Data science in data librarianship: Core competencies of a data librarian
Bibliographic record
Abstract
Currently, data are stored in an always-on condition, and can be globally accessed at any point, by any user. Data librarianship has its origins in the social sciences. In particular, the creation of data services and data archives, in the United Kingdom (Data Archives Services) and in the United States and Canada (Data Library Services), is a key factor for the emergence of data librarianship. The focus of data librarianship nowadays is on the creation of new library services. Data librarians are concerned with the proposition of services for data management and curation in academic libraries and other research organizations. The purpose of this paper is to understand how the complexity of the data can serve as the basis for identifying the technical skills required by data librarians. This essay is systematically divided, first introducing the concepts of data and research data in data librarianship, followed by an overview of data science as a theory, method, and technology to assess data. Next, the identification of the competencies and skills required by data scientists and data librarians are discussed. Our final remarks highlight that data librarians should understand that the complexity and novelty associated with data science praxis. Data science provides new methods and practices for data librarianship. A data librarian need not become a programmer, statistician, or database manager, but should be interested in learning about the languages and programming logic of computers, databases, and information retrieval tools. We believe that numerous kinds of scientific data research provide opportunities for a data librarian to engage with data science.
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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 | 0.048 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.012 | 0.021 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".