Bio-IT World is proud to bring together innovative data scientists and developers from across the industry to solve real-world data challenges using the principles of Open Source & FAIR Data.

For the past three years, the Bio-IT World Hackathon has delivered a new level of collaboration to the annual Bio-IT World Conference & Expo in Boston. Our 2019 FAIR Data Hackathon brought together a record breaking 8 teams and over 100 participants to solve real data challenges. Learn more about the 2019 Hackathon at Bio-IT World. 

The fourth Bio-IT World Hackathon has been postponed to 2021 and will continue in the tradition of uniting life science and IT teams to tackle actual bioinformatics projects with maximum impact potential. Projects at the event will feature either Open Source tools or some or all aspects of making data findable, accessible, interoperable, and reusable. All projects will be broadly applicable to the data science community. 

Examples of previous projects include:

DOE JGI Genomics Data Set
U.S. Department of Energy Joint Genome Institute
The DOE Joint Genome Institute has a wealth of environmental genomics data that is available for public use. The JGI has been working on a new search and download system to ensure the data are findable and accessible in order to address concerns from the community. This is an open project and hackathon participants will be able to help assess the 'FAIR'ness of the data access point, and link it to other community efforts.

Bringing the Power of Synthetic Data Generation to the Masses
The Broad Institute
Everyone from tool developers and educators to researchers publishing their own work or trying to build on someone else’s, is hamstrung by the lack of open access genomic datasets appropriate for reproducing biologically meaningful analyses at scale (as opposed to plumbing testing, ie « will it run? » for which we have data in spades). The solution involves generating custom synthetic datasets, but current tools to do so are complex and require a lot of computational work that ends up being redundant when applied to multiple studies. This project will build on existing tools to provide community resources and streamlined tooling for generating custom synthetic data efficiently.

FAIR Beyond Data – Applications as FAIR
The Jackson Laboratory
The Jackson Laboratory is working with the registration of an application through the specification of inputs and outputs and the expected transformation as a POC accomplishing two things. One, being agnostic to platform and two being FAIR. Extending the principles of FAIR to applications which transform input to another form by algorithms, e.g. machine learning algorithm, normalization or transformation algorithms.