 Okay. Good evening. Oh, good morning. Hello, everyone. Thank you for being here. Welcome to this meta science session on cloud fight for thin collaboration by making visible invisible health. My name is Sarah ganda. I will be the moderator of this session. I am a researcher of the King, Juan Carlos University in Spain. I'm also co founder of crowd fight. A crowd fight is an initiative born during the pandemic. Who's aim is to match experts with researchers that may need their help. But I'm so very good at that maybe can say hello, if it's possible if not, we'll see everyone. I'm the director of crowd fight and he's also a researcher at the research center of animal cognition in France. And in the first part of this session, he will tell you more about this initiative. In the second part, and Alberto Pascal Garcia, you can say hello to everyone. You don't want to. Hello everyone. Thanks for joining. And he's a board member of the fight and he's a researcher at the ETA to suit it in Switzerland. And he will evaluate the current situation of our project and will describe our current challenges. But before that I wanted to ask the audience, they want to if you want to leave question please write it in the question and answer chat and we will try to answer them later after the presentation. And now I give floor to Alfonso. Okay, thank you very much. I'm going to share my screen. Okay. I hope you're seeing this line now. So, my job today to tell you what is kind of fight and why we are. And this is the outline first. I'm going to try to give you the big picture of what we are trying to do. And I'll tell you why we thought it was impossible at the beginning, why we tried anyway, even though we thought it was impossible and how we're doing it and why we actually think it may be possible even though it's not. So, the big picture comes from the difference between collaboration and health. And to us these two words are very important collaboration happens when two people help each other so a healthy and at the same time be a while help is when only a healthy. I am sort of a biologist and a study cooperation in animals, and these two types of interaction have different names. The first one collaboration is called direct reciprocity, because both of them reciprocate the health, while the second one is what we call indirect reciprocity. Because for this to help. In general, and long term a does need to get some reward from the process. So, the situations in which the second team work are those in which actually, in some way, a gets a reward, usually because somebody else is helping a. The important thing to understand here is that these two types of interaction have very different rules. They both exist at the same time in any society, and they work differently. Collaborations are inflexible. If we think about science, a collaboration in which both scientists benefit is very hard to establish you need to people who have interest in the same question and are going to end up publishing a paper together to both of the benefit. But still they are different people they have different expertise. In the second case in the health. You don't need both to be interested in the same question, maybe researcher be needs a protocol and researchers a can help with that. And any researcher who knows the protocol is a valid partner in the case. The drawbacks is that while the collaborations are self sustained, because both people are interested, and therefore a collaboration can emerge naturally in a situation. The cases with the helping cases require a community require a set of rules where, for example, we make sure that they are not cheaters that there are there are no people who are getting a lot of help without giving anything in exchange. They may even require some infrastructure. One of the reasons the claim I want to make is that science is extremely biased towards the left side of this slide. When we talk about collaboration in science, we always have in mind this direct reciprocity case. When a foundation gives a grant for collaborative project. They are thinking that the researchers are going are going to end up publishing together. One of the things is that shifting a little bit the equilibrium towards more health towards more indirect reciprocity would benefit science a lot would make it more efficient would make it even more agreeable for those who do science. And that it is actually possible to do that. At the beginning, we did not think it was the reason we thought it was impossible or at least I did. I'm not going to go into a lot of detail, but the incentives in science are what they are publication grants, etc. And they are not very well suited for interactions in which one person is going to selflessly help another one without getting any immediate reward. But then what happened is that we did not try to solve this problem, we tried to solve a different problem. What happened was that the pandemic hit, and we had an idea of building a platform just so that any scientist could help other scientists who could make an actual different fighting the pandemic. And then we worked with following. If you have a researcher who's already working on COVID, or at least have the means to start the break working seriously on COVID. But this scientist needs something which is generating us that another scientist can help. So for example, this person is a literature search on Coronavirus for reading mechanism. We would write this request in our webpage, and then we have this person, the coordinator who would understand more or less what they want, and would look for another scientist who voluntarily donate his time or her time to help in this request. And then we would put them in contact. The actual system is more complicated. It goes through these steps. So, first, the request that makes the request, then at the reception stage, we understand what they want, we ask for information if needed. Then the task distribution means that we send it to many volunteers, thousands of them sometimes, we send the brief summary, and each volunteer self assesses whether they think they are capable or not to help. Then we have a person that we call the advisor who shortly the volunteers choosing the perhaps 10 or 20 most suitable one. And then it goes to the coordinator who will actually pick the good one or sometimes it's two or three, but really more and make the contact and follows up to make sure that everything works. So this was the idea. And it worked surprisingly well. It grew super fast. We have more than 40,000 volunteers, we have hundreds of requests, and we have many successful requests in this. The types of requests that we got are very bright from people who needed reagents and were putting contact with someone who could provide them to translations access to data and many more. An interesting thing to note is that many of these requests while they started at something quick concrete in one way. Almost half of them developed into longer collaborations in many cases with quality so even though we started on the side of the health, let's say, in many cases we move to the side of longer collaborations that benefit both sides. And so we thought that it would be nice to give you some concrete examples of, of requests and when to stop sharing my screen. And for example, Alberto has acted as coordinator of many requests and maybe he can share his point of view for some of it. Thank you Alfonso this should be prepared. So, I mean I have many, many favorite request I would say in in mind I remember for instance, one, one company from South Africa asking us for help, because they developed some antibodies from alpaca and they were looking for a BS3 lab which is a high security lab in which you can use, you can test COVID there. And this there are very few labs of this kind in the world so we were able to find a lab in Scotland that they were willing to have them. And these guys were able to neutralize it to show that that this antibodies were able to neutralize the virus there, and it's now following up into into a positive potential in a drug. I like well I was also involved in one in which I particularly like, which is the NGO from Syria that they were asking us to help on how they could rearrange informal camps in northwest Syria which is the area which is now occupied still by the opposition leaders. And so it's a very, it's an area completely immersed in a conflict. How they could rearrange informal camps in order to minimize the spread of the virus so we set up a group of modelers and this ended up also into into a nice paper and also into a policy report with conversations also with opposition leaders in Aleppo. And yeah, I don't know also one in which in Uganda they asked us for help to on how to use a PCR machine that they acquire for for performing tests, and we were able to find one person that were that was able to help them with this with this training. So yeah, I don't know they have many many examples what are you a fun so give me one favorite. So myself for simple I coordinated one that was very interesting it was a translation. So there was a group that they was doing a poll to track the spread of coronavirus through polling, and they wanted to translate it to as many languages as possible. And then the way we set this up was that completely distributed so we, there were like 3000 volunteers who were willing to collaborate. So what we did was we set up many copies of the document to translate one for each language. We send them and we just said, choose your document and choose your language and then translate or review the translation of other volunteers. We had some rules of how to collaborate on this document, but it was completely self organized. And then we thought it was going to be a mess, but actually it worked amazingly well and actually to give an example so we have a system to when you need to send 3000 emails you need like a special system to send them. And it takes a while it takes maybe five minutes not much more. And in this case, so we started to send the email. And before the last email was sent some of the translations were already done. They were already somebody translated somebody reviewed. You could see all the comments of how suggestions of oh I would change this sentence in this way super polite, but everyone super enthusiastic. So it was a really nice experience because also we saw the volunteers interact in real time was really amazing. And in the end we managed to provide a translation to more than I think more than 60 languages. And the system is right now working is called Corona service, and they are tracking COVID in many, many countries. Okay, and so this is maybe the point of view of the coordinators, but Sarah has some videos prepared with other participants. So, yeah, so thank you for saying this stories they are great. So now we are going to search videos that we recorded in a recent symposium that we are nice about the science of collaboration. These videos are from participants. We have to cut some of them because for the sake of time. So the first one. I want to start sharing the screen. This is by Fran Robson, who is a plant molecular biologist at the University of Bristol and see in these videos is telling her experience as sorry. On social media, a friend shared this page on Facebook volunteer your time and skill. If you're a researcher not currently involved in COVID-19 research, you can help. And I thought, you know what, I'm sitting here at home. I'm a molecular biologist. This is something that I can do. So I sought permission from my boss who very generously said that I could have all of the time that I needed to help out, which was great. This is task 241 E as it landed on my email inbox. I'll just summarize for you volunteers were needed to do a literature search and summarize on the proofreading mechanism of coronavirus and research ideas including drug design were also welcome. Coronavirus is tricky. They can resist antiviral nucleosides by carefully proofreading during RNA synthesis and literally just snipping them out. So the research involved wanted to know more about that molecular mechanism in order to help him design better drugs to fight COVID-19. So the requester was while I'm also known as Billy assistant professor at in chemical biology and drug discovery at the Chinese used university of Hong Kong. So he's a medical chemist designing small antiviral molecules and he wanted molecular biology advice. So I was not the only volunteer. And in fact, what Billy decided to do, rather than just asking one scientist to put together a review into proofreading, he decided to ask a team of us coordinated by me and make it a really collaborative effort. So Billy was joined by his very talented research assistant, Kavija in Hong Kong. And then we were also joined by Palmer Russia's lab in France. Her PhD students, Clement Parry, and from Vietnam at Khan Lee. And from Turkey PhD students in him, who was also getting to grips with having to do online tutoring for her for her colleagues. And from from the USA, Peter, who was normally working in France, but had found himself stuck in California when all flights were canceled. So, how did we, how did we go ahead. So, fortunately, my partner Rob, shown in a picture here is a software engineer, which is very handy because I am a molecular biologist, but I'm not very techie. And he suggested, okay, why did you do this using Google Docs might sound simple to some of you, but I've never used it myself. And it was absolutely brilliant. Because in real time, we could all edit, add comments, questions, answer questions, and also use an online reference manager, to take a pile, which again was brilliant because it didn't matter where we were in the world, what the time zone was, we could just put all of the references from our research into a central dropbox. Everyone could access them, download them, read them, and then add them to the growing document. So we decided we did divided the document up into chunks, introduction, middle bit, techie bit at the end, and we all got going. We also use Skype a lot more than I had ever done before in my life, which was zero times. And, and we had regular meetings. I, because I was a coordinator, usually I would choose a very nice time for Bath UK where I live 10 in the morning. But that sometimes meant that poor Peter got the short straw and was, was Skyping with us at 3am. This is just a screenshot of one of our meetings with just a few of the eight participants. And I think we're probably trying to decide if we should give ourselves a deadline. So to cut a long story short, we put together all of our research and published our first review coronavirus RNA proofreading molecular basis and therapeutic targeting. And largely due to the, the ambition and determination of Billy, who was our requester, who would not take no for an answer and wanted this review to make it into one of the top journals as high as possible. We managed to publish this last September in molecular cell, which has got a really high impact factor. And as of today, we have Google scholar citations up to 99. And as you can see from the matrix, there was a lot of interest on social media, something like over 140,000 shared likes and comments so far. So, there were quite a few of us, and it became clear when we were writing the review that we actually had enough material for two reviews. And so we ended up publishing a second review which just came out last month. Again, in a very high journal, thanks to the determination of Billy trends in biochemical sciences. And we published our review called nuclear acid based technologies targeting Chrome viruses, things like antisense oligonucleotides, small interfering RNAs, CRISPR technology, and of course something very current messenger RNA vaccines. So this has only been out for officially just over a month, very high impact factor and we've already got a few citations and again a lot of interest and shares on social media. Also, we were really fortunate in making the front cover of trends in biochemical sciences last month. And that's because a friend of Palmer, an artist called Luigi Russo put together this beautiful painting entitled messenger RNA COVID-19 vaccine. And it shows a robot and locking the key to messenger RNA vaccines and handing it to a medic at the frontline. So obviously all of this has been personally very satisfying, good for our CVs, and hopefully especially for the younger researchers involved, the PhD students, great for their career. But the reason we obviously got involved was so that we could hopefully make a difference to the pandemic. And hopefully these two reviews will help people to navigate the frankly vast body of literature that's out there, both historic literature and current literature. And hopefully that will point them in the right direction to making some more breakthroughs. Hello again. Now I'm going to share another video by another person that participated in this initiative. It was a van San Parisi. He is the director of research of a micro microbiology lab in Bordeaux, France. And in this video, he will be sharing his experience as a requester. One second. I'm losing that. Sorry. Okay. I'm having a problem to share this screen. Wait a second. I'm sorry. I don't know what's going on. Guys, I'm having a problem. I don't know if you prefer to. I don't know what's happening. We can continue perhaps Alfonso, could you maybe follow with the. And I can do later. Okay. Or we can. Okay. And. Okay, so then I will continue. I'm sorry you couldn't see the, the other video from a requester. So actually, there's a question from Andrew Miles. Thank you very much for the question who asks if we can do the same thing we are doing for coronavirus, but in more in general, in other fields. And I'm going to answer this exact question for the next five minutes. So let me share my screen again. Okay, so initially, as usual, we thought we would not be able to extend this to any other field. We thought that our system was something that could work during the pandemic because of the exceptional situations, but it was very efficient because you had. A PI like me, for example, acting basically as the secretary of another researcher. So this is okay because we all want to fight the virus, but in general, it's not going to work. However, we were wrong. And we discovered that it can work in general. And this is because there were two surprises that we did not anticipate. The first one is that this image represents what we expected people were going to ask, which means boring and mechanical jobs. We expected people to want other people to transcribe data from a notebook or to do a search or this kind of thing. But what people actually wanted was very technical, highly skilled help. So for example, in many cases, somebody was stuck with the protocol, and they wanted to chat with someone who could help them troubleshoot the protocol. And this is extremely efficient. It saves a lot of time, maybe a couple of hours back and forth in Zoom can save you three weeks of trial and error in your lab. So this creates a lot of value for the scientific community. The second surprise was that we expected that the requesters would be very happy, and because we were helping them with the project, and they were very happy, but the happiest people probably were the volunteers. And you might think that it's because they were helping in the pandemic, but this was only a small part of the story. There is a very strong effect which is the following. If you're a scientist, you will know what I'm talking about. So when you're a scientist, you have your set of skills that you have worked on for years. Everyone in your field has similar skills, and everyone in your field is working on very hard problems and making very little progress. So basically you go to the lab every day and you fail. That's your life. Then one of these skills, you take it and you put it in a different field. So somebody needs this protocol that you master, and with very little effort on your side, you help this other person a lot. You see how your contribution makes a big difference. And this is something that feels really great. This is something that makes you feel, makes you have a real impact in the advancement of science. So even though you are not getting a direct reward in that case, this from a personal point of view is very reward. Because of this, we have found that the system is actually very efficient and it can be expanded to other fields because of four basic ingredients. The first two are the ones I have mentioned. The request or at least part of the request are what we call high value requests that are very efficient save a lot of time to the community. Because of this high value, actually the volunteers are incentivized to help because they see that they have it really useful. And then we find that it's a great way to make useful contacts. So in science, we are very encouraged to work on networking and make contacts that are very important. And usually the way you do this is you go to a conference, you go to the coffee break, you try to talk about the weather with somebody, at least for me, it's one of the most stressful parts of my job. And this is a much more fun to make contact because you will be put in touch with somebody who needs your help, you will discuss something very concrete, very focused for whatever time, and then you will make a friend. As we mentioned actually in some cases, this develops in a long term collaboration and you end up publishing a paper together like in the case we just saw in the video, but even if that's not the case, it's a great way to make a contact with another side. We are actually already used in our culture, it's very common to donate our time to the community. We do this whenever we do peer review. And if you think about it, we only donate our time to control each other. So we review each other's papers, we review each other's grant. And I think we just need to change a little bit the idea and get used to from time to time donating a little bit of time just to help each other to help another side. For this reason, indeed, we have already decided and we have already implemented an expansion. So now we are accepting requests from any field of thought, anybody, any researcher can make a request and we will find an expert to help them. How is it going? So the system works just like it was working for COVID, it's working now. Thanks to the fact that the volunteers are still enough, they still show enough engagement. This shows that, as I said, the incentive for the volunteers is not so much a problem, they are, the incentive is enough even in the current situation. We have, however, important challenges and several limiting factors. The first one is organization. We, in the core team, we are all volunteers, we are scientists that have our own job and our time is very limited. We need more funding, basically, because we have not been proactive enough until now to get it. And surprisingly, we are having less demand than we expected. So we are giving a service for free. And still, the limiting factor is not the response from the volunteers that, as I say, is excellent, is the amount of requests that we are getting. And this could be because our service is not as useful as we think, but the fact is that we have several requesters who are coming back, they make one request, and then once it's finished, they make another. So it's obviously helpful for them. We feel that we are having trouble communicating what we do and sharing with the world what we are. And this is the main challenge that I think we will be discussing over the rest of this session. So this is all on my side. I'm going to stop the sharing and thank you very much. I'm going to try to share one time policy video now. Because we think it's very interesting to do it. I think it's working. Is it working? Okay, I will mute myself. So thanks a lot for inviting me for this meeting. I will just start by introduce myself so I'm working in the microbiology lab. And we are mainly interested in pathogenic genome mobility, including retroviruses replication and integration of the retroviruses genome into the host genome. And when we met as a COVID pandemic, we were working on a different kind of models, including pathogens from humans like retroviruses, bacterial and eukaryotic and prokaryotic transposages. And we developed some models for biochemical assays, recapitulating the activity of the enzymes involved in the process, cell biology approaches, hemacology approaches and structural biology. So when the first lockdown came, what we wanted to do is to participate to the fight against COVID, of course, and to share the different models that we developed in the lab, focusing on the entry stage of the virus, which involves a complex between the virus, spike protein and the H2 receptor from the cell. And the idea was to monitor and quantify the interaction and develop further system to select molecule able to disrupt the complex. So that's why we, we asked to the crew fight network, and we asked some requests. The first one was to get some material, biological matter to start the project. So we asked for expression, plasma, then JC Bloom from the French answer to us and send us all the plasma required for the expression of the proteins. So that is the expression of the protein. This led us to develop in humans, different models for monitoring the association between the two protein using biochemical approaches like perlton alpha screen blitz, and also several systems like viral infectivity, I says, and Sarah interaction essay between the two proteins. So this led us to start a project for screening drugs against this complex. We started to start a virtual docking, asking a new request to provide and Sergio Sousa from Porto and swear to us and developed an individual or in silico model to monitor the association between two protein, leading to monitoring, docking monitoring systems and allowing him to identify jugable sites within the complex between the two protein. So this allowed us to start virtual screening of molecules. So more than 70,000 molecule have been screened led us to select less than 10 drugs and finally two drugs that were able to inhibit the infectivity of the various in different kind of cells. We were ready to answer to new requests based on the different tools that we have developed and we answer to several requests from the qualified coming from different lab from Marseille, Bordeaux, but also Abidjan in Africa, and also in Asia. And so lab asked us to test different kind of molecule using all the systems that have been developed and this led to the identification of new drugs and especially to one that are currently under studies and we're fun to be able to block the entry steps of the virus in different kind of cell, including the natural cell pulmonary cells. So, as you can see, and I hope I convinced you that our interaction with the qualified peoples led to a very efficient collaboration in few months less than one year we were able to develop new tools thanks to the interaction with the different teams from qualified. We selected drugs from about 70,000 initial compound and several manuscript are being published or are under preparation. And more importantly, we have one or two patterns under progress, progress about source new molecules that can be efficient on the verification. We are currently answering to additional requests based on the model that we have developed thanks to provide interaction, especially for studying the entry stage of the bar replications or protein protein infestage. And also we're currently working on bar entry. We are fully ready to work with all the additional people that could be interested in the context of the new profile network. So, again, thanks a lot. I just want to mention that in addition to new information about bar replication, we also increase our own collaborative network additional group have joined us with very specific skills that were not available before in our team like physical physics, but also in silicon civilization, and of course, people that are experts in current of our application analysis and study. So again, thanks a lot and we are ready to answer to new requests based on our skills and expertise. Thanks. Okay, so here I'm back again. Now we are going to pass to the second part of this session. So I give floor to Pasco who will tell us about how is our current situation and our current. Thank you, sir. Okay. So I'm going to share my screen as well. And yeah, a little bit. So the next session will focus a little bit more into the challenges on new perspectives what we have learned so far from from our platform know and what I think there's a few things that that could be very relevant for for meta science. First of all, we have recently transition from crowdfied COVID-19 as Alfonso explained to a more general branding like crowdfied with we extended our collaborative model to any scientific area. And in doing so, we feel that the number of requests we are receiving are still far from what we believe we should receive as to be a real game changer know on this. So, which are the reasons for that. There are some obvious reasons know as someone that has mentioned in the in the chat. So crowdfied COVID-19 has a sense of urgency people was really willing to help. Well, now if we are open to any scientific question this is harder to find know indeed. It was a clear target. It was COVID-19. Now it's difficult to know what we are crowd fighting if we are open to any scientific area. And of course there was a perfect timing for for COVID-19. And now I would say that even people is a little bit tired know people wants to rest a little bit of science all of those involved in COVID-19 as well. But I think that there are some questions that go beyond these obvious reasons and this I think what could be interesting for meta science. First of all, the credit. So nowadays in science we are living a very hard competition this is this graphic illustrates how the number of PhDs awarded increases a much faster page than the number of faculty positions so if you're thinking on working in academia you will experience a very, very hard competition. And more importantly, how typically decisions are taken on who will get these positions are focused on a single item which is the number of publications so we are living in this published or various world, which with very, very bad consequences right the increase of predatory journals for instance the rise of misconduct cases. So we think that a very important thing is that every little aspect of the activities of scientists should be visible. We should try to make these things visible. And from our perspective, the thing that we are trying to do is the most basic thing with we can think of is, well, let's report all the activities that our users are having in our platform. So we created this community in Senado in which every request is recorded with a document in which we describe what happened in the request we describe the people involved to give them credit and importantly, we are giving them a digital object identified with which they can, they can use in their curriculums. In addition, I think we are, we are looking a little bit forward and thinking we like this particular kind of model you probably know a stack exchange. And in this communities, this is the profile of one user and you can very clearly see the activity of this user how he's involved in different communities, the different batches that this person got for his or her activity. And we think that this is a very important mechanism to be created to people and we are considering the possibility of using this kind of profiles. In our workflow, as Alfonso explained, we have very different roles, and all these roles can be recorded every single activity is put that can potentially be recorded. And what we are working now with is more in the user interface technology to try to give credit in every single step to users and record it in a single in a single profile. The second question is the scope. What I mean is that in the beginning when we were crossing COVID-19, as you can see here, these are the numbers of the, the number of people involved in the different steps in red that are those related with the internal organization as you see since several people is involved in the same in several roles with less than 50 people we were able to manage 1000 requests or not currently more. With the help of tens of thousands of volunteers. Now when we transition to crowdfied, we were expecting increasing one order of magnitude this is what we would say that we are really a game changer or we are not at that point. Sorry. And we think that one of the reasons is that increasing the scope, we have reduced the feeling of belonging of our users. And this is also translated in the social dynamic the social dynamics that does not feedback as fluently as before the sense of community, perhaps is being lost with this increasing the scope. And so, we are considering now, perhaps to re branch the crowdfied into to redesign crowdfied using different different branches know for instance focusing on particularly urgent questions like climate change, or in a small community that may help us to spread the spread the the project in word to mouth manner, perhaps supported through partnerships of scientific societies foundations that will help us to feed these these communities. Another question is about engagement and scaling up. I think that when we when you're working in a project like this one that you feel that it's really important and that we tend to think that the things will happen naturally organically and that nothing else should be done. But think that uses spend a lot of time in front of a screen. And so, in a way we need that this interaction with their screens are interesting. And let me come back to this profile of a static chain and to emphasize that some of the of the badges that this person is getting are in a way fun know this person is a guru is a citizen pattern. These are things that, well, perhaps for a is not is too informal for an evaluation agency know, but they think that we could try to work in this direction and to start also to look at least for solutions similar in a speed have more formal that than this know but I think it's important to get this this kind of incentives for the users. And indeed, one thing that we are considering is that if we will be able to increase this number of requests one order of magnitude. The next bottleneck would be here in these steps in which we is one of the distinctive traits of crowd fight that these steps here the advisor and coordinator steps are done by scientists that we trust, we know them, we train them. We are doing their job properly, but it's heavily centralized. So, we could transition to a model in which we have just this final step, let's say a checkpoint of quality, which is centralized, and we could try to transform these two steps here in which in a way to use volunteers, as well as advisors in a self organized How could we convert helpers into advisors. One possibility would be to create peer to peer games. So, since volunteers are those that are answering to our requests, we could use them to evaluate the request themselves in a peer to peer manner. In order to build a consensus answer, then a comparison between the answers that everyone has provided and the consensus answer, give us a very easy way to evaluate its, its volunteer participating to incorporate their responses and to give a score of the importance of of their of their responses and in this way increase their reputation. So we think that this could be a way to scale up and also engage users in this kind of of platform. And to finish, I would like to just mention one of the of our main challenges which is funding. When we started, we were saying, well, we have to do this, it's important, who cares about funding, and indeed all craft members are volunteers. Most of our members work on public institutions that is not trivial because there might be incompatibilities to do other stuff. And it's a nonprofit organization registered in Europe, which is somehow relevant as well because in Europe, I think we do not have the culture of donations that is happening for instance in the USA. So indeed we do care because if we want to scale up, the organization is running for costs that might be unaffordable if we grow, and we really think that professional stuff is needed. And we are doing the basic things. Of course, we are asking for donations we have in our website you can donate. We also have merchandising you can you can buy and we are asking for public and private grants. For instance, our main funder so far is the European Open Shorts Cloud. And still we think that we need more funds for the long term. So we wonder now if it is a free and independent service be able no indeed Wikipedia could be one of the models that we would like to follow but it might be an exception more than a rule. So should be should we perhaps transition to a hybrid model like many open source software companies in which part of the services are free and part have a cost. So these are these are things that we are also eventually considering. So as take home messages. I think that the four things are credit I think it's important to make visible in science invisible help the invisible activity. The scope if you have one organization of this kind and you want to have a nice social dynamics perhaps sometimes a small community is better less is more. The scale up your organization perhaps social interaction and engagement are important questions to consider. I'm funding this is an open question for us as well that we will be very happy if you can give us feedback on that. So if you want to help us. Of course you can sign up as in crowdfight as helper you can make a request you are very welcome. Please tell people about us if you can give you invite is to give a talk, donate, give us feedback and ideas I will give you our slack channel in the chat as well if you have further questions that we are unable to answer now. And I will finish to with a big thing to the core team. I can tell you that all these people has slept very few hours during the pandemic. And a big thing as well to the crowd fight helpers because they are our community and without them we cannot, we cannot perform our service. And thank you for your attention. I will stop sharing now. Yeah, we'll be very happy to answer your questions. Yeah, so there are a few questions I'm going to read them and you can choose who answer. Okay. So the first one is, did you have any pathological or otherwise destructive request or and how did you manage that. So maybe I can take this one. So, in general, I would say very few that the, the general impression I want to emphasize is, is super positive. Requestors are usually extremely grateful at the end. Actually, this is one of the things that is best about what we're doing the feeling that you get at the end of per request when everyone's super happy. We have had a some case where there was a conflict. It's well below one person. It's very rare. And in these cases. So what happens is we have very little actual power because we are so we basically make contact between people but we have little, little power after that. And what we have decided to do, which I think is the best we can do is to try to set the expectations as clearly as possible. So we, we are trying to clarify a bit more what is expected from the interaction what kind of ethical rules should be followed and so on and so forth. We always emphasize people at the border request and the volunteer to discuss the details of the collaboration in advance but besides that we try to set their expectations. And we are also trying to surrey more actively so to ask actively whether there are any conflict. Sometimes not directly but make questions that would make conflict visible so that we are aware. And then we are also considering ways to in in the case that actually, yeah, it might arrive a case where we want to basically ban a given requested because we think that their behavior was unacceptable. This is very hard to do because who decides. But actually, we are considering for example something which could be agnostic. In the sense that at the end of the request, we send a little story both to the requester and to the volunteer, what how it was, whether the request was sold, and so on and so forth, and how kind how nice people work. And what we are thinking we could implement something where if a volunteer say that you were not good, then I'm sorry we don't judge, but you cannot make another request even that the service is for free and this is more like something you get than a right or whatever. It would be okay that if you don't get a very good rating, then you cannot come back. This is not implemented yet these are just ideas, and we haven't. So, we are, we are working on them but as I say, it's very rare to have any conflict in general the response is extremely positive. So we are implementing this but yeah, it's not not yet finished. Thanks, so we have a ranking of questions with the likes so I'm just following this. So, Rose Franzen is asking what has a long question, while ensuring documentation of the work done is in is an important step. I think perhaps one of the bigger issues is that oftentimes the invisible work, especially if it is coordination related data management library science work. It's under evaluated with within academia, not seen as a valuable scholarly contribution, at least in terms of prestige status promotion, etc. Do you have any thoughts on that on whether or not increasing credit documentation alone will be enough to increase incentivization. Right. I think it's completely it's completely right this this question it's what we are offering is what we are suggesting is that documenting is a necessary condition of course it's not sufficient, because the evaluation agency should start considering all the kind of activities. I think, little by little this is changing for instance in the European Union, at least it's more and more important to have outreach in in in our scientific activity. And what we hope is that if we make visible these invisible steps. This will start having more presence also in the city so of people and people will start putting this forward. I think this is something that I don't know if in the short term perhaps it's just the drop that you know, balance towards one person or the other one. But we hope that at least in the long term this will change our vision of what excellence means, which by the way there is a session on that after our session that might be very interesting to follow. Roberto. So we have another interesting question by money a baker. She's asking, can you compare contrast crowd fight with other ways that researcher could ask a stranger for help, like protocols.io Twitter called email various targets like conversation. What else. Yeah, I could perhaps say a few words on this. I think that the mostly perhaps in important signature let's say of crowd fight is that is this steps that are centralized that there is people behind looking for the right expert for you so this will not happen. But of course you can you can have an idea of who is an expert and you can write an email to this person and this person may ignore you and may ignore us as well of course, but the good thing is that with the help of the helpers. We have a very large variety of experts that may help this person and sometimes the first one does not work that but the second one will work. Another interesting feature I think is that this is happening in a way it's private. So there is people that it's perhaps a little bit more reluctant to share their research or, or to ask for help. And this is an easy way to do it. I think it's a very, very convenient in this way. I mean, I would like to add a thought to this, just because and so the question. I mentioned two specific ways, and we have been thinking about them so Twitter, that's great many people get a lot of good help there, but it correlates a lot with how popular you are. So it's great for famous people, but if you're not famous, that's worth one. And the other is called emails, which means you just send an email to someone who doesn't know you and you are for help. We should be doing this. So sometimes when a request is hard, we do this, we send an email to someone who doesn't know, who doesn't know us, and people actually respond and they are very helpful. So here is a question of culture, people are not used to just asking for help. And even if this is the only thing we do. Like, change this culture, I think we will be successful. It's just, sometimes it's just changing the mindset. It's not that we are doing something particularly special. Great, thank you Alfonso. We have a comment, and I think you probably want to comment something about that. It's by Andrew Miles, and here this is more of a comment. A theme of this conference has been the need to improve the methodological rigor of science, possibly by putting subject matters part into contact and collaboration with methodological experts. It seems to me that there would be a huge market for a platform like Crawfide that could facilitate this connection. So feel free to comment on a comment. And I completely agree that this is true. Actually, I think that a little bit is what happened in the video of Vincent Parisi. He was seeking for a computational biologist expert and he found it through Crawfide. But let me tell you one example we are considering as well, which is in a way similar to what this person is commenting, which is Crawfide fake news. So Crawfide would be as well a good way to look for experts that determine whether a new that is willing to be published is scientifically sound or not. So very last question from Olavo. Can you give a general panorama of the main kind of scientific skills that are currently included in the request just to give an idea of how community specific the platform at stake has been? Yeah, so I would like to emphasize we have no limits to any field. We accept requests from any field. We have to volunteer from any field, even non-science. But it's true that we have a very strong bias to biology and especially would say molecular biology and bioinformatics. Those have been, I think, the most, maybe 80% of requests are in those areas. Of course, virology, etc. But yeah, I would say those are the heaviest. But again, every field is welcome in Crawfide. Great. So I think we don't have any more questions on this time. So we want to thank everyone for being here. Both of you for giving this great talk. And I hope everyone enjoyed as much as I have done and probably you. That's probably all. Maybe see you soon in another meta science or science of collaboration meeting. Thank you very much, everyone.