A collection of datasets published on Mendeley Data that recognize researchers or research groups who make their research data available for additional research and do so in a way that exemplifies the FAIR data principles – Findable, Accessible, Interoperable, Reusable.
Datasets in this collection have been selected by Elsevier's independent Research Data Management Advisory Board.
Read Elsevier's community blog - Elsevier Connect - to discover interviews from researchers who published these datasets.
* Prof. Zhiyong Shao, Fudan University China: https://www.elsevier.com/connect/spotlighting-fair-data-and-the-researchers-behind-it
* Prof Ricardo Sánchez-Murillo, UNA Costa Rica: https://www.elsevier.com/connect/we-dont-want-data-sitting-in-our-desk-says-tropical-cyclone-researcher
* Dr. Vanessa Susini, University of Pisa, Italy: https://www.elsevier.com/connect/for-mendeley-data-winner-sharing-fair-data-helps-researchers-learn-from-each-other
A collection of open datasets created by different groups across Elsevier in collaboration with our research collaboration partners. In line with FAIR data practices, these data are openly shared to foster research and promote reproducibility.
Our current research in data science spans natural language processing, fact extraction and entity identification. We also have projects studying research itself through the lenses of gender, researcher mobility, FAIR data use, peer review and the impact of sustainable development goals.
Contributors:Anastasia Nesterova, Peter Linsley, Sergey Sozin, Eugene Klimov, Maria Zharkova et al
-> The Human Disease Author Aid Collection combines information about rare and common diseases in standardized, easy-to-navigate overviews and tables.
-> The Author Aid Collection includes clinical, molecular, and pharmacological data from several Elsevier and public sources.
-> Author Aid Templates can be a helpful guide for authors, researchers, clinicians, and students, especially those interested in rare diseases, because they highlight the latest updates, findings, and basic disease information from several sources on one page.
-> The Human Disease Author Aid Collection is published in parts with 5-10 diseases grouped by therapeutic areas, except Part 1. Tables are planned to be updated with the latest metadata and citations quarterly.
-> Part 1 includes opening examples for common and rare human diseases: hemophilia, phenylketonuria, alpha-1 antitrypsin deficiency, migraine, and COVID-19.
-> Each disease template overview in Part 1 includes six sections: Terminology; Epidemiology/Demographics; Clinical presentation/Diagnosis; Etiology/Pathology (genetics, biomarkers, pathways); Treatment/Follow-Up; and Case studies. Each subset of data is linked to a list of publications with relevant citations.
This is the dataset supporting the publication Discovering Gene-Disease Associations with Biomedical Word Embeddings.
Finding the right target for a disease is critical in the drug development process. This paper presents a machine learning approach for predicting gene-disease associations that (i) employs biomedical word embeddings as features for a classifier trained on Open Targets Platform (OTP) data that (ii) generalises beyond a specific disease or gene class. We train, evaluate and compare different word embedding models and classifiers for the task at hand. In addition, we validate the approach by training on a past OTP release and show that it can assist in identifying probable positive associations among current low evidence associations, confirmed by a recent OTP release. Furthermore, we train word embedding models on different time slices of biomedical articles from ScienceDirect and demonstrate that the trained classifier predicts associations that have not explicitly been mentioned in the training corpus, 5 years into the future.
Please send a message to Elsevier describing briefly your request on how you would like to use the assets with a short justification. Elsevier will connect directly with you for the elaboration of a personalized license. The contact information can be found in the license information.