librarianship and help craft a new relationship between the library and scientists” (p. 217). As an example, in health science libraries, the demand for RDM services has been very strong. As indicated by Martin (2013) , “As biomedical science becomes more data-intensive, researchers are faced with a range of data management challenges, problems, and needs. Health sciences librarians are ideal partners for offering scientists at their institutions a range of data management services” (p. 1). Data services provided by health science libraries may vary from creating “a
D. L. Carey, K. Ong, M. E. Morris, J. Crow and K. M. Crossley
Gallo, T. F., Cormack, S. J., Gabbett, T. J., & Lorenzen, C. H. (2016). Pre-training perceived wellness impacts training output in Australian football players. Journal of Sports Sciences, 34(15), 1445-1451.
Gaudino, P., Iaia, F., Strudwick, A., Hawkins, R., Alberti, G., Atkinson, G., & Gregson, W. (2015). Factors Influencing Perceptionof Effort (Session-RPE) During Elite Soccer Training. International Journal of Sports Physiology and Performance, 10(7), 860-864.
Hawkins, D. M. (2004). The problemof
Abernethy, B. (1987). Review: Selective attention in fast ball sports: Expert-novice differences. The Australian Journal of Science and Medicine in Sport, 19 , 7-16.
Abernethy, B. (1990). Expertise, visual search, and information pick-up in squash. Perception, 19 , 63-77.
Abernethy, B. (2001). Attention. In R. N. Singer, H. A. Hausenblas, & C. M. Janelle (Eds.), Handbook of sport psychology (2nd ed., pp. 53-85). New York: Wiley & Sons.
Adolphe, R. M., Vickers, J. N., & Laplante, G. (1997). The effects of training visual
. Int. J. Man. Mach. Stud ., 35(3):363–377, Sept. 1991.
 F. Bowden, D. Lockton, R. Gheerawo, and C. Brass. Drawing energy: Exploring perceptionsof the invisible. 2015.
 C. Bravo-Lillo, L. F. Cranor, J. Downs, and S. Komanduri. Bridging the gap in computer security warnings: A mental model approach. IEEE Secur. Privacy , 9(2):18–26, Mar. 2011.
 J. K. Burgoon, R. Parrott, B. A. Le Poire, D. L. Kelley, J. B. Walther, and D. Perry. Maintaining and restoring privacy through communication in different types of relationships. J. Soc. Pers. Relat
Nathan Malkin, Joe Deatrick, Allen Tong, Primal Wijesekera, Serge Egelman and David Wagner
Conference (EISIC) , pages 172–175. IEEE, 2016.
 Varun Chandrasekaran, Kassem Fawaz, Bilge Mutlu, and Suman Banerjee. Characterizing privacy perceptionsof voice assistants: A technology probe study. arXiv preprint arXiv:1812.00263 , 2018.
 Eugene Cho. Hey Google, Can I Ask You Something in Private? In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems , CHI ’19, pages 258:1–258:9. ACM, 2019.
 Paul Cutsinger. How to Improve Alexa Skill Discovery with Name-Free Interaction and More, September 2018.
 John Travis Butler and Arvin Agah. 2001. Psychological effects of behavior patterns of a mobile personal robot. Autonomous Robots 10, 2 (2001), 185-202.
 Ryan Calo. 2011. The drone as privacy catalyst. Stanford Law Review Online 64 (2011), 29-33.
 Ann Cavoukian. 2012. Privacy and drones: Unmanned aerial vehicles. Information and Privacy Commissioner of Ontario, Canada.
 Reece A Clothier, Dominique A Greer, Duncan G Greer, and Amisha M Mehta. 2015. Risk perception and the public acceptance of drones
Kopo M. Ramokapane, Anthony C. Mazeli and Awais Rashid
. Chin, S. Hanna, D. Song, and D. Wagner. Android permissions demystified. In Proceedings of the 18th ACM conference on Computer and communications security , pages 627–638. ACM, 2011.
 A. P. Felt, S. Egelman, and D. Wagner. I’ve got 99 problems, but vibration ain’t one: a survey of smartphone users’ concerns. In Proceedings of the second ACM workshop on Security and privacy in smartphones and mobile devices , pages 33–44. ACM, 2012.
 A. P. Felt, E. Ha, S. Egelman, A. Haney, E. Chin, and D. Wagner. Android permissions: User attention
Chad Spensky, Jeffrey Stewart, Arkady Yerukhimovich, Richard Shay, Ari Trachtenberg, Rick Housley and Robert K. Cunningham
 I. Leontiadis, C. Efstratiou, M. Picone, and C. Mascolo, “Don’t kill my ads!: balancing privacy in an ad-supported mobile application market,” in MobiSys 2012.
 B. Ur, P. G. Leon, L. F. Cranor, R. Shay, and Y. Wang, “Smart, useful, scary, creepy: perceptionsof online behavioral advertising,” in SOUPS 2012.
 Z. Xu, K. Bai, and S. Zhu, “Taplogger: Inferring user inputs on smartphone touchscreens using on-board motion sensors,” in WiSec 2012.
 L. Cai and H. Chen
Eric Zheng, Yong Tan, Paulo Goes, Ramnath Chellappa, D.J. Wu, Michael Shaw, Olivia Sheng and Alok Gupta
time to find their causal relationships. It is believed that probably, the inner loop of the hierarchical models is machine learning driven, while the outer loop is econometrics driven. In my view, they are just techniques and tools for us to attack all these interesting problems.
Idea 1: Machine learning and econometrics are different in perception and understanding ofproblems
I have used some components of machine learning when I was an engineer. According to my experience and understanding of machine learning and econometrics
physical or chemical reactions. Digitalizing these heritages and constructing digital libraries of the digitized materials are considered to be viable solutions to these problems ( Evens & Hauttekeete, 2011 ; Piccialli & Chianese, 2017 ). Libraries, museums, and research organizations are currently developing collections of digitized heritages Digital Dunhuang is one of the largest digitizing cultural heritage projects in the world, involving scholars from many countries and several disciplines ( Zhou, 2011 ; Wang et al., 2015 ).
Dunhuang is an ancient city in