Reuse remix recycle: repurposing archaeological digital data

Preservation of digital data is predicated on the expectation of its reuse, yet that expectation has never been examined within archaeology. While we have extensive digital archives equipped to share data, evidence of reuse seems paradoxically limited. Most archaeological discussions have focused on data management and preservation and on disciplinary practices surrounding archiving and sharing data. This article addresses the reuse side of the data equation through a series of linked questions: What is the evidence for reuse, what constitutes reuse, what are the motivations for reuse, and what makes some data more suitable for reuse than others? It concludes by posing a series of questions aimed at better understanding our digital engagement with archaeological data

Refactoring intermediately executed code to reduce cache capacity misses

The growing memory wall requires that more attention is given to the data cache behavior of programs. In this paper, attention is given to the capacity misses i.e. the misses that occur because the cache size is smaller than the data footprint between the use and the reuse of the same data. The data footprint is measured with the reuse distance metric, by counting the distinct memory locations accessed between use and reuse. For reuse distances larger than the cache size, the associated code needs to be refactored in a way that reduces the reuse distance to below the cache size so that the capacity misses are eliminated. In a number of simple loops, the reuse distance can be calculated analytically. However, in most cases profiling is needed to pinpoint the areas where the program needs to be transformed for better data locality. This is achieved by the reuse distance visualizer, RDVIS, which shows the intermediately executed code for critical data reuses. In addition, another tool, SLO, annotates the source program with suggestions for locality ptimization. Both tools have been used to analyze and to refactor a number of SPEC2000 benchmark programs with very positive results

Big data: the potential role of research data management and research data registries

Universities generate and hold increasingly vast quantities of research data – both in the form of large, well-structured datasets but more often in the form of a long tail of small, distributed datasets which collectively amount to ‘Big Data’ and offer significant potential for reuse. However, unlike big data, these collections of small data are often less well curated and are usually very difficult to find thereby reducing their potential reuse value. The Digital Curation Centre (DCC) works to support UK universities to better manage and expose their research data so that its full value may be realised. With a focus on tapping into this long tail of small data, this presentation will cover two main DCC, services: DMPonline which helps researchers to identify potentially valuable research data and to plan for its longer-term retention and reuse; and the UK pilot research data registry and discovery service (RDRDS) which will help to ensure that research data produced in UK HEIs can be found, understood, and reused. Initially we will introduce participants to the role of data management planning to open up dialogue between researchers and library services to ensure potentially valuable research data are managed appropriately and made available for reuse where feasible. DMPs provide institutions with valuable insights into the scale of their data holdings, highlight any ethical and legal requirements that need to be met, and enable planning for dissemination and reuse. We will also introduce the DCC’s DMPonline, a tool to help researchers write DMPs, which can be customised by institutions and integrated with other systems to simplify and enhance the management and reuse of data. In the second part of the presentation we will focus on making selected research data more visible for reuse and explore the potential value of local and national research data registries. In particular we will highlight the Jisc-funded RDRDS pilot to establish a UK national service that aggregates metadata relating to data collections held in research institutions and subject data centres. The session will conclude by exploring some of the opportunities we may collaboratively explore in facilitating the management, aggregation and reuse of research data

Crowdsourcing Linked Data on listening experiences through reuse and enhancement of library data

Research has approached the practice of musical reception in a multitude of ways, such as the analysis of professional critique, sales figures and psychological processes activated by the act of listening. Studies in the Humanities, on the other hand, have been hindered by the lack of structured evidence of actual experiences of listening as reported by the listeners themselves, a concern that was voiced since the early Web era. It was however assumed that such evidence existed, albeit in pure textual form, but could not be leveraged until it was digitised and aggregated. The Listening Experience Database (LED) responds to this research need by providing a centralised hub for evidence of listening in the literature. Not only does LED support search and reuse across nearly 10,000 records, but it also provides machine-readable structured data of the knowledge around the contexts of listening. To take advantage of the mass of formal knowledge that already exists on the Web concerning these contexts, the entire framework adopts Linked Data principles and technologies. This also allows LED to directly reuse open data from the British Library for the source documentation that is already published. Reused data are re-published as open data with enhancements obtained by expanding over the model of the original data, such as the partitioning of published books and collections into individual stand-alone documents. The database was populated through crowdsourcing and seamlessly incorporates data reuse from the very early data entry phases. As the sources of the evidence often contain vague, fragmentary of uncertain information, facilities were put in place to generate structured data out of such fuzziness. Alongside elaborating on these functionalities, this article provides insights into the most recent features of the latest instalment of the dataset and portal, such as the interlinking with the MusicBrainz database, the relaxation of geographical input constraints through text mining, and the plotting of key locations in an interactive geographical browser

Revisiting Qualitative Data Reuse

Secondary analysis of qualitative data entails reusing data created from previous research projects for new purposes. Reuse provides an opportunity to study the raw materials of past research projects to gain methodological and substantive insights. In the past decade, use of the approach has grown rapidly in the United Kingdom to become sufficiently accepted that it must now be regarded as mainstream. Several factors explain this growth: the open data movement, research funders’ and publishers’ policies supporting data sharing, and researchers seeing benefits from sharing resources, including data. Another factor enabling qualitative data reuse has been improved services and infrastructure that facilitate access to thousands of data collections. The UK Data Service is an example of a well-established facility; more recent has been the proliferation of repositories being established within universities. This article will provide evidence of the growth of data reuse in the United Kingdom and in Finland by presenting both data and case studies of reuse that illustrate the breadth and diversity of this maturing research method. We use two distinct data sources that quantify the scale, types, and trends of reuse of qualitative data: (a) downloads of archived data collections held at data repositories and (b) publication citations. Although the focus of this article is on the United Kingdom, some discussion of the international environment is provided, together with data and examples of reuse at the Finnish Social Science Data Archive. The conclusion summarizes the major findings, including some conjectures regarding what makes qualitative data attractive for reuse and sharing. </jats:p