2023 Best Negative Data Prize in Clinical Neuroscience
Cohen Veterans Bioscience (CVB) and the European College of Neuropsychopharmacology (ECNP) welcome your submissions for the 2023 Best Negative Data Prize in Clinical Neurosc
The prize recognizes the researcher or research group whose publication in clinical neuroscience best exemplifies clinical data where the results do not confirm the expected outcomes or original hypotheses or results that challenge a long-standing clinical precede
All submissions must meet the following criteria:
- Written in English and published or accepted for publication in a peer-reviewed journal that is currently listed by Web of Science and/or Scopus
- Published after March 1, 2018
- Reports the results of a human study (or set of studies) in the field of neuroscience
- Not a review, meta-analysis, systematic review, short communication or case report
- Is a full-length randomized clinical trial with detailed materials and methods section(s) (within the body of the paper or as supplementary information).
In addition to the above-mentioned criteria, each submission will be rigorously evaluated by a Prize Review Committee, comprised of members of the ECNP prize committee and CVB. The multi-step review process will evaluate each submission based on the data analysis and statistics, adherence to research rigor standards, a technical review of the materials and methods used in the study, and a field-specific scientific review by two subject matter experts.
Submissions for the 2023 Best Negative Data Prize in Clinical Neuroscience will be accepted from March 30, 2023, and close on May 21, 2023. The award itself is a monetary prize of $10,000, made available through the generous sponsorship support provided by CVB. The review committee will select and inform the winner by August 1, 2023. The public announcement and presentation of the prize will take place at the ECNP Congress in Barcelona, Spain, on October 7-10, 2023, where the winner will be expected to present a talk about his or her negative data results paper.