Accessing research literature: A mixed-method study of academics in Higher Education Institutions in Nepal

Background: Research in Higher Education (HE), particularly in health and medical sciences plays a crucial role, not only in enhancing knowledge and skills of students and academics, but also in helping to develop managers and policy makers who rely on evidence-based planning and programming. This paper reports university teacher’s knowledge and practices in accessing electronic research-based evidence in health and medical sciences in Nepal. Material and Methods: This was mixed-methods study comprising a self-administered questionnaire completed by HE teachers and informant key interviews with authorities of HE institutions. A total of 15 out of the total 40 institutions offering HE above Bachelors level on health-related subjects were included for the study. Results: The response rate was 68%; 244 out of 360 HE teachers completed self-administered questionnaire. The respondents comprised nurses (36%), followed by doctors (23%), public health practitioners (18%), dentists (17%) and pharmacists (12%). Most of the HE teachers reported that they have computer skills and possess their own computer. Two-thirds (66%) of the HE teachers had work email and almost all (93%) have a personal email ID. All institutions had a computer lab and/or library. Almost all teachers had internet access at work but the internet was reportedly slow. Each institution had a librarian to support to the students and staff but only a third of teachers sought their help. Many do not know what kind of support librarians can provide. Less than half of the staff had accessed electronic research materials. This proportion varied between HE institutions ranging from 13 to 83%. HINARI and PubMed were the mostly used research databases. Less than half of respondents (48%) had published research papers in peer-reviewed journals, and only 19% published a paper based on a systematic review. Female HE teachers were less likely to publish (32%) than males (68%). More readers and professors had published (75%) than instructors/assistant lecturers (30%) and lecturers (45%). Conclusions: Accessing electronic research literature provides an opportunity to gathering up-to-date research-based information that should be core to all health curricula. We call upon curriculum developers and university authorities in Nepal to revise health curricula and help build electronic searching skills among staff and students

Assessing the potential replacement of laurel forest by a novel ecosystem in the steep terrain of an Oceanic Island

Biological invasions are a major global threat to biodiversity and often affect ecosystem services negatively. They are particularly problematic on oceanic islands where there are many narrow-ranged endemic species, and the biota may be very susceptible to invasion. Quantifying and mapping invasion processes are important steps for management and control but are challenging with the limited resources typically available and particularly difficult to implement on oceanic islands with very steep terrain. Remote sensing may provide an excellent solution in circumstances where the invading species can be reliably detected from imagery. We here develop a method to map the distribution of the alien chestnut (Castanea sativa Mill.) on the island of La Palma (Canary Islands, Spain), using freely available satellite images. On La Palma, the chestnut invasion threatens the iconic laurel forest, which has survived since the Tertiary period in the favourable climatic conditions of mountainous islands in the trade wind zone. We detect chestnut presence by taking advantage of the distinctive phenology of this alien tree, which retains its deciduousness while the native vegetation is evergreen. Using both Landsat 8 and Sentinel-2 (parallel analyses), we obtained images in two seasons (chestnuts leafless and in-leaf, respectively) and performed image regression to detect pixels changing from leafless to in-leaf chestnuts. We then applied supervised classification using Random Forest to map the present-day occurrence of the chestnut. Finally, we performed species distribution modelling to map the habitat suitability for chestnut on La Palma, to estimate which areas are prone to further invasion. Our results indicate that chestnuts occupy 1.2% of the total area of natural ecosystems on La Palma, with a further 12\u201317% representing suitable habitat that is not yet occupied. This enables targeted control measures with potential to successfully manage the invasion, given the relatively long generation time of the chestnut. Our method also enables research on the spread of the species since the earliest Landsat images

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

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