For now, I will present myself. I'm Gilla with Sardi. I'm new. I can still call me brand new E-Life Innovation Officer. I started working for E-Life on the 7th of January of 2021. So, yeah, I'm still brand new. And this is the first public appearance in the E-Life vest. Pretty happy for it to be for the open-source commutical. So, yeah. Okay. I think we can start since we have a lot of panelists that are going to join us today and it's because they're going to tell us a lot about their projects. So, the open-source commutical of today is about COVID updates. This time, it's hosted by us, but as you probably know, if you have participated before, this is a giant venture between us, Data Dryad, and First 11. In these slides, I'm going to say a little bit about what this is, what is the call, and what are we going to talk about, and how the call is going to work. I'm going to also present our speakers, and I'm going to tell a little bit about them, but I'm going to let them the stage right after. So, yeah. So, what are the open-source commuticals? If you ever participated before, probably you know better than me, but these calls are really informal way to share and discuss efforts and promote open approaches to research communication. This means that we tend to focus on emerging projects and significant updates for ongoing ones. Usually, the calls are organized three times a year. So, this is the first one. They're going to happen another one around June and another one in autumn. And we host these interns in between us, Elife, Data Dryad, and First 11. In the next hour, it will happen a lot of things will happen, actually. If you have access to the open agenda from the invitation, you're going to see a list of presenters that are going to be presenting their projects and updates on their projects. This lightning talk will happen really fast. Each presenter is going to speak for five minutes, and each presentation is going to be followed by two minutes for questions. We know that two minutes is not even enough, and five minutes is not even enough to go through projects, but we hope to start a conversation and to start, like, something. We kindly ask you to write your question down in the open agenda. So, even if we don't have time to go to your question during this call, our presenters are going to have time to respond under the agenda, and you can check all the notes later. If you want to ask a question in the specific time that it's possible, please write it down and then raise your hand using the Zoom button, and we will let you voice your question if you raise your hand, or if you only write it down, we will voice it for you. So, no problem for that. Participants of the call can interact on Zoom in the chat. We have, like, Tracy, that it's going to moderate for us today with Anya as well, and you can also interact on the agenda. Remember, please, now in the beginning, to write your name and your organization in the corresponding section of the agenda. We also already wrote our name, and you can write it, like, just after hours. Leave questions and comments for each presenter in the space provided under each item. These help us immensely because it's kind of like, you know, we're going to have a lot of presenters, so it's going to be better for us to have everything organized to go through. And, yeah, as I said before, you can read these out loud, or we can write the question and comments for you. We will take notes of the answer, and we'll leave them in the agenda, and also we will ask the presenters to look at them, so if we got anything wrong, they can check on those. And also, please, we ask you to be mindful and follow our code of conduct, so basically respect the order of the question and interact really well with others. Yeah, obviously. So thank you a lot. I'm going to start with presenting you, our presenters. As you can see from the agenda, our first presenter, let me, wait a second, let me do this. Our first presenter is going to be Akimek Fermilane. She's going to present a call for an open research phone. Then we are going to have Nakome from Sensila, that is going to talk about executable research articles. Then we're going to have Peter Krocker and Giriya Goyal from Open Knowledge Maps and Refigure. They're going to talk on Covis, and then we're going to have Zang from Covid Preprints. Then we're going to have Nate from FlashBob.io, and then we're going to have Daniela from Pre-Review. Then we're going to have Joan from Adgin on the Covin 19 collection. So yeah, I think we have a pretty busy agenda, and we can start with the presentation. So I'm going to stop sharing my screen and let Aki start with our presentation of the call. Great. Thank you, Julia. Just let me share my screen. Yeah, so I'm actually here on a non-COVID focused mission today to tell you about the Open Research Fund call that is currently open for concept notes until the 12th of March. So there's still a few more weeks to put something together if this piques your interest. So Open Research Fund aims to support small-scale pilots developing new approaches to search openly, and also contributing to the evidence based on what actually works in this context. Several of you are on the call here. It'll be familiar with the previous calls of the Open Research Fund. So I wanted to make clear here that the focus for this third round is different, narrower, focusing on incentivizing openness and research, increasing adoption of open research practices amongst researchers. And with various multinational and multi-stakeholder initiatives that are going on at the moment, there is a growing commitment to moving towards a world where all research is openly available where possible. But we really need the researchers and teams to feel motivated to adopt these approaches, which is what we're trying to address. That's the part of the problem we're trying to address with this call this year. There is a non-exhaustive list of examples on the Welcome website, which I've linked in the open agenda and I'll try and show up the link a bit later in the slides as well. But for example, this could be developing a new metrics for assigning credit or new types of support networks for researchers. Just a couple of ideas. And very interesting to hear what any of you might have up your sleeves. So don't hesitate to get in touch if you want to discuss whether your idea would fit in or not. It is very important to us, as I mentioned, that you think about how you're going to assess the successes and challenges of the approach you've proposed and really we want to hear your justifications for why you've chosen this particular evaluation approach in the concept notes as well as full applications. So I'll skip over this quite quickly. We're not necessarily looking for the same things as the previous rounds and this isn't a problem for ongoing core support of a tool or resource generally. We are looking for a defined project with a research question within it. I'll run through this pretty quickly as well. If you don't catch all of these details, this is all available on the website which is linked to the bottom here. The concept notes, as I said, we are accepting them until the 12th of March. And then if you're successful at that stage, we will invite you to submit a application and we hope to inform you of that. And then you will have until the 21st of May to submit a full application if you are successful in getting to that stage for decisions in July of this year. Okay, let's see if there is any question. I don't see question on the open agenda, but if someone has some question, remember you can raise your hand. Or just if something comes to mind later, you can just start it with agenda. And I'm sure that Aki, we can ping Aki and be sure she can read those and maybe reply and follow up on the agenda. And if there's something that's relating to your personal idea that you don't want to type in the agenda, so it is about your personal employment situation, you can get in touch directly by email as well. Giri Azar, okay. Wait a second. How do I, Giri? Can you speak? I think you are in a mute. This is Giri Ja. So Aki, I just, you would mention that there are some differences compared to past rounds. And here you're looking really at incentivizing communities that are not currently openly sharing. I think if you had an example in mind that might be helpful to illustrate the difference, that would be great. Yeah. So I'm not necessarily trying to pull out a particular research community, not a particular discipline or anything like that. I'm thinking more of a situation where, you know, someone has the choice between, for example, preprints, the hot topic, between posting a preprint and simply not posting that preprint. And we're looking to target the people who aren't feeling motivated to post a preprint rather than they don't post it because they don't have the requisite tool. I think there is a difference there. It's a sort of changing hearts and minds is more what we're looking for this year than previous years. Does that make sense? Yeah, that was helpful. Thank you. Do we have any other questions? Okay. I don't see any other raised hand. So thank you, Aki, a lot for presenting this opportunity. I think it's amazing that you're sharing this with everyone. And I hope that a lot of people will join and try to participate to the call. Okay, Dan. I think we can... Oh, we have an action. Okay.  So, Nakame, if you want to share your screen. Okay. Thank you so much. Good morning. I'm Nakame Bentley. I'm going to be talking about our project, Stancila, today. Stancila is a platform for authoring, collaborating on, and publishing open source interactive documents. And we've been partnering with Elife on executable documents and particular executable research articles. And I'm going to give you a bit of an update on that project and show you some of the examples of eras that we've been publishing. So on the Stancila Hub, you'll see a number of eras that are published. And eras, executable research articles are, in the first step, articles that research has already published with Elife that have been published. But the author has gone back and enriched the article with code that they used originally to do their analyses. So if you head over to the Stancila Hub, you'll see there's a page for Elife and there's a number of examples of eras that are either published or in the pipeline. One nice example that we've got was published by Matteo Mancini and Adele a few months ago. And this one's, in particular, is a nice example because it's got some nice interactive visualizations that are developed and you can explore and hover over and so forth. So the way that these documents are produced in the first place, as I said, was to enrich the Elife article. You can go and click on the source for each article and see how they were produced in some of this pipeline. So in this case, Matteo took his original Elife articles source XML and converted it into a Jupyter notebook. That was a format that he originally used for this research. And he took the Jupyter notebook that was produced from the JAPS XML and enriched it with code. So I'll show you something that looks like this is a Jupyter notebook, which is just a raw JSON from the Jupyter notebook. And you can see it's got the normal structure of a Jupyter notebook with markdown cells and so forth. But how this differs is that we've also included all the metadata. So if I scroll down to the bottom, you'll see that a lot of the metadata for how the article, the references of the article and the identifiers for the article are all included within the Jupyter notebook. So we're embedding that into the Jupyter notebooks metadata so that when we come to publish this era, it's all included in the published HTML, including all the references down below. So we've improved our support for Jupyter notebooks over the last few months and we're really happy with where that's going. But we also support markdown. So here's another example of an era that was published several months ago. It's fairly simple example. It has one interactive figure where you can look at the code for the article for this one figure that's interactive. This is R code. But this article was published from a markdown document. And again, you can see the metadata for the article that's embedded within the R markdown that the authors uploaded. And then the actual R markdown that has the code for the figures. The other thing that I just wanted to update you on quickly was we, as well as Eli, we're also supporting other publishers and looking to work with other publishers to publish extra research articles. And we've just got a one article now with gigabytes that were in the process of publishing that we're really excited about. And so this article actually presents some research on COVID. And one of the things that we have with the Stensilah Hub is the ability to theme articles in different ways. And you can see for this particular article, instead of using the Eli theme, we're using the Gigaspike theme. So we're generating the same HTML, but applying a different theme to it. I just wanted to finish off by just shouting out or calling out some of our core open source repositories that are behind some of the things I've demoed today. In particular, Encoder, which is our conversion library that's available on GitHub. It's what's responsible for doing all those conversions between the different formats, including the published HTML. But also FEMA, which is a CSS library, which has the different themes. And it's got a nice demo interface where you can have a look at the various themes. Here's the Eli theme. And there's the Gigascience one down here somewhere. We've got various demos for different general themes. Thank you. Sorry if I stop you right away, but unfortunately, the five minutes is up. And we have a question for you. I don't know if the person who asked the question wants to buy it. If not, I will go for it and ask it to you. How do these compare to the F-1000 research living figure? And this person also linked a link. I don't know if you have the agenda open, but I don't know what this person is talking about or if you want to take the question later after looking at it. Maybe I'll take the question later. Okay. Yeah, because I think since there is a link, it would be better for you to check it and everything like that. There's someone else who wants to ask a question regarding a comments presentation. Yeah, there is a question in the chat. I don't know if you can read that. If not, I can read it to you. Yeah, if you can write a place. Sure. If the goal were to be science literacy, executable articles as potential for use by member of the public, especially students who would like to experience life science to illustrate applications of science to ongoing issues, health, vaccination, etc. That can be useful to them? Yeah, I think so. We try and support a number of different fields. And a lot of our examples so far have been around the life sciences, so certainly. Okay. Okay. Thank you, Kate, for the question. Okay, then. Sorry, we have a few time, but we have like a lot of projects, so we have to go on. Now we have a double presentation, Open Knowledge Maps and Refigure. So I leave the room to Peter and Kirisha. And yeah, it's your turn. Thank you very much. I'm going to share my screen with you. Yeah, and I'm very happy to talk to you about Open Knowledge Maps today and also about our COVID related project COVIDs for which we teamed up with Refigure. So Kirisha is going to follow my talk and talk about a bit more about their side of the project. And I'd also like to mention that these slides were co-created together with fellow Open Knowledge Maps board member Maxi Schramm. Yeah, the motivation for us in Open Knowledge Maps, so I think very well summed up in this picture, we just seem to be drowning in a lot of papers. There are far too many papers out there that anyone can read. And especially in the coronavirus pandemic, we've seen that in the beginning, the amount of knowledge doubled every 20 days. And that has slowed down a bit, but we're far north of 100,000 research papers at this point. And that makes it very difficult to get an overview of the topic. And once you have it, to then keep it. And I think it's not just coronavirus research. I think in all disciplines, we feel a little bit like the deeper in this boat, we just swamped with the amount of knowledge. And that was the reason why we started Open Knowledge Maps, because we thought there must be better ways to provide an overview of a scientific topic, of a scientific field. And our idea is to use knowledge maps for discovery. And they have a number of advantages over list based approaches in that they give you an instant overview. You can see all the areas or the subtopics at a glance. And you already have resources that are related to each of these topics. So you can get immediately started. If you go to our website, open knowledge maps.org, you can create a knowledge map of your own, can type in any topic, for example, digital education. And we will then create a knowledge map for you. And as you can see, it looks very similar to the example. And the overview is then based either on PubMed, or the most relevant papers from PubMed, or from the Bielefeld Academic Search Engine. And we use this technology now also for the QOIS project, as I already said. We teamed up with Refiger for this in the early days of the lockdown last year. And we were grateful to receive support from the European Commission and EOSC Secretariat project in particular for this. And the goal here was to give researchers an overview of the most reliable research on the topic because many people switched from one day to the other to this topic, many labs overnight switched to coronavirus research. And so they had the problem of identifying the pertinent information within a sea of papers that was being published every day. And so if you go to a website, open-knowledge-maps.org-slash-coviz, you can access a knowledge map on the topic. So you can see here we have the key areas within biomedical research that are related to COVID-19. And you can then look at these bubbles. If you find one interesting, you can zoom into it. You can see the papers and the other outputs that are related to it. You can also see that the curation team has contextualized many of these items in order to make it, yeah, to give more context, whether you want to read this paper or not. We do have datasets, general articles, pre-prints, and reviews, but also refigures. And Kirish is going to talk a lot more about this, but essentially the refigure comes into play when a single question cannot be answered by a single paper anymore. And for example, around hydroxychloroquine, the refigure team put together this visual dashboard that synthesizes the knowledge around the efficiency of this question. The big difference between COVID-19 and open knowledge maps is the data source. So open knowledge maps are completely automatically created while COVID-19 is actually curated data source by a dedicated curation team. And for that we have connected a spreadsheet to the knowledge map. And this is used by the curation team. They select the articles in there. They nominate articles. And once they green light an article, it's then automatically added to the knowledge map. Yeah, so that was a cool thing, a nutshell from my point of view. Going out, if you want to support open knowledge maps, the platform, if you want to contribute to our maintenance, then I would advise you to become part of our supporting membership group. These organizations are already on board. And this gives you also the ability to suggest features and influence the future of open knowledge maps. Another possibility is to directly fund our roadmap, which you can find on GitHub. And big shout out to Eli's Innovation Initiative, with whom we're just wrapping up a refactoring of our front end. So they supported directly our roadmap and the technical platform. With that, I say thank you for your attention. And now I hand over the screen to Kirisha. Thank you, Peter, for a wonderful presentation and the introduction both to refigure and to Covis. I just wanted to introduce our small team based in Boston, U.S. here, which includes my co-founder Dr. James A. came in to very talented interns, Stephanie and Elena. I also want to take 30 seconds of my five minutes to remark on our collaboration with Open Knowledge Maps. We have been attending Eli's supported events and Marzela's supported events for many years. And fortunately, unfortunately, the pandemic and the COVID literature gave us really a chance to collaborate. So some collaborations take time, even with very sympatical people to develop. And yeah, this is a wonderful collaboration with the Open Knowledge Maps team that took that was many years in the making. The refigure mission is to make science democratic, remixable, crowdsourced and rapid. And this might sound self-evident, but this is not the current case, particularly in biomedical sciences. This is what how science is currently communicated in the form of very long form papers. Sometimes in, for instance, in my field, the papers can have 10 main figures, each of which is hundreds of experiments. And further supplemental figures, which would be could run to 30 or 50 pages of PDF supplements. This represents years of data, each figure in itself being an actionable finding which should have been shared early on. The storytelling format of the that is very was sort of came about, you know, more than a century back, so that it could be human interpretable. But unfortunately, it also introduces bias, leaving the authors to actually connect these findings in the form of a story. The hundreds of findings in each paper are often behind a paywall. So a refigure very simply is a very simple web app which will allow users to actually collect figures from different sources. This encourages remixing and discussion of science based on many different factors. Refigure can be downloaded from the Chrome store as a browser extension and use. I'm just going to share a quick demo here. So let's say if I was reading this paper, and I had the refigure extension downloaded here, and this was the experiment. I wanted to save and compare to another experiment. I could just create a new refigure and all that's required is a title, but the value is really in the user annotated notes and descriptions. And I can quickly just collect this figure. And it will basically show up in essentially as a visual dashboard. And Peter just showed you one that we had created on COVID. I'm going to go back to my presentation now. So the COVID actually was a wonderful use case because preprints were coming out really rapidly. And sometimes a single preprint actually did not answer the question that we were interested in. So just like the example Peter showed you, we curated all the studies on hydroxychloroquine early on to confirm the findings that it was largely ineffective in COVID clinical trials. This format of review is very visual and allows public commentary. And readers, which include experts and forms of citizens, librarians can comment on reproducibility. I'm sorry, I'm having some trouble with my screen here. Students have also used a refigure to create research projects around various topics. This was one created by Stephanie as part of her internship where she was very interested in studying the COVID-19 infection and severity in different sexes. We've also been talking to publishers as to use refigure as a way to increase engagement with their content. Sorry, I need to cut you unfortunately because we went over your 10 minutes. I asked you if it's possible you publish these slides on the document so people will be able to ask you questions because I know there are a lot of people interested in that. Unfortunately, having all of these projects in a few minutes. So we have to go on with the next presentation and it's going to be Zhang with the COVID preprints. So I leave you the room Zhang. Hi, hello everyone. Good afternoon. So I'm Zhang. And today I'm going to talk about covidpreprints.com. So this was an initiative that the team, so I was speaking on behalf of the team and we did this during the E-Life Sprint in September last year. So let me go through a little bit of the backstory behind this project. We wanted to combine everything given the covid pandemic. We wanted to combine all the information into a one-stop centre that would bring together all this information into a single location. And the keyword here is that it's for all audiences. We wanted our website to feature landmark studies and all these key global events on a single timeline. And finally, we wanted to highlight preprints with short digests and references to expert discussions on social media. So given these three aims, we came up with a website and this is the preprints snapshot. So this is how the website looked like prior to the E-Life Sprint. And you can see that actually it was not very easy to navigate and visualise. So what happened during the Sprint was that we wanted to fulfil three main aims. The whole project was based around sustainability. So we wanted our project to be sustainable. And this was because we were a small team of people and we wanted to, and we had to come up with a way to mitigate the increasing number of literature that was published during the covid-19 pandemic. So there were three main aims during the Sprint. First we wanted to make this whole project scalable. We wanted to be able to crowdsource it and we also wanted to automate it. So previously the way that we updated the website was to key everything in into an Excel and then we would export this Excel and then via GitHub and then we would, the website would be updated from there. And so we hoped that with the expertise that would be present during the E-Life Sprint we would be able to keep up with the expanding volume of literature through crowdsourcing and through automation. The other aim that we wanted to achieve during the E-Life Sprint was to present the information well for everybody. I think we saw in an earlier slide that the data was not easily visualised and we hoped that through the E-Life Sprint we would be better able to present all this data that we wanted to share. So this is the scalable workflow that we came up with during the E-Life Sprint and basically this is some crowdsourcing suggestions. So basically the user, all the user needs to do is to update the DOI of the preprint and everything else will be automated. So the reading of data associated with the preprint, the adding of the preprint info, basically this is a short two-line summary of what the preprint is about and the exporting of the preprint to the server. And so we also acquired this very interesting logo design designed by Bruno during the E-Life Sprint and I especially like these designs because they were, they pay some homage to the original team that came up with this idea in the first place, which is we were a small group of pre-liters. So this is how the final timeline looks like now. I think it is a lot more readable and makes things easy to visualize. The prelights are over here on the left and there are some important events that are associated with the pandemic over there on the right. So there were a few other suggestions that we came up, that the team came up at the sprint and that is to cluster the preprints around certain themes and the most important thing that we gained from the sprint is that we develop a set of tools that can be easily repurposed. So because this was always at the back of our minds to moving on from the pandemic after the pandemic, finally lies down what would happen to this website. So I think the most important thing is that we have developed this set of tools that we hope that other people can pick up in the future. So I think the most important thing that we learned during the E-Life Sprint and that I would like to share with you all today is that the crowdsourcing part is really challenging. The interest and discussion on social media on Twitter most of usually does not necessarily equate to a nomination or preprints for our website. And we realized this because we designed the whole website and then it didn't necessarily translate to an increase in the crowdsourcing part. So I think this is something very important that we learned. And in fact, just recently, I think just last week or the week before, Johnny, one of the people who came up with this idea in the first place, he actually has archived the website. So it's not active anymore. We also learned that establishing this scalable workflow that I talked about is especially important and that the literature grows exponentially. So I think to understand how we came up with this lesson, covidpreprints.com originally started with a small group of pre-lighters trying to highlight, come up with a list of preprints that we would highlight on prelights. But after that, we realized that with just a small team, maybe two to five people, we couldn't keep up with the exploding preprint, covid19 preprint. Sorry, I have to stop you as well. I'm sorry, we're running out of time. We have to rush. Thank you. Thank you. So this is, I just want to thank all these people. Yeah, sure. Thank you so much. Leave it on the screen for now. Thank you a lot. And yeah, we have to go on with Nate. Thank you. It's your time. Thank you. Okay. All right. Hey, everybody. My name is Nate. I'm the founder of flashbook.io and I will be quick. I will rush through here and yeah, definitely leave any questions you have for me and they sounds like we want to leave it in the agenda and I can touch these people. Yeah. So yeah, so like a lot of people here, we are interested in, you know, problems around publishing. It's really expensive to publish. It takes a really long time to get published and even if you're publishing immediately, the long format of it means that you're publishing a law, many years worth of work in one kind of go. And so what we've really focused on at flashbook.io and what really kind of motivates us and drives us is to reducing the size and scope of each contribution to the smallest possible unit. And we're still in that experimental phase to kind of see how short and how small we can get it. But the basic format that we're shooting for is about the length of a single figure. So it's actually really cool to see some of the work that Refigure is doing. I'd love to touch base with you guys afterwards. But yeah, so the length is about the single figure and more so than that, it's really about communicating just a single finding. So basically stripping out the narrative context, stripping out, you know, the intro of the discussion, like not really needing to know how it fits into the bigger picture, just having a really robust individual finding and then kicking the impact and narrative structure to the community level. And so the first part of what we did, the project where we got going was just building a platform that could do these micro publications, working with the structure designs and basically breaking down the narrative to these individual chunks, which is really fun and really awesome. But that was sort of one pillar and then that created this new problem, which was, you know, once you get rid of that narrative, you lose a lot of the really necessary user experience of how do you consume information, how do you, like what's the, why should you even publish this, what's sort of the meaning of it all. And so that's really what we're working on right now. And we're sort of in the throes of this right now. So we'd appreciate any sort of feedback or ideas that people have on this. But yeah, basically what we're doing is we are trying to organize these things called research campaigns, which allow people to then build up a narrative again, but instead of waiting until you have all the data and publishing a paper, you know, you know, a couple of years after it's done, you're sort of publishing it in the moment. And you're also inspiring and inviting other people to contribute to the campaign as you're going down that path. So kind of the analogy is a GitHub repo as opposed to releasing the software all at once. And so a big part of that is, you know, data visualization and things like that. I'll give an example in a second. We're really just focused on one community right now. We just have this community outbreak, which is our only micro publishing community. And so that's live and just we're kind of slowly building that up right now. And it's focused on COVID informatics. So forecasting, modeling, anything on more on the computational side of things. And so this is just a, this is just a mock up. This is not actually live. This is one that we're kind of designing and working on right now. This is basically an example of a data visualization that does this, that thing that I was talking about, where you let people publish individual chunks, and then you bring it together in a data visualization to sort of capture and follow the story as it emerged. So there's a lot of synergy with what other groups are doing here with, you know, a three figure and the executable are the, you know, kind of the living figures and things like that. Can you guys see my pointer when I'm pointing or no? Yeah. Okay. Cool. Yeah. So, so the basic idea is each of these dots are represented individual micro publication. And what allows us to bring them together to a single plot or single visualization is that structured claim that goes along with each individual micro publication. So in this case, these are estimates for the efficacy of a particular vaccine, associated with a particular strain of COVID. And that allows us to basically plot it into just a simple scatter plot so that you can start to get an idea for the trends that go across it. And so this is just made up data. This is just a total, a total mockup. But, you know, as we go forward with a pandemic, you know, what we're expecting to see is, you know, basically there's going to be reductions in efficacy for the original vaccines as the new strains build and stuff. So this is really an ongoing emerging story. And you want to allow people to contribute as it's happening and then also sort of capture the larger trends as it's going on. So these research campaigns would be led by individual research labs. It would be instead of waiting to publish a paper, they'd be kind of leading a research campaign. Yes. Like I said, our only community right now is Outbreak, but we're looking at launching a few more depending on how many, you know, how big the team is and things like that. We're pretty, we're pretty small right now. There's, you know, me and one other guy. So we're doing, going as quickly as we can through all of this. Thank you, Nate. Sorry, I have to stop you. Oh, but you were in the end. So I hope at least I got not too much over your presentation. Thank you for going super fast. But this is the thing of today. So yeah, we'll go on with Daniela since we are rushing. Thank you. If you share your slides, we will be having everyone asking your questions. Thank you so much. Hi. I'm going to try to share. I had, I've been having problems sharing. I don't know why. But let me try. The only problem is it might not be a presentation mode. But all right. So thank you, everyone. First of all, I wanted to have a demo and some data that I have been without power for two and a half days. So that's my excuse for not having the latest updates, but I'm just going to go and run through some concepts here. So I'm Daniela. I'm the co-founder and director of Pre-Review. Pre-Review is an open project that aims at bringing more diversity and equity to peer review through openness and collaboration. Let's see if I can actually move forward. So we're inspired by some of the issues that all of you have touched already on, but also the broad lack of diversity and representation in the peer review pool. And we really see preference as an opportunity. And all of the projects that you have actually talked about, I think there is always this component of engaging more a broader community to the review for a fast dissemination of knowledge and also assessment of it for broader consumption. So our team has focused in the past couple of years on building community through training programs and also organizing events for the collaborative review of preference. And we have done a few women collaboration with E-Life and Plus and Jamie are on some COVID preferences here. And then the discussion generated reviews to preference aware release in different places that we also build open infrastructure. And we see this really as a home for the community that we want to support and see flourish. I wanted to, the demo that I haven't had the chance to show you today was on the new platform, which is Preview has been around for a few years, but for a couple of years, actually, oh my gosh, it's been just a year for the new platform. But this is a new version of the platform that is really marriage between two projects that we were running in parallel. I'm going to tell you a little bit more about it, but the main concept and the things that are most excited about this new infrastructure is that we are just going to foster some of the, hopefully, the behaviors that we want to see change and the behaviors that we want the communities to take, which is really like being more collaborative in the writing of reviews and providing ways for the rapid reviews of preference as well as more long form reviews and then present the aggregated visualization of the reviews themselves and all in a way that actually promotes constructive criticism and allows for a safe space for community reviews to be displaced and consumed. So I only just had the chance to take a couple of screenshots here and as I was mentioning this new code base, there's all available on GitHub, has, is a merge between these two projects and Preview and Aurex Science, Rapid Review and the main features that we're going to bring to the community is this ability to have a rapid, to run a rapid review alongside the long form review and presented alongside with a, with a prep print. Apologies again, this is just a screenshot. It's not super informative, but what I wanted to show is that we're still working on presenting a useful visualization of the aggregated rapid reviews that can come from different community members and also ways to filter that content in a way that actually promotes expertise and valued for their contributions of self and not for the prestige of the reviewer or the background that all those metrics that we believe while they might be informative, they actually carry a lot of bias and so we're really working through the platform on designing features that can highlight constructive criticism rather than other metrics that might not have anything to do with the ability to peer review. I also want to end presenting just a few ideas that we've been part of this COVID-19 rapid review publisher initiative that started last April and this was a joint, is a joint initiative that any life is part of and we are as well and the goal for the whole project was to bring more community reviews to COVID-19 manuscripts so that that could be shared across all of these publishers engaged in this initiative. We were there as the only project really looking at footprints and so our commitment has been to trying to engage the 1800 volunteers, researchers that have signed up to rapidly review COVID-19 footprints and engage them in the rapid review on our site. Sorry, I need to stop you because we have only eight minutes left so we have our last presentation. Thank you so much, Daniela, thank you a lot. Joan, it's your turn. Thank you. Okay, hopefully you can all see my slides. All right. Well, I'm going to try and go really fast. You know, five minutes, five slides, that's the rule. So, Agene is a materials repository, slightly different from the material, the repositories you've been hearing about or the projects you've been hearing about. The big development is what we're doing in COVID so I'm going to skip a lot about what Agene does and hope for that you can look that up. Our mission for 16 years has been to accelerate research and we do that by helping scientists share openly. Our main project is Plasmids but we also have a lot of products derived from Plasmids over the years and we're about to launch a very exciting new project in 2021. I hope I'll be able to join another call in the future and talk about that. So, we're talking about making biomaterials shared, quality-controlled, standardized and available to the community. To date, including all types of samples, we're almost at 1.5 million distributions. That's kind of a huge scale. That's one of the things that makes Agene successful is the scale and the service we can offer around the distribution. Also, the democratization of sharing. We share to 103 countries. The most recent is Tanzania and we also make materials available when scientists can't afford to request. We make that accessible for them. There's a lot of benefits. This community may be interested in incentives. I won't have time, obviously, to talk about that but Agene has a lot of incentives that make people deposit and share. I'd love to talk to people after the call about that offline. So, of course, this saves money. It saves mistakes and makes science more reproducible, obviously. We also have a lot of data and information on the portal. We have one of the top 100 websites in biology in the world as far as accessing a number of accesses. We provide a huge amount of free and open information to go with the materials, which is what the distribution fees support is really supported distribution of materials, not just throwing them out but having them supported with technical support and other customer support. So, in the time of COVID, what happened? I thought this group would be interested. We had to close, obviously, to make our workplace safe but within the first few days, scientists were beating down our door to deposit materials related to SARS-CoV-2. We've already have closer to 5,000 new deposits in one year in the repository related to the study of COVID-19 specifically. And we're actually at about 12,000 distributions of COVID-19 related materials to 62 countries. So, if that's any comfort, the world of science is working on this problem. Good for all of us. And the availability of Agene to be there to accelerate this work, I think you can all see how immeasurable that is from an access perspective, rapid access. This was how quickly materials were distributed and how quickly materials became popular in the repository. Some of the people that requested things deposited already again within the first year. So, innovated on what they requested and then re-deposited so that other scientists could continue to share. So, we're growing this collection. In addition, most of the materials deposited for COVID-19 were made available to for-profit requesters as well. So, scientists working in companies. This is kind of new and it's an area that we're trying to move in and something I would also be very happy to talk to you about. We would like to bridge this academia industry barrier in a big way. And so, this is a project for us in the coming years. So, thank you for having me and I'm happy to talk with anybody offline after the event. Thank you so much. Thank you everyone for being here. I think you can leave all the questions you have for all the speakers in the agenda. I think they would be really happy to reply to every question you can post to them. Some of them left their contact. I invite everyone else to leave their contact just to create this network of people discussing on what we started to discuss today. And to keep the conversation going. I have just two last items to touch upon. One is Tracy. Do you want to join in? Tracy from Data Dryad. She has an event to briefly talk about. If you in one minute can say, well, what is it about? Sure, yes. I put it in the upcoming calls at the bottom of the agenda, but on March 4th. So, Dryad just announced an integration with Zenodo to support easier and better sharing of data and code. So, at that March 4th webinar, we are going to be presenting on how to do that and be available to answer any questions. So, go down there, sign up through the link there. And we'd love to see you there. Thank you so much, Tracy, for adding this in. I just add on our side, on the side of eLife and a little bit on the side of ERA, like to expect some updates on the 1st of March. I want to spoil more than that. I would like to find out at that point. So, I would like to thank you everyone who participated in the call. I would like to thank you all of the panelists. I cut short. I'm really sorry because of that. I hope to have you again on the call. And yeah, I hope to see you in June in the next call. And yeah, have a great night, evening, morning, or whatever you are in this moment, wherever you are in the world.


Hello and welcome to the session. I am Deepika here. Let's discuss the question which says prove that the corpandicular at the point of contact to the tangent to a circle passes through the center. We know that a tangent to a circle is a line that intersects the circle. Only one point. The common point of the tangent circle is called the point of contact. This is the key idea behind our question. We will take the help of this key idea to solve the above question. Let's start the solution. We have given pt is a tangent to this circle with center o is a point of contact. We have to prove that the corpandicular at the point of contact to the tangent to a circle passes through the center. That is pt is corpandicular to ot and corpandicular to ot. That will be a point of intersection with the circle. Because according to our key idea a tangent to a circle is a line that intersects the circle at only one point. The point of intersection with the circle then not a tangent to the circle contrary to our assumption. Because we have assumed that pt is a tangent to the circle is corpandicular to ot. That is the corpandicular at the point of contact to the tangent to a circle passes through the center. This proved. I hope the solution is clear to you. Bye and take care.


Hello everyone, I am Mrs. Meenakshi Shrigandhi, Assistant Professor from Walchin Institute of Technology, Shalapur. Welcome to the video lecture on sequential search. Learning outcome. At the end of this session, students will be able to implement sequential search using C programming. What is searching is? Searching is the process of finding a given data in a list of data. It decides whether a search key is present in the data or not. It is the algorithmic process of finding a particular item in a collection of items. Searching methods. Sequential search or linear search. A sequential search is a method for finding an element within a list. It sequentially checks each element of the list until a match is found or the whole list has been searched. A binary search also known as half-interval search, logarithmic search or binary chop is a search algorithm that finds the position of a target value within a sorted array by repeatedly dividing the search interval in half. Hashing. Hashing is a process of converting a given key into address. A hash function is used to generate the address according to a mathematical algorithm. The result of a hash function is known as a hash value or simply a hash. Sequential search. A sequential search or a linear search is a method for finding an element within a list. So given a list of elements, it sequentially checks a target element with each element of the list until a match is found or the whole list has been searched. Sequential search is usually very simple to implement and is practical when the list has only few elements. This is because the number of comparison in sequential search is more if the list size is more and it is also practical when performing a single search in an unordered list. Let us see illustration of sequential search. Now consider an array A with this 8 elements and we want to find an element with value 97. So this is how the array is storing the elements. It is having it is starting with the index value 0 and it is ending with the index with the size minus 1 and our role is to find the target element with value 97 from this array. So for that we are setting a pointer with at the index 0. Now I am comparing A of 0th element which is 24 with value with the target element that is 97. So here we can understand that A of 0th element is not equal to 97. In that case we are moving this pointer to the next index that is 1. Again A of 1th element that is 18 is compared to 97. Again we can see that A of 1th element is not equal to 97. For that we are moving the pointer to the next index that is 2. Now A of 2 element is compared to 97 and A of 2 element is minus 25, 25 is not equal to 97. For that we are moving the pointer again to the next element that is with index 3. A of 3 element is now compared with the target element 97. Again we can see that A of 3 element is 32. 32 is not equal to 97. So the pointer is moved ahead now with value with index value 4. Again we can see that A of 4 is not equal to 97 because A of 4 is 87. 87 is not equal to 97. So the pointer is moved again to the next index. Now the next index is 5. A of 5 is minus 56. So minus 56 is not equal to 97. This pointer is moved ahead with index 6. A of 6 is now equal to 97. So we can say that our target is found at A of 6. So here in this case we can see that the number of comparisons are more when the target element is placed far ahead and the number of comparisons are less if the target element is placed at the beginning of the array. So based on this we are going to see the illustration of best case when it is achieved for sequential search. So consider the array again and here I want to find the target element that is 24. The array is represented in this form with index 0 to 7 and here in this case the target element is 24. Again the pointer is set to the 0th index. Now the comparison is done with A of 0 and the target element and we can see that A of 0 is equal to 24. So the element which I want to search is found at the very first place. So we can see that this is the best case of sequential search because in only one comparison the target element has been found. When the search is done only with one comparison the best case is achieved. So when the number of comparisons are less the best case is achieved. Let us just see the illustration of worst case of sequential search. Again we can consider an array with 8 elements. Now in this case I have the target element 22. Array is again represented in this form with index 0 to 7 and the target element now in this case I want to find the element with value 22. For this the pointer is again set to index 0. A of 0th element is compared with the target element and we can see that A of 0 is not equal to 22. So this pointer is moved ahead to the index 1. A of 1 is not equal to 22. So we can see that the pointer is moved ahead again. A of 2 is not equal to 22 because A of 2 is minus 25 and minus 25 is not equal to 22. So the pointer is again moved ahead to the index 3. A of 3 that is 32 is not equal to 22. So the pointer is moved to the next index that is 4 and A of 4 is not equal to 24 because A of 4 is 87 and that is not equal to 22. So the pointer is moved to the next index with 5 and A of 5 is not equal to 22. So this pointer is now moved to the next index that is 6. A of 6 is not equal to 22 as A of 6 is 97 and this is not equal to 22. So this pointer is now moved to the next index that is index 7 and here we can see that A of 7 that is 22 is equal to 22. So we can say that the target has been found at A of 7. So the worst case of sequential search is achieved when the target element is placed at the last place. So in the worst case the number of comparisons are equal to the size of the array. So let us see the advantages and disadvantages of sequential search. Advantages, sequential search is simple and is very easy to understand and implement. It can be implemented on an array of any order let it be sorted or unsorted. Disadvantages, it is much slower compared to other searching method as it takes lot of comparison to find a particular element from an array. It is used only for small amount of data. Worst case is achieved if the target element is not there in the array and still n number of comparisons are done. Try to think an answer. What is the algorithm for sequential search? Pause the video for some time and note down the answer in your book. Algorithm for sequential search. At the very first step input an array of n element and target to be searched. Then initialize the pointer i equal to 0 and repeat step 3 if i is less than the size of the array that is n by incrementing i by 1 with each iteration. And in each iteration check if target is equal to a of i. If target is equal to a of i then we can print the message that target is found and you can use a break statement to come out of the loop. Now after coming out of the loop check if i is equal to n. If i is equal to n then we can print the message as target is not found. Let us see the implementation of sequential search using C programming. Here in this case so here in this case we have declared some variables. We have declared an array of size 50 and n i is the pointer and the target is to take the element to be searched. At the very beginning we are asking the user to how many elements he wants to enter in the array and that is collected that value is collected in the variable n. And after that we are using a for loop and it will execute till it reaches i reaches equal to n. So we are taking n elements from the user to enter into the array and with each iteration we are asking the user to enter an element and that element is entered at the ith position of the array. After that we are taking the element to be searched and that element is collected in the target variable. We are using the for loop. We are using the for loop where i is initialized to 0 and it will repeat till i reaches to n. And with each iteration every time the comparison is done between the target element and the ith element of the array. Whenever the comparison is equal then we can say that the target element is found and we are printing the message that the target element is found at the ith plus 1th position. And after that use a break statement to come out of the for loop. This for loop is repeated till the element is found or it will reach to the end of the array. If the element if i reaches equal to the size of the array then we can say that the element is not found in the array. So this was the implementation of sequential search using C programming. These are my references. Thank you.


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Okay let's try a slightly harder one. What's the molar mass of aluminium hydroxide? As with the water example the first thing you need to do is write down the formula for the compound. Now aluminium has a valence of 3 plus, it's in group 3. The hydroxide ion has a charge of minus 1. So when we put those together that gives us the formula of AL brackets OH3. Now to work out its molar mass you can see I'm going to need the molar masses of aluminium, oxygen and hydrogen. So I'm going to write those down first. Now I have oxygen and hydrogen already, 15.999 and 1.008. Let me add aluminium to that, 26.981. Alright now I can put them together. Now one unit of aluminium hydroxide, remember I can't say a molecule because this is an ionic substance, contains 1 aluminium ion, 3 oxygen atoms and 3 hydrogen atoms. So to work out the molar mass of aluminium hydroxide I need to put together the mass of the aluminium plus 3 times the mass of the oxygen plus 3 times the mass of the hydrogen. And that gives me a final molar mass of 78.002 grams per mole. Alright now let's try a problem that involves us finding out how many moles of aluminium hydroxide are in a certain mass. I'm going to set this out in two different ways. One is just to use the formula I gave you before. The other will use the kind of format that I've also used in the videos on unit conversions. Now here's the problem. If I have 23.4 grams of aluminium hydroxide, how many moles of aluminium hydroxide is this? So first of all write down what you know. You know that the mass is 23.4 grams. You also know the molar mass of aluminium hydroxide because we've just calculated it. You might come at a problem like this cold and not know the molar mass straight away but it's always something that you can calculate as long as you've got a periodic table handy. So I have the mass, I have the molar mass and what I want to know is the moles. Now we know from before that the molar mass equals the mass over the moles. I can rearrange that to make moles the subject. I multiply both sides by moles and divide both sides by molar mass. And that gives me that the moles equals the mass over the molar mass. Then all I have to do is substitute in the values that I know and get the answer. And because my mass had three significant figures I'm going to round this off to three sig figs. Now I'm going to do the same problem but I'm going to set it out in a slightly different way. Again I need to write down what I know. So I have the mass, I have the molar mass and I want to know the moles. But I'm going to set it out a bit like a unit conversion. So first of all I will put down what I know as a fraction. 23.4 grams over one. And the conversion factor is my molar mass in grams per mole. But I have a mass that's in grams and I want to cancel out those grams and turn it into moles. So I'm going to flip my molar mass upside down. The 78.002 grams will go on the bottom and the one mole will go on the top. Just take a minute to look at this if this is confusing you. All I've done is take a conversion factor that looks like that and flipped it on its head. I've inverted it like that in exactly the same way as you invert your conversion factors when you're doing unit conversions. I can now cancel out the grams and I'll be left with moles when I do this conversion. So I multiply out the top and bottom of the fraction. I do the calculation and I come out with exactly the same answer as before. Now which of these two formats you use is up to you. My advice is if you have a tendency to mix up algebraic conversions and are a bit sloppy with your units use the second format, the one that's like unit conversions. It will keep you on the straight and narrow. Ideally though you would practice rearranging equations until you felt confident with it because we will use bigger and more complicated formulae later on in the course that will definitely need rearranging. However as you see both formats work perfectly.