1. Information Science to Data Science: from the Aspect of Research┓
Information Science to Data Science: from the Aspect of Research
Wei Jingzhu
Professor, School of Information Management
Sun Yat-sen University,China
Researcher, Advanced Research Center of Intellectual Property of Wuhan University

2. Data
Data Resource: China Academic Journal Network Publishing Database
Theme of retrieval: Data Science
Period:
Valid Record: 579
Analysis Tool: Citespace
collection

3. High frequency keywords
PART 1
High frequency keywords

4. High frequency of data science research papers(Frequency>7)
Serial Number
Keywords
Frequency 1
Big Data 88 20
Data Literacy 12 2
Science Data 86 21
Data Reference 3
University Library 82 22
Data Integration 4
Library 70 23
Open Access 11 5
Data Management 59 24
Bibliometrics 6
Data Sharing 32 25
Scientific Research Service 10 7
Scientific Research Data 30 26
University 8
Digital Library 27
Data Regulation 9
Science Data Management 28
Cloud Computing
Data Service 29
Information Service
Management 19
Subject Librarian
Data Curation 18 31
Open Data 13
Resource Integration 17
Institutional Knowledge Base 14
Data Mining 16 33
Ontology 15
Metadata 34
Science Data Service
Associated Data 35
Research Hotspot
Knowledge Service 36
Digital Source
Knowledge Map 37
Integration
Big Data Era 38
Data Literacy Education

5. The timezone graph of the sample keyword

6. The hotspot of 2016:
Data Literacy
Integration
Data Literacy Education
The hot words of 2014: Big Data
Science Data
University Library
Library
Data Management
Data Sharing
Scientific Data
Digital Library
Science Data Management
Data Service
Data Curation
The hotspot of 2015: Knowledge Map
Data Integration
Open Access
Scientific Research Service
From extensiveness to refinement. That is, from a number of research hotspots to several focuses of research.

7. PART 2 Research branches 1.Open Scientific Data and its Access
Current researches on open access to scientific data mainly focus on the barriers of public access to scientific data, policies related to open scientific data, open registration and citations of scientific data, open scientific data warehousing and data sharing platform.
Research branches
2.Data Curation (DC)
Domestic research mainly focus on：
foreign data curation institutions, university libraries and other DC-related meetings, trainings, projects, practice and its latest progress.
DC service provided by domestic libraries.
DC methods and implementation ways.
PART 2

8. PART 2 Research branches 3.Research data and scientific data services
Contents and methods of data services.
Technology Application of data service.
Library participation in data services.
Research branches
4. Data mining and analysis
Many scholars have explored how to improve the efficiency and precision of mining by using traditional data mining methods, including decision tree-based methods, neural network-based methods, genetic algorithm-based methods, and Bayesian methods.
PART 2

9. PART 2 Research branches 5.Scientific data management
Case study of other countries
Management tools and models
Policy
Training in scientific data management
Research branches
6.Data literacy
Connotation & requirement
data utilization behavior
data literacy education
PART 2

10. High cited literatures
High Cited Literature about Data Science (Frequency> 14)
Serial Number
Frequency
Author
The name of the paper
Time 1 52
Meng Xiangbao; Li Aiguo
Scientific Data Literacy Education in Overseas Academic Libraries
2014/03 2 45
Chen Chuanfu; Qian ou
Study on the Construction of Digital Library in the Age of Big Data
2014/07 3 34
Deng Zhonghua; Li Lirui
Analysis on Model of Information Services Embedded Process of Scientific Research in
Big Data Environment
2014/01 4 22
Shen Xiuqiong
Discussion on the Resource Construction Strategies for University Library Oriented on MOOC
2014/22 5 20
Song Yanhui;
Wu Yishan
A Comparative Study on Author Bibliographic-coupling Analysis and
Author Keyword-coupling Analysis Based on Scientometrics 6 17
Zhang Jilong;
Yin Shenqin
Social Scientific Data Sharing and Serving
-An Example of Fu Dan University Social Scientific Data Platform
2015/01 7 16
Zhang han;
Wang Zhong
Comparison Research on Open Data of International Government
2015/08 8 15
Dong Zhenge; Chen Huilan
Investigation into Library Service Model of University Discipline
Evaluation on the Basis of ESI and InCites Databases
2014/11 9
Shen Tingting
Data Literacy and Its Impact on Scientific Data Management
PART 3
High cited literatures

11. Cases cited in research of data science in LIS
International Cases
Purdue University
Warehousing and its support for scientific data management services
New Mexico University
Data ONE
Oxford university
Embedding Institutional Data Curation Services in Research, EIDCSR
Data Preservation Alliance for the Social Sciences,DataPASS
Domestic Cases
Fu Dan University
Social science data sharing platform
Renmin University of China
Chinese Social Survey Open Database，CSSOD
Sun Yat-sen University
Sharing Plan of Academic research Database
Wu Han University
China Academic Scientific Data Service
Shang Hai Library
New Visualization Data Service

12. Data science has become a hot topic in scientific field in China, and interdisciplinary research of library science and information science, computer science, management, finance, statistics, medicine, geography and other disciplines, together with data science, has already show rich research output.
Summary

13. Conclusions
Not much research output in focus on data science in LIS both in the aspect of quantity and quality
Not systematic
Most are introduction or expansion of international research or experience
Focus more on application but theoretical level

14. Not much discussion about the core and the scope of data science, no architecture of the data science realm.
There are research projects chaired by individual scholars in LIS, most are entrusted by government departments and big enterprises,which have need to data analysis to support their decision and add value,reflecting social needs of data analysis from LIS,which often cooperated with scholars from other disciplines such as computer science and economy science.
Not much research on how to reform the current curricula system and how to construct a new one for data science.

15. While undoubtedly, data era is upon us, so how LIS contribute to the social development and scientific field and embody its value is what we should think about.

16. At the year of 2016, the World Intellectual Property Organization (WIPO), collaborated with Cornell University, Business School for the World(INSEAD), issued the report of Global Innovation Index Rankings at Geneva, Switzerland, China ranks 25 across the world.
At the year of 2016,an officer of the Ministry of Industry and Information Technology of the People’s Republic of China pointed out that the total amount of data in China is increasing at 50% per year and is expected to be accounted for 21% of the world in China is now becoming a country holding huge data resource.

17. Data-driving is a different path to innovation, which provides a new chance for China if talent need of data science can be satisfied.

18. Further directions for iSchools in research and curriculum in DC
Data user
Data need
Data policy
Data management(classification, organization,aggregation,visualization)
The model data enable knowledge creation and innovation
The model data facilitate prompt and better decision
How library and other institutions provide data service to scientists to improve their scientific process
* Data itself; data need and use; data management; data policy; data service

19. Thank you!