373
© Springer Nature Switzerland AG 2020
P. Saundry, B. L. Ruddell (eds.), The Food-Energy-Water Nexus, AESS
Interdisciplinary Environmental Studies and Sciences Series,
https://doi.org/10.1007/978-3-030-29914-9_14
Chapter 14
Data
Benjamin L. Ruddell
14.1 Introduction: Framing Data Between Metrics
and Models
Data provides the practical means by which we conduct scientific investigation of
metrics (Chap. 13) that facilitate informed operational decision-making, and of the
construction and parameterization of predictive models (Chap. 15). Especially
today in the computer age where computing power is abundant, adequate data now
tends to be the limiting factor on the quality of our estimation, modeling, understanding, decision-making, and prediction. Fortunately, there is a lot of data available about FEW systems. But, this data is not easy to locate, access, or utilize. This
data is often privacy-restricted and privileged. This data is patchy with surprisingly
large gaps for critical layers and scales of FEW systems. The lack of seamless, highquality, synthetic datasets describing FEW systems across sectors and scales is one
of the major practical barriers to FEW systems work at the present time. Most data
sets were collected to answer a single question about a single layer, process, and
scale in the FEW system, but systems science and systems management requires
data that interoperates across layers and scales.
In the abstract sense data are simply facts and assumptions, and in the digital era,
these facts and assumptions are usually (but not always) quantitative and numeric in
nature. Data may be observations gathered from the real world, in which case the
data are empirical. However, empirical observations still imply both a conceptual
model (a question) and an observational model (an apparatus). Data are increasingly, these days, the outputs of complicated computer models; these model output
data are predictions (or post-dictions) that assimilate empirical observations and
B. L. Ruddell (*)
School of Informatics, Computing, and Cyber Systems, Northern Arizona University,
Flagstaff, AZ, USA
e-mail: Benjamin.Ruddell@nau.edu
© Springer Nature Switzerland AG 2020
P. Saundry, B. L. Ruddell (eds.), The Food-Energy-Water Nexus, AESS
Interdisciplinary Environmental Studies and Sciences Series,
https://doi.org/10.1007/978-3-030-29914-9_14
Chapter 14
Data
Benjamin L. Ruddell
14.1 Introduction: Framing Data Between Metrics
and Models
Data provides the practical means by which we conduct scientific investigation of
metrics (Chap. 13) that facilitate informed operational decision-making, and of the
construction and parameterization of predictive models (Chap. 15). Especially
today in the computer age where computing power is abundant, adequate data now
tends to be the limiting factor on the quality of our estimation, modeling, understanding, decision-making, and prediction. Fortunately, there is a lot of data available about FEW systems. But, this data is not easy to locate, access, or utilize. This
data is often privacy-restricted and privileged. This data is patchy with surprisingly
large gaps for critical layers and scales of FEW systems. The lack of seamless, highquality, synthetic datasets describing FEW systems across sectors and scales is one
of the major practical barriers to FEW systems work at the present time. Most data
sets were collected to answer a single question about a single layer, process, and
scale in the FEW system, but systems science and systems management requires
data that interoperates across layers and scales.
In the abstract sense data are simply facts and assumptions, and in the digital era,
these facts and assumptions are usually (but not always) quantitative and numeric in
nature. Data may be observations gathered from the real world, in which case the
data are empirical. However, empirical observations still imply both a conceptual
model (a question) and an observational model (an apparatus). Data are increasingly, these days, the outputs of complicated computer models; these model output
data are predictions (or post-dictions) that assimilate empirical observations and
B. L. Ruddell (*)
School of Informatics, Computing, and Cyber Systems, Northern Arizona University,
Flagstaff, AZ, USA
e-mail: Benjamin.Ruddell@nau.edu