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<article article-type="research-article" dtd-version="1.0" xml:lang="en"
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<front>
<journal-meta>
<journal-id journal-id-type="issn">1683-1470</journal-id>
<journal-title-group>
<journal-title>Data Science Journal</journal-title>
</journal-title-group>
<issn pub-type="epub">1683-1470</issn>
<publisher>
<publisher-name>Ubiquity Press</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.5334/dsj-2015-001</article-id>
<article-categories>
<subj-group>
<subject>Editorial</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Big Research Data and Data Science</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Jianhui</given-names>
</name>
<xref ref-type="aff" rid="aff-1"/>
</contrib>
</contrib-group>
<aff id="aff-1">Secretary-General, CODATA-China, China, Director, Scientific Data Center, Computer
Network information Center, Chinese Academy of Sciences, China</aff>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2015-05-22">
<day>22</day>
<month>05</month>
<year>2015</year>
</pub-date>
<volume>14</volume>
<elocation-id>1</elocation-id>
<permissions>
<copyright-statement>Copyright: &#x00A9; 2015 The Author(s)</copyright-statement>
<copyright-year>2015</copyright-year>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/3.0/">
<license-p>This is an open-access article distributed under the terms of the Creative Commons
Attribution 3.0 Unported License (CC-BY 3.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and source are credited. See <uri
xlink:href="http://creativecommons.org/licenses/by/3.0/"
>http://creativecommons.org/licenses/by/3.0/</uri>.</license-p>
</license>
</permissions>
<self-uri xlink:href="http://www.datascience.codata.org/article/view/dsj-2015-001/"/>
</article-meta>
</front>
<body>
<p>CODATA China was very pleased to organize the 1st Scientific Data Conference &#8211; Scientific
Research Big Data and Data Science, which was held in Beijing, China on 24&#8211;25 February 2014.
Participants included more than 400 domestic experts, scholars and students from universities,
research institutions and industry.</p>
<fig id="F1">
<label/>
<caption>
<p>Group Photo of 1st Scientific Data Conference.</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="figures/Fig01_web.jpg"/>
</fig>
<p>The Conference aimed to improve understanding of the central issues in the era of Big Data, to
promote multidisciplinary communication and collaboration, to help the development of young data
scientists, to encourage the revitalization of traditional research approaches and to contribute to
and support the Chinese national strategy to promote innovation. Around the world, there is talk of
a &#8216;data revolution&#8217; &#8211; this conference aimed to place China at the forefront of
this revolution, providing the communication, skills and training to help Chinese scientists seize
the opportunities of Big Data and &#8216;ride the wave&#8217; of increasing data volumes, velocity
and variety. Spanning two days, the conference featured two plenary sessions and fourteen breakout
sessions. There were four major keynotes, three invited reports and three reports on projects and
initiatives. The keynote lectures focused on the hot issues in the Big Data era, including
integration and notation of Big Data, development opportunities and major challenges for science and
technology. The breakout sessions also included technical sessions and open forums, with topics
including:</p>
<list list-type="bullet">
<list-item>
<p>Big data applications in earth and spatial science</p>
</list-item>
<list-item>
<p>Big data analysis and processing technology</p>
</list-item>
<list-item>
<p>Life science and medicine data and</p>
</list-item>
<list-item>
<p>applications</p>
</list-item>
<list-item>
<p>Materials science data and applications</p>
</list-item>
<list-item>
<p>Big data technologies and applications in agriculture and rural informatization</p>
</list-item>
<list-item>
<p>Linked data and information recommendation</p>
</list-item>
<list-item>
<p>Scientific data visualization</p>
</list-item>
<list-item>
<p>Physics and chemistry data and applications</p>
</list-item>
<list-item>
<p>Cloud computing and data discovery</p>
</list-item>
<list-item>
<p>DOI registration and release</p>
</list-item>
<list-item>
<p>Open forum: data science and data scientists</p>
</list-item>
</list>
<fig id="F2">
<label/>
<caption>
<p>Academiacan GUO Huadong made keynote lecture.</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="figures/Fig02_web.jpg"/>
</fig>
<fig id="F3">
<label/>
<caption>
<p>Plenary Session.</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="figures/Fig03_web.jpg"/>
</fig>
<fig id="F4">
<label/>
<caption>
<p>Breakout Session.</p>
</caption>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="figures/Fig04_web.jpg"/>
</fig>
<p>At the closing ceremony Prof. LI Jianhui, secretary-general of CODATA-China, made a concluding
speech thanking the participants and described CODATA-China&#8217;s aspirations for the future. He
also announced the forthcoming book <italic>Scientific Discovery in Big Data Era</italic> (now
published) and that the 2nd Scientific Data Conference - Data, Science and the Silk Road Economic
Belt would be held in Lanzhou in August 2015. The conference was one of the most successful academic
activities to have been convened in the 30 years since CODATA-China was set up.</p>
<p>This collection of the <italic>Data Science Journal</italic> brings together the papers presented
at this conference, providing insight into the current state of the art in China for data. The
papers were selected and reviewed by the conference organizers as a fitting and representative
collection of the discussions and insights presented at the conference. The organizers would like in
particular to thank Diane Rumble for her assiduous editorial work for this special conference
collection.</p>
<p>The selected papers consist of research work in three categories: 1) common technologies in data
science, 2) data science research and 3) applications of data science in different domains.</p>
<p>Seven of the selected papers are concerned with the critical need for common technologies in data
science:</p>
<list list-type="bullet">
<list-item>
<p><xref ref-type="bibr" rid="B1">Cai and Zhu</xref> construct a dynamic assessment process of data
quality in the big data era;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B5">Jiang et al.</xref> propose search strategies using synchronous
search and asynchronous search to conquer the problems of search speed difference and to combine the
two separate search results;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B10">Shen et al.</xref> introduce OpenCSDB, which is a solution of
applying Linked Data in the Scientific Database, which will promote the sharing of scientific data
and play a greater role in the &#8220;Twelfth Five-Year&#8221; program;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B13">Xiang et al.</xref> propose an invocation sequence of web service
composition and its related invocation policies based on Petri net and analysis of structural
relationships;</p>
</list-item>
<list-item>
<p>An approach is proposed by <xref ref-type="bibr" rid="B17">Zhong et al.</xref> to increase the
accuracy and efficiency of seeding algorithms of magnetic flux lines in magnetic field
visualization;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B14">Xie et al.</xref> propose an ontology-based agricultural
knowledge fusion method to enhance identification and fusion of new and existing data sets to make
big data analytics more possible;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B18">Zhu and Xiong</xref> introduce the new discipline of Data
Science, which provides a type of novel</p>
</list-item>
<list-item>
<p>research method (data-intensive method) for natural and social sciences and takes research on
data largely beyond computer science.</p>
</list-item>
</list>
<p>Data science has attracted the attention of researchers from different domains:</p>
<list list-type="bullet">
<list-item>
<p><xref ref-type="bibr" rid="B6">Li and Lu</xref> propose a computational method to integrate both
biomedical scientific data and literature for drug discovery and new uses of existing drugs;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B8">Meng et al.</xref> describe DarwinTree, which provides an
integrated bioinformatics platform that supports all phases of the analytical pathway for
phylogenetic study from data collection, phylogenetic tree construction, visualization of the tree
of life and web-based rendering, and to specific application service &amp; data mining;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B15">Zhang and Zhao</xref> introduce data mining software and tools
being applied in big data issues of Astrostatistics and Astroinformatics;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B2">Guo et al.</xref> introduce the National Rural Comprehensive
Information Service Platform (NRCISP), supported by national science and technology support
program.</p>
</list-item>
</list>
<p>There are novel applications in data science and related research areas:</p>
<list list-type="bullet">
<list-item>
<p><xref ref-type="bibr" rid="B9">Peng et al.</xref> review recent advances of geophysical data and
geophysical informatics developed in China;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B3">Guo et al.</xref> provide comfortable bus routes recommendation
methods for passengers using an approach that combines multi-objective programming and a genetic
algorithm in personalized information recommendation services;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B4">Huang et al.</xref> propose a novel trigger model with data mining
techniques for sales prediction;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B7">Liu et al.</xref> offer an information filtering method and an
aggregation model to provide a solution for how to choose appropriate experts for peer review;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B11">Wang Zhongya et al.</xref> introduce a novel
personalization-oriented academic literature recommendation method to meet the user&#8217;s
preference in multiple dimensions simultaneously;</p>
</list-item>
<list-item>
<p><xref ref-type="bibr" rid="B12">Wang Haitao et al.</xref> propose an analysis architecture to
make full use of data on natural environment corrosion of materials: the approach includes grey
relational analysis, artificial neural network, fracture mechanics calculation;</p>
</list-item>
<list-item>
<p>A knowledge model for literature data mining is proposed by <xref ref-type="bibr" rid="B16">Zhang
et al.</xref> and it is applied to analyze the correlation between earthquake events and
multidisciplinary data types.</p>
</list-item>
</list>
<p>These papers introduce the development of data science from a range of different perspectives.
They help us get an overall understanding of the common or the most popular techniques used in data
science research and applications. I hope you find them interesting and illuminating.</p>
<p>Prof. Li Jianhui</p>
<graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="figures/Fig05_web.jpg"/>
<p>Secretary-General, CODATA-China</p>
<p>Director, Scientific Data Center, Computer Network information Center, Chinese Academy of
Sciences</p>
</body>
<back>
<ref-list>
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<label>1</label>
<mixed-citation>Cai &amp; Zhu (2015) The Challenges of Data Quality and Data Quality Assessment in
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<mixed-citation>Guo et al. (2015) A Study of the Application of Big Data in a Rural Comprehensive
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<mixed-citation>Guo et al. (2015) How to Find a Comfortable Bus Route &#8211; Towards Personalized
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<mixed-citation>Huang et al. (2015) A Novel Trigger Model for Sales Prediction Data Mining
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<mixed-citation>Jiang et al. (2015) The Development of a Combined Search for a Heterogeneous
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<mixed-citation>Li &amp; Lu (2015) An Integrative Approach for Discovery of New Uses of Existing
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<mixed-citation>Liu et al. (2015) How to Choose Appropriate Experts for Peer Review: An Intelligent
Recommendation Method in a Big Data Context. <italic>Data Science Journal</italic>,
<bold>14</bold>:16. DOI: <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5334/dsj-2015-016">http://dx.doi.org/10.5334/dsj-2015-016</ext-link></mixed-citation>
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<mixed-citation>Meng et al. (2015) DarwinTree: A Molecular Data Analysis and Application Environment
for Phylogenetic Study. <italic>Data Science Journal</italic>, <bold>14</bold>:10. DOI: <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5334/dsj-2015-010">http://dx.doi.org/10.5334/dsj-2015-010</ext-link></mixed-citation>
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<mixed-citation>Peng et al. (2015) From Geophysical Data to Geophysical Informatics. <italic>Data
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<mixed-citation>Shen et al. (2015) OpenCSDB: Research on the Application of Linked Data in
Scientific Databases. <italic>Data Science Journal</italic>, <bold>14</bold>:4. DOI: <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5334/dsj-2015-004">http://dx.doi.org/10.5334/dsj-2015-004</ext-link></mixed-citation>
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<mixed-citation>Wang et al. (2015) A Personalization-Oriented Academic Literature Recommended
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<mixed-citation>Wang et al. (2015) Effective Utilization for Data of Natural Environment Corrosion
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<mixed-citation>Xiang et al. (2015) The Executable Invocation Policy of Web Services Composition
with Petri Net. <italic>Data Science Journal</italic>, <bold>14</bold>:5. DOI: <ext-link ext-link-type="uri" xlink:href="http://dx.doi.org/10.5334/dsj-2015-005">http://dx.doi.org/10.5334/dsj-2015-005</ext-link></mixed-citation>
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<mixed-citation>Xie et al. (2015) Research on Agricultural Knowledge Fusion Method for Big Data.
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<mixed-citation>Zhang &amp; Zhao (2015) Astronomy in the Big Data Era. <italic>Data Science
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<mixed-citation>Zhang et al. (2015) Correlation Analysis Model on Multidisciplinary Data for
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<mixed-citation>Zhong et al. (2015) An Improved Seeding Algorithm of Magnetic Flux Lines Based on
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<mixed-citation>Zhu &amp; Xiong (2015) Towards Data Science. <italic>Data Science Journal</italic>,
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</back>
</article>
