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Data Management Case Studies

Sensitive data

Impressive Library has a great conservation department. The specialists there have created a system for documenting the repairs they make to rare books that ensures the process is reproducible. The data they collect through their process is of interest to other conservation departments at other libraries as well as to book historians and possibly some chemists. However, the department has not yet made the data available because if bad actors had access to the data, they could more successfully create forgeries, remove pages from rare books, or learn about valuable items that could be stolen from the library.

It's not always immediately obvious what someone could do with data that would put the subject of the data at risk. Data about people is particularly vulnerable as Big Data science makes reidentification of participants easier. Even if the only risk is privacy violations, that's still a pretty bad thing to have happen to your research subjects. 

Other types of sensitive data include archeological dig sites and recovered items and data about endangered species. Some data may not be vulnerable to the mustache twirling bad actors we can envision with these examples but may be sensitive because of the ways colonialism has impacted some populations. Data about Indigenous peoples, their languages, their beliefs, and their land is one example of this kind of data.

How can we limit the risks to our research subjects and data we collect about them? Being conscientious about the tools we use is one thing we can do. Storing our data appropriately and using appropriate data collection and analysis tools are paramount.

There are also ways to share sensitive data with the research community and others presumed to be good actors. Some repositories, like ISCPR, are designed with sensitive data in mind. They limit who can access some types of data to faculty at member institutions. 

Another option is to share only metadata files about your data. This allows the research community to know what data has been collected and how those data are organized. That will allow them to build on your work by collecting data that can be interoperable with what you've collected. 

Read more about collecting sensitive data on the Sensitive Data page of the Creating Inclusive Surveys guide.