Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

THE RATE OF BINARY BLACK HOLE MERGERS INFERRED FROM ADVANCED LIGO OBSERVATIONS SURROUNDING GW150914

A transient gravitational-wave signal, GW150914, was identi fi ed in the twin Advanced LIGO detectors on 2015 September 2015 at 09:50:45 UTC. To asse ss the implications of this discovery, the detectors remained in operation with unchanged con fi gurations over a period of 39 days around the time of t he signal. At the detection statistic threshold corresponding to that observed for GW150914, our search of the 16 days of simultaneous two-detector observational data is estimated to have a false-alarm rate ( FAR ) of < ́ -- 4.9 10 yr 61 , yielding a p -value for GW150914 of < ́ - 210 7 . Parameter estimation follo w-up on this trigger identi fi es its source as a binary black hole ( BBH ) merger with component masses ( )( ) = - + - + mm M ,36,29 12 4 5 4 4 at redshift = - + z 0.09 0.04 0.03 ( median and 90% credible range ) . Here, we report on the constraints these observations place on the rate of BBH coalescences. Considering only GW150914, assuming that all BBHs in the universe have the same masses and spins as this event, imposing a search FAR threshold of 1 per 100 years, and assuming that the BBH merger rate is constant in the comoving frame, we infer a 90% credible range of merger rates between – -- 2 53 Gpc yr 31 ( comoving frame ) . Incorporating all search triggers that pass a much lower threshold while accounting for the uncerta inty in the astrophysical origin of each trigger, we estimate a higher rate, ranging from – -- 13 600 Gpc yr 31 depending on assumptions about the BBH mass distribution. All together, our various rate estimat es fall in the conservative range – -- 2 600 Gpc yr 31