In the academic world, young students are taught about the importance of sharing and working together to accomplish a goal. However, as we get older, we also start to begin seeing the distinction between sharing for the good of everyone and the problem of one person using that knowledge to further themselves. This becomes clearly evident in competitive fields like research, where the credit for valuable data can enhance one’s scholarly reputation so much that one is sometimes willing to be, well, less than totally honest about the steps one took to get there, such as using data that wasn’t theirs to begin with.

Yet, there are plenty of advantages to sharing data when you’re a researcher, and you can even earn coveted open science badges from some journals. So how do you know when to share your data, and when to avoid confidential dilemmas and scooped data sets? These are valid concerns with answers that, like the debate itself, aren’t always black and white. 

Concerns About Data Sharing

There’s a solid debate that has been raging for years as open science becomes more popular. Should a researcher make their data public, or are there legitimate reasons to keep one’s data set private, particularly when they want to use it for personal studies?

This is where science remains, somewhere stuck in the shades of gray of partially open-source, partially closed databases. Your institution likely has policies and guidelines for you to follow, but those will change if you move careers, try to publish in a new journal, or talk to someone from another institution or country. Although the US National Academics of Sciences, Engineering, and Medicine and the European Commission are on board with science becoming open, there aren’t any clearcut ways to make it happen.

Right now, many publishers and institutions follow the FAIR standards. These are the set of data-management procedures that the US NASEM and European Commission have endorsed, and they’re based on the principles of making research findable, accessible, interoperble, and reusable. If you’re using a government funding source in the US, Europe, or Australia, you’ll be required to showcase how you’re managing your data and the protocols for sharing it, and many private funders and journals are following suit.

You can host your data sets in an open-access repository, where it can be shared amongst other researchers. The advantages of this in terms of how data sharing could benefit the greater world of science should outweigh the disadvantages of one scholar’s reputation, but does it have to be either/or?

When to Share Your Data

Many researchers believe in the idea of greater openness in theory, but they don’t want to be scooped out of their hard work, and that’s understandable. Maybe, just maybe, there’s a middle ground?

You spend so much of your important time curating the data necessary to complete your research. You know how beneficial it is to use other scholarly data sets available on open source databases, and you don’t mind sharing your work with others. However, when is it too soon to add your information to open-science, and how do you do it? 

If you don’t have a lot of expertise in the field of data sharing, curation, and metadata collection and dispersion, not sharing can be more of a matter of uncertainty and confusion rather than a choice not to do so. They key is to ensure your data is useful to others by making it easy to site. This is done with a digital object identifier (DOI) that makes your data discoverable amongst the endless other files in a database.

How and when to do this depends on your field. Neuroscience and biomedicine fields, for instance, have limited data, which makes everything you can provide potentially beneficial. These researchers spend a lot of time generating their data set because it is all unique. This is an expensive undertaking, and those same researchers likely want to hold onto their data until it has paid for itself before they share it with others. They do want to share it, they just want to ensure their investment is covered, first, and that’s also understandable.

If you’re in a field where scooping is common because credit for an important discovery can mean anything from an impressive scholarly reputation to big bucks, you want to hold your data even closer to your chest. When that’s the case, you should consider keeping access to your data set limited until you’ve published your findings, unless they have the potential to benefit others on such a wide scale that, ethically and morally, your reputation should take a backseat.

Regardless of when you share your data, you should always follow your work’s influence on your target field. This is easily done with Impactio’s data analysis tools. Choose the factors you want to monitor, and watch their growth using Impactio’s complex and impressive report features. Your new knowledge can be a springboard to your next level of academic success.