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Computational methods have gained importance and popularity in both academic research and industrial applications in recent years. Since 2014, our team has consistently worked on reforming our Materials Science and Engineering curriculum at the University of Illinois Urbana-Champaign by incorporating computational modules into all mandatory undergraduate courses. Here, we investigate the impact and effectiveness of these computational modules in light of our recent graduates’ feedback. We surveyed alumni who graduated between 2017 – 2021 and asked them about the benefits of the computational curriculum and the significance of computation for their career. “data analysis” was reported to be the most significant computational practice, followed by “programming” and “simulation tools”. Python is the most prevalent programming language, and half of the respondents have reported to use it for their work. Particle based simulation tools are rarely used by our alumni, whereas continuum methods are more relevant, especially for alumni in industry. Graduates who pursued Ph.D. or Master’s degrees benefited more from the existence of computational modules and would also benefit the most from qualitative improvements of the modules. Alumni have reported limited benefits of computational modules during their job search, but note a slightly positive impact on their job performance. Overall, our Alumni think that the current amount of computational material in the curriculum is ideal, but further analysis indicates there is still room for qualitative improvements. We find the perspective provided by alumni to be a valuable tool to evaluate the computational reform of the MatSE curriculum at the University of Illinois Urbana-Champaign and it is a useful guide on how to reshape and improve its effectiveness further.
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tobac v1.5: introducing fast 3D tracking, splits and mergers, and other enhancements for identifying and analysing meteorological phenomena
Abstract. There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed.
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- Award ID(s):
- 2019939
- PAR ID:
- 10566810
- Publisher / Repository:
- Copernicus Publications on behalf of the European Geosciences Union
- Date Published:
- Journal Name:
- Geoscientific Model Development
- Volume:
- 17
- Issue:
- 13
- ISSN:
- 1991-9603
- Page Range / eLocation ID:
- 5309 to 5330
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gmd-17-5309-2024
