Big Code

Big Code
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大码

DOI:
10.1111/gean.12330
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发表时间:
2022
影响因子:
3.6
通讯作者:
Rey, Sergio J.
Rey, Sergio J.
中科院分区:
地球科学3区
文献类型:
--
作者:
Rey, Sergio J.

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大数据,现代数据科学时代的“新石油”,在地理信息系统科学界备受关注。然而,在这场现代淘金热中,我们忽视了代码在推动大数据革命中的作用。相反,代码受到的关注集中在计算效率和可伸缩性问题上。相比之下,我们错过了代码的更具变革性的方面提供的机会,作为组织我们科学的方式。这些“大代码”实践有可能解决大数据的一些不良影响,这些影响受到了正确的批评,例如算法偏见、缺乏代表性、把关以及我们社区的权力失衡问题。在本文中,我考虑了来自开源社区的经验教训可以帮助我们发展成更具包容性、生成性和可扩展性的GIScience的领域。这些涉及行为准则、数据管道和可再现性的最佳做法,重构我们的归属和奖励系统,以及重新改造我们的教学方法。
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.
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