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Data-Driven Discovery: A New Era of Exploiting the Literature and Data
Ying Ding, Jevin West, Min Song, Guilin Meng, Qi Yu
1Indiana; 2Washington; 3Other
In the current data-intensive era, the traditional hands-on method of conducting scientific research by exploring related publications to generate a testable hypothesis is well on its way of becoming obsolete within just a year or two. Analyzing the literature and data to automatically generate a hypothesis might become the de facto approach to inform the core research efforts of those trying to master the exponentially rapid expansion of publications and datasets. Here, viewpoints are provided and discussed to help the understanding and challenges of data-driven discovery. The purposes of this SIE are: 1) to showcase data-driven discovery research by highlighting several applications; 2) to discuss the challenges and potentials about data-driven discovery research, especially focusing on how information scientists and data scientists should work together; and 3) to create community of data-driven discovery in iSchool so as to facilitate collaboration and inspire innovation.