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data refuge paths

These are the paths we used at Data Rescue Philly. There are many paths to data refuge

Guides will take you on these Paths: choose one according to your interests and skills. A fuller description of the workflow is available at https://datarefuge.github.io/workflow/

  • Seeding & Sorting Path (to feed the End of Term Archive): This is the widest path and requires a variety of skill levels. Consider this path if you are a coder, hacker, have front end web experience, or just have a great attention to details.

  • DataRefuge Path:the various interwoven paths to get "uncrawlable" data into DataRefuge
    • Researchers (to review URLs the Seeders & Sorters mark as Uncrawlable): Consider this path if you have a strong front end web experience and like to find out more information about thing.

    • Harvesters (to figure out how to capture the uncrawlable data): Consider this path if you're a hacker

    • Checkers (to inspect a harvested dataset and make sure that it is complete): The main question the checkers need to answer is "will the bag make sense to a scientist"? Checkers need to have an in-depth understanding of harvesting goals and potential content variations for datasets.

    • Baggers (to do a quality assurance check and package the data): Consider this path if you have data or web archiving experience, or have strong tech skills AND attention to detail.

    • Describers (includes a few people from the Baggers path): Consider this path if you have experience working with scientificdata (particularly climate or environmental data) or with creating metadata. Trained librarians and scientists will be very helpful on this path.

  • Documentation & Storytelling: Consider this path if you’re on social media (Facebook, Instagram, Twitter, whatever), if you can use Storify, if you have good listening and writing skills, and/or if you can make creative and engaging materials.

  • The Long Trail: Consider this path if you’d like to build DataRefuge into the future. Future projects will also call attention to all the data that exists but can't be captured in a single weekend as well as data that doesn't exist, but should. Many kinds of skills needs. Experience in public engagement projects and informal STEAM education settings will be especially helpful.