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A Data Science Course for Undergraduates: Thinking with Data [PDF]

, 2015
Data science is an emerging interdisciplinary field that combines elements of mathematics, statistics, computer science, and knowledge in a particular application domain for the purpose of extracting meaningful information from the increasingly ...
Baumer, Ben
core   +3 more sources

Greater data science at baccalaureate institutions [PDF]

, 2017
Donoho's JCGS (in press) paper is a spirited call to action for statisticians, who he points out are losing ground in the field of data science by refusing to accept that data science is its own domain. (Or, at least, a domain that is becoming distinctly
Baumer, Benjamin S., Horton, Nicholas J., McNamara, Amelia   +2 more
core   +3 more sources

Population Data Science: The science of data about people [PDF]

International Journal of Population Data Science, 2018
IntroductionSocietal and individual benefits of data-intensive science are substantial but raise challenges of balancing individual privacy and public good, while building appropriate governance and socio-technical systems to support data-intensive science.
Kim McGrail, Kerina H. Jones
openaire   +4 more sources

Big-Data-Driven Materials Science and its FAIR Data Infrastructure [PDF]

, 2019
This chapter addresses the forth paradigm of materials research -- big-data driven materials science. Its concepts and state-of-the-art are described, and its challenges and chances are discussed.
A Agrawal, A Gulans, A Jain, A Jain, A Rahman, A Seko, A Siebes, A Ziletti, AH Larsen, AK Singh, AP Bartók, AP Bartók, BJ Alder, BJ Alder, BJ Alder, BR Goldsmith, C Draxl, C Draxl, CE Calderon, Claudia Draxl, D Lazer, DL Donoho, EJ Candro, EJ Candès, EJ Candès, ER Scerri, F Faber, F Herrera, G Kresse, GE Hinton, GE Hinton, IJ Nelson, IY Zhang, J Enkovaara, J Pearl, J Pizzi, J Saal, JC Slater, JC Slater, JC Slater, JC Slater, JC Slater, JH Friedman, K Hansen, K Hansen, K Lejaeghere, K Reuter, KF Garrity, KT Schütt, L Hedin, LM Ghiringhelli, LM Ghiringhelli, LM Ghiringhelli, M Atzmueller, M Rupp, MD Wilkinson, MJ Setten van, MT Yin, N Metropolis, Nature editorial, NV Chawla, P Blaha, P Hohenberg, P Pyykkö, R Ouyang, R Tibshirani, S Curtarolo, S Lorenz, S Lorenz, TB Blank, V Blum, VL Moruzzi, W Duivesteijn, W Kohn, Y Zhang   +74 more
core   +3 more sources

Data-driven predictions in the science of science

Science, 2017
The desire to predict discoveries—to have some idea, in advance, of what will be discovered, by whom, when, and where—pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the “science of science” and what ...
Aaron Clauset, Aaron Clauset, Roberta Sinatra, Roberta Sinatra, Daniel B. Larremore   +4 more
openaire   +4 more sources

Prospecting (in) the data sciences [PDF]

Big Data & Society, 2020
Data science is characterized by engaging heterogeneous data to tackle real world questions and problems. But data science has no data of its own and must seek it within real world domains. We call this search for data “prospecting” and argue that the dynamics of prospecting are pervasive in, even characteristic of, data science.
Stephen C Slota, Andrew S Hoffman, David Ribes, Geoffrey C Bowker   +3 more
openaire   +4 more sources

Effects of chronic browsing on life‐history traits of an irruptive large herbivore population

Population Ecology, EarlyView.
This study aimed to determine the relationship between diet quality, body mass, and size (hind foot length), and female reproduction and sought to identify the mechanism by which high density under severe food limitations is maintained. Our results demonstrated that sika deer introduced to Nakanoshima Island have maintained high densities through high ...
Koichi Kaji, Kazutaka M. Takeshita, Hiroshi Takahashi, Hiromasa Igota, Mayumi Ueno, Yukiko Matsuura, Takashi Ikeda, Ryo Yoshizawa, Hino Takafumi, Tsuyoshi Yoshida   +9 more
wiley   +1 more source

A Science Cloud for Data Intensive Sciences

Data Science Journal, 2013
It is often discussed that the fourth methodology for science research is "informatics". The first methodology is a theoretic approach, the second one is observation and/or experiment, and the third one is computer simulation. Informatics is a new methodology for data intensive science, which is a new concept based on the fact that most scientific data
Ken T. Murata, Yasuhiro Murayama, Kazunori Yamamoto, Hidenobu Watanabe, Keiichiro Fukazawa, Shinichi Watari, Kazuya Muranaga, Osamu Tatebe, Yasufumi Kubota, Manabu Kunitake, Tsutomu Nagatsuma, Takuya Tsugawa, Kentaro Ukawa, Eizen Kimura, Hisao Kato   +14 more
openaire   +4 more sources

Dendritic cells steering antigen and leukocyte traffic in lymph nodes

FEBS Letters, EarlyView.
Dendritic cells are key players in the activation of T cells and their commitment to effector function. In this In a Nutshell Review, we will discuss how dendritic cells guide the trafficking of antigen and leukocytes in the lymph node, thus influencing T‐cell activation processes. Dendritic cells (DCs) play a central role in initiating and shaping the
Enrico Dotta, Agnieszka Katarzyna Maciola, Tania Baccega, Giulia Pasqual   +3 more
wiley   +1 more source

Mechanism Design for Data Science

, 2014
Good economic mechanisms depend on the preferences of participants in the mechanism. For example, the revenue-optimal auction for selling an item is parameterized by a reserve price, and the appropriate reserve price depends on how much the bidders are ...
Chawla, Shuchi, Hartline, Jason, Nekipelov, Denis   +2 more
core   +1 more source