e‐Science Central for CARMEN: science as a service
Bibliographic record
Abstract
Abstract Scientists face many severe challenges in extracting value from the increasingly large volumes of data they generate. In this paper we describe the requirements we have derived from working across a wide range of e‐science projects. In particular, the CARMEN neuroinformatics project has exposed a range of challenges due to a need to analyse and share large volumes of data. We have identified the four key activities required by scientists with whom we work, and designed an integrated system—e‐Science Central—to provide them. This exploits three emerging technologies: software as a service to avoid the need for users to deploy and maintain any of their own software; social networking to allow users to collaborate by sharing data, services and workflows in a controlled manner and Cloud computing to provide scalable compute resources. The system can not only be used through any web browser, but also provides an API so that applications can build on the core functionality. We describe the requirements, and the design that flows from them. This includes data storage with in‐built versioning and signing, an in‐browser workflow editor and a job scheduling system that allows workflows to be run both on local ‘private’ clouds and the Microsoft Azure Cloud. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".