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Prieto, D. and Sabnis, S.C. and Amer, S.E.D.R. and Liew, A.W.C. and Perco, P. and Rahimi, F. and Riva, G. and Zhang, C. and Devkota, H.P. and Ogami, K. and Basharat, Z. and Fierz, W. and Siebers, R. and Tan, K.H. and Boehme, K.A. and Brenneisen, P. and Brown, J.A.L. and Dalrymple, B.P. and Harvey, D.J. and Ng, G. and Werten, S. and Bleackley, M. and Dai, Z. and Dhariwal, R. and Gelfer, Y. and Hartmann, M.D. and Miotla, P. and Tamaian, R. and Govender, P. and Gurney-Champion, O.J. and Kauppila, J.H. and Zhang, X. and Echeverría, N. and Subhash, S. and Sallmon, H. and Tofani, M. and Bae, T. and Bosch, O. and Cuív, P.O. and Danchin, A. and Diouf, B. and Eerola, T. and Evangelou, E. and Filipp, F. and Klump, H. and Kurgan, L. and Smith, S.S. and Terrier, O. and Tuttle, N. and Ascher, D.B. and Janga, S.C. and Schulte, L.N. and Becker, D. and Browngardt, C. and Bush, S.J. and Gaullier, G. and Ide, K. and Meseko, C. and Werner, G.D.A. and Zaucha, J. and Al-Farha, A.A. and Greenwald, N.F. and 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Singh, M. and Spearman, P. and Subramaniapillai, M. and Swali, R. and Tan, C.M. and Tellechea, J.I. and Thomas, L.-M. and Tong, X. and Vavvas, D.G. and Veys, R. and Vitriol, V. and Wang, H.-D. and Wang, J. and Wang, J. and Waugh, J. and Webb, S.A. and Williams, B.A. and Workman, A.D. and Xiang, T. and Xie, L.-X. and Xu, J. and Xu, T. and Yang, C. and Yoon, J.G. and Yuan, C.M. and Zaritsky, A. and Zhang, Y. and Zhao, H. and Zuckerman, H. and Lyu, R. and Pullan, W. and RELISH Consortium", journal = "ACM SIGMIS Database", year = "2019", volume = "2019", pages = "1-67", publisher = "Oxford University Press", issn = "0095-0033", doi = "10.1093/database/baz085", keywords = "article; benchmarking; human; medical research; Medical Subject Headings; Medline; nonhuman; okapi; plant seed; search engine; statistical bias; systematic review", abstract = "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." }