Repositioning Data Management Near Data Acquisition

Repositioning Data Management Near Data Acquisition

Paolo Diviacco (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Italy), Jordi Sorribas (CSIC, Spain), Karien De Cauwer (Royal Belgian Institute of Natural Sciences, Belgium), Jean Marc Sinquin (Ifremer, France), Raquel Casas (CSIC, Spain), Alessandro Busato (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale - OGS, Italy), Yvan Stoyanov (Royal Belgian Institute of Natural Sciences, Belgium) and Serge Scory (Royal Belgian Institute of Natural Sciences, Belgium)
DOI: 10.4018/978-1-5225-0700-0.ch008
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Abstract

This chapter intends to propose a solution to the progressive paradigmatic drift that emerges when data is part of a workflow where processes and contexts are not accessible. In this case end users can have difficulties in identifying possible anomalies, or events that might be very important for the dataset and its interpretation. Since sustainable development is based on cross-disciplinary studies, this can revert in misunderstanding and difficulties to work collaboratively. To address this issue it is proposed to fill the gap between the data and its acquisition through a logging system named EARS that records underway data such as for example: meteo or swell, and events such as: anomalies or acquisition milestones. Once all these information are stored, they can be linked to the data through an OGC compliant metadata model and gathered as summaries as required by several data management initiatives.
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Background

Science serves to ground all activities that shape the study of sustainable development, and therefore science foundations are overwhelmingly important and condition all aspects of what is built on top of them. Any shortcoming in the foundations of Science can, therefore, potentially, undermine how sustainable development is addressed.

The main problem that can be envisioned is that there is a gap between what is needed by stakeholders, such as: directives, norms, decisions, protocols on one hand and, on the other, the underlying scientific theories. In the first case facts and objective positions are requested, while if we climb backward to the scientific research that is supposed to produce them we realize that things become complicated.

Objectivity is a complex concept to handle in itself, but in Science this complexity becomes even more difficult to deal with. A detailed review of the role of objectivity in Science and in particular in environmental sciences, can be found in Diviacco (2015), Diviacco (2015b) and Diviacco (2012). Here it is worth mentioning the main idea reported there, which is that scientific objectivity can be explained only as something built from the judgment of a group of sane, rational and sufficiently informed people. It is not based on an unbiased analysis of external facts; rather, it is something related to the convergence of a community. The subjective is private and the objective is public. This means that any position, idea, concept or representation has to be negotiated with other people who take part in the activity.

This perspective is evidently contrary to the popular viewpoint where Science grants facts that cannot be biased by human intervention.

We will not explore here the issue of how many other forms or definitions of objectivity are possible. We will instead highlight that the main hinge on which such distinction can be made is the role of the “experience”, meaning the observation of events. Where experience is necessary to understand a process or a system, since each one of us can have a different experience, the public aspect of objectivity is raised.

Scientific research is of course based on observations and measurements, but while the standard view of the scientific method prescribes in the deductive/inductive loop a verification phase wherein an experiment could verify (or following Popper (2004) only falsify) a theory upon the result of the experiment, many recent thinkers have criticized this approach.

Following Hanson (1958, p19), for example, Observation of x is shaped by prior knowledge of x. Experiments themselves are devised by researchers upon a pre-existent set of theories and assumptions and that can condition them. This means that researchers cannot avoid projecting their backgrounds and way of thinking (in a word their paradigms) even on their very basic activities of observation.

This process applies at all levels and is called bias.

A wider view would see this phenomenon in a cognitive perspective, where researchers are able to understand concepts only if they match and fit their cognitive models. Studies on cognitive aspects of scientific research can be found in Diviacco (2015).

This raises the problem that since each scientific field, if not each school of thought or institute, tends to develop its own specific cognitive model, concepts derived within one field cannot be easily understood by researchers from another.

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