That's the question we asked our latest research published in the J Dent Res (open access here)
Raw data allows replication and validation of results. Additionally, if it is in a machine-accessible format, this would allow new hypotheses to be explored using the available data. So, in order for machines to access data, it must be Findable, Accessible, Interoperable and Reusable. This is known as #fairdata
Sharing data in #fairdata allows other researchers to access it, but it is primarily intended for, as they see the data in a different way.
In this way they can catalog, process, or reuse this data or join it with other data, maximizing the effects of the research.
We programmatically analyzed the availability and quality of data available in dental publications. We found that of 7 549 available publications, 112 (1.5%) shared research data; Table 1 shows the results by journal and year. Of 165 journals, we found data in only 21. We did not find a trend by year
When evaluating compliance with FAIR criteria, we found that the average compliance was 32.6%. The items with the lowest compliance were reusability (24%) and interoperability (27%)
We observed that there was no clear trend in #FAIRdata compliance for dental publications between 2016 to 2021 nor for journals.
When analyzing the detail, the item with the least compliance refers to the description of the metadata and whether the data is in a format that can be reused (open format such as csv) or closed (such as xlsx, doc, etc).
These results explain the urgency of major research funding agencies to start forcing researchers to publish the raw data obtained from publicly funded research.
Recent research showed that even when authors put "Data available upon request", 93% of the contacted authors refused or not answered the request ( https://jclinepi.com/article/S0895-4356(22)00141-X/fulltext ) so journals should start demanding data or authors justify why they can't share it.
On the other hand, the fact that the few data are shared in unstructured format instead of #FAIRdata hinders their use by other researchers, limiting their reproducibility as well as their machine-actionability. What can researchers do to improve this situation?
When you publish your data, make sure that you clearly indicate whether you allow the use of the data and under what conditions by means of a license.
The fact that your data is #FAIRdata does not mean that everyone has access, and there may be conditions (confidentiality, commercial agreements) that may well limit access, but the *data should be as open as possible and as closed as necessary*
When you share your data in FAIR format in you ensure that your results can be independently replicated and validated, and that's just what doing science is all about!