Research In Action
Research In Action
The FAIR Guiding Principles for scientific data stewardship state that data should be Findable, Accessible, Inter-operable, and Re-usable (FAIR). Why do these principles matter? When we make our research data more FAIR, other investigators (and our future selves):
- F: are able to find relevant data and datasets
- A: have a clear means to gain (or request) access to those data
- I: have data that are readable and interoperable across various software systems (now and into the future)
- R: have enough information about variables and data collection methods to allow re-use of those data
The FAIR principles are part of a growing global movement, across all scientific disciplines, toward more open and transparent science. But it’s important to remember that FAIR data are not the same as “open” data. Even though much data from human subjects research cannot be made completely public and open, there are ethical and practical ways for de-identified data from health and behavioral studies to be findable and accessible for re-use by other investigators. Re-using data can allow researchers to ask new questions that could not otherwise be addressed.
The Child Trauma Data Archives
- In 2010, the Prospective studies of Acute Child Trauma and Recovery (PACT/R) Data Archive grew out of an NIMH-funded international project harmonizing individual participant data from multiple prospective studies in order to develop predictive algorithms for child traumatic stress. As the project grew to include 19 datasets from five countries, we realized that we had created a unique, re-usable, and share-worthy research resource.
- In 2017, NICHD funded the expansion of the PACT/R Archive, which currently includes 30 datasets representing over 5,000 children exposed to acute trauma. PACT/R data have been used by investigators around the world to examine novel research questions, resulting in peer-reviewed publications.
- In 2021, with new funding from NICHD, we are building on the PACT/R framework to launch the Child Trauma Prevention and Treatment (CTPT) Data Archive. This project will develop new methods for harmonizing study- and participant-level intervention data in the child trauma field and enable novel analyses of prevention and treatment effectiveness and moderators. We expect to include over 20 intervention study datasets, representing over 2,000 children, in the initial stages of this archive.
FAIR Data Efforts in the Global Research Community
Our work to advance FAIR data practices has also grown beyond child trauma. The Global Collaboration on Traumatic Stress (GCTS) is a coalition of 11 scientific societies around the world that coordinates joint efforts to advance research and practice. In 2019, the GCTS adopted “Collaborating to Make Traumatic Stress Research Data FAIR” as one of its key themes, and I am honored to lead the GCTS FAIR Data Workgroup. Current efforts include:
- An international survey of traumatic stress researchers to better understand current knowledge and practices in data sharing and re-use
- An online index of datasets and a toolkit to help researchers implement FAIR data practices
- Supporting data archiving and harmonization efforts, with a current focus in two areas of traumatic stress studies: child trauma and traumatic grief
What Can You Do to Advance FAIR Data Practices?
As researchers, making our data FAIR is not an all-or-nothing proposition – it is useful to think of “FAIR-ness” as a continuum. To the extent that we can work towards making our research data more FAIR, we are promoting better science and ultimately benefiting the children and families we seek to serve.
What can you do?
- In your lab: Examine your own data practices across the research lifecycle: Where can you be more FAIR? Consider benefits, as well as challenges and how you can address them. Learn more here.
- As a mentor and colleague: Think beyond publication when considering scholarly impact. Support the idea that developing re-usable data resources is a key research outcome that advances science.
- In your field: Get familiar with the state of FAIR data in your discipline and research area and advocate for greater adoption of FAIR data practices.