Module 1: Making Data Accessible, Discoverable and Useful
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Abstract
This module focuses on the importance of make materials data discoverable, interoperable, and available and best practices to doing so. Data generation is both time consuming and costly, thus, making the available, as appropriate, with the community is critical to accelerate innovation. This is particularly important with the increasing use of data science in engineering and physical sciences. We discuss FAIR (findable, accessible, interoperable, and reusable) data principles and online resources to deposit and acquire data. These principles apply equally to models and scientific workflows. We introduce how nanoHUB enables users to develop models and workflows online and share them with their community.
This end-to-end module is designed to be self-contained and easy to incorporate in existing courses or used for self-study. The module consists of two components:
- Pre-recoded lecture
YouTube | Video Download (MP4) | Slides (PDF) - Homework Assignment
Download (PDF)
This module is part of a series on data science and machine learning for engineering and physical sciences. Users will be able to run interactive code online using nanoHUB, no need to download or install any software.
Learning objectives. After completing this module, you will:
- Be aware of and able to adopt FAIR principles in your own work
- Know how to add metadata to your results
- Be able to contribute your own results to data repositories
- Be able to document and share models and scientific workflows
Pre-requisites
- None
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References
Jha, D., Ward, L., Paul, A. et al., ElemNet: Deep Learning the Chemistry of Materials From Only Elemental Composition, Sci Rep 8, 17593 (2018). https://doi.org/10.1038/s41598-018-35934-y
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