Title |
Big data - a 21st century science Maginot Line? No-boundary thinking: shifting from the big data paradigm
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Published in |
BioData Mining, February 2015
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DOI | 10.1186/s13040-015-0037-5 |
Pubmed ID | |
Authors |
Xiuzhen Huang, Steven F Jennings, Barry Bruce, Alison Buchan, Liming Cai, Pengyin Chen, Carole L Cramer, Weihua Guan, Uwe KK Hilgert, Hongmei Jiang, Zenglu Li, Gail McClure, Donald F McMullen, Bindu Nanduri, Andy Perkins, Bhanu Rekepalli, Saeed Salem, Jennifer Specker, Karl Walker, Donald Wunsch, Donghai Xiong, Shuzhong Zhang, Yu Zhang, Zhongming Zhao, Jason H Moore |
Abstract |
Whether your interests lie in scientific arenas, the corporate world, or in government, you have certainly heard the praises of big data: Big data will give you new insights, allow you to become more efficient, and/or will solve your problems. While big data has had some outstanding successes, many are now beginning to see that it is not the Silver Bullet that it has been touted to be. Here our main concern is the overall impact of big data; the current manifestation of big data is constructing a Maginot Line in science in the 21st century. Big data is not "lots of data" as a phenomena anymore; The big data paradigm is putting the spirit of the Maginot Line into lots of data. Big data overall is disconnecting researchers and science challenges. We propose No-Boundary Thinking (NBT), applying no-boundary thinking in problem defining to address science challenges. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 36% |
United Kingdom | 2 | 9% |
Spain | 1 | 5% |
Italy | 1 | 5% |
Switzerland | 1 | 5% |
Finland | 1 | 5% |
France | 1 | 5% |
Unknown | 7 | 32% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 11 | 50% |
Scientists | 8 | 36% |
Practitioners (doctors, other healthcare professionals) | 3 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Japan | 1 | 1% |
Unknown | 68 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 12 | 17% |
Researcher | 11 | 15% |
Student > Ph. D. Student | 10 | 14% |
Professor | 8 | 11% |
Student > Doctoral Student | 6 | 8% |
Other | 16 | 23% |
Unknown | 8 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 10 | 14% |
Biochemistry, Genetics and Molecular Biology | 8 | 11% |
Agricultural and Biological Sciences | 8 | 11% |
Business, Management and Accounting | 6 | 8% |
Medicine and Dentistry | 6 | 8% |
Other | 21 | 30% |
Unknown | 12 | 17% |