There is little doubt that we have entered an era where data underpins modern science and research in general. In support of this, numerous infrastructures have been designed and built, ranging from proprietary on-prem systems through to distributed commercial clouds. Such implementations provide a range of functions during the research lifecycle from provisioning and cataloguing data assets through to storing and presenting data to computing platforms. In this paper we analyse the underlying principles of such systems and develop a high-level Research Data Reference Architecture (RDRA). Specifically, we identify eight key features of a RDRA that can guide the design, construction, and procurement of implementations without mandating any domain, approach, technical solution, or product choice. As a result, it allows implementers to make local and commercial decisions while still meeting the core requirements of a research data management platform. The intended audience is teams charged with implementing infrastructure in research organizations.

The first parallel sort algorithm for shared memory MIMD (multiple-instruction-multiple-data-stream) multiprocessors that has a theoretical and measured speedup near linear is exhibited. It is based on a novel asynchronous parallel merge that evenly partitions data to be merged among any number of processors. A benchmark sorting algorithm is proposed that uses this merge to remove the linear time bottleneck inherent in previous multiprocessors sorts. This sort, when applied to data set on p processors, has a time complexity of O((n log n)/p)+O((n log p)/p) and a space complexity of 2n, where n is the number of keys being sorted. Evaluations of the merge and benchmark sort algorithms on a 12-processor Sequent Balance 21000 System demonstrate near-linear speedup when compared to sequential Quicksort.>