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

Integrated evaluation and attribution of urban flood risk mitigation capacity: A case of Zhengzhou, China

Study region: The Zhengzhou City in China Study focus: Urban flood risk mitigation (UFRM) refers to the runoff retention capacity of land surface in urban regions. Previous studies have focused on assessing the UFRM change in central urban areas. Nevertheless, analysis of the UFRM change considering the heterogeneity of urban and rural gradient is scarce. This study aims to evaluate the spatiotemporal change of UFRM and its influencing factors in different urban functional zones. An integrated framework combining the hydrological model and geographic information system (GIS) has been established to assess the evolution of UFRM in Zhengzhou between 2000 and 2020. New hydrological insights for the region: The findings illustrate the following: (1) The UFRM of Zhengzhou experienced a 10.76% increase from 2000 to 2010, followed by a subsequent 3.38% decrease from 2010 to 2020.(2) UFRM increased in ecological areas while decreasing in urban regions, revealing a disparity between UFRM supply and demand. (3) The adverse impact of impervious surfaces on UFRM surpasses the influence of precipitation intensity in urban regions. As a result, this study suggests that urban areas should avoid extensive expansion of impervious surfaces and prioritize implementing low-impact development practices. The ecological zones should implement rigorous policies to protect natural preservation. These findings provide policymakers a scientific foundation for devising sustainable urban development strategies to enhance urban flood resilience

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