A Test in Reproducibility

During his time as Editor-in-Chief for Molecular Brain, Fujita Health University’s Tsuyoshi Miyakawa┬ámade requests for raw data for 41 papers marked for revision prior to publication, and the majority of authors failed to respond to this request (in fact, only one article from this 41-article set was eventually accepted for publication). This experience prompted Miyakawa to pen an essay entitled “No raw data, no science: another possible source of the reproducibility crisis” and to call for journals to include raw data requests as part of their editorial process, describing his journal’s new policy for datasets:

I propose that all journals should, in principle, try their best to have authors and institutions make their raw data open in a public database or on a journal web site upon the publication of the paper, in order to increase the reproducibility of published results and to strengthen public trust in science. Currently, the data sharing policy of Molecular Brain only ‘encourages’ all datasets on which the conclusions of the manuscript rely to be either deposited in publicly available repositories (where available and appropriate) or presented in the main paper or additional supporting files, in machine-readable format (such as spread sheets rather than PDFs) whenever possible. Building on our existing policy, we will require, in principle, deposition of the datasets on which the conclusions of the manuscript rely from 1 March 2020. Such datasets include quantified numerical values used for statistical analyses and graphs, images of tissue staining, and uncropped images of all blot and gel results.

In my own recent experience with journal editing, I can say that having the ability to access underlying data for any study would provide me with more confidence when considering the results discussed in a paper, regardless of discipline.

Miyakawa does recognize the need for more robust data infrastructures for supporting raw data provision by authors and calls for better infrastructures for this:

…institutions, funding agencies, and publishers should cooperate and try to support such a move by establishing data storage infrastructure to enable the securing and sharing of raw data, based on the understanding that ‘no raw data, no science.’

Read more:

Miyakawa, T. No raw data, no science: another possible source of the reproducibility crisis. Mol Brain 13, 24 (2020). https://doi.org/10.1186/s13041-020-0552-2

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