Bibliographic record
Abstract
Within FENS, the CARE committee was originally created to better inform the neuroscientific community and homogenize the actions taken to improve animal well-being in our research and apply the 3Rs (Refine, Reduce, Replace). In parallel, however, the actions taken against animal use in research forced the European parliament to take action and re-evaluate animal experimentation. As CARE committee, we have thus engaged in activities to better coordinate with other organizations (such as EARA [European Association on Research using Animals], EBC [European Brain Council], CAR of SFN) to meet and inform politicians, the public, and the scientific community at large, regarding the continued need to use animals in research. Our message is that animal experiments are an essential part of progress in neuroscience and are being done with great responsibility and maximal care to reduce harm and improve well-being, in line with the 3Rs. In neuroscience, nonanimal methods currently complement animal experiments, as each model offers unique strengths for addressing different aspects of research questions. However, phasing out animal research remains on the agenda and it is important that the next generation of scientists is prepared to take responsibility and advocate for the use of animals in brain research for as long as necessary. In order to make this generation of scientists share their reflections about this often highly emotional topic, the CARE committee launched a writing competition under the motto: “Why do we still need animal research in neurosciences?”. Two categories of assays could be submitted “scientific assays” or “literary assays”. The call for participation was launched via the FENS newsletter, our CARE webpage (https://www.fens.org/about-fens/our-organisation/fens-committees/care-committee) and various social media platforms (Facebook, X, LinkedIn etc.). A total of 43 entries were received from 23 different countries as diverse as Canada, India, Nigeria, Denmark, Ireland, and so on. All members of CARE acted as member of the jury and all converged towards the same impression that all assays reflected a deep thinking and a great sense of responsibility from each participant. One winner and two runners-up of each category have been nominated and are published in the FENS official journal EJN as a special issue. The two winners also received a €500 prize, and their text was highlighted at the FENS Forum 2024 in Vienna. The winner of the best scientific assay is Cassandre Vielle (current PhD student in ethology at Concordia University, Montreal, Canada; holder of a PhD in Neurosciences from Aix-Marseille University, France). She has written an assay on the use of animals in drug-addiction research and how it could be improved for better translatability towards human patients suffering from addiction. The winner of the literary assay is Ivan Montiel (current post-doc at Institut Pasteur, Paris, France). He has written an assay entitled “Beyond the Use: The Paradox of Scientific Animal Utilization” in which he describes the difficult guilty feelings after his mice were sacrificed, while wandering around in the Paris streets, leaving the Institut Pasteur. We will also have the opportunity to discover the runners-up of each category. For the scientific essay, Ana Isabel Guimarães, a Master student from University of Porto, Portugal, explores the disadvantages of animal models and considers alternatives in a balanced way. Ingvild Bjerke, PhD student from University of Oslo, Norway, suggests in her essay that we change our routines to increase data-sharing and reuse in order to reduce the number of animals used. Finally, the two runners-up of the literary essays are Lene-Marlen Wessel, Master's student from University of Copenhagen, Denmark, who took alternating perspectives of a laboratory rat and a student on the day of sacrifice to lead us to a moving discussion on “useful death”. The second runner-up of the literary category is Rand Suleimann, Master's student from the University of Dresden, Germany, who places a young neurobiologist in front of her professor, questioning her about animal use in her research and then initiating a complete change in her research. Because all these questions raised by young scientists are timely and require deep thought, we can only encourage you to read these essays and motivate your early-career collaborators to think about the need of animals in neuroscience research and consider participation in the next competition in 2026 for the FENS Forum in Barcelona. The author declares no conflicts of interest. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/ejn.70201.
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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".