Supporting FAIR Data Management Planning Across Different Disciplines at the University of Sheffield

Recognition is growing sector-wide of the importance of FAIR data management planning in facilitating the sharing and reuse of research outputs. Indeed, research funders increasingly mandate practices such as data sharing or inclusion of a data availability statement in publications issuing from funded projects, practices which need to be anticipated at the data management planning stage. Nevertheless, there is a shortage of discipline-specific guidance to support researchers seeking to make data and software FAIR within a given domain and to a meaningful degree. This article outlines a project which explored ways to address this shortage. In Spring 2022, the University of Sheffield Library worked with researchers in seven disciplines to develop subject-specific FAIR checklists for the use of colleagues before, during and at the end of their research project. We chart the outcomes and implications of the project and the ways this will inform future support for researchers in planning for FAIR data at this institution

Model-based groupware solution for distributed real-time collaborative 4D planning via teamwork

Construction planning plays a fundamental role in construction project management that requires team working among planners from a diverse range of disciplines and in geographically dispersed working situations. Model-based four-dimensional (4D) computer-aided design (CAD) groupware, though considered a possible approach to supporting collaborative planning, is still short of effective collaborative mechanisms for teamwork due to methodological, technological and social challenges. Targeting this problem, this paper proposes a model-based groupware solution to enable a group of multidisciplinary planners to perform real-time collaborative 4D planning across the Internet. In the light of the interactive definition method, and its computer-supported collaborative work (CSCW) design analysis, the paper discusses the realization of interactive collaborative mechanisms from software architecture, application mode, and data exchange protocol. These mechanisms have been integrated into a groupware solution, which was validated by a planning team in a truly geographically dispersed condition. Analysis of the validation results revealed that the proposed solution is feasible for real-time collaborative 4D planning to gain a robust construction plan through collaborative teamwork. The realization of this solution triggers further considerations about its enhancement for wider groupware applications

Establishing, Developing, and Sustaining a Community of Data Champions

Supporting good practice in Research Data Management (RDM) is challenging for higher education institutions, in part because of the diversity of research practices and data types across disciplines. While centralised research data support units now exist in many universities, these typically possess neither the discipline-specific expertise nor the resources to offer appropriate targeted training and support within every academic unit. One solution to this problem is to identify suitable individuals with discipline-specific expertise that are already embedded within each unit, and empower these individuals to advocate for good RDM and to deliver support locally. This article focuses on an ongoing example of this approach: the Data Champion Programme at the University of Cambridge, UK. We describe how the Data Champion programme was established; the programme’s reach, impact, strengths and weaknesses after two years of operation; and our anticipated challenges and planned strategies for maintaining the programme over the medium- and long-term

Building information modelling project decision support framework

Building Information Modelling (BIM) is an information technology [IT] enabled approach to managing design data in the AEC/FM (Architecture, Engineering and Construction/ Facilities Management) industry. BIM enables improved interdisciplinary collaboration across distributed teams, intelligent documentation and information retrieval, greater consistency in building data, better conflict detection and enhanced facilities management. Despite the apparent benefits the adoption of BIM in practice has been slow. Workshops with industry focus groups were conducted to identify the industry needs, concerns and expectations from participants who had implemented BIM or were BIM “ready”. Factors inhibiting BIM adoption include lack of training, low business incentives, perception of lack of rewards, technological concerns, industry fragmentation related to uneven ICT adoption practices, contractual matters and resistance to changing current work practice. Successful BIM usage depends on collective adoption of BIM across the different disciplines and support by the client. The relationship of current work practices to future BIM scenarios was identified as an important strategy as the participants believed that BIM cannot be efficiently used with traditional practices and methods. The key to successful implementation is to explore the extent to which current work practices must change. Currently there is a perception that all work practices and processes must adopt and change for effective usage of BIM. It is acknowledged that new roles and responsibilities are emerging and that different parties will lead BIM on different projects. A contingency based approach to the problem of implementation was taken which relies upon integration of BIM project champion, procurement strategy, team capability analysis, commercial software availability/applicability and phase decision making and event analysis. Organizations need to understand: (a) their own work processes and requirements; (b) the range of BIM applications available in the market and their capabilities (c) the potential benefits of different BIM applications and their roles in different phases of the project lifecycle, and (d) collective supply chain adoption capabilities. A framework is proposed to support organizations selection of BIM usage strategies that meet their project requirements. Case studies are being conducted to develop the framework. The results of the preliminary design management case study is presented for contractor led BIM specific to the design and construct procurement strategy

Incremental scoping study and implementation plan

This report is one of the first deliverables from the Incremental project, which seeks to investigate and improve the research data management infrastructure at the universities of Glasgow and Cambridge and to learn lessons and develop resources of value to other institutions. Coming at the end of the project’s scoping study, this report identifies the key themes and issues that emerged and proposes a set of activities to address those needs. As its name suggests, Incremental deliberately adopts a stepped, pragmatic approach to supporting research data management. It recognises that solutions will vary across different departmental and institutional contexts; and that top-down, policy-driven or centralised solutions are unlikely to prove as effective as practical support delivered in a clear and timely manner where the benefits can be clearly understood and will justify any effort or resources required. The findings of the scoping study have confirmed the value of this approach and the main recommendations of this report are concerned with the development and delivery of suitable resources. Although some differences were observed between disciplines, these seemed to be as much a feature of different organisational cultures as the nature of the research being undertaken. Our study found that there were many common issues across the groups and that the responses to these issues need not be highly technical or expensive to implement. What is required is that these resources employ jargon-free language and use examples of relevance to researchers and that they can be accessed easily at the point of need. There are resources already available (institutionally and externally) that can address researchers’ data management needs but these are not being fully exploited. So in many cases Incremental will be enabling efficient and contextualised access, or tailoring resources to specific environments, rather than developing resources from scratch. While Incremental will concentrate on developing, repurposing and leveraging practical resources to support researchers in their management of data, it recognises that this will be best achieved within a supportive institutional context (both in terms of policy and provision). The need for institutional support is especially evident when long-term preservation and data sharing are considered – these activities are clearly more effective and sustainable if addressed at more aggregated levels (e.g. repositories) rather than left to individual researchers or groups. So in addition to its work in developing resources, the Incremental project will seek to inform the development of a more comprehensive data management infrastructure at each institution. In Cambridge, this will be connected with the library’s CUPID project (Cambridge University Preservation Development) and at Glasgow in conjunction with the Digital Preservation Advisory Board

Education alignment

This essay reviews recent developments in embedding data management and curation skills into information technology, library and information science, and research-based postgraduate courses in various national contexts. The essay also investigates means of joining up formal education with professional development training opportunities more coherently. The potential for using professional internships as a means of improving communication and understanding between disciplines is also explored. A key aim of this essay is to identify what level of complementarity is needed across various disciplines to most effectively and efficiently support the entire data curation lifecycle

Searching Data: A Review of Observational Data Retrieval Practices in Selected Disciplines

A cross-disciplinary examination of the user behaviours involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data. Two analytical frameworks rooted in information retrieval and science technology studies are used to identify key similarities in practices as a first step toward developing a model describing data retrieval

The Data Management Skills Support Initiative: synthesising postgraduate training in research data management

<p>This paper will describe the efforts and findings of the JISC Data Management Skills Support Initiative (‘DaMSSI’). DaMSSI was co-funded by the JISC Managing Research Data programme and the Research Information Network (RIN), in partnership with the Digital Curation Centre, to review, synthesise and augment the training offerings of the JISC Research Data Management Training Materials (‘RDMTrain’) projects.</p> <p>DaMSSI tested the effectiveness of the Society of College, National and University Libraries’ Seven Pillars of Information Literacy model (SCONUL, 2011), and Vitae’s Researcher Development Framework (‘Vitae RDF’) for consistently describing research data management (‘RDM’) skills and skills development paths in UK HEI postgraduate courses.</p> <p>With the collaboration of the RDMTrain projects, we mapped individual course modules to these two models and identified basic generic data management skills alongside discipline-specific requirements. A synthesis of the training outputs of the projects was then carried out, which further investigated the generic versus discipline-specific considerations and other successful approaches to training that had been identified as a result of the projects’ work. In addition we produced a series of career profiles to help illustrate the fact that data management is an essential component – in obvious and not-so-obvious ways – of a wide range of professions.</p> <p>We found that both models had potential for consistently and coherently describing data management skills training and embedding this within broader institutional postgraduate curricula. However, we feel that additional discipline-specific references to data management skills could also be beneficial for effective use of these models. Our synthesis work identified that the majority of core skills were generic across disciplines at the postgraduate level, with the discipline-specific approach showing its value in engaging the audience and providing context for the generic principles.</p> <p>Findings were fed back to SCONUL and Vitae to help in the refinement of their respective models, and we are working with a number of other projects, such as the DCC and the EC-funded Digital Curator Vocational Education Europe (DigCurV2) initiative, to investigate ways to take forward the training profiling work we have begun.</p&gt