Recording Link: https://youtu.be/Z3T_ERHaGHw
The NIH HEAL Initiative℠
Data Ecosystem aims to transform data, findings, and publications from a research portfolio of unparalleled depth and breadth into a virtual, annotated, searchable catalog in which datasets and findings from different studies can be analyzed, compared, and
To do this, the HEAL Data Ecosystem is employing a cutting-edge approach that will integrate HEAL data from a wide variety of existing data repositories into a unified HEAL web
platform that will allow users to find, access, and analyze data from across the HEAL data landscape.
The first step in successfully gaining access to the HEAL Data Ecosystem is to choose a repository - or home - for your research data that will ensure their persistence,
enforce data management best practices for your research domain or data type, and ultimately allow your data to become findable and accessible through the HEAL Data Platform.
In this webinar, Dr. Alex Waldrop of the HEAL Data Stewardship Group will present key principles
for finding the best home for your data and answer your questions about how to make this first important decision on the path to
joining the HEAL Data Ecosystem.
Presenter Name and Bios:
- Dr. Alex Waldrop - senior bioinformatics and machine learning scientist at RTI International
Dr. Alex Waldrop is a senior bioinformatics and machine learning scientist in the Genomics, Bioinformatics, and Translational Research Center at RTI International. He also serves as the Data Engagement Lead for the HEAL Data Ecosystem Stewards team. A computer
scientist by training with a PhD in bioinformatics and more than 10 years of experience in big data analytics, his work focuses on leveraging the power of machine learning to solve real world problems across the biological, environmental, and social science
domains. His scientific background includes various applications of high-throughput sequencing data to biological systems, including projects in microbiomics, population genetics, cancer genomics, transcriptomics, and statistical behavioral genetics. His computational
background has focused largely on leveraging Natural Language Processing (NLP) techniques to improve data findability and harmonization through the development of tools like Dug, a semantic Search engine for biomedical datasets developed for the NHLBI’s BioData
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