My Favourite Tipples from a biopharma librarian and taxonomist
Jinfo Blog

By Mary Chitty

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My Favourite Tipples are shared by Mary Chitty of life science network Cambridge Healthtech Institute. She shares some of her preferred resources in areas from biomedical to innovative medicines and leadership skills.

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Here are some of my go-to sources for keeping both clients and myself abreast of the latest developments on data science in the life sciences.

One of the biggest ironies of "big data" is that we need even more of it. Biological data is often highly dimensional (think gene expression data), relatively sparse, and longitudinal data with health outcomes is particularly challenging. Simply having lots of data is not necessarily a good thing. (Has your data lake turned into a data swamp?) Data provenance, integrity and quality are crucial.

• Go FAIR (Findable, Accessible, Interoperable, Reusable): These are 14 guiding principles to improve FAIRness and machine actionability. Findable requires rich metadata in a searchable resource. Accessible requires open protocols, authentication and authorisation. Interoperable requires data integration, in a formal shared and broadly applicable language. And Reusable requires richly described metadata, detailed provenance and domain-relevant community standards. There's a video explaining why we need FAIR data on YouTube.

• National Institutes of Health: This website includes links to BISTI (Biomedical Information Science and Technology Initiative) and Big Data 2 Knowledge (BD2K) funding sources and news, and the June 2018 NIH Strategic Plan. It discusses FAIR data, data infrastructure, modernised data repository, data management analytics and tools, workforce development, and stewardship and sustainability. It aims to enhance data sharing and interoperability, ensure security and confidentiality, and develop and promote data standards.

• The FDA's Framework for Real World Evidence (RWE) Program 2018: Recruiting sufficient numbers of patients for randomised controlled trials is already challenging. As we learn to better stratify patients, there is hope for faster, cheaper, more definitive clinical trials. This document discusses the current use of Real World Data (RWD) for evidence generation and a framework for evaluating RWD/RWE.

• The GetReal Initiative: This is a two-year project of the Innovative Medicines Initiative (IMI), an EU public-private consortium consisting of pharmaceutical companies, academia, health technology assessment agencies and regulators, and patient organisations. The GetReal Initiative has the ultimate goal of driving the adoption of tools, methodologies and best practices and increasing the quality of real-world evidence (RWE) generation in medicines development and regulatory/HTA processes across Europe. The site also links to a glossary of definitions (PDF).

For fun:

• Bob Sutton's blog: The author of the book "The No Asshole Rule", Bob also writes about scaling up excellence (an underappreciated challenge) and has a new "friction project" focusing on "Building organisations that make the right things easy to do and don't drive people crazy". Keeping up with technology is easier than changing cultures and managing expectations!

An article in Jinfo I found particularly interesting:

• I found "How technology is transforming pharma - key challenges and transformational changes" by Jeanette Eldridge very practical, with some excellent resources and straightforward about the challenges of differentiating hype from reality. It was good to see discussions of ethics and standards.

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