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OK, I think, I think we'll just crack on. Um So, so welcome everyone. Um Thanks for joining today. Uh And as you can see, as you all know, we're gonna be talking about um we're going through, I see a uh three lecture. This is gonna be done by AC. Um I don't say much more but just questions and stuff. So um feel free to post in the chat. I'll keep an eye on it as well. Um As I can and let him let him know if there are any questions throughout. Um But yeah, other than that, I just hand on over to him. Um Yeah, no worries. Uh Thank you. Uh Hi, everyone. Uh I'm Abhishek, I'm fifth year and Imperial and I'll be doing the uh ICA three talk for me today. So let's get side um Obviously a bit of a disclaimer. I'm not uh I'm not no longer doing a BSC, finish my BSC. Uh I don't mark on the BSC course, I'm not like an official administrator for it. This is all kind of unofficial student, student advice. Um You will have had or will be having uh official uh kind of office minutes with your course leads hopefully at some point where you can ask more specific questions. Um And I'm sure they're very responsive to emails as well if you have any specific questions about, about your course and the and the data you've been given. Um And in terms of slide, there's a lot of there, it's I'm quite bad um because there's, there's a lot of writing on these slides. Um However, slides and the recording will be available afterwards. Um There are some links on the slides that you can access later on as well and any specific questions, feel free to email me. Uh and just one thing, I'm sure none of you will, but uh please don't send the IC three to look at. I'm sure that would be an issue uh that the university will not take too kindly to uh in terms of the context of this talk, go over. What is IC three. And then quite importantly, we'll go over what the point of my talk is. Um How should you approach writing a brief bit on data analysis and statistics and then some tips on writing uh and then tips on writing the introduction, introduction, abstract and lay summary. And then there are some useful like resources and tips that I have at the end as well. So, well, first of all, what does I see for you? I'm sure you've all seen your student guidance document called the data analysis task. You're given some amount of raw data. Uh I know some courses like Neurology, Neuroscience, you have to collect it yourself. Uh You're given a brief idea about why these, why they did the experiments that they did? Typically, it's like a pure science uh kind of molecular lab based type experiment. Um You asked to analyze the data, do a statistical testing, forgot why they were done uh make some figures and then write a 1500 word document says you mini paper about these experiments. So I guess one of the main questions is what's the point of this talk? Um For a uh like you've had in previous years, uh let's say you have AAA formative or summative coming up on some content, there's content to learn. So a talk makes sense because it can distill all that content down uh into some useful slides that you can learn from. You learn about, you know, high yield facts. But really IC three is quite a special one because if we ask ourselves, if I ask myself in my experience where, where do most students struggle with ICA three? Even though it's called the data management task, the, the data management only takes up about 10% of it. And the more difficult part is writing um which will be the focus of my talk. Um This is not going to be an exhaustive statistics lecture. Um We're all you're very intelligent. Um Statistics is something that. Um Yes, it's, it's there in my talk. Um And there are some useful resources linked, but so I'll be on the scope of both my, my expertise and what you kind of what you need for this. I CAA more extensive lecture would be very useful for the final ICA um which you guys will either get or, or just find on youtube or learn about yourself. Um Yeah, it's far, far more important for the final ICA. And really, I want what I want this talk to be is I don't want it to be kind of uh a bubble where you just learn how to write your ICA three and then you take nothing else away from it. I want it to be a good stepping stone for you to do well in this ICA group literature assignment later on. Uh and then your final ICA which is the project right up at presentation. And I've got a bit about one of the hardest parts about this ICA. Um funnily enough referencing uh can be a real struggle with both with this one, your group, uh literature review and your final ICA. Uh However, I have some, I have some tips to make it easy. I don't, I don't think statistics in making the graphs, making the figures is particularly difficult. At least not for this. I ti think there are more challenging aspects of it cramming everything in 1500 woods uh can seem impossible. At times figuring out why they bother doing these experiments. Uh I think is the main, is one of the main challenges of this ICA. And then at the very top, you have the thing on all the ma schemes and that you've been told counter signs in your, in your lectures in your course, critical synthesis, critical analysis, critical summarization, critical appraisal. What is this? It's slightly nebulous term. And hopefully I'll, I'll give my view on it. Maybe you'll understand a little bit better. Maybe I don't know. OK, so some quick points. So some people have already done their ICA three which, which means kind of don't, you know, you, you obviously can ask them, how did they find things? Um may maybe that will help if you've got some friends who did it uh earlier on in the term. Um but don't rely too heavily on, on their advice. Um Importantly about the data, you probably already have your data uh which is quite important and undoubtedly, uh you have been discussing your data and its analysis. This is a kind of inevitable uh consequence of everyone getting the in the course, getting the same data. So what that means is probably already, you probably already know what statistical tests you need to do because you know, from discussion, you've looked at your data, you've googled it. Uh You already kind of have a general idea about the result, what the results are. If you haven't, you'll get across this very quickly. It's only about six or seven experiments. They, they're generally all kind of the same statistical test. Um What do you do now? Because you have, let's say, give or take two weeks to write it and that's what we're talking about now. So, structure of your IC. So you have the lay summary, which is this, uh it's written, uh It's very, it's, it's a bit hard to describe. Uh but it's essentially a kind of general understanding of your paper. It's your entire paper uh or the entire ICA all the experiments distilled down into 500 words that anyone can understand. So it's missing out the super technical details, maybe not mentioning some results which aren't relevant. It's the main point of a paper written in a uh understandable way, a scientific abstract which most of you will be familiar with. Um It's the introduction methods, results, conclusions, uh the key findings, key results and short and sweet. And we have these results companion which is 1500 words for your methods, results and your discussion and your conclusion, results. And I wanna say, don't worry, papers not written in this format. Um Ever full papers ever. Uh They're written everything is its own section and the lay summary is, is kind of optional. It's often for that um in terms of approach, um you've got your data, no doubt you've started playing around with it if you've had time Um So that's a good place to start because you've been given your data for each experiment start by uh looking at the data importing into whatever software you're using, which I'll talk about. Um And make some, make some rough graphs, do some rough statistical tests, uh then double check the tests you've done. All right. Um And as you're doing that, another thing you can do is start thinking about the uh hypotheses that these experiments are based on. Uh So why were these experiments done? You have to figure it out for yourself? That's the main kind of crux of this ICA. Um And that requires a bit of time, requires a bit of thinking, uh requires you to have the results and requires you to have a logical chain of thinking. Um And that obviously comes once you've got your results and then as you're kind of doing all that in the background, you're collecting references, you're thinking about the broad literature which will help you write the bulk of your introduction. Uh You can, if you somehow managed to write the abstract and lay summary first, but ge generally, they come absolutely lost. Uh when I say lost. I don't mean, you know, morning of submission, I mean, uh enough time to make it good because they do care a lot about your late summary. Uh And they want that to be good and that, that's kind of the scientific communication part of this. Uh I see it. So uh this is my first statistic slide and doesn't come up, but that's fine. So, statistics, um I've got a bit of humor here. Um A lot of statistics is outsourced and statistical experts are experts for a reason and we ask for their help all the time. We being uh doctors and future doctors um because there are about a million ways to do one thing and it's an incredibly dense field. Uh Even uh the simple statal test you expected to do kind of complicated. Um However, I have some top tips. So as a kind of baseline, the type of data you have uh is it categorical, is it continuous categorical, is it or ordinal binary uh et cetera um assess normality. So this is whether uh a variable is, is normally distributed or not, there are two tests or the KS test. Um This may be an optional step in your course. Um If they've told you it's an optional step, then assume it's an optional step. Uh If they haven't, then just do it and report it. It's very easy. Um I'll talk about how you can do them kind of software wise. Um And then conduct test, this would probably be a lot of independent T tests. Um The paired T tests are one for the same group at two different points in time and you probably won't end up using that. Um I remember that there are tests used for uh a variable that is normally distributed between parametric. Uh and then there are, there are tests for variants which are not normally distributed respectively. Um I appreciate that this isn't a huge amount of detail. We're not going, you know, through each type of data, we're not going through kind of um you know, going step by step in Excel or Prism uh how to do these tests. Um Ultimately, that isn't the hardest part of the ICA. Um And that isn't uh kind of what I wanted to focus on the talk to be. Um There is a very helpful cheat sheet slash handbook, uh which I've linked to the end of the talk, which essentially explains all the tests you'll ever need for the most part, um which you can take a look at. And uh this is quite an important one. It ultimately, you probably will have more than two groups um in your, in your data, at least I know cardiology did last year. Uh Endocrinology did as well. Um When you do lots and lots of uh physical tests between groups, uh you know, you compare one against 22 against three, then one against three. for example, um the more tests you do, uh the greater likelihood that you're gonna get a significant p value somewhere. So you have to correct for the multiple testing and the most popular method to do that is the one for only multiple test correction um, if I remember correctly last year, uh, on the course I did endocrinology, they told us, don't bother with this, just do your PT test. They actually told us what, what tests to do, uh, and, and how to do them in Excel, which is very helpful. Um And they told us don't bother with the multiple test correction, uh, in other courses, they expect you to just do it. So it's just a disclaimer. If you are doing multiple, multiple uh t tests, then make sure you've checked whether you need a correction or not and importantly for your figures. Um Make sure you list absolutely everything you've done. I've got a quite detailed list of things that you should include in your figures, um and make sure to give the stars for the corresponding P values. Um And I got a bit more on statistics. So, yeah, undoubtedly you've, you've already had a look at um in terms of resources that cheat sheet that I've linked. Um I've linked a youtube video as well which goes over between group differences. Um Let's talk a bit about software. So this is, this comes even even before you've uh before you start to make anything because you need to figure out in what you're gonna make them. Um There are lots of options. Um SSS starter, you have Prism, grandpa Prism, they're the same thing. Uh You've got R programming languages like R and Python and then you've got Excel. Um, what I will say is, of course, it's entirely up to you. Um, what I would say it's, it's not worth your time to go and learn an entirely new programming language just to make seven figures. Um, if you have the time and you want to learn a programming language for whatever reason. Um, oh, well, I'd say for whatever reason, it probably will come in quite handy. Um, if you're interested in academic, academic medicine and you want to take those skills over into your, into your final project. Um I would recommend uh uh with the tiny collection of packages. That's a good place to start just with this ICA though, I would say absolutely use whatever is uh easiest to get your hands on or whatever your course is recommended. Um Prism is quite good because you can download a uh a free version which expires in 30 days after you download it. Um Prism makes really nice looking graphs, like really, really publication worthy nice graphs uh with minimal effort. Um And it also does the uh bars uh for the statistical test automatically. Um And it does the stars automatically and everything and it's really quite easy and probably more importantly, it does the statistical test, it has a statistical test embedded into the software. So you just need to click one button for which test you want and then it will run it and it will spit out P value and that's all you care about. Um And it's very, very painless. Um So you've got the free trial for the super nice upgraded version on your home laptop and then you've got the imperial version, which is available for free on all the imperial computers uh in the library or, or anywhere you go, I guess, um, both are fine. Both, both will work. Um I recommend Prism, I think is very good. Um However, Excel also does everything that Prism does. It makes slightly less nice looking graphs. Um But if it's, if it's, if you're in a time crunch or you simply don't care, um you can use Excel. Uh don't let anyone tell you otherwise it's fine. Uh Excel is quite powerful. Uh probably need to look up how to do the different testing in there, but it definitely works. Um Yeah, that's a bit about the statistical testing. Um you know, case in point. Um Absolutely feel free to, to learn whatever you like about statistics um about, about the kind of software side of things. Um But for this ICA try and keep it simple, don't get out of your depth in terms of you, you know, using the software before you've made your figures. Um So yeah, that was the two slides I got statistics. Um I know it wasn't, uh it wasn't a super in depth statistical run through, but it's what it's, it's what's important for this ICA. Um And there are some helpful resources at the end uh and a youtube video which you can, which you can watch as well. Um The rest of this talk hopefully will be uh very helpful because it gives all the kind of tips and tricks that you, that the course leads. They don't really tell you these things, but I hope will be very useful. So, figures, how do you go about making figures? So once you made your graphs, um you've got a limit of seven figures, I think. So, let's say you've got your data in, you've done the testing, you have all your P values. Uh You've got the kind of bars to show the, the significance of the P value, whether it's not significant uh between the groups. Um Once you got your graphs, this is kind of standard regardless of where you make them export your graphs as png's or JP ES. Uh get open a powerpoint presentation with seven slides, delete everything from them, import them in there and then construct the rest of your figure. Um And then you can export each individual slide as a kind of package of slides to your computer uh at really high quality uh which makes really nice looking uh figures. So that won't be any blurring us or kind of technical issues later on down the line when you come to submit and turn it in important points about figures. So the figure title, right? So you've got the body of the figure which is a bunch of which I which I'll talk about. But the figure title really, it's not a description of the experiment. Uh It's not what you did. It's actually the main takeaway from that experiment. So someone should be able to look at the, look at the graph uh the bar chart that you made probably uh and then look at the title and then understand what's going on, right? The title should be a one line distillation of what is the main takeaway from this experiment. I think people forget this. They uh they write their figure titles as kind of what, what the experiment was or how it was conducted and they, they make it three lines long. It should be really simply what is the conclusion from this experiment? Keep it really nice and short and succinct uh don't include a huge amount of detail, but just enough to make it understandable. And then below that, your figure legend should give a brief methodology now. So you have, this is an important point. You have 1500 words for this results. Compendium which is not a lot. Um I think what people have done in previous years is they've looked at these figures and they've said, hm I can put a lot of methodology in here. Um So they've shrunk it down to like a font size three or something and they've written, you know, seven lines about the methods for that experiment um that will not run with the examiners, they're privy to things like this. So please give, give a brief methodology which includes the names of mice or whatever cells you use. Um timing dosages, what they got, what medication they got, et cetera. But don't, don't go overboard. Uh Keep it simple, keep it three or four lines, keep the font sides readable. Um And then kind of in that you should have one run after that. You've got title. Uh you've got that brief methodology at the very end, it's quite an important part. It's kind of consistent in papers. They expect to see this um give the gender of the animals uh if applicable. So for example, all mice were female or male, all cells were of a certain variety uh where they were obtained, for example, give a sample size that brackets N equals how many uh how the data was presented statistically. Um So normally distributed data, typically you would give the mean plus some deviation, non normally distributed data, you'd give the medium plus minus interquartile range. Um And also an important note note for the statistics don't do anything to outliers. This is well beyond the scope, this IC three is not looking for you to do super complicated statistical analysis, don't do anything to outliers. Um And just stick to uh analyzing the data, doing the, doing the test, doing the test correction, no need for anything else. Um Say what his test was used. So that includes whether you, if you assess the no uh whether the data was normally distributed. Um correction, if you use one that would be bone for any, for most of you and then give the final, the kind of the final line should be one star means uh this level of P value, two stars means below this level of P value uh three stars, et cetera and that's kind of standard. Um And then any abbreviations. So H FD high diet list, any abbreviations you use in your figure or in your paper in general, if it's relevant to the figure. OK. So once you, once you've done the figures, so we'll assume that you've uh you've made your figures, you've exported them, they look nice right now. You have to get on to actually writing the ICA, which is a difficult part. So there, there's a couple of steps to this. So there's figuring out what the hypotheses were some of the experiments. There's figuring out kind of what critical synthesis is, critical summary, critical analysis, uh they call it many things. Um what is it? And then this c which is actually getting done in writing. So in terms of probably the most confusing part of this ICA is figuring out why these experiments were done. Unfortunately, I can't give huge amounts of specific advice because it's specific to your data and the papers that you find. So what I would say is you have to use your own situation. You have to be like a detective. Take the experiments you've done, you know what they used, what medications or compounds, cells, mice, et cetera, put those into public. See what's, what's happening recently. There's really no point. Don't find a paper from like 1975. Find a paper that's recent, that's done something similar. Don't have a read of that whole paper but have a read. I don't mean the abstract actually read it, read the methods, read the results, read the discussion and then ask yourself. OK. So was there a gap left by this paper? Was there a scope for more work? Does, does the experiments I've been given for this ICA fit into that scope? If so then you have a kind of narrative hypothesis to work on. Um And that's, and those papers you can reference in your introduction, look at the key papers in the field. You should have a general idea about what's going on in the field from your lectures. Um But get all of these papers up on pub met, read all of them, make notes about them and try to remember that this is a story, right? Your the experiments that uh have been done uh are part of a are part of a narrative, make sure there's a cohesive narrative from experiment one through to experiment seven that can be followed in your writing. Uh And this is true. Both this ICA, any ICA you do any paper you write. Um And also also from your introduction, uh you're not describing seven ency experiments. You, you're describing a story about uh a group of scientists trying to figure something out. So remember that um and kind of as you as you're going through take note of the papers, save them in a document, save links to them in a document, save them in your reference manager as well. We'll, we'll talk about reference managers as well. Um Always ask, you know, why was this done? Was there a gap here? Um And then once you found that out, settle on, on a couple of ideas, whittle them down and then you can, you can start writing in more detail. So aren't you worry about, about critical synthesis or critical analysis, critical summarization? It has many names. Um If you want to do well, this ii would be bold as to say, this is probably the skill that they want you to take away from uh the entire BSC as much as possible because it's, it's what separates a good scientific writer from a really, really excellent scientific writer. Um writing code, collecting, analyzing data, anyone who could do that. Um A lot of people can do that. Um And there are a lot of people more specifically trained to do those things but actually analyzing appropriately, writing about it, appropriately writing it in a logical manner. Communicating it properly. Um Thinking about the discussion, the implications, the limitations. That's, that's really the crux of what, what this ICA is trying to teach you indeed, what the whole course tries and teaches you as well. Is that writing abilities? And so what is it? So I'll show you an example of what it's not um A L did this bl, did that, uh conversely cl did something different and they found out something different that is not Critical Synthesis. And it's really tempting to write that because it's, it's, it's quite easy because, you know, it shows that you read the papers. Um if you read the papers, you'll have understanding of, of what they did and what they found. And so it's really tempting to just go and write this author. They did this experiment and they, then they, then they found this, the, the second author, they did this, they found that. But on the other hand, you know, someone else did something else and they found something else, unfortunately, listing out authors and what they did is not Critical Synthesis. And it is, it was, is not what will kind of elevate your writing to the next level. It's unless there are only like two papers in your field of study, listing out the names of the individual authors and writing about them is not, is not what Critical Synthesis are about. Critical Synthesis is. Oh it's very difficult to explain, but it's, it's asking and answering the question, how does your, how does this work? How does your work fit into the broader context of the literature? And that means many things um what do the results mean? Have they actually showed something? Have they been inconclusive? What's the reason for that? Uh And what's the reason for that is answered by looking at other papers? Has the me is the methodology different from other papers. Why is the methodology different from other papers? Um has technology improved? Have the, have the uh the means to get those methods done improved in recent years? Uh Was it not available, you know, 10 years ago when other other people were working on it are the result results surprising? Are they in line with what you'd expect from kind of kind of mechanistic understanding of the biology? Are there confounding variables? This is an important one. Are there other factors that play which could be leading to the hypothesis? And a lot of experiments are designed to eliminate confounding variables which is um but that's, that's still something to think about. Um Could there be some other mechanism that we don't understand one of the limitations of my work? Um Or, or indeed of the other papers, are there limitations to all of them? Have we improved upon the work that's been done? And that's critical synthesis um kind of explained uh hopefully succinctly um uh important to remember just because one thing has improved or changed, doesn't mean everything else will, don't assume, you know, things have to be demonstrated experimentally uh in order for you to come to a conclusion. If it hasn't been demonstrated, then you can only, you can in a discussion, you can hypothesize that it may create a change in something else, but that requires future work, which is a point that you can get. Um honestly, you know, it sounds terrible because um I'm giving a lecture on it but the absolute best way you can get really, really good at this is having a read of really good papers and the discussions. Um new nature is an example because that, that's kind of the background and the style of writing. But when you're, you know, just having a read of them, seeing how they link together work, they talk about their limitations but they, they don't list out papers but they, they summarize all of this work. You know, they'll reference 10 papers in one sentence and they'll, and they'll say something like previous results have been inconclusive, likely due to very methodology, they'll link 10 papers. That is, that is critical synthesis because you've taken all that information, you realize that they're all very similar and so you've love them all together and have reading more and more, uh the more you read, the, the better you'll write, uh reading more and more gives you an understanding of how er scientific writing is done at a really high level. Um And that's the really, really the best way to get better at critical synthesis. Not help uh not just helpful for this ICA but for every other ICA that you do in the future, um in terms of discussion, the best, this is probably where critical synthesis comes in the most. Because in your methodology and results, you're, you know, you're giving your results. Uh and you're saying what was done fine, you don't need to do any critical synthesis there. In your introduction, you need to do a little bit. You need to distill probably a quite a lot of literature into not that many words. So you need to, you need to pick out what's important and what's not. Again, we talked about, you know, a bunch of experiments, a bunch of papers, they all do the same thing. It's fine, you can lump them together reference and they won go. What was the main point? What did they all show? Um Were the results? Were they different? Why? Um And then why did those papers leave a gap for our work? What, what question was left unanswered, unanswered? Um So as you're going along and you're writing, ask yourself why? Like what's the relevance of this? So for example, uh you know, with that sentence I gave you a L did this bi did this and found this, you know, if you ask yourself why? Well, it's not really, you haven't written it, you instead of writing what they found what the, what the numbers of the results were in however many mice or cells, for example, uh write what they found in plain English and then answer for yourself, why was it important or why was it relevant? What did they show? So, taking one whole 3000 word paper, you know, that's probably very dense with a lot of detail, ti it down into maybe a sentence worth what's the key takeaway? Always ask yourself what is what I'm writing relevant. You know, if I, when I'm reading it, when I'm thinking about it, ask why, always ask why. And then I've got a handy list of things that your discussion should do. Most of all. It should, it should answer simply because you've got your results section, it should answer simply. What does that work? Well, you know, what's the key takeaway? So within your discussion, there's like a, you know, maybe a 33 line conclusion because people read papers out of uh you know, out of sync, they don't go from introduction to uh conclusion. So very simply what does your work show? How does that work fit in with the rest of the literature? So what this means is how does it compare to previous experiments? Has anyone done these experiments? If they have done these experiments? Um What did they do differently? What did they find differently? And importantly, why did they find these things differently. You can apothesar and connections between papers and methodologies. You can uh draw on your understanding of the mechanisms and the biology and you can reference those papers as well. So it's like this interconnected spider diagram. You know, you're constantly pulling references from uh here and here to supplement the the picture of the the picture of your, of your paper. What your paper is showing, why are the results significant? Um This is quite an important one like, you know, what does it mean? This is quite important for the lay summary because in the lay summary, you know, people don't really care, you know about cells, they don't want to hear about cells, they wanna hear about um clinically. What does it mean? Actually, this is actually very relevant to kind of um why our results in? Can you wanna put this in a term that I'm sure all of you will be familiar with kind of the clinical aspect? Um How does this relate to clinical medicine or clinical, you know, health or health intervention or global health or epidemiology, things like that? And then kind of at the end you wanna talk about, OK, where do we go from here? All work has done this, maybe there was some limitations, maybe things could have been done better. How would it be expanded to the point in the future? Um And then you want to talk about limitations, however, you want to, you want to not, you know, shoot yourself in the foot, um Talk about your limitations but don't write, you know, 20 lines about why your results or why your methodology was awful. Um You wanna write a little bit about what could have been improved, what could have been done better, but don't go overboard with it because then you're not making a good case for your, for your paper uh for your experiments. OK. And in terms of writing, yeah, I think this is, it's kind of like a metaphorical kind of wishy washy understanding. But I of nature papers uh one's relevant in your field. I'm sure you've been uh have been sent some by your by your course leads. But the really good critical syntheses that you read uh are those discussion sections which take the experiments and then they fit them in with the rest of the literature and they create connections and they create uh that kind of interconnected web because you're the research that you're doing the experiments they fit in somewhere in that web. Um There's a reason why they were done and it's up to you to find those things. And yeah, I in your introduction, you will undoubtedly have, you know, 50 references. Are you going to write 50 lines, each line dedicated to one of those papers? Absolutely not. You can summarize quite a lot of them. Um integrated example, pro demonstrated X to be a vital component of health related interventions and then in that reference, you have like five papers which all show the same thing in slightly varying cohorts of people or animals. For example. Um, this is an important point for writing. You're not writing a novel even though it's a story. Um, it's a scientific story. Uh, you're not writing a novel, you may think that you have the ability to write six line long sentences with, you know, lots of hyphens and commas and semi colons. Uh I'm sure I'm sure you do. However, for the purpose of, of the ICA S, keep them short uh as you get better and better, you'll be able to craft kind of longer sentences. Um but try to keep them within two lines, linking words. However, therefore, conversely, furthermore, moreover, et cetera, they have maintained the flow of your writing and they allow you to introduce new concepts uh which is very helpful for the introduction and the discussion. OK. So now, now we've covered the uh kind of quick synthesis, what's going in your discussion, how you should approach it, how you can get better at it uh to live about the introduction. This is where you introduce your story, right? So you give a really basic understanding of where did this? Because at some point, someone had to start doing this research and you probably have some references from like 19 seventies or something like that. Find out why uh why they were doing that? Well, when was the interest, what was the interest in this particular field of study? I think you kind of do a story about how that research adapted, what was done. And then most importantly, what was the pertinent question left over which your work answers? Uh All the experiments you've been given answer. That's the most important part of the introduction. Um No need to have a separate aims. Section. Aims are hypotheses, kind of the same thing that should be your last paragraph, right? What were the aims? Uh What did you hypothesize? You don't, I don't think you need a formal sentence saying we ize that we would see this but it's you, you can do if you want it's your liking. Um And then you give a brief idea, give a brief idea about what you did um about what the methodology was, what animals used. Uh For example, or cells, uh free instruction is really helpful to gather whatever references you can ahead of time. Um Or you know, some, you're probably already doing this because you've got your data. So you've been looking at the research um in terms of references, II, didn't realize I forgot to put in a slide here. Um I highly recommend using a reference manager uh Endnote or men, they both integrate with word. They both have decent enough plugins. Endnote requires a little bit more set up, but I think it's much more reliable than men. Um If you're short on time or you can't be bothered men is just fine. Uh They make referencing a breeze uh and really, really simple. And for your introduction, you will have to condense a fair amount of literature, try and find the pertinent gap question in the literature. Everything else is based off of this, everything that follows your introduction. So the abstract and lay summary, I would recommend write your scientific abstract, but this is the introduction methods results conclusion. Um This is where you introduction, you get a couple lines, introduction of what what is the basis of this work? What are the different molecules and things you're looking at? Um your methods is is a general understanding is like it is a very broad overview of what experiments were done. Um And then your results section um give the results in plain English, then give ap value of or the P value for that experiment if it was significant or it wasn't significant less the P value. If it's less than naught point naught five, you can just put less than a 0.05. You don't have to put the whatever the exact decimal number was uh that's fine. Um It's greater naught 0.05. Yeah, same thing. Um You may give, you know, like percentages, there was this much percentage decrease or whatever, but that's entirely up to you really, they're looking for you to communicate your results in a really succinct short uh punchy way. And once you've done that, write your conclusions, obviously, that should be like, you know, three lines of what is the main takeaway from your work from the work. Uh What did you find? And a brief bit about if you have words about what could be done in the future? Lay summary. This is actually, I think, I think people stress a lot about the lay summary because they don't have any idea how to write it. Look up a nature short communication. It's linked on the final side of this powerpoint. Um They're really good. It should be written in this style. They're a condensed, simplified in plain English, your entire work, your entire ICA 500 words simply. Uh Yeah. Imagine someone non scientific with not a scientific background. All right, sorry. A basic scientific background is what they say. Um He's reading the book and they should be able to understand it. If you have the time you can have someone read it and check it. That's fine and you can throw some humor in there as well. Um I did last year. They, they quite like that. Um Obviously if it's not a humorous topic, then please don't. Um However, ours was about olive oil and so I threw in a thing about, you know, you might wanna add olive oil to your next meal. They found that funny. Um It's part of the whole, you know, lay summary aspect to it. So resources So that is actually most of the advice I have uh right on time. I have some resources here. So I've got this uh really excellent PDF that goes over pretty much every physical test you would ever use and covers things like regression as well. If you're interested, it's nice to have on hand because sometimes you just need to check what test you're using. Um I've linked to nature short communication here as well. Have a read of it, have a look at the style that it's written in. Um your lay summary should be written in the same style and I link some youtube videos from a good youtube channel um as well. And that's the end of my talk. Let me check if there are any questions. Nope, no questions. Uh If you do have questions actually, um feel free to put them in the uh in the chat and I'll get around to answering them. Yeah. Um Perfect. Thank you so much for that. Um II thought that was really good. I was sure everyone else did as well. Um Just so I think for questions, I got some feedback last time just saying that that we didn't leave enough time. So I think we'll just wait for like a couple of minutes. I cos I guess people got to type out their questions. Yeah, of course. Um And then I as well, obviously, um I left his um email as well so you could hopefully if that's fine if you can even after it. Yeah, of course. Ok, perfect. We'll just give it a couple of minutes before we uh finish things off. Oh, you want the, the links in the chart? Uh Yes, of course. Uh Give me one second and I'll put them in the, in the truck. Um, also as a note guys, I just, everyone watching, so I'll below the slides, um, and the recording as well onto meal after. Um I should be done by this evening. Um So you can see the slides and stuff as well. I think it's a non question. Yeah, this is a good question. Um If you haven't received a this. So yet, how do you suggest going on about researching the topic once you receive the data? Um We probably have to infer the topic from our data as they don't give out a specific topic. So they'll uh yeah, you're right. Um I know some courses are a bit late to give up the data. Um Once you've got the data, you should, it should come with a, uh you know, a description of what each experiment was, tell you what, what they're using, what they're doing. Um And then from that, you kind of have to infer why they were doing this. Obviously, they follow logic. The experiment one will be some kind of, they'll do like a baseline experiment to show that they are doing what they say they're doing, they'll be checking variables and things like that. Um I would say have a look at the the pieces of text that come with that they, that the course gives to you with each experiment. Highlight the key words like what mice, for example, like what cells are they using? What mice are they using? Put everything into PUBMED and just have a look, right? Find out what they do, um Google it, um find out what everything is just to give you a basic understanding. And once you've got a basic understanding, figure out what, what each experiment is showing because they'll do things, but they won't say what it showed. That's for you to infer once you've got the results, the results should also of each experiment should guide you towards why they did something um because II didn't really say, but they're not gonna give you a data set where they did a bunch of experiments and they didn't get significant values and it was really disappointing and it was really bad. They'll give you a data set where they got the results the day people who did the experiments were expecting there will be significant results there, there will be positive findings that you can comment on. It's not gonna be a case of all the, all the experiments are gonna come out nonsignificant and you're gonna have to both find out why they did the experiments and then justify why the experiments didn't work Right. So, yes, you do have to infer those, get out the keywords, put everything into PUBMED, find papers, um and try it, try piece it all together. It's really difficult and it's all course dependent as well as, you know, every course is different. They have different experiments. Um My key tip would be, it would be the, the people giving you the data uh will give you data and experiments which are similar to what they've been telling you about already. Um Chances are it will come out of their lab or someone they knows lab, so it will be related to what they do. The course leads do because they are academics. Um Yeah, have a look at that. No worries. Ok. So, II mean, I think that's probably enough time for anyone you had a question um to, to type it up. So if there's no more questions, um we could probably call it a day. Um Yeah, once again, thank you very much. Um Before that talk Abha um Yeah. Uh and I guess if no one has anything else um then have a nice evening everyone. Uh Good luck. Good luck. I see. Alright. Thank you so much. Good luck everyone.