Thanks very much for coming. My name is Alex Storr and I'm the Director of Data Analytics and Research Computing at the Stanford Graduate School of Business. I'm Christina Maymon. I'm the Research Data Services Lead with Research Computing Services at Northwestern and we're here to talk to you today about Data Science Support Service. What it entails, what why social scientists need such a service, and what we've learned about making this type of service successful. So to start with, we're going to talk about an example researcher who we're going to call Sarah, and she has a research goal. She wants to compute political polarization between parties in the North Carolina State Legislature before and after the Civil War. She knows that you can compute political polarization if you can get roll call voting data from the legislature, right? Who voted yes or no on which bill? It's a standard political science methodology. So she sets out to get this data. After a good amount of research, she finds it. It's in the records of the State Assembly are each year is in a separate PDF and just sort of this long discourse of everything about the legislature for the year. She looks through the files and see that the actual voting data, interspersed in a lot of text, will sort of be a paragraph with lists of mostly last names that were the people who voted for a given bill. So the data she needs is there, but what she needs is a spreadsheet, right? To feed it in to get the political polarization scores and figure out where each of the members of the legislature fit on a political dimension. If Sarah and her RA try and extract the votes from the files into a spreadsheet by hand, it's going to take a really long time, right? They're probably going to make a lot of mistakes. And if she outsources the work to a transcription company, usually talking about about five cents per vote per line in the spreadsheet. So that's $50,000 in the case here. So a researcher Sarah knows that there are ways to extract extract text from documents like this, but she doesn't really know where to begin. She's got a background in quantitative methods and knows how to model the data, but her programming experience is limited. So should she take the time to learn how to do this herself? Should she try and find an RA with the technical skills? Should she get a grant to do the transcription? Is this project even worth it when she doesn't know what she's going to find and clearly it's going to take a lot of time and effort in some form? And this really is the problem that we're here to talk about. Working with big, diverse collections of data is just hard. It's hard in a general sense, not just for Sarah and not just for social scientists. It requires specialized skills to do well. Social scientists aren't the only ones who need support. There are researchers in other fields who need data science support as well, but social science is really in a transformative period where there's a lot more data available than there ever used to be, but there's still really big barriers to incorporating that data into research. Interesting innovative projects are not getting done because of these technical obstacles to incorporating the data into the research. And this fundamentally is why we need data science support available to researchers as a standard service. If your institution wants to support innovative, impactful social science research, social scientists need a reliable way to get assistance with the challenges of working with the data. So what does this mean? First, what do we mean by data science? It's a term that get used in a lot of different ways. Like a broad definition of data science that includes all phases of working with data. So starting from data gathering, preparation, exploration, includes collecting the data, getting into the right format, starting to see what's there, what does it look like? How messy is it? Data representation and transformation includes deciding how data is stored and how different pieces of data are related to each other. This can include deciding what type of database may be needed, determining the database schema, but it can also include deciding how to encode or represent information. So if you have text data, how are you going to transform that into variables and features that you can summarize and model? Computing with data includes a wide range of activities from writing scripts to accomplish other tasks, to choosing the appropriate computational resources to do the model that you're doing, to developing software packages to share your algorithms or your functions, and you often need multiple different programming languages for different parts of the process. Data modeling includes both traditional statistical modeling, where you care about the estimated coefficients and what the model looks like. And it also includes predictive modeling, often usually referred to as machine learning, where you care more about the outcomes and how well the model can predict on a separate data set. In data science in general, we'd also include things like data visualization, building tools to work with data as well. So we have the data science part. What does this look like as a service? Thanks, Christina. So so if we go back to Sarah, and we think about what what it would be like for Sarah to come engage the service on her North Carolina voting project. We'll we'll see how it starts. So first, she has to get in touch with us get in touch with the service. So in this case, she might email us, she might fill out some sort of intake form, so that the service and the people who staff it can know what the request looks like. Then the project will get assigned to somebody. And how that works will depend on how many people are on the team, what their skills are, and how busy their plates are. Then the staff who's assigned to this project will write a clear description of their engagement in the project. So this way, there can be an understanding of what the service will and won't provide a general timeframe and an indication of what's still needed in order to start. So now I'm going to walk through some of the steps for this particular project kind of under the hood to see what had to be done to get to the finish line. So we started by writing code to download these documents. And on the North Carolina State Assembly webpage, it's a little bit tricky, so you have to sort of reverse engineer how to download those documents. Then once you have them, you have to figure out how you're going to store them. So if you're downloading 100 documents, it's kind of no big deal. But if you're downloading a million pages, you really want to think about how they're going to be stored, so you're going to be able to do some sort of computing with them. Maybe when you download a million things, they're not all going to download successfully. So you have to keep track of that and make sure that you're ready to have a complete data set to work with. Then once you've downloaded everything, you have all of these pages, but not every page has the data you're looking for on it. So you have to identify the pages with the data and then which part of that page has the specific roll call vote text on it. But then that's not enough either because you have to kind of create a clean data set in this rationalized format. So Christina showed you the kind of snippet of the roll call votes, but each one of those names is not actually a unique identifier for a legislator over a hundred plus your period. So you have to build kind of a database of what's in this data in terms of both the votes, which were the bills, the legislators, who they are, what sessions they were in. And then once you have all of that data cleaned up, then you're able to use this legacy FORTRAN software that's used in the political science field to compute this political polarization. So in this case, you have your clean data, but the way it has to be represented to go into the software is sort of non trivial, especially if you're not used to working with some of these more esoteric data formats and you don't like reading documentation for FORTRAN code. So once we get to the end of this project, the service is going to deliver a professional summary of what they did. So that's going to include data. It's going to include code. It's going to include the results. It's also going to highlight any potential issues that came up along the way. There might be some things that were mistakes or things that weren't able to be downloaded. Reasons the data set isn't perfect. And then there's an opportunity to give feedback on the service to make sure that we're able to provide a high level of support. So for this whole process, when engaged with us, this took about 120 hours of human time. So there was a lot of work in terms of the computer doing stuff, but that sort of doesn't count. And if you compare this to using undergraduate RAs or outsourcing it, we're able to provide this sort of data set a lot more quickly and probably more accurately and definitely for less money once the service is employed. So we've talked about Sarah's case and why in this one circumstance it's valuable for her to engage a service. But in general, I think it's really valuable for such a service to exist. So we're going to kind of step through some of these reasons and make it really clear why we think that is. So one value to the institution of having a service is that we can tackle similar problems again more efficiently. So instead of having dozens of researchers all solving the same problem on their own over and over again, we have staff who are building expertise, we're using less time overall to do this and using less researcher time to do this when researchers are supposed to be teaching classes and writing, you know, grants and writing papers. Alex and I work at different schools, but when we discuss a project, we can quickly see the same types of needs and challenges that we've seen in other projects before. They have common components. So for example, one step in a North Carolina project here was the data gathering stage. And this is a general class of problem that includes web scraping, collecting data from APIs, or systematically downloading data from databases. It's one of the most common types of problems that we help people with. These projects require similar skills and steps regardless of the subject matter. So we've applied these skills to diverse sources of data, ranging from financial filings about public companies to movie reviews to newsletters that members of Congress email their constituents. They all have the same form of a problem. Similarly, another step in our process was converting text to data into a format that the researcher can analyze with the methods of their field. So this involves identifying the relevant parts of the documents with the information of interest, extracting and standardizing that data and then converting it to the format the researcher can use. Again, it applies across many different types of data. There's always challenges, unique cases, specific details, but understanding the general form of the problem and the likely issues, steps you have to go through, makes it more efficient for somebody who's done this type of problem before to tackle a similar one in the future. Right, so if you have this sort of high performing professional service, you can really expand the scope and ambition of the researchers and what they're able to do. So we can come up with these effective solutions and we can do it pretty quickly and we can deploy it on large data sets. So what that means is that if you are trying to code Facebook ads, for example, instead of hiring an undergraduate to watch Facebook ads and code them for are there people in this frame, you know, does this have Donald Trump in it or not, you can instead download all of these ads, figure out how to send it to a cloud service or do some sort of machine learning on it locally to extract the information you need from all of them. So it can be much more efficient. And then, of course, when you figure out new things as new challenges come along, you've kind of integrated those skills and you can deploy them really quickly. So Christina mentioned briefly that the job of social scientists is not to be data scientists, it's to be social scientists. So there's all sorts of things that, you know, a good social scientist has to do. They have to keep up with the literature in their field. They have to, you know, advise. They have to be parts of committees. They have to kind of be good scholars and part of being a good scholar is not going to be debugging FORTRAN code unless that's your field of interest. So it's really nice to have a service that can take this aspect of your research problem that maybe isn't that relevant to the substance of what you're researching and then extract the data in a way that makes it easy for you to pick it up and move on with. Having a professional service also means that the researcher doesn't have to try and hire a magical RA or postdoc or staff member who knows all of the necessary skills for their project. They don't have to become an employer, right? They don't have to write a job description, advertise it, interview and vet candidates for skills that they themselves don't have. It's a difficult task. They don't have to manage them when they show up. And they don't have to pick up the pieces when that student or postdoc leaves in a year or two and try and figure out how to transition the project to somebody else. You know, they're engaging with a service with reliable experienced staff instead. Beyond not having to hire people, it also means that researchers can get help with a wider variety of projects. It doesn't have to take the form of a project where I need one person to support it. It can be a smaller project where all of that overhead just wouldn't be worth it to engage with the project. It could be a larger project where multiple people might be needed over a short period and that might make a difference. Yeah, so we've talked about why having a service in general is really valuable, but I think it's also really useful to have a team. So let's kind of walk through some of what we see as the valuable pieces of having a whole team of people rather than just an individual. So by having more people, you can have people coming from different backgrounds and perspectives. So part of that is that you might bring different kind of technological expertise. You can have people who are graded optimizing code. You can have people who are wizards at GIS stuff, but then you can also have people that come from domain backgrounds that can prove really relevant. So Christina was just talking to me about a project where she had to collect Russian politics information. If there were a Russian scholar, that would be super valuable and obviously you can't get everybody, but if you have a team of people that kind of broadens the scope of what people have seen and what they're able to do. The other side of having one person not to know everything is that there isn't just one person who knows everything. You know, what happens if that one person working on your project is sick or leaves or is just busy when you have an entire team supporting the service and you have procedures in place for documentation and sharing information about your projects that isn't as much of a concern. Having a team also means that there's people around to collaborate with and get feedback from. This is really important as a data scientist, you know, working in an office all by yourself. You can get stuck on a problem for a good week if you don't have anybody else to work through it with or to see what their experience has been with a similar issue before. This leads to, you know, higher quality projects and also more efficient because you don't have people just getting stuck with a problem on their own. When there's multiple people in the same role, this also usually means that there's a position for a team leader, a manager. Some of this is just purely logistical benefit, right, of having somebody who is managing the folks directly and they aren't working for the researchers. But it should be more than that, right? So a good manager is going to make sure that the team is pursuing the right strategies, they're making smart decisions about the project, they're going to ensure that those people are developing, that they're getting the technical skills and the professional skills, that they need to provide better service. And there's going to be somebody who is developing networks and relationships with the university, right, who's connected to other services and people and who's looking forward to make sure that the service is offering, what the needs of the researchers are, is they evolve. Okay, so we talked about why the service is valuable, why it's nice to have a bunch of people on your team that are that are able to staff this. But there are still lots of questions about how it's going to be implemented and this is going to vary on a number of important dimensions. So, so there's a question of what does this service do exactly? So you can have, you could have a service that trains people to try and elevate skills where you have experts in certain things, you could have short consultations where people can come and ask for help or you can be part of a project for kind of a short period of time, or you could be on a project for a pretty long period of time and all of those fit into the scope of this kind of professional data science service. In terms of who the clients are, there's questions about do you help every single person at the school? Do you help students who are having problems in their classes? Do you help graduate students who are working on their research projects? Do you help postdocs who are really committed to a single project for a long period? Or do you only help faculty members? Furthermore, there's questions about who you want to make this service available to in terms of domain. So it might be available to one department, it might be available to one school, or it might not be bounded by that and it might be for a whole discipline. You could imagine a GIS consulting service which is available to everyone with GIS problems, but isn't just for somebody in the geography department or something. And then of course you could make it general, available to everyone at the entire university. Then there's of course the question of who pays for this service. And there are options here. So it could be centrally supported. So somebody at the library or in research computing or in some or other organization could foot the bill for this. It could be a fee for service model where you do charge back based on the length of your engagement to the researcher. Or the service itself could be written into grants to help support the service over time. So then you have to find people and there are questions about who those people are going to be. So they could be professional staff members who work full time on this sort of data science team. Or you could engage contractors for short periods if you have to expand what you need to do. Or you could have students who are involved, especially for these short term consultations as part of their growth and education and also to provide those skills. So I work at the graduate school of business. So for me, we mostly do consultation style projects. We want our projects to always have a clear beginning and a clear end. We try not to be involved in anything for all that long so that we can keep providing this consultation service rather than getting bogged down with the resources that we have. Our mandate is just to help faculty members. Of course this gets really fuzzy when faculty members say oh yeah yeah yeah help my graduate student it's my project. That's a different question. And we help only people at the graduate school of business. So there are 125 faculty members at the GSB in all fields in the social sciences. So it still feels pretty broad to us. But if you have to support the whole university I think it feels pretty narrow. Our funding is centrally supported, one of the benefits of being a business school. And then in terms of our staff we have almost all professional staff with some temps and outsourcing that we do when there's some extra data collection that needs to happen or something like that. So Kellogg Northwestern's business school has a research support department similar to that Stanford business school but I work with the university wide research computing group which is part of IT and we provide support to the entire university. Because we are providing support across all of these fields and from students to faculty our model looks different. Like the business school we are medium and sort of long-term project support so anything more than about eight hours of effort for a rough cutoff is for faculty. But we do consultations and training workshops and online training for students postdocs anyone and again focused on researchers here because we're research computing so we're not helping people with classwork but anything from undergraduate students working on you know their honors theses through graduate students or faculty researchers are included. Teaching workshops helps us expand the number of people we can help with the limited staff and we find that we're in a unique position to be able to provide this training that's not available to the students elsewhere in their curriculums or in their departments. Because we're supporting a larger set of people we do charge fees for some of our longer-term engagements or specialized projects or ask the faculty to find grant funding for the project. And in addition to professional staff we have student workers who provide consultation services to other students as well. Right so both Christina and I have been the professional staff on these teams and we've also had the opportunity to lead some of these teams. So we want to kind of walk through what we think it takes for this service to be successful. So the job itself is actually pretty hard. So there are important skills that you want these people to have when they come to work on a team like this. So they have to understand academic research. They also have to be able to collaborate with faculty members which I think is a completely different skill from understanding academic research. They have to have outstanding technical skills. They have to be extremely professional. You can't just be a you know a developer because you have to interface with people and they have to work with uncertainty and figure out lots of diverse things. So the job changes a lot. The projects are never going to be the same and there's going to be tons of new challenges to figure out. So I talked about these magic right people and we're not necessarily the magic right people but since we've done the job we'll tell you a little bit about our backgrounds. So undergrad I did a social science research major and a computer science minor. I've been programming since I was little but I got interested in social science at college and combined the two. Went to grad school for political science and I focused on political methodology and statistics. And then after grad school I knew I did not want to be faculty and I worked for a computational social science firm for about eight years before I found my way to this type of work. So I did my undergrad in computer science and cognitive science and then I got my PhD in computational neuroscience where I learned how to read a bunch of papers and work with faculty members and do lots of really esoteric data stuff, scraping brain websites things like that and kind of built my skills out of that and then transitioned into social science afterwards in this kind of role on one of these teams. So both of us have PhDs and it's valuable to have a PhD but it's definitely not necessary and we've worked with people who have gotten this research expertise either in industry or by doing RA ships with faculty members around other research projects. So there aren't that many people with both the data science expertise and the research experience that you really want to have in this role and these are skills that are in high demand. You're asking these folks to do complex work supporting cutting-edge research to essentially help faculty with the tough problems in the research that they can't handle themselves. So if you want to attract and retain these folks it's really important to respect them. I had a talk yesterday from Patrick here but about the turnover in research computing roles. They can be pretty high. So if you get somebody good you don't want them to go away. This means that you really do need to provide career pathways for folks with stable employment you know competitive compensation and opportunities to grow. This is not hey here's a one-year postdoc here's a one-year temporary position and we're going to try and get you as cheap as possible because those folks are not going to stick around. They're going to go get a data science job in industry. Moving beyond the people on the team we found that good project scoping and project management practices are really key to serve as success it's based on really obvious but we found that the fundamentals practices for this are often missing and they're not trivial to get right. The details here are a whole other talk actually that I'm working on but why are these practices so important? Properly evaluating a scoping a project is really important to avoid scope creep right? So research is messy and uncertain and goes in unexpected ways so scoping a project be willing to abandon a project and start a new one when the research direction changes is important. Actively managing a project helps build trust with researchers being clear about the communication what you need from them giving them the information that they need and then tracking project metrics helps you ensure that the service is going to remain sustainable and impactful and you can communicate what it is that you're doing and why the funding that you're getting should continue to come your way. Right so your service is also going to have to stay dynamic the work because of its nature is going to be continuously changing. So by providing this sort of service in a continuously changing environment it has to be super dynamic so your people are going to need time and space to figure out new things try new techniques read papers go to conferences learn what is state of the art and kind of cast a wide net and then of course the things that you are supporting might kind of shift over time as there's a new technique that comes out maybe people really want to do deep learning models and want to use GPUs to train them. So somebody on a team like this could figure out a good workflow for doing that and then reproducing it in the future. Or you know you want to do text mining but what if you want to do text mining on medical records suddenly it's a completely different problem where you'd like to be prepared for this sort of thing as it comes up so that you're not figuring out a year's worth of infrastructure planning as things happen. So being dynamic is super important. Furthermore your service is only successful if your people are going to use it and the only way they can use it if they is if they know that it exists. So you have to tell people what it is that you do and this is actually over and over again over and over again it's really pretty hard. So you have to kind of tell the story of what it is that you do because if you talk to a faculty member and you say oh yeah we help with research they're like whatever like I don't know what you do you're not going to help me and you walk through a project and suddenly the light bulb start to go off and they say like okay like this part is really hard you could just do that part and then come back to me oh that's that would be really useful. So you need to have a communication plan about how you're going to get the word out about your service. Furthermore you have to make sure that the communication is both ways. So a faculty members are like we are really looking for somebody to do XYZ and you guys aren't doing it it'd be nice to incorporate that into your service rather than just providing a static set of things over time. Then you have to be able to engage with other people. So outside of your own unit you have to be aware of the evolving policies and technology that are available on your campus. So if you're going to do text analysis on medical records is there a place on campus where you can store high-risk data is there a place on campus where you can do analytics on high-risk data that's really good to know if you're going to have to use it and you can only know that by communicating really effectively with the people on the ground in your school. And then if you guys saw the plenary opening session yesterday then you know that it's really important to be generous with your skills and I think there's a lot of leveling up that can happen on a team like this and you can generate lots of useful stuff. So useful frameworks useful code and it's nice to be able to share that back to the academic community in whatever way is effective for your group. All right we're near the end with a couple takeaways if you're going to remember a few things about our presentation. First is that there's lots of data in the world but working with it is hard. This is a challenge for social science that is only going to continue to grow. Also having a professional service for data science support is efficient and enables innovative research without clients getting bogged down in hiring assistants or learning specialized technology. Finally providing the service requires that you are attracted to retaining data scientists with research experience. These folks are looking for professional positions with opportunities to grow and use their skills. This isn't a service that can realistically be provided only by students or postdocs.


What's up everyone takedown here welcome back to another hockey card video today is another mystery pack from eBay video these I absolutely love getting because a lot of them give really great stuff for a really low price and last time I got from this seller I gots the Adam Foots Nordiques card, which was valued at $20 now this seller sells them at $5 per card So I paid $20 for four cards last time and I one of them was worth $20 So I'm hoping to push my luck today and see what I get from him if it's really nice stuff once again I'm gonna order more from him in the future. I'm really excited to do so so let's get into it up first is a jury secak Numbered out of 250 ice premieres Not really a fan of Montreal Canadians.  So I'm not really familiar with this player But that is still a nice ice card. I do like him Next up is a marquee rookie for Quinn Hughes That's really cool. It's my first Quinn Hughes card. I Do like OPG platinum Which is what this one is so I'm not sure what that is worth, but I'm assuming it's worth possibly more than $5 But that one there We have a young guns for Philip Roineck probably butchered that I do apologize not familiar with the player and The last card is once again a hit So I love getting from this seller because you get some really nice cards from him. It is a anthology Materials quad jersey for Steve Zerman. That is amazing. It's numbered even it's numbered out of 99 That is even better. So I'm definitely gonna have to look this one up I'm assuming since I had luck last time in the card that I got was a $20 card I'm assuming this is easily $20. It looks really cool. It's my first actually might not be my first quad jersey But that is a really really nice jersey card numbered out of number nine number numbered out of 99 Don't really collect this player, but that is amazing. So this one is staying in my personal collection. All of these ones are Actually, you know what? These two may not I might keep this young guns at least for now I tend to get rid of young guns if they start to not do so hot on the ice But this one here this ice premieres I don't really collect ice premieres other than it being a numbered card It has no appeal to me because it is Montreal Canadians might be for a giveaway on the channel or it might even be on my ebay at one point I am gonna be doing giveaways more and more throughout the year. So that one might be included in the future, but yeah Those were really really nice cards this seller I'm gonna try to leave them linked down below in the description. Check them out. These are $5 mystery cards. So One card per mystery pack. He pulls them out of his own stock So basically $5 for one card and you have a chance to get things like you just got Here and you've seen in this video. So I am gonna order more in the future Hopefully a few more to see if maybe he can work out a deal or other things Yeah, so I'm really excited. This seller is a great seller on eBay. Definitely recommend them go and check him out I don't know how to pronounce his name or I'd be saying it, but I'll leave it down in the description So you guys can check him out. I'm gonna leave this one here. This was a quick mystery card video Hope you guys enjoyed. I'm gonna see you guys in the next video. Please take care. Peace


Jignesh, welcome and thank you very much for talking to NewsClick on this eve of another historic march, hopefully it will be another historic march. All the best to you for that. I want to start with revinding a bit. May I ask you, what is slogan? But not much has been achieved, one would argue, in terms of the real impact of that slogan and in terms of actually getting land from the state. What is the new slogan for this phase Una 2.0 movement? First of all, I want to respond to this. Two or three aspects of this movement. For a long time, Dalit and Andhra and Dalit were in politics. They were not talking about material issues. At that time, after land reforms, you were regenerating, recreating, resurfacing. When land to the tiller has already become land to the tycoons, they were demanding land reforms at a very difficult time. There was such a complex and difficult question of land. That there was just a sword out of the field of forces. So in all these circumstances, it is very obvious that we did not get the same success. But we did get a very small success. When all the Dalit citizens, the people of the Dalit community, and the Dalit party, initiated long-term struggles outside of Gujarat. Right? So ask the people of the village, if we give this land to them, we could capture people's imagination. It created some vibes. People were enjoying it, people were also wandering around the world. But actual length struggles were not initiated. For sure, the slogan had a taste and people remembered it for long after it. And that is also why we are talking about it. But on the ground level, every Dalit has 5 acres of land. This was started after the Una lynching. There are two issues in this, Jignesh. First of all, the people whose families were lynched, why did they leave your indolence? Second, the lynching started with a very protracted way of talking about the land. Are you expecting to be able to complete it? Or will you be able to reach it to the end in this next 2.0 struggle that you are starting from tomorrow? Two questions. First of all, the victims of the Una lynching, they did not leave their indolence. They are with the MSP. They say it publicly. That is my distance from them. So if they come as victims, then always we are with them. In fact, on my own words, a legal matter of theirs is arguing with my advocate. And I will always be with them in their struggle. But if they have any political affiliation, then I am not for that. But I will always be with them. Second, the question of the land. As I said, it is a very difficult task. In the coming next 2-4 months, there will be no freedom in the next few months. In fact, the whole life will have to be given to it. Only then it will be successful. Because all the issues related to structural change, the entire dominance of the upper caste in the village, and the land ownership of the land, if it is dismantled, then the whole structure will be destroyed. But you just hinted a little bit that the Una victims and their families are now supporting the BSP. That is why their movement is getting a little helpless and is breaking down. Their movement is getting helpless and not breaking down. It is their political affiliation. You are taking the cost, but the reason for their helplessness was that the one who spoke to them, he said that Jignesh Mewani left Una and then went to Delhi and Bombay. And this lynching has become a very different issue for them. Then they did not return. So they abandoned us. I don't think they have said anything like this. But if anyone feels like this, or if they have said it somewhere, then my position is that we are not only for the Una Kandurans, but for the 4 victims of the Una Kandurans. For the Una Kandurans, for the Una, for Alwar, for Dadri, for Jamshedpur, for Lathihar, for Junaid, for Jati Adherit Hinsa, for Mobile Lynching, for Jati Neemuland, for the Dalit Mukti, then I believe that it is about the victims of the Una, but it is about the land reforms, the whole of Misaralpur, Rohit Vemula, then I will also work in Gujrat, I will also go to UP, Bihar, I will also go to the whole country. But now the Una movement, it was not just the Dalit movement, after today's program, it seemed that from a long time, let's not say left unity, but we were talking about the unity of social movements, and many people tried to do it. Today's image, it was definitely concrete, what is the vision of your next start of the rally? Is it expanding from the mere space of Dalit politics to a larger opposition politics? Is it a larger social movement, unity? How do you see this? See, the purpose of the freedom movement is the same, last time we went to Una from Ahmedabad, then Dalit was going to Asmita, this time it is the freedom movement, so our clear intention is that its first Dalit, Dalit dimension intact, let's talk about the questions of the Dalits, but not only about the issue of the Dalit, about GST, the way the business was beaten in Surat, their talks, the way the bullets were fired on the farmers in Madhya Pradesh, their talks, the talks of the mob lynching, the way of the free Dalit, it is not possible to pick up all the questions, and the people who ask the other questions, if they don't keep the Dalit, then there will be no revolution, so these are two interconnected things, so we want to pick up all kinds of questions, so that a larger unity, which is formed against this and the BJP, which wants to destroy the society, which wants to take out secular words from the society, and replace it with a Hindu Russia, and introduce a corporate democracy as socialism, so we have an attempt, that all those powers, all those people, who are ready to open a corner in front of the BJP, become a broad democratic front of that, which is not related to the electoral politics, but it is important to intervene in the electoral politics. There are two things, the immediate aim is to defeat the BJP in the Gujarat elections, or the larger counter narrative, the unity that you are talking about, to fight it more, to affect a social fabric. I mean, there is no such thing as launching a election platform, there is some coalition within the electoral politics, and then there is no such thing as voting for elections, but if you believe that there is some kind of indolence, and Patel invites me, then I will definitely go and talk to him, in the same way, we are doing something free, so I will also tell the Patel community people that this is welcome for you too, what can we say together, that when Mr. Modi said during the election of Vidhan Sapa, that he will make 50 lakhs of votes, what happened to him? Why should I ask him alone? Why should I ask him alone? Both the castes, I belong to the Dalit society, Patel's society belongs to them, beyond that castes, 6 crore people, we can ask together, what happened to that 50 lakh house, that should be a unity, then in that process, if a political platform emerges, or there is so much power, that if we can defeat the BGP, then it is better. So you are open for that? My second question is, all encompassing in a narrative, Patel also joined, and all the people came, who are representing this movement. But the fragmentation already exists in the Dalit movement. How will you apply it? First of all, the second is, even today, not many women Dalit representatives were there. So how will you encompass that? So those are also emerging challenges within the Dalit movement. Definitely, this is also a big challenge. We have been doing this effort since the last year, that even the sisters came in this position, but they are also searching for it. Although in our team, the length struggle of Saroda village, we had two sisters in it, Naina and Lakshmi. But I will admit, in that case, this is our weakness. In general of the Dalit movement, and in particular of us, we have to overcome this weakness. We can still hope, that in the coming days, more sisters are in the leadership position, and more are in the Dalit Andhra. And our team is connected to our struggle. When you say, the fragmented Dalit movement, I mean, the different Dalits, the fragmentation, how to resolve it? To resolve it, I have only one way, I think, that we should talk about material issues. Not just to solve the fragmented Dalit movement, but to bring all the movements together. To bring all the movements together. Because the only thing that caste knows, is to divide. And what can unite its class? If we make unity on the economic question, then all the Dalit movements can come together. One more question, Jignesh. Even from Saharanpur, a kind of Dalit movement, in the form of Bhima and Chandrasekhar's leadership, came down in the last few days. But the main voice of it, was more aggressive. And, I mean, it was also militant. So, in the freedom of violence, how many times it was done, when it was done, you also said, that we are not putting the aggression in a completely different way. We are not putting the violence in a completely different way. We can give the answer to the eight stones, if needed, we can do that too. So, in the freedom of violence, what is the scope of violence? What is the movement of the Bhima army? What is the movement of the Bhima army that comes at the point of freedom? Or does it come at the same time? Very fairly and objectively, the people of the Bhima army didn't have any violence. I don't want them to have violence. I am not talking about violence. I am talking about their approach and style. So, in the approach and style, if I display bravado, then people can see it but until I don't do violence, then how can I say that you are violence? Until I don't even slap anyone, until I talk I mean display bravado, you cannot say that I believe in violence or I will do violence. Jigresh, you yourself have said that the answer to the eight stones can be given. Theoretically, what is the kind of openness you have right now to this kind of a philosophy? I believe in this, I have never slapped anyone in my life. But I believe in this ideologically and clearly that the civil society is not in general, but the casteist elements physically hit the Dalits they should give shock treatment to the Dalits. They should give shock treatment to the Dalits. They should give relief, they went to court and put petitions. They have done all the things that they have done. Now, this is a practice. It was a shock treatment experiment to stop the train. So, some experiments like this should be done by thinking out of the box. But I will repeat on the part of violence that I really believe Dalits are a minority in the village. There are three houses in the village. In some villages there are 15, in some there are 19, somewhere there are 12, 6. The civil society becomes one. Practically in many places wherever it is possible and wherever the casteist elements physically hit the Dalits the Dalits should answer those elements in that language. To answer the last question in the next Gujarat election what will your movement play? We have a clear agenda. First of all, the demand of the land that we have been raising since last year we have got some small achievements and we should consolidate the Dalit movement. Along with the Dalit movement we should also connect the people of other communities. We should expose the Gujarat model.  Thank you very much Jignesh. Thank you so much.


to the next speaker, who is Coincenters Peter van Valkenburg. And I will speak for a few minutes first, because as executive director of the foundation, I am also the main point person for compliance and regulatory and legal issues. And we pay a lot of attention to these things because it does make a difference. We live in a real world where there are real concerns for any type of blockchain company startup organization. And every jurisdiction is different. We operate in a number of them. So something that I strongly believe people should take seriously for your own benefit, for your own protection, and to be able to further the innovation technology that we all do here, that we all support. We are not that organization that is more like a 501C4. We are truly a, you know, we're Swiss, Stiftung, which is closer to a 501C3, and we support research and development and the education and outreach that surrounds it. However, we still have to deal with regulatory considerations. And I've personally invited CoinCenter to come and make this presentation. So that we can address it and get it out of the way and enjoy the rest of the technical presentations that we have lined up for the next three days. So, Peter. Thank you so much, Ming. Thank you. So it is a tremendous honor to be sharing the stage with all of you. I didn't know you'd still be here. But honestly, the work you all do is incredibly inspiring to me, a humble, regretting liberal arts graduate who foolishly went to law school. But thank you. So thank you, Ming, and thank you to the Ethereum Foundation for inviting me. My name is Peter. I come from CoinCenter. I come from the government, but I'm here to help.  I am a lawyer, but I'm not your lawyer. The following talk is for educational purposes. Sorry. If you need a lawyer, you should hire a lawyer. Does this clicker work? It does. Oh, okay.  CoinCenter is a nonprofit that's based in Washington, D.C., and I'm gonna put our mission up here, but it's probably better if I just talk about a little bit of my background. So I, as I said, decided to go the liberal arts route, went to law school. While I was in law school, I started becoming very interested in Bitcoin. This was around 2012. I got very close to setting up a pretty powerful mining rig, and then there were exams and other things, and I got distracted, and it's one of my big regrets in my life. But nonetheless, learning about the technology, while also learning about the law, gave me a unique perspective. I thought I always wanted to work in copyright law or digital privacy, because those are areas where the internet has already disrupted a lot of expectations as to what is possible to enforce from a government policy perspective and what is not. But Bitcoin came around and gave me an opportunity to work in a very new area that's being disrupted, which is financial services regulation, which is the internet as far as truly decentralizing it and building it the way it should have been built from the beginning. So, I got a Google Fellowship to go work in Washington, D.C. in policy. I worked first for a think tank that was doing copyright and surveillance. This was around the time of the Snowden Lynx, and then a guy named Jerry Brito, who I share a number of mutual friends with, came up to me and said, hey Peter, do you want to be the director of research at a Bitcoin think tank? And I laughed at that, and I said, that's ridiculous, nobody would do that and no such thing exists. But then I said yes, and we've been around now for two years. So, our passion is defending these open networks. So, in a nutshell, mission aside, we want to be to decentralize computing networks with open consensus mechanisms, to cryptocurrencies in general, what the EFF was to the developers of the early internet. A point where you can find a go-between with policy makers in D.C., where we can communicate the value of the technology, where we can set an anchor bias in some policy makers so that they don't see this as just another way to sell drugs or subvert authorities. We have a full-time professional staff with three attorneys on staff. We have support from a number of key businesses in the space, some of the big Bitcoin startups among them, but also some of the venture capital firms that are financing a lot of the work in this space. But actually over half of our support comes from individuals who are passionate about this technology and want to see it flourish. Additionally, I should point out that like the EFF, we're not a trade association. We set our own agenda, we don't represent specifically any of these companies. As I said, we want to help smooth the path to decentralizing all the things. And so if people want to support us in our mission and believe in the work we do, we take their support. Now what do we do? We do education, policy research and advocacy. Education is sort of the big part of this because especially when we started two years ago, the amount of knowledge on the ground in Washington, D.C. and in policy circles globally was very poor when it comes to what a cryptocurrency is and how it works and what it can do. We also do policy research where there is a existing law that potentially applies to something you're doing using the technology and there is an open question as to how that existing law should be enforced because the existing law is drafted in a vague or a broad way. We develop policy research that advocates for a way to enforce that law that allows regulators to still meet their statutory obligations because that's what Congress put them there to do. That's what their legislatures and their respective states put them there to do but still provides the freedom to innovate as much as is possible. So we're trying to find a peaceful way forward basically. We have a series of plain language explainers of discrete topics in this space. We have a fantastic backgrounder by Vitalik explaining Bitcoin in a way, I mean, whoa. Explaining Ethereum in a way that will make sense to policy makers. We deal with a number of different topics also at the legal intersection of these technologies. Some of these are also useful resources for developers aside from policy makers because they explain a difficult legal construct like OFAC which is US sanctions law. We develop policy reports that deal with bigger topics like securities regulation, access to banking for companies and consumer protection laws particularly difficult in the United States with the state by state money transmission licensing setup. And periodically, most of our advocacy is done face to face meetings with policy makers but we do periodically file and regulatory proceedings. So just recently for example, we filed with the European Securities and Markets Authority. I didn't know if any of you have seen the paper they put out, they basically said something like we're really excited about blockchain for securities but we understand that it will only ever be closed or permissioned ledgers that will be used in these systems. Personally, I think there is value in permissioned ledgers. I think there is low hanging fruit in reconciling siloed data structures even if it's through a consortium consensus mechanism but I think that the conclusion that only a closed consensus mechanism is suitable for high grade financial services is premature. So we filed a comment in this explaining how so many of the advances that are essential to financial services are being developed in the open space. The pioneers here are the people working on Ethereum are the people working on Zcash for example with zero knowledge proofs. You mentioned ring signatures earlier. The only way that they'll be privacy for financial services data is through the things that are being developed in large part on these open networks. We testify, we've briefed members of the European Parliament. We've had several different engagements in our two years and we love Ethereum. We really do. So in the early days we focused mostly on Bitcoin and there's a reason for that. This was about two years ago. It was hard enough to get a meeting with someone in Washington DC to discuss Bitcoin but at least Bitcoin was something they already knew about and they already wanted some answers about. That is beginning to change now and we start to talk more about just open consensus mechanism driven networks and what they can do. And the idea I think of having a general purpose decentralized computing system is incredibly exciting. So again, thank you for all the work that you do and explaining that to policy makers is one of the most worthy things I think I can do with my time in DC. So with that, I'm going to turn to the less happy side of the talk and talk about some regulatory considerations for people who are developing decentralized applications. And that means this talk is going to be about securities laws. So why securities laws? First because they're heavy duty regulation. These are laws that say seek permission, do not seek forgiveness and if you didn't seek permission there will be strict penalties. Additionally, a lot of crowdsales and presales may subject developers of these technologies to securities regulation. Several scams over the years have drawn attention to this area already. And several vocal pundits, many of them from the closed or consortium consensus mechanism side of the technology will freely tell you that they think that all at-point or crypto crowd sales qualify as unregistered securities issuance. And I think that's wrong. And I think we need to correct those misunderstandings. Now why US securities laws? There's a couple things here. If you have any purchasers for say an app token in the US, any of them, you are potentially subject to US securities regulations. And if you're in a country, if you're based in a country that has friendly relationships with the US, extradition could be possible. So this is some serious stuff. The US securities laws are by far the most broadly applied of all securities laws and there's a couple reason for that. So in other jurisdictions there's generally a statute, a piece of legislation that enumerates specifically which financial products are regulated as securities and then usually which are not, what are excluded. In the US there is no such enumerated list in the statute. There's an undefined term that says investment contracts. And the federal courts have interpreted that term and continue to interpret that term to apply to various different schemes. Additionally, the US Securities and Exchange Commission is already investigating the scammy cryptocurrency pay coin, pay coin fans, and the Dow I can say has gotten the attention of staff at the SEC and they are actually surprisingly well informed. We've tried to help correct some misapprehensions they had about it and generally flesh out their information but they are surprisingly well informed. So why are US security laws broadly applied? The definition of security includes an undefined term and as I said the term has been defined by federal courts. Courts have sought to ensure that the definition is inclusive in order to reach and this is a quote from a case the countless and variable schemes devised by those who seek the use of the money of others for the purpose, the promise of profits. And that language comes from a case called the SEC versus Howie and it's from that case that we get the Howie test for what is a security or not. So in order to really understand these issues we need to quickly actually look at the Howie case I think. Like a lot of strange and vaguely alarming stories it begins in Florida. Howie was a, he owned an orange grove in a hotel and he would invite rich New Englanders down to Florida, is my mic, this mic's working now, right? This is working now. And he would invite rich New Englanders down to his hotel in Florida and give them a tour of the orange groves and he'd say look behold the beauty of Florida's orange fields. And then inevitably the New Englander would say something along the lines of oh the orange groves they're so beautiful and Howie would say well it's funny you asked that the orange groves are for sale and then he'd proceed to sell him one acre of the many acre orange groves that he owned. Basically he was partitioning his property into a number of parcels. We're doing time keeping back there. Into a number of parcels. And selling them as property. Now when you bought this property you'd also be pressured into signing a contract and that contract would say howie, we'll just go with this. So you'd be signing a, you'd be signing a contract that says you look to Howie in order to maintain the fields, the soil, to pick the oranges to sell them at market for a profit. Just working now. Okay good. Louder, okay good. So you look to Howie to maintain the soil, the trees, pick the oranges, sell them for a profit. And he will give you the profits from the land that you bought. So it needless to say the orange grove turned out to be less profitable than many might have expected. There was a lawsuit. It went all the way up to the Supreme Court. And the Supreme Court says this is the exact kind of thing that we want the Securities and Exchange Commission to have jurisdiction over. So that's why this is the formative case. And from that they created the Howie test. Which has a number of elements I won't go into specifically. But what's notable is that it is immaterial whether the shares in the enterprise are evidenced by formal certificates or by nominal interests in the physical assets employed by the enterprise. And sales of such nominal interests in physical assets have since that case been extended to all manner of things. Sales of beaver pelts, sales of condos, sales of minks, of gold bullion, of golf course memberships and condominiums. All of these have been interpreted as issuance of securities rather than just sales of a physical object or asset. And with the SEC's interest in pay coin it is not unreasonable to assume that cryptocurrencies or some cryptocurrencies may be added to that list of sales of nominal assets or transfers of nominal rights to assets that qualify as securities. And it's because of their interest in pay coin that we created a framework for securities regulation. So if you are interested in this area I highly recommend you go to our website coincenter.org and you look at the whole framework it's posted there and you read it. If you're a developer you will not need to read the first two sections which explain these technologies to policy makers. You can skip to the third which draws legal conclusions based on the applicable legal tests and the aspects of the technology that are sailing into that test. So I'm gonna skip through this. Here are the key findings for people who are developing app coins or DAP tokens. The following are less likely to be treated as securities. If the token was purchased for its use value rather than merely with the expectation of profits. So the line of cases here that build on the Howie test deal with condominiums actually. So in New York for example plenty of people will buy a share of a housing co-op. Now is the person issuing those shares issuing a security? The Supreme Court and some of the federal circuit courts have found no that is not the case because while people may expect the condominium that they buy to go up in value they also may be buying it to live in it, to rent it, it has utilitarian value and those are not the things we regulate as securities. Another factor, the token was purchased after, oh by the way on this previous one the utilitarian value. Ethereum or Ether is a very good example of this. So yes, lots of people speculate as to the price of Ether. Why isn't Ethereum a security? Well one reason is that Ether is essential to using the Ethereum virtual machine. You can't run contracts unless you fund them with gas basically. And there's this utilitarian core value proposition that saves it potentially among other factors from being a security. Token was purchased after application is already up and running. So in other words pre-sales are going to be more suspicious. And the line of cases here comes actually from golf courses and country club memberships. So basically if you were a developer and you were building a golf course and you wanted to sell memberships to the golf course but the golf course wasn't built yet, you were actually going to likely be found as an issuer of a security. On the other hand if the golf course is already built and you were selling memberships, in those cases courts have said that that is not a security. So I don't know if this is interesting to other people or if it's just my being a huge dork for the law. But these are two things that the community actually I think would agree on as far as ways to prevent scams or as ways to judge what is real and what is a scam. So if you haven't built anything, if you have no hope of necessarily proving anything but you're asking for money, more likely to be a scam. And if you are selling something that is purely speculative, that has no obvious use case, more likely to be a scam. So the law actually lines up pretty well. Finally, the token's value is dependent on the purchaser's own efforts or the efforts of a large number of other unaffiliated investors, users or developers. The intuition here is that if I buy gold bullion from an open market, I am not buying a share of the gold bullion industry or the gold industry. There's multiple people who make gold valuable, people who dig it out of the ground, people who find industrial uses for it, people who find jewelry type uses for it. So if there's really a widespread community that gives value to the thing, less likely to be a security. And then the last thing I wanna talk about is some things to avoid. So one big one is just using language that suggests that this is a securities issuance. This shouldn't ever happen, initial coin offering. That comes from initial public offering. It's almost like you're playing a joke on the SEC, which is funny to some people, I guess, but as a community, I think we should find it more frustrating or annoying. Profit sharing is another term that commonly comes up in securities issuance. So if you're using this language to describe your application and the tokens that you're going to be giving for that application or selling for that application, you're basically asking for scrutiny from the SEC. And that's fine. I think freedom is freedom. I love the open internet. I love the emergence of amazing things and technologies and I'm not here to tell you not to do things. I just wanna say you should consider the regulatory risks when you do them and not including language like that is one easy way to hedge some of your regulatory risk. And this is the big one. Endorsing risky ventures or claiming endorsements. So the securities laws penalize people who are promoters of unregistered securities just as much as they penalize issuers. So if you're thinking should I endorse, should I put my face on this particular decentralized application, think carefully. Consider your appetite for regulatory risk. And then more importantly, if you are building something and thinking of putting someone else's endorsement onto some public materials, think very carefully. Have a conversation with them, make sure they understand what they're potentially signing up for. It's sort of be a mensch because these have potentially dire consequences. And so an endorsement should only come when there's truly been an endorsement. So in conclusion, please, please, please, think about your regulatory risk appetite when you develop these things, when you contemplate having a crowd sale or a presale and please feel free to contact me personally at CoinCenter if you have questions related to this. We'd love to learn what you're building and talk about the path forward because there are definitely legal ways to go forward by structuring the way you actually develop your technology from an issuance point of view. Thank you very much, Peter VanValkenberg of CoinCenter.


The BSU women's soccer team continues to climb up the charts after a pair of victories this past weekend. The Beavers moved up to sixth in the latest Division II coaches' poll and they remain at number one in their region. Rachel Norton was named NSIC Offensive Player of the Week, scoring three times this weekend and Anaphobie was named goalkeeper of the week after pitching a pair of shutouts. The Beavers will take on Minot's Nate Friday night looking to continue their perfect season. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


Good day, fellow investors. Today was a good day for me. A little bit also said. I'll explain that later. But Neveson Resources got an offer to be acquired from Lundin Mining. The problem is that they rejected the offer because the management wants more money probably and they're negotiating, which is a good deal and shows that the management is good. However, the stock is now trading at $3.43 but the stock was close to $2 just a few months ago and I want to take this example of Neveson to show what is my investing strategy and how I go about investing. What I seek is a margin of safety first, so where I cannot lose money or the likelihood of losing money is very, very low and where there is the potential for 2050 100% returns over the next, let's say, short term to medium term, few years. With Neveson, I started buying it above $3 per share, a long time rebalanced a lot of times, bought a lot when it was 220, 240, 209 remember, 202 and rebalanced according when it went up. But it was my biggest position and now that there is an offer from Lundin, I have closed it down, I have left a small position that is okay for the merger arbitrage play but it isn't the asset play it was anymore. So let me explain what Neveson is, what happened, what's going on and how I went about it and how I reached my satisfying investment returns in this case. So Neveson is a Canadian mining company with a copper zinc mine in Eritrea and a copper gold project in Serbia. I was bullish both on zinc and copper which made Neveson a perfect sector play. Further, Neveson has no debt and had a cash balance of above 400 million that fell to around 150 million after it acquired reservoir minerals and the TMock project in Serbia. So the cash balance, the no debt, the Eritrean mine that was cashflow profitable forever and will continue to be so for the next four years, made Neveson a very low risk play and the extremely high grades, the extremely high grade ore body in TMock also made it likely that the project will be acquired by somebody as it is tempting when the pre-feasibility study is made. As soon as the pre-feasibility study was made there you have an offer by London which shows that high grades recently it is always a risk reward but high grades usually win. However, the stock price as you can see was very, very volatile and this shows how Wall Street is short-term oriented, myopic and that's what we do it yourself investors have to take advantage of. 2015-2016 you see the stock price go from above for to below 250 that was due to the commodity crisis then it recovered a little bit was volatile for 2016 but then in 2017 it really crashed. Neveson acquired TMock and then when it tried to switch from copper mining and the secondary ore to the primary ore at Bisha they didn't manage to have good recoveries and the stock price dropped and the stock price dropped especially when there was the dividend cut. Dividend cut nobody likes that because the investors that are there for the dividend start selling and nobody wants to buy so the stock price fell from 3 to I think 260-230 immediately. I managed to sell and this is also what I do. I managed to sell in the first half an hour that the dividend cut was announced and then rebuy the whole position and even more lower so if you are really applying yourself to the stock market you can also take advantage of such situations so that further increased my return on Neveson. However you can see here further it was again bad in 2018 the copper prices were volatile but then suddenly good news started to arrive recoveries in Eritrea were better cash flows from Eritrea were better the pre-visibility study at TMock was good and you can see how the stock price really ramped up up till now what is 340 however what did I do I open a position which is a small part of my portfolio when I see okay there is value then I'm ready okay what's the worst thing that can happen that's what you always have to think and then you say okay if that happens if that happens if that happens okay and then you adjust your portfolio exposure let's say you start with 2% and then when there is really panic you increase it to 10-15% 20% 25% depending on how good is the margin of safety and the bargain and that's what I did with Neveson I started with a relatively good portfolio position traded rebalanced around but the bulk of my position was opened between 2 and 240 because that was an extreme bargain for me just to summarize Neveson paid about 500 million for TMock which gives it a minimum value of 1.7 per share when you add the 0.5 in cash there was a margin of safety of 2.3 and when the stock price traded below that don't forget that London also offered to buy TMock two years ago so there was really a margin of safety and of course Eritrea was completely free even if it was cash flow positive and there is still exploration potential and there is still exploration potential from the lower TMock zone plus exploration potential around TMock that we have seen now good results coming in so margin of safety with so much exploration potential and potential coming from the lower zones and other projects which made it a low risk high return investment now the sad part unfortunately I saw Neveson also as a long-term player long-term investment unfortunately the company was quality which means that some a bigger player takes it over that's how things go so no long-term return I will look at London mining to see and take the volatility there if there is value but that's a completely different story now if you followed me on Neveson you might be wondering what to do as I said I reduced my position read sold most of it just left the margin arbitrage exposure portfolio exposure that I have as I'm very very happy with the returns a year yearly returns the bulk was I don't want to talk about it I have to be happy and be grateful for what I got however if you want to take advantage of the merger arbitrage traders that will be coming in the offer is what 260 the price is 240 so there is potential for what's 8% there will probably be a new offer from London or from somebody else as the management is negotiating there will try to push the price as high as possible so perhaps it will go to four for something but that is what 20 25% upside while the downside there is always downside if London says no I don't want to deal with you anymore and there is no other buyer then Neveson could be put under pressure and the stock price could go lower so that's a different play it's not an asset play anymore it's a merger arbitrage play the points that I want to share from Neveson from this experience find find value with a margin of safety the rest will come with time if the value is there never buy a full position immediately always wait for a better entry points that always come in volatile sectors like commodities and especially mining having investment strategy set up beforehand and look at all that can go wrong and think ahead okay what will I do if things go wrong this allows you to take advantage of dividend cuts and other irrational moments panic sales from other people always take advantage advantage of other people's irrationality so never fall in love with the stock listen to what the market is doing and take advantage of that do research the more research you do the better we will understand the investment and more opportunities will come your way in whatever market and of course always rebalance a little bit around the intrinsic value you have a value have calculated the value for me it was 2.9 for Neveson with expecting a 20 return over the next five years so I always rebalanced around that really bought a lot when the price went down and took advantage of the volatility thank you for watching looking forward to your comments if you have been with me on Neveson for the past six months or eight nine months since I have recommended the stocks on the channel let me know in the comments below don't forget to take a look at my book that is available on Amazon and I thank all of you that have bought and supported me with the book see you in the next video