Data Management and Preservation Planning for Big Science

Abstract

‘Big Science’ - that is, science which involves large collaborations with dedicated facilities, and involving large data volumes and multinational investments – is often seen as different when it comes to data management and preservation planning. Big Science handles its data differently from other disciplines and has data management problems that are qualitatively different from other disciplines. In part, these differences arise from the quantities of data involved, but possibly more importantly from the cultural, organisational and technical distinctiveness of these academic cultures. Consequently, the data management systems are typically and rationally bespoke, but this means that the planning for data management and preservation (DMP) must also be bespoke.These differences are such that ‘just read and implement the OAIS specification’ is reasonable Data Management and Preservation (DMP) advice, but this bald prescription can and should be usefully supported by a methodological ‘toolkit’, including overviews, case-studies and costing models to provide guidance on developing best practice in DMP policy and infrastructure for these projects, as well as considering OAIS validation, audit and cost modelling.In this paper, we build on previous work with the LIGO collaboration to consider the role of DMP planning within these big science scenarios, and discuss how to apply current best practice. We discuss the result of the MaRDI-Gross project (Managing Research Data Infrastructures – Big Science), which has been developing a toolkit to provide guidelines on the application of best practice in DMP planning within big science projects. This is targeted primarily at projects’ engineering managers, but intending also to help funders collaborate on DMP plans which satisfy the requirements imposed on them.

Digital curation and costs

“…In terms of: a) Data production: Bicarregui et al [8] and Santos [47] discuss scientific research costs in the Big Science -since it deals with a data volume that can hardly be depleted in published papers -by introducing management problems that qualitatively differ from other disciplines and that, as a result, must forcibly be large-scale projects familiar with costs estimates. b) Appraisal and Selection: it needs to consider the materials' cost, type, condition, quantity, accessibility, singularity and possibilities of future use [39].…”

“…Kejser et al [30] argue that these represent continuous costs that depend on the range of services provided by an institution and that are hard to isolate from other costs associated with the life-cycle, such as production and access/dissemination. Since the fact that, as expressed by Bicarregui et al [8], most curation costs stem from preservation, the proper management of this activity would erase a significant set of practical problems concerning the release of data. Migration and emulation strategies [28] are included in this stage.…”

“…In Donnelly & North's [19] opinion, research councils and information service providers must aim their policies and strategies to the needs of the research communities and to the optimization of information use and exchange. Bicarregui et al [8] mention the choice of a project's products as representing, for the scientist, a commitment comprising the amount of time available to invest in understanding those data, the amount of support he is given by his colleagues and the data owners, and the delicateness of the issue he aims to research. Machado [33] reports the lower costs of new research conducted upon shared scientific data, a value identified by Whyte and Pryor [55] as a saving in researchs' indirect costs, enabled by the reuse of congregated resources (avoiding harvesting exhaustion) and by an "exchange of available presents" as a way of accelerating derived scientific research, leading to an increase in efficiency.…”

“…g) Dissemination: Bicarregui. et al [8] stand against a wide data dissemination because it is not free, which instead implies significant costs identified by Whyte & Pryor [55] as relating to data understandability (preparation and documentation using acknowledged meta-information schemes -revision costs) to conclude that sharing should increase the cost, instead of decreasing it. Dürr et al [20] recommend research on displaying tools and on recovery of data sets, whereas Wilson & Jeffreys [56] advocate costs in disseminating data management services.…”

“…Queiroz [41] considers it an activity that aims to obtain best practices regarding the processing of digital objects, depending on production and technological contexts affected by human, socioeconomic, financial variables, amongst others. Bicarregui et al [8] approach to curation underlies management's planning and data preservation for the Big Science for on-going use, and Wilson & Jeffreys [56] argue that specialized repositories possess an acknowledged experience in the curation skills of their human resources, while Dillon [17] and Strasser et al [50] highlight the need for researchers to receive training in data management and digital curation in order to acquire good practices. From an applied linguistics and journalism perspective, Barros [6] states that digital curation is related to contents' selection, organization, understandability and evolution, representing a new practice that media professionals and volunteering users have to partake in, and that can only be understood when considering its cultural framework.…”