Evaluation of unique identifiers used as keys to match identical publications in Pure and SciVal – a case study from health science

Limitations

We analyzed a case study from the health sciences and our results should be interpreted in context of the case. The results cannot be generalized, e.g. to other research areas. Furthermore we analyzed information on coverage, precision, and characteristics to detect errors in the matching process between Pure and SciVal. This type of information did not allow us to detect all types of errors. If a publication was assigned a wrong UID in one of the databases and thus wrongly matched we may not have detected this. In a sample of the apparently correctly matched publications we checked if the matched publications were identical and found no errors.

Throughout the study we use the term publication even though we did not analyze the full content of a publication but only parts of its metadata representations in Pure and SciVal; mainly UIDs, publication type, language, and journal. If a publication had more UIDs assigned to it in one of the databases we assumed that each of the UIDs represented the same publication. We do not know the details of the matching process between Pure and SciVal, but assumed that if one UID did lead to a match then the other UIDs assigned to the same publication were not taken into account. From SciVal we did not get any reports on contradicting UIDs for a publication. Such contradictions could question if there was a match or not.

We describe the characteristics of two publication sets from Pure and the two matched publication sets from SciVal, but it is beyond the scope of this paper to do a thorough analysis of the metadata quality of the Pure and SciVal publication sets. Also we limit the discussion of bibliometric indicators to three basic bibliometric indicators. More advanced indicators and their construction in SciVal are not discussed.

Literature review

Evaluations of UIDs as match keys to detect identical publications in different databases or duplicates in one database were identified. Information on search terms, search strategy and databases are given below. UIDs as match keys have been used for many applications, but our focus was on research publications, with a particular interest in how the method may affect bibliometric analysis. Thus, the search was limited to studies where UIDs of publications are used as the match keys or part of the match key, and in which the method is analyzed and discussed in some detail.

An exploratory search showed that the terminology for applying match keys to detect identical publications is not consistent. It is called citation linking or reference linking or simply matching or linking. Matching within the same database is called deduplication. The term citation matching is also used, but often for the more specific purpose where citing and cited publications are matched. We also saw examples of more general terminology, namely integration or interoperability between databases or retrieval strategy. In our subsequent searches the different terms for applying match keys to identify identical publications were combined (Boolean AND) with different terms for UID: unique, identifier, DOI, PMID. This gave us an idea of which databases use UIDs as match keys, e.g. CrossRef, Mendeley, and Altmetric.com. We also included these databases as search terms and combined them with the different terms for UIDs.

The searches were conducted in WoS (https://login.webofknowledge.com/), Scopus (https://www.scopus.com/), and Google Scholar (https://scholar.google.dk/). No range of years was specified. If no relevant publications were found in WoS and Scopus, we continued the search in Google Scholar. This means that not only peer-reviewed research but also a few reports were included in the literature review. In relevant publications, we manually scanned references and citations for other relevant publications. The searches were done in August and September 2015 followed by later supplementary searches based on the references found in August and September.

Case study

In the case study we explored information about coverage, precision, and characteristics of publications matched versus not matched in the integration between Pure and SciVal to detect errors, if any, in the matching process. Our publication set is from the Department of Clinical Medicine (DoCM) at University of Copenhagen (UCPH). DoCM registers approximately 2,000 research publications in the UCPH Pure database per year. The majority are peer-reviewed journal publications in English. As this type of publication and the health sciences are well-covered in Scopus/SciVal (Mongeon & Paul-Hus, 2016, p. 218–219+222; Valderrama-Zurián et al., 2015, p. 570–571), we expected the DoCM publication set to be well-fitted for our purpose, namely to explore the matching process, rather than how well Scopus/SciVal covers publications from a department.

The publication set was limited to research publications published in 2014, registered and validated in Pure. Publications published before 2014 were not included as these have been validated at department or group level and the data quality is not consistent as no common practice was in place. The validation of publications from 2014 was undertaken by the authors of this article and three information specialists from the University Library as a service for the Faculty of Health and Medical Sciences. As part of the validation process, language and publication type was determined according to the categories available in Pure. This information is utilized in the Results section. However, the focus of the validation was not UIDs as match keys, and fields for UIDs were not mandatory. If PMID or EID was registered in Pure, it is most likely because the publication was imported from PubMed or Scopus. A publication in Pure without a UID may not have a UID, or the UID is simply not registered in Pure. As mentioned in the Introduction, from October 2015 Elsevier matches publications in Pure with Scopus and attributes EIDs to new publications and retrospectively.

Our choice of case implies some limitations. The publication sets have too few non-journal publications to draw conclusions on their coverage and the precision in publications matched versus not matched with UIDs as match keys. Furthermore, the publication year 2014 gives the publications too short of a time since publication to obtain robust citation counts.

The case study alone did not lead to generalizable results, but the results were compared to findings from the literature review to identify trends and compatibility with previous studies.

Before we analyzed the outcome of the matching process based on UIDs as match keys, we downloaded, merged, and cleaned data from Pure and SciVal. This process was carried out in August 2015 (n=2068) and repeated in December 2015 (n=2066). It is possible for researchers and administrative staff to make retrospective changes to the registrations in Pure; this is the most plausible explanation for the lower number of publications in December.

Data software

Data download, merging and cleaning

Raw data was downloaded from Pure in August and December 2015 using the following filters:

To fit relevant data in just one worksheet in Excel and be able to create a .csv file, most of the data columns were deleted, and only the following kept:

Due to an error in the Copenhagen University Pure at the time, it was not possible to download a full data report of publications with the DOI column. Instead, first an ungrouped raw data report was downloaded, then the same report grouped on DOI. The two reports were matched on Pure ID to create one list with DOI data where available.

The Data set 1 DoCM Pure data August.csv and Data set 2 DoCM Pure data December.csv files comprise our "raw" Pure data – ever so slightly tidied to a) create one full data report with DOI where available, b) fit relevant columns in one worksheet to be able to create a .csv file.

The Pure "raw" data was furthermore cleaned by:

After the Pure data was sent to SciVal for analysis, the resulting SciVal publication sets (August and December) were downloaded from SciVal with the following information:

The Data set 3 DoCM SciVal data August.csv and Data set 4 DoCM SciVal data December.csv files comprise our raw SciVal data.

The SciVal raw data was furthermore cleaned by:

A note on some Article in Press occurrences in the SciVal data:

In the Results section, characteristics for three groups of publications are shown: Publications with UID exported from Pure to SciVal and matched, publications with UID exported from Pure to SciVal and not matched, and publications without UID not exported from Pure to SciVal. The publications without UID (DOI, PMID, or EID) were extracted from the cleaned Data set 1 and 2 with Pure data. To identify publications exported from Pure to SciVal and matched, we compared UIDs (DOI, PMID, or EID) in the cleaned Data set 3 with UIDs in the cleaned Data set 1 (August download). Publications in Data set 1 with no corresponding UID in Data set 3 constitute publications with UID exported from Pure to SciVal and not matched. This was repeated for Data set 2 and 4 (December download). The EIDs attributed automatically to publications in Pure were not visible in our raw data. We found 32 publications in Data set 4 from SciVal which must have an EID in Pure and SciVal as no other UID was assigned to them. Finally we compared UIDs in the cleaned Data set 3 and 4 to identify publications matched in SciVal in December but not in August.

Results of the literature review

The literature review shows two trends. Firstly, the publication year of relevant studies is 2011 or later. Older UIDs such as arXiv ID and PMID do not seem to have the same momentum as DOI. Secondly, the use of UIDs as match keys in bibliometric studies and citation indexes seems under-reported. A possible explanation is that the commercial players do not publish their methodologies in full detail (Olensky, 2014, p. 3). However, in a study from 2015, two bibliometric research groups provide documentation for how they use DOI as part of their match keys (Olensky et al., 2015, p. 7–9).

If we do not focus on the matching process in citation indexes and bibliometric analysis alone, we find an increasing number of tools for handling and analyzing research publications, e.g. CrossRef’s cited-by links (CrossRef, 2015a) and Altmetric.com’s embeddable badges (Altmetric.com, n.d.). Evaluations of these databases were also included in our literature review. But also for these tools evaluations of UIDs as match keys are rare.

We included 17 studies in the literature review. Two of the studies’ main focus was to evaluate the integration of UIDs in existing systems and are in that sense comparable to our objective. For a small publication set, Zahedi et al. (2014) evaluated the quality of Mendeley metadata by a comparison with Web of Science metadata. The DOIs did not match for 15 publications (8%). Missing DOI or erroneous special character recognition were identified as error types. Franceschini et al. (2015) showed that a DOI in Scopus can point to multiple not identical publications. These error types were included in the overview in Table 1.

The rest of the studies used UIDs as match keys for different purposes and in doing so discussed how well the matching process worked. Different from our study, the main focus regarding the UIDs was the coverage. For all the studies, UIDs did not cover all publications potentially relevant for the analyses. Some studies only included publications with UIDs, and others supplemented the match keys with other types of bibliographic information to include publications without UIDs. We provide a short summary of how UIDs were used in the studies. Keep in mind that UIDs are used in many other studies and tools, but with very limited or no documentation and discussion of the UIDs.

The vast majority of the studies (ten) analyzed altmetric scores or altmetric methods. They used UIDs (DOI, PMID, and arXiv ID) sometimes in combination with other match key elements to match a publication set from WoS, Scopus, arXiv.org, a local database, or specific journals with identical publications in Mendeley, Almetric.com, Twitter, Wikipedia, Impact Story, CiteULike, BibSonomy, Connotea, or a selection of Elsevier databases (Bar-Ilan et al., 2012; Costas et al., 2014; Haunschild & Bornmann, 2016; Haustein et al., 2014; Haustein & Siebenlist, 2011; HEFCE, 2015; Kraker et al., 2015; Nuredini & Peters, 2015; Zahedi et al., 2014a). Two studies analyzed deduplication via UIDs (DOI, PMID, and arXiv ID) and other match key elements in Mendeley (Hammerton et al., 2012) and for a metasearch engine which covered five biomedical datadases (Jiang et al., 2014). One study created a citation index and used DOIs and other match key elements to match citing and cited publications (Kim & Kim, 2013). One report tested the interoperability between Researchfish and local CRISs via DOI or PMID (Research Councils UK, 2015). Finally, two studies reported on missing citations in WoS and Scopus. DOI was used to match identical publications in the two databases (Franceschini et al., 2013; Franceschini et al., 2014). Nine studies, in addition to the coverage of UIDs, also addressed the precision or types of errors when UIDs are used as match keys. This information is central for our study. The types of errors are summarized in Table 1. As shown above the studies have used UIDs for different purposes. Still, we recognized the error types as relevant for our objective.

Results of the case study

In the case study we analyzed research publications (co-)authored by the Department of Clinical Medicine (DoCM) at the University of Copenhagen, published in 2014, and registered and validated in Pure. We evaluated the matching process based on information about coverage, precision, and characteristics of publications matched versus not matched with UIDs as the match keys to detect errors, if any, in the matching process.

The share of publications matched between Pure and SciVal, or the coverage, is 85.6% in August and 89.3% in December.

There are precision issues for a minor part of the publication sets. Three of the error types reported by other studies (Table 1) are also present in our publication sets.

Duplicate DOIs (Table 1): An automatic report from SciVal states that 1837 publications (August) and 1876 publications (December) are matched with our Pure publication sets. These numbers are inflated due to DOI duplicates (Table 2).

From August to December the number of duplicates decreases partly due to Scopus’s automatic cleaning process, where an Article in Press is deleted after the published version is registered in Scopus. We have discussed our results with consultants from Elsevier’s SciVal team and this has led to a correction of some of the other duplicates. It may also have had an effect that Elsevier in October 2015 started adding EIDs automatically to publication records in Pure.

Incorrect DOIs in reference lists and databases & DOIs not registered by the database where a bibliometric analysis is performed (Table 1): In the August and December publication sets, respectively 5 and 2 of the DOI duplicates are examples of publications assigned a wrong DOI in Scopus (Table 2). For a 10% sample of the remaining matched publications in the August and December publication sets we verified the DOIs. The publications were sorted by DOI and every tenth publication was searched in Scopus, PubMed, and CrossRef where title, authors, journal, and start page were compared. No errors were identified, but two publications did not have their DOIs registered in Scopus. Furthermore, we checked the 77 publications not matched in SciVal in August but matched in December. Of these, 36 publications have a DOI in our Pure publication set. No errors were found in Scopus. But as the publications were unmatched in August, DOIs or other UIDs must have been missing or been incorrect in Scopus in August or the publications were not indexed in Scopus in August.

We now turn to the characteristics of the publications in our publication sets. In Table 3–Table 7, the general characteristics of publications matched versus not matched in the integration between Pure and SciVal are presented. We have a particular interest in the publications’ UIDs as these are essential for a possible match. Publication type and language can give us an indication of whether all potential matches are made. We expected journal publications in English to be matched because they are well-covered in Scopus/SciVal. Table 3 gives an overview of how many publications were matched and unmatched. For the unmatched publications we also show how many have a UID.

In Table 4a & Table 4b we focus on the types of UIDs for the matched and the unmatched publications.

DOI is the most common UID (Table 4a) but nearly as many publications have a PMID. This was expected as the majority of the publications were imported from PubMed to Pure in our specific publication set. In the August publication set, very few publications had an EID; most likely because Scopus is not commonly used for import to Pure by DoCM. In the December set we could not analyze the EIDs, as automatically attributed EIDs are not shown in our Pure reports of raw data. According to this report, 10 publications had an EID. But at least 32 additional publications in our Pure publication set from December had an EID as no other UID is assigned to them in our Pure raw data and they were matched in SciVal.

The unmatched publications with a UID are shown in Table 4b. PMID is the most common UID, up to 90%. Close to 40% of the publications have a DOI. For the December publication set, we assume that the unmatched publications have no EID, otherwise they should have been matched.

In the following three tables we analyzed publication type as registered in Pure. Notable, but not surprising, is that close to 100% of matched publications are journal contributions (Table 5a), as these are usually well-represented in Scopus/SciVal. What is surprising, however, is that practically the same percentage of unmatched publications with a UID is journal contributions (Table 5b). For the publications without a UID (Table 5c) there are still many journal publications, approximately 60%, but a much lower share than for the publications with a UID. The distributions among publication types do not differ substantially between the August and December publication sets. All publication sets include very few non-journal publications.

We also analyzed the languages of the publications. Concerning the matched publications, 99% are written in English. Interestingly, the absolute number of matched publications in other languages increased from 4 to 27 between August and December (Table 6a). Elsevier’s automatic assignment of EIDs may improve the match for these publications in our specific setting. However, our publication set is far too small to draw any conclusions from. For the unmatched publications with and without UID in the August and December publication sets, the ratios between English and other languages are close to fifty-fifty (Table 6b and Table 6c).

Our analysis reveals more journal publications in English not matched in SciVal than we expected. Therefore we extracted lists of the top journals according to number of publications from our publication sets. For the unmatched publications a large share is published in the two journals of the Danish Medical Association (Ugeskrift for Læger and Danish Medical Journal). Both are indexed by Scopus. Interestingly, we see Ugeskrift for Læger represented among the matched publications, the unmatched publications with UID, and the unmatched publications without UID. Also PLOS ONE publications are among both the matched and the unmatched publications, but not among publications without a UID. Three of six unmatched PLOS ONE publications from the August publication set are matched in December. The remaining three PLOS ONE publications were still not registered in Scopus in December. Table 7b and Table 7c includes more journals which are indexed by Scopus but the publications are not matched. For example Clinical Nutrition (cf. Table 7c) with 299 publications from 2014 indexed in Scopus, and Clinical and Translational Allergy (cf. Table 7b) with only 4 publications from 2014 indexed in Scopus. This may indicate some shortcomings in the Scopus indexing procedures. For our publication sets this seemed to be the biggest problem for a successful matching of publications with an UID. These results suggest that the missed matches were due to missing publications in Scopus. Another explanation could be missing UIDs in Scopus. This was detected in a study from 2012 (Bar-Ilan et al., 2012, p. 101). In addition to this, 35 publications from the August and December publication sets were from journals not indexed by Scopus according to Scopus’ Content Coverage Guide.

In summary, the literature review shows that only a few studies report findings on UIDs as match keys. Results on coverage are reported and errors in the matching procedure are less frequently addressed (Table 1).

The findings from the case study show that the majority of the publications were matched (85.6% in August and 89.3% in December). Almost all the matched publications have a DOI and are journal publications in English. Among the matched publications, 67 (3.8%) in the publication set from August have a duplicate DOI, whereas 32 (1.7%) from December do. Other error types (Table 1) were observed which lowered the precision of the match between Pure and SciVal. Still, duplicate DOIs are the most prevalent problem. However, both coverage and precision have improved from August to December. This can be explained to some extent by Scopus’s automatic merging of Article in Press and the published version. Elsevier’s procedure of adding EIDs to publications in Pure may correct other duplicates and improve the coverage. Finally, duplicates may have been corrected manually by Elsevier in Scopus.

The unmatched publications also include journal publications. Close to half of these are in Danish and published in the journal Ugeskrift for Læger of which the indexing in Scopus is highly irregular. Our analysis indicates that journals with publications in English also suffer from similar irregular indexing but to a much lesser extent.

The matching of publications across databases can be used to create publication sets for bibliometric analysis. As an example we now briefly discuss how publication sets formed by using UIDs as match keys may affect the bibliometric indicators number of publications, number of citations, and the average number of citations per publication. This is to our knowledge only discussed briefly in the two studies. They both conclude that duplicate DOIs can lead to errors in bibliometric analysis (Franceschini et al., 2015, p. 2186; Valderrama-Zurián et al., 2015, p. 575).

The coverage can affect bibliometric indicators. Results from our case study indicated that the majority of the publications from Pure are matched correctly in SciVal. Yet, the difference between the August and the December publication sets and the analysis of top journals (Table 7a–Table 7c) show that coverage can be improved. This means that the number of publications and citations could be higher in a bibliometric analysis based on our publication set. We do not know the number of citations for the publications not matched but based on our knowledge about journals not indexed fully by Scopus we discuss the potential consequences for number of citations. Ugeskrift for Læger has over 100 publications that are not covered in Scopus/SciVal. The journal is not highly cited (Scopus 2014 IPP = 0.127, SNIP = 0.109) so inclusion of the missing publications would probably increase the number of citations a little, but lower the average number of citations per publication. However, inclusion of the missing publications for other journals could potentially have the opposite effect and increase the average number of citations per publication. An example is PLOS ONE (Scopus 2014 IPP = 3.270, SNIP = 1.034).

The precision of a bibliometric indicator is distorted by the fact that some DOIs are matched multiple times in SciVal. In most cases it is due to a duplicate of the same publication, but we also observed instances of publications in our Pure publication set with a DOI duplicate in SciVal not present in the Pure set (Table 2). The duplicates have several implications for the bibliometric indicators number of publications, number of citations, and the average number of citations per publication:

The number of publications becomes inflated by inclusion of duplicates. In our publication sets from August and December the publication count increased by 3.8% and 1.7%, respectively. Therefore we recommend that when the number of publications is calculated, duplicates should be removed whether the duplicate publication is in the original Pure publication set or not.

Before citations are counted, all duplicates not present in the Pure publication set must be deleted. For the remaining duplicate pairs we found that sometimes both duplicates were cited independently. In all instances except one there was no overlap between the citations. Citations divided between duplicates in Scopus are also reported in another study where variations of a journal name results in duplicates in Scopus. It is suggested that databases like Scopus can improve verification of DOIs to solve the duplicate problem (Valderrama-Zurián et al., 2015). Publications were from 2014 and the export from Pure to SciVal was done in August and December 2015. So the publications had a very short time to attract citations. In total the publication set from August had 4,982 citations, but if citations for the duplicates were included the number was 5,047. This is an additional 1.3% citations. For the publication set from December the total number of citations has increased to 7,695, and to 7,720 if citations for duplicates are included. The duplicates account for 0.3% additional citations. In our study, many of the duplicates removed were Article in Press whereas the Article version was kept.

The calculation of the average number of citations per publication should not include duplicates in counting publications but include duplicates in counting citations. If duplicates are kept the average number of citations per publication will be too low. If the duplicates are removed some of the citations may also be discarded and again the average number of citations per publication will be too low.