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Chinese Journal of Library and Information Science Vol. 5 No. 3, 2012
14
Research Paper
National Science Library,
Chinese Academy of
Sciences
occur “by themselves”, scientists collect and handle huge amounts of data within
the framework of highly visible international projects. Examples are CERN’s Large
Hadron Collider (LHC), the Human Genome Project, ENCODE (a consortium and
project that has built an encyclopedia of functional DNA elements)
[3,4]
, the Interna-
tional HapMap Project, the 1,000 Genomes Project, the Human Variome Project,
the Sloan Digital Sky Survey, etc., with many big projects collecting and re-
analyzing climate data such as the 20
th
Century Reanalysis Project, and many more.
Yet, already more than 13 years ago a warning about the coming tidal wave of data
was published in Nature
[5]
, while the main theme of Nature (2008, 455: 729, pub-
lished in September 2008) was “science in the petabyte era”. Hilbert and López
discussed the enormous growth in information content made possible by the use of
digital data
[6]
.
Big data is not only an issue for scientists, but even more so for companies. They
all collect data such as shopping data and try to make good use of them
[7]
. As this
contribution is mainly aimed at scientists we just mention one example: TomTom,
a Dutch firm of personal navigation systems, collected real travel times in Europe
and North America and from these massive amounts of data constructed the TomTom
Congestion Index (TTCI). This index compares travel times during non-congested
periods (free flow) with travel times during peak hours
[8,9]
. The difference is expressed
as a percentage increase in travel time. All types of roads are taken into account
(local roads, arterials and highways) and raw data are actual global positioning
system (GPS) based measurements. The most congested cities in Europe and North
America are shown in Tables 1–2.
Table 1 Most congested European cities according to the TTCI – 2012
[8]
Rank City Country TTCI (%) Morning peak (%) Evening peak (%)
1 Warsaw Poland 42 89 86
2 Marseille France 41 79 81
3 Rome Italy 34 76 66
4 Brussels Belgium 34 82 86
5 Paris France 32 72 63
6 Dublin Ireland 30 70 62
7 Bradford-Leeds UK 28 63 60
8 London UK 27 48 48
9 Stockholm Sweden 27 65 62
10 Hamburg Germany 27 49 42
As big data are often produced by government institutes or projects supported by
governments, governments, e.g. the Obama administration, also support big data
studies
[10]
.