Openness is one of science’s fundamental ethical norms, but it should not take precedence over the obligation to protect the confidentiality of data. Deidentifying the data obtained from human genomic research as a condition of providing open access to research data is a strategy to promote scientific openness while protecting research participants’ confidentiality interests. However, given recent advances in methods of reidentifying individuals whose deidentified data are in genomic databases, the best way to (...) balance scientific openness and protection of human subjects in genomic research is to permit access to genomic data on a restricted basis. (shrink)

Deficiencies or unclear statements in consent forms used for research with human participants may lead to publicly-funded research data being unsuitable for sharing with other researchers. Long-term data use is encouraged or required by many of the UK research councils and other funding bodies. Researchers may fail to address long-term use and sharing of data when obtaining informed consent and when arranging to keep data obtained from participants confidential. Sharing data should not (...) compromise confidentiality if care is taken to anonymize information where this is needed or has been explicitly requested. Whilst researchers should consider all potential uses of their research data in confidentiality and consent agreements, research ethics committees can provide better guidance by pointing out to researchers that long-term use, preservation and sharing of research data should be taken into account when informing participants and obtaining their consent. (shrink)

This dataset documents results and code for the paper "Interdisciplinarity in the 17th Century? A Co-Occurrence Analysis of Early Modern German Dissertation Titles" by Stefan Heßbrüggen-Walter, forthcoming in *Synthese*. The data to be processed are contained in four files, derived from a larger dataset related to German dissertations and sourced from the national bibliography of 17th century German prints *VD 17* that will be released at a later date. More information can be found in the file `README.md`.

ABSTRACTThere has considerable interest in bringing low/middle-income countries scientists into discussions on Open Data – both as contributors and users. The establishment of in situ data sharing practices within LMIC research institutions is vital for the development of an Open Data landscape in the Global South. Nonetheless, many LMICs have significant challenges – resource provision, research support and extra-laboratory infrastructures. These low-resourced environments shape data sharing activities, but are rarely examined within Open Data (...) discourse. In particular, little attention is given to how these research environments shape scientists’ perceptions of data sharing incentives. This paper expands on these issues of incentivizing data sharing, using data from a quantitative survey disseminated to life scientists in 13 countries in sub-Saharan Africa. This interrogated not only perceptions of data sharing amongst LMIC scientists, but also how these are connected to the research environm... (shrink)

Research data produced in both universities and the NHS are subject to the Freedom of Information Act 2000. This article examines the practical and ethical implications of freedom of information for research data, arguing that increased openness is both here to stay and is ethically justifiable. Researchers need to learn how best to cope with this.

Research integrity is core to the mission of higher education. In undergraduate student samples, self-reported rates of data fabrication have been troublingly high. Despite this, no research has investigated undergraduate data fabrication in a more systematic manner. We applied duplication screening techniques to 18 data sets submitted by psychology honors students for assessment. Although we did not identify any completely duplicated cases, there were numerous partial duplicates. Rather than indicating fabrication, however, these partial duplicates are (...) likely a consequence of poor measure selection, insufficient data screening, and/or participant characteristics. Implications for the teaching and supervision of honors students are discussed. (shrink)

Since January 2016, the Mahidol Oxford Tropical Medicine Research Unit has trialled a data-sharing policy where requests to access research datasets are processed through a Data Access Committee. In this paper, we share our experiences establishing data management systems and data-sharing infrastructure including a data-sharing policy, data access committee and related procedures. We identified a number of practical and ethical challenges including requests for datasets collected without specific or broad consent to (...) class='Hi'>data sharing and requests from pharmaceutical companies for data to support drug registration applications. We also encountered significant resource constraints which required the development of appropriate human resources and infrastructure. We suggest a research agenda to promote responsible and equitable data sharing while safeguarding the rights and interests of research participants and researchers. (shrink)

Although data sharing is one of the primary measures proposed to improve the integrity and quality of published research, studies show it remains the exception not the rule. The current study examines the availability of data in papers reporting the results of analyses of empirical data from original research in high-impact addiction journals. Thirteen high-impact journals with data sharing policies were selected from those included in the substance abuse category of the 2018 Clarivate Analytics’ (...) Journal Citation Report. The first 10 full or short original research reports that included empirical data in the most recent complete issue of each journal were electronically searched and reviewed for reference to where their data can be obtained and for a formal data sharing statement. Only eight of the 130 papers contained a data sharing statement in their text or supplementary online materials, and just one contained a direct link to the data analyzed. Data sharing was rare in the 13 high-impact addiction journals reviewed. The nature of the data reported in addiction journals might partly explain this. Currently, data sharing is not a procedure likely to improve the quality and integrity of published addiction research. (shrink)

The purpose is to provide researchers with reliable Scientific Research Data from the massive amounts of research data to establish a sustainable Scientific Research environment. Specifically, the present work proposes establishing an Intelligent Recommendation System based on Machine Learning algorithm and SRD. Firstly, the IRS is established over ML technology. Then, based on user Psychology and Collaborative Filtering recommendation algorithm, a hybrid algorithm [namely, Content-Based Recommendation-Collaborative Filtering ] is established to improve the utilization efficiency of (...) SRD and Sustainable Development of SR. Consequently, the present work designs literature and SRD-targeted IRS using the hybrid recommendation under the background of SD. The proposed system’s feasibility is analyzed through experiments. Additionally, the system performance is analyzed and verified from accuracy, diversity, coverage, novelty, and recommendation efficiency. The results show that the hybrid algorithm can make up for the shortcomings of a single algorithm and improve the recommendation efficiency. Experiments show that the accuracy of the proposed CBR-CF algorithm is the highest. In particular, the recommendation accuracy for the single-user system can reach 82–93%, and the recall of all recommended algorithms falls between 60 and 91%. The recall of the hybrid algorithm is higher than that of a single algorithm, and the highest recall is 91%. Meanwhile, the hybrid algorithm has comprehensive coverage, good applicability, and diversity. Therefore, SD-oriented SRD-targeted IRS is of great significance to improve the SRD utilization and the accuracy of IRS, and expand the achievement value of SR. The research content provides a reference for establishing a sustainable SR environment and improving SR efficiency. (shrink)

BackgroundWe assessed potential consent bias in a cohort of > 40,000 adult patients asked by mail after hospitalization to consent to the use of past, present and future clinical and biological data in an ongoing ‘general consent’ program at a large tertiary hospital in Switzerland.MethodsIn this retrospective cohort study, all adult patients hospitalized between April 2019 and March 2020 were invited to participate to the general consent program. Demographic and clinical characteristics were extracted from patients’ electronic health records (EHR). (...) Data of those who provided written consent (signatories) and non-responders were compared and analyzed with R studio.ResultsOf 44,819 patients approached, 10,299 (23%) signed the form. Signatories were older (median age 54 [IQR 38–72] vs. 44 years [IQR 32–60], p <.0001), more comorbid (2614/10,299 [25.4%] vs. 4912/28,676 [17.1%] with Charlson comorbidity index ≤ 4, p <.0001), and more often of Swiss nationality (6592/10,299 [64%] vs. 13,813/28,676 [48.2%], p <.0001).ConclusionsOur results suggest that actively seeking consent creates a bias and compromises the external validity of data obtained via ‘general consent’ programs. Other options, such as opt-out consent procedures, should be further assessed. (shrink)

Making research data readily accessible during a public health emergency can have profound effects on our response capabilities. The moral milieu of this data sharing has not yet been adequately explored. This article explores the foundation and nature of a duty, if any, that researchers have to share data, specifically in the context of public health emergencies. There are three notable reasons that stand in opposition to a duty to share one’s data, relating to: (i) (...) data property and ownership, (ii) just distribution of benefits and burdens and (iii) the contemporary ethos of science. We argue each reason can be successfully met with corresponding rationale in favour of data sharing. Further support for data sharing has been echoed in policies of health agencies, funding bodies and academic institutions; in documents on the ethical conduct of biomedical research; and in discussions on the nature of public health. From this, we ascertain that sharing data is the morally sound default position. This article then highlights the key roles reciprocity and solidarity play in supporting the practice of data sharing. We conclude with recommendations to regard public health research data as a common-pool resource in order to build a framework for stable data sharing management. (shrink)

A powerful feature of global health research is data‐sharing with regions which bear the heaviest burden of disease. It offers novel opportunities for aggregating data to address critical global health challenges in ways higher than relying on individual studies. Yet there exist important stratifiers of the capacity to share data, particularly across the Global North‐South divide. Systemic challenges that characterize sub‐Saharan Africa and disadvantage the region's scientific productivity threaten the burgeoning data‐sharing culture too. Like all (...) endeavors requiring equal commitments under unequal circumstances, a strong ethical impetus is needed to help reduce inequities and imbalances to encourage adherence. This article discusses mandatory data‐sharing in relation to peculiar challenges faced by sub‐Saharan African scientists to suggest ethical principles for rethinking and reframing solutions. We propose six principles which mirror guidelines from the Institute of Medicine and encapsulate principles from the Emanuel Framework, Nairobi Data Sharing Principles, and the COHRED guidelines. (shrink)

BackgroundInstitutions, funding agencies and publishers are placing increasing emphasis on good research data management (RDM). RDM lapses in medical science can result in questionable data and cause the public’s confidence in the scientific community to crumble. A fledgling medical school in a young university in Singapore has mandated every funded research project to have a data management plan (DMP). However, researchers’ adherence to their DMPs was unknown until the school embarked on routine data auditing. (...) We hypothesize that research data auditing improves RDM awareness, compliance and reception in the school.MethodsWe conducted surveys with research PIs and researchers before and after data auditing to evaluate differences in self-reported RDM awareness, compliance and reception. As it is mandatory to deposit research data in a central data repository system in the school, we tracked data deposition by each laboratory from 2 weeks before to 3 months after data auditing as a marker of actual RDM compliance.ResultsResearch data auditing had an overall positive effect on self-reported RDM awareness, compliance and reception for both research PIs and researchers. Research PIs agreed more that RDM was important to scientific reproducibility, were more aware of proper RDM, had higher RDM strength in their laboratories and were more compliant with the DMP. Both research PIs and researchers believed data auditing helped them to be more compliant with data deposition in the repository. However, data auditing had no significant impact on laboratories’ data deposition rates over time, which could be due to the short sampling period.ConclusionsResearch PIs and researchers generally felt that data auditing was effective in improving RDM practices. It helped to evaluate their RDM practices objectively, propose corrective actions for RDM lapses and spread awareness of the university’s data management policies. Our findings corroborated other studies in medical research, geosciences, engineering and ethics that data auditing promotes good RDM practices. Hence, we recommend research institutions worldwide to adopt data auditing as a tool to reinforce research integrity. (shrink)

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

Answering important public health questions often requires collection of sensitive information about individuals. For example, our understanding of how HIV is transmitted and how to prevent it only came about with people's willingness to share information about their sexual and drug-using behaviors. Given the scientific need for sensitive, personal information, researchers have a corresponding ethical and legal obligation to maintain the confidentiality of data they collect and typically promise in consent forms to restrict access to it and not to (...) publish identifying data.The interests of others, however, can threaten researchers' promises of confidentiality when legal demands are made to access research data. In some cases, the subject of the litigation is tightly connected to the research questions, and litigants' interest in the data is not surprising. Researchers conducting studies on tobacco or occupational or other chemical exposures, for example, are relatively frequent targets of subpoenas. (shrink)

This article describes Cosa Nostra bosses’ psychological characteristics starting from research data collected over almost twenty years. Through the use of fragments of interviews to justice collaborators and their wives, children, and sisters-in-law, we analyze the boss’ role and relational context. This article also reports the results of a recent research on the relationship between boss and white-collar workers, carried out through the analysis of environmental tapping recorded at a famous Sicilian Mafia boss’ house: Giuseppe Guttadauro. This (...) research highlights the psychological diversity the relationship between boss and white-collar worker is based on. The analysis of environmental tapping transcriptions has also allowed discovery of the main relational modalities used by the boss and white-collar worker in their conversations. (shrink)

Thirty-Five Years of Research on Neuro-Linguistic Programming. NLP Research Data Base. State of the Art or Pseudoscientific Decoration? The huge popularity of Neuro-Linguistic Programming therapies and training has not been accompanied by knowledge of the empirical underpinnings of the concept. The article presents the concept of NLP in the light of empirical research in the Neuro-Linguistic Programming Research Data Base. From among 315 articles the author selected 63 studies published in journals from the Master (...) Journal List of ISI. Out of 33 studies, 18.2% show results supporting the tenets of NLP, 54.5% - results non-supportive of the NLP tenets and 27.3% brings uncertain results. The qualitative analysis indicates the greater weight of the non-supportive studies and their greater methodological worth against the ones supporting the tenets. Results contradict the claim of an empirical basis of NLP. (shrink)

Pharmaceutical companies can dramatically improve their understanding of how certain drugs work by having access to data from prospective research participants and those enrolled in clinical trials. Yet can data legitimately be used in ways that these individuals have not specifically authorized? In some cases it is ethically acceptable to share data with pharmaceutical companies even if there was no specific consent to do so by appealing to the principles of beneficence and respect for persons. These (...) principles require us to look not just at how well the process and documentation of consent conform to regulatory requirements, but also at how well they minimize harm and maximize benefit for research participants and future beneficiaries of the research. (shrink)

This paper examines the consequences of a culture of “personal ethics” when using new methodologies, such as the use of social media sites as a source of data for research. Using SM research as an example, this paper explores the practices of a number of actors and researchers within the “Ethics Ecosystem” which as a network governs ethically responsible research behaviour. In the case of SM research, the ethical use of this data is currently (...) in dispute, as even though it is seemingly publically available, concerns relating to privacy, vulnerability, potential harm and consent blur the lines of responsible ethical research behaviour. The findings point to the dominance of a personal, bottom-up, researcher-led, ‘ethical barometer’ for making decisions regarding the permissibility of using SM data. We show that the use of different barometers by different researchers can lead to wide disparities in ethical practice - disparities which are compounded by the lack of firm guidelines for responsible practice of SM research. This has widespread consequences on the development of shared norms and understandings at all levels, and by all actors within the Ethics Ecosystem, and risks inconsistencies in their approaches to ethical decision-making. This paper argues that this governance of ethical behaviour by individual researchers perpetuates a negative cycle of academic practice that is dependent on subjective judgements by researchers themselves, rather than governed by more formalised academic institutions such as the research ethics committee and funding council guidelines. (shrink)

Responsible data management is a multifaceted topic involving standards within the research community regarding research design and the sharing of data as well as the collection, selection, analysis and interpretation of data. Transparency in the manipulation of images is increasingly important in order to avoid misrepresentation of research findings, and research oversight is also critical in helping to assure the integrity of the research process. Intellectual property issues both unite and divide academe (...) and industry in their approaches to data management. Central to the realization and promulgation of responsible data management is clear and careful communication of standards and expectations within the research community to trainees as well as among colleagues. These topics are examined and explored in depth in a special issue of Science and Engineering Ethics on responsible data management. (shrink)

Research funders, regulatory agencies, and journals are increasingly expecting that individual-level data from health research will be shared. Broad consent to such sharing is considered appropriate, feasible and acceptable in low- and middle-income settings, but to date limited empirical research has been conducted to inform the design of such processes. We examined stakeholder perspectives about how best to seek broad consent to sharing data from the Mahidol Oxford Tropical Medicine Research Unit, which implemented a (...) data sharing policy and broad consent to data sharing in January 2016. Between February and August 2017 qualitative data were collected at two sites, Bangkok and the Thai-Myanmar border town of Mae Sot. We conducted eighteen semi-structured interviews. We also conducted four focus group discussions with a total of nineteen people. Descriptive and thematic coding informed analysis of aspects of data sharing that are considered most important to inform participants about, and the best ways to explain complex and abstract topics relating to data sharing. The findings demonstrated that clinical trial participants prioritise information about the potential benefits and harms of data sharing. Stakeholders made multiple suggestions for clarifying information provided about data sharing on such topics. There was significant variation amongst stakeholders’ perspectives about how much information should be provided about data sharing, and it was clear that effective information provision should be responsive to the study, the study population, the individual research participant and the research context. Effectively communicating about data sharing with research participants is challenging in practice, highlighting the importance of robust and effective data sharing governance in this context. Broad consent should incorporate effective and efficient explanations of data sharing to promote informed decision-making, without impeding research participants’ understandings of key aspects of the research from which data will be shared. Further work is required to refine both the development of core information about data sharing to be provided to all research participants, and appropriate solutions for context specific-challenges arising when explaining data sharing. (shrink)

The practice of conducting research online is in its infancy. Consequently there is debate concerning the ethical implications of online data collection. We outline three approaches to online data collection and focus specifically on the issues of consent and anonymity of participants. We conclude that ethical issues raised when planning and implementing online data collection are no different to those raised by more traditional approaches to data collection.

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

This article examines the possible basis for legal liability of researchers who use the Internet in the collection of research data. In particular, it examines the potential legal issues associated with the protocols of ethnographers who use listserv, discussion board, blog, chat room and other sorts of web or Internet-based postings as the source of their data. The author assumes that the forum for participation is legitimate, in that the list, board, blog, chat, etc. is not created (...) or otherwise concocted by the researcher, but involves situations akin to off-line practices where a researcher would be listening to the conversation or watching the behavior of subjects in a public place. Since these practices do not involve direct interview or interaction with or treatment of "human subjects", much of the precedent involving negligence, informed consent, and related issues is not relevant; however, it is reviewed initially for guidance in suggesting the applicable, legal standards of conduct. This article does not discuss the use of the Internet as a collection device, e.g., in online surveys. Issues of legal harm arising from claims in negligence, privacy, and defamation for subsequent disseimination of such postings are evaluated. Although it is concluded that the likelihood for a success of such claims is small, analysis will include steps that researchers can take to ensure that such risk remains negligible. (shrink)

Qualitative data typically contain multiple identifiable characteristics about people, places and events, in the unique voice of each participant. This short report considers sensitivity and security of audio-recordings, drawing attention to a lack of guidelines for researchers on the preservation or destruction of such data. The authors urge debate on this issue, with due consideration to both ethics and scientific rigour.

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

Appropriate information and consent has been one of the most intensely discussed topics within the context of biobank research. In parallel to the normative debate, many socio-empirical studies have been conducted to gather experiences, preferences and views of patients, healthy research participants and further stakeholders. However, there is scarcity of literature which connects the normative debate about justifications for different consent models with findings gained in empirical research. In this paper we discuss findings of a limited review (...) of socio-empirical research on patients’ and healthy research participants’ experiences and views regarding consent to biobank research in light of ethical principles for appropriate information and consent. (shrink)

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

In this paper, we present a case study of how well subject metadata (comprising headings from an international classification scheme) has been deployed in a national data catalogue, and how often data seekers use subject metadata when searching for data. Through an analysis of user search behaviour as recorded in search logs, we find evidence that users utilise the subject metadata for data discovery. Since approximately half of the records ingested by the catalogue did not include (...) subject metadata at the time of harvest, we experimented with automatic subject classification approaches in order to enrich these records and to provide additional support for user search and data discovery. Our results show that automatic methods work well for well represented categories of subject metadata, and these categories tend to have features that can distinguish themselves from the other categories. Our findings raise implications for data catalogue providers; they should invest more effort to enhance the quality of data records by providing an adequate description of these records for under-represented subject categories. (shrink)

Public-private partnerships are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public (...) antipathy to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service providing patient data to Google’s DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR. Please refer to that article for more information on 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. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data. (shrink)

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

Qualitative researchers sometimes talk about objectivity in relation to qualitative data sets. In this paper, I defend a reconstructed notion of objective qualitative data sets that may serve as a useful and reachable guiding ideal in qualitative data generation. In the first part of the paper, I develop the ideal. According to it, a qualitative data set is objective to the extent that it, in conjunction with true assumptions, possesses a combination of good-making features in virtue (...) of which the data set is suited to serve as evidence base for a satisfying answer to the research question under study. In the second part of the paper, I examine and reject two possible lines of objection to this ideal: One is that it picks out the wrong good-making features. The other is that the very focus on good-making features is misguided: the objectivity of a qualitative data set should instead be seen as a matter of how it was generated or evaluated. (shrink)

Medical research data is sensitive personal data that needs to be protected from unauthorized access and unintentional disclosure. In a research setting, sharing of data within the scientific community is necessary in order to make progress and maximize scientific benefits derived from valuable and costly data. At the same time, convincingly protecting the privacy of people participating in medical research is a prerequisite for maintaining trust and willingness to share. In this commentary, we (...) will address this issue and the pitfalls involved in the context of the PEP project1 that provides the infrastructure for the Personalized Parkinson’s Project,2 a large cohort study on Parkinson’s disease from Radboud University Medical Center, in cooperation with Verily life Sciences, an Alphabet subsidiary. (shrink)

To address the ethical challenges in big data health research we propose the concept of systemic oversight. This approach is based on six defining features and aims at creating a common ground across the oversight pipeline of biomedical big data research. Current trends towards enhancing granularity of informed consent and specifying legal provisions to address informational privacy and discrimination concerns in data-driven health research are laudable. However, these solutions alone cannot have the desired impact (...) unless oversight activities by different stakeholders acquire a common substantive orientation. (shrink)

This essay examines issues involving personal privacy and informed consent that arise at the intersection of information and communication technology and population genomics research. I begin by briefly examining the ethical, legal, and social implications program requirements that were established to guide researchers working on the Human Genome Project. Next I consider a case illustration involving deCODE Genetics, a privately owned genetics company in Iceland, which raises some ethical concerns that are not clearly addressed in the current ELSI guidelines. (...) The deCODE case also illustrates some ways in which an ICT technique known as data mining has both aided and posed special challenges for researchers working in the field of population genomics. On the one hand, data-mining tools have greatly assisted researchers in mapping the human genome and in identifying certain “disease genes” common in specific populations. On the other hand, this technology has significantly threatened the privacy of research subjects participating in population genomics studies, who may, unwittingly, contribute to the construction of new groups that put those subjects at risk for discrimination and stigmatization. In the final section of this paper I examine some ways in which the use of data mining in the context of population genomics research poses a critical challenge for the principle of informed consent, which traditionally has played a central role in protecting the privacy interests of research subjects participating in epidemiological studies. (shrink)

We employ data envelopment analysis on a series of experiments performed in Fermilab, one of the major high-energy physics laboratories in the world, in order to test their efficiency (as measured by publication and citation rates) in terms of variations of team size, number of teams per experiment, and completion time. We present the results and analyze them, focusing in particular on inherent connections between quantitative team composition and diversity, and discuss them in relation to other factors contributing to (...) scientific production in a wider sense. Our results concur with the results of other studies across the sciences showing that smaller research teams are more productive, and with the conjecture on curvilinear dependence of team size and efficiency. (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)

Case Study research is characterized by the employment of multiple data gathering methods. In this paper, I examine the concurrent use of participant observation and qualitative interviews. The question I examine is: what is the rationale behind their combination in case study research? In the literature on case study research, the two most common reasons for using multiple methods appeal to comprehensiveness and convergent confirmation respectively. I argue that there is a third significant, yet overlooked, way (...) to motivate the joint use of participant observation and qualitative interviews: the methods may generate complementary evidence and this puts the researcher in a better position to confirm that her data manifest central epistemic values and so are suitable as basis for providing an adequate answer to her research question. I refer to this as the rationale of blended epistemic value validation. (shrink)

In this paper we argue the need to reimagine research ethics frameworks to include notions of environmental sustainability. While there have long been calls for healthcareethics frameworks and decision-making to include aspects of sustainability, less attention has focused on howresearchethics frameworks could address this. To do this, we first describe the traditional approach to research ethics, which often relies on individualised notions of risk. We argue that we need to broaden this notion of individual risk to consider issues (...) associated with environmental sustainability. This is because research is associated with carbon emissions and other environmental impacts, both of which cause climate change health hazards. We introduce how bioethics frameworks have considered notions of environmental sustainability and draw on these to help develop a framework suitable for researchers. We provide a case study of data-driven health research to apply our framework. (shrink)