The reproduction and replication of novel scientific results has become a major issue for a number of disciplines. In computer science and related disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it in a new codebase frequently requires local knowledge missing from the published manuscripts and project websites. Alongside this issue, benchmarking, and the development of fair, and widely available benchmark sets present another barrier.<\/p>\n\n\n\n
In this paper, we outline several suggestions to address these issues, driven by specific\u00a0 examples from a range of scientific domains. Finally, based on these\u00a0 suggestions, we propose a new open platform for scientific software\u00a0development which effectively isolates specific dependencies from the individual researcher and their workstation and allows faster, more powerful sharing of the results of scientific software engineering.<\/p>\n","protected":false},"excerpt":{"rendered":"
The reproduction and replication of novel scientific results has become a major issue for a number of disciplines. In computer science and related disciplines such as systems biology, the issues closely revolve around the ability to implement novel algorithms and approaches. Taking an approach from the literature and applying it in a new codebase frequently […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Tom Crick"},{"type":"text","value":"Benjamin A. 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