Finding the Tipping Point in Automated Markup During Up-Translation

Caitlin Gebhard

Inera Incorporated

Copyright © 2017 by Inera Incorporated. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY) 4.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Finding the Tipping Point in Automated Markup During Up-Translation

Up-Translation and Up-Transformation: Tasks, Challenges, and Solutions
July 31, 2017

Up-Translation for Scholarly Publishing

While researchers continue to write primarily in Microsoft Word (with the exception of some disciplines preferring LaTeX), most systems surrounding the publication of scholarly manuscripts are driven by XML. Submission systems use XML to capture author and article metadata. Further, XML drives initiatives such as Crossref for digital object identifiers (DOIs), ORCID for author identifiers, and FundRef for funding information, not to mention the web and e-book platforms on which the content is published.

The scholarly publishing community, particularly in the STEM fields, has adopted the JATS (Journal Article Tag Suite) DTD and its sibling, BITS (Book Interchange Tag Suite) DTD, as the standard XML formats for published journals and books, respectively. JATS is the most recent incarnation of the NLM DTDs, originally released in 2003 by the National Center for Biotechnology Information (NCBI) of the National Library of Medicine (NLM). The NLM DTDs evolved from 2003 to 2008, at which time it was clear that they needed to be formally standardized. The DTDs were submitted to the National Information Standards Organization (NISO) in 2008, and in 2012, the ANSI/NISO Z39.96 (informally known as JATS) standard was published. The most recent version of this standard is JATS 1.1, revised in 2015 (ANSI/NISO Z39.96-2015).

Tagging Complex Content

The JATS and BITS models are quite rich and complex, befitting the varied and complicated content scholarly organizations publish every day. While this complexity allows publishers to be more flexible and quite granular with their content, it makes automated up-translation challenging. At the 2017 Journal Article Tag Suite Conference (JATS-Con 2017), a member of the International Standards Organization (ISO) discussed his experience in finding a vendor to convert ISO’s back-catalogue content to ISOSTS XML, a standards DTD based on JATS:

During the RFP process, we heard statements such as “we can provide 100% accuracy and 100% automation”. This is simply impossible, and Innodata were one of the more open providers initially offering 98% accuracy and being quite upfront this would be a semi-automated process, ie, a large part of the operation would require manual tagging. (Galichet 2017)

ISO sought an XML conversion process that would guarantee at least 99.95% accuracy, but found that this was impossible to accomplish with automation alone.

Likewise, the complexity of the content, the richness of the models, and what can only be called the human factor make it extremely difficult to perform high-quality, accurate up-translation through manual processes alone. Completely manual tagging is prone to headache-inducing inconsistencies and one-off errors that do not occur in an automated process For example, this author has seen instances where the publication-type attribute “journal” was misspelled:

<mixed-citation publication-type="jornal".../>

Because the publication-type attribute allows any value, the misspelled “jornal” type is valid according to the DTD, but it would throw a wrench in any system trying to access journal references tagged correctly:

<mixed-citation publication-type="journal".../>

Rather than attempt the herculean feat of high-quality up-translation through either fully automated or fully manual processes, scholarly content requires a careful balance of both. However, finding the tipping point between the two processes can be a challenge. In this author’s experience, a semiautomated process is is most effective for capturing certain types of metadata. In particular, publishers are often sensitive about the metadata located in the front matter. It is important for such metadata to be accurate so that citations to that record are (hopefully) correct. Bibliographic citation data are likewise important because they drive the statistics around the scholarly record, the journal, and the publisher.

Contributor Metadata

The accurate tagging of contributor metadata is not just a courtesy to the authors; it also drives many of the systems surrounding and influencing the scholarly community. An incorrectly tagged author name can result in overlooked credit or misattribution of credit, incorrect bibliographic references to the author that appear in subsequent papers, and confusion at funding organizations. As part of their Science Editor series on international naming conventions, the Council of Science Editors published several articles with guidelines for editing (and, therefore, parsing) author names from around the world. In one article on Spanish and Portuguese naming conventions, Bill Black highlights the importance of editing and parsing contributor names correctly: “It can help to ensure that other researchers and bibliographic database services that cite the articles give the credit due to the authors and to the editors’ journals and that other researchers who see the piece cited can easily locate abstracts of the articles or their full text” (Black 2003). Contributor metadata fuels many different aspects of the research and publishing ecosystem, and it is important to get it right.

Bibliographic Citation Metadata

Accurate, granular tagging of the manuscript’s references is just as important as tagging the manuscript’s author information. As Carol Anne Meyer explains in her article, “Reference Accuracy: Best Practices for Making the Links,” reference data accuracy is the key to providing better experiences for readers and for authors. She writes:

Reference citations in scholarly publishing have never been more important than in the age of electronic distribution. Not only do references cite relevant previous work, they also provide one of the most valuable functions in electronic publishing: an actionable, clickable link to more information about the citation. (Meyer 2008)

Reference linking impacts a publication’s measurability and discoverability, criteria that benefit both the author as well as the publisher. As Meyer 2008 explains, “any problems with reference accuracy can underestimate the impact of an author’s work or of a journal.” Editors can spend hours copy editing and correcting bibliographies, but errors introduced during the conversion to XML can undo all of that work in seconds.

Idiosyncrasies: Limitations of Automation

When we (human readers) look at this byline:

D Carol Bird MD, editor

we can identify patterns that inform how we would tag this paragraph according to the JATS XML model:

<given-names>D Carol</given-names></name>

We use white space to parse the individual parts of the byline and context to recognize what those parts are. For example, we know that when “MD” appears at the end of the author name, it is likely a degree and not an abbreviated given name. In theory, smart software could do all of this tagging automatically.

The problem is that the pattern-recognition algorithms used in smart software can only get you so far. What happens when smart software encounters a pattern that it is not programmed to recognize? Even worse, what happens when it encounters a pattern that looks exactly like something it can recognize but is actually something completely different?

Multicultural Contributions

Due to the variety of human naming conventions around the world, it is nearly impossible for a machine to correctly parse every author name it encounters. It is also fair to say that a human reader will not accurately parse every name she or he encounters, but humans have the added benefit of common sense, flexibility, and the Internet. When in doubt, a human reviewer can check her or his understanding of a name against reference sources (or ask the author). It is difficult to program a piece of software to question itself in the same way.

Here are some examples of cases that cannot be handled by automation alone.

Example 1: Multipart Names

A machine can easily be trained to parse names that have multiple given names and/or surnames, but without understanding the author’s background, the results will never be 100% correct. For example, a smart machine can use white space and a bias toward Western naming conventions to automatically parse and tag the multipart name “Jonathan Taylor Thomas” without error:

<contrib contrib-type="author">
<given-names>Jonathan Taylor</given-names></name>

If the same algorithm is applied to names from other cultural contexts, it will likely mis-parse the name. For example, if the machine uses the same white space identification and Western name bias as in the previous example, it will mis-parse the Spanish name “Federico García Lorca”:

<contrib contrib-type="author">
<given-names>Federico Garc&#x00ED;a</given-names></name>
In fact, “García Lorca” is the surname and not the given name; the correct XML is:

<contrib contrib-type="author">
<name><surname>Garc&#x00ED;a Lorca</surname>

In Science Editor’s article on editing author names from Spanish- and Portuguese-speaking countries, Black 2003 explains that names of Spanish origin often use two family names, “the first being the father’s and the second the mother’s.” He continues:

When a woman marries, she drops her mother’s name but keeps her father’s name, to which she appends the preposition de (“of”) followed by her husband’s father’s surname. For example, if María Esquivel López marries Juan Sánchez Mendoza, she drops her mother’s surname (López), keeps her father’s surname (Esquivel), and adds “de Sánchez,” becoming María Esquivel de Sánchez. (Black 2003)

There are also variations in how the parts of the compound surname are presented; “some women omit the de when adding the husband’s surname, such as using María Esquivel Sánchez instead of María Esquivel de Sánchez ” (Black 2003). On top of this, Hispanic given names may contain multiple parts, such as “María Cristina” or “José Fernando” (Black 2003).

Using the same automated parsing rules as illustrated earlier, and if the software is programmed to identify “de” as a surname prefix, the name “María Esquivel de Sánchez” would be incorrectly parsed as:

<contrib contrib-type="author">
<name><surname>de S&#x00E1;nchez</surname>
<given-names>Mar&#x00ED;a Cristina Esquivel</given-names></name>
The correct XML is:

<contrib contrib-type="author">
<name><surname>Esquivel de S&#x00E1;nchez</surname>
<given-names>Mar&#x00ED;a Cristina</given-names></name>

Black 2003 summarizes that “for persons unfamiliar with Spanish names, the use of compound given names can make it difficult to decide whether a word that falls in the middle of a name is the second part of the given name or is the paternal surname.” It is part of an editor’s job to know this kind of information and get the metadata right. However, low-skilled manual taggers and semi-smart software may not have the nuances of international naming conventions incorporated into their workflows, and errors can easily be introduced in the author metadata.

Example 2: Surname or Suffix

Inappropriate translations can also introduce errors. In the name “Edward Araujo Júnior,” the author’s full surname is “Araujo Júnior,” but we can imagine software—or low-skilled manual taggers—incorrectly parsing “Júnior” as the English generational suffix “Junior”:

<contrib contrib-type="author">

The author “Araujo Edward Jr.” is quite a different name from “Edward Araujo Júnior.” The correct XML is:

<contrib contrib-type="author">
<name><surname>Araujo J&#x00FA;nior</surname>
Left uncorrected, such an error would inevitably cause subsequent errors in citations to Mr. Araujo Júnior’s work.

Example 3: Eastern Order of Names

In some Eastern and East European cultures, the family name and the given name are presented in the opposite order in which Western cultures present the names. In Science Editor’s article, “English Versions of Chinese Authors’ Names in Biomedical Journals: Observations and Recommendations,” Sun et al. 2002 explain that “in Chinese the surname precedes the given name. Thus, for example, in the Chinese name Zhou De-An, Zhou is the surname and De-An is the given name.” Automation tools whose algorithms are configured to parse primarily Western-style names will inevitably mis-tag such names. Unfortunately, the author Zhou De-An is easily mis-tagged as:

<contrib contrib-type="author">

The correct tagging is:

<contrib contrib-type="author">

While it is possible to switch the order of elements in the algorithm, what happens when it encounters a group of contributors that consists of both Western- and Eastern-style names? If Han Solo (given name “Han”) and Han Sunghee (given name “Sungee”) were to publish a paper together, how would a single algorithm tag these names correctly?

The problem can become even more complicated, because some East Asian authors will attempt to westernize their names and change, for example, “Lee Choon Shil” to “Choon Shil Lee” when submitting a paper to a Western (or English-language) journal. Worse, sometimes publishers do not realize when an author has converted their Eastern name to Western order, and the name is further mangled as the editor tries to Westernize an Eastern name that is already in Western order.

For editors without access to proper training—or a solid understanding of proper tagging—East Asian names may be left completely unparsed; the full name is tagged as the <surname> without any effort to disambiguate the given name and family name. For example, the Korean authors of a conference paper deposited with Crossref (doi:10.1109/RFIC.2003.1213947) are incorrectly tagged as:

<person_name sequence="first" contributor_role="author">
<surname>Jae-Hong Chang</surname>
<person_name sequence="additional" contributor_role="author">
<surname>Yong-Sik Youn</surname>
<person_name sequence="additional" contributor_role="author">
<surname>Mun-Yang Park</surname>
<person_name sequence="additional" contributor_role="author">
<surname>Choong-Ki Kim</surname>

Some research into Korean naming conventions and other publications by the same authors indicate that the authors submitted their paper using Western naming order; the correct tagging should be:

<person_name sequence="first" contributor_role="author">
<person_name sequence="additional" contributor_role="author">
<person_name sequence="additional" contributor_role="author">
<person_name sequence="additional" contributor_role="author">

Subsequent editors—or reference-linking software—trying to properly parse or link to this paper will be unable to use the DOI metadata of this publication to disambiguate the names.

Problem References

Automated parsing algorithms can encounter even more variety when parsing bibliographic references. Hundreds of different editorial styles are used throughout the publishing community, and when you take into account typos and inconsistencies introduced by authors and the reference management software that authors use, developing software that can correctly identify each piece of a reference 100% of the time is a herculean feat. Even the most sophisticated algorithms will undoubtedly encounter references that can confuse even the brightest human editor.

Example 4: Immunoglobulin A

This particular reference follows an author’s unique (perhaps accidental) editorial style. Semicolons are used to separate each author as well as to separate the author list from the article title.

Watanabe A; Kitamura M; Shimizu M; Immunoglobulin A (IgA) with properties of both cryoglobulin and pyroglobulin. Clinica Chimica Acta. 1974;52(2):231–237.

Although the article title begins at “Immunoglobulin,” someone unfamiliar with biology or an automated tagging algorithm could easily misidentify this as the last author—“Immunoglobulin A”:

<ref id="b1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><string-name><surname>Watanabe</surname> <given-names>A</given-names></string-name>, <string-name><surname>Kitamura</surname> <given-names>M</given-names></string-name>, <string-name><surname>Shimizu</surname> <given-names>M</given-names></string-name>, <string-name><surname>Immunoglobulin</surname> <given-names>A</given-names></string-name></person-group>. <article-title>(IgA) with properties of both cryoglobulin and pyroglobulin.</article-title> <source><italic>Clin Chim Acta</italic></source>. <year>1974</year>;<volume>52</volume>(<issue>2</issue>):<fpage>231</fpage>-<lpage>237</lpage>.</mixed-citation></ref>

The correct XML is:

<ref id="b1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><string-name><surname>Watanabe</surname> <given-names>A</given-names></string-name>, <string-name><surname>Kitamura</surname> <given-names>M</given-names></string-name>, <string-name><surname>Shimizu</surname> <given-names>M</given-names></string-name></person-group>. <article-title>Immunoglobulin A (IgA) with properties of both cryoglobulin and pyroglobulin.</article-title> <source><italic>Clin Chim Acta</italic></source>. <year>1974</year>;<volume>52</volume>(<issue>2</issue>):<fpage>231</fpage>-<lpage>237</lpage>.</mixed-citation></ref>

Example 5: False Duplicates

When algorithms that parse out complex content are created, it can be difficult to account for what appears to be duplicate information. Take this reference:

Morcos MW, Al-Jallad H, Hamdy R. Comprehensive review of adipose stem cells and their implication in distraction osteogenesis and bone regeneration. BioMed research international. 2015 Sep 13;2015.

At first glance, an editor might question which “2015” is the print date. An automated up-translation might try its best:
<ref id="b1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><string-name><surname>Morcos</surname> <given-names>MW</given-names></string-name>, <string-name><surname>Al-Jallad</surname> <given-names>H</given-names></string-name>, <string-name><surname>Hamdy</surname> <given-names>R</given-names></string-name></person-group>. <article-title>Comprehensive review of adipose stem cells and their implication in distraction osteogenesis and bone regeneration.</article-title> <source>BioMed research international<source>. 2015 Sep 13;<year>2015</year>.</mixed-citation></ref>
But it will stumble on untangling the dates, and it might incorrectly tag the second instance of “2015” as the <year>, when in this reference, in fact, it is the volume number. The correct tagging for this reference is:

<ref id="b1"><mixed-citation publication-type="journal"><person-group person-group-type="author"><string-name><surname>Morcos</surname> <given-names>MW</given-names></string-name>, <string-name><surname>Al-Jallad</surname> <given-names>H</given-names></string-name>, <string-name><surname>Hamdy</surname> <given-names>R</given-names></string-name></person-group>. <article-title>Comprehensive review of adipose stem cells and their implication in distraction osteogenesis and bone regeneration</article-title>. <source>BioMed research international.</source> <year>2015</year> <month>Sep</month> <day>13</day>;<volume>2015</volume>.</mixed-citation></ref>

Example 6: Name Segmentation

As previously discussed in author bylines, name segmentation differs in different cultures, and without guidance from knowledgeable editors, errors can be easily introduced into the tagging of contributor names. This problem is more common in bibliographic references, because the editors cannot simply query the contributors to the reference as easily as they can contact the author of the paper. Furthermore, errors introduced at other publishers can be reinforced and transmitted to other publications if the erroneous record is deposited at a metadata service such as Crossref. When an editor or automated reference linking tool uses such a service to validate the accuracy of their references, they may be verifying their references against incorrectly tagged data.

Name segmentation errors are one such error category that propagates throughout the publishing world. The following reference was published in an article in Trace Elements and Electrolytes:

Saiki M, Alves ER, Jaluul O, Sumita NM, Filho Jacob W. Determination of trace elements in scalp hair of an elderly population by neutron activation analysis. J Rad Nucl Chem. 2008; 276: 53-57.

The last author, “Filho Jacob W,” was mis-parsed at some point during the editorial process. The correct name is “W. Jacob Filho.” Represented in JATS XML, the contributor is incorrectly tagged as:

<string-name><surname>Filho Jacob</surname> <given-names>W</given-names></string-name>

For this reference author, the name may be easily parsed by either a manual tagger or machine into the given name “W. Jacob” and surname “Filho” instead.. However, further research into this particular author found that the surname is actually “Jacob Filho” (sometimes represented as “Jacob-Filho”), and the given name is “W” (or, “Wilson”). So, while the publisher’s reference parsing tools segmented the name correctly, the order of surname elements was incorrect. The correct tagging is:

<string-name><surname>Jacob Filho</surname> <given-names>W</given-names></string-name>

This and other reference errors were examined in a series of Letters to the Editor in Trace Elements and Electrolytes, in which one researcher identified errors in references and Inera responded with explanations of the possible causes. Inera summarizes the most common errors:

The most systematic error occurred in author name segmentation (the separation of name elements into given name(s) and surname(s)). Publishers and their vendors may need to review and improve their processes for capturing correct author name information in the publication workflow and verifying that all metadata is correct, including not just the spelling but the order and segmentation of author names. (Dunford et al. 2017)

Example 6: Vital Health Stat 10

There are also references that automated conversion software can do a better job of tagging than a human editor. Take the following reference:

Benson V, Marano MA. Current estimates from the National Health Interview Survey, 1992. Vital Health Stat 10 1994 Jan;(189):1-269.

In this case, “10” is part of the journal title Vital Health Stat 10 (the full name is Vital and Health Statistics. Series 10. Data from the National Health Survey). Using a well-curated database of possible journal title names, automation can solve this particular problem, but it is almost impossible to hand-tag correctly without previous knowledge of this unusual journal title.

Balancing Automation with Human Review

In order to mitigate the errors that may be introduced by a fully automated process and create a workflow that ensures accuracy, publishers must find a way to incorporate manual human review without sacrificing the time saved by automation. This might be addressed in two ways.: proof the middle steps and flag suspect cases.

Proof the Middle-Steps

In cases where there is no room for error, automated conversion to XML can happen “under the hood.” For example, the parsing of submission history dates from

(Published online: 16 April 2009)

<pub-date pub-type="epub"><day>16</day><month>04</month><year>2009</year></pub-date>
can happen silently without human review. However, when processing more complex content, such as author lists or references, it is prudent to include an intermediary human-review step. For example, in the Word document color-coded character styles could be applied to identify individual components before the document is converted to XML. For example, after the initial parsing stage, each element of the author paragraph is marked with yellow “au_fname” and green “au_surname” character styles:

Figure 1

png image ../../../vol20/graphics/Gebhard01/Gebhard01-001.png

By using color-coding and intuitive character style names, the editor can quickly scan the byline and confirm or correct the parsed names.

This step is even more important for references, where the nuances of an editorial style may render errors in the machine’s results invisible. For example, for the following reference:

2. Kuck D, Caulfield T, Lyko F, Medina-Franco JL, Nanaomycin A Selectively Inhibits DNMT3B and Reactivates Silenced Tumor Suppressor Genes in Human Cancer Cells. Mol Cancer Ther 2010;9:3015–23.

automation might incorrectly parse “Nanaomycin A” as an author name (similar to the error that we see in Example 3: Immunoglobulin A). If the resulting XML for this reference is rendered according to the same editorial style, it would appear as:

2. Kuck D, Caulfield T, Lyko F, Medina-Franco JL, Nanaomycin A. Selectively Inhibits DNMT3B and Reactivates Silenced Tumor Suppressor Genes in Human Cancer Cells. Mol Cancer Ther 2010;9:3015–23.

The only evidence that the XML is incorrect are the comma preceding and period following the false author, “Nanaomycin A.” With color-coded character styles in the Word document, the editor can identify and correct the error before the manuscript is converted to XML:

Figure 2

png image ../../../vol20/graphics/Gebhard01/Gebhard01-002.png

In this case, the editor would manually correct the character styles applied to the article title before submitting the manuscript to the final stage of XML conversion.

Flag Suspect Cases

In addition to displaying the results of automated parsing using color-coded character styles, publishing tools can add an additional piece of communication between the automation and the human editor. In cases where it is known that algorithms may introduce errors when parsing content, the software can insert Word comments informing the editor that manual review is necessary. In other words, software developers can take steps to help algorithms recognize when they are unsure of the results rather than silently introduce errors.

For example, when the software detects that there is a lot of unstyled text between what it recognized as the author list and what it recognized as the article title, the software is “aware” that it was unsuccessful in automatically parsing the author list. In addition to providing color-coded character styles, the software inserts a Word comment alerting the editor, or tagger, that there is a problem. The error message may read: “The process was unable to fully identify and edit the author list, most likely because of incorrect punctuation in the original text. Please correct the entire author list by hand.”

Figure 3

png image ../../../vol20/graphics/Gebhard01/Gebhard01-003.png

Fully self-aware XML conversion software is a long, long way down the road. However, we can mitigate errors introduced by automation if we program software to be somewhat aware of its limitations. When software communicates to the human user whenever it has encountered a hurdle, it is then much easier for the user to perform a targeted review of the machine’s work.

Post-Conversion Quality Assurance

In addition to proofing the initial parsing stages of a semiautomated Word-to-XML workflow, it is often recommended that production teams proof the final XML output. In the 2011 JATS-Con paper, “Reality Check: What to Expect from Automated Conversion to NLM XML,” Bloom et al. 2011 explain the ways in which their team employs XSLT stylesheets to display a WYSIWYG version of the tagged content . By using a variety of colors, fonts, and point sizes, the visual representation makes it easier for editorial and production teams to review the tagging of complex elements. This visual scan ensures that the “XML is not just valid but correctly represents the data” (Bloom et al. 2011). They conclude:

Pre- and post-conversion effort pays off in the end. No conversion to XML can be a totally hands-off/lights-out silver bullet. The analysis performed up-front will lead to a better result in the end. The final XML will still inevitably need some adjustments but the effort put in before and after conversion will minimize the amount of tweaking necessary.

Coupling manual review and analysis before and after conversion helps ensure that the final XML is valid and accurate.


When working with human-created content, it is important to realize that it is impossible to get completely accurate results from a fully automated up-translation process. The nuances of and complexities inherent in such content can never be fully appreciated by a machine. Alternatively, attempting to perform this up-translation manually is prone to inconsistencies and one-off errors, not to mention the expense of labor.

For publishers of complex content, up-translation should be accomplished with a careful balance of automated software and manual review. Finding the tipping point between what a machine can do well and what a human can do well is a challenge. In most cases, this tipping point is triggered by content that requires a more sophisticated and flexible approach. In this author’s experience, the scholarly content that requires a more sophisticated eye, and therefore a “cyborg” approach, is the metadata. There is simply too much variation in this data—differences in naming conventions, inconsistent styles, and human error—for a machine to parse and tag this content accurately.

To address this, we recommend combining the efficiency of smart software with the flexibility and knowledgeable application of manual review. One way to do this is to include an intermediary step between parsing and XML tagging to present the results of automated parsing to the user for a quick review and possible correction. User-friendly warnings to alert the user that the software has encountered content that requires manual review and possible hand tagging can also help. The human editor can review the machine’s parsing results (and correct them) before finally translating the document to XML. Sophisticated algorithms can save time and provide granular tagging, but nothing beats the flexibility of a human reviewer.


[ANSI/NISO Z39.96-2015] ANSI/NISO Z39.96-2015. JATS: Journal Article Tag Suite. 2015.

[Black 2003] Black, B. (2003). Indexing the Names of Authors from Spanish and Portuguese-Speaking Countries. Science Editor, 26(4), 118–121. Available from

[Bloom et al. 2011] Bloom, D., Friedman, B., & Kupferstein, G. (2011). Reality Check: What to Expect from Automated Conversion to NLM XML. In: Journal Article Tag Suite Conference (JATS-Con) Proceedings 2011. Bethesda, MD: National Center for Biotechnology Information. Available from

[Dunford et al. 2017] Dunford, R., Izzo Hunter, S., & Rosenblum, B. (2017). RE: Seifert M. How Accurate Are References in Trace Elements and Electrolytes? Trace Elements and Electrolytes, 34, 139–140. doi:10.5414/TEX01495/

[Galichet 2017] Galichet, L. (2017). Beware of the Laughing Horse: Managing a Back-catalogue Conversion. In: Journal Article Tag Suite Conference (JATS-Con) Proceedings 2017. Bethesda, MD: National Center for Biotechnology Information. Available from

[Meyer 2008] Meyer, C. A. (2008). Reference Accuracy: Best Practices for Making the Links. The Journal of Electronic Publishing, 11(2). doi:10.3998/3336451.0011.206

[Sun et al. 2002] Sun, X. L., & Zhou, J. (2002). English Versions of Chinese Authors’ Names in Biomedical Journals: Observations and Recommendations. Science Editor, 25(1), 3–4. Available from