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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.

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  • This repository contains supplementary data from the publication: The microbiota of Malaysian fermented fish sauce https://doi.org/10.1101/2020.03.10.986513
    Data Types:
    • Tabular Data
    • Dataset
    • File Set
  • 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.
    Data Types:
    • Software/Code
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    • Text
  • 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 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.
    Data Types:
    • Collection
  • Supplementary material for the manuscript "Wavelet-based fuzzy clustering of interval time series". This includes additional figures and tables referred to in the manuscript as well as details of scripts and data files used for the simulation studies and the application. All scripts are in .R format and data files are is in EXCEL (. xlsx) format.
    Data Types:
    • Software/Code
    • Tabular Data
    • Dataset
    • Document
  • The dataset includes models, sub-networks, and signaling pathways depicting roles of histone modification sites in cell biology and cancer. Pathways were manually reconstructed in Pathway Studio software and based on Resnet - literature extracted network of molecular interactions. Sub-networks were built based on information from publicly available databases. All models have rich annotations including identification for objects and references for interactions between them.
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
    • File Set
  • Supplementary data for JAAD-D-20-01405R4 - Evaluation of the Effect of Store-and-Forward Teledermatology on In-person Healthcare System Utilization in a Safety-Net Public Health and Hospital System
    Data Types:
    • Dataset
    • Document
  • 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.
    Data Types:
    • Dataset
  • This source repository includes all the design files of our proposed all-in-one sensor for monitoring the level, EC and temperature of urban stormwater drains. The design files include the circuit board design, sensor case's 3D model and a component list. For more details, please check http://www.bosl.com.au/wiki/Main_Page
    Data Types:
    • Other
    • Software/Code
    • Tabular Data
    • Dataset
  • This data set consists of the in-lab and field collected data that used to prove the performance and understand the uncertainties of the developed water depth and EC sensor.
    Data Types:
    • Tabular Data
    • Dataset
  • -----> 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!
    Data Types:
    • Image
    • Tabular Data
    • Dataset
    • Document
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