
NSF Org: |
OAC Office of Advanced Cyberinfrastructure (OAC) |
Recipient: |
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Initial Amendment Date: | September 6, 2018 |
Latest Amendment Date: | September 6, 2018 |
Award Number: | 1761805 |
Award Instrument: | Standard Grant |
Program Manager: |
Martin Halbert
OAC Office of Advanced Cyberinfrastructure (OAC) CSE Directorate for Computer and Information Science and Engineering |
Start Date: | September 15, 2018 |
End Date: | August 31, 2021 (Estimated) |
Total Intended Award Amount: | $20,126.00 |
Total Awarded Amount to Date: | $20,126.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
110 8TH ST TROY NY US 12180-3590 (518)276-6000 |
Sponsor Congressional District: |
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Primary Place of Performance: |
110 8th Street Troy NY US 12180-3590 |
Primary Place of
Performance Congressional District: |
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Unique Entity Identifier (UEI): |
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Parent UEI: |
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NSF Program(s): | BD Spokes -Big Data Regional I |
Primary Program Source: |
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Program Reference Code(s): |
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Program Element Code(s): |
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Award Agency Code: | 4900 |
Fund Agency Code: | 4900 |
Assistance Listing Number(s): | 47.070 |
ABSTRACT
Frequently research on data integration carried out by computer scientists and resulting tools must be modified to fit the needs of domain practitioners (ecologists in this case). This challenge is a socio-technical, collective action problem that can be addressed through a combination of tools and incentives. The project proposes to holding a series of workshops along with proofs-of-concept implementations. These workshops will result in approaches to decentralize the sharing of data in the long tail, through socio-technical approaches that appropriately incentivize and facilitate data integration by smaller labs. Such an interdisciplinary community will provide crucial real-world input to computer science researchers, which will give their research into tools the potential for larger impact in ecological practice and will yield better tools for ecologists.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The goal of this project was to bring computer scientists and ecologists together to address the data integration challenge of the long tail of environmental and ecological data sets. This long tail includes thousands of data sets of scientific value often collected for disparate and relatively small-scale research projects, often by individual groups or labs. However, the full value of these data is challenged because their ad hoc and heterogeneous nature challenges analysis for integrative, systems-oriented, and regional questions. This challenge limits the usefulness of past research efforts. A large and active body of computer scientists researches data integration, but their research often cannot be directly applied by smaller labs due to its highly technical nature and the lack of tools and expertise on the part of domain scientists. This project sought to address this challenge by creating novel software tools to facilitate data publication by individuals and small groups and publishing articles and a book chapter that provided best practices and perspective on environmental sensors, sensor data, data management, and data sharing. Efforts to address this key issue in ecological data science should yield future benefits in terms of great scientific output and scientific transparency.
During the project, our team collaborated with external partners such as the NSF-funded Environmental Data Initiative (EDI) and external domain scientists. Our team included multiple universities and experts in both computer science and ecology, as well as an industry collaborator. The project provided funding for student training, software development, workshops, and publication of papers related to ecological data.
Last Modified: 11/29/2022
Modified by: Kevin C Rose
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