 Okay, thanks very much everyone for joining us today for this masterclass on information governance, making it work. It will be delivered by Professor Awebron, my colleague from University of Aberdeen. She's joining us from Japan today, so Awebron, now it's all up to you. Fantastic, thank you. I hope everyone can hear me. And thank you everyone for coming to session today. I'm very much looking forward to it. Thank you particularly to Virju and to John and to all the IT team in Cambridge who are hopefully making this work. Fingers crossed it is going to work. We are exploring information and technology, so hopefully it will work. I'm going to start off. I actually prepared this for what happened to now be in Japan, but I prepared most of this presentation when I was on research lead in Melbourne. So I would like to acknowledge the traditional owners of the land on which I prepared this work, the unceded land and with respect to the elders past, present and emerging. So this is the second time I've given a masterclass to the NERC CDE gathering, which now we're called the digital gathering. And last time we do a really fantastic session and we had some really great discussion. I felt that that session was a bit more about me sharing information from my side of things, and I'm hoping that this will move things on a little and it will be more focusing on what perhaps more the science informed you and I hope very much to learn from you. I certainly did a lot last time. I'm hoping we'll have a very interactive session. We're going to be exploring how information governance can really work. I did quite a lot of research. Maybe some of the people who are in the room, some of the other CDE senior experts, some of the people who are working in this space. And some of the common themes were, it's all really, really complicated. And I just want to do my job. I don't want to have to become a legal expert. How can we actually make it work? So I'm hoping that this, both as a session today and can set down some future foundations as to how we might as a community try to make it work. So I'm going to try and explore and learn from you, some of the relevant laws and regulations to working in this space and information governance, some funder industry and scientific norms, which are of course changing, which is very exciting. And also, particularly, can also be a bit of a challenge if this is not generally your daily priority. I'd really like to learn from you, your challenges and successes. Again, I've heard some experiences and I'd really like to hear more. I'd really like to hear from you, what would help you, and also to share some of the research I've been doing on what to help is out there, whether we find that this helpful or not. I'm going to be a little bit more of a science focus rather than a more legal perspective, although we had a really valuable discussion generally last time. So to perhaps preempt that you may already have had a look, don't worry, I'm not going to ask you if you have, but on the website for this session, we've got a link to the slides which were updated taking to account some of the points that we discussed last time to the CDE 2022 master class. So there's a lot there, more sort of legal background side of things. So if you haven't had a look at that, and you do have an understanding that, please have a look at that. And obviously, if you would like to speak to me about anything, please do get in touch. I'm hoping as well as the science focus will have a real cross cutting theme. I'm really sorry. I so enjoyed being in Birmingham last time. I'm hoping this can time with the rest of discussions in Cambridge, looking your themes, of course, familiar next generation fencing data science tools and techniques, environmental data and building confidence and trust people and skills. So that's a very broad overview. I'm going to talk for maybe about 20 minutes. I'm setting out some things that I see as particularly relevant. I've drawn together some checklist type things as well that from some of the research and discussions I've been having that people might find helpful. If anyone has any comments at any time, if you are in the room, please put up a physical hand and virtue will tell me to shut up. If you are, if you are online. Let me have a look at the chat. I've got the chat function open. So if you are online and would like to put something into that please do I've also got the Slido open in front of me. That's CD. Did you 23 and then looking for this master class. So if you have any questions in there, please do put them in. I'm going to be leading this, you hopefully will be leading and challenging me in this the of British on an IT support in the room, questions in the main by a Slido, and we are recording. I'm not going to stop anyone participating eyes and find last time and I've built up my notes from last time into some other work I've done in the network but I think this would be really great to have this as a resource which we can share with others in our community. So hopefully people are comfortable with that and we can have a very open and collaborative and sharing discussion as we go forward. And Berju, if you could click to the next, the next slide please. Great. So key themes as we're going through a really strong focus on the growing importance of open research, open data, open access, but what of course do they actually mean in different people's day jobs which I suspect there's probably quite a lot of variety in the room. So we can really vary whether we're looking at metadata, whether we're looking at data sets, whether we're looking at outputs and publications. We'll have a look at funders, NERC, obviously I keep priority but some others as well. We'll look at the sciences taken by governments, particular focus on the UK government but so many of us are working internationally so I think it's important to have that wider viewpoint. So we'll look at the issues which are being taken at that national level, such the Geospatial Commission and its work in relation to Geo6, which I know many of you have shared with me that this is highly relevant to some of you, but it is only limited. There's some really valuable work which has not been covered by the Geospatial Commission. We'll look at some information sharing regimes and the regulatory and legal side of things. So those are things that I tend to think of broadly as more things that are what a scientist we distinctively think of and you're very welcome to challenge me on that, although I'm not in the room, but spent a lot of time with scientists recently, particularly in the ocean governance space. There are a lot of discussions about the importance of fair, fair data, the Dora new evaluation process, relation to open science, the fourth lauderdale approach, that careful balance of results being immediately available because this is so important for a collaborative open scientific process, as against also the wide recognition across the international science community about the need to be to, or perhaps we may discuss this, but I view that there's a need for one success to be recognised and to be respected and to be able to get that initial publication in. And the final theme running through this will be the different business models if one is in business. Again, some of the room may think you're not there to be businesses, but we are there to be academic scientists. But if we are looking at business models, what should be driving this? Is it sharing? Is it reward? What type of reward is that? Is that a salary? Is that a grant? Is that a great publication? And the different mixes which might come within that. And who says which is going to apply in which situation and how widely is that shared, particularly again, if this is not perhaps one person's day job and this is only something which pops up from time to time. So for you if you could press on please. Fantastic. So going to spend some time just exploring a little bit of that wider context of that scene setting that I was talking about. So what do we mean by open? And this is one thing I've had many interesting discussions over the years with lawyers, with scientists and lots of different types of scientists. What do we really mean by open? As a starting point, I'm an information and technology lawyer. And I, when I first left professional legal practice to work at the Academy, we set up a genuinely online open access peer reviewed freely available journal. And it's highly respected in its field. This is a very exciting thing to do, but it also meant for a long time when I heard the term open access. I thought that was what that meant. I've very much been learning over the years that this of course is not necessarily what it always means. And I think a very interesting example, some of you which I suspect are a lot more familiar with this than I am, but the open, for example, could be quite legitimately used as a term to apply to the approaches taken by the ordinance survey in relation to their data and their maps. But actually within that, we have the master map, which is free, genuinely free immediately. But for the rest of that, you have to pay, you have to pay a premium service. And without for now getting the discussion about whether this is fair enough or not. It's open, but it's not necessarily free or it's not necessarily free immediately. So an interesting question when one is thinking about open, what is it actually meaning here. And we can see the balances around which this might mean explored also in the UK's geospatial strategy. So mission one is talking about safeguarding data. UK needs to create the right market conditions and incentives for innovation. I will come back and talk about that later because again this, this is where we are but this might seem controversial or interesting to some. But it's talking about we must incentivize innovation, we must make use of information, but we must also safeguard national security, intellectual property rights, which we looked at a lot of last time, and individual privacy. So we're talking a lot about sharing a lot of making this more widely available. But there's an awful lot of caveats in there. Now they're fantastic to a lawyer. They're really annoying, I suspect, to people which isn't really their day job. We can also see a refer before to fair open cannon effect refer to different qualities of data. Is it fair, findable, accessible, interoperable and usable? Does it mean free at the point of use, genuinely free? Or does it mean simply that it's got clear licensing conditions? So there might be quite a lot of restrictions on what you can do with it and you might have to pay quite a lot, but you can get it. And it's coming perhaps in an interoperable format. So that might suffice to be seen as open data. Now, whether you're doing a funding application, which I was doing before I came on this call, or whether you are trying to plan a dissemination process, work on a data management plan, the different meanings of openness I think are extremely, extremely important. And what we also see in the juice fish strategy, which I think summarizes quite nicely says to unlock the power of location, we need to take a balanced approach that considers the costs, value and sensitivity of individual types of data. Now, again, lawyers love this type of thing. In the first version, you can tell me later what the body language is in the room of whether this is just something which is really annoying for scientists. I put in some, some bits and brackets after that, that wording on the slide. Value, value, I think, in the year or so since I've been working in this space, this seems to become a much, much, much more topical term. I really welcome thoughts that people might want to share on that. It's seeing the golden thread throughout. It's all about value, but also what are we meaning in value? Are we meaning financial value? Are we meaning recognition? Are we meaning short term, longer term value? So I think that's again something in which a lot of things can need to be unpacked. We're talking about the sensitivity of data. Now, is this someone's individual personal information? Is the fact that someone type of trade secret? Is it the fact that you're a scientist and you and your team have had this massive breakthrough? And you simply think that you should be able to control information tied in with location. So all of these are within a strategy which is really trying to set out. We're going to safeguard data and we're going to incentivize it as effective as we can. There's lots and lots hidden within there. And again, if this isn't normally what you do, we've got a case study we're coming on sharply to explore together, this might be quite confusing, or it might be extremely straightforward. I'm really interested in hearing from everyone. There's the concept of the race. So the concept of are you all perhaps within teams as individuals or between teams? Are you under pressure? Are you competing with each other? You're under pressure to publish. You're under pressure to get more grants. So you really want to get that information out there as quickly as you can. You want to get out there for your shareholders depending on the size of your business. You want to get it out more widely in the market to raise further funds. So you want to publish it. Now, there are some exceptions in information control regimes, Freedom of Information Act, with environmental ones and the wider ones. We had a really interesting discussion about that last year. There are some exceptions to needing to share that information so people can come to you. They can say I want to know that and receive that information as part of freedom of information. You can say no, no, I'm not going to do that. I can take advantage of that exception. Tying in, recognising perhaps that race that scientists might perceive themselves as being part of. And also perhaps linking with that is the fact that open is great. Open, open as a free beer, open as we get it immediately, open as unrestricted, open from a pure perhaps academic view of science. That's fantastic. Someone is having to pay. And we do really great discussion at the University of Aberdeen as part of an open research workshop a few months ago now saying that one might think it's great you don't have to pay, but someone is having to pay, whether that's coming off a block grant, whether that's coming off the budget that you're spending out is trying to put together someone is having to pay. And are we almost losing that within the concept of too much openness. And ultimately, how is this actually working yet again. We're working in the context of Ref 28. You may or may not be immersed in that I've certainly been having a look at that. So Ref 28 for UK universities. We're looking at a really strong focus on an environment on culture and on collaboration. There's still some consultation to go by that seems to be where we're going. It's interesting to see where we're the future of openness and information governance and sharing as opposed to control ties in within all of that. And a final point on this slide. And this is the lawyers cheats sheets truth all coming out now. And the importance to check all the time. You're either looking to know you're working with information governance and you're trying to find an answer, or you haven't a clue what is going on. And this is this really strange world. Check, check, check, which is really painful because you might not think it's your priority or your day job, looking at the bottom of pages is a really, really good idea. But looking at funder policies, looking at the license, look at standard operating procedures. If you're not a university research manager or data research manager, then there is some fantastic resource which available if you are in a university, if you're in a small spin out. This is obviously is a lot more difficult because it might be coming back into you. There's evolving standards all the time. There's there's lots of different national forms of infrastructure support which is there. We'll be touching on that a little bit more detail, but I wanted to set that through and continue in similar vein on the next slide, because there's a whole jigsaw, I think, of information which is there but also whole jigsaw sort of slightly competing strategies which are there and it's very much relevant to what we're exploring particularly, which we're exploring. Through the network, but it can also seem slightly unclear about if anyone actually in charge at it's certainly not me so really interested to discuss this. Okay, so as you mentioned this particular national differences now some of this is is intra UK, Scotland, England difference that differences noticeably, but what we'll touch on actually in a second but also if you're on a global project Brexit has a huge implications for things so something really important to be aware, maybe also if you've been working working in rivers and you're going to going to move into to something else perhaps more coastal work. The regimes might be operating slightly differently there's important to be aware of that. When I spoke to you all last time. I started a little bit of a fight where I said I didn't believe in the concept of owning data. I don't quite believe in the concept of owning data, but it's been really fascinating in this data journey in the environments community to see the, the ever present concept of owning data so we're a lawyer, we're a philosopher may not think that one can own data it is absolutely very much within the space that you are all working. So maybe no one really owns the data, but some people certainly think they own the data and they have power and responsibility in respect of the data, and this has given them the power to create different forms of funding regimes. So sometimes that's a government sometimes it's a funder sometimes it's individual scientists, sometimes it's company. Sometimes it goes right back to some, some historical points I'm not sure if you were a runner in the room but we have some great discussions over the years about taking this as an example so James Hutton Institute in Aberdeen. We have the open data license the approach of it is taken to soil in Scotland is very different from the approach which is taken in relation to England, because of the situation back in back in the 20s now, when we have crown field ending up very broadly with the soil databases and crown field is there depending on the status of the user in a situation to looking at to commercialize the release of that data. The same thing can happen in relation to Scotland but on a different form of framework, a different type of license, and that's just the type of thing that it can be really quite painful if this isn't your priority to realize that one might have to navigate. So everyone is looking at licenses if you've dug them out from the small print hidden away in a website. Key things which I think are quite interesting to look for and again really interested in learning experiences that you might have been encountering. So what can you do right now. What can you do with your results what do they actually say about the use that you can make of data now, and also what why do you might be able to share the data in the future can you pass that on can it be part of further improvement or next generation work. What does it say does it say about publication doesn't make any difference if your work is commercial or non commercial. I think this can be really fascinating because particularly if you're say in a in a spin out company, or indeed you're in a business, but you're doing a lot of research, you'd say this is not really focused on getting a product the market, that non commercial commercial decision which is very deeply embedded, often simply doesn't really work anymore so something to look out for, but also today in question again I look forward to starting whether this is actually something which works effectively just now. There's a focus of what what I and others I've noticed talking about almost raw data as opposed to value added data. Huge debates whether there is any data, which is raw, whether everything is always the result of some form of expertise to bring that out to to analyze soil to value soil again run if you're in there and we've had some great discussions on that over the years about how you can try to get the different types of knowledge which are relevant to something which might seem to be raw. And then if we're adding value to something who says what are the different types of value which might be added and what are the different requirements which are a license when if you're trying to get that data in to be part of your research. What might you be able to do with that. And a really important point. And again, we've had discussions about this in the past. And I know certainly the open government license which we will touch on shortly and some other licenses often do say you are taking this data as it is we're not giving you any sort of warranty as to its maturity or completeness and whatever you go away and do with my data in the future, we take no responsibility for that. Now for my lawyer's point of view that's that's great because that's quite clear. That's of course lawyers and we don't think we're that perfect. You can put one thing in a license. You know, with some really great discussions over the years about well, if someone is taking your data and they're being part of a project that you don't feel comfortable with that. Is that in some way going to rebound on you and what might you be able to do about that. One thing that I've been learning a lot from discussions with colleagues recently and through some of my own research is if you're looking at a license and if you're indeed if you're drafting a license in a situation where you do have any more autonomy. I would actually say about what you're trying to do with information and how you may share it and when you may share it, both in relation to the publication, but also in relation to the actual data sets and what requirements are being set out now that it may be the funder it may be by your university about getting a new DIY minting new DIY for a data set, really making that clear. And several people have said to me, you know, we read publications, but there's a DIY but we don't know that do we don't know if it's for the data set. I think we're very much in a situation when it should be that it's very clear from the licenses about when you get the data in what you can do with it and make that clear in your publication, both for the output and for the data set. But my sense also and again, you're on the ground and really welcome your thoughts that we're maybe in a bit of an interim transition stage about actually really learning to make that work. So the rules I think are pretty clear, but that doesn't mean that everyone is actually following them or people might not need guidance to do that. An interesting question again welcome your thoughts are people doing do as you would be done by do you think others are doing as you would be done by are you what barriers are there. Of course, everyone is trying to do the very best what barriers are there perhaps out there to people being able to do that if your perception that people aren't a fact always doing that. What what more support is needed because that within universities that within funders is there a space for for the for the NERC NERC and this environment group to help as well in relation to data management and dissemination plans for example. If I can ask you to have a couple of questions from. Would you like to take them now or later on. Yes, why don't we take why do we take them now thank you. Is that is that on Slido. Yeah. So I don't yet. Excellent thank you. Oh, so wonderful. So we have one from Matt hello Matt. And could I describe the Fort Lauderdale thing now maybe the best thing if I can do is it's going to be too much multitasking. So the Fort Lauderdale was a sort of declaration about about sharing sharing sharing outputs. So let me just call this up. And so if we have Dora which is about evaluating the quality of your work Fort Lauderdale was this balance of ensuring that the results of scientific research should be made freely available. And it was very much driving that but also it's got this huge exception in that. So maybe it's a balance I see more is more is an exception on that what I think I'm going to try and do. Now, can I do this. I'll try my other computer. It might be helpful if I can get that link posted just that. Just while that's thinking about it. Let me just see what else we have in the slide. So Richard Osler. Hello Richard. And isn't it the license which determined if data can be considered open or not an unfair, the findable metadata must be open. Richard, absolutely. Another important thing is finding the license which is relevant to what you are doing, and then seeing what it says. Now, most data will say if it is being open. But I think it's actually very important because if you haven't got the license in front of you, and if someone has said to you, this data is open. And if some examples I've been including that could mean so very many different things. And if the license said it must be fair. And it's got to be open under a particular creative common type of license. Absolutely. And, and it sounds like you're very much on top of this, but it's knowing that. Welcome to other thoughts in the room. Get the license, read it, know that people really, really mean it and then work out how that can be more widely disseminated. Richard, does that, does that, does that help or is that, is that kind of the angle that you were, you were looking at? I'm not sure if you're online or in the room. Yeah, yeah, it does. I think it's the better data. It hasn't got a license. I understand we've got any data. This is the license. So we said that data cannot be considered open because we haven't got a license to spy on our community. Is that a license or something? Is the agreement between you as a user and the data provider? So the data provider doesn't provide a license. There is no pronouncement. Maybe we'll just say leave me with that. That is my place. Yes, absolutely. I can't hear you particularly, I'm sorry, but yes, I got, I got the gist of that. Absolutely. And I think if you so find the data, see what it says and be aware of the limits which are on that and be aware, I think of the idea that just because we say it's open, the open is fair is great. Fair is a huge step forward over what it might have been in the past, but that doesn't mean it's free beer to do whatever you might want to do, you might want to do immediately. Matt, my computer is really, really misbehaving, but I will, what I think I'll probably do afterwards anyway is like I did last year, I will update the slides based on the point that we have been discussing so far. And I'll put out some information of that fort, the Fort Lauderdale Declaration in that. What I will say though is that it's not real. That's a terrible thing for a lawyer to say. It's not binding. It was a very valuable step forward because the community got together, scientific community and said this is why we think good practice should be operating within our community. But it's, it's more of a practice statement, perhaps, rather than being particularly binding in itself. So I'll share some more information about that. So thank you Matt for that. Okay, shall we? We also have a follow up question on slide. Wonderful, thank you. Thank you, Bursha. This is so above your day job. Richard. Hello Richard. One person's metadata can be another person's data. Is it really reasonable to force it to be a particular creative creative commons license in the case that Richard mentions here. Yeah. So that I think is a is a really, really important point. And without getting too esoteric and also think coming to what we really are talking about, about metadata and data in relationship to particular data sets and actually comes down to the question of control and the question of ownership. Does anyone have, have any experiences on this that they would, that they would, they would like to share on this. Richard, I know we spoke, we spoke before. Do you want to say anything about some of the experiences that you've had? Andy, type type more in type more in this slide. Yeah, I mean I can do. I mean partly it's, it's a recognition that when people are often some of the metadata associated with our data can just be some vocabulary or ontology that somebody's put enormous amount of work is generating and so saying that the metadata associated with that should just be CC zero that they don't even get credit for it when you're using it seems extraordinary. But, but it also it gets worse than that I mean in that case I think we, we certainly felt that it was completely reasonable to say that this metadata is CC by or whatever you know it's just a different license and we don't care if somebody disagrees with that. But, but there's a worst case that we, that we had dealing with the medics where they felt that some of the metadata was itself disclosing. And, and, and so, and so they needed to keep the metadata inside their TREs, and we couldn't, we could only refer to that and the abstract and basically at that point we just broke down and crying, and we didn't really have a solution to that. Yeah, those are the two cases that I was thinking. Thank you. And that was, so it's not situation. What was driving the medics, for example, is that their funder requirements or their confidentiality of what you know is almost what they would. Yeah, that was everyone free or people aren't free because they've, they've got their own rules hidden in their license conditions or, or funder policies. And we couldn't quite get to the bottom of the detail but I think the idea was fundamentally that some of the metadata would refer to specific conditions that were so rare that it was disclosing to you mentioned that the conditions were in the data. Even though the, the information about them was, was, was kind of formally metadata. Yeah, so they, they felt that under the, under their regulations that they operate under some mystery, see, you know, patient confidentiality stuff they couldn't release the metadata. No, I understand. And that's, and that actually brings home so, so, so much of this that we're not. And I mean, fortunately, because you've all got much more exciting things to do and then negotiate individual licenses but you are getting licenses which just exist and are in some ways actually really limiting what you can do, and you don't know the full, the full story. And that comes to light, as Richard said, forcefully, a long way down into your project. So I mean, in an ideal world, it's when you're starting out, you know, go to research managers, go to funders, really try and work out what what this is what this is going to happen, try and preempt. So perhaps one of those clauses I've put in as well is, you know, is it metadata? Is it not? What might be the strictest other people would say you're going to work with other people's data sets? What are their drivers? But then who's got the time? I mean, this is a genuine question. Is that something that people have the time to do when you are planning your projects together? Or is it something which is almost arising accidentally because you weren't working together? Could we plan our way out of this in the ideal world? Or is it not really something that happens like that? Any thoughts on that? Because, you know, we could do our own declaration having said that they were just statements, but they're valuable statements. So this could be something we could work towards. Or is this a theory pointer? Is this actually a real point of detail? Any thoughts, Richard? Richard or other Richard or anyone else at all? Yeah, I mean, so our conclusion at the end of this somewhat agonising process was that we basically had two classes of metadata, a kind of fundamental set of metadata that we couldn't imagine any circumstances under which this was disclosed or indeed data that was basically authors and associations and this kind of much more generic metadata, my mind has gone blank about the word for that kind of data, but anyway, you know, there was that kind of data and then all the other forms of metadata we treated just as data. It was data that was associated with other data in our data sets in a way that made it metadata, but it was actually just primary data in our data sets. And so, and in our data analysis so that we could then apply our standard licensing rules to how that data was being treated, even though there was a sense in which we were kind of breaking fair principles about the metadata being CC0 or whatever. You know, but we thought that was the only way that we could really move forward was to essentially separate out these into two classes of metadata. Interesting. Thank you. What I'm going to suggest, would you know, is what I've tried to do, building on very much the help for discussions, including had with Richard Richard a few weeks ago now. I have tried to put together some starting checklists, which have some sets of information and such training and things like that. I certainly find it very useful to me in trying to map the landscape in my head about the questions that people might want to ask themselves when they are starting and working with information governance. I must give this massive health warning and again I'm probably quite relieved I'm not in the room. This might be you baby way ahead of me and you may think we know all this Abby don't be ridiculous in which case, apologies, but if that is the case, I guess also useful for you to think about. Is this the type of thing if someone was brand new to the space just just coming in working working with maybe a new grant holder or maybe started to work with it with a new company, something like that. Is this the type of thing that they might find helpful. And it's at third level so it's might be helpful to you might help newcomers, it might be helpful to just contextualize where we seem to be right now, and then we are going to do a little bit of a case study. So the first thing I found, and it may be that some of you in the room were actually involved in creating this, but now I've got a really good data tree training online training funded by now it was launched in 2018. I have done some of it, not all of it, but I have done some of it and I found the toughest 13 and six were really, really good. So, if anyone has done this or indeed wrote it please let me know, but I thought it was, it was really quite good. So that is something that perhaps really might be useful if people are coming in I find it helpful to practical level the difference in metadata and data sets, the policy drivers engaging with industry engaging with policymakers. Also what I put up here and again I will work with with John and it seems to get these, these slides circulated round to everyone afterwards updated with our discussion. UKRI has a fantastic set of resources, including publishing your research funding, making your data open. What I did find particularly interesting the lawyer in me is that it doesn't refer to intellectual property rights. The UKRI version did refer to intellectual property rights. In some ways I think this is really interesting because it shows that there's much more of a focus on making data open. The other side of me kind of thinks okay so what are we going to do with the intellectual property rights then. We should record and make metadata available and discoverable to other researchers in a way that helps them understanding the research and reuse potential of the data. Publish results should always include information about how to access supporting data to make sure you get the appropriate recognition. You may be entitled to a limited period of privilege use of the data you have collected and analyzed to publish the results. The length of time depends on the research discipline and on the position of the research council and that's actually very similar to what we see in the court law, the jail declaration, which I'll circulate in more detail. So that's that's good, very lawyerly again and it's still it's just that bit of a balance so I don't know if you feel in your daily life if this is actually helpful or not if you find it straightforward to find out what actually your funder requirement is, for example, and if we're rich experience we suggest it's really not always easy to have that distinction in between metadata and data. Then we have UKRI policies and standards again a lot of really good stuff there if you have the time to do it and again fully accept that again this might be something else we might want to work with to perhaps suggest we might work with funders in the future how this might be made a bit more user friendly. But there's a whole set of resources there on open research for you if we could click on Lee. We have the NERC data policy, which again you may be intimately familiar with. I will confess to having not been intimately familiar with it beforehand. It's central to policy that NERC funded scientists must make their data openly available within two years of collection, and perhaps within the situation you might have been in Richard I could see that could that could be really quite a challenging challenging situation. You must deposit it in a NERC data center for long term preservation. The aim is that all NERC funded data are managed and made available for everyone to use without any restriction. And I think that's the type of statement that, you know, it that can be taken out of context and that people could read that and suggest I can use anyone's data immediately for any purpose I like. And given the purple balances that we've been seeing, I don't think that really is what what is being being meant or it's certainly within the caveats that we saw. The UKRI site also has polluting licensing and charging information, which I rather enjoyed because that's the kind of thing I like but really good set of resources. We also have the UK government licensing framework, and that's much wider framework National Archives, for example, open government license can be can be fine there. Again, really welcome thoughts of whether this is something that you are engaging with with a lot of whether you're working more only with say the then the NERC guidance and policy approaches. We are seeing a real drive by, for example, the Geospatial Commission to making geospatial data more accessible but it's very much very much more. It's not fully, it's only applying to the Geo6 and we do have the data exploration license, and that is a situation upon you which you can freely access data for the purposes of exploration, very much pre commercialization. But that is very much seen as a step by the Geospatial Commission to try to deliver some of the geospatial strategy which we find. And again, really welcome thoughts on whether this is something which people are finding helpful on the ground or indeed you may have been involved in drafting it or whether this is seen as something that's frankly just a bit too complicated and hidden away hidden away in websites. We have the open government license reusing public sector information as well. As we mentioned, and that can, that can be extremely important that as I spent a lot of time analyzing this and that is not. There's a lot of caveats in there as well. So simply because you're getting that information, don't blindly assume that everything that you are getting under that can be used on a creative common spaces. There's a lot of caveats, particularly if information is being obtained, which is actually the subject of someone else's intellectual property rights. Thank you, if we could move on. Thank you. And so now it started to dig in some of the nerks funded bodies for the appropriate term. Fantastic set of resources British Geological Survey, the National Geoscience Data Center with the open Geoscience set of pages we have big licensing page with page and intellectual property rights. Again, love to know if these are things that you are all intimately familiar with, or if it's something that is just noise, which you don't have time to do and how might we be able to move forward in this basis. So we have the British on a graphic data center web page. It's got guidelines and how you might submit data and it's got information in relation to copyright. So if we could click on again, sorry, way on my screen. Then we have the center for environmental data analysis and last time we spent a lot of time talking about Jasmine, the terms conditions of use of the Jasmine resource. We've got the deposit and the acquisition policies which apply, and then very close to home obviously now. So we've got to give my love to Cambridge. I was at Newhall, as I still call it Murray Edwards around the corner from Churchill so not very far so lots of happy memories of Cambridge. British Atlantic Survey UK Polar Center data, metadata guidance from them, data citation and publishing, and also the public sector geospatial agreement, which can be a very important part depending on what you're doing, depending on the ordinance survey work. And on to our last checklist for you. I quite like this top resource. So this is the open knowledge foundation talking about what do we mean by open now this doesn't have one answer and the open knowledge foundation are particularly keen on information being really freely widely available to everyone for all purposes. I think it's an important reminder that whether that's working with a partner, whether that's working with with a new funder, perhaps if you're if you're working with some activist groups. They may have a particular view, and they might think that fair is not is not enough. And how does that time with what with what you are trying to do. And then we've got the public sector Scotland end user license, the JNCC has a set of resources the creative commons license we spent a lot of time talking about a couple of creative commons licenses. There are many, many available again it sounds like some people in the room are very familiar with some of them. So but do be careful it doesn't mean free to everyone for for any purposes. So this is that final resource in the world, meteorological organization, they've been doing a lot of work in relation to open standards. And again, it's not, it's not totally open. There are some different restrictions which are free to place depending on the type is it core information, for example, or is it not, but really interesting to see some some new policies which have been developed after lots of international negotiation in relation to that. Those are things that I was wondering if they were of any interest to you, or what else might be of any interest to you, or in thinking about I'm going to work in information governance space, and I simply don't know where to start. I also accept that these things are structured might not be particularly helpful either. But is there something else that anyone is using that they find particularly helpful just do any universities or any funders have other funders have particularly great training that people go on any, any thoughts anyone would like to share in relation to any of that. So what we might do. Sorry. I've got a question. It's the camera. Hello. That's my camera there. That's the one way that you know. I've got to try and remember it was now. I think the point about ownership is quite interesting. And I was wondering if there's a way to help people almost sort of replace that feeling of ownership with the feeling of ownership of ownership or the kind of recognition of the do I is that is that is that how we can make that move from people feeling that's that's their data set to they own the data to the fact that they own or they're kind of they own the responsibility for that the publication of the data set. Yes, I think that's a really nice idea because there are and I mean, I haven't I've never mentioned a do I am sure some of you in the room have, but to go through that there's a real process of your being integrity the process which which you have gone through. And to have been awarded a do I, particularly for your data set is a is a big thing and that. And that brings with that not not that it's something that can be, I mean ownership can have real connotations of power of control and exclusivity and you can't let anyone else use it which is really not where where we are looking at going. The fact that it's some form of recognition that you have that people will know it's. I get quite quite quite question back to you I suppose but it seems that that really could be a way of the fact that you have a do I in relation to that and that's a really big deal and that maybe just as important, or as getting a grant or get getting a publication. I think that does seem to tie into the general movement of, of an open approach a stewarding approach, and even an unlocking the value which we noted, which could be seen as having a more commercial connotation but it's, it's, it's respecting but it's finding a different way of creating that that link so I think that could could really work and is that something Matt's and others that that you're seeing just now that or is it just seen as a more perhaps administrative side of things. So, talking people around to the idea, well, people who are already obliged to public sense it and talking them around to the idea of doing it, because I think that the character is, is the DOI in that kind of recognition. And getting a DOI again so you know I've read all these policies I'm sure you're all very familiar with them as well but is. Do people get do eyes as a matter of course is that where we are, or is there more of a, why do I have to do this I've got a million and one other things to do type of thing. So, the DOI is publishing it into the data centers so they get, they get that much of course but then you get the other, well, I have to do all this work to get it to. But that isn't that's already stated in the policy that they have to do that. Yeah, absolutely. So I mean it seems very clear what is to be done, but does that so it sounds that it's embedded up to that point, but perhaps beyond that there's a bit less. Okay, so where's you. I think a couple of questions are from the room. Yeah, I just, I mean the DOI thing is great. But I'm just a bit concerned that the policies that enforce freedom to use data in short periods of time are massively massively disturbing trick. And because, because what happens is that there are some very very big groups out there and everybody's field there are some very very big groups out there with lots of resources to do analysis but they may not have access to specific sites and specific areas where smaller groups with very little resources spend decades collecting data sets and then a force to release them and then somebody else just hoovers it up and publishes it and acknowledges the DOI. And it's like yeah okay fine you acknowledge my DOI but you also stole my next three research papers and and and that's not a fair balance. I think it's really problematic and you know whilst you know a lot of what I do is about making trying to improve the understanding of the importance of the day sets and the people who generate the day sets in this process. We can't dismiss the fact that the primary publications being nicked by big groups from small groups is exactly the opposite of what we want to achieve here even if it means that research happens a year or two quicker. And progress that's made a year you know a year or two faster. If what we're doing is undermining those small groups and making it possible for them to become, you know big groups themselves and that's not a win. No, thanks. And is this a debate which has taken place with with funders? I know as an analogy in the UK and right now in Australia where I've just been, there's been debates about how quickly you have to release your details about where the oil is under the sea. And what is a fair period of time for someone to have a period of exclusivity and then when she did be shared in Australia, they're looking at shortening it. Was there ever that sort of debate with any of the funders? You know we've seen reference to two years. I mean I'm certain that debates being had many times and you know but the pressure is always to bring that number of years down to allow people to have more access quicker. And I totally understand the motivation behind it so long as it's accompanied by really fair credit for the data generation itself but actually this you can go too far I think you can just go too far because, you know, I mean maybe you have to have a special clause for small groups so they don't have to really But you see this all the time in some domains, something all domains, but in some domains of large groups, heavily computational large groups hoovering up data and publishing more sophisticated analysis than the generators of the data were able to in such a short time. And it's really problematic for the ability of those small groups to develop and to get credit for their work, no matter how much we value the data. Yeah, no, that's really interesting. And again, some of that maybe takes us back to whether we're calling ownership, whatever we're talking about, what are we rewarding and what are we needing to reward. And if we are still in a place and again maybe even with a new ref, if publication is still whatever it is really really after and it's the most substantial and it's the most substantive and maybe the second and third publication that are equally important as that first one. And this system, which is trying to achieve great things, but is actually very unfair, as you have set out, because someone, someone, someone does the work. It's been seen as a public asset, but is it really a public asset, and are the balances which are being struck in, in the most sensible of places. So that's really, really interesting. I'm very sick time for what Matt was saying, because, but so you could get more credit for getting that do I could have proper acknowledgement but if the real benefit actually still in that second publication that you'd really like to do, then we can see that that very deep unfairness which might be coming out, coming out in there. Interesting. So we are normalising the use of DIYs publishing data within our research institute and one of the reasons we're doing that is to give the recognition and reward so it's part of that that door that open, full of research assessment of raising data set the profile of data sets as a research output. And often that is a greater benefit to people who traditionally are on the publications. So the technicians and so on so that you know that they are getting real recognition from using things like DIYs. I think that the, yeah, the poaching aspects can be problematic. I think it's very overstated. So we're working on data for on something for 1020 years and you've still not published are you ever going to publish. So what I think that the context of frame it is more around collaboration and again it's making that data. The value of research output that you get credit for is the producer. So whether that is through the cycle, the correct citation DIY, not as an acknowledgments which you can't really get the metrics from, but a correct citation about DIY, or being a collaborator, you know, co-author on the papers that are produced. The data is really that impactful. I don't think it's right, especially if it's publicly funded to be sitting on that data because you may publish from it in the future. What I'm saying at all just to be clear, the data sets I'm talking about are generated in the field by people doing sampling work year in, year out to create longitudinal data sets that take longer as a time to collect in order to produce significant results. And then they get one paper out of it and it's gone and other people are publishing on it. And it's fine to say, well, you should have published the paper after five years, but there would never have funded a 10-year longitudinal study if they thought five years was going to be enough. So they might have got a half-ass paper after five years and then another half-ass paper after 10 years. And that's fundamentally the problem. It's not about people sitting on data. It's about once the stuff is actually in a state where you can then publish it, you publish it, and then the next paper is gone because somebody's hooded it up. And I say this as somebody who's, I'm a data consumer. My background's AI, machine learning, computing. I'm a data analyst. I don't generate data, but I'm horrified by the exploitation of people who generate data. You know, in my community, it's absolutely mortifying seeing what people are doing to the people, slaving the fields, collecting the data. And it's just wrong. And they know it's wrong, but they get a next paper out of it and it's like, well, that was wonderful. So I'll go back to the back of this bit. So I'm one of the authors of research and we have several, and we've all been national in the past two researches. The, the researchers have been well, we've talked to several equipment, to match with the consultants. And they have a really important stage that we sense is the data there. And you have to demonstrate the data that is being used. and he can't demonstrate that he's been abused, but he was possibly one of the people, because that's how he gets funded, obviously. Yeah, so it's going to vary, a tiny bit of a lot of background is, you know, but the people who research it in the field, who are academics, and you know those departments who will get research from, the people who take the projects, it's not all done so, you know, it is all done so in a sense, but yeah, it's going to go like that in a way. So it sounds almost as if we spoke about possible carve out for smaller teams, but maybe it's even about just a bit of a granular approach to what is the type of the data, the longitudinal study, you might be looking at something different, or is it about different forms of recognition, if someone is focusing more on data generation, can there be a different form of recognizing that? Or should we all, are we, you know, heretical statements, are we stuck with the fact that the publication is the most important thing? And then if so, should there be a way of trying to find different times we can do that? You know, I've written down the mortifying exploitation, and that's really resonated with me. So, yeah, I think you know much more than I do, which was definitely the end of the game, so I'm learning a lot from that. But yeah, maybe that's something when we come right back into what we might want to do, is that something to feed back into funders, not challenging open, not challenging fair, but just this isn't working in some particular situation. It seems very counterproductive from what is being done. Can I, I think we'd one final question for now, and then we could move to a case study, or did we pick that up? Yeah, just one. So I want to respond to this, because the, this issue of data being put away and then worked on later, it's, I think it's an unproblem, and it's used as an, where I've seen it used has been used as an excuse. If you get money from Research Council Grant, in the UK, it has been the data policy for many years. The NERC data policy applies if you're collecting environmental data, no matter which Research Council you, is actually paying your grant. The NERC data policy is the policy that applies. And that states, and has said for at least the last decade, that you will deposit your data within two years. And if you don't want that money, don't take that, if you don't want that condition on you, don't take that money. That's the answer. I've just, I'm a grant holder in the UK UH program, and I've just had my red notice to say I haven't done it, which keeps me at the backside to get on with and do it, because it says very clearly that under the terms of that policy, that we've all signed if we've taken money out of NERC, that we are liable to cessation of grant funding and blacklisting for future grant funding. Those are the rules of the system, and that is the rule we are working. Anybody who behaves badly deserves to be pilloried by their community, and I will be the first person to support any actions or somebody's been stealing data, but I haven't seen it in my own area. I just want to make a specific one issue we do need to get a handle on is data quality. As British Geological Survey, we hold a legacy data. We've just had some data published by a researcher, which supplies some data to him saying, this is all we've got on this. We know it's rubbish, but this is what we have, and he then published it with our permission and has now rescinded it because we pointed this out to him. But we do, there does have to be a point to say, we're very happy to give you data, but don't publish it on if we know that there are methodological flaws in the way it was discovered. And this is particularly a problem with legacy data. So there are rounds to say, we really advise you not to, but data has to be open if the public are paid for it, because they expect that of us. Fantastic, thank you. Anyone wants to come back on any of that or with any other points? I'm just going to have one more go at my quick cross. I'm absolutely in favour of open data. This is what I work on. Half of my job is working on open and fair data. I'm not opposed to open data. I'm just opposed to not getting the correct credit for your work. And the, and I'm not opposed to the data policy. I'm just opposed to, to, to, I'm just concerned that as we, the more, the more we push earlier and earlier publication data, the more there's the risk that I dive out. I certainly know many people who've experienced this. I've worked with MRC WHO collaborating centres who are screaming about this, that their data is being stolen off them day by day by day and being published by groups that don't understand the data they're working with, but it's getting published in nature anyway, even though they're the principal world experts on this subject, but they're taking time to do the analysis because they're busy trying to choose the next flu vaccine for the seasonal flu job. And this, this stuff is going on as I speak. And I, you know, I'm not, I'm not making it up because I benefit from this process. I benefit from being able to take these data off these, off these, off these resources. I'm just saying that it's a, it's a real concern and point to one thing that demonstrates what the problem is here. So talking to all the people that I work with who do field work. You really don't doubt that it's the same with in the Newark area, but this is BBS, I'll see it at MRC I'm talking about the, they say it's now impossible to get funding for surveillance, because surveillance just generates data and you don't guarantee you're going to get any results out of it. So you may have to do this for quite a long time before you get anything interesting out in the meantime, all that data has been hooded up by other people. You've had to release it and they, they've done like minor Nazis on it and you were looking for some outbreak that might happen a couple of years in the future, but they, you know, the data is hugely valuable, but because you can't hold on to it and you can't gain credit for it, they go, well, look, there was no, there were no research outputs from this, from, from the surveillance study you did. It's like, well, there was a risk of being literally nothing because it's surveillance and again what's going to happen. But also these other people are based on our data because we released it because of an earlier draft that we have to put out of the data and these kinds of things. I mean, I'm not, you know, it's a real problem and the fact that surveillance is so hard to find out is, I think all comes back to, to how, to how quickly the people doing that drudgery are having to release a data and not getting credit for it. And so it's like, thank, thank you. Yes, so that sounds maybe in that in that particular space, there's, there's the need to share, there's then the fact that others are benefiting on that data, the grant holder, isn't they able to show the outputs which came from that and so the next so they're being less successful getting the next type of grant if I'm understanding right. And, and maybe that's an example of the particular differences that we are seeing. That might not be the case in other experiences that are other speakers or I can't pick up on the names was talking about but we definitely see that in the BBS or C and MRC space before we move on. So to talk about, yeah, you know, people should be if this practice is going on a clear test for some but not for others, that they should be reviled by their peers, they weren't your words but that's how I understood it. I mean, does that happen? I spoke before about sort of instinctive and emerging and very clear scientific norms. I mean, if people do. I know there's new practices which are emerging in terms of retraction and things like that. If people are using other people's data, how does the sector respond? I mean, one example as an analogy, a serious analogy, but I know that some comedians don't believe in copyright law. But if someone steals anyone else's jokes, no one is allowed in the club and that's a real reality and it's a real, real, real threat and it's much more effective than anything the courts might be able to do. I mean, what, how does it work in the scientific world if someone did encounter some of the things that Richard's been experiencing? So I can tell you what happens in practice in the MRC, BBS and C-groups that I've been involved in. So FAO, WHO, RAF labs that I know, that I know the people who run the labs personally, they say they will never work with any of these groups again and they'll name the groups of people who they think just steal their data, willy-nilly and publish nature paper after nature paper without ever collaborating with them, just citing the data that they put in their open literature and they say the papers are wrong because they don't understand the data and they're getting no credit for it and it drives them absolutely insane. So they should not be refused to do it, but these are senior professors. They can choose not to have a work with these people. You know, a lot of the junior researchers don't have that luxury and so, you know, so that's the end result of the process that I'm a little bit angry about. I don't know. I get that. And what about anyone else? I mean, any other things that people have seen, any experiences, director or indirect, of this has been happening and the community is responding? Yeah, I think there's a big gap between theory and practice when it comes to reuse of data. So we make lots of data freely available. It's all under, well, yeah, the vast majority is under Creative Commons attribution license. But the problem we see is that is at the journal side and the editorial side is that the journals aren't enforcing the practice that they're meant to be enforcing in their journal based policies around data citation. And if you don't have that correct data citation, you can't get the metrics on how your data is being used. And that's what we need to report back to our funders to show that impact. That's really interesting. That's fascinating. Thank you. Sorry. One more thing about the end results of this process. So there are five global seasonal flue graph labs. They all used to release their data. Only one does now. It's the UK. Everybody else will stop. It's all private because they're fed up with having their data stolen. That's interesting. Okay. Wow. 50 minutes to go. So we're going to tell you about virtue if you go on the next slide. So this slide doesn't have been massively overtaken but humor me. We are going to talk about an example. A researcher wishes to explore experiences of flooding on the cam for old times sake. And he's thinking of using four databases. One which is a research council resource. That's an air data policy, maybe, maybe a relevant another net approaches, one which has been developed by by a public body. So separately mutually, mutually termed. One was developed by a large multinational corporation. One database was organically developed developed by someone and has now been bought by a large multinational corporation. And the resources not involving any personal information. The researcher wants to share the data to support their, their publication and indeed depending on the funder which is coming around they may of course be absolutely obliged to do it but they're wanting to share the data. They're also wanting to combine data coming from the four data sets and they're wanting to add data for a next generation approach. And they're very much having in their mind how they can look more widely in their own project and everything that they are doing Mr Flexi and one of the bits of feedback I got from someone how, how is there a space for people to look more widely than their own project and the data that they are generating. They want to make a publication immediately open access. They would like to pass the new data set on to partners to some for money and some for not. They want to enable a new spin out company and they would also like to address an imminent climate related emergency and yes this is exactly how I write exam questions for my students. So, I thought this might be probably overtaken some of this in our discussion but I thought this might be quite interesting to see, but if you could just click on to the next slide just momentarily. I'm wondering what you would do if you were placed with something like this, you mean you'd have experienced this or something similar. And what do you think you would need to think about something solving this type of problem. And is it there. Are we talking about about new types of policies but deliberately been trying to move into that more private private sector type of approach, what might we want to do. So I'm going to give us just just a few minutes to reflect individually, but if you could flip, flip back. Thank you. And just what what what this type of fact pattern, which may or may not be time to experience may or may not be realistic but I think it draws together quite a few things that I've been coming about and some of the people I think consulting with as well. What, what, what would happen, what could work where might where might you look so a couple of minutes just to to reflect on that, and then we'll, we'll chat it through. And Richard, thank you for your Slido post on the LXC or RDM resources. Oh, sorry. The second database is developed by developed by public body. Yeah, I was going to say that on that basis. I think we might have to move things on a bit more rapidly because I think people have been sweating in the other two rooms are quite keen to stretch the legs. We may need to wrap up in about five or so minutes. We can absolutely wrap up in five. Okay, seeing the discussion has started. So what do we think research plans for databases research country resource we've we've chatted through and we've noted the challenges within that someone shared the experience of the public body. And you've been trying to negotiate for five years. So the virus, they're very good at having open data sets, but if it's not already in their pipeline to many of them, then chances of them, you know, bringing through that pipeline in a reasonable time practically zero. So they've got good, they've got good existing open resources. But once they are open, there's a lot of time involved in the season. Yeah, I think I think that that that's only times and a lot of things I've been doing. I mean, Owen was worried is one person. I'm sure you know it's been a lot of work, you know, tracking when people say open access what they're really meaning. And yeah, things are locked. They're either shared or they're not shared on picking that I think is, is like to be to be really difficult. Anyone an experience of working with the private sector. No, okay, so maybe that that's not. Sorry, please. I guess it depends on their interest of the project. Yeah, that might be the last point where you're actually trying to do something for money. They probably want to know all the details about how much it was going to bring in and how much you might be able to pass it to them. Again, it also slows it down. I think that's right. And I think then we're looking at the difference between this is a business we're all going to we're all going to make money or we're wanting to do data because we're wanting to address a climate emergency. We're not really much money flying around about that. And also that has a lot of echoes of what's happened with COVID. And certainly what's been seen from the IP side certainly in relation to health and vaccines and data was that basically they're the public spiritedness seem to disappear pretty quickly, which is not criticized people have to make money that they have their metrics to recognise how the business models are run, but they weren't very quickly back into a traditional private sector reward model. So perhaps this isn't going to isn't consistent with that fair and open type of approach. What worries me, I will confess to some of you know, then is that we also have the data which was developed on an open basis, and suddenly that ends up being bought by the private sector. And then it's shut down again. And that's something that really worries me. I'm not aware of so much of that something which has happened yet, but I think that's probably something to kind of, to kind of look out for. So when it happens, you can remember that I said, I said that first. Okay, first you can click on just thank you to john to john Warren if you click on to the next one as well. I'm really quickly going to run through this and some of the points and then the cycle from around after lots of discussion going in other spaces. Digital Twins pre do some of your way ahead of me in relation to the NERC possibility of digital management framework. I've spoken about discussions and oil and gas in the UK about sharing information quickly and how we balance those same interests. I'm at conferences in Japan we're doing a lot of talk about AI. Who owns that liability reward regulation totally unclear everywhere in the world and it's changing it's highly relevant to what we're doing in this space but that will continue to change. And I think also relevant is the EU data strategy which also is very keen on public use of private use of public data and vice versa and is trying to develop some regimes to make that happen. It's a nice game on standards of interoperability and funding keeps carving out IP and trade secrets and the court and respect for private power, and there's no data access right. So I think that this whole balance about public private reward time is going to continue there for you if we could click on again. Great. Term of the moment. I noticed before is unlocking value. Everyone is very keen on unlocking value. This idea of this, there's this mountain of cash just hidden under the data. Now you're much, much, much closer to that than I am whether that's really likely to be true. In relation to Scotland and health there's talk of moving away from a culture of caution that people have been too scared to share information because you think they think it belongs to someone. And I also think you're working that different space. A lot of this, not necessarily always as we saw with the private sector examples, or something is publicly funded and that perhaps that is something which should be freely available. But how do we fairly unlock that value. And we've also had some experiences that people feel things are being done very unfairly. Now, I'm noticing a lot of work on this, no through the digital solutions program. I know some of our community have been involved in this. The delivery plan going forward, improving connectivity is one of its focus digital strategy, making the best use of digital access maximizing value again, making it accessible, make it interoperable. The issues are there they are being recognized but it doesn't yet sound I would say from our discussion today that the real practicality of how we're really making that work completely I'm really into the point the points of funders or publishers and the respect for individual situations perhaps aren't there. And the next one I don't worry we don't have to read it all, but this, this is the results of the very recent use spatial commission call for evidence about opportunities across the economy. And I find this really interesting is basically saying ordinance survey as an example might be hindering innovative applications because I need to demonstrate commercial returns and Netherlands are making more information much more freely available. We should do this here. So that's one, one, one type of angle. People saying access to use spatial data is one of the key driver of change we need to integrate more data sets, but people are lacking resources and lack of incentives to share data, high cost associated with implementing data sharing, but also more geospatial data is being increasingly collected by multiple private sector actors, rather than a single public authority, even the notice that they often won't give us the data either. And if you could click on again. Value change doesn't differentiate between public and private sector value chains. Now again, you may know this because this is what's coming from the marketplace anyway but I think it's a very interesting angle that Narcan and other funders they have that particular particular angle which is so important but also existing alongside that private private sector space. I think we can leave it at that if you want the last one. So thank you. We are, we are done. I hope that's all right john thank you for that fantastic discussion I've learned so much from that. I will I will work up the slide and pick up some some of the points that have been made. I think we can definitely keep going afterwards and in the bar would be at dinner and and do you get in touch with me afterwards you what might we be able to do as a as a community is there anything that we think we might be able to put is that pressure on on journals for for example. I think we have some sort of new new declaration what might help to practical level to ensure that the present system works as fairly as the can but perhaps also maybe as we're moving to that bit more of that public private intersection which does seem to be certainly part of of the landscape. And I that that's all I wanted to say any final points anyone would like. Very, very quickly. Very, very quickly. Thank you. Thank you. Thank you so much. Apologies I couldn't be there really well discussion and Bridget and john thank you so very, very, very much.