Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering
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Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering: The BIGDATA program seeks novel approaches in computer science, statistics, computational science, and mathematics, along with innovative applications in domain science, including social and behavioral sciences, geosciences, education, biology, the physical sciences, and engineering that lead towards the further development of the interdisciplinary field of data science. The solicitation invites two types of proposals: "Foundations" (F): those developing or studying fundamental theories, techniques, methodologies, and technologies of broad applicability to big data problems; and "Innovative Applications" (IA): those developing techniques, methodologies, and technologiesof key importance to a Big Data problem directly impacting at least one specific application. Projects in this category must be collaborative, involving researchers from domain disciplines and one or more methodological disciplines, e.g., computer science, statistics, mathematics, simulation and modeling, etc. While IA proposals may address critical big data challenges within a specific domain,a high level of innovation is expected in all proposals which should, in general, strive to provide solutions withpotential for a broader impact on data science and its applications. IA proposals may focus on novel theoretical analysis and/or on experimental evaluation of techniques and methodologies within a specific domain. Proposals in all areas of sciences and engineering covered by participating directorates at NSF are welcome.
While notions of volume, velocity, and variety are commonly ascribed to big data problems, other key issues include data quality and provenance. Data-driven solutions must carefully ascribe quality and provenance to results in a manner that is helpful to the users of the results. For example, in some cases, such as in education research,data quality may aggregate to test or measurement instrument quality, where a composite of variables may be used to describe one or more constructs.
In addition to approaches such as search, query processing, and analysis, visualization techniques will also become criticalacross many stages of big data use--toobtain an initial assessment of data as well as through subsequent stages of scientific discovery. Research on visualization techniques and models will be necessary for serving not only the experts, who are collecting the data, but also those who are users of the data, including “cross-over” scientists who may be working with big data and analytics for the first time, and those using the data for teaching at the undergraduate and graduate levels. The BIGDATA program seeks novel approaches related to all of these areas of study.
Before preparing a proposal in response to this BIGDATA solicitation, applicants are strongly urged to consult other related programs and solicitationsand review 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 Campus Cyberinfrastructure - Data, Networking, and Innovation Program (CC*DNI) program (<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504748&org=ACI&from=home">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504748&org=ACI&from=home</a>). Applicants should also consider the Computational and Data Enabled Science and Engineering (CDS&E) program(<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813</a>) for work not specifically addressing big data issues, and the Exploiting Parallelism and Scalability (XPS) program (<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504842">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504842</a>)for work focused on scaling of software.
While notions of volume, velocity, and variety are commonly ascribed to big data problems, other key issues include data quality and provenance. Data-driven solutions must carefully ascribe quality and provenance to results in a manner that is helpful to the users of the results. For example, in some cases, such as in education research,data quality may aggregate to test or measurement instrument quality, where a composite of variables may be used to describe one or more constructs.
In addition to approaches such as search, query processing, and analysis, visualization techniques will also become criticalacross many stages of big data use--toobtain an initial assessment of data as well as through subsequent stages of scientific discovery. Research on visualization techniques and models will be necessary for serving not only the experts, who are collecting the data, but also those who are users of the data, including “cross-over” scientists who may be working with big data and analytics for the first time, and those using the data for teaching at the undergraduate and graduate levels. The BIGDATA program seeks novel approaches related to all of these areas of study.
Before preparing a proposal in response to this BIGDATA solicitation, applicants are strongly urged to consult other related programs and solicitationsand review 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 Campus Cyberinfrastructure - Data, Networking, and Innovation Program (CC*DNI) program (<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504748&org=ACI&from=home">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504748&org=ACI&from=home</a>). Applicants should also consider the Computational and Data Enabled Science and Engineering (CDS&E) program(<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504813</a>) for work not specifically addressing big data issues, and the Exploiting Parallelism and Scalability (XPS) program (<a href="http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504842">http://www.nsf.gov/funding/pgm_summ.jsp?pims_id=504842</a>)for work focused on scaling of software.
Federal Grant Title: | Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering |
Federal Agency Name: | National Science Foundation |
Grant Categories: | Science and Technology |
Type of Opportunity: | Discretionary |
Funding Opportunity Number: | 16-512 |
Type of Funding: | Grant |
CFDA Numbers: | 47.041, 47.049, 47.050, 47.070, 47.074, 47.075, 47.076 |
CFDA Descriptions: | Engineering Grants; Mathematical and Physical Sciences; Geosciences; Computer and Information Science and Engineering; Biological Sciences; Social, Behavioral, and Economic Sciences; Education and Human Resources |
Current Application Deadline: | Feb 9, 2016 |
Original Application Deadline: | Feb 9, 2016 |
Posted Date: | Nov 10, 2015 |
Creation Date: | Nov 10, 2015 |
Archive Date: | Mar 10, 2016 |
Total Program Funding: | $26,500,000 |
Maximum Federal Grant Award: | $2,000,000 |
Minimum Federal Grant Award: | $400,000 |
Expected Number of Awards: | 35 |
Cost Sharing or Matching: | No |
- Applicants Eligible for this Grant
- Unrestricted (i.e., open to any type of entity above), subject to any clarification in text field entitled "Additional Information on Eligibility"
- Link to Full Grant Announcement
- NSF Publication 16-512
- Grant Announcement Contact
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