We Need a (Responsible!) Data Science Rapid Response Network
We Need a (Responsible!) Data Science Rapid Response Network
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我们需要一个（负责任的！）数据科学快速响应网络
DOI：
10.1162/99608f92.2794e78d
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Parker, Micaela S.
中科院分区：
文献类型：
--
作者：
Kolaczyk, Eric;Lee, Meredith M.;Liu, Jing;Parker, Micaela S.
在2019冠状病毒病大流行期间，无论好坏，我们都对自己这个社会有了更多的了解。Sabina Leonelli（“数据科学时代的数据科学”，本期）从数据科学的角度对迄今为止我们所学到的许多东西进行了广泛、令人信服和发人深省的反思。她选择的方法——通过对数据使用的想象——在平衡已经发生的和可能发生的感觉方面特别有效（对大多数数据科学家来说，这是一种令人耳目一新的陌生）。这些想象包括对大流行期间数据科学贡献的两个广泛领域的陷阱的一个受欢迎的框架：人口监测和预测建模。与类似地关注“数据科学对流行病应对的贡献是如何被想象和预测到未来的”不同，我们在这里集中并强调了促进、甚至理想地优化这些贡献的适当机制的问题。我们提倡建立一种机制，使数据科学界在危机时刻能够产生协调和负责任的快速反应，而不是匆忙的反应，沉浸在对社会背景不断发展的理解中。具体而言，我们提出以下几点：1)具有可推广框架的数据科学快速反应网络不仅有助于解决当前大流行所揭示的差距，而且有助于解决未来许多类型危机中的预期挑战；2)数据科学快速响应网络应利用现有的联盟、中心和网络，增强本地、区域和更广泛的能力；3)数据科学快速反应网络可以汇集人才，支持科学诚信，并以独特的方式帮助将科学发现转化为行动，促进相关和及时的协调。
During the COVID-19 pandemic, for better or worse, we are learning much about ourselves as a society. Sabina Leonelli (“Data Science in Times of Pan (dem) ic,” this issue) provides a broad, cogent, and thought-provoking reflection on much of what we have learned to date from a data science perspective. Her chosen device for doing so—through imaginaries of data use—is particularly effective (and refreshingly unfamiliar to most data scientists) in balancing a sense of both what has been and what might be. The imaginaries include a welcome framing of the pitfalls of two broad areas of data science contributions during the pandemic: population surveillance and predictive modeling.Rather than similarly focusing on “the ways in which the data science contributions to the pandemic response are imagined and projected into the future,” here we center and emphasize the question of appropriate mechanisms for facilitating, or ideally even optimizing, such contributions. We advocate for mechanisms that will yield a coordinated and responsible rapid response, not a rushed response, from the data science community in times of crises, steeped in an evolving understanding of societal context. Specifically, we make the following points: 1) A data science rapid response network with a generalizable framework would help address not only gaps illuminated by the current pandemic, but also anticipated challenges in many types of future crises; 2) A data science rapid response network should leverage existing consortia, centers, and networks—strengthening capacity locally, regionally, and more broadly; and 3) A data science rapid response network can bring together talent, support scientific integrity, and help translate scientific discoveries to action in unique ways, facilitating relevant and timely coordination.
影响因子：
30.8
作者：
Portelli, Stephanie;Olshansky, Moshe;Ascher, David B.
通讯作者：
Ascher, David B.
影响因子：
3
作者：
Peek, Lori;Tobin, Jennifer;Mathews, Mason Clay
通讯作者：
Mathews, Mason Clay