Bibliographic record
Abstract
Purpose Performing efficient literature searches and subscribing to the most comprehensive databases for interdisciplinary fields can be challenging since the literature is typically indexed in numerous databases to different extents. Comparing databases will help information professionals make appropriate choices when teaching, literature searching, creating online subject guides, and deciding which databases to renew when faced with fiscal challenges. The purpose of this paper is to compare databases for searching the chemical engineering literature. Design/methodology/approach This paper compares journal indexing and search recall across seven databases that cover the chemical engineering literature in order to determine which database and database pair provide the most comprehensive coverage in this area. It also summarizes published, database comparison methods to aid information professionals in undertaking their own comparative assessments. Findings SciFinder, Scopus, and Web of Science, listed alphabetically, were the leading databases for searching the chemical engineering literature. SciFinder-Scopus and SciFinder-Web of Science were the top two database pairs. No single database or pair provided 100 percent complete coverage of the literature examined. Searching a second database increased the recall of results by an average of 17.6 percent. Practical implications The findings are useful since they identify “best bets” for performing an efficient search of the chemical engineering literature. Information professionals can also use the methods discussed to compare databases for any discipline or search topic. Originality/value This paper builds on the previous literature by using a dual approach to compare the coverage of the chemical engineering literature across multiple databases. To the author’s knowledge, comparing databases in the field of chemical engineering has not been reported in the literature thus far.
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 | 0.109 | 0.485 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.058 | 0.059 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.026 | 0.022 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".