Helping researchers understand COVID-19 and combat misinformation.
Applying Nebuli’s robotic co-worker model to help researchers worldwide read through thousands of research papers within seconds, instead of weeks.
Deep data mining.
Cognitive Data Research and Trends Analysis.
Scientists and researchers around the world have united in the fight against COVID-19 by publishing hundreds of research papers every day about their findings in both peer-reviewed journals and pre-print servers. It is truly an unprecedented moment in the history of academic research. However, these daily publications are scattered across several sources and, unfortunately, no single individual can go through tens of thousands of full-text documents efficiently and rapidly enough to deal with this global emergency.
More critically, out of these thousands of papers, only a few may hold the key to unlock the cure or provide new molecular insights for a COVID-19 vaccine, or even a new way to treat patients and prevent the spread of the virus. Thus, such papers could be missed out unintentionally or discovered much later than need be.
For this reason, our team at Nebuli is setting up a cognitive deep data mining project, called Researcher.AI, applying our robotic co-worker model to help researchers worldwide read through thousands of research papers within seconds, instead of weeks. We have published further details in our official announcement here.
While we are currently focusing on the imminent COVID-19 crisis, Researcher.AI is designed to support researchers in monitoring and dealing with future outbreaks and other unforeseen emergencies, such as political instabilities and environmental catastrophes. COVID-19 will not be the last outbreak. Hence, Researcher.AI can be utilised within government and academic communities to observe emerging epidemiological trends that could support their efforts in preparing and planning well in advance, compared to what we have seen with COVID-19 to date. Not to mention, the system’s API integration with social media platforms could also assist them in quickly identifying content that has not been scientifically verified. Thus, supporting their efforts in significantly reducing the spread of fake content on their platforms.
Nebuli’s Key Role:
Connect as many research paper ecosystems and research data sources as possible for Nebuli’s smart indexing process and generate critical trends analysis based on specific parameters inputted by researchers and government agencies.
Creation of several of Data-Driven Worlds (DDWs) from thousands of data collections.
Cognitive Search of specific data elements within the DDW.
Data clustering and segmentation of specific data parameters defined by individual researchers within each DDW.
Creation of Data maps of DDW (Visualisation) using self-organising map (SOM) models and data vectors that can be displayed on Researcher.AI‘s UI and loaded within an organisation’s internal data visualisation software via API libraries.
Creation of an isolated system with its own database for each DDW that allows for more in-depth analysis of targeted traits of the virus, particularly when new traits are discovered and reported in various journals.
Outcomes and Objectives:
Researcher.AI platform (website + API) for Researchers and Government Agencies: Specialist algorithms designed specifically to mine research paper abstracts and full-text to focus on all historical and newly published research outcomes and critical data related to Coronavirus and COVID-19 pandemic, allowing researchers and government agencies to easily and quickly monitor trends based on their specific parameters. We believe this model can help with planning and monitoring current and future pandemics well in advance.
Researcher.AI for the General Public (mobile/social media apps): The same algorithms can be applied through a dedicated API gateway that enables developers to build mobile and social media apps (e.g. Facebook app) that help users validate the COVID-19 stories and claims based on our indexed research papers. An important part of this public API is that when someone reads something online or shares it on social media, our solution could show exactly how much of the posted claims are backed up by real science (with citations). Having an embedded social media app integration would allow users to verify feeds or texts from WhatsApp, enabling them to dismiss incorrect or misleading claims of others. The API library could also recommend links to more accurate sources on the fly, thus not requiring much extra effort to perform an effective and community-lead fact-checking process. This should put fake news into isolation!