You are not alone: selecting your group members and leading an outstanding research team
Bibliographic record
Abstract
Being hired in a faculty position is the pot of gold at the end of the scientific training rainbow.After years of education and postdoctoral training, this first faculty job is a thrilling progression in a scientific career that allows us to develop our own research program and pursue the questions that most interest us, but starting a lab from scratch comes with a unique set of pressures and struggles.Luckily, Principle Investigators (PIs) don't have to walk alone, as most build teams to work with.Thus, one of the first and most important things to do at this critical career stage is to recruit team members.Subsequently, the PI leads and guides the group both in terms of the scientific projects and facilitating the career progression of the team members.While scientists are generally very well trained in designing and running experiments, most of us do not receive much, if any, training in the organizational skills needed for managing a group of people and inspiring them to do great work and plan for the future.This is a problem because proper selection, training, and mentoring of team members are essential to achieve scientific aims.The Federation of European Neurosciences (FENS) and the Kavli foundation have established a new group called the FENS-Kavli Network of Excellence (http://www.fens.org/Outreach/FENS-Kavli- Network-of-Excellence/) to provide peer support for early career neuroscientists and to provide a voice for people at this career stage in shaping the future of Neuroscience.Part of this support is this series of Opinion Articles in EJN to provide advice about different aspects of career progression in neuroscience.The first in the series was a piece about getting hired and negotiating a group leader position (Karadottir et al., 2015).Here we will discuss the next stage of the process, building and effectively leading a research team.There is a glaring omission in this article, the elephant in the lab, which is how to get funding, but don't worry, the next in the series of Opinion Articles will be entirely dedicated to funding.For now, we will focus on recruitment, leadership, mentoring, and handling problems in the team.Unfortunately there is not a magic recipe for cooking up the perfect research group, but we hope that our experiences will at least help avoid some of the common pitfalls and provide some tips that have helped us along the way.
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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 | 0.060 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.025 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.039 |
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".