This section provides guidance on and resources related to study design, consent, privacy and security when research uses human specimens or data, management of clinical and experimental data, and a review of factors to consider when choosing from some of the common large scale molecular data generating platforms.
This section provides guidance for researchers looking to develop a hypothesis that will have reasonable statistical power, identify the appropriate set of samples, and execute a large scale data production from those samples. There are the two general types of studies using large scale molecular data sets, which can loosely be thought of as “investigative” and “comparative.” The two aren’t completely extricable and can each can serve as groundwork for future experiments for the other. The process to perform these types of studies, however, can be very different. The details specific to each study type are best addressed prior to generating materials or data sets.
The policies and processes that relate to the human subject components of any large scale data generating or analyzing study are continually evolving as new issues arise and become more clear. Keeping up with the particular issues that do or do not apply to a given research project can sometimes be a challenge, and these pages contain relevant guidance and links to the necessary information researchers need before, during and after a research project involving human specimens or data.
For a each study, the particular covariates associated with large scale data sets typically come from clinical or laboratory data. When these data are originating from human samples, certain protections need to be in place to ensure patient privacy. There are resources at the Fred Hutch which can help researchers effectively manage these data so that they can be associated with downstream molecular data sets more consistently and securely. This section also contains guidance about generating or handling large scale data from a variety of sources, highlights the particularites of each, and include information for researchers interacting with various Fred Hutch Shared Resources.
Some of the challenges associated with executing large scale data projects can be mitigated by implementing good laboratory management and data systems before beginning the project. These sections relate to the particular systems available to Fred Hutch researchers that help them manage data generated in laboratories and also specific types of guidance about good laboratory practices with large scale data generation in mind.
Updated: May 18, 2022Edit this Page via GitHub Comment by Filing an Issue Have Questions? Ask them here.