Management of a Multidisciplinary Research Project: A Case Study on Adopting Agile Methods
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it