Recent large-scale personal data loss incidents highlighted the need for public bodies to more securely handle confidential data. We surveyed trainees from all specialties in the Welsh Deanery for their knowledge and practice. All registered trainees were invited to participate in an online anonymised survey. There were 880 completed and non-duplicated responses (52.9% response rate). Responses were analysed using Microsoft Access. Over 40% (388/880 (44.1%)) did not use formal guidelines on storage or disposal of confidential data. The (...) majority appeared to dispose of confidential paper documents securely, that is, using shredders and white shredder bags. However, there were significant numbers of unmarked responses. Clinical documents, such as theatre lists, were taken home by 281/880 (31.9%) of trainees. The majority secured their computers (569/871 (65.3%)) by either not keeping patient identifiable data on them or using encryption. However, 302/871 (34.7%) did not adequately secure their computers. The surgical and anaesthetic specialties were least aware of formal confidentiality guidelines (95/178 (53.4%)) and 52/102 (51.0%) respectively) and least secured their computers (106/178 (59.6%) and 63/102 (61.8%) respectively). Education is needed to improve knowledge and practice of confidential data handling. This may be delivered through workshops during induction programmes or as part of European Computer Driving Licence (ECDL) modules. Training is especially indicated for the surgical and anaesthetic specialties. (shrink)

Natural languages come in two different modalities. The impact of modality on the grammatical structure and linguistic theory has been discussed at great length in the last 20 years. By contrast, the impact of modality on linguistic data elicitation and collection, corpus studies and experimental (psycholinguistic) studies is still underinvestigated (van Herreweghe/Vermeerbergen 2012; Orfanidou et al. 2015). In this paper, we address specific challenges that arise in judgement data elicitation and experimental studies of sign languages. These challenges are (...) related to the socio-linguistic status of the Deaf community and the larger variability across signers within the same community, to the social status of sign languages, to properties of ;he visual-gestural modality and its interface with gesture, to methodological aspects of handling sign language data, and to specific linguistic features of sign languages. While some of these challenges also pertain to (some varieties of) spoken languages, other challenges are more modality-specific. In addition, the special combination of the challenges discussed in this paper seems to be a specific facet empirical research on sign languages is faced with. In addition, we discuss the complementarity of theoretical approaches and experimental studies and show how the interaction of both approaches contributes to a better understanding of sign languages in particular and linguistic structures in general. (shrink)

This paper presents and discusses empirical results from a survey about the research practice of Danish chemistry students, with a main focus on the question of anomalous data. It seeks to investigate how such data is handled by students, with special attention to so-called ‘questionable research practices’ where anomalous data are simply deleted or discarded. This question of QRPs is of particular importance as the educational practices students experience may influence how they act in their future professional (...) careers, for instance in research. The ethical evaluation of QRPs however is not univocal. In parts of the literature QRPs are seen as unquestionably bad, while in other parts of the literature certain QRPs are seen as a necessary aspect of scientific practice. Results from the survey of Danish chemistry students shows that many students engage in certain types of questionable practices, and that a large minority of the students have been actively encouraged by their teachers to engage in such practices. The paper discusses to what extent and under what circumstances such instructional practices can be defended and suggests how the instructional practice connected to the handling of anomalous data can be improved. (shrink)

Virtual screening is the most critical process in drug discovery, and it relies on machine learning to facilitate the screening process. It enables the discovery of molecules that bind to a specific protein to form a drug. Despite its benefits, virtual screening generates enormous data and suffers from drawbacks such as high dimensions and imbalance. This paper tackles data imbalance and aims to improve virtual screening accuracy, especially for a minority dataset. For a dataset identified without considering the (...) data’s imbalanced nature, most classification methods tend to have high predictive accuracy for the majority category. However, the accuracy was significantly poor for the minority category. The paper proposes a K-mean algorithm coupled with Synthetic Minority Oversampling Technique to overcome the problem of imbalanced datasets. The proposed algorithm is named as KSMOTE. Using KSMOTE, minority data can be identified at high accuracy and can be detected at high precision. A large set of experiments were implemented on Apache Spark using numeric PaDEL and fingerprint descriptors. The proposed solution was compared to both no-sampling method and SMOTE on the same datasets. Experimental results showed that the proposed solution outperformed other methods. (shrink)

As with other areas of the social world, academic research in the contemporary healthcare setting has undergone adaptation and change. For example, research methods are increasingly incorporating citizen participation in the research process, and there has been an increase in collaborative research that brings academic and industry partners together. There have been numerous positive outcomes associated with both of these growing methodological and collaborative processes; nonetheless, both bring with them ethical considerations that require careful thought and attention. This paper addresses (...) the ethical considerations that research teams must consider when using participatory methods and/or when working with industry and outlines a novel informed consent matrix designed to maintain the high ethical standard to which academic research in the healthcare arena has traditionally adhered. (shrink)

In modern day technology, the level of knowledge is increasing day by day. This increase is in terms of volume, velocity, and variety. Understanding of such knowledge is a dire need of an individual to extract meaningful insight from it. With the advancement in computer and image-based technologies, visualization becomes one of the most significant platforms to extract, interpret, and communicate information. In data modelling, visualization is the process of extracting knowledge to reveal the detail data structure and (...) process of data. The proposed study aim is to know about the user knowledge, data modelling, and visualization by handling through the fuzzy logic-based approach. The experimental setup is validated through the data user modelling dataset available in the UCI web repository. The results show that the model is effective and efficient in situations where uncertainty and complexity arise. (shrink)

Conflict resolution practitioners consistently struggle with access to structured armed conflict data, a dataset already rife with uncertainty, inconsistency, and politicization. Due to the lack of a standardized approach to collating conflict data, publicly available armed conflict datasets often require manipulation depending upon the needs of end users. Transformation of armed conflict data tends to be a manual, time-consuming task that nonprofits with limited budgets struggle to keep up with. In this paper, we explore the use of (...) a deep natural language processing model to aid the transformation of armed conflict data for conflict analysis. Our model drastically reduces the time spent on manual data transformations and improves armed conflict event classification by identifying multiple incidence types. This minimizes the human supervision cost and allows nonprofits to access a broader range of conflict data sources to reduce reporting bias. Thus, our model contributes to the incorporation of technology in the peace building and conflict resolution sector. (shrink)

Legal documents, like contracts or laws, are subject to interpretation. Different people can have different interpretations of the very same document. Large parts of judicial branches all over the world are concerned with settling disagreements that arise, in part, from these different interpretations. In this context, it only seems natural that during the annotation of legal machine learning data sets, disagreement, how to report it, and how to handle it should play an important role. This article presents an analysis (...) of the current state-of-the-art in the annotation of legal machine learning data sets. The results of the analysis show that all of the analysed data sets remove all traces of disagreement, instead of trying to utilise the information that might be contained in conflicting annotations. Additionally, the publications introducing the data sets often do provide little information about the process that derives the “gold standard” from the initial annotations, often making it difficult to judge the reliability of the annotation process. Based on the state-of-the-art, the article provides easily implementable suggestions on how to improve the handling and reporting of disagreement in the annotation of legal machine learning data sets. (shrink)

One of the important issues in mobile robots is finding the position of robots in space. This is normally achieved by using a sensor to locate the position of the robot. However, relying on more than one sensor and then using multisenor data fusion algorithms tends to be more reliable than just using a reading from a single sensor. If these sensors provide inconsistent data, catastrophic fusion may occur, and thus the estimated position of the robot obtained will (...) be less accurate than if an individual sensor is used. This article uses an approach that relies on combining modified Bayesian fusion algorithm with Kalman filtering to estimate the position of a mobile robot. Two case studies are presented to prove the efficiency of the proposed approach in estimating the position of a mobile robot. Both scenarios show that combining fusion with filtering provides an accurate estimate of the location of the robot by handling the problem of uncertainty and inconsistency of the data provided by the sensors. (shrink)

The ritual handling of serpents remains an unnoticed cultural form for the explanatory aims and theoretical insights desired by cognitive scientists of religion. In the current article, we introduce the Hood and Williams archives at The University of Tennessee at Chattanooga that contains data culled from Hood’s 40-plus year career of studying serpent handlers. The archives contain hundreds of hours of interviews and recordings of speaking in tongues, handling fire, drinking poison, and taking up serpents by different (...) congregants and congregations. The archive remains a rich but untapped source of data for building, testing, and refining cognitive theories of ritual in general, and serpent handling in specific. We connect Hood’s work to current cognitive theories and engage critically with research on the social functions of ritual. Finally, we discuss several further reasons to pay more attention to SHS communities and practices in cognitive theories of ritual. (shrink)

Research involving minors has been the subject of much ethical debate. The growing number of longitudinal, pediatric studies that involve genetic research present even more complex challenges to ensure appropriate protection of children and families as research participants. Long-term studies with a genetic component involve collection, retention and use of biological samples and personal information over many years. Cohort studies may be established to study specific conditions (e.g. autism, asthma) or may have a broad aim to research a range of (...) factors that influence the health and development of children. Studies are increasingly intended to serve as research platforms by providing access to data and biological samples to researchers over many years. (shrink)

Even a small percentage of missing data can cause serious problems with analysis, reducing the statistical power of a study and leading to wrong conclusions being drawn. In the case of monitoring a woman’s monthly cycle, missing entries can appear even in a woman experienced in fertility awareness methods. Due to the fact that in a system of controlling a woman’s fertility, it is the most important to predict the day of ovulation and, ultimately, to determine the fertile window (...) as much precisely as possible, much attention should be paid to the quality of the used data. This paper presents the results of handling missing observations as far as predicting the time during the cycle when a woman can become pregnant is concerned. Data taken from a multinational European study of daily fecundability was used to learn the quantitative part of the variety of a higher-order dynamic Bayesian network modeling a woman’s monthly cycle. The main goal of this paper is to examine whether omitting observations has an influence on the model’s reliability. The accuracy of comparison was examined based on two measures: the average percentage length of the infertile time during the monthly cycle and average percentage of days inside the fertile window classified as infertile. (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)

Nanomedicine is a rapidly growing field in the academic as well as commercial arena. While some had predicted nanomedicine sales to reach $20.1 billion in 2011, the actual growth was much more rapid, with the global nanomedicine market being valued at $53 billion in 2009, and forecast to increase at an annual growth rate of 13.5% to reach more than $100 billion in 2014. In 2006, more than 130 nanotechnology-based drugs and delivery systems had entered preclinical, clinical, or commercial development. (...) The European Medicines Agency reviewed 18 marketing authorization applications for nanomedicines in 2010. In 2011, 22 drugs that had been approved by the FDA, and 87 Phase I and Phase II clinical trials were listed in the U.S. National Institutes of Health data base, www.clinicaltrials.gov. Although the fastest growing areas of nanomedicine are applications in medical imaging and diagnosis using contrast-enhancing agents, most nanomedicine research and commercialization is in the area of cancer drug therapy, including nano gold shells. (shrink)

Since its onset, scholars have characterized social media as a valuable source for data collection since it presents several benefits (e.g. exploring research questions with hard-to-reach populations). Nonetheless, methods of online data collection are riddled with ethical and methodological challenges that researchers must consider if they want to adopt good practices when collecting and analyzing online data. Drawing from our primary research project, where we collected passive online data on Reddit, we explore and detail the steps (...) that researchers must consider before collecting online data: (1) planning online data collection; (2) ethical considerations; and (3) data collection. We also discuss two atypical questions that researchers should also consider: (1) how to handle deleted user-generated content; and (2) how to quote user-generated content. Moving on from the dichotomous discussion between what is public and private data, we present recommendations for good practices when collecting and analyzing qualitative online data. (shrink)

Background: In end of life care, ethical problems often come to the fore. Little research is performed on ways or strategies for handling those problems and even less on obstacles to and possibilities of using such strategies. A previous study illuminated stroke team members’ experiences of ethical problems and how the teams managed the situation when caring for patients faced with sudden and unexpected death from stroke. These findings have been further explored in this study. Objective: The aim of (...) the study was to illuminate obstacles and possibilities perceived by stroke team members in using strategies for handling ethical problems when caring for patients afflicted by sudden and unexpected death caused by stroke. Research design: A qualitative method with combined deductive and inductive content analysis was utilized. Participants and research context: Data were collected through individual interviews with 15 stroke team members working in stroke units of two associated county hospitals in western Sweden. Ethical considerations: The study was approved by the Regional Ethics Review Board, Gothenburg, Sweden. Permission was also obtained from the director of each stroke unit. Findings: All the studied strategies for handling of ethical problems were found to have both obstacles and possibilities. Uncertainty is shown as a major obstacle and unanimity as a possibility in the use of the strategies. The findings also illuminate the value of the concept “the patient’s best interests” as a starting point for the carers’ ethical reasoning. Conclusion: The concept “the patient’s best interests” used as a starting point for ethical reasoning among the carers is not explicitly defined yet, which might make this value difficult to use both as a universal concept and as an argument for decisions. Carers therefore need to strengthen their argumentation and reflect on and use ethically grounded arguments and defined ethical values like dignity in their clinical work and decisions. (shrink)

BackgroundConsent for data research in acute and critical care is complex as patients become at least temporarily incapacitated or die. Existing guidelines and regulations in the European Union are of limited help and there is a lack of literature about the use of data from this vulnerable group. To aid the creation of a patient-centred framework for responsible data research in the acute setting, we explored views of patients and next-of-kin about the collection, storage, sharing and use (...) of genetic and health-related data for observational research.MethodsWe conducted qualitative interviews (n = 19) with Dutch sudden cardiac arrest survivors who donated clinical and socio-economic data and genetic samples to research. We also interviewed their next-of-kin. Topics were informed by ethics literature and we used scenario-sketches to aid discussion of complex issues.ResultsSudden cardiac arrest survivors displayed limited awareness of their involvement in health data research and of the content of their given consent. We found that preferences regarding disclosure of clinically actionable genetic findings could change over time. When data collection and use were limited to the medical realm, patients trusted researchers to handle data responsibly without concern for privacy or other risks. There was no consensus as to whether deferred consent should be explicitly asked from survivors. If consent is asked, this would ideally be done a few months after the event when cognitive capacities have been regained. Views were divided about the need to obtain proxy consent for research with deceased patients’ data. However, there was general support for the disclosure of potentially relevant post-mortem genetic findings to relatives.ConclusionsSudden cardiac arrest patients’ donation of data for research was grounded in trust in medicine overall, blurring the boundary between research and care. Our findings also highlight questions about the acceptability of a one-time consent and about responsibilities of patients, researchers and ethics committees. Finally, further normative investigation is needed regarding the (continued) use of participants’ data after death, which is of particular importance in this setting. Our findings are thought to be of relevance for other acute and life-threatening illnesses as well. (shrink)

The speed of development in Big Data and associated phenomena, such as social media, has surpassed the capacity of the average consumer to understand his or her actions and their knock-on effects. We are moving towards changes in how ethics has to be perceived: away from individual decisions with specific and knowable outcomes, towards actions by many unaware that they may have taken actions with unintended consequences for anyone. Responses will require a rethinking of ethical choices, the lack thereof (...) and how this will guide scientists, governments, and corporate agencies in handling Big Data. This essay elaborates on the ways Big Data impacts on ethical conceptions. (shrink)

Scientific knowledge production is currently affected by the dissemination of data on an unprecedented scale. Technologies for the automated production and sharing of vast amounts of data have changed the way in which data are handled and interpreted in several scientific domains, most notably molecular biology and biomedicine. In these fields, the activity of data gathering has become increasingly technology-driven, with machines such as next generation genome sequencers and mass spectrometers generating billions of data points (...) within hours, and with little need for human supervision. Given the relative ease and low costs with which datasets can be produced (that is, once a laboratory has been able to afford .. (shrink)

Educational research is dominated by a particular model: data is gathered and analysed. Much literature on methods concerns either ways of processing data, or ethical issues regarding its collection and handling. The present paper looks beyond these matters to the taken‐for‐granted idea of data itself. What can be meant by ‘data’? How does this connect with ideas of the given? What is the place of giving in education—in teaching and learning, in research itself? These issues (...) are explored in the light of information technology’s impact on research, uncovering ethical dimensions that the seeming naturalness of data otherwise obscures. (shrink)

This book provides an overview of computer techniques and tools — especially from artificial intelligence (AI) — for handling legal evidence, police intelligence, crime analysis or detection, and forensic testing, with a sustained discussion of methods for the modelling of reasoning and forming an opinion about the evidence, methods for the modelling of argumentation, and computational approaches to dealing with legal, or any, narratives. By the 2000s, the modelling of reasoning on legal evidence has emerged as a significant area (...) within the well-established field of AI & Law. An overview such as this one has never been attempted before. It offers a panoramic view of topics, techniques and tools. It is more than a survey, as topic after topic, the reader can get a closer view of approaches and techniques. One aim is to introduce practitioners of AI to the modelling legal evidence. Another aim is to introduce legal professionals, as well as the more technically oriented among law enforcement professionals, or researchers in police science, to information technology resources from which their own respective field stands to benefit. Computer scientists must not blunder into design choices resulting in tools objectionable for legal professionals, so it is important to be aware of ongoing controversies. A survey is provided of argumentation tools or methods for reasoning about the evidence. Another class of tools considered here is intended to assist in organisational aspects of managing of the evidence. Moreover, tools appropriate for crime detection, intelligence, and investigation include tools based on link analysis and data mining. Concepts and techniques are introduced, along with case studies. So are areas in the forensic sciences. Special chapters are devoted to VIRTOPSY (a procedure for legal medicine) and FLINTS (a tool for the police). This is both an introductory book (possibly a textbook), and a reference for specialists from various quarters. (shrink)

This article discusses the status of transcripts in Conversation Analysis. Repeatedly, the function and the epistemic state of transcripts have been the subject of discussions and reflections in Conversation Analysis. Drawing on a range of empirical examples taken from various authors, this article discusses the question of how present forms of visuality and multi-modality in the data material or the handling of artifacts can be captured in transcripts and how the problem of ‘representation’ of complex and interactive situations (...) can be solved. It turns out that the evolution of recording media along with the medium shift in Conversation Analysis has altered the status of transcripts, effecting a twofold ‘in-between-ness’: transcripts inhabit the conceptual space between orality and literacy on the one hand and between artwork and figure on the other hand. This oscillating hybrid nature proves to be the central property of transcripts in present-day Conversation Analysis. Transcripts are described as scientific ‘objects of knowledge’ and discussed with respect to their methodological and epistemic status. (shrink)

How ethical praxis is shaped by different contexts and situations has not been widely studied. We performed a follow-up study on stroke team members’ experiences of ethical problems and how the teams managed the situation when caring for patients faced with sudden and unexpected death from stroke. A number of ways for handling ethical problems emerged, which we have now explored further. Data were collected through a three-part form used as base for individual interviews with 15 stroke team (...) members and analyzed using both quantitative and qualitative content analysis. In the analysis, the approaches in the form were condensed into strategies, and the two different ways those strategies were preferred and used by the team members were shown. Hindrances perceived by the team members to impede them from working the preferred way were also revealed and grouped into eight categories. (shrink)

The publication of the latest contribution to the alcohol-in-pregnancy debate, and the now customary flurry of media attention it generated, have precipitated the renewal of a series of ongoing debates about safe levels of consumption and responsible prenatal conduct. The University College London (UCL) study’s finding that low levels of alcohol did not contribute to adverse behavioural outcomes—and may indeed have made a positive contribution in some cases—is unlikely to be the last word on the subject. Proving a negative correlation (...) is notoriously difficult (technically, impossible), and other studies have offered alternative claims. The author is not an epidemiologist, and the purpose of this article is not to evaluate the competing empirical claims. However, the question of what information and advice healthcare practitioners ought to present to pregnant women, or prospectively or potentially pregnant women, in a situation of uncertainty is one to which healthcare ethicists may have a contribution to make. In this article, it is argued that the total abstinence policy advocated by the UK’s Department of Health, and even more stridently by the British Medical Association, sits uneasily with recent data and is far from ethically unproblematic. In particular, the “precautionary” approach advocated by these bodies displays both scant regard for the autonomy of pregnant and prospectively pregnant women and a confused grasp of the principles of beneficence and non-maleficence. (shrink)

Data management raises a number of issues, both regulatory and non-regulatory. Researchers should understand how data are defined by their particular institutions and regulatory authorities. Data are the bases of scientific communication and provide a strong defense against allegations of scientific misconduct. Authorization is often necessary before collection of data can commence. Proper handling, retention, and storage of data, especially that involving humans, are crucial for the researcher. Data ownership by the institution leads (...) to a responsibility by the institution to educate all its researchers in responsible data management practices. (shrink)

Recent studies of scholarly data work argue that whatever researchers handle as data and, in particular, what researchers consider as potential evidence for supporting claims, counts as data. In this article I extend the relational approach towards data to the various ways of dealing with data in the course of a single research project. Relying on an example from ecology, I argue that data gain presence for and occupy researchers in manifold ways: for example, (...) as a promise, desire or pressure, or as a problem of trust and strength. In this way we notice that data, in the eyes of researchers, have more than one mode of existence, each of which is linked with its own expectations, challenges and actions. And we notice that these modes of existence not only coexist in the framework of a single project, but can also compete with each other. (shrink)

In many real-life situations, we are interested in the value of a physical quantity y that is difficult or impossible to measure directly. To estimate y, we find some easier-to-measure quantities x1, … , xn which are related to y by a known relation y = f. Measurements are never 100% accurate; hence, the measured values equation image are different from xi, and the resulting estimate equation image is different from the desired value y = f. How different can it (...) be? Traditional engineering approach to error estimation in data processing assumes that we know the probabilities of different measurement errors equation image. In many practical situations, we only know the upper bound Δi for this error; hence, after the measurement, the only information that we have about xi is that it belongs to the interval equation image. In this case, it is important to find the range y of all possible values of y = f when xi ∈ xi. We start the paper with a brief overview of the computational complexity of the corresponding interval computation problems: Most of the related problems turn out to be, in general, at least NP-hard. In this paper, we show how the use of quantum computing can speed up some computations related to interval and probabilistic uncertainty. We end the paper with speculations on whether “hypothetic” physical devices can compute NP-hard problems faster than in exponential time. Most of the paper's results were first presented at NAFIPS'2003 [30]. (shrink)

The starting point of this work is the gap between two distinct traditions in information engineering: knowledge representation and data - driven modelling. The first tradition emphasizes logic as a tool for representing beliefs held by an agent. The second tradition claims that the main source of knowledge is made of observed data, and generally does not use logic as a modelling tool. However, the emergence of fuzzy logic has blurred the boundaries between these two traditions by putting (...) forward fuzzy rules as a Janus-faced tool that may represent knowledge, as well as approximate non-linear functions representing data. This paper lays bare logical foundations of data - driven reasoning whereby a set of formulas is understood as a set of observed facts rather than a set of beliefs. Several representation frameworks are considered from this point of view: classical logic, possibility theory, belief functions, epistemic logic, fuzzy rule-based systems. Mamdani's fuzzy rules are recovered as belonging to the data - driven view. In possibility theory a third set-function, different from possibility and necessity plays a key role in the data - driven view, and corresponds to a particular modality in epistemic logic. A bi-modal logic system is presented which handles both beliefs and observations, and for which a completeness theorem is given. Lastly, our results may shed new light in deontic logic and allow for a distinction between explicit and implicit permission that standard deontic modal logics do not often emphasize. (shrink)

The global distribution of laboratory mouse strains is valued for ensuring the continuity, validity and accessibility of model organisms. Mouse strains are therefore assumed mobile and able to travel. We draw on the concept of ‘animal mobilities’ to explain how attending to laboratory mice as living animal, commodity and scientific tool is shaping how they are transported through contemporary scientific infrastructures and communities. Our paper is framed around exploring how animal strains travel, rather than animals, as we show that it (...) is only through understanding strain mobility that we can explain how and why live animal movement can be replaced by germinal products. The research is based on qualitative fieldwork in 2018 and 2019 that included 2 weeks ethnography and interviews with key informants involved in the movement of laboratory animals. The empirical analysis discusses practices that relate to managing biosecurity and animal welfare concerns when moving laboratory animal strains. In closing we reflect more broadly on the contemporary ‘ethico-onto-epistemological’ entanglement that shapes who or what travels to support laboratory science data-making practices, and the intensity of care ‘tinkering’ practices that facilitate the movement. We explain how a laboratory animal strain exceeds its value solely as a mobile and thus exchangeable commodity, illustrated in how values that relate to animal sentience and infection-risk supports its material transformation. Consequently, it is becoming increasingly common for non-sentient germinal products – embryos and gametes - to replace live sentient animals when being moved. (shrink)

Identity governance is an emerging concept for fine-grained conditional disclosure of identity information and enforcement of corresponding data handling policies. Although numerous technologies underlying identity management have been developed, people still have difficulty obtaining a clear picture of how their identity information is maintained, used, and propagated. An identity management framework is described for tracking the history of how a person’s identity information is handled after it is transferred across domains of control and for enforcing meta-policies related to (...) managing identity information distributed over the Internet. With this framework, organizations that manage identity information can improve accountability for their data practices and thereby increase their trustworthiness. The framework also enables users to control and optimize the propagation of their identity information in a user-centric manner. (shrink)

While much of the scholarly work on ethics relating to academic authorship examines the fair distribution of authorship credit, none has yet examined situations where a researcher contributes significantly to the project, but whose contributions do not make it into the final manuscript. Such a scenario is commonplace in collaborative research settings in many disciplines and may occur for a number of reasons, such as excluding research in order to provide the paper with a clearer focus, tell a particular story, (...) or exclude negative results that do not fit the hypothesis. Our concern in this paper is less about the reasons for including or excluding data from a paper and more about distributing credit in this type of scenario. In particular, we argue that the notion ‘substantial contribution’, which is part of the International Committee of Medical Journal Editors (ICMJE) authorship criteria, is ambiguous and that we should ask whether it concerns what ends up in the paper or what is a substantial contribution to the research process leading up to the paper. We then argue, based on the principles of fairness, due credit, and ensuring transparency and accountability in research, that the latter interpretation is more plausible from a research ethics point of view. We conclude that the ICMJE and other organizations interested in authorship and publication ethics should consider including guidance on authorship attribution in situations where researchers contribute significantly to the research process leading up to a specific paper, but where their contribution is finally omitted. (shrink)

The aim of this work is to identify and analyze a set of challenges that are likely to be encountered when one embarks on fieldwork in linguistic communities that feature small, young, and/or non-standard languages with a goal to elicit big sets of rich data. For each challenge, we (i) explain its nature and implications, (ii) offer one or more examples of how it is manifested in actual linguistic communities, and (iii) where possible, offer recommendations for addressing it effectively. (...) Our list of challenges involves static characteristics (e.g., absence of orthographic conventions and how it affects data collection), dynamic processes (e.g., speed of language change in small languages and how it affects longitudinal collection of big amounts of data), and interactive relations between non-dynamic features that are nevertheless subject to potentially rapid change (e.g., absence of standardized assessment tools or estimates for psycholinguistic variables). The identified challenges represent the domains of data collection and handling, participant recruitment, and experimental design. Among other issues, we discuss population limits and degree of power, inter- and intraspeaker variation, absence of metalanguage and its implications for the process of eliciting acceptability judgments, and challenges that arise from absence of local funding, conflicting regulations in relation to privacy issues, and exporting large samples of data across countries. Finally, the ten experimental challenges presented cover a broad typological spectrum, encompassing both spoken and sign, extant and nearly extinct languages. (shrink)

The analysis of data from electronic health records aspires to facilitate healthcare efficiencies and biomedical innovation. There are also ethical, legal and social implications from the handling of sensitive patient information. The paper explores the concerns, expectations and implications of the National Health Service England care.data programme: a national data sharing initiative of linked electronic health records for healthcare and other research purposes. Using Nissenbaum’s contextual integrity of privacy framework through a critical Science and Technology Studies (...) lens, it examines the way technologies and policies are developed to promote sustainability, governance and economic growth as the de facto social values, while reducing privacy to an individualistic preference. The state, acting as a new, central data broker reappropriates public ownership rights and establishes those information flows and transmission principles that facilitate the assetisation of NHS datasets for the knowledge economy. Various actors and processes from other contexts attempt to erode the public healthcare sector and privilege new information recipients. However, such data sharing initiatives in healthcare will be resisted if we continue to focus only on the monetary and scientific values of these datasets and keep ignoring their equally important social and ethical values. (shrink)

Airports are crucial channels of mobility for the global citizens of the twenty‐first century. They are points of entry and exit for tourists, business persons, workers, students and of course, for some refugees as well. The scale of operations is huge ‐ international passenger travel increased twelve‐fold in the second half of the twentieth century and the vast majority of this is accounted for in air travel. In the USA alone there are two million daily airtravelers on 20,000 flights. Airports (...) are ‘placeless’ sites of temporary sojourn, air‐lock chambers for nomadic executives or sun‐seekers. But they have profound social and political significance, particularly in personal data handling. (shrink)

Advances in applications of artificial intelligence and the use of data analytics technology in biomedicine are creating optimism, as many believe these technologies will fill the need-availability gap by increasing resources for mental health care. One resource considered especially promising is smartphone psychotherapy chatbots, i.e., artificially intelligent bots that offer cognitive behavior therapy to their users with the aim of helping them improve their mental health. While a number of studies have highlighted the positive outcomes of using smartphone psychotherapy (...) chatbots to handle various anxiety related problems no conclusive data illustrate their effectiveness or warrant their use in mental illness diagnosis and treatment settings. Yet smartphone psychotherapy is highly endorsed by experts in the field of mental health research. In this paper, I focus on the specific features of smartphone psychotherapy chatbots intended for the diagnosis and treatment of mental illness and criticize three popular promises; i.e., they enable early diagnosis and intervention through digital phenotyping; they defy the stigma of mental illness diagnosis and treatment; they offer increased access to mental health treatment globally. Going against the popular enthusiasm, I argue smartphone psychotherapy chatbots have epistemic and ethical limitations in the diagnosis and treatment of illnesses. In light of these, I encourage researchers, clinicians, policy makers, patients, and caregivers to pause before jumping on the artificial intelligence bandwagon to seek solutions for mental illness on the grounds of these three promises. (shrink)

Background Research on the impact of ethics reflection groups (ERG) (also called moral case deliberations (MCD)) is complex and scarce. Within a larger study, two years of ERG sessions have been used as an intervention to stimulate ethical reflection about the use of coercive measures. We studied changes in: employees’ attitudes regarding the use of coercion, team competence, user involvement, team cooperation and the handling of disagreement in teams. Methods We used panel data in a longitudinal design study (...) to measure variation in survey scores from multidisciplinary employees from seven departments within three Norwegian mental health care institutions at three time points (T0–T1–T2). Mixed models were used to account for dependence of data in persons who participated more than once. Results In total, 1068 surveys (from 817 employees who did and did not participate in ERG) were included in the analyses. Of these, 7.6% (N = 62) responded at three points in time, 15.5% (N = 127) at two points, and 76.8% (N = 628) once. On average, over time, respondents who participated in ERG viewed coercion more strongly as offending (p < 0.05). Those who presented a case in the ERG sessions showed lower scores on User Involvement (p < 0.001), Team Cooperation (p < 0.01) and Constructive Disagreement (p < 0.01). We observed significant differences in outcomes between individuals from different departments, as well as between different professions. Initial significant changes due to frequency of participation in ERG and case presentation in ERG did not remain statistically significant after adjustment for Departments and Professions. Differences were generally small in absolute terms, possibly due to the low amount of longitudinal data. Conclusions This study measured specific intervention-related outcome parameters for describing the impact of clinical ethics support (CES). Structural implementation of ERGs or MCDs seems to contribute to employees reporting a more critical attitude towards coercion. Ethics support is a complex intervention and studying changes over time is complex in itself. Several recommendations for strengthening the outcomes of future CES evaluation studies are discussed. CES evaluation studies are important, since—despite the intrinsic value of participating in ERG or MCD—CES inherently aims, and should aim, at improving clinical practices. (shrink)

The storage and access of genetic testing results have unique considerations for medical records. Initially, genetic testing was limited to patients with single gene diseases. Genetic medicine and testing have expanded, as have concerns about appropriately handling genetic information. In this study, we surveyed the management of genetic information in general hospitals in Japan using a questionnaire on access restrictions. Our questions included whether any other medical information was managed in a unique way. We identified 1037 hospitals designated for (...) clinical training located throughout Japan and received responses from 258 hospitals, and 191 reported that they handle genetic information and results of genetic tests. Of the 191 hospitals that handle genetic information, 112 hospitals implement access restrictions to genetic information. Seventy-one hospitals, one of which uses paper medical records rather than electrical medical records, do not enforce access restrictions. For eight hospitals, it was not known whether access restrictions were enforced or not. The responses from these hospitals indicated that access restrictions and storage methods varied across institution type (e.g., general vs. university hospitals), institution size, and the presence of a clinical genetics department. Other information, such as infectious disease diagnosis, psychological counseling records, abuse, and criminal history, was also subject to access restriction in 42 hospitals. The disparity in how medical facilities handle sensitive genetic information demonstrates a need for discussion between medical professionals and the general public on the storage of sensitive records, including genetic information. (shrink)

This paper addresses the growing problem of retractions in the scientific literature of publications that contain bad data, also called “false science.” While the problem is particularly acute in the biomedical literature because of the life-threatening implications when treatment recommendations and decisions are based on false science, it is relevant for any knowledge domain, including the social sciences, law, and education. Yet current practices for handling retractions are seen as inadequate. We use the metaphor of a virus to (...) illustrate how such studies can spread and contaminate the knowledge system, when they continue to be treated as valid. We suggest drawing from public health models designed to prevent the spread of biological viruses and compare the strengths and weaknesses of the current governance model of professional self-regulation with a proposed public health governance model. The paper concludes by considering the value of adding a triple-helix model that brings industry into the university-state governance mechanisms and incorporates bibliometric capabilities needed for a holistic treatment of the retraction process. (shrink)

Currently, most studies are moving towards Big Data Analytics because they are important in research, and this is becoming increasingly important as Internet and Web 2.0 technologies become increasingly popular and how to handle this massive data. Moreover, this proliferation of the Internet and social media has revolutionized the search process. With this Big Data of data generated by users using social media or electronic platforms, the use of these details and daily activities is integrated with (...) tools designed for analysis. The topic of analyzing big social media will be discussed and an intensive explanation will be given to the topic of Big Data. This paper compares Big Data analysis techniques using several methods of analysis, the first technique using neural networks and the second technique using data clustering. The purpose of this study is to infer the ages that use social media and what are their interests in writing and in the end, who are the most widely used social media males or females. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance and inform (...) ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

The pressure control for a liquid nature gas ship is vital for the cargo handling system which is a nonlinear, unstable, and controllable complex system accompanied by the dynamics of time delay. To improve the control effect, this article proposed a robust controller based on closed-loop gain shaping algorithm by mirror-mapping approach. In addition, the complete mechanism models for the system are established to predict the changes of temperature, pressure, and liquid inventory in the cargo tank. The heat exchanges (...) and evaporation of liquid are also considered. By collecting the data from the LNG ship “Dapeng,” the system models are validated. At the same time, the comparative experiment is introduced to verify the reliability and effectiveness. The scheme has been compared with the 2-DOF structure control law of modified Smith predictor. The comparative experimental results show that the scheme proposed in this note has a strong disturbance rejection ability and steady-state performance. The controller designed in this note has advantages of simplified construction method, satisfactory control effect and robustness. (shrink)

Matrices that cannot be handled using conventional clustering, regression or classification methods are often found in every big data research area. In particular, datasets with thousands or millions of rows and less than a hundred columns regularly appear in biological so-called omic problems. The effectiveness of conventional data analysis approaches is hampered by this matrix structure, which necessitates some means of reduction. An evolutionary method called PreCLAS is presented in this article. Its main objective is to find a (...) submatrix with fewer rows that exhibits some group structure. Three stages of experiments were performed. First, a benchmark dataset was used to assess the correct functionality of the method for clustering purposes. Then, a microarray gene expression data matrix was used to analyze the method’s performance in a simple classification scenario, where differential expression was carried out. Finally, several classification methods were compared in terms of classification accuracy using an RNA-seq gene expression dataset. Experiments showed that the new evolutionary technique significantly reduces the number of rows in the matrix and intelligently performs unsupervised row selection, improving classification and clustering methods. (shrink)

In China, the increasing traffic pressure has created traffic policing problems such as the heavy workloads of traffic police, as well as more conflicts between traffic police and road users. The treatment of traffic policing as a semiotic practice provides both a new perspective in and a helpful approach to dealing with these problems, yet few studies have investigated how semiotic resources are used in traffic policing interactions. This article examines the supportive role of semiotic resources in traffic policing, and (...) draws upon genre and exchanges involved as an illustration. The data include three audio recordings of traffic accident handling cases in Shanghai, China. Through qualitative analysis of the data from the perspective of Systemic Functional Linguistics, this article identifies the genre stages in traffic accident handling and their logico-semantic relations, and situates the typical exchanges and moves used by traffic police in different stages. It also discusses the usefulness of semiotic resources for traffic policing and the implications of these resources in tackling the existing problems of traffic policing in China. (shrink)

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

The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ (...) privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively, voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. (shrink)

A self-perceived lack of training in ethical theories and related pedagogy has kept many engineering faculty members from teaching data ethics, an important aspect of engineering research that has become more salient in recent years. This paper describes the development of a module, which includes concepts, cases, policies, and best practices, to support the teaching of ethical data practice. Based on a user-oriented design approach and a moral literacy framework, the module was designed to be used in different (...) courses and co-curricular activities for students of varying levels of competence to improve their ability to identify and analyze ethical problems associated with the handling of research data. This work seeks to encourage ethical reflection on researchers’ data practice through the idea of an “ecology of data,” which highlights the co-production of data by multiple, interconnected, and heterogeneous actors. This paper also presents online and in-class evidence about the impact and limitations of the module, which is now available for interested researchers and instructors to browse and use. (shrink)

This article explores how both corporate governance and corporate social responsibility can be improved by using insights from complexity theory. Complexity theory reveals that decentralized governance architecture is required for firms to absorb competently the increased intricacies, variety of variables, and objectives introduced by CSR. The current predominant form of centralized governance based on command-and-control hierarchies copes with complexities by reducing data inputs. This approach results in firms reducing their objectives, concerns, and insights about CSR. Firms with a decentralized (...) “network” form of governance architecture are used to illustrate how the data inputs of each manager can be reduced through the decomposition of decision-making labor to improve the capability of the firm to intelligently absorb and manage complexity. Network governance also introduces a division of powers with stakeholders to facilitate information flow and strengthen incentives to manage the enterprise to enhance both shareholder value and CSR. (shrink)