Preprints & COVID-19

“This work is being maintained by Gautam Dey, Srivats Venkataramanan, Sundar Naganathan, Debbie Ho, Zhang-He Goh, Kirsty Hooper, Lars Hubatsch, Mariana De Niz, Sejal Davla, Mate Palfy & Jonny Coates. For questions or queries please contact prelights@biologists.com or Jonny Coates jc2216@cam.ac.uk.”

Comment: Today (April 2, 2020) it hadn’t been updated since January 2, 2020.

KNOWLEDGE BASE ON EPIDEMICS FROM AMELICA/REDALYC JOURNALS

“In the face of a global health contingency, the vital role of Open Access is endorsed: to bring knowledge to all corners of the world, to allow science to be quickly and timely accessible so that its contribution is reflected in the improvement of the quality of human life , in saving lives and in the development of a better society for all. Open Access initiatives such as Redalyc have been working towards this goal for 18 years. Today, the AmeliCA/Redalyc alliance reaffirms its commitment to Open Access and continues to develop technology which it is now applied to the semantic dissemination of articles published on topics of interest in epidemiology, pandemics and related topics. This development enable to publish more than 6 thousand articles in Linked Open Data (LOD) format so that they can be processed and interconnected in the LOD knowledge cloud and allow users to browse content and access to full-texts in a thematic discovery service….”
 

AmeliCA/Redalyc1 run an ontology-based algorithm, previously developed called OntoOAI (Becerril-Garci?a & Aguado-Lopez, 2018), on their databases to extract epidemics-related content. The results include: an ontology representation of the knowledge published in 6,557 scientific articles including concepts and relations, as well as their attributes, a directed-graph thematic content browser to access to full-texts and a dataset available at SPARQL endpoint to query the results as part of Linked Open Data….”

 

 

ZB MED COVID-19 Hub

“This page aims to support researchers and interested individuals by providing tools and data sets related to the Coronavirus disease 2019 (COVID-19) outbreak and the SARS-CoV-2 virus.

Please contact us if you need help or have suggestions for further tools or data sets. We are experienced in bioinformatical data analysis, text mining, data visualization, (FAIR) research data management as well as in hosting information services….”

Epidemic Calculator

“At the time of writing, the coronavirus disease of 2019 remains a global health crisis of grave and uncertain magnitude. To the non-expert (such as myself), contextualizing the numbers, forecasts and epidemiological parameters described in the media and literature can be challenging. I created this calculator as an attempt to address this gap in understanding.

This calculator implements a classical infectious disease model — SEIR (Susceptible ? Exposed ? Infected ? Removed), an idealized model of spread still used in frontlines of research….”

Keep up with the latest coronavirus research

“An open-resource literature hub known as LitCovid curates the most comprehensive collection of international research papers so far on the new coronavirus disease COVID-19 (see go.nature.com/3almd5p). Developed with the support of the US National Institutes of Health’s intramural research programme, LitCovid is updated daily with newly published articles. The aim is to provide timely insight from the scientific literature into the biology of the virus and the diagnosis and management of those who have been infected.

LitCovid has a more sophisticated search function than existing resources. It identifies roughly 35% more relevant articles than do conventional keyword-based searches for entries such as ‘COVID-19’ or ‘nCOV’. Furthermore, the articles are categorized by topic — overview, disease mechanism, transmission dynamics, treatment, case report and epidemic forecasting — as well as by geographic location for visualization on a world map…..”

Keep up with the latest coronavirus research

“An open-resource literature hub known as LitCovid curates the most comprehensive collection of international research papers so far on the new coronavirus disease COVID-19 (see go.nature.com/3almd5p). Developed with the support of the US National Institutes of Health’s intramural research programme, LitCovid is updated daily with newly published articles. The aim is to provide timely insight from the scientific literature into the biology of the virus and the diagnosis and management of those who have been infected.

LitCovid has a more sophisticated search function than existing resources. It identifies roughly 35% more relevant articles than do conventional keyword-based searches for entries such as ‘COVID-19’ or ‘nCOV’. Furthermore, the articles are categorized by topic — overview, disease mechanism, transmission dynamics, treatment, case report and epidemic forecasting — as well as by geographic location for visualization on a world map…..”

Mapping the Scholarly Literature Found in Scopus on “Research Data Management”: A Bibliometric and Data Visualization Approach

Abstract:  INTRODUCTION Since the 2000s, interest in research data management (RDM) has grown considerably. As a result, a large body of literature has discussed a broad variety of aspects related to data management. But, few studies have examined and also interpreted from visual perception the intellectual structure and progressive development of the existing literature on RDM. METHODS Guided by five research questions, this study employed bibliometric techniques and a visualization tool (CiteSpace) to identify and analyze the patterns of the scholarly publications about RDM. RESULTS Through CiteSpace’s modeling and computing, the knowledge (or network) structures, significant studies, notable topics, and development trends in the literature of RDM were revealed. DISCUSSION The majority of the literature pertinent to RDM was published after 2002. Major research clusters within this interdisciplinary field include “scientific collaboration,” “research support service,” and “data literacy,” while the “scientific collaboration” research cluster was the most active. Topics such as “digital curation” and “information processing” appeared most frequently in the RDM literature. Additionally, there was a sharp increase in several specific topics, such as “digital library,” “big data,” and “data sharing.” CONCLUSION By looking into the “profile” of the literature on RDM, in terms of knowledge structure, evolving trends, and important topics in the domain, this work will add new information to current discussions about RDM, new service development, and future research focuses in this area.

Mapping the Scholarly Literature Found in Scopus on “Research Data Management”: A Bibliometric and Data Visualization Approach

Abstract:  INTRODUCTION Since the 2000s, interest in research data management (RDM) has grown considerably. As a result, a large body of literature has discussed a broad variety of aspects related to data management. But, few studies have examined and also interpreted from visual perception the intellectual structure and progressive development of the existing literature on RDM. METHODS Guided by five research questions, this study employed bibliometric techniques and a visualization tool (CiteSpace) to identify and analyze the patterns of the scholarly publications about RDM. RESULTS Through CiteSpace’s modeling and computing, the knowledge (or network) structures, significant studies, notable topics, and development trends in the literature of RDM were revealed. DISCUSSION The majority of the literature pertinent to RDM was published after 2002. Major research clusters within this interdisciplinary field include “scientific collaboration,” “research support service,” and “data literacy,” while the “scientific collaboration” research cluster was the most active. Topics such as “digital curation” and “information processing” appeared most frequently in the RDM literature. Additionally, there was a sharp increase in several specific topics, such as “digital library,” “big data,” and “data sharing.” CONCLUSION By looking into the “profile” of the literature on RDM, in terms of knowledge structure, evolving trends, and important topics in the domain, this work will add new information to current discussions about RDM, new service development, and future research focuses in this area.

Ivissem | Information Visualization & Social Scholarly Metric

“The mere identification of the most relevant Scientific Knowledge Objects (SKOs) in a particular topic is increasingly difficult due to the existing interfaces, returning massive lists of results. It is recognized that researchers are not merely producers of knowledge. Instead they are social actors who play a preponderant role in the discovery and filtering of scientific knowledge. The data that results from this social interaction provides an important basis for the design of various usage metrics, also known as aka altmetrics.

Access to the right and relevant information is paramount for scientific discoveries. IViSSEM aims to develop and test a new altmetric, called Social Scholarly Experience Metric. This metric will result from the application of Machine Learning techniques to different combinations of altmetrics and profiles of researchers. Its application will reflect the individual preferences in the process of finding a specific topic. The current massive lists of results will be replaced by an innovative interface based on advanced visualization techniques.

 

Objectives…

To design and develop a Linked Open Data based solution architecture that ensures data interoperability, data accessibility, data integration and data analytics with full aligned with international best practices.
To dynamicaly relate SKOs and researchers with knowledge organizations systems.
To clean, transform, store and give access to collected data in a triplestore….”