Guillaume Cabanac
email: guillaume.cabanac@univ-tlse3.fr

James Hartley
email: j.hartley@keele.ac.uk

Issues of Work-Life Balance Among JASIST Authors and Editors

Many dedicated scientists reject the concept of maintaining a "work-life balance." They argue that work is actually a huge part of life. In the mind-set of these scientists, weekdays and weekends are equally appropriate for working on their research. Although we all have encountered such people, we may wonder how widespread this condition is with other scientists in our field. This brief communication probes work-life balance issues among JASIST authors and editors. We collected and examined the publication histories for 1,533 of the 2,402 articles published in JASIST between 2001 and 2012. Although there is no rush to submit, revise, or accept papers, we found that 11% of these events happened during weekends and that this trend has been increasing since 2005. Our findings suggest that working during the weekend may be one of the ways that scientists cope with the highly demanding era of "publish or perish." We hope that our findings will raise an awareness of the steady increases in work among scientists before it affects our work-life balance even more.

Work is play when it's something you like.

Introduction

A decade ago, Guest (2002, p. 255) was already stressing that "work-life balance has become an important topic for research and policy." We use the concept of "work-life balance" in this article in preference to that of "workaholism," largely because the concept of workaholism has a more specific usage than we intend here, particularly in the areas of occupational and industrial psychology (see, e.g., [START_REF] Andreassen | Development of a work addiction scale[END_REF][START_REF] Harpaz | Workaholism: Its definition and nature[END_REF][START_REF] Ng | Dimensions, antecedents, and consequences of workaholism: A conceptual integration and extension[END_REF][START_REF] Van Beek | Workaholic and work engaged employees: Dead ringers or worlds apart?[END_REF]. In a recent study, [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF] attempted to investigate this work-life balance among scientists. Instead of relying on classical methods, such as questionnaires or interviews, they suggested monitoring a routine activity for any scientist: searching the literature. Thus, they recorded the worldwide downloads of research articles published online by Springer for a period of 5 weekdays and 4 weekends in mid-April 2012. Subsequent data mining performed on the 1,800,000+ downloads suggested that many scientists worked overtime, especially during the weekends. More recently, [START_REF] Magnone | A scientometric look at calendar events[END_REF] examined the publication records of 660,191 scientific articles published from 1990 to 2010 and available from Elsevier's ScienceDirect to show that scientists published practically every day of the week, including weekends and holidays. Although the methods used by these authors are appealing with regard to their original use of publicly available metadata to better understand the rhythms of scientists' lives, at least three caveats should be raised concerning their assumptions:

• An unknown number of downloads may have been performed by programs instead of people. For instance, web search engines rely on indexing programs that harvest the contents of web pages. This may explain the "extremely abnormal" number of downloads that were recorded by [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF] for Tianjin city during a 10-minute time. These outlying values were eventually discarded. Still, less aggressive programs may have performed downloads that might not have been detected and then discarded from the study. • Downloading an article does not always imply that it is read.

For instance, scientists may download articles in their office at the university before going home, simply because most of Springer's articles lie behind a pay wall whose access is granted only to university networks. As a result, the number of people working during the weekends might have been underestimated. • Finally, the 9-day life span of the study by [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF] may have been too short a period to obtain results that would apply to the whole year. Our own study (see later) and that of [START_REF] Magnone | A scientometric look at calendar events[END_REF] show that special events (e.g., conference deadlines, spring breaks, national holidays) may have biased their study.

In this brief communication, we intend to investigate the work-life balance of scientists while overcoming some of the above concerns. Unlike [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF], we do not rely on real-time downloads as a trace of scholarly dedication to work. We rely instead on the analysis of the publication history printed on every published article. This informs the reader about when the article was originally submitted, revised for the last time following the referees' comments, and then accepted by the journal's editor. Of course, these events may happen on weekdays, weekends, and during public holidays.

Nonetheless, it seems reasonable to assume that there is no actual rush to submit or revise a journal paper as far as the authors are concerned, although some might like to get on with it because of upcoming commitments. Likewise, there is no rush to accept a paper as far as editors are concerned. Yet, despite these factors, the data suggest that many authors and editors will be working during the weekend. We believe that working during the weekend on tasks that could be deferred without serious consequences until the next week starts (i.e., 2 days later at most) can be considered as an example of scientists maintaining an inappropriate work-life balance. This brief communication reports on the traces of these problems that we found among the JASIST community. We hope that our findings will raise awareness of the steady increase in weekend working among such scientists and how this might affect their work-life balance in the long run.

Data and Method

This study relies on publication metadata that are publicly available on the JASIST website. 1 Besides appearing on the first page of each JASIST publication, the "publication history" of papers published since 2001 is also provided on their dedicated web pages. Histories are composed of the following metadata, with example values from Oyarce (2008): Note that these dates are assigned by JASIST's editorial manager (called ScholarOne 2 ) upon manuscript reception, revision, and acceptance with no third person involved.

Our study is concerned with the first three fields: dates of manuscript submission, revision, and acceptance. Articles published in JASIST usually go through two or three rounds of revision [START_REF] Cronin | Rigor and speed[END_REF][START_REF] Cronin | Peer review[END_REF]. Notice, however, that only the date of last revision before acceptance is provided in print and online issues.

We extracted the publication histories of the 2,402 articles that were included in issue 52(5) of 2001 to issue 63(11) of 2012. Notice that publication histories were inconsistently reported before issue 52(5). Next, 839 articles with missing dates for one or more of the considered fields were discarded. These were mostly book reviews, editorials, errata, letters to the editor, and obituaries. Another 30 articles were discarded because of chronological flaws. For instance, [START_REF] Kim | Describing and predicting information-seeking behavior on the web[END_REF] appeared to have revised her manuscript (28 APR 2008) before she initially submitted it (26 SEP 2008). Eventually, we determined the day in the week for the remaining 1,533 valid JASIST papers. All these data are released as online supporting information (Appendix S1).

Results

We first discuss how author-and editor-related events are distributed across weekdays and weekends. Then we take a broader perspective in studying these events longitudinally to determine whether there have been any increases in weekend working over time.

Daily Submissions of JASIST Authors

If authors consider all days equally appropriate for work, then the distribution of the original submissions per day would be uniform. This is clearly not the case, as the distribution is positively skewed instead (Figure 1). Weeks are clearly divided into two parts according to these data. Authors initially submit their papers more during weekdays than during weekends. Nonetheless, there is a slight decline in the number of submissions as the week elapses, and weekends show 11% of all incoming submissions. The distribution of the final revised versions received per day (Figure 2) is similar to the distribution of initial submissions. Revised versions were also posted throughout the week, but with a slight preference for the beginning of the week. Again, several authors spent parts of their weekends revising and submitting papers, because 11% of the completed versions were posted on Saturday and Sunday.

We wondered whether the submissions and revisions happening during the weekends were higher for single authors. It might be expected that weekends would be chosen more by single authors to advance their research, as they might enjoy the lack of interruptions from family, coworkers, other clerical work, or teaching duties. Among the 1,533 articles under study, 29% (n = 449) are single-authored articles. However, the significant and strong relationship (r 2 = 0.98, p < 0.001) between the distributions of revised and submitted articles of multiauthored versus single-authored articles does not support this hypothesis: The rhythms of solo versus collaborative researchers do not differ significantly.

Our study of submission and final revision dates thus revealed some trends among JASIST authors. It must be remembered that there is no deadline for submitting a paper to JASIST, and authors are allotted a whole year to improve and resubmit their manuscript. Thus, there is no time pressure (i.e., no rush) in submitting or revising papers. And yet, 11% of submissions and revisions happen during weekends.

Weekend Working Among JASIST Editors

Two editors-in-chief managed JASIST during the period under study (2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012). Donald H. Kraft served from 1985 to 2008 [START_REF] Meadow | A new editor for JASIST[END_REF], and then Blaise Cronin took over in 2009 (Cronin, 2009a). The distribution of the acceptance dates by the two editors (Figure 3) shows a peak on Mondays. This peak may result from them handling authors' revisions submitted during the previous weekend. Traces of weekend working were also found among JASIST editors. Figure 3 suggests that editors sent 7% of all notifications of acceptance during weekends. Unfortunately, there is no way to refine this study by differentiating when rejection happened: directly without review (about 30% of the time according to [START_REF] Cronin | Rigor and speed[END_REF]) or after several rounds of review. However, a final example of anecdotal yet indisputable evidence of working during the holidays lies in three articles [START_REF] Lazarinis | Engineering and utilizing a stopword list in Greek web retrieval[END_REF][START_REF] Stvilia | A framework for information quality assessment[END_REF][START_REF] Talja | Impact of research cultures on the use of digital library resources[END_REF]. These three JASIST articles were accepted on December 25, 2006, which is a national holiday in the U.S. home of the journal's editor-in-chief.

Longitudinal Study of Changes in Work-Life Balance in JASIST

Having found evidence of issues of work-life balance in the JASIST community, we wondered about the evolution of this condition among authors-the case of editors would include too few people to be informative, and moreover, such people are deemed to be incurable workaholics [START_REF] Aguinis | What does not kill you (sometimes) makes you stronger: Productivity fluctuations of journal editors[END_REF]!

Figure 4 shows the distribution of original submissions and final revisions from authors across weekdays and weekends for 2001 through 2012. Although the weekends used to be quiet up until 2004, the data suggest an increase in paper submissions and revisions during the weekends since then. This phenomenon has been increasing by an average of 3% a year since 2009. Overall, the number of submissions and revisions during weekends has been increasing by a 1% margin per year, as shown by the linear regression plotted as a solid line, and this year (2012) it reached 20%. The findings of this study complement those of [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF], who probed research activity in April 2012 through the study of paper downloads from Springer's digital library. Although seeking to explain this phenomenon is beyond the scope of this study, we may speculate that the globally increasing pressure to "publish or perish" is a factor producing these hardworking weekenders [START_REF] Garfield | What is the primordial reference for the phrase 'Publish or Perish[END_REF].

Limitations

This study has mined publication histories to raise our understanding of the work-life balance of scientists. It should be stressed, however, that the dates that we retrieved must be considered in the light of ScholarOne, whose server is located in New Jersey, according to the traceroute command. 3 We also know from [START_REF] Cronin | The changing profile of JASIST authors[END_REF][START_REF] Cronin | Language matters[END_REF] that more than 50% of the JASIST papers come from outside the United States. For authors who were in different countries and time zones than ScholarOne's (i.e., UTC -5), the date d recorded by the system may differ from the date d′ experienced by the author in his or her time zone by a 1-day margin at most, that is, d -d′ ∈ {-1, 0, 1}. For instance, a manuscript submitted from France by the first author (a workaholic scientist) on Saturday at 4 a.m. would get recorded on Friday at 10 p.m. in New Jersey. Unfortunately, we were unable to account for difference in countries and time zones, because exact timestamps and ScholarOne user location at submission, revision, or acceptance are undisclosed. We do not think that this particular problem is likely to have affected our results a great deal, but the possibility has to be borne in mind.

No doubt, our study actually underestimates the amount of work completed by JASIST authors, as we only assessed this through a weekday versus weekend dichotomy (and we assumed that it is universal practice to rest during the weekend when, of course, in some cases it may not be so). Furthermore, some authors may have submitted papers during weekdays when they are on holiday. [START_REF] Ladle | Come all ye scientists, busy and exhausted. O come ye, O come ye, out of the lab[END_REF], for instance, using Google Scholar, found a 600% increase in the number of submissions received on Christmas Day in 2006 compared with the same day in 1996. The paper by Oyarce ( 2008) provides yet another extreme example of a dedicated author and editor. According to our data, this revised JASIST paper was submitted on December 31, 2006, and accepted on January 1, 2007!

Summary and Conclusion

This brief communication has investigated the work-life balance of scientists from the perspective of their publication histories. We have focused on the case of JASIST authors and editors. Appendix S1 presents the data that we collected and releases it as an online supporting information. A dichotomy between weekdays and weekends was introduced as a proxy for "work" and "life." Possible evidence of increases in "work" at the expense of "life" was recorded for submissions, revisions, and acceptance of manuscripts during the weekends. It was assumed that the work could normally have been deferred to the following week because JASIST does not set any deadlines for paper submission and allots a whole year to researchers for revision. Nonetheless, we found that 11% of manuscript-related events happened during the weekends. This finding is in accord with [START_REF] Wang | Exploring scientists' working timetable: Do scientists often work overtime[END_REF] results about overworking scientists. Finally, our longitudinal study of the past decade showed that working during weekends has been increasing among JASIST authors since 2005. We hope that the light we have shed on these issues will raise readers' awareness of these problems and how they might affect them. But it is hard not to forget that, for some:

•

  Manuscript received: 24 JUN 2007 • Manuscript revised: 31 DEC 2007 • Manuscript accepted: 1 JAN 2008 • Article first published online: 14 MAY 2008 • Issue published online: 9 JUN 2008

  Percentages of new submissions posted by authors by days of the week (percentages are rounded). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

		21%	18%	19%	18%
						15%
							5%	6%
		Mon Mon	Tue Tue	Wed Wed	Thu Thu	Fri Fri	Sat Sat	Sun Sun
		FIG. 1.
	1 http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-2890/
	issues	2 http://mc.manuscriptcentral.com/jasist

  Percentages of final submissions posted by authors by days of the week (percentages are rounded). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Composite data from two JASIST editors showing the percentages of manuscript acceptances by days of the week (percentages are rounded). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

						26%
	20%	20%	17%	16%	17%		19%	17%	16%	16%
					5%	6%				3%	4%
	Mon Mon FIG. 2. Mon Tue Wed Thu Fri Sat Sun Tue Wed Thu Fri Sat Sun Mon FIG. 3.	Tue Tue	Wed Wed	Thu Thu	Fri Fri	Sat Sat	Sun Sun

Supporting Information

Additional supporting information may be found in the online version of this article:


Guillaume Cabanac
email: guillaume.cabanac@univ-tlse3.fr

Thomas Preuss
email: preuss@fh-brandenburg.de

Capitalizing on Order Effects in the Bids of Peer-Reviewed Conferences to Secure Reviews by Expert Referees

Peer review supports scientific conferences in selecting high-quality papers for publication. Referees are expected to evaluate submissions equitably according to objective criteria (e.g., originality of the contribution, soundness of the theory, validity of the experiments). We argue that the submission date of papers is a subjective factor playing a role in the way they are evaluated. Indeed, program committee (PC) chairs and referees process submission lists that are usually sorted by paperIDs. This order conveys chronological information, as papers are numbered sequentially upon reception. We show that order effects lead to unconscious favoring of early-submitted papers to the detriment of latersubmitted papers. Our point is supported by a study of 42 peer-reviewed conferences in Computer Science showing a decrease in the number of bids placed on submissions with higher paperIDs. It is advised to counterbalance order effects during the bidding phase of peer review by promoting the

Introduction

Scientific conferences are premier venues for disseminating the results of late-breaking research. Published papers are selected through the peer review process, also known as the referee system [START_REF] Zuckerman | Patterns of evaluation in science: Institutionalisation, structure and functions of the referee system[END_REF]. Nowadays, peer-reviewed conferences increasingly rely on paper bids. These are composed of the five stages depicted in Figure 1.

• S1. Authors prepare a paper according to the call for papers issued by a given conference. This document states the topics of the conference, as well as the opening and closing dates for paper submission. Authors are allowed to submit papers any time between these two dates. • S2. Scientists acknowledged for their expertise in the conference domain (i.e., peers) are invited to review the submitted papers. They are allowed to bid for the papers they would like to evaluate. • S3. The program committee (PC) chairs assign submissions to peers according to their bids, whenever possible. • S4. Peers act as referees to evaluate the submissions they were assigned to. They grade papers according to several predefined criteria, such as originality and significance of the contributions. • S5. PC chairs select the papers with best quality according to the evaluations, comments, and recommendations of the referees. These papers are then published in the conference proceedings.

The scientific community and the general public have expectations for peer review. On the one hand, it is expected to act as a fair filter to select papers according to only objective criteria assessing their scientific qualities. On the other hand, peer review is expected to enforce equality among papers regarding their acceptance or rejection. Unfortunately, various inadequacies affect peer review, such as status bias, gender bias, objectivity bias, and conflict of interests [START_REF] Zuckerman | Patterns of evaluation in science: Institutionalisation, structure and functions of the referee system[END_REF][START_REF] Benos | The ups and downs of peer review[END_REF].

Regarding scientific conferences, we argue that the submission date of papers is one extra factor at play during the peer review process. We hypothesize that order effects [START_REF] Becker | Why an order effect[END_REF] are introduced when papers are chronologically presented to referees (during S2 and S4) and PC chairs (during S3 and S5). Paper evaluation would then result from objective judgment and from a subjective feature of submitted papers: their submission date. Although undocumented to date for peer-reviewed conferences, we believe that this phenomenon is detrimental to the integrity of peer review.

The article is organized as follows. We first discuss how submission dates may affect peer review supported by paper bids through order effects. Then we design a method to check the existence of such order effects on a sample of 42 peer-reviewed conferences from the Computer Science field. The results bolstering this hypothesis are then presented. Finally, we propose to capitalize on order effects to secure reviews from expert referees. This contribution is intended to contribute to ensuring the integrity of peer review.

How Do Submission Dates Challenge Peer Review?

Peer-reviewed conferences have entered the digital age. Most of them are supported by online conference manage-ment systems nowadays. These systems assign a sequential number called "paperID" to each submitted paper. Then, for any given conference, the list of submissions to review is sorted by paperID. This order conveys chronological clues: the list starts with early-submitted papers and goes on to last-minute submissions at the end. In this section, we argue that order effects may lead scientists to favor (or unfavor) some papers because of their submission date. Such an unfair treatment questions the integrity of peer review.

Peer Review and Conference Management Systems

Conferences have become major scientific events attracting worldwide audiences. In Computer Science, for instance, "highly selective" conferences receive several hundred submissions, less than 30% of which getting accepted for publication [START_REF] Chen | Conference paper selectivity and impact[END_REF]. This is the case of the three leading conferences shown in Figure 2, for instance. Publishing in such conferences is also appealing because papers in their proceedings reach a large readership. [START_REF] Freyne | Relative status of journal and conference publications in Computer Science[END_REF] reported that these papers receive more citations than journals listed in the bottom half of the ISI Web of Knowledge impact ranking for journals.

Peer review now has become the standard procedure to select papers for publication [START_REF] Benos | The ups and downs of peer review[END_REF]. While peer review supports both journals [START_REF] Cronin | Peer review[END_REF] and conferences [START_REF] Hartvigsen | The conference paper-reviewer assignment problem[END_REF], the present study focuses on conferences supported by bids. Papers submitted to conferences are assigned to at least two expert referees drawn from the conference PC-when not rejected outright by PC chairs for not complying with the topics of the conference call for papers. Referees assess each paper according to several criteria (e.g., novelty, contribution) before issuing a recommendation (e.g., weak or strong accept, neutral, weak or strong reject). In the end, the scientific community expects these recommendations to inform PC chairs when selecting papers with best quality and impact.

Running a peer-reviewed conference with several hundred submissions implies the synchronization of the tasks of thousands of people who act as authors, referees, or both. Fortunately, several conference management systems, such as EasyChair, ConfManager, ConfMaster, ConfTool, and Precision Conference (see http://easychair.org, http:// confmanager.com, http://confmaster.net, http://conftool.net, and http://precisionconference.com) have been developed to cope with this daunting task. These websites support the researchers involved in the five stages of the peer review process (Figure 1). For instance, most conference management systems allow referees to bid on submissions prior to paper assignment [START_REF] Rodriguez | Mapping the bid behavior of conference referees[END_REF]. Then PC chairs rely on referees' areas of expertise and preferences (deduced from the bids) to assign papers manually or with the help of various algorithms [START_REF] Wang | A survey on reviewer assignment problem[END_REF]. Nowadays, virtually all conferences rely on a conference management system.

What Is Behind PaperIDs?

Conference management systems assign an identifier to each paper submitted to a given conference. This identifier, called paperID, is usually a number starting with 1 if this is the first submission received. Then it is incremented for subsequent submissions. In other words, the n papers submitted to a conference are numbered with paperIDs from 1 to n.

The chronology of submissions is revealed by paperIDs, since they give a clue about the submission date of each paper. Early-submitted papers get low paperIDs (i.e., close to 1), whereas last-minute papers receive high paperIDs (i.e., close to n).

Order Effects on Choice

Various studies have shown that people are influenced by the order of presentation when having to select items from a list according to their preferences.

When desirable items are randomly distributed throughout a list, people still tend to select early positions. [START_REF] Becker | Why an order effect[END_REF] studied the chance of selection of five radio program types (e.g., news, popular music, sports) among 16 radio program types presented to 16,193 people. Two parameters were controlled in this experiment: the order of presentation and the popularity of the program types. These were counterbalanced respectively by a Latin square and by an analysis of variance. The results showed that the position of items influenced their chance of selection, with early positions attracting significantly more votes than later positions. Becker (1954, p. 276) even noticed that "the later on a check-list that a program type is listed, the less the chance that someone will select it as one of his five favorites." Such order effects were also found to influence the selection of the names of candidates to elections held in the three largest counties of Ohio in the USA [START_REF] Miller | The impact of candidate name order on election outcomes[END_REF].

When desirable items are expected to be ranked at the top of the list, people tend to select early positions even if this condition is not satisfied. For instance, [START_REF] Craswell | An experimental comparison of click position-bias models[END_REF] conducted an experiment with manipulated results of web search engines. The authors found that users kept clicking on documents from top to bottom of the result list, even if most relevant documents were not at the top of the list.

The "direction of comparison" also has an influence on item evaluation regarding one's preferences. For instance, when being presented with the sequence of items A and B, subjects are more likely to comment on the positive (i.e., desirable) features of B that were not present in A, unlike when presented with the sequence of items B and A (Bruine de [START_REF] Bruine De Bruin | Order effects in sequentially judged options due to the direction of comparison[END_REF]. This has direct implications for the fairness of competitions using jury evaluations (Bruine de [START_REF] Bruine De Bruin | Save the last dance for me: Unwanted serial position effects in jury evaluations[END_REF][START_REF] Bruine De Bruin | Save the last dance II: Unwanted serial position effects in figure skating judgments[END_REF].

The familiarity of the person with the judged items seems to play a role in item selection too. About wine tasting, [START_REF] Mantonakis | Order in choice: Effects of serial position on preferences[END_REF] found a primacy effect for the general public (i.e., selection of earlier items), whereas expert tasters showed a recency effect (i.e., selection of later items). Various experiments with paintings, American Idol audition clips, jellybeans, and female faces were conducted by [START_REF] Li | When the best appears to be saved for last: Serial position effects on choice[END_REF]. The authors found serial position effects, since people tended to select early items when being presented with globally undesirable items. Conversely, they tended to select later items when presented with globally desirable items.

Finally, people tend to underestimate the time spent choosing from a long list of items [START_REF] Fasolo | The effect of choice complexity on perception of time spent choosing: When choice takes longer but feels shorter[END_REF]. Attention decrement and boredom are also human factors contributing to order effects on choice [START_REF] Mantonakis | Order in choice: Effects of serial position on preferences[END_REF].

Potential Implications for the Integrity of Peer Review

Throughout the peer review process, conference papers are processed by PC chairs and referees. They work with a submission list produced by the conference management system. The n papers of this list are presented chronologically, as the list is sorted by paperID. This order may influence the scientists involved during the various stages of peer review depicted in Figure 1 as follows.

• During S2, referees skim through the submission list, bidding on a handful of papers according to titles and abstracts (full text is usually not provided in S2). Here, referees may feel it tiresome to get a sense of each paper, as it requires reading titles and abstracts. We may recall the findings of [START_REF] Fasolo | The effect of choice complexity on perception of time spent choosing: When choice takes longer but feels shorter[END_REF] and [START_REF] Mantonakis | Order in choice: Effects of serial position on preferences[END_REF] about the difficulty of item selection and resulting boredom to support this point. As a result, referees may focus their efforts on papers presented at the top of the list (primacy effect). In addition, since each referee is expected to review k << n papers, the paper pool seems globally undesirable to them. According to the findings of [START_REF] Li | When the best appears to be saved for last: Serial position effects on choice[END_REF], this may motivate referees to select early-submitted papers (those with low paperIDs). Such a phenomenon is all the likelier with leading conferences totaling hundreds of papers. • During S3, PC chairs go through the submission list to assign k << n papers to each referee. According to our experience, it seems that k = 5 Ϯ 2 for most conferences. Again, order effects may arise if PC chairs process the submission list sequentially. Hence, early-submitted papers may be assigned to qualified referees willing to review them, for they have bid on them. Meanwhile, late-submitted papers may be assigned to referees who have not bid on them or who do not have their share of papers to review yet (i.e., assignment by default instead of preference-wise). • During S4, referees are given the k << n papers for evaluation purposes, with no guarantee that their preferences (i.e., bids) were satisfied. Assigned papers are usually sorted by paperID in the list presented to referees. Here the order in which papers are then evaluated may affect their evaluation (Bruine de [START_REF] Bruine De Bruin | Save the last dance for me: Unwanted serial position effects in jury evaluations[END_REF][START_REF] Bruine De Bruin | Save the last dance II: Unwanted serial position effects in figure skating judgments[END_REF]). • During S5, PC chairs repeatedly go through the list of evaluated papers to review the referees' evaluations, to engage discussions on papers with diverging evaluations, and eventually to decide their rejection or acceptation.

To sum up, order effects may lead referees and PC chairs alike to favor papers submitted early (i.e., listed in low positions) at the expense of papers submitted later (i.e., listed in high positions). Such a different treatment would be detrimental to the peer review process, which is expected to ensure the integrity of science and public trust [START_REF] Wing | Reviewing peer review[END_REF].

In this article we focus on the issue related to the bids collected during S3. It is our hypothesis that order effects play a role in the way referees place bids on submissions. In the next sections we bolster this hypothesis through exploratory statistics applied to the logs of peer-reviewed past conferences.

Method

The hypothesis under study applies to conferences implementing (P1) a peer-review process supported by (P2) paper bids. We intend to check it against data from past conferences complying with P1 and P2. These data currently exist, since they are generated by conference management systems. The UML class diagram in Figure 3 models the data considered in the current study. We discuss it according to the stages of the peer-review process introduced in Figure 1 (terms in italics refer to attributes in the UML diagram).

• S1. Authors submit papers to conferences. Papers are assigned a sequential paperID: the paper submitted first is assigned the value 1, the paper submitted second is assigned the value 2, and so on. • S2. Referees are presented with the list of the n papers submitted during S1. They are given the opportunity to bid on papers whose subject is interesting or related to their area of expertise. Conversely, referees can state conflicts of interests, limited expertise, or disinterest. These preferences are stored in the interest attribute (positive, neutral, or negative) of the Bidding class. • S3. PC chairs assign k << n papers to referees according to paper topics, referee areas of expertise, and referee preferences acquired during S2. Each paper is assigned to at least two referees. • S4. Referees mark papers globally (overall attribute) and detail their evaluation according to several criteria (e.g., value of the contribution, paper originality and quality, relevance to the call of papers, significance of the results). In addition, referees evaluate how confident they are in their review, as a way to state their degree of expertise regarding the evaluated paper. These marks kept in the Evaluation class are usually given on a 7-point [START_REF] Likert | A technique for the measurement of attitudes[END_REF] scale (e.g., strong reject, reject, weak reject, neutral, weak accept, accept, strong accept). • S5. PC chairs proceed through the papers to accept or reject them. They may rely on the average score computed from referees' weighted marks-the relative importance of each criterion is up to each conference. In the end, each paper has a given status (e.g., accept as full paper, accept as short paper, accept as poster, reject).

We intend to check whether the submission date of papers (conveyed by paperIDs) is related to the number of bids received. A scatter plot akin to Figure 4 is generated for each conference. The independent variable (x axis) is the position of the paper (i.e., paperID in Figure 3), while the dependent variable (y axis) is the number of bids. In addition, the linear regression y = m ¥ x + p is computed with standard least squares fitting for estimating m and p. The coefficient of determination R 2 ∈ [0,1] associated with the linear regression measures the goodness of fit (i.e., it is inversely proportional to the squared difference between data points and the trend line). The slope of the trend line (i.e., m) informs us about the relationship between x and y, while the magnitude of R 2 informs us about the quality of the approximation of the data points by the trend line (R 2 → 1 shows a better approximation). Figure 4 shows a decreasing number of bids as paper positions increase. In other words, papers with low positions (i.e., early submissions) attract more bids than papers with higher positions (i.e., later submissions).

Having approximated the data points of each conference by a single trend line with associated R 2 , a broader perspective is then taken by plotting the conference trend lines altogether (e.g., see Figure 14). Each trend line starts at position x = 1, and ends at the highest paper position of the considered conference (i.e., x = n with a different n for each conference). Consequently, conferences with a larger number of papers are represented with longer trend lines.

Finally, the quality of the approximations by trend lines is shown with varying line weights: the stronger the goodness to fit R 2 , the bolder the trend line. This is to emphasize trend lines that better approximate the initial data points. Four groups of trend lines are computed according to their R 2 . The group with the 25% highest R 2 is labeled "First quarter of R 2 " and plotted in weight 4, while the following 25% is labeled "Second quarter of R 2 " and plotted in weight 3, and so on. The overall tendency is then visible by observing all the slopes. In addition, the distribution of slope values is studied via box plots a.k.a. box-and-whisker diagrams [START_REF] Mcgill | Variations of box plots[END_REF] to assess the skewness of data. Slope values skewed towards negative values show a decrease in the dependent variable when the independent variable (i.e., paper position) increases, thus supporting our hypothesis.

In the next section we introduce our findings based on the logs of peer-reviewed past conferences with paper bids.

Data and Results

The working hypothesis was tested with data from conference logs. Among the various available conference management systems we retained ConfMaster, for it has been used by 324 conference editions (Conference list: http://confmaster.net/references.php) as of May 2012. Most of them are conferences in Computer Science. The following sections report statistics about 42 randomly selected peer-reviewed conferences with 157,332 bids placed by 2,989 referees, who wrote 19,108 reviews about 7,351 papers.

Characteristics of the 42 Peer-Reviewed Conferences

The ConfMaster team agreed to provide us with anonymous data related to 42 peer-reviewed conferences drawn from its pool of 324 conferences. These 42 conferences were held between 2003 and 2011 with supporting paper bids. These data match the UML class diagram shown in Figure 3, except for some attributes that were not provided (e.g., conference name, reviewer identity, review contents) for anonymity concerns. We publish this data set called ConfMaster2003-2011 as an Additional Supporting Information (see Appendix A). This ensures the reproducibility of our results, while enabling other researchers to investigate other aspects of the peer review process from this data set.

A total of 7,351 papers were submitted to the 42 conferences under study. The box plot in Figure 5 depicts the distribution of the number of papers per conference. As shown by the green box, the middle half of all conferences attracted between 70 and 204 papers (Mdn = 102). The black dots show four outlying conferences that have attracted more than 387 papers.

The distribution of acceptance rates for the 42 conferences is shown in Figure 6. Regarding full papers, the middle half of the conferences features a selectivity lying between 14% and 31% (Mdn = 19%). There are three outlying conferences with selectivity greater than 51%. Provided that highly selective Computer Science conferences have a 30% or less acceptance rate according to [START_REF] Chen | Conference paper selectivity and impact[END_REF], our data set is mainly composed of highly selective conferences.

Characteristics of the 157,332 bids

ConfMaster allows referees to bid on submissions in order to signal positive preference (denoted by + for "interested" and by ++ for "very interested"), negative preference (denoted byfor "dislike") or conflicts of interests (denoted by ). Overall, 76% of all referees placed a bid on at least one paper, with a median of 12 bids per referee per conference, which corresponds to a median of 5% of all submissions. Figure 7 shows that negative bids and conflicts of interests (56%) were slightly more frequent than positive bids (44%). It is worth noting that nearly half of all bids are negative (-), which suggests that it is simpler for referees to disqualify than to pick papers during S2.

Let us focus on the papers assigned by PC chairs during S3. Figure 8 shows the distribution of the 19,108 reviews with respect to their associated bids, if any (i.e., some papers attracted no bids, others were assigned to referees although they placed no bids on them). More than half of all reviews were written by referees who placed a positive bid on the evaluated paper. Only 1% of the reviews were done by referees who placed a negative bid on the paper. Only two reviews were done by referees who declared a conflict of interest ( ). This suggests that the current use of negative bids and conflicts of interests (see Figure 7) succeeds in avoiding to assign unwanted papers to referees. Notice that no bids were placed on papers evaluated in 42% of the reviews under study.

It is generally assumed that referees place positive bids on the submissions for which they feel qualified to review [START_REF] Rodriguez | Mapping the bid behavior of conference referees[END_REF]. We wondered whether reviews preceded by a positive bid were likelier to exhibit a strong referee confidence. This point is supported in Figure 9, where reviews following positive bids seem to be associated with higher referee confidence values. As a consequence, encouraging referees to place positive bids may be an effective way to raise reviews with strong confidence.

One way to improve the quality of reviews would be to reduce the large proportion of "orphan" papers, namely, the 42% bid-less reviews (Figure 8). Many factors can lead to bid-less submissions. On the one hand, the topics of some papers simply appeal to no bidders at all. On the other hand, some papers are displayed at the bottom of biddable papers, for they were submitted quite late. In the latter case, the referee may have failed to bid on interesting papers only because of their submission date. Hence, the evaluation of these papers would partly depend on their submission date.

Characteristics of the 19,108 Reviews

Each review conveys a referee's evaluation of a paper to which an overall mark is assigned (i.e., an integer value between 1 and 10). Marks 3 and 4 are the most frequent ones, accounting for 37% of all marks (Figure 10). Such a high proportion of low overall marks suggests that most submissions show poor quality and do not deserve publication. This quality issue may be one factor explaining the low acceptance rates reported by [START_REF] Chen | Conference paper selectivity and impact[END_REF] for Computer Science.

In addition to their comments and overall mark, referees also estimate their confidence in the review through an integer value ranging from 1 to 10. The distribution of confidence values shown in Figure 11 suggests that 50% of all referees were not very confident (i.e., confidence values from 1 to 5) in their review. Half of all reviews were thus written by researchers who admitted to being marginally expert and who signaled that their review was possibly marginally relevant. In our view, this finding calls for a more effective assignment of papers to referees in order to increase the number of reviews with high levels of confidence.

Having papers reviewed by confident referees is preferable for authors (who get more trustworthy remarks) and for PC chairs (who make wiser decisions when relying on trustworthy recommendations). The study of overall marks with respect to referee confidence suggests that confident referees are willing to give higher (lower) marks when papers deserve it. Moreover, confident referees seem to use a larger palette of marks to grade papers. These points are illustrated in Figure 12, where bullets show the mean overall mark (m) per confidence value and the bars extend to the standard deviation (s) from the mean (i.e., a bar is m + s long). In other words, it seems that confident reviewers use the strong reject and strong accept marks more often than less confident referees, who stick to the less controversial weak accept, weak reject, and neutral marks.

We extended our study by looking at the number of best paper nominations according to referee confidence. Such nominations appeared in 1% of all reviews only. As shown in Figure 13, nearly half of all nominations are granted in reviews from confident referees (with confidence values between 8 and 10, which only accounts for 26% of all reviews). This finding supports the idea that confident referees are keen on voicing strong support for the papers showing best quality.

This article questions order effects in the referee bids. We investigate this issue in the next section.

Evidence of Order Effects in the Referee Bids of Conferences

Let us recall the hypothesis under study: order effects play a role in the way referees place bids on submissions. The relationship between the submission dates of papers (as [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] an independent variable) and the number of bids received during step S2 of the peer-review process (as a dependent variable) is studied in this section. As a reminder, note that referees can only consult the titles and abstracts of the submissions during S2, which gives no clue to them about their quality. Consequently, paper quality plays no role in the bidding behavior of referees.

Figure 14 shows the 42 bidding trend lines. There are 33 conferences (79%) with trend lines showing a negative slope. For these conferences, the number of bids decreases with higher paperIDs. In order to further the analysis, the distribution of trend line slopes is plotted in Figure 15. The box plot shows that the middle half of the 42 conferences has negative slopes. Moreover, the distribution of slopes is skewed towards negative values. This finding supports the aforementioned hypothesis, since early submitted papers seem to generally have received more bids than later submissions.

What are the implications of this finding for the integrity of peer-reviewed conferences? First, papers with positive bids tend to get reviewed by referees with higher confidence (Figure 9). Second, referees with higher confidence are keen on grading good quality papers with higher marks (Figure 12). They even nominate best papers more frequently (Figure 13). According to these two points, we may conclude that it is best for a paper to attract a large number of bids. Yet, because of order effects related to their submission date, the papers at the bottom of the submission list are likely to attract fewer bids than the papers at the top of the list. This difference in treatment due to a feature of papers unrelated to quality (i.e., their submission date) is detrimental to the integrity of peer review, which is expected to be impartial.

Limitation

There is a convenient feature in ConfMaster that enables users to filter submission lists according to given keywords. Consequently, some of the referees involved in the ConfMaster2003-2011 data set may not have skimmed a submission list for bidding; they may have filtered it thanks to keywords representing their domain of expertise instead. Unfortunately, there is no way to know how each referee performed the bidding task: by skimming papers, by filtering papers, or both. As a result, our findings may underestimate the effects of submission dates on the peer review process.

Discussion

One may believe that order effects in referee bids do not apply to the conferences run without any bid process (i.e., no S2). Yet this task is devoted to PC chairs, who are in charge of assigning papers to each referee according to their estimated area of expertise. It is likely that PC chairs skimming repeatedly through the list of submissions (sorted by PaperID) are sensitive to the order effects that were at stake for referees when bidding (during S2). As a result, peer-reviewed conferences with or without a bid process alike may need to control order effects.

Let us introduce a solution to prevent order effects from affecting peer review through conference management systems. The proposed solution is threefold, as we recommend:

1. The generation of random alphanumerical paperIDs instead of current numerical paperIDs generated sequentially. The comparison of paperIDs should not inform people about which paper was submitted before others, hence the need for randomization. Since people may still be tempted to (wrongly) infer precedence from the comparison of numbers, we advise to generate random alphanumerical strings (e.g., r3a). Such identifiers of three characters can encode up to (26 + 10) 3 = 46,656 submissions, which seems enough for any peer-reviewed conference. 2. The promotion of bid-less papers during S2. We intend to counterbalance order effects to improve peer review. Since we know that positions at the top of the submission list attract more bids (Figure 14), we should make the most of these positions to advertise "orphan" papers (i.e., those attracting few or no bids). In practical terms, submissions should be sorted by the number of bids they have attracted so far (in ascending order). Tied documents (i.e., those with the same number of bids) should be randomized to avoid order effects once again. The submission list should be re-sorted anytime it is accessed by a referee during S2. Then, it is expected that the advertised (i.e., top-listed) orphan papers will attract more bids from the referees. Eventually, the number of bids per submission will stabilize (unlike the current negative trend slopes in Figure 14). This will help PC chairs when assigning papers manually or automatically [START_REF] Wang | A survey on reviewer assignment problem[END_REF]. This simple manipulation of the submission list will contribute to counterbalancing the aforementioned undesirable order effects. cannot reduce potential order effects, but it does give candidates an equal chance of being assigned to preferred serial positions."

These three recommended changes seem fairly easy to implement in current conference management systems. We believe that they would help improve the integrity of peerreviewed conferences by ensuring equal treatment of all submitted papers.

Conclusion

The scientific community and the general public trust the peer review process to select high-quality scientific papers. However, it is now established that peer review suffers from several inadequacies [START_REF] Zuckerman | Patterns of evaluation in science: Institutionalisation, structure and functions of the referee system[END_REF][START_REF] Benos | The ups and downs of peer review[END_REF]. It is of paramount importance to study and report such issues. Scientists, aware of these pitfalls, can and should take action to avoid them, thus contributing to the integrity of peer review.

In this article we focused on peer-reviewed conferences supported by paper bids. We developed the idea that order effects play a role in the way referees place bids on submissions. We failed to find any work documenting this issue in the literature. A random sample of 42 peer-reviewed conferences in Computer Science totaling 7,351 papers was studied. We found a decreasing trend in paper bids with respect to paper submission dates. This result has several implications for the integrity of peer review. Indeed, papers attracting many bids tend to be assigned to expert referees, who are keen on voicing sharp recommendations (rejection or acceptation). Consequently, late submissions (attracting fewer bids than early submissions) are at a disadvantage compared to early submissions because of order effects related to submission dates.

We discussed a solution to prevent this issue during the bidding phase (S2) of conference peer review: counterbalance the observed order effects by manipulating the order of papers listed in submission lists. We advised using first positions to promote the submissions with fewer bids. The list should be reordered according to this criterion any time a referee skims it for bidding purposes. This intends to globally smooth the number of bids per paper, which fosters in turn the distribution of papers to expert referees willing to review the papers (as they placed bids on them). As a result, we capitalize on order effects in referee bids to secure reviews by expert referees. In addition, we suggest assigning randomized paperIDs to overcome order effects during S3, as well as the randomization of evaluated papers during S5 whenever they are accessed through conference management systems.

Following the recommendations of [START_REF] Hanson | Making data maximally available[END_REF] and [START_REF] Borgman | The conundrum of sharing research data[END_REF], the ConfMaster2003-2011 data set used in this study is published online as an Additional Supporting Information (Appendix A). This is to ensure reproducibility, while fostering research on peer review as implemented by current scientific conferences. Further research should address a larger body of conferences from Computer Science, as well as from all other domains of science.

FIG. 1 .

 1 FIG.1. The five usual stages of the peer review process for conference paper selection supported by paper bids.

FIG. 3 .

 3 FIG.3. UML class diagram modeling the data generated by peer-reviewed conferences supported by a bidding process.

FIG. 4 .

 4 FIG. 4. Scatter plot for conference number 3,903 (see Appendix A)showing the number of bids received per paper position, as well as the trend line for the data points (linear least squares regression with associated coefficient of determination R 2 ). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FIG. 7 .

 7 FIG. 7. Distribution of the 157,332 bids placed by referees with respect to the interest value (i.e., , -, +, or ++). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FIG. 10 .

 10 FIG. 10. Distribution of the 19,108 reviews with respect to the overall mark that referees gave to papers. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

FIG. 14 .

 14 FIG. 14.Trend lines for bids given to papers submitted to the 42 peer-reviewed conferences. Line width is proportional to goodness of fit (R 2 ). Note that the x axis is cut at position 550 for readability concerns (only one conference has 831 papers). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

  3. The randomization of submission lists anytime they are displayed during S3 and S5. Order effects in choice should be avoided by shuffling submission lists used by both referees and PC chairs. As Bruine de Bruin (2005, p. 245) puts it about jury evaluations, "randomization FIG. 13. Distribution of the 243 best paper nominations (among 19,108 reviews) with respect to referee confidence. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

Acknowledgments

We thank Prof. Claude Chrisment and Dr. Gilles Hubert of the University of Toulouse for valuable suggestions and discussions. We also thank Dr. Nadia Yassine-Diab of the University of Toulouse for improving the linguistic content of the article.

Appendix A. The ConfMaster2003-2011 Data set of 42 Peer-Reviewed Conferences

The data set is supplied as an XML file, and released as an online Additional Supporting Information to this article (Appendix S1). An excerpt of the data set is shown in Listing 1, where the document type definition (DTD) precedes the records of the 42 peer-reviewed conferences.

Listing 1 ConfMaster2003-2011ConfMaster2003- : 42 peer-reviewed conferences held between 2003ConfMaster2003- and 2011 License: Creative Commons Attribution-ShareAlike 3.0 Unported License. (see http://creativecommons.org/licenses/by-sa/3.0) @version 08-MAR-2012 @author Guillaume Cabanac (guillaume.cabanac@univ-tlse3.fr) @author Thomas Preuss (preuss@fh-brandenburg.de)

<confidence>3</confidence> <overall>6</overall> <bestPaperNomination>N</bestPaperNomination> </review> <bid idReferee="6399" interest="2" /> <bid idReferee="6400" interest="1" /> <!--. . . --> <bid idReferee="8809" interest="-1" /> </submission> <!--. . . --> <submission id="869"> <!--. . . --> </submission> </conference> <!--. . . --> </confmaster>


Guillaume Cabanac
email: guillaume.cabanac@univ-tlse3.fr

Gilles Hubert
email: gilles.hubert@univ-tlse3.fr

James Hartley
email: j.hartley@keele.ac.uk

Solo Versus Collaborative Writing: Discrepancies in the Use of Tables and Graphs in Academic Articles

The number of authors collaborating to write scientific articles has been increasing steadily, and with this collaboration, other factors have also changed, such as the length of articles and the number of citations. However, little is known about potential discrepancies in the use of tables and graphs between single and collaborating authors. In this article, we ask whether multiauthor articles contain more tables and graphs than singleauthor articles, and we studied 5,180 recent articles published in six science and social sciences journals. We found that pairs and multiple authors used significantly more tables and graphs than single authors. Such findings indicate that there is a greater emphasis on the role of tables and graphs in collaborative writing, and we discuss some of the possible causes and implications of these findings.

H3: There are more figures in multiauthor articles than in single-author articles.

. Visual inspection reveals differences between the two distributions for each journal. The middle 50% of the distributions is lower for singleauthor articles when compared with multiauthor articles.

 (see Appendix) confirm this visual observation. For instance, multiauthor articles in JASIST contain 1.60 more figures (on average +52%) than single-author articles on average. The difference found between single-author and multiauthor articles is significant for all journals. H3 is thus supported.

H4: There are more figures in two-author articles than in single-author articles.

. Visual inspection reveals differences between the two distributions for each journal. The middle 50% of the distributions is lower for single-author articles compared with multiauthor articles.

 (see Appendix) confirm this visual observation. For instance, two-author articles in JASIST contain 1.07 more figures (+35%) than single-author articles on average. The difference found between singleauthor and multiauthor articles is significant for all journals but JASP and WE, which nonetheless show an 8% increase on average. H4 is thus supported.

Discussion and Concluding Remarks

The two main findings from this study are the following:

• Authors in groups use significantly more tables than single authors (H1). Indeed, this is also noticeable between paired and single authors (H2). For instance, in JASIST, authors in groups use 50% more tables than single authors in their articles. • Authors in groups use significantly more figures than single authors (H3). This is also noticeable between paired and single authors (H4). For instance, in JASIST, authors in groups use 52% more figures than single authors in their articles.

Introduction

It is a well known fact that the numbers of authors of individual scientific articles have been increasing. Indeed, as Price (1963, pp. 86-91) put it for chemistry, perhaps tongue in cheek, "The proportion of multiauthor articles has accelerated steadily and powerfully, and it is now so large that if it continues at the present rate by 1980, the single-author paper will be extinct." Today, commentators are more circumspect. Abt (2007, p. 358) for example, writes, "We conclude that single authored papers will decrease in frequency in the coming years, but will not disappear . . . [because] . . . the rapid increase between 1900 and 1960 did not continue, but changed into an exponential that will never reach zero."

Currently, we estimate that about 30% of the articles in JASIST are single authored [START_REF] Cabanac | Issues of work-life balance among JASIST authors and editors[END_REF].

The numbers of authors contributing to scientific articles covers a considerable range-from single authorship to hyperauthorship [START_REF] Cronin | Hyperauthorship: A postmodern perversion or evidence of a structural shift in scholarly communication practices[END_REF][START_REF] Cronin | The hand of science: Academic writing and its rewards[END_REF]. There are articles with more than 100 authors in some domains (e.g., see [START_REF] Adiga | An overview of the BlueGene/L supercomputer[END_REF][START_REF] Foster | The Grid2003 production grid: Principles and practice[END_REF], and, of course, there is the spectacular contribution by [START_REF] Aamodt | First proton-proton collisions at the LHC as observed with the ALICE detector: Measurement of the charged-particle pseudorapidity density at s = 900 GeV[END_REF] with its 1,055 coauthors.

Together with this increase in the numbers of authors there has been an increase in the numbers of articles about the effects of coauthorship. Table 1 lists the findings from some of these studies. Many of these articles were published from the 1960s to 1990s (see Speck, Johnson, Dice, & Heaton, 1999), and these predate the electronic revolution that now facilitates collaborative writing.

Of course, different authors collaborate in different ways. Indeed, for the purposes of this article, it might be of interest for the reader to know that the article was initially proposed to James (Hartley) by Guillaume (Cabanac) working with Gilles (Hubert), who had suggested the study. Guillaume and Gilles carried out the data collection and analysis; then the article was initially written in six parts. James drafted the Introduction and squabbled over various titles. Guillaume and Gilles wrote up the Methods and Results sections. James wrote the Discussion and the Conclusions, and Guillaume completed the References section. But in all of these stages the manuscript passed backward and forward electronically between us numerous times with suggestions for improvement and agreement on every section. Then the final version was checked by James for appropriate English before it was checked by Gilles and Guillaume for submission by Guillaume.

Nonetheless, various patterns of collaboration need to have certain features in common. There has to be a senior author responsible for the submission. Everyone has to agree with the final version. Different authors contribute different things, so the more authors there are the more areas there are for discussion and perhaps disputation. Some authors are seen as more expert than the others on different issues. Therefore, deciding on the order of the authors on the title page can sometimes present problems [START_REF] Kosmulski | The order in the lists of authors in multi-author papers revisited[END_REF][START_REF] Waltman | An empirical analysis of the use of alphabetical authorship in scientific publishing[END_REF], and sometimes, as in the present article, the author who proposed the study comes last.

There have to be negotiations, too, about the amount of detail to contain in the Introduction, Methods, Results, and Discussion sections. There needs to be agreement over the numbers and suitability of the tables, graphs, and references. Also, after submission, the criticisms of editors and referees have to be discussed and responded to by all or by a selection of the authors. 1 In this article, we focus on the numbers of tables and graphs in single and coauthored articles. There have not been, as far as we are aware, any previous articles on this topic. So we have no specific hypotheses generated by earlier research, but we believe that as "more heads are better than one," there might be more tables and graphs in articles written with more authors. The earlier research summarized in Table 1 suggests that increasing the numbers of authors appears to lead to increases in other key features in academic publications.

Accordingly, we made the following predictions:

• On the use of tables:

-Hypothesis 1 (H1): Multiauthor articles feature more tables than single-author articles.

-Hypothesis 2 (H2): Two-author articles feature more tables than single-author articles. • On the use of figures: -Hypothesis 3 (H3): Multiauthor articles feature more figures than single-author articles. -Hypothesis 4 (H4): Two-author articles feature more figures than single-author articles.

Methods

We tested these hypotheses using the following six-step method. It relies on processing all of the articles published in each issue of selected research journals during a specific time period.

1. Retrieve the full text of each article and count the following: number of pages, number of authors, number of tables, and number of figures. 2. Discard articles with less than four pages to eliminate nonresearch articles, such as book reviews, editorials, errata, letters to the editor, notes, and so on. 3. Group articles according to their number of authors. We thus defined "Group 1A" and "Group 2A+" for singleauthor and multiauthor articles, respectively. 4. Use box plots to inspect visually the differences in the distribution of the number of tables and figures between Groups 1A and 2A+ for each journal. 5. Test the statistical significance of these differences with the nonparametric Mann-Whitney U test on two independent samples. The null hypothesis H0 assumes that the distribution of the variable under study (e.g., number of tables) is not statistically significant between single-author (Group 1A) and multiauthor articles (Group 2A+). When H0 is rejected, we report the level of significance of the test (two-tailed) according to the classical three levels: *p < .05, **p < .01, and ***p < .001. 6. Further analyze the data to check whether any such difference can also be observed between single-author and two-author articles (instead of multiauthor articles). This analysis repeats Steps 3 to 5 to compare the use of tables and figures among single-author (Group 1A) and twoauthor articles (Group 2A).

1. The journals had to publish a large number of articles per year for our analyses. We thus focused on the top journals of JCR categories according to the "Articles" field.

1 We wonder how [START_REF] Aamodt | First proton-proton collisions at the LHC as observed with the ALICE detector: Measurement of the charged-particle pseudorapidity density at s = 900 GeV[END_REF] collaborated in these respects! Hence the need to differentiate between contributors and coauthors, as suggested by [START_REF] Rennie | When authorship fails: A proposal to make contributors accountable[END_REF]. 2. The journals had to publish at least 60% of articles featuring tables and figures. This was not the case of some fields, such as pure mathematics. 3. The journals had to publish a reasonable ratio of singleauthor versus multiauthor articles. Journals that mainly publish multiauthor articles (or only single-author articles) do not meet this requirement (e.g., often in physics and biology). 4. The journals had preferably to publish articles by researchers involved in a diversity of scientific domains. Multidisciplinary journals were of particular interest in this respect. 5. The journals had to publish articles in HTML format. This pragmatic requirement allowed us to count the number of tables and figures systematically and in the same way for each journal. 6. The journals had to have no restrictions on the numbers of tables and figures allowed per article, as sometimes occurs in medical and science journals (e.g., Journal of Biological Chemistry and International Journal of Pharmaceutical Science and Research).

Table 2 shows our six selected journals that complied with these criteria. Journals listed in only one category of the JCR are Area (AREA), Journal of Applied Social Psychology (JASP), and Journal of Informetrics (JOI). Note, however, that the researchers publishing in JOI come from various backgrounds (e.g., economics, chemistry, computer science, psychology, sociology). There is one journal listed in three categories of the Social Sciences edition, namely, The World Economy (WE), and two journals appear in both JCR editions, Journal of the American Society for Information Science and Technology (JASIST) and Scientometrics (SCIM). Similarly to JOI, these multidisciplinary journals feature authors from different backgrounds.

The journal parts considered in this study are shown in Table 3. For each journal, we retrieved the latest and every part published in HTML format. The number of retrieved articles ranged from 389 to 1,834. JOI, the newest journal in our data set, had the lowest number of articles. In addition, we note that focusing on recent articles controls for a bias related to any potential lack of up-to-date software used for designing tables and figures in earlier sources.

Figure 1 shows that the share of single-author articles (12%-62%) was not uniform across our journals. These substantial differences may be because of the varying numbers of contributors required to complete a piece of work in the various scientific domains. Indeed, [START_REF] Barrios | Scientific production in psychology: A gender analysis[END_REF] have documented a limited number of single-author articles in psychology (9%). In our data set, more than two thirds of the articles are multiauthored, except for the journal AREA (geography) in which single-author articles prevail (62%).

As far as the multiauthor articles are concerned, the distribution of articles per number of coauthors is not uniform across journals (Figure 2). For all journals, the number of published articles is inversely proportional to the number of coauthors. In addition, there are notable differences in the formatting of articles (e.g., number of columns [one, two, or even three], fonts, and type sizes). Moreover, in this study, each journal contributes a different number of articles. Thus, it was necessary to sample articles at the journal level, as opposed to studying the distribution of tables and figures regardless of the journal in which they appeared.

We used the SOFA statistical package in this study (http://www.sofastatistics.com). For reproducibility concerns, the data used in this study are released as an online Supplementary Appendix, following the advice of [START_REF] Hanson | Making data maximally available[END_REF].

Results

Differences in the Use of Tables

We first consider the case of tables by addressing the following question: Are there differences in the use of tables in single-author articles versus multiauthor articles (H1), as well as two-author articles (H2)?

H1: There are more tables in multiauthor articles than in single-author articles.

Multiauthor articles feature more tables than single-author articles, as suggested in Figure 3. Visual inspection reveals differences between the two distributions for each journal, except for JASP. The middle 50% of the distributions, as showed by the boxes, is lower for single-author articles compared with multiauthor articles. Statistics reported in Table A1 (see Appendix) confirm this visual observation. For instance, multiauthor articles in JASIST contain, on average, 1.82 more tables (+50%) than single-author articles. The difference found between single-and multiauthor articles is significant for all journals but JASP, which actually shows an average 6% decrease. H1 is thus supported for five of the six journals selected.

H2: There are more tables in two-author articles than in single-author articles.

Two-author articles still feature more tables and figures than single-author articles, as suggested by Figure 4. Visual inspection reveals differences between the two distributions for each journal. The middle 50% of the distributions is lower for single-author articles compared with multiauthor articles for all journals but JASP and JOI. Statistics reported in Table A2 (see Appendix) confirm this visual observation. For instance, two-author articles in JASIST contain, on average, 1.67 more tables (+46%) than single-author articles. The difference found between singleand multiauthor articles is significant for all journals except JASP and JOI. H2 is thus partly supported.

Differences in the Use of Figures

Having found a difference in the use of tables between single-author and two-author (H1) or multiauthor (H2) articles, we now repeat our study by focusing on figures (H3 and H4). This balance between the use of tables and graphs by single and multiple authors is present in JASIST. Some other journals, however, feature a different balance, such as WE.

In WE, the difference in the use of tables (48%) is larger than the difference in the use of figures (13%).

These findings, although clear, require some explanation. We need to consider the role of tables and figures in academic articles and, more especially, features of writing together versus writing alone.

Role of Tables and Figures

There is considerable literature about what tables and figures (and their derivatives) are actually for, and when a table is more appropriate than a graph, and vice versa (e.g., see [START_REF] Durbin | Effective use of tables and figures in abstracts, presentations, and papers[END_REF][START_REF] Kastellec | Using graphs instead of tables in political science[END_REF][START_REF] Vessey | Cognitive fit: An empirical study of information acquisition[END_REF]. The Publication Manual of the American Psychological Association provides recommendations regarding the use of tables and figures, and how they should be presented (American Psychological Association, 2010, Chapter 5). Generally speaking, it is suggested that tables are best when one wants to present/retrieve exact numbers and that graphs are best at showing trends. However, some authors (e.g., [START_REF] Gelman | Let's practice what we preach: Turning tables into graphs[END_REF][START_REF] Kastellec | Using graphs instead of tables in political science[END_REF] advocate turning tables into graphs to improve the presentation of results. Gelman et al. demonstrate the effects of doing this with graphs and tables from The American Statistician. As an aside, we might note here that Tartanus, Wnuk, Kozak, and Hartley (2013) found that agricultural journals containing a higher number of graphs had higher impact factors than did those with a smaller number. Furthermore, [START_REF] Gelman | Let's practice what we preach: Turning tables into graphs[END_REF] and [START_REF] Hartley | New ways of making academic articles easier to read[END_REF] both argue that increasing the caption lengths to explain more fully what the data actually show can increase the readers' comprehension of both tables and figures.

Collaborative Writing and Authors' Features and Skills

The earlier findings suggest that groups of authors use more tables and figures in their articles than single authors. The question is, of course, why do such results occur?

Earlier (see Introduction) we outlined how we as authors had set about writing this article. We now want to note how this description, although perhaps helpful at the time to the reader, appears to give no more than one freeze frame from a lengthy film and that any bland description of how two (or more) authors collaborate cannot be the same for everyone. As [START_REF] Noël | Empirical study on collaborative writing: What do co-authors do, use, and like?[END_REF] point out, there are too many sets of multiple and overlapping variables. Different authors in a team may differ in terms of age, sex, nationality, background knowledge on the topic, discipline, and mathematical, computing, statistical, and verbal skills, and so on. Some may work together in the same office (e.g., Gilles and Guillaume), some may never have met in person the other authors with whom they collaborate (e.g., James and Gilles), and some may be close friends (e.g., Guillaume and Gilles). Some may prefer graphical complexity (e.g., Guillaume and Gilles) to verbal simplicity (e.g., James). And, finally, none of us has used any of the more complex computer-based tools written to facilitate coauthorship (e.g., see [START_REF] Churchill | Anchored conversations: Chatting in the context of a document[END_REF][START_REF] Noël | Empirical study on collaborative writing: What do co-authors do, use, and like?[END_REF][START_REF] Sharples | Writing together[END_REF].

The more authors there are the more substantial is the mix of these multifaceted attributes. In writing this article, Gilles and Guillaume have tended to talk about tables and graphs supporting collaboration in writing research articles as though they emerge in some way out of the collaboration. In contrast, James has preferred to think that those with a visual bent can help make the verbally-oriented writer clearer, and that such people will bring these tools "ready-made" as it were. It would indeed be interesting to discuss these issues further with other groups of coauthors or trace the history of particular articles written in different ways by different groups of authors. Table 4 lists some recent related studies in this respect, and it is interesting to note that these studies about how and why tables and figures support collaboration in academic writing call for additional, qualitative research.

  FIG.1. Distribution of single-author and multiauthor articles for the six journals under study. All journals but one publish more multiauthor articles than single-author articles (between 12% and 30%). [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

  FIG.3. These box plots show the number of tables in single-author versus multiauthor articles. Visual inspection and significance tests (*p < .05, **p < .01, ***p < .001) show that there are more tables in multiauthor articles when compared with single-author articles for five of six journals. H1 is thus supported.[Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

TABLE 1 .

 1 Findings from previous research comparing multiple with single authors.

	Findings	References
	• Receive more citations	Bahr & Zemon, 2000; Figg
		et al., 2006; Skilton, 2009
	• Are not always of higher quality	Bridgstock, 1991
	• Require less revision	Bahr & Zemon, 2000
	• Are accepted more quickly for native	Tregenza, 2002
	English authors
	• Have fewer acknowledgments to others	Hartley, 2003
	• Take longer to be reviewed	Hartley, 2005
	(pre-electronic conditions)
	• Have longer titles	Lewison & Hartley, 2005;
		Yitzhaki, 1994
	• Have longer texts	Lewison & Hartley, 2005
	• Use fewer colons in their titles	Lewison & Hartley, 2005
	Note. Articles on research collaboration published before 2005, although
	of general interest, do not reflect the changes in academic writing brought
	about by new technology.

TABLE 2 .

 2 JCR editions and categories of the six journals under study.

		Editions and categories in the JCR 2011
	Journal
	abbreviation	Social Sciences edition	Science edition
	AREA	Geography
	JASIST	Information science and	CS, information
		library science	systems
	JASP	Psychology, social
	JOI	Information science and
		library science
	SCIM	Information science and	CS, interdisciplinary
		library science	applications
		Business, finance
	WE	Economics
		International relations
	CS = computer science.

TABLE 3 .

 3 Source of the 5,180 articles under study with features of the considered six journals.

		Volumes (issues) considered
	Journal			No. of	Single-author
	abbreviation	Earliest	Latest	articles	articles (%)
	AREA	35(1) of 2003	45(1) of 2013	714	62
	JASIST	52(14) of 2001 64(3) of 2013	1,834	30
	JASP	36(12) of 2006 43(2) of 2013	1,010	12
	JOI	1(1) of 2007	7(2) of 2013	389	28
	SCIM	82(2) of 2010	92(3) of 2012	684	26
	WE	29(12) of 2006 36(1) of 2013	549	30

  FIG. 5. These box plots show the number of figures in single-author versus multiauthor articles. Visual inspection and significance tests (*p < .05, **p < .01, ***p < .001) show that there are more figures in multiauthor articles when compared with single-author articles for all journals. H3 is thus supported. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

					Single-author papers		Multi-author papers
		30
	Number of Figures	20
		10
		0
		AREA	JASIST	JASP	JOI	SCIM	WE
		***	***	***		***	*
					Journal

*

TABLE 4 .

 4 FIG.6. These box plots show the number of figures in single-author versus two-author articles. Visual inspection and significance tests (*p < .05, **p < .01, ***p < .001) show that there are more figures in two-author articles when compared with single-author articles for four of six journals. H4 is thus partially supported. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.] Some representative reports on how jointly written articles and book chapters have been written.

	Single-author papers	Two-author papers

[START_REF] Katz | What is research collaboration?[END_REF][START_REF] Sharples | Writing together[END_REF][START_REF] Noël | Empirical study on collaborative writing: What do co-authors do, use, and like?[END_REF][START_REF] Macarthur | The effects of new technologies on writing and writing processes[END_REF][START_REF] Rigby | Peering inside research networks: Some observations on the effect of the intensity of collaboration on the variability of research quality[END_REF] Moore & Barrett, 2010 Wyatt, Gale, Gannon, & Davies, 2010[START_REF] Nevin | Working with coauthors[END_REF][START_REF] Hurford | Working collaboratively: Reflecting on unexpected outcomes[END_REF][START_REF] Badenhorst | Writing relationships: Collaboration in a faculty writing group[END_REF][START_REF] Egghe | Measuring co-authors' contribution to an article's visibility[END_REF]

Data

We considered several journals related to various scientific domains to have a range of different types of journals for our studies. We selected six peer-reviewed journals listed under the two editions of the Thomson Reuters Journal Citation Reports (JCR 2011), namely, the Social Sciences and Sciences editions. Moreover, these journals appear in various categories of the JCR, some of them being listed in more than one category (Table 2).

The following criteria were considered in selecting these journals:

Supporting Information

Additional supporting information may be found in the online version of this article at the publisher's website:

Appendix. Detailed results. Note. The table reports means (M), standard deviations (SD), difference in means (ΔM), and the significance of the difference between the two distributions (p value) according to the U test (two-tailed). Significance: **p < .01, ***p < .001.