A major contributor to the scientific reproducibility crisis has been that the results from homogeneous, single-center studies do not generalize to heterogeneous, real world populations. Multi-cohort gene expression analysis has helped to increase reproducibility by aggregating data from diverse populations into a single analysis. To make the multi-cohort analysis process more feasible, we have assembled an analysis pipeline which implements rigorously studied meta-analysis best practices. We have compiled and made publicly available the results of our own multi-cohort gene expression analysis of 103 diseases, spanning 615 studies and 36,915 samples, through a novel and interactive web application. As a result, we have made both the process of and the results from multi-cohort gene expression analysis more approachable for non-technical users.
Reproducibility is a defining feature of science. Lately, however, serious concerns have been raised regarding the extent to which the results of research, especially biomedical research, are easily replicated. In this Editorial, we discuss to what extent reproducibility is a significant issue in chemical research and then suggest steps to minimize problems involving irreproducibility in chemistry.
To make replication studies more useful, researchers must make more of them, funders must encourage them and journals must publish them.No scientist wants to be the first to try to replicate another’s promising study: much better to know what happened when others tried it. Long before replication or reproducibility became major talking points, scientists had strategies to get the word out. Gossip was one. Researchers would compare notes at conferences, and a patchy network would be warned about whether a study was worth building on. Or a vague comment might be buried in a related publication. Tell-tale sentences would start "In our hands", "It is unclear why our results differed …" or "Interestingly, our results did not …".
The Alan Turing Institute Symposium on Reproducibility for Data-Intensive Research was held on 6th - 7th April 2016 at the University of Oxford. It was organised by senior academics, publishers and library professionals representing the Alan Turing Institute (ATI) joint venture partners (the universities of Cambridge, Edinburgh, Oxford, UCL and Warwick), the University of Manchester, Newcastle University and the British Library. The key aim of the symposium was to address the challenges around reproducibility of data-intensive research in science, social science and the humanities. This report presents an overview of the discussions and makes some recommendations for the ATI to take forwards.
The study of diet quality in a population provides information for the development of programs to improve nutritional status through better directed actions. The aim of this study was to assess the reproducibility and relative validity of a Mexican Diet Quality Index (ICDMx) for the assessment of the habitual diet of adults.
The latest policy developments require immediate action for data preservation, as well as reproducible and Open Science. To address this, an unprecedented digital library service is presented to enable the High-Energy Physics community to preserve and share their research objects (such as data, code, documentation, notes) throughout their research process. While facing the challenges of a “big data” community, the internal service builds on existing internal databases to make the process as easy and intrinsic as possible for researchers. Given the “work in progress” nature of the objects preserved, versioning is supported. It is expected that the service will not only facilitate better preservation techniques in the community, but will foremost make collaborative research easier as detailed metadata and novel retrieval functionality provide better access to ongoing works. This new type of e-infrastructure, fully integrated into the research workflow, could help in fostering Open Science practices across disciplines.