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Abstract 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.

Data collaborations have gained currency over the last decade as a means for data- and skills-poor actors to thrive as a fourth paradigm takes hold in the sciences. Against this backdrop, this article traces the emergence of a collaborative subject position that strives to establish reciprocal and technical-oriented collaborations so as to catch up with the ongoing changes in research. Combining insights from the modernity/coloniality group, political theory and science and technology studies, the article argues that this positionality engenders epistemic obedience by bracketing off critical questions regarding with whom and for whom knowledge is generated. In particular, a dis-embedding of the data producers, the erosion of local ties, and a data conformism are identified as fresh sources of obedience impinging upon the capacity to conduct research attuned to the needs and visions of the local context. A discursive-material analysis of interviews and field notes stemming from the case of astronomy data in Chile is conducted, examining the vision of local actors aiming to gain proximity to the mega observatories producing vast volumes of data in the Atacama Desert. Given that these observatories are predominantly under the control of organisations from the United States and Europe, the adoption of a collaborative stance is now seen as the best means to ensure skills and technology transfer to local research teams. Delving into the epistemological dimension of data colonialism, this article warns that an increased emphasis on collaboration runs the risk of reproducing planetary hierarchies in times of data-intensive research. [ABSTRACT FROM AUTHOR]

Objective Population data scientists are committed to research that has public value. Much of this research is applied; it is undertaken in partnership with the public, patients, families, as well as policy- and decision-makers. Working directly with policy-makers (who are often also data providers) has advantages, but presents challenges as well. Approach We offer four provocations to stimulate thinking about the relationship between research and the “systems” that research is trying to influence. These provocations include: 1) assessing the implications of “partnership” and who is expected to change or accommodate others’ views, and how this affects researchers’ ability to challenge current practice; 2) challenging the emphasis on short-term over longer-term challenges in systems; 3) moving beyond post-implementation evaluations of policies; and 4) critiquing the current project-specific orientation to assessing return on investment (ROI). Results The current focus on partnership in applied research tends to suggest that it is researchers who need to be empathetic to the timelines and needs of policy makers. True relationships, however, are bi-directional, and more importantly need to be open to tough conversations and constructive feedback. Further, focusing on priorities of “systems” will emphasize short-term issues. These are important to address, but can crowd out more systemic and structural considerations. This leads to researchers often engaged in post-implementation evaluation where they have had little involvement in policy or intervention design, which may not be evidence-based. Finally, a focus on single-project ROI will tend to undervalue riskier – but also potentially more rewarding – research. Conclusion It is important to recognize that valuable research might challenge current thinking and practice, and/or address issues that are not short-term priorities. More early testing of policies before broad implementation will advance evidence. ROI should be viewed as an emergent property rather than an attribute of each individual project.

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. [ABSTRACT FROM AUTHOR]

Data governance and data literacy are two important building blocks in the knowledge base of information professionals involved in supporting data-intensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data. [ABSTRACT FROM AUTHOR]

Computer-aided design (CAD) logs provide fine-grained empirical data of student activities for assessing learning in engineering design projects. However, the instructional sensitivity of CAD logs, which describes how students respond to interventions with CAD actions, has rarely been examined. For the logs to be used as reliable data sources for assessments, they must be instructionally sensitive. This paper reports the results of our systematic research on this important topic. To guide the research, we first propose a theoretical framework for computer-based assessments based on signal processing. This framework views assessments as detecting signals from the noisy background often present in large temporal learner datasets due to many uncontrollable factors and events in learning processes. To measure instructional sensitivity, we analyzed nearly 900 megabytes of process data logged by our Energy3D CAD software as collections of time series. These time-varying data were gathered from 65 high school students who solved a solar urban design challenge using Energy3D in seven class periods, with an intervention occurring in the middle of their design projects. Our analyses of these data show that the occurrence of the design actions unrelated to the intervention were not affected by it, whereas the occurrence of the design actions that the intervention targeted reveals a continuum of reactions ranging from no response to strong response. From the temporal patterns of these student responses, persistent effects and temporary effects (with different decay rates) were identified. Students' electronic notes taken during the design processes were used to validate their learning trajectories. These results show that an intervention occurring outside a CAD tool can leave a detectable trace in the CAD logs, suggesting that the logs can be used to quantitatively determine how effective an intervention has been for each individual student during an engineering design project. [ABSTRACT FROM AUTHOR]

Data-intensive research using distributed, federated, person-level datasets in near real time has the potential to transform social, behavioral, economic, and health sciences--but issues around privacy, confidentiality, access, and data integration have slowed progress in this area. When technology is properly used to manage both privacy concerns and uncertainty, big data will help move the growing field of population informatics forward. [ABSTRACT FROM PUBLISHER]