Reviewer has chosen to be AnonymousOverall Impression:
UndecidedTechnical Quality of the paper:
Clear noveltyData availability:
All used and produced data (if any) are FAIR and openly available in established data repositoriesLength of the manuscript:
The authors need to elaborate more on certain aspects and the manuscript should therefore be extended (if the general length limit is already reached, I urge the editor to allow for an exception)
Summary of paper in a few sentences:
Paper aims to give listing of ten commandments translational teams should follow when considering data for projects and how best to manage that data. THey stress the importance of consistency and planning BEFORE a study actually begins, which will enable easier analysis at the end of the study.
Reasons to accept:
This type of information is extremely useful, especially to newly formed teams. Having worked in the field for years, I frequently encounter resistance to several of these topics from PI's, and it is often challenging to perform data analysis when some of these steps are not thought of at the beginning of a study. Having something to reference would be very helpful.
Reasons to reject:
It is unclear who the intended audience is. The author seems to imply the commandments would be useful for new teams, however much of the language is in acronyms which a new team might not know. The audience should be better defined, and that should be thought of when explaining each commandment. Along those lines, including examples for some of the commandments (i.e. commandment 1 would be nice to include or reference a work package). The author mentions this is for translational research, yet the focus seems to be mainly on clinical data. Basic science data needs to be mentioned/included more to make it more relevant to the entire translational research spectrum, and not just clinical research which is how it largely reads at the moment.
Explain acronyms...there are many, and some are not defined.
Commandment 1 - work package is strange phrasing, as Ive never heard that term, and I suspect many others have not either. SOP (standard operating protocol) might be a better choice. A reference should be included for Horizon 2020. It would be nice to include what a WP should include in this commandment as well. Examples would be helpful for people forming new teams.
Commandment 2 - What is EDC? SInce reference NCATS earlier, would be nice to include REDCap in references here as well. How does this commandment apply to EMR or basic science data? Can you extract data directly from machines to ease this problem?
Commandment 3 - in addition to a codebook, it would be good to consider restricting the data types for some variables to diminish errors (such as gender should not be recorded as numeric). Also good to include plausible values for some variables to diminish errors as well. This is in addition to having a codebook. What is eCRF? ANd how does that apply to basic science research/teams?
Commandment 4 - would be good to mention places that one can share data as well as how to go about the sharing process as it is not always straightforward.
Commandment 5 - Would be good to list, or reference, specific imaging tools mentioned in the last sentence.
Commandment 6 - Table 1 would benefit from a better description of the software...such as what each tool actually does, or what specific field (analytic area) do the resources apply to. Potentially along the lines of why someone would want to visit each of the websites (i.e. what can one use i2b2 to help them with?)
Commandment 7 - should also mention importance of dissemination of these tools so that others know they actually exist, and can easily find
Commandment 8 - it would be good to include an example of how to apply/follow the FAIR guidelines
One additional thing to consider is that it is often a good idea to include in the team/data management plan to consult with someone that will be doing the downstream data analysis from the acquired data. This is to ensure that the fields collected will be in a format needed for easier access, and that the statistician/bioinformatician won't have to perform magic before analysis can be performed.