Overview of biomedical big data science and biomedical, clinical, and translational research.

Examples of issues of measurement and scale; avoid “big data hubris” with examples from Google Flu Trends

Discussion of the major ethical issues surrounding the use of data

Overview of clinical data standards related to big data.

Standards for biomedical data, participating in communities that develop them, aiding reproducibility

Overview of public health and its use of big data

“Team Science” will define team science in the context of modern bioinformatics research. The module will highlight some of the most pressing considerations facing team science, and provide a basic framework for understanding and establishing high functioning teams.

Overview of the secondary use (or re-use) of clinical data

Overview of the process of publication of scientific data and information, including peer review, journal literature, and limitations of the scientific literature

Overview of information retrieval (search) for knowledge-based information.

Introduction to identifiers (modeling, identifying and describing resources and objects.)

Data preparation, developing standardized quality assurance processes and pipelines

Tutorial for implementing the SMART on FHIR standards for clinical data

Introduction to OWL2 and data reasoning

Data set descriptions, ISA-TAB, and W3C standards for versioning, provenance, and attribution

Tools to semantically integrate data

Current approaches for modeling and mining Big Data; Streaming and parallel algorithms; Optimization considerations

Streaming data visualization; Visual data mining; Visual search and recommendation; Big data storytelling using visualization; Scalable parallel visualization methods; Advanced hardware and architecture for data visualization and analysis; Human computer interaction

The spectrum of reproducibility; Replication vs. Validation; Data Integrity

Research regulations; Privacy regulations; Compliance

Workshop materials and documentation for leading workshops pertaining to NIH guidelines, best practices for data management, data standards, data linking, public repositories, and your scholarly footprint

Review of current policies and how they pertain to different organizations; Data sharing versus data reuse; being a good big data reviewer