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    • Funding Sources

    Bioconductor: Open Source Software for Genomics

    Providing Bioconductor training by redeveloping the website and developing global infrastructure to deliver high-quality community-led training in local languages.

    About Bioconductor

    Bioconductor is an open-source, open-development software project in the R statistical software language and is widely used in bioinformatics, immunology, microbiology, and single-cell genomics. Over 1,200 developers have contributed software that is downloaded by users over 500,000 times per year.

    In April 2020, we established a Community Advisory Board (CAB) that includes elected representatives from America, Europe, Africa, Asia, and Latin America to outreach to the growing global Bioconductor community. The CAB identified accessible high-quality training materials and delivery methods as an urgent, impactful need. Fundamental components for a successful training platform include:

    • Skilled and effective program management.
    • Excellence in instruction.
    • Quality of training materials.
    • Equity in access.

    This proposal will redevelop the Bioconductor web presence to ensure Bioconductor communities have equitable access to quality, well-structured project information and training materials. This work will also deliver structured, high-quality, culturally-aware, inclusive global training by developing a platform for community-led instruction.

    Publications

    • (Reimers and Carey 2006)

    Collaborations

    Our lab supports the open-source bioinformatics community and is working with:

    • ELIXIR: European Infrastructure for life-science information.
    • LERO: Ireland’s open-source software research center.

    ELIXIR Logo LERO Logo

    Funding Sources

    This project is funded by a Chan Zuckerberg Initiative CZI Science Essential Open Source Software (EOSS) award to Aedin Culhane.

    References

    Reimers, Mark, and Vincent J. Carey. 2006. “[8] Bioconductor: An Open Source Framework for Bioinformatics and Computational Biology.” In, 119–34. Elsevier. https://doi.org/10.1016/s0076-6879(06)11008-3.
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