The Industrial Ecology Digital Lab
Bibliographic record
Abstract
The dramatic growth in data intensity and software requirements in recent years demanded the sharpening of coding skills of individual scientists and the establishment of a sophisticated digital infrastructure within and across research groups. Several initiatives aim to help individual research to hone their skills, from online programming tutorials and classes (e.g. Khan Academy) to global non-profit organizations like software or data carpentry dedicated to teaching computing and data skills to researchers. Although improved coding skills of individual scientists usually lead to a good vertical reuse of software (by individual researchers and consecutive projects of related topics), horizontal interconnection (between researchers in or across research groups) often remains limited. To address this issue, the Industrial Ecology Programme at the Norwegian University of Science and Technology (NTNU, Trondheim - Norway) recently established a Digital Laboratory to foster a common digital infrastructure for the group. Industrial ecology (IE) is in the core of sustainability science, connecting environmental, economic and data research to assess global environmental issues, analyse the life cycle of individual products and the material/energy flows at different geographical scales. IE uses four main methodologies: Life Cycle Assessment, Environmentally Extended Multi-Regional Input-Output analysis, Impact Assessment and Material Flow Analysis. The Industrial Ecology Programme at NTNU is one of a few places in the world covering all four methodologies, therefore providing an unique opportunity to establish an overarching digital infrastructure for coupling and integrating analytic tools and datasets across the research group. However, despite accumulating a vast amount of data and analysis tools, these were mainly developed for singular research tasks or projects; model integration, reuse of developed software and gathered data across the whole group remained limited. This motivated the establishment of the Digital Laboratory of the IE Programme with the main objectives to (a) consolidate available infrastructure and ease the integration of newly developed tools by providing code and data exchange standards across the group (b) develop novel software based on common needs across the IE programme, thereby building an IE software and data toolbox (c) explore synergies with other research groups by connecting to similar initiatives in the sustainability and environmental research community Currently, the permanent staff of the IE Digital Lab consist of a Lead Researcher and a trained software engineer. Specific issues we faced during the first year included the different skill levels of individual researchers, the massive backlog of available software, the use of several programming languages and different data formats in the group as well as the different modelling philosophies across the group. We used a combination of techniques to solve these issues, ranging from establishing a knowledge exchange platform across the group, setting up common code standards and developing digital tools working across programming languages and operation systems. Here, we want to present these first steps taken by the IE Digital Lab in order to (1) share our experience and provide guidance for similar efforts, (2) build a network of Digital Laboratories of groups involved in sustainability/environmental research and (3) give an overview about developed tools which might be of use for other groups and individual researcher for managing a digital infrastructure.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.449 | 0.282 |
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".