 Okay, thanks very much Matias and I'm really pleased to be here. It's a different audience for me, it's nice to have a connection to the CTA project and to see some friendly faces. Matias, it's very nice to continue the work with you. Thank you very much also for the introduction, touched on a few topics. That's true, I work at the CDS in Strasbourg in France. And I will, I put the slides on onto the escape template because most of what I'm going to talk about today concerns things that have happened in the escape project. But I also tell you a little bit at the beginning just about my background and, and a little bit about the CDS. And I should also say that I've stolen a few slides from colleagues of mine in the escape project, Matthew Serbia and Matias and as well as my colleague from ESO Alberto Nicole. I use a lot of liberty to demonstrate things using some of the CDS tools and so you'll see that as we as we go along. Yes, so just to add to the introduction so my background is as an astronomy researcher on active collective nuclei. And I've somehow found my way to working in a data center and open science. So I'm the director of the CDS and as Matias says we have services that hopefully are well known and used throughout the community. And last year we celebrated the 50th anniversary of the CDS. And so I mean contributing also to the idea way because it relates directly to sharing data, and where I've led the virtual observatory activities in the escape project. I mean, just a couple more words on CDS it's a it's a French research infrastructure. I'm going to be using the word research infrastructure a few times so I sort of thought I'd point out that the one that we have here is a national level research infrastructure at the University of Strasbourg in the CNRS. And we have our reference databases simbed which has some 14 billion objects in it. We collect catalogs from the published literature there are some 23,000 of those 80 billion rows that we we serve as our role in astrophysics. And we have a visualization tool called Aladdin, which has all sky surveys in it. It's the fastest growing service we've got almost half a petabyte of image data in accessible by Aladdin and other services and API's that serve those tools. And we make them interoperable by standards by using the standards of the idea way. And that's the International Virtual Observatory Alliance. And this is kind of the link into the work that we do in a scale, and in the virtual observatory. So there's a whole landscape of these type of activities I mean this is a CDS centric view of what we do at CDS we have connections to the to the infrastructures like the big telescopes and the space missions who have their own archives. We also collaborate with astronomy data centers like ADS that we all know, as well as Ned and Canadian and American data centers. We participate in this activity which we call virtual observatory. It's kind of a idea that the data that we publish in the electronic services in astronomy kind of make this digital sky of data that we can observe and so this idea of virtual observatory came out in about 2000 and we've been using that word. We're participating horizon 2020 projects, you'll hear about escape, and we also sort of connected to some of the data infrastructures that are out there like the research data alliance, and in Europe, what we call European open science cloud. At CDS we have a kind of special link to the journals in astronomy, because we process the data that comes from the journals. Okay, so this is what the CDS does is part of a much bigger enterprise, which we can call open science. And if I think of some of the main elements of that it's about how we share the data. And that in terms of something being open, you know there's sort of no barriers to that that's seamless it's it's the idea that we can reuse that research data in a relatively free and easy way. Not spending most of our time writing programs or logging into websites or things like that so that's the idea that things are open and seamless. And it's quite well known now to use the word fair and I like it a lot because it gives us a kind of common language for what we mean about open science and data sharing that the data should be findable. It should be accessible that it should be, you know, kind of usable together interoperable. And the whole point of doing that is that we don't, you know, we can reuse a lot of the data that comes from observatories. In many ways, not just for the original PI science but for all types of activities and astronomy it's really a common way of working because most of the physics questions that we need to ask today in astronomy need data from multiple telescopes so we really need to be able to combine data and reuse it. Another aspect of that is that you know this doesn't happen automatically. There's a lot of computers involved of course there's a lot of technical things, but there's also a very human side to open science and we call we can call ourselves data stewards, because it's the human aspects of being able to, you know, provide quality data to manage the data to provide those those services. So the fair is this acronym which is being well abused everywhere now and I, I kind of wanted to jump into a sort of visual example of fair to sort of show what I think it means for for some aspects of astronomy. So if you're interested, for example in active for interacting galaxies, you probably know about the antennae and NGC 4039 is one of those interacting galaxies and I, I kind of want to show you an example of fair with with the antennae galaxy. So, so how is that findable if you're using a tool like, like the one we provide from the CDS called Aladdin, you can basically type in the name of the object, it gets resolved to an astronomical coordinate. It then queries the databases that are in astronomy. And it can tell you where you can find that data. So this application is fairly detailed. And you can see on the on the left panel. It shows you the data collections and actually that lights up in green when there's data available for that object in the field of view that you're looking at. And this is an interactive thing you can zoom in and zoom out, and those colors will change, depending on which services are able to give you data on that object. So that's, that's telling you, you know, data that's found. And it's also telling you where it's not found, which is, I find it extremely useful thing to do. And that's because behind those data services that are listed on the left hand side. There is a kind of indexing, and it's a coverage of those different services on the sky, using a map like what you see here that allows those services to tell you whether they have data or not at that point in the sky. So that's finding that's f accessible. Once you've got, you know, and you found that the data exists, you can, you can access it, you can look at it, you can visualize it, you can download it. So that's the A. So the interoperable. You want to use data, not just from one source, but from many and tool like a level allows you to pull those data into register them astrometrically so that they're on the same zoom scale, you can combine them overlay them, do what you want. And so that's kind of one level of interoperability, maybe a fairly simple one, but one that we certainly need in astronomy. And it's reusable. So I've shown you Aladdin, but a lot of those services which are available through Aladdin are also available through various application programming interfaces, and you can query them for example through Python notebooks. So here's just one simplified example where you are using that data that's there accessing it programmatically in a Python notebook and doing things like making cutouts of the data so you're reusing them for some other purpose so so that's my kind of visual example of a fair for astronomy using just one one object. Now, how do what makes that possible. How does that work. And one is the standardization that's behind for the all sky coverage, we use a system called hips that stands for hyper hierarchical progressive surveys based on hill pics, you split up the sky into regions and then you keep on dividing those regions four by four, as far as you can go. It's a system on the sky that's easier to use than astronomical coordinates. It's a system where you can support this idea that the more you zoom in the higher resolution images you get or the higher detailed information you get. So that's been standardized in 2017 by the way and we have a paper published about that. And this is, you know the technical side of it what you, you know, you have a complex table like this that shows you the indexing of those reviews on the sky and what's the reason why I show this table is because it sort of illustrates that you can combine all of the different W map images have a resolution of only about seven arc minutes on the sky. But in the same system, you can describe, you know, an image from the Hubble Space telescope using, you know, with 25 million second pixels. So you have this system that allows the description of all those different sites up to data in the same way that allows a tool like a Latin to use them all together. So it's standardized with a lot of effort to make kind of agreements at international level about how you do that through the idea way and you end up with standardized documents like the one for hips that's shown there. And that becomes a kind of versatile container. Anything that you can describe on the sky you can make into a hips you can make it for images you can make it for catalogs. You can make it for density maps or flux maps you can see the guy density map on the bottom there. You can see the footprint of observations there and because it's actually because it's a sphere you can even use it for planetary services. Okay. And here's my video I've got for this move this slide, this some talk, whereby, you know, in escape, for example, we've used the path of the data of for SKA using the cat. There's some noise coming through. I think maybe someone needs to mute in the background, but okay so this video was just demonstrating the use of the use of that standard where you can deal with the whole sky of data. The more you zoom in, the more you get. And it allows you to access those terrible petabytes of data that you would not be able to download onto your computer and zoom into. But here you get you're being streamed just the data you need to for what you need to see a given moment. And you might have noticed that we were changing the stretch on that image. And that's because it uses not just a visualization format like JPEG but behind that is the fits files of that mere cat image of the center of the galaxy. And you can actually change the stretch, because you're dealing with the real scientific values not just the, not just the visualization. That indexation of the sky is extended to not just the images like you saw but also to describing the coverage of a given data set in space like what part of the sky does it cover. So let me just mention to that that we've done, which is based on time so you can say, you know what is in what region of the sky observed within a certain time range. And you can use these things to make the intersection between hundreds of surveys in space and and time. So that's the I just wanted to sort of give the idea of standardization and how it relates to some of the data that we're using on the sky. So that's the concept of the standardization that's been done at the international level through the IVOA. This is a kind of overview of all of the standards there and the architecture. There are about 50 standards that are approved over the lifetime of the IVOA. And let me just sort of show you what the idea is I mean, we can split this architecture up into different levels this is the most simple level, where you say, you know at the top and at the bottom you have your providers and you want to be able to find that data, get that data, use that data, share it and what happens in the center is basically what needs to happen in the VO. This is the sort of next level of that architecture where you put the language of the virtual observatory onto that diagram. So when you talk about finding, we're talking about the virtual observatory registry. When you're talking about getting, we're talking about the data access protocols. And then everything kind of in the middle are things like the formats, the semantics, the data models, the query languages. And then also around that there's the applications, the metadata, the collecting of the actual storage and things like that. And then the IVOA has built standards that relate to different areas of that architecture. And so I want each blue box here is a standard that relates to some aspect of that architecture. And there's a whole process for that. You can access all of the standards through the IVOA, they're published through the ADS they have DOIs for the standards themselves. And we also list all of those standards in a thing called fair sharing it's a database for standards across all different areas of science. And here's just another idea, you know, aspect of how you, you know, what that enables you're able to have your applications that can talk to one another based on the standards that data is accessible by the standards. And there are a number of tools that support them, not just ones from the CDS like I've shown at the top but also others like, like top cat and, and various libraries for Python notebooks and the like. Okay, so the standard don't much good unless they're actually used. So there's been a big push of course to foster the adoption of standards in research infrastructure so I'm going to give at first a couple of examples of those. And in fact, the request for the, for the presentation in this webinar was, was asking, you know, what, what can CTA learn from the experience of implementation of these types of technologies in other infrastructure so I tried to highlight now how those standards have been used in different places. And then I'll get on to how we've supported that in escape. So, so here's one nice example. It's Easter sky, it's from the European space agency. They have a data center, which has influenced virtual observatory standards and they make things accessible through their tool called the supply it uses the hips images that you've used, but it uses the other standards for tables called table access protocol that uses queries to the astronomy data query language, and it provides a sort of semantics of the data using a standardized thing called UCD which is unified content description. So that's taken the approach at ESA to integrate VO fairly deeply into the archives, even the internal workings of the archives, make use of the table access protocol, and of the various standards and then they expose those data models and those, those data through applications and query formats, like Easter sky but also other programmatic things. So that's one example. Another example is the recent use of some of the virtual observatory standards for gravitational wave follow up observations and the localizations of those gravitational waves on the sky so they have applications that enable you to visualize using those standards. The coverage on the sky where the gravitational wave may have came from you know so those are probably probability maps that they display on the sky and they use the VO to manipulate those sky regions and to do various things for their users. So that's an example where the VO has kind of been implemented as a layer, you know they've already got their services working, and they're able to provide a kind of VO layer on top of those to then interact with that interaction through tools like what you see there. Okay, so there's been a few ways we've been trying to support the adoption of the VO in Europe. There have been a series of what's called Euro VO projects that have run for, you know some 20 years actually, since 2002 and of course there's been many projects that have run for that time. The most recent project is called escape and that is a project which is not just VO VO is one part of escape and that project ran for four years between 2019 and and January this year. And that's a big project, which was addressing the open science challenges of many large research infrastructures in astronomy, astro particle physics and also in particle physics. So there's the whole set of logos you can see on the side there and in fact that project came to a conclusion in January. So these are the logos again of those things where it's various particle physics projects, and what we're really concerned with are the astrophysics ones, and I prefer this diagram, which, which shows the actual pictures of the existing and sometimes future infrastructures that we're concerned like the square kilometer array, like the joint, like the VLBI network in Europe, the ELT of course plan for the future ESO telescopes including Alma that you see in the picture, the Europeans solar telescope and the Chinkoff telescope. All right. You know on the particle physics side. There's their projects like concern, and, and the project called fear, but also the gravitational wave from partners for ego and Virgo and cosmic ray neutrino. So escape was a big project that had many aspects to it. I think I've got it. Yeah, okay. So there is aspect about data storage with the data leg. There was a work package on science platforms for processing data. There was a science aspect of the project. There was a work package that built an open source software repository that is in operation. And there was the video so the video part concerned the things I've sort of already started to mention interoperability of data, publishing data in a standard way so that it can be accessed, and what metadata and protocols and I needed to make that that work. And basically what I just said the virtual observatory part was to use the VO standards to make the data fair. And the idea was that we expanded from what had happened before to to new partners, the solar telescope for example is completely new application of virtual observatory standards. It's fairly new for radio astronomy. And I would say that's also high energy astrophysics concerning CPA is also a relatively new area in which the standards were sort of maturing and we helped them mature during escape. Let me go fast through some of the objectives. This is a project level things that we were trying to do to. Oh yeah, let me let me move on a bit and just say the approach that we took was to combine the partners who already have expertise in building things like virtual observatory standards and tools like ones from the CDS but also from the University of Heidelberg and Spain and and combining those partners with the partners from the research infrastructures like ESO, SKA, GIVE, CTA and putting those together in a way that enabled us to assess the needs of those infrastructures for VO standards and then to do some level of implementation. So the way we did that we did it through having technology forums. We did it by bringing those partners into the IVLA itself. And the key part of course was to implement to develop to prototype. Each infrastructure was really at a different level of maturity. So that needed to be taken into account. And then we also combined that development by making sure that those services are really usable and we took them to various training events where early career researchers would come and participate in schools for using that data. And then we also had training events for the, for the data providers themselves where they would come and share the experience of how, you know, what difficulties were they having to implement VO standards or what did they need from those VO standards. And I've really stolen some slides from those events to, to highlight the aspect of, you know, learning from the other implementations. So one of the relatively mature implementations of VO standards is that the European Southern Observatory. They have an archive and they have, you know, we have documented the experience that they have had in adopting VO technologies. And we're doing that so that we show what's possible. Other infrastructures can assess from that what is relevant for them. And really important, you know, we highlight what the strategies are for adoption, and what are the difficulties, you know, it's not easy really. So what are the difficulties that come about so at ESO, for example, when addressing the IVA standards, they basically have a process where they define high level requirements. They selected which standards were necessary. And they went through a process of implementation. So that's that's a very simple thing to say. In the documented case, there's really a lot of detail about how they did that through analyzing the constraints, working out what evolution of their existing archive infrastructure would have to happen. What database selections they needed to use. What could be used off the shelf and what you'd have to be built from scratch based on their needs, the cost, and how to build that into their process at a big infrastructure like ESO. The example is fairly good because they have a successful science portal with a web interface. And they integrate those real aspects, they use a lot of existing tools, integrate them into their services, and as well as library so they use tools like ones that they might get from the CDS, but also libraries that are shared between different partners around the world for accessing and querying data models and building up their services. What's really important on in the ESO example is that they show how you can have alongside of the interactive web interfaces. You can have the programmatic tools and access. So they have a lot of documentation about how you can query their services using ADQL, this language which is used for accessing tabular data. They have an interactive way of using on their website, but then you can use that programmatically as well and they highlight which IDLA standards they use, and then which, which software has been used to implement those. So I think for other research infrastructures assessing how deeply how much to use their tools, and if it's useful for them, it's a really good example, because they really highlight what tools they've used internally there. And, of course, they provide feedback, and there was, you know, I don't want to hide from this in any way. They have a big assessment, which they call, you know how bumpy was the road. In some cases, of course, it's a bit bumpy, because it's not like implementing DO is just implementing a toolkit or, or something off the shelf. It's usually requires adaptation to the real needs that you have there, you know, you have a certain kind of data. It might be characteristics, it might be lots of small files, it may be a number of big things it may be distributed in different ways. So I'm not going to go through this slide but just highlight that you can read the, how, you know, what sort of issues they have to overcome. The other example I wanted to highlight a little bit more quickly is, is in radio astronomy from Jive. They were a relatively newcomer to the virtual observatory. And they were able to build up expertise using those standards, because some of them are complex, and also understanding what the dependencies were, especially if you're in an operational environment, and you need to implement something new. So what I want to highlight a little bit more quickly is, is in radio astronomy from Jive. They were a relatively newcomer to the virtual observatory, where, you know, radio astronomy and the bureau is in its early stages, and Jive comes with, you know, lots of legacy data archives, and in particular different types of data. And I've just directly stolen some of their slides presented at the escape events where we did this type of analysis. They defined what their video protocol use cases were, for example, accessing the historic data, which would then be used for high resolution follow up with, with the LBI that related to gravitational waves to various things. And they wanted to have a standardized access to that archival data that you could use through science platforms, and they wanted to do that through Jive, the lab environment. And they really had to face the fact that the VLBI data is different to say the data that's from a classical spectrograph or from a from a optical survey done by ESO, for example. So it's visibility data. The observation properties are less well defined. I mean, there are footprints. And there can be multiple sources in a given data set. So, part of the process there was discussing with the video partners about how to get the metadata about that, right. And they evolved to being able to use the OPSTAP approach, which is for exposing the tables of data in a standardized way, using the OPSCOR data model. And they use a lot of this standard called data link to link together the different components. They implemented using tools such as the DAX suite, the ACHS provided by Heidelberg, which enables them to expose their database in like in the picture that you see there. There's, you know, there's software in there that allow them to ingest data to, to, to integrate with their system. In particular, it's another case of a kind of layer because they say that they run on top of an existing archive. It's not like they rewrote the whole archive. It's like they put a layer on top of that. They just did their service, which means they make an access it through the tools like, like, like a ladle. And in summary, at the end of the escape project, they were able to say that, you know, the European VLBI network data archive is more findable. These protocols were used to implement services that can access the access to the science platform, and that had helped from from the partners in the project for doing that. So this is my overall sort of results table that we that we produced for the escape work where for each of the different infrastructures that were involved. And results for helping them to use the video. So for ESO, it was, you know, the video standards in the archive and well basically using them as an example. For the gravitational wave, it was the development of the mock 2.0 standard and the associated library that allows you to deal with that in Python called mock pie. For the radio astronomy, one of the biggest things there was really just getting radio astronomy going in the eye of the way with the creation of an interest group there. I skipped to EST. I mean, they really faced for the first time, how to describe the metadata about solar physics that they have. And for high energy for CTI and for KM three net. There was a lot of effort, putting the project about data provenance standards, and for helping the implementation of those so here I'm really referring to the work of data. And there were, there's a reference paper published about that and there were workshops held about data provenance. And so I've taken a few slides from Matthew just, I mean it's not certainly not for me to lecture to this group about the high energy physics but clearly it's this, you know, these different types of that physics that you can access for in high energy science. CTA, of course, is about protection of events, a kind of indirect detection of the astrophysical source. I certainly won't lecture you on on those, but I think we should. As Matthew explains, recognize what is the complexity that comes with that there are many telescopes. It's an indirect detection. There's a strong influence of the atmosphere in the state of your instruments. It's a transient sky that you're observing. And the data acquisition involves a reconstruction, a detailed reduction of the data. And so, at the IDO level, or for VO and CTA, I'm extracting here information from Matthew, I mean the main thing is to be able to handle event lists that are appropriate to what is observed, or what will be observed with the CTA. There's a data discovery aspect where, you know, how do you describe the core elements of observation for a high energy event list. And what is the connection between the data, the detailed data model that you will have in CTA to the standardized more simplified data model that you would use in as an IDO and OPS core. So these are some of the technical details that have been addressed at the level of like within the escape project working out what are the observational core data model fields that are relevant to the CTA data. And here you're getting some very technical things of course about calibration level about how you describe the coordinates and the regions. You know how do we map the CTA versions on to the IDO version. Of course, there's examples for surfaces like the HES data. This is just an example of using some of the services that are already there for accessing the tabular data from HES and being able to display it in tools like Topcat. And one of the results of the escape work was to really standardize based on, you know, CTA requirements, things for the provenance of the data. What happened to the data because it's such an important part of the CTA pipeline and processing to be able to capture that. And that was something that was brought in by the CTA project, asked, you know, and working with partners like Observatory of Paris and CTAO to come to the standardization necessary. And so it's been quite a good success actually to have that standardized and to publish papers and have workshops about it. So I'm coming to the end now. And we do all of this so that our, you know, astronomers can use the data and do science with it. And in the escape project, we've tried to foster that through the Virtual Observatory schools where people come, and they bring their own projects. We support them before, during and after the school. And I guess I'm highlighting that slide here because there's a whole lot of training materials. There's interactive, interactive tutorials that use tools like Top Cat and Aladdin and, and the different services that are out there. But other results are Python notebooks that can run in the escape platform and integrate with the wider aspect of escape. So this is just a screenshot of what we've been doing in the last years, lots of virtual events for training. And in conclusion, I hope that I've shown you that the VO is a mature framework of standards for data sharing and interoperability and astronomy. The use of those standards has been supported by projects like escape, where we've brought the VO experts together with the partners from those experiments from those large infrastructures. It's not necessary because you need that combined expertise to make progress. Also, the people involved with developing, you know, the archives and the data servers of those instruments. It makes a big difference if they also participate in the standardization themselves that way, the standards that are built are really relevant to what you need. And I encourage really being involved in the IPA. And I think the use of examples of implementation are a valuable way to identify other for infrastructures that different levels of maturity to work out what they should do based on the experience of others and hopefully feedback themselves. You know what they learned and what they achieved. And I have provided a whole set of references at the end of the talk with links to some of the information and notebooks and documentation, but I'll go back on the conclusion slide and I'll finish there. So thanks very much for your attention.