 And good morning everybody. It's a real pleasure for me to be making this presentation today. So I'm going to hopefully share my screen and we will kick off with this presentation. So I'm going to talk a bit today about open science and, you know, what is in it for me, but by me, I actually mean you as the researcher or indeed as somebody who's involved in some aspect of data stewardship. So let's start off by looking at some of the terminology and definitions. So what is open science? A lot of people think that open science is just open data, but actually open science is far more. Open science is actually open data, but it's also open software. It's about open methodology, and it's also about open peer review and open access journals, but also open educational resources for the next generation of researchers as well. There are a number of definitions around for open science. This rather dusty one from OECD actually comes from 2015. And the OECD defines open science as about making primary outputs of publicly funded research and results of the publications and the research data publicly accessible in digital form with no or minimal restriction. But let's sort of talk about this in more general terms. What is open science? Well, really it's about methodology, observation and the collection of the data itself. It's also about public availability and reusability of scientific data, and in fact all research assets. It's about the public's ability to really access the scientific communication, so there needs to be transparency in the way we communicate our science. But ultimately it's about removing the barriers to sharing any form of research output to allow anyone who wants to use these research assets to support their own research in the future. Let's reface that really to open science. If we start talking about benefits, there are the benefits to society. There are also the benefits to the research community. But today, as you might expect from the title of this presentation, I'm actually going to talk about what are the benefits for you as a researcher or a data steward. So let's start thinking about some of the benefits of open science for researchers. Open science actually allows increased research efficiency. If you're able to share research resources with others, this allows you to make better use of your time and effort. It also allows greater transparency, quality and reproducibility. But reproducibility is actually a bit of a challenge. I was exemplified by the discussion that quite kicked off in 2016 about whether there's a reproducibility crisis. And this actually came from a survey that was run by Nature, who actually sent out a survey, and of the respondents, there were 1,576 of those. They didn't say that they tried and failed to reproduce another scientist's experiments. Slightly more alarming was 50% of those who responded actually said they couldn't reproduce their own experiments when they revisited them. I was a little bit surprised that so many were willing to actually make that admission. There has been a lot of work to done to look at what causes this failure to reproduce research. Well, part of it is around the way that we report our research. At the moment, we selectively report our results through journal publications. And I'll come back to that in a moment. But there's also the quality of the original research as you might expect. But another aspect is that pressure to publish. People are in a hurry to publish their results as quickly as possible for a variety of reasons. But fundamentally, the failure to reproduce is actually about the availability of associated research assets. So when someone looks at a journal article or some other form of output. Not being able to access the associated data software, the workflows, the documentation, anything that allows somebody to reproduce that experiment or that result. If that's not available to them, then it's almost impossible to reproduce that experimental result or that observation in the future. But there's also other benefits to open science. Open science actually increases the visibility of your work. It allows reuse, but it also creates greater impact for your research. So more people that are seeing it, the more opportunities there are for people to actually know what you're doing and to actually try to collaborate with you in the future. And this need for open access was highlighted during an international open access week a few years ago now, in fact 2017, when Professor Stephen Hawking, who is sadly no longer with us, who was a great advocate for open science, stated that anyone anywhere in the world should have free and unhindered access to my research. And he went on to say, but, but he went on to say, sorry, he went on to say that the importance of open science is about also being able to share the research of every great and inquiring mind across the spectrum of human understanding. And he backed up this statement by releasing his PhD thesis from 1966, via the Cambridge University Library web service, and at the time that web service had 60,000 downloads in 24 hours before it crashed. But it demonstrated the level of interest and the level of impact that an eminent scientist could have by releasing his research to the wider community. But not all of us are Stephen Hawking. However, evidence does show that publishing open articles openly can help reach the researchers get noticed. A study of over 2000 articles published in Nature Communications showed that those published openly received nearly double the tweets and Mendeley reads as any closed article. So this demonstrates that actually by publishing your research as open access, you will get more credit and you will get more citations. There have been a number of exercises to try and quantify the impact of open access publishing on the level of citation for specific articles. So, for example, there was a comparison of open access and non open access articles in PNAS. And this research shows that on average, open access articles are actually cited earlier, because they're available to a wider range of researchers, and they're also cited more often. And in fact, they're twice as likely within the first four to 10 months of publication to have been cited, and three times more likely within 10 to 16 months after publication. And this does show that open access publishing has a major impact on the visibility of your research. There are also more benefits of open science for researchers. There is the accelerated research process, the opportunity to actually speed up the research process by working with others outside your own institution. I don't think there's been any greater example of this than the collaboration to develop the COVID-19 vaccines. The development of the COVID-19 vaccines is a clear demonstration that by working together, it can accelerate the scientific process. And also open access allows greater access to published material for all, even those who can't afford to access open access journals that currently are hidden behind paywalls. But as I've already mentioned, open science also gives you more opportunities for collaboration and community building. And at the same time, improved opportunities for funding, because you, when others can see what you're working on, and they're looking to build a team perhaps for the next proposal, they're more likely to approach you to ask you whether you want to be part of this team. Why do researchers think open science is a bad idea? And part of this is because many research efforts require a significant investment of resources in terms of both people and financially. The investment required to collect and conduct research often leads to a strong sense of attachment for many researchers. And a very good friend of mine, Leslie Wyborn, at the Australian National University, coined the term data mining, meaning the data's mine and I don't want to share it with you. There's more to this than just this sense of attachment, which is an issue. It's the reason that some researchers still carry their research around on a pen drive that when they lose it, then it becomes a major catastrophe. But researchers have this attachment and this concern about open science because they're worried about inappropriate secondary reuse of their research, which quite recently was identified as the data parasite. And what this really means is that researchers are concerned about being scooped. They're worried that someone else will actually take their data and publish their research before them. But they're also concerned that others may unfairly benefit from using shared research assets without little additional effort and also may not give the original researchers or the original team that can't collect the data, any credit whatsoever. But also by making data open, many researchers are worried that by having the data and the research assets available to all other researchers, it potentially steals the opportunity for them to do further research. And this can be a real problem for future proposals where they may be competing with other researchers. The reality is that all researchers are competing for jobs, they're competing for funding and they are competing for recognition. But there's also the issue that sharing research assets takes extra resources and distracts from what some still consider to be the real job of publishing papers. And at the moment, there is currently little or no incentive or reward for other research, for other forms of research outputs, although this is rapidly changing as we'll see later on. But the reality is that the ingrained published or perished doctrine is still largely alive and well, even though it's largely a product of a different era when scientific publishing and peer review was entirely restricted to paper-based journals. However, unfortunately, this paradigm does continue to place excessive strain, particularly on young researchers, who are still required to publish prolifically in order to actually gain career progression or tenure to secure funding or even to gain future employment. But I have to say, this is particularly noticeable in some regions where this has now got to the level where researchers are actually rewarded for the impact factor of the journal in which they publish, and they get preferentially rewarded for publishing their articles in journals such as Nature or Science, for example. But the truth is that citation or H-index and journal impact factors are still used to assess the research contribution by many. And for this reason, publishing in academic journals still remains the principal way to communicate scientific findings, and authorship on manuscript scripts is still seen as an indicator of an individual researcher's contribution through the list of authors on peer-reviewed papers. And for that reason, the list of authors on many peer-reviewed papers continues to grow. I'm sure many of you have seen journal articles with more than 20 authors. And there are some extreme examples out there as well. So, for example, the most recent report in the physical sciences from the Large Hadron Collider Research had 5,154 authors, which was published in the physical review letters. But how many of those authors were given honorary credit, and how many of the key PIs have been lost in this huge list? For that reason, there are a number of initiatives that are seeking to redress the balance. And credit or the contributor roles taxonomy shown here is an example of just one of these that is widely being adopted by a number of the high profile journals. Credit is a high level taxonomy that includes 14 roles that can be used to represent the roles typically played by the contributors in a particular scientifically scholarly output such as a paper published in a journal. I think it's fair to say as this becomes more widely used, you'll be seeing far more of this type of taxonomy in the future. But the reality is that science is becoming increasingly more complex and compliance with good research practices is more of a requirement than it's ever been. We are increasingly talking about the need for data management plans and also ensuring that our data and research assets are lodged with an appropriate repository rather than hidden away on the hard disk of our computer. But one of the big issues is that the contribution by some researchers are also becoming more intangible and difficult to quantify. So we need to move beyond the current paradigm and we need to start thinking about how can we encourage greater adoption of open science. Because by through adoption of open science, we will actually be able to reward other forms of research outputs, such as the data software and workflows to name but a few. This requires us to encourage researchers and institutions to adopt these open science practices. But we also need to make sure that we're providing the tools and technologies that are needed to support open science. A good example of one of these tools is the open science framework, which is being widely used throughout the scientific research community. But it is just one example of a collaborative tool that can be used for researchers across the world to share their work. And this is this type of collaborative platform has come into its own in the last couple of years, particularly whilst we are unable to travel to work with our colleagues. And just to say a little bit about the open science framework, it's as I've already said it's an open source collaboration platform so it's not proprietary. But it provides functionality across the complete research lifecycle. And by this I mean that it supports everything from the initiation of the project right through to the publication of your research. One of the powers of the open science framework is that it allows integration with other third party tools and services. So many of your favorite tools are probably already shown around this graphic here just to pick out a few of those that I use there is GitHub here there's Google Drive. There's also DMP tool for data management planning. And this, this diagram is slightly out of out of out of date, because Jupiter notebooks is now also integrated into the open science framework, which I know is being widely used by many researchers. But I also want to say a little bit about some of the tools that you might use that actually are part of the open science framework. So in order to actually link together many of the research assets, the open science framework makes comprehensive use of persistence identifiers. Now unique persistent identifiers are important, because they provide a long lasting reference to the two research assets. They also provide information necessary to reliably identify, locate and verify research assets. So for example, let us look at the digital object identifier, and I'm sure all of us have come across these in some context or other. But I want to just quickly break them down a little bit to explain what they really mean. Basically a digital object identifier is internationally recognized and supports its standards so it is incredibly widely used. The persistence identifiers are used to uniquely identify an object in a digital environment, but just to emphasize the object itself can be physical or digital. So it's not just about data, software, journal articles, it can also be a material sample as well. A digital object identifier is made up of two components. And I'm going to come back to CERN as the example and again, the output of data and resources and reports from the Large Hadron Collider Experiments. So a DOI is basically made up of two components, which ensures long term accessibility. The first part, which begins 10 dot, actually identifies who the publisher is. And it also identifies the individual article or book. The second part actually references the specific data set. But this two component digital object identifier also references a service to locate this resource. So if you would like to have a look at that report that has over 5,000 authors that came from the Large Hadron Collider Experiments, this is the DOI for you. But let's say a little bit more about the importance of unique persistent identifiers. Unique persistent identifiers allow citation of research assets. So they allow citation of the data, software, anything else that's created as part of the research endeavor. But they also provide a permanent and an ambiguous link between the researcher and their outputs. This is facilitated by the use of a personal persistent identifier. And I'm going to just give an example of one that is increasingly widely used and I know is currently recommended by both by NERC and UKRI for researchers. And I should say that ORCID is becoming increasingly mandated by a range of funding agencies, but also by many journals, at least for the primary author. Normally when I give this talk in person, I actually pause at this point and ask people to indicate whether they actually have already registered for an ORCID or not. So to say a little bit about what ORCID is for those of you don't know. ORCID is a persistent digital identifier that basically distinguishes you from every other researcher. It's an alphanumeric code. And if anybody chooses to reference this particular code that's shown here, you'll discover it's mine. I quite like this because mine's quite easy to remember having four fours at the end. But these persistent identifiers as alphanumeric codes also support automatic linkages between you and your professional activities. But one of the things that I do need to highlight as I've already indicated ORCID is researcher driven, so you need to register yourself. At the moment, there is no mechanism for bulk registration of teams or members of staff within an organization. So it's important if you want to be able to be linked to your research outputs that you register yourself. And at this point I would encourage you to make sure that if you register yourself as a minimum information. Please do make sure that as well as your name, you include your affiliation and that you keep your affiliation updated if and when you move to another institution. The reason for this is that ORCID does not have any mandatory fields apart from name. But if you happen to be one of those people who is Peter Jones, for example, there are probably hundreds if not millions of Peter Joneses in the world, and you want to be uniquely connected to your research. This is also another strength from using persistent identifiers, and this is about linking your research assets and you to your research assets. And this is open research graphs. Open research graphs are actually important because they're a mechanism for linking different elements of research lifecycle, the research lifecycle. The research graphs allows citation of research assets, but it also allows that automated link between the different research assets and the researcher. It also supports the credit for non-traditional research outputs, and it provides that additional metric for assessing contribution to the scientific endeavor. One other aspect of the tools and techniques that I want to mention in this talk that I think is incredibly important for researchers is to mention that open science is not just about disclosure. Giving your data, a DOI or publishing your research in a journal is not making your data open. That is only publishing your data. In order for you to actually be practicing open science, you need to be thinking about how findable, accessible, interoperable and reusable your research assets are. The fair principles, as they are known, provide a set of concise guiding principles, and they're important because they are increasingly widely adopted, and you will hear a lot about fair in a range of different spaces. But this is because they are domain independent, they are high level, and they are applicable to a wide range of research outputs. I'm actually going to say a huge amount about fair at this point, simply because this is a whole different talk, and there are actually 14 different fair principles. But just to say a little bit about what the principles are, they were developed by Force 11, who are an open data advocacy organization, and you can find more about them on their website. But basically these principles allow you to define the characteristics necessary to aid discovery and reuse of your research assets. As I said, they're basically a set of guiding principles, and they're applicable to data, metadata, they're applicable to tools, vocabularies and infrastructure. The point of these principles is that you are able to make your interpretation of how they're used, and they are quite powerful in terms of referencing the different discoverability of your research assets. I should also say, there are a number of tools online available where you can assess the fairness of your research assets. But also, I want to say something about open access publishing, because this is also an aspect of fair. As I already mentioned, disclosure is not the same as open science, and this also applies to open access journals. Open access journals are increasingly being used by researchers in terms of where they're publishing their research. But as I've already mentioned, there is still an issue for many researchers that the journal impact factor is a criteria for a measure of success for them as an individual researcher. However, open access journals are becoming increasingly popular. In August 2020, there were 15,000 open access journals listed in the directory of open access journals. And a recent study by Heather P. Weller demonstrated that about 20% of scholarly literature is now open access. Another real shift is that many funding agencies are increasingly mandating open access publishing. It's also worth noting that one of the reasons that many open access journals have relatively low journal impact factors at this point in time is because many of them are quite new. None of them have the longevity of nature or science or PNAS, for example. But it's also worth saying something about open access journals in terms of publishing models, because although many funding agencies are mandating open access publishing, it is quite a complex field. And this is due to the different open access models, which have actually promoted adoption, but there is still a long way to go. The models include gold, and this is where the final published version of the article is permanently and freely available online. But the issue with the gold open access model is that there is a charge associated with publishing your original article. And this is termed the article publishing charge, and it actually can be quite high and is therefore a barrier for many people to publishing in these open access journals. There's also the green open access model, and there are an increasing number of journals who are now adopting this model. And this is where the individual researcher archives an early version of the manuscript in a repository and online. And as you might expect, this is free. Another model that's less well known is also the bronze model. And this is a bit of a strange one really because the publisher provides a free to read version of the paper on their website, but there is not a clearly identifiable license, which then in itself becomes a barrier to being reused by other researchers. As I mentioned, there is a hybrid model, and this is because some journal publishers currently use a blend of all three of these different models within one journal. And this is becoming an increasing problem, and actually is making this landscape incredibly challenging for researchers themselves. In regards to the green self archiving, I also want to mention the increased use of preprint servers. Preprint servers are services that are allow up that are increasingly becoming available, which allow open access to papers prior to peer review or to publication. They're free for authors and for those accessing the paper. One of the strengths of these preprints is also that they're citable and indexed by Google scholar. This means that researchers are able to get credit for their research at a very early stage and indeed before the peer review processes completed. It's also worth noting that unlike when preprint servers were first launched, many journals now permit preprints, including Nature, Science, PNAS, and all of the EGU and AGU journals. It's also worth mentioning that there are a number of preprint servers for the Geosciences, including Earth Archive. And in fact, Archive provides preprint servers for other disciplines and domains as well. There's also the newly launched AGU Earth and Space Science Open Archive, ESOR, and I have to mention that the European Geosciences Union early next year will be launching EGU Sphere as its own preprint server. So I just want to wrap up this presentation today with a few thoughts. I'm very aware that I've covered a lot of ground in a very short period of time today. But for that reason, I just wanted to leave you with three important takeaways. Open Science Practices benefit researchers, institutions, and society, and this is being increasingly recognised from the researcher through to the policymaker through to the funding agency. I think everyone is now in agreeance that we have to move towards Open Science Practices and that we have to mandate these in order to allow many researchers to be able to get the due credit that they should receive for those non-traditional research outputs. There are many tools and techniques supporting Open Science and I've mentioned a very small number of those in this presentation today. But they provide different metrics for assessing research contributions beyond traditional academic publishing. But I have to say that anecdotal evidence that I've received firsthand from early career researchers in a range of the roles that Gary referenced in his introduction to me, that show that young researchers are still being discouraged from adopting good practices with regards to Open Sciences by senior researchers in their own institutions and in fact their PhD researchers in some case. Many institutions still want to prop up the existing measures for measuring productivity rather than actually facilitating a fundamental change to how we measure research contribution and how we measure career success. Wider adoption of Open Science actually requires a fundamental change in the research culture. And this is has to be led by the institutions, the funding agencies, but also the researchers themselves, they have to be willing to adopt many of the practices and change some of those ingrained things that they have done for so many years, such as hiding their data on their data sticks or on their computers in their office. There are many people with the necessary expertise both within NERC and UKRI and widely in the informatics community who can help and support with the adoption of Open Science. And with that, I'd like to thank you for the opportunity to talk to you today and and I hope you've learned something. And if there are any questions, I'm more than happy to take them. Thank you very much. Thank you Helen. I think that was excellent. We thought there was a huge amount of detail there and I'm sure the participants will be very keen to ask some questions and we've got some questions coming to the Q&A and I've had a number passed to me by the chat area so that we will we will we will press ahead. I'll take the convener's prerogative here to ask a quick question because this is one that's interesting and close to my heart. I've heard about the data parasite scenario from many different angles now. I've heard and talked about in your global travels and your different roles. Have you ever seen any research to evidence and back that up? It seems to be more of a perception than something that I've ever seen any evidence to back up. That's actually a really good point. I think it's more of a perception by some researchers and used as a justification for some ingrained practices. There is certainly some justification where those people feel that by publishing their data or their research, particularly the research assets, before they've had a chance perhaps to put in that second research proposal that follows on from the first. When I've tried to find some real exemplars where people really have been scooped and they feel that somehow they've lost out on opportunities, I actually think they're quite few and far between. It is more of a perception than a reality, but it is driving certain behaviors. We should be addressing over time then and trying to make sure that people understand that while the risk probably does exist, it is very small and shouldn't be standing in the way of what we're trying to achieve with open science and open data. Absolutely. Let's step to some of the questions and answers that have been asked so you have an interesting first question here Helen. They were wondering what your thoughts were around the issues of research integrity and open access. Do you think that whistleblowers have importance in maintaining research integrity in open access. Yeah, that is a thorny question, whoever the anonymous person was who posted that. So, that's it. So I'll give you an example. I think there is, there is a, an issue with regards to protecting research integrity, and therefore there is a role to be played by individuals in highlighting poor practice. And also, I'm being very careful what I say here because I think I think this is a very thorny issue. I think one of the key things is that we do need to have to feel that if we see, there is poor practice, or that you know there are sort of plagiarism, or, you know, you mentioned the data parasite, we should feel able to be able to call that out. But we also need to make sure that doesn't become something destructive that becomes a barrier to open science as well, because there is that danger that one of the, the, the, some of the research that I looked at that I referenced at the beginning of my talk with regards to PNAS, and what they looked at in terms of sort of open access publishing, but also researchers behavior did show that there is a real sensitivity to people's willingness to, you know, to recognize bad practice, they don't want to be seen to be that person who's the whistleblower. So I think in answer to the question, I think there is a role to be played in calling out bad practice. I think that is an important part of protecting research integrity, but we also need to make sure that then doesn't become a self fulfilling prophecy that damages the whole open science paradigm. No, absolutely, absolutely. Good answer to a challenging question. Our second question links to another one that I had in my mind, which is what are your thoughts on how NERC can support and encourage researchers in publishing in open access and working on open access tools. And my take on that was around, what would you suggest UKRI and NERC do to encourage open science? What would you say is the primary steps we should be doing now? So I think that's a great question actually because I think this comes back to this whole idea of career credit. We need to make sure that we're not focusing on sort of, you know, how many journal articles have you published? Where have they been published? So we need to start more focusing on the overall contribution to the research effort. And so that comes down to, I think in the context of NERC and UKRI, we, in another space, we had a conversation about carrot and stick and I think that's quite relevant here as well. I think we need to make sure that we don't overemphasize the importance of things like journal impact factor so that people feel empowered to publish in open access journals. But we also should be rewarding the, you know, the adoption of open science practices. But I have to say, I think one of the other things that we do need to do, and I'm always reluctant to say this, there is a lot of evidence to show that mandating some of these good practices works. But I have to, it's, I think it's obvious that this can be done in tandem with journal publishers, for example, you mentioned in the introduction Cogdes that I'm involved with. This is an initiative for those people who don't know that brings together researchers and repositories and journal publishers to talk about how to improve open science practices and the implementation of metrics. And I think what one of the things that NERC and UKRI can do is support the journals approach to mandating some of these things like mandating orchids, for example, very shortly it's going to become almost impossible to submit an article to a journal without having an orchid, for example. I know UKRI and NERC already recommend these. I don't think there's any need, for example, for them to be made mandatory necessarily, but I think we should maybe also be informing people, why is this important, what is the benefit for you. And also, I think one of the things I would say is, and that maybe this comes from 30 years of being with BGS. Researchers are clever people. They will find a way around mandatory things that they need to do. I know Gary, you and I have had conversations in the past about researchers who've been very creative in trying to avoid things we've told them they have to do. So I do think it's about awareness, it's information, it's encouraging people, but ultimately it's about making sure we're not using the wrong metrics to reward career output. Thank you. Thank you. So a slightly different question we've had is how best to search for open data. And I imagine in a world where you can find data everywhere if you go hunting for it. That's a very good question. Would you like to start? I'm going to say, yeah, I think maybe, yeah, this is a question perhaps for you, Gary, rather than for me, but I think the important thing is that, as Gary's rightly pointed out, there are a range of resources available. For example, I'm currently involved with the Group on Earth Observations and the GIS platform provides a whole range of open access data that's available for reuse. But that's, you know, closer to home, you know, there are a number of repositories. There are also the NERC data centers which provide a number of open access resources. So what I would say is, if we're looking for open data, I would start with the domain repositories within your own discipline. We have experts in-house who can help you to identify what you're looking for, but also advise you on how to use it appropriately because that's one of the important things is that when you find data on the internet, you need to make sure that you are making appropriate use of it. So I would say the first port of call is a domain repository or an initiative such as Geo where we have people on, you know, on call who can assist you with using the data because making appropriate use is one important facet of open science. And if I jump in there as a head of a data center, one of NERC's data centers, the National Geoscience Data Center, yeah, I think trusted repositories is the way that you will get your hands on data that you can rely upon and step forward. And I think that's where the word domain and community comes in because that's where some of the best data is stored. Yes, you can find lots of data in many different places, but it's having the confidence around the metadata and the contextual metadata and understanding what the limitations are when you try to reuse that data in the future. And I think that's where domain repositories are really have their home and can give you the best advice and guidance. So Gary, I think it's also I think it's also worth saying Gary that there are, if you want to go up a level. There are lists that you can search for trusted domain repositories as well. Many of the publishers now have them because the reality is many publishers are requiring their mandating that the data is available for you to publish a journal article. But as well as the publishers, there are many other initiatives that now have lists. So for example, there is the W3C list of domain repositories that you can search. And all of these will actually lead you to good reliable sources of open data. Yeah. And again all of these sources quite often have their own certification to demonstrate their professionalism and their delivery of that information. So we have a question around is open data always the right way to go. And I think that's probably driven by the fact that all of us are quite happy to stand here and say that there are scenarios where open data is not the right way to go. Would you like to comment. Yeah, absolutely. I think within the community that's talking about open data, we all recognize that there is data that cannot be in the public domain for a whole range of reasons, whether that's, you know, personal privacy of the individual we all know about GDPR. But there's also civil security. There's also international security. So yes, of course there is that level where open, open science is not the way to go. I think one of the things that most practitioners who are advocating for open science recognize there is a balance to be struck between the benefits of open science, but also the need to sometimes recognize that it's not the way to go for a variety of reasons. There are also mechanisms in place that strike that balance. So for example, I didn't mention it today. But within the open access journal models, there's also the option for embargoes. So you can actually lodge your data with a data center or seek to publish it with a journal article, but it's actually embargoed and there's a whole range of periods that vary from months to years with regards to embargoes. So I think really what I'm saying is you have to balance the benefits against the, you know, what are the pitfalls, and there will always be things that cannot be open access for a range of reasons. Definitely. And your answer there really addresses another question that we've received which was how would we balance open science with trusted scientific research, where trusted scientific research is the is the guidance that we're getting from UK or I around what can and can't be shared. And I think you quite clearly highlight that that's, that's going to be a forever changing landscape and one that we have to make assessments on on a case by case basis. But we're not about going to either end of those spectrums but understanding the landscape and where we sit within it. Go on. Go on, Gary. No, I was going to say I think the only thing to really emphasize what you just said is it is a rapidly involving landscape you only have to look back five years and look at, you know, how things have changed in five years in terms of the the advent of, of, you know, data journals, for example, you know, they, they where you can actually publish data sets. Now, this is something very new that has come along, but it's been a game changer. So another question then what we've had pop in. So persistent object identifiers as part of your presentation. What would you recommend for historical data or document resources. Yeah, that that's a toughie that that's probably, I have to say that's probably going to be outside my, my spectrum of expertise. I suspect there are other people participating or attending this, this, this presentation today who are much better than I would like to, to answer that question. But I think the clear message is that even historical material can be given a unique persistent identifier which makes them more visible and available. It can be an analog form, they can still be referenced and identified. Because I know for many, many repositories they have a massive backlog of analog information that they would love to be more widely used because the reality is many, many archives have to justify their existence based on how much the material they hold gets used. And a lot of what gets used for some archives is the physical material. And so by actually being able to expose even the catalogs is a major step forward. But I think in terms of what is the best persistent identifier for, you know, legacy information. I would say that's probably outside my field of expertise to recognize anyone. Suppose it, it kind of depends on the sub domain of environmental science or the wider environmental domain itself that we're talking about. Maybe we should flag IGSN as a as an option within that. Yeah, well, absolutely. I mean, in terms of samples, obviously the international Geo sample number is increasingly gaining adoption. IGSN is actually in the process of transitioning to an organization that will be in partnership with data site, which is one of the biggest organizations that mince DOIs for a range of research assets. And in fact, IGSN will become part of that suite of DOIs that will be available from data sites in the future. But yes, IGSN I think is increasingly being adopted for physical material internationally. And interestingly enough is being mandated by a number of the largest journals now, including nature. But to go to a point that you raised within your presentation, a very good demonstration about how PIDs are being used to link these different research data assets together and provide that landscape where people can navigate across. Yeah, absolutely. I mean, I think this is the power of persistent unique identifies is that that linking of everything together, whether it's physical, whether it's digital but also being able to link it to the individual researcher where that's appropriate as well. Definitely, definitely. Do you feel there's a role for data trusts to take on and manage and control access to data that maybe can't be open in origin within a community. Oh, yeah. So, yeah, that's another that's another very prickly question. I think there, there is always a role for different types of repository or initiatives. And many of them fulfill specific purposes. And, and I think this is evidenced by the comment you made a moment ago Gary about the fact that, you know, this is a rapidly evolving landscape, but I think the role of data trust is still to be seen. And I think the question really, I don't know, I have to admit, I don't know a huge amount about them. But I, at the moment, I wonder where they fit in against the landscape of the of the trusted repositories themselves. Yeah, yeah, I think there's probably some more work that we need to do understanding how they would fit within the landscape of what we're talking about. So, how would we recognize excellence in science in the future. So that, that, yeah, that, that's an interesting question. So I think, I think this comes back to one of my original points in my presentation actually, it's about the fact that we need to move away from this idea that somebody's research excellence is about the papers they've published and where they published them. Really what it's about is, you know, what are those research outputs that have had the most impact. And I'll give you a perfect example. I did some work with somebody a couple of years ago now pre pandemic, who published a data set that has ended up being one of the key data sets within a previous IPCC report. And I don't have the reference to the blog that a blog was written about the fact that basically they got no credit for the fact that they were the ones who collected this particular data set that ended up in an IPCC report, because they had no way of actually highlighting the fact that they were the original author of this data set. And I think in answer to that question. It comes back to this idea of, we need to think about what are the metrics that we can use to measure the impact of somebody's research, and that is going to be about how much it's been reused. How many downloads has that individual data set had, for example? It is very much about looking at reuse and impact rather than how many papers have you published and where were they published. And I think that's really where we need to be focused on.