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)

Ontologies, as the term is used in informatics, are structured vocabularies comprised of human- and computer-interpretable terms and relations that represent entities and relationships. Within informatics fields, ontologies play an important role in knowledge and data standardization, representation, integra- tion, sharing and analysis. They have also become a foundation of artificial intelligence (AI) research. In what follows, we outline the Coronavirus Infectious Disease Ontology (CIDO), which covers multiple areas in the domain of coronavirus diseases, including etiology, transmission, epidemiology, (...) pathogenesis, diagnosis, prevention, and treatment. We emphasize CIDO development relevant to COVID-19. (shrink)

We describe a strategy that is being used for the horizontal integration of warfighter intelligence data within the framework of the US Army’s Distributed Common Ground System Standard Cloud (DSC) initiative. The strategy rests on the development of a set of ontologies that are being incrementally applied to bring about what we call the ‘semantic enhancement’ of data models used within each intelligence discipline. We show how the strategy can help to overcome familiar tendencies to stovepiping of intelligence (...) data, and describe how it can be applied in an agile fashion to new data resources in ways that address immediate needs of intelligence analysts. (shrink)

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. A sample of 602 lecturers were accessed during data collection and responded to two questionnaires (Data Management Practices Questionnaire – DMPQ and "Educational Research Effectiveness Questionnaire – EREQ). Multiple (...) regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness of university lecturers in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%. The five data management practices compositely influences the educational research effectiveness of university lecturers in terms of proper citations (F=152.25,p<.05), problem-solving (F=33.90,p<.05), knowledge creation (F=62.78,p<.05), and the generation of testable data (F=69.65,p<.05), is statistically significant. Based on the findings of this study, it was concluded that data management practices (storage, security, retrieval, sharing, and re-use) jointly and relatively contributes to the total variance in educational research effectiveness in terms of proper citations, problem-solving, knowledge creation and generation of testable data at varying extents. It was recommended, among others that, lecturers should practice the habit of managing research data in their personal computers and through manual channels. These records could become vital in the future for further problem-solving. (shrink)

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)

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 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)

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)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating (...) to the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds (...) in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

Access to data in crime and place research has traditionally been reserved for those who have the means to collect fresh data themselves, pay for access, or obtain data through formal data sharing agreements. Even when access is granted, the usage of these data often comes with conditions that circumscribe how the data can be used through licensing or policy (Kitchin, 2014). Even the public dissemination of findings which emerge from analysis might be (...) subject to restrictions. This can lead to unequal access, controlled usage and curb the diffusion of findings, severely limiting the insight that can be obtained from data. (shrink)

The sharing economy is a new and underdeveloped phenomenon in Lithuania, starting from the definition of the concept in a state’s legal framework and scarce statistics. The aim of the paper is to describe the trends of the digitally supported sharing economy in Lithuania. Available national and international information and data were analysed. It was shown that the most popular services in Lithuania there is the transport sector, in the second place there is the accommodation sector, in (...) the third—food-related services. The reasons why Lithuanians offer services via collaborative platforms mostly concern additional sources of income and flexible working hours. Over two-thirds of the habitants express their positive attitudes towards sharing economy and collaborative platforms, and over ninety per cent would recommend other services offered via collaborative platforms. Though 97% of the Lithuanians have never offered the services via sharing economy and collaborative platforms, and it is mostly because of no item or interest, and two-fifths do not know at all those collaborative platforms are. The development of the sharing economy in Lithuania as far is gaining speed, and in the future, the principles of these phenomena are going to be used in an even broader scope of the sectors. (shrink)

Shared conceptualization, in the sense we take it here, is as recent a notion as the Semantic Web, but its relevance for a large variety of fields requires efficient methods of extraction and representation for both quantitative and qualitative data. This notion is particularly relevant for the investigation into, and construction of, semantic structures such as knowledge bases and taxonomies, but given the required large, often inaccurate, corpora available for search we can get only approximations. We see fuzzy description (...) logic as an adequate medium for the representation of human semantic knowledge and propose a means to couple it with fuzzy semantic networks via the propositional Łukasiewicz fuzzy logic such that these suffice for decidability for queries over a semantic-knowledge base such as “to what degree of sharedness does it entail the instantiation C(a) for some concept C” or “what are the roles R that connect the individuals a and b to degree of sharedness ε.” . (shrink)

The dishonest practices in the conduct of educational research in South-South region particularly, and Nigeria generally, has called for urgent intervention. This study assessed data management practices and educational research effectiveness of University Lecturers in South-South Nigeria. A sample of 602 lecturers that were accessible during data collection responded to two questionnaires (Data Management Practices Questionnaire – DMPQ and "Educational Research Effectiveness Questionnaire – EREQ). Multiple regression was employed in the analysis of data. Findings revealed amongst (...) others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations (F=152.25,p<.05), problem-solving (F=33.90,p<.05), knowledge creation (F=62.78,p<.05), and generation of testable data (F=69.65,p<.05), is statistically significant. Based on the findings of this study, it was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification. This study contributes to the existing body of knowledge by addressing the gaps arising from the paucity of research literature on educational research effectiveness. This study shows how data management practices affects the quality of educational research output of university lecturers using empirical evidence from Nigeria. (shrink)

This article focuses on Makerere University and Kyambogo University to highlight stakeholders’ concerns pertaining to the shared governance framework for public universities in Uganda. It is anchored in the interpretivist lens and the data was derived from three state-sponsored reports on the two public universities. The secondary data was analysed using content analysis. The findings demonstrate that the size, composition, authority and effectiveness of the university council and the senate and the mode through which leaders assume office, are (...) the salient governance concerns in the two universities. The results further show that, stakeholders’ concerns regarding the current shared governance framework for public universities relate to the substance of the framework rather than its form or the framework itself. This suggests that the governance framework comprising the university council, the university senate and the administration is fit for purpose and in sync with the nature of the academy. Arguably, these governance organs (and the nature of their work) set a university apart from other organisations. Finally, without being prescriptive, the article sketches options for reform. (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)

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)

Wearable devices are increasingly present in the health context, as tools for biomedical research and clinical care. In this context, wearables are considered key tools for a more digital, personalised, preventive medicine. At the same time, wearables have also been associated with issues and risks, such as those connected to privacy and data sharing. Yet, discussions in the literature have mostly focused on either technical or ethical considerations, framing these as largely separate areas of discussion, and the contribution (...) of wearables to the collection, development, application of biomedical knowledge has only partially been discussed. To fill in these gaps, in this article we provide an epistemic (knowledgerelated) overview of the main functions of wearable technology for health: monitoring, screening, detection, and prediction. On this basis, we identify 4 areas of concern in the application of wearables for these functions: data quality, balanced estimations, health equity, and fairness. To move the field forward in an effective and beneficial direction, we present recommendations for the 4 areas: local standards of quality, interoperability, access, and representativity. (shrink)

Most people are completely oblivious to the danger that their medical data undergoes as soon as it goes out into the burgeoning world of big data. Medical data is financially valuable, and your sensitive data may be shared or sold by doctors, hospitals, clinical laboratories, and pharmacies—without your knowledge or consent. Medical data can also be found in your browsing history, the smartphone applications you use, data from wearables, your shopping list, and more. At (...) best, data about your health might end up in the hands of researchers on whose good will we depend to avoid abuses of power.2 Most likely, it will end up with data brokers who might sell it to a future employer, or an insurance company, or the government. At worst, your medical data may end up in the hands of criminals eager to commit extortion or identity theft. In addition to data harms related to exposure and discrimination, the collection of sensitive data by powerful corporations risks the creation of data monopolies that can dominate and condition access to health care. -/- This chapter aims to explore the challenge that big data brings to medical privacy. Section I offers a brief overview of the role of privacy in medical settings. I define privacy as having one’s personal information and one’s personal sensorial space (what I call autotopos) unaccessed. Section II discusses how the challenge of big data differs from other risks to medical privacy. Section III is about what can be done to minimise those risks. I argue that the most effective way of protecting people from suffering unfair medical consequences is by having a public universal healthcare system in which coverage is not influenced by personal data (e.g., genetic predisposition, exercise habits, eating habits, etc.). (shrink)

Immunology researchers are beginning to explore the possibilities of reproducibility, reuse and secondary analyses of immunology data. Open-access datasets are being applied in the validation of the methods used in the original studies, leveraging studies for meta-analysis, or generating new hypotheses. To promote these goals, the ImmPort data repository was created for the broader research community to explore the wide spectrum of clinical and basic research data and associated findings. The ImmPort ecosystem consists of four components–Private (...) class='Hi'>Data, Shared Data, Data Analysis, and Resources—for data archiving, dissemination, analyses, and reuse. To date, more than 300 studies have been made freely available through the ImmPort Shared Data portal , which allows research data to be repurposed to accelerate the translation of new insights into discoveries. (shrink)

The predominant view is that a study using health data is observational research and should require individual consent unless it can be shown that gaining consent is impractical. But recent arguments have been made that citizens have an ethical obligation to share their health information for research purposes. In our view, this obligation is sufficient ground to expand the circumstances where secondary use research with identifiable health information is permitted without explicit subject consent. As such, for some studies the (...) Institutional Review Board/Research Ethics Committee review process should not assess the practicality of gaining consent for data use. Instead the review process should focus on assessing the public good of the research, public engagement and transparency. (shrink)

The existing body of knowledge has witnessed gaps arising from the paucity of research literature on the quality of educational research output in higher education. This study shows how the management of data in higher education affects the quality of academic research conducted by university lecturers in South-South Nigeria. The study adopted a factorial research design. The population of this study includes all the Faculty of Education academic staff in both public and private universities situated in South-South Nigeria. Using (...) results of a survey administered to a sample of 602 accessible lecturers during data collection, one research question and one null hypothesis were resolved in the study. Multiple regression was employed in the analysis of data. Findings revealed amongst others that; data storage, data security, data retrieval, data sharing, and data re-use jointly contributed to the total variance in educational research effectiveness in terms of proper citations by 56.25%, problem-solving by 22.14%, knowledge creation by 34.50%, and generation of testable data by 36.88%; and their composite influence on educational research effectiveness in terms of proper citations, problem- solving, knowledge creation, and generation of testable data, is statistically significant. Based on the findings of this study, it was concluded that data management practices play an important role in higher education that significantly influences the quality of research output in universities in South- South Nigeria. It was recommended among others that, there should be an up-to-date database or data warehouse in Ministries of Education where researchers can easily access data in order to increase the quality of researches by eliminating the problems of data fabrication and falsification. (shrink)

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and (...) SSA domain knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations. (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)

The oil and gas industry is a complex data-driven industry with compute-intensive, data-intensive and business-intensive features. Cloud computing and big data have a broad application prospect in the oil and gas industry. This research aims to highlight the cloud computing and big data issues and challenges from the informatization in oil and gas industry. In this paper, the distributed cloud storage architecture and its applications for seismic data of oil and gas industry are focused on (...) first. Then,cloud desktop for oil and gas industry applications are also introduced in terms of efficiency, security and usability. Finally, big data architecture and security issues of oil and gas industry are analyzed. Cloud computing and big data architectures have advantages in many aspects, such as system scalability, reliability, and serviceability. This paper also provides a brief description for the future development of Cloud computing and big data in oil and gas industry. Cloud computing and big data can provide convenient information sharing and high quality service for oil and gas industry. (shrink)

Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the (...) Science, Health and Policy-relevant Ethics in Singapore Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. (shrink)

The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer (...) and expanding our knowledge of near-Earth space. To facilitate data-sharing interoperability among distinct orbital debris and space object catalogs, and SSA information systems, I proposed ontology in (Rovetto, 2015) and (Rovetto and Kelso, 2016). I continue this effort toward formal representations and models of the overall domain that may serve to improve peaceful SSA and increase our scientific knowledge. This paper explains the project concept introduced in those publications, summarizing efforts to date as well as the research field of ontology development and engineering. I describe concepts for an ontological framework for the orbital space environment, near-Earth space environment and SSA domain. An ontological framework is conceived as a part of a potential international information system. The purpose of such a system is to consolidate, analyze and reason over various sources and types of orbital and SSA data toward the mutually beneficial goals of safer space navigation and scientific research. Recent internationals findings on the limitations of orbital data, in addition to existing publications on collaborative SSA, demonstrate both the overlap with this project and the need for data-sharing and integration. (shrink)

The COVID-19 pandemic demonstrated the benefits of international data sharing. Data sharing enabled the health care policy makers to make decisions based on real-time data, it enabled the tracking of the virus, and importantly it enabled the development of vaccines that were crucial to mitigating the impact of the virus. This data sharing is not the norm as data sharing needs to navigate complex ethical and legal rules, and in particular, the (...) fragmented application of the General Data Protection Regulation (GDPR). The introduction of the draft regulation for a European Health Data Space (EHDS) in May 2022 seeks to address some of these legal issues. If passed, it will create an obligation to share electronic health data for certain secondary purposes. While there is a clear need to address the legal complexities involved with data sharing, it is critical that any proposed reforms are in line with ethical principles and the expectations of the data subjects. In this paper we offer a critique of the EHDS and offer some recommendations for this evolving regulatory space. (shrink)

According to the PubMed resource from the U.S. National Library of Medicine, over 750,000 scientific articles have been published in the ~5000 biomedical journals worldwide in the year 2007 alone. The vast majority of these publications include results from hypothesis-driven experimentation in overlapping biomedical research domains. Unfortunately, the sheer volume of information being generated by the biomedical research enterprise has made it virtually impossible for investigators to stay aware of the latest findings in their domain of interest, let alone to (...) be able to assimilate and mine data from related investigations for purposes of meta-analysis. While computers have the potential for assisting investigators in the extraction, management and analysis of these data, information contained in the traditional journal publication is still largely unstructured, free-text descriptions of study design, experimental application and results interpretation, making it difficult for computers to gain access to the content of what is being conveyed without significant manual intervention. In order to circumvent these roadblocks and make the most of the output from the biomedical research enterprise, a variety of related standards in knowledge representation are being developed, proposed and adopted in the biomedical community. In this chapter, we will explore the current status of efforts to develop minimum information standards for the representation of a biomedical experiment, ontologies composed of shared vocabularies assembled into subsumption hierarchical structures, and extensible relational data models that link the information components together in a machine-readable and human-useable framework for data mining purposes. (shrink)

This study examined the determinants of trust in artificial intelligence (AI) in the area of asset management. Many studies of risk perception have found that value similarity determines trust in risk managers. Some studies have demonstrated that value similarity also influences trust in AI. AI is currently employed in a diverse range of domains, including asset management. However, little is known about the factors that influence trust in asset management-related AI. We developed an investment game and examined whether shared investing (...) strategy with an AI advisor increased the participants’ trust in the AI. In this study, questionnaire data were analyzed (n=101), and it was revealed that shared investing strategy had no significant effect on the participants’ trust in AI. In addition, it had no effect on behavioral trust. Perceived ability had significantly positive effects on both subjective and behavioral trust. This paper also discusses the empirical implications of the findings. (shrink)

Replicability and reproducibility of computational models has been somewhat understudied by “the replication movement.” In this paper, we draw on methodological studies into the replicability of psychological experiments and on the mechanistic account of explanation to analyze the functions of model replications and model reproductions in computational neuroscience. We contend that model replicability, or independent researchers' ability to obtain the same output using original code and data, and model reproducibility, or independent researchers' ability to recreate a model without original (...) code, serve different functions and fail for different reasons. This means that measures designed to improve model replicability may not enhance (and, in some cases, may actually damage) model reproducibility. We claim that although both are undesirable, low model reproducibility poses more of a threat to long-term scientific progress than low model replicability. In our opinion, low model reproducibility stems mostly from authors' omitting to provide crucial information in scientific papers and we stress that sharing all computer code and data is not a solution. Reports of computational studies should remain selective and include all and only relevant bits of code. (shrink)

This study assessed the partial as well as the collaborative impact of age and gender on academic staff preparedness to adopt Internet tools for research sharing in African universities during Covid-19. Although evidence abounds in the literature on gender and age as they affect relatively, scholars’ utilisation of digital tools for research communication, such studies did not examine scholars’ preparedness to adopt from a broad perspective of Africa. This study was conducted based on the argument that the preparedness of (...) scholars may affect their future interest to utilize digital tools for research sharing. A quantitative method, based on the descriptive survey research design, was adopted to provide answers to four prevailing research questions. The examination focused on a populace of 8,591 staff in African universities, nonetheless, information was gathered from 1,977 of them, who deliberately took part from 24 African nations. A validated electronic rating scale, which was mailed/posted to targeted participants, was used as the instrument for data collection. Gender and age significantly affected academic staff preparedness to adopt Internet tools for research sharing partially and interactively in African Universities during Covid-19. Female staff were more prepared than males to adopt internet tools for research sharing during the pandemic. Older lecturers reported a higher rate of preparedness than their younger colleagues to adopt Internet tools for research sharing during Covid-19. In light of these proofs, ramifications and proposals for future exploration are discussed. (shrink)

Lewis et al. (2011) attempted to restore the reputation of Samuel George Morton, a 19th century physician who reported on the skull sizes of different folk-races. Whereas Gould (1978) claimed that Morton’s conclusions were invalid because they reflected unconscious bias, Lewis et al. alleged that Morton’s findings were, in fact, supported, and Gould’s analysis biased. We take strong exception to Lewis et al.’s thesis that Morton was “right.” We maintain that Gould was right to reject Morton’s analysis as inappropriate and (...) misleading, but wrong to believe that a more appropriate analysis was available. Lewis et al. fail to recognize that there is, given the dataset available, no appropriate way to answer any of the plausibly interesting questions about the “populations” in question (which in many cases are not populations in any biologically meaningful sense). We challenge the premise shared by both Gould and Lewis et al. that Morton’s confused data can be used to draw any meaningful conclusions. This, we argue, reveals the importance of properly focusing on the questions asked, rather than more narrowly on the data gathered. (shrink)

Social media has triggered an increase in fake news spread about different aspects of modern lives, society, politics, societal changes, etc., and has also affected companies’ reputation and brands’ trust. Therefore, this paper is aimed at investigating why social media users share fake news about environmentally friendly brands. To examine social media users’ behavior towards environmentally friendly brands, a theoretical research model proposed and analyzed using structural equations modeling in SmartPLS on a convenience sample consisting of 922 questionnaires. Data (...) was collected by means of a quantitative-based approach via a survey conducted among social media users from an emerging market. The results show that social media flow has a mediated impact on sharing fake news about environmentally friendly brands on social media. Considering the critical consequences of fake news, the paper argues that understanding the dissemination process of this type of bogus content on social media platforms has important theoretical and managerial implications. Understanding the psychological mechanisms that influence people’s behavior in sharing fake news about environmentally friendly brands on social networking sites (SNS) could help in better understanding the factors and the effects of this phenomenon. The originality of this research consists of proposing flow theory from positive psychology to be used as a theoretical framework to explain users’ behavior of sharing fake news about environmentally friendly brands on social media. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

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

This paper builds off the Our Data Ourselves research project, which examined ways of understanding and reclaiming the data that young people produce on smartphone devices. Here we explore the growing usage and centrality of mobiles in the lives of young people, questioning what data-making possibilities exist if users can either uncover and/or capture what data controllers such as Facebook monetize and share about themselves with third-parties. We outline the MobileMiner, an app we created to consider (...) how gaining access to one’s own data not only augments the agency of the individual but of the collective user. Finally, we discuss the data making that transpired during our hackathon. Such interventions in the enclosed processes of datafication are meant as a preliminary investigation into the possibilities that arise when young people are given back the data which they are normally structurally precluded from accessing. (shrink)

Managing complex disaster situations is a challenging task because of the large number of actors involved and the critical nature of the events themselves. In particular, the different terminologies and technical vocabularies that are being exchanged among Emergency Responders may lead to misunderstandings. Maintaining a shared semantics for exchanged data is a major challenge. To help to overcome these issues, we elaborate a modular suite of ontologies called POLARISCO that formalizes the complex knowledge of the ERs. Such a shared (...) vocabulary resolves inconsistent terminologies and promotes semantic interoperability among ERs. In this work, we discuss developing POLARISCO as an extension of Basic Formal Ontology and the Common Core Ontologies. We conclude by presenting a real use-case to check the efficiency and applicability of the proposed ontology. (shrink)

This article identifies and compares meanings of wildfire risk mitigation for stakeholders in the Front Range of Colorado, USA. We examine the case of a collaborative partnership sponsored by government agencies and directed to decrease hazardous fuels in interface areas. Data were collected by way of key informant interviews and focus groups. The analysis is guided by the Circuit of Culture model in communication research. We found both shared and differing meanings between members of this partnership (the ‘‘producers’’) and (...) other stakeholders not formally in the partnership (the ‘‘consumers’’). We conclude that those promoting the partnership’s project to mitigate risk are primarily aligned with a discourse of scientific management. Stakeholders outside the partnership follow a discourse of community. We argue that failure to recognize and account for differences in the way risk mitigation is framed and related power dynamics could hamper the communicational efforts of the collaborative partnership and impact goals for fuels reduction. We recommend ways that both groups can capitalize on shared meanings and how agency managers and decision makers can build better working relationships with interface communities and other external stakeholders. (shrink)

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective (...) data in other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

This article is a knowledge technology case study of DNAOS, a distributed platform for Knowledge Resource Entitlement, Modeling, Management, and Sharing (KREMMS). Some historical aspects of its design, development, and release are briefly discussed, after which the DNAOS technology is commented upon from the specific viewpoint of KREMMS. At the core of this platform is the conception of knowledge as a natural phenomenon, which conception is reflected in the ontology of this technology: Fundamental knowledge structures and structuring principles, believed (...) to be “natural,” including qualification, resources, and relationships, are considered theoretically and modeled computationally. Finally, with the help of images, the reader is briefly introduced to some DNAOS capabilities, touching on transaction, application, data, model, and interface. (shrink)

Humans think and communicate in very flexible and schematic ways, and a Spatial Data Infrastructure (SDI) for the Amazon and associated information system ontologies should reflect this flexibility and the adaptive nature of human cognition in order to achieve semantic interoperability. In this paper I offer a conceptual investigation of SDI and explore the nature of cultural schemas as expressions of indigenous ontologies and the challenges of semantic interoperability across cultures. Cultural schemas are, in essence, our ontologies, but they (...) are markedly different than classical formal ontologies. They shape our ontological commitments to what exists in the world as well as the ways in which we approach and engage the world. And while they help structure our understanding of the world in which we are embedded, they are associative and flexible. They help to focus our attention to particular details of our experiences and give them salience, yet they cannot be simply reduced to a series of extracted features. They allow us to make meaning of the contextualized, cultural experience in which we are always immersed. An SDI is a shared social-technological-informational structure that, if it is to be useful and successful for sustainability in the Amazon, must incorporate and use indigenous cultural schemas. Indigenous communities must have the ability to contribute to the collection of geospatial data and their contributions recognized as legitimate forms of knowledge. In order for the SDI to work, it must recognize the larger cultural landscape to which cultural schemas can connect to the ready-to-hand elements of salient cultural experiences. (shrink)

This commentary records, through notes taken by Hermias, Syrianus' seminar on Plato's Phaedrus, one of the world's most influential celebrations of erotic beauty and love. It is the only Neoplatonic commentary on Plato's Phaedrus to have survived in its entirety. Further interest comes from the recorded interventions by Syrianus' pupils - including those by Proclus, his eventual successor as head of the Athenian school, who went on to teach Hermias' father, Ammonius. The second of two volumes of Hermias' commentary, the (...) chapters translated here begin with a discussion of how the discarnate soul is visualised as a winged chariot team whose charioteer may gain some glimpse of beauty itself, which can explain subsequent erotic longing. This volume provides a translation is accompanied by explanatory notes, an introduction detailing the significance and context of the treatise and a scholarly apparatus including multiple indexes, glossaries and a bibliography. (shrink)

Trust in social media information is gaining in importance and relevance for both companies and individuals as nowadays contemporary society is confronted with a wave of fake news about daily life situations, brands, organizations, etc. As it becomes more difficult to accurately assess social media information and to determine its origin or source, as well as to be able to double-check information spread across different Social Networking Sites (SNS), businesses must understand how individuals’ perceived control, concentration, and time distortion enhances (...) the social media usage, thus allowing them to correctly assess online information. Therefore, the scope of the paper is to assess, based on a conceptual model, the antecedents of trust in online information about companies by considering users’ perceived control, concentration, and time distortion, while browsing social media networks and sharing fake news about companies in SNS. With the help of an online survey, data was collected from social media users, later being analysed with SmartPLS. The findings suggest that social media usage and sharing of fake news mediate the relationship between users’ perceived control, concentration, and time distortion (i.e., flow characteristics) and trust in online information about companies. (shrink)

Abstract: One of the most contentious challenges facing the Nigerian federal experiment today is that of revenue allocation. Right from the colonial era till present, finding an acceptable revenue formula has remained intractable as the various attempts have not gained wide acceptance. This paper chronicles the various revenue commissions in Nigeria and the formulae derived there from. Data for the paper were mainly from secondary sources. The theoretical nerve of the paper is based on the nature of intergovernmental fiscal (...) relations. The findings indicate that the derivation principle has been the main bone of contention since the discovery of oil in Nigeria. Based on this, the paper recommends a serious restructuring of the Nigerian federation to grant the various component units enough autonomy to develop at their own pace, while economic viability should be the main basis for creation of states and local governments. These states and local governments should also be encouraged to develop other revenue sources outside oil, and finally the issue of revenue allocation should be made open for all Nigerians to contribute in the true spirit of democracy. (shrink)

In the last decade there has been a movement towards facilitating Open Access to academic outputs via the World Wide Web. This movement has been characterised as one that embodies corporate citizenship because such sharing has the potential to benefit all stakeholders: academics, policy makers, charitable sectors and the wider public. In the UK, the Economic and Social Research Council are implementing Open Access compliance guidelines for research that they fund, which is interpreted by individual institutions in their school (...) regulations. In the case of doctoral theses, there is now a requirement for students to provide an electronic format of their final work to be included in their school's online digital repository. In a number of UK institutions, University Awards and Progress Committees will only consider awarding the doctoral degree once these requirements have been satisfied. Although this move to Open Access can be considered as an egalitarian endeavour, this paper argues that an important stakeholder may have been overlooked in the march towards progressive dissemination. The temporal space between gaining informed consent from research participants and the changing nature of the accessibility of outputs can both engender a breach of ethics in terms of the initial agreements negotiated with participants and raise issues around representation in the ongoing dissemination and reformulation of the original work, particularly where visual images are central to research outputs. The paper utilises autoethnography and poetry to reflect on my own encounter with the requirement for Open Access and the ways in which this brings up concerns around ethics, obligations and integrity. (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.

This paper will attempt to reposition Ludwig von Mises’s methodological Apriorism and the Austrian economic method firmly in the Aristotelian realist tradition of Apriorism, rather than the more problematic Apriorism associated with Kantian idealism. The author will argue that the Misean method whilst aesthetically Kantian, is far more nuanced than semantics suggest.

Translation and commentary on the only surviving sustained work on Plato's Phaedrus from antiquity.