Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy.
This content will become publicly available on February 22, 2025
Do Machines and Humans Focus on Similar Code? Exploring Explainability of Large Language Models in Code Summarization
- Award ID(s):
- 2211428
- NSF-PAR ID:
- 10495855
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- 32nd IEEE/ACM International Conference on Program Comprehension, RENE
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This release covers the state of the data and associated analysis code for determining code sharing between cryptocurrency codebases funded through the end of the original NSF CRII award. This material is based on work supported by the National Science Foundation under Grant CNS-1849729.
