DataRescue Workflow -- Overview
This document describes the workflow we use for the DataRefuge project, both at in-person events and when people work remotely. It explains the process that a url/dataset goes through from the time it has been identified by a seeder & sorter as "uncrawlable" until it is made available as a record in the datarefuge.org ckan data catalog. The process involves several distinct stages, and is designed to maximize smooth hand-offs so that each phase is handled by someone with distinct expertise in the area they're tackling, while the data is always being tracked for security.
Before you begin
We are so glad that you are participating in this project!
If you are an overall Coordinator:
- See the description of some of the work overall coordinators do, including the role of the Spreadsheet Minder, here
If you are an Event Organizer:
- Learn about what you need to do to prepare the event here.
If you are a regular participant:
- Get a role assignment (e.g., Seeder, or Harvester), get account credentials needed for your role, and make sure you have access to the key documents and spreadsheets needed to do the work. The Event/Remote organizers will tell you how proceed to do all this.
- Go over the workflow documentation below, in particular the pages corresponding to your role.
Plan Overview
1. Seeders/Sorters
Seeders and Sorters 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/Sorters nominate them to the End-of-Term (EOT) project, otherwise they add them to the Uncrawlable spreadsheet using the project's Chrome Extension.
2. Researchers
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. Research.md describes this process in more detail.
We recommend that a Researchers and Harvesters (see below) work together in pairs, as much communication is needed between the two roles. In some case, one same person will fulfill both roles.
3. Harvesters
Harvesters take the "uncrawlable" data and try to figure out how to actully capture it based on the recommendations of the Researchers. This is a complex task which can require substantial technical expertise, and which requires different techniques for different tasks. Harvesters should see the included Harvesting Toolkit for more details and tools.
4. Checkers
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.
5. Baggers
Baggers do some quality assurance on the dataset to make sure the content is correct and corresponds to what was described in the spreadsheet. Then they package the data into a bagit file (or "bag"), which includes basic technical metadata and upload it to final DataRefuge destination.
7. Describers
Describers creates a descriptive record in the DataRefuge CKAN repository for each bag. Then they links the record to the bag, and make the record public
Partners
Data Rescue is a broad, grassroots effort with support from numerous local and nationwide networks. DateRefuge thanks EDGI in particular for their contributions, and to our numerous supporters for their hard work. See more of our institutional partners on our home page.