 Hello, I'm Korra Korzadz, Head of Communities at E-Life. It's my pleasure to welcome you all to our February's ECR Wednesday webinar. This series aims to give early career researchers a platform to discuss issues important to you and your research career. You can follow us on Twitter at E-Life community and with the hashtag ECR Wednesday. The session is being recorded and we will make it available on YouTube in the near future. Now it's my pleasure to invite Yara Bidiakho, a research fellow at the West African Center for Cell Biology of Infectious Pathogens at the University of Ghana and a member of the Early Career Advisory Group to introduce today's session and our panelists. Thank you Korra and hello everyone. Thanks for joining our ECR Wednesday webinar on promoting inclusion in science. My name, as Korra said, is Yara Bidiakho and I'm a member of the E-Life Early Career Advisory Group or the ECAG and I'll be moderating this webinar. E-Life is a nonprofit organization that is operating a platform to improve all aspects of research communication by encouraging and recognizing the most responsible behaviors in research. The role of the ECAG is to influence and support E-Life's work to catalyze broad reform in the evaluation and communication of science and in particular to represent the needs and aspirations of researchers at early stages of their careers for a research culture that is healthy for science and for scientists. We have applications open currently actually for five vacancies in the ECAG and so I'd encourage everybody listening who is interested to check out more details and apply at elivesciences.org slash community. We open 2021 with a webinar dedicated to exploring diversity and inclusion within academic research. Today our speakers will review selected literature on this subject and we'll discuss manifestations of bias and discrimination in academia. They will also share resources and inclusive practice examples with a view to support researchers to improve practices in recruitment, peer review and mentoring. So I'll just start with a little bit of housekeeping. Following the presentation will be relaying your questions to the panelists. So to ask a question you can type your question in the Zoom chat box or you can tweet us. We are at E-Life community using the hashtag ECR Wednesday. I will read out your name and question in the Q&A at the end of the webinar. So we just ask that during the webinar you please be respectful, honest, inclusive, accommodating, appreciative and open to learning from everyone else. Please do not attack, demean, disrupt, harass or threaten others or encourage such behavior. If at any point you feel uncomfortable or unwelcome on any of these webinars, so not just this week's, please contact E-Life by emailing events at elivesciences.org. And of course we reserve the right to ask anyone to leave and or to deny access to a subsequent webinar if you fail to follow these rules of conduct. So if you need help, please send a chat message directly to Cora Corsac. And to ask questions, as I said again, please use the Zoom chat box or tweet us at elivecommunity and use the hashtag ECR Wednesday at any point during the webinar. So without further ado, I'd like to welcome our three speakers to take us through this webinar. And I'll come back again to moderate the questions at the end. Thanks for the introduction. Before we get started today, we'd like to take an opportunity to state our support for marginalized groups, especially black academics in the US and across the world, who experience enhanced discrimination every day. This needs to change and we can all do that together by holding ourselves and each other socially accountable to address racial abuse. So let's get started. My name is Sarah Hayner. My pronouns are she, her, my assistant professor at the University of Pittsburgh, but originally from Canada. Hi everyone. I'm Renuka Kudva. I'm currently a project coordinator at the National Genomics Infrastructure at PsyLife Lab in Sweden. I have a PhD in postdoctoral research experience in biochemistry. I'm Indian and I currently live in Stockholm. And hello, I'm Freya Olavstotte. I'm an IU and assistant professor and a Donters-Morpem fellow at the Donters Institute in the Netherlands. I'm originally from Iceland and my preferred pronouns are she and her. So we are all scientists and all of us have been passionate in promoting equity within our spaces. We have worked together with others to put together resources and information to improve inclusion and equity in academic science. This presentation is centered around the work of other scientists who have identified disparities in STEM, mostly in the US and Europe. We have put it together as a resource to inform and challenge ourselves, our audience and policymakers to make scientific environments more inclusive and nurturing to everyone. Regardless of their unique identities or circumstances that have shaped them, being unique is a good thing. So let's celebrate that. Our main goal today is to highlight why it is important to be inclusive in academic science. Specifically, we will present data on how ignoring the importance of equity and inclusivity contributes to weaknesses in academia. We will also provide a shared language for understanding inclusion, reflect on biases that we have, and provide suggestions for fully supporting our colleagues. It's important to know upfront that individuals who are marginalized within any work setting, including academia, often leave these settings. This is not only an issue for that individual, but rather an issue for retaining great talent within academia. There have been several scientists who were discriminated against for their differing identities and these contributions to science have been rediscovered in recent times. We would like to begin by taking a step back in time to highlight a few of them. Perhaps the most widely known scientist who experienced discrimination during their scientific career is Rosalind Franklin. During her career, Dr. Franklin was subjected to gender-based discrimination by her colleagues. Many of us probably know how she performed seminal work in solving the structure of DNA, receiving little credit in her lifetime. And even now, textbooks still often do not refer to her data. Beyond Dr. Franklin, let's take a moment to take a look at a few other well-known scientists who experienced biases for a range of individual or multiple identities. Now we have overlaid the identity discriminations these individuals experienced during their scientific careers. How many more scientists' contributions will be overlooked, lost or forgotten before we make science more inclusive? Can a change in existing policies and our own behaviors make academia a welcoming place and make scientists want to stay? Policies are inspired and shaped by data, and data is the result of years of research. There has been a lot of work on studying discrimination of different social identities over the past decades. This research has largely focused on the discrimination of women, ethnic minorities, and individuals with a disability. The following slides will describe evidence we know for these social identity groups. For now, we are a limit to the data we have, but we hope future iterations of this webinar may include data that better reflects the kinds of discrimination that exist in society. Starting with gender. Just to clarify, when we speak about gender in these subsequent slides, we're referring to the self-reported binary biological sex of individuals, which therefore doesn't include non-binary individuals and may differ from a person's gender identity. So please bear this caveat in mind as we discuss data around gender discrimination. The so-called leaky pipeline metaphor has been used extensively to describe the underrepresentation of women in academic science. This phenomenon is best illustrated by the low proportional women in senior academic ranks, shown here in orange, compared to junior ranks. Indeed, data from the European Union shows that in STEM occupations, women are not only across the board underrepresented, but the underrepresentation becomes greater in senior ranks. For example, in 2018, only 15% of grade A academic positions, i.e. the most senior positions such as professorships, were held by women. Now, if we analyze gender representation on an individual lab basis, we find that so-called elite and feeder labs, these are labs where the PI is an awardee of a prestigious prize or as a sort of powerhouse in terms of generating quality, and which are shown in white bars in this figure, do the worst in terms of gender representation, namely, these labs are less likely to train female scientists. By the way, this is true for both male and female elite PIs, although male PIs on the whole seem to have poorer gender ratios. This finding is particularly concerning as these labs contribute disproportionately to training our future leaders in science. Another form of gender-based discrimination is paying men and women doing the same job a different amount. Again, data from the EU shows female scientists earn on average just 17% less than male scientists. Similarly, in the US, women researchers also underpaid relative to men. There, the pay gap is even larger, namely, the average salary of a female scientist is almost 27% lower than the average salary of a male scientist. Importantly, the gender pay gap increases when we look at higher academic ranks and older age groups. For example, for researchers aged 55 years older or older, the average gender pay gap in the EU is almost 22%. And what's even more striking is the gender pay gap grows even more in the more researched tons of countries in Europe. For example, in Germany, the average gender pay gap for this oldest group of researchers is over 30%. The underrepresentation of women also can also be observed for publication authorship, scientific recognition and presence at seminars and conferences. For example, women shown in green bars here make up only 30% of senior authors of published papers. They are also much less likely to be the recipients of awards or to be invited to speak at seminars or conferences. Moreover, the rate of change seems to be painfully slow. Before revealing what the estimated time is for closing the gender gap, at least in terms of senior authorship, we would like to ask you how long you think it will take using this anonymous poll. So how long do you think it will take before there's gender parity in terms of senior authorship? Please provide your best guess. The answers are of course anonymous. Can we see the results? Okay. Interesting. If I just close this. So many of you think it will take 20 years for the gender gap to be closed. However, based on the current rate of change, it will actually take another 50 years. However, gender representation seems to be improving on some measures. Here, the figure shows that between 2012 and 2016, the proportion of male and female speakers became more equal. Another form of identity discrimination that has been studied extensively is racial discrimination. In the US, scientists belonging to an underrepresented minority, such as being black, mixed race or Latinx, are less likely to work in scientific occupations than white scientists. So the main refraction of senior academics are black, namely only 0.6% of all UK professors are black. Although black people are a racial minority in the UK, this number should be at least six times higher if it were to be representative of the general population. Finally, racial inequality can also be observed at the trainee level. A recent survey found that underrepresented minority PhD students published at significantly lower rates than underrepresented minority students. Racial disparities also exist for pay. In the US, underrepresented minority scientists are paid 13% less on average than their white counterparts. In the UK, black academics are paid 10% less on average, as seen highlighted on the screen. Finally, another source of discrimination is the disability status of an individual. Disability in this context refers to both physical and mental disabilities. In the United States, scientists and engineers for the disability are less likely to work in STEM occupations than scientists who do not have a disability. Moreover, in the UK, disability disclosure rates have been found to decrease with academic rank, meaning they are particularly low among senior academics. And this is particularly the case in some subjects. Finally, academics in the UK who have disclosed a disability in the high-weight bracket, as is shown highlighted on the screen, suggesting they're also discriminated against in terms of pay. Of course, many other forms of inequalities exist. For example, based on individual socioeconomic background, immigrant status, religious affiliation, marital status, LGBTQIA plus status, and many others. However, the data we've described so far puts individuals in discrete social identity categories, ignoring overlaps that invariably exist in any society which may have important duplications for how people are treated. For example, the experience of white and black women scientists may not be the same, and the same goes for white male scientists who come from different socioeconomic backgrounds. Or immigrant scientists belonging to a minority group in their host country. Indeed, there is data suggesting that multiple identities can form a basis for enhanced disparities. For example, data from the UK shows that of all black female academics, just under 2% are professors. This compares to almost 6% of white female academics. Although both of these numbers are low, it seems the representation of black women is particularly low. Moreover, the representation of men in senior academic positions also depends on race. Namely, of all black male academics, only 4.5% are professors. While for white males, this number is almost 15%. I hope that these data Freya presented and pressed upon you that biases exist in academic science. What can we do to change it? Let's begin by developing a common language to discuss issues governing inclusion and then get back into what has been done and what can be done to counter bias in academic science. First, let's discuss the basic terms of inclusion. Diversity, inclusion, equality and equity. Four terms often used, but also often misused. Diversity means respect for and appreciation of differences in ethnicity, gender, race, age, national origin, ability, sexual orientation, education, religion and more. But it's even more than this. We all bring with us diverse perspectives, work experiences, lifestyles and cultures. Diversity is all the things that differentiate people from one another. Inclusion, on the other hand, is about focusing on the needs of groups with these diverse attributes. It is valuing, respecting and supporting all people no matter their identities. In a perhaps more memorable description, diversity is the mix, meaning the range of identities every person in a group has. And inclusion is getting this mix of people with different identities to work well together. So why does diversity matter? Of course, diversity matters because it is how to be fully inclusive and is morally proper to be so. But beyond this, diverse groups have been shown in many disciplines to better, to promote better critical thinking, higher productivity, more innovation and creativity. In addition, diverse groups enhance individualist experiences by exposing others to alternative perspectives and strengthen communities. These benefits are true for academic science, but in many other fields as well. All these advantages that diverse teams provide are important skills that lead to success across disciplines. Additional terms often used when discussing inclusion are equality and equity. Equality is when each individual receives the same resource. So in this example on the right, three individuals with different needs are all given the same equal stand to see over the fence when clearly this doesn't work for each of those individuals. Equity, on the other hand, is when resources are provided based on individual needs. And in this example, each of the three individuals are given stands that assist that individual best. While these definitions seem straightforward, in practice, differences between equality and equity can be more nuanced, but hopefully this covers the basics. We want to highlight that equitable systems are required for permitting the opportunity for equal outcomes. In this example, when the three individuals were given the same stand, they could not all see over the fence. When provided stands that are designed best for that individual, they could then see over the fence. Equity is what works towards providing resources based on individual needs. Therefore, we should strive to provide equity in all opportunities. In order to be inclusive and to promote an equitable environment, it is first necessary to think about why there are disparities in academia. These come from our biases and we all have them. Even children have them. So let's try to understand what they really are through understanding a few basic terms. Discrimination simply put is the objective unequal treatment of different groups or individuals. For example, paying men and women a different salary for the same job. Discrimination can arise because of prejudice that we have for certain groups or individuals. Then there's privilege. In this context, privilege refers to access to advantageous resources or rights simply because one belongs to a particular social group. For example, children of parents who are of a lower socio-economic status are more likely to get into better schools than children of parents who have a low socio-economic status. Prejudices on the other hand represent our negative personal beliefs about social groups, which leads us to have preconceived ideas about the qualities and characteristics of individuals in those groups disregarding individual variations. For example, believing women are not good in science. As I mentioned earlier, these prejudices can lead to discrimination. Finally, a bias can be described as a preference or inclination that leads to impartial and discriminatory judgments, actions or policies. For example, preferring to hire someone that comes from the same country as oneself. These biases stem from our prejudices. Importantly, many biases are implicit, meaning individuals are not aware they have them. Yet, even though we're often not aware of our biases, it doesn't mean we can be complacent about them. The fact biases can lead to discriminatory and unfair decisions means it is essential we try to address them, even if they are unintentional. So let's have a look at how biases affect academia. Again, we will focus on biases around gender and race, as these identities have been studied most intensively. Implicit biases have been extensively studied in the context of recruitment. A seminal study by Maas Rakhazan and colleagues showed that people rate identical CVs differently, solely if the name on the CV changes from a female to a male name. Namely, in the study, participants were given fictitious CVs from student applicants to assess for a lab managerial. All CVs were identical, except half of them had a male name on them, and the other half had a female name. The participants then rated the candidates on various measures. Across all measures, the CV with a female name, shown in light gray here, was rated worse than a CV with a male name. The participants even suggested a lower starting salary for female applicants compared to male applicants. Similar results were also obtained when the name on the CV was changed from an African-American name to a name more often associated with white individuals. Moreover, groundbreaking and seminal work by Benaras involved found striking levels of gender bias in the evaluation of grot applications. The office ranked the impact of applicants independently and compared their impact rank to the competency score that applicants actually got from the Grot Review Panel. The authors found women, shown in yellow in this figure, had to receive a 2.5 times higher impact score in order to receive the same competency assessment from the Review Panel. In practice, this means that women scientists have to be more than twice as productive than men in order to be ranked equally to men. The authors also found evidence of nepotism, namely applicants that had an affiliation with one of the Grot Review Panels got higher than those that didn't, even though the affiliated person was removed from the official review. Similarly, Hop and colleagues found African-American researchers were less likely to receive R01 funding, one of the more prestigious type of funding in the US than their white counterparts. More specifically, Hop and colleagues found application from African-American researchers were less likely to be discussed, meaning they'd passed the initial review stage, received a lower impact score, and were more likely to belong to less well-funded research areas than application from white researchers. Now, institutional biases also exist. For example, closet and colleagues found that the vast majority, so this is 70 to 90% of junior faculty in the United States, comes from only 25% of all academic institutions. In practice, this suggests that the likelihood of obtaining a faculty position seems to depend a lot on where you do your training. Furthermore, closet and colleagues found new faculty were hardly ever recruited from less prestigious institutions. Only about 10% of trainees moved up the academic hierarchy. More biases around institutional prestige also seem to influence an institute's ability to obtain research funding. Before going over the evidence for this, we'd like to do another quick anonymous poll to assess how biased you think scientific funding is. So what proportion of NIH grant money, so the NIH is one of the main grant funding bodies in the U.S., so what proportion of their money do you think goes to the wealthiest 10% of universities institutes in the U.S.? Give us your best guess so we can see how biased you think the system is, see the results. Interesting. So there's a bit of a variety here, but actually the... So many of you actually think that 80% of... So that 80% of NIH grant money goes to the wealthiest 10%, which is actually correct. So you seem to have quite a realistic view of the funding landscape in the United States. But what was even more interesting is that only 10% of PIs received what being 40% of the grant money. And what's even more interesting is considering these findings is that these more prestigious institutes and labs are actually less productive than the less prestigious institutes than other institutes. Now, so it's evident biases are a strong influence in academic life, both at the individual level as well as at the level of institutions. Importantly, discrimination is bad for science. The benefits of diversity for decision-making and productivity have been well documented. For example, Al Sheblin colleagues found a clear correlation between the diversity of a research team shown on the x-axis and the impact of published research shown on the y-axis. Similarly, a fascinating study of research groups, such as being female or non-white, were more likely to make novel discoveries in their doctoral studies than individuals belonging to the majority group. For example, Lillian Bruch, who was one of the leading researchers who discovered HIV infections, came from non-human primates, seven of finding by all accounts. And she did this at a time when female biologists were highly underrepresented. In fact, Hofstra and colleagues found that the more underrepresented researchers was, the more innovations they produced. The graph on the left shows the correlation between the number of innovations introduced in a PhD thesis by female researchers and how represented female researchers were at the time in the scientific subject. The graph on the right shows the same, but for non-white PhD researchers. Both graphs show a clear negative correlation suggesting a minority status is associated with a higher degree of scientific innovation. So what can we do? First, we need to be aware of our biases in a set that we all, and that we are all biased. Except in this then means we should always strive to be self-reflective and second guess our decisions and actions, even though this may lead to uncomfortable feelings or tension. We should try to challenge biases when we see them and be the ally of those who we identify as being discriminated against, even if it means we are shaking out of our comfort zones. To help educate ourselves about our own biases, one can take implicit bias tests and participate in privileged walks. So now that Freya has mentioned how we can all be less ignorant about our own biases, over the next few minutes, I'm going to talk about a form of targeted discrimination termed microaggressions that can result from biases. Let's face it. We have all at some point likely related our perception of an individual's ability to their accent, the color of their skin, their gender, their marital status, their age, et cetera. We've also perhaps misguidedly thought we were complimenting someone or trying to be funny and being completely unaware that our actions and words have been hurtful. Well, we can all learn why this is a problem in the next few slides and think about how we can work on that together. The term microaggressions was first coined by the US psychiatrist Chester Pierce in 1970 to describe the constant put-downs that African-Americans had unleashed on them every day. This term was subsequently popularized to include a broader range of identities by Daryl Wing-Sue, who defines them as the everyday slides indignities, put-downs and insults that people of color, women, LGBTQ plus populations and other marginalized people experience in their day-to-day integrations. The use of the term microaggressions has been recently debated because micro might make it sound like these comments are minor when in fact they are discriminatory. And telling a receiver that we meant something as a compliment or as a joke and that they're being too sensitive for being hurt makes it worse. So we can all commit to recognizing that, especially if and after it's been pointed out to us. Now to move forward, here are some common examples of microaggressions that have been borrowed from the racial microaggression photo series from a few years ago. You don't speak Spanish? So what does your hair look like today? So like, what are you? So you're Chinese, right? You don't act like a normal black person. So what do you guys speak in Japan? Asian? By the way, microaggressions can also be non-verbal and can include actions such as eye-rolling or using dismissive body language. Scientists can encounter microaggression in all of their workspaces in the lab, at conferences, during manuscript reviews, at job interviews, et cetera. And now here are a few examples of microaggressions that we've encountered in our scientific careers. I think you're smart despite being a woman. Does your bachelor's degree even count? You give good presentations for someone who has an accent. You're too pretty to be a scientist. Of course, you received that award. How did you get your job? It must have been harder for you to do your PhD. People from your country are normally lazy, aren't they? You look so good for our lab. You're the ultimate diversity candidate. You people. We mentioned early on that discrimination should be addressed through stronger policies that promote responsible and inclusive behaviors. But we as individuals can also play a part. And this starts with taking responsibility and being accountable for our actions. Now taking responsibility means not getting defensive, outraged, or being dismissive if we are alerted to a microaggression that we've engaged in. Rather, we must listen, put ourselves in another shoes, reflect, and apologize. We can also refrain from telling individuals how they should and should not feel because feelings are one's own. And then we can all be allies, but it's important, and especially today when everything is virtual, to remember that these actions need to go beyond declarations on social media and should find their way into real life. We can all be discriminatory in our work, anti-discriminatory in our workspaces and challenge others to do the same. Speak to aggressors and victims. Support those that take the initiative to do so because that can be really challenging. And use these opportunities to reflect on how we as scientists can be more effective, like mentors, recruiters, managers, peer reviewers, collaborators, et cetera. However, it's also helpful to remember that not everyone wants or needs someone to stand up for them, but we are all responsible for providing a respectful and supportive workspace for our colleagues. Now to get started, we recommend taking this dignity pledge, and if you are interested in more resources, please let us know. So that was change at the individual level. Now in this last section of the webinar, we'd like to highlight what we know so far is being done by institutions and organizations to make science more inclusive. We also make some suggestions for what can be done to affect inclusive policies at all levels of academia. So at the level of hiring, there have recently been a number of initiatives that aim to promote diversity and inclusivity when hiring new faculty. For example, some universities in the US are now making it a requirement that new faculty applicants write a diversity statement that aims at describing the candidates' experience and plans to promote diversity in their new roles. Numerous universities and funding bodies across Europe also have women-only fellowships that aim to rebalance the historical underrepresentation of women in science, particularly at the faculty level where they are underrepresented. And you can see some examples of those on the screen. Now I'd like to add that these fellowships that aim to increase the representation of a minority group are helpful and can be useful in addressing inequalities in academia. However, applications for any minority focused opportunities need to clearly state this in their application requirements to ensure a fair process. And what can funding agencies do to be less biased? Well, here's an interesting study from the Canadian Institute for Health and Research. The authors conducted a study to highlight the consequences for gender representation for awarded grants when the grant review explicitly describes the caliber of the candidate called a foundation grant on the screen versus when it focuses on the quality of the proposed project. The candidate focused approach was biased towards male candidates shown here in dark green on the screen when compared with the project focused approach, although both showed an advantage for males. Now, although the male advantage appears small for each grant application, over time this accumulates to a very large advantage for male scientists on the right in green. This plot simulates the cumulative grant money a researcher of each gender would acquire over time given unequal success rates. Grant money awarded to women is shown in red. The solid line simulates money accumulated for candidate focused grants and the dashed line shows the same but for project focused grants. And the figure clearly shows that over time women accumulate dramatically less grant money if the grants are candidate focused. This suggests that evaluating funding applications based on the quality of the proposed project rather than on the candidate might indeed be a stepping stone for making scientific funding fairer. And then some funding agencies are actually trying to be more inclusive. So the NSF in the US and the Wellcome Trust in the UK have a policy that funding will be taken away from scientists who engage in workplace bullying. And the NSF explicitly also encourages collaborations that bring together groups through their includes program. And then finally in the UK, the Athena Swan Charter has started to encourage institutions to apply policies promoting gender equality and in fact even awards institutions that do so. And then journals have started also to try and adopt inclusive practices such as editors are now explicitly encouraging women underrepresented minorities and only career researchers to sign up to be peer reviewers. And some journals have started to provide statistics on submissions and reviews. So for example through the revealing the gender of reviewers and submitting authors which can promote transparency in review and highlight biases that exist. And yet others have started to implement review methods that include an open discussion among the peer reviewers through a panel or also offer open or double-blind reviews. And these have all been taken with the view to produce and highlight biases during the process of peer review. Renuka just covered some of what is being done in different sectors of academic science. Now we want to take a bit of time to discuss what you and others can do to be as inclusive and fully supportive as possible. Our group released a few resources last year pictured here which are guidelines for inclusive practices in hiring, mentoring, grant review, references and manuscript review. We chose these five resources as we think they are vital components within academic science that also largely introduced bias ultimately resulting in hindered success of individuals that experienced this discrimination. So we'll take the last few minutes here to go through what individuals can do at different career stages focusing within the structure of academic science. So what can members of research labs do to be as inclusive as possible? Renuka mentioned that you can be an advocate to marginalized groups by helping to identify and reduce discrimination through speaking with colleagues about inappropriate behavior, supporting individuals when they have been discriminated against among other actions. This holds for within a lab if your co-worker is acting in a discriminatory way speak out either to that individual or to the lab head. In addition to formal training spend time educating yourself through reading and conversation with your colleagues, mentors and friends, we are all continuously learning for our scientific endeavors and enhancing knowledge for better inclusion is no different. It is important to be inclusive and equitable toward all lab members for social events and within lab structure. For social events, for example, make sure it is one in which individuals can attend and would feel welcomed through selecting foods, activities and more that are inclusive to all lab members. And for within the lab, share lab duties equitably. Don't assume a lab member should be doing a specific or more lab chores due to a certain organizational trait or stereotype you or others have toward that person's identity that suggests a particular chore is a fit for them. Importantly treat others how they want to be treated. Do not assume everyone wants to be treated the same and do not assume how someone wants to be treated because of a stereotype or prejudice associated with one of the identities of that individual. At some point or perhaps even now you may supervise individuals such as new lab members. So be fair and inclusive in your supervision now. A PI or lab head is the leader of the group. This means you have very important requirements of your position in the way that you have a large amount of power and influence over an individual's current position and future career. You should make sure you hire diverse trainees. As Freya mentioned there is literature showing that individuals tend to hire people who are reflections of themselves and therefore it is an important bias to avoid. It is extremely important as a supervisor of any kind to be fair and equitable in your treatment of trainees. For example, it can be easy to spend additional time speaking with more social members of the lab. However, setting up timed meetings with trainees provides support in a more equitable way. Generating and using a lab expectations manual can provide transparency for your thoughts on both the scientific and social environment of your lab including the importance of inclusion. And last year we released an inclusive template for a lab expectations document that is available in individual labs to modify and use however they would like. Relatedly, every individual does not require the same type of mentorship and so you should alter your mentoring style to best assist each of your trainees' needs and their career goals rather than having trainees fit into one mentorship style. This also means always supporting your mentees in public and casual conversation with colleagues and to your other trainees. So beyond members of the lab and lab heads, what can our institutions do? Institutions should make it a primary goal to increase the number of diverse staff, faculty and students. This means altering how we both recruit and accept individuals. Furthermore, we need to focus not just on recruiting those individuals but providing support to allow those individuals to succeed. It is our purpose as educators and mentors to provide the resources and accommodations for all to succeed. Job postings for all university hires should be inclusive. For example, using gender-neutral pronouns within the job ad and posting the ad to diverse host locations. Another important modification that institutions can make is to be more family friendly through making work hours compatible with day care in schools, having day cares on site for university members, permitting work from home as able and also providing full parental leave. Finally, making sure specific quotas for different identities are reached on various committees or boards. For example, recent data has shown that the percent of female speakers at a conference directly relates to the percent of females on the organizing committee confirming the importance for diverse organizational committees. Publications containing scientific data are currency and academic science. We succeed and fail by the rate, by the impact and acceptance of our scientific publications. Careers advance largely due to success in publishing. Therefore, it is important to reduce the bias that publishing journals and manuscript reviewers have for underrepresented groups. One of our short guidelines is designed to reduce bias in manuscript review. Finally, what about funding agencies? Hand in hand with publications for academic success goes acquiring research funding. Academic science is also fraught with discrimination, however. We also generated a short guideline for grant review that can help reduce bias in this vital component of academic science. But briefly, funding agencies can alter their review processes to make this process more fair and reduce biases. Again, aspects of these more fair reviews can be altered by us when we review grant applications which we outline in this guideline. All the data we've presented is based on the disparities faced by individuals due to their different identities. But discrimination is more complex because it can be multidimensional. So it makes us think that an intersectional approach to inclusivity could be a means to building equitable and inclusive academic environments. We, and of course others, think it could because intersectional inclusion goes a step further to focus on the needs of individuals rather than grouping them together. Intersectionality is a theoretical framework that is the work of Dr. Kimberlay Crenshaw who, in her 1989 paper for the University of Chicago Legal Forum, focused on how the experiences of two identities cannot be understood by evaluating each identity independently. But rather must include the interactions of these two identities. We would now like to play a clip of Dr. Crenshaw describing what intersectionality means. Eli has procured the permission to play this clip in this presentation. Intersectionality is just a metaphor for understanding the ways that multiple forms of inequality or disadvantage sometimes compound themselves and they create obstacles that often are not understood within conventional ways of thinking about anti-racism or feminism or whatever social justice advocacy structures we have. To summarize, focusing on identifying underrepresented groups is valid but even more important is doing the work to understand the distinct layers of injustice that people of specific identities face and the layers within these identities. Parsing out the implications of these differences is what it means to be intersectional. We are glad that there have been individuals who have set some of that change in motion because it highlights some of the immediate areas that need to be worked on. This is one of the things that we have presented here today and those that have made it their life's work to highlight the gaps and advocate for equity in professional environments. We hope to have more data that focuses on overlapping identities in the future. We would like to thank the team that helped put together the resources we presented today. We have different identities but a common goal and we would like to thank you for listening and we would be happy to take questions now. We would like to thank the team for a really great webinar. Thank you so much. It's been really interesting. I believe that the chat has a number of questions, some of which actually I think have been answered by panelists as we were going. So the first question I see here I don't think has been answered yet is regarding gender equity and senior authorship, is the percentage of female less than the percentage of lab heads that are female? I can maybe answer this because I presented that slide. So undoubtedly this under-representation of female senior authors is to some extent a reflection of their under-representation of senior positions. I should caveat that by saying that a senior author is less than the percentage of lab heads that are female. I should caveat that by saying that a senior author doesn't have to be a lab head, right? So it's not a one-on-one correspondence but yes, we do think that the fact that women are less likely to be senior authors meaning they are less likely to be leading research projects is certainly one of the symptoms of the fact that they are less represented at higher academic ranks. Okay. So I hope that satisfies the question. She says the 80% of NIH for 10% of universities and the 10% of PI gets the 40% if she remembered the numbers correctly. I think that's on her right. Would introducing a funding cap allow more funding opportunities? And I think she continues to say as those PIs are less productive the additional funding can provide opportunities in early careers and diverse applicants with more risk and creative ideas. So I guess the question is do you think a funding cap potentially would help? Maybe Sarah, she and her team put together some guidelines on how to reduce biases in funding. So maybe she can address this question. Sure. I think a funding cap is one way to go about it. There's a lot of difficulty with funding caps because science is of course collaborative and so how do you assign funds if somebody has many collaborative grants or for example, if a junior scientist requires a senior author many people have senior collaborators to help strengthen their grants and if that senior author is capped on funds then they can't maybe help a junior scientist in a way. So there are some difficult things that would have to work out for a cap but certainly it's been shown that the number of people in labs you have a steady increase in productivity and publication and then there's a plateau. And so having caps could probably help but I think we need to think about creative ways to address how to make the caps because I don't think having a flat cap would work. But there are many other ideas on how to help funding. We actually also with a number of people in the E-Life Ambassadors put together a document to help increase their funding ideas such as creating lottery based systems for middle scoring grants or thinking about providing a bump like the NIH provides for example for early career researchers. So I think there's many approaches that institutes can make that those are just a couple of examples and I think really a lot of them need to be tested out to see which ones actually do reduce fires but I think a funding cap could be one of them for sure. Those are the only questions I may add to that. No please go ahead. Sorry just to add to that. So Renuka also she described a study that was really interesting from the Canadian Research Institute where they were trialing a different way of reviewing grants with the review to see how it affects biases in reviews and what they found interesting is that if you I'm sure you were listening but just to reiterate for the sake of making this point is that what they found is that if the reviewer was asked to focus on the quality of the project rather than the applicant the gender ratios were more equal women basic were more likely to get grants if they are kind of identity wasn't focused was being a criteria for success. So you know that there are lots of ways I think that we can think about that may help improve the fairness of funding. Okay. I don't know if there anymore I think there are a number of resources that have been dropped in the chat that I guess if you know we might make available the team might make available if they haven't already. So a number of people have commented if they don't see any other questions and missed anyone but I just had one since we have a little we have a couple more minutes is there a lot of work on intersectionality from outside of North America and Europe. So obviously intersectionality is often viewed as the remit of you know ethnic minorities living in the global north I'm just curious if in your research you came across anything about intersectionality done by people in Latin America, Africa or Asia in terms of how those concepts because in many ways we publish in a global environment right so depending on where you're from you're still publishing in journals that are maybe housed in Europe or North America and so come across some of these issues but I was just wondering is there any literature or any statistics or data looking at intersectionality from the lens of somebody outside of Europe or North America? So most of what we focused on was in academics and unfortunately there's very little that focused on intersectional identities in general even if we focus just in North America or Europe Europe. I do know in the US there's been a few studies that have looked at for example African-Americans versus people who come from Africa so people who were born in the US and perhaps descendants from slaves versus people who come from Africa later in terms of their status in academia and that's the only one where I think they've looked at the geographical difference in combination with race for example. That's the only study that I'm familiar with that is even closely related to what you're talking about but there might be studies outside of the realm of academia outside of the realm of academic science I should say that we're not familiar with or at least I'm not I just like that I just like to add in the context so I looked a lot for the concept because I mean it's being applied also in India where I'm from the context of the caste system but there weren't any studies on that with regards to academia I tried to look I looked a lot extensively actually when we were preparing this presentation and I haven't found it but I can definitely think that it is something that would be useful to study the disparities that exist in the culture that I come from. Yeah I think it's an important point that we tend to look at it as a northern problem but disparity exists everywhere and it can translate to academia anywhere so I think that might be an interesting to look at. Sorry I missed a question from Cindy. How can funders help dispel the perceptions in the academic community that providing resources equitably is not unfair? I think yeah I think this is the idea that for example affirmative action is hurting other people and things like that that is a complete myth so people have shown that diverse groups are more productive are more innovative are better overall for everyone it doesn't hurt anyone and actually people for example have studied the NIH funding system and crunched the numbers and if there were an increase in funding for example for black academics it would barely diminish funds for other people and so what you're enhancing is far greater of this inclusion which promotes innovation and creativity and propels other people and advances science and it helps everyone so I think this is just a myth that people have in their heads that needs to be dispelled and so the way to dispel that is to just show the data even through action type of approaches people who benefited from them for example in the US were largely white females so I mean yeah people need to look at the actual data yeah I mean I think this happens a lot especially the idea of quality dropping if you try to include more diverse people I mean that's also a misconception because oftentimes you look at those who actually perform well and it's the people you've sort of got to so I think that goes across the board we're almost out of time but there's a final from Anahi who says if it's so obvious that blind applications where January is hidden would help the share of grants why is this not taking place yet and why is it taking so long for reviewers to not be able to see the name of the applicant well I guess you could ask that to funders I mean if you have that so please go ahead well I do think this is a little bit of a difficult thing making things completely anonymous is hard because as scientists everybody knows that your science typically builds on itself you refer to your own papers you refer to things like that so even if we remove the candidate like where they did their training and who they are in that way a specific statement will likely in some ways reveal who you are which makes it a hard thing to tackle now one thing for example we proposed in the fair funding document that I think Cora put in the chart is if we separate the CV from the research statement for example then you would probably either have to be extremely familiar with the field to identify from the citation who the person is or you'd have to spend a lot of time digging into that so maybe we can separate them in that way but to be truly anonymous would actually be very difficult to do I think although I think there are steps to take to help reduce the blatant bias that exists from names and things like that okay well it's five o'clock GMT I'm not sure whatever time it is where you're watching but I think we've come to the end of our time I think it's been a really interesting webinar if I look at the comments a lot of the comments are not even questions it's just appreciation to the team for putting together such an insightful and informative piece so thank you very much to Renuka Freya and Sarah for doing this and I hope it sparks future discussion I think this is just meant to be a conversation starter on how each of us can contribute to this area so we hope you've enjoyed today's webinar the next ECR Wednesday webinar will be announced soon so please look out for the announcement and we hope you join us then but at this point I'd just like to say thank you again to the team this is quite a bit of work that's gone into this so we are really grateful for this and we thank everybody for tuning in and we hope that you will go away and think about it a little bit and perhaps ask yourself if you're contributing to this problem or helping to alleviate it maybe even worse anyway I'll leave that to you to think about so thank you very much and I guess we'll just end here goodbye thank you thank you for listening yes the slides are available