This article argues that personal medical data should be made available for scientific research, by enabling and encouraging individuals to donate their medical records once deceased, similar to the way in which they can already donate organs or bodies. This research is part of a project on posthumous medical data donation developed by the Digital Ethics Lab at the Oxford Internet Institute at the University of Oxford. Ten arguments are provided to support the need to foster posthumous medical (...) data donation. Two major risks are also identified—harm to others, and lack of control over the use of data—which could follow from unregulated donation of medical data. The argument that record-based medical research should proceed without the need to secure informed consent is rejected, and instead a voluntary and participatory approach to using personal medical data should be followed. The analysis concludes by stressing the need to develop an ethical code for data donation to minimise the risks, and offers five foundational principles for ethical medical data donation suggested as a draft code. (shrink)

Fixing the problem won’t be easy, but humans’ sharpened focus on an emerging philosophy of data might give us some clue about where we will be heading for.

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)

Global poverty, hunger, and lack of access to save water raise problems of how to organize human society so that everyone's needs can be met. Philanthropic proposals, such as Peter Singer's and Peter Unger's, are based on a false analogy to duties of rescue and encourage philanthropic responses, thus closing the discourse to discussion of the causes and remedies of poverty. Radical criticism of capitalist social structures are put off the table, and this is a profound error.

Between 2006 and 2011, humanity accumulated 1,600 EB of data. As a result of this growth, there is now more data produced than available storage. This article explores the problem of “Big Data,” arguing for an epistemological approach as a possible solution to this ever-increasing challenge.

The potential to collect brain data more directly, with higher resolution, and in greater amounts has heightened worries about mental and brain privacy. In order to manage the risks to individuals posed by these privacy challenges, some have suggested codifying new privacy rights, including a right to “mental privacy.” In this paper, we consider these arguments and conclude that while neurotechnologies do raise significant privacy concerns, such concerns are—at least for now—no different from those raised by other well-understood (...) class='Hi'>data collection technologies, such as gene sequencing tools and online surveillance. To better understand the privacy stakes of brain data, we suggest the use of a conceptual framework from information ethics, Helen Nissenbaum’s “contextual integrity” theory. To illustrate the importance of context, we examine neurotechnologies and the information flows they produce in three familiar contexts—healthcare and medical research, criminal justice, and consumer marketing. We argue that by emphasizing what is distinct about brain privacy issues, rather than what they share with other data privacy concerns, risks weakening broader efforts to enact more robust privacy law and policy. (shrink)

Intergenerational transfers are a core feature of the practice of private philanthropy. A substantial portion of the resources committed to charitable causes comes from transfers (either during life or at death) that continue to pay out after death. Indeed, much of the power of the charitable foundation lies in its ability to extend the life of an enterprise beyond the mortal existence of its initiating agents. Despite their prevalence, whether and in what way the instruments of intergenerational philanthropy (...) can be justified is controversial. Many have argued that these instruments unfairly privilege the interests of the dead at the expense of the living and unborn. More recently, others have argued that intergenerational charitable transfers comport with the demands of distributive justice and are therefore legitimate. This paper contends that both of these perspectives fail to see the problem for what it is. Intergenerational charitable transfers may indeed promote justice in certain respects, but they do so at the cost of imposing the judgments of the dead onto the living. Respecting the wishes of the past conflicts with an interest in “generational sovereignty.” The paper concludes that properly accounting for this interest in generational sovereignty doesn’t require the abolition of intergenerational philanthropy. But it does tell in favor of a different regulatory orientation than most legal systems currently adopt. (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)

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)

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)

Data subject rights provide data controllers with obligations that can help with transparency, giving data subjects some control over their personal data. To date, a growing number of researchers have used these data subject rights as a methodology for data collection in research studies. No one, however, has gathered and analysed different academic research studies that use data subject rights as a methodology for data collection. To this end, we conducted a systematic (...) literature review that searched, compiled, and analysed 32 academic studies that use data subject rights as a data collection method. We find that the right of access is the most commonly-used data subject right by researchers, most studies are interested in measuring data subject rights compliance, and that a variety of difficulties exist in conducting research studies with data subject rights. We conclude that researchers should explore other data subject rights for alternative purposes, ease the process of exercising data subject rights, and improve the scalability of these studies. (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.

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)

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)

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)

Lexicon entry on Kant's Essay "On a Supposed Right to Lie from Philanthropy.".

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...) last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

Data is very important in any research experiment because it occupies a central place in making decisions based on findings resulting from the analysis of such data. Given its central role, it follows that such an important asset as data, deserve effective management in order to protect the integrity and provide an opportunity for effective problem-solving. The main thrust of this paper was to examine data management practices that should be adopted by scholars in maintaining the (...) quality of their research data. In achieving this, various concepts related to this paper were clarified. Various data management practices were also discussed beginning from data generation to data shredding. Based on the underlying observations from the light of the discussions made in this paper, it was recommended among others that: tertiary institutions in every part of the world should endeavour to establish a data management unit that will be saddled with the primary duty of formulating research data management policies, and the hosting of research data; every Journal should as a matter of compulsion, require the submission of data set corresponding to empirical papers submitted by authors and scholars. (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)

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)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

“Big Data” and data analytics affect all of us. Data collection, analysis, and use on a large scale is an important and growing part of commerce, governance, communication, law enforcement, security, finance, medicine, and research. And the theme of this symposium, “Individual and Informational Privacy in the Age of Big Data,” is expansive; we could have long and fruitful discussions about practices, laws, and concerns in any of these domains. But a big part of the audience (...) for this symposium is students and faculty in higher education institutions (HEIs), and the subject of this paper is data analytics in our own backyards. Higher education learning analytics (LA) is something that most of us involved in this symposium are familiar with. Students have encountered LA in their courses, in their interactions with their law school or with their undergraduate institutions, instructors use systems that collect information about their students, and administrators use information to help understand and steer their institutions. More importantly, though, data analytics in higher education is something that those of us participating in the symposium can actually control. Students can put pressure on administrators, and faculty often participate in university governance. Moreover, the systems in place in HEIs are more easily comprehensible to many of us because we work with them on a day-to-day basis. Students use systems as part of their course work, in their residences, in their libraries, and elsewhere. Faculty deploy course management systems (CMS) such as Desire2Learn, Moodle, Blackboard, and Canvas to structure their courses, and administrators use information gleaned from analytics systems to make operational decisions. If we (the participants in the symposium) indeed care about Individual and Informational Privacy in the Age of Big Data, the topic of this paper is a pretty good place to hone our thinking and put into practice our ideas. (shrink)

The theory of knowledge in early twentieth-century Anglo American philosophy was oriented toward phenomenally described cognition. There was a healthy respect for the mind-body problem, which meant that phenomena in both the mental and physical domains were taken seriously. Bertrand Russell's developing position on sense-data and momentary particulars drew upon, and ultimately became like, the neutral monism of Ernst Mach and William James. Due to a more recent behaviorist and physicalist inspired "fear of the mental", this development has been (...) down-played in historical work on early analytic philosophy. Such neglect assumes that the "linguistic turn" is a proper and permanent effect of twentieth-century philosophy, an assumption that distorts early analytic historiography, and begs a substantive philosophical question about thought and cognition. (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)

Big Data must be processed with advanced collection and analysis tools, based on predetermined algorithms, in order to obtain relevant information. Algorithms must also take into account invisible aspects for direct perceptions. Big Data issues is multi-layered. A distributed parallel architecture distributes data on multiple servers (parallel execution environments) thus dramatically improving data processing speeds. Big Data provides an infrastructure that allows for highlighting uncertainties, performance, and availability of components. DOI: 10.13140/RG.2.2.12784.00004 .

What is data? That question is the fundamental investigation of this dissertation. I have developed a methodology from social-scientific processes to explore how different people understand the concept of data, rather than to rely on my own philosophical intuitions or thought experiments about the “nature” of data. The evidence I have gathered as to different individuals' constructions of data can be used to inform further inquiry of data and the design of information systems. My research (...) demonstrates that people have different constructions of data. The methodology of the SDFN, created for this dissertation, has proven able to probe those understandings. The SDFN, loosely based on a DFD and combined with ideas from SNA, provides a way of discovering practical definitions of hard-to-operationalize terms like data. The process of repeatedly categorizing various items as data allows the methodology to explore how participants actually use the term, rather than relying on theoretical dictionary-based definitions. Analysis of the interviews found three different constructions of data: data as communications, a container for meaning; data as subjective observations, sense-impressions filtered by knowledge; and data as objective facts, measurements revealing the relationships of reality. (shrink)

The article distinguishes between charity and philanthropy and answers those who argue that monies spent for either are an inefficient deployment of monies for present consumption that could better be deployed by investing in the production of future wealth. It closes by arguing that philanthropists provide a key leadership role in the free-market economy. -/- The author owns the copyright, and there was no agreement, express or implied, not to use the publisher's PDF.

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

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene (...) on semantic information, and misinformation is not a tyoe of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates the important implications of the revised definition for the analysis of the deflationary theories of truth, the standard definition of knowledge and the classic, quantitative theory of semantic information. (shrink)

Since the release of OpenAI's ChatGPT, the world has entered a race to develop more capable and powerful AI, including artificial general intelligence (AGI). The development is constrained by the dependency of AI on the model, quality, and quantity of training data, making the AI training process highly costly in terms of resources and environmental consequences. Thus, improving the effectiveness and efficiency of the AI training process is essential, especially when the Earth is approaching the climate tipping points and (...) planetary boundaries. It is evident that AI can function better when trained to mimic certain human mental processes. Based on insights from quantum mechanics and the informational entropy-based notion of value, we suggest AI developers can enhance the AI training processes' effectiveness and efficiency by creating and implementing parameter systems that are capable of effectively assigning probabilities to informational quanta. Such systems can help reduce entropy within the system, which lowers the energy required for data storage and processing while decreasing the likelihood of information loss during training. Successfully applying the concept of information entropy-based value in AI development will be crucial for advancing generative AI and achieving AGI in a sustainable manner. (shrink)

This review delves into the influence of big data analytics on project management effectiveness and project success rates. By examining applications, accomplishments, hindrances, and emerging developments in the context of big data analytics and project management, this review provides insights into its transformative potential. Results indicate that big data analytics fosters improved project performance, more robust risk management, and heightened adaptability. However, challenges related to data quality, privacy, and project manager training remain to be addressed. This (...) review underscores the value of data-driven decision-making for both practitioners and researchers in the project management field. (shrink)

Since W. B. Gallie introduced the notion of essentially contested concepts (ECCs) in 1956, social science scholars have increasingly used his framework to analyze key concepts drawing “endless disputes” from contestant users. Despite its merits, the ECC framework has been limited by a neglect of social, cultural, and political contexts, the invisibility of actors, and its ahistorical character. To understand how ECCs evolve and change over time, I use a conceptual history approach to study the concept of philanthropy, recently (...) labeled as an ECC. Using France during classical modernity as a case study, I analyze key events and actors from the concept’s inception in 1712 as a virtue of the Enlightenment to its triumph after 1789 as a secular alternative to Catholic charity, until its decline at the end of the nineteenth century as a new consensus emerged around the concept of solidarity. By introducing the notion of historically contested concepts, I make several contributions to research on ECCs, conceptual contestation, and conceptual change. (shrink)

In the article charity as a factor in building a strong organizational culture is considered; examples of the charitable activities of the world’s leading companies, including health, tourism and hospitality, are demonstrated; the survey results of these industries employees understanding of the culture of philanthropy features are presented; the existence of a link between the characteristics and types of a strong organizational culture, on the one hand, and the charitable donations policy, on the other hand, is proved; recommendations for (...) creating a culture of philanthropy in a company are made. (shrink)

The word data (sing. datum) is originally Latin for “things given or granted”. Because of such a humble and generic meaning, the term enjoys considerable latitude both in its technical and in its common usage, for almost anything can be referred to as a “thing given or granted” (Cherry [1978]). With some reasonable approximation, four principal interpretations may be identified in the literature. The first three captures part of the nature of the concept and are discussed in the next (...) section. The fourth is the most fundamental and satisfactory, so it is discussed separately, in section three. On its basis, some further clarifications about the nature of data are introduced in sections four to six. A reminder about the social, legal and ethical issues raised by the use of data concludes this entry. (shrink)

Surveillance studies scholars and privacy scholars have each developed sophisticated, important critiques of the existing data-driven order. But too few scholars in either tradition have put forward alternative substantive conceptions of a good digital society. This, I argue, is a crucial omission. Unless we construct new “sociotechnical imaginaries,” new understandings of the goals and aspirations digital technologies should aim to achieve, the most surveillance studies and privacy scholars can hope to accomplish is a less unjust version of the technology (...) industry’s own vision for the future. (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)

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)

Ontological realism aims at the development of high quality ontologies that faithfully represent what is general in reality and to use these ontologies to render heterogeneous data collections comparable. To achieve this second goal for clinical research datasets presupposes not merely (1) that the requisite ontologies already exist, but also (2) that the datasets in question are faithful to reality in the dual sense that (a) they denote only particulars and relationships between particulars that do in fact exist and (...) (b) they do this in terms of the types and type-level relationships described in these ontologies. While much attention has been devoted to (1), work on (2), which is the topic of this paper, is comparatively rare. Using Referent Tracking as basis, we describe a technical data wrangling strategy which consists in creating for each dataset a template that, when applied to each particular record in the dataset, leads to the generation of a collection of Referent Tracking Tuples (RTT) built out of unique identifiers for the entities described by means of the data items in the record. The proposed strategy is based on (i) the distinction between data and what data are about, and (ii) the explicit descriptions of portions of reality which RTTs provide and which range not only over the particulars described by data items in a dataset, but also over these data items themselves. This last feature allows us to describe particulars that are only implicitly referred to by the dataset; to provide information about correspondences between data items in a dataset; and to assert which data items are unjustifiably or redundantly present in or absent from the dataset. The approach has been tested on a dataset collected from patients seeking treatment for orofacial pain at two German universities and made available for the NIDCR-funded OPMQoL project. (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)

Data security is, protecting data from ill- conceived get to, utilize, introduction, intrusion, change, examination, recording or destruction. Cloud computing is a sort of Internet-based computing that grants conjoint PC handling resources and information to PCs what's more, different gadgets according to necessity. It is a model that empowers universal, on-request access to a mutual pool of configurable computing resources. At present, security has been viewed as one of the best issues in the improvement of Cloud Computing. The (...) key issue in effective execution of Cloud Computing is to adequately deal with the security in the cloud applications. This paper talks about the part of cryptography in cloud computing to improve the data security. The expectation here is to get bits of knowledge another security approach with the usage of cryptography to secure information at cloud data centers. (shrink)

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)

Big Data ethics involves adherence to the concepts of right and wrong behavior regarding data, especially personal data. Big Data ethics focuses on structured or unstructured data collectors and disseminators. Big Data ethics is supported, at EU level, by extensive documentation, which seeks to find concrete solutions to maximize the value of Big Data without sacrificing fundamental human rights. The European Data Protection Supervisor (EDPS) supports the right to privacy and the right (...) to the protection of personal data in the respect of human dignity. DOI: 10.13140/RG.2.2.30867.43041. (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)

The ability to collect fine-grained energy data from smart meters has benefits for utilities and consumers. However, a proactive approach to data privacy is necessary to maximize the potential of these data to support low-carbon energy systems, and innovative business models.

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)

The first two sections of the paper characterize the nineteenth century respect for the phenomenal by considering Helmholtz’s position and James’ and Russell’s move to neutral monism. The third section displays a moment’s sympathy with those who recoiled from the latter view -- but only a moment’s. The recoil overshot what was a reasonable response, and denied the reality of the phenomenal, largely in the name of the physical or the material. The final two sections of the paper develop a (...) third way, which retains a healthy respect for the mental and for the mind–body relation, does not attempt to equate objects with congeries of sensations, and does not attempt to deny the reality of the phenomenal. In fact, I will claim that on some conceptions (and not merely idealist-phenomenalist conceptions), the phenomenal is a fact of nature, and hence a part of the natural world. Some aspects of this third way are familiar in the various representational and critical realisms of the twentieth-century. But the realization -- or, more neutrally, the conception -- that the natural might include the phenomenal is less familiar. Yet this position has its predecessors too, not only among the physicists and psychologists of the nineteenth century, but among major physicists (as opposed to physicalist philosophers) and psychologists of the twentieth. [A re-edited version of this paper appears in Gary Hatfield, Perception and Cognition: Essays in the Philosophy of Psychology, Clarendon Press, 2009.]. (shrink)

In modern data centers, managing the distribution of workloads efficiently is crucial for ensuring optimal performance and meeting Service Level Agreements (SLAs). Load balancing algorithms play a vital role in this process by distributing workloads across computing resources to avoid overloading any single resource. However, the effectiveness of these algorithms can be significantly enhanced through the integration of advanced optimization techniques. This paper proposes an SLA-driven load balancing algorithm optimized using methods such as genetic algorithms, particle swarm optimization, and (...) simulated annealing. By focusing on both resource utilization and SLA compliance, the proposed approach aims to reduce latency, improve throughput, and maximize overall system efficiency. The research introduces a novel framework that incorporates real-time monitoring, dynamic resource allocation, and adaptive threshold settings to ensure consistent SLA adherence while optimizing computing performance. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed (...) to accommodate both individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

In this paper we investigate if sentences presented as the result of the application of statistical models and artificial intelligence to large volumes of data – the so-called ‘Big Data’ – can be characterized as semantically true, or as quasi-true, or even if such sentences can only be characterized as probably quasi-false and, in a certain way, post-true; that is, if, in the context of Big Data, the representation of a data domain can be configured as (...) a total structure, or as a partial structure provided with a set of sentences assumed to be true, or if such representation cannot be configured as a partial structure provided with a set of sentences assumed to be true. (shrink)