Virtualization of Research Data Services during the COVID-19 Pandemic as an Opportunity to Enhance Research Data Support
Bibliographic record
Abstract
For Canadian academic libraries, the emergence of the COVID-19 pandemic necessitated an unprecedented switch to virtual services. An abrupt halt to in-person activities required almost all libraries to utilize new technologies in order to continue serving patrons. While the Academic Data Centre (ADC) has traditionally offered both physical and online services, with the emergence of the pandemic, the ADC pivoted to exclusively online service provision. Through new initiatives such as remote desktop access to statistical software, embedded virtual spaces for consultation and breakout discussions, online workshops and teaching, and the use of social media--the Academic Data Centre emerged successful in supporting student and faculty data needs. While virtually scaling up data services was essential to avoid disrupting researchers working with data, the shift to online services also presented an unexpected opportunity to reflect meeting the data needs of users and, in turn, strategize innovative future data service delivery. Three themes emerged from our reflection: emphasis on greater accessibility; more flexible instruction; and the benefits for cultivating a data community. As emerge from the pandemic, the ADC expects to further embrace newly implemented technologies and virtual services to further scale and augment research data service support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".