[ Science ] Call for transparency of COVID-19 models2020/05/01
OpenGMS has joined with many other modeling science teams around the world to publish an open letter in Science, calling on our colleagues to please share your expertise, insights, and model code on open, FAIR aligned repositories.
Thirty scientists have written an open letter to Science advocating for increased transparency in the modelling behind COVID-19.Among these, Prof. Min Chen has signed on behalf of we OpenGMS team. Read the full article here: https://science.sciencemag.org/content/368/6490/482.2
A hallmark of science is the open exchange of knowledge. At this time of crisis, it is more important than ever for scientists around the world to openly share their knowledge, expertise, tools, and technology. Scientific models are critical tools for anticipating, predicting, and responding to complex biological, social, and environmental crises, including pandemics. They are essential for guiding regional and national governments in designing health, social, and economic policies to manage the spread of disease and lessen its impacts. However, presenting modeling results alone is not enough. Scientists must also openly share their model code so that the results can be replicated and evaluated. Given the necessity for rapid response to the coronavirus pandemic, we need many eyes to review and collectively vet model assumptions, parameterizations, and algorithms to ensure the most accurate modeling possible. Transparency engenders public trust and is the best defense against misunderstanding, misuse, and deliberate misinformation about models and their results. We need to engage as many experts as possible for improving the ability of models to represent epidemiological, social, and economic dynamics so that we can best respond to the crisis and plan effectively to mitigate its wider impacts. We strongly urge all scientists modeling the coronavirus disease 2019 (COVID-19) pandemic and its consequences for health and society to rapidly and openly publish their code (along with specifying the type of data required, model parameterizations, and any available documentation) so that it is accessible to all scientists around the world. We offer sincere thanks to the many teams that are already sharing their models openly. Proprietary black boxes and code withheld for competitive motivations have no place in the global crisis we face today. As soon as possible, please place your code in a trusted digital repository (CoMSES Network) so that it is findable, accessible, interoperable, and reusable.