fantastic panel who'll be presenting some critical knowledge on the work that they've been doing in the movement and on the project to, of course, model, map, and bridge knowledge gaps. The first presentation will be from Dr. Netta Hussein, who describes the process of mapping knowledge gaps on Wikipedia, drawing experience from a Wiki project, Women's Health. The second presentation will be from Danielle Metilli and Tiara Paolini, which explores the Wikidata Gender Diversity Project. The project explores gender diversity in Wikidata and focuses in particular on marginalized gender identities, such as trans and binary people. The presentation will explore how the current Wikidata ontology model represents gender, including quantitative methodology. And it explores looking at how the Wikidata community has evolved from very narrow interpretations of gender's binary towards the inclusion of a wider spectrum of gender identities. The third presentation by Galder Gonzalez explores the current list of 2,000 biographies that every Wikipedia should have, a list which has been created collaboratively by Wikimedians. The presentation shows how the list is far from being universal and how diversity is a contested topic when adding a new biography to the list. The presenters will analyze gender, location, time and language coverage and discuss a way to improve the diversity of the items proposed on that list. Finally, I will be presenting the foundation's plans to work together with communities to address the gender gap through more effective community programs. Questions covered will include what we have done in recent years to move the needle forward on the gender gap, what we are hoping to do in the new year to support gender organizers in the work and how people can find and reach out to us for more information and support. First, I would like to welcome to the stage Dr. Neta Hussein, who's a volunteer since 2010. Thank you so much, Neta. Thanks, everyone. My name is Neta Hussein. I'm a volunteer mostly working with articles related to women's health on the English language Wikipedia. So on the English language Wikipedia, we have a portal, a Wiki project for women's health, where we look at articles related to women's health, where people who work with women's health come together and talk about new articles for creation, saving articles from deletion, and increasing the quality of existing articles. So this project was started in 2015, but it did not create a lot of momentum. It was quite inactive as of 2022. And then I wanted to change the situation and I wanted to do more of mapping and bridging these knowledge gaps surrounding women's health on English Wikipedia. So I first started it off as a Wikipedia, in the Wikipedia namespace, with the title, Knowledge Gaps in Women's Health.  I also tried to touch a little bit on social and cultural issues surrounding women because health care and society and culture are very widely, very tightly interrelated, particularly in countries with low access to resources. So I began the process by mapping the existing knowledge gaps that we have in terms of women's health. So I used NAO boxes to be able to map all these knowledge gaps. So you can see that there's an hour box on women's health. It was already existing on English Wikipedia, but there are also NAO boxes on feminism, for example. So I mapped some of the existing knowledge related to women's health through NAO boxes. And then now it was time to find what missing articles exist. So I looked at 20 textbooks related to obstetrics and gynecology, women's health, and looked at the appendix section of the textbooks. I grabbed the keywords related to women's health and compared it with the corpus of information that we have on English language Wikipedia. And I found that you can see that many of them are red links. Some of them could be redirects. Some of them could be stub articles. I mean, all these red links are the articles that do not exist, but it doesn't necessarily mean that these articles could be created because sometimes this article would be existing in a different name so that it could be a redirect also. So I arranged them in alphabetical order, and this is the list that I collated by comparing external information with Wikipedia information and seeing what is missing with us. And because women's health is so vast, it was not possible to talk about knowledge gaps in everything. So I focused on three main important areas, misinformation, gynecological cancers, and abortion. And here I can show you some of the knowledge gaps related to misinformation. And it was surprising for me that we still do not have an article about misinformation related to abortion or misinformation related to birth control, for example. And a lot of gynecological surgical procedures also did not have an article. And this is quite important because we know that historically women's health has been marginalized in many countries, and the only access for them to know about the surgeries they are probably going to have would be from the internet. And probably it's Wikipedia that they are going to to look up more about the surgical procedures that they have to undergo. And you can see that all these articles are not present when it comes to gynecological surgeries, mortality, epidemiology, and I also try to map some knowledge gaps related to the economic impact of breast cancer and unsafe abortion, for example. There were some themes that were emerging. We have some good articles about sex and gender differences in different aspects of medicine, but we do not have an article about say gender pay gap in medicine, for example, or gender differences in healthcare access. And along with mapping these knowledge gaps, I also found references that could be used to bridge these knowledge gaps. And in the process, I also looked at how knowledge gaps vary between countries. So here is the article about abortion in Albania, and it's a start class category and its size is such and such. But when it comes to, you can see that when it comes to Europe, you have, you possibly have articles about most of the countries, but when it comes to Africa, you do not have an article about abortion in Gabon or abortion in Gambia or abortion in Mali, for example. So these are the knowledge gaps that I started to see when I looked at articles in terms of geography when it comes to abortion. And I did it with many other topics as well. You can go into the page if you're interested to know more about it. And one of the other areas that was missing is female predominant diseases. Some diseases affect women disproportionately than men. And I try to see how these articles lie in terms of quality when it comes to having description about how this disease affect differently between men and women. And the results are here. And after that, I also used Wikidata to find out what Wikidata knows about women's health and what English Wikipedia doesn't know about it. So there is this list of articles that Wikipedia knows. About women's health, but English Wikipedia doesn't know. For example, Corio-Carsinoma of ovary, it's given in italics, which means that this article doesn't exist on the English language Wikipedia, but Wikidata knows about it, for example. Yes, there is still so much more to do. This project could be replicated in different languages, not only in English. I need more tools to be able to, you know, do these lists for lists of articles that require cleanup, for example, could be done in a much better way. Right now, I'm using only the existing tools for mapping these knowledge gaps. So it would be really great if there is more support in terms of both tools, as well as, you know, participation from everybody speaking different languages to be able to, you know, do the same project in many different languages. That's all, thank you. Thank you so much, Neta. We will have a Q&A session after all of our panelists have spoken. We are now moving to, or not myself, I'm the last panelist. Next up, we have Danielle Metelli and Chiara Paolini, who are online. Yeah, hello. Hello, Anna. Clearly the connection will work. It just closed for a second, but we will try now to share the screen and start the presentation. Okay, so our presentation is titled, How does Wikidata model our gender? Findings from the Wikidata Gender Diversity Project. And I'm presenting today with my colleague, Chiara Paolini. So our project is called Wikidata Gender Diversity. It is a one-year project funded through the Wikimedia Research Fund. It will actually last a bit more than a year because it has been extended now until the end of December. So we are studying gender diversity in Wikidata. We are, instead of focusing like previous projects on the gender gap between men and women, we are focusing mostly on marginalized gender identities from a queer intersectional feminist perspective.  The motivation for this project is that these identities, any identity that falls outside the gender binary in some way, suffers discrimination and systematic injury in society. And in general, any kind of gender or sexual diversity is impacted by this. So this is what we are focusing on. Now, the traditional way to look at the gender gap in Wikidata, but also in Wikipedia, for example, as I said, is overly simplistic because it is something like this where you have millions of men and millions of women and you measure the gap and you say, we need to fix this by adding more data, right? About missing women, for example, which is good. I mean, I'm not saying it's a bad thing to add more data about women specifically or about non-binary people, for example. But however, this is not everything because to actually fix the gender gap in a good way, we believe we need to look at why, what caused the gender gap, why is the situation like this? Can it be solved and how and what is missing here, right? So this is why we have these three research questions and three strengths in the project. So the first is about the modeling of gender, how gender is modeled in Wikidata, which is very important and somewhat of a prerequisite to understand then the data. So the second question is about the gender data that is actually there, who are the people and how is the gender identity represented? And the third question is about the community. So mostly looking at user discussions around gender and this tells us why gender has been represented in this way. So the three project outcomes will be a timeline of gender modeling in Wikidata. So this roughly corresponds to the outcome of the first strand about the model. But then also there is a gender dashboard showing statistics about the gender identities in Wikidata. And then there is a corpus of user discussions about gender, which will be described later by my colleague Chiara. Now about the model, first of all, we need to say a data model is always an interpretation of reality. So as such, it cannot be unbiased. It is biased because any interpretation of reality and any description of reality is biased in some way. So we need to be aware of this. We also need to be aware of the fact that reality changes and our understanding of reality changes. So the model is subject to change. However, a model that is very unstable is also problematic. So the initial approach to modeling by the Wikidata community was that every human must have a sex or gender and only one of the allowed values and the allowed values in the beginning were only made female and intersex. So it was based mostly on sex and not on gender identity. But it is somewhat ambiguous because the property that is used to represent gender is called sex or gender. So it is a bit unclear if it refers to sex assigned at birth or if it refers to gender identity. So this is a source of confusion for the users. Of course, there are other properties that are also involved in the modeling of gender, such as family relations, personal pronouns, et cetera, but we don't have time to go into them. And now the model evolved over time and there is a complex taxonomy and we are looking in detail at the taxonomy and trying to understand the issues and how it can be improved. But again, we don't have much time today to discuss this. When it comes to the data, Wikidata represents about 8 million people using statements that describe them, so biographical data and also gender. And when it comes to gender, the situation is like this, where we have 74, almost 75% men and 25% women, but then we have non-binary people and we have intersex people. Non-binary people is actually an umbrella, so there are many identities under non-binary in the current classification. And also I want to highlight that there are 400 men and 106, almost 700 women, but all these identities are severely underrepresented in the knowledge base compared to their prevalence in society. So it is important that this needs to be addressed and of course also the under-representation of women in general needs to be addressed. But then this is a more in-depth analysis of non-binary identities where you can see that there are gender-fluid people, gender-queer and many other identities. So the population of gender data was problematic because it was done initially manually by the user through the interface, but then importing from Wikipedia, which is not a reliable source, imported from external databases, which may or may not be reliable, but they have issues as well, especially with regard to the gender binary or that they have a binary view of gender. Gender is also imported from personal names, very problematic because this cannot be done in any reliable way and from pronouns as well. So these initial mistakes then had to be fixed, but some of them still persist. I am concluding my part of the presentation by saying gender needs to be carefully sourced. So it is not okay to source it by saying imported from Wikipedia, for example. But of course, the most reliable source for gender identity given that this is identity is the person. So the most reliable source about my identity is me. It is not the state or my document, which does not, for example, for myself, does not describe correctly my gender identity. It is also not a newspaper article, for example, or an encyclopedia. So this needs to be taken into consideration. And this conflicts somewhat with the traditional Wikipedia model, for example, of reliable sources. Now I will leave the floor to my colleague, Chiara Paolini, who will talk about the user discussion, which is the third strand of the project. Oh, hello everyone. I will try to be very quick. So yeah, this is the third part of our project that has a main focus, the users and specifically their discussions. So we are interested in how the users discuss gender and the issues around gender and how the narrative around gender has developed during time. So in Wikipedia, we have a big problem. We don't have biographical data of the Wikipedia users.  For this reason, we created, we use this corpus-based approach because specifically I'm a linguist, so I'm mostly interested in linguistics. And what we did was to create a corpus about the discussion around gender. So we first build this corpus using specific keywords around gender, so like male, female, trans, non-binary, the different properties, different codes and so on. And this corpus is very useful because it helps us to keep track of the narrative around gender identities in the Wikipedia community, see how the narrative around gender identity changed during time and studying the impact of the LGBTQI plus movements and how they kind of bring some kind of awareness or the events around the world helped Wikidata to improve their representation of gender. Next, please. Dani, can you go next? Good slide. Dani, can you go next? Okay, sorry. So while the qualitative analysis is still ongoing, we did some kind of corpus analysis and specifically we did a topic modeling analysis. I think we are out of time, so I will keep this for the Q&A. So if you can go to the last slide, Dani, and we can finish this. Yeah. So one of the main outputs of this project is our chapter in the book Ethics in the Lincoln Data that is coming out very soon, apparently in December, if I'm not mistaken, where we described the first part, the first main part, the structure of the project and some of the results, among them the one that is for the corpus related study. And if you want, it's freely available on our website, widget.com. So thank you for listening and sorry, because we are out of time. Thank you. Can you use this? Okay, so I'm Galdar González from the BAS Wikimedia User Group. And this actually is a part of our research that he knew for my PhD. And it was sent to a journal in February, but still no answer from the peer review. So, well, I'm presenting it in any way. The idea was to observe the universalism diversity in the list of 2000 biographies every Wikimedia's will have. Is this to go to the next slide or yes? Okay. And it's a demographic research, okay? I will explain what I have done. So, short history, the list of 10,000 articles every Wikimedia's will have. I don't know if you know that, but it exists. And it's a project that this month, for the first time a Wikimedia has done, that is serving Wikimedia in August, 2023. In 2004, a list of 1000 articles was created to invigorate smaller Wikimedia's and ensured that each contain a minimum of information that will be of use to users. That was a project created in 2004. It had 140 biographies, including 32 that in a a paragraph called Women in History. In 2008, it was created the list of 10,000 articles just to make a wider list. And handle more voice and more diversity. This is what it was claimed when it was created. And we will center on biographies because there are topics. I mean, there are science topic, history topics, it is it that can be also research it, but we are going to center in biographies. There should be, and what I mean, should is what it states is like. There should be 2000. Currently there are 1,913 individual humans, but there are also some humans that are, some biographies that are not humans, like, I don't know, biblical people or Superman or this kind of biographies that are not actually humans that could be in fiction, but that's another topic. So I will define universalists as they believe that some interests are equally valuable for everyone. Like this is universal because everyone should be interested in this topic. And diversity, that the idea that local knowledge might be relevant for anyone, for everyone. There could be other definitions of universal and diversity, but I'm using this for the sake of clarity on what I'm saying. Okay, so how the analysis was made. First, I started out the biographies and the Wikidata items for those biographies. I built a patch pile where you can do other things with that. And then with Quica statement, I added P5008 to its unfocused list of Wikimedia project with time start to the items. So we can research in Wikidata in the future also if we change when this something was changed and how this list varies on time. There is a problem when we are defining location and this problem arise at first. In most of the biographies, mainly all of the biographies, we have a birthplace. But I mean, you can be born in one place and then migrate when you are one year old. So maybe it's not the best statement. There is also P27 that is nationality. And this is more problematic because of political change. I mean, without changing your place, you can be born Russian, then Soviet, then Lithuanian, for example. And you have three nationalities, but you didn't even move from your home. And this happens a lot. I mean, a lot of people from Singapore will be from the British Empire and so on. So, and the third one is the less useful that is P551, which is residence because it's only filled when someone is known to have reside in another place that is not the birthplace. So this is not the best one. So I chose P19, the birthplace. But this gives another problem that is the problem of continents, like Rudyard Kipling. Well, he's a famous English writer, but he's Indian. So he is from Asia. Barack Obama was born in Hawaii. So he's from Oceania. Cristiano Ronaldo is the most African, the best African football player in the world because he was born in Madeira. Or people from France can be actually from any continent because you choose someone who was born in France. You can say that his Caribbean are also from Oceania because he's from Caledonia or something like that. So continents will be traced by hand in most of cases, also in ancient Rome. So the results. Well, in the list there are 1,726 males and 185 females. I'm sorry for the last presentation because there was also more diversity, but I chose in this way. There are no other unknown. You have Judy Butler and Jack the Ripper, who we assume he's a man, but we don't know. So we have one other and one unknown. This is the location of the people. This is birthplace. So you can actually see quite good. Okay, you can argue that here and here, very few or actually here, very few people live, but that's not an argument. I mean, the list is quite, well, you can see it. It's quite easy to see. And location by gender is even more problematic because we have only these women here, step one. All of them are ancient Egyptian pharaohs. And we have only one African contemporary woman, I think not two because there is another one in here and only one woman in all Oceania. Okay, and here you have Barak Obama. Okay, so that's the map. Location by current sovereign country. I mean, the birthplace of this person with sovereign country belongs to actually, so United States of America, United Kingdom, France, Germany, Italy, Russia, India, Spain, Austria, People's Republic of China, Poland, Turkey, Netherlands. Okay, you can see this, United States and United Kingdom. And location by gender and continent. You can see that females, how is distributed and you can see the only non-binary is North American and you have nine people in Oceania and only one is sovereign. I think that this is mostly clear that this is not the diversity we should be pursuing. For information, most of the biographies are from the 20th century, like nearly a half, more than a third. And the most, the most representative decade is people born in the 1900s, I don't know how to say it, 1,900, I don't know how to say it in English properly. It's one in eight, it was born in that decade. So these people came with the template when I donated it. Okay, and it's perfect because it represents the current status. For language coverage, we have that for gender, this is quite complex, how is coverage. For females, there are 96 links per biography for males 98, most linked women is Elizabeth II, most linked man is Barack Obama, not Jesus. This is quite suppressive. Are there better options? Well, the problem of this is that they are finite. I think someone requires discussion who gets out. So, BTS, this is the most known K-pop group in the world. They are 0.5 of Korean GDP, but all other pop stars in the list are Anglophone. They are not in the list. Okay, but we have, for example, Beatles, John Lennon and Paul McCartney. So, I mean, we can't read one of them, like the Bad Beatles. Okay, Regoberta Menchus is an overpriced low-rate. There are only two women from Latin America, both are Argentinian, and there is no indigenous American woman in the list. So, why not, I'm Regoberta Menchus? Karina Kapoor, more than 60 films, the best box office actress of India of all times, but there are 34 youth actors and only two are Indians and both are classical. So, why not? I don't know, Humphrey Bogart, who cares? Karina Kapoor. Oxanachu Sobitina, she's the only person with eight Olympic participation in the history, and there is no women for Central Asia in the list, but there are eight boxers from the United States, okay, and there are only nine boxers in total. Or Ramon Yul, he's a medieval Catalan philosopher, theologian and encyclopedist. He's a precursor of combinatorie, well, he's a European, yes, but a fifth of all Europeans are from the British Isles. So, making a list is taking sides. There is no view from nowhere, there is no way to say, no, I'm completely neutral and objective, so we should take sides and say that, okay, there are views from some other places, and our view is profoundly Anglo-centric. The wisest man from London doesn't exist. I mean, the idea that there is someone in London who grew the British Encyclopedia and is the wisest man in the world, and we should take care of what his opinion is. It's not real, and I say this because if you propose like, okay, let's get rid of Paul McCartney to add BTS, you will have lots of from-raids, English people say, no, Paul McCartney is very important. Yes, he is, but BTS is also important, and so the London, maybe the one from London has to seat one of the seats to other. But this is a Zoom zero game, so the only way to win is if everyone wins and someone has to lose for that. It's not something additive, and this is important to, if we want to make this discussion, we have to be able to know that this is a Zoom zero. We can't win. I can't win, I mean, as a European male, heterosexual and white, I can't win forever, okay? So finalizing, a universalist presenting more people from New York State than from all Africa doesn't seem universal at all. There is more people from New York State, actually, there is more people from New York City than from Africa, but there is more people from New York State than from all Africa, and this is not something we should be promoting that way. And I think that that's it. It can be done, but we need to do everyone together and not just sit. The privilege to do the list to the people in Anglo-centric countries. Thank you, thank you. Thank you so much to our fantastic panelists for such thought-provoking contributions. I think in this current year, the foundation has been trying to actively listen to these concerns and build a better infrastructure to respond to some of the challenges and opportunities and I think uplift the work that gender organizers have been doing. So I do wanna apologize in advance to folks who are at the Mind the Gap event at Google. Earlier this week, a lot of this content is from there, but this is part of our attempts to really hear and reconcile what the movement is asking of us. So for those who might not be aware, a brief history of the gender gap is that it first became apparent in 2008 through a global study that found that 13% of all editors were female. This figure in 2011 was revised downwards to 9%. And at the time, Sue Gardner, who was a foundation executive reflected that equity in terms of gender was about making them psychopedia as good as it can be. Of course, an intersectional lens recognizes that quality isn't just about gender and gender binaries, but also intersectional gender identities. So class, location, ethnicity, ability. And wanting that to be at the heart of quality, which our fantastic panelists have pointed out. Today, they have been some improvements, but mindful that 2008 was a decade and a bit ago, we still see that 67% of Wikipedia readers on any given day are male, that men make up 80% of active editors and 80% of biographies on Wikipedia are about men. We center the male as the primary statistic here because, again, as Danielle pointed out in their presentation, that 20% within that gender grouping is so much diversity that needs to be accounted for. I'm trying to go as fast as I can so that they space for questions. But our movement is a reflection of the world. We do not exist in a vacuum. And in fact, when we look at the feminist movement and what has been taking place around the world, a lot of the same struggles that have been amplified and made real in the rest of the world are still things that our movement is trying to grapple with. Exogenous variables that lead to this include digital penetration, which is vastly gendered across the world. Smartphone access, social reproduction, so care, care work, unpaid care work, and what it looks like to have time for volunteering. Epistemic justice, which Galder and Netav pointed to, what knowledge is considered real knowledge or valid knowledge, and what are the power structures that underpin the validity of that knowledge? Patriarchal norms and values and how those shape, how mods and admins interact with content that is written about women and non-binary people. We also have very endogenous variables that are unique to our movement. What are our movement norms and culture? What does notability, verifiability, and neutrality look like in a space where it might be a quality issue to talk about gender equity and diversity? So we need to make it safe for women and non-binary people to do more than just join our movement. We want them to be able to feel like they can shape our movement and the foundation is putting our energy and our effort towards that. Many contributors, in fact, have led in this work and we recognize that we are, this is not a work that the foundation is championing in fact, you know, I see Maria here in the room and informed by conversations we've had. We recognize that this is a lesson that we have to embody based on the efforts of existing gender organizers, and I think that's not even a fair term, organizers in the movement who have committed their work to gender. So this is an interesting page. I have to give credit to Ben Bershbow for the slide, which is, you know, this question on what quality looks like. We actually see that through engagement with different feminist or gender user groups and affiliates in the movement, that quality and page views actually increase from those contributions. So as women and non-binary people are stepping into the movement and being welcomed into a community, they're having a direct impact on the quality of page views that are written and also, sorry, the quality of articles that are written in the number of page views that those receive, which is really exciting. This is, in fact, an effort that we want to be able to support. So we're listening and responding in the last minute that I have. I'm gonna get into the plan. I wanna say that part of this has looked like what we've been seeing in the movement, which is 65% of all general support and Alliance funds have as their focus gender content gap and 51% of programming focused on bringing in contributors from different gender identities into the movement. And we are actively trying to support that kind of resourcing in the movement. That is what the movement is telling us at once. But we know, for example, based on the six best practices that gender organizers have identified through a study done by Rosie Stephenson-Goodnight and the Foundation, is that gender organizers are hosting in person events focusing on content creation, specifically biographies, because creating a lot of non-biographical content focused on women, for example, the sustainable development goals, gender and climate, which is a norm in practice and the rest of the world in our movement is still met with a lot of resistance, particularly on English Wikipedia. We know that gender organizers and contributors care extensively about tracking progress of data is critical to the work. They want to be able to see the impact and also to be able to measure the impact. Recruiting new participants is really important, as well as ensuring that they have active partnerships with key stakeholders in society. Interestingly enough, because a lot of on-wiki communication feels unsafe to gender organizers, the study found that 80% of communication amongst those who are working on gender content is happening off-wiki. It doesn't feel safe to talk about gender in talk pages, and we are really interested in hearing from you what support looks like to ensure that there's a culture of change around that. We know that there are systemic biases and policies within the movement, and this is leading to poor community health. We're losing a lot of women and non-binary people, and we also know that issues around representation and leadership and decision-making structures within our movement needs to change. That's why this year we've committed to addressing gender. I'm gonna take just one more minute with this, and this is going to look like, I wish I had time to go into more detail. We will be having office hours where I can go a bit deeper with this, specifically building support for more intersectional content. So NETA's project on women's health has been identified as an experiment with an existing infrastructure of mapping that we want to be able to support. We want to hear from all of you who are working in these areas, what support you need so that we can begin to build out an infrastructure of support for content, including biographies, but also beyond biographies. We also want to be able to tell the story of what it looks like to be an organizer in this movement working on gender diversity and intersectional feminism, and make that part of a change narrative that is echoed out into the world. And we also want to be able to support skills and tactical development. This year, the foundation's organizer lab will focus specifically on supporting organizers who want to work on gender with tactical skills for growing the movement access to partnerships and resources in their organizer work. And also my first experience at an in-person movement event, we want to be able to support events like Wiki Women Summit, Wiki Women Camp, because we see how difficult it is for you to extend your unpaid care work into the movement. So what does it look like for us to take on that grant work and do the running for you so that you can show up and focus on the content? This is just a start. A frightening statistic is in 2021, the World Economic Forum in their global gender gap found that it would take 135 years to close the gender gap in society, but we don't have that much time. In fact, you see in our movement where we've sort of gone from 13% to 20% since 2008 that we actually are running out of time and we must avoid regression. But we're also mindful that this is a very lean approach in this financial year. That's because we don't want to take up space where we need to be listening and we need to be accountable. So we want to hear from you. Please do feel free to find me on mlawudzi-wmf on my talk page or by email at the same address or in person. I'm here to help, as is my team, online and in person. And thank you for the space. I don't want to take up much more time. Thank you for the small round of applause. I would love to open the floor so that you can ask questions to our fantastic panel. Thank you. Yes, please, if you have a question, the microphone stand is here. I'm gonna... Thank you so much to all the panelists for the very insightful session. And that was very much well detailed and researched. I think for me, while all the panelists were presenting, what was coming in my mind was then, how, how do we do this? The last panelist, also moderator, was Moon. I lighted about the gaps that exist on... I'm just trying to find it here. The gap that exists on the women, the Wikipedia readers, about, I mean, 67% are men, and then looking at also the active editors who are also men. So I'm from Uganda, and my name is Sandra, and I'm also an organizer for campaigns. And one of the campaigns is Wikigap. So this is what happened. I started being active in this contribution in 2020, and it was during the pandemic. So we are organizing Wikigap.  Women are encouraged to write about women and on women. So then you see that most of the people who are interested are men. So I was stuck in between there being an organizer and also being able to help editors to contribute on Wikipedia. So I see all the editors were men. So I was like, no, no, no, no, no, I can't do this. I can't see men writing about women issues. Women are supposed to write about that issue and they can write it more or better than anyone. So then I got challenged and I had to participate in the editor's own. I won industry that year. So I was like, how do we then get more women? So you will get about three women participating in the event. That was now 2021. And out of the blue, the women will disappear from participation on the editor's own. Now you get also the task that, oh, no, no, no, you can't see the men winning. So you get into the participation and then I win again. I'm tired, you know? So that happened again in 2021, 2022. So what was more challenging was in 2023. So I was like, you know what? I'm tired of winning. I need to see how best do we then empower more women to participate or to contribute on Wikipedia. So we try to bring more women to participate. Again, they were not participating. So what I really want to ask to discuss is how do we do this?  So how do we do this? The how it should be discussed in this room. Thank you. I will start trying to answer that question. I mean, your question has many facets, one about editor retention and one about, you know, how do you approach women and try and make them editors? But this one comes from my past research based on Indian women and we tried to see what motivates them to participate on Wikimedia projects and what challenges they face. And one of the recurring challenges that they said were not based on Wikipedia, but very many cultural issues. They did not have discretionary volunteering time to be able to travel and go to these editathons. Somebody said that Wikipedia was their third job. Their first job was in the office. The second job was to take care of things at their home, child rearing and the third job was Wikipedia. So not having enough time was the repeated answers that I got from different women from different parts of the world. And of course, we also have problems within Wikipedia. We have issues of harassment, we have issues of bias and for some people it's off-putting that they have to contribute anonymously for fear of being harassed or such and such. And there has also been concerns about our interface. The interface is not sufficiently suitable for not sufficiently welcoming for women to come and contribute. So there's a multitude of issues. I don't know the solutions for this, but we have been talking about this way since 2009, 2010. Some changes have happened. We have got more women than before, but it's quite hard for me to put everything into one box and give one simple solution for everything. Editor retention has always been a problem, not only for women, but also for other minorities. When it comes to India, the Dalit community participates less, black community participates less. So we have this editor retention problem not only in terms of women, but also in terms of all other minorities. So I don't have an answer to your question, but I think mapping the issues again and again, revising the issues, doing research on these things would get us a clearer picture and probably some solutions might emerge from that. Ciel, do you have something to add to that? To this question. To this question as well. And I also want to share an example that really helps me to frame what is actually wrong with the gender gap. But now maybe Galdar also wants to... Yeah, time is up, unfortunately. Okay, but then I'm going to respond because we've been doing this, indeed. I've been around since 2012, as were you. We've been doing this for a long, long time. And it's a word of warning to the movement as a whole, to WMF in doing things, and to us as volunteers, to Sandra, to myself, to all of us gender gap volunteers, as long as we are still with 80% males and only 20% of women and non-binary people in the movement, we cannot expect from ourselves to have equal representation on every committee, in every project, like it is not fair. If you expect that from yourself, you're taking too much of the weight on your shoulders as that small 20%. It will not be, it is not fair to expect that we can have 50-50 representation as long as it is 80%, 20% in the movement still. So it's a word of warning. Be gentle, be kind, and look for allies. It is not only a woman thing. We need everyone. Absolutely. Thank you. I think these comments, these questions are really the summary of this conversation. I will say then as well, just to echo and to close, is we can learn so much from other movements, especially feminist movements, where women and non-binary people can tap in and tap out based on their capacity and maybe review what it looks like to be an active contributor in our movement and have space for different types of capacity as long as we require the same metric of success, that people who have ample free time and get that level of visibility, we will still run into some of the same structural issues. But we wanna talk about this as well. What do you need to get to 50%? Please find me. Please.


God they're showing his backstory. You know what that means. That means he's dying. Oh, no, don't show his backstory I don't want it Vinland saga season 2 January 9th, 2023. Yeah, we're finishing it this year This year I heard it's gonna be a little slow these next few episodes, which I honestly don't mind I don't need Marvel like action every second to keep me entertained You don't need a rattle keys in front of me every five seconds.   Oh see I thought it was Oscar live. Oh My god, oh, I would I feel like I'm there the gamble we're going to the casino I trout her that and pigs. I don't want to see any pigs or sheep being eaten Bro, that's how Kobe gets around you're sharing the same bottle over here. This is where it started No, no, no, that's not right. That's her arm. I gotta pray Jesus aka Thorfinn They're making fun of God Where Jesus they called Jesus frail honestly, I don't believe in like religion or anything But I do like Odin, I do believe in Odin and Thor so maybe hopefully they're real This is nice a conversation about religion with with with what's his name for cool Is this guy still praying? Is that what he's been doing this entire time? Is that how long his rosary be it is?  I'm just gonna pray. Maybe I'll be ignored Is this gonna be the whole episode I wouldn't mind that at all The question is How are you gonna fight the Danish army with one man because the only one man matters and the other character that has a name I want to go to Valhalla If I see a horse flying in this show it's jumping right ahead of chainsaw man in attack on Titan Okay Damn, we're getting straight into anime now this show has been pretty normal so far now We have Pegasus and the Millennium I How would he let it in talk bro his head would be off make that egg head scrambled eggs It's Jesus Bro, this is like when I went to church with my cousins.  Literally come on you from the sky.   Oh My god, thank god, it's like person. I thought it was gonna be a bird Oh my god That is so sick What are you guys no, what are you doing? I don't care how many you have You don't talk to a main character like this Thousand there were two thousand men. I think we know All right imagination said okay releases him and they just continue On what world would know I don't know what this show this show come through this show gets me sometimes I never know what he's gonna do and that's why I like him is unpredictable Whoa, wait a minute guys.  But now try to keep him that It's the mind of door kills so you never know what he's gonna do.   Oh, is this Finland saga at a slow pace.  It's cuz he's not born You can never predict this show That is here.  He's getting hotter and hotter.  That's why he's the main character Oh, hey Throw water. Oh, I thought they said throw water at door kill Saki Tome ni Mita Look at it Come on link.  Hi No No, you even heard the screams would they burn a cow in the studio just for a scream That looks like real smoke. It looks like the Titans are coming Now like he's gonna run at this point, I think he wants to be abducted Where's Jesus telling them to get abducted looked way too cool to be just sitting there in silence Do something now. Are you gonna fly?   That's like right now if I'm doing a reaction See I'm that fast He looks like Mufasa now Look at him charging up his final smash That's what I'm talking about. Yeah, he's taunting He's charged to get up. The meter is almost there. I can't believe he just said hi with his fingers missing What is he happy Wait wait a minute wait a minute Everyone knows doors he's looking down on y'all right now from Valhalla I knew he was stronger. Yeah, there he is. Let's bring him back.  All right, so they're not related How do you know Helga? Is he gonna anime disappear too? Oh Son of Thorfinn all right now we established that okay. That was that map of fire going on there. Oh Oh That's right.  Is it a girl? Jesus Christ, it's a girl. I was right It's a girl That's why he wasn't talking. It's a princess. I was joking. All right, so now I don't feel really I don't I don't feel that gay anymore. Okay, cuz I I said it was a girl I said it and I was right. I was right Nicholas right TV Is he is he about to get a spear through his head? Oh God, no, what are you gonna do this about man?  Oh my god the sound design in this show is so good, man I feel like I'm I'm on the countryside right now Would ask a lot He said stop talking don't you see this guy has his ear to the ground This guy's like I hear 500 people wait, no 501 people I noticed a shift that a footsteps on the grass and had a grass was more caved in God Fuck Michelangelo. This is our Sure, this guy isn't Thor kind of looks like Triple H from wrestling If only we had a ship just steal one that's what y'all best at Find a village destroy it and steal their their ships I wouldn't sleep in this world like I'd be afraid to shut my eyes for two seconds You better have somebody out to watch. Oh, it's us glad. Okay, you're fine. I'd sleep. No, and he's uh, he's on look out I'd walk blindfolded with him by my side. That's how much I just Now that's a map of fire right there Hunter Aw venting session, is this a therapy session with us a lot It's because he's in love with her uh, oh ask a lot Is he gay? Oh, no, it's the princess He could tell if somebody's clever or a bag. Yeah, he said One look I wish I had that look I wonder what he would think of me when he saw me would I be a bag? It is a woman Man, I don't know what's going on anymore speak say your words. I know what you are God, I thought we thought we were watching airbender for a second with this sound. Okay, better sees angst swooping Oh god, no, yes Wow Now that's a map of sky Those are map of shadows a map of rain and a forest Yes Look at these little scenic shots. I oh my god. Now. That's a map of forest Look at this. You can hear the mud Mapa took their studio out in a real forest when it rains and recorded the footsteps in the mud Yo, the music is getting intense. I'm getting worried man. This is getting spooky Now that's a main character spot right there Move Looks like a predator like a local brooklyn predator They don't use an ost like this during a side a filler scene.   So I stopped talking to her anyways Can I look at this map instead of the map I was looking at in school I'd rather know about this world than my own Why are you showing a bird?  Oh god dolphins with the prince Oh god. Well, this motherfucker put down the drink for one second They're the same age imagine they fist bump each other Ooh, does she does she like oh, that's cute. No, that's cute. That's cute I still think it's a girl that's the princess. That's what I'm calling him Y'all can cancel me say I'm misidentifying somebody purposely.   Somebody put this dude on the cross already Now he's a hard-boiled egg look at him he's getting right like me Oh, they were talking to each other and ever I see a close-up of a bird. I'd of a bird.  You are not getting gold Love Looks like mario.  I don't even know what it means I don't even want to know what it means. I belong with this group not with him This guy's boring if I want a monk. I'll hang out with aang not this guy Stop stop it doesn't have to be every episode Whoever did this OST bring them back for season two. I swear to god if you don't we're gonna have a problem I'ma bring it back to the Vinland saga era Avalon It's the master story. It's a doors hammer in the ground. All right ear. What are you here? Ear hurt from three oceans away that there's 503.7 men on the way Damn he couldn't hear that a mile away he heard everything else from an ocean away Yeah What does Thorfinn have an ear to? Got him getting PTSD from Thor's dying.  Come on. He's gonna pull out a pistol Next episode yep. Oh my god. It's getting crazy now.  You change it mid-season. What is this attack on titan? Who are these people what is this a new cast is that her as a kid? We can't just move on from him.  Looks like he's getting older That wasn't that good Am I a hater? Because I'm too attached to mukanjiyo, but not even it wasn't that good of a opening song I don't know like I don't like it.   It doesn't affect how I feel about the show, but no Does it go swim?   Why did we have to show that angle? You couldn't raise the camera up a bit. You couldn't start from a bum in front of the horse What are they gonna do here? What in the anime are they about to do? Y'all are all speaking Japanese. I don't know what we're talking about here Why do we have to show the horse?  He has no friends just throw the anime kid at them This show is haram What were y'all just watching on tv where you're watching the anime that's how you know Or do you have ear on your side? Ear's brother knows We'll capture the prince and use him as a shield. Jesus christ.  This is some Broadway performance here. They're gonna start busting out in a dance The prince wouldn't even talk to one man.  So it's cool. It's cool I'm gonna get emotional when this guy dies because he's definitely dying eventually Otsukawa wants to swing his sword so bad right now I don't mind the prince that much It's interesting I like this friendship. This might bet this might get really interesting Yeah, that's good to inspire him Thorfinn's gonna inspire the prince.  He has to die. That's basically what he said. Just kill Wagner in his sleep Side characters one through eight leave us immediately That's 99 that's 98 the Danes And one anime kid and ask a lot aka god himself. Okay. That's what you meant to say Oh god when ask a lot laughs. I told y'all once in this video do not trust his laugh his smile That means death I wouldn't know because they're all speaking Japanese I keep thinking they're saying the n-word but then but then I know I think Korean and Japanese they have words They're similar to the n-word. Don't he called ask a lot the n-word But we're gonna wait the season to hear what he said I'm both well as in Danish.  We're learning more about him Bro, I need like a family tree of inland saga immediately Damn Even the horse looks like a main character horse. Look at that. He has his own shirt I know king odds right now.  I just learned that a pope still existed Yes, yes go through his whole life. We're gonna see baby.  Oh, we're getting a little oh god the show in his backstory You know what that means that means he's dying. Oh, no, don't show his backstory. I don't want it. I don't want it Showing somebody's backstory is bad. You see what happened to Thor's and every other anime character second They showed elves backstory. I knew he was dying That is super interesting man, but I don't like that. We're learning more about ask a lot I'm getting really nervous now that that spells no good I feel like we're gonna replace ask a lot with this bitch Yeah, speechless now, bub I'm gonna start laughing. I'm not sure she's like that in my reactions.  Hmm. You might actually do it I don't like this I don't want to know more about ask a lot. We all know what that means. I'm like 80% sure ask a lot is dying Maybe like 40% this season. I don't think he's dying this season.  I'm the heck. I like the four of them. The four of them will cool together. I like this. I like him her I don't know.  No, not sheep again.  Oh my god. I dare you do it.   You better get off twitter, bro.   This is cool Uh-oh the night walkers are coming It's no season We went through fall and now winter has arrived Nothing phases him unless his father comes back down dies again Yeah, cone head stay in your place That's his new name cone head. I'm not calling him Wagner Oskala's judgment is not wrong. It's like he's god. It's ask a guide This is way too focused on Oskala though. And that's why I'm really nervous Oh god, they're showing his back which are manic.   All right guys, I'm throwing my bets in I'm throwing the towel and I think I'm 99.100% sure that's not even possible I'm 100% sure. I ask a lot of dying This is this is bad. This is really bad. No, I can't say 100% I'm gonna say I'm gonna be I'm gonna say 90. No, no, no 80 80 percent. I'm 80 sure he's dying.  You never know. I don't think they would kill another father figure.  I don't know man I feel like my percentage is gonna go down or maybe it'll go up throughout the season But I don't think he's dying this season like I'm like 95 percent sure he's not dying this season You can't kill him now. He can he's just getting started. Who knows though?  I'll bring him down Bye


Steve Paul Jobs, February 24, 1955 to October 5, 2011, an American billionaire, was a CEO and co-founder of the world's leading computer and smartphone company, Apple Incorporation. Subsequently, Steve had some challenges with his partner at Apple and resigned to Found Next, a computer hardware and software company in 1985. In the 1990s, Apple had a lot of financial troubles and had to bring back Steve. When he returned, he launched the Think The Friend campaign and released several products until he restored the company to profitability. Apple is currently one of the world's largest tech companies, alongside Amazon, Google and Microsoft. In this video, I am going to show you the 9 books recommended by Steve Jobs while he was alive. One, King Lear, by William Shakespeare. In the book, the agent monarch, King Lear, decides to step down from the throne and divide his kingdom among his three daughters. Two of them made speeches of how much they loved him. While the third maintained that she couldn't express her love in words, the king had the third daughter sent away. The two daughters, whom he favored for proving their love to him through speeches, however it turned out to be treacherous.  The book depicts the folly of trusting people based on what they say. The book was full of death and tragedy, but the moral lesson is clear. Action speaks louder than words. Two, Moby Dick, by Herman Melville.  One of his legs had been taken by the whale, so he sixed it at all costs despite several warnings about the whale. The relentless captain continues to seek Moby Dick at sea. When he eventually meets it, their struggle lasted three days. So many people were killed in the process. The captain in his final stand throws harpoon at Moby Dick, but becomes entangled with the line. He goes underwater with the fish and doesn't return. Steve read a lot. He arrived at the perspectives it brought to Apple through a lifetime of reading. According to the Business State Financial Post, Job's lifelong interest in the humanities gave Apple a human touch. Also, according to Isaacson Walter, the president of the Aspen Institute, who wrote Steve's biography, Moby Dick's skill lesson is to not become obsessed with a particular goal and lose out on life. This lesson may have been what guided Steve to resign from Apple in 1985 and found it next the same year. He was able to let go of the fact that he was a co-founder of Apple, even though at the time Apple was competing with IBM for the computer hardware and software market in America. He let go and focused on building something new. This is perhaps why this book comes highly recommended by Steve. It portrays the futility of senseless struggle. Have you read Moby Dick? The book is currently free to download on Google Books. Using Google can however be a bit tricky. To download the PDF, type Asterisk Moby Dick Asterisk File Type PDF on Google. It will take you to the search result that will allow you to download and access the book. 3. The Poems of Dylan Thomas by Dylan Thomas Steve was a big fan of Dylan Thomas, according to Daniel Smead, author of How to Think Like Steve Jobs. Thymuses poems drew Steve Jobs in with its striking new forms and an erringly popular touch. The poems of Dylan Thomas are typically lyrical and packed with emotions. He is known for his popular poems. Do not go gentle into that good night, which is widely cited as Steve's favorite. Steve had an ideology centered on creativity. He sought to sell dreams and not products. According to Forbes, Steve Jobs is the world's greatest storyteller, turning product lunches into an art form. It is likely that Dylan Thomas' appeal for popular art forms influenced Steve Jobs' techniques on selling tech. Unlike his billionaire counterparts, Warren Buffett and Bill Gates, who recommend mostly business and management books, Steve had a deep love for the arts, according to CNBC. Steve Jobs had his engineers engraved his signatures inside each sheet in the Macintosh computer. Poetry has intrinsic power to inspire change than other art forms. It is therefore not surprising that Steve would recommend a book of poetry. The poems of Dylan Thomas are also available online and can be read for free. 4. Be Here Now by Ram Daz Steve was an undergraduate at Reed College in the U.S. in late 1972. He spent six months in school before dropping out. After he dropped out, he stuck around on campus and attended classes that caught his fancy. He also started reading spiritual books. In 1974, Steve traveled through India seeking spiritual enlightenment and studying Zen Buddhism. According to the Business Financial Post, Steve Jobs and his friends were transformed by reading the book, Be Here Now. The book is structured into three parts. The Journey, the transformation of Dr. Richard Alpert, PhD, into Baba Ram Daz. From Bindu to Ojas, the core book and cookbook for sacred life. A Manual for Conscious Being The book explores spiritual teachings and promotes the view that there are no accidents, the significance of surrender, and living in the present. It might not be your regular Tuesday read, but hey, Steve, one of the world's brightest minds in tech and business recommended it. It's worth taking a look. 5. Diet for a Small Planet by Francis Moore LaPei In Steve's autobiography, written by Walter Isaacson, he acknowledged that after reading Diet for a Small Planet, his dieting habit changed. He was recorded to have lived off Roman male cereal. Dates, almonds and carrots in his freshman year at Reed's College. Steve was historically careful with what he ate. He was known to eat very little and fast very often. According to Max LeGervier, an American journalist, foods that are high in two-plant pigments, Lutin and Zeke Zantin, have been linked to greater crystallized intelligence, food that has rich plant content can improve intelligence. Steve recommended the book, Diet for a Small Planet, to people as part of the book that had a strong influence on him. Even if you love junk food a lot, reading the book, Diet for a Small Planet is what a shot. 6. Mucusless Diet Healing System by Arnold Eritt This was one of Steve's favorites. In his book, Prof. Eritt explains how a plant-based diet combined with intermittent fasting is the most powerful way to help the human body of illness. In the book, Prof. Eritt showed that mucus-forming foods are natural for us to eat and are the fundamental causes of many human illnesses. The author healed himself from Bright's disease with a diet based on fruits. When Steve was very particular about his health and diet, this book would make a great read for anyone looking to understand their body system and how to stay healthy better. 7. The Autobiography of Yogi by Paramhansa Yogananda This book recommended by Steve was written by an Indian yoga guru, Paramhansa Yogananda. Steve read it while on a sojourn in India. According to Isaacston, Steve Jobs, after an incident in India in which the book kept him company, we read the book every year. This book is one of the world's most acclaimed spiritual classics. If you want to enter the minds of Apple's pioneer and visionary, reading the book can be a good start. 8. Zen Mind, Beginner's Mind by Shonryu Suzuki After Steve went back from India, his interest in meditation picked. He started attending Buddhism classes held by Shonryu Suzuki, the Japanese monk who authored Zen Mind, Beginner's Mind. He had a strong interest in spiritual enlightenment while he was alive. His perspectives and tag and calmness in times of trouble may be credited to his state of mind. 9. The Innovator's Dilemma by Clayton M. Christensen This book is a must read for innovators and startups. In his biography, Steve credited his view on innovation to the ideas explained in this book. According to Steve, it is important that we make this transformation because of what Clayton Christensen calls the Innovator's Dilemma, where people who invest something are usually the last ones to see past it and we certainly don't want to be left behind. The Innovator's Dilemma is certainly a book to read. In conclusion, Steve lived a full life as one of the most successful entrepreneurs in the world. We can each learn something he did differently by reading some of the books he recommended.


I guess we can just start a little bit I won't like be starting I hope still that some people will come 9.15 but we were we wanted to kind of give you a little bit background introduction into why we're doing dealing with like these 3D brand atlas and what we're using them from and kind of our perspective on this so what we're aiming to do with the human brain product we're aiming to integrate heterogeneous neuroscience data into one platform and making possible to find it and find data and access it and have them quite intraproportable to be able to use it together and analyze it further in the data curation team we receive a lot of different types this disparate neuroscience data from data producers within the human brain project and we try to integrate together and registering similar metadata and one of like the common denominators that we are very yeah and that we use and is of course location in the brain because all data sets often like have an association to a position within the brain like very specific or maybe a little bit more broad and I guess this this is kind of the main topic also for today that we use position in the brain to think about and understand the brain so I guess when when you search for information online what we have found is that research results can be very overwhelming and a little bit unstructured you can find articles images tables diagrams but they often have various use of nomenclature formats units yeah so it can be a little bit difficult and to understand how how I'm gonna like build this into something I can make sense of and put beside and I guess this can feel like an overload like both for experienced researchers and and youngest researchers because it does take a lot of time to do like a thorough screening of all information available for the topic that you're interested in and so this is definitely a challenge today and and I guess on the other hand and today only kind of a small portion of all research is available for the reader so these are often interpretations or research results presented in scientific papers while the data that this is built on is sometimes on a dusty shelf somewhere and not presented and we it's maybe lost which is a pity I think so currently could almost say that links between researchers are a bit blocked there's like paywalls if you want to read an article and sometimes you can't and there's also need to personally get in touch with each other to just to know if what another group or another researcher is doing is of interest to you and I guess this can be a bit frustrating because we want to be efficient and yeah optimize like research and I guess this can actually slow down important research yes so the data creation team this is kind of the challenges that we're trying to tackle and we're trying to make newer scientific research data more discoverable accessible reusable and to get the most out of research that's kind of big goal and we do this by curating all the data that we receive and that we want to make available to users and creation is defined as the action or process of selecting organizing in looking after the items in a collection and of course in this context a collection of newer scientific data yes so trying to integrate all of our data into a knowledge graph a graph system and we register metadata and so data about data and such a species or location in the brain as I mentioned and link a data set collection of data to metadata categories so that the data set can be queried and accessed and downloaded for further use around the world and we strive to follow the fair principles which you might have heard of before I hope and so the fair principles for data management and stewardship and when we tag a data set with metadata we make it findable you can search for it in a search engine and we try to make our data accessible and downloadable via persistent identifiers and giving clear terms of use and by presenting the data files in open formats they can also be interoperable with tools and yeah to open in different programs that you want to use them in and to integrate with other data of similar formats and I guess all of this including like more detailed descriptions of methods and materials makes a data set more reproducible and reusable so when data is created and made available for other researchers to use it yeah so the data creation and tries to create a research infrastructure so that we can connect different people in the field together from students to professors clinicians and modelers and I guess we we want to ultimately unleash the potential of all the scientific contributions that are delivered to our team and we want to yeah help answer the big questions of course about the brain and its diseases and yes so we use location in the brain as kind of one of our main common denominators and how do we do this so I don't know if you can see this but we receive together with the data set we we receive some information about where in the brain the data was from so we call this used to define location documentation and this can be terms and descriptions for example hippocampus ci1 maybe they reference a reference atlas maybe they don't they can also give us coordinates this can be linked to a stereotactic brain atlas or a landmark such as breakbound or lambda or it can be defined in a coordinate space that we can interpret and translate to the coordinate spaces that we are using or it could be in form of a brain section image where we can see where they took the sample or maybe the whole brain section image is actually the data maybe it's histology it could also be like a brain volume and we have several atlas integration procedures so if we receive a term we try to translate it as good as we can to one of the atlas that we use in HPP so we use Vaxxon space atlas of the red brain we use all mouse brain atlas and we have several human brain atlas I guess you will learn more about that but what we're going to talk about today is the the image registration and so both 2d to 3d image registration but also 3d to 3d image registration and we have some tools and workflows analysis analytic workflows that also builds on this registration yes and when we have done what we have done we register the metadata and we integrate the location metadata then we're able to present the data sets in our search engine it's called the knowledge graph search you can find it from human brain project.eu website and we also have a brain viewer and there you can also query a for data set from the viewer so we will look a little bit more at this


and the very first lecture i talked about why we need to do statistical mechanics is a difficult subject very difficult subject and in the in the evolution of statistical mechanics physicist and chemists almost you know they were hand in hand many many things were done together when Willard Gibbs many times is one of the father was considered to be chemist when i was doing phd he was referred to as a american chemist and now i think he is claiming as much as now so very fast lecture that we did we did the preliminaries these the we talked about how if you take a undergraduate physical chemistry or even ms level physical chemistry book what you will find is that the chapters except the the web mechanics one or two chapters and spectroscopy if there are 32 chapters you will find 20 25 chapters are things like kinetic theory of gases thermodynamics entire four three four chapters of thermodynamics then you have a phase equilibrium then you have a phase transition then you have binary mixture your solution electrochemistry all these chapters that you study they are all and you know remember that when you study of your conductivity of the ions lithium sodium potassium then you plot it against the size they are supposed to go as one over size of the ion crystallographic radius but it just goes instead of going like that straight line it just goes the maximum and falls back that if you remember that was called conductivity times viscosity is called faulted product and that non-monotering behavior was called breakdown of the faulted product the reason i am talking of the breakdown of the faulted product is that because i want to make a point and a very important point which is the following so i this is limiting ionic conductivity in electrochemistry you have right d by hooker remember and viscose at one over r sorry one over r ion since i am this is nothing but the what is called the diffusion and diffusion is inversely proportional to viscosity so this product should vary as one over r ion which is like this instead of that what happens that it just comes down like that and this lithium then sodium then potassium it goes like that and this is here where cesium rubidium and all these guys are maybe potassium will be closer to that now so in the undergraduate textbook of castellan or wood or glass tone all other atkins they call this breakdown of halden's product and give to some pictorial description like iceberg formation around the small ions so basic idea of my telling this is that in march of physical chemistry in undergraduate whenever we have anything interesting going on we have a picture that came but that picture was mostly very approximate however behind all these pictures there is a quantitative theory that was largely developed in the post 1950 and or maybe 1960s 70s and aided enormously by the computer simulations so potassium mechanics has come of age now and like in quantum chemistry you have all the packages so a student working in a quantum chemistry laboratory they can always have these packages and they use these packages for example doing organic chemistry calculations and other things statistical mechanics those kind of packages are just coming now in terms of grommocks or amber or the different force fields that have come so it has become much more institutionalized now so there are a lot more calculations going on and it has played a very important interesting thing that it has gone it has gone again okay it's a rapidly developing field huge amount of it's not that much visible still in India though there are now significant number of people doing statmaking chemistry physics always the huge huge in physics so this was the preliminaries that I explained that why that you need to invoke statistical mechanics to understand the what you call the large-scale phenomena large-scale phenomena many particles like phase transitions like this conductivity or understanding thermodynamics or so phase equilibrium phase transition all these things we discussed that in the first lecture second lecture we had little bit of mathematics where we did probability and statistics and because this name is statistical mechanics which combines mechanics mechanics is very deterministic it starts with neutron situation and you know everything is initial conditions given you can predict the the future but as I told you that the unfortunately that the we cannot even solve what is called a three body problem even if you have three particles we cannot solve that analytically even two particle having a little in a complicated potential not in a complete radial potential say lennard jones potential even that has to be done through a quadrature yeah so the i the whole a of so this is a interaction between a and b r a b this is the lennard jones potential we call it 6 12 potential the form is this is a universal notation sigma r by 12 minus sigma r by 6 yeah i let me say a and b are the same two particles both are a then i don't need the b i can do without this index then is a separation between them that is r and this is the form it very important to understand these things interaction potential between two particles when they come from a distance they attract each other but when they come too close electrons overlap and there's a huge repulsion this simple potential apparently simple potential plays a very important role in the understanding of many many phenomena a solvation phase transition many things however coming back to the point even these potential in a two body we have to do by quadrature and by the time i go to three body potential if i bring one more i cannot solve the newton's equation anymore okay but however when you think of the properties of water then you think of properties of water in a glass which has avocado number or molecules and they are strongly interacting and we cannot use newton's equation anymore to solve them if we want some really very complicated and sophisticated effect like polarization that one molecule is polarizing another molecule then we have to do quantum mechanics that is even more difficult okay so we are faced with then a situation where we have to explain natural phenomena like why ice males and then why steam 100 degree centigrade becomes steam why i put a solute there then depression of freezing point and elevation of boiling point that you have learned in school and these kind of things if you want to understand you cannot on from first principles and what do i mean by first principle these are term we use again and again statistical mechanics you also you know quantum mechanics quantum mechanics when you see first principles we think that we are starting with third anger equation and interaction potential and going about it of course they are looking at electronic properties and there are there are there are approximations and there are answers in statistical mechanics why one can do quantum statistical mechanics right now let me focus on classical statistical mechanics that because the large amplitude large scale phenomena like phase transition or as i am saying why steam bites you more than water at the same temperature depression of freezing point like you add salt that is what the principle of ice cream that you get you can go to minus 20 degree centigrade that is where ice cream forms you can do that experiment doing that now you can ice cream ice cream maker and there is a liquid there which is can goes very low temperature and you put water and you can make the ice cream and not water milk and water now so these the many many large amplitude phenomena that you want to understand we cannot do by following neutron situation anymore so then we need but how do you go so neutrons mechanics classical mechanics means the neutron situations or hamptons equation or whatever let's continue with the neutron situation which we know neutron's equation then is thus now is not going to help us because we cannot even solve a three body problem that is where the statistical mechanics come seen it was formulated starting with Maxwell Boltzmann then we did Gibbs now so what is then i do i cannot do mechanics as i know it so i have to get there this was a huge huge conceptual and philosophical problem in the 19th end of 19th century when people started introducing the concepts of statistics and the what it all started with the work of Maxwell you know Maxwell all of you know the distribution Maxwell is the first guy who told okay if i have in a glass jar a bunch of atoms and molecules at a temperature T i do not need to follow at each of their each properties of each atom or molecule instead i can talk in terms of distribution so Maxwell said okay what are the Boltzmann also what are the properties we want to know i want to know the viscosity of the gas and later viscosity of the liquid i need to know the pressure equation of state you know how is the pv equal to nrt is the ideal gas but when you go to little high density ideal gas now breaks down and then comes the virial equation okay how do i get then virial coefficients so and so then Maxwell said i don't need to know individual atoms and molecules in a departure striking departure from classical mechanics instead i'll talk of a probability distribution as soon as the concept of probability distribution came in then came the question of statistics how do i define probability i need statistics to define probability okay so that is what it was the second lecture and which we discussed probability and statistics then the last lecture third lecture we started talking the postulates so fundamental concepts and postulates i'll revise a little bit of that but then i'll not do liable theorem in this lecture i'll do it later i'll go to directly to something which is little bit more application ensembles and partition functions and what these are from my book and what i written there that actually earlier i have this from postulates to formulation so we'll go to the formulation of statistical mechanics today so it's a very important class in that sense these are next class and what i had before the title is that from promises to realization because there was this promise that was made by bolchmann wheeler gifts or wheeler gifts mostly realized it so the postulates both bolchmann and wheeler gifts played a role in giving us the main postulates of statistical mechanics from there we'll directly go now because this is more to do with dynamics and we'll do that at some stage but not now so the postulates there are two postulates what are connected by the one one hypothesis and based on that statistical mechanics promises to explain the natural phenomena which is a very high a so first postulate so there are postulates that i did last but this is so important it's no harm done doing it once more so first postulate is called time average and equal to ensemble average and second postulate is called equally probability and what is not told in a not any book is that why we need the hypothesis and these two are connected by our godly hypothesis i i'll spend five next five minutes talking about it then we'll go to the starting the partition function and ensembles how did that come it came because bolchmann tried very very hard to develop a kinetic theory of gases so you when you read the i mentioned this last time also you know like in sardingar equation in in quantum mechanics if you know noticed i didn't notice but i realized later partly because of a book i picked up in one and a half rupee in presidency college the old books next to presidency college a that fans that when you study quantum mechanics is all the way from hydrogen molecule you don't have any name you know there is no name because particle in a box rigid rotator harmonic oscillator hydrogen atom hydrogen molecule that because everything was done by sardingar alone you know when you first hit upon the idea you went to a resort area and stayed three months there and solved everything so the whole quantum mechanics as you read in msc you know is that done by one man similarly kinetic theory of gases again there is no name almost entirely of the kinetic theory of gases was done by Maxwell and a bit here and there by bolchmann later so when this beautiful paper of Maxwell appeared there are other papers around that but not as clear as Maxwell and also not meant to English speaking world then bolchmann fell completely in love with that paper until the end of his life he died early 1906 he carried that paper and then he tried to extend the Maxwell's Maxwell had this funny mix of assumptions that particles are like billiard ball but then on the other end he doesn't he is also talking of an ideal gas ideal gas don't interact they pass through each other so there was the discontinuctions in all the ideal gas in our study of kinetic theory of gases so bolchmann set out to extend it to real gases and separate and there's a famous equation on bolchmann kinetic equation but bolchmann did not fully succeed he tried very hard he could go only to very dilute gas and he also made some assumptions which were heavy criticized to the extent that probably cost his death now when bolchmann tried very hard and could not take into account even with the he is the first one Maxwell had it the probability distribution concept of probability but he did not explicitly stated that in his formulation and that was done by bolchmann he explicitly added probabilistic concepts he said okay if i have a probability of a particle at a position r with momentum p i call it f r p t another particle at position r1 i say this r1 p1 this r2 p2 then i have then the together i have a two particle distribution like i call up f1 f2 subscript to r1 p1 r2 p2 t that a given time a particle has r1 and p1 another particles r2 p2 that is a two particle distribution but it is so difficult because it has r1 and r2 all are three dimension three three six and p1 and p2 another three three six what a beast it is so he made an approximation that f2 r1 p1 t2 this was called is random random chaos approximation and that he was immediately and hugely criticized for making this approximation but here are no other option once subsequent whole century people have tried to 100 years people have tried to extend that and have done to some extent so when bolchmann tried this he could extend only a little on the other side of atlantic one person who looked with concern at the difficulty faced by bolchmann who was equal fan of maxwell and maxwell distribution his name is wheeler gibbs wheeler gibbs then thought okay what bolchmann did is not tenable anymore we cannot go that way because we cannot do two particle three particle is out of question and there are many other complexity so then wheeler gibbs make the important observation that as i told last class also then let me have 10 glasses 10 glass of water half full now now if i look into by that time the microscopic motions and all these things were somewhat guided theory of gases was already understood so if this 10 glasses all of the glasses if i think of the microscopic state and a microscopic state now is defined by if giving the position and momentum of each particle so i have n particles then i give you r1 r2 rn i give you p1 p2 pn that together determines my microscopic state of the system now my 10 glasses always have water now all of these microscopic all of them have the same properties they have the they are in same temperature they have the let me copy same volume they have the same pressure they have the same specific heat they have the same conductivity same entropy every property is the same but certainly the microscopic state of all of them would be different because there's such a huge number of microscopic states atoms and molecules are moving around so then wheeler gibbs realized that if i can now have a mental replica i have just one this is my system in question this is my real system now i mentally construct billions and billions of my mental optical such that they are thermodynamically same but their microscopic states are different so they are mental construction these mental construction is called ensemble then he said okay my over if i wait for a long time then my this system my system in question going to go through all the microscopic states who are essentially same microscopic states which are these particles are these systems are so now if i now in my mental replica i create a mental billions and billions of copies of these things which i call ensemble the collection is called ensemble if i can take average of that then that would be same if i now study what boljman tried to do study the detailed trajectories trajectory means the path that the particle takes all the particles take together that you will be the same that means if i can do a time averaging over infinite time with boljman tried and if i can replace by the ensemble averaging averaging over all these my mental i'll be little bit more exact because sometimes words don't language don't carry the difficult but so now they so that was the first assumption first postulate of statistical mechanics that time average equal to ensemble average now as soon as gives did that there is a problem came problem came that when i'm doing time averaging yeah i have one system which i'm studying for a very long time and i'm studying the motion of atoms and molecules their positions and momenta and then i'm averaging a property for example pressure i'm averaging a property for example the internal energy their enthalpy entropy but now i have replaced by the my mental construction but what is the guarantee that my mental construct my my what is the guarantee that the system i'm following for infinite time will go through all the microscopic states and second even if they go through all the microscopic states what is the what how do i how do i give a probability to it so that is the time the he introduced that i am going to talk of the systems with a constant number n constant volume v and constant energy so all my mental replica have a constant energy so the all the microscopic states have the same energy if all the microscopic states have the same energy then i can now assume and i was no other option but to assume under these ensembles that there is equally probability all the microscopic states are equally probable there's no other option than to do that it turned out it's connected worked out but that means all the subsequent works said that is okay all the microscopic states with equal energy are equal people still work on this so so these are the two first postulates first postulate was introduced the ensemble the brain what are the most brilliant idea that mankind has ever come up with it probably doesn't get the sufficient credit that how brilliant this construction is the ensemble and then time average equal to ensemble average but as soon as this was made here to average over all the microscopic states and he needed a probability of being in a microscopic state but since they are all the same energy the natural postulate was that they are all equal problem that's equal right they are now no other option but it turned out to be okay now came what us in in between started talking about that now came the problem following or then gives face the following problem okay i have done time average ensemble average and i have said equal probability but what is the so in ensemble average i am going to billions and billions of microscopic states but what is the guarantee that when i do a time averaging the system goes through all the microscopic states in a given time capital t say what is that guarantee then comes introduce the constrain of the hypothesis which is called algorithmic hypothesis which means the system indeed given sufficient amount of time it visits every microscopic state equal like this so first postulate required the second postulate because to average over ensemble and once this is set in but then i have to make sure that my system visits every state so this is then the made the ergodic hypothesis which connects these two hypothesis the these two postulates so this is the very important point of the statistical mechanics and why it is important because armed with these two postulates and one hypothesis that is where everything whole of statistical mechanics is built on is amazing you know this is what you can say equivalent to there is no equation equations will come later it is equivalent to you are saying that okay this is a wave function and the wave function has to have these properties satisfies Schrodinger equation psi square has to be positive and square integrable the similar kind of things that comes here in this in this statistical mechanical formulation this very very important to understand this part is more important now to understand than before because when we do now as i was talking you a while ago about the different packages time average is the what we call now the more molecular dynamic simulation and this is ensemble average what is called Monte Carlo so these are the two major branches of computer simulations that we do and the same problem in research by Gibbs so many years ago more than 100 years ago is exactly the problem we face now so when i do equilibrium statistical mechanics equilibrium properties like phase transition i land up the problem by one phase to other phase that cannot be done in molecular dynamic simulation because it cannot explode all these things it gets stuck in the minima we'll talk about it now that's where you go to Monte Carlo which is much better to do time independent equilibrium properties now so what she was asking me today why we do mix these two the reason we mix the two is that many times molecular dynamics get stuck so we follow a hybrid where you start with a multiple not too many maybe 10 or 20 initial condition then do molecular dynamics on that and then probability of initial state given by the energy so the hybrid simulation is a very popular thing now to study really really really complex problems like in biology or like in like in water in a supercooled in a very low temperature there are lot of interest now in water in low temperature because there is some presumably liquid transition and people have gone banana over that okay so but now i'll slightly talk if i have okay now this is the kind of thing that i want to point out say i have a particle which is undergoing some kind of a so this is a particle but i have a a a a kind of a landscape energy like this in that case the particle can get stuck here this is a very simple thing a one-dimensional walk random walk but in ordinary random walk drunkards random walk you don't have the you have a flat energy but you have a call equal to rugged energy landscape such a simple problem one-dimensional with the energy distribution it just hell to get a molecular dynamic simulation going into these kind of a system because it becomes non-argodic very quickly and so if you want to calculate equilibrium properties you do a Monte Carlo you just sample that's very easy to do on the other hand if you want to dynamical properties it becomes very very difficult so this is an example what we call compromised ergodicity so the ergodic hypothesis was particularly made to make sure these kind of situations are not done now why it does not is less serious many of the cases in nve because all of them are same energy like this will be ruled out by if i can do the simulation nve because these kind of things are not allowed however in real system we do not do nve we do the other ensemble which i am now going to going to discuss