NIH Big Data to Knowledge (BD2K) Initiative by Shar Steed.
From the post:
The National Institutes of Health (NIH) has announced the Centers of Excellence for Big Data Computing in the Biomedical Sciences (U54) funding opportunity announcement, the first in its Big Data to Knowledge (BD2K) Initiative.
The purpose of the BD2K initiative is to help biomedical scientists fully utilize Big Data being generated by research communities. As technology advances, scientists are generating and using large, complex, and diverse datasets, which is making the biomedical research enterprise more data-intensive and data-driven. According to the BD2K website:
[further down in the post]
Data integration: An applicant may propose a Center that will develop efficient and meaningful ways to create connections across data types (i.e., unimodal or multimodal data integration).
That sounds like topic maps doesn’t it?
At least if we get away from black/white, match one of a set of IRIs or not, type merging practices.
For more details:
A webinar for applicants is scheduled for Thursday, September 12, 2013, from 3 – 4:30 pm EDT. Click here for more information.
Be aware of this workshop:
August 21, 2013 – August 22, 2013
NIH Data Catalogue
Chair: Francine Berman, Ph.D.This workshop seeks to identify the least duplicative and burdensome, and most sustainable and scalable method to create and maintain an NIH Data Catalog. An NIH Data Catalog would make biomedical data findable and citable, as PubMed does for scientific publications, and would link data to relevant grants, publications, software, or other relevant resources. The Data Catalog would be integrated with other BD2K initiatives as part of the broad NIH response to the challenges and opportunities of Big Data and seek to create an ongoing dialog with stakeholders and users from the biomedical community.
Contact: BD2Kworkshops@mail.nih.gov
Let’s see: “…least duplicative and burdensome, and most sustainable and scalable method to create and maintain an NIH Data Catalog.”
Recast existing data as RDF with a suitable OWL Ontology. – Duplicative, burdensome, not sustainable or scalable.
Accept all existing data as it exists and write subject identity and merging rules: Non-duplicative, existing systems persist so less burdensome, re-use of existing data = sustainable, only open question is scalability.
Sounds like a topic map opportunity to me.
You?