Abstract: There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders-representing academia, industry, funding agencies, and scholarly publishers-have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Abstract: To increase transparency in science, some scholarly journals are publishing peer review reports. But it is unclear how this practice affects the peer review process. Here, we examine the effect of ...

Abstract: Importance In peer-reviewed medical journals, authoring an invited commentary on an original article is a recognition of expertise. It has been documented that women author fewer invited publications than men do. However, it is unknown whether this disparity is due to gender differences in characteristics that are associated with invitations, such as field of expertise, seniority, and scientific output. Objective To estimate the odds ratio (OR) of authoring an invited commentary for women compared with men who had similar expertise, seniority, and publication metrics. Design, Setting, and Participants This matched case-control study included all medical invited commentaries published from January 1, 2013, through December 31, 2017, in English-language medical journals and multidisciplinary journals. Invited commentaries were defined as publications that cite another publication within the same journal volume and issue. Bibliometric data were obtained from Scopus. Cases were defined as corresponding authors of invited commentaries in a given journal during the study period. Controls were matched to cases based on scientific expertise by calculating a similarity index for abstracts published during the same period using natural language processing. Data analyses were conducted from March 13, 2019, through May 3, 2019. Exposure Corresponding or sole author gender was predicted from author first name and country of origin using genderize.io. Main Outcomes and Measures The OR for gender was estimated after adjusting for field of expertise, publication output, citation impact, and years active (ie, years since first publication), with an interaction between gender and years active. Results The final data set included 43 235 cases across 2549 journals; there were 34 047 unique intraciting commentary authors, among whom 9072 (26.6%) were women. For researchers who had been active for the median of 19 years, the odds of invited commentary authorship were 21% lower for women (OR, 0.79 [95% CI, 0.77-0.81];P Conclusions and Relevance In this case-control study, women had lower odds of authoring invited commentaries than their male peers. This disparity was larger for senior researchers. Journal editors could use natural language processing of published research to widen and diversify the pool of experts considered for commentary invitations.

Abstract: Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006–2016) to gauge the state of research and identify gaps warranting further work. We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens. We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals. The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

Abstract: Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, we describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here we explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work.

Abstract: Designing good multiple choice questions (MCQs) for education and assessment is time consuming and error-prone. An abundance of structured and semi-structured data has led to the development of automatic MCQ generation methods. Recently, ontologies have emerged as powerful tools to enable the automatic generation of MCQs. However, current question generation approaches focus on knowledge recall questions. In addition, questions that have so far been generated are, compared to manually created ones, simple and cover only a small subset of the required question complexity space in the education and assessment domain. In this paper, we focus on addressing the limitations of previous approaches by generating questions with complex stems that are suitable for scenarios beyond mere knowledge recall. We present a novel ontology-based approach that exploits classes and existential restrictions to generate case-based questions. Our contribution lies in: (1) the specification of procedure for generating case-based questions which involve (a) assembling complex stems, (b) selecting suitable options, and (c) providing explanations for option correctness/incorrectness, (2) an implementation of the procedure using a medical ontology and (3) and evaluation of our generation technique to test question quality and their suitability in practise. We implement our approach as an application for a medical education scenario on top of a large knowledge base in the medical domain. We generate more than 3 million questions for four physician specialities and evaluate our approach in a user study with 15 medical experts. We find that using a stratified random sample of 435 questions out of which 316 were rated by two experts, 129 (30%) are considered appropriate to be used in exams by both experts and a further 216 (50%) by at least one expert.

Abstract: A cross-disciplinary examination of the user behaviors involved in seeking and evaluating data is surprisingly absent from the research data discussion. This review explores the data retrieval literature to identify commonalities in how users search for and evaluate observational research data in selected disciplines. Two analytical frameworks, rooted in information retrieval and science and technology studies, are used to identify key similarities in practices as a first step toward developing a model describing data retrieval.

Abstract: Strategic Management in the International Hospitality Industry: content and process, is a vital text for all those studying cutting edge theories and views on strategic management. Unlike others textbooks in this area, it goes further than merely contextualising strategic management for hospitality and tourism, and avoids using a prescriptive, or descriptive approach. It looks instead, at the latest in strategic thinking and theories, and provides critical and analytical discussion as to how and if these models and theories can be applied to the industry, within specific contexts such as culture, profit and non-profit organisations.Key features: Cutting edge approach: applies advance and recent strategic management views into tourism and hospitality field. Critical treatment: provides critical discussions about whether and how strategic models/theories can be applied into the hospitality and tourism field. Sensitive to specific contexts: As the tourism and hospitality industry has become one of the largest industries worldwide, discusses how strategic management concepts can be applied in different cultures and profit and non-profit tourism organizations. With supporting case studies related to the strategy content, context and process, from international industries such as, Radisson, McDonalds, Carnival Cruiselines and Disney, this text consist of five main sections: introduction, strategy content, strategy context, strategy process and cases. Each of the chapters within these sections has a thorough pedagogic structure consisting of a bulleted introduction, examples and vignettes, discussions points, exercises, case studies and further reading and web sites. Strategic Management in the International Hospitality and Tourism Industry: content and process also provides online support material for tutors and students, in the form of guidelines for instructors on how to use the textbook, PowerPoint presentations and case studies plus additional exercises and web links for students.

Abstract: In December 2016, after several years of development, Elsevier launched a set of transparent, comprehensive, current, and freely-available journal citation metrics called CiteScore metrics. Most of the CiteScore metrics are static, annual indicators calculated from the dynamic Scopus citation index. In the spirit of recent public statements on the responsible use of metrics, we outline the desirable characteristics of journal citation metrics, discuss how we decided on the cited and citing publications years and document types to be used for CiteScore metrics, and detail the precise method of calculation of each metric. We further discuss CiteScore metrics eligibility criteria and online display choices, as well as our approach to calculating static indicators from the dynamic citation index. Finally, we look at the feedback the metrics have so far received and how CiteScore is already developing in response.

Abstract: In commercial research and development projects, public disclosure of new chemical compounds often takes place in patents. Only a small proportion of these compounds are published in journals, usually a few years after the patent. Patent authorities make available the patents but do not provide systematic continuous chemical annotations. Content databases such as Elsevier’s Reaxys provide such services mostly based on manual excerptions, which are time-consuming and costly. Automatic text-mining approaches help overcome some of the limitations of the manual process. Different text-mining approaches exist to extract chemical entities from patents. The majority of them have been developed using sub-sections of patent documents and focus on mentions of compounds. Less attention has been given to relevancy of a compound in a patent. Relevancy of a compound to a patent is based on the patent’s context. A relevant compound plays a major role within a patent. Identification of relevant compounds reduces the size of the extracted data and improves the usefulness of patent resources (e.g. supports identifying the main compounds). Annotators of databases like Reaxys only annotate relevant compounds. In this study, we design an automated system that extracts chemical entities from patents and classifies their relevance. The goldstandard set contained 18 789 chemical entity annotations. Of these, 10% were relevant compounds, 88% were irrelevant and 2% were equivocal. Our compound recognition system was based on proprietary tools. The performance (F-score) of the system on compound recognition was 84% on the development set and 86% on the test set. The relevancy classification system had an F-score of 86% on the development set and 82% on the test set. Our system can extract chemical compounds from patents and classify their relevance with high performance. This enables the extension of the Reaxys database by means of automation.

Abstract: In light of increasing calls for transparent reporting of research and prevention of detrimental research practices, we conducted a cross-sectional machine-assisted analysis of a representative sample of scientific journals' instructions to authors (ItAs) across all disciplines. We investigated addressing of 19 topics related to transparency in reporting and research integrity. Only three topics were addressed in more than one third of ItAs: conflicts of interest, plagiarism, and the type of peer review the journal employs. Health and Life Sciences journals, journals published by medium or large publishers, and journals registered in the Directory of Open Access Journals (DOAJ) were more likely to address many of the analysed topics, while Arts & Humanities journals were least likely to do so. Despite the recent calls for transparency and integrity in research, our analysis shows that most scientific journals need to update their ItAs to align them with practices which prevent detrimental research practices and ensure transparent reporting of research.

Abstract: Multimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: (1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; (2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; (3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.

Abstract: Chemical patents are an important resource for chemical information. However, few chemical Named Entity Recognition (NER) systems have been evaluated on patent documents, due in part to their structural and linguistic complexity. In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents. We compare word embeddings pre-trained on biomedical and chemical patent corpora. The effect of tokenizers optimized for the chemical domain on NER performance in chemical patents is also explored. The results on two patent corpora show that contextualized word representations generated from ELMo substantially improve chemical NER performance w.r.t. the current state-of-the-art. We also show that domain-specific resources such as word embeddings trained on chemical patents and chemical-specific tokenizers have a positive impact on NER performance.

Abstract: Biomedical Named Entity Recognition (NER) is a challenging problem in biomedical information processing due to the widespread ambiguity of out of context terms and extensive lexical variations. Performance on bioNER benchmarks continues to improve due to advances like BERT, GPT, and XLNet. FLAIR (1) is an alternative embedding model which is less computationally intensive than the others mentioned. We test FLAIR and its pretrained PubMed embeddings (which we term BioFLAIR) on a variety of bio NER tasks and compare those with results from BERT-type networks. We also investigate the effects of a small amount of additional pretraining on PubMed content, and of combining FLAIR and ELMO models. We find that with the provided embeddings, FLAIR performs on-par with the BERT networks - even establishing a new state of the art on one benchmark. Additional pretraining did not provide a clear benefit, although this might change with even more pretraining being done. Stacking the FLAIR embeddings with others typically does provide a boost in the benchmark results.

Abstract: From 2005 to 2010, we conducted 2 randomized studies on a journal (Medicina Clinica), where we took manuscripts received for publication and randomly assigned them to either the standard editorial process or to additional processes. Both studies were based on the use of methodological reviewers and reporting guidelines (RG). Those interventions slightly improved the items reported on the Manuscript Quality Assessment Instrument (MQAI), which assesses the quality of the research report. However, masked evaluators were able to guess the allocated group in 62% (56/90) of the papers, thus presenting a risk of detection bias. In this post-hoc study, we analyse whether those interventions that were originally designed for improving the completeness of manuscript reporting may have had an effect on the number of citations, which is the measured outcome that we used. Masked to the intervention group, one of us used the Web of Science (WoS) to quantify the number of citations that the participating manuscripts received up December 2016. We calculated the mean citation ratio between intervention arms and then quantified the uncertainty of it by means of the Jackknife method, which avoids assumptions about the distribution shape. Our study included 191 articles (99 and 92, respectively) from the two previous studies, which all together received 1336 citations. In both studies, the groups subjected to additional processes showed higher averages, standard deviations and annual rates. The intervention effect was similar in both studies, with a combined estimate of a 43% (95% CI: 3 to 98%) increase in the number of citations. We interpret that those effects are driven mainly by introducing into the editorial process a senior methodologist to find missing RG items. Those results are promising, but not definitive due to the exploratory nature of the study and some important caveats such as: the limitations of using the number of citations as a measure of scientific impact; and the fact that our study is based on a single journal. We invite journals to perform their own studies to ascertain whether or not scientific repercussion is increased by adhering to reporting guidelines and further involving statisticians in the editorial process.

Abstract: Although epigenetic mechanisms are important risk factors for allergic disease, few studies have evaluated DNA methylation differences associated with atopic dermatitis (AD), and none has focused on AD with eczema herpeticum (ADEH+). We will determine how methylation varies in AD individuals with/without EH and associated traits. We modeled differences in genome-wide DNA methylation in whole blood cells from 90 ADEH+, 83 ADEH−, and 84 non-atopic, healthy control subjects, replicating in 36 ADEH+, 53 ADEH−, and 55 non-atopic healthy control subjects. We adjusted for cell-type composition in our models and used genome-wide and candidate-gene approaches. We replicated one CpG which was significantly differentially methylated by severity, with suggestive replication at four others showing differential methylation by phenotype or severity. Not adjusting for eosinophil content, we identified 490 significantly differentially methylated CpGs (ADEH+ vs healthy controls, genome-wide). Many of these associated with severity measures, especially eosinophil count (431/490 sites). We identified a CpG in IL4 associated with serum tIgE levels, supporting a role for Th2 immune mediating mechanisms in AD. Changes in eosinophil level, a measure of disease severity, are associated with methylation changes, providing a potential mechanism for phenotypic changes in immune response-related traits.

Abstract: Many common pathological features have been observed for both autism spectrum disorders (ASDs) and obsessive-compulsive disorder (OCD). However, no systematic analysis of the common gene markers associated with both ASD and OCD has been conducted so far. Here, two batches of large-scale literature-based disease-gene relation data (updated in 2017 and 2019, respectively) and gene expression data were integrated to study the possible association between OCD and ASD at the genetic level. Genes linked to OCD and ASD present significant overlap (P-value <2.64e-39). A genetic network of over 20 genes was constructed, through which OCD and ASD may exert influence on each other. The 2017-based analysis suggested six potential common risk genes for OCD and ASD (CDH2, ADCY8, APOE, TSPO, TOR1A, and OLIG2), and the 2019-based study identified two more genes (DISP1 and SETD1A). Notably, the gene APOE identified by the 2017-based analysis has been implicated to have an association with ASD in a recent study (2018) with DNA methylation analysis. Our results support the possible complex genetic associations between OCD and ASD. Genes linked to one disease are worth further investigation as potential risk factors for the other.

Abstract: The technical debt (TD) concept inspires the development of useful methods and tools that support TD identification and management. However, there is a lack of evidence on how different TD identification tools could be complementary and, also, how human-based identification compares with them. To understand how to effectively elicit TD from humans, to investigate several types of tools for TD identification, and to understand the developers’ point of view about TD indicators and items reported by tools. We asked developers to identify TD items from a real software project. We also collected the output of three tools to automatically identify TD and compared the results in terms of their locations in the source code. Then, we collected developers’ opinions on the identification process through a focus group. Aggregation seems to be an appropriate way to combine TD reported by developers. The tools used cannot help in identifying many important TD types, so involving humans is necessary. Developers reported that the tools would help them to identify TD faster or more accurately and that project priorities and current development activities are important to be considered together, along with the values of principal and interest, when deciding to pay off a debt. This work contributes to the TD landscape, which depicts an understanding between different TD types and how they are best discovered.

Abstract: Distributed representation of words, or word embeddings, have motivated methods for calculating semantic representations of word sequences such as phrases, sentences and paragraphs. Most of the existing methods to do so either use algorithms to learn such representations, or improve on calculating weighted averages of the word vectors. In this work, we experiment with spectral methods of signal representation and summarization as mechanisms for constructing such word-sequence embeddings in an unsupervised fashion. In particular, we explore an algorithm rooted in fluid-dynamics, known as higher-order Dynamic Mode Decomposition, which is designed to capture the eigenfrequencies, and hence the fundamental transition dynamics, of periodic and quasi-periodic systems. It is empirically observed that this approach, which we call EigenSent, can summarize transitions in a sequence of words and generate an embedding that can represent well the sequence itself. To the best of the authors’ knowledge, this is the first application of a spectral decomposition and signal summarization technique on text, to create sentence embeddings. We test the efficacy of this algorithm in creating sentence embeddings on three public datasets, where it performs appreciably well. Moreover it is also shown that, due to the positive combination of their complementary properties, concatenating the embeddings generated by EigenSent with simple word vector averaging achieves state-of-the-art results.

Abstract: Alzheimer's disease (AD) is associated with self-assembly of amyloid β-protein (Aβ) into soluble oligomers. Of the two predominant Aβ alloforms, Aβ40 and Aβ42, the latter is particularly strongly linked to AD. Longitudinal studies revealed a correlation between AD and type 2 diabetes (T2D), characterized by abnormal insulin levels and insulin resistance. Although administration of intranasal insulin is explored as a therapy against AD, the extent to which insulin affects Aβ dynamics and activity is unclear. We here investigate the effect of insulin on Aβ42 self-assembly and characterize the capacity of insulin, Aβ42, and Aβ42 co-incubated with insulin to disrupt the integrity of biomimetic lipid vesicles. We demonstrate that quiescently incubated insulin, which does not form amyloid fibrils, over time develops membrane-disrupting capacity, which we propose to originate in misfolded insulin monomers. These hypothetically toxic misfolded monomers might contribute to the development of insulin resistance in early stages of T2D that are associated with abnormally high insulin levels. We show that in contrast to quiescent incubation, insulin incubated under agitated conditions readily forms amyloid fibrils, which protect against membrane permeation. Insulin quiescently incubated with Aβ42 attenuates both Aβ42 fibril formation and the ability of Aβ42 to disrupt membranes in a concentration-dependent manner. Our findings offer insights into interactions between insulin and Aβ42 that are relevant to understanding the molecular basis of intranasal insulin as a therapy against Aβ-induced AD pathology, thereby elucidating a plausible mechanism underlying the observed correlations between AD and T2D.

Abstract: Supervised models of NLP rely on large collections of text which closely resemble the intended testing setting. Unfortunately matching text is often not available in sufficient quantity, and moreover, within any domain of text, data is often highly heterogenous. In this paper we propose a method to distill the important domain signal as part of a multi-domain learning system, using a latent variable model in which parts of a neural model are stochastically gated based on the inferred domain. We compare the use of discrete versus continuous latent variables, operating in a domain-supervised or a domain semi-supervised setting, where the domain is known only for a subset of training inputs. We show that our model leads to substantial performance improvements over competitive benchmark domain adaptation methods, including methods using adversarial learning.

Abstract: There is anecdotal information available that some reviewers attempt to increase their citation counts by using the peer review process, adding references to reviewed publications. There have been studies around citation coercion from the perspective of journal editing and boosting of journal indicators. This study builds further on that work, with a different angle: by measuring excessive citation manipulation at the level of reviewers. In order to assess the extent of this behaviour, access to a large pool of peer-review records is required: connections between authors and reviewers, connections between reviewers and reviewed work and so forth. This study explores this area in two phases. In phase one we detect the overall patterns of citations from reviewed material to reviewers, and assign a value to the proportion of citations originating from reviewed work. The second phase further explores the citation patterns, by taking the outliers from phase one and identifying the citations that have been added during the review process. We find in the results that highly suspicious cases of this behaviour can be successfully detected and that the scale of suspicion of clear misconduct behaviour is relatively limited (0.79%).

Abstract: U.S. university engagement with Africa in various areas including global health, education, environment, and agriculture has grown in the last 15 years. However, there is limited literature that acknowledges the scholarly output and impact of U.S.-Africa university research collaboration in support of their respective institutions’ comprehensive internationalization goals. Using Michigan State University (MSU)—a public, research-intensive, land grant university with long history of engagement with Africa, as a case study, this paper aims to measure the research output, describe the collaboration patterns and research trends, and assess the impact of MSU’s research engagement on Africa for the last 10 years (2006–2015). It also attempts to determine the gender dimension of MSU’s research focus on Africa. Our scientometric and social network analysis provide strong quantitative evidence of scholarly success, research focus and diversification, collaboration, and impact on MSU’s engagement on Africa, especially in science, technology, engineering, and math-related disciplines. The co-authorship data provides evidence of greater collaboration of MSU with non-African countries on African-focused research but a growing number of partner institutions from African countries. This growing collaboration provides significant benefits on strategic research conducted and its impact, as well as increasing the recognition of African researchers as contributors to impactful international research collaborations and improving the ability of MSU and its global and African partners to co-generate knowledge and innovation that can help solve global problems more effectively. Finally, this study provides additional evidence of the increasing involvement of women in advancing knowledge and global innovation to address Africa’s socio-economic development challenges. The paper, overall, contributes to the science, research management, and gender perspectives of university internationalization goals and initiatives.

Abstract: Research Development Professionals (RDPs) play an increasingly important role in the development of successful team science research. Research Development as a professional field in its own right is relatively new; however, the scientific literature has long recognized the role of research administration in building and supporting successful collaborations; and there is a small but growing body of literature that focuses specifically on the role played by RDPs. A variety of research development strategies, programs, and services can help institutions thrive in an increasingly competitive environment. The research development enterprise can also contribute to the growing importance of teamed scientific collaboration that includes representation from the broader community and external nonacademic institutions in order to address increasingly complex scientific questions.