Put it Simply: Tools and Tips for Communicating Library Collections Data
Bibliographic record
Abstract
In communicating library collections data to our stakeholders and administrators, our main goals are to be impactful, to make our points clearly and concisely, and to provide data that can move decisions forward. Accomplishing these goals requires time and creativity to experiment and refine—assets that can be hard to come by for busy librarians. This presentation will provide an introduction to a few easy-to-use data visualization tools and how they can be applied for communicating data about library collections. The main tools included in the overview will be Google Spreadsheets and Tableau Public (including the pros and cons of each) as well as guidance on how to tame Microsoft Excel’s graph design biases. We will also review basic tips to take into account when graphically communicating data on the use and value of library collections to stakeholders. Real world library data such as usage statistics and collection expenditures will be included in the demonstrations of these tools and tips. No programming skills will be needed! By the end of this session, attendees will be equipped with some practical strategies and tools that make it easier to share and make sense of library collections data.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".