There we go. Great. Welcome to our Birds of the Feather on geospatial capabilities for the next decade. We're very glad to have you along for our exchange today. This is brought to you by the geospatial capabilities community of practice. You may or may not have heard of us. The community we put together in 2019 with ARDC, Oren and Citrax. We formed initially to facilitate the exchange of knowledge around the management discovery and use of geospatial data. I coordinate this group. I work for the ARDC as data management consultant. Our chairs here are Kieran, Michael and Sanjeev. You can find us online at this website here. We've created as an Odo community of the same name for a lot of our resources. They're mainly presentations. And we have a Google group. We have 99 members so far. And here on this Google group, we post announcements as to when drafts might be available relevant to our topics for you to review. We might have announcements about papers that have been published. We always encourage our members to post to our group. We'd really like to have discussion in here. We also announce relevant events. And for example, this BOF and other conferences or other communities of practice that might be relevant. So for example, sensitive data. I'm going to track pads a bit touchy here. Sorry about that. Okay, there we go. Ah, here we go. So, so far our community practice we in the year that we established we ran above at this conference. And here we gathered feedback from participants around the availability management of geospatial data. And we had a discussion about current circumstances and challenges. We also ran a session mapping a landscape where we wanted to learn what was accessed from what metadata was around. Similarly for the discovery and use of geospatial data. And then we set off another spin off working group on that to put some work into that area. So, then we had an additional BOF in 2020, where again we wanted to discover further requirements and suggested solutions. And then during all of our meetings, we've had various presentations by specialists on various topics. So for example, location index, the A&Z metadata working group, and others that you can see here. And then we've always uploaded the presentations and associated resources into the Zanoda community. So, you're able to go back and keep looking at those. So today you're going to hear from the chairs of the community of practice. So that's Karen and Michael and Sanjeev who are here. So in addition to our chairs contribution, we're very grateful to two additional authors of our abstract for today's session and for the planning of this session. So that's Jens Klopp from Syro and Leslie Wyblon, AENU and ARDC. Jens isn't able to attend because of our concurrent session. And Leslie might hopefully be able to attend a little bit later in the session for when we get to the panel. We're very pleased to have presenters for you today. Alison Kealy has joined us from RMIT. And we have Simon Costello from GAA invited, but he might not be able to attend. So instead, Irina and Matt are going to attend to present. So this is the overview of our session today. We've got the introduction that you'll hear from our chairs. And then we'll have our presentations. And then we very much welcome your participation in our panel discussion. And then we'll just finish off withdrawing the threads together and conclusions, which will include as much discussion as we can on the NRIR. So I will move on now for Michael to continue. Great. Thank you, Melanie. It's lovely to be here today and have such a great group of people here to be part of this discussion. So as we move into the introduction around the geospatial capabilities community practice, obviously being here at eResearch, we're very much focusing on the research infrastructure side of things. I understand that brings in the data with the analytics models visualizations and all the other services, which actually make the use of that geospatial domain possible. Thank you for meeting your microphone. I thought the start of this presentation would be quite important that we focused on the breadth of geospatial within research infrastructures and then kind of work our way down through the funnel, so to speak, understanding that spatial is inherently special, describing a number of different aspects across the surface of the earth and how we observe and measure phenomena. And while data is seen as being the central representation of what's actually going on, geospatial capabilities do draw in a range of other services into this piece. And looking at the geospatial data and services landscape. But first, we do understand that geospatial is inherently cross cutting and supporting and working across various domains, whether it's transport health, all the way through to education industry. And this breadth in terms of the domains includes a real broad range of stakeholders from data curators and custodians all the way through to the research and government, academia industry they're engaged in a wide range of these tasks, which may draw on and actually direct and connect a range of different capabilities. Now, as we're looking into the new future, I guess picking up on some of the items we've discussed in the key notes just earlier. Technology is really spurning on quite a lot of change in the geospatial area. And this change is really needing to be addressed, particularly from a research infrastructure perspective, but really looking at the Australian Academy of Sciences advancing data intensive research in Australia report that was recently released. They document a range of different challenges in that. And obviously opportunities there for research infrastructures. And if we sort of go back to the initial energy of looking at the big V's and big, big data, we could sort of come across variety as being one of these really large important ones for spatial understanding that variety may include differences in data capture all the way through to differences in spatial temporal scales and the attributes or fields that comprise a particular data set and the way that it's used on a research infrastructure platform. And with this huge variety, it comes in a huge opportunity to allow, you know, researchers or users to choose from an increasing array of capabilities to actually help them do scientific activities, which today is almost seeing as a way of customizing these whether that's actual data sets themselves or the pipelines through which we push this data. Now looking at the current way things are being done, there's been a lot of prepackaging of data and services, whether that's through data sets which are foundational released by government or the private sector, but those services which are prepackaged in the sense of being commercial applications or tools available online which are very flexible and can't really interoperate in the ways that we'd like them to maybe they're constrained by governance or licensing, or just the actual technical decisions that he put there on the way. So looking at I guess these challenges in the new era, around how we're going to do things we've got a whole bunch of different things that have to discuss as days off drawing in aspects such as the reliance on some of these foundational data sets or services and how we actually going to move them are they actually fair for research. And in the geospatial community practice we've been hearing a number of concerns around well, is it kind of actually get access to the underlying raw data which is being used here. So I'm going to encounter a whole bunch of other issues around data sharing agreements or cost of trying to access these things. And how do we actually engage with these new capability as I'm saying advanced, and how to actually develop research infrastructure which can use things. And in the geospatial community is a practice, I guess our role is really trying to keep the strain government academic industry at the forefront of innovation. This requires that we develop geospatial research infrastructures that can really try and overcome these challenges I've just briefly mentioned in my opening and facilitate seamless connectivity between platforms and services through a whole bunch of different meetings such as standards, all the way through to backhand capabilities and services to really meet the needs of researchers. So hopefully that provides a bit of a motivation for today's boss. And I'll hand over to Karen for the next phase of this session to really dive into some great use cases from which we can unpack this discussion further. Thanks Karen. Thanks Michael. So in the next session we've got a couple of presentations. This presentation will be from Alison Kealings from RMIT who's going to with the introduction to space 2030 and the geospatial capabilities sector. Alison, did you want to take over? Yes, thanks Karen. I'm going to share my screen. Okay, can you see that Karen? Yes, we can. So this is my first boss. So it's lovely to be here and to be part of this on this discussion. I'm Alison Kealy and I'm both at RMIT University in the geospatial group there but I also work part time for the smarts at CRC. It is the convergence of these two worlds that has kind of given rise to this concept of the space and spatial roadmap for 2030. So I'm part of a working group that are building this roadmap which is a consultative process towards developing and securing Australia's interest in the integrated ecosystem of space and spatial. It's often really interesting when speaking to a group of people who are already committed to geospatial because the space ecosystem and community don't really understand very clearly what we do. And in some ways we're trying to understand why space is suddenly getting all this attention and where that becomes relevant for us in the geospatial domain. So the space and spatial industries as we know already play a significant role in Australia in responding to catastrophic events, climate change that recently the post-pandemic recovery and the growth of these industries will profoundly impact Australia's well-being in the future. So that's what's driving this activities. We know they are already inherently synergistic. We know that the space sector provide us with our Earth observation sensing platforms. We know that we use it for things like global navigation satellite systems so that whole positioning navigation and timing service. And we also know that space provides a lot of the comms that enables a lot of the IoT distributed data access and capabilities. Spatial on the other hand gives us our data infrastructure. It gives us the value added analytics. It gives us the product and it basically takes that information and puts it in the hands of the decision makers in the way that they needed to effectively make decisions. The issues arising is that the optimization of the combination of these two ecosystems will strengthen for Australia's sovereignty, defence security, environment and ultimately our well-being. The challenges are how do we do that. The space sector itself has its own challenges in that it is newly emerging and fragmented. But we already know we can see that it'll create opportunities because we're already doing and leveraging some of that capability. So the roadmap itself is focused on achieving these outcomes for Australia by highlighting the strategic impact of space and spatial capabilities. By identifying the critical characteristics of such a capability and to try and outline the steps to realise an integrated ecosystem as a national asset. And that's what this roadmap has been doing. The intention is not to craft another roadmap. It's not to kind of come up with things that override or doesn't take into account other strategies. What its intention is to look at the other strategies that are out there. Look at what's happening in Geoscience Australia. Look at what the new smarts at CRC and the space agency are already developing in terms of roadmaps for space. We know that there are defence strategies to build more resilient space architectures for remote sensing, for comms. How do we identify the gaps in there and align the activities so that it focuses on the growth potential for earth oriented applications. Recognising that there's a whole suite of things that'll be happening in space. But the intersection of space and spatial is where we focus on earth oriented activities. So when we talk about space, we talk about the regions surrounding the earth where atmospheric friction has little or no impact on the motion of objects. It is technically defined by an imaginary line called the common line, which is about 100 kilometres above mean sea level. And that's normally considered the start of space. So we're looking really at the objects that are being put into that environment. But more recently we're looking at things that sit in a suborbital environment. So these are things like using high altitude balloon platforms to create sensing, sensing architectures that are broadly covering both space and near space environments. When we look at the spatial environment, we know what that is. It's the mechanisms by which we collect and look at and analyse and create products based on information that has a location enabled capability. It allows us to answer the where and when in some of the decision making processes that we have to deal with. And so it's the intersection of space and spatial where we're trying to craft this roadmap for. What are the opportunities that sit in there that if we were to leverage the developments and innovations in space, what does it unlock for the geospatial community in terms of new capabilities and new opportunities for innovation? What new infrastructures are going to be required in order for us to maximise the potential benefits of this? And the potential benefits, we kind of can quantify these both in terms of the economic value of it. We know that space and spatial make a big contribution to digital transformation. And this is expected to reach a value of 2.3 trillion dollars by 2023. We know that the Australian space sector's forecast to be valued around 3.9 was valued about 3.9 billion in 2019 and expected to grow exponentially. Direct economic benefit to Australia from the use and application of it is observation dated from space alone is predicted to reach 1.6 billion dollars by 2025. And as everybody in this audience probably already knows that spatial directly impacts on a number of sectors, very important and critical sectors and other countries are showing similar growth. A great way to think about some of this and where the value of space and spatial are being connected to everyday life is to think about, you know, the example we use as a cup of coffee. Your cup of coffee, if you think of where it started from in terms of the product, the milk, it starts on a farm. It's an agricultural environment in which we're setting up virtual fences for animals where we're monitoring the quality of the agricultural process using spatial technologies. We're connecting that to the transport and logistics markets to move that out of the farm into a production environment. We're then moving that out into the hands of the retailers and ultimately into your hands. It is the synergy and the connectedness between the space technologies used to track and trace the product right down to the analytics that assures the quality of the product that ends up in your hand. So the value chain of space and spatial is really well understood. It's just not really well connected as a single ecosystem for growth. So a coordinate strategic approach to the integration of Australia's space and spatial sectors will result in all of these things. If you go through this, it's almost fairly intuitive as to how we can benefit as a society from leveraging some of these activities that are happening. I already mentioned the food industries, but simply by coordinating the activities more strategically, we actually accelerate the maturation of both sectors. And I think that's a really important cause. The maturation of both the space and spatial industries by coordinating the activities of the key leading agencies and the academic R&D sector will create the environment through which the innovation that is being unlocked in the university and the academic sector can rapidly find its way into the hands of industries and ultimately into the hands of the decision makers. It's really quite powerful language because it is the powerful nature of the impact if we were able to achieve this that is really compelling. And so when I talk about what is the power of space and spatial, I'm really looking at that intersection, right? I've heard all of those developments in there that are taking us from where we are now into more enhanced capabilities. We know we can do positioning. We know GPS works. We know that that is something we have come to rely on in a number of ways, digital twins as well. But the future is when we have these digital twins, how do we use them in a way that creates an asset from them? What about positioning? What do we need the next generation of PNT type capabilities to allow us to do? We hear things about low earth orbit in satellites being used for positioning, broader, more diverse options for positioning in environments where GPS doesn't work. How do we quickly leverage the innovation that we know exists, connect that to what is happening in the comms environment and push that out into all the spatial domains that we know can benefit from it? So it's looking in two directions. How can space enable spatial? And what are some of the things we know already in spatial that are going to enable space? The digital twin is a really good one. The geospatial world has great understanding of digital twin and the potential for digital twin. How do we move that into space, that knowledge, so that when we design satellites using digital twin type capabilities, we secure the resilience. So we have satellites in space that have a longer lifespan. There are only four areas that are driving the purpose of this roadmap. If the purpose is to build and accelerate Australia's capability leading to improve national outcomes and sustain economic growth, there are really three focus areas that this roadmap is focused on. One is national security. We know that Australia is currently heavily reliant on other space mastering nations for their capability. GNSS is a good example. Australia doesn't own its own GNSS capability. So we're looking at where does the concept of sovereign capability relinquish Australia's vulnerability to foreign-owned technologies. And how do we leverage our own strengths? How do we leverage the fact that we have good infrastructure capabilities here in Australia to secure our own sovereign wealth in space and spatial? Economic development, accelerating industry growth three years ago, we had very few startups in the space ecosystem. Now we have over 60. Enhancing decision-making for critical spatial data and systems for economic benefit, digital twins, 3 and 4D data sets of the future built around robust AI technologies and advanced intelligence sensing capabilities. And of course societal environmental well-being, stem and diversity. How do we develop the workforce of the future to support the space and spatial ecosystems? And how do we build the essential systems required for things like climate resilience and handling some of the catastrophic events that plague Australia almost annually? To support this, a number of key topics were identified. There were 24 key areas and for this group, I think one of the key ones in here is a dedicated R&D section within the roadmap. The R&D section recognizes the role that the research sector, not just the academic research sector, but the industry research sector has to play in creating and crafting the inputs to a synergistic space and spatial capability. And so out of these 24, there are some priorities in here that are relevant to us in identifying not just what is required now in terms of technologies and data sets and data analytics too, but it's kind of looking into the future and looking at the research questions that need to be answered by each of these 24 domains and what is unlocked in those 24 domains that creates new opportunities for the spatial sector. And then feed that back. You know, if the spatial sector kind of identifying that with greater data, with greater opportunities for higher fidelity and higher granularity in information, what is it that we need to happen in the space sector? What is the idea of onboard processing? Do we want to distribute some of the processing to the onboard systems so that what we're actually extracting from the space environment are information sets that allow us to make more timely decisions during disasters. We don't want to have to deal with some of the huge data sets. So how do we distribute that? How do we build resilient architectures to identify failure and engineer greater trust in the data sets that we're building? So these 24 priorities were part of the discussion document sent out to the stakeholders in the space and spatial environment to get them to respond to these and tell us what were the priorities. Obviously the academic sector has jumped up and said hands down. It's going to be a really robust and well supported R&D sector because all of these things are where the starting point for broader future discussions are going to happen. This is a broader, this roadmap is developed by a broader steering committee. All of the key players in geospatial and space are part of the steering committee and have contributed to those 24 ideas to the focus group. So this has already gone through broad consultation to ensure that what we're talking about here really sits at the forefront of what Australia's needs are going to be over the next decade. And most of these people here are probably going to look familiar to you in terms of steering committee members. And then there's also the working group with people like myself who play a role in putting together this information that we then send out to our stakeholders. So I'm going to stop there. I've gone very quickly. I know, but it's a very exciting topic and lots of exciting things to cover in here. So thank you very much for the opportunity. Thank you Alison. That was really interesting. We might take one or two questions before we move on. If anyone's got a quick question, if you want to put it up on their chat since we've got a lot of people, we might take questions in there. If not, we will move on and we'll come back to it. So if anyone's got questions through these sessions, if just put it on the chat and then we'll try and answer them as we go along. Next up we've got Irina and Matt who's going to talk to from GSW Australia who's going to talk to us about the digital Atlas of Australia and what they are doing. Thanks Kieran and good afternoon everyone. I'm just going to attempt to share my screen here for everyone to see these presentations slides. Okay, it should be coming through now. Okay, so my name is Matthew Jacob. I'm an acting director of the coordination team within the National Location Information Branch in GSW Australia and presenting on behalf of Simon Costello who wasn't able to join us this morning on the digital Atlas of Australia. I'd like to start first by giving a brief introduction into GSW Australia. GSW Australia is, in essence, Australia's national mapping organization. We are the National Geoscience Public Sector Organization, part of the industry science, energy and resources portfolio. Our mission is to be the trusted source of information on Australia's geology and geography for government, industry and community decision making. Through partnerships and national leadership, GSW Australia provides authoritative location information and geographic services, spatial analysis and advice, including advice on relevant policy. Our free and open location data and products enable evidence based decision making, aid the delivery of government policy and assist industry development needs and support community wellbeing. Recent national emergencies have highlighted that national or even global events require a response that is tailored to local settings. Be that response around economic, demographic, health, infrastructure, environmental issues or national resource availability. For example, bushfires and drought present crisis situations to large parts of Australia, but their impacts vary widely in different locations, meaning that both during the event itself and in the recovery phase, governments need to get the right services to the individuals, businesses and communities that are going to meet their specific needs, based on local infrastructure, industry sectors, health and education services and many other factors. Another example we're all very familiar with is the global COVID-19 response and recovery. Again, the pandemic has a national indeed global footprint, but the impacts vary across jurisdictions, cities and communities and therefore health and economic responses must be tailored to local settings. COVID-19 has accelerated the take up of digital technology, particularly location information and highlighted the role that can play to support and enhance business operations across every sector of the economy and improve the delivery of government services and generally make life easier for Australians. The pandemic and recent natural disasters have reinforced to the Australian government that the ability to connect data through location is a powerful tool to inform decision making, policy design and service delivery that supports economic growth, appropriate environmental settings and the wellbeing of all Australians. As a result, the Australian government has embarked on an ambitious digital economy strategy to make Australia a leading global digital economy by 2030, along with an Australian data strategy to ensure government data is in the best state to feed this future digital economy. Location information and technology are key components to realising this goal as collectively the governments of Australia hold a wealth of information, a wealth of authoritative data about Australia geography, but need to put it in place in place capabilities for decision makers to connect, access and analyse those data assets. In this year's federal budget, the government committed $40 million to build a digital Atlas of Australia, which will be led by GS Science Australia. This is part of a $1.2 billion digital economy strategy package that has a range of measures focused on improving the accessibility and discoverability of the wealth of data held by Commonwealth organisations. The Digital Atlas of Australia will be a free, online, secure and interactive map of Australia. You will be able to find rich, authoritative and trusted data about Australia as it is now, as it was in the past, and how it might look into the future. It will be the next generation of the Australian government's location-based data infrastructure that has data on Australia's population, the economy, employment, infrastructure, health, land and the environment. You will access tools that let you analyse that data, such as comparing data for different places for the same place or for the same place through time. You will be able to personalise that information to suit your interests and your decision-making needs, including downloading what you create or taking APIs to put into your own applications, helping to make faster, cheaper and smarter decisions from trusted open data of Australia's geography and more. GS Science Australia will be partnering with a number of Australian government organisations in the first instance to deliver the Digital Atlas of Australia, but later with state and private sector organisations to connect and make accessible a wealth of data. This announcement is the culmination of work that GS Science Australia has been undertaking with our partners across the Australian government, including through the recently formed location Interdepartmental Committee, a partnership of over 15 departments coming together to join up our geospatial people, platform policies and partnerships. The Australian spatial sectors achievements over many years have advanced Australia's geospatial use and have also been instrumental in paving the way for the Australian government support of the Digital Atlas of Australia. So thank you. Some everyday examples of how the Digital Atlas of Australia will be used include things like this. A working family with young kids making a tree change move from a to Wagga Wagga can use tools in the Digital Atlas of Australia to show you which suburbs are nearest to their preferred schools and childcare centres, as well as how close they are to public transport links, recreation parks, green spaces and playgrounds. A retired couple planning an interstate holiday to the Sunshine Coast can use the Digital Atlas of Australia to create a customised map showing them where are the nearest healthcare services from their accommodation, such as hospitals, GPs, pharmacies, as well as other public amenities, including disabled toilets. A land holder in the Barclay Table Lands trying to get their goods to market can use tools in the Digital Atlas of Australia to help them calculate the best transport route to take and to see what transport routes need upgrading. A Southern based business wanting to expand north can use the Digital Atlas of Australia to bring together data to make the best decision. Figures on population growth and employment trends in the region together with site specific information on land use and land ownership will all be available. An infrastructure developer in Far North Queensland can use interactive 3D slope and elevation data from the Digital Atlas of Australia to help them work out where roads, dams, buildings can go. Or how much earth to move or how high to build or where's the best view and use historical aerial images of the area over time to better understand its history for more in depth due diligence and de-risking. An Indigenous land holder in Catherine looking over their country can use the Digital Atlas of Australia to help them work out what to do with their land, where to develop, what to protect and how to inform their development agenda in the region. The Digital Atlas of Australia will be developed and managed with appropriate security protocols. The platform will be developed using a secure by design approach, building on the learnings and implementation of the existing national map. A risk will be managed by limiting access to sensitive data sets through the two authenticated government users and building the public interface to only include data that is nonsensitive and unrestricted for use. The initial version of Digital Atlas of Australia will be available and accessible to the public business and government from late 2022. And in the years following its initial release Geosites Australia will enhance Digital Atlas of Australia by adding modern analytical tools and richer national data sets. Through to June 2024 we will see the Digital Atlas of Australia supporting strong growth in digital decision making across business, government and community. In our partnership, in a partnership with our state and territory colleagues and through including through the Australian and New Zealand Land Information Council. The Commonwealth sees digital twin technology as a big part of the Digital Atlas of Australia in the future. Digital twins offer a 3D and 4D immersive and dynamic environment where faster, smarter and cheaper decisions can be made. For the Commonwealth government, using the proactive and predictive powers of digital twin technology will revolutionise policy and program design and implementation, extracting greater value from the data. As the platform matures, data in the Digital Atlas of Australia will be accessible where appropriate humans and machines enabling innovative uses through new applications and technologies including but not limited to artificial intelligence and machine learning. The detail of how these new technologies will be integrated into the Digital Atlas of Australia will come from an evaluation of the platform at year three. This evaluation will inform a roadmap for the future incorporation of industry partnerships, data from smart devices and artificial intelligence. A successful Digital Atlas of Australia will rely on better leadership of spatial data coordination, federally through the location IDC with jurisdiction through AINSLIC and ICSM and with the spatial private sector. Improvements in government data sharing will also be key to the Digital Atlas of Australia, which is why Geoscience Australia welcomed the signing of the Intergovernmental Agreement on Data Sharing by National Cabinet on the 9th of July 2021. This agreement on data sharing commits all jurisdictions to share public sector data with one another as a default position where it can be done securely, safely, lawfully and ethnically. The agreement recognises data as a shared national asset and aims to maximise the value of data to deliver outstanding policies and services for Australians. The agreement applies to a wide scope of public sector data including spatial and location information. Data and digital ministers are finalising the first national data sharing work programme in consultation with portfolio ministers. Geoscience Australia will eagerly follow the development of the first work programme of national priority areas by the data and digital ministers. Given data sharing is an important factor in the success of the Digital Atlas of Australia. Geoscience Australia also continues to assist in developing the Australia data strategy to ensure location data is included in the strategy and that Digital Atlas of Australia remains aligned with this work. Continued government support for location information is critical to the success of the Digital Atlas of Australia. Having location data concepts embedded in the Australian data strategy will help ensure that there is continued recognition drive and support behind the importance of location information from successive governments in Australia. The Digital Atlas of Australia is further affirmation of the relevance and the importance of geospatial information in transforming Australia into a modern and leading digital economy by 2030. And pleasingly the government's Digital Atlas of Australia budget announcement has generated a genuine boost in excitement, positivity and support across the spatial sector. Since the announcement Geoscience Australia has been approached and has had discussions with many of you in the spatial sector wanting to learn more about the Digital Atlas of Australia and how to get involved. Geoscience Australia is in active planning for the program and technology strategy and we are engaged with our core partners across federal and state governments. There will be more to come in early 2022 on how to get involved. Thanks very much for your attention. Thank you Matt. That was really interesting to see those and exciting to see those developments happening. There will be lots of questions as we go along. There was one question that came up as to whether there was an existing URL with details for the Digital Atlas. Yeah, I don't believe we have a URL for that at this time. So Kieran, just initial information, I put a link to the digital economy strategy so some initial description of the Digital Atlas can be found there. But we are developing a communication package at the moment. Right, okay, thank you. Okay, is there any other quick questions before we move on to the next section for either Matt or Alison? Alright, we will move on then to the next section. So we've got a panel discussion happening now because we've got a bit more of an interactive time here. So we're going to have Michael, Bigby, Sanjay, Alison, Irina and Matt to join the panel session. We'll get all of you on the screen, that'll be useful. And I will try and facilitate that. So to start off with, if I can pause the question to the panelist now that we've had a bit of a talk about this. What are the geospatial capabilities that we see are becoming essential as we move into the next decade? I might just get each of the panelists to answer that and I might start with Irina if you don't mind. Yeah, no, that's good. Thanks. Wow, where to start? So we just heard all these presentations from Alison and Matt and they described quite a few. And as we all know, Australia is moving quite rapidly towards digital transformation and adoption of modern technologies. We're looking at moving closely with federated access to data, to more standardized access to data, moving towards being able to use dynamic capabilities. Also, we are many agencies and datasets which were traditionally known, spatial like health or education. They are now looking at how they can bring their data together and gain great insights through application of spatial data or bringing together spatial data. So capabilities of spatial data will be more and more important every day. So what do we need to look at? How we can standardize data? It's extremely important. How we can integrate it dynamically on fly and rapidly. So we need not just the data structures, look at the capabilities, understanding from metadata, what this data can provide to us. All this information needs to be in machine actionable way. So the data is structured and can be interpreted by machines rather than humans. We need a great set of analytical tools. So when we integrate data across multiple domains, multiple portals, these tools need to be able to pick up all these pieces of information, and analyze it dynamically and provide feed for purpose advice to people who want to use it. So that's probably a start from me. Thank you very much. I might go to Sanjay next. Now I see everything as moving between two extreme, like more and more, we are getting two extreme. On one hand, we are getting data set which are designed for citizens who have got very less geospatial capabilities. On the other hand, we have got people who have got very high geospatial capabilities and they are capable of dealing with raw data. Now in between, we are getting many other expertise level where people are able to handle different set of raw data. Because for example, once your data is processed and it is presented as a process product, then you can't derive all the information if you compare it with the raw data. And if I will compare it with the earlier paradigm shift that we had in geospatial sector, like in 70s, we used to deal quite a lot with topographic map. And when GIS came, then people started looking for more and more raw data. So all the export is started looking to raw data and then they start pulling information out of that. Now again, we are moving towards more and more customized products where information is being designed for citizens who have got much less geospatial capabilities. So I feel that in the next decade, it will be very important to label all the geospatial data products as who can, for whom it is meant for, whether it is meant for an expert or it is meant for a lay person who is simply a surgeon who is looking for where is the nearest school or a person who is trying to identify where, how the schools are distributed in a state or at the national level. So this is what I feel that in coming days, we have to label all the data set with the level of expertise of the end user. Thank you, Sandivya, for that ability to understand who the data is meant for is going to become one of the interesting talents, I think. Can I ask Michael to go next? Thanks, Kiran. From my side, just drawing on what Irina mentioned there around data integration. Another thing that we've been seeing in the urban regional and social sciences is the way to work across the physical and social assets in this space and really looking at how we're going to bring these data sets together, not just for things like analytics and modeling, but also things like modeling and how to do that in a way which is usable and provides not just researchers but all the other stakeholders in this space with a great user experience. And I guess here we're heading towards, I guess, what Alison pointed out in a fantastic presentation on space and spatial, this idea for an integrated ecosystem as a national asset and how we realize that and allow people to move seamlessly between the different areas, which is more of an infrastructure platform perspective, but it flows into these aspects quite a bit in terms of the actual spatial data side of things. Thanks, Kiran. Thanks Michael. Alison, did you want to go next? Yeah, thanks for that. Yeah. It's a really interesting question. And I think rather than focus on some of the data usage, I think if we were able to look at what the requirements of spatial data are going to be in the future, as applications become more safety and liability critical or the decisions that are being made become more safety and liability critical. I think aspects of data related to security, aspects of data that's related to trust, aspects of data that are related to reusability and interoperability. I think these are characteristics of spatial data information that are going to be really important, especially when we start combining data sets, because we want traceability of the uncertainty in all of those data sets so that when we make decisions, based on aggregated or federated or cascaded systems, then we want to be able to trace the uncertainty through all of those systems. And I'm not necessarily a data person. I sit more on the sensor side of things. And I think it also, you know, it also comes back to, are we building sensors to collect the right thing? We have more capacity now to build cheaper, smaller sensor platforms based on very sophisticated quantum technologies on board AI. So are we now able to measure more closely to the level that we actually want the actual thing we want to measure? So no longer measuring proxies for things, but co-locating in-situ sensors for water quality, for bushfire detection, autonomous platforms. Are we able to sense it better? So I think it's the descriptors of quality of the spatial data that I think will be something urgent in the future. Thank you also. That's quality is an interesting one that we keep grappling with as we grow with the data. I think it's likely to become a much bigger challenge as we go forward and more important as we rely on these data. Matt, what's your thoughts on this? Well, I had a couple of thoughts, one that very much echoes others on the panel that have talked about data integration and the ability to enable people, whether they're experts or whether they're novices, to be able to bring data together to really add value so that they can start to either analyse in a very sophisticated way or to try to gain insights in perhaps a not so sophisticated way so that they can understand a situation or understand a challenge of their problem or an opportunity indeed. So I think that's a really important point. Another one, I think, is around advocacy for making data available. And by that, I mean trying to encourage the provision and the publication of information that can be joined up so that those users can get the value that they want and add value to related data sets. And so that advocacy comes from really understanding what are the problems that people are trying to solve or all the opportunities that they're trying to create. So starting sort of from their outcomes and working backwards to try to understand what are the ingredients of the evidence that they're going to need to be able to make a good decision. And then once that evidence is really well understood and the intended outcomes are really understood, can then start to identify what are the high priority, high value data sets that need to be available for decision makers to join up. So it's perhaps not so much a technical skill but more of a softer skill for practitioners and for people that are supporting the platforms and the capabilities going forward. That's really valuable. I think that soft skills is something that gets missed easily and the focus of the user focus. I had the need for focusing on this from a couple of different angles there. But I like the cycle to what you started off with that. You said being able to have that sophisticated analysis as well as the single one. And then that also cycles back to what the Sanji was highlighting that as we make things productize and make it easier for people to use. You can't lose the option value of that data could be the most sophisticated analysis that you get from the road data and there's this contention that that seems to be there. As we get more data and more sophisticated platforms and tools and products coming through, but at the same time making it easy for being able to be available for that analytics and advanced analytics. I just wanted to understand just get everybody's the panels we want on this, you know, how do we handle this this challenge of having access to the open road data that you can have all these options and can do that sophisticated analytics. But at the same time the need for data products and this whole growth of data products coming out and then products from products coming out, which reduce the option value and becomes very opaque on what the data is behind that. Maybe I'll start Sanjeev to start and then we can. I think we have to do some sort of classification where we are just labeling the data according to the capability of the end user. Plus, there have to be some sort of hierarchy for all the algorithm which are used for all the data set to derive any products. And the only trouble is like we always see examples of different for example earth observation data products where we have got one data appearing multiple times, and each one has got a different labels and which indicate at what stage of processing that data set is. But that definitely create a lot of task for the custodian of the data set because they have to have that much of space to make all the form of data available to the end user. So that is one aspect. So, did I answer your question. Matt I'm interested to hear your views on that and maybe in response to what Sanjeev said as well. If we're thinking about the difference between raw data and data products. I guess we need to confront the question well how raw do the users actually want raw data. So is it raw as in straight off the sensor, as observed by the sensor or is it data that's gone through some sort of transformation to make it machine readable into a format that other machines can read or is it data that has then undergone through some sort of quality control. So I guess it's a little bit about establishing some data levels. And so you can imagine a power user, a very sophisticated user might want a level one or a level two data that's not too far from from what the sensor had observed or as the data had been as measured. Whereas a someone who's perhaps not so sophisticated and just wants to gain some insights might need something that's a few levels away from that. So I think maybe we should be thinking about those different user needs and trying to understand what levels people might want to come to the data at. And I suppose there is a question in there too around the, not just the suitability but the sensitivity of data at those different levels. And perhaps the development of data products through those levels can have different different grades of sensitivity, if you will. And also this put a question on what level of geo special capabilities we require in the coming era, because we need to have expert who are capable of differentiating between different form of data. And they should be able to identify data set which is suitable for certain analysis, because sometime when we process the data, it generalized information, and it hides some information, which doesn't render it for certain sort of analysis, like for example, if you want to do say a photogrammetric analysis. And if your data is processed, sometime you have to dig for the raw data only then you can proceed. So this is just one one example and that has got some implication on what sort of geo special capabilities we require in the next decade. Thanks, and did anybody else want to add to that. Yeah, probably extend on it a little bit. So access to raw data can be sometimes basically impossible or impractical to have. So if we're talking about say satellite imagery, or some observations from Bureau of astrology, it's like terabytes and terabytes of data. So how you provide access to this vast amount of information. The ideal case is when you have your processing capabilities, or tools, like modeling tools associated with those data repositories. However, we all know that we have quite well organized research infrastructure, government has its own different research facility, different facilities for data storage. So I guess question here is how you can build, maybe partnerships with those storage facilities where we can actually apply both store this raw data and be able to process it between different repositories. This is something which probably still quite not utilized or agreed on. So that's a question on on the chat, which I'd like to ask in relationship with some of these. I know is asked if they are planning to make available the data products generated by user based on integration of data sets available to digital address Australia. Yes. So basically the short answer is yes. And once we start developing those products, we will be publishing them through number of ways. So one of previous question was about what the relationship between national maps and the digital address of Australia. The national maps for us is one of their communication channels where people can access the data and associated tools and services. So, and the product development will be developed through the lenses of various use cases so we'll try to take it for different groups of people. So another related question that was in there was something that Alison touched on earlier as we have this data coming through the need for security and handling sensitive data becomes a question from an event about how might we think about handling the sensitive data and how is there thinking that that's happened in saying Alison in your space that around the security and how you deal with the sensitivity of the data that's been produced. Yeah, I think I think for me some of these or a lot of these issues are all tied up in the question of quality. I think we have it's when we start integrating data sets that all of these problems become more intractable. Right. So we might have highly secure data sets being sensitive data being integrated but not so sensitive data. And then what is what is that that that traced as. And so I think in some ways it's kind of understanding I think somebody's already put this in the chat there about provenance and the propagation of provenance as part of the integration process. And for me that is is kind of the way this this this could be handled in terms of saying okay, this is all just a question of quality if I have raw data, then I have raw data as a certain quality. I preserved that as a set of information related to it. And as I integrate data set we move away from a thinking of having a single quality metric that describes an entire data set. We can now handle more more finely grained aspects of quality, because we have greater computational power, or we're able to distribute the computational power across distributed networks or in the cloud, and handle some of these issues in a in a more robust way. And so I think for me that is that is the big change that is coming in the future we're moving away from this idea of large centralized systems of handling data to really small intelligent and many more sensors and distributed processing capability. So I think that that change will certainly trace into the changes around statistical measures of quality and how we write liability into data sets as well. Thank you Alison. Any thoughts on that on data quality and the sensitivity you did that as well. I should have another question for arena and maps was around the foundational layers from and slick and I CSM just wondering about in the past we've had a foundational layers be published if we were to go to different models. How would that change things, or is there a plan in place already to encompass such a perspective. I was wondering if you could perhaps ask that question in a slightly different way Michael. Yeah sure no problem. So currently we have the foundational spatial data framework which was set up a number of years ago. And I was wondering what the next steps are for that, and how maybe some of these different threads have been talking about my drawing to that. Modernization of the foundation spatial data framework is one of the features in the strategic plan for the for and slick the Australian New Zealand Land Information Council. I am not able to comment on how far progressed they are with that. Or the, or how the things that we're talking about today, you know might influence that but it's, yeah, it's a very relevant question. So for me there are a couple of aspects here. So for the modernization of foundations, special data framework. So what the working groups are doing they're looking at different themes and aspects of data and see how they can improve interoperability of it between different stakeholders providing this data. So we can improve capabilities of access to this data. And eventually when these capabilities and infrastructures and architectures are developed, they will form the basis for those particular data sets at national level within the digital atlas. And particularly there's sort of preparational part of it where people are getting agreements and then part where we actually incorporate this new data sets into the digital atlas and be able to provide access to it for common share. There were a couple of other questions about digital atlas looks like a very popular topic. Relationship between the and the digital atlas. So for me with development of digital atlas and our understanding of different use cases and requirements from stakeholders, we will be incorporating more and more lace into it. So initially it's probably more focused on topographic data but definitely DA with its maturity of capabilities will be included as part of digital atlas. There's another question about location index where it fits with digital atlas as well. So location index for people who are never heard about location index. It was a project which looked at how we can use modern technology like clean data and tgs to enable integration of data from federated sources. There's a partnership between a BS just science Australia CSI role and do and we've been facing this modern technology for inclusion. So location index and the methodology and capabilities which were developed through it will be part of the digital atlas of Australia is one of the available capabilities. I'd like to cycle back to a question to Alison. In your talk you mentioned that one of the goals of the plan was to One of the plans was to see how you can accelerate and make it easy for the work that's been done in academia to flow into industry. I want to ask the other question going the other way because a lot of work happened in industry as well. How does that flow back into benefiting academia. Yeah, that's I think you're right and I think certainly with with the space environment. We see that a lot more because the space the space industry is made up of a large number of startups. So they're they're really kind of developing a lot of R&D or trying to to push R&D out to the the higher end of the the TRL or the technical readiness levels in there. So you have things like the smarts at CRC, which is trying to, you know, it's got $245 million to try and push these these things together. And craft, you know, these unique opportunities for engagement between industry and academia. What are the problems that industry have that they don't have the capacity to to build the solutions for or where they build an innovative solution. How do how do we advance that into to demonstrators so we have activities like water quality monitoring like emergency response. You know you have you have opportunities larger science Australia with their SBAS and their NPI work, National Positioning Infrastructure work where the space based augmentation system really looked at where industry was driving the need for higher positioning accuracy and integrity and using that as a lever to to to get money out of government for a space based infrastructure as well. So I think this there's a real awareness that the ecosystem for innovation in Australia in space and spatial is no longer this one way. Directionality, it's actually a feedback mechanism that that that's being being built. And what we want to do is we want to accelerate that we want to accelerate that so that the nations that are already more sophisticated in the space environment are, you know, we're we're not left behind or reliant on what they're doing to develop our own industry growth in Australia. Thanks Alison. Michael Sanjeev, we've seen a similar thing in other areas. Because I will start with what Michael commented right in the beginning of this session. One thing he mentioned that special is always special. And in academia, we have got few special thing which we ensure that all the special people acquire, like all the information come come from curve surface. And when we generalize any information to make it available to citizens, we often introduce a lot of error into it which affect the quality like a special auto auto correlation etc. So on one hand we had the challenge to make sure that all the basic things are incorporated into the curriculum. But at the same time, there have to be a constant feedback from the industry to update the curriculum so that all the people who are going to do a special workforce, they are capable of understanding all the new format of data, new technology, new data stream, and how to match multiple data stream together. Like in this time we have one very good example on one hand we are getting a huge amount of data set from other observation platforms, but at the same time we are getting lot of information from sensors which are put on the ground. Now how to match the two things together on a routine basis that is always going to be a challenge. Can I respond to that as well? So yeah, I think you're absolutely right. The interesting thing is that I always laugh when we say special is special because the space people think they're super special. Right, so you have this interesting system where the space people are kind of in some ways, as we have often had with spatial, people don't really understand why we know we're special and we know why we're special. But the rest of the world are not part of that understanding. And so in a strange way the space people often don't nearly understand as well what spatial is trying to achieve. And so you're constantly in this ecosystem, trying to push these two things together, because we see the synergies but it's not often clear to the other people. It's the same with cyber security, you know, cyber security is really important. You try and push that with spatial and the cyber security people are kind of going well why are we doing this, you know, that's something you have to do. And it's really getting that connection of what and I'll say this again, it's about what quality means for data and information and how those quality values affect decision making so that when I draw uncertainty around the decision, it actually has a meaning to the person who's going to use it. And so it's a really, yeah it's a really hard thing to do practically. Thanks. I was in there. I agree that there's some very interesting challenges coming out in there. We're running out of time. The lovely to have this a bit longer because there's a few other things that come up, we're talking about it that would be interesting to explore but in the interest of keeping this on to time I'm going to say thank you to everybody for that lovely panel discussion and highlighting the different things that we'll need to consider as we go forward. So hopefully we can take all of this and feed it into into our thinking on our own places as well as the timing is that we're thinking about the next next 10 years five years of investment. So hopefully we'll start to feed into the thinking in there. So once again, thank you all for that. I'm going to hand over to Michael to do the next session and bring it all together. Thank you, Karen. And I guess it's my challenge now to, I guess, look at the research lens and look at, I guess, all the different aspects we've been discussing so far. And what when I do that, obviously a number of different things stand out. And obviously these great opportunities and I guess the, the challenges do create more opportunities for us to have a look at the people we're having a research hat on in terms of how we're going to tackle some of the data challenges. Whether we're looking at the data capture side of things and space based remote sensing or drones or distributed sensor networks and citizen science and all that sort of stuff. All the way through to the infrastructure, which is facilitating the data flows, whether it's storage and compute or the actual applications themselves to facilitate all of that. And looking at the research space. The big things obviously discussed in this meeting, I think it's been great for Alison to really drill into this is around data quality. And looking at obviously what this means for data, and going back to this idea of almost what's what's the what's the requirements, how we have feedback loops almost the design of sensor which is actually quite exciting. The flows between space and spatial industry and government academia, which are being built now, what are the different opportunities for researchers to be involved at that at those different points. And there's been a really great discussion around that. And I think there's a lot of different bits and pieces in this space because it'll take forward in a piece which I'll come to in a moment. So looking at, I guess, what does all this mean for the research space. And if we are going to construct this up high in terms of looking at ecosystem based approach for connecting systems of systems together. How will that actually happen. How do we move from this monolithic infrastructure based approach to more of a, you know, module based approach that I think we've touched on a bit today. There's a lot of different connections between these modules and how will this actually be realized. So we're going to draw on the Federation of data where that's coming out of national map data to go to you or just allow us to Australia and connect it up with some of the analytic pipelines behind this seems to sort of allow connections to happen from all these different domains and disciplines across the space to ultimately enable innovation to happen. One of the big things we were discussing in preparation for this fall was around the national research infrastructure roadmap which is being going on this year. And for those who who don't know much about the roadmap exercise going on and very much affects the research team. And this is activity being going on at the highest levels of the research infrastructure space looking at what are the opportunities that are out there at the moment and really setting goals and targets out to 2030, as well as looking at some of the opportunities and challenges in this space and currently, the National Research Infrastructure Roadmap I'll just paste the link there in the chat window to have a look at that. So we are kind of right in the middle of this process, and we're looking at what we call in phase three, which is actually developing the exposure draft so we are hoping, I guess it's geospatial capabilities community practice members that this exposure draft we released sometimes soon so we actually have a look at seeing some of the ideas going on in the minds of obviously the expert working group is put this together and obviously all the contributors in phases one and two to see well what can we look forward to in the future and phase four, which is the next phase after the exposure draft has been released is an opportunity for people to comment on the exposure draft. And I think drawing on a number of these different components we've heard about today. It will require obviously looking at the exposure once it's released looking at some of the recommendations within that and trying to understand where they're coming from and what are some of the technical and social challenges around those so we can really act on it from the research perspective. And I think this NRIR piece at the moment does provide us a way to funnel some feedback into this so we can ultimately develop capabilities in Australia national research infrastructure which can really meet the needs of the different domains drawing on spatial. There's a number of people on this call from all over the place whether it's in the social sciences across to engineering or the geosciences and there's lots of different crossovers who have been speaking about today, up to state of quality data levels application space but I didn't really want to hop on this too much, but just putting it out there in terms of the capability group. I think it's really ready for us now to take some of this discussion forward, just having a voice in the roadmap piece where that comes out later in October obviously we'll have to wait and see but make it will make a really good reading hearing and invitation for all I have to have a separate meeting on this one to really unpick some of these different bits and pieces here to figure out what's required. I think the discussion today has really given us a good sort of direction in terms of discussion points and future ideas to consider for the next five to 10 years. So back to you Karen. If they really are the wrap up part of it and we've got a few minutes so we might open it up to a question one more questions if anybody's got any comments or feedback. If not, I'd like to throw a question at at the panelists again, because Michael you talk, you know we're talking about the roadmap, but just as a thought experiment, you know, if you had $10 million to invest, where would you invest it in the spatial space to make the 10 years of the future, the one place you'd invest it in. Human resources, the skills gap. And I would invest it in creating a framework for training the next generation of spatial industry experts who I am an academics I will say this but but it's about kind of creating pathways for younger people to move into a very diverse application where they speak the spatial language, they understand the inherent specialness of spatial data, and they're able to connect it in a really robust way to the application domains that that that needed so I think we need. We need to build capacity by identifying the gaps that spatial need to fill into the future. Anybody else. Sanjeev, I think you're on mute. I will put at least 10% money for curriculum design for US special capabilities and US special science. So it's about the human side of it that the skills. Yeah. I actually think that the preparation is extremely important, because at the moment, while we can build all capabilities. A lack of prepared data for being able to use these capabilities is quite alarming still. Yeah, thank you. Yeah, that's that's another big piece. I think we'll bring it to a wrap there. I just wanted to wrap up by saying thank you to everybody. And as as Michael highlighted, the National Research Infrastructure Roadmap is coming in, we're hoping to see a draft there coming up soon and Michael's already posted that link up so what. And finally, you know, this was posted by the GeoFest spatial capabilities community of practice. So if any of you are not part of that, please join in. And we can continue this dialogue in our future conversations and meetings. So with that, thank you everybody and thank you very much for the endless part in it as well.


So, I'm a professor of computer science at University of Illinois in Urbana-Champaign and I also run a startup company which is licensed by the university. The company is called Runtime Verification and what we do, both in my lab at the university and at the company is to develop software analysis tools and techniques based on formal semantics of programming languages. And at the company we are working mostly with large institutions and companies which are very interested in making sure that their code is safe, like NASA, Boeing, Toyota. And also, more recently, we started working with companies in the blockchain space like IOHK and the Ethereum Foundation. Initially we didn't have a plan to get into the blockchain space, we were just developing our general purpose, language-dependent program verification tools. When we received an interesting message from Charles Hoskinson who said that, hey guys, you know, your techniques could also apply in the blockchain space. You just have to switch to another language. And this is something that we preached for so long that program verification and software analysis tools can be language-dependent. You just have to plug and play your programming language. And Charles really understood that and he said, you can plug and play languages that are used in the blockchain space like Solidity or Ethereum Virtual Machine and then you can verify smart contracts which is so important. And then the first question I asked myself was, who is Charles Hoskinson? And then I read a bit about him and then I started getting a bit deeper into the blockchain literature and space and I realized that actually he's right. We can do that. Let's go for it. And then we started collaborating with IOHK and it was like a very fast ride since then. There are the same problems that we encounter in any software, bugs. So software has bugs and the larger the software is, the more bugs it has. It's very hard to write software without bugs. The question is how to detect them or at least how to make sure that hackers cannot exploit the bugs. If you think about airplanes and cars, they all run software, lots of software. But that software cannot be easily attacked by hackers because software running in cars is not even developed by the car developers. Like Toyota doesn't write the software, there are others who write the software and Toyota doesn't even know what that program, they know what the program does but they don't know how it is implemented, they don't see the code. So what is different in the blockchain space is that all the code is public. Everybody sees the code. So if it has a bug and it can be exploited, somebody will exploit it. And that raises unprecedented challenges in terms of security and using techniques like the ones that we have developed for many, many years and using the context of safety critical or mission critical software. Suddenly, all those techniques can be applied to the blockchain space as are. We don't even have to make any changes except to define formal semantics to languages in the blockchain space. Back in 2003, I started teaching programming languages at the University of Philenoise and I took the lecture notes from previous professors and they were mainly like many other universities about implementing interpreters for programming languages, ad hoc interpreters for programming languages. So implement an interpreter for a programming language in another programming language and this is how people learn programming languages and how they design programming languages. And I always felt that something is missing because when you implement an interpreter, you just implement a program. It's not like you have a mathematically grounded definition of your language and what you really want is to have a mathematical definition of your language which you can use for everything you want to do with that language to verify programs, to execute programs, to even compile programs. So I always thought that that's what a language framework should be like and I decided back in 2003 to not follow the approach that was used previously to teach programming languages and which is still being used at many universities. Instead, what I decided to do was to use a language called MOD which is a precursor of the K framework. And I defined rewrite theories, mathematical theories, encompassing semantics of programming languages in that language mode. And then I realized that all definitions follow a certain pattern and it didn't take long until we realized that actually we can somehow frame that pattern into a domain-specific language for languages and that's how K was born. Basically, just adapting initially MOD to do what we wanted it to do for programming languages. And then we sugar the notation and we defined several large real languages like we started defining languages like C, Java, JavaScript and before we realized we had a framework and people started using it and students started defining real languages, large languages in it. And then we just had to maintain it and to make it better and better and better and now we've got to a point where it's kind of stabilized. So the current version of K is stable, now we just need to implement it to be faster and better. So K has nothing to do with EVM. So K is a framework for defining languages or implementing languages, all kinds of languages including EVM. In the simplest possible language I would say that K is a language for languages, a meta language. It allows you to implement programming languages in a very useful way. Once you implement a programming language in K then K will give you a suite of tools that you can use to analyze programs in that language. One major advantage of using the K framework when designing a language is that you understand your language better from early stages when it is really the most important moment to understand your language. You can understand the syntax of your language, you can play with the syntax of a language, you can do what designers do, you can design and play with the design of your language and experiment with it right away. So that's the very immediate benefit of implementing a language in K. So imagine this, you want to design a language, how would you start? You have to define some syntax, then you write some programs and then you parse the programs, then you want to execute the programs. So for each of these you would have to implement some ad hoc tool, you have to implement a parser to parse the programs, then you realize that I don't like these syntax, I have to change the parser and that may not be immediately trivial, how to change the parser or to make the parser do exactly what you want. And then when you run programs, you also realize that, oh, I didn't mean this program to do that, I have to change how I run the program. So basically you change the design and K makes it really easy for you. You can add new semantic features to your language without having to go back and change the existing features. That's typically very inconvenient for language designers. And unfortunately most semantic approaches out there do not take that into account. And one of the reasons I believe personally, and this can be subjective, is that one of the reasons we do not have large programming language definitions in other frameworks or in many other frameworks is exactly because of their lack of modularity and reuse. Each time you add a new feature to the language, you have to go back and change what you have in order to accommodate the new feature. And that's very demotivating, you do not feel like adding a new feature to your language because there is a lot of fault that comes with it. While in K, you just add a new feature, you add additional components to the configuration, you add additional rules to take into account your new feature, and you have the feature. So it's like plugging and playing features into your language.  So that's the first contact with K, the first impression that you get when you start using K. And most people actually like it right then. But that's only the beginning, the bigger benefits come after because once you do that, once you have your language defined or implemented in K, then you can ask the K framework to give you tools for your language, not only a parser or an interpreter to execute programs, but tools like a model checker, for example, or a symbolic execution engine or a deductive program verifier. Tools which allow you to reason about programs in your language. You can prove that programs do what they are supposed to do using this framework. So you get everything from one formal definition, one implementation of your language in K. So why implement your language or different tools for your language in lots of different other languages and then to have to keep up with updates and changes in the language when you can do it only in one place and then generate all the tools automatically from it? So that's the promise of K, and I think it got to a point where the promise started to be delivered. So formal semantics is a very, very old field in computer science, probably as old as computer science, almost. The first semantics approach is what are proposed in the 60s, early 70s, and they are mainly related to how to compile or translate a program into a mathematical object. Like in the notational semantics, you put these double brackets around your program and you get a mathematical object. And then once you get a mathematical object, you can reason about programs using mathematics, which we understand because we developed mathematics for 5,000 years, where programming language is only for 50 years or so. So programming language semantics, yes, it's an old field of study and there are lots of different semantics approaches proposed by various famous scientists. And what we do in K, really, we build on their work. So we stay on the shoulders of giants. So what we did with K was to understand all the very semantics approaches. I think we literally looked at more than 10 different semantics approaches developed by different groups, different researchers and tried to get from each of them what was really nice and useful, trying at the same time to avoid limitations of each of the frameworks. So I could say that K was engineered. It was engineered from the existing semantics approaches in order to get a semantics engineering framework. So the framework now allows you to engineer your languages, but the K framework itself was engineered from the existing approaches. And then once that happened, then theory came. So K now has a very solid theory in place, which all the other semantics gives meaning to your programs once you have a semantics in K. Take Java, for example, to have a Java semantics in K. Now what K will allow you to do is to take a program in Java and interpret it as a mathematical entity. And then the K tools are generic. They can reason about such mathematical entities. And this way you can use the K framework to reason about your Java program. The K framework was used to define large languages like C, Java, JavaScript. And then the resulting tools were used to analyze programs in these languages. And as I mentioned, Charles Hoskinson came with the idea that you can use a K framework actually for lots of languages in the blockchain. Then when we looked at the languages in the blockchain space, actually all of them compiled or translated their high-level programs to the Ethereum Virtual Machine, which is a binary stack-based language, very low-level language. And if you want to verify smart contracts at that level, then you need a semantics of the Ethereum Virtual Machine. And this is how we started working on formal semantics in K for the Ethereum Virtual Machine. Interestingly, it started as a class project. So a couple of students defined the first version of the EVM. Actually, it started even before Charles raised the issue. And then when Charles came and he looked at the project, he said, let's accelerate this. Let's get EVM all done quickly and then let's use the K suite of tools, the K ecosystem, to verify smart contracts based on the semantics. And that's what we've done. And why you want to verify EVM programs? First place, because many security weaknesses, security attacks are rooted in errors, coding errors in the program. So if you eliminate those errors, at least you know that there are no security loop holes related to errors in the coding. There could still be security weaknesses or leaks due to the overall protocol that is implemented. Some cases that were missed by the designers of the protocol. And that's unavoidable unless you formally specify exactly what you want, which is not always easy. But at least verifying the code based on formal semantics of the language allows you to eliminate the silly errors that you may have in your code, simply because you didn't realize as a programmer that there was a little case, like a division by zero, for example. You didn't realize that there is a certain value for the inputs for which the program will run into a division by zero. So this kind of error should not happen. They are not meant to be part of the design. When we define the semantics of the EVM, we realize that certain features of the EVM were not very easy to formalize semantically. And one lesson we learned in time, defining many different languages, was that when some features are not easy to formalize, maybe those features are not very clear or they may have problems. They may also be problems in programmer's mind when they program. So we decided that certain features of the EVM could be possibly replaced with simpler features that can achieve the same objectives or even more secure objectives. And also we verified lots of smart contracts as part of our business in runtime verification. And we learned that many of the verification attempts that gave us a hard time or even failed were due to certain features in the EVM that if they were different, then verification would be easier. So based on all these lessons that we learned from formally defining the semantics of the EVM and from verifying smart contracts, EVM smart contracts, we kind of formed our own opinion how a virtual machine for the blockchain should be like. Also, since we come from the University of Illinois where LLVM was born, we like LLVM, so we thought that it would be great to have an LLVM-like virtual machine for the blockchain. And that's how LLVM was created with this in mind. So we wanted LLVM to be modified for the blockchain, taking into account everything we learned from the previous experience with defining the semantics of EVM. And I am personally very proud of LLVM. I think it is exactly what we wanted. It's even better than what we initially planned. And if we were to do it again, I would do it exactly the same way. That's how I think a virtual machine for the blockchain should be right now. So one thing that I particularly like about LLVM is that it is the first time in the history of formal semantics when a real language has been designed using formal methods from bottom up. And not only that, but also the implementation, the virtual machine itself, has been generated completely automatically from the specification. So in the past, we either defined formal semantics to existing languages like C, Java, JavaScript, or we did what I just said before for toy languages, but not for real languages. So now it's the first time when we show that programming language research, fundamental research, semantic research in programming languages, finally got to the point where it can deliver the promise on the promise. Because that's how we want to design languages, formally, mathematically, and then to get the implementations automatically correct by construction from the specification. And LLVM is the first time this happens. And as a programming language researcher and formal methods researcher, I cannot be more excited than I am about this. But now once LLVM is there, it opens the door for a lot of opportunities. For example, we are working on an LLVM back end 4k, which means that it will take programming language semantics and generate LLVM implementations for those languages. Since LLVM is LLVM-like, it is very easy to switch to LLVM instead of LLVM. And we are going to do that. And when that happens, then you can take any programming language for which you have a semantics in K and generate a LLVM interpreter for it. And these combined with semantics-based compilation, where you can take a semantics and the program and generate another semantics, which captures the semantics of the programming language only. And then we generate LLVM out of it. What you get is a correct by construction compiler to LLVM from any programming language. So this way with K in the middle, you can write smart contracts in any programming language out there. All you need is a formal semantics for that language. All right. And then semantics-based compilation will take that program you write in a programming language, which you already have on the blockchain, whose semantics you already have on the blockchain, and generate a LLVM binary that then will be executed with the LLVM machine that has been itself generated automatically from its specification. So if you look at the whole picture, everything is either a formal specification or generated automatically, correct by construction, from a formal specification. There is no line of code written by humans, which has not been verified. And notice that you need formal specifications anyway, right? You can say that, well, now you don't write programs, but you have to write formal specifications. Well, you have to write formal specifications anyway if you want to verify properties about programs. Because without formal specifications, you do not know what programs are supposed to do. So what I think the vision can be here, it's my vision, I hope everybody will embrace it, is that you can actually go only with formal specifications and all the code to be generated automatically. And the technology that we are developing now will take us in that direction. And I'm very excited about the opportunities in the blockchain space where the need for correctness of software is at another level compared to safety-critical systems.  Because without formal semantics, you do not have formal verification. And without formal verification, you cannot have security.


Welcome to Rainfresh Harvest. My name is Barry Adler. I'm growing on approximately a little less than an acre total. This up front here is only maybe a quarter acre total with the greenhouses. I've got about another half acre to three quarters acres back the other side of the house. I've got some fruit trees. I've got field tomato crops out there, jalapenos, basil. Down here out in the field I've got a few tomatoes, basil, blackberries, a variety of peppers, some cucumbers, and in the greenhouses here I've got mizuna, arugula, cucumbers, spearmint, and I've got a variety of fish aquaponics operations in there too. Yellow perch, bluegills, koi. I originally looked at setting up a hydroponic organic tomato and lettuce operation. Crop King has had at the time some of the first greenhouses with that design but as I did my due diligence I found out my water was so terrible I would have to treat the water or run 35% of the water and leech every time I watered the plants so I decided that wasn't going to work because when I looked at the cost of reverse osmosis it was going to cost me two cents more per pound for the tomatoes than I was going to be able to sell them for. Due to the water issues I decided I had to design my own greenhouse and I had been working at Scots as a researcher for 22 years and I had my job was outsourced so I had some severance money to look into a new business and at the time I was interested in renewable energy but back in 2004 there was absolutely no market for it. So my background's in horticulture I've got a master's degree in horticulture I decided well I've got this land I might as well grow things and do that as my business so I started in and decided well I've got to design my own greenhouse to catch the water and I thought well I can put renewable energy on it also because at that time was the first year that the grants were available through the state of Ohio so I combined both of my interests and designed this and took about a year and a half of figuring out all the ins and outs and looking at designs that seemed to work and also different types of aquaponics that seemed to work and I'd kind of designed and took off from there and designed my own system. This is an off-the-grid bio-integrated greenhouse and it's powered by the Sun and the wind we have a one kilowatt Berge XL wind turbine 2.1 kilowatts photovoltaics there's 12 Siemens they were originally 175 watts each but one of them had to be replaced because I got a bullet hole in one of them about three years ago and that's a 190 watt panel because it didn't make them 175 watts 10 years later so in fact those same size panels now you can get almost about 400 watts in the same square footage. These are solar thermal collectors I have a hot a water drain back system originally start with propylene glycol system but it ended up because of the way it was designed it was letting air in the lines and the copper was starting to degrade so another guy came along who had had more experience in this area and he said no get the propylene glycol out of there and just use straight water and it's worked fine ever since so at 34 degrees everything drains back into the building so the pipes don't freeze in the wintertime and this is hooked into a heat exchanger and I'll show you when we get in there it pumps the hot water or takes the heat out of the water puts it in a storage tank or when that storage tank reaches a certain temperature then I have pex tubing in a concrete floor so the floor itself acts as a thermal storage. I've got two tanks that I catch water in one's underground sister ones on the second floor each of them has a capacity to catch 500 gallons so I catch about a thousand gallons of water all it takes is about a half inch of rain and that fills up that 500 gallon tank. If there's no wind or no sun I've got battery backup storage that'll hold about three to four days running everything but then when I know I'm not going to have sun or wind I start shutting down things I got about 40 different circuits in there I can flip everything off and only run the essential systems the bubblers for the fish and irrigation and fans inside the greenhouse so I can stretch it out to five six days if I need to.


The Supreme Court on Thursday, later December 15, to deliver its judgment on the appeal that is seeking to compel the federal government to release the embattled leader of the indigenous people of Biafra, I-POP Namde Kano from detention. A five-member panel of the APS court headed by Justice Kudirat Kekere-Kon-Okage is the matter for judgment after counsel for both federal government and the detained I-POP leader adopted their final briefs of argument. While the federal government was represented by a team of lawyers that were led by an acting director civil appeals at the Federal Minister of Justice T. A. Ghazali, Kano's legal team was led by former Attorney-General of the Federation and Minister of Justice Kano Agabi. Even though Agabi led Kano's team of lawyers, it was Professor Mike Ozekome that presented his appeal to the APS court panel. Ozekome prayed the court to not only order the immediate release of his clients from detention but to equally award very heavy and punitive costs against the federal government. We now humbly appeal to the government of the day to immediately, without any further delay, with the urgency of yesterday, release Namde Kano that it may come out of his illegal and unconstitutional detention, which the courts, up to the court of appeal, have since pronounced upon, but is still illegally being held there today, so that it will come out and join hands with the rest of us in rebuilding this nation that is frantically yearning for Nishamput.