Vesicle Viewer – Data Analysis and Visualization Software
Bibliographic record
Abstract
As progress is made toward more detailed methods of experimentation, larger volumes of data are generated. Much the critical information inside that data is inaccessible to researchers, who have limited time to analyze results by hand. Easy to use data visualization software can allow them to take full advantage of valuable data and maximize the use of limited resources. In this project, a web application is being developed to visualize data generated in the study of lipid bilayers. Small angle scattering (SAS) techniques are used to analyze the structure of generated bilayers. This data is then fitted to an appropriate model, after which it is visualized into various graphs. In this way composition, lipid volume and bilayer thickness can be determined and utilized in further study.\nThis application will primarily use Django, a python package specialized for the development of robust web applications. In addition, several other libraries are used to support the more technical aspects of the project – notable examples are MatPlotLib (for graphs), NumPy (for calculations) and Pandas (for advanced data structures). The decision was made to develop a web application to allow scientists all over the world to take advantage of this solution. Without the barrier of downloading and installing software, users can take advantage of the application regardless of which operating system they use. This also allows for a shorter development cycle by eliminating the need to push updates or prepare multiple versions.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.028 |
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".