Towards Automatic Capturing of Manual Data Processing Provenance

Often data processing is not implemented by a work ow system or an integration application but is performed manually by humans along the lines of a more or less specified procedure. Collecting provenance information during manual data processing can not be automated. Further, manual collection of provenance information is error prone and time consuming. Therefore, we propose to infer provenance information based on the read and write access of users. The derived provenance information is complete, but has a low precision. Therefore, we propose further to introducing organizational guidelines in order to improve the precision of the inferred provenance information

Towards a service-oriented e-infrastructure for multidisciplinary environmental research

Research e-infrastructures are considered to have generic and thematic parts. The generic part provids high-speed networks, grid (large-scale distributed computing) and database systems (digital repositories and data transfer systems) applicable to all research commnities irrespective of discipline. Thematic parts are specific deployments of e-infrastructures to support diverse virtual research communities. The needs of a virtual community of multidisciplinary envronmental researchers are yet to be investigated. We envisage and argue for an e-infrastructure that will enable environmental researchers to develop environmental models and software entirely out of existing components through loose coupling of diverse digital resources based on the service-oriented achitecture. We discuss four specific aspects for consideration for a future e-infrastructure: 1) provision of digital resources (data, models & tools) as web services, 2) dealing with stateless and non-transactional nature of web services using workflow management systems, 3) enabling web servce discovery, composition and orchestration through semantic registries, and 4) creating synergy with existing grid infrastructures

Standing together for reproducibility in large-scale computing: report on reproducibility@XSEDE

This is the final report on reproducibility@xsede, a one-day workshop held in conjunction with XSEDE14, the annual conference of the Extreme Science and Engineering Discovery Environment (XSEDE). The workshop's discussion-oriented agenda focused on reproducibility in large-scale computational research. Two important themes capture the spirit of the workshop submissions and discussions: (1) organizational stakeholders, especially supercomputer centers, are in a unique position to promote, enable, and support reproducible research; and (2) individual researchers should conduct each experiment as though someone will replicate that experiment. Participants documented numerous issues, questions, technologies, practices, and potentially promising initiatives emerging from the discussion, but also highlighted four areas of particular interest to XSEDE: (1) documentation and training that promotes reproducible research; (2) system-level tools that provide build- and run-time information at the level of the individual job; (3) the need to model best practices in research collaborations involving XSEDE staff; and (4) continued work on gateways and related technologies. In addition, an intriguing question emerged from the day's interactions: would there be value in establishing an annual award for excellence in reproducible research? Overvie

Digital cultural heritage: Collaborating with students and discovering lost museums

Our paper consists of two parts. First, we review the history of the Royal United Service Institute (RUSI) museum, its collections, its closure and the dispersal of its collections. Second, we synthesize this analysis with a summary and reflection on the challenges of undertaking a collective memory project that represents the rise and fall of empire. To synthesize these two points, we discuss the museum’s history and highlight how digital cultural heritage initiatives have catalyzed an interest in digitizing and archiving RUSI’s collection records. Following our review of RUSI and its museum collection, we discuss the value of academics forming partnerships with cultural heritage institutions, and we analyze our experiences managing two student projects hosted at RUSI. Our discussion of student work will reflect on methods for designing engaging curriculum that encourages students to practice record keeping for cultural heritage institutions

KAPTUR: exploring the nature of visual arts research data and its effective management.

KAPTUR (2011-2013), funded by JISC and led by the Visual Arts Data Service (VADS), is a highly collaborative project involving four institutional partners: the Glasgow School of Arts; Goldsmiths, University of London; University for the Creative Arts; and the University of the Arts London. The preservation and publication of research data is seen as positive and all UK Research Councils now require it as a condition of funding (RCUK 2012). As a result a network of data repositories are emerging (DataCite 2012a), some funded by Research Councils, others by institutions themselves. However, research data management practice within the visual arts appears ad hoc. None of the specialist arts institutions within the UK has implemented research data management policies (DCC 2011a), nor established research data management systems. KAPTUR seeks to investigate the nature of visual arts research data, making recommendations for its effective management; develop a model of best practice applicable to both specialist arts institutions and arts departments in multidisciplinary institutions; and apply, test and refine the model with the four institutional partners. This paper will explore the nature of visual arts research data and how effective data management can ensure its long term usage, curation and preservation