Opening Letter of RILEM TC SDM: The current situation of data and metadata management in construction materials research - Emerging demands and viable solutions
Bibliographic record
Abstract
To meet growing demands of stakeholders of the construction materials research field, new practices and methods must be established in handling metadata and raw data. Modelling approaches, simulation calculations and a targeted use of machine learning can save time and resources and is increasingly used in the industry as a decision-making tool. These techniques will play a crucial role in achieving the Net-Zero CO2 deadline of 2050. However, a better collaborative effort is required to ensure that data can be successfully reused. The task is challenging, as methods and strategies for data collection in the field of construction materials are diverse. The RILEM TC SDM (Scientific Metadata Management of Construction materials) aims to lay the foundation for a formal approach to metadata collection and management. As an initial step, a metadata collection framework and the associated input tool will be designed. Along with a best-practice guideline for the storage of raw data, this will help data producers establish a robust routine that aligns with the FAIR data principles, facilitating the data's reuse. The TC will provide the bridging element – the metadata file – that links the journal publication to the raw data. The metadata file can be easily stored and searched.
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 | 0.016 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.027 |
| Insufficient payload (model declined to judge) | 0.013 | 0.012 |
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".