Version 1
: Received: 20 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (10:36:36 CEST)
Version 2
: Received: 27 April 2023 / Approved: 2 May 2023 / Online: 2 May 2023 (04:13:23 CEST)
How to cite:
Keskinoglu, E. Plan for Constructing DataDiscoveryLab: Creating DataBases for Well-Rounded Searches. Preprints2023, 2023041074. https://doi.org/10.20944/preprints202304.1074.v2
Keskinoglu, E. Plan for Constructing DataDiscoveryLab: Creating DataBases for Well-Rounded Searches. Preprints 2023, 2023041074. https://doi.org/10.20944/preprints202304.1074.v2
Keskinoglu, E. Plan for Constructing DataDiscoveryLab: Creating DataBases for Well-Rounded Searches. Preprints2023, 2023041074. https://doi.org/10.20944/preprints202304.1074.v2
APA Style
Keskinoglu, E. (2023). Plan for Constructing DataDiscoveryLab: Creating DataBases for Well-Rounded Searches. Preprints. https://doi.org/10.20944/preprints202304.1074.v2
Chicago/Turabian Style
Keskinoglu, E. 2023 "Plan for Constructing DataDiscoveryLab: Creating DataBases for Well-Rounded Searches" Preprints. https://doi.org/10.20944/preprints202304.1074.v2
Abstract
The abundance of information in academic articles, reports, and studies can make it challenging for researchers to gain insights from the existing literature. To address this issue, there is a growing demand for tools that can help researchers effectively parse and analyze large volumes of data. One such tool is DataDiscoveryLab, a software system that utilizes computer vision algorithms and NLP techniques to parse academic articles into text and figures, creating three separate databases. These databases allow researchers to quickly identify articles that may be relevant to their research questions, gain a deeper understanding of the research presented, and analyze visual data. The integration of article mining and computer vision in the DataDiscoveryLab software system provides researchers with a powerful tool for navigating the vast amount of scientific literature available today. Yet, as we will discuss in the latter papers these databases’ purpose is to create a bridge between researchers’ data and practically unlimited scientific publications. Yet, in this article, we will discuss how we plan to do that, and our efforts on integrating deep learning modes. After all, unlike already existing AI models, DataDiscoveryLab can be their combination and the first Generative AI in academia that can encompass every part of the natural sciences.
Keywords
data analysis; computer vision algorithms; visual data; natural language processing; scientific research
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Elbek Keskinoglu
Commenter's Conflict of Interests: Author