Towards a standard for soil and terrain data exchange: SoTerML

Soil and landform information is needed for a wide range of applications but available data are often inaccessible, incomplete, or out of date. Within the European FP7 project ‘e-SOTER’, following recognised harmonisation principles, we developed an XML schema to serve as an exchange format for soil and terrain data derived from e-SOTER methodologies (SoTerML). It encompasses existing SOTER database conceptual modelling as well as the WRB (World Reference Base of soil resources) and the FAO soil data structures and classifications, therefore covering major soil and terrain databases such as the European Soil Database (ESD). The flexibility of the modelling achieved is demonstrated from legacy data integrated in the new scheme and made available using an OGC Web Feature Service. Along with the description of SoTerML, the paper aims at pointing out the modelling approach and the modelling principles used for soil and terrain observations, which extracted from our proposal, could prove useful for emerging initiatives towards defining an exchange standard framework for the soil them

On the inclusion of dissipation on top of mean-field approaches

International audienc

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 science. © The Author(s) 2019. Published by Oxford University Press