Data Governance Community Webinar

Webinar Summary

Data science is becoming a key discipline in pharmaceutical research. A successful Data Science strategy requires high-quality structured, integrated data collected from internal sources (for example bioassays) and external sources (literature, patents, drug labels, etc). Manual curation by domain experts is the key approach that allows companies to get from unstructured data spread across thousands of sources to high-quality datasets in order to gain new insights, make discoveries, and speed up drug development.

Curation at scale is a complex process that involves the development of strict protocols and standards, sophisticated infrastructure, and management of a large number of curators. The complexity of manual curation makes it a costly – but necessary – process, and that is why it is so important to get it right. This webinar will explore the processes, best practices, and pitfalls of manual data curation.

Data Science is the future: Join this webinar to learn about how to get there with high-quality, manual curation.

Featured Topics

• Key learnings about manual curation best practices from the leader in manual curation based on 20 years of experience in curating biological knowledge and data.
• FAIRification of Clinical Trial Data at Roche

Learning Objective

At the conclusion of this session, participants should be able to:

• Employ key strategies and avoid pitfalls when curating data to generate high-quality structured knowledge and datasets.

Sponsored by:

Speaker Bios

Frank Schacherer, PhD, VP Products and Solutions, QIAGEN Digital Insights, QIAGEN GmbH
Dr. Frank Schacherer leads QIAGEN’s program for information systems, knowledge content and machine learning in discovery research. He joined QIAGEN in 2014 with the acquisition of BIOBASE where he served as a managing director. Dr. Schacherer has more than 20 years of experience in management, software, and database development. He holds a Ph.D. in bioinformatics. His current interest is in translating the promise of data science and AI into useful solutions for understanding biological systems.

Rama Balakrishnan, PhD, Biomedical Ontology Specialist, Genentech
Rama received her Ph.D. in Biophysics from SUNY Buffalo(NY) and was a post-doctoral researcher in the Biochemistry Department at Stanford University(CA). She then moved to managing genomics databases and developing ontologies for biomedical domains also at Stanford. She continues to contribute to data curation and ontology development at Genentech/Roche.

Joshua Bernal, Data Curator, Genentech
Josh studied Biology at UC Berkeley and moved into Data Management shortly after. He has 15 years of combined CRO, Vendor, and Pharma Data Management and Data Curation experience.

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