IEEE Geoscience and Remote Sensing Magazine - June 2015 - 53

the process of knowledge transfer, reduce redundancy
of effort amongst researchers, and improve productivity. One technological challenge is to develop seamless
collaboration solutions fostering knowledge exchange.
These solutions should support sharing of all scientific
resources amongst individual researchers within both a
team of researchers and the larger scientific community.
These solutions should be compatible with researchers'
existing scientific analysis workflow rather than expecting the researchers to learn and use a new tool or site.
Adopting The Next New Technology - Every few years, a
new technology enters the so-called Gartner's hype cycle
[4]. This technology generates enormous interest within
both the science and informatics community, and promises to radically improve processes within the knowledge
generation life cycle. An example is the "Big Data" technologies supporting scalable server side analysis/querying on vast amounts of data and potentially removing the
need for researchers to create local data copies. Big data
technologies leverage the wide adoption of "shared nothing" architecture, distributed file storage systems and
have industry supported open source middleware to support reliable (fault tolerant) processing. The challenge for
the Earth Science research community is to evaluate these
new technologies by asking the right questions. What is
this new technology enabling/providing that is new and
different? Can one justify the adoption costs with respect
to the research returns? Nothing comes for free - utilizing a new technology entails adoption costs that may
outweigh the benefits. Some technologies may require
formulating new funding models, especially if there are
substantial operational costs associated with providing
this new functionality.
IV. END USER PERSPECTIVE
Changing End User expectations - Google, social networking
sites, and mobile apps have all changed the user's expectations regarding the ease of use of research tools. For example, one of the most common complaints against existing
data search tools is that these tools need to be more like
Google. Users now expect disparate data sets to seamlessly
integrate together to support their research objectives. Both
the process of discovery, access, and exploration, and moving between these phases is expected to be easy. In addition, tools for data analysis and visualization need to be
simpler and more intuitive to enable ready adoption by the
user community.
Growing Interdisciplinary Research Needs - As more and
more research crosses disciplinary boundaries, there is a
greater need for research infrastructure to support crossdiscipline data discovery, access, integration and analysis. Most interdisciplinary users are not proficient in the
"vocabulary" used within other disciplines, and thus
require data systems to provide "cross-walks" between
vocabularies to enable discovery of data across domains.
In addition, rich information models that capture syntacjune 2015

ieee Geoscience and remote sensing magazine

tic, content, semantic, quality and provenance elements
are required for interdisciplinary users to support the use
of these datasets in their research.
Verticalization of Tools/User Experience - Verticalization
refers to the customization of a tool [7] based on specific
science use or domain application. Different domains/
sub-domains within science
ThE kNOwLEDgE
have specific needs and consequently most tools require
gENERaTION LIfE CYCLE IS
customizations of both user
hIghLY DYNamIC, DRIVEN
interface and functionality.
bY NUmEROUS faCTORS
The process of "verticalizawhICh CaN bE
tion" forces integration of
CaTEgORIzED baSED ON
domain information and
fOUR PERSPECTIVES: DaTa,
needs into the tool design,
TEChNOLOgY, END USER
a nd he lps i n prov id i ng
aND POLICY.
an intuitive user experience
of data discovery, access,
and analysis.
V. POLICY PERSPECTIVE
Reproducibility and Preservation - New requirements mandated by different agencies can influence changes on the
knowledge generation life cycle. New requirements such
as ensuring there is no/minimal information loss as data
bits move across systems as well as over time, readability
of the datasets over time, long-term understandability,
and finally repeatability of previously obtained results are
some current changes. The role of provenance research is
integral for enabling these capabilities. While it is easy to
design new data systems where both provenance capture
and production are integral components, the challenge
is how to instrument provenance capture into the legacy
systems without impacting these existing systems. In addition, while provenance can be a central thread for both
reproducibility and contextual understanding, ensuring
full reproducibility entails capturing upstream data production algorithms as well downstream analysis workflows along with all the critical science artifacts.
Archiving Long Tail of Research - Agencies funding
research are now requiring individual researchers to have
a Data Management Plan. This plan must address issues
such as a strategy for long term archival, preservation and
sharing of data collected and created during a lifetime of
a project. However, new infrastructures with adequate
data stewardship services are required to support these
individual data producers. Who runs these infrastructures
and who pays for this functionality? Can one trust that the
organization providing the data stewardship will not use
the data inappropriately? Will the organization providing
the infrastructure have financial support for long term
preservation and archiving? The Earth Science research
community is struggling to find right answers to these
questions. Other issues such as requiring the data producers to provide rich metadata is still a challenge. Experience
53

Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - June 2015