Translation to plain English of selected portions of Longo and Drazen's editorial on data sharing

Longo DL, Drazen JM (2016) Data sharing. New England Journal of Medicine 374:276-277.

The aerial view of the concept of data sharing is beautiful.

This open science thing seems to be popular.

However, many of us who have actually conducted clinical research, managed clinical studies and data collection and analysis, and curated data sets have concerns about the details.

We are scared.

The first concern is that someone not involved in the generation and collection of the data may not understand the choices made in defining the parameters. Special problems arise if data are to be combined from independent studies and considered comparable. How heterogeneous were the study populations? Were the eligibility criteria the same? Can it be assumed that the differences in study populations, data collection and analysis, and treatments, both protocol-specified and unspecified, can be ignored?

Our methods sections are not complete.

A second concern held by some is that a new class of research person will emerge — people who had nothing to do with the design and execution of the study but use another group’s data for their own ends

Whoever said "If I have seen further, it is by standing on the shoulders of giants" should probably be arrested for stealing those giants' shoulders for their own ends.

possibly stealing from the research productivity planned by the data gatherers

We literally can't think of a way to plan our analyses so this doesn't happen.

or even use the data to try to disprove what the original investigators had posited.

Science is all about confirming preconceived notions.

There is concern among some front-line researchers that the system will be taken over by what some researchers have characterized as "research parasites."

We don't really have any data on this happening, but there are probably lazy dishonest people out there who will benefit from our work and we call these people "research parasites". Hopefully this won't turn into a hashtag!

This issue of the Journal offers a product of data sharing that is exactly the opposite. The new investigators arrived on the scene with their own ideas and worked symbiotically, rather than parasitically, with the investigators holding the data, moving the field forward in a way that neither group could have done on its own.

This issue of the Journal contains a report where the original authors were offered co-authorship which makes us happy.

How would data sharing work best? We think it should happen symbiotically, not parasitically.

We want to be co-authors.

Start with a novel idea, one that is not an obvious extension of the reported work.

We have dibs on all obvious extensions. Who knows, one of our students may need a project.

Second, identify potential collaborators whose collected data may be useful in assessing the hypothesis and propose a collaboration. Third, work together to test the new hypothesis. Fourth, report the new findings with relevant coauthorship to acknowledge both the group that proposed the new idea and the investigative group that accrued the data that allowed it to be tested.

We really, really, really want to be co-authors.

What is learned may be beautiful even when seen from close up.

Let's hope this data sharing thing is just a fad.

UPDATE: Translation to plain English of NEJM clarification on data sharing

(Hat tip to Daring Fireball for inspiration)