Dataset and selected figures of biomedical publications on Ebola in 2014
Bibliographic record
Abstract
The database contains data about the biomedical publications on Ebola in 2014.The volumes of publications and the classifications were determined by using the PubMed search engine. The data are ordered according to different criteria. Columns/row headings and embedded comments describe the contents of the columns/rows. Columns A-K of sheets ‘Clinical Trial term’, ‘Canada’, ‘China’, ‘France’, ‘Germany’, ‘Guinea’, ‘Liberia’, ‘Sierra Leone’, ‘UK’, ‘USA’ were from csv files downloaded from PubMed searches.<br>Further information about the methodology and the data is contained in the associated article. All data and figures mentioned in the associated article and this figshare entry are linked to a sheet of this database. Figure 3<br>Numbers of citations with ‘ebola’ or ‘ebolavirus’ in title from 1995 to 2014. Figure 4<br>Numbers of citations (abstract available) with ‘ebola’ or ‘ebolavirus’ in title from 1995 to 2014. Figure 5<br>Proportions of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available from 2005 to 2014. Figure 15<br>Proportions of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available with the indicated search terms in the title/abstract. Year 2014. Figure 16<br>Proportions of citations (‘ebola’ or ‘ebolavirus’ in title) with the search term ‘clinical trial’ in the title/abstract. The number of publications about original clinical trial studies is also indicated. Year 2014. Figure 17<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) per month. Year 2014. Figure 18<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available per month. Year 2014. Figure 19<br>Subjective classification for ‘current outbreak’ focus of 2014 citations (‘ebola’ or ‘ebolavirus’ in title). Remaining citations were assigned to one (and only one) of the indicated discipline/area categories. Year 2014. Figure 20<br>Subjective classification for current outbreak focus or, alternatively, for the indicated discipline/area categories of 2014 citations (‘ebola’ or ‘ebolavirus’ in title) per month. Citations were assigned to one (and only one) category, similarly to Figure 19. Year 2014. Figure 21<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) during 2014 with search term ‘outbreak’ in title/abstract. Figure 22<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) during 2014 with search term ‘Africa’ in title/abstract. Figure 23<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) during 2014 with search terms ‘vaccine’ or ‘vaccines’ in title/abstract. Figure 24<br>Numbers of total biomedical citations of the 20 countries with most total biomedical publications. Year 2014. Figure 25<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) of the 20 countries with most total biomedical publications. Year 2014. Figure 28<br>Numbers of total citations with abstract available of the 20 countries with most total biomedical publications. Year 2014. Figure 29<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available of the 20 countries with most total biomedical publications. Year 2014. Figure 31<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) of the six countries with most Ebola-related publications. Citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available are also shown. Year 2014. Figure 32<br>‘Manual’ control test of the method for country affiliation attribution.<br>Numbers of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available of the six countries with most Ebola-related publications. The numbers of publications automatically retrieved or with real ‘any author’, ‘first author’ or ‘last author’ with the proper country affiliation are indicated. 100% of the citations had at least one author (‘any author’) with the proper country affiliation, thus indicating accuracy of the method. Year 2014. Figure 33<br>Subjective classification for article type of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available of the United States and Canada, the two countries with most Ebola-related publications with abstract available. Year 2014. Figure 34<br>Subjective classification for discipline/area of citations (‘ebola’ or ‘ebolavirus’ in title) with abstract available of the United States and Canada, the two countries with most Ebola-related publications with abstract available. Citations were assigned only to the more relevant category, except citations related to specific aspects of the 2014 outbreak that were assigned optionally and in addition to the other categories. Year 2014. Figure 35 Numbers of total biomedical citations and citations (‘ebola’ or ‘ebolavirus’ in title) of Sierra Leone, Liberia and Guinea, the three countries with most Ebola cases. Year 2014.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.106 | 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; both teacher heads agree on what is shown here.
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".