For the last couple years, faculty from the OHSU Department of Medical Informatics & Clinical Epidemiology (DMICE) and Library have been developing open educational resources (OERs) in the area of Biomedical Big Data Science. Funded by a grant from the National Institutes of Health (NIH) Big Data to Knowledge (BD2K) Program, OERs have been produced that can be downloaded, used, and repurposed for a variety of educational audiences by both learners and educators.
Development of the OERs is an ongoing process, but we have reached the point where a critical mass of the content is being made available for use and to obtain feedback. The OERs are intended to be flexible and customizable and we encourage others to use or repurpose these materials for training, workshops and professional development or for dissemination to instructors in various fields. They can be used as “out of the box” courses for students, or as materials for educators to use in courses, training programs, and other learning activities. We ultimately aim to create 32 modules on the following topics:
- Biomedical Big Data Science
- Introduction to Big Data in Biology and Medicine
- Ethical Issues in Use of Big Data
- Clinical Standards Related to Big Data
- Basic Research Data Standards
- Public Health and Big Data
- Team Science
- Secondary Use (Reuse) of Clinical Data
- Publication and Peer Review
- Information Retrieval
- Version Control and Identifiers
- Data Annotation and Curation
- Data Tools and Landscape
- Ontologies 101
- Data Metadata and Provenance
- Semantic Data Interoperability
- Choice of Algorithms and Algorithm Dynamics
- Visualization and Interpretation
- Replication, Validation and the Spectrum of Reproducibility
- Regulatory Issues in Big Data for Genomics and Health Semantic Web Data
- Hosting Data Dissemination and Data Stewardship Workshops
- Guidelines for Reporting, Publications, and Data Sharing
- Terminology of Biomedical, Clinical, and Translational Research
- Computing Concepts for Big Data
- Data Modeling
- Semantic Web Data
- Context-based Selection of Data
- Translating the Question
- Implications of Provenance and Pre-processing
- Data Tells a Story
- Statistical Significance, P-hacking and Multiple-testing
- Displaying Confidence and Uncertainty
At the present time, 20 of the above modules are available for download and use. We are encouraging their use and seeking feedback from those who make use of them. The feedback will be used to improve the available modules and guide development of those not yet released.
We have also been developing mappings to research competencies in other areas, such as for the NIH Clinical and Translational Science Award (CTSA) consortium research competency requirements and the Medical Library Association professional competencies for health sciences librarians. To this end, we have been able to link these materials to existing efforts, and provide training opportunities for learners and educators working in these areas. We ultimately aim to complete this mapping across all of the BD2K training offerings, to align with other groups, avoid redundancy and to ensure we are meeting the needs of these various groups.
This project is actually one of several projects that have been funded by grants to develop and provide education in biomedical informatics and data science. The other projects include:
- Update of the ONC Health IT Curriculum that includes focused training of 1000+ incumbent health IT and healthcare professionals in healthcare data analytics. (Also described in a recent posting in the blog of one of the PIs.)
- Development of data science skills courses funded by a second grant from the BD2K program that makes use of some of the OERs as well as other materials.
We hope that all of these materials are useful for many audiences and look forward to feedback enabling their improvement.
For more information, please contact Nicole Vasilevsky.