EN100-web

Special theme: Scientific Data Sharing and Re-use
Metadata Assisted Canonical Brokering:
The next step has canonical metadata
outside the conversion software with
which the metadata of any dataset of
interest is matched/mapped. Again, the
kinds of tools mentioned above are used.
This reduces the n*(n-1) problem to n.
However, a canonical metadata scheme
for all datasets is a huge superset.
Restricting the canonical metadata
(based on commonality among
entities/attributes) to that required for
contextualisation (of which discovery is
a subset) maintains the benefits of the
reduction from n*(n-1) to n for much of
the processing and only when connecting
data to software with detailed,
domain-specific schemas is it necessary
to write or generate specific software.
Metadata Assisted Canonical Brokering
at Domain Level: The above technique
for contextual metadata may be applied
at detailed level restricted to a domain.
This is not applicable for multidisciplinary
interoperation. In any particular
domain there is considerable commonality
among entities/attributes e.g. in
environmental sciences many datasets
have geospatial and temporal coordinates
and time-series data have similar
attributes (e.g. temperature and salinity
from ocean sensors).
The advantage of Metadata
The use of metadata and associated
matching/mapping in brokering either
provides a specification for the programmer
writing or generates or partially
generates the mediating software.
Data interoperability – researched intensively
since the 1970s – remains without
automated processes. Partial automation,
based on contextual metadata has
reduced considerably the cost and time
required to provide interoperability.
Metadata Assisted Brokering in RDA
Metadata is omnipresent in RDA activities;
four groups specialise in metadata
as mentioned in an accompanying
article. These groups are now working
closely with the Brokering Governance
WG (https://rd-alliance.org/groups/brokering-governance.html)
to promote
techniques for dataset interoperation.
Link:
https://rd-alliance.org/groups/
brokering-governance.html
References:
[1] S. Nativi, M. Craglia, J. Pearlman:
“Earth Science Infrastructures
Interoperability: The Brokering
Approach”, IEEE JSTARS, Vol. 6 N.
3, pp. 1118-1129, 2013.
[2] M. J. Franklin, A. Y. Halevy, D.
Maier: “A first tutorial on dataspaces”,
PVLDB 1(2): 1516-1517 (2008).
http://www.vldb.org/pvldb/1/1454217.pdf
[3] K. Skoupy, J. Kohoutkova, M.
Benesovsky, K.G. Jeffery:
“‘Hypermedata Approach: A Way to
Systems Integration”, in proc. of
ADBIS’99, Maribor, Slovenia, 1999,
ISBN 86-435-0285-5, pp 9-15.
Please contact:
Stefano Nativi
CNR, Italy
E-mail: stefano.nativi@cnr.it
Asking the Right Questions - Query-Based Data
Citation to Precisely Identify Subsets of Data
by Stefan Pröll and Andreas Rauber
Data underpins most scientific endeavours. However, the question of how to enable scalable and
precise citation of arbitrary subsets of static and specifically dynamic data still constitutes a nontrivial
challenge.
Although data has been the source of
new knowledge for centuries, it has
never received the same attention as the
publications about the derived discoveries.
Only recently has it been recognized
as a first-class citizen in science,
earning equal merit (see Link JDDCP
below). Beyond mere referencing for the
purpose of acknowledging the creators,
it is the underlying basis and evidence
for many scientific discoveries. With the
increasing focus on repeatability and
verifyability in the experimental sciences,
providing access to the underlying
data is becoming essential.
Data used to be condensed into human
readable form, by aggregating source
data into tables and graphs.
Alternatively, the specific subset and
version of data used in a study was
deposited in a repository for later reuse.
With the arrival of data driven science
[1], the increasing amount of data
processed and the increasing dynamics
of data, these conventional approaches
are no longer scalable.
Research datasets can be huge in terms
of contained records. Scientists are
often interested in a particular view of
their data, using subsets tailored to a
specific research question. An experiment
can only be reproduced and verified
if the same subset can be retrieved
later. Depositing the specific subset in a
repository does not scale to big data settings.
Also providing the metadata
helping users to find, interpet and
access specific datasets again can be a
challenge. Textual descriptions of subsets
are hardly precise enough, require
human intervention and interpretation
of ambiguous descriptions in re-creating
the dataset, limiting reproducibility
of experiments and re-use in
meta-studies.
Furthermore, many research datasets
are highly dynamic: new data is added
continuously via data stream, rendering
conventional versioning approaches
useless unless one wants to revert to
old-fashioned time-delayed batch
releases of annual/quarterly versions of
the data. Additional dynamics arise
from the need to correct errors in the
data, removing erroneous data values,
or re-calibrating and thus re-computing
values at later points in time. Thus,
researchers require a mechanism to
retrieve a specific state of the data
again, in order to compare the results of
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ERCIM NEWS 100 January 2015