Methodology and Practice on Quantifying 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 measure the impact of the datasets, as well as the data authors, and 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 DIS of each dataset among the 192 cited datasets published by the Global Change Research Data Publishing & Repository (GCdataPR) and the Geoscientific Data & Discovery Publishing System during 2014–2018 were calculated using the DIS model. Besides, the DIS of each author among the 565 dataset authors and two data publishing centers were also calculated using the DIS models. There are totally 324 citations with the DIS of 134.069,4 during 2014–2018. Taking 5%, 15%, 30% and 50% as the critical values, all the datasets were classified into Q1, Q2, Q3 and Q4 levels. 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 centers. The DIS methodology can 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 spatial-temporal scales (from global to regional and local, from instantaneous to monthly, yearly, …, historical and geological), and in different data formats(vector, raster or table based). The data is a necessary resource for global change, 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 Data Impact Score (DIS) methodology^[1] since 2018. Through quantitatively measuring with Data Impact Score, the first 5% and the first 5%–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 during 2014–2018. The aim of the call is to encourage open good-quality data to be reused by citation in the broad fields of earth system science 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 can be archived in the data impact measuring database, are as follows:

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

(2) The dataset is 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 is openly accessible through 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 (the first 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 datasets published through it 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 Science

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

According to the process of data impact assessment of global change and earth system science 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 citing journals^[5] collected, the citing journals’ IF Sub-database was established; finally, according to DIS model^[1] and the above information, the DIS_dataset (2014–2018) was calculated.

Taking the first 5%, 15%, 30% and 50% of DIS_dataset (2014–2018) as the critical values, the Q1, Q2, Q3, and Q4 of DIS_dataset (2014–2018) were identified.

Based on the DIS_dataset (2014–2018) and DIS models^[1] for measuring the data authors, funding agencies and projects, the author affiliations, and data publishers, the DIS for each of them was calculated separately.

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

3 Database Establishment for Measuring Impact of Published Datasets 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 dataset;

(3) Impact Factors of each citing journal;

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

(5) DIS results and ranking for each of dataset, data authors, data authors’ affiliations, funding agencies, funding 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 datasets were not included, because no dataset was published along with the data papers.

The contents of dataset information include: dataset title, 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 Dataset

For establishing the data citation sub-database for each dataset, the information of citing papers for each dataset from 2014 to 2018 was collected, including the title of dataset, title, DOI, and authors of citation paper, year of the paper published, journal title, journal ISSN, etc.

3.3 Impact Factors Sub-database of Each Citing Journal

The IF information of each citing journal in each year from 2014 to 2018 was collected to establish the IF sub-database for each citing journal.

3.4 DIS Model Sub-database

The DIS models are composed of several models, including:

(1) DIS model for dataset;

(2) DIS model for data publisher;

(3) DIS model for data author;

(4) DIS model for funding agencies;

(5) DIS model for funding projects;

(6) DIS model for ranking (Q1, Q2, Q3, Q4).

3.5 DIS and Ranking in 2019 (Based on Data During 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 funding agencies;

(5) DIS ranking for funding projects.

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

4.1 Dataset Impact Score (DIS_dataset)

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 authors of these datasets were 565 individuals.

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

                      (1)

where DIS_Dy 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 1  Statistics of 192 cited datasets in five years (2014–2018)

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

4.3 DIS_dataset Ranking for Four Portions

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

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

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

 (3) Q3: the 5-year DIS_dataset is ranking between 15%–30% with DIS more than 0.2, and there are 41 datasets on the list of Q3.

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

4.4 Analysis on DIS_dataset in Q1

There were 10 datasets ranking in Q1 as listed in Table 4. As can be seen from Table 4, the 10 datasets in Q1 were cited 115 times in total, with 75 times cited by English journals, accounting for 65.22%, and 40 times cited by Chinese journals, accounting for 34.78%. DIS_dataset calculated by English journal citations is 246.206, accounting for 78.02%, and by Chinese journal citation is 69.368,3, accounting for 21.98%. It is about 3.55 times for English journals as that for Chinese journals. The average IF of citing journals is 2.744,1. For both the citing times and DIS_dataset, English journals is higher than Chinese journals.

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

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

Table 4  List of DIS ranking top 5%–15% (Q2) datasets in five years (2014–2018)

5 DIS_A Ranking

According to the DIS model of data author^[1] and statistics data in 2019, the 5-year DIS_A (2014–2018) were obtained for each of the 565 data authors. There were 28 authors ranked Q1 (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 are 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 are from the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, accounting for 25%.

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

6 DIS_p Ranking

Based on the statistics of datasets published by each data publisher and the DIS_dataset of each dataset, the DIS_p for each publisher in 5 years was calculated. The calculation method is as follows:

                           (2)

where DIS_p 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 impact score of the i^th dataset 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 Geographic Sciences and Natural Resources Research (IGSNRR), CAS, and the Geographical Society of China (GSC) published 161 datasets, with credited DIS_p of 416.001,9, while the Geoscientific Data & Discovery Publishing System^[6] (DOI: 10.23650/) operated by the National Geolog-ical Archive and superintended by the Ministry of Natural Resources of P. R. China, published 31 datasets, with the DIS_p of 30.048,5 (Table 6).

Table 6  Statistics of DIS for data publishers in five years (2014–2018)

7 Examples of the Most Impact Dataset

Figure 3  Statistics 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, Jingying, Jang, Dong, and Huang, Yaohuan. During the five years from 2014 to 2018, it was cited for 42 times, and the total DIS_dataset was 113.508,6, 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 PM_2.5 in China via space-time regression” (DOI: 10.1016/j.rse 2017.12.018) published in the journal of Remote Sensing of Environment (ISSN 0034-4257) in 2018, and the journal’s IF was 6.457. The number of dataset citations increased year by year in the last five years (Figure 3). More detailed records of citations are listed in Table 7.

Table 7  Statistics of data citation on “1 km grid population dataset of China”??2014–2018??

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

Figure 4  Statistics of data citation on “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 are Zhang, Yili, Li, Bingyuan, and Zheng, Du. During the five years from 2014 to 2018, it was cited for 23 times, and the total DIS_dataset was 94.067,2, which was listed as top 5% (Q1) in 2019.

Among the 23 citing papers, the highest IF paper is “High-resolution mapping of global surface water and its long-term changes” (DOI: 10.1038/ nature20584) in Nature (ISSN 0028-0836) in 2016. The IF of Nature in 2016 was 40.137. The detailed records of the dataset citations are listed in Figure 4 and Table 8.

Table 8  Statistics of data citation on “Datasets of the boundary and area of the Tibetan Plateau” (2014–2018)

8 Discussion

The practice of data impact measuring with the DIS methodology for global change and earth system science data in 2019 shows that the quantitative assessment of the data impact can reach to every dataset, data author, and data publisher. There is a phenomenon that the data citation is normally delaying a few years than that of discovery papers. From Tables 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 is not well-known at its earlier stage, but it is more and more popular 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 a longer time than the paper directly adopting the discovery viewpoints. Third, it is possible that data users are not very familiar with how to cite the dataset, and some of users mention 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 standardization will 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 international 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 cite data in references, so they can’t 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 standard 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 5-year statistics; does it happen in the fifth year or later? Does different datasets 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, collected and processed the data.

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