The year 2020 began with the extremely fast spread of the COVID-19 pandemic and is expected to reach a second peak in the upcoming months (Moore et al., 2020; World Health Organization, 2020) . Countries such as the US, India, Brazil and many others are struggling to flatten the curve. The pandemic has impacted almost every aspect of life ranging from the economy to tourism, political affairs, the arts and sports, thus there is global effort in searches for ways to understand and cope with it. These efforts lay, in great part, in the hands of the scientific research community. As such, scholarly research and its publication patterns have also been greatly effected by this current crisis.
• How has international (co-)authorship changed? We hypothesise that the pandemic has caused a significant increase in international co-authorship and a wide diversity of collaboration in scholarly research.
To address these questions we employ both a short-term analysis technique, focusing on the first 6 months of 2020 (the first six months of the outbreak), and a longitudinal analysis technique through which we compare the publication patterns across the last five years (2016) (2017) (2018) (2019) (2020) . Our study employs a set of statistical tests in order to ascertain statistically significant changes. These tests are conducted at both the short-term and longitudinal levels. At the short-term level, these tests indicate if any statistically significant differences exist when comparing COVID-19 papers to non-COVID-19 ones. At the longitudinal level, these tests indicate if any statistically significant differences exist when comparing papers published prior to the pandemic and during the pandemic. We focus on two main types of venues for research publication: peer reviewed Journals and Preprint servers. Preprint servers are becoming widely used in other fields of research, such as Computer Sciences and Physics, but up until the pandemic the usage of such publication venues in the fields of Medicine and Biology was limited (Desjardins-Proulx et al., 2013; Maslove, 2018) .
Understanding changes in publication patterns during the pandemic is valuable due to their possible implications. As the pandemic does not seem to come to a stop, these changes, for good and for bad, may have prolonged effects that should be considered by journal editors, recruiting and promotion committees, funding agencies and others. This paper is organized as follows: Section 2 presents related work in scientometric analysis of pandemic related research. In Section 3 we describe the data and tools we use. Section 4 presents the results to the research questions we posed. We conclude the paper with a discussion in Section 5.
Numerous scientometric studies have examined how publication patterns vary during or post a pandemic (see Zhang et al. (2020) and references therein). These studies commonly focus on one or a few scientometrics aspects such as growth of publications in various databases, research funding agencies' countries, average time to acceptance and international collaboration patterns. In these works, two standard techniques are often used: short-term technique in which publication pattern changes are analysed during a pandemic and a longitudinal technique in which the publication pattern analysis is focused on a collection of papers related to viral pandemics, written over a long period, usually several years.
Recent studies on the COVID-19 pandemic follow these two techniques as well. Adopting the short-term analysis technique, da Silva et al. (2020) have examined publication volumes of COVID-19 papers and identified top journals, countries and authors. Similarly, Costa et al. (2020) performed keyword analysis and identified most productive countries, institutions, authors and journals, and Lou et al. (2020) observed publication types, journals and publication countries. Chahrour et al. (2020) focused on the international distribution of COVID-19 publications compared with the number of COVID-19 cases in the respective countries, again adopting a short-term analysis approach. Common to the above studies is the focus on COVID-19 related publications and they do not consider the possible changes in publication patterns of papers unrelated to the COVID-19 pandemic which are published at the same time of the pandemic. One exception is Homolak et al. (2020) who do focus on both COVID-19 related papers and non-COVID-19 related papers in their short-term analysis.
Adopting the longitudinal approach, Kun (2020) observed the extremely short time to acceptance for COVID-19 papers. The author focused on the first three months of 2020 for COVID-19 papers and compared them to papers on other coronviruses. Ahmad & Batcha (2020) have taken a different approach, focusing solely on COVID-19 papers yet examining them over the years 2011-2020. Similarly, (Mao et al., 2020; Tao et al., 2020; Zhai et al., 2020) studied the same for the years 2000-2020 and they examined the publications' countries of origin, collaboration networks, authors, keywords and additional publication characteristics. In the same vein, Kagan et al. (2020) analysed publications related to multiple nCov viruses and compared those to influenza and additional viruses, and Zhang et al. (2020) did a comparative bibliometric study of multiple outbreaks and performed a preliminary analysis of the COVID-19 outbreak.
Our study complements the above in three respects: 1) By providing both a short-term analysis as well as a longitudinal one, our study gives a wider perspective in which the influence of the pandemic on current research can be observed; 2) We focus on both COVID-19 and non-COVID-19 papers, providing an assessment of how the effects of the pandemic differ in respect to these two types of papers; and 3)
To the best of our knowledge, we provide the most extensive scientomteric-based research on COVID-19 publications to date as we analyse the first six months of the pandemic.
Our study further compares preprint servers and peer-reviewed journals as possible dissemination venues for COVID-19 research output. Prior research by Krumholz et al. (2020) and Johansson et al. (2018) have shown an increase in the usage of preprint servers in previous pandemics. Recently, evidence was provided to support their findings in the current pandemic as well (Fraser et al., 2020; Fry et al., 2020) . While the focus of these studies was papers related to the pandemic in preprint servers, our work extends these works by observing longitudinal changes in both COVID-19 and non-COVID-19 publications in these venues as well as assessing the influence the pandemic had on these changes.
The data for this research was obtained from four main sources:
• Elseviers' ScienceDirect and Scopus. ScienceDirect is a full-text scientific database which is part of SciVerse. Scopus is an abstract and citation database of peer-reviewed literature. Utilizing both ScienceDirect and Scopus API we extracted data on COVID-19 related journal articles.
• medRxiv (pronounced "med-archive") is a free online archive and distribution server for complete but unpublished manuscripts (preprints) in the medical, clinical, and related health sciences. The server was founded by Cold Spring Harbor Laboratory (CSHL), a not-for-profit research and educational institution, Yale University, and BMJ (mostly referred to as the British Medical Journal), a global healthcare knowledge provider.
• bioRxiv (pronounced "bio-archive") is a free online archive and distribution service for unpublished preprints in the life sciences. It is operated by CSHL.
• arXiv is an open archive for scholarly preprints in various fields. It is maintained and operated by Cornell University.
In addition to the above described datasets we have also extracted supplementary data from PubMed which is a web-portal of the medical database MEDLINE; and ScimagoJR -a publicly available portal which includes journals and country scientific indicators developed from the information contained in the 
To understand the publication behaviour in the first months of the pandemic, we analysed the data which was extracted from the sources described in Section 3.1. The search in ScienceDirect was done using the search query "COVID-19" OR Coronavirus OR "Corona virus" OR Coronaviruses OR "2019-nCoV" everywhere in the document. The journals with the highest numbers of COVID-19 related publications were selected for further analysis. For each journal both COVID-19 and non-COVID-19 related papers' data was downloaded separately. Data was downloaded for each year in our analysis (2016-2020) and split up by months according to the online availability date of the paper. Additional data for each paper was extracted via its DOI from PubMed Entrez and through Scopus API. Duplicates as well as papers with missing data were removed automatically. Records for the examined papers were analysed according to various attributes including publicizing journal, authors, publicizing countries and numerous dates.
In order to retrieve and analyse COVID-19 data from bioRxiv, medRxiv and arXiv, we queried the archive servers with the search query -"COVID-19" OR Coronavirus OR "Corona virus" OR Coronaviruses OR "2019-nCoV". This query was executed separately for each of the first six months in 2020. In addition we queried the archive servers for all papers published in each of the first six months of the years 2016-2020. The retrieved results were downloaded and automatically analysed using designated scripts written by the authors. A subset of the results were manually tested to ensure both accuracy of the data and the scripts. As the arXiv server is used for many fields of research and our study focused on Biological and Medical papers, the data extraction from the arXiv server was limited only to the "quantitative biology" field.
We analyse three main aspects of our data: 1) Publication growth that has occurred during the pandemic;
2) Changes in the time to acceptance of peer reviewed papers; and 3) Changes in authors' countries of affiliation and international collaborations. These three aspects are analysed and reported for both COVID-19 papers as well as "standard" non-COVID-19 papers published during the pandemic. This comparative approach allows us to identify the effects of the pandemic on the three examined aspects both for pandemic-related research as well as the standard biological and medical research published during that time. The above three aspects are further analysed for pre-pandemic papers published in the years 2016-2019 and compared against the COVID-19 papers and non-COVID-19 papers published in 2020. This part of the study allows us to identify the effects of the pandemic on the three examined aspects in a longitudinal view. Some of the following analyses, especially regarding time to acceptance, authors' country of affiliation and international collaboration, require a large volume of publications.
Thus, for these analyses we selected a subset of biological and medical journals with the highest number of COVID-19 paper publications. These are shown in Table 2 . Figure 1 shows that preprint servers are considered a legitimate and even valuable source of dissemination at this time. Interestingly, the publication growth was observed for both COVID-19 publications as well as for non-COVID-19 related papers in all of the preprint servers we analysed. Furthermore, the growth in COVID-19 publications increased in a similar fashion to the international spread of the pandemic, although some decline was apparent in June in the medRxiv preprint server.
The sharpest increase in publications was observed in medRxiv, which was created as a preprint server in mid-2019. While the total number of papers published in it has increased from 200 in January 2020 to nearly 1800 in June 2020 (a factor of 9), the number of COVID-19 related papers increased from 40 in January 2020 to 1350 in June 2020 (a factor of 34). COVID-19 papers showed an increase in percentage from 19.5% to 75% of total papers published on the server during these months.
Focusing on the arXiv preprint server, which is well known for publications in Physics and Computer Science, reveals that the number of publications in biology (quantitative-biology field in the arXiv, q-bio for short) was low over the last four years with a slow, almost flat, increase. However, from January 2020 and until June 2020, the total number of publications in the q-bio field had almost doubled and the number of COVID-19 related papers had increased by a factor of 8. The percentage of COVID-19 papers increased from 20% in the month of February to 35% in June, of total papers published in the q-bio field of the server in these months 1 .
Turning to the analysis of peer reviewed journal publications, we observe that the growth in publication rates is correlated with the journals' "Scimago Journal Rank", (SJR) (González-Pereira et al., 2010) .
Specifically, in low ranking journals, which are journals in the fourth quartile of their subject area or not in any quartile, hardly any COVID-19 papers were published. In contrast, the vast majority of COVID-19 papers were published in the highest ranked journals. Specifically, using the SJR score as a sorting criteria, 31% of the COVID-19 papers we analysed were published in top ranking journals (∼20% of the analysed Similar to Figure 1 which presents the growth in preprint papers, Figure 2 shows the COVID-19 publication growth for journals in Table 2 as compared with publication growth over the last four years. While we can see a large surge in the total number of publications as compared to previous years, COVID-19 publications seem to account for virtually the entire growth. Specifically, the number of non-COVID-19 related publications follows the same pattern of previous years. 
Following the results reported in Section 4.1.2, we now turn to investigate how this publication growth has affected the time to acceptance for the papers. Naturally, time to acceptance is only applicable to journal publications and not for preprint ones. For each of the journals analysed in Table 2 , we calculated the time to acceptance in each of the six examined months for the years 2016-2020 by calculating mean and standard deviation (STD) of the time to acceptance. The time to acceptance is defined here as the period between the "date received" and the "date accepted" or the "date online", whichever is earlier. Data entries for which the "date received" or both the "date accepted" and the "date online" were missing or corrupted were omitted from our analysis. We obviously omitted inaccurate data entries for which the "date received" was later than (or the same as) the "date online" or the "date accepted". We performed a series of one tailed t-tests comparing mean time to acceptance of COVID-19 papers to non-COVID-19 papers. The test was performed for each of the months February-June of 2020 2 as well as for the average time to acceptance from months January-June of 2020. Values are shown in Table 3 .
As can be seen, all of these tests revealed a statistically significant difference in mean time to acceptance with p < 0.05. In February 2020, for example, the average time to acceptance of non-COVID-19 papers was almost 10 times longer than that of COVID-19 papers. Across the examined period, on average, non-COVID-19 papers suffered from an average time to acceptance of 91.3 days compared to 19.3 days for Additional support for the above phenomena can be seen in Table 4 as well. Taking a longitudinal approach, we compare each pair of consecutive years as to the mean time to the acceptance of papers from our examined journals. As can be seen in the table, until 2019, inclusive, no significant changes were observed. For 2019 and 2020 we see that papers published in 2020 suffered from a longer time to acceptance, both when considering the entire publication set as well as when focusing on non-COVID-19 papers alone.
Due to our previous finding that high ranked journals yield a higher number of COVID-19 papers, we expected this to affect the time to acceptance as well. Two contradicting hypotheses can be assumed:
1) The high volume of papers submitted to high ranked journals yields a longer acceptance time; and 2)
The high volume of accepted papers to high ranked journals implies a faster review process and a shorter acceptance time.
In order to examine these hypotheses, we calculated the Pearson correlation between the SJR score of a journal and the mean acceptance time of COVID-19 related papers for that journal. The correlation was found to be weakly negative, as would be expected from the second hypothesis, but not statistically significant (r = −0.266, p = 0.43). Table 2 .
The results are also depicted in Figure 4 , which displays the mean time to acceptance and the Standard Error of the Mean (SEM) averaged over all analysed journals from Table 2 in the first six months of each of the years 2016-2020. The figure also presents the mean and SEM for COVID-19 and non-COVID-19 related publications in 2020. We first observe an apparent trend for COVID-19 publications which is first declining sharply from January to February, mainly attributed to the relatively low number of COVID-19 papers in January and thus the high SEM in January, and then inclining moderately from February onwards, mainly due to the increase of COVID-19 papers. However, despite the trend, COVID-19 publications enjoy a shorter time to acceptance period compared both to non-COVID-19 publications in 2020 and to publications in 2016-2019. As we speculated before, non-COVID-19 publications in 2020 have "paid the price" for the shortened time to acceptance of the COVID-19 papers by exceeding the trend of previous years.
We focus on the countries with the highest COVID-19 related publications and compare them to the top publishing countries of non-COVID-19 papers in the first six months of 2020, as well as to the historical data from the last four years. In Table 5 we report the comparison between COVID-19 and non-COVID-19 publications for the first six months of 2020. A longitudinal perspective over last four years is presented in Figure 5 . Table 2 ). SEM is calculated and displayed separately for each month. As can be seen from both figures, while the top publishing countries have remained almost the same over the past five years, a consistent growth in the number of papers is evident. This is consistent with our previous findings showing the total growth of publications over the years and most significantly in 2020. It is interesting to note that despite the fact that the same countries are top publishing countries throughout the years regardless of the Diversity of international collaboration. As before, we employed both a short-term analysis, comparing COVID-19 related publications to non-COVID-19 related ones, and a longitude analysis, where the diversity in international collaboration is measured over the past four years and compared to the year 2020. related papers. This was performed in each of the months February-June of 2020 3 separately and also for the total first six months of 2020. For each of these periods, the mean number of collaborating countries was calculated and a t-test was performed. The results are shown in Table 6 . Our findings showed that for all periods tested, except for the month of May, the mean number of collaborating countries in non-COVID-19 papers was found to be statistically significantly greater than the mean number of collaborating countries for COVID-19 papers, with p < 0.05. Table 7 . The results show that for all examined periods, the number of collaborating countries is steadily increasing. The differences are statistically significant from the 2017-2018 period onward, p < 0.05. In addition, we ran t-tests comparing international collaboration in 2019 compared with 2020 but observed collaboration only for COVID-19 related publications and similarly for only non-COVID-19 related publications. While both showed a statistically significant difference, the direction is reversed as the mean number of collaborating countries for 2020 COVID-19 papers is statistically significantly smaller than that of 2019, but the mean number of collaborating countries for 2020 non-COVID-19 papers is statistically significantly larger than that of 2019, p < 0.05 for all accounts.
An increase in collaboration diversity over the years has been demonstrated in previous works as well (Leydesdorff & Wagner, 2008; Wagner & Leydesdorff, 2005) . The results presented here complement these findings by showing that the year 2020 has experienced the largest increase in the number of collaborating counties. However, the findings from the comparison of 2019 to the 2020 COVID-19 papers along with the findings when comparing COVID-19 and non-COVID-19 collaborations (as shown in Table 6 ) show that collaboration in COVID-19 papers is, surprisingly, low. Specifically, it is lower when compared to non-COVID-19 papers, lower when compared to collaboration in the past and lower than what we would expect during a pandemic, where collaboration is of increased significance.
Volume of international collaboration As before, we employed a short-term analysis, where the volume of COVID-19 related collaborated publications were compared to non-COVID-19 related ones, and a longitudinal analysis where the volume of internationally collaborated publications is measured over the past four years and compared to the year 2020. We start by analysing two extreme cases, papers written with no collaboration at all, and papers written in any form of collaboration, meaning, at least two countries collaborated on the authorship of that paper. For (Pearson, 1900) for comparing single country authored papers to multi-country authored papers. The test was performed for each of the months February-June of 2020 4 and as a sum over the first six months of 2020. In each time period we examined how many papers were published by a single country (all authors from the same country) and how many were in collaboration with other countries. This was done for COVID-19 papers and for non-COVID-19 papers. The results are shown in Table 8 . Based on the χ 2 statistic and p values we can conclude that for the months of April, May and June 2020 and for the total over the six months, the type of paper (i.e., COVID-19 or non-COVID-19 related) is significantly associated with the authorship by a single country or multiple countries. Specifically, authorship by a single country is indicative of COVID-19 related papers while co-authorship by multiple countries is indicative of non-COVID-19 related papers.
Additional χ 2 tests were performed to analyse if the pandemic affected the number of papers internationally co-authored compared to previous years. In this test we measured the mean number of papers authored by single countries and the mean number of papers authored by multiple countries in the first six months of each two consecutive years in our study, comparing 2016-2017, 2017-2018, 2018-2019, 2019-2020 . This can be seen in Table   9 . Our results for the longitudinal χ 2 test are statistically significant at p < 0.05 in two specific cases: When comparing international co-authorship for 2019 papers to 2020 COVID-19 papers; and when comparing authorship for 2019 papers to 2020 non-COVID-19 papers. In order to better understand the previous findings, we performed a series of short-term analysis t-tests and a similar series of long-term analysis t-tests. In the short-term analysis we measured, for every country, the number of papers written in international collaboration. This was performed in each of the months February-June of 2020 5 and for the total first six months of 2020 for COVID-19 related papers and for non-COVID-19 related ones. The results are displayed in Table 10 and show that for the examined periods of February, March and June and the total first six months of 2020, the mean number of COVID-19 collaboration papers is statistically significantly smaller than for non-COVID-19, with p < 0.05.
In the longitudinal analysis we measured, for every country, the number of papers written in international collaboration. This was performed by summing, for each country, the number of internationally collaborated papers over the first six months in each of the years 2016-2020. Figure 9 displays the countries with the highest number of papers written in international collaboration over the last five years. We can observe two interesting collaboration patterns here. The first is that the number of papers written in collaboration has continually increased for the top collaborating countries. This is consistent with our findings of growth in total publications over the past five years as can be seen in Figure 2 . The second finding that can be observed is that although the US and UK have remained the top two collaborating countries, when observing collaboration for COVID-19 publications, China is extremely collaborative and Italy is in the top 5 collaborating countries. This can be explained by the pandemic originating in China and its wide spread in Italy.
We performed pairwise t-tests for each two consecutive years in our study, comparing 2016-2017, 2017-2018, 2018-2019, 2019-2020 for all internationally collaborated papers published in the first six months of these years.
From Table 11 we can observe that a statistically significant difference exists only for the period 2019-2020. The mean number of internationally collaborated papers in 2019 is statistically significantly smaller than the mean number of internationally collaborated papers in 2020, with p < 0.05. However, this difference is not statistically significant when comparing 2019 collaborated papers separately against 2020 COVID-19 papers and against 2020 non-COVID-19 papers. These findings indicate that when international collaboration is measured by the volume of collaborated papers, the COVID-19 pandemic does not seem to affect the increase in international collaboration.
In this study we have analysed how the COVID-19 pandemic effected the publication patterns in biological and medical literature. We employed two types of analyses to address each of our research questions -short-term analysis and a longitudinal analysis of preprint servers and peer reviewed journals.
Our analysis showed a significant increase in published papers both in peer reviewed journals and in preprint servers compared to previous years. The new MedRxiv preprint server especially stands out with an exceptionally large increase in publications and we expect this preprint server to continue this pattern post-pandemic. Notably, while the increase in publication in preprint servers has occurred for both COVID-19 and non-COVID-19 related papers, this is not the case for the journals we have analysed. In these journals virtually the entire growth was due to COVID-19 papers while, on average, the volume of non-COVID-19 papers has remained similar to previous years. Our results also showed that high ranked journals publish more COVID-19 papers than low ranked journals.
It is further apparent that journals had responded quickly to the pandemic by lowering the time to acceptance for COVID-19 papers. Unfortunately, this has come at a cost for non-COVID-19 papers, whose time to acceptance was longer than that was observed in previous years (and obviously longer than that of COVID-19 papers).
Taken jointly, the non-increasing volume and longer time to acceptance of non-COVID-19 papers may lead to a significant slow down in non-COVID-19 related research, at the very least in journal publications. While such research may not be "urgent", it is, presumably, of no less importance than the current crisis. On the other hand, we do observe an increase in the use of preprint servers irrespective of COVID-19 publications. This may indicate that the community is recognizing the above phenomena and adjusting their publication behaviour accordingly.
Turning our focus to the countries authoring these papers, we observed the top publishing countries and international collaboration patterns. We observed that while the US and UK have remained the top publishing and collaborating countries, Italy, Brazil and Hong Kong have produced a significant amount of COVID-19 related papers as well, disproportional to their lower ranking in non-COVID-19 papers today and in the last four years.
This can be explained by the major impact this pandemic has had on these countries. Our results further showed consistent growth over the past five years in international collaboration when examining both collaboration diversity and volume. However, contrary to our original hypothesis, international collaboration diversity in COVID-19 papers was lower than in non-COVID-19 papers and lower than the past four years. We observed that most COVID-19 papers were authored by a single country or only a very few countries. This can be explained by the complexity of conducting international studies during the pandemic. However, this may also suggest that countries are doing a significant amount of COVID-19 research nationally and knowledge is officially shared only after the research has been published. Although the number of countries whom a single country has collaborated in COVID-19 related papers was lower than expected, the number of such papers written in collaboration had increased compared to non-COVID-19 papers and compared to previous years.
We recognize that the current study is limited by the amount, quality and diversity of the data used. While this work is, to the best of our knowledge, the most extensive one on all three accounts when discussing scholarly publications during the COVID-19 outbreak, a larger analysis may reveal additional or other trends, which were not captured here. We plan to extend this work further to include the long-term effects of this pandemic on scholarly research, in biological and medical literature and in other fields as well. Such research would complement this work by analysing (hopefully) post-pandemic changes in citation patterns and the usage of preprint servers.
6 References