Reliable novelty: New should not trump true is a research paper published in PLOS Biology (2019). On theSindex it has a DataRank of 0.515. It has been cited 30 times. Its calibrated FAIR score is 13/100.
Although a case can be made for rewarding scientists for risky, novel science rather than for incremental, reliable science, novelty without reliability ceases to be science. The currently available evidence suggests that the most prestigious journals are no better at detecting unreliable science than other journals. In fact, some of the most convincing studies show a negative correlation, with the most prestigious journals publishing the least reliable science. With the credibility of science increasingly under siege, how much longer can we afford to reward novelty at the expense of reliability? Here, I argue for replacing the legacy journals with a modern information infrastructure that is governed by scholars. This infrastructure would allow renewed focus on scientific reliability, with improved sort, filter, and discovery functionalities, at massive cost savings. If these savings were invested in additional infrastructure for research data and scientific code and/or software, scientific reliability would receive additional support, and funding woes-for, e.g., biological databases-would be a concern of the past.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.515
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.