Management of simulation studies in computational biology

Data management is a well defined task in computer science which investigates methods for organising and controlling the information generated during (research) projects. It comprises several tasks, including data storage, search, retrieval, version control and provenance. Effective data management strategies for computational biology are needed to handle the increasing amount of data that is being generated and processed: High-throughput experiments generate large amounts of data; computational models become complex; novel methods for model coupling enable researchers to combine models into even larger systems; increasing computational power allows for complex simulations; and the availability of data at different scales demands clever integration techniques. However, recent studies showed that the rate of reproducibility of scientific results in the life sciences, including computational biology, is not acceptable [Ioa14]. As a consequence, efforts have been launched to improve reusability and reproducibility of biomedical results (e. g., [M14, Ioa14]), and results of simulation studies in particular [W11a, B14, C15]. Today, paths towards improved data management are discussed by funders and publishers, in large scale projects and by individual researchers. For example, funders established policies such as the ERASysAPP Data Management Guidelines; the German Network for Bioinformatics Infrastructure, de.NBI (http://www.denbi.de) has dedicated data management centers; and projects are funded to develop support for sustainable data management, e. g., FAIR-DOM (http://fair-dom.org).

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