Pre-registration of mathematical models is a research paper published in Mathematical Biosciences (2022). On theSindex it has a DataRank of 0.449. It has been cited 19 times.
Pre-registration is a research practice where a protocol is deposited in a repository before a scientific project is performed. The protocol may be publicly visible immediately upon deposition or it may remain hidden until the work is completed/published. It may include the analysis plan, outcomes, and/or information about how evaluation of performance (e.g. forecasting ability) will be made, Pre-registration aims to enhance the trust one can put on scientific work. Deviations from the original plan, may still often be desirable, but pre-registration makes them transparent. While pre-registration has been advocated and used to variable extent in diverse types of research, there has been relatively little attention given to the possibility of pre-registration for mathematical modeling studies. Feasibility of pre-registration depends on the type of modeling and the ability to pre-specify processes and outcomes. In some types of modeling, in particular those that involve forecasting or other outcomes that can be appraised in the future, trust in model performance would be enhanced through pre-registration. Pre-registration can also be seen as a component of a larger suite of research practices that aim to improve documentation, transparency, and sharing-eventually allowing better reproducibility of the research work. The current commentary discusses the evolving landscape of the concept of pre-registration as it relates to different mathematical modeling activities, the potential advantages and disadvantages, feasibility issues, and realistic goals.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.449
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.