Recognising the importance and impact of Imaging Scientists: Global guidelines for establishing career paths within core facilities
Bibliographic record
Abstract
In the dynamic landscape of scientific research, imaging core facilities are vital hubs propelling collaboration and innovation at the technology development and dissemination frontier. Here, we present a collaborative effort led by Global BioImaging (GBI), introducing international recommendations geared towards elevating the careers of Imaging Scientists in core facilities. Despite the critical role of Imaging Scientists in modern research ecosystems, challenges persist in recognising their value, aligning performance metrics and providing avenues for career progression and job security. The challenges encompass a mismatch between classic academic career paths and service-oriented roles, resulting in a lack of understanding regarding the value and impact of Imaging Scientists and core facilities and how to evaluate them properly. They further include challenges around sustainability, dedicated training opportunities and the recruitment and retention of talent. Structured across these interrelated sections, the recommendations within this publication aim to propose globally applicable solutions to navigate these challenges. These recommendations apply equally to colleagues working in other core facilities and research institutions through which access to technologies is facilitated and supported. This publication emphasises the pivotal role of Imaging Scientists in advancing research programs and presents a blueprint for fostering their career progression within institutions all around the world.
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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 | 0.092 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.015 | 0.021 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".