Hey T. The fourth paradigm: Data- intensive scientific discovery[M]. Redmond, WA: Microsoft Research, 2009: 109-130.
[2]
维克托·迈尔-舍恩伯格, 肯尼思·库克耶. 大数据时代:生活、工作与思维的大变革[M]. 盛杨燕, 周涛, 译. 杭州: 浙江人民出版社, 2013: 2-18. Mayer- Sch？nberger V, Cukier K. Big data: A revolution that will transform how we live, work and think[M]. Sheng Yangyan, Zhou Tao, trans. Hangzhou: Zhejiang People's Publishing House, 2013: 2-18.
[3]
Xiang X D, Sun X, Briceno G, et al. A combinatorial approach to materials discovery[J]. Science, 1995, 268(5218): 1738-1740.
[4]
Liu Z K, Chen L Q, Raghavan P, et al. An integrated framework for multi- scale materials simulation and design[J]. Journal of Computer- Aided Materials Design, 2004, 11(2-3): 183-199.
[5]
Olsen G B. Pathways of discovery designing a new material world[J]. Science, 2000, 228(12): 933-998.
[6]
Yang Y, Lin T, Weng X L, et al. Data flow modeling, data mining and QSAR in high- throughput discovery of functional nanomaterials[J]. Computers & Chemical Engineering, 2011, 35(4): 671-678.
[7]
王盼盼, 杨迪. 阿里巴巴拟以大数据推动智能制造[EB/OL]. 2014-11- 01. http://news.xinhuanet.com/fortune/2014-11/01/c_1113074082.htm. Wang Panan, Yang Di. Alibaba intends to promote smart manufacturing with big data [EB/OL]. 2014-11-01. http://news.xinhuanet.com/fortune/ 2014-11/01/c_1113074082.htm.
[8]
师昌绪. 材料大辞典[M]. 北京: 化学工业出版社, 1994. Shi Changxu. Materials comprehensive dictionary[M]. Beijing: Chemical Industry Press, 1994.
[9]
36大数据. 中国大数据行业面临的五大挑战以及应对策略[EB/OL]. 2014.11.21. http://www.36dsj.com/archives/17137. 36 big data. The strategies for the five challenges the big data industry are facing[EB/OL]. 2014.11.21. http://www.36dsj.com/archives/17137.
[10]
Kietzmann J, Pitt L, Berthon P. Disruptions, decisions, and destinations: Enter the age of 3-D printing and additive manufacturing[J]. Business Horizons, 2014. http://dx.doi.org/10.1016/j.bushor.2014.11.005.
[11]
Chou D T, Wells D, Hong D, et al. Novel processing of iron-manganese alloy-based biomaterials by inkjet 3-D printing[J]. Acta Biomaterialia, 2013, 9(10): 8593-8603.
[12]
胡良霖. 科学数据资源的质量控制和评估[J]. E-Science, 2009(1): 50-55. Hu Lianglin. The quality control and evaluation of scientific data resources[J]. E-Science, 2009(1): 50-55.
[13]
Sargent P. Data quality in materials information systems[J]. Computer- Aided Design, 1992, 24(9): 477-490.
[14]
Bhagwat S A, Patterson K Y, Holden J M. Validation study of the USDA's data quality evaluation system[J]. Journal of Food Composition and Analysis, 2009, 22(5): 366-372.
[15]
罗芳. 土地利用数据综合结果的质量评价[D]. 武汉: 武汉大学, 2013. Luo Fang. Quality evaluation for land use data generalization[D]. Wuhan: Wuhan University, 2013.
[16]
苗海芳. 多元交通信息数据质量评价和控制方法研究[D]. 长春: 吉林大学, 2014. Miao Haifang. Research on evaluation and control methods on multisource traffic information data[D]. Changchun: Jilin University, 2014.
[17]
Curtarolo S, Morgan D, Persson K, et al. Predicting crystal structures with data mining of quantum calculations[J]. Physical Review Letters, 2003, 91(13): 135503.
[18]
黎建辉. 科学数据共享的新机制与新趋势[C]//第507次香山会议. 北京, 2014-10-22. Li Jianhui. New mechanism and trend of scientific data sharing[C]// No. 507 Xiangshang Science Conference, Beijing, 2014-10-22.
[19]
Morgan D, Ceder G, Curtarolo S. High-throughput and data mining with ab initio methods[J]. Measurement Science and Technology, 2005, 16(1): 296.
National Research Council (US). Committee on Integrated Computational Materials Engineering. Integrated computational materials engineering: A transformational discipline for improved competitiveness and national security[M]. Washington, DC: National Academies Press, 2008.
[22]
Robinson L. New TMS study tackles the challenge of integrating materials simulations across length scales[J]. JOM, 2014, 66(8): 1356- 1359.
[23]
Sally T, David L M, Amanda B, et al. Sharing data in materials science[J]. Nature, 2013, 503: 463-464.
