Whether the details of data processing affect the reproduction of results should be published in detail

Data processing details affect the reproduction of results; can follow the example of the 1,000-person genome project

The "Nature" magazine published on February 17 published an editorial, "Devil in the details", focusing on the impact of the detailed level of scientific research published in the field of genome sequencing on the recurrence of its results. The following is the main content of the article:

As computer analysis of massive amounts of data is increasingly becoming an indispensable part of research, a new question arises: How detailed are the research findings published by scientists so that others can repeat their research results?

In the field of genomics and sequencing analysis, biologists process more and more complex data. However, they often only publish the results, so some people say that the content of how the author analyzes and processes these data is not in most cases. detailed.

A recent survey examined a total of 14 articles on sequencing published in Science, Nature, and Nature-Genetics last year, and found that these articles lacked the necessary details for others to reproduce their results. Content-For example, only the name of the analysis software used is mentioned, but the version and key parameters are not mentioned.

Another study also pointed out that among the 18 microarray experimental articles surveyed, it may be because of the lack of relevant information that the research results of 10 articles could not be reproduced.

Some people claim that only the original data and final results are published, which is enough; others argue that it is meaningless to publish the version of the analysis software-the difference between different versions of the software is not much different. This is not always the case: some people used different versions of the same software, which made the results very different and were finally rejected.

Even in the face of complex analysis processes, its transparency is still achievable. For example, the 1000 Genomes Project (1000 Genomes Project) will carefully announce its detailed workflow, including raw data and processing procedures, as well as software details. This approach is worthy of academic study and very important.

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