SEIS, the seismometer of the InSight mission, which landed on Mars on 26 November 2018, is monitoring the seismic activity of the planet. The goal of the Mars Structure Service (MSS) is to provide, as a mission product, the first average 1-D velocity model of Mars from the recorded InSight data. Prior to the mission, methodologies have been developed and tested to allow the location of the seismic events and estimation of the radial structure, using surface waves and body waves arrival times, and receiver functions. The paper describes these validation tests and compares the performance of the different algorithms to constrain the velocity model below the InSight station and estimate the 1-D average model over the great circle path between source and receiver. These tests were performed in the frame of a blind test, during which synthetic data were inverted. In order to propagate the data uncertainties on the output model distribution, Bayesian inversion techniques are mainly used. The limitations and strengths of the methods are assessed. The results show the potential of the MSS approach to retrieve the structure of the crust and underlying mantle. However, at this time, large quakes with clear surface waves have not yet been recorded by SEIS, which makes the estimation of the 1-D average seismic velocity model challenging. Additional locatable events, especially at large epicentral distances, and development of new techniques to fully investigate the data, will ultimately provide more constraints on the crust and mantle of Mars.

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.