logo

Critical Techniques and Technologies for Advancing Big Data Science & Engineering

Sector: Commercial • Location: United States of America

Source: Grants.gov

Project
Archived

This year, the solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental techniques, theories, methodologies, and technologies of broad applicability to Big Data problems; and "Innovative Applications" (IA): those developing techniques, methodologies and technologies of key importance to a Big Data problem directly impacting at least one specific appl

Project Information FAQ

Project Information

5 Q
The project “Critical Techniques and Technologies for Advancing Big Data Science & Engineering” is an infrastructure initiative in the Commercial sector, located in United States of America. Taiyo aggregates data on it from Grants.gov.

Want to explore the full details? View the full report

Participants

Sponsoring Agency

Obfuscated Data

Company

Obfuscated Data

Status

Original status

archived

Taiyo status

Obfuscated Data

Taiyo last update

00-00-0000

Available timestamps

00-00-0000

Available timestamp type

Obfuscated Data

Contact

Contact name

Obfuscated Data

Phone

0000000000

Email

ObfuscatedData@email.com

Address

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Description

Description

This year, the solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental techniques, theories, methodologies, and technologies of broad applicability to Big Data problems; and "Innovative Applications" (IA): those developing techniques, methodologies and technologies of key importance to a Big Data problem directly impacting at least one specific application.?? All proposals must address critical challenges for big data management, big data analytics, or scientific discovery processes impacted by big data. These techniques, methodologies and technologies can be computational, statistical, or mathematical in nature, and proposals may focus on novel theoretical analysis or experimental evaluation of these techniques and methodologies. A high level of innovation is expected in all proposals. Proposals in all areas of science and engineering covered by participating directorates at NSF are welcome. This solicitation is a part of a larger national "Big Data Initiative", which covers a wide range of topics: big data infrastructure; education and workforce development; and multi-disciplinary collaborative teams and communities that address complex scientific, biomedical and engineering grand challenges.?? Before preparing a proposal in response to this BIGDATA solicitation, applicants are strongly urged to consult the list of related solicitations available at: http://www.nsf.gov/cise/news/bigdata.jsp??and??consult the respective NSF program officers listed in them should those solicitations be more appropriate.?? In particular, applicants interested in deployable cyberinfrastructure pilots that would support a broader research community should see the Data Infrastructure Building Blocks (DIBBS) solicitation (http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504776 ). Applicants should also consider the Computational and Data Enabled Science and Engineering (CDS&E, PD 12-8084) solicitations for potential fit (http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813&org=ENG&sel_org=ENG&from=fund ). Proposals submitted to the "Innovative Applications" (IA) category must specify one or more relevant participating NSF directorates in the Project Summary.

Original sub-sector

Obfuscated

Original Currency

USD

Original budget

000000000000000

Procurement method

Obfuscated Data

Budget

000000000000000

Location

Region

Obfuscated

Country

Obfuscated

State

Obfuscated Data

County

Obfuscated

Location

Obfuscated Data, Obfuscated data, obfuscated data, Obfuscated data

Source

Source reliability

High

Data quality score

100%

Source

Obfuscated Data

URL

obfuscated_data,obfuscateddata.com

More Details

Project Type

Obfuscated Data

Article Published Date

Obfuscated Data