Journal of Global Change Data & Discovery2019.3(3):207-226

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Citation:Liu, C., Zhang, Y. H..Methodology and Practice on Measuring the Im-pact of Global Change & Earth System Science Data in 2019 .Journal of Global Change Data & Discovery2019.3(3):207-226 .DOI: 10.3974/geodp.2019.03.01 .

Methodology and Practice on Measuring the Impact of Global Change & Earth System Science Data in 2019

Liu, C.*Zhang, Y. H.

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract:With the coming of data driven sciences, the importance of re-using scientific research data is increasingly significant. How to quantitatively measuring the impact of the datasets, as well as the data authors, data centers is particularly urgent. Based on the statistics of the data citation from 2014 to 2018 and the method of Data Impact Score (DIS) models, the Data Impact Score (DIS) of each datasets among the 192 cited datasets published by the Global Change Research Data Publishing & Repository (GCdataPR) and the Geological Data Publishing Center (GDPC) from 2014-2018 were identified by the DIS model. Besides, each authors’ DIS among the 565 dataset authors, two data publishing centers were measured also by the DIS models. There are total 324 cites with 134.069,4 of DIS from 2014-2018. Taking 5%, 15%, 30% and 50% as the parameters, all the datasets were identified into Q1, Q2, Q3 and Q4 parts. As the result, 10 datasets were ranked into Q1 with the DIS above 6, 24 datasets were ranked into Q2 with the DIS between 2 and 6, 41 datasets were ranked into Q3 with the DIS between 0.2-2, and 117 were ranked into Q4 with the DIS less than 0.2. There were 28 data authors ranked into Q1, and the GCdataPR was ranked the top one of the data publishing center. The DIS methodology could be used for more broad datasets from the data repositories or data publishers in national and international levels.

Keywords: big data; data impact score; statistics; data author; data center

1 Background

Global change and geoscience data are related to all spheres of the earth, multiple scales in time and space from global to regional and local, from instantaneous to monthly, yearly, …. historical and geological time scales, and in different data formats, vector, raster or table based. The data is a necessary resource for global change and earth science research and sustainable development. Dataset is a basic body of data publishing and dissemination, as well as the data citation. In order to promote the research data sharing and re-using, the Big Data Working Committee of the Geographical Society of China (BigData_WC/GSC) decided to have a quantitative measuring action with the Liu’s DIS methodology[1] since 2018. Through quantitative measuring with Data Impact Score, the first 5% and the first 15% of the most impact datasets are ranked into Q1 and Q2 respectively.

In early 2019, the BigData_WC/GSC had a call for dataset impact measuring based on the datasets published from 2014-2018. The aim of the call is to encourage open quality data and data re-using by citation in the broad fields of earth system sciences and global change studies. The BigData_WC/GSC shall release the data impact ranking based on the data citation[2] census and DIS models.

The qualifications for the dataset, which may be archived in the data impact measuring database, were as follows:

(1) The contents of the dataset are relevant to the fields of global change or earth sciences;

(2) The dataset was already published with DOI (Digital Object Identifier) and archived in any of the regular members of World Data System[3], or National Data Center, or any qualified and registered data publishers;

(3) The dataset was openly accessible though internet;

(4) The dataset was published from January 1, 2014 to December 31, 2018.

Any of the followings is eligible to submit his/her application:

(1) Dataset author: the dataset authors including the principle author, corresponding author or any of the co-authors are eligible to submit the application for the dataset impact measuring;

(2) Data publisher: the data publisher is eligible to submit its application for all published datasets through the publisher for the dataset impact measuring.

The deadline of the applications was May 15, 2019.

The Geographical Society of China will officially release the measuring datasets impact result at the 2019 Conference of the BigData_WC/GSC (September 21, 2019, Dalian) [4]

2 The Flowchart of Data Impact Assessment of Global Change and Earth System Sciences

The flowchart of data impact assessment of global change and earth system sciences is shown in Figure 1.

According to the process of data impact assessment of global change and earth system sciences in 2019, the first step is to identify each valid dataset, which will be measured, and then put it into the Assessment Sub-Database; secondly, a comprehensive survey was conducted based on the dataset citation (including published title, author, journal, volume, year, DOI), and a Data Citation Sub-Database was established; then, based on the Impact Factors (IF) of cited journals[5] collected, the cited journal’ IF Sub-Databased was established; finally, according to Data Impact Score model[1] and the information above, the DISdataset (2014-2018) was calculated.

Taking the first 5%, 15%, 30% and 50% of DISdataset (2014-2018) as parameters, the Q1, Q2, Q3, and Q4 of DISdataset (2014-2018) were identified.

Based on the DISdataset (2014-2018) and DIS models[1] for measuring the data authors, founding agencies and projects, the author affiliations and data publishers, the DIS for each of them will be calculated separately.

Figure 1Flowchart of data impact assessment of global change and earth system sciences

3 The Database for the Published Datasets Impact Measuring in 2019

The database for the published datasets impact measuring in 2019 consists of five tables, including:

(1) Valid datasets for impact measuring;

(2) Data citation information of each datasets;

(3) Impact Factors of each cited journals;

(4) DIS models for datasets, data authors, data author’ affiliations, founding agencies, founding projects and data publishers

(5) DIS results and ranking for each of datasets, data authors, data author’ affiliations, founding agencies, founding projects and data publishers

3.1 Valid Datasets for Impact Measuring (2014-2018)

There are 282 valid datasets for the data impact measuring among the 284 applications; two were not included, because no datasets published although the data papers published only.

The contents of dataset information include: title of the dataset, publisher, DOI, publication year, website, the authors, author’s affiliations, funding projects, data format, data size, data policies, data retrieval information etc.

3.2 Data Citation Information of Each Datasets

For establishing the data citation information data table for each datasets, the contents were from a census and statistics of cited papers for each database from 2014 to 2018, including the cited title of dataset, DOI of published paper, authors of the paper, year of the paper published, journal, journal ISSN etc.

3.3 Impact Factors of Each Cited Journals

For establishing the Impact Factors (IF) data table of each cited journals, the IF information of each cited journals in each of the five years (2014-2018) were collected.

3.4 DIS Models

The DIS models are composed of several models, including:

(1) DIS mode for dataset

(2) DIS mode for data publisher

(3) DIS mode for data author

(4) DIS mode for founding agencies

(5) DIS mode for founded projects

(6) DIS model for ranking

3.5 DIS Results and Ranking in 2019 (for 2014-2018)

The DIS results and ranking database include:

(1) DIS ranking for dataset in Q1, Q2, Q3, Q4

(2) DIS ranking for data publisher

(3) DIS ranking for data author Q1, Q2, Q3, Q4

(4) DIS ranking for founding agencies

(5) DIS ranking for founded projects

4 Practice on Measuring the Impact of Global Change & Earth System Science Data in 2019

4.1 Dataset Impact Score (DISdataset)

Among the 282 datasets from two data publishers, which were valid application for data impact measuring, 192 datasets were cited, with a citation rate of 68.09%. The data authors of these datasets were 565 individuals.

The method of calculating the DIS for each of the datasets for five years is based on two steps, first step is to calculate the DIS for each of the datasets for one year[1] and then summarize DIS for the five years from 2014-2018. The model of DIS for a datasets for one year is shown in the following formula:

(1)

Where, DISDy is the Dataset Impact Score in y year.

4.2 DIS Summary for the 192 Cited Datasets in Five Years (2014-2018)

The DIS for the 192 cited datasets in five years (2014-2018) is summarized in Table 1.

Table 1Statistics of 192 cited datasets in five years (2014-2018)

Year

Cited No.

 

DIS

Mean of Jouranl’s Impact factor

Journal in Chinese

Journal in English

Sum

 

Journal in Chinese

Journal in English

Sum

2014

30

0

30

 

69

0

69

2.300,0

2015

2

5

7

 

2.786

15.144

17.93

2.561,4

2016

6

9

15

 

7.913

68.302

76.215

5.081,0

2017

20

82

102

 

18.080,5

78.533

96.613,5

0.947,2

2018

30

140

170

 

36.289,9

150.122

186.411,9

1.096,5

Sum

88

236

324

 

134.069,4

312.101

446.170,4

1.377,1

From 2014 to 2018, 192 datasets were cited 324 times in total, and the DIS in 5 years totaled 446.170,4, and the average cited journal IF was 1.377,1. In English journals, 236 articles were cited, accounting for 72.84%, while in Chinese journals, 88 articles were cited, accounting for 27.16%. DIS calculated from the citation by English journals is 312.101, accounting for 69.95%, and by Chinese journals is 134.069,4, accounting for 30.05%. There are 2.33 times more in English journals than in Chinese. From the analysis of the cited times and DIS, the citation ratio of English journals is higher than that of Chinese journals.

4.3 DISdataset Ranking for Four Portions

Based on the DISdataset calculated results, 4 portions were divided as the following:

(1) Q1: the 5-year total DISdataset is ranking 5% with DIS more than 6, there are 10 datasets on the list of Q1 (Table 2).

(2) Q2: Q1: the 5-year total DISdataset is ranking 15% with DIS more than 2, there are 24 datasets on the list of Q2 (Table 3).

(3) Q3: Q1: the 5-year total DISdataset is ranking 30% with DIS more than 0.2, there are 41 datasets on the list of Q3.

(4) Q4: Q1: the 5-year total DISdataset is ranking in the last 50% with DIS less than 0.2, there are 117 datasets on the list of Q4.

4.4 Analysis on DISdataset Results in Q1

There were 10 datasets which were ranking in Q1 listed in table 4. As can be seen from table 4, the 10 datasets in Q1 were cited 115 times in total, cited 75 times by English journals, accounting for 65.22%, and cited 40 times by Chinese journals, accounting for 34.78%. DISdataset in English journals cited is 246.206, accounting for 78.02%, and Chinese journals cited is 69.368,3, accounting for 21.98%. There are 3.55 times more in English journals than in Chinese. The average IF of cited journal is 2.744,1. From the statistics of the cited times and DISdataset, the citation ratio of English journals is higher than that of Chinese journals.

Table 2Statistics of DIS ranking Top 5% (Q1) Datasets in five years (2014-2018)

Year

Cited No.

 

DIS

Mean of Jouranl’s Impact factor

Journal in Chinese

Journal in English

Sum

 

Journal in Chinese

Journal in English

Sum

2014

17

0

17

 

39.1

0

39.1

2.300,0

2015

1

4

5

 

0.932,8

12.222

13.154,8

2.631,0

2016

6

7

13

 

7.913

65.441

73.354

5.642,6

2017

7

17

24

 

6.715

55.059

61.774

2.573,9

2018

9

47

56

 

14.707,5

113.484

128.191,5

2.289,1

Sum

40

75

115

 

69.368,3

246.206

315.574,3

2.744,1

Table 3List of DIS ranking Top 5% (Q1) Datasets in five years (2014-2018)

No.

Title

Author(s)

Dataset’s DOI

Publisher’s DOI

DIS

1

1 km grid population dataset of China[6]

Fu, J. Y., Jiang, D., Huang, Y. H.

10.3974/geodb.2014.01.06.V1

10.3974/

113.508,6

2

Datasets of the boundary and area of the Tibetan Plateau [7]

Zhang, Y. L., Ii, B. Y., Zheng, D.

10.3974/geodb.2014.01.12.V1

10.3974/

94.067,2

3

1 km grid GDP data of China (2005, 2010)[8]

Huang, Y. H., Jiang D., Fu, J. Y.

10.3974/geodb.2014.01.07.V1

10.3974/

55.676,3

4

Time series of land ecosystem classification dataset of China in five-year increments[9]

Xu, X. L, Liu, J. Y., Zhang, Z. X., et al.

10.3974/geodb.2015.01.01.V1

10.3974/

11.766,6

5

Eco-regional boundary data of the roof of the world[10]

Liu, C., Shi, R. X., Chen, W. B.

10.3974/geodb.2014.01.01.V1

10.3974/

9.560,0

6

Elevation cluster dataset covering the eco-region of the roof of the world [11]

Liu C., Shi R. X., Lv, T. T., et al.

10.3974/geodb.2014.01.03.V1

10.3974

6.900,0

7

Land slope cluster dataset of the eco-region of the roof of the world[12]

Liu, C., Shi, R. X., Lv, T. T., et al.

10.3974/geodb.2014.01.02.V1

10.3974

6.900,0

8

Cropping rotation system data of China[13]

Xu, X. L., Liu, L.

10.3974/geodb.2014.01.08.V1

10.3974/

6.868,6

9

1 km/5-day NDVI product over China and the association of southeast Asian nations for 2013[14]

Li, J., Zeng, Y. L., Liu, Q. H., et al.

10.3974/geodb.2015.01.16.V1

10.3974/

6.697,0

10

1 km/5 day surface reflectance product over China and the association of southeast Asian nations for 2013[15]

Zhong, B., Wu, S. L., Liu, Q. H., et al.

10.3974/geodb.2015.02.02.V1

10.3974/

6.697,0

Table 4List of DIS Ranking Top 15% (Q2) Datasets in Five Years (2014-2018)

No.

Title

Author(s)

Dataset’s DOI

Publisher’s DOI

DIS

11

Dataset of mangroves in southeast Asia[16]

Lv T. T., Zhou X., Liu C., et al.

10.3974/geodb.2015.01.08.V1

10.3974/

5.994

12

The 60 hm2 plot in situ multi-scale species dataset in Jianfengling tropical montane rainforest, Hainan island, China[17]

Li, Y. P., Xu, H., Li, Y. D., et al.

10.3974/geodb.2017.02.05.V1

10.3974/

5.172

13

Pollutant coefficients data of livestock industry at provincial level in China[18]

Zhou, T. M., Zhu, Y. Q., Fu, Q., et al.

10.3974/geodb.2014.01.10.V1

10.3974

5.041

14

Historical monthly temperature data of China at 1km resolution[19]

Jing, W. L., Yang, Y. P., Feng, M., et al.

10.3974/geodb.2015.01.02.V1

10.3974

4.706

15

1 km / 5 d fractional vegetation cover dataset in the belt and road and surround region (2015)[20]

Li, J., Liu, Q. H., Zhao, J., et al.

10.3974/geodb.2017.04.18.V1

10.3974/

4.61

16

Global land surface water dataset at 30m resolution (2010)[21]

Chen, J., Liao, A. P., Chen, L. J., et al.

10.3974/geodb.2014.02.01.V1

10.3974/

4.0032

17

Remotely sensed dataset of grassland degradation on the Qinghai-Tibetan plateau[22]

Wang, L., Xu, X. L., Liu, L.

10.3974/geodb.2014.01.11.V1

10.3974/

4.0024

(To be continued on the next page)

 

(Continued)

No.

Title

Author(s)

Dataset’s DOI

Publisher’s DOI

DIS

18

1 km/daily evapotranspiration product over China and the association of southeast Asian nations for 2013[23]

Jia, L., Hu, G. C., Zheng, C. L., et al.

10.3974/geodb.2015.01.11.V1

10.3974/

3.646

19

Boundary data of loess plateau region[24]

Wang, Z. X.

10.3974/geodb.2015.01.09.V1

10.3974/

3.526

20

Time series of land ecosystem classification dataset of China in five-year increments[25]

Xu, X. L., Liu, J. Y., Zhang, Z. X., et al.

10.3974/geodb.2015.01.01.V1

10.3974/

3.51

21

Suitability assessment data on marginal land resources for cultivating bio-energy crops of Asia[26]

Jiang, D., Fu, J. Y., Huang, Y. H.

10.3974/geodb.2014.01.04.V1

10.3974/

3.338

22

Global artificial land surface dataset at 30 m resolution (2010)[27]

Chen, J., Liao, A. P., Chen, L. J., et al.

10.3974/geodb.2014.02.02.V1

10.3974/

3.162

23

Dataset of percentile-based Etccdi temperature extremes indices for cmip5 model output[28]

Yang, C.

10.3974/geodb.2017.02.09.V1

10.3974/

3.1

24

Remote sensing based forest phenology data of northeast of China[29]

Yu, X. F., Zhuang, D. F., Wang, Q. K.

10.3974/geodb.2014.01.14.V1

10.3974/

2.42

25

Hydrothermal conditions change data of China in 1km resolution in decades from 1951 to 2010[30]

Ning, X. J., Qin, Y. C., Cui, Y. P., et al.

10.3974/geodb.2016.01.07.V1

10.3974/

2.329

26

Time series of snow cover data of northeast of China (2003-2013)[31]

Chen, S. B., Yang, Q., Xie, H. J., et al.

10.3974/geodb.2014.01.15.V1

10.3974/

2.3

27

The gridded dataset of spring phenology of Fraxinus Chinensis in China from 1952 to 2007[32]

Ge, Q. S., Dai, J. H., Liu, H. L., et al.

10.3974/geodb.2014.01.19.V1

10.3974/

2.3

28

Typical plant phenological observation dataset of Chinese phenological observation network, Beijing (19632012)[33]

Ge, Q. S., Dai, J. H., Wang H. J.

10.3974/geodb.2014.01.09.V1

10.3974/

2.3

29

Professor Lin Chao - pioneer of Chinese geographers in GU—— remembering Lin Chao in his 100th birthday[34]

Hong, S. R., Lin, Y. X.

10.3974/geodb.2014.01.20.V1

10.3974/

2.3

30

Dataset of post stamps on rocket and satellite (1957-1959)[35]

Liu, C.

10.3974/geodb.2014.01.05.V1

10.3974/

2.3

31

Soil sample data for locust plague analysis around Baiyangdian lake in 2002[36]

Shi, R. X.

10.3974/geodb.2014.01.16.V1

10.3974/

2.3

32

Average soil potential productivity of Hunan province, China, 1981-2010[37]

Sun, W., Guo, C. X., Zhu, Y. Q.

10.3974/geodb.2014.01.18.V1

10.3974

2.3

33

Dataset of winter fallow land in middle and lower reaches of Yangtze river (2007-2008)[38]

Xu, X. L., Zhai, M. Y, Liu, L.

10.3974/geodb.2014.01.17.V1

10.3974

2.3

34

Dataset of regional vegetation changes from 1981-2009 in the Qinghai- Tibet plateau[39]

Yu, B. H., Lv, C. H., Lv, T. T., et al.

10.3974/geodb.2014.01.13.V1

10.3974

2.3

5 DISA ranking

According to DIS model for data author[1] and statistics data in 2019, the five years (2014-2018) DISA were obtained for each of the 565 data authors. There were 28 authors ranked Q1 by top 5% in 2019 (Table 5). The certificate was given to each Q1 dataset and data authors at the 2019 BigData_WC/GSC Conference (Figure 2). Among them, 16 were from the institute of geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, accounting for 57.14% of the authors in Q1, and 7 were from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, accounting for 25%.

Table 5List of DISA ranking Top 5% (Q1) authors (28 among 565) in five years (2014-2018)

No.

Author

Organization

DIS

1

Fu, J. J.

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS)

71.507,9

2

Huang, Y. Y.

Institute of Geographic Sciences and Natural Resources Research, CAS

57.049,8

3

Zhang, Y. L.

Institute of Geographic Sciences and Natural Resources Research, CAS

47.033,6

4

Jiang, D.

Institute of Geographic Sciences and Natural Resources Research, CAS

43.969,0

5

Zheng, D.

Institute of Geographic Sciences and Natural Resources Research, CAS

23.516,8

6

Li, B. Y.

Institute of Geographic Sciences and Natural Resources Research, CAS

23.516,8

7

Liu, C.

Institute of Geographic Sciences and Natural Resources Research, CAS

16.579,3

8

Xu, X. L.

Institute of Geographic Sciences and Natural Resources Research, CAS

13.230,7

9

Shi, R. X.

Institute of Geographic Sciences and Natural Resources Research, CAS

9.021,3

10

Li, J.

Institute of Remote Sensing and Digital Earth (RADI), CAS

5.777,3

11

Lv, T. T.

Institute of Remote Sensing and Digital Earth (RADI), CAS

5.084,5

12

Liu, L.

Xinjiang Institute of Ecology and Geography (XIEG), CAS

5.009,9

13

Wang, Z. X.

Institute of Remote Sensing and Digital Earth (RADI), CAS

4.197,3

14

Zhong, B.

Institute of Remote Sensing and Digital Earth (RADI), CAS

3.856,0

15

Liu, J. Y.

Institute of Geographic Sciences and Natural Resources Research, CAS

3.819,2

16

Chen, J.

National Geomatics Center of China

3.582,6

17

Chen, W. B.

Keio University

3.432,5

18

Yang, C.

Beijing Normal University

3.220,0

19

Liu, Q. H.

Institute of Remote Sensing and Digital Earth (RADI), CAS

3.003,7

20

Li, Y. P.

Research Institute of Tropical Forestry, Chinese Academy of Forestry Sciences

2.586,0

21

Zhou, T. M.

Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS)

2.520,5

22

Jing, W. L.

Institute of Geographic Sciences and Natural Resources Research, CAS

2.353,0

23

Ge, Q. S.

Institute of Geographic Sciences and Natural Resources Research, CAS

2.300,0

24

Wang, L.

Institute of Geographic Sciences and Natural Resources Research, CAS

2.001,2

25

Wu, S. L.

Institute of Remote Sensing and Digital Earth (RADI), CAS

1.960,3

26

Zhou, X.

Institute of Remote Sensing and Digital Earth (RADI), CAS

1.959,8

27

Zhu, Y. Q.

Institute of Geographic Sciences and Natural Resources Research, CAS

1.925,3

28

Zhang, Z. X.

Institute of Remote Sensing and Digital Earth (RADI), CAS

1.909,6

6 DISp ranking

Based on the statistics of datasets published by each data publishers and the DISdataset of each datasets, the DISp for each publisher in 5 years were calculated. The calculation method is as follows:

(2)

Where, DISp is the sum of DIS in 5 years for the data publisher, and n is the number of dataset published from 2014 to 2018. The DISi dataset was the ith dataset impact score from 2014 to 2018.

Figure 2 Prof. Liao Xiaohan, Chair of BigData_WC/GSC and Prof. Lu Huayu, Vice President of GSC presented the Certificates to each of the datasets and data authors in Q1 (September 21, 2019, Dalian, China)

According to the above calculation results, the Global Change Research Data Publishing & Repository (GCdataPR)[5] (DOI: 10.3974/) co-operated by the Institute of Geographical Sciences and Natural Resources Research, CAS and Geographical Society of China (GSC) published 161 datasets, and was credited DISp 416.001,9. While, the Geological Science data Publishing System[6] (DOI: 10.23650/) operated by the National Geological Data Library of the Ministry of Natural Resources, PRC, published 31 datasets with the DISP 30.048,5 (Table 6).

Table 6Statistics of DIS for Data Publishers in Five Years2014-2018

Publishing center

Sponsor

Organization

DOI

Number of cited datasets

Citing number

DISp

Mean of jouranl’s impact factor

Global Chang Research Data Publishing & Repository

IGSNRR, GSC

CAS

10.3974/

161

291

416.001,9

1.429,6

Geoscientific Data & Discovery Publishing System

National Geological Archive, China

Ministry of Natural Resources, China

10.23650/

31

31

30.048,5

0.969,3

7 Examples of the Most Impact Dataset

Figure 3Statistics of data citation on 1 KM Grid Population Dataset of China

7.1 Example 1:1 KM Grid Population Dataset of China

The 1 KM Grid Population Dataset of China (DOI: 10.3974/geodb2014.01. 06.V1) was published by GCdataPR in June 2014. Three authors were Fu, J., Jang, D., Huang, Y. H. During the five years from 2014 to 2018, it was cited 42 times, and the total DISdataset was 113.5086, which was listed as top 5% (Q1) in 2019.

Among the 42 citation papers, the highest IF paper was Satellite-based Mapping of Daily High-resolution Ground PM2.5 in China via Space-time Regression (DOI: 10.1016/j.rse 2017.12.018) published in the journal Remote Sensing of Environment (ISSN 0034-4257) in 2018, the journal’s IF was 6.457. The number of dataset citations was increased year by year in the five years (Figure 3). More detailed records of citations were listed in Table 7.

Table 7 Statistics of data citation on 1 KM Grid Population Dataset of China2014-2018

No.

Title

DOI of Citing Paper

Citing Journal

ISSN

Citing Date

Impact Factor

1

1 km grid population dataset of china[40]

10.11821/dlxb2014S006

Acta Geographica Sinica

0375-5444

2014

2.3

2

Daily estimation of ground-level pm2.5 concentrations over Beijing using 3 km resolution modis aod[41]

10.1021/acs.est.5b01413

Environment Science & Technology

0013-936X

2015

5.393

3

An estimation model of population in china using time series DMSP night-time satellite imagery from 2002-2010[42]

10.1117/12.2207575

Int. Conf. on Intelligent earth Observing and Application

2015

0

4

Contamination of phthalate esters, organochlorine pesticides and polybrominated diphenyl ethers in agricultural soils from the Yangtze river delta of china[43]

10.1016/j.scitotenv.2015.12.012

Science of the Total Environment

0048-9697

2016

4.9

5

Comparison of socioeconomic factors between surrounding and non-surrounding areas of the Qinghai-Tibet railway before and after its construction[44]

10.3390/su8080776

Sustainability

2071-1050

2016

1.789

6

Study on grid refinement for population distribution based on DMSP/OLS [45]

10.3969/j.issn.1000-0666.2016.02.020

Journal of Seismological Research

1000-0666

2016

0.4171

7

Preliminary numerical simulation of air pollution in Hefei with GIS data [46]

10.13671/j.hjkxxb.2016.0100

Acta Scientiae Circumstantiae

0253-2468

2016

1.2175

8

Forest fire risk assessment for China under different climate scenarios[47]

10.13287/j.1001-9332.201603.034

Chinese Journal of Applied Ecology

1001-9332

2016

1.6795

9

Predicting malaria vector distribution under climate change scenarios in China: challenges for malaria elimination[48]

10.1038/srep20604

Scientific Reports

2045-2322

2016

4.122

10

The air quality and health impacts of domestic trans-boundary pollution in various regions of China[49]

10.1016/j.envint.2016.08.004

Environment International

0160-4120

2016

7.297

11

The relief degree of land surface and its correlation with population distribution of Chaohu watershed[50]

-

Science Technology and Engineering

1671-1815

2016

0.12

12

A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China[51]

10.5194/acp-17-2839-2017

Atmospheric Chemistry and Physics

1680-7316

2017

5.509

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(Continued)

No.

Title

DOI of Citing Paper

Citing Journal

ISSN

Citing Date

Impact Factor

13

Development of a high-resolution emission inventory and its evaluation and application through air quality modeling for Jiangsu province, China[52]

10.5194/acp-17-211-2017

Atmospheric Chemistry and Physics

1680-7316

2017

5.509

14

Impacts of 1.5 and 2 °c global warming on water availability and extreme hydrological events in Yiluo and Beijiang River catchments in China[53]

10.1007/s10584-017-2072-3

Climatic Change

0165-0009

2017

3.537

15

Water scarcity under various socio-economic pathways and its potential effects on food production in the yellow river basin[54]

10.5194/hess-2016-188

Hydrology andEarth System Sciences

1027-5606

2017

4.256

15

Fire dynamics under monsoonal climate in Yunnan, SW China: past, present and future[55]

10.1016/j.palaeo.2016.10.028

Palaeogeography, Palaeoclimatology, Palaeoecology

0031-0182

2017

2.375

17

Impacts of sanitation improvement on reduction of nitrogen discharges entering the ENV. from human excreta in China[56]

10.1016/j.scitotenv.2017.03.177

Science of the Total Environment

0048-9697

2017

4.61

18

Relationship between provision and reception of ecological service of carbon fixation and oxygen release in Beijing-Tianjin-Hebei region[57]

10.11934/j.issn.1673-4831.2017.09.003

Journal of Ecology and Rural Environment

1673-4831

2017

0.9112

19

Emission inventory of water soluble ions in fine particles from residential coal burning in China and implication for emission reduction [58]

10.13227/j.hjkx.201707063

China Environmental Science

1000-6923

2017

1.7143

20

“Ghost cities” identification using multi-source remote sensing datasets: a case study in Yangtze river delta[59]

10.1016/j.apgeog.2017.02.004

Applied Geography

0143-6228

2017

3.117

21

Global change research data publishing & sharing rankings[60]

10.3974/geodp.2018.03.01

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

22

Analysis to the highest impact dataset 2018 from the geographical society of China[61]

10.3974/geodp.2018.03.02

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

23

Data impact score (dis) —a quantitative method of data performance to the

Data-driven sciences [1]

10.3974/geodb.2018.02.02

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

24

Optimal redistribution of an urban air quality monitoring network using atmospheric dispersion model and genetic algorithm[62]

10.1016/j.atmosenv.2018.01.011

Atmosphere Environment.

1352-2310

2018

3.708

25

Mapping human influence intensity in the Tibetan Plateau for conservation of ecological service functions[63]

10.1016/j.ecoser.2017.10.003

Ecosystem Service

2212-0416

2018

4.395

26

Pedodiversity and its controlling factors in mountain regions - a case study of Taihang Mountain, China[64]

10.1016/j.geoderma.2017.09.027

Geoderma

0016-7061

2018

3.74

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(Continued)

No.

Title

DOI of Citing Paper

Citing Journal

ISSN

Citing Date

Impact Factor

27

Resilience analysis of countries under disasters based on multisource data[65]

10.1111/risa.12807

Risk Analysis

0272-4332

2018

2.898

28

Simulation and projection of the spatial sattern of the Population in Beijing under the background of non-capital function extraction[66]

10.12082/dqxxkx.2018.170368

Journal of Geo-information Science

1560-8999

2018

0.7989

29

Application of the LUR model in the prediction of spatial distributions of soil heavy metals[67]

10.13227/j.hjkx.201704024

Environmental Science

0250-3301

2018

1.7318

30

Research on the geo-environment in the era of big data[68]

10.3981/j.issn.1000-7857.2018.03.005

Science and Technology Review

1000-7857

2018

0.2923

31

Potential threats from variations of hydrological parameters to the Yellow River and Pearl River Basins in China over the next 30 years[69]

10.3390/w10070883

Water

2073-4441

2018

2.069

32

Impact of land-cover layout on particulate matter 2.5 in urban areas of China[70]

10.1080/17538947.2018.1530310

International Journal of Digital Earth

1753-8947

2018

2.746

33

Satellite-based mapping of daily high-resolution ground PM2.5 in China via space-time regression modeling[71]

10.1016/j.rse.2017.12.018

Remote sensing of Environment

0034-4257

2018

6.457

34

The impacts of climate change and human activities on alpine vegetation and permafrost in the Qinghai-Tibet Engineering Corridor[72]

10.1016/j.ecolind.2018.04.067

Ecological Indicators

1470-160X

2018

3.983

35

Development of an asset value map for disaster risk assessment in China by spatial disaggregation using ancillary remote sensing data[73]

10.1111/risa.12806

Risk Analysis

0272-4332

2018

2.898

36

Study of car-sharing diffusion criticality conditions based on human traveling network[74]

10.1142/S0129183118400090

International Journal of Modern Physics C

0129-1831

2018

0.919

37

Spatiotemporal variations in satellite-based formaldehyde (HCHO) in the Beijing-Tianjin-Hebei region in China from 2005 to 2015[75]

10.3390/atmos9010005

Atmosphere

2073-4433

2018

1.704

38

Satellite-based high-resolution pm2. 5 estimation over the Beijing-Tianjin-Hebei region of china using an improved geographically and temporally weighted regression model[76]

10.1016/j.envpol.2018.01.053

Environmental pollution

0269-7491

2018

4.358

39

Assessing local resilience to typhoon disasters: a case study in Nansha, Guangzhou[77]

10.1371/journal.pone.0190701

Plos one 

1932-6203

2018

2.766

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(Continued)

 

No.

Title

DOI of Citing Paper

Citing Journal

ISSN

Citing Date

Impact Factor

40

Spatial distribution and change of population in Guizhou based on stratigraphic and topographic conditions[78]

-

Guizhou science

1003-6563

2018

0

 

41

Impacts of sectoral emissions in china and the implications: air quality, public health, crop production, and economic costs[79]

10.1088/1748-9326/aad138

Environmental research letters

1748-9326

2018

4.541

 

42

Are all cities with similar urban form or not? Redefining cities with ubiquitous points of interest and evaluating them with indicators at city and block levels in china[80]

10.1080/13658816.2018.1511793

International journal of geographical information science

1365-8816

2018

2.37

 

Sum in five years (2014-2018): cited 42, DIS 113.5086, mean of the citing journal’s impact factor 2.70

 

Figure 4 Statistics of data citation on Datasets of the Boundary and Area of the Tibetan Plateau

7.2 Example 2: Datasets of the Boundary and Area of the Tibetan Plateau

The Datasets of the Boundary and Area of the Tibetan Plateau (DOI: 10.3974/geodb.2014.01.12.V1) was published by GCdataPR in June 2014. Three authors of it were Zhang, Y.L., Li, B.Y., Zheng D. During the five years from 2014 to 2018, it was cited 23 times, and the total DISdataset was 94.067,2, which was listed as top 5% (Q1) in 2019.

Among the 23 citation papers, the highest IF paper was High-resolution Mapping of Global Surface Water and its Long-term Changes (DOI:10.1038/ nature20584 in Nature (ISSN 0028-0836) in 2016. While its IF in 2016 is 40.137. The detailed records of the dataset citations were listed in Figure 4 and table 8.

Table 8Statistics of data citation on Datasets of the Boundary and Area of the Tibetan Plateau (2014-2018)

No.

Title

DOI of citing paper

Citing journal

ISSN

Citing date

Impact factor

1

Remotely sensed dataset of grassland degradation on the Qinghai-Tibetan Plateau[81]

10.11821/dlxb2014S011

Acta Geographica Sinica

0375-5444

2014

2.3

2

Datasets of the boundary and area of the Tibetan Plateau [82]

10.11821/dlxb2014S012

Aata Geographica Sinica

0375-5444

2014

2.3

3

Dataset of regional vegetation changes from 1981 to 2006 in the Qinghai-Tibet Plateau [83]

10.11821/dlxb2014S013

Acta Geographica Sinica

0375-5444

2014

2.3

(To be continued on the next page)

(Continued)

4

Eco-regional boundary data of the roof of the world[84]

10.11821/dlxb2014S001

Acta Geographica Sinica

0375-5444

2014

2.3

5

Changes in the timing and duration of the near-surface soil freeze/thaw status from 1956 to 2006 across China[85]

10.5194/tc-9-1321-2015

Cryosphere

1994-0416

2015

4.906

6

Recent changes in wetlands on the Tibetan Plateau: a review[86]

10.1007/s11442-015-1208-5

Journal of Geographical Sciences

1009-637X

2015

1.923

7

Characteristics and protection effectiveness of nature reserves on the Tibetan PlateauChina[87]

-

Resources Science

1007-7588

2015

0.9328

8

High-resolution mapping of global surface water and its long-term changes[88]

10.1038/nature20584

Nature

0028-0836

2016

40.137

9

Reconstruction of the historical crop land data in China using the partition and layering-based gridded method [89]

10.11821/dlxb201607005

Acta Geographica Sinica

0375-5444

2016

2.799,4

10

Tracing changes in atmospheric moisture supply to the drying southwest China[90]

10.5194/acp-17-10383-2017

Atmospheric Chemistry and Physics

1680-7316

2017

5.509

11

The dynamic response of lakes in the Tuohepingco basin of the Tibetan Plateau to climate change[91]

10.1007/s12665-017-6446-7

Environmental Earth Sciences

1866-6280

2017

1.435

12

Glacier changes on the Tibetan Plateau derived from Landsat imagery: mid-1970s-2000-13[92]

10.1017/jog.2016.137

Journal of Glaciology

0022-1430

2017

3.2

13

Spatial distribution and variation of precipitation in the Qiangtang Plateau[93]

10.11821/dlyj201711002

Geographical Research

1000-0585

2017

2.5263

14

GIS dataset of boundaries among four geo-eco regions of China [94]

10.3974/geodp.2018.01.08

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

15

Global change research data publishing & sharing rankings[60]

10.3974/geodp.2018.03.01

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

16

Analysis to the highest impact dataset 2018 from the geographical society of China[61]

10.3974/geodp.2018.03.02

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

17

Mapping human influence intensity in the Tibetan Plateau for conservation of ecological service functions[63]

10.1016/j.ecoser.2017.10.003

Ecosystem Services

2212-0416

2018

4.395

18

Spatiotemporal patterns of vegetation greenness change and associated climatic and anthropogenic drivers on the Tibetan Plateau during 2000-2015[95]

10.3390/rs10101525

Remote Sensing

2072-4292

2018

3.406

(To be continued on the next page)

 

 

 

(Continued)

 

No.

Title

DOI of Citing Paper

Citing Journal

ISSN

Citing Date

Impact Factor

19

Scenarios simulation of vascular plant species abundance distribution on Qinghai-Tibet Plateau[96]

10.11821/dlxb201801014

Acta Geographica Sinica

0375-5444

2018

3.015,7

20

Soil carbon stocks are underestimated in mountainous regions[97]

10.1016/j.geoderma.2018.01.029

Geoderma

0016-7061

2018

3.74

21

Grassland ecosystem responses to climate change and human activities within the Three-River Headwaters region of China[98]

10.1038/s41598-018-27150-5

Scientific Reports

2045-2322

2018

4.122

22

Current challenges in distinguishing climatic and anthropogenic contributions to alpine grassland variation on the Tibetan Plateau[99]

10.1002/ece3.4099

Ecology and Evolution

2045-7758

2018

2.34

23

The ecosystem carbon storage dataset in Tibet (2001-2010)[100]

10.3974/geodp.2018.01.11

Journal of Global Change Data & Discovery

2096-3645

2018

0.12

Sum in five years (2014-2018): cited 23, DIS 94.067,2, mean of the citing journal’s impact factor 4.1

8 Discussions

The practice of data impact measuring with the DIS methodology for global change and earth system sciences data in 2019 shows that the quantitative assessment of the data impact can reach to every datasets, data authors and data publishers. There is a phenomenon that the data citation is normally delaying a few years than that of discovery papers. From table 3 and 4, the two cases published by GCdataPR, either a single dataset or a data publisher, the citation delaying is at least 5 years. There may be several reasons. First, it may be that the data published was not well-known at its earlier stage, while more and more popular with year by year. Second, the scientific achievements made by the dataset re-using are different from the scientific discovery. It requires a process of data re-application, re-integration and then discovery, which takes longer than the time of the paper directly adopting the discovery viewpoints. Third, it is possible that data users are not very familiar how to cite the dataset, some of users mentioned their uses in acknowledgement instead of references. Because the data citation is a new specification, as time goes by, the data application and citation in reference standardization could increase.

As the current statistical data are all datasets published in China, according to the statistics of users’ source of download usage, the Chinese users are far more than that of foreign users. However, in terms of data citation, the number of citations of Chinese journals is less than that of English journals. This phenomenon indicates that Chinese users probably do not clear how to have data citation in references, so they cannot be counted in the DIS model. Therefore, it is necessary to further strengthen the publicity and requirements of new regulation and scientific ethics to promote the data citation in China and the editors of each journal also need to strictly check and take the data citation as one of the important part of the paper review.

The peak of the data citations occurs in the last year of the five-year statistics; does it happen in the fifth year or later? Does different dataset have different lag periods? How long could the data citation peak lag periods be, and when does the peak of data citation occur, it seems need more practice and take a longer time, as well as to have more sufficient data and cases to answer these questions.

Author Contributions

Liu, C. designed the DIS model and data processing procedure, and wrote the paper; Zhang, Y. H. set up the database framework, collect and process the data.

References

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[20] Li, J., Liu, Q. H., Zhao, J., et al.1 km / 5 d fractional vegetation cover dataset in the belt and road and surround region (2015) [DB/OL]. Global Change Research Data Publishing & Repository, 2017. DOI: 10.3974/geodb.2017.04.18.V1.

[21] Chen, J., Liao, A. P., Chen, L. J., et al.Global land surface water dataset at 30m resolution (2010) [DB/OL]. Global Change Research Data Publishing & Repository, DOI: 10.3974/geodb.2014.02.01.V1.

[22] Wang, L., Xu, X. L., Liu, L.Remotely sensed dataset of grassland degradation on the Qinghai-Tibetan plateau. Global Change Research Data Publishing & Repository, 2014. DOI: 10.3974/geodb.2014.01.11.V1

[23] Jia, L., Hu, G. C., Zheng, C. L., et al.1 km/daily evapotranspiration product over China and the association of southeast Asian nations for 2013 (2013) [DB/OL]. Global Change Research Data Publishing & Repository, 2015. DOI: 10.3974/geodb.2015.01.11.V1.

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[26] Jiang, D., Fu, J. Y., Huang, Y. H.Suitability assessment data on marginal land resources for cultivating bio-energy crops of Asia [DB/OL]. Global Change Research Data Publishing & Repository, 2014. DOI: 10.3974/geodb.2014.01.04.V1.

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Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum