I take a couple of minutes now to build upon what Pialogen told you about regression. The first topic here, polynomial regression, is actually multiple regression. It's simply a form of multiple regression. A form that allows us to fit one or more explanatory variables to a response variable in a nonlinear way. So basically we are trying to be nonlinear with linear regression. And this will be extremely helpful in some cases as you will see. The principle is to add to, let's take the simple case where you have one response variable, y, and one explanatory variable, x. So polynomial regression consists in adding higher degree terms of this x-explanatory variable. So polynomial terms, x squared, x three, fourth power, and so on. And here we have an intercept term as an usual regression. Well, the story could stop here. That's simple. But there are some points to examine. It's not always so simple and there are some traps and pitfalls where you are not supposed to fall into. So we'll see this now. So depending on the degree of the polynomial, the second, third, and third you obtain a regression called regression of order two or three and so on. The idea of doing this is to be able to fold the straight line that is actually a model of first degree regression when you have a simple linear regression with one explanatory variable. Each new degree actually adds a fold to the model so it becomes more subtle. So let's take this example here with artificial data that I built on purpose for it. Clearly the first degree regression here simplifying the x variable to the response variable is absolutely unsuccessful. It barely takes up a little bit of a trend because those values here happen to be a little higher on average than those on the other side. But as you see, the adjusted r square, now you know what adjusted r square is. The adjusted r square is zero. It may be, well, this is one little point about the adjusted r square that consequently to the way it is computed. It may happen that you obtain negative values, but those are simply considered as zero. It's just depending on the exact mathematics of it, but it's not important. I mean, as long as you are below zero, consider simply that as zero value. And of course, if I test the relationship between x and y, I get something highly non-significant in this case. You know, probably know the code in r to run a simple linear regression. So lm for linear model responds depending on x. And this is the result when I plot it. If I add a second term, second-degree polynomial term, x squared, you see that the model begins to catch, to capture an appreciable part of the variation in y. Our adjusted r squared has increased to 0.23. The model is significant. And, well, you see that we have already something interesting here. Note here how I have coded this in r. I have not simply written x plus x squared, because if I did such a thing, r would consider this as an interaction in terms of an over. It would misinterpret the second-degree term. So you put it between brackets with this i, meaning take this as it is. It is an x squared and nothing else. So don't forget, if you want to run a polynomial regression directly by hand coding this in r, don't forget this i and the brackets here. So adding a third-order term allows us to bring a new fold into our model. And as you see, this further increases the fit here of the model to the data. A little bit, r squared is now 0.32, with p-value very low. So here we have indeed a highly significant model. And as you see, I have added the third polynomial term as before with the i and the x. And the third power here. Should I continue? I shall. I shall continue here up to the fourth degree here of x. And here, obviously, I've still gained something. Square is 9, almost 0.7. And of course the p-value gives a highly significant model. And then, should I still go up, that may become a problem. So at some point, we'll see that you have to put some stopping rules to this play. Because otherwise, your model, for one, becomes extremely cumbersome. It actually, it quickly goes out of hand because you cannot interpret the various terms. And then, of course, there lurks the risk of overfitting that Professor Liscardi mentioned yesterday. Overfitting is always a risk. It's a risk in statistics as well as in machine learning. So at this point, let us stop here. It looks like really, we have captured everything that may be interesting in this data. And perhaps even some more, but well, in any case, it looks good for now. So this is for the principle. The first problem with this simple way of doing it may be that polynomial terms of a variable, when you put, when you square an x variable, for instance, those terms are highly correlated. So, to one another. So here, for instance, if I construct an x variable here by asking for 100 values extracted from a uniform distribution with a range between 0 and 10. And I correlate this variable to its squared values. I get this, 96%. If you run this same piece of code, of course, you will obtain slightly different values because each of your runs will end up with different random values. But the general idea is this. Those two terms are extremely correlated. You don't like this in regression because when you have highly correlated explanatory variables, then the regression coefficients that are computed on those variables become unstable. What do we mean by unstable? It's simply that in a real situation where you have several... It's not only true for variable that are squared and so on, but in general cases it's a problem in multiple regression when you have a bunch of variables that are highly correlated among others, then the risk is that from one sample to the other in the same statistical population, you can get regression coefficients that are widely different from one sample to the other. This is the instability that is referred to here. It's a risk in general terms. I just mentioned it in this case because we are here speaking of polynomial regression, but it's a general risk. Again, this refers to the excellent advice of Michael Skardy of yesterday when he advised to resort to sound ecological thinking. Before throwing into your models any variables simply because it may be interesting, first think of those that may be most interesting and possibly check whether a couple of them do not tell approximately the same story, which may be the case for instance in vegetation science when you have different means of assessing nitrogen in soils, which is a difficult task. You may have different possibilities or phosphorus, total phosphorus or other forms, and then of course we have the different values, so just throw in the corresponding variables in the equations and no, this is not good because you run into this problem, but this is a little bit broader than the question here of polynomial regression, but in any case it's also lurking here. You have a couple of possibilities because when you have different variables like in my phosphorus case here, of course you can resort to thinking or possibly test one and the other and decide to retain only one, this is possible on the basis of thinking, but here we are stuck with the problem, we want to keep both, so we must find a means to avoid this correlation. So there is a first easy way of well alleviating the problem without eliminating it completely except in some really limited cases and this is simply to center the X variable before squaring it and put it in on third and so on and so forth, but at least for the second degree when you center the variable, then after that your second term will be almost uncorrelated to the first degree, at least as long as the variable is reasonably symmetrical in its distribution. When it's perfectly symmetrical in its distribution, then the second term is perfectly non-correlated with the first degree. As long as you have those usual mild asymmetries then you are not completely uncorrelated but at least it helps a lot and you can certainly work with such a case here. The other more general possibility which is fortunately offered in a very easy way in R is to use orthogonal polynomials. So mathematical literature offers a couple of possibilities to scale and well the principle begins also with the centering of the X variable but then you have various manipulations and computations so you obtain polynomials first, second, third and fourth degree in this case that are non-correlated to one another. As you see here I have asked here for a polynomial of fourth degree of my variable X and here I have correlated all those resulting terms to one another and what I obtain is of course here in the diagonal, it's the value of themselves correlated with themselves, it's a correlation matrix here and everything else is zero, it's related to the machine precision here when you are at minus 17, you are at zero. So this is the most elegant way to resort to polynomial regression and in terms of R squared you don't lose anything, it's equivalent to using the non-orthogonal polynomials, it works perfectly. And just to show you that it indeed produces the polynomial that you want, this one, I have used those four terms that I have obtained here and simply plotted them against X which is my original variable. So you see that the first degree has been somehow centered, you see that now instead of having from zero to ten, you have something that is centered around zero, it has been centered. And then the second degree is this and the third one is this and the fourth one is this. So we indeed have all the terms with all the folds that we need to adjust our data as well as possible. But now as Pierre suggested earlier, is it really the aim to get to the perfect nirvana of an R squared equal to one in our data? You know, when people are extremely proud and come to me, yes, I get this and that analysis and I have an R squared of 0.95, 98. Ooh, this gets me nervous. I'm practically certain that there is something wrong somewhere in the process and I think I... Well, I never found a real situation where something sound had been done and you get such a thing. Well, natural variation is such huge that it's simply not possible. So you probably heard in the direction of too many explanatory variables. Of course, the reason why with, in Pierre's example here, Petru's example, 100 points and you had 99 random variables and then you obtain an R squared equal to one. The simple geometrical reason is that you need 99 dimensions to position 100 points, one against the other, you know? To position two points, you need only one dimension, which gives you the distance between two points in an absolute, not as a reference of an external referential, but one in function of the other. You need one dimension for two points, you need two dimensions for three points, okay? You always can fit the plane in three points and so on and so forth. Hence the N minus one degrees of freedom you always start with in any data set. So if you have 100 points and 99 degrees of freedom in your model, well, it just amounts to a transformation of the data in terms of your new variables, but you have as many dimensions, you provide as many possibilities to fit your data as you have dimensions to start with. So you don't, as Pierre put it, you explain everything with nothing, okay? That's the reason. So we have to avoid this. Keep this in mind in general cases, not only here for polynomial regression. But now what is the use of polynomial regression in ecology? A remark, of course, a preliminary remark here, is that I present this now in the frame of multiple regression with one single response variable because this is a two-days topic, and of course as with the presentation made by Pierre earlier today, this is because we will need this in the multivariate frame from tomorrow on in RDA and in CCA. So basically we know that frequently species show unimodal responses to gradient, to an ecological gradient, to a constraint of maybe against temperature or calcium content or whatever, pH. So you have something like an optimum of the species where you will find most individuals per square meter or what your measure was, or grams per square meter if it comes to biomass. And then of course when you get farther away from this optimum, then you have less individuals because the conditions are more difficult. That's the basis of this unimodal response. Using a second-order polynomial term provides you that possibility to put that necessary fold in your model for that situation to capture this feature, this unimodal feature. So using a regression with a second-order term but still keeping the first order is a way to capture any linear trend. Linear trend in this context probably generally meaning that you have not captured the whole range of your species when along the trend, you cannot switch into the multivariate case. Of course when you sample communities, you will most likely sample part of the species in the whole range of their presence, hence the zeros outside this range on both sides, and a part of those species maybe at the left or at the right part of the range and you capture only part of this unimodal response. So if you are just a little bit at the edge, like in this example here, we have captured probably most of it at the left part but not quite here at the right part and of course there are still some variability here. So the linear part may still be useful to make the adjustment a little bit better to take this slight asymmetry into account. Of course here the second-degree term will capture the parabolic shape here of the response curve. So this is the basic reason why we could be interested in using polynomial regression in ecology. If we do it in a multivariate term, a case like tomorrow or the next day in RDA, then we could use that procedure to improve our modeling. So you can also verify, if you are working on one single species since we are in regression today, I'll now go to these cases where we have a single response variable. You can combine those. It's easy to plot scatter plots between your response variable and all the explanatory variables in turn. If you observe that for one given explanatory variable, you seem to have a linear response here. Maybe you are at one end of the gradient here and you have something that is maybe not quite linear but at least reasonably monotonic here. Then you can dispense with the second order. Of course it would be useless here. So in this case I would have here my model. I would put it with a linear relationship. While here in this case I would keep the second order term or put in at the second order term to have the unimodal relationship. So it's perfectly possible of course to combine linear model for one of the explanatory variable and unimodal one for second order for the other one. Okay, up to now. Now of course this is at least when you work on it. Maybe you have time to do so or you want to do so because you are very careful about one's particular species and you can afford the time to go in that direction. But another possibility also is to resort to some kind of variable selection. I'll come to that at another point in this course, variable selection. But the way of actually finding automatically which degree is the best fit for your model, for your explanatory variables would be at the outset to provide the model with the second terms. So here if I have a situation with my response variable here and three explanatory variables here I build a regression model with the second term for x1, x2 and x3. So this would be complete. Well, you could go even further and build interaction terms or a combination where you multiply the different terms. I'll not go into that for now. Let's simply consider each of the explanatory variables separately. But in any case this already gives you something more consistent but as we saw in the previous slide it's always necessary. So there are ways of selecting the variables that really contribute to the model here. And if you apply one of those variable selection procedures you may for instance end up with the elimination of those two terms which this is completely fictitious of course but it could happen that for one term you have only the squared value because you happen to have sampled your species in the complete range of this explanatory variable and so your unimodal response shape is completely symmetrical so you don't need the first degree. Here it could be the reverse meaning you have something monotonic and in the third variable you could have used both to obtain an optimal fit for your model. So at the end you have a thinned out model, a reduced model here where you have kept only those variables that are adequate that are significant. Of course there are tests associated with this. We'll see all this tomorrow with a model that has a much better fit than a model built only on the first degree variables. It's up to the point that even in RDA we recommend especially in early phases when you are at an exploratory stage of your study to use to add those second degree at least second degree polynomial terms to all your environmental variables to be able to eventually identify those situations where a second degree may be useful. So second degree is easy to interpret. As you saw it has an obvious role to play when you have a unimodal response. Third degree may still be useful because well it's an approximation, but if your response looks like this because you are slightly asymmetrical maybe well you may have the first degree that captures part of it but then a third degree may produce such a thing here. This is not fully appropriate of course but by no means you could extrapolate such a model out of the range where it has been fitted. But this is also a general rule in regression. You model your and maybe well outside regression as well when you calibrate a model when you determine its parameters within a certain range of your variables the model is valid inside this range but not outside and Pierre has an extremely funny example of this. You may have noticed in the material for today something about Miss America an undernourished model. What are these? Actually the idea here was precisely to give you some idea of what happens when you extrapolate a model because basically the ratio between height and weight of Miss America tended to decrease during the 20th century and if you fit a linear model here and go to 2016 your Miss America is really really undernourished as to have negative weights. Something like that. So it's the danger of extrapolation. You can always interpolate meaning you are within the range but don't extrapolate otherwise it becomes a very risky business. So well for my example here the third degree may be marginally useful but you can hardly go beyond that in ecology without well I mean of course as I've shown you you can always add up and fit a little bit more but it makes no point going beyond the degree that you can explain and if you want to go progressively from first to second to third degree to fourth degree that way it's always a possibility to test if the addition of a new term brings something significant to the R square. So the gain in R square is significant. You can test this. It's easy to do in R and in my script for this afternoon there is a procedure. I show you how to do this. It's also in Pierre's material actually that part I think I took from Pierre's material just adopted it a little bit with a small example where you add terms and then at some point it stops to be seen the addition stops to be significant but this is as always I prefer first resorting to sound ecological thinking and if you really don't have particular reasons to stop at some point then well you can resort to statistical testing to see where you are supposed or when you can stop or not. Another okay always you know if you have a question and simply raise the hand I'm very likely to not see the hand and this is because physiologically our sensory system is made to detect changes not constant values. This is why for instance frogs when they wait for their mosquito to pass by are completely they don't move at all because even the eyes don't move so when the mosquito goes here you have a sudden change and it reacts extremely happily and captures the mosquito of the dragonfly and we are built the same way constant signals like the pressure or friction of our clothes or well the smell in this room you don't feel this anymore because these are constants so if you make this with your hand you don't see the movement because this is a change okay Pierre briefly mentioned trans-surface analysis before trans-surface analysis is a particular use of polynomial regression where the basic explanatory variables are spatial coordinates in the simplest case if you are along the transect it is the first example that I showed you with the folds adding up up to the fourth degree so you could do this on a one-dimensional transect but here I am especially speaking of a two-dimensional well on an area okay you have a couple of some off-sites you have your sites on an area and you have their x, y coordinates so you have those two variables that you could use here as a first-order polynomial well it's not yet polynomial but in any case you have your binomial term here x and y and this using this is equivalent to fitting a plane across your data so maybe if your area your sampling area was this room the samples distributed maybe randomly don't need to be systematic of course randomly across this room and your response variable happens to be have larger values there and smaller there for instance then fitting a plane would give something that goes up in this direction which is a combination of x and y so this is a simple way a very crude way I agree but a very useful way in some circumstances as I will mention later but then you could envision at this at the early stages of our thinking about capturing and modeling spatial structures in ecological data we went along this line order two, order three here and this was actually at the basis of our paper in 1992 about partition and variation partition in multivariate case where we had one explanatory matrix made with environmental variables and the other one made of coordinates and their second and third degree third order form here so this was what we had at that time so of course things have now drastically changed we have other possibilities as you will see in the two last days of this course but nevertheless you have that possibility and adding the second order here also adds a fold in both directions you see you can have a fold in this one and another one in that in the other direction and since you have the possibility here of even multiplying both usual polynomial construction then again you have the possibility of for instance a uniform bump here at the middle of or somewhere in the room or a saddle shaped so maybe this way and then that way in the other direction so this gives you one more fold and the third order one more fold again in both directions but as you see it becomes rapidly extremely cumbersome for a third degree polynomial you have nine terms here not counting the nine terms of course you can resort to variable selection and as I explained earlier you can try to select those that are significant but in any case you have a large number of terms quickly and even for the third degree it still is an extremely crude way of modelling very broad-scale spatial structures so we shall not go any further here because much more powerful methods exist to analyse spatial patterns and we will come to them yes Cosimo Urban myth Urban myth Urban myth ok I am extremely happy that you remind me because I thought of that one hour ago but forgot to mention it ok the requirement about normality is about the residuals of your model the residuals of the model one way to obtain this normality of residual may be to normalise the response but there is absolutely no reason to do anything about the explanatory variables and the proof being in the pudding Pierre mentioned earlier that a regression a multiple regression could be constructed with dummy 01 binary variables which are of course absolutely not normal but take the extremely simple case where you have one response variable and one dummy variable simply well separating actually separating your data set into two groups this actually is equivalent to the famous if you run it classically with a simple regression that's equivalent of running a key test for two groups for the means so what are the residuals if you have two groups say I'll draw them separately here on my on this you have two groups of data with the same response variable here but one group has a mean here observed mean and the other one has an observed mean okay the requirement for normality is for the residuals why? because here what are the residuals the residuals are the deviations from the respective means so running your regression amounts to superimpose here actually it's like centering your two groups upon the respective means so both become zero and you have something like this and the model being here this is the common mean actually and of course the test is whether this here is significantly significantly different from zero but be it or not the residuals here are simply the superimposition of those two curves here and if this is normally distributed then you are in business but of course my zero one variable is absolutely not normal it does not have to be so this is an extreme case but just to show you okay so it's a well it was a I think it's a thing we should repeat as a mantra in all courses variables don't have to be normal, it's the residuals and that holds for an over that holds for multiple regression that holds for anything when you call for normality concerns actually conserve the residuals and this is of course in the purpose of this is another point that I can add this is for a purpose of a parametric test so if you use, if you want to use the t distribution students t distribution to test your different between means or your model of regression then this normality is a requirement if like we will do in RDA and CCA if you test by permutations then you don't even have to hold to this requirement of normality of residuals because permutations will take care of that by generating their own reference distribution some other requirements but then no I go not further into this but you'll see that it doesn't mean that permutation test means it's a free for all you have to be careful about some other points because when permutations test were invented many people thought yes I can do anything I want since I generate my own reference distribution who cares no it's not okay in ANOVA or in comparison of two groups for instance you still have to verify that the two variances are homogeneous or two or more k variances for ANOVA are homogeneous and this is the reason why I did not build this example something like this which could not even be submitted to a permutation test it wouldn't work you would have you would go into the how is it called the Berenz Fischer problem yes thank you Berenz Fischer problem you still go into that problem but in any case well we are out of the subject here but thank you for having asked this question because it's extremely important that everybody understand this okay normality is for residuals paint it on your walls above your bed I don't remember it as I told you before one useful still now extremely useful use of what polynomial between brackets actually regression in ecology is the trending in we'll see in the last days of this course that we have to identify spatial structure but actually not only in most cases those spatial structures that interest us because they are most difficult to bring out are those that are at intermediate spatial scales not the one that cover the whole surface which be those ones maybe general trend because there is something larger process is larger than your sampling area that produces a trend within your area biographical trends for instance could produce such an effect they are beyond they act at scale that are beyond your sampling scale but never or your area but nevertheless they produce a trend within your area now the methods most methods in our identifying spatial structures including the ones we develop to identify those structures at all scales work better or work at all if a condition called stationarity is respected satisfied now to be very simple about that that stationarity basically means that at every point on your surface the differences between close values if you take two close values there and there and there and there and there and you average those values plus minus that must be zero and you must have finite variance over the area so that you don't have something widely different in different areas so basically you can obtain something very close to that and satisfy conditions for most of the tests we are running or they are running in geographical geology for instance in those different tests for spatial structures when you remove those broad scale trends from the data before running those those tests for other reasons and now specific to what we have developed in terms of DBMEMs and MEMs that we will address later is that yes those methods are able to capture broad scale trends but they will waste many variables because those variables we will create are basically sine waves or something like this so it's extremely cumbersome and actually useless to try to model something linear with a bunch of sine waves ok it works we tried it but then you use half of those you have already created just to model this and this is of course a linear trend is easily removed from the data beforehand so this is another use of course it's a first order polynomial but I included this notion here because it just fitted in the general pattern here so this is as I told you before equivalent to do this to do in this you you fit a model you fit first order x y and z being your response variable and you use your x y coordinates as explanatory variables nothing else test if it is significant and sometimes we test it in other cases we simply use it and decide that we want everything flat and so what you retain are the residuals which are here represented by those vertical lines above and below the plane that I have fitted on my surface ok this all this may already be known by many of you collectively I'm quite sure that everybody knows a little bit of everything that I have now yesterday last night I decided to include a couple of more slides about another use of polynomial regression that has been presented by Kajot Erbrach in a book that he published with co-authors in 95 which was one of our reference books at the early ages of canonical ordination and this is a use of how to be able to run a Gaussian regression so a regression fitting a normal curve on species abundance data you know those kind of data with a unimodal response actually the two model is not the course second degree polynomial that I showed you because it would make negative abundances at both ends of the distribution the real model here if the distribution is symmetric around the optimum here would be a normal curve like this you can do this easily to do that you have first to transform your response data, your abundances into logs because fitting a second degree polynomial function on log transform data is equivalent and then you can go from one to the other to fitting a normal model, a normal curve to the corresponding raw data so this is how you do it let's show you here some words in French because I borrowed this last minute yesterday from another course I'm giving so you have here a fictitious species for the French speaking people I've called it bidonia because it's a species that doesn't exist well so you have the distribution here a fake species actually so you have here a fictitious series of sites here along a wetness gradient or water content, soil water content gradient here where I've captured just about the whole gradient so these are the true values here you see the raw abundances the first step is to well you transform the data to well could be neperian logs usually it's what we use of y plus 1 this transformation is so often used in ecology that in R, in the standard R installation you can so yp for instance yprime could be log 1p log 1p does exactly that so you don't have to write log y plus 1 if you write log 1p it does the same job automatically just the information here so you have now the logs and you fit your second degree polynomial equation to these log transform data now we will work with the B0, B1, B2 terms well actually B1 and B2 B0 as well for the last stage you take those and now with B1 and B2 you can compute the value of the optimum of your species meaning along this x axis this value here is the U and you obtain this from those regression coefficients you can also compute what in ecology we would call the tolerance of that species around this optimum tolerance being here defined of course it's one definition you couldn't multiply it by two if you are very generous tolerance being defined as one standard deviation of the two sides here so this is again obtained but this time only with the B2 coefficients you don't need the other one so that second important feature of your species distribution can be obtained in this simple way and the third and last one is the abundance at the optimum so here tolerance here is the abundance at the optimum it's that easy I did not take time last night to write a little r function that would do this automatically for you but I suggest you for those interested in writing r functions to do it as an exercise and finally well here I have only the model not the data I didn't put this back into this representation here those terms and of course to draw the curve itself you just insert those parameters that we just computed the C U and T you just put them into the the normal probability probability density function here when I read this the first time nice how elegant now of course as I told you already polynomial regression has its limits so avoid the use of I'm just a little bit summarizing what I have told you up to now avoid the use of higher order polynomials if you cannot explain them ecologically so second term okay second degree third degree to some extent further than that it becomes a little bit strange because I'm not sure whether you have really clear explanation of that and if you prefer using an objective criterion then as I told you also add degrees but test the added explained variance at each step and stop if nothing significant is added in terms of r squared and beware of overfitting so the situation that I explained with Pedro's example I took up Pedro's example that Pierre mentioned earlier so you don't explain beware to explain everything with nothing


So we're gonna be ditching that garbage right there Just go ahead and Get that trash out of there big dog in the house gonna try to put the mat in Let's see what you got Chuck Can he get it in let's see if it's easy enough? Oh, man. Look at you go. How old are you? You're five. Are you sure? You're five really you're five. All right. There you go. So that was so simple a five-year-old Can do it. What's up, Mopar fam? I hope everybody's having an awesome day out there and in the box you see behind me We have a set complete set of lace fit floor mats for the Ram 2500 and I am So glad we got these floor mats because Desperately need a good set of floor mats for this truck as this is my daily driver and my work truck and obviously this truck toes The race truck back there. I was really looking forward to getting these mats in the truck So let's get to it But first let me show you the garbage that I had in here to start with So we're gonna be ditching that garbage right there Just go ahead and Get that trash out of there. All right guys. So as you know, we have the same lace fit floor mats in my 2015 Dodge Durango And again, this is the complete back section driver side passenger side custom fit for the floor on The Ram 2500 pickup trucks and They have floor mats for pretty much every vehicle out there. So go check them out The link to these things will be in the description Below so click below. I'll have the link to it Take you right to lace fit where you can find these floor mats for your Ram truck All right. So we have some helpers here today to help us get some floor mats put in So let's get these floor mats out and unbox them. What we got Take them out and put them on the top of the trailer. All right, this one is I'm gonna say the jur passenger side. All right, we got the front mats out Trying to get the rear one out now. You got it. There we go All right, so we have the floor mats out from lace fit And this is going to be the driver side. I believe right here And as you can see these things are really nice they go up Really deep pockets holds in the mud and the dirt and the water really well Custom fit Very nicely cut This is going to be the passenger side. I believe right here And then this is going to be the rear section completely custom cut I'm gonna be a super super clean fit. They're gonna fit and these are gonna work awesome and I'm super stoked that we have them for the new truck now What I do want to mention real quick and I mentioned it in the video When we put these in the Durango Not long ago We just unboxed these things for the first time out of the plastic package in the box Absolutely no smell.  So you don't have to worry about putting them in your car and then dealing with A rubbery tire kind of smell for a few weeks until it goes away These things have absolutely no smell whatsoever. Don't have to worry about that All right, so with that said we're going to grab these floor mats You think you put some floor mats in? Yeah, all right. Let's put some floor mats in the truck All right, we're going to do the driver's side real quick. We have Big dog in the house going to try to put the mat in Let's see what you got chuck Can he get it in? Let's see if it's easy enough. Oh man.  That was so that was so simple That was so simple. Come here. That was so simple How old are you? You're five. Are you sure? Yes You're five really you're five. Yes. All right. There you go. So that was so simple a five-year-old Can do it they fit great guys look at that Up against the panel pretty tight Up to side of the carpet here real nice the back fits good Locks into the little lock holder Super awesome now this part's important too With floor mats always make sure you can pressure your gas pedal down and it not get stuck in a floor mat some mats are super thick And you'll actually sometimes can jam them into the floor And it looks like we have no problem It barely touches the the uh The mat which is another reason I love these mats from lace fit. They're super thin, but they're super super durable so driver side Fits like a glove. Let's go do the passenger side. All right. Now we got the passenger side to do so We got another helper you got to get the old one mat. You got to get the old mat out What do you see what you got?  Oh You're almost there You're almost there There we go. Good job. We got the passenger side in Fits well got it popped in down here in the holder nice fit Now however, my truck is actually missing the little hook That goes under here as you can see I gotta get one of those mine's missing So not a big deal, but mine's missing. So it will be a little bit looser than it's supposed to be right here But we can deal with that Mat looks nice Matches the interior of the truck really good. I like it looking good We think good job mat lady All right.  You ready? All right, let's see. Let's get it over here All right, you take this in take that in Nope Come over here There you go. Pick that side up And put it in and hand it to the tell you sister Hand it to your sister. There you go Oh, you got it Let's see if y'all can team tag team this thing make it work So y'all have a funny method going on here, but let's let's see what you got going on. I mean Looks like we're getting it. I mean it looks like we're getting it We're getting it All right. I think we got it Oh, did you get it? Let's see. There we go Lay's down really nice Oh, you're you're hidden hiding spot It's going to be hiding under the mat for now on No more covering it with mud when we get when you guys climb in there You know what I mean? Because you keep getting my truck muddy All right, so we got the rear section isn't as you can see Drops in fits real good Looks like it fits up around the back of the carpet pretty good Long the door seal pretty good Goes over the hump I like it guys Nice mats from lace fit So there you go. That's the lace fit mats for the ram 2500 trucks They have mats for pretty much everything out there Durangos your trucks your vans Cars everything if you have a 2500 ram The link to these exact floor mats will be in the video description below so scroll down Link will take you right to these mats and again, I'm loving them. We have these same We have the same mats from lace fit in our in our 2015 Dodge Durango and they have Literally saved the carpet in that vehicle And uh, yeah, when you have kids, you know how it is not to mention, you know, we have pets as well The mats or lifesaver not only that they're a must just for everyday needs You can take these things out. You can hose them off with a water hose pressure wash them whatever And they are ready to go. So as always guys Stay safe out there. Don't forget hit that subscribe button and we'll see you on the next one


Hi, this is your host, Sableem Khartia, and welcome to a brand new episode of our series, TFR Topic of the Month, aka T3M. And this month's topic is platform engineering is DevOps there. And today, we have with us Dothan Horowitz, Principal Developer Advocate at Logs.io. Dothan, it's great to have you on the show. Thank you for having me, pleasure being here. Before we get jump into the discussion of platform engineering versus DevOps versus SREs, how would you define platform engineering? I definitely don't think that DevOps is dead. And I actually view platform engineering as an evolution. I would say that we all know DevOps as the combination of tools and practices that enable the release of the software all the way to production. In a way, I would say that platform engineering is the productization of these tools and practices. And packaging them in a way into reusable services that can be used across the organization, across business units, across product teams, across the house, obviously very relevant, mostly relevant to large organizations where you have lots of them. And you rightly said DevOps is not a job title, it's practices, it's tools. Talk a bit about your companies, tell how do you fit into this context, Logs.io. Logs.io is a company in the DevOps and observability space. What we do is we take the best of breed, open source tools for monitoring and observability, such as Prometheus, OpenSearch, OpenTelemetry, Yega and provide them as one unified managed platform built for scale. So in essence, our customers get the unified open platform and we call it Open360. So it provides a 360 degrees view of their infrastructure and the applications across logs, metrics, tracing, even security, and they enjoy the best of both worlds. So they keep on using the open source that they know and love, but without the hassle of running and managing it. And of course, without the risk of vendor lock-in as in closed source solutions. And when we look at it from the platform engineering perspective, the platform engineering teams look for consolidated ways to see the infrastructure and application and then make it available for the different teams, the different product teams, the different infrastructure teams, the DevOps teams across the board. And that's where we fit in. We allow these teams to keep their practices, their open source, their PromQL query language or Lucene or whatever, what not. And still do that in a consolidated unified manner. We're also talking a lot about developer experience these days. First of all, let's talk about how would you define developer experience? And then we'll talk about when we look at platform engineering, how does that enforces that view or helps developers? So I come from a developer background. I've been primarily a backend developer for many years. And the thing is that for many, many years, developers were not treated as users in many respects. So we needed to do hard things about CLI and the unfriendly tools and lots of tweaking in order to get a grasp of what's going on in our systems and managing them and configuring them. And I think now there's a strong shift into developer experience where developers are actually treated as users and also as a very valuable resource within the company that we don't want to waste the time of the developer doing all sorts of boilerplate types of activities. We want to facilitate as much as possible. And as I mentioned, make platforms to enable the developer as much as possible so the developer can focus on the core business value that he brings to the company or she brings to the company. So that's developer experience for me and Acha. Can you talk also about the importance of building this developer experience because when we talk about the whole ownership from the time application is created versus deploy and also kept running, kept secure. And that's where we can also talk a bit about as you were talking earlier about the whole culture movement DevOps SREs because these are, once again, sometimes these are tools, sometimes these are practices. So I think that we see a very strong emphasis on the shift left movement. So people expect the developers to take ownership of the application all the way to production and beyond but developers need to be enabled to do so. So, and then developers are interested in what happens to their code the moment after they release it in the GitOps process whichever that might be. But in order to be effective, they need to understand what goes on there and that's where I think observability comes into play. And this is what platform engineering comes into play because platform engineering, unlike you mentioned PaaS, unlike PaaS that was off the shelf product that was taken and provided externally through the organization, platform engineering is part of your organization. It's actually engineering within your organization sitting closely with the product engineering understanding the processes, the constraints, the tools, the methodologies, the compliance requirements and then find the best ways to facilitate these in a way that fits the DNA of the organization and even the specific teams. So it needs to be flexible enough to adapt and to make the tweaks and adjustments to the specific team. So this is where the power of platform engineering enables this movement because it is a team within the organization that provides this experience to the developers. Since you are a huge proponent of open source, you are also getting a share with this cost cutting which is going on, which also means that folks will be doing less of do it yourself or not invented hair syndrome more and more companies. I mean, they're already leveraging a lot of open source. They will be adding their own proprietary as a Tabasco sauce on top of addition or not the whole sauce is Tabasco sauce, which means not the whole code is proprietary with the growing adoption of open source and this cloud native technologies, what do you see will be the impact on cultural shift? I think that the leaning towards open source is significant. I see, for example, I'm very active at the open telemetry project that aims to unify the different observability signals, logs, metrics, traces, even newer signals such as the continuous profiling and others. And you see everyone aligning behind this. This is the second most active project actually in the CNCF in the cloud native computing foundation after Kubernetes itself, which is amazing. And even the vendors that made their bread and butter out of selling agents, proprietary agents have realized that and started shifting to supporting open telemetry in one way or the other. So I think this movement is here to stay and actually amplifies. On the other hand, I do see that at least for large organizations that need to scale out these deployments, it starts becoming burdensome. Some organizations see that handling and elastic search or open search cluster at scale and managing the indexing and sharding and all of that, it requires a lot of manpower and headcount and then they're looking for ways to consolidate, to outsource and to make it more cost efficient. This is where I think managed solutions that support the open source bring additional value in this age of financial stress. Let's look at logs.io. How are you enabling teams to kind of embrace these practices? As I mentioned before logs.io provides a cloud native observability platform. So in a way, we allow these teams to understand. We then allow the developers in essence to connect to their production environment and very easily see what goes on, where the exceptions are. You just released a new drop, a new version of your microservice into production and things start breaking. How do you understand what breaks? So you have a very easy way to sift through the noise, understand where the errors come from, which exceptions, see aggregate of the logs into patterns that repeat themselves, see the trends over metrics over time, both the infrastructure and the application, understand the path of requests through a distributed system with distributed tracing and even being able to spot issues, security issues, someone now trying to do malicious work or do some Bitcoin mining. This sort of visibility and observability into production is what enables developers to take this shift left movement of taking ownership on their microservices and the drop software all the way to production. Dutton, thank you so much for taking time out today and talk about this topic today. And as usual, I would love to have you back on the show. Thank you. Thank you. It's been a pleasure talking to you.


Hello everyone, this is Amit Dhan and today I'll be presenting the MUHAL R89 Razor. If this is your first time or if you visit, thank you for your support on this hybrid channel. I'm always trying new and different ideas to take you beyond boundaries. If you're into thinking outside the box, this is the channel for you. If you're looking for more Razor and Shave videos, there will be links in the description box below. So today I'm going to review MUHAL R89 which I have it right here and at the same time I'm just going to show you a comparison between these two Razors. This is Edwin Jagger R86 but they also have R89 version which is just a slightly, you know, there's one thing which is not like this and I'm going to tell you that in a second. So I'm going to review this one first thing and then we'll look at some of the comparisons between these two Razors. So what happened? I made a shaving review video, you know, showing my face and shaving with this Razor and that was my full review but my file got corrupted and I lost all the video. So now I decided not to like, you know, shave and do a shaving video anymore, rather I've just like, you know, do a dry video. I'll just like, you know, go over some of the specs and stuff here and at the same time because this looks so much similar to this Razor so I'll just, you know, show you some comparisons and stuff. So this is MUHAL R89 made in Germany and some of the specs we have here are, this is a mild Razor and closed comb and this is a chrome plated brass and the ounce are the weight is 2.36 amps and 76 and the length is 9.7 inches or 9.4 centimeter and the handle diameter is 13.3 millimeter. Now what I noticed is that when you are actually looking at different websites, there are like, you know, weight discrepancies between these two. So I just picked up from the MUHAL website but, you know, the weight doesn't match with many other websites so, you know, do not just like, you know, go by the weight what you see, the actual weight would be different but whenever I'm touching these two, definitely I have a feeling that this is more heavier than Erwin Jagger but looks like some websites are mentioning that Erwin Jagger is heavier. So that's something I wanted to let you know. So now this is a closed comb Razor. So what it means that, you know, the combs here, they're closed, they're not open and this is very suitable for, you know, new or first-time double-edged Razor users. It's going to give you a mild and comfortable shape. So the moment you pick up the Razor, you can see the quality in it, the like, you know, gnarling face here. It prevents slipping out of your fingers and then this is a three-piece Razor. So what it means that, you know, if you detach it, it's going to break into three pieces. You can see that there are three pieces here, one, two, and three.  So when you're closing it, if you look here very carefully, like right here, you can see that very little gap. So that tells you right where that, you know, this is going to be a very smooth and mild shave and this is very suitable for if you're new or if you're a pro because this is going to, like, you know, your skin is going to appreciate it because there's not that much opening here. So this is going to give you very, like, you know, gentle, nice, mild shave. Now the quality is all over. This is, you know, made in Germany and you can tell. And the price range of this Razor is about $45 to $100. I do not provide any affiliated links. So you cannot buy it from my website, from my YouTube channel. But if you're looking online to buy, my suggestion would be do a search and check that other, you know, retailers may give you better price than Amazon because when I was checking, I saw that, you know, Amazon has it for $100, like, Canadian dollars. But there are many other online sites offered $45, $55. And I actually didn't buy this from Amazon. I bought it from another retailer and I paid much less. So just, you know, do your research. So the thing is, you cannot go wrong with this Razor.  And on the side, it has a little bit tab opening. So what it means that when you insert the blade, the blade is going to be out very little, not too much, but that's not an issue for, like, you know, me or probably for many of us, not every blade can hide the side or edge of the blade. But that's how the blade works. And this is Muhal R89. Now, if you look at this Razor, which is Edwin Jagger R86. And if you look at the specs side by side, everything looks very identical. Both are, you know, chrome-plated brass, both are mild-closed chrome. The weight is very similar, length is very similar, handle. The Edwin Jagger is a little bit shorter than the Muhal. For example, if you take a look here, you can see that this is much, much shorter. But now one thing is, this has a rubberized grip, which is right here. But you can buy without the rubberized grip, which is going to be R89. And even if you look at this version, this is R89 as well. And even if you look on the front and the back side by side, you can see that they're very same and identical. So I'm just going to, you know, remove the head just to show you what I mean by that. Okay, so if you're taking a look at here and look at here, they're exactly the same except Edwin Jagger has the logo here. And then if you take a look here, and if you take a look here, exactly the same. So when I first bought the Razer, and I didn't know much about Razer, I thought someone copied each other. But then later on I found out that Edwin Jagger and Muhal has a joint venture and some of their, like, you know, Razers share the same blade head. So like, you know, they have the same head, but different handle. So that's why they look very identical. So you cannot go wrong with buying any or not. One of these, you know, both are very mild and smooth Razer because you can see that, you know, they have the same head. But this is made in England, which is the Edwin Jagger and the Muhal is made in Germany. And if you wish, you can also buy Muhal, same like, you know, metal handle. But this is like, you know, rubberized handle and it feels very smooth to hold. Another thing about the Muhal is the handle thickness is much better than other Razers.  So the handle diameter I was able to get it from a website was 13.3 mm or millimeters.   But you know, this is the Muhal R89 and Edwin Jagger R86 or 89, they're same thing. And you cannot go wrong buying any one of these. And when I shape with these, like, you know, I feel very comfortable and even now I can go fast without, without, like, you know, cutting or scratching myself because they're really smooth Razers. So the Muhal is made in Germany, but it's kind of, you're going to find Edwin Jagger more. This is, it has more availability than Muhal and also, like, you know, Muhal on some websites, they're very high priced, for example, $100 or around that range. But other retailers, you might be able to find it cheaper. Someone told me that actually, if you buy, if you're buying from Muhal in Europe, it's always cheaper. If you're buying Muhal from North America, it's always expensive. So I have a feeling that some of these websites who are selling Muhal, probably they're like in European websites. But if you walk into a store and buy Muhal, so definitely you're paying more in North America than in Europe. So it's a, Europe is a good place to buy Muhal because that's where they sell it for cheaper. But definitely I like Muhal. This is a very good razor and I like also the Edwin Jagger. So this is the review of the Muhal and at the same time, I wanted to just give you some quick, you know, similarities between these two razors because they do a joint venture and some of their, you know, blades are, sorry, razors have the same head. So it's not, they're copying each other.  So thank you for watching this video. For the next double edge safety razor video, I'm going to put a link on the screen. I'll see you in the next video. Thank you for watching.


I'm here to talk about the Australian Research Data Commons Party Infrastructure, one of our core infrastructure projects at ANZ. And the purpose of this particular infrastructure project, and I'll briefly mention some others at the end of this presentation, is to improve the discovery of data sets in research data Australia. And the way it attempts to do that is by linking research data sets via the researchers that are involved with them, and also by expanding the amount of information that might be available in research data Australia about that researcher or that research organisation, which helps people understand the usefulness or the authority of the data. And you may, the first question probably is, well, you know, why do we need an infrastructure, the people who are providing the collection activity and service records to ANZ will also provide party records, which says who this person is. The problem there is that there's a fairly common scenario where, you know, Dr. John Smith might be a researcher at Monash University, he's responsible for a number of data sets that are published by Monash University. He's also working on a collaborative research project. He's also responsible for data sets that are published by Queensland University of Technology and also works as an advisor to a program at CSIRO, and there are data sets published by CSIRO with which he's also involved. So you can see there that we won't be able to link those data sets together unless there is some sort of common identifier, and this is where the party infrastructure comes in. So it attempts to enable this linking by persistently identifying researchers and research organisations using a public identifier, and we heard Nick talking earlier about what an identifier is. It means that somebody is taking responsibility for managing that identifier. And by having a public identifier for researchers and research organisations, it just improves overall management of researchers in the research sector, and has a lot of spin-offs. And to a certain extent, the major beneficiaries of having this infrastructure is not necessarily research data Australia. The beneficiaries will be the academic publishers, the research grant authorities like ARC and NH and MRC. There are a lot of people who are trying to move towards this nirvana of being able to say who an identifier is, no matter in what context they are producing outputs, and no matter who they're affiliated with. So we needed to choose a public identifier scheme, and since you know it's not an ongoing organisation, we're not going to be here in the future, we needed to use a scheme that was going to continually be supported and managed by an ongoing organisation. And so there was already the NLA People Australia Service providing a public persistent identifier for Australians and Australian organisations. They already had 880,000 records based originally on the Australian name authority file. So basically the people in there are people who publish or people who are published about. But they also then included contributors such as the Encyclopedia of Australian Science, the Australian Parliamentary Library for Politicians, the Australian Women's Register, the Australian Dictionary Biography Online, they now have Queensland University of Technology researchers in there. So we are getting more, we have contributors in that space that will give us more information. So Dr John Smith at the Queensland University of Technology who also works at the Monash University, will also may have an entry because he is an Australian scientist in the Encyclopedia of Australian Science, which gives quite a lot of biographical information about him that's not going to come in through the party record that you're supplying in URIF CS feeds. So they have already established an aggregation service. They have got software and hardware and processes around the managing of aggregation from multiple contributors around Australia. They have contributors from most, because of libraries Australia, Australian universities are already contributing records to the National Library of Australia about publications and they're already submitting, most institutional repositories are already submitting records out of their institutional repositories to the National Library of Australia now for Australian research online. So it was good to sort of have all of these things connected in the one space. And also they're committed to integrating with international identifier services such as a virtual international authority file, I think the international standard name identifier is another initiative and ORCID, which I think is an initiative of the academic publishers to try and establish an international identifier. So because they're integrating with those, it means the identifier that the Australian public identifier we're using for researchers will map to these other identifiers and will help connect everything together, the research data sets, as well as publications from the same person or organisation. So how ANS established this project was to separate it into two stages, because this was a difficult one. It was in the sense that it was an infrastructure that was already in place. It was uncertain as to whether the research sector, in particular the universities who were the predominant research organisations and people like CSIRO, Geoscience Australia, for example, would actually feel about this idea of assigning a public persistent identifier. And I stress public, the only information it will link to is public information. It's not a Australia card type identifier. So it was important that stage one of the project involved a lot of consultation about gathering requirements and establishing with representatives from that sector that a infrastructure and processes that they actually felt would work and at least would be supported by the majority of institutions. We're not anticipating that we'll get complete coverage here. There will always be whatever reason, perhaps some organisations that may opt out. But unless the majority was going to eventually participate in this, then it wasn't worth doing. And this is why we had to have stage one, stage two. Now, the advisory group met for the final time about, I think, last week, a week before. And they have endorsed the architecture that's been described in the first stage and recommended to Anne's that we proceed with this project. And so we are now moving into stage two, which is the implementation phase. There's a project wiki which you may go to which has more information. Also, the advisory group now is terminated, but a lot of the people in the advisory group are now in our early implementers group. And they will help Anne's and the NLA define the requirements of the sorts of tools, the exchange methods for information, some of the matching rules may need to be varied in order to accommodate the needs of the universities. So the implementation stage, the second stage of the contracted project with the NLA, is adapting their existing infrastructure, which is designed for librarians at the NLA to match sort of identifiers amongst contributors that we spoke of. Not really designed for what would be needed to make the efficient, the matching of large numbers of researchers. So that infrastructure needs to be adapted so that institutions can use the tools and the identity matching service needed to be modified and the tool that's used for identity matching enhanced. And so in order for this to be implemented and the tool improved, we need to ingest work with a number of early implementers. And this is the stage two, which will probably finish in the first part of next year. And they will produce technical documentation and guidelines. And after that, the project has funded has ended. But of course, there is ongoing support needed for this infrastructure. For Anne's, that will be providing training guidelines to contributors about how to use it. Liaison and some tools, for example, making sure that the metadata store projects integrate with the party infrastructure project. For the NLA, they will continue because they have to for all sorts of other reasons. It's their mandate to maintain the infrastructure. They will register contributors and the harvest arrangements with the universities and the government agencies and continue to support and improve the data administration tool. So that's just an ongoing commitment. So just a little bit of a pictorial view. On the left, we'll have a ARDC contributor. Could be an institution, could be part of an institution, somebody like yourselves who's contributing collection service and activity records to the ARDC. Now, at the moment, you also may need to include party records. But the purpose of the infrastructure is that the party records can be submitted independently to the party infrastructure. So information about your researchers, about your research groups, your departments can be submitted directly to the party infrastructure. And in return, a persistent ID and otherwise an NLA party identifier is assigned. And that means that we don't need your party records anymore. What we need is in your collection service and activity records, a related object which says this NLA party identifier is the manager of, is the chief investigator or whatever the relationship is to your collection or to your service or to your activity. Once you have that relationship, you know that there is already a record in the ANS registry for that identity. So there is no need then to supply party records. But of course, you do need to supply party records or someone needs to supply party records. It may not be the individual project or data capture project. It may be that your library, your research office is already providing information about all of your researchers that are involved with your projects to the NLA. And so you really just need to be able to link to those records. But I'll talk about those implications later. And then from the party infrastructure, the ANS collection registry collects party records, which is linked to the collection records that we also receive from the contributors. So it's a little bit of a little bit of a split instead of in the sort of model that you've seen so far. Just a little bit about people Australia or, which is now a trove, if you're familiar with trove, they're people and organisations view. They of course don't use RIFCS as the underlying schema for records about people and organisations because RIFCS was designed for a data registry, a collections and services registry. And it only captured enough information about people to provide a little bit of context. Whereas what they're looking at is getting a lot of literature information about people from the sorts of data sources that they're talking to, Australian Dictionary of Biography, Australian Scientists, it's like a pity of Australian Science, et cetera. And so they use a well-used or well-known standard in their library and archives sector for describing corporate bodies, persons and families called EAC-CPF, which is the new version, which they're just now moving to from the older version, it's just called EAC. And as well as supporting names, it supports rich descriptive information, it supports related parties, it supports related resources, and it supports the description of functions and activities. And that's a link to that standard, if you're interested. So just a little bit of a, so again, another diagram, the model, there's the data providers providing party information. The NLA would obviously hope that most people have an OAI repository, we know that's not true just yet, but most of you are building towards one, if you hope, don't have one already. But they will accept party information that's in a static XML file, accessible over the internet as well. And they will negotiate with you when you set up a relationship, or you, or the institutional, whichever body within the institution is providing the party information, will sort of contact them and say, I've got party records, information to give to you to get identifiers for, and they'll sort of say, well, you know, do you want to use send us your records in EAC format? Do you want to, have you already got them in RIFCS format? We'll take that if that's what you've got. But they can also work with you, and whatever internal, you might use another directory standard that's for describing people and organisations within your research management software or your staff directory system, and they can also use that format and map it to EAC. So that's just the negotiation phase. They take that information in to their harvester via their harvester into the People Australia database if it's automatically matched. Now, at the moment, the rules for automatic matching are very, very tight, and most of your records would not get automatically matched because, you know, an identical first name and surname is not sufficient to say that that's the same person. They use things like birth and death dates and other ways to do automatic matching, but most would not match unless they have a common identifier. So without some sort of common identifier to go with the name, then probably it will end up not being matched and will go into the party data administration tool where someone will have to manually say, yes, that's the same person, or no, that John Smith is, it's not the same person. There's no one of that name already in People Australia. It's a new person and it's a new identity. And when it's in the People Australia database, it's not only available via Trove where you can search and browse people and organizations, it's also available via machine query services using the SRU standard API, and it's also available for harvest via their AI repository. So you can harvest those records back again and you can harvest it with the NLA identifier. So your own systems can automatically populate your systems with the NLA party identifier against that person's entry in your system. And from there, you can harvest it back again or you can send a request. And the change now because we're moving into using this service in the research data context is that the party administration tool will now be available to the data providers as well as to the National Library for them to do their own matching, for their own records. And this is an example of what the party administration tool currently looks like. It'll be released at the end of October or mid-October for a small number of early implementers who are going to provide their records to the National Library about their researchers for them to use and basically feedback improvements. And as the tool is improved, then we'll release it more widely next year. And so you can see that the entry there, that's the entry for Thomas Huxley. And you can see on the side that there were actually three contributors. So there were three sources of information about Thomas Huxley. One was the Library of Australia, in other words, the Australian name authority file, and the Encyclopedia of Australian Science and the Australian Dictionary Apography Online who all provided records about Thomas Huxley. And you can see there's some biographies there, related people and organisations, and resources, things that either about Thomas Huxley or by Thomas Huxley. Now if we were to look at a researcher, for example, someone from QUT, you can see here now the contributors are the name authority file and the Queensland University of Technology. And when the Queensland University of Technology supplied their records, it was out of their institutional repository. So the National Library of Australia was also able to harvest from them related, sorry, publications in their repository that were connected to Greg Hearn. And so you now see the selected resources from their repository, as well as other publications that were already in People Australia by or about Greg Hearn. And then if you look at the, back in the People Australia database, this is what happens. They don't change anyone's, they just take what the person contributes, it stays, or the organisation contributes, it stays in there. So there's one identity for Douglas Mawson, one identity authority. That's the party ID. But within the database, there will be three quite separate records that are each being provided. And the local ID that was submitted as the identifier by the local institution when they submitted that data. And that can always be matched against to the, they will keep that match point. So the Encyclopedia of Australian Science could always send an SRU request to Trove with their local ID, and it will return back the NLA party identifier. So, and also if they then send an updated feed and they have expanded the information about Douglas Mawson, it knows that it's the same record because it's got the same local ID and so it will replace that record rather than create a new one. It will do an automatic match. And so what this means for data providers to the ARDC is that they're encouraged to provide party information to the NLA party infrastructure service and to obtain NLA party identifiers. In this way, we will build a common set of parties for the Australian research sector rather than a slab of identities from this institution, this institution, this institution, this institution, many of whom will overlap. Over time, affiliation information can also be captured in EAC so that people will be able to see that this person was at Queensland University of Technology until sort of 2008 and then they moved to the Monash University. This sort of information is a benefit in the research and innovation context. And so what we also then want to see is that the collection and activity and service records that are provided to Ann's contain links to NLA party records. So inside those collection records or dataset records, for example, there would just be this related object element. And you can be confident that when you see your dataset record in RDA that there will be a lot of information available there about that person who is managing that dataset without you having to provide that information in your dataset records. So for data capture projects, they're a little bit different than from sitting in the commons projects because you're often working in a quite specific subset or area of the institution. So you really need to work with other groups within your institution who are also got contracts to provide data records to Ann's so that you actually come to a common agreement on what the key structure you'll use for party records and who was going to provide those party records. Are you each going to provide party records to Ann's or is one area like the research management officer going to do it on behalf of all of the areas institution? It doesn't really matter. It's quite valid for the party infrastructure to get a set of party records from a particular research center which has very rich information in their system about those researchers much richer than perhaps their staff managed personnel system or their research office system has. And so if you provide records about that person the research office provides records about that person they all get all that information gets connected into the one identity. So even though you're two different contributors you can be referring to the same person and you get a sort of an amalgam of information about the person, what you provide and what somebody else has provided. But to make that happen easily so you don't have to do a lot of manual matching you want to sort of agree what key will you use what is some unique key within your institution that you can use so that that record that you provide about the researcher in your research program can be matched with the record that might be provided by another area of university about that person because you've chosen to use the same key structure for identifying. If you don't that's okay too but somewhere along the line someone may have to manually match because there's not enough information if you like to do an automatic match because the name being the same alone is not enough. But as part of this project the NLA and ANS will be looking at other information we can use to do probabilistic matching such as publications, field of research codes there are other things that might be able to be used for automatic matching so that people don't have to do manual matching and we'll look into that as we go along.


So k plus q by this distance plus k minus q by that distance, so it becomes 0. Sir, if we like this, we can put this. It can't be both curvatures. Only once and then each curvatures are this. Sir, if we like this, we can put this. It will be same? Exactly same. Exactly same? No. That's all right. It will be same? Yes. What do you think? The way I did this, I distributed on this ring. q charge is uniformly distributed on the ring of radius r. It's a point charge, right? It's a small charge. No strings. Yeah. So this will be... So any charge distribution is potential at a distance z, radius is r, or a charge is q. The ring is like this and you're finding along this line. It's a ring. A ring.


Hello everyone, welcome back to my second channel. It's been a while since I posted on here, hasn't it? Actually, how long has it been? I haven't posted on this channel in six months, but in my defense, I've been busy. Dang, I didn't realize it's been that long, but my last video on this channel got 1.5 million views. That's pretty good for this channel. I don't know why I haven't posted on here in a while. I guess I feel like I have been prioritizing the main channel more and stuff like that. So I apologize if you live for the second channel content, but here I am again, I've got a LaCroix and I want to try to do a Reddit video. It's kind of a thing that other YouTubers do. Shout out to Cody, kind of his thing on his second channel. I think PewDiePie does it too, so it's sort of his thing. A lot of people have it as their thing and so now it's gonna be my thing too. Now, on this channel, maybe. I don't know, I'm just gonna, I'm gonna do one and we'll see if it's fun and then maybe I'll do more in the future. I just, I want more things to post on this second channel because I do feel like I did put in a bit of an effort to make this second channel and to get it to where it is today and then I stop posting on it. So if you don't know, I have a subreddit. It's r slash Danny Gonzalez and people post on there fairly often and I check it out once every couple of days just to see what people are saying and sometimes there's funny, excuse me, and sometimes there's funny stuff on here and sometimes there's like questions that people want to know about me and stuff like that so maybe I can answer those and it would be interesting or interesting to see my reaction to some of the things on here. So let's go ahead and look at my subreddit. Should I start by hot or new? I feel like we probably want the cream of the crop for our first try, especially since there's not that many people posting on here right now. So we'll probably want to go for hot. This is the top thing on my subreddit. Behold the Gregma nation. It was posted 15 hours ago. It says Alpha, Sigma and Gregma. Yeah, I guess I am sort of the pinnacle of man, the absolute peak male that there could be. Maybe I should start making tutorials on how to be a Gregma male and it'll basically just be a tutorial on how to act like me all the time. So basically what you're gonna want to do in any social situation is try to avoid conversation as much as possible because you know you're not very good at making conversation. If you're at like a restaurant or something and the waiter makes a mistake, maybe they bring you the wrong drink. It's important for you to apologize to them even if they misheard you because it does feel like your fault even if it's not your fault. If you want to be on the top of the male hierarchy, it's important that you cannot grow beard. I think that's the clear progression from Alpha to Sigma to Gregma. He starts off with a pretty full beard. It gets a little patchy when you get to Sigma.  Danny should have one of these in every single corner of his room.  I don't know why that's necessary. When have you ever thought that the one thing missing from my Christmas decorations is that their eyes can't move around and like follow me around the house? That's so scary. It does have beautiful blue eyes though. Did his pupils change size too?  I don't understand why his eyes would need to move and I definitely don't understand why his pupils would be getting bigger and smaller. Okay, this comment agrees with me. Oh, that's terrifying. I think I've seen that exact same eye animation on some Halloween animatronics. So yes, he absolutely should. Should I get one of those? I do have a giant nutcracker that I put in one video. It was a video with Drew.  I still own this and I actually still have all of these nutcrackers also. They're just in my basement. So in a sense, I do kind of have this. I guess not in every single corner of my room. I was thinking a while ago it would be kind of funny if for one video instead of having that in the background, I just hire somebody to dress up like that and like paint their face with like the smile and the rosy red cheeks. It just stand perfectly still in the background for an entire video. But then I kind of feel like it would be like a waste of an entire video.  Actually, you know what? I should plug that in. I just put the bulb back in this guy. A lot of people might not know this is a light up Christmas toy. Toy. Decoration. It's not a toy. It's serious. There we go. That's kind of nice, huh? I'm kind of worried even that might be too distracting. Is that? You turn.  Why did it turn off?  The light bulb just burned out. That was so weird. It was like the second I looked away. Okay, well, I guess we're not doing that. This post is called, Am I Crazy or? And the post reads, Is Danny kind of fine? I started watching Drew Gooden recently and then found Curtis and Danny's videos and I'm surprised I never see fans mentioning his looks other than to say he looks young or that he's just Drew. Am I some strange outlier or just not part of this normal demographic? I don't think Drew or Curtis are attractive. It's just Danny. This is driving me insane. Yeah, I'm not going to click on that. Especially since it only has seven upvotes. Just going to upvote that. Here's a post that's a petition for me to do a Reddit video and that's actually the reason why I'm doing this Reddit videos because I saw this post and it's got 300 upvotes. So how could I not? And it's got 50 comments all being like, Yes, please, I vote. Infinite votes. What does Reddit video mean? Okay, I'm sure if he knew he would have voted yes. Okay, this post says, Sup, I like Greg and it looks like a selfie. I don't know what this is supposed to be. Is this just a guy posting a selfie? This looks like a selfie from like 2006 if it is. What's up? Just wanted to say hi from 2006. I know Danny hasn't started making YouTube videos yet but I just wanted to hop in and say what's good. Love the content dude and I like Greg. Part of me thinks that this is maybe supposed to be a doppelganger because this guy has a big nose and anytime anyone sees someone with a big nose they posted on the subreddit saying it's a doppelganger but there is a doppelganger label for this subreddit that this person did not use. They just put it in general. Okay, let's see what the comments say. Maybe someone can glean what this is supposed to mean. Someone said, A, it's Curtis. I guess they're probably talking about the guy with the curly hair. Is this meant to look like me and Curtis? Don't really think it particularly looks like either of us. Oh, this is when me and Curtis met in 2006. This is when this is from that video where we sort of linked up as 12 year olds and made a vlog together. I forgot, this was actually our first collab. I was so nervous, you can tell by the way I'm standing. First and said screw Apple products, we're using potato products over here. And then the original poster commented back, indeed.  Okay, this is sick. I actually commented on this. This is from three days ago. I built Danny's old setup in Minecraft. This person like modeled this room in Minecraft but the way that I used to have it set up added all the decorations and stuff.  Maybe they have it modded or something but they got the Greg letters and the shelf and the nutcracker, the curtains. It looks very good. I commented on it and I said, why is my comment not top comment? My comment is like five comments down, even more than that. Wait, where is my comment? My comment's like almost at the bottom. You guys know this is me, right? Hey, people on my subreddit, this is me, Danny Gregg Zollis. I even have a thing under my name that says it's me. Danny, I'm Danny. If I comment on something, upvote it, all right? How embarrassing is that? I don't even have the top comment on my own frigging subreddit. Let's see what other people are saying.  That's solid callback. Someone responded like honestly, I assume chisels and bits mod. Like this person is really so aggressively asking where they got the little cactus on the shelf.  I've been searching in Minecraft for years for a little cactus. Person's probably like, dude, take it easy, man. It's probably just a mod. Dude, that's amazing. How long did it take? No response. He's probably all craft in something else, someone else's room. Maybe he'll do a different room in my house. Maybe my bedroom. I don't think I've ever showed that in a video so it would actually be very disturbing if they were able to do that. Oh, someone said if you subscribe and don't turn on notifications, are you potentially Greg instead of truly Greg? Or are you just Gerr? The EG can only be acquired by clicking on the bell icon. Okay, so are you like potentially Greg or you like half Greg? You're just Gerr. I think I voted on this. I think I voted GR. But now that I think about it, I mean like, I guess you're probably more just like a potential Greg or like a Greg in training or like a beta Greg. You know what? I'm gonna comment on this. I'm gonna set the record straight. You're beta Greg. And the Alpha Gregs and the Sigma Gregs have their notifications on. Actually, maybe Alpha Gregs are subscribed with notifications on and Sigma Gregs are also subscribed to this channel with their notifications on. I think that makes the most sense. It's that time of year again.  Did you guys know that this is my most viewed video on YouTube? I think just a few months ago it passed one of my Troom Troom videos and now it's my most viewed video. I'm gonna kill Santa Claus has 17 million views compared to 15 million, 13 million are my other most viewed videos. 17 million views. And it's not even like one of my regular videos. It's a music video. I should make more music videos. Haven't made one in a long time. I have a few songs that are like straight up done and I've just been kind of like putting off making music videos for them. I should do that. Just takes a lot of time to plan, you know? But I have a lot of fun doing them and it could become my new most viewed video. So why not? Maybe I should give it a go. Danny has spooky songs and this song. It's official. We need a Thanksgiving song from him ASAP. I think I thought about doing that once. I thought about like releasing a project of like spooky songs and one of them was gonna be called November 1st.  Actually, let me see if I can find that because I know that I started making part of it and I just have it private on my SoundCloud. This was like years ago. Yeah, November 1st, two years ago. I don't think I finished making this, but forgive this singing. This was definitely a rough draft. Yesterday I saw so many sexy cats but today I just eat normal cats. What's up with that?  Why, why can't I? So that probably answers your question of whether or not I'll make a Thanksgiving song.  Someone commented under this, I don't have Thanksgiving, what's it like? And then he responded with kind of a sweet little explanation of Thanksgiving. Well, it's just a lot of food in your family getting together. Turkey is normally the go-to for the main dish, but my family makes stuffing and mashed potatoes and roast some veggies and we eat until we can physically eat no more. Then we have some pumpkin pie. This post just turns into like a really sweet post about the meaning of Thanksgiving. It's about giving thanks, that's really the heart of it. Wait, what was this post about again? Killing Santa Claus, that's right, okay. Does anyone still watch Danny's videos that came out two or three years ago? Looks like everyone does watch my videos that came out two or three years ago. That's kind of sweet. I have a few of my videos that are like favorites that I go back to and watch just for like nostalgia every once in a while, especially videos that I filmed with other people like my collabs with Drew or Curtis or like videos from tour. If I'm feeling especially nostalgic, I guess some of them aren't even my videos, but like I'll go back and watch the Gooby video every once in a while if I want a good laugh. It's just kind of fun. And sure, maybe it is a little bit narcissistic to be enjoying my own videos, but I do it for nostalgia, just to kind of remember what it was like to be on the road with my boys or hanging out with my boys and having a good time. How did Danny get into music? I know he majored in computational media and that he's always wanted to do comedy in videos, but I don't know if he's ever mentioned how he got into playing guitar, writing or producing music. I'm really into music, so I would like to know more about that. That's actually kind of a good question. I don't think I've ever been asked that before or I don't think I've ever answered that before. I don't think he's ever answered that, but I know that he made music on one of his childhood YouTube channels. So I think it's safe to say he's been into it for a while. That's true. I did. I wonder what the first like musical thing I ever posted on YouTube was that I produced. I think the first bit of music I ever posted on YouTube was this video. Hope we don't scare. We're hitting kids. Produced by yours truly. I started making music for two reasons actually. One, because I wanted to make videos like this, I wanted to make music for videos that I was gonna make with my friends because we had a YouTube channel together. But I think that the reason that I actually downloaded FL Studio, which is what I started making music in, is because I listened to Skrillex in high school and I wanted to make dubstep like Skrillex. Let me see if I can find some of that actually. I know I posted it on an old SoundCloud account. Let's see, this is the first thing I ever posted on SoundCloud. So probably one of the first things I ever made. This was 10 years ago, sometime in 2011. I would have been a junior in high school at the time, probably. This is when I started making music.  I don't know what vibe that is. It's got a little something to it. Let's see, happy song. I didn't know what I was doing at the time. I don't think I understood how music genres worked. I think I just sort of like just played notes. Okay, here's dubstep. Yeah, this was 10 years ago. I've got tracks called dubstep, dubstep two, dope step. This is when I was getting into Skrillex. Dude, I haven't listened to this in so long, but like I remember making this. That's so weird. What is SpongeBob remix? I hope my horrible ugliness won't be a distraction to you. Not a no boy, not a no boy.  Oh my God. So yeah, that's how I got into music. Okay, this is the kind of meme I like to see.  Why doesn't this have any comments? This should have more upvotes and more comments. This is an actual meme related to my videos, topical. Very nice. Danny looking a little different here. Okay, I've gotten a lot of comments saying I look like the new Buzz Lightyear. And honestly, I think I see it. Let's compare here. This is a picture of my face from a thumbnail. And then here's the new Buzz Lightyear. We both have the blue eyes. I think the eyebrows are pretty similar. I get it. I see why people are saying it.  Dude, that does look like me. It's me. Wow. That actually could be a cartoon version of me. I should say though, this happens every time Pixar comes out with a new movie. People tweet me saying that the main character looks like me. For some reason, every male Pixar character are like, I feel like any animated movie now for some reason has to have like brown hair and blue eyes and a pronounced nose. Of course, people send me this guy all the time from Cloddy with a Chance of Meatballs. People have sent me this guy before, the guy from Ratatouille that I look like. People are even sending me this guy. He's not even a human being. I don't know why Pixar style is just me. Oh shit, we got a controversial post over here. This person is aggressive. Rain Deers don't have paws. This actually makes me so mad in the song I'm Gonna Kill Santa Claus. He says when I hear those reindeer paws, I swear to God it makes me so angry. He was so pissed off that he put an extra syllable in angry. I am angry. Okay, here's the thing. I know reindeer don't have paws now. The funny thing is I wrote this line because there's that Christmas song that's like up on the rooftop, reindeer, paws, whatever. So I wrote that part kind of as a reference to that song and because I needed something that rhymes with Santa Claus. Come to find out. That line isn't even talking about their paws. It's talking about reindeer pausing. Like up on the rooftop, the reindeer, they pause. They stop for a second. I thought it was someone hearing clap, clap, clap reindeer paws. But I don't think I've ever officially written out the lyrics to that song anywhere.  So for all this person knows and for all anyone knows, I could be also saying pause as in when I hear the reindeer pause. Like when I hear them stop on my roof, that's when I'm gonna break the law. It's true that I was misinformed while writing the lyric, but I do think that it holds up because you can just change the version of pause and it's fine. Santa's reindeer are just built different. You know what? That's a fair response too. They can also fly.  So maybe that he can fly and he also has paws instead of hooves for some reason. This video is sponsored by Raycon, specifically Raycon's new everyday earbuds. These are them. It's the same great company that have been making these high quality earbuds that are half the price of other premium audio brands. But now they're even more convenient. They've got a better design, better sound quality and improved rubber oil look and feel and a built-in microphone for taking calls. All in all, they're great earbuds. These things fit really well. Both Laura and I will go for runs wearing these. I could be running for like an hour with these things in my ears and they won't fall out. They actually come with a bunch of different size of the gel ear tips so that you can find the right size for you. And all in all, these are just high quality earbuds. They're designed well. They don't like feel like cheap and like they're gonna fall apart. They feel sturdy. They come in a nice case that charges them. They offer eight hours of playback time and 32 hours of battery life. And if for some reason these aren't the right earbuds for you, they do offer a 45 day happiness guarantee. So if you're looking for some new wireless earbuds, whether you have wired ones right now, which are not ideal for any sort of like active activity or if you just need to replace your current ones, I definitely recommend you check out the Raycon everyday earbuds. All you gotta do is click the link in the description or go to buyraycon.com slash furious to unlock some exclusive deals up to 20% off of your Raycon order. Again, that's the link in the description or buyraycon.com slash furious and you can get up to 20% off your order on some Raycons. Thanks to Raycon for sponsoring this video and thank you to you guys for checking out Raycon. All right, well, you know what? I think that I had a pretty good time making this video and maybe I'll do another. Let's see how it does. Let's see if people like it. Please post some more on the subreddit. Give me a little bit more material to work with and I'll see you guys next time. Bye.