Fostering Research and Innovation with the Researcher Expertise Dashboard

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The Innovation Partnerships team, in collaboration with the UBC Cloud Innovation Centre (UBC CIC), has developed a prototype to help identify potential research collaborations. The prototype aggregates available data, primarily from the STEAM area, on research activity into one dashboard.

The Challenge

The University of British Columbia (UBC) is a hub for research and innovation, with over 7,000 researchers located across the university’s two campuses. Identifying UBC researchers in areas of interest to potential research sponsors can be challenging given potentially relevant data about areas of expertise is spread amongst a variety of sources.

Approach

The Researcher Expertise Dashboard uses up-to-date data to deliver multiple data points relating research activity and outputs. The dashboard is meant to help identify researchers who may be interested in specific funding opportunities, partnerships, or more. Data in the dashboard is aggregated from institutional as well as other external data sources. The dashboard consists of multiple components including researcher affiliations, advanced search filters, a publications tab, and reactive data visualizations.

Dashboard Features

The landing page of the dashboard, which displays the tabs, the search bar, the logout button, and the default list of researcher profiles.
The landing page of the Researcher Expertise Dashboard.

The profiles consist of publicly available researcher contact and academic affiliation data, including name, campus, faculty, department, and email address; along with publication data and areas of interest, based on the most frequently occurring keywords in the publication data. (Note: for the purpose of prototype development, the project used the publicly available data sources of Scopus and SciVal to demonstrate data ingestion.)

The search feature can be used to find researchers and publications. In addition to searching by name, title, and keyword, filters can be applied to narrow the scope and optimize search results. Applicable filters include faculty and department, publication journal, year published, and content type.

The publication data tab displays a researcher’s publications. Visual tools such as a word cloud and a donut graph within metrics show the most frequent areas of interest and publications by academic unit.

Supporting Artifacts

Architecture Diagram

The architecture diagram of the Researcher Expertise Dashboard.

User Interface

Screenshots of the Researcher Expertise Dashboard’s user interface. Due to the static nature of the screenshots, personal data has been redacted.

The researcher search bar, with "Climate change" typed in. Below it are the search results of different researchers who are related to the topic.
The search function of the Researcher Expertise Dashboard.
A list of similar researcher profiles to a current researcher from the dashboard.
A list of similar researchers to a current researcher from the dashboard.
A word cloud from the dashboard that displays the top 100 keywords from 2019 to 2022.
The word cloud displays the top 100 research keywords within a selected time period.

Technical Details

Click below to read more about the four primary technical elements that make up the solution’s architecture.

Name Match Step Function

The Researcher Expertise Dashboard uses several AWS components to aggregate, store, and query the data from institutional and external sources. Raw Scopus and Institutional Researcher Profile data are fetched from an Amazon S3 bucket in the form of comma separated values (CSV) files. Both datasets are standardized before being stored within the S3 bucket. The standardized names are compared to match Scopus IDs to institutional researcher profile data using a string metric called Jaro-Winkler distance in order to determine if two names are the same. Researchers whose Scopus IDs are identified have their data stored in the PostgreSQL database.

Data Fetch Step Function

For each Scopus ID in the database, metrics such as a 5-year h-index as well as the number of documents, number of citations, and ORCID ID are fetched from the SciVal and Scopus APIs respectively. This data is stored in the PostgreSQL database, alongside the number of filed patents listed on ORCID from the ORCID API and each researcher’s publication data from the Scopus API. The publication data includes each publication’s title, associated keywords, author names and Scopus IDs, journal title, and the number of times the publication has been cited. From there, the “start replication” Lambda uses AWS Data Migration Service (DMS) to replicate this data into AWS OpenSearch so that users can search for the data in the web app’s search function.

Update Publications

A Python docker container hosted on AWS Fargate labelled “Update Publications” is regularly run to update the publications of the researchers in the database, researchers’ h-indices, and number of publications. Update Publications will also add newly published publications to the database and remove publications with no current institutional researchers. When any changes are made to the PostgreSQL database, AWS DMS will replicate the new changes from the database to OpenSearch to keep searches up to date. When queried, the Lambda communicates with AWS OpenSearch and executes the search required before AWS Appsync triggers the OpenSearch GraphQL resolver and passes the correct variables needed to execute the query.

Front End

AWS Web Application Firewall (WAF) helps prevent malicious users from unauthorized access to data or from disrupting the website with DDOS attacks. Users connect to the webpage where access to AWS resources is granted through authentication using AWS Cognito. From there, users navigate to the application in their web browser.

Visuals

An infographic titled "How Does the Researcher Expertise Dashboard Work?" that lists the steps "Log In," "Search," "View," and "Connect".

Acknowledgements

UBC Office of the Vice-President, Research and Innovation

Photo by Martin Dee from the UBC Photo Library.

About the University of British Columbia Cloud Innovation Centre (UBC CIC)

The UBC CIC is a public-private collaboration between UBC and Amazon Web Services (AWS). A CIC identifies digital transformation challenges, the problems or opportunities that matter to the community, and provides subject matter expertise and CIC leadership.

Using Amazon’s innovation methodology, dedicated UBC and AWS CIC staff work with students, staff and faculty, as well as community, government or not-for-profit organizations to define challenges, to engage with subject matter experts, to identify a solution, and to build a Proof of Concept (PoC). Through co-op and work-integrated learning, students also have an opportunity to learn new skills which they will later be able to apply in the workforce.