projects within Sierra Nevada (Fig. 16.4). Preliminary results have shown a spatial
pattern with a high density of research activities at the western side of the mountain
range (Fig. 16.4b). In particular, the areas with high densities of research projects
correspond to high-mountain terrestrial and aquatic ecosystems, mountain rivers,
and forests and shrublands. We also identified a lack of research activities in the
central region of the range. The distribution of the research projects shows that the
most arid ecosystems of Sierra Nevada (located on the eastern side) have scarcely
been studied (Fig. 16.4c).
16.7 Data Management
An increasing demand for more detailed, high-quality data and information about
natural resources and ecosystems functions requires trained personnel (resource
specialist, data manager, researchers) working in collaboration to steward data and
information assets (Cook and Lineback 2008). Information systems are fundamental for managing a large amount of data (Rüegg et al. 2014). Here, we distinguish two types of information: first, raw data collected directly in the field by
scientific methods; second, structured information found in scientific papers and
reports, slide presentations, videos, etc.
Raw data collected from the field is stored in relational spatial databases. To store
processed information, we use relational databases that allow the fuzzy classification
of the information into categories using the facilities of the web 2.0. For large
amounts of information, it becomes critical to have a catalogue that highlights the
main features of this information. This data about data is known as metadata or
documentation. Metadata should answer prime questions about data, such as who
created them, where they were collected, what method was used, and what organizational principle was used (Michener 2006). Metadata are very useful to share
information between different information systems (Schildhauer et al. 2001).
Once an information system is established for the storage of documented data, the
next step is to process and analyse raw data to provide information. From a functional and structural standpoint, an information system must be able to document and
execute the algorithms that we use to process the data. Ideally, these tasks are run
automatically using a scientific workflow software (Barseghian et al. 2010;
McPhillips et al. 2009; Bonet et al. 2014). These tools allow the creation and
execution of complex workflows by linking several computational steps (algorithms)
in order to produce a given final product. Scientific workflow software can be used to
run a spatial distribution model or even a simple query to a relational database. The
results are also documented using the same standards described above.
We have developed an information system for the Sierra Nevada Global Change
Observatory (Fig. 16.5). This system, called Linaria (https://linaria.obsnev.es—free
access upon registration), acts as a repository storing raw data gathered by the
monitoring programme as well as information generated through the processing of
16 Monitoring Global Change in High Mountains
395
pattern with a high density of research activities at the western side of the mountain
range (Fig. 16.4b). In particular, the areas with high densities of research projects
correspond to high-mountain terrestrial and aquatic ecosystems, mountain rivers,
and forests and shrublands. We also identified a lack of research activities in the
central region of the range. The distribution of the research projects shows that the
most arid ecosystems of Sierra Nevada (located on the eastern side) have scarcely
been studied (Fig. 16.4c).
16.7 Data Management
An increasing demand for more detailed, high-quality data and information about
natural resources and ecosystems functions requires trained personnel (resource
specialist, data manager, researchers) working in collaboration to steward data and
information assets (Cook and Lineback 2008). Information systems are fundamental for managing a large amount of data (Rüegg et al. 2014). Here, we distinguish two types of information: first, raw data collected directly in the field by
scientific methods; second, structured information found in scientific papers and
reports, slide presentations, videos, etc.
Raw data collected from the field is stored in relational spatial databases. To store
processed information, we use relational databases that allow the fuzzy classification
of the information into categories using the facilities of the web 2.0. For large
amounts of information, it becomes critical to have a catalogue that highlights the
main features of this information. This data about data is known as metadata or
documentation. Metadata should answer prime questions about data, such as who
created them, where they were collected, what method was used, and what organizational principle was used (Michener 2006). Metadata are very useful to share
information between different information systems (Schildhauer et al. 2001).
Once an information system is established for the storage of documented data, the
next step is to process and analyse raw data to provide information. From a functional and structural standpoint, an information system must be able to document and
execute the algorithms that we use to process the data. Ideally, these tasks are run
automatically using a scientific workflow software (Barseghian et al. 2010;
McPhillips et al. 2009; Bonet et al. 2014). These tools allow the creation and
execution of complex workflows by linking several computational steps (algorithms)
in order to produce a given final product. Scientific workflow software can be used to
run a spatial distribution model or even a simple query to a relational database. The
results are also documented using the same standards described above.
We have developed an information system for the Sierra Nevada Global Change
Observatory (Fig. 16.5). This system, called Linaria (https://linaria.obsnev.es—free
access upon registration), acts as a repository storing raw data gathered by the
monitoring programme as well as information generated through the processing of
16 Monitoring Global Change in High Mountains
395