has claimed over 400,000 lives worldwide and disrupted almost all aspects of human society. Stay-at-home orders, lock downs and school closures have affected scientists as well, and especially those caring for children or elder family members (1, 2) . As a result, the productivity of female scientists appears to have decreased (3) (4) (5) . Early evidence suggests that the proportion of publications with female authors is lower during the pandemic (6, 7) ; furthermore, the proportion of female scientists publishing specifically about COVID-19 is dropping much lower than expected, by almost 23% (8, 9) .
To investigate whether COVID-19 exacerbates the gender gap in scientific publishing, we collect data about 80,875 papers from biorXiv (43,459 papers), medrXiv (5,080 papers) and selected high-impact Springer-Nature journals (32,348 papers). By using state-of-the art gender inference techniques, we identify the most likely gender of the 445,633 authors. For those publications that do not provide the location of the author's affiliated institution, we infer it by using an ad hoc localization method. We build a set of statistical models to estimate the expected prevalence of female authors publishing during the COVID-19 pandemic and compare it to observed data.
Our analysis focuses on 361,234 authors (228,449 male and 132,789 female) whose gender could be inferred with high accuracy. Even after correcting for temporal trends, we observe an average drop of 5% in the proportion of female authors during the COVID-19 pandemic. For research topics related directly to COVID-19, the proportion of female scientists in first author positions drops by 44%. Male authors are more likely to publish papers about COVID-19 as the first authors on preprint servers. Overall, during the pandemic, scientists published at increasing rate on preprint servers. On average, we observe 22% more publications than expected and 28.7% increase in number of authors (26% increase of females and 29% increase for males). Although the relative increased productivity applies to both genders, male are experiencing it at higher rates than female scientists, further widening the gender gap.
When data is disaggregated by country, gender disparities become even more apparent, as high as 40% in some cases. Furthermore, the gender gap widening during the pandemic is inversely correlated with the country's GDP per capita: large developing countries like India and Brazil exhibit the largest gap widening.
The gender gap in research during the COVID-19 pandemic.
First, we establish the baselines, that are the expected proportions of women that appear either as the first author and as the author regardless of the authorship order. The expected proportions are calculated using the OLS model and the historical data from the year 2019 (see Materials and Methods). We then calculate the true observed proportions of female authors that publish during the COVID-19 pandemic in 2020 and compare it to the baselines.
The aggregate results suggest that the proportion of female authors publishing on all topics as the first author has decreased by 4.9%, as illustrated in Fig.1A (expected arithmetic mean: 0.37; observed arithmetic mean: 0.35). The percentage drop becomes much more prominent when we analyze the papers about COVID-19. The proportion of women that write on COVID-19 related topics as the first authors has decreased by 44.5% (expected arithmetic mean: 0.37; observed arithmetic mean: 0.2). When looking at the authors regardless of the order, the proportion of women writing about COVID-19 decreased by 15.4% (expected arithmetic mean: 0.34; observed arithmetic mean: 0.29). The standard errors of the aggregate analysis are relatively small and can be seen as the vertical lines on top of the bars.
We observe an overall increase in number of papers published on preprint servers (Fig. 1B ) during the pandemic (26% for biorXiv and 363% for medrXiv), and the drop of publications on Springer-Nature journals (84%). Such a trend suggests that during the pandemic the researchers are trying to make their results available as quickly and widely as possible, often circumventing lengthy peer-review process. Despite the absolute increase in numbers, the fraction of women publishing on preprint servers drops significantly. That is particularly evident for COVID-19 related papers where the relative drop in proportion of females is 35% and 21% for medrXiv and biorXiv respectively. The trend for Springer-Nature journals is slightly different. We still observe the drop in proportion of female authors as the first authors across the disciplines. However, the fraction of women authors regardless of the authorship order is increasing from 0.33 to 0.38, that is the relative increase of 14%. Note that only 0.3% of all papers from Springer-Nature journals deals with COVID-19 related topics. That is much smaller than 2.3% in biorXiv and 48% in medrXiv.
We see the evidences that women are getting underrepresented in COVID-19 research, especially in papers published on preprint servers. That confirms some earlier suggestions that female first authors contribute less to COVID-19 studies than research in other areas (9) . Women remain underrepresented even though we observe the increased publishing rate for both genders during the pandemic (Fig. 1B) .
Country-level Analysis. The global pandemic has touched almost every nation on the planet. Countries, however, responded differently to contain the spread of the disease. The variability of the measures and their timing, combined with differences in cultural norms and outbreak severity, have had a variable impact on scientific communities across the world. Country-level analysis better reveals global trends, as the aggregate data can be skewed by the countries with disproportionately large number of publications such as the US with almost 29% of all authors in the data set. Additionally, it can reveal regional, political and cultural differences between the nations.
We identify the most likely country of the author based on their affiliation (see Materials and Methods) and measure the difference between the expected and observed proportion of female authors during the pandemic. Figure 1C shows the pandemic gender gap across countries. The values represent percentage difference between the expected and observed fraction of female authors publishing in biorXiv, medrXiv and selected Springer-Nature journals between February and April 2020. Points to the left (orange) of the mid-line represent countries with less than expected fraction of female authors, and points to the right of the mid-line (in green) represent an increase in the fraction of female authors. Papers dealing explicitly with the topic of COVID-19 (middle panel) show a greater gender gap than papers on other research topics (right panel). In Italy, for example, the relative drop in the proportion of female authors is 40% (expected arithmetic mean: 0.43; observed arithmetic mean: 0. 26 affiliated with Italian institutions are publishing disproportionately more than their female colleagues about COVID-19. Similar result applies to Australia, United Kingdom, France and Germany. The opposite is true for Switzerland and Japan, where the proportion of women publishing about COVID-19 increases by 5.6% and 15% respectively. Missing points indicate that there was not enough data points during the pandemic to calculate the observed mean. The gender gap for non COVID-19 related research (right panel) exists during the pandemic, but it is smaller than for COVID-19 research. Again, we observe a stark contrasts between the countries, with proportion of female authors (regardless of author order) publishing during the pandemic decreasing in Australia, Brazil and Sweden, and gender gap shrinking in South Korea and Japan.
Gender disparities in research are strongly associated with a country's wealth (10) . Figure 2 shows that wealthier countrieswith higher per capita gross domestic product (GDP)-have proportionally fewer women in research, with Asian countries having consistently fewer women researchers. In addition, wealthier countries show smaller pandemic-related drop in women's participation in research than poorer countries, with wealthier Asian countries experiencing an increase in the proportion of active women researchers. This suggests that women experience bigger life disruptions in poorer countries, which affect their productivity.
Data. The data on published papers is collected from three separate sources: (i) biorXiv (43K papers and 232K authors), provided by the Rxivist, the API provider for biorXiv publications (11);
(ii) medrXiv (5K papers and 35K authors), scraped directly from medrxiv.org; (iii) Springer-Nature (32K papers and 210K authors), data from 70 journals that have H-index larger than 12, collected using the Springer-Nature OpenAccess API. For each source, we collect the meta-data of all the papers published between January 1 st 2019 and May 3 rd 2020. The earliest publication on medrXiv is from Jun 25 th 2019. For each paper in biorXiv and medrXiv, we keep the date of publishing. For Springer-Nature journals, we keep the date of manuscript submission. We additionally store the title and the abstract of the papers. For each author, we preserve the name, affiliation and the authorship order. Additionally, we use socio-economic data on countries, including their respective GDP per capita provided by Our World in Data (ourworldindata.org).
As a heuristic to identify locations of authors' institutions, we use data provided by GRID (grid.ac).
To measure the discrepancy between expected and observed proportion of female authors, we first establish a baseline model. Using historical data before January 31 st 2020, we calculate the proportion of female authors publishing each week. We train a linear model f = bt + c, where f , the proportion of female authors, serves as a dependent variable, t is time measured in weeks, b and c are the slope and the intercept. We train the separate model depending on the level of disaggregation (country, publisher...). The model is illustrated in Fig. 1D . From the model, we derive the expected fraction f Exp = f /n, that is the mean fraction of all predicted values for the observed period and f Obs = ftrue/n. The error for the predicted value is the mean standard error of the prediction. The error of the observed value is calculated as the standard error of the mean SE = σ/ √ n. The errors for the percentage drops in Fig. 1C are calculated as the total sum of the errors of predicted and observed values.
To infer the author's gender from their name we use a state-of-the-art tool, namely the genderizer.io API (12) . Given an input name, the model returns a gender and a confidence score between 0.5 and 1. The uncertainty is greater for Asian names that often are not gender-specific (13) . We filter out all authors for which the confidence score is lower than 0.8. Overall, 19% of names yields scores below such threshold, with Chinese and Korean topping the ranking with 54% and 41% respectively. To identify authors' location, we first locate a toponym in the author's affiliation. If there is no toponym, we query the GRID.ac database and find the institution with the most similar name and assign the institution's location to the author.
Identifying COVID-19 papers. The papers that deal specifically with COVID-19 and similar topics are identified by the set of keywords that appear in their title or the abstract. ACKNOWLEDGMENTS. DARPA support via W911NF1920271.