Skip to feedback

Award Abstract # 1933803
Collaborative Research: Social Dynamics of Knowledge Transfer Through Scientific Mentorship and Publication

NSF Org: SMA
SBE Office of Multidisciplinary Activities
Recipient: SYRACUSE UNIVERSITY
Initial Amendment Date: July 31, 2019
Latest Amendment Date: July 31, 2019
Award Number: 1933803
Award Instrument: Standard Grant
Program Manager: Mary Feeney
SMA
 SBE Office of Multidisciplinary Activities
SBE
 Directorate for Social, Behavioral and Economic Sciences
Start Date: October 1, 2019
End Date: September 30, 2023 (Estimated)
Total Intended Award Amount: $176,475.00
Total Awarded Amount to Date: $176,475.00
Funds Obligated to Date: FY 2019 = $176,475.00
History of Investigator:
  • Daniel Acuna (Principal Investigator)
    daniel.acuna@colorado.edu
Recipient Sponsored Research Office: Syracuse University
900 S CROUSE AVE
SYRACUSE
NY  US  13244-4407
(315)443-2807
Sponsor Congressional District: 22
Primary Place of Performance: Syracuse University
312 Hinds Hall
Syracuse
NY  US  13244-1190
Primary Place of Performance
Congressional District:
22
Unique Entity Identifier (UEI): C4BXLBC11LC6
Parent UEI:
NSF Program(s): SciSIP-Sci of Sci Innov Policy
Primary Program Source: 01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7626
Program Element Code(s): 762600
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.075

ABSTRACT

Mentorship plays a critical role in the careers of scientific researchers. Most researchers spend several years training under just one or two graduate and/or postdoctoral mentors, suggesting that these few relationships can have a large impact on scientific careers. Mentorship can provide both direct intellectual benefits to the trainee, through the learning of new skills and concepts, and indirect social benefits, through engagement with the social network of the mentor. Given the importance of mentorship in scientific career development, this aspect of training may play an important role in determining access of underrepresented groups to scientific careers. The purpose of this study is to characterize the impact of demographic variables--such as gender, ethnicity and socio-economic background--on the outcome of scientific training.

Networks of mentors and trainees can be represented by a directional graph resembling a traditional family tree. This project develops a large crowdsourced database of academic mentorship relationships, and links that data to databases that measure scientific productivity (publications and grants) and demographic variables. Graph theoretic and semantic tools will be used to determine if and how demographic variables, associated with both of the mentor and trainee, impact scientific productivity. A preliminary analysis of gender replicates previous reports of bias toward representation male researchers, especially at more senior career stages. Accurately modeling effects of demographic variables requires accounting for other variables that impact scientific productivity metrics, namely differences between fields and long-term temporal trends. This project will use semantic analysis of publication data to develop the concept of the "intellectual neighborhood" of mentors. and incorporate this into the modeling of career outcomes. Data will be made open-access for general use by the public, providing a new resource for studying the dynamics of research fields.

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

Note:  When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

Acuna, Daniel E. and Teplitskiy, Misha and Evans, James A. and Kording, Konrad "Author-suggested reviewers rate manuscripts much more favorably: A cross-sectional analysis of the neuroscience section of PLOS ONE" PLOS ONE , v.17 , 2022 https://doi.org/10.1371/journal.pone.0273994 Citation Details
Ke, Qing and Liang, Lizhen and Ding, Ying and David, Stephen V. and Acuna, Daniel E. "A dataset of mentorship in bioscience with semantic and demographic estimations" Scientific Data , v.9 , 2022 https://doi.org/10.1038/s41597-022-01578-x Citation Details
Schwartz, Leah P. and Liénard, Jean F. and David, Stephen V. "Impact of gender on the formation and outcome of formal mentoring relationships in the life sciences" PLOS Biology , v.20 , 2022 https://doi.org/10.1371/journal.pbio.3001771 Citation Details
Xu, Huimin and Liu, Meijun and Bu, Yi and Sun, Shujing and Zhang, Yi and Zhang, Chenwei and Acuna, Daniel E. and Gray, Steven and Meyer, Eric and Ding, Ying "The impact of heterogeneous shared leadership in scientific teams" Information Processing & Management , v.61 , 2024 https://doi.org/10.1016/j.ipm.2023.103542 Citation Details

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 project aimed to explore the dynamics of scientific mentorship and its impact on the transfer of knowledge within academic environments. One of the central objectives was to characterize how demographic variables such as gender, age, and institutional affiliation influence mentorship relationships and, subsequently, the professional success of trainees. Through a combination of graph theory and semantic analysis tools, our research successfully mapped out the “intellectual neighborhoods” of mentors to model and predict the career outcomes of mentees.

Our findings have significantly advanced the understanding of how these demographic factors interplay to affect academic productivity and career trajectories. Notably, one of our studies uncovered patterns of gender bias in mentorship, particularly at senior career stages, and identified critical factors that influence the likelihood of trainees pursuing academic research careers. These insights are backed by comprehensive databases developed during the project, incorporating previous work from the Eileen.io project and extending the scope of available datasets for future research.

Broader impact

The broader impacts of this project are many. First, it has enhanced our understanding of the role mentorship plays in shaping scientific careers, especially for underrepresented groups. The insights gained from this study have potential implications for developing policies and practices that promote equity in scientific training.

Additionally, the project has produced valuable resources for the scientific community, including the Academic Family Tree (AFT) database, which integrates mentorship data with publication records. This integration facilitates a deeper analysis of mentorship networks and research topics and will serve as a novel resource for future studies.

The project also emphasized capacity building through the organization of two Science of Science Summer Schools in 2021 and 2022. These events brought together over 100 students from around the globe, providing them with training in advanced research methodologies and exposure to the frontiers of the science of science research.

Dissemination and Publications

The research team has actively disseminated the findings through various channels. Several manuscripts detailing the project’s results have been submitted and published in peer-reviewed journals, the materials of the summer school are available online, and the software and datasets are also available through NSF Public Access Repository.

Science of Science Summer School

MENTORSHIP dataset and software

 


Last Modified: 04/23/2024
Modified by: Daniel E Acuna

Please report errors in award information by writing to: awardsearch@nsf.gov.

Print this page

Back to Top of page