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

Attitudes towards autonomous vehicles among people with physical disabilities

Autonomous vehicles (AVs) represent a technological revolution in the transportation sphere. The efficient and orderly introduction of AVs will require their acceptance by the public. Although the perspectives of members of the general public concerning AVs have been surveyed, no academic research has to date been completed on the views of AVs held by people with disabilities. The present study employs a mixed methods research methodology to assess attitudes towards AVs among a UK sample of individuals with physical disabilities that affected their mobility. Participants were asked open-endedly to express their thoughts about AVs and their responses were analysed using a structural topic modelling (STM) procedure. Outputs to the STM analysis were then employed in a structural equation model (SEM) constructed to predict the sample members’ willingness to travel in driverless vehicles. The results were compared against those obtained from a control group of people without physical disabilities. The attitudes towards AVs of people with disabilities differed significantly from the attitudes of the respondents without disabilities. Attitudes towards AVs among people with disabilities were significantly influenced by their levels of interest in new technology, generalised anxiety, intensity of a person’s disability, prior knowledge of AVs, locus of control and action orientation. A latent class analysis confirmed the relevance of these variables as determinants of the views of people with disabilities regarding AVs