Data Citation: Let's Choose Adoption Over Perfection | Zenodo

"In the last decade, attitudes towards open data publishing have continued to shift, including a rising interest in data citation as well as incorporating open data in research assessment (see Parsons et al. for an overview). This growing emphasis on data citation is driving incentives and evaluation systems for researchers publishing their data. While increased efforts and interest in data citation are a move in the right direction for understanding research data impact and assessment, there are clear difficulties and roadblocks in having universal and accessible data citation across all research disciplines. But these roadblocks can be mitigated and do not need to keep us in a consistent limbo. The unique properties of data as a citable object have attracted much needed attention, although it has also created an unhelpful perception that data citation is a challenge and requires uniquely burdensome processes to implement. This perception of difficulty begins with defining a ‘citation’ for data. The reality is that all citations are relationships between scholarly objects. A ‘data citation’ can be as simple as a journal article or other dataset declaring that a dataset was important to the creation of that work. This is not a unique challenge. However, many publishers and funders have elevated the relationship of data that “underlies the research” into a Data Availability Statement (DAS). This has helped address some issues publishers have found with typesetting or production techniques that stripped non-articles from citations. However, because of this segmentation of data from typical citation lists, and the exclusion of data citations in article metadata, many communities have felt they are in a stalemate about how to move forward...."