D1.6 SSHOC Data Management Plan
Bibliographic record
Abstract
The Deliverable 1.6 Data Management Plan (DMP) provides structured description of data collected, created, and used for and by the SSHOC project. It also describes the procedures and policies as well as data protection and preservation mechanisms used during the implementation of the SSHOC project and planned in the post implementation period. This document follows the structure of Horizon 2020 DMP template1 and outlines the key points regarding: ● purpose of the data collection in relation to SSHOC project objectives and activities, ● types and formats of SSHOC project data, ● reuse of existing data, ● origin of data and data usefulness, ● alignment with FAIR principles, ● resources needed and responsibilities within the project, ● data security, ● ethical and intellectual property aspects related to data. The SSHOC DMP describes the overall methodology, standards and technical aspects to enable sound and tenable data management system for the SSHOC project. It presents the SSHOC project datasets, the basic rules of conduct in handling project data, and serves as a guide for members of the SSHOC project consortium. SSHOC recognizes the following data handled throughout the project duration: ● Survey data ● Case studies / pilots data ● Tools and Service data ● SSHOC Marketplace data ● SSHOC user communities data ● Other data Descriptions of the recognised data follow the same structure throughout the document giving the overview of the key points for each type of data. The SSHOC Data Management Plan is a living document and shall be updated continuously throughout the project in line with the new information gathered via conducting the project activities.
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 | 0.030 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.162 |
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".