Speeding up Science¶
"Speeding up Science" is a series of workshops focused on developing application-specific Jupyter notebooks which are executable/launchable via Binder. The goal of these workshops is to “reverse engineer” common data visualization approaches used for biological data analysis and commonly published in scientific journal articles (heatmaps, read/OTU summaries, etc.).
Workshop goals/products include:
- Compile reproducible code workflows for the three common environmental -Omics approaches (metabarcoding, metagenomics, or metatranscriptomics). Jupyter notebooks will contain functional code and customizable parameters (with documentation/explanation) that users can adapt and deploy on their own data sets, assuming import of “standard” data formats that are typically generated during standard -Omics pipelines (e.g. FASTQ files of reads/contigs, OTU tables for metabarcoding).
- Gather information from end users in the life sciences about their computational needs and ongoing challenges. Where do the gaps exist in terms of your data analysis needs? Where do existing tools, pipelines, tutorials and trainings fall short?
- Lay the foundations for new open-source online lessons focused on “analyze your own data” training (where participants come to a workshop to specifically learn to analyze their own in-hand environmental -Omics datasets).
The first workshop happened on May 8-10, 2019 at UC Davis, and produced a number of analysis and visualization notebooks for metagenomics, metatranscriptomics, and metabarcoding. A static version of these workflows can be viewed on this site, and each workflow contains a link to an executable version hosted on Binder.
You can find us on twitter at #SpeedingUpScience.