Preclinical Development – Chemical
2019 PharmSci 360
Pharma R&D is becoming increasingly data-driven. Scientific insights and innovations are catalysed by the effective exploitation of data, using knowledge discovery from data technologies. Data quality and data readiness are essential prerequisites for successful data science. A fundamental enabler for this digital transformation is FAIR, a set of principles developed to ensure that data is Findable, Accessible, Interoperable and Reusable by humans as well as by machines [https://doi.org/10.1038/sdata.2016.18]. FAIR is an internationally renowned brand, widely adopted by a number of research communities in the academic and private sector [https://doi.org/10.1016/j.drudis.2019.01.008], as well as publishers, funders and other global organizations. Although the ecosystem of FAIR-enabling tools and resources is still work in progress, the principles are a game charger for data management, highlighting essential activities and a skill for today's data science , and guiding the development of new processes and tools set to save time, effort and money on ‘data wrangling’.