Structuring supplemental materials in support of reproducibility

Easyreporting simplifies the implementation of Reproducible Research layers in R software

During last years "irreproducibility" became a general problem in omics data analysis due to the use of sophisticated and poorly described computational procedures. For avoiding misleading results, it is necessary to inspect and reproduce the entire data analysis as a unified product. Reproducible Research (RR) provides general guidelines for public access to the analytic data and related analysis code combined with natural language documentation, allowing third-parties to reproduce the findings. We developed easyreporting, a novel R/Bioconductor package, to facilitate the implementation of an RR layer inside reports/tools. We describe the main functionalities and illustrate the organization of an analysis report using a typical case study concerning the analysis of RNA-seq data. Then, we show how to use easyreporting in other projects to trace R functions automatically. This latter feature helps developers to implement procedures that automatically keep track of the analysis steps. Easyreporting can be useful in supporting the reproducibility of any data analysis project and shows great advantages for the implementation of R packages and GUIs. It turns out to be very helpful in bioinformatics, where the complexity of the analyses makes it extremely difficult to trace all the steps and parameters used in the study.

Making supplemental information more accessible

Supplemental information is difficult to organize, review, and understand. Genome Biology has listed some new recommendations for the organization of supplemental data.

Role of supplementary material in biomedical journal articles: surveys of authors, reviewers and readers

OBJECTIVE

Many journals permit authors to submit supplementary material for publication alongside the article. We explore the value, use and role of this material in biomedical journal articles from the perspectives of authors, peer reviewers and readers.

DESIGN AND SETTING

We conducted online surveys (November-December 2016) of corresponding authors and peer reviewers at 17 BMJ Publishing Group journals in a range of specialities.

PARTICIPANTS

Participants were asked to respond to one of three surveys: as authors, peer reviewers or readers.

RESULTS

We received 2872/20340 (14%) responses: authors 819/6892 (12%), peer reviewers 1142/6682 (17%) and readers 911/6766 (14%). Most authors submitted (711/819, 87%) and 80% (724/911) of readers reported reading supplementary material with their last article, while 95% (1086/1142) of reviewers reported seeing these materials sometimes. Additional data tables were the most common supplementary material reported (authors: 74%; reviewers: 89%; readers: 67%). A majority in each group indicated additional tables were most useful to readers (61%-77%); 20%-36% and 3%-4% indicated they were most useful to peer reviewers and journal editors, respectively. Checklists and reporting guidelines showed the opposite: higher proportions of each group regarded these as most useful to journal editors. All three groups favoured the publication of additional tables and figures on the journal's website (80%-83%), with <4% of each group responding that these do not need to be available. Approximately one-fifth (16%-23%) responded that raw study data should be available on the journal's website, while 24%-33% said that these materials should not be made available anywhere.

CONCLUSIONS

Authors, peer reviewers and readers agree that supplementary materials are useful. Supplementary tables and figures were favoured over reporting checklists or raw data for reading but not for study replication. Journals should consider the roles, resource costs and strategic placement of supplementary materials to ensure optimal usage and minimise waste.

TRIAL REGISTRATION NUMBER

NCT02961036.

Reproducible Research Using Biomodels

Like other types of computational research, modeling and simulation of biological processes (biomodels) is still largely communicated without sufficient detail to allow independent reproduction of results. But reproducibility in this area of research could easily be achieved by making use of existing resources, such as supplying models in standard formats and depositing code, models, and results in public repositories.

Reproducible research and GIScience: an evaluation using AGILE conference papers

The demand for reproducible research is on the rise in disciplines concerned with data analysis and computational methods. Therefore, we reviewed current recommendations for reproducible research and translated them into criteria for assessing the reproducibility of articles in the field of geographic information science (GIScience). Using this criteria, we assessed a sample of GIScience studies from the Association of Geographic Information Laboratories in Europe (AGILE) conference series, and we collected feedback about the assessment from the study authors. Results from the author feedback indicate that although authors support the concept of performing reproducible research, the incentives for doing this in practice are too small. Therefore, we propose concrete actions for individual researchers and the GIScience conference series to improve transparency and reproducibility. For example, to support researchers in producing reproducible work, the GIScience conference series could offer awards and paper badges, provide author guidelines for computational research, and publish articles in Open Access formats.