Hi, welcome to chapter six, Creative and Critical Thinking in the Small Group. In the next few slides, what we're going to do is talk a little bit about some of the methods and techniques that you can use to enhance creative as well as critical thinking and your group interactions. All right, so a couple of things here at the banding to consider is that groups are often considered a excellent source of ideas, right? One of the main reasons people join groups is that as members of the group, there is more ideas than you would have just functioning as individuals. That said, when it comes to the group's creative process, that we can see group creativity as being both a divergent and convergent process. So divergent thinking is when ideas vary inside the group, right? People go out, they think outside of the box, they try different things. The idea behind convergent thinking, however, is that eventually these ideas must come back together for the group to be successful. And I think this makes sense when you think of most group interaction, it's good early on to think outside the box, but as you conclude and move towards the actual completion of tasks, you wanna make sure that you and your group members are coming back together with your ideas. The next couple of slides will talk about some of the ways that you can do that. So there is a wide variety of ways to increase and create creativity inside the group. Let's briefly talk about three. One of the most common ways that groups engage in enhancing creative thinking is the process of brainstorming. Brainstorming is a technique in which group members generate a wide variety of ideas to ultimately come up with solutions, or topics, or directions for group interaction. There's a couple of things though about using brainstorming in an effective systematic way. One is to set aside some time to actually engage in the brainstorming process. During that time, you should try to focus on quantity and not necessarily quality of ideas. The next part of this is that don't evaluate any of those ideas at that point in time. The goal of brainstorming is to get a lot of ideas in the table and then to later, at a separate point in time, to evaluate those ideas. So brainstorm those ideas, don't evaluate, but make sure that you record them. It might even be good to set out a digital recording device or have somebody take notes. Then, at a separate setting, you wanna go ahead and evaluate those. A couple of tips, don't end too early, don't evaluate, and make sure that you have a chance to fully gain everybody's participation inside of that. Another really good technique is a technique called mind mapping. And mind mapping is a process of visually depicting an issue or a task or directions for a project that a group is in. And it usually involves starting with like a center idea and then drawing lines and processes and kind of flowing them out inside of that and connecting things together. Ultimately, through the process of creating a mind map, it allows the group's ability to understand the complexity and the nuance and the multiple parts of an issue before they are able to go ahead and address and look at that. And again, similar to brainstorming, don't be too evaluative as people come up with ideas, put them on the mind map, see where they fit, and then identify orphans and things that don't necessarily fit at a later point in time. One final really useful method for creating creativity is to engage in changing perspective. And this is a process where oftentimes people can get too locked into one way of thinking or about the situation and the constraints that exist for a given process. And it involves actually moving yourself out from your perspective, temporarily suspending reality and allowing yourself to consider other possibilities and ideas that you wouldn't, and then bring things back in to be able to gain a more cohesive idea as to what you're looking at. All right, now that we have a little bit of an idea as what encompasses creative thinking, let's talk about critical thinking and why it's important. So critical thinking is a type of systematic thinking that uses evidence, reasoning, and logic to make decisions. So when we talk about evidence inside of here, what we're talking about is the use of materials from credible sources, right? We're talking about empirical research that might have been published in peer reviewed journals or published by subject experts that make strong arguments that something is the way that we say it is and it is true. Reasoning and logic means that we are looking at things logically, that we are making sure there are things that are sound and that they ultimately are void fallacies, which we're going to get to in a couple of minutes here. When we put these things together to engage in systematic critical thinking, this allows us to be more effective and avoid some of the pitfalls of lacking critical thinking, such as groupthink. All right, the next concept we're talking about is that of argumentation. Oftentimes in life, many of the communicative things that we engage upon are actually arguments. What an argument is is a claim, a statement about what is or what ought to be that is supported with evidence and sound logic. So again, we're bringing back the tenets of creative thinking to ultimately put things together. So if I was to make a claim for that claim to become an argument, I would have to support that claim. I would have to explain why something is the way it is and then perhaps cite some sources or bring some evidence to pull it in without drifting into ill sound logic, which we'll talk about when we get to fallacies. There are a few different ways to kind of enhance critical thinking in a group. One of the most fundamental ways to do this is to have the right attitude. Having the right attitude includes being open-minded and being willing to consider new information and ideas. It can include having a sense of what we would call show me skepticism, which includes having members think for themselves, be open to hearing ideas, but be like, okay, I like your idea, it's interesting, but show me how it works. Make me understand what you're trying to say. Ultimately, you wanna be a good skeptical decision maker, which means that you wanna ask probing questions that are designed to help critical thinking by examining information and reasoning with more depth. Remember that critical thinking is an active rather than a passive process of testing information. Another thing that you can do is to evaluate information. And this means that you need to determine the meaning of what is being said, right? What is the information actually trying to say? What claims are it making and what is being supported inside there? An important skill to be effective at this is to be able to distinguish the difference of fact and from opinion and inference. So facts are things that can be verified through empirical observation and aren't necessarily arguable, right? Whereas opinion are inferences that go beyond facts and contain some degree of probability, right? We think that this might be true in this given situation. So go ahead and identify that. You wanna make sure that if there are any ambiguous terms that you've clarified those so that you can understand where those different possible meanings and probability comes to them. Last, you need to get good at determining the credibility of information, right? So this is evaluating the opinions and determining the credibility of the sources that facts and opinions are coming from. Some things that you should do here are that you should ask questions to decide how much trust a place in an opinion. You should think about, for example, is there any reason to suspect the person supplying the information of bias, right? Is there something that could be influencing them? Are there other sources that disagree with what they're saying? Are they an outlier of information or are what they say is what they're saying, you know, kind of consistent with other research that you've seen? And then finally, you wanna take a look at the source of the information to see if the individual that is speaking is actually a qualified, credible expert on the information that they are looking at. This is why in your presentations, just saying Dr. Smith tells us blah, blah, blah, blah, blah, isn't enough. We need to know who Dr. Smith is and why he's credible to be speaking on your presentation topic. All right, with that, that leads us into our discussion of logical fallacies. So first of the outset, you should know that there are well over 100 different fallacies. What they are are these logical statements that kind of show erroneous logic or reasoning and might even be manipulated or you know, perhaps on accident inside of there. Generally, logical fallacies are bad. They distract from the argument. Sometimes we see them on commercials and they're obvious and meant to be funny, but you should be careful because sometimes fallacies and arisen reasoning can be very subtle. There are a few different fallacies that we talk about in the chapter. I'm just briefly going to cover them, but it's good to kind of gain some more insights and reasoning. The more you understand about fallacies, the better you'll be able in terms of identifying them and separating good arguments and good information from ones that are logical. So one of the most common types of fallacies that we see there is the fallacy of a hasty generalization or of overgeneralization. And what this means is that you're drawing a conclusion from insufficient evidence, right? You're making a conclusion at an end point when in reality the evidence doesn't really support that the argument was reaching that. So you're kind of making a jump in logic in there. Another common fallacy is what we would call the either or fallacy. This is a fallacy that creates the false dilemma or binary by limiting the choice to two alternatives when there are more options, right? So then you can either do this or you can do that when there's 10 other things that you might be able to do inside there. My visual aid here in the United States of America, love it or leave it, is an either or false dilemma fallacy. The ad hominem fallacy is a fallacy that translates into against the man for Latin. And what this means is that instead of attacking your opponent's ideas, that you attack them directly, right? So something about them as opposed to the validity of the argument that they're making. So attacking the person instead of their ideas. Next, continuing on this discussion is the fallacy of the incomplete comparison. And this involves comparing two disparate things when the comparison is invalid, right? So it's kind of like making a false metaphor inside of here by looking at things that are differently related but different than this is the argument of fallacy from false cause. And this claims that one event leads to the next event but there is no link between these two things. If then, then this, but no explanation why if this one thing happens, this next thing does not follow. So the false cause leads us to problems inside of them. And the last fallacy that we'll talk about here is that of the slippery slope. And the slippery slope fallacy is an argument that suggests that there will be like a domino effect event that leads to disaster. If you do this, then this will happen which will lead to this, which will lead to this, which will lead to this, which in some ways is a chain of false cause fallacies that ultimately lead us to horrible things. For a false cause or for a slippery slope fallacy to be valid, there has to be evidence and support between every event mentioned to actually explain why we would end up in a disaster scenario at the end. You can't just make those jumps without evidence inside of there. All right, one final important concept to discuss as we wrap up this chapter is the avoidable but very dangerous concept of groupthink. What groupthink is, is a process by which members of a group cease to think critically and ultimately just develop an unhealthy norm of going in and conforming to the status quo and not questioning their ideas, right? So this is where groups tend to overestimate their power. They become closed-minded and there's a lot of cultural pressure inside the group to conform. One of the most famous examples of groupthink ending very tragically is the organization of NASA. During the Challenger disaster, there were individuals who were engineers and working on that that knew of some of the problems with the space shuttle before it happened but there had been multiple delays to the launch and so ultimately when the launch took place and led to the death of all of the members of the crew, this is something that would have been avoidable had groupthink not pressured people to keep quiet and not address those issues. So some of the techniques that you and your groupmembers can use to kind of prevent groupthink from happening is encourage a culture where groupmembers constantly discuss problems, right?  Don't rush to get things done just for the sake of getting things done. Take the time to slow down and work through effective group processes. Establish norms inside the group of critically evaluating decisions and information inside of there. Like don't just rush through to get things done but take the time to critically look at things that are before the group. If there are leaders in the group, whether they're informal or formal leaders, leaders in general should step back from expressing their preferences too early because oftentimes in a group that's beginning to suffer from groupthink, a leader will say this is what we should do and no one else will question that. So if you find yourself acting in a leadership position, you need to keep your mouth closed for a while and allow people to organically reach some ideas and some considerations before offering your two cents. Then finally, make sure that you're not insulating the group from information. So try not to become too close to the system where outside information cannot come in and influence and impact the decisions that the group needs to make. All right, once again, thank you for watching. If you have any questions, please feel free to contact me at one of the many methods made available to you.


Hello everyone, this is Amit Dan of AmitDan.com. Today is my last day in Melbourne and I'm heading to Auckland, New Zealand. I'm at Melbourne Airport and I'm taking it to the Qantas Business Class Lounge. After passing airport security, I was searching for the Qantas Business Class Lounge. It took me a while because this lounge was located on the basement level and I was not expecting it. The Qantas International Business Class Lounge is big and you'll find everything you need including a business center with printers, a kids' zone and a shower facility. I didn't have to use any of them. I was rushing to complete taking video and pictures so I could have some time to eat or drink a coffee. There were plenty of food and snacks. I saw the alcohol and coffee sections were impressive. There was even barista coffee. In my next video, I'll take you inside one of the world's best business class suites, Qantas Airbus A330-200. Thank you for watching this video. Forget about subscribing this channel. Forget about liking this video. There will be more videos coming up so I'll see you shortly.


from NCCU and an MA from UNCG and has occurred doctoral candidate at UNC Chapel Hill. Her professional focus is on supporting faculty and graduate students with their research and serves as the embedded librarian with NCANT's Office of Research Development, which includes the sponsored programs. This embedding brings scholarly communications, info, literacy, and reference consultations into campus sponsored research programs. So thank you very much, Nina, for doing this. Well, thank you, Linda. The health series has been such a fantastic tool for everyone. So I'm super glad to be able to contribute a little bit to it. So yes, I am Nina from North Carolina Agricultural and Technical State University. We are in the same city as UNCG, but we are separate and we have a heavy research focus. And because of that, we started a lot of scholarly communications, public services stuff, and that is my job as researcher and grant support services librarian. And today I'll be talking to you about the NSF. I figured that we'll have roughly three groups of people that are independent from thinking. One that's just like, huh, I wonder what this is about. And one group that is mostly interested in the stats side of it and one group that is mostly interested in the grant seeker and faculty liaisoning side of it. And so I'm going to kind of split the time between the stats side and the supporting your faculty. I am better at the supporting your faculty and grant seeker resources side, but I think the stats side is super important because they are one of the big stats and data groups and that's something that everyone should know a bit about. I stumbled into being an accidental, if you will, research and grant support services librarian out of public services, representation of scholarly communications, and I'm very into that. So if any of you happen to ever want a researcher and grant support services librarian to come talk to you about your public services side of grant support and faculty liaisoning and scholarly communications, that is something that I do, especially if you're in North Carolina or nearby, I'm happy to come and talk to you individually. So that's my self-pimping. Also, I am part of the STEM link group that was mentioned earlier. So this is sort of indirectly sponsored as a pre-STEM spring session for that. And I hope that you all be able to come back for our STEM spring series. So the NSF, lots of people hear about the NSF. You probably have most been familiar with seeing it because you'll see a lot of times some kind of journal article or event will have the little sponsored by the NSF logo on it or a disclosure. So they're best known as a funding agency, one of the top funding agencies for promoting the advancement of science for economic reasons, for health, but not immediate health, like not clinical outcomes. It's more like we think someday down the line that the study of cell motility might have some long-term effect on health. And that's very much the kind of stuff that they do is both promote and track what we call basic research, the stuff that you have to know before you can know the things that are commercializable. So for those of you that have faculty in areas of math or theory of engineering or basic engineering concepts, physics, theoretical or not immediately commercializable things in computer science, basically any science, any engineering, any technology, especially those that aren't the kinds that you could right away get a commercial sponsor for because, oh, we could definitely sell this. And it's the kind of stuff that will help build our overall capacity. The NSF is super, super interested in that stuff. They fund about a quarter of 24% of the funding to colleges and universities.  And one of their guidelines is that human health directly oriented go tickets to the NIH because they got more money than us. The DOD, of course, also does a lot of practical engineering and physics. But the more basic stuff, and so that's a lot of times what comes out of universities, is the stuff that can't go to market immediately, but will lead to overall improvements. That's the stuff the NSF really loves. They are one of the 13 federal data agencies. And when I say that, I mean the people who like not just the census, but say the Bureau of Labor Statistics or the National Center for Education Statistics, those groups like that that are the big data collectors. The NSF runs the NCSES, the National Center for Science and Engineering Statistics. And the biggest point is just knowing that they're there. And you don't usually think of the NSF in the context of the NCSES, but they are there. And you can find them on that website. And they produce a couple of major reports. One is Science and Engineering Indicators, which is probably their best known report, and the underlying data sets that run like that. And they also track diversity issues that's another of their big reports and data sets. And then they also keep state-level profiles, infrastructure things, funding levels, where people are getting money to do research. Also, there's a really wide variety of stuff around STEM and the U.S. R&D machine. And so you can use that for all kinds of stuff in workforce planning, in industry, in assessing your universities. There's just a huge amount of data in there, but very narrowly defined to STEM. This is the NSF website, and that NCSES site is under the document library, which I don't think would necessarily be obvious. They're some part of the Social Behavioral Economic Services Directorate. And once you get under the document library, then you see that link for the NCSES. So if you can't remember the name of the NCSES, to Google it, they're going to be under the document library. They produce large-scale data sets, several reports. They have some data tools that you can use, including a great one called Webcast Bar that cross-links NSF educational data with IPEDS educational data. And apparently I forgot to finish the sentence there, but it's very useful for any kind of outcome assessment that you might do on campus. Another tool they have is SESSTAT, which focuses on the labor force. And then those of us who support data scientists always are being asked for micro-data files. They do have both public and private use micro-data, mostly focused around the educational types of data, so educational outcomes. But some really interesting stuff on once you get a doctoral degree, which in most of the STEM fields you have to get a doctorate to really do intense research, what happens to those people after they get out. So there's really some great stuff there. Let me pop into it so that you can see what I'm talking about. So this is the NSF website, and most of their important stuff is under their header here. Of course, like most websites in the government, you've got tons of news and how we're helping the taxpayer. But most of the stuff is going to be up in this top bar, and the NCSES is under the document library. They have all kinds of other stuff under the document library too. And here's that key science and engineering indicators. And then you see that they take their data and combine it with a lot of the other data units to come up with special reports and briefs that are a little bit more reader-friendly than just looking at the data sets. But they do also have some statistical tables that they produce. So pretty similar to what everyone else does, to be honest. And you can see there's a digest version of this, but the tables are where you're going to go if you want to really dig into the data. And they've got lots of good tools and tables available, and of course you can run your own from the data sources if you want to dig into it. So you can see that out of these, the state indicators are kind of a great place for those of you who do regional economic development types of stuff. In the list I saw some people that I know are parts of government agencies. So these are really good for those government agencies that are looking for economic development kind of opportunities. There's a lot on the academic body, so those of you that are university librarians like me are probably going to be interested in how the university is affecting research and development and what that part of the body is like. They divide it into people getting degrees and education into them and those that are producing research. And that's a really important division. Educating researchers and being researchers is a really important division to them. They do have K-12 for those of you that support a teacher education program, so I know that would be super helpful with a lot of the current trends that are going on in the Department of Education. And then there's these national and international issues around labor force and how much research we're producing, how much development we're producing, our industrial capacity, our industrial product that comes out. And then public attitudes sort of covers the things that you might do related to polling, related to what people's thoughts are. And so there's a lot of good stuff there for sociology and public policy, political science, because these are currently kind of hot topics because we like to think of ourselves as being one of the leaders in innovation. And these science and engineering indicators are a lot of the stuff that we use to figure out, well, are we really leading in innovation or are we just thinking of ourselves that way? If you're not a data librarian, you don't want to dig into the data sources, I recommend either the digest or the figures. They kind of pull out and highlight a lot of nice graphs that are super useful, and they include the Excel if you want to take that and make your own visualization. But if you're just looking for something to give some students, maybe teachers in training going into STEM, some good insights that are already kind of prepackaged. So the science and engineering indicators do a really good job of spanning intense data for people who work with data and then kind of prepackaged reports, prepackaged visualizations for people who want to know about science but who aren't really into data and aren't good with data. And that's one of the things I like about it. I don't feel that way when I work with the Bureau of Labor Statistics, for example. I think the NSF tries to be very conscious of the public view and the usability of what they put out and really highlight some things that are helpful for the more casual user. So basically any other sector that is also interested in STEM is going to get some value from that. So it's good for a STEM librarian like myself, but it's also good for people who are supporting education. I have a previous life as a business librarian, and this is really good for the kind of upper level, high cost, high education requirements, high infrastructure requirements studies about either the workforce or the business capacity building kinds of stuff. There's a lot of stuff going on right now and the workforce and the size of the STEM workforce is very useful for that. So it may be valuable for your institutions if they are looking to justify influxes of dollars either through development and fundraising or through grants and chips. Say look here the statistics to show where the critical gaps in our workforce are, where the growth in industry are, where the jobs that our students could get, those are really valuable. And then also for regional economic development and public libraries that are supporting their counties, there's just a lot of stuff that can be used here to identify gaps that would be helpful for community colleges that are developing new programs for universities or public areas looking to where to invest their money, what kind of infrastructure is most promising for economic development. There's a huge amount there that can be dug into. The challenge with working with it is because it's so focused on science and engineering that you can't kind of look across an industry. So you really have to find ways to take it and cross it with either the state level or the county level or with a specific IPEDS code or some other way there if you want to look at STEM compared to employment in general or STEM compared to education in general. It doesn't have those comparative data. It's just a very small slice of the overall educational or industrial data status. So you find that sometimes you run into a piece of data that you're like oh well and I really wish I could compare this to something in the arts for STEM purposes maybe and you find that it's not there. So it gets a little bit deeper into some of the data but in just this very, very bounded set of interests. So that can be a little awkward sometimes and taking it and crossing it with other data is a good way to try to get around that a little bit. Of course that requires a lot more expertise because you have to find somewhere to link it up. So it can be a little awkward to work with. Does anyone have any questions about the data part of this before I move on? So okay I'm going to go on to the grants part of it. And to give you a quick idea because sometimes when I talk to folks first of all I'm going to be talking about grants mostly for your faculty, not for your libraries although I'll touch briefly on library grants for NSF. But when I talk to people about grants a lot of times they're kind of fuzzy on it. It's money you get, right? But there's a little bit of more granularity to it. It is specifically when you're getting it from the government tends to be given to stimulate some sort of success. And a grant specifically requires reporting and monitoring which is why most of you will have some sort of sponsored programs office or sub-office or sub-sub-office to do that monitoring. So it's not the same as money that falls from the sky. It's money that has a whole bunch of strings and rules attached to it. If you want money that has no rules you want to talk to your development office and talk about gifts. It's also different from contracts. We hear about government contractors a lot. Contracts have a lot more rules than grants is. So grants are in between as far as how ruley they are. The grants are kind of in the middle of the ground. They're more ruley than gifts and less ruley than contracts. And somewhere on your campus you will have someone who works with grants. It may be a part-time accounting person. It may even just be one faculty member that does a little bit of proposal development. Or if you're bigger then there's probably research development or proposal development or sponsored research, or sponsored programs and lots of different names behind it. There's an office somewhere or person or part of a person that cares about this stuff on your campus. And so if you're ever interested in starting that conversation and trying to get your library kind of involved in how can we help with this stuff, research development or proposal development is usually a great place to start. I also have had a lot of success with RCR or Responsible Conduct and Research, the ethics office. And they usually know who else works in sponsored research or sponsored programs too, so that can just be a place that's been to talk to. So the NSF grants, as I mentioned, are grants specifically around science, especially basic science, and technology, especially technology theory. And if you do any scholarly communications or if you do any liaison that is not 100% around the topic of education and information literacy instruction, then you should care about grants. Because even if you are at a smaller comprehensive or community college, if you have biology faculty, you have chemistry faculty, anyone working in STEM cares about the NSF. The NSF is the people who pay to get labs set up and who help people create new outreach programs. Also all of the kind of technology innovation programs, all of the building of labs, especially if it's a new lab for a new technology, all of that kind of stuff, even if you're a predominantly undergraduate or community college institution, that stuff usually comes from the NSF in part or in full. So anyone who does electronics, biology, chemistry, physics, the whole deal, NSF may have a role for helping your faculty and your student researchers. And helping those faculty find stuff or keep up with stuff from the NSF is a great way for the library to build those faculty relationships. So it's not just, oh, well, I don't see where the library can teach my students. I mean, for those faculty that you already have instructional relationships with, that's great. But sometimes you've got those faculty that are like, I don't need the library to do instruction. There's nothing that we need from the library for information literacy. But then you've got the second way that you can try to relate to them. Well, what about if I could help you with your research? So it's another way to find ways to build relationships with your faculty members. The NSF makes grants by directorates, which are kind of subparts of the NSF that are focused on specific types of science. You can see that those directorates are biology, chemistry, information science, engineering, education, which is where if you're doing STEM education trying to build a better undergraduate or build a better graduate student, that's where you would go. They also do have one program that is focused on what they call informal STEM education, which is libraries and museums. So if you have a special library or if you have a campus museum STEM education program, that informal STEM education branch within the EHR directorate would be where you'd want to look. The engineering, environmental, geosciences, integrative, which is things that are cross-disciplinary and interdisciplinary, especially if they're really going to make two or more disciplines work together. They have an international branch, math and physics. Well, the physical sciences, which are going to include physics. And then social behavioral and economic, which is sort of a catch-all for them of stuff that they feel is sciencey but not falling into one of the other directorates. So if you understand the directorates, you have a much better chance of identifying where you want to be when you're looking at grants or looking at talking to a faculty member about grants. The big plus is you probably know what your faculty members do. So this is a very liaison-friendly set. I really only have to go into a few of these directorates, biological sciences, EHR, and engineering. I never have to deal with the math and physical sciences directorate because I don't work with any of those or liaison with any of those areas. So you can really concentrate in on areas that match your liaison responsibilities, or if you're in a special library that match in with your special library responsibilities. And each directorate kind of has its own flavor, so you can get a good feel for the relevant directorate for your faculty. And I showed you the research areas within my PowerPoint, and that shows you the directorates here. But under the about us, they have this organization list, which will lead you to this organization list page here. And this gives you a more granular view of the directorate. So one of my areas is biology. Under the directorate of biological sciences, they have various divisions that are their more focused areas. And when you get a grant, it tends to be from a division or cross-divisional within a given directorate. So you can really get into where your faculty interested. And here's that formal and informal settings, the informal settings part of the division of research and learning, informal and informal settings within the directorate of education and human resources. I'm getting very governmental here. This is where the stuff that focuses on libraries and museums is at. Which isn't to say, and you can see that there's a lot of stuff on multidisciplinary. And you saw that there was a whole kind of sub-office for interdisciplinary. There is a lot of stuff that focuses on multidisciplinary and interdisciplinary work. But most of the time, even if you go for multidisciplinary activity like this, they're going to end up primarily flooding it under one specific division, not just leave it floating between a whole bunch of divisions. So they're very top-down and very much want to get everything into a specific directorate into a specific division and then assign it a specific lead person within that division to kind of work with it. So finding these NSF grants is just like any other information resource. You can search, you can browse, or you can set up alerts, just like you would in any other types of databases. Searching on the website under the funding tab where we just wore that top bar. There's a fine funding to search on their website. NSF Fastlane has a search function. You can Google NSF Fastlane. That's where we submit our grants, but that's also they have a search function within that. And then grants.gov is a website that does all kinds of funding. Not every government funding is listed in there, but it's much more than just NSF. So if you're going to learn just one search tool, one database, if you will, grants.gov is probably the one to learn. For some funding agencies, you stay in the grants.gov, mail you. But with NSF, you can find it in grants.gov. And if you find an NSF thing, it'll bounce you back to Fastlane to actually put the grant in. And that's usually something. That putting it in process is something that you trade off to your sponsored programs office. Unfortunately, we don't need to know about that unless we're doing one for ourselves. Then if you don't want to search, browsing is also an option, just like with journal databases. There's a browse for all funding opportunities, A to Z, if you want to get the feel of them. Or as I showed you the directorates before, you can browse within each directorate. So let me see if I'm going in the education and human resources into the formal and informal learning they're going to have. And they show you your dig down over here. So this left-hand bar is really good for navigating NSF. The programs and then funding are both good places to go. Programs will usually give you the better browse. This advancing informal STEM learning is the most library-focused. But you can see there's lots of different ways of getting better K-12 for those of you that are doing teacher education. The eye test seems to be really popular right now, bringing teachers on campus with students for summer programs and stuff. And so once you dig down into a specific function, you can browse through their programs. And that's a valuable backup because predicting the search can be very, very hard until you've gotten a sense of what kinds of words that they use. So we're used to searching for journal articles which are kind of much smaller, whereas these are much bigger terms. So they're things like observatories or informatics. They're not going to be terms that are down to cell motility. It'll be broad things like biological instruments, biological sample collections. So it takes a little bit of practice to get a feel for what their terms are, to get a feel for what their terms are before being able to search. So starting with either the A to Z list or the Directified Directorate Program search to get a feel for what kinds of terms you're seeing is a good way to get a feel for that. So I recommend the browsing. And then again, just like with journals, we get set of alerts. They have an email or RSS system. You can do it through grants.gov. If you just go to grants.gov, there's a managed subscriptions option. NSF has a great email system that sends out a weekly set of newsletters. And because they've got so many different newsletters, you can just use checkboxes to control which ones you want. So if you only want your biology liaison, all you want to see is new program announcements in the Biology Directorate. You can get it down that narrow or you can turn on a whole bunch of stuff and see I want to see the Education and Human Resources, and I want to see biology, and I want to see physics, and I want to see engineering. You can turn on a lot. You can turn on a little. So it gives you a lot of control options for how much you want in that email they send you each one. And so when you were reading this, you might have wondered, what does a librarian do to help with grants? Because I just mentioned you can help faculty search, browse, or set up alerts just the same way as working with journal databases. On most campuses that are large enough to have any proposal development professionals over in their sponsored research program, those people are teaching a search class, and we're teaching some sorts of search strategies. And so working together with them to improve how the search can work. They may not understand about Boolean. They're getting very granular into the click here, click here, click here, but not really necessarily talking about Boolean terms to improve searching, to narrow things down, or to broaden things out. So a lot of times you can improve the workshops that they do just by bringing search expertise to what they're working on. And the NSF especially, but all government databases, are very classical types of non-discovery platform search interfaces. You've got your ands and ors and nots. You've got sometimes very, very close word matching that you have to use truncation and oil cards with if you wanted it to find plurals. So think like 2002 technology for the search interfaces, and a lot of times you'll be there. The big problem, as I mentioned, is the keywords are so different because instead of being very specific to a given study that the faculty member wants to do, you have to find a much larger umbrella over top of it. So a lot of times you need to do a different kind of brainstorming or term mapping process to figure out what potential terms might umbrella over what they're doing, what kinds of outcomes they might come up with.  But it's a great way if you've got STEM faculty, which a lot of times are not really interested in getting their students in. They really want to get their students to the bench. They don't necessarily want them to be spending a lot of time analyzing their searches and using information literacy from their articles and so on. They just want to hand them articles and then get them to the lab bench, get them into the field, wherever. So for me, I find that sometimes information literacy is a little bit harder to sell to the STEM folks. And so this is a different way to start a conversation with those faculty for areas that don't have as much traction with them. Also, once a faculty member finds a grant that they're interested in, they have to put literature in throughout it to prove that there is some evidence that their study holds promise. So these are not studies that are already completed. These are studies that they're hoping to find the money to see whether the study will find something. So they need to use a lot of literature to support the probability that the study is well conceptualized, is cutting edge, is going to really have an impact. And so a lot of times it's a good opening to a conversation about broadening their literature search and having some consultations around their literature and so on. So a lot of the NSF structures, once you get into them, you find they're very librarian-friendly. And if you didn't already know this, you probably did since a lot of you are data and government librarians. But if you didn't already know this, there has been a change in the last year about the data of repositing open access to data and also public access reporting of articles after the study is completed. And we have traditionally known for the last several years that if they're funded by the NSF, they will need to reposit their data in a reusable format. And they have to have a data management plan that goes into their grant application. With the new rules, after they complete the study, any publications that result from funded activities will have to be reposited or filed with the NSF. And that filing process means that they need to work with specific field codes and work with the metadata to make sure that their articles are properly described. They have accurate keywords that are in line with the way NSF wants the keywords set up. Right now, the data management has been growing across all of the government agencies. NSF's been doing that for a while, so you're probably already familiar with that if you do any NSF work at all. But new to the NSF is going to be this public access of articles. And from past experience, when agencies have started to require faculty to reposit their articles, they've needed a lot of help from librarians for how to file them, because it's not necessarily a familiar interface to them, although it's like selling off a log for us. And so that can be another great conversation to have. So there's a lot that we can offer to our grants office and to our grant seeking faculty members. All right, so I have about 15 minutes left now for Q&A. Public libraries. Yes, the ASL, so an advancing informal STEM learning, includes anything that's not attached to a classroom, and they strongly support STEM learning for the public in informal, which is to say non-classroom environments. So that's going to be very public libraries, and as I said, museums. Most of their applications tend to come from museums, but their language is really clear that they want public libraries to. Could I talk a little more about Webcast Bar? I can talk some about it. So Webcast Bar is especially interesting because it links everything through IPEDS numbers. So you can say I want to have an IPEDS, maybe the IPEDS salary, and I also want to look at earned doctorates. I want to look at, actually, institutional characteristics might be more interesting. I want to look at how the library institutional characteristics survey and earned doctorates relate to each other. And then I can download it as comma or SAS. So this is nice because they do let you do a SAS file. You don't have to take it into Excel and then read. So you see I didn't get a whole lot out of this. I've got my averages. I probably would want to filter. I'm not sure why the library stuff isn't in there, which is very disappointing, because I wanted to see whether I could get it to spit out library funding, higher than average library funding, and how many doctorates that got. Process of filing articles. Yes, that I do know. You can see that I am not a data librarian. I apologize. So this is going to be the NSF PAR. This is wonderful naming, but not as bad as some of the ones that I've seen. So NSF PAR is, and you can see that this is in beta right now, the PAR is going to be their version of PubMed. They're kind of taking this from the PubMed model, although the infrastructure under it comes from the Department of Energy structures. This is where the actual articles are going to be. So eventually as we deposit more stuff here, it'll be like a science-y version of PubMed. So a lot of people are going to get their free articles in there, and that's what their goal is. As far as how to do the repositing, they will have to set up an account. These are the rules. So if you're starting from the PAR, the public access page linked down here will take you to this. These are their, like, why are we doing it? What are we doing? How are we doing it? So anyone who submitted their proposal after January 25 of this year, so what they call the 2016 proposal and award policies, when they get to the end of their thing. So they submitted their thing in February. They submitted it to say, I want to get some money. It went to the NSF. NSF debated through their peer review for maybe six months and said, yes, you can get some money. Then it has to go to the campus and the NSF, and there's a whole debate and a whole bunch of forms they fill out. So if they filed in February, they're probably not actually getting the money until like a month or two from now, October, November. And then they have to do the study or the program or whatever that was funded. And then when they write that up, so this is going to be a year or two from now probably, when they write that up and get it published, then that article will have to be repositive, which is why the PAR can still be in beta because anyone who is affected probably hasn't written much yet. They're very disappointingly to me personally, they're not allowing local repositing to replace it. So if you have in your local institutional repository some way to directly ingest into the PAR, since it's still in beta they haven't told us how they're going to handle yet, that may be something that you can tie your institutional repository in with. But for right now, they're not requiring, they're not allowing us to use institutional repositories for NSF. But when they put it in, they are going to have to have basic metadata and the metadata has to be attached to the article. So when they file the article, it's going to need to have basic metadata attached. Now when I say basic, I'm talking name, author, institutional provenance, funding provenance, keywords, that kind of stuff. But still, that can really throw faculty for a loop. So once they publish, they have a year to get it into PAR with its metadata and then PAR will ensure long-term preservation. But that means that if there's an errata or something that comes out, they need to have a system to put that in. And then the persistent identifier will be issued by the PAR and they can stick that in. With their final publication. Just like with PubMed, several of the big publishers are already trying to set up automatic ingestion into the PAR. But of course, you have to keep an eye on the whole licensing issue with that too. So frequently asked questions, probably going to be the place that you would take here. And then you can actually see their frequently asked questions. This is probably where you would want to take your talking points from if you were going to go out and talk to people about this. One of the big things that's a good faculty talking point on this is that the publisher cannot charge an additional article processing charge for open access through the PAR. So they can't say, well, because you're going to be open access with NSF, we're going to charge you half of our open access fee because you won't be able to access the rest of your build and access through someone. They cannot do that. So there's no allowed additional charges for the PAR version of it. So that may be something that's worth trying to enforce early on. They can choose to put in open access fees for the journals open access, but not for the added open access required for their government repositing. Most of the information we have, they've done some webinars and stuff. NSF does a lot of webinars, but most of the information that we have is through the public access rules, either the fact or their initial statement that they released back when they were initially planning it. And once they have a fast lane or NSF account, they'll also have access to the PAR. So you'd have to be working alongside the faculty members to help them go through the repositing process. And I see I'm at my time. Does anyone have any last questions? Please feel free to email me either if you have general questions or if you want me to come and talk more directly to you and your library. Thank you very much, Nina. Is there any last questions?


Pikeville Medical Center is celebrating both Hospital and Nurses Week. Staff members at the hospital are being recognized for their outstanding work in the medical field. The slogan for the week at PMC is We Are Health Care, signifying how much each employee their values, their work and community. Well, the theme for National Hospital Week this week is We Are Health Care, and that is exactly what Pikeville Medical Center is. Each of our team members are very important to the care of the region and our patients. And of course our nurses work hand in hand with our patients to ensure they receive excellent care here at Pikeville Medical Center. This is just a great time for us to honor all of those wonderful people that are dedicated to the care of our patients. Cancer, it's a very scary word, but an outstanding treatment facility was here at home. I was led to the Lawson Cancer Center. As a nurse practitioner at the Lawson Cancer Center, we are champions for our patients. I am so happy for Robert. The treatment worked. In addition to the recognition of everyone's labor, PMC will be hosting an award ceremony on Tuesday May 9th. Justice expresses her gratitude toward everyone involved in upholding the hospital's reputation. There's special recognition going on throughout our organization this week. We have a lot of nice treats going out to all of our clinics, all of our locations, all of our units and floors here at Pikeville Medical Center. We also have an special awards night planned tomorrow night where we will be honoring certain employees for their accomplishments throughout the year. I am truly honored to work with the 3,000 plus employees here at Pikeville Medical Center, whether it be our people who answer the phones, our therapists, our technicians working across Pikeville Medical Center, of course, our doctors and our physicians. It is great to see a group of people come together for the well-being of the region and be so passionate for that cause. So it's great to celebrate them this week. Reporting from Mountain Top News, I'm Nick Colum.


Good afternoon. My name is Heidi Thorpe and I've worked for a government agency in Australia as a data scientist and today I'm going to be talking to you about Fuzzing and how you can use this in your testing How you can use fuzzing to harden your systems mainly against cyber attacks. So First of all, I'm going to introduce the subject What are we going to do? What is fuzzing? It is the automated generation of data for testing for fuzzing to work best It has to be more than just randomly generated invalid data So for instance, you have an image classifier That you're going to train to look for horses If you were going to use random test data Then you might have pictures of an orange or a chair Random pictures random data that you might use You test the classifier with the orange and you say no no that orange is not a horse You test the classifier with the chair and you say well, it's a bit closer.  A donkey is a donkey a horse well not really but Closer than an orange is a unicorn a horse well Still not quite, but it's still closer It's invades closer to what we're expecting, but it's still Unexpected data and could cause unexpected results So what we're going to be doing is creating XML or JSON that looks almost right But gives you substantially more edge cases than you would get using just manual testing The objective is to come up with data that superficially looks correct But it's not because it is machine learning you can automate the generation of the data and the execution of the tests The grade of greatest vulnerabilities Crashes null pointer exceptions buffer overflows you can run thousands of tests and in this way look for more exceptions, I can't show you real Client data, so what we'll be doing is we'll be using MS word data because internally it's XML If you take an MX word document, you'll notice that it's a zip file and you can unzip the zip file And this is the set of data Files that you get when you unzip the zip file It's not a practical example, but it's a demonstration of fuzzing and how to generate XML with the added advantage that we can Import the data back and see the results at the end This is an example of one of the XML files from a Word document and since many rest interfaces Portals, etc. Expect XML or JSON. It's still a valid test So what is neural fuzzing neural fuzzing is a technique using neural networks for fuzz testing in this case using neural Networks to generate test data Why is fuzzing a good idea well fuzzing can save money, but it's not just about saving money We'll start at the programming step the programmer makes a mistake the software then moves through the QA and test cycle The bug is not found The testers are not using fuzzing, but maybe they're not using enough testing Maybe they're not using the right data The software is released and at some point in the future an attacker finds Finds the bug probably they're using fuzzing maybe However, they do not However, they do find the vulnerability they find it and attack the system They send data to the software they cause it to crash they can then steal money data Property whatever they like It could quite literally be death and destruction If an attacker crashed the software in your car and this has actually happened not necessarily XML, but it has happened to people in their car At some point the owner of the software realizes there's something wrong They realize that the computer vulnerability has been found Computer security experts are consulted and they trace the attack back to the original bug Now it is listed as a known vulnerability and the vendor can fix the bug a patch can be released and This is the worst-case scenario and it happens all the time A better bug lifecycle would be this instead of an attacker finding the bug it is found by a security expert using fuzzy They can responsibly discreetly Disclose the vulnerability before it is found by an attacker and it can be the vendor can put a player patch and the bug is not exploited The best-case scenario would be this one Somebody writes a bug the QA testing find it the bug is fixed and it's not part of the software release This lowers the cost of the software and increases the satisfaction for the software users functional testing alongside fuzzing Increases the possibility that a bug is caught before the software is released So how does it work? first testing involves Inputting massive amounts of random data in an attempt to make the software crash During fuzzing a program is executed many times and after each execution the result is recorded The input is modified. Sometimes the whole data Input is changed sometimes only some of it the modified data is then sent To the program and the result recorded along with the input data if the software crashes the result can be Analyzed otherwise the process just continues until a failure happens There are two types of software Fuzzing techniques done fuzz testing where the input data is randomly changed and the reaction to the random data is observed This doesn't may not provide useful results As most applications expect the data to be in a particular format the technique Should still be employed it is as it's reasonable to expect that sometimes the software will receive this random input The other type of Software fuzz tech testing technique is smart fuzz testing changing specific values leveraging knowledge of the underlying format Or expected behavior In this case the application expect input that is all numbers For instance a phone number these users all abide by the rules and input numbers Fuzz testing systematically simulate a User who does not abide by the rules So here we have an analogy For instance a security guard at a party you send a message with your name He looks on a list and sends a reply. Yes or no. Will you get into the party? To fuzz test this guard you need to do something unexpected you say look over there Unexpected behavior look over there. Is it valid input? It's not expected, but it's not random either The unexpected behavior may trigger a failure Fuzz testing systematically simulate a user who does not follow the rules and could cause a failure a Good fuzzer decides what the malformed inputs should look like and generates test cases It then automatically Executes the tests and records any failure it needs to keep any failure It needs to keep records of any failure so that it can be a repeatable process The whole point of fuzzing is to find vulnerabilities in the software before anyone else does This is important from both security and a robustness point of view Sheer power of having fuzzing generate test cases is that you're going to have useful test cases showing up Now we're going to implement a long short-term memory network a neural network and This is how we're going to generate the invalid data Specifically, I'm using recurrent neural networks and Python libraries with TensorFlow to generate new inputs Traditionally on neural networks start from scratch and cannot use prior knowledge People don't start from scratch and every time every time they read or hear something they use knowledge that they have from prior experience Recurrent neural networks are networks with loops Allowing information to flow between the layers and to persist the most important factor with an RNN is The recurrence the loop to the internal state also known as the hidden layer. You just keep looking for as long as there are inputs Consider what happens when the loop is unrolled there is a box for each time step or input in the sequence LSTMs are a special type of RNN and this is what we're using in this instance The chain reveals that recurring neural networks are related to see lists and sequences Consider when trying to predict the last word in some text. I grew up in France, but I do not speak Something recent information implies the next word would be that of a language But which language we need the context of France from further back As the gap grows between the relevant data RNNs are unable to learn the connected information In theory RNNs are capable of connecting these long-term dependencies, but in practice, this is not the case This is an example of an LSTM network X of t is given where t is time Y of t is what we're trying to find and h of t is the hidden layer Long short-term memory networks Usually just LSTMs are a special form of RNN They're designed to avoid the long-term dependency problem that RNNs have Remembering long-term information is their default behavior. The repeating module of an RNN contains a single layer This is the four interacting layers Of an LSTM the key to the LSTM is the cell state the horizontal line running through the unit The cell state runs straight down the entire chain with only some minor linear Interactions, it's easy for information to just flow along it unchanged The LSTM has the ability to forget or remember information This is carefully regulated by the structures called gates Gates are a way of optionally remembering or forgetting information They're composed of a sigmoid neural net layer and a point-wise multiplication operation The first step in that LSTM is to decide what information we're going to throw away from the cell state This decision is made by the sigmoid layer the forget gate layer It looks at t minus one one step in the past and Inputs a number between zero and one to decide what it's how much it's going to forget and how much it's going to remember a One represents completely keep and a zero means completely forget The cell state may for instance remember the name of the present Subject and when it finds a new subject it may throw away the old subject and just remember the new one The next step is to decide which information we're going to store in the cell state This has two parts first the sigmoid layer called the input gate Decides what value we're going to update next the tan layer Creates a vector of the new candidate values. This could be added to the state In the next step, we're going to combine these two to create an update to the state In the example of our language model We'd want to add the name of the new subject to the cell state to replace the old one that we're forgetting is Now time to update the old cell state C minus C of t minus one Into the new cell state C of t The previous steps already decided what we're going to do. We just need to actually do it We multiplied the old cell state By f of t forgetting the things that we decided to forget earlier and then we add C of T this is the new candidate values Scaled by how much we decided to update them in the case of the language model This is where we actually decide what information we're forgetting or remembering and this is the actual implementation of the previous step Where we actually implement what we're forgetting and what we're remembering Now we're going to look at some code and Because most people are not really very good at looking at XML and deciding whether it's correct We're going to look at some Shakespeare sonnets and we're going to generate some Shakespeare sonnets using LSTMs so So what we've got here is We've got the Keras model the sequential model We're loading some okay We're loading some sonnet text and what we've got to do first is turn the sonnets into Sequences and in this case we're turning them into sequences of Ten characters and this is what the neural network is going to use to learn we build the model We're using a hyper parameter called temperature which Depending on the value of it is used to predict the control the randomness so what we've got is All of the different characters that we're using So we have that is our character set and Usually if you're using words then you'd use word to vex or something that's already been trained But in this case we're just using characters and we're going to generate the The sonnet one character at a time So we have the neural network and you can see It's pretty gibberish and There's just random letters and then as it's learning It discovers the word the So we've found found a word and We're learning a bit more and A bit more and this is just one character at a time We're learning and then when we get to About here we say something death not to me so furloughness Dibbinent So it's it's finding words and it's almost kind of like a Sonnet and then as we go further Further down here. We say okay the stars of summer state and then Betreat of all two sir one gives and sing and this is one character at a time Generating sonnets as it goes and then as we get towards the end So we say here We've got something that actually looks Kind of like Shakespeare the brave day sunk In hideous night when I behold the violet past prime and sable curls all Silvered or with white when lofty Trays I see Baron and you say well that that's one character at a time and this of course is The code that I used for XML is exactly this code But as you can see with with sonnets you can actually see it doing something But with XML it's just that much harder and this is exactly the same code But instead of having the sonnet it has some XML as input and you can see it has gibberish Gibberish and then as it gets towards the end it's actually looking like real XML and so that is What I've used not exactly this code not exactly this data But something similar and that's how I've been generating data for testing because generating it manually just Is just too hard whereas generating it automatically generate thousands of records And that's all thank you


I'm waiting Yonja I called you too late, man. I called you that's on me yonja. That's on me How's it going everybody doc today here? We are once again back with some more dragon ball fighters this time playing the main team and we are once again up against the incredible player Fred now we are doing another first to three today with Fred if you didn't see our last set no spoilers, but uh Okay, now I'm gonna spoil it. I got cooked and I'm not getting cooked this time. Try something All right. Well, yeah, we go ahead and get him in the next one and by next one. I do mean next game I'm accepting loss on this one already Now I'm just kidding of course. I never give up never surrender. That's my ninja way something like that. I'm a spark Nice nice. I needed something cheap There's no way there's no way. I'm that bad at the game. Oh, I couldn't even think fast enough My brain don't move that quick down tech. Oh, what a confirm. I can't believe that hits You got a jump. That's super crazy. Also. I'm dead. Wait. No, he can't go on to go. Go on. Can you come right back out?  Oh That was very lucky It had to be like four frames away from me getting hit, but I will take that. Are you kidding me? All right, this time.  I don't think he sparks here. I knew it I knew it. He's too good of a player to spark. He was gonna wait. He was gonna wait or block low. I'll take it I'll take it. Whoo. All right. Now the only difference is he has spark Nice, I'll take I'll take that. We've got to get that wolf bang back step every now and again yamcha buffs It should hit in the corner. We're gonna force his hand again. We're gonna force his hand This time I actually do think he sparks. Oh He tried to he tried to cook me There's no way there's no way. I'm bad at the game. We've got to appreciate a nice two-way each especially in this weather Dude, you've got to appreciate it. Oh, no All right, he can't get out though. I should have solar flared I should have solar flared. It would have been sick. It would have been sick 21 at least lost the blue health So we're in a decent position. This 21 has a lot of health I don't know what he can do here. I'm honestly considering just mashing But I should play defense. Oh Command grab I should have mashed. I knew it. I knew there was my primal it damn I got kicked sideways my primal instinct told me that mashing was the wave and I didn't believe it Damn, I definitely tried something there. I was going for a quick little level three. I'll admit it I'm man enough to admit when I was going for raw level three. Don't take my damage my damage My damage.  This is getting crazy. Oh, okay. We have we've got a little potential here Just a little bit. Did I lose my I thought I lost it that round. Oh Yamcha why oh Yamcha why oh, I mean the young justice call was good, but I just didn't play around it I didn't have faith in Yamcha Okay, what do we do here? I'm honestly thinking about sparking Yeah, we sparked her we sparked there. We just we need the momentum momentum is so big for me And that's not momentum. Oh Beautiful beautiful. I called you too late man. I called you. That's all me. That's all me. That's all me I'm to know you have to live you have to live. No, yeah, I'm sorry. Oh He's dead. I lost I did it way too late. All right Krillin. You got me though Krillin if you got me then let me know you got me then So the player this is bad. This is bad. This is not great. Okay focus up We level three here cuz we just got seven bars. We might as well spend some We're gonna double overhead. I don't think that would have killed double overhead Double overhead. It's a little you know it Krillin's got some gimmicks. Damn. She hits so far man The be the beam seems so safe in my mind that genuinely jumpscared me like bro, what could he possibly do to counter full-screen beam? Yeah, get me out of there bro. Get me out of there. I'm not playing don't don't don't do it again Did I learn my lesson the last time? No, oh wait Krillin can play around this. I just have to survive He's gonna try to command grab me. I'm mashing here No Krillin could have got all that help back to No Okay, okay, it's already 2-0 It's already two nils. Oh, we're gonna have to make it a more than a best of three because I'm getting cooked too fast This is bad. This is so bad I'm getting cooked too fast for videos Okay, risky. Okay, just stay on them stay on them stay on them Okay, he had to spark That was a beautiful spinning blade. Oh into the combo extender huge damage huge. Oh My hitboxing stars. Oh my hitboxing stars that just was not necessary. Oh my goodness Arxis Arxis somebody get Arxis on the phone. This can't be legal Combo and I didn't even tech I I didn't even see myself get hit I didn't see myself get hit so I didn't walk low. Damn. I thought I'd walk low for sure All right Again, I can't really I can't really I can't I can't really use a beam full screen like I want to because I'm gonna get cooked again I'm not trying to get cooked again Uh, do you wake up level three here? Wow, I missed my attack.  That was great Damn, I was so bad at the game that I became good at the game. That was awesome We love to see that we love to see that I was mashing level three. I'll admit it. I'm again. I'll admit it when I get caught mashing level three I'll admit it. Damn. He was ready for me. He was ready for the instant crossover. Damn I should have stayed. I should have stick stay true to just normal thing fast level one into level five Damn My god, this man brought his whole father out to yell at me God it's like life back on the cul-de-sac. That's crazy. All right. We should be fine Oh, he didn't jump forward this time. He didn't jump forward and I over committed Yeah, I know about you, bro. I know about you. I know you like to kick people gohan I know I know what you get you get into. All right. Stay in the corner gohan Stay in the corner gohan. I know you also own a dp gohan is a proud owner of the dp nice Still very winnable still very winnable I'm not jumping forward.  Oh, that was sick. Okay.  That was sick.  I'm gonna fake it. I'm gonna fake it.  That's how you play the game That's how you play the game right there. Damn That's how you play. Whoo Oh, I was clenching my controller extra tight. Damn. All right. We good That's how you play the game right there, baby. That's how you play the game Oh, that's how you play the game now. I'm playing with pure confidence. Oh my goodness. Oh The confidence right now is insane. Oh This is just how you play Dragon Ball fighters right now. God, this is just how you play fighters God, damn, I'm playing fighters right now. That's all I really wanted to see That's all I wanted to see.  Oh sit down. I'm just so into the game right now. I am so into the game right now Damn, I wish that would have hit me. I was still a spark. We still spark. I wish I would have done kept that way shorter Jiren don't die. No, Jiren. Oh last forever in a day. It lasts forever in a day Damn. Oh a little bit shorter arxist arxist. Oh, there's no way.  That was just bad. That was just awful. All right, we've we've really thrown a nice lead here for ourselves Krillin jumping him from behind. Is that even legal? Yo Krillin's breaking rules Krillin is just breaking rules right now. And I actually do not care Krillin just broke a rule. Whatever shout out Krillin Do it do what he's got to do to win. I'm level one Okay, that was sick That was almost very clean. I messed up at the end, but Dude, that was so clean That was me try to be petty. I was trying to be petty. I'll admit I was trying to be petty. I was trying to be petty. I was trying to be petty I was trying to be petty. I was trying to be petty. I was trying to be petty I was trying to be petty. I was trying to be petty. I'm not doing that again I won't do that again. I won't do that again. Look sometimes in order to play fight if that He just DM me. He just DM me instead of that rock killed it.   I'm so focused in order to play fighters. Good.  I'm not cocky. I I took I took away all my pride and arrogance and what it one of my left with bad gameplay.  I'm getting cooked. Oh, let's go. He overstepped his line literally overstep God damn it a little more armor on that. We would have been fine Okay, bad start go on, please six head The double overheads working pretty well. Can't be upset.  Oh, what up Android 21? Last time I read the cross-up. Do you think you would try to cross me up again? You know what? I want to look like I'm good at the game. So I'm gonna read it. Oh, come on I write it. I got keyblast instead of reflect. Damn. I didn't read it though. No Now I look like a scrub.  Oh my god. I'm playing so bad.  He read me like a book. He was waiting for the guard cancel I didn't want to give it to him, but then I thought he over committed day. Oh, I thought he missed Right there. I am dead by the way. This is the end of the set. It was a good set. We lost our confidence Come on.  Well, that's one of them game of fighters. My entire muscles are clenched right now But hell of a set that's gonna wrap it up with Fred again check him out at the links below That's gonna do it for me. I'm exhausted these kind of fighters videos I'm just exhausted afterwards. Hopefully you guys enjoyed it though If you did definitely leave a like comment down below if you want to see more stuff like this and check out some of The other videos that should be on your screen right now other than that I have been dotted away and I will see you in the next one