| NSF Org: |
DMS Division Of Mathematical Sciences |
| Recipient: |
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| Initial Amendment Date: | September 10, 2018 |
| Latest Amendment Date: | June 17, 2022 |
| Award Number: | 1839307 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Christopher Stark
DMS Division Of Mathematical Sciences MPS Directorate for Mathematical and Physical Sciences |
| Start Date: | October 1, 2018 |
| End Date: | July 31, 2023 (Estimated) |
| Total Intended Award Amount: | $199,859.00 |
| Total Awarded Amount to Date: | $199,859.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
845 N PARK AVE RM 538 TUCSON AZ US 85721 (520)626-6000 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
888 N Euclid Ave Tucson AZ US 85719-4824 |
| 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): | TRIPODS Transdisciplinary Rese |
| 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): | 47.049 |
ABSTRACT
Scientists in diverse domains from astronomy and atmospheric sciences, to earth sciences and genomics are generating massive datasets at an unprecedented scale. Rapidly evolving computational and data management technologies for harnessing value from these datasets are providing the foundation for a vibrant ecosystem by establishing robust collaborations and building communities of domain scientists, data scientists, and engineers. These collaborations are central for transforming these datasets into information and knowledge. Barriers of both a technical and non-technical nature can hamper productivity for such transdisciplinary teams and collaborations, especially when highly productive teams with diverse expertise and computational backgrounds work on common problems. These barriers are often associated with frictions at the boundaries of computational technologies and human communications, especially when working at scale. Overcoming such challenges is critical for ensuring successful outcomes.
This project will bring together participants representing thought-leaders and practitioners in data-driven open science projects, TRIPODS+X project teams, and participants from the astronomy and earth sciences communities through two Innovation Labs. The first Lab will introduce participants to the national NSF-funded cyberinfrastructure and commercial cloud infrastructure, providing the opportunity to evaluate and learn from exemplary projects that have utilized these platforms for their collaborations, allowing participants to explore how their communities can extend these platforms for their data science projects in a reliable, scalable and reproducible manner. The second Innovation Lab will establish an early prototype TRIPODS Commons, a cohesive platform for showcasing, experimenting with, and sharing research products (code, data, methods), eventually becoming an avenue that provides visibility to the vibrancy and productivity of projects occurring at all NSF TRIPODS Institutes. Through these Innovation labs, the project will provide pragmatic approaches and pathways for establishing successful transdisciplinary collaborations that enable teams to work across domains and institutional boundaries, and at scales essential for addressing the research, education, and advanced cyberinfrastructure needs as outlined in NSF's 10 Big Ideas.
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.
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.
In the present day, proficiency in working with large datasets is a fundamental skill for many scientific disciplines, especially in fields like astronomy, earth sciences, and genomics. These fields are producing massive datasets at an unprecedented rate, and they are making this data publicly available. However, in order to turn these datasets into useful information and knowledge, it takes a team of highly skilled domain experts that can actively collaborate with data scientists to produce the needed novel tools and methods.
There are a number of new computational infrastructures and data science resources that are becoming available thanks through NSF-funded projects and centers. Additionally, community standards are emerging that make it easier for these systems to interact with data and automation. There are also open-source tools and platforms being contributed by industry. Despite all of these advances, many disciplines and projects are unable to take full advantage of the vibrant ecosystem of tools and platforms.
Our project aims to help improve transdisciplinary collaborations by sharing best practices from projects that have successfully leveraged the data science ecosystem. We also aim to identify key components for projects of all sizes, from small, single-laboratory efforts to large consortia.
To accomplish these goals, we held two workshops in the style of innovation labs. In these workshops, participants were encouraged to share their innovative approaches to building data science tools and platforms through transdisciplinary collaborations. The first workshop focused on the challenges that prevented projects from reaching their full potential. This led to the development of a paper titled "Ten Simple Rules to Cultivate Transdisciplinary Collaboration in Data Science." The second workshop focused on identifying the key components that form an ideal toolbox of technologies and expertise for a given discipline. Participants learned from each other about these capabilities and explored ways to combine them into a unique platform that could be readily assembled for new projects.
The outcomes of these two workshops have provided a roadmap and technical guidance for future transdisciplinary data science collaborations. This guidance will help ensure that the software products and methods that are developed meet open science and reproducibility standards, and these outcomes can be adopted by a wider community of users.
Last Modified: 01/29/2024
Modified by: Nirav C Merchant
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