The Ten Commandments of Translational Research Informatics

Tracking #: 564-1544


Responsible editor: 

Manisha Desai

Submission Type: 

Position Paper


Translational research applies findings from basic science to enhance human health and well-being. In translational research projects, academia and industry work together to improve healthcare, often through public-private partnerships. This “translation” is often not easy, because it means that the so-called “valley of death” will need to be crossed: many interesting findings from fundamental research do not result in new treatments, diagnostics and prevention. To cross the valley of death, fundamental researchers need to collaborate with clinical researchers and with industry so that promising results can be productized. The success of translational research projects often does not only on the fundamental science and the applied science, but also on the informatics needed to connect everything: the translational research informatics. This informatics should enable the researchers to store their ‘big data’ in a meaningful way, to ensure that results can be analyzed correctly and enable application in the clinic. The author has worked on the IT infrastructure for several translational research projects in oncology for the past nine years, and presents his learnings in this paper in the form of ten commandments. These learnings are not only useful for the data managers, but for all involved in a translational research project. Some of the commandments deal with topics that are currently in the spotlight, such as machine readability, the FAIR Guiding Principles and the GDPR regulations, which might mean that they no longer need to be mentioned in an overview like this within a few years. The other commandments might still be noteworthy in many years to come.



  • Reviewed

Data repository URLs: 


Date of Submission: 

Saturday, March 23, 2019

Date of Decision: 

Friday, April 26, 2019



Solicited Reviews:

1 Comment

Meta-Review by Editor

Thank you for your submission. While we believe you present important principles for data management, we cannot accept the manuscript in its current form. We would therefore like to invite you to revise your manuscript based on the reviewer’s suggestions at which point we will reassess the piece’s suitability for publication.

Your paper attempts to address issues that are essential to the field of data science, but it is missing some critical pieces that could enable much greater impact. Overall the commandments were too general and simplistic to be helpful in practice and could benefit from more concrete details with specific examples (perhaps of good practice contrasted with poor practice). Importantly, previous work on data stewardship should be described and referenced so that the ideas or principles described here can be distinguished from past practices. It is especially important to acknowledge what has been done and how (if at all) these ideas put forth here are new or different from previous work. In addition, the commandments are provided for translational research, however, the guidelines seem tailored to clinical research. Explicit inclusion and consideration of basic science would allow for a more impactful piece. The language used (including the excessive use of acronyms) is quite specialized indicating a narrow audience and should be made more general so that the principles can be adopted by the more general data science community. Whenever possible, examples should be used for illustration. An important rule not listed includes engagement with an expert in study design or statistics for downstream analysis. This point was notably missing.

We believe this is an important and timely topic and we look forward to reviewing a revised version of the manuscript if you are interested in submitting one that addresses the reviewers' concerns.

Manisha Desai (