Watch the Recording
There is an urgent need for better integrating physical objects into the digital research data ecosystem, both in a global and in an interdisciplinary context. This webinar series is aimed to help facilitate cross-domain exchange and convergence on key issues related to the digital representation of physical samples and collections.
The second webinar in this series will outline how RRIDs (Research Resource ID’s), persistent identifiers used to track key biological resources, i.e., "physical samples" for the biomedical field, successfully drove improvements in resource identification. It will highlight lessons learned, and how any research field could learn from the challenges that the RRID project faced in implementing RRID’s. Particularly the successful adoption of RRIDs by publishers and the research community is of interest to anyone interested in persistent identifiers.
The project that developed RRIDs started in 2012 and in 2014 launched the first RRID identifiers. Today, RRIDs are used in thousands of research papers, are included in 1000+ Journal instructions and guidelines, and have made a significant impact across the scientific literature.
In this webinar, we will share information about the RRID project including the things that worked well and the things that we might have done better. We will also discuss the data showing the impact of RRIDs across the scientific literature and how and when RRIDs should be used.
What types of samples are included in the RRID project?
What types of samples are not included in the RRID project?
Data, pipette tips, salts, and other "stuff" that mostly works as it is supposed to. RRIDs track the tricky bits in biology that do not necessarily work the same way from lab to lab, and end up being the cause of many reproducibility issues.
Attendees will learn about why RRIDs were created, when RRIDs should be used in a manuscript, and how to obtain and use an RRID. Computational scientists can learn how to obtain data on resource usage, as this is now becoming an interesting dataset in and of itself (see RRIDs in Hypothes.is).
If you are interested in persistent identifiers, if you like reagents, but do not like how they are usually described in your friends’ papers, or you are someone within or outside the biomedical field and could learn about the implementation of persistent identifiers in scholarly communication then you should attend!