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www.know-center.at
Exploring Coverage and Distribution of
Scholarly Identifiers on the Web
14th International Symposium of Information Science
Zadar, 21 May 2015
Peter Kraker, Asura Enkhbayar & Elisabeth Lex

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Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
The Emerging Eco-system of Scholarly Services on the Web

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Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
The Emerging Eco-system of Scholarly Services on the Web

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Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Research Questions
• How are scholarly identifiers distributed in crowd-
sourced systems on the Web?
• Does the provision of different identifiers have an
influence on findability of scientific publications in other
bibliographic and bibliometric sources?
• Who are the top providers of identifiers?
• Which identifier combinations are the most common?
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Indentifiers on the Scholarly Web
Article level
Publication levelAuthor level
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Data & Method
Data Sources: arXiv, CrossRef
and Mendeley
arXiv discipline of quantitative
biology (q-bio), 1992-2014
(n=14,195 metadata records)
Data collection: 17/11/2014
arXiv discipline of astrophysics
(astro-ph), 2009-2014
(n=81,814 metdata records)
Data collection: 04/03/2015
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
arxiv-ID, DOI
DOI
• arXiv-ID
• DOI
• Scopus-ID
• Pubmed-ID
• ISSN

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Distribution of arXiv Articles Over Time: q-bio
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Distribution of arXiv Articles Over Time: astro-ph
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Results of the Data Collection Process
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
36.1%
50.0%
81.5%
29.8%
76.8%
86.1%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
docs with DOI on
arXiv
docs with DOI docs found on
Mendeley
q-bio (1992-2014; n=14,195) astro-ph (2009-2014; n=81,814)
Change in the
metadata lookup

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Findability on Mendeley: q-bio (1992-2014)
11Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Total documents
14,195
Documents
with DOI & arXiv-ID
7,100 (50.02%)
Documents
with arXiv-ID
7,095 (49.98%)
Documents retrieved
on Mendeley
with DOI
6,492 (91.44%)
Documents retrieved
on Mendeley
with arXiv-ID
4,896 (69.01%)
Documents retrieved
on Mendeley
with arXiv-ID
5,414 (76.25%)

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Findability on Mendeley: astro-ph (2009-2014)
12Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Total documents
81,814
Documents
with DOI & arXiv-ID
62,800 (76.8%)
Documents
with arXiv-ID
19,014 (23.2%)
Documents retrieved
on Mendeley
with DOI
54,093 (86.1%)
Documents retrieved
on Mendeley
with arXiv-ID
12,812 (67.4%)
Documents retrieved
on Mendeley
with arXiv-ID
45,586 (72.6%)

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Findability on Mendeley & DOI Coverage: q-bio
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Findability on Mendeley & DOI Coverage: astro-ph
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
2009 2010 2011 2012 2013 2014
findability
DOI availability

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Identifier Frequency and Mean Readership on
Mendeley
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
arxiv doi scopus pmid issn
q-bio (n=11,570)
frequency
10,351
(89.5%)
8,321
(71.9%)
8,409
(72.7%)
5,477
(47.3%)
8,119
(70.2%)
mean readership 20.4 25.4 25.4 32.4 25.9
median readership 9 13 13 19 14
astro-ph (n=70,459)
frequency
65,719
(93.3%)
61,667
(87.5%)
58,780
(83.4%)
1,454
(2.1%)
60,985
(86.6%)
mean readership 7.2 7.3 7.5 20.6 7.3
median readership 5 5 13 5 6
StdDev: 33.4

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Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
Distribution of Disciplines on Mendeley (March 2011)
[Kraker et al.
2012]

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Identifier Combination Frequency on Mendeley
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
39.2%
22.8%
18.2%
4.4%
4.1%
2.7%
0.0%
0.0%
0.0%
8.5%
1.3%
10.2%
74.5%
0.0%
0.0%
4.5%
2.6%
1.7%
1.5%
3.7%
0% 20% 40% 60% 80% 100%
arxiv-doi-issn-pmid-scopus
arxiv
arxiv-doi-issn-scopus
doi-issn-pmid-scopus
arxiv-scopus
doi-issn-scopus
doi-arxiv-issn
doi-issn
doi-arxiv
Other
astro-ph (2009-2014; n=81,814) q-bio (1992-2014; n=14,195)

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Conclusions
• As expected, crowd-sourced systems show big differences
in identifier coverage and distribution  crowd-sourced
data needs to be amended automatically
• When retrieving arXiv articles from Mendeley, we were able
to obtain more articles using the DOI than the arXiv-ID
• BUT: a single arXiv-ID is the second most popular identifier
combination on Mendeley. This suggests that pre-prints are
being read – if at a lower level
• There is, however, a certain time lag concerning the
adoption of articles from arXiv on Mendeley
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Conclusions
• The distribution of identifiers in a collection of papers
gives hints at the nature of papers in this collection
• As with citations, field normalization will be essential for
cross-comparison of altmetrics scores
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Limitations & Future Work
• Choice of arXiv as primary data source introduces a bias
• We only looked at two disciplines from natural sciences
• Using a random sample of 381 articles from Web of Science,
Zahedi et al. (2014) report that they were able to retrieve only
47.7% of articles on Mendeley using the DOI or the title.
Expand this study to further disciplines and fields using
more data sources
 Look more deeply into the reasons for disciplinary and
tool-related differences
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics

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Open Source Crawling Framework
Know-Center GmbH • Research Center for Data-Driven Business and Big Data Analytics
https://github.com/Bubblbu/crawling-framework

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SAVE THE DATE: i-KNOW Conference
Special Track on Science 2.0 & Open Science
21-23 October 2015, Graz, Austria
Submission Deadline (extended): 22 June 2015 (Abstracts due: June 8)