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

Health as a Social-technical Enterprise Anchored in Social-ecological Justice and Stakeholder Collaboration: Insights from Mexico-Lerma-Cutzamala Hydrological Region

Part of the Climate Change Management seriesHuman health and wellbeing depend on the health and integrity of ecosystems we co-inhabit with other species and the ways we use natural capital in our economy. They depend on the ways people are able (or not) to satisfy basic needs: breathing clean air, drinking clean water, eating healthy food, and having access to healthy housing, a safe and secure neighborhood, a stable livelihood, and healthcare. Science grapples with deciphering how environmental, biological, and lifestyle/behavioral factors interact to co-determine health. Meanwhile, major promoting and degrading factors are highly unevenly distributed across populations and landscapes; significant social-ecological health injustice prevails. We present four innovations to address recognized limitations of existing health research and practice: (1) an integrative framework to tackle innate conundrums and conceptualize important domains; (2) an integrative operational process for designing health-water-ecology-climate projects that enable multi-component research and its translation; (3) coupled mixed methods systems modeling-GIS/geospatial analysis, plus exposure vs. response/risk curves for groups with differential vulnerability to stressors—to reveal promoters and degraders of health, and structural injustices; and (4) a social-technical capacity building enterprise model to frame health-sustainability projects based on prior successful multi-stakeholder experience in Mexico. Our ongoing project—“Climate Change Impacts & Resilience in the Mexico-Lerma-Cutzamala Hydrological Region”—illustrates their application. We argue health, sustainability and climate resilience challenges be reframed as opportunities to co-create social-technical enterprises at different spatial, temporal and human scales, firmly anchored in the moral pursuit of social-ecological justice, and enabled by stakeholder partnerships

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