| NSF Org: |
OISE Office of International Science and Engineering |
| Recipient: |
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| Initial Amendment Date: | August 22, 2022 |
| Latest Amendment Date: | August 22, 2022 |
| Award Number: | 2233430 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Kara C. Hoover
kchoover@nsf.gov (703)292-2235 OISE Office of International Science and Engineering O/D Office Of The Director |
| Start Date: | January 15, 2023 |
| End Date: | August 31, 2023 (Estimated) |
| Total Intended Award Amount: | $87,233.00 |
| Total Awarded Amount to Date: | $87,233.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
2550 NORTHWESTERN AVE # 1100 WEST LAFAYETTE IN US 47906-1332 (765)494-1055 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2550 NORTHWESTERN AVE STE 1900 WEST LAFAYETTE IN US 47906-1332 |
| 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): | AccelNet - Accelerating Resear |
| 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): | 47.079 |
ABSTRACT
Scientific networks are at the heart of the National Science Foundation?s (NSF) AccelNet program and its goals to accelerate the process of scientific discovery and to prepare the next generation of U.S. researchers for multi-team, interdisciplinary, international collaborations. Networks in this case are not just a metaphor but can also be a quantifiable object of study that can be documented, analyzed, evaluated, and acted upon. To that end, this project will offer an intensive three-part workshop series for participating AccelNet Teams to: 1) learn how their goals related to increasing collaboration can be thought of in the terms of social network analysis and network science, 2) gain the skills and experience to collect and curate network data related to their project; and 3) analyze and visualize their data to make informed decisions about their AccelNet network to inform their project?s strategy and assessment.
In these three virtual workshops, intended to be taken in sequence, each team will be shown examples of published scientific collaboration studies (and ongoing case studies of two AccelNet Teams the investigators are involved in: GLASSNET and AccelNet-MultiNet) and will be introduced to ways they can collect their own data through existing sources (like bibliometric databases or administrative records) and/or through new sources (such as network surveys or observation). Participants will be given detailed workflows and example scripts for collecting and analyzing data and provided technical support and office hours after each workshop to complete their work. The project will also disseminate an assessment template that the AccelNet teams can utilize to establish baseline graphs and to assess and report the growth and development of their networks over time by updating and monitoring their networks using a common approach. Workshops will be recorded, and materials archived and open for public use at the conclusion.
The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.
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.
Collaboration is at the heart of the National Science Foundation's AccelNet program and its goals to accelerate the process of scientific discovery and to prepare the next generation of U.S. researchers for multi-team, interdisciplinary, international collaborations. Representatives from two current AccelNet projects, GLASSNET at Purdue University and MultiNet at Indiana University, developed and delivered a series of three, half-day, online workshops to teach the AccelNets to create scientific collaboration graphs and conduct basic network analysis that helped them to improve their understanding of collaborations currently happening within their networks; to leverage the strength of expertise and connectedness within their networks to help them to accomplish their goals; to create strategies to address gaps in expertise and connectedness to strengthen within their networks; to identify and evaluate other networks to collaborate and connect with to optimize their network-of-networks; and to assess and report the growth and development of their networks over time. Participants were selected from thirteen current AccelNets and participated in the three workshops from January to May 2023 as a cohort.
In the first workshop, Data Sources for Analyzing Scientific Collaborations, participants learned about commonly available data for assessing scientific collaboration networks and how they can curate and collect that data for their own team. Examples from GLASSNET and MultiNet demonstrated how scientific publication data, survey data, and other observational data were used to both evaluate change efforts and to guide decision-making for future activity.
In the second workshop, Tools for Analyzing and Visualizing Scientific Collaboration Networks, participants learned about three major approaches to using their network collaboration data to assess their collaborations by visualizing their network data, finding important actors, and assessing and finding potential opportunities to increase cohesiveness.
In the third workshop, Evaluating the Growth of Your AccelNet and Strategic Decision-Making, the AccelNet PIs were invited to join the cohort to learned what kinds of questions related to their AccelNets can be answered through network graph analysis; how to establish a baseline scientific collaboration network graph; practical considerations for quantifying network analyses into measurable units; and how to utilize the assessment template for annual reporting to NSF and for informing strategic decision-making to guide the growth and development of their AccelNets.
Participating AccelNets included the Alliance for Tropical Forest Science (ATFS), ALSNet, US-Africa Sustainable Food Systems through Water-Energy-Food Nexus Networking (SustainFood), International Network for Bio-Inspired Computing, Extreme Light in Intensity, Time, and Space Network (X-Lites), Institute for Complex Adaptive Matter (ICAM), Crustal Ocean Biosphere Research Accelerator (COBRA), VR(Ex)Change, GLASSNET, Global Union of Bat Diversity Networks (GBatNet), Implementing a Deep Ocean Observing Strategy (iDOOS), MultiNet, and Transboundary Groundwater Resilience (TGR). Colleagues from the Potsdam Institute for Climate Impact Research in Germany also participated and provided an international perspective.
Workshops included code examples and hands-on exercises with participants gathering and analyzing data from their respective AccelNets using tools such Clarivate Web of Science, OpenAlex, VOSViewer, Visone, and SuAVE2. Support and consultation was offered virtually in between workshops through weekly office hours. Workshop materials including video excerpts, tutorials and examples, assessment template, and exercises have been made openly accessible on the website for the series, https://www.sciencecollaborations.net.
Last Modified: 12/20/2023
Modified by: Michael Witt
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