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Drawing upon ethnographic observations of staff working within a research laboratory built around research and clinical data from twins, this article analyzes practices underlying the production and maintenance of a research database. While critical data studies have discussed different forms of ‘data work’ through which data are produced and turned into effective research resources, in this paper we foreground a specific form of data work, namely the affective and attentive relationships that humans build with data. Building on STS and feminist scholarship that highlights the importance of care in scientific work, we capture this specific form of data work as care. Treating data as relational entities, we discuss a set of caring practices that staff employ to produce and maintain their data, as well as the hierarchical and institutional arrangements within which these caring practices take place. We show that through acts of caring, that is, through affective and attentive engagements, researchers build long-term relationships with the data they help produce, and feel responsible for its flourishing and growth. At the same time, these practices of care – which we found to be gendered and valued differently from other practices within formal and informal reward systems – help to make data valuable for the institution. In this manner, care for data is an important practice of valuation and valorisation within data-intensive research that has so far received little explicit attention in scholarship and professional research practice.

Purpose This paper aims at understanding the current situation of research support services offered by academic libraries in world-leading universities and providing useful implications and insights for other academic libraries. Design/methodology/approach Of the top 100 universities listed in the QS World University Rankings in 2017, 76 libraries were selected as samples and a website investigation was conducted to explore the research support services. The statistical method and visualization software was used to generalize the key services, and the text analysis and case analysis were applied to reveal the corresponding implementation. Findings Research support service has become one of the significant services of academic libraries in the context of e-research and data-intensive research. The research support services can be generally divided into seven aspects, as follows: research data management (62, 81.58 per cent), open access (64, 84.21 per cent), scholarly publishing (59, 77.63 per cent), research impact measurement (32, 42.11 per cent), research guides (47, 61.84 per cent), research consultation (59, 77.63 per cent) and research tools recommendation (38, 50.00 per cent). Originality/value This paper makes a comprehensive investigation of research support services in academic libraries of top-ranking universities worldwide. The findings will help academic libraries improve research support services; thus, advancing the work of researchers and promoting scientific discovery.

With the widespread application of big data, artificial intelligence, and machine learning technologies in the medical field, a new paradigm of data-intensive clinical research is emerging as a key force driving medical advancement. This new paradigm presents unprecedented challenges for graduate education in clinical professions, encompassing multidisciplinary integration needs, high-quality faculty shortages, learning method transformations, assessment system updates, and ethical concerns. Future healthcare professionals will need not only to possess traditional medical knowledge and clinical skills, but also to master interdisciplinary skills such as data analysis, programming, and statistics. In response, this paper proposes a series of countermeasures, including curriculum reconstruction, faculty development, developing and sharing resources, updating the evaluation and assessment system, and strengthening ethics education. These initiatives aim to help clinical graduate education better adapt to this new paradigm, ultimately cultivating interdisciplinary talents in medical-computer integration.

Background The rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social licence for achieving such ethical governance. Main text We performed a narrative review of the social licence as presented in the biomedical literature. We used a systematic search and selection process, followed by a critical conceptual analysis. The systematic search resulted in nine publications. Our conceptual analysis aims to clarify how societal permission can be granted to health research projects which rely upon the reuse and/or linkage of health data. These activities may be morally demanding. For these types of activities, a moral legitimation, beyond the limits of law, may need to be sought in order to preserve trust. Our analysis indicates that a social licence encourages us to recognise a broad range of stakeholder interests and perspectives in data-intensive health research. This is especially true for patients contributing data. Incorporating such a practice paves the way towards an ethical governance, based upon trust. Public engagement that involves patients from the start is called for to strengthen this social licence. Conclusions There are several merits to using the concept of social licence as a guideline for ethical governance. Firstly, it fits the novel scale of data-related risks; secondly, it focuses attention on trustworthiness; and finally, it offers co-creation as a way forward. Greater trust can be achieved in the governance of data-intensive health research by highlighting strategic dialogue with both patients contributing the data, and the public in general. This should ultimately contribute to a more ethical practice of governance.

Data-intensive research, including policy modelling, poses some distinctive challenges for efforts to mainstream public involvement into health research. There is a need for learning about how to design and deliver involvement for these types of research which are highly technical, and where researchers are at a distance from the people whose lives data depicts. This article describes our experiences involving members of the public in the SIPHER Consortium, a data-intensive policy modelling programme with researchers and policymakers working together over five years to try to address health inequalities. We focus on evaluating people’s experiences as part of Community Panels for SIPHER. Key issues familiar from general public involvement efforts include practical details, careful facilitation of meetings, and payment for participants. We also describe some of the more particular learning around how to communicate technical research to non-academic audiences, in order to enable public scrutiny of research decisions. We conclude that public involvement in policy modelling can be meaningful and enjoyable, but that it needs to be carefully organised, and properly resourced. Plain English summary Actively involving members of the public is less common in ‘data-intensive health research’ (health research which does not create new data but focuses on analysing big existing datasets of statistics) than in conventional health research. ‘Computational policy modelling’ is an example of data-intensive health research where public involvement is not yet standard practice. This article describes our experiences involving members of the public in the SIPHER Consortium, a policy modelling programme with researchers and policymakers working together over five years to try to address health inequalities. This paper focuses on evaluating people’s experiences as part of Community Panels for SIPHER. We brought together people with lived experience of health inequalities into three Community Panels, and we met for half a day 3-4 times a year to discuss and give feedback on the research. At first, it was difficult for Panel members to understand the research. Researchers had to try harder to avoid jargon, explain their work in plain English, and focus on the impact of the research in the ‘real world’. Both the researchers and the Panel members learned how to communicate better over repeated meetings. Over time, we managed to have meaningful discussion of the choices researchers were making, so Panels could see their impact on the research. It was important that details of the meetings – planning meetings carefully so everyone feels welcome and valued, providing support with digital technology, financially rewarding people for their time – were taken seriously. We conclude that public involvement in policy modelling can be meaningful and enjoyable, but that it needs to be carefully organised, and takes time and money to get right.

This article reframes the potential of generative AI in qualitative analysis, shifting from a focus on efficiency and automation toward opportunities to deepen analysis and strengthen core commitments of qualitative inquiry. We introduce “AI-in-the-loop analysis” as a term to describe the intentional incorporation of computational capabilities into analytic processes that remain grounded in human sensemaking, interpretation, and reflexive judgment. Building from foundational commitments of qualitative inquiry such as sustained attention to fine-grained data in relation to its larger context and intentional engagement of positionalities to support noticing and interpretation, we examine how properties of large language models (LLMs) can be mobilized to extend these practices. We focus on affordances provided by AI’s large-scale pre-training, rich semantic representations, attention mechanisms, long-context capacities, and interactive prompting, and describe ways that thoughtful engagement with these capabilities can help researchers maintain close attention to the details of the data across multiple iterations while situating interpretations in context, expand interpretive perspectives in dialogue with each other to layer meaning, and surface both confirming and disconfirming evidence across complex datasets. We connect these possibilities to established criteria for trustworthiness such as credibility, dependability, confirmability, transferability, and authenticity, showing how AI-in-the-loop approaches can offer new mechanisms for achieving and demonstrating analytic rigor. Rather than replacing human interpretive labor, generative AI can be used to augment researchers’ capacity for noticing, questioning, and synthesizing across large and complex qualitative data sets. When used critically and transparently, AI-in-the-loop analysis offers the possibility to expand the methodological repertoire of qualitative researchers for supporting rigorous, trustworthy, reflexive, contextually grounded analyses.

Dynamic consent forms a comprehensive, tailored approach for interacting with research participants. We conducted a survey study to inquire how research participants evaluate the elements of consent, information provision, communication and return of results within dynamic consent in a hypothetical health data reuse scenario. We distributed a digital questionnaire among a purposive sample of patient panel members. Data were analysed using descriptive and nonparametric inferential statistics. Respondents favoured the potential to manage changing consent preferences over time. There was much agreement between people favouring closer and more specific control over data reuse approval and those in favour of broader approval, facilitated by an opt-out system or an independent data reuse committee. People want to receive more information about reuse, outcomes and return of results. Respondents supported an interactive model of research participation, welcoming regular, diverse and interactive forms of communication, like a digital communication platform. Approval for reuse and providing meaningful information, including meaningful return of results, are intricately related to facilitating better communication. Respondents favoured return of actionable research results. These findings emphasize the potential of dynamic consent for enabling participants to maintain control over how their data are being used for which purposes by whom. Allowing different options to shape a dynamic consent interface in health data reuse in a personalized manner is pivotal to accommodate plurality in a flexible though robust manner. Interaction via dynamic consent enables participants to tailor the elements of participation they deem relevant to their own preferences, engaging diverse perspectives, interests and preferences.

Background: Patients and publics are generally positive about data-intensive health research. However, conditions need to be fulfilled for their support. Ensuring confidentiality, security, and privacy of patients’ health data is pivotal. Patients and publics have concerns about secondary use of data by commercial parties and the risk of data misuse, reasons for which they favor personal control of their data. Yet, the potential of public benefit highlights the potential of building trust to attenuate these perceptions of harm and risk. Nevertheless, empirical evidence on how conditions for support of data-intensive health research can be operationalized to that end remains scant. Objective: This study aims to inform efforts to design governance frameworks for data-intensive health research, by gaining insight into the preferences of patients and publics for governance policies and measures. Methods: We distributed a digital questionnaire among a purposive sample of patients and publics. Data were analyzed using descriptive statistics and nonparametric inferential statistics to compare group differences and explore associations between policy preferences. Results: Study participants (N=987) strongly favored sharing their health data for scientific health research. Personal decision-making about which research projects health data are shared with (346/980, 35.3%), which researchers/organizations can have access (380/978, 38.9%), and the provision of information (458/981, 46.7%) were found highly important. Health data–sharing policies strengthening direct personal control, like being able to decide under which conditions health data are shared (538/969, 55.5%), were found highly important. Policies strengthening collective governance, like reliability checks (805/967, 83.2%) and security safeguards (787/976, 80.6%), were also found highly important. Further analysis revealed that participants willing to share health data, to a lesser extent, demanded policies strengthening direct personal control than participants who were reluctant to share health data. This was the case for the option to have health data deleted at any time (P<.001) and the ability to decide the conditions under which health data can be shared (P<.001). Overall, policies and measures enforcing conditions for support at the collective level of governance, like having an independent committee to evaluate requests for access to health data (P=.02), were most strongly favored. This also applied to participants who explicitly stressed that it was important to be able to decide the conditions under which health data can be shared, for instance, whether sanctions on data misuse are in place (P=.03). Conclusions: This study revealed that both a positive attitude toward health data sharing and demand for personal decision-making abilities were associated with policies and measures strengthening control at the collective level of governance. We recommend pursuing the development of this type of governance policy. More importantly, further study is required to understand how governance policies and measures can contribute to the trustworthiness of data-intensive health research.

Cloud computing infrastructure is now widely used in many domains, but one area where there has been more limited adoption is research computing, in particular for running scientific high-performance computing (HPC) software. The Robust Application Porting for HPC in the Cloud (RAPPORT) project took advantage of existing links between computing researchers and application scientists in the fields of bioinformatics, high-energy physics (HEP) and digital humanities, to investigate running a set of scientific HPC applications from these domains on cloud infrastructure. In this paper, we focus on the bioinformatics and HEP domains, describing the applications and target cloud platforms. We conclude that, while there are many factors that need consideration, there is no fundamental impediment to the use of cloud infrastructure for running many types of HPC applications and, in some cases, there is potential for researchers to benefit significantly from the flexibility offered by cloud platforms.

Abstract Background Advances in next generation sequencing make it possible to obtain high-coverage sequence data for large numbers of viral strains in a short time. However, since most bioinformatics tools are developed for command line use, the selection and accessibility of computational tools for genome assembly and variation analysis limits the ability of individual labs to perform further bioinformatics analysis Findings Findings: We have developed a multi-step viral genome assembly pipeline named VirAmp, which combines existingV tools and techniques and presents them to end users via a web-enabled Galaxy interface. Our pipeline allows users to assemble, analyze, and interpret high coverage viral sequencing data with an ease and efficiency that was not possible previously. Our software makes a large number of genome assembly and related tools available to life scientists and automates the currently recommended best practices into a single, easy to use interface. We tested our pipeline with three different datasets from human herpes simplex virus (HSV). Conclusions VirAmp provides a user-friendly interface and a complete pipeline for viral genome analysis. We make our software available via an Amazon Elastic Cloud disk image that can be easily launched by anyone with an Amazon web service account. A fully functional demonstration instance of our system can be found at http://viramp.com/. We also maintain detailed documentation on each tool and methodology at http://docs.viramp.com.