I believe it is important to ask and mandate all research journals to force authors to release all successful and failed experiments in full details, which in essence is your entire log book. It is absolutely essential for your readers to know what you tried, what worked, what didn’t, and what was ambiguous. On top of everything, it will probably help to reduce result fabrication or falsification and pressure the researchers to keep better records of themselves for more reproducible experiments – and yes, I am looking at you who’s embarrassed of yourself! You know, assuming no fabrication of data, better records and the release of full results might have saved Dr. Chunyu Han from retracting his paper on a potential revolutionary gene editing method (NgAgo) from Nature Biotechnology6 that made a big fuss in academia as other researchers couldn’t replicate his results7. In the end of the day, natural sciences are systematic sciences that need a complete, fully explainable and predictable system, not just success or spectacularly unexpected failures that lead to new theories.

Surely, some may argue that by doing this, not only will the manuscript be much lengthier, many other problems that may cause reproducibility issues of a paper which aren’t solved here, as of Figure 2 – besides, sometimes it is really a one-time technical error, and no one wants to spend a paragraph on the manuscript just for that. To those people, I say, maybe a part of what you are asserting is true. From my point of view, however, the drawbacks are nowhere as significant as the advantages we harvest. For opener, by throwing your raw data into the supplementary information and by discussing the failures to an appropriate extent only, we would avoid two of the problems above. More importantly, while the proposal here doesn’t solve all the issues with reproducibility, it is a step in the right direction. The publications will now at least tell us what went good, what when wrong, and by doing what. By leaving all of your procedure and data to the readers, we can now make appropriate and informed judgments on your raw data ourselves – It shouldn’t