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How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications

Abstract

In this paper an analysis of the presence and possibilities of altmetrics for bibliometric and performance analysis is carried out. Using the web based tool Impact Story, we collected metrics for 20,000 random publications from the Web of Science. We studied both the presence and distribution of altmetrics in the set of publications, across fields, document types and over publication years, as well as the extent to which altmetrics correlate with citation indicators. The main result of the study is that the altmetrics source that provides the most metrics is Mendeley, with metrics on readerships for 62.6 % of all the publications studied, other sources only provide marginal information. In terms of relation with citations, a moderate spearman correlation (r = 0.49) has been found between Mendeley readership counts and citation indicators. Other possibilities and limitations of these indicators are discussed and future research lines are outlined.

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Notes

  1. Being immediately available compared to citations that take time to accumulate.

  2. Previously known as Total Impact, we use IS in this study to refer to Impact Story. For a review of tools for tracking scientific impact see Wouters and  Costas (2012).

  3. For a full list see http://impactstory.org/faq.

  4. A REpresentational State Transfer (REST)(ful) API (Application Programming Interface) used to make a request using GET (DOIs) and collect the required response from impact Story.

  5. In the previous study, the data collection was performed manually directly through the web interface of IS. Manually, IS allowed collecting altmetrics for 100 DOIs per search and maximum 2000 DOIs search per day in order to avoid swamping the limits of its API, for details see Zahedi, Costas & Wouters (2013).

  6. The additional functionality from the “proc groovy” which is a java development environment added to SAS (Statistical Analysis Systems) environment for parsing and reading the JSON format and returning the data as an object.

  7. From IS one DOI was missing. We also found that 301 DOIs were wrong in WoS (including extra characters that made them unmatchable, therefore excluded from the analysis). Also 61 original DOIs from WOS pointed to 134 different WOS publications (i.e. being duplicated DOIs). This means that 74 publications were duplicates. Given the fact that there was no systematic way to determine which one was the correct one (i.e. the one that actually received the altmetrics), we included all of them in the analysis with the same altmetrics score resulted in: 20,000 − 1 − 301 + 74 = 19,772 final publications. All in all, this process showed that only 1.8 % of the initial DOIs randomly selected had some problems, thus indicating that a DOI is a convenient publication identifier although not free of limitations (i.e. errors in DOI data entry, technical errors when resolving DOIs via API and also the existence of multiple publication identifiers in the data sources, resulted in some errors in the full collection of altmetrics for these publications).

  8. It means that publications without any metrics were left out of the analysis.

  9. This was the only PLOS paper captured by our sample.

  10. Non-citable document type corresponds to all WOS document types other than article, letter and review (e.g. book reviews, editorial materials, etc.).

  11. In Delicious, articles, non-citables, letters and review papers have the highest number of metrics orderly.

  12. Average metrics per publications calculated by dividing the total numbers of metrics from each data source by total number of publications in the sample. For example, in Mendeley, average number of readers per publication equals to 99,050/19,772 = ~5.

  13. In the previous study, we used the NOWT (Medium) with 14 subject fileds. For more details see: http://nowt.merit.unu.edu/docs/NOWT-WTI_2010.pdf.

  14. Here publications can belong to multiple subject categories.

  15. According to the Global Research Report by Mendeley (http://www.mendeley.com/global-research-report/#.UjwfTsanqgk), coverage of Mendeley in different subjects are as follows: the highest coverage are by publications from Biological Science & Medicine (31 %), followed by Physical Sciences and Maths (16 %), Engineering & Materials Science (13 %), Computer & Information Science (10 %), Psychology, Linguistics & Education(10 %), Business Administration, Economics & Operation Research (8 %), Law & Other Social Sciences (7 %) and Philosophy, Arts & Literature & other Humanities (5 %).

  16. For 9 fields (8 fields from Art and Humanities and 1 field from Science) CPP and RPP scores were exactly the same.

  17. In 2005, the two most tweeted papers are from the field of Physics, they received more than half of the total tweets in this year (472 tweets), thus showing a strong skewed distribution.

  18. Calculating Spearman correlation analysis in SPSS for large datasets gives this error: "Too many cases for the available storage", for overcoming this limitation, we followed the process we mentioned in the text. For more details see: http://www.ibm.com/support/docview.wss?uid=swg21476714.

  19. Impact Story, was in an initial stage of development (i.e. in a ‘Beta’ version) at the moment of development of this study.

  20. For current limitations of IS see: http://impactstory.org/faq#toc_3_11.

  21. The time interval between the first and the second data collection was 6 months and data collection done manually versus the second one which done automatically using RESTAPI calls.

  22. Reasons for these differences can be the changes/improvements in the identification of publications by Mendeley (e.g. by merging version of the same paper, identifying more DOIs, increments in the number of users in Mendeley, etc).

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Acknowledgments

This study is the extended version of our research in progress paper (RIP) presented at the 14th International Society of Scientometrics & Informetrics Conference (ISSI) Conference, 15-19 July, 2013, Vienna, Austria. We thank the IS team for their support in working with the Impact Story API. This work is partially supported by the EU FP7 ACUMEN project (Grant agreement: 266632). The authors would like to thank Erik Van Wijk from CWTS for his great help in managing altmetrics data. The authors also acknowledge the useful suggestions of Ludo Waltman from CWTS and the fruitful comments of the anonymous referees of the journal.

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Zahedi, Z., Costas, R. & Wouters, P. How well developed are altmetrics? A cross-disciplinary analysis of the presence of ‘alternative metrics’ in scientific publications. Scientometrics 101, 1491–1513 (2014). https://doi.org/10.1007/s11192-014-1264-0

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  • DOI: https://doi.org/10.1007/s11192-014-1264-0

Keywords

  • Altmetrics
  • Impact Story
  • Citation indicators
  • Research evaluation