The Griffindale University research beacons are examples of pioneering discoveries, interdisciplinary collaboration and cross-sector partnerships that are tackling some of the biggest questions facing the planet.
We selected Vaccine, prevention, diagnosis & treatment datasets indexed by the Mendeley Data Search engine on the 2019-present COVID-19 / Coronavirus pandemic. The aim was to make it easier to find potentially relevant datasets for this specific topic
Contributors:Maxime Rivest, Yury Kashnitsky, Alexandre Bedard-Vallee, David Campbell, Paul Khayat et al
The United Nations Sustainable Development Goals (SDGs) challenge the global community to build a world where no one is left behind.
Since 2018, Elsevier have generated SDG search queries to help researchers and institutions track and demonstrate progress towards the targets of the United Nations Sustainable Development Goals (SDGs). At the end of 2018, Elsevier worked on 2 versions of the SDG queries. One version was created by the Elsevier Analytical Services group and another by the Science-Metrix group, who had recently become part of Elsevier. At that time Science-Metrix was creating queries for 5 of the 16 SDGs, as part of pro-bono work for UNESCO.
In 2020 inspired by the earlier queries, Elsevier, through its Science-Metrix group, used a new approach to mapping publications to the SDGs. Taking customer feedback into account, they significantly increased the number of search terms used to define each SDG. Those queries were then complemented by a machine learning model, which helped increase the recall by approximately 10%.
As a result, this year’s “Elsevier 2021 SDG mapping” captures on average twice as many articles as the 2020 version, while keeping precision above 80%. The mapping also has a better overlap with SDG queries from other independent projects. Times Higher Education (THE) are using the “Elsevier 2021 SDG mapping” as part of their 2021 Impact Rankings.
The documentation below, describes the methods used and shares the queries.
The attached file contains R code which encompasses and describes the process of loading data, cleaning data, selecting variables, imputing missing values, creating training and test sets, model building and evaluation. Additionally, the code contains the process to create graphs and tables for data and model evaluation.
The goal was to build a logistic regression model to predict outcomes after surgery for colon cancer and to compare its performance with machine learning algorithms. An XGBgoost model, a Random Forest model and an XGBoost model from oversampled data using SMOTE were built and compared with logistic regression. Overall, the machine learning algorithms had improved AUC.
Human Disease AuthorAid Collection combines information about rare and common diseases in standardized, easy-to-navigate overview templates and tables. It includes clinical, molecular, and pharmacological data from several Elsevier's and public sources. Tables are planned to be updated with the latest citations quarterly.
AuthorAid Templates can be a helpful guide for authors, researchers, clinicians, and students, especially those interested in rare diseases, because it highlights updates and findings from several sources on one page.
Contributors:Anastasia Nesterova, Sergey Sozin, Eugene Klimov, Pavel Golovatenko-Abramov, Peter Linsley et al
-----> Human Disease Author Aid Collection combines information about rare and common diseases in standardized, easy-to-navigate overview templates and tables. It includes clinical, molecular, and pharmacological data from several Elsevier's and public sources. Tables are planned to be updated quarterly.
-----> Author Aid Templates can be a helpful guide for authors, researchers, clinicians, and students, especially those interested in rare diseases, because it highlights updates and findings from several sources on one page.
-----> Each disease template overview includes 6 sections: Terminology; Epidemiology/Demographics; Clinical presentation/Diagnosis; Etiology/Pathology (genetics, biomarkers, pathways); Treatment/Follow-Up; Case studies. Each subset of data is linked to a list of publications with relevant citations.
-----> Monogenetic Rare Diseases
(Human Disease Author Aid Collection, Part 2)
In the current part, monogenetic rare diseases were chosen based on their classification, prevalence, and degree of data availability. By "monogenetic" we mean diseases that are caused by one or few known mutations. We considered worldwide point prevalence between PLEASE download files to read them and open the links!
Contributors:Anastasia Nesterova, Pavel Golovatenko-Abramov
-----> Chromosome Microdeletions Rare Disease (Author Aid Human Disease Collection, Part 3)
This is an addition to Author Aid Human Disease Collection (see in Mendeley datasets). Part 3 includes available at the moment automatically generated text-mining information for the list of selected rare diseases with microdeletions. Files are planned to be updated quarterly.
----->Please download the files to read and open links!
NUOnet Vision: Efficient use of nutrients to optimize production and product quality of food for animals and humans, fuel and fiber in a sustainable manner that contributes to ecosystem services.
Best nutrient management practices are critical for maintaining profitable economic returns, sustaining higher yields, lowering environmental impacts, optimizing nutritional quality, and providing ecosystem services. Best management practices that improve nutrient use efficiencies can reduce nutrient losses from agricultural systems. However, we need to improve our understanding of biological, physical and chemical influences on nutrient processes. For instance, crop use efficiency of nitrogen (N), the primary macronutrient regulating yield and protein content, can be reduced by processes such as denitrification (N2O and N2 emission), leaching (NH4-N, NO3-N, and organic-N), ammonia (NH3-N,) volatilization, surface runoff and erosion, disease, and non-crop competition. Similarly, we need to obtain more information about biological and physical cycles of nutrients, especially phosphorus (P), including factors that influence nutrient availability from fertilizers, crop residues, cover crops, manures, and other byproducts. We need a better understanding of relationships between soil biological communities and ecosystems, including plant roots and root exudates, and availability and uptake of macro- and micro-nutrients. In addition, we need information regarding how these practices impact yields, organoleptic qualities, and the macro- and micro-nutritional composition of plants. This information will improve our ability to develop best nutrient management practices.
See the NUOnet Home Page for more information about this database and strategic goals.
The Agricultural Collaborative Research Outcomes System (AgCROS) is a growing “network of networks” that presently consists of multiple agricultural data networks: Nutrient Uptake and Outcome Network (NUOnet), the Greenhouse gas Reduction through Agricultural Carbon Enhancement Network (GRACEnet), Resilient Economic Agricultural Practices (REAP), Dairy Agriculture for People and the Planet (DAPP; Dairy Grand Challenge), Soil Health Assessment Network (SHAnet), Agricultural Antibiotic Resistance (AgAR), and the Long-Term Agroecosystem Research (LTAR) Network. By integrating these diverse database networks, AgCROS facilitates the flow of information and increases the cooperation among researchers participating in these networks.
Alopecia areata recurrence patterns in children and young adults while on systemic tofacitinib therapy