Blog DFH meetup - Finding research data: how to find your way!
|Datum:||27 augustus 2018|
When I think of the findability of research data 16th-17th century maps of Asia and Australia (or what was considered ‘new’ land by Europeans) come to my mind. The vast emptiness always strikes me. It shows some pieces of detailed information on the shores and lines indicating that there probably is something there. It emphasises the large unknown areas. I can’t image how they were able travel. A lot happened in the field of mapping and cartography. How different is it today when Google maps can show you how to walk, bike or drive to every corner of the world. And you can already see how it looks out there without leaving your desk.
Similarly, in regard to research data, there are publications, presentations, and products coming out of universities and research institutes. So, probably there is data there. But what we often do not see are these large body of collections of research data in various forms and shapes that form the basis for all those publication and results. Like the maps of 400 years ago, some part are described, but there is still a large emptiness of directions. There is no Google maps for finding data for research, there is not a paved route available. We still need to rely on peddling up to the shore, have a look around, peddle back, try a different place 10 km further up, etc. Sending an email to an author of a publication is still the main way of finding data. Of course there are collections that have invested in findability and some collections have fulfilled this need from the start; for instance: CBS, EGA, DANS, BBMRI, LifeLines, and smaller studies like 1000IBD.
Why should we explore this data field, map it and make data findable for humans and machines? There are a number of reasons. For some scientific question combining data is essential, because the numbers in any individual dataset are too small (for instance rare diseases). Also, for replication of scientific results, finding similar data is crucial. Society asks researchers and research institutes to open up, use the data more efficiently and work in a more transparent way so research can be reproduced. By re-using data, society does not have to pay again for collecting the same information over again. Also, to make steps ahead in data intensive research and let the current methods for data analyses be used optimally for research, we need to work with larger datasets. Re-use of existing data is a valuable resource to further scientific knowledge
How more effective would it be if researchers could find relevant research data globally and evaluate its relevance and quality without leaving their desk. The Data Federation Hub actively supports findable data and strives to facilitate transparent and data intensive research. During the next meetup on September 18, we present the endeavours of our spokes and invited you to think with us on this topic and share your ideas. Please join us and let us know how we can mutually help each other with the challenges.