Paper submitted to Recomputability 2014: “Share and Enjoy”: Publishing Useful and Usable Scientific Models

Last month, me, Ben Hall, Samin Ishtiaq and Kenji Takeda (all Microsoft Research) submitted a paper to Recomputability 2014, to be held in conjunction with the 7th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2014) in London in December. This workshop is an interdisciplinary forum for academic and industrial researchers, practitioners and developers to discuss challenges, ideas, policy and practical experience in reproducibility, recomputation, reusability and reliability across utility and cloud computing. It aims to provide an opportunity to share and showcase best practice, as well as to offering a platform to further develop policy, initiatives and practical techniques for researchers in this domain.

In our paper, we discuss a number of issues in this space, proposing a new open platform for the sharing and reuse of scientific models and benchmarks. You can download our arXiv pre-print; the abstract is as follows:

The reproduction and replication of reported scientific results is a hot topic within the academic community. The retraction of numerous studies from a wide range of disciplines, from climate science to bioscience, has drawn the focus of many commentators, but there exists a wider socio-cultural problem that pervades the scientific community. Sharing data and models often requires extra effort, and this is currently seen as a significant overhead that may not be worth the time investment.

Automated systems, which allow easy reproduction of results, offer the potential to incentivise a culture change and drive the adoption of new techniques to improve the efficiency of scientific exploration. In this paper, we discuss the value of improved access and sharing of the two key types of results arising from work done in the computational sciences: models and algorithms. We propose the development of an integrated cloud-based system underpinning computational science, linking together software and data repositories, toolchains, workflows and outputs, providing a seamless automated infrastructure for the verification and validation of scientific models and in particular, performance benchmarks.

 
(see GitHub repo)