Data and Model Sharing
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Science is a social activity requiring transparency, collaboration, and critical evaluation of published research. The bulk of research today is still communicated via peer-reviewed publication, and journals represent the primary gateway by which research is disseminated. One of the core aspects of the scientific method is the need to reproduce results. This is one of the primary distinguishing features of the scientific method and is what makes it such a successful enterprise.
One of the consequences of this is that the reproducibility of published research becomes less important. This diminishes the stature of science as a reliable methodology and can affect policymakers in government and industry and instill a lack of trust by the general population.
One approach to remedy this situation is to encourage journals to make sure that published work is reproducible. This means developing
policies and a more open culture to freely share empirical data, and models alongside the publication.
Data- and model-sharing
To make it easier to conduct reproducible biochemical modeling, the Center for Reproducible Biomedical Modeling is developing tools that simplify model building, annotation, simulation, and visualization. However, if the model, results, and documentation associated with published modeling studies are not accessible, and the modeling workflow is not transparent, reproducing the model and its simulation results remains difficult and reuse is impossible.
Therefore, we recommend that all model artifacts produced during the modeling workflow be publicly shared to facilitate reproducibility and reuse, as emphasized by the Findability, Accessibility, Interoperability, and Reusability (FAIR) principles. Following the FAIR principles will ensure that models can be downloaded and manipulated by independent research groups, allowing published results to be validated, and will allow complex models to be built by integrating previously-published work on components of the system. We encourage modelers to disseminate packages of artifacts alongside publications with an open-source license and to deposit these packages in version-controlled public repositories.
Alignment with the NIH Strategic Plan for Data Science