Environmental Data Science

Managing Data in Digital Marketing

“…Sundaresan (2017) in his article on the history of data science, points to Bill Cleveland in the International Statistical Review, as an important turning point, calling for the establishment of data science as an area that covers but extends the scope of the area of statistics. After more than 50 years from the beginning of the discussion on the possibility of self-determination in the field of science, the votes are divided (Ceri, 2018;Gibert et al, 2018;Olhede & Wolfe, 2018;Reid, 2018;Shi, 2018;Smirnova et al, 2018;Zhu & Xiong, 2015). On the pages of the Data Science Journal Zhu and Xiong (2015) state "A new discipline called Data Science is coming.…”

“…Data Science is characterized by a new approach to data and their understanding. In 1997, Turkey pointed to a new approach to statistics, where more emphasis was put on using data as a source of hypothesis for testing (Gibert et al, 2018). Agarwal and Dhar (2014) suggest that the current development of research tools with unprecedented access to diverse data and taking into account their numbers, promotes the creation of opportunities in which computers are sufficient not only to test hypotheses but also to suggest theories.…”

Assessing the state of research data publication in hydrology: A perspective from the Consortium of Universities for the Advancement of Hydrologic Science, Incorporated

“…The increasing practice of Data Science in environmental applications—i.e., transforming data into understandable and actionable knowledge relevant for informed decision making (Gibert, Horsburgh, Athanasiadis, & Holmes, )—is also influencing hydrology, particularly with the application of machine learning and deep learning techniques to emerging large data sets generated by in situ sensors and by aerial and satellite remote sensing (Shen, ). Advancing and comparing these methods requires the availability of shared example, training, and benchmark datasets, a pattern that has been demonstrated across many domains where Data Science methods are employed (e.g., Deng et al, ; Wu et al, ).…”

Environmental timeseries lifecycle in the Internet of Things era : Lowering e-science barriers

“…Knowledge is produced when raw data are processed through scientific tools, as environmental models. Domain scientists curate relevant datasets, feed them as input to scientific tools, and interpret tool outputs into actionable knowledge (Gibert et al, 2018;Athanasiadis & Mitkas, 2007). Nowadays, e-scientists and environmental practitioners are confronted with the ever-growing amount of environmental datasets, and new data produced by the Internet of Things (IoT).…”

“…The required technological skills is a barrier which hinders e-scientists to acquire and integrate heterogeneous environmental datasets in order to analyze them. Nowadays, an environmental e-scientist besides their domain expertise should have certain computer science skills in order to process, analyze and transform raw data into actionable knowledge (Gibert et al, 2018). In the environmental science literature, there is a number of efforts which utilize computer science skills in order to acquire and transform syntactically and semantically diverse environmental timeseries (Woodard, 2016;Porter et al, 2014;Stadtmüller et al, 2013;Harth et al, 2013).…”