Management of a Multidisciplinary Research Project: A Case Study on Adopting Agile Methods
Bibliographic record
Abstract
Agile methods, initially used by cross-functional teams in software development projects, can also facilitate teamwork in collaborative research processes. For this, project management-related issues need to be addressed, including the challenge of finding practical means for coordinating scientific collaboration, while garnering commitment from all participants. This article explores the utilisation of agile methods by a semi-distributed scientific team, for coordinating a multidisciplinary research project. It examines how these methods can contribute to task coordination in scientific research and highlights key factors for successful adoption of the agile framework in collaborative research projects. Data are collected from a research team, after a 10-week phase of implementing agile methods. Data analysis focuses on the effectiveness of team dynamics and the digital tools used for communication and coordination during the project. The findings indicate a perception that agile methods contribute to improved coordination and teamwork during project development, with less agreement on the utility of some of the tools used. Also, it suggests the importance of involvement of the Principal Investigator and the role and contribution of a Facilitator.
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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 | 0.034 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".