenous Peoples are developing and implementing new strategies to examine and change assumptions about which agents (...) should count as salient participants in scientific projects, especially in projects that build and manage large digital data portals. Two notable efforts are the FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority, Responsibility, Ethics) Principles for scientific data management and governance. To characterize how these principles influence the governance of biodiversity data portals, we develop an account of fit-for-use data that makes explicit its social as well as technical conditions in relation to agents and purposes. The FAIR Principles, already widely adopted by biodiversity researchers, prioritize machine agents and efficient computation, while the CARE Principles prioritize Indigenous Peoples and their data sovereignty. Both illustrate the potency of an emerging general strategy by which groups of actors craft and implement governance principles for data fitness-of-use to change assumptions about who are salient participants in data science. (shrink)

The term “Complex Systems Biology” was introduced a few years ago [Kaneko, 2006] and, although not yet of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science. Although broad generalizations are always dangerous, it is safe to state that mainstream biology has been largely dominated by a gene-centric view in the last decades, due to the success of molecular biology. So the one gene - one trait (...) approch, which has often proved to be effective, has been extended to cover even complex traits. This simplifying view has been appropriately criticized, and the movement called systems biology has taken off. Systems biology [Noble, 2006] emphasizes the presence of several feedback loops in biological systems, which severely limit the range of validity of explanations based upon linear causal chains (e.g. gene → behaviour). Mathematical modelling is one the favourite tools of systems biologists to analyze the possible effects of competing negative and positive feedback loops which can be observed at several levels (from molecules to organelles, cells, tissues, organs, organisms, ecosystems). Systems biology is by now a well-established field, as it can be inferred by the rapid growth in number of conferences and journals devoted to it, as well as by the existence of several grants and funded projects.Systems biology is mainly focused upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. We know indeed that there are some principles of this kind, biological evolution being the most famous one. The theory of cellular organization also qualifies as a general principle. But the main focus of biological research has been that of studying specific cases, with some reluctance to accept (and perhaps a limited interest for) broad generalizations. This may however change, and this is indeed the challenge of complex systems biology: looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviours in various kinds of systems. The hope to find such general principles appears well founded, and I will show in Section 2 that there are indeed data which provide support to this claim. Besides data, there are also general ideas and models concerning the way in which biological systems work. The strategy, in this case, is that of introducing simplified models of biological organisms or processes, and to look for their generic properties: this term, borrowed from statistical physics, is used for those properties which are shared by a wide class of systems. In order to model these properties, the most effective approach has been so far that of using ensembles of systems, where each member can be different from another one, and to look for those properties which are widespread. This approach was introduced many years ago [Kauffman, 1971] in modelling gene regulatory networks. At that time one had very few information about the way in which the expression of a given gene affects that of other genes, apart from the fact that this influence is real and can be studied in few selected cases (like e.g. the lactose metabolism in E. coli). Today, after many years of triumphs of molecular biology, much more has been discovered, however the possibility of describing a complete map of gene-gene interactions in a moderately complex organism is still out of reach. Therefore the goal of fully describing a network of interacting genes in a real organism could not be (and still cannot be) achieved. But a different approach has proven very fruitful, that of asking what are the typical properties of such a set of interacting genes. Making some plausible hypotheses and introducing some simplifying assumptions, Kauffman was able to address some important problems. In particular, he drew attention to the fact that a dynamical system of interacting genes displays selforganizing properties which explain some key aspects of life, most notably the existence of a limited number of cellular types in every multicellular organism (these numbers are of the order of a few hundreds, while the number of theoretically possible types, absent interactions, would be much much larger than the number of protons in the universe). In section 3 I will describe the ensemble based approach in the context of gene regulatory networks, and I will show that it can describe some important experimental data. Finally, in section 4 I will discuss some methodological aspects. (shrink)

In recent years, the growing academic field called “Data Science” has made many promises. On closer inspection, relatively few of these promises have come to fruition. A critique of Data Science from the phenomenological tradition can take many forms. This paper addresses the promise of “participation” in Data Science, taking inspiration from Paul Majkut’s 2000 work in Glimpse, “Empathy’s Impostor: Interactivity and Intersubjectivity,” and some insights from Heidegger’s "The Question Concerning Technology." The description of (...) Data Science provided in the scholarly literature includes “the study of the generalizable extraction of knowledge from data” (Dhar 2013, 64), “data stewardship and data sharing…access to data at higher volumes and more quickly, and the potential for replication and augmentation of existing research” (Hartter et al., 2013, 1), and “personal information, health status, daily activities and shopping preferences that are recorded and used to give us instant feedback and recommendations based on previous online behavior.” (Shin 2013) United States universities have begun to offer graduate programs in “data science”, anticipating the growth of this field for marketing, national security, and health industries. These universities include New York University, Columbia University, Stanford, Northwestern, and Syracuse. (shrink)

The Galilean explanatory style is characterized by the search for the underlying structure of phenomena, the positing of "deep" explanatory principles, and a view of the relation between theory and data, on which the search for "crucial data" is of primary importance. In this paper, I trace the dynamics of adopting the Galilean style, focusing on the science of episodic memory. I argue that memory systems, such as episodic and semantic memory, were posited as underlying competences producing (...) the observable phenomena of memory. Considered in idealized isolation from other systems, episodic memory was taken to underlay the ability of individuals to remember events from their personal past. Yet, in reality, memory systems regularly interact, standing in many-to-many relations to actual memory tasks and experiences. Upon this backdrop, I explore a puzzle about the increasing prominence of the notion of autonoetic consciousness in Tulving's theory of episodic memory. I argue that, contrary to widespread belief, the prominence is not best explained by the purported essential link between autonoetic consciousness and episodic memory. Rather, it is explained by the fact that autonoetic consciousness, hypothesized to uniquely accompany episodic retrieval, was considered a source of crucial data, predictable only from theories positing a functionally distinct episodic memory system. However, with the emergence of a new generation of theories, positing wider memory systems for remembering and imagination, the question of the relation between episodic memory and autonoetic consciousness has been reopened. This creates a pressing need for de-idealization, triggering a new search for crucial data. (shrink)

Open science will make science more efficient, reliable, and responsive to societal challenges. The European Commission has sought to advance open science policy from its inception in a holistic and integrated way, covering all aspects of the research cycle from scientific discovery and review to sharing knowledge, publishing, and outreach. We present the steps taken with a forward-looking perspective on the challenges laying ahead, in particular the necessary change of the rewards and incentives system for researchers (for (...) which various actors are co-responsible and which goes beyond the mandate of the European Commission). Finally, we discuss the role of artificial intelligence (AI) within an open science perspective. (shrink)

Research, innovation, and progress in the life sciences are increasingly contingent on access to large quantities of data. This is one of the key premises behind the “open science” movement and the global calls for fostering the sharing of personal data, datasets, and research results. This paper reports on the outcomes of discussions by the panel “Open science, data sharing and solidarity: who benefits?” held at the 2021 Biennial conference of the International Society for the (...) History, Philosophy, and Social Studies of Biology, and hosted by Cold Spring Harbor Laboratory. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes (...) of data, resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

Many philosophers are skeptical about the scientific value of the concept of biological information. However, several have recently proposed a more positive view of ascribing information as an exercise in scientific modeling. I argue for an alternative role: guiding empirical data collection for the sake of theorizing about the evolution of semantics. I clarify and expand on Bergstrom and Rosvall’s suggestion of taking a “diagnostic” approach that defines biological information operationally as a procedure for collecting empirical cases. The more (...) recent modeling-based accounts still perpetuate a theory-centric view of scientific concepts, which motivated philosophers’ misplaced skepticism in the first place. (shrink)

The modern abundance and prominence of data has led to the development of “data science” as a new field of enquiry, along with a body of epistemological reflections upon its foundations, methods, and consequences. This article provides a systematic analysis and critical review of significant open problems and debates in the epistemology of data science. We propose a partition of the epistemology of data science into the following five domains: (i) the constitution of (...) data science; (ii) the kind of enquiry that it identifies; (iii) the kinds of knowledge that data science generates; (iv) the nature and epistemological significance of “black box” problems; and (v) the relationship between data science and the philosophy of science more generally. (shrink)

It has been argued that ethical frameworks for data science often fail to foster ethical behavior, and they can be difficult to implement due to their vague and ambiguous nature. In order to overcome these limitations of current ethical frameworks, we propose to integrate the analysis of the connections between technical choices and sociocultural factors into the data science process, and show how these connections have consequences for what data subjects can do, accomplish, and be. (...) Using healthcare as an example, attention to sociocultural conversion factors relevant to health can help in navigating technical choices that require broader considerations of the sociotechnical system, such as metric tradeoffs in model validation, resulting in better ethical and technical choices. This approach promotes awareness of the ethical dimension of technical choices by data scientists and others, and that can foster the cultivation of 'ethical skills' as integral to data science. (shrink)

AI-powered records science is revolutionizing the way facts are analyzed and understood. It can significantly improve the exceptional of information evaluation and boost its speed. AI-powered facts technological know-how enables access to more extensive, extra complicated information sets, faster insights, faster trouble solving, and higher choice making. Using the use of AI-powered information technological know-how techniques and tools, organizations can provide more accurate outcomes with shorter times to choices. AI-powered facts technology also offers more correct predictions of activities and (...) developments and superior accuracy in sample reputation.Furthermore, AI-powered facts science techniques can help agencies discover anomalies in datasets, learn new patterns, and better understand the points. This paper discusses the advantages of AI-powered statistics science and how it may enhance data analysis. It additionally discusses a number of the challenges going through organizations in imposing AI-powered records science projects and gives excellent practices for deploying and coping with records technology projects. (shrink)

In this paper, we analyse the relation between the use of environmental data in contemporary health sciences and related conceptualisations and operationalisations of the notion of environment. We consider three case studies that exemplify a different selection of environmental data and mode of data integration in data-intensive epidemiology. We argue that the diversification of data sources, their increase in scale and scope, and the application of novel analytic tools have brought about three significant conceptual shifts. (...) First, we discuss the EXPOsOMICS project, an attempt to integrate genomic and environmental data which suggests a reframing of the boundaries between external and internal environments. Second, we explore the MEDMI platform, whose efforts to combine health, environmental and climate data instantiate a reframing and expansion of environmental exposure. Third, we illustrate how extracting epidemiological insights from extensive social data collected by the CIDACS institute yields innovative attributions of causal power to environmental factors. Identifying these shifts highlights the benefits and opportunities of new environmental data, as well as the challenges that such tools bring to understanding and fostering health. It also emphasises the constraints that data selection and accessibility pose to scientific imagination, including how researchers frame key concepts in health-related research. (shrink)

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using various formats. It (...) is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute …. (shrink)

In Husserl’s Missing Technologies, Don Ihde urges us to think deeply and critically about the ways in which the technologies utilized in contemporary science structure the way we perceive and understand the natural world. In this paper, I argue that we ought to extend Ihde’s analysis to consider how such technologies are changing the way we perceive and understand ourselves too. For it is not only the natural or “hard” sciences which are turning to advanced technologies for help in (...) carrying out their work, but also the social and “human” sciences. One set of tools in particular is rapidly being adopted—the family of information technologies that fall under the umbrella of “big data.” As in the natural sciences, big data is giving researchers in the human sciences access to phenomena which they would otherwise be unable to experience and investigate. And like the former, the latter thereby shape the ways those scientists perceive and understand who and what we are. Looking at two case studies of big data-driven research in the human sciences, I begin in this paper to suggest how we might understand these phenomenological and hermeneutic changes. (shrink)

Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal (...) knowledge is necessary for the project, both as a source for handling complexity and as an output for meeting the project’s goals. Consequently, I argue that data-driven claims about causality are fundamentally flawed and causal knowledge should be considered a necessary aspect of Big Data science. In addition, I present the consequences of this result on other data-driven claims, concerning the role of theoretical considerations. I argue that the importance of causal knowledge and other kinds of theoretical engagement in EXPOsOMICS undermine theory-free accounts and suggest alternative ways of framing science based on Big Data. (shrink)

Since Circular 34 from the Ministry of Science and Technology of Vietnam required the head of the national project to have project results published in ISI/Scopus journals in 2014, the field of economics has been dominating the number of nationally-funded projects in social sciences and humanities. However, there has been no scientometric study that focuses on the difference in productivity among fields in Vietnam. Thus, harnessing the power of the SSHPA database, a comprehensive dataset of 1,564 Vietnamese authors (854 (...) males, 705 females) with 2,410 publications in the 2008 – 2018 period was extracted and analyzed. Various factors were considered including age, gender, new authors, leading authors, co-authorship, and Impact Factor. The findings suggest a high level of contribution from authors at the age of 40 – 44 in economics (858 publications) in a 12-years period, which is equivalent to the social medicine total output, and two times more than the total output of the education. Moreover, the presence and reinforcement of male researchers are still dominating in Economics and other fields, with the only exception of education. Despite the rapid rise in the number of Vietnamese lead authors, gender disparity among disciplines is an issue. Contrary to the strong international collaboration-oriented tendency in social medicine, economics, and other fields, educational authors are not open to international collaborating. Finally, most of the publications in economics belong to the group with JIF from 0 to 2, in contrast with the high number of social medicine publications with JIF from 2 to 5, which suggesting the field of economics is fulfilling the quantity, but still, need more quality publications. (shrink)

In this paper, I critically evaluate several related, provocative claims made by proponents of data-intensive science and “Big Data” which bear on scientific methodology, especially the claim that scientists will soon no longer have any use for familiar concepts like causation and explanation. After introducing the issue, in Section 2, I elaborate on the alleged changes to scientific method that feature prominently in discussions of Big Data. In Section 3, I argue that these methodological claims are (...) in tension with a prominent account of scientific method, often called “Inference to the Best Explanation”. Later on, in Section 3, I consider an argument against IBE that will be congenial to proponents of Big Data, namely, the argument due to Roche and Sober Analysis, 73:659–668, that “explanatoriness is evidentially irrelevant.” This argument is based on Bayesianism, one of the most prominent general accounts of theory-confirmation. In Section 4, I consider some extant responses to this argument, especially that of Climenhaga Philosophy of Science, 84:359–368,. In Section 5, I argue that Roche and Sober’s argument does not show that explanatory reasoning is dispensable. In Section 6, I argue that there is good reason to think explanatory reasoning will continue to prove indispensable in scientific practice. Drawing on Cicero’s oft-neglected De Divinatione, I formulate what I call the “Ciceronian Causal-nomological Requirement”, which states, roughly, that causal-nomological knowledge is essential for relying on correlations in predictive inference. I defend a version of the CCR by appealing to the challenge of “spurious correlations,” chance correlations which we should not rely upon for predictive inference. In Section 7, I offer some concluding remarks. (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)

Big Data has the potential to enable unprecedentedly rigorous quantitative modeling of complex human social relationships and social structures. When such models are extended to nonhuman domains, they can undermine anthropocentric assumptions about the extent to which these relationships and structures are specifically human. Discoveries of relevant commonalities with nonhumans may not make us less human, but they promise to challenge fundamental views of what it is to be human.

We critically engage two traditional views of scientific data and outline a novel philosophical view that we call the pragmatic-representational view of data. On the PR view, data are representations that are the product of a process of inquiry, and they should be evaluated in terms of their adequacy or fitness for particular purposes. Some important implications of the PR view for data assessment, related to misrepresentation, context-sensitivity, and complementary use, are highlighted. The PR view provides (...) insight into the common but little-discussed practices of iteratively reusing and repurposing data, which result in many datasets’ having a phylogeny—an origin and complex evolutionary history—that is relevant to their evaluation and future use. We relate these insights to the open-data and data-rescue movements, and highlight several future avenues of research that build on the PR view of data. (shrink)

ICTs, personal data, digital rights, the GDPR, data privacy, online security… these terms, and the concepts behind them, are increasingly common in our lives. Some of us may be familiar with them, but others are less aware of the growing role of ICTs and data in our lives - and the potential risks this creates. These risks are even more pronounced for vulnerable groups in society. People can be vulnerable in different, often overlapping, ways, which place them (...) at a disadvantage to the majority of citizens; Table 3 in this guide presents some of the many forms and causes of vulnerability. As a result, vulnerable people need greater support to navigate the digital world, and to ensure that they are able to exercise their rights. This guide explains where such support can be found, and also answers the following questions: - What are the main ethical and legal issues around ICTs for vulnerable citizens? - Who is vulnerable in Europe? - How do issues around ICTs affect vulnerable people in particular? This guide is a resource for members of vulnerable groups, people who work with vulnerable groups, and citizens more broadly. It is also useful for data controllers1 who collect data about vulnerable citizens. While focused on citizens in Europe, it may be of interest to people in other parts of the world. It forms part of the Citizens’ Information Pack produced by the PANELFIT project, and is available in English, French, German, Italian and Spanish. You are welcome to translate this guide into other languages. Please send us a link to online versions in other languages, so that we can add them to the project website. (shrink)

The issue of the definition and position of archaeology as a discipline is examined in relation to the dispute which took place from 1980 to 2009 between the archaeologist Jean-Claude Gardin and the sociologist Jean-Claude Passeron. This case study enables us to explore the actual conceptual relationships between archaeology and the other sciences (as opposed to those wished for or prescribed). The contrasts between the positions declared by the two researchers and the rooting of their arguments in their disciplines are (...) examined: where the sociologist makes use of his philosophical training, the archaeologist relies mainly on his work on semiology and informatics. Archaeology ultimately plays a minor role in the arguments proposed. This dispute therefore cannot be considered as evidence for the movement of concepts between archaeology and the social sciences. A blind spot in the debate, relating to the ontological specificities of archaeological objects, nevertheless presents itself as a possible way of implementing this movement. (shrink)

While the term “reactivity” has come to be associated with specific phenomena in the social sciences, having to do with subjects’ awareness of being studied, this paper takes a broader stance on this concept. I argue that reactivity is a ubiquitous feature of the psychological subject matter and that this fact is a precondition of experimental research, while also posing potential problems for the experimenter. The latter are connected to the worry about distorted data and experimental artifacts. But what (...) are experimental artifacts and what is the most productive way of dealing with them? In this paper, I approach these questions by exploring the ways in which experimenters in psychology simultaneously exploit and suppress the reactivity of their subject matter in order to produce experimental data that speak to the question or subject matter at hand. Highlighting the artificiality of experimental data, I raise the question of what distinguishes a genuine experimental result from an experimental artifact. My analysis construes experimental results as the outcomes of inferences from the data that take material background assumptions as auxiliary premises. Artifacts occur when one or more of these background assumptions are false, such that the data do not reliably serve the purposes they were generated for. I conclude by laying out the ways in which my analysis of data quality is relevant to, and informed by, recent debates about the replicability of experimental results. (shrink)

My belief that Socrates was wise, and your belief that Socrates was mortal can be said to have a common focus, insofar as both these thoughts are about Socrates. In Peter Geach’s terminology, the objects of our beliefs bear the feature of intentional identity, because our beliefs share the same putative target. But what if it turned out that Socrates never existed? Can a pair of thoughts share a common focus if the object both thoughts are about, does not actually, (...) really exist? Object-centric accounts of intentionality which explain the aboutness or directedness of thought in terms of the intentional object the thought in question is about, contend that thoughts which share a common focus do so in virtue of both thoughts simply being about the same intentional object. However, Alexander Sandgren contends that such theories face difficulties in explaining a puzzle of intentional identity put forward by Walter Edelberg, in which a pair of sentences seem to differ in truth value but are purportedly logically equivalent on the object-centric theory. If this is right, then it seems that any account which explains intentionality with reference to an intentional object is threatened by this result, whether this object be abstract, merely possible, Meinongian, or otherwise. In this paper, I argue that Edelberg’s Puzzle is analogous to Frege’s Puzzle and the same tools conventionally used to solve Frege’s Puzzle can be used to solve Edelberg’s Puzzle. I then propose a new object-centric solution to Edelberg’s Puzzle which takes into account modes of presentation and which is able to accommodate all the relevant linguistic data. (shrink)

Was there a concept of data before the so-called ‘data revolution’? This paper contributes to the history of the concept of data by investigating uses of the term ‘data’ in texts of the Royal Society's Philosophical Transactions for the period 1665–1886. It surveys how the notion enters the journal as a technical term in mathematics, and charts how over time it expands into various other scientific fields, including Earth sciences, physics and chemistry. The paper argues that (...) in these texts the notion of data is not used merely as a rhetorical category, and also cannot strictly be identified with the category of evidence. Instead, the notion comes with an associated epistemic structure, one that is in line with its development from an early mathematical use. (shrink)

Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality on (...) data scientists, or data engineer during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

Motivation: • Breaking barriers in publishing demands a proactive attitude • Open data, open review and open dialogue in making social sciences plausible .

Medicine is often thought of as a science of the body, but it is also a science of data. In some contexts, it can even be asserted that data drive health. This article focuses on a key piece of data technology central to contemporary practices of medicine: the medical record. By situating the medical record in the perspective of its history, we inquire into how the kinds of data that are kept at sites of (...) clinical encounter often depend on informational requirements that originate well outside of the clinic, in particular in health insurance records systems. Although this dependency of today's electronic medical records on billing requirements is widely lamented by clinical providers, its history remains little studied. Following the archaeology of medicine developed by Michel Foucault in The Birth of the Clinic and expanding his methodology in light of more recent contributions to the field of media archaeology, this article excavates some of the underexplored technological conditions that help constitute today's electronic medical record. If in some contexts, it is true that data drive health, then an archaeology of medical records helps reveal how health insurance records often impact clinical care and, by extension, health and disease. (shrink)

Despite an enormous philosophical literature on models in science, surprisingly little has been written about data models and how they are constructed. In this paper, I examine the case of how paleodiversity data models are constructed from the fossil data. In particular, I show how paleontologists are using various model-based techniques to correct the data. Drawing on this research, I argue for the following related theses: first, the ‘purity’ of a data model is not (...) a measure of its epistemic reliability. Instead it is the fidelity of the data that matters. Second, the fidelity of a data model in capturing the signal of interest is a matter of degree. Third, the fidelity of a data model can be improved ‘vicariously’, such as through the use of post hoc model-based correction techniques. And, fourth, data models, like theoretical models, should be assessed as adequate for particular purposes. (shrink)

This paper demonstrates the practical and philosophical strengths of adopting Luciano Floridi’s “general definition of information” (GDI) for use in the information sciences (IS). Many definitions of information have been proposed, but little work has been done to determine which definitions are most coherent or useful. Consequently, doubts have been cast on the necessity and possibility of finding a definition. In response to these doubts, the paper shows how items and events central to IS are adequately described by Floridi’s conception (...) of information, and demonstrates how it helps clarify the muddy theoretical framework resulting from the many previous definitions. To this end, it analyzes definitions, popular in IS, that conceive of information as energy, processes, knowledge, and physical objects. The paper finds that each of these definitions produces problematic or counterintuitive implications that the GDI suitably accounts for. It discusses the role of truth in IS, notes why the GDI is preferable to its truth-requiring variant, and ends with comments about the import of such a theory for IS research and practice. (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)

Model-data symbiosis is the view that there is an interdependent and mutually beneficial relationship between data and models, whereby models are not only data-laden, but data are also model-laden or model filtered. In this paper I elaborate and defend the second, more controversial, component of the symbiosis view. In particular, I construct a preliminary taxonomy of the different ways in which theoretical and simulation models are used in the production of data sets. These include (...) class='Hi'>data conversion, data correction, data interpolation, data scaling, data fusion, data assimilation, and synthetic data. Each is defined and briefly illustrated with an example from the geosciences. I argue that model-filtered data are typically more accurate and reliable than the so-called raw data, and hence beneficially serve the epistemic aims of science. By illuminating the methods by which raw data are turned into scientifically useful data sets, this taxonomy provides a foundation for developing a more adequate philosophy of data. (shrink)

Decision-making by a Data and Safety Monitoring Board (DSMB) regarding clinical trial conduct and termination is intricate and largely limited by cases and rules. Decision-making by legal jury is also intricate and largely constrained by cases and rules. In this paper, I argue by analogy that legal decision-making, which strives for a balance between competing demands of conservatism and innovation, supplies a good basis to the logic behind DSMB decision-making. Using the doctrine of precedents in legal reasoning as my (...) central analog will lead us to a decision-theoretic analogy for much more systematic documentation of decisions in clinical trials. My conclusion is twofold: every DSMB decision should articulate a clear general principle (a ratio decidendi) that gives reason for the decision; and all such decisions should be made public. I use Bartha’s (2010) theory of analogical arguments to both frame and assess my argument by analogy. (shrink)

Increasingly, in data-intensive areas of the life sciences, experimental results are being described in algorithmically useful ways with the help of ontologies. Such ontologies are authored and maintained by scientists to support the retrieval, integration and analysis of their data. The proposition to be defended here is that ontologies of this type – the Gene Ontology (GO) being the most conspicuous example – are a part of science. Initial evidence for the truth of this proposition (which some (...) will find self-evident) is the increasing recognition of the importance of empirically-based methods of evaluation to the ontology development work being undertaken in support of scientific research. Ontologies created by scientists must, of course, be associated with implementations satisfying the requirements of software engineering. But the ontologies are not themselves engineering artifacts, and to conceive them as such brings grievous consequences. Rather, ontologies such as the GO are in different respects comparable to scientific theories, to scientific databases, and to scientific journal publications. Such a view implies a new conception of what is involved in the authoring, maintenance and application of ontologies in scientific contexts, and therewith also a new approach to the evaluation of ontologies and to the training of ontologists. (shrink)

In this essay, we advocate that the issue of health data ethics should no longer be considered on the level of individual scientists or research labs, but rather as a problem involving all stakeholders, from publishers, funders, ethical committees to governments, for the sake of research integrity.

There is an ongoing methodological debate in philosophy of science concerning the use of case studies as evidence for and/or against theories about science. In this paper, I aim to make a contribution to this debate by taking an empirical approach. I present the results of a systematic survey of the PhilSci-Archive, which suggest that a sizeable proportion of papers in philosophy of science contain appeals to case studies, as indicated by the occurrence of the indicator words (...) “case study” and/or “case studies.” These results are confirmed by data mined from the JSTOR database on research articles published in leading journals in the field: Philosophy of Science, the British Journal for the Philosophy of Science (BJPS), and the Journal for General Philosophy of Science (JGPS), as well as the Proceedings of the Biennial Meeting of the Philosophy of Science Association (PSA). The data also show upward trends in appeals to case studies in articles published in Philosophy of Science, the BJPS, and the JGPS. The empirical work I have done for this paper provides philosophers of science who are wary of the use of case studies as evidence for and/or against theories about science with a way to do philosophy of science that is informed by data rather than case studies. (shrink)

Open science (OS) is considered the new paradigm for science and knowledge dissemination. OS fosters cooperative work and new ways of distributing knowledge by promoting effective data sharing (as early and broadly as possible) and a dynamic exchange of research outcomes, not only publications. On the other hand, intellectual property (IP) legislation seeks to balance the moral and economic rights of creators and inventors with the wider interests and needs of society. Managing knowledge outcomes in a new (...) open research and innovation ecosystem is challenging and should become part of the EU’s IP strategy, underpinning EU policies with the new open science–open innovation paradigm. (shrink)

DNA sequencers, Twitter, MRIs, Facebook, particle accelerators, Google Books, radio telescopes, Tumblr: what do these things have in common? According to the evangelists of “data science,” all of these are instruments for observing reality at unprecedentedly large scales and fine granularities. This perspective ignores the social reality of these very different technological systems, ignoring how they are made, how they work, and what they mean in favor of an exclusive focus on what they generate: Big Data. But (...) no data, big or small, can be interpreted without an understanding of the process that generated them. Statistical data science is applicable to systems that have been designed as scientific instruments, but is likely to lead to confusion when applied to systems that have not. In those cases, a historical inquiry is preferable. (shrink)

The knowledge-centric Theory of Mind research program suggested by Phillips et al. stands to gain significant value by embracing a neurocognitive approach that takes full advantage of techniques like fMRI and EEG. This neurocognitive approach has already begun providing important insights into the mechanisms of knowledge attribution, insights which support the claim that it is more basic than belief attribution.