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

Observações anatômicas em plantas de Coffea arabica L. obtidas por enraizamento de estacas

Uma forma para se obter diminuição significativa de tempo e recursos despendidos nos programas de melhoramento de Coffea arabica L. é a clonagem de híbridos F1 por meio de estacas caulinares. Alguns estudos, em diferentes instituições, foram realizados buscando-se definir um método eficiente para esse tipo de clonagem. Com o objetivo de verificar-se a presença de barreiras anatômicas ao enraizamento de estacas caulinares do cafeeiro e a origem das raízes adventícias, bem como compara-las às raízes provenientes de plantas obtidas por semeadura, foram realizadas análises anatômicas no Departamento de Biologia da Universidade Federal de Lavras (UFLA), Lavras, MG. Utilizaram-se estacas caulinares de cafeeiro dos cultivares Acaiá e Rubi e mudas obtidas por semeadura direta e por estaquia. Os cortes realizados nas estacas caulinares mostraram não existirem barreiras anatômicas ao enraizamento adventício. Nas estacas enraizadas, a origem do primórdio radicular foi próxima aos tecidos vasculares. Cortes histológicos nas raízes formadas nas estacas e nas raízes de mudas obtidas por semeadura confirmaram que elas apresentam as mesmas estruturas primárias

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