The Global Preclinical Data Forum (GDPF) fosters the exchange of unpublished negative data between scientists. The practice of sharing failed results is rare, effectively leaving scientists with only half the story. Negative data are rarely published in journals while positive data are several times more likely to be published. Why is this important? Negative findings may contribute to invaluable scientific insights, but this data is currently lost to science. Estimated costs of lost data in the US are $28 Billion annually, similar to the $35 Billion National Institute of Health annual budget**.

For these reasons, the Best Negative Data Prize was established as an incentive for preclinical researchers to publish “negative data” results to ensure neuroscience studies properly advance knowledge.

In 2018, the first Negative Data Prize was awarded to Laura Luyten and Tom Beckers (KU Leuven, Belgium) for the following paper:
Luyten L, Beckers T (2017) A preregistered, direct replication attempt of the retrieval-extinction effect in cued fear conditioning in rats. Neurobiology of Learning and Memory 144: 208–215.

The GPDF opens a call for submissions for the 2020 Best Negative Data Prize in neuroscience beginning April 7, 2020 through May 31, 2020. The award is 10,000 made possible through the support of Cohen Veterans Bioscience. Travel and accommodation expenses will be covered. 

Who May Apply

First or corresponding authors may submit applications that:

  • Were published or accepted for publication in English by a peer-reviewed journal
  • Date of publication is not older than April 7, 2015
  • Report results of a non-clinical study/set of studies in neuroscience, not a review

The paper will detail materials and methods within the body of the paper or supplementary information. Authors must be confident technical failures are not the reason for their negative results. They must also be willing to share their raw data with third parties. GPDF is grateful to all colleagues supporting this project as reviewers, experts and advisors!

Prevention of Conflict of interest

The paper reviews, communication between reviewers and authors and decision-making related to the nominations will be handled by those without a conflict of interest. Neither the sponsors of the Prize, nor the publishers represented on the Advisory Board will be involved.

The review committee will inform the winner by August 1, 2020. The public announcement and presentation of the Award will take place at the ECNP Congress in Vienna, Austria on September 12, 2020 where the winner will present a talk about his or her negative data results paper. Travel and accommodation expenses will be covered. 

According to Dr. Thomas Steckler (ECNP Preclinical Data Forum co-Chair, Janssen Pharmaceutica NV):

“Science is historically self-correcting. This process is most effective when both positive and negative results are published. However, negative results are less likely to get published because they are often believed to generate less “value” for an individual scientist, organization or journal. Indeed, compared with the positive data, negative data may appear less exciting, are less likely to open new avenues of research and therefore new funding opportunities. Unpublished data is effectively a waste of valuable real and human capital, particularly in the face of the reproducibility challenge currently discussed in various fields of science: reproducibility in neuroscience has come under particular focus in recent years. It’s startling to realize that over 50% of published biomedical data cannot be reproduced*”.

Dr. Anton Bespalov (ECNP Preclinical Data Forum Co-Chair, PAASP), added:

There are hundreds of drug trials which have failed in the last few years. Analysis of the factors that led to these failures is very often compromised by the biased representation of the early, preclinical work. The prize aims to emphasize to scientists and academic publishers that there is real value in publishing all the results, not just the headline-grabbing positive results”.