JINR is an electronic journal, which brings to the fore research in Natural Language Processing and Machine Learning that uncovers interesting negative results.
They idea is that while we see a lot of publications about successful attempts, it is not clear which paths are not promising directions.
Much can be learned by analysing why some ideas, while intuitive and plausible, do not work. The importance of counter-examples for disproving conjectures is already well known.
Negative results may point to interesting and important open problems. Knowing directions that lead to dead-ends in research can help others avoid replicating paths that take them nowhere. This might accelerate progress or even break through walls!