 the ability to adapt trainings to match the trainee cohort and so on. There's a framework to follow. The skills landscape is the first step in developing a national skills framework to enable a coordinated and cohesive approach to skills development across the Australian e-research sector. It is also a first steps set towards helping to analyse current approaches to data training to identify siloed skills initiatives and finding ways to build partnerships and improve collaboration, identifying skills deficits and working to address the gaps in data skills and also identify areas of skills development to invest in by skills stakeholders like universities, research organisations, skills and training service providers, AIDC and many others. I should note here which it's already been spoken about in the previous session but I'll reiterate it, AIDC is not a training organisation and it's not what we're funded to do. But we do have an interest in skills uplift across the e-research sector because of the cultural change a skilled workforce can bring. Hence, AIDC's interest in leading the development of the data skills landscape. So we're hoping the skills landscape will help us home in on the areas we should and need to focus on and help other skills providers do the same. So now I'm finally getting to the bit you're here for. I do see the skills landscape as a taxonomy. So a classification of skills into an ordered set of categories. So our own data skills taxonomy although semantic specialists might disagree but we'll let them. So what does the skills landscape help us do? After significant consultation on e-research skills development it was time for AIDC to connect many of the dots from multiple skills community conversations. The e-research and data skills landscape diagram aims to represent many of those conversations. We're simply laying a foundation on which to drive discussions on the national e-research skills agenda. The concrete hasn't set yet so there's still opportunity to shape it further. And I'll give a shout out here to Natasha Simons who kick-started these conversations with skills communities and was the person along with the skilled workforce team at that time who put the skills landscape diagram together and I think it was a, you know, well I would think it's a non-trivial task. So the landscape diagram aims to assist in identifying the skills needed for data intensive research and I think the skills landscape largely answers this question although we may have missed some skills. So let us know and actually some skills may have dropped off of the landscape that you might think actually belong back in there so let us know about that as well. So we could also use it to highlight cross-cutting skills and we've outlined, well we've used the landscape to outline and describe a generalised set of data-related roles and it also indicates who needs these skills and at what level of competence. And the work that stems from the skills landscape such as creating role profiles and skills learning paths could help to answer this question a little further as well. And I'll provide an example of each in subsequent slides. And finally, I think identify, the skills landscape can identify training providers and the skills that they cover. You know, is this overlap and duplication are their gaps? I have an activity later in the session that I really am praying and hoping won't fall over but anyway we'll see how we go. That hopefully helps to capture some of this information from you. We assume there is significant overlap and duplication but interestingly I've been involved in a lightweight working group that is looking at just this question and the data from each of the organisations involved in this working group indicates very little duplication so far. This could change once we have a more comprehensive sample of course metadata and overlaps and duplications could start to become more apparent. So this work will be highlighted in community action session number three at 2pm Australian Eastern Daylight Time on Wednesday. So if you're interested in this area, get along to that session. So this is the beginning slide of the skills landscape. This slide hopefully provides some scope or context and I'll just point out a couple of things from here. I've already talked about why developers skills landscape so we won't go there again. Who is the skills landscape for? Generally anyone who wants to understand the skills needed to work with research data, build capability and improve current skills development offerings across their organisations and the e-research sector. The skills landscape slides themselves are a generalised identification of skills, not roles. So try not to get the two mixed up when you're looking at them and thinking about the skills. And the skills landscape, we've tried to put it, look at it through the lens of course unit and it'll be interesting to see whether you think we've actually managed that. So comments about that would be really useful too. This is the higher level structure of the landscape and shows the entire, well you know, data skills curriculum if you like with the stream's data governance, fair data principles, data management, data generation and use. So I'd like to also point out here that we've grouped data governance, fair data principles and data management under the higher level stream of data stewardship. So you can see it's in a different colour. So we feel that these all sort of belong in that data stewardship skills stream. We'd welcome comments on that grouping as well. And as we go through the slides, please add suggestions, including the skills we've missed, skills that you feel are in the wrong stream or any general comments you'd like to make in the collaborative doc, which hopefully I think has been shared. Or do I need to share it? It's been shared? Okay, cool. Thank you. Okay, so for the first question, I'll grab the Mentimeter code and pop that in there. Okay, so if you go to menti.com and I'll just get this up here and present that slide. So if you go into there, so the question is what skills do you think ARDC should focus on to enable users to gain greatest benefit from the comments? And depending on how you answer, it may mean significant expectation management on ARDC's behalf, so it'll be interesting to see what you have to say. So I'll refresh this. We might start to see some answers. Is it working? Oh, yes, here we go. Right, very good. This is really good to capture this information from you guys. So while you're populating that, I'll keep going because I tend to overload my presentations. So Rosie has already painted the picture for ARDC moving forward with skills development in her talk, but I think it makes sense to revisit some things again. So the emphasis is on working with stakeholders and partners around policy and infrastructure, national skills coordination initiative, so tackling skills training challenges with a hands-on approach through the facilitation of working groups that we're hoping and I guess expecting to form out of this skills summit. Continuing to build on our existing skills trainers community of practice and many adjunct data skills development activities such as maintaining our commitment to the carpentries through varied activities, including running train-the-trainer refresher courses for new instructors, advising on good pedagogical practice with respect to preparing and delivering training, facilitating trainer community activities such as regular community discussions, event planning for things like carpentry connect or adjacent collaborations such as RESBAS, leading by example with the development of open source and practically fair training materials for example our own fair data 101 and library carpentry fair lesson. And looking to the future technology space and supporting partners by conducting and facilitating training and leading edge technologies and also not reinventing the wheel by ensuring that we connect with what's happening internationally around good practice. And last but not least is providing support to the skills related efforts of all four ARDC themes. And wow, that's really great. So thank you everyone for answering that question. We'll have a really good look at that and see what comes out of it. Safe API use. That's an interesting one. Okay. Very good. All right. In the interest of time, thank you. We'll move on to the data governance skills slide and people are still going there, which is great. And it might help if I share back my slides. Okay. So these are the skills and knowledge needed for the processes of creating and complying with data standards and policies that manage the availability, usability, integrity, use and security of data. We've decided that this is the core set of things you need to know about to ensure good data governance. Now on this slide, you'll see the data stewardship skills badge across, you know, on the top of data governance. As I said before, we feel that this is kind of encompassed within the data stewardship skills. And I'll just explain train trainer, train the trainer and materials, even though they may be reasonably self-explanatory. But from an ARDC perspective, trainer educator is where we would provide training for a particular skill or capability to the people who will use it. This training can be in the form of courses or workshops or one-on-one meetings. Train the trainer. This would be where ARDC provides skills activities to support and develop a trainer network in support of a skill or capability. And then of course, materials and infrastructure would be ARDC's developing reusable materials, infrastructure and resources to support training activities around a skill or capability. And you can see here that we've only really put materials and infrastructure related to institutional policies, funders policies and also government policy legislation and trust certification, which is an area that we do have interest in. But there are some caveats here. The areas we've coloured are those we have historically been active or see as key things, but this doesn't mean that they will remain key areas long-term. And also the highlighting of these skills does not reflect that we'll tackle all skills uplift in that particular area. And really in a sense, we probably only need to colour in a portion of each of these to sort of indicate our involvement and influence there. But in many of these areas, ARDC does have interest in the influence in shaping change. And this influence generally won't come through training, but through the development of materials such as guides, information videos, decision trees, meetings with stakeholders and other skills development mechanisms. Okay, so I obviously stuffed up before you, hopefully you didn't miss any of the slides. Okay, so this is the Fair Data Principles skills stream. So skills that are useful to create and use fair data outputs and infrastructures that enhance the ability of machines and people to find access and use or reuse data. It's no surprise that ARDC has widespread interest in upskilling around fair principles, given the services that we provide. So research data Australia, research vocabularies Australia, the PID services and cloud. So this slide shows that ARDC delivers skills development via either train the trainer and this is only where necessary. So what we mean by this is where we have the unique expertise in this area and the training is not offered elsewhere. But mostly we'll be offering training through the development of materials and of course our infrastructure. We've run a couple of very well-received fair training courses this year, Fair Data 101 and Fair Data Express, but as we see the continuing trend of institutions and other research organisations delivering their own research skills development activities, we'll step back from this training and instead provide support for fair in other ways. We know there are institutions out there including fair in their current trainings. We just spoke to UQ the other week and I also know that Siro and others are including fair in their research data management training offerings. So once again I've got another question and I'll just get out of that one if I can with the lag. Okay, I think I've somehow clicked on the other one. Sorry, here we go. So it's the same code again. I'll just present that. So this one is at your organisation are you including fair data principles in your data training? Oh, it's adjusted down. Oh, here we go. There's some nos. Wow, this is good to know. Just give you a couple of minutes to populate your answers. I actually wasn't expecting that. I was expecting that there would be more thinking about it. But that's really good to know. All right. So as you continue there I'll go back to my slides and check the chat. Are there any questions? I really am like a freight train when I present. So if there are any comments, lives or questions or we can just keep going otherwise and probably best to given the timing constraints. So the next set of skills is data management skills and these are the operational management and they offer skills around operational management and oversight of data assets to help provide users with high quality data that is easily accessible in a manner consistent with the data governance framework. So you can see from this slide that we are again mainly concerned with providing materials and infrastructure and the trainer educator aspect is related to our training in using our own in the use of our own retention and discovery infrastructures. So that's why we have a training a trainer educator focus there. Hopefully I'm not going too fast through these slides but the slides are in the collaborative docs. So while you're listening to this presentation if you want to go back to a previous slide in the doc and make comments and things like that please feel free to. So the next set of skills is data generation and user skills. These skills are useful for researchers and other data generators to ensure their data is at the outset structured and managed in such a way as to facilitate use, reuse, high quality and reflection of impact. Now in previous iterations of the skills landscape we had data generation and data use separated onto two slides but it seemed a little nonsensical as data generators generally use data and data users generally generate data and it's sometimes difficult to delineate. So now they're grouped together so any comments on that decision would be welcome as well. And the data stewardship skills are a duplication on this slide but we thought it was important to highlight that data generators and data users still need to have some level of data stewardship knowledge. Now your thoughts here, does this muddy the water? Should these skills be removed from this slide and we simply represent this connection in another way? So please provide any comments that you have and the skills themselves, I mean if anyone sort of has any questions about the skills please put those through as well because of the terminology in particular. So now we're onto the roles slide and this normally comes right at the end of the pack but I've done it a little bit differently here. This is a generalized list of roles and it's reasonably self-explanatory but it's worth pointing out the people in their day to day roles often cover more than one of these roles. It's rare for these roles to be specifically dedicated except possibly in larger organisations or in a large scale project where you can find data specialists. And the next slide looks at who needs data skills and at what level of competence based on their role and I'll unpack that in two slides but I just want to sort of reiterate these are generalized roles so they're not the kind of roles that you would see a person's job title. Well not necessarily, you might have people who are data governance experts or whatever but so we look at who might need these skills and the short answer is those who use or will use the data comments. On the following slide there is a more descriptive breakdown of the previous slide of the types of roles that we're looking at but while I'm on this slide this explains the legend found on the next slide. It describes the competency levels of a skill, awareness, beginner, intermediate and advanced. The part of the work surrounding the skills landscape will be to standardise the terminology for skills development to ensure we are all on the same page and talking about the same things and to help with this skills landscape Glossary is currently in progress but we will need community input from that once it's at a point that it's consumable and as an example I've used these terms for competency levels, awareness, beginner or foundational, intermediate and advanced and I notice others are using slightly different terms for example intersect use introductory rather than beginner. One step would be to establish the competency levels, their descriptive names and the description of the competence required at each level that we can all agree on. Now this slide is, so you can see the roles across the top here, researcher, data scientist, et cetera and then the skills landscape skills on the left vertically. So what we're doing here is based on the four competency levels, this mapping actually you will have noticed but the legend here is awareness, beginner, intermediate and advanced. So based on these four competency levels this mapping highlights who needs what skills and at what level of competence and by the way I'm more than happy to be challenged on the rankings that I've made here. This kind of mapping provides us with a general idea or starting point from which to identify the skills development focus for each target audience and it would be really interesting to know if people find this reasonably useful. Just to quickly point out too we're not the only ones looking at skills frameworks. There are a number of international efforts that we should be connecting our work with such as SFIA, so skills framework for the information age and this is for those working in information and communication technologies, software engineering and digital transformation and what I'll do is I'll just pop the link to that in the chat. I've gone out to everybody. So another step in developing a national skills framework is to create a number of key profile, key role profiles starting with those highlighted in the who needs these skills matrix from a previous slide. These profiles highlight relevant responsibilities and related tasks for the specific role and are then matched to the skills from the skills landscape. So you can see here responsibilities tasks and then here we have the list of the skills landscape skills. And I'd also like to say, oh and sorry, I've got myself lost. We now have a skills. So from this basically what we're trying to do is create a skills requirement list for a role and in the example on the screen this role profile is for data repository managers which kind of equates also out to data curators, data archivists and data services librarians. This is still a work in progress and so if you've got any, it's probably a bit hard for people to read on the screen but if anyone is interested in it I'm more than happy to share and have comments made. I'd also like to say that some work has already been done on role and skills profiles by the European Open Science Cloud, EOSC through their skills and training working group and FAIR for S Skills Pilot Project. Irina Kuchma from Eiffel, the Electronic Information for Libraries and she's also the working group's repertoire. We'll be providing a presentation in our final session on Friday at 1pm Eastern Daylight Time. So be sure to come along to that session because I think it'll be really interesting. And of course they're not the only folk that are working on roles and skills profiles and other aspects of skills frameworks. And I will also pop that in the chat. Link to the FAIR stuff there. So coming back to the role profile you'll notice the persistent identifier is hyperlinked here in a couple of places actually. So this actually links out to a learning path for PIDs or persistent identifiers that we've created. It still needs to go through a review by our PIDs experts here at ARDC but is part of a proof of concept to illustrate how the skills landscape can be used to connect trainers and learners to coursework, training events and associated skills development materials. You can see it is fairly stock standard for a learning path providing information to assist the potential learner. So description, time to complete which hasn't been determined in this one. Skill level, learning outcomes. Who the learning path is for with a table to indicate the level of competency for the specific role or required for that role. And then a breakdown of the resources to use for each level of competency and learner user that learner requires. And so there's the beginning part of the journey so for awareness and the introductory level then learn a bit more so that you start getting into that intermediate level and then building on your expertise to where you can actually apply those skills at the more advanced level. So in this case all the resources were available for free online in other learning paths we wouldn't by necessity point to training platforms that may require subscription, a fee or negotiated agreement. And some examples might be data camp or online learning, LinkedIn learning causes of materials through training providers like Intersect, QCIF and others training provided through universities that could potentially be shared based on agreements and so on. So this is another way of raising the visibility of trainings and materials available across the Australian research sector and these learning paths can also be seen as a trusted resource because they will be curated by experts in the topic areas. So now for the scary bit I will just grab because the only reason why it's scary is because I think it may potentially fall over but I'll pop a link to a spreadsheet that I'd like you to all go to and it might take a little bit of time to actually load up but we'll see. And in this spreadsheet what I'd like you to do is answer the question who is responsible for the development and delivery of skills in the areas below. So what we've got are four tables for each of the skills. Now what you need to do is actually put a comma after each so that it will be able to register. So if you're putting a number in just follow it with a comma for the next person. And so you can start to see that these radar graphs or charts are starting to populate and I think it's just useful as a skills and training community to see where we think these particular skills should sit where the responsibility should sit for the training and materials development and so on. Now if it gets a little bit too slow or if there's lots of people in one cell just move to another chart if you want to another table and start populating there. So I'll give you, what have we got? A little bit of time. So I might give you five or more minutes to get in there and populate your answers and I might just try and catch up on chat if I can. And obviously we could change the charts depending on how this data sits in these radar charts. So the data is person so not an expert in terms of what really works for this kind of data in here as well. Catherine, we've got a question from the chat from if asking do libraries count as skills sorry skills service providers? I would so as separate to Unis and research institutions is that the question or is it so for me if I'm defining well yeah a skills service provider I probably am thinking in terms of paid services so I would put in relationship to in relationship to this particular activity I would include library service skills providers or provision of services training services from librarians in the Unis and research institutions column. Sorry my dogs are barking in the background so very distracting. I don't know if that really answered the question Liz. I would be asking from a university perspective yes. Actually Belinda that's a good point some comments too around you know whether this is even covering all of the various service providers because there's also commercial training providers as well like Microsoft and those folks too so it doesn't really cover the full gamut you know in the end it was more of my brain function was going into how do I actually get people to repeat this data than the nitty gritties of who each group was but anyway this was a kind of I guess really we're looking at trying to ensure there's an appropriate coverage of skills across the sector and so we need to start mapping this out to do that and this is kind of my cursory or high level attempt at doing that so someone with much better statistical skills please talk to me at some stage and we might do this on a larger scale. Yes that's a good point too Fiona that publishers do some training too okay so we can this document is open as well so anybody who wants to contribute to this post this session you're more than welcome to keep adding to it what we'll do is we'll come back to this on Friday we've got a drop-in session just a very informal session on Friday before the final international perspective session and so we could come back to this and have a bit of a chat about this in the drop-in so I'm getting close to actually winding up my presentation so I've got to move away from this and get into the final slide there we go so encircling back to the beginning to provide James and many thousands of other Australian researchers with the best possible advantage through data they need the most relevant data skills training delivered by people with expertise and in a variety of skills development formats the ARDC skills focus is evolving we're going to be narrowing but deepening our focus on specific skills areas and looking at our approach to skills development delivery this approach will be much more targeted to our remit capacity and importantly towards enhancing the use of the commons we're aiming to partner on skills development where our expertise and services at best value so we're hoping that our skills providers will do the same together with the help of the skills landscape and associated work communities within the sector can identify their areas of responsibility for data skills uplift and begin to surface current and future envisaged skills activities to enable coordinated approaches to skills development and delivery reduction of duplication and address deficits in coverage of skills and competencies and so I guess you know my sort of call to action here is we need a national approach that's what's required so