 Thank you. That comes through. Nice. Okay. Okay. So thanks. So as I said, I'm such a guru. So I lead the data source analytical platform in turn. So today I will talk about the sum of the link to open capabilities we have built for the ecosystem science community. So the talk is purely on the work we have done as part of turn and then the vocabulary we have built and then how we have built it. So just to provide a bit of a perspective. So they must have people probably know turn certain is a is a one of the increased project. So the main intention of the turn is to collect collect and publish the terrestrial ecosystem data. So if you look at the terrestrial site, it is of anything on the line. So we, we do a substantial data collection. So, you know, across turn the major component of the turn is like an observing platform. So we observe at the three different scales. The one is at TV what we call it as a landscape scale so this is like a complete continental scale observation typically the remote sensing observations and the model derived data products. The next scale is what is called what we call does like a turn ecosystem surveillance. So these are all the regional scale observation. So these are typically the 100 meter 100 meter plot created all across Australia. Collect the vegetation baseline vegetation and then the soils information. This includes the samples as well samples of the soils and then the vegetation. The next scale of observation is the terrestrial ecosystem processes. So this week, you can call it as models like an intense monitoring. So these are all about 14 sites highly sensorized the within and then additional to that around 34 flux towers collected the carbon energy flux the phenocam acoustic sensors. And every fire every fires they do the vegetation survey as well as part of the site. And then previously there was some the bird survey was done as well in that side. The sensors you know depending on the type of sensors the observation is made from every, every, every minute to every hour as well. So just to provide a perspective so in return. If you want to put in one picture this is how I think at least in the current observation looks you know we have a favorite of flux towers. And then the remote sensing satellites, and then we do an aerial observation platform. So is a terrestrial is a scanner, and then the survey sites, these are typically 100 meter, 100 meter blocks. And then when there's a multiple observation has been done, and then you can have a similar data products that may, that may derive, for example, using a terrestrial in a ground based as a scanner. You can scan the entire site and derived vegetation structure. And then when you are, when you do the human observation you can do the way you can derive vegetation structure as well. So when they using the remote sensing data products, you know they they use the, they use the ground based calibration validation to derive the vegetation structure at the continental scale. So, so even at the single variable can be measured at a different scale at a different platform. So that's why you know we always struggle during the organization aspect and how effectively we describe the data. So if you look at. So that's why it's the complexity is quite high. There is a multiple of the human observation and the sensor observation, and then, you know, some of the observation is at the point for example the fluxed over. And then the models are the grid at you know raster images. And then the data collection is, you know, from a different platform instruments and then the methods, but use multiple methods for us, measuring the same thing. So just to provide a bit of a context. So this is in a structural growth form. So on the left hand side on the table so that is the table generally they use if they do the human observation. And then the based on that the target okay what is the structural growth form. And then the same similar data is derived from the remote sensing which is on the right hand side as well. So they are the comparable data set but the measurement and then the scale is is completely different. Similar to the vegetation height. The left hand side is typically on the vegetation stratum that was measured in a plot in a multiple layers and then the very similar thing using the remote sensing data products that is derived as well. The main reason we build all this vocabulary is to harmonize all this, you know, the different multi scale of observation. So that the user when they come to the data discovery portal, they know where this actually the observation is coming from kind of a thing. And then so that it also enables us for the harmonization of the of the data set that is that we absorb. So we control vocabulary as an harmonization point at a different scale. So as part of that what we do is you know we build a vocabulary to describe each and every artifacts of the observable property. We describe what is the platform where the data is measured the instrument that is used, and then the spatial regions, you know, because we index the site on a different spatial region including the Hebrew, ecoregion states and territories, etc. So and then we, we, we describe it as a spatial temporal scale resolution, etc. and the people were there to collect the data organizations the procedure, the methods and then the observer property. So, what, what we have done is, you know, we follow the sociology to build this, you know, especially the instance of platforms. So most of the if it's nothing of a non instance thing we use a, you know, scores, a capillaries to build the ontology otherwise, and then the effect of the ontology is if there's already there, the terms, we reuse it for example in the platforms, you know, we reuse a fair bit of already there in the GCMD, and then we, if there is any certain instance of a platform. We describe that, for example, in our case even the ecological site is a platform, and then the flux tower you saw in the picture that is also a platform and then the instrument is attached to that one so which we describe using a social sensor. If we are describing the instance of an instrument, otherwise we use just the use a score for capability describe a type of a sensor. For the unit of measurement we use a QDT ontology, and then we work very closely with them if there is some unit of measurement is not there, we just send a pull request to them, they add that as part of the ontology as well. So all the spatial resolution, temporal resolution everything you know we just use what is in the GCMD because that fits well for us, you know, we don't have to do much extension. The observable properties are tricky ones, we have like a wide list of classifier even in the observable property we classify the classification we do. And then we try to align with the end with this and then the CF conventional capabilities. So partly because you know the lot of the, the flux data they use a CF convention. So we use reuse of those vocabulary if it's not there or the definition doesn't mean especially ecology it's a funny thing. You know the definition, you know, there are a lot of like a regional central definitions for how they do it, especially terminology that they use if there is anything if there is a conflict so we update that, but we provide a relationship. You know, in this using a small relationship so that you know, if anybody the users would know the, you know, the relationship between the different categories. The other thing what we do is you know even though we use a CF conventional capabilities, the capabilities as a one to one, we just create an instance. We create a vocabulary of that one, and then we put it as like an exact match. This is purely for the operational perspectives. If we just, you know, run through the third party URIs. Sometimes their system is down the URI doesn't resolve then again we need to do the favorite of a work. So that is in that way, you know, a lot of things are beyond our control if how they manage the thing. So in this way, at least, you know, it results to the URI, what is it what is in our control, and then the user can know okay it is an exact match to the CF recap that is kind of the thing. So just to provide a bit of an example of, you know, if you if you go to the, you know, for example if somebody is looking at the, you know, the flux server data set. So the, you know, it should have like the e blue echo regions everything where the tower is, and then the platform under the platform is described the, you know, which talk about flux tower is assumed that you know if we are looking at the, you know, we can observe property radiation, the instrument that was used, and then they observe properties radiation, and the procedure. So most often we provide a different procedure, or the special resolution to, you know, what is it, it is a point resolution and it's a stationary tower. We tell it and then the temporary solution and because it data is about 30 minutes we provide that as part of the temporary solution. And then the content we publish is from the net CDF is the content time. So if you if you look at for example in our data discovery portal, this is assume that even of the random one I picked up in a great ocean woodland flux tower one of the data release. In the platform it says that the greatest and woodland flux station. And then, you know, if you click on that link and it shows the complete details of the tower, and what it is and everything. And then the, if you click on any of those instrument it will tell what what that instrument is kind of a thing. So here this one so it tells you the exactly how we classify the thing so basically the flux towers comes under the classification is a fixed fixed platform in our case and then we. It's a flux tower it's part of the flux tower the instance of the flux tower. And that is how we classify in in our technology. Again in the field ecology, even the each of the site is a is a platform for us, and then assume that if if you are looking at the vegetation I this is the basically the pipeline you can see in each of the, how the, you know, the one of the data is tagged as part of the metadata when we are writing up the metadata. So, in, so in summary so we use a LOV is for the consistent representation of the data. And then, so it is substantially improve the search and access and the reusability aspects of the thing as well. And then the lot of take what we have done is a reusable artifacts and then you know that can be used by anybody. So we have instances where the, you know, we have the capitalized a lot of the state government protocols, and then, you know, they have downloaded from our link data website and then reusing that as well. So the we use this vocabulary is to across our multiple platform what what we use to publish a different varieties of data including the data discovery portal the echo plots which is a data integration platform to publish the plot data and then the images which is the image repository of all the ecological images that was that is collected at the time. So I brought a link to the, the vocabulary is you can have a look at that one. So finally I would like to acknowledge the traditional owners and that the stewardship of the land on which we turn operates we pay our respect to their ancestors and descendants who continue cultural and spiritual connection with this country. So finally I would like to acknowledge the our team, then I put some URLs where you can go and look at the favorite of the vocabulary is we have built not only for the time for the other projects as well. So I just wanted to insist that you know so what what you can see is from the, the vocabulary says of the type, the instances we are still working on to integrate that into the viewer. What I mean is like and if I'm describing the particular instance of a platform that probably is not discoverable there so we're still working on it, but all the vocabulary is type, you know, is available predominantly those are source for camp that is on those you can, you can, you can search in that the link that we are. I will stop there. Thank you very much.