DataRescue Workflow
This document describes DataRescue activities both at in-person events and remotely, as developed by the DataRefuge project and EDGI. It explains the process that a URL/dataset goes through from the time it has been identified, either by a Seeder as difficult to preserve, or "uncrawlable," until it is made available as a record in the datarefuge.org data catalog. The process involves several stages and is designed to maximize smooth hand-offs. At each step the data is with someone with distinct expertise and the data is always being tracked for security.
Most of this document is generic to the entire #DataRescue effort. For New Haven-specific information, navigate to the documentation on the left.
Event Attendees
- Join the event Slack team recommended by event organizers, this is often the DataRefuge Slack. During the event people share expertise and answer each other's questions here.
- Pick your role from the paths below, get account credentials, and make sure you have access to the key documents and tools you need to work. Organizers will instruct you on these steps.
- Review the relevant sections(s) of this workflow.
Path I. Website Archiving
Seeding
Seeders canvass the resources of a given government agency, identifying important URLs. They identify whether those URLs can be crawled by the Internet Archive's webcrawler. If the URLs are crawlable, the Seeders nominate them to the End-of-Term (EOT) project. Otherwise, they add them to the Archivers app using the project's Chrome Extension.
Path II. Archiving More Complex Datasets
A. Researching
Researchers inspect the "uncrawlable" list to confirm that Seeders' assessments were correct (that is, that the URL/dataset is indeed uncrawlable), and investigate how the dataset could be best harvested. Researching.md describes this process in more detail.
We recommend that Researchers and Harvesters (see below) work together in pairs, as much communication is needed between the two roles. In some cases, one person will fulfill both roles.
B. Harvesting
Harvesters take the "uncrawlable" data and try to figure out how to actually capture it based on the recommendations of the Researchers. This is a complex task which can require substantial technical expertise and different techniques for different tasks. Harvesters should also review the Harvesting Toolkit for tools.
C. Checking/Bagging
Checkers 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.
Note: Checking is currently performed by Baggers and does not exist as a separate stage in the Archivers app.
Baggers perform some quality assurance on the dataset to make sure the content is correct and corresponds to the original URL. Then they package the data into a bagit file (or "bag"), which includes basic technical metadata, and upload it to the final DataRefuge destination.
D. Describing
Describers create a descriptive record in the DataRefuge CKAN repository for each bag. Then they link the record to the bag and make the record public.
Partners
DataRescue is a broad, grassroots effort with support from numerous local and nationwide networks. DataRefuge and EDGI partner with local organizers in supporting these events. See more of our institutional partners on the DataRefuge home page.