Preserving Digital Humanities Projects Using Principles of Digital Longevity
Bibliographic record
Abstract
Academic libraries and archives struggle to preserve Digital Humanities (DH) projects due to the divide between faculty expectations and operational realities and the lack of capacity to preserve bespoke software and web-based scholarly communications. Consequently, over 40% of DH projects have disappeared from the internet, with 50% remaining unarchived, posing a threat to their longevity. Obstacles such as leadership changes, inadequate documentation, funding scarcity, and obsolete technologies contribute to these challenges. As funding agencies increasingly demand preservation plans, academic institutions must address these issues. This chapter reports on a large, global survey undertaken by the Endings Project and introduces the “Endings compliance” toolbox, guiding librarians and archivists in assisting DH scholars to frame their work for cost-effective preservation. The chapter argues that collaboration among technologists, scholars, librarians, and archivists throughout the project lifecycle is essential to address longevity challenges in DH work, particularly for preserving complex web applications. Clear indicators of project completion are necessary, along with contingency plans for potential disruptions. Libraries and archives can avoid the pitfalls of complex software stacks through such collaboration, and by adhering to known preservation principles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".