YouTube Video tz--Ctx0CiU  ## Metadata - Author: [[YouTube]] - Full Title: YouTube Video tz--Ctx0CiU - Category: #articles - Document Tags: [[networking]] [[research]] [[thesis]] - Summary: YouTube Video tz--Ctx0CiU - URL: https://www.youtube.com/watch?v=tz--Ctx0CiU PhDs can feel so weird and overwhelming,like there's no structure to them. But infact, what we don't like to admit toourselves is that there is a structureto a PhD, and if you follow this, it'llfeel like cheating. So, the first thingyou need to know is that there are fivesteps, and I call it the scale framework or blueprint. The S in the framework stands for starting with theend in mind. So, what we've got to do islook at where we're ending up, andthere are two levels in which we need tothink about where we want to end up. Thefirst thing we want to know is what aPhD thesis or PhD outcome in your fieldlooks like, and it's really simple. Weneed to turn it into the basic math youneed to go through a thesis, or something that you want to emulate inyour field. It needs to be kind of likeadjacent to your field, if nothing else,and you want to go through and have a look at how long it is. So, thisone is like, you know, maybe 205 to 260 pages. I noticed, and it's got lots offigures; it's got lots of text. I'll gothrough and be like, okay, how manyindividual figures are there in here? Howmany individual kind of like studies areThere in here, maybe it's one study perchapter, like it is in here, that tells mewhat I need to do throughout my three, four,five plus years during my research. Then I can think about managingthat backwards and go, okay, if I needto do four big studies, one per chapter,and each chapter, each study has got like20 figures, how does that look on aday-to-day basis or a week-to-week basis?Do I need to produce one figure per week?Do I need to produce one figure everymonth? What is it that actually getsme to this bit? Starting with the endin mind means that you'll have somewhere to aim for,because we don't like to admit that, in fact, a lotof them are very similar. If you were to look at someone else in this field, evenin my research group, their thesis wouldbe of a similar thickness; it would have asimilar amount of stuff inside, becausethat's what it takes to convince someonethat you're worthy of a PhD. So startwith the end in mind. Have a look attheses from your research group and fromother people in your research fieldaround the world, because that's what ittakes to get a PhD in your field. Look atfigures, look at the schematics... How many individual studies are therethat will give you something to aim for?And it’s very important becausesometimes we feel like a PhD is justthis unstructured unknown thing thatwe’ve got to kind of scramble together.Someone's already done it for you; youjust need to find the data and thesecond level to this: start with the endin mind, which arguably is more important. What do you want to do at the end ofyour PhD? Do you want to go into academia?Do you want to go into industry? Do youwant to do something else completely?Because your PhD is going to lookdifferent from someone who wants to gointo academia. If you, like me, wanted togo into science communication or intoindustry or somewhere else, so you needto then know what you need to do throughyour PhD to ensure that your exitout the other end of a PhD is actuallysort of like not stressful, and you’vegot all the skills and the networks thatyou need to make sure that thattransition into your next career is seamless. Now, the problem about this is, ifit’s outside of academia, it takes alittle bit more work to get your fingersin those pies and get those little digits. Those disgusting little sweaty digitsinto those pies, but it's well worth itbecause if you know from the start whereyou want to go, if you've got an inkling,if nothing else, you'll know what youneed to do to get to the end. To behonest with you, so many PhD studentsdon't think about this, and it causes somuch stress throughout their PhD. You're notgoing to be one of them; start with theend in mind in those two ways. The C in scale stands for create. Now weneed to create things at three differentlevels throughout your PhD. You need tocreate ideas, so let's have a look at alittle bit of kind of a funnel, if youwill, a little funnel in it goes: ideas.You need to be thinking about ideaspretty much all the time. You come upwith an idea and then you go and dosomething about that idea. You come upwith an idea; you evaluate it. Is it worthdoing? Is it not worth doing? Then youpursue that idea to see what happens. Ideas are the currency, the backbone, thesoup that you slurp up during your PhD.You need to be constantly slurpingthat idea soup, because if you don't,you're just going to run out of thingsto do, and you're going to feel lost. You're going to feel like you're justsort of wandering around. Ideas arewhat keep things going, so create themregularly. Set aside time for looking atthe literature and coming up with ideas.That's why we read the literature; it'snot about sitting in thebath and thinking, "Oh, that's a brandnew idea that no one's everthought about." You build it off the backof someone else's work, and you think toyourself, "Oh, I wonder if they could havedone this. I wonder if they did do this. Iwonder if that is an awesome thing thatI should explore." That is what ideas areall about. So, you need to create ideasall the time, or this stands forcreation. There we are: create number one. That's the first level: create ideasthroughout your PhD, especially importantin the early part. Then we put ideasinto our data. So, we want to be creatingdata. So, first of all, we create ideas.Secondly, we create data, data, data, data.Create, create, create. Data takes a numberof forms, whether or not it's raw data.Well, I'll write this down foryou so it looks like I know what I'mtalking about. Okay, so we got raw data.This is important because you need to goOut and just produce raw data, and thenafter that we look at analysis. Analytical work like that, um, and then we look atanalysis, and then three we talk aboutsort of presentation, you know, creatinggraphs, figures, things that we canpublish, and really sort of like persuadepeople that what we've done shows whatwe're saying it does. So then we end upwith, I don't know, let's just say figuresor outputs down in the bottom.So then we're creating this — creating ofdata should be something you set asidetime for every single week. Remember, wesaid start with the end in mind; we knowhow many figures we need to produceroughly for our research field. We know how many times we need to produce sortof like data. So make sure you areconstantly producing data. If you havea regular research — sort of like seminaror regular meeting with your supervisors,make sure you have new data and newideas to put forward to them everysingle time because that means you arecreating a PhD, which relies on creating,creating, creating; and that brings us tothe last bit: we've got output. So hereis the output for creatingoutput. To create output, what does that looklike? It looks like figures. Oh, I'vedropped mypen. I'm back. It looks like creatingfigures; it looks like creating papersright before you are ready. Look at thefigures you've got put together. Storiesand output is a story. Let's put thatthere: an output is essentially aresearch story that you can tell. It hasthis idea, it has this data, and it hasthis conclusion. Super, super easy. Youneed to be creating these throughoutyour PhD. If you’re not creating, you’renot in a PhD program. The next thing we need to lookat is A, which stands for automate.You need to automate the processes asmuch as possible. Now, with AI tools andsort of code that you can createyourself, you can do that with AItools as well. Create your own littlecode micro things. Um, anyway, what I meanwith that is each one of these you canactually automate. Ideas; we are nowreally good at coming up with ideas. Whyis that notworking? Donk.Don. Okay.Ah, that was white. Out all right. Let’s have a look. We need to automate theidea generation. Automating the ideageneration means going to AI tools likeElicit, like SI Space, like Consensus, andjust coming up with ideas using semanticsearch. Go to these tools and think,"Is this an idea that I should pursue?Is it interesting to me? Does it fit inwith my research?" That is so veryimportant. So, automate the idea creationby going to these AI tools and askingsemantic questions. If you have an ideapop into your head, boom, put it in. Ifyou’ve got an interesting paper, put thatpaper into an AI tool like ChatGPT, Deep Seek, or wherever you want to put it, and say,"Hey, what are some ideas that can begenerated from the back end of thisresearch? It will help you come up withthose ideas because sometimes caffeinejust isn’t enough. We also can automatesome of the data. Now, I don’t mean comeup with the data; I mean presenting thedata and analyzing it. Toolslike Julius AI are great at getting that first touch,Julius—oh no, there’s a “u” in there, okay.Like thison bonon, okay, Julius AIis fantastic at helping you with that. First, pass over your data, so upload thatdata to Julius AI and think to yourself,"Hmm, I wonder what I can do with this data?"What does this show you if you're noteven sure you can say, "I'm doing this aspart of this research project," andexplain the research project? Whatconclusions do you think you can draw fromthis data? Use that as your sort ofdiving into the data. Create data, analyze that data, andyou can even create the outputs and thefigures. Now, with Julius AI, you can evencreate lovely little bar charts. You caneven create nice schematics, scattergraphs— all sorts of stuff is now available to be automated. Go check itout. Um, and also, then we can automatethat output as well. We can automate thatoutput by having our research story andthen saying, "I need to create thisresearch story. Create it for me." You canuse loads of AI tools. I prefer ChatGPT,Canvas, but I know loads of academicsabsolutely love Claude for helpingautomate that output. You need to putyour own ideas in, your own data, sort ofconclusions in, but it will help youcreate that first draft of your research. Project automation has never beenso easy in a PhD; you just need to makesure you use it ethically and you use itto support your writing and creating rather than doing the like-from-nothing creation process. I think that'swhat I mean. Yeah, that's what I mean. Allright, then that is automation. AI toolsthis whole channel is about it, so gocheck out my channel, go have a lookat all of the AI tools I've reviewed andalso check out this video where Italk about the top AI tools for 2025, andI think you'll absolutely find somethingin there that will automate the outputof your PhD. The "L" in scale stands for leverage, so there we are. Oh, we'restarting to run out of space; I'll use anew color. Let's have a look, let’s gothisone leverage. All right, as a PhD student,you don’t have much leverage, but whatyou do have as leverage is time andfreedom. You've got a number of years toactually explore ideas that areinteresting to you and your researchsupervisor. The way you can leverage thatis by having a multi-threaded project. Now this is what I mean; as you'regoing along with your project, you onlyYou need one idea to start it off. You'relike, "Oh, I like that idea. Let's carry on."I'm going to sort of explore that. I'mgoing to get data from that, and whenyou've got data from that, then you think,"Oh, actually, I also think I shouldexplore this thing." And then you end upwith these parallel research projects. These parallel research projects areyour leverage and also your insurance tomake sure that your PhD isn't just onebig sort of like book of failure. Minenow is a book of lies because this isall been superseded with new information.However, that is what every good thesisturns into eventually. Anyway, I amsidetracked. When we end up with leverage,we end up, uh, okay down here withmulti-thread. And then we continue thisone and this research project, and thenmaybe we get some more data. Maybe itcomes off this project, and we're like, "Oh,we like that idea." And you're always sortof like keeping track of all of thesedifferent sort of ideas, and we'recreating data and outputs for each oneof them. And every so often, somethingjust won't work. It just will not work. Itwill fail, and that's when we can get ridof it. Here, I'll make it a nice big red color. So we're not confused; we get ridof that research track. Maybe that oneand that one for some reason just didn’twork. We don’t have the time, we don’thave the money, we don’t have theequipment. The actual idea didn’t sortof pan out in the end. But by havingthese multi-threaded research projects,it means that when something goeswrong, we’re not panicking. And it’simportant that even in the smallest of research fields, you canalways take a little bit of a side stepout and follow a new idea in parallel towhat you’re doing. And by doing that, as you get closer to completion, you’ll havethe things that are working. Another thing in leverage that Iabsolutely love and I’ve talked about onthis channel a lot is the 80/20 principle,where you look every so often atthe research that you’re doing and youask yourself, "What 20% of my effortsare giving me 80% of the results?" Thoseare going to be the things that carry you through to a PhD, and it’sgoing to get rid of the things thatyou’re spending a lot of time and efforton but actually aren’t giving you therewards, which are the data and the outputs. That you need to get to a PhD, you maylike some of these more than the others,but it's better to kill it early so itdoesn't sort of eat into thatleverage, which is your time and freedom.And then at the end, you'll see that allof these things come together into anice PhD. Oh, there we are, nice PhD.Everyone's happy, your supervisor's happy,and you haven't wasted all that timechasing stuff that isn't working. So,leverage. You don't have much of it, butthat's what you do have, and it'sprobably the most important thing: timeand freedom. Use it wisely. And the last thing you need to know about the E inscale is expertise. You need to exploitexpertise. What does that mean? Well,during a PhD, you are surrounded by minds,by people's minds, by experts. Thoseexperts can be postdocs, they can be sort oflike third-year PhD students near completion,they can be actual tenured professors. But you aresurrounded by minds, and so few PhDstudents actually leverage the expertisearound them and exploit it to the pointwhere it's sort of valuable for them. Onething I like to do during any sort ofresearch project — I've done it heaps. Just sort of arrange a meeting withsomeone who's adjacent to yourresearch field in your department or askto speak to the senior researcheror senior postdoc in your lab and say, "Hey,could we sit down and talk about this?"Everyone's going to absolutely lovedoing that because it's going todistract them from their own internalthoughts about why they're even doingall this research and academia stuff inthe first place—a welcome distraction ifyou ask me. So here we have, um,exploit,exploitexpertise. All right, this is whereyou're going to reach out to people andyou're going to exploit them for theirminds. This means that if you have aresearch project or a paper that youjust want someone to look over, send itout to them. Say, "Hey, would you mindtaking a look at this?" Obviously ifyou're allowed to, and all the sort ofdata security stuff is all right. But you can say, "Hey, is this good?"Actually, some of the best things Ilearned during my PhD weren't fromreading, weren't from sitting down with athesis, weren't from sitting down lookingOver sort of Google Scholar, it wasactually conversations with academicsand experts in different fields. I usedto sit down with sort of like a mentorregularly who wasn’t even in my field,but he would often bring anotherviewpoint. It’s not about the techniques;it’s not about the individual kinds ofsystems and methods you use. It’sabout having someone clever look at whatyou’re doing and say, "Oh, what about this? What about this? What about this?"Exploit it as much as you can. This is probablyone of the only times in your life, ifyou don’t decide to stay in academia,where you are surrounded by brilliantminds. They may be weird; they may havehairy ears and their nose hair is long, butthey have brilliant minds. It’s kind oflike a balancing game: you get a goodmind, but you get hairy ears. We’ve allbeen there. Anyway, this is your time toactually exploit that expertise. If youhave a paper that you’re not sure isquite ready just yet, send it to them andsay, "Hey, I’m not quite sure this is ready,but would you take a look at it?"They'll absolutely do it because onething academics love is being supercritical. Be critical about other people'swork; they absolutely love it, and theywill jump at the chance to be a littlebit mean to you in writing. Don't takeit personally; they just love doing it, soexploit that expertise. This brings us tothe end scale: start with the end in mind.Create, create, create; ideas create data,create outputs, automate everything youcan. This is the world and time ofautomation, automation, automation. I'vebeen talking for too long. Leverage; makesure you leverage the two things PhDstudents have, which are the freedom toexplore what you want, quite often,depending on your supervisor, and alsotime. Make sure that you're able to sortof create a multi-threaded project thatyou weave together at the end, killingoff stuff that doesn't work as you go.Exploit that expertise because it'sthere to be exploited. They absolutelylove it, and it will help you no end. If you like this video, check out this onewhere I talk about getting published inScopus-indexed journals fast andefficiently. I think you'll love it, andit's very important because publishingis the backbone of academia, and it'sgoing to make everyone, and you, lookAwesome! Anyway, go check it out., YouTube Video tz--Ctx0CiU, YouTube, 01kac2vfzc54zkcdpnhmjq7p6m.