A Solicitation of the National Institutes of Health (NIH) and The Centers for Disease Control and Prevention (CDC) for Small Business Innovation Research (SBIR) Contract Proposals

Active
No
Status
Closed
Release Date
August 25th, 2023
Open Date
August 25th, 2023
Due Date(s)
November 14th, 2023
Close Date
November 14th, 2023
Topic No.
NIH/NIAID 136

Topic

Software or Web Services to Re-Represent Existing Scientific Data and Knowledge into a Knowledge Graph Format

Agency

Department of Health and Human ServicesNational Institutes of Health

Program

Type: SBIRPhase: BOTHYear: 2023

Summary

The Department of Health and Human Services, specifically the National Institutes of Health (NIH) and The Centers for Disease Control and Prevention (CDC), are seeking proposals for Small Business Innovation Research (SBIR) contracts. The solicitation, titled "Software or Web Services to Re-Represent Existing Scientific Data and Knowledge into a Knowledge Graph Format," focuses on the development of software or web services that can convert scientific data and knowledge into a knowledge graph format. The goal of this research is to address the challenge of digesting and analyzing the increasing volume of scientific data and knowledge. Researchers are increasingly relying on computer algorithms to assist with data analysis and the discovery of new information. The COVID-19 pandemic highlighted the need for efficient data analysis, as a large number of papers were published in a short period of time. The public health response to emerging threats and the treatment of diseases require continuous analysis of new data, which is complicated by the large volume of data being generated. Knowledge graphs (KGs) have emerged as a valuable technology for data representation and integration. KGs represent information about entities and their relationships in a semantically rich way, enabling efficient data retrieval and analysis by computational algorithms. KGs have shown promise in various scientific domains, such as analyzing collaborations between authors, exploring pathways in molecular biology, and drug repurposing. However, a major bottleneck in leveraging KGs for biomedical research is the difficulty of representing data and knowledge in a KG-compatible format. Many biomedical data are stored in formats that are not compatible with KGs, such as spreadsheets and databases. The solicitation seeks proposals for the development of software or web services that can convert existing scientific data and knowledge into a KG-compatible format. The funding for this project is as follows: Phase I proposals can receive up to $300,000 for a duration of up to 1 year, while Phase II proposals can receive up to $1.5 million for a duration of up to 3 years. The anticipated number of awards is 1-3. Overall, this solicitation aims to address the challenge of representing scientific data and knowledge in a KG-compatible format, enabling more efficient data analysis and knowledge discovery in the biomedical research field.

Description

Fast Track Proposals will be accepted. Direct-to-Phase II will be accepted. Number of anticipated awards: 1-3 Budget (total costs): Phase I: $ 300,000 for up to 1 year. Phase II: $ 1.5 million for up to 3 years. Background Scientific research produces more data and knowledge than ever before and it is becoming increasingly challenging for researchers to digest and analyze the data to derive new knowledge and insights. Increasingly, researchers will depend on computer algorithms to assist them with data analysis and with the discovery of new information and knowledge. For example, during the COVID-19 pandemic, the discrepancy between the speed of data generation and the ability to digest and analyze the data was acutely illustrated with almost 25 thousand new papers published in the first half of 20201 . The public health response against emerging pandemic threats and the treatment and control of existing infectious- and immune-mediated diseases requires the continuous analysis of newly emerging data, information, and knowledge; a task that is increasingly complicated by the large volume of data that is being generated. Knowledge graphs (KG’s) have emerged as a prominent technology for data representation and integration and are widely used in the industry. KG’s represent information about entities and relationships between entities in a semantically rich way that enables efficient data retrieval and analysis by computational algorithms, such as automated reasoners and AI, and have shown great promise for data management and knowledge discovery. A familiar use case for KG’s in the scientific domain is for the analysis of collaborations between authors based on published papers, for the exploration of pathways in molecular biology, and for drug repurposing. A major bottleneck for leveraging KG’s for biomedical research and discovery is the difficulty to represent data and knowledge into a KG-compatible format. Many biomedical data are stored in formats that are not compatible with KG’s, such as spreadsheets and databases, and cannot be used by automated reasoners and other computer-assisted knowledge discovery methods, unless these data are represented in a KG format. Given the complexity of accurately representing semantic information on scientific data and related methods, automated methods and software will be essential to scale up the amount of data on infectious- and immune-mediated diseases available in KG’s for accelerating scientific discovery