Data as first-class citizens in the Science of the 21st century
Masuzzo, Paola
The last science paradigm has marked the beginning of the e-Science, or Science 2.0: we are immersed in an enormous amount of data and are equipped with the computational resources and infrastructure needed to make sense of these data. However, the process of scholarly communication and especially the one of research evaluation need to still shift the focus from the traditional research outputs (aka, the paper) to data. In this talk, I will make the case that the 21st century academic production can no longer be PDF-centric, but needs to look at data as first-class citizens of science, recognizing that the publishing system, as well as the assessment criteria, need to move towards dataset publication, citation, evaluation.
 Allow me to start by saying that I'm extremely happy to be opening the digital research data of the Geneva workshop on innovation and scholarly communication. This entirely online edition of 2021. I would rather be somewhere with you all talking about research data, open science and all the things that I'm very passionate about. But hopefully this will be the last time we'll have to do this via the computer. So I'll be talking today to you about data, how data have changed the way that we do science and that we publish and hopefully that we evaluate the science as well in the last few years. Now, if there is a message that I would like you to take home or wherever you are to take with you at the end of this presentation, I would break down this message in three parts. I would definitely say that we cannot talk about research the way we did years and years ago. So we now conduct research in a much different way than 10 or 20 years ago. So the way we talk about it also needs to be different. The other thing is that given that this is the workshop on innovation and scholarly communication, we can only innovate once we feel free to experiment. And I believe that this is absolutely crucial for the challenges ahead of the entire scientific process. And then the last part of this message, which is also a little bit the pillar, the theme of this edition of the workshop, is the future of open science. The future of open science, I believe, is already happening. Could it be happening faster? I think so. But if I look back at the last five or six years, a lot of things have happened and the future is already present. Speaking of open science, of course, I cannot start talking about research data if I don't put them underneath the big umbrella of open science. I could be talking for hours about open science, people who have listened to me talk before know that that would be absolutely the case. A lot of things can be said. But the thing here that I want to highlight is that the open science movement really fights and encourages for the sharing of research output beyond the contents of what we call an article or a published paper. So I will say to this as, let's say, the starting point for the rest of this presentation. And what I would like to address with you during the next 25 or 30-minute stop is that the data needs to be started, treated like first-class citizens. And hopefully, in a few minutes, you will understand what I mean with that. I will also talk a little bit about why there are so many barriers to data sharing and what can we actually do to overcome this. But I will already have to tell you that I don't have a solution for everything. And last but not least, because I'm not a traditional researcher, of course, I'm an independent researcher affiliated with ICTORE, the Institute for Globally Distributed Open Research and Education. And I'm also a professional data scientist full-time in a profit organization in industry. I also would like to talk to you about the potential and the benefits of sharing data outside of the academic bubble, so really within the society. But let's start with the first point. Science has certainly evolved to the last centuries, perhaps even the last thousands of years. If it's true that a long time ago, we would be talking about science from an empirical point of view, driven by experiments. A hundred years ago, we finally were capable of drawing most of the laws that are now regulating almost all the theory that we know about science. In the last few decades, thanks to a lot of computational resources, we've also been able to look at science from a simulation point of view, using computational science. And in the last few years, we have definitely assisted into a completely new paradigm of doing science, which some people would say that it's driven by data. I prefer to talk about science that it's informed by data. I'm a data scientist. I collect and analyze. I have to interpret a lot of data every day. I don't like to think that this data will drive the business that I work for and with, but better than we inform the decisions that we're going to make. Of course, the fact that we collect all this data and that we are now capable of doing what it's called, science 2.0 or eScience also means that we have to face new challenges. And these new challenges are, first and foremost, that it's very difficult to actually produce data that is not only human, but also machine-readable, and then to use this data as a substrate for knowledge discovery. Because let's be honest, we don't produce data just for the sake of it. We produce data because we want to understand how the world works. Of course, reproducibility also comes with it and all the issues that we know about that. We'll go to that in a moment. And research assessment, given that we're now producing more data, analyzing more data, and we're also expanding somehow the portfolio of the digital skills that every researcher needs to have these days. Also, research assessment should be changed and shifting likewise in the same direction. Let's talk, let's think for a moment. We really need to spend too much time here about the research method. This would be, of course, slightly different depending on the discipline that you're studying or that you're researching in. But generally, we start from hypothesis, we design our study, then we have to run the study, which mostly means collecting some sort of data, which we then analyze. And finally, we write a report to tell the world and to tell all our peers and our colleagues at different universities and research institutions, whatever it is that we have found. This also means that research produces much more than just a PDF. And sometimes we tend to forget about this. So what I have sketched here is really, I like to think about the final paper where we write our findings as the paper that does the advertising for our research. And I don't mean this necessarily in a bad way. We need to have a mean to tell society and to tell our peers that, you know, we did an experiment, we did a study, and these are the conclusions and these are our doubts and the next questions that we have. So we need to tell the story, absolutely. But the science, the research behind the story is an entire package that it's not only made of this narrative, this text, but it's also made of all the data, the code, the version, every digital object that we are capable of producing. Of course, I say digital objects because to share physical object, it's something different. And so when we talk about the data in this context, I really refer to every single unit of digital information that we produce in our research cycle, as I have just highlighted. This also means that we have a certain reproducibility spectrum that goes really to very, very, very minimum to almost 100%, depending on how much we share what we work on and how well, of course. And just to give you a little bit of background here, but I'm pretty sure everybody is by now familiar with the concept of reproducibility is really the minimum standard for research validity. But as a minute means that if we take the same data sets and we rerun the same analysis, we should be able to reproduce the study, the workflow, and this workflow should give us the same results. And really, if you think about it, if you can't replicate an experiment, how are you going to trust its results? Right? So we have all these objects, they are all scattered, but somehow all these objects that belong together should be linked to each other and should also be linked to other objects so that they can be discovered on the web and so that they can produce this substrate on which we can enable the knowledge discovery and everything that we need to progress science and to progress discoveries. But how do we do that? It's rather complicated. We came up with this beautiful concept of the fair principles, which really provide guidance for data stewardship. Now, I'm not going to go into details, especially given that the rest of the talks you left during the session on digital research data are going to look at the fair principles from different angles and across different research disciplines. The only thing that I want to mention here is that their goal is really to assist the discovery and the reuse of all these objects through the web. We talk about the fundable, accessible, interoperable, and reusable. That doesn't necessarily mean open data. The fair principles come in degrees. This is something for me very important and I'm sure you will hear about this later during the day. And they are completely diagnostic of technical implementation. So they're out there. Somebody has come up with this beautiful idea and they are really a nice set of principles to make sure that we can achieve the discovery and the reuse of all these research objects through the web. But if we go back now for a moment to the research cycle or the research method, if you prefer that I quickly highlighted before, between the step three and four, when we run the study, we collect our data and we analyze the data. Of course, this is a little bit simple, the way I'm depicting it. If you were to zoom just inside that part, we would actually see that the data have a life cycle on their own. So we collect them, we process them, then we go through data analysis, then we need to make sure that we publish the data and we enable the access of this data. In the long term, data also needs to be preserved so that if you use this can trigger new research ideas, which turns can become more research data planning and design and the cycle comes on and so on and so forth. So you might already start to think that getting into this all entire big world of research data also means that we need to have to deal with very different specific aspects and a lot of skills are needed in terms of what does it mean to preserve a dataset, what does it mean to access it, and even the data analysis, how do I make sure that it's then reproducible, I need to be able to code, I need to be able to do versioning control. So it's really not that fair and that's something that I would like, not that fair, I mean not that simple, and that's something that I would like to keep in mind because I will go back to this in a moment. And this brings me to why I believe that data sharing is still not so common and the uptake is not as we would like it to be. So there are some barriers for sure, but as I said a little bit at the beginning of this presentation, I don't ever suggest all of them, but I would definitely like to stress and to point out what the possible solutions are already out there and so really to try to have a positive spin on the subject. Mostly when I talk about data sharing, especially within universities and research institutions, some of the things that always come back to me are that, well first it requires additional skills and it is correct. Sometimes people say that, wow you're already so very busy, we have to do so many things, we really don't have time, I mean it's time we need to take away from other things and we don't have the time, we don't have the time actually. It's not rewarding them or perhaps it's not rewarding enough. And the last but not least reason I believe it's really that we tend to hold ourselves higher standard than we tend to hold other people and I'm going to go to that in a moment. Let's start with this case. So I said before that if we want to make sure that the dataset is fair or that the dataset can be, it's not only free, open on the web but can actually be accessed so that it's accessible and that somebody can reuse it, we need to be able to check all sorts of items on a checklist. Starting from data management plan, then thinking about annotating the metadata, reporting all the tools that we have used. So we could really talk about a fair journey where we start from our data, we annotate our code, we also show and share whenever possible the tools that we used and in the end we are capable of saying look these are my results, if you take this nice data package you can reuse it as you wish, as you like, with this specific open license for example. In practice this might not be so simple to do, so easy and it's a bit what happens every time that we have a new innovation that is going to be diffused in the field. So with the innovation diffusion you always have that at the very beginning you need to kind of overcome a sort of technical obstacles or limitations because not everybody simply has the skills or knows how to do specific things and that's what I believe investing in infrastructures and in tools and in communities for good practice is crucial to help everybody through their own fair journey. In this sense I cannot avoid of course to bring to you this fantastic project, the Fair Cookbook that really aims at turning fair into practice. It's completely open source, developed really following collaborative methods, was created by researchers and by data managers professionals. Right now it's targeted at life scientists but I believe it's easily extendable to other disciplines as well and it's really like a cookbook with recipes that you would follow the same way you would follow recipes to make a specific dish, a specific meal. So it really puts things together to tell you how is it that you actually can be successful in your fair journey or as they say the fairification journey because as I said a little bit before fair really comes in degrees. Having a data set or your work fair there is no zero and a hundred percent, even 20 percent is good, even five percent is good. So this fair cookbook it's really full of recipes, example recipes on the four principles FA, I and R as you see on this slide and basically the idea is that it makes it too easy not to do. So every time somebody now will tell you I don't care about fair because it requires too much skills and I'm really not good at it, you can tell them look out to the community, look up online, there are a lot of resources and I am a hundred percent sure there would be some resource that even if it's not fitting your use case a hundred percent it goes very close by and so you can do half of the job ready for free, you can get it done ready for free. So you will see recipes for example about unique persistent identifiers which are crucial to make sure that the people can actually find your data set. How do they access it, what does mean that it transfers data with specific protocols, how to make sure the data is machine readable so interoperable between different systems, what does it mean to take care of metadata notation or controlled vocabularies or ontologies but not only theoretically but even practically with specific use cases and specific examples and of course last but not least usability. So we talk there about what does it mean to release a data set with an open science, with an open license, sorry, what are the uses that are permitted and so on and so forth. So really make it just too easy not to do it, there is no excuse. The other aspect that I believe we really need to start focusing more is the aspect of investment. I know it's not very elegant to talk about money and some people don't want to talk about money but I believe it's crucial and we are now at a stage where we need to choose how we spend our money and with our I mostly mean the public money. So I guess you have all read and if not please do read it, the report from the European Commission of a couple of years ago that investigated what is the cost, the estimation of the cost of not having fair research data. This cost has been estimated in 10 billion euros each year just in the academic sector with plus and super plus of 16 billion euro in the lost innovation opportunities. So it's a lot of money that we're wasting if we really do not have fair research data and so there is this idea, somebody that is a month that is pushing for investing research funds in ensuring the data reusable and I must say that I absolutely agree with it. It's still a shame that we care so much about transformative agreements and now to make a specific company in the publishing sector richer and richer and more profiting from public money and we still don't care enough about research data usability and fairness. So it's irresponsible to support research but not data stewardship and what does it mean? This means that most of the obstacles in my personal opinion right now are really of a cultural type. I believe that TU D'Aft is one of the few universities out there at least in the European scenario that it's really showing the way to go when it comes to research data management. I think for a moment about that cycle that I showed you about the data, you collect the data, you analyze them, you need to make sure that you publish them, you need to make sure that they are reusable, you need to make sure that the data stay there in the long term, so long-term preservation and then you need to make sure that this data can be reused by other people which in turn will generate new idea, you need to make sure that you tell people how you did share the data and how did you run specific analysis, I mean you need to do a lot of things and I really believe that expecting the researchers, every researcher this day to know everything there is to know about digital skills and about data preservation and data management is absolutely crazy. I would say it the way I think it's crazy. What I think needs to happen for a for a fair data world to become a reality is not only to make it easy, so to really establish these tools and infrastructure and good practices and communities that are built on these good practices like I showed a little bit before with the Fair Cook book, but first and foremost I think we need to invest in people. So every research institute, every university these days should have data stewards and data managers on campus which will across different faculties help the researchers address their data management needs. They will also need to have software developers because to some extent some faculty is more than others, there is a need of coding tasks, a need of moving into software development and skilled professionals also need to be able to assist the researchers with this. Of course open champions, so if you have people that are already engaging in open research practices, in open research practices around digital research data these champions are there and are super crucial. Their voice is very important because they can teach, they can support and they can inspire other researchers in embracing the same research practices. And of course the head of the research data services it all goes there, it goes back to people who believe in people and people who believe that specific budget and specific funding needs to be invested in making sure that these human resources are available and they're there to help the community grow. I want to focus for a moment on the open champions. Of course open champions you would think immediately that how do you become a champion of open data or fair data? Well first because you think it's good, maybe because the technical barrier for you is not so high to overcome, maybe because you feel the responsibility for your data to be open as much as possible but definitely to be fair. Of course it would also be nice if you felt that you were assessed because of these open research practices and that you were credited and that you were rewarded. So what still needs to happen is really a shift where we start value reproducibility but also transparency and open research practices as much as we have always valued the number of papers that we publish and unfortunately the input factor over journals where we publish those papers which is now becoming less important I would say in the common conversations but still of course present. So it's really time for this culture shift to change because it's only with the systematic system, systematic practices of research assessment and rewarding metrics that will incentivize researchers to shift towards a higher data reproducibility and sharing. The other thing of course it's the data publishing. This is something that where definitely a lot of work has been invested in the last few years. What's the idea here? The idea is that the credit needs to be given where the credit is due. We are too much used into thinking about the currency in science as a PDF but it's a bit what I say at the very beginning, right? We produce when we do research so much more than the final story, why should we receive credit just for the PDF? Only for the final part. There is an entire process, an entire list of struggles we had to go through to produce the paper and those should be recognized as well. So ideally what would happen and this is already happening somehow in some journals that allow specific publication of data, you identify yourself with an orchid of course with a digital identifier as an open researcher. You also have a persistent identifier associated with the data set that you have produced. You produce this data set and you deposit it with its own digital identifier and now from this moment on forever the two objects, you as a digital object in the sense of your digital identity on the web and your data sets are forever linked to each other. Then the data set is cited in a paper with a specific digital object identifier with a specific UI and this citation is tracked in an article publication and so on and so forth. So you actually know that this data set is already being used by other people who are perhaps building extra analysis, data reparposes and other things. And of course this opens up the possibility for data reuse because this data set then can generate an additional data set perhaps with some extra dimensions that are complementary to the first data collection and this can be analyzed in another paper, in another paper, in another paper and it has already acquired like four or five citations by now which means that the credit is given really what it's due and this is a mechanism of recognition to you as a researcher which in turn will of course also make sure that you will stick to this type of approach and you will stick to this type of data publishing and verification of your data. Sometimes I have heard this, I don't want to share my data because I'm afraid people will find mistakes and here with data of course you need to read also code. I don't want to open up the source code that I used for my analysis because I'm afraid people will think I'm stupid and people will think I made mistakes. Human mistakes, sincere, honest mistakes are okay. I'm getting a little bit, I feel a little bit stupid having to say this because sometimes I think about shouldn't this be natural, shouldn't this be obvious to everybody but unfortunately I don't think it is. It's okay to be human and it's okay to make honest mistakes. That's why the peer community is there, that's why other researchers are there to tell you hey I think this is a mistake, perhaps we should look at it and make it better. Actually the reason you should open up your data is also for other people to make sure that they can correct you because making mistakes is absolutely fine and possible. Which I also believe goes back to the fact that we should make it okay to be slower, to go a bit slower. I'm a big fan of this low science movement that has come and that has emerged in the last few years especially now after the pandemic or I would say in the pandemic still era where we know that what we do when we are within traditional academic path and careers it's way too much. Researchers are expected to excel and to be able to take care of everything that's on their plate, produce as much as they can while I actually believe that science needs to slow down a little bit. So maybe if we started recognizing and giving credit to researchers for all that even the research outputs that they produce also this space would change and the future of academia would look a bit brighter. And last but not least I want to close and I think I'm good on my time the last five minutes stop on why I advocate for open data, why I think it's so important and what's the value of it even outside of the academic bubble and I want to start with a message to all of you and my message here is that sometimes when I read about research discoveries or when I read about articles that talk about society and science or even when I see on televisions people citizens that are being interviewed about science and we have seen a lot of that especially in the last year because you know COVID-19 and then the vaccination and the fear of the vaccination and I could go on for hours unfortunately. Sometimes I believe that science seems to have lost its connection with society and to the needs of society but this connection of course is crucial to address the challenges that we need to address and to build better realities and this connection is made of dialogue and collaboration and when I look at science and society I believe the one of the first things that we really need to start investing more is the availability of data and digital products. So this synergy where science really goes back working with and for society it's a synergy that can happen with collaboration and transparency. So open access in terms of open access publishing of research papers of research articles is absolutely crucial because the citizen has all the rights to have access to the scientific literature which is being paid with their taxes and on top of that the availability of every digital product that it's produced on both sides. On one side within for example governmental institutions and on another side within research institutions. So the first study that I want to talk to you about it's a story that it's very very close to my heart and it narrates a campaign that was launched by Ondeta which is an Italian association that promotes the opening the publishing of government public data so of which I am an associate together with transparency international Italia and Action 8 also with Italian chapter and basically this campaign that you can find on datipenicommune.it it translates into English with the data common goods and it basically what it does it asks the Italian government to open up all the data around the pandemic the epidemiological crisis and to release this data in a fair according to the fair principles. Why is this so crucial? This is not because we want to have this data because God knows what are we going to do with that. This is so crucial because public trust in this type of data it's absolutely key to ensure that there is first a correct risk perception which in terms is followed by proper behavior from the citizens and these two things together are absolutely fundamental to make sure that we get out of this situation as fast as we can and actually to have had this data one year ago would have made a huge difference according to me and the sharing and the accessing of this data is absolutely key to build this public trust and the last but not least point of this campaign and all these conversations around public data an opening of this data that he has produced is that when people tell you I want my data go to this website you will find a list of all the PDF files and even some interactive dashboards those are not data and they absolutely certainly are not fair data so we also need to start educating educating and educating institutional roles about what data means and especially what is human and machine-readable data so if you're curious this is something that as I say is very close to my heart and I would like to have a look at if you want and the second story which really comes back to the fact that data are not PDF, PDF are not data it's a story of a civic hacking if you want or citizen science in the region where I was born and where I grew up in Sicily in the south of Italy which talks about how a group of activists opened up Sicily as implemented a system that automatically scrapes all the PDFs that are released on the official site of the region in Sicily to make sure that all the information contained in these PDFs is no longer tracked but it's made available in data sets that are in an open format in a machine-readable format that then can be used for example to make a map like this in this specific case we talk about the PDF where the region the institutional office of the region would list all the cities that were in red code COVID-19 specific lockdown measurements in place there was a delay in the publication of these documents but the most important thing is that these documents were not even accessible with people with specific disabilities so this is really not okay and to make this data actually available opens up an entire list of possibilities in terms of understanding what's going on and how to live better fundamentally and the last thing that I want to mention to you before I conclude is the story about the fair toolkit for the life science industry so I also would like to point this out because as I said I'm an independent researcher, a civic hacker but I am also a person that works within the data industry and there the most relevant benefit of having fair data is really the reusability beyond the denation and primary purpose when people tell me I don't care about fair because I cannot make my data open I really try to convince them of the opposite fair data is not about opening data it's okay if you cannot open your data not every data can be opened up the goal of fair is really to be capable of reuse your own data beyond what you had conceived in the first place so it's really a benefit for every organization out there I recommend you to have a look at all the use cases all the stories that are reported on the fair toolkit on the epistoy alliance because they're really beautiful and the other thing it's also that when you have linked metadata on top of data even if because of the life cycle the data has been deleted the linked metadata is still there and it's still a permanent scientific record which will stay forever and that has really really great value so with this I conclude what I wanted to talk about I think I did a good time we'll open data change the world we'll fair data change the world we'll make a better reality no I think that's still up to us that's up to us as a bunch of you know human beings hopefully good human beings can we do that through data through data that it's open and that it's fair and it's accessible and reusable I think the answer is yes I'm pretty sure we can and with this I thank you very much for your attention and I look forward to the session with our Q&A thank you
 Allow me to start by saying that I'm extremely happy to be opening the digital research data  of the Geneva workshop on innovation and scholarly communication. This entirely online edition  of 2021. I would rather be somewhere with you all talking about research data, open science  and all the things that I'm very passionate about. But hopefully this will be the last time we'll  have to do this via the computer. So I'll be talking today to you about data, how data have  changed the way that we do science and that we publish and hopefully that we evaluate the science  as well in the last few years. Now, if there is a message that I would like you to take  home or wherever you are to take with you at the end of this presentation,  I would break down this message in three parts. I would definitely say that we cannot talk about  research the way we did years and years ago. So we now conduct research in a much different way  than 10 or 20 years ago. So the way we talk about it also needs to be different. The other thing is  that given that this is the workshop on innovation and scholarly communication, we can only innovate  once we feel free to experiment. And I believe that this is absolutely crucial for the challenges  ahead of the entire scientific process. And then the last part of this message,  which is also a little bit the pillar, the theme of this edition of the workshop,  is the future of open science. The future of open science, I believe, is already happening.  Could it be happening faster? I think so. But if I look back at the last five or six years,  a lot of things have happened and the future is already present. Speaking of open science, of  course, I cannot start talking about research data if I don't put them underneath the big umbrella  of open science. I could be talking for hours about open science, people who have listened to  me talk before know that that would be absolutely the case. A lot of things can be said. But the  thing here that I want to highlight is that the open science movement really fights and encourages  for the sharing of research output beyond the contents of what we call an article or a published  paper. So I will say to this as, let's say, the starting point for the rest of this presentation.  And what I would like to address with you during the next 25 or 30-minute stop is that the data  needs to be started, treated like first-class citizens. And hopefully, in a few minutes,  you will understand what I mean with that. I will also talk a little bit about why there are so many  barriers to data sharing and what can we actually do to overcome this. But I will already have to  tell you that I don't have a solution for everything. And last but not least, because I'm not a  traditional researcher, of course, I'm an independent researcher affiliated with ICTORE, the  Institute for Globally Distributed Open Research and Education. And I'm also a professional  data scientist full-time in a profit organization in industry. I also would like to talk to you  about the potential and the benefits of sharing data outside of the academic bubble,  so really within the society. But let's start with the first point. Science has certainly evolved to  the last centuries, perhaps even the last thousands of years. If it's true that a long time ago,  we would be talking about science from an empirical point of view, driven by experiments.  A hundred years ago, we finally were capable of drawing most of the laws that are now regulating  almost all the theory that we know about science. In the last few decades, thanks to a lot of  computational resources, we've also been able to look at science from a simulation point of view,  using computational science. And in the last few years, we have definitely assisted into a  completely new paradigm of doing science, which some people would say that it's driven by data.  I prefer to talk about science that it's informed by data. I'm a data scientist. I collect and  analyze. I have to interpret a lot of data every day. I don't like to think that this data will drive  the business that I work for and with, but better than we inform the decisions that we're going to  make. Of course, the fact that we collect all this data and that we are now capable of doing  what it's called, science 2.0 or eScience also means that we have to face new challenges.  And these new challenges are, first and foremost, that it's very difficult to actually produce data  that is not only human, but also machine-readable, and then to use this data as a substrate for  knowledge discovery. Because let's be honest, we don't produce data just for the sake of it. We  produce data because we want to understand how the world works. Of course, reproducibility also  comes with it and all the issues that we know about that. We'll go to that in a moment. And  research assessment, given that we're now producing more data, analyzing more data, and we're also  expanding somehow the portfolio of the digital skills that every researcher needs to have these  days. Also, research assessment should be changed and shifting likewise in the same direction.  Let's talk, let's think for a moment. We really need to spend too much time here about the research  method. This would be, of course, slightly different depending on the discipline that you're  studying or that you're researching in. But generally, we start from hypothesis, we design our study,  then we have to run the study, which mostly means collecting some sort of data, which we then  analyze. And finally, we write a report to tell the world and to tell all our peers and our colleagues  at different universities and research institutions, whatever it is that we have found. This also means  that research produces much more than just a PDF. And sometimes we tend to forget about this.  So what I have sketched here is really, I like to think about the final paper where we write our  findings as the paper that does the advertising for our research. And I don't mean this  necessarily in a bad way. We need to have a mean to tell society and to tell our peers that, you  know, we did an experiment, we did a study, and these are the conclusions and these are our doubts  and the next questions that we have. So we need to tell the story, absolutely. But the science,  the research behind the story is an entire package that it's not only made of this narrative,  this text, but it's also made of all the data, the code, the version, every digital object that we  are capable of producing. Of course, I say digital objects because to share physical object, it's  something different. And so when we talk about the data in this context, I really refer to every  single unit of digital information that we produce in our research cycle, as I have just highlighted.  This also means that we have a certain reproducibility spectrum that goes really to very, very, very  minimum to almost 100%, depending on how much we share what we work on and how well, of course.  And just to give you a little bit of background here, but I'm pretty sure everybody is by now  familiar with the concept of reproducibility is really the minimum standard for research validity.  But as a minute means that if we take the same data sets and we rerun the same analysis, we  should be able to reproduce the study, the workflow, and this workflow should give us the same results.  And really, if you think about it, if you can't replicate an experiment, how are you going to  trust its results? Right? So we have all these objects, they are all scattered, but somehow all  these objects that belong together should be linked to each other and should also be linked to other  objects so that they can be discovered on the web and so that they can produce this substrate  on which we can enable the knowledge discovery and everything that we need to progress science and  to progress discoveries. But how do we do that? It's rather complicated. We came up with this  beautiful concept of the fair principles, which really provide guidance for data stewardship.  Now, I'm not going to go into details, especially given that the rest of the talks you left during  the session on digital research data are going to look at the fair principles from different  angles and across different research disciplines. The only thing that I want to mention here is that  their goal is really to assist the discovery and the reuse of all these objects through the web.  We talk about the fundable, accessible, interoperable, and reusable. That doesn't necessarily mean open data.  The fair principles come in degrees. This is something for me very important and I'm sure you  will hear about this later during the day. And they are completely diagnostic of technical  implementation. So they're out there. Somebody has come up with this beautiful idea and  they are really a nice set of principles to make sure that we can achieve the discovery and the  reuse of all these research objects through the web. But if we go back now for a moment to the  research cycle or the research method, if you prefer that I quickly highlighted before, between the  step three and four, when we run the study, we collect our data and we analyze the data. Of  course, this is a little bit simple, the way I'm depicting it. If you were to zoom just inside  that part, we would actually see that the data have a life cycle on their own. So we collect them,  we process them, then we go through data analysis, then we need to make sure that we publish the data  and we enable the access of this data. In the long term, data also needs to be preserved  so that if you use this can trigger new research ideas, which turns can become more research data  planning and design and the cycle comes on and so on and so forth. So you might already start to think  that getting into this all entire big world of research data also means that we need to have  to deal with very different specific aspects and a lot of skills are needed in terms of what  does it mean to preserve a dataset, what does it mean to access it, and even the data analysis,  how do I make sure that it's then reproducible, I need to be able to code, I need to be able to do  versioning control. So it's really not that fair and that's something that I would like, not that  fair, I mean not that simple, and that's something that I would like to keep in mind because I will  go back to this in a moment. And this brings me to why I believe that data sharing is still not so  common and the uptake is not as we would like it to be. So there are some barriers for sure,  but as I said a little bit at the beginning of this presentation, I don't ever suggest all of them,  but I would definitely like to stress and to point out what the possible solutions are already out  there and so really to try to have a positive spin on the subject. Mostly when I talk about data  sharing, especially within universities and research institutions, some of the things that  always come back to me are that, well first it requires additional skills and it is correct.  Sometimes people say that, wow you're already so very busy, we have to do so many things,  we really don't have time, I mean it's time we need to take away from other things and we don't  have the time, we don't have the time actually. It's not rewarding them or perhaps it's not  rewarding enough. And the last but not least reason I believe it's really that we tend to hold  ourselves higher standard than we tend to hold other people and I'm going to go to that in a  moment. Let's start with this case. So I said before that if we want to make sure that the  dataset is fair or that the dataset can be, it's not only free, open on the web but can actually  be accessed so that it's accessible and that somebody can reuse it, we need to be able to check  all sorts of items on a checklist. Starting from data management plan, then thinking about  annotating the metadata, reporting all the tools that we have used. So we could really talk about  a fair journey where we start from our data, we annotate our code, we also show and share  whenever possible the tools that we used and in the end we are capable of saying look these are  my results, if you take this nice data package you can reuse it as you wish, as you like,  with this specific open license for example. In practice this might not be so  simple to do, so easy and it's a bit what happens every time that we have a new innovation that  is going to be diffused in the field. So with the innovation diffusion you always have that  at the very beginning you need to kind of overcome a sort of technical obstacles or limitations  because not everybody simply has the skills or knows how to do specific things and that's what  I believe investing in infrastructures and in tools and in communities for good practice is crucial  to help everybody through their own fair journey. In this sense I cannot avoid of course to bring  to you this fantastic project, the Fair Cookbook that really aims at turning fair into practice.  It's completely open source, developed really following collaborative methods,  was created by researchers and by data managers professionals. Right now it's targeted at life  scientists but I believe it's easily extendable to other disciplines as well and it's really like  a cookbook with recipes that you would follow the same way you would follow recipes to make a specific  dish, a specific meal. So it really puts things together to tell you how is it that you actually  can be successful in your fair journey or as they say the fairification journey because as I  said a little bit before fair really comes in degrees. Having a data set or your work fair  there is no zero and a hundred percent, even 20 percent is good, even five percent is good.  So this fair cookbook it's really full of recipes, example recipes on the four  principles FA, I and R as you see on this slide and basically the idea is that it makes it too  easy not to do. So every time somebody now will tell you I don't care about fair because it requires  too much skills and I'm really not good at it, you can tell them look out to the community,  look up online, there are a lot of resources and I am a hundred percent sure there would be some  resource that even if it's not fitting your use case a hundred percent it goes very close by  and so you can do half of the job ready for free, you can get it done ready for free.  So you will see recipes for example about unique persistent identifiers which are crucial to make  sure that the people can actually find your data set. How do they access it, what does mean that  it transfers data with specific protocols, how to make sure the data is machine readable so  interoperable between different systems, what does it mean to take care of metadata notation  or controlled vocabularies or ontologies but not only theoretically but even practically with  specific use cases and specific examples and of course last but not least usability. So we talk  there about what does it mean to release a data set with an open science, with an open license,  sorry, what are the uses that are permitted and so on and so forth. So really make it just too easy  not to do it, there is no excuse. The other aspect that I believe we really need to start focusing  more is the aspect of investment. I know it's not very elegant to talk about money and some people  don't want to talk about money but I believe it's crucial and we are now at a stage where we need to  choose how we spend our money and with our I mostly mean the public money. So I guess you  have all read and if not please do read it, the report from the European Commission of a couple  of years ago that investigated what is the cost, the estimation of the cost of not having fair  research data. This cost has been estimated in 10 billion euros each year just in the academic sector  with plus and super plus of 16 billion euro in the lost innovation opportunities. So it's a lot of  money that we're wasting if we really do not have fair research data and so there is this idea, somebody  that is a month that is pushing for investing research funds in ensuring the data reusable  and I must say that I absolutely agree with it. It's still a shame that we care so much about  transformative agreements and now to make a specific company in the publishing sector richer  and richer and more profiting from public money and we still don't care enough about  research data usability and fairness. So it's irresponsible to support research but not data  stewardship and what does it mean? This means that most of the obstacles in my personal opinion  right now are really of a cultural type. I believe that TU D'Aft is one of the few universities out  there at least in the European scenario that it's really showing the way to go when it comes to  research data management. I think for a moment about that cycle that I showed you about the data,  you collect the data, you analyze them, you need to make sure that you publish them, you need to  make sure that they are reusable, you need to make sure that the data stay there in the long  term, so long-term preservation and then you need to make sure that this data can be reused by other  people which in turn will generate new idea, you need to make sure that you tell people how you  did share the data and how did you run specific analysis, I mean you need to do a lot of things  and I really believe that expecting the researchers, every researcher this day to know  everything there is to know about digital skills and about data preservation and data  management is absolutely crazy. I would say it the way I think it's crazy. What I think needs to happen  for a for a fair data world to become a reality is not only to make it easy, so to really establish  these tools and infrastructure and good practices and communities that are built on these good  practices like I showed a little bit before with the Fair Cook book, but first and foremost I think  we need to invest in people. So every research institute, every university these days should  have data stewards and data managers on campus which will across different faculties help the  researchers address their data management needs. They will also need to have software developers  because to some extent some faculty is more than others, there is a need of coding tasks,  a need of moving into software development and skilled professionals also need to be able to  assist the researchers with this. Of course open champions, so if you have people that are already  engaging in open research practices, in open research practices around digital research data  these champions are there and are super crucial. Their voice is very important because they can  teach, they can support and they can inspire other researchers in embracing the same research  practices. And of course the head of the research data services it all goes there, it goes back to  people who believe in people and people who believe that specific budget and specific funding needs  to be invested in making sure that these human resources are available and they're there to help  the community grow. I want to focus for a moment on the open champions. Of course open champions  you would think immediately that how do you become a champion of open data or fair data?  Well first because you think it's good, maybe because the technical barrier for you  is not so high to overcome, maybe because you feel the responsibility for your data to be open  as much as possible but definitely to be fair. Of course it would also be nice if you felt that  you were assessed because of these open research practices and that you were credited and that  you were rewarded. So what still needs to happen is really a shift where we start value reproducibility  but also transparency and open research practices as much as we have always valued the number of  papers that we publish and unfortunately the input factor over journals where we publish those papers  which is now becoming less important I would say in the common conversations but still  of course present. So it's really time for this culture shift to change because it's only with  the systematic system, systematic practices of research assessment and rewarding metrics  that will incentivize researchers to shift towards a higher data reproducibility and sharing.  The other thing of course it's the data publishing. This is something that where definitely a lot of  work has been invested in the last few years. What's the idea here? The idea is that the  credit needs to be given where the credit is due. We are too much used into thinking about  the currency in science as a PDF but it's a bit what I say at the very beginning, right?  We produce when we do research so much more than the final story, why should we receive credit  just for the PDF? Only for the final part. There is an entire process, an entire list of struggles  we had to go through to produce the paper and those should be recognized as well. So ideally  what would happen and this is already happening somehow in some journals that allow specific  publication of data, you identify yourself with an orchid of course with a digital identifier  as an open researcher. You also have a persistent identifier associated with the data set that you  have produced. You produce this data set and you deposit it with its own digital identifier and  now from this moment on forever the two objects, you as a digital object in the sense of your  digital identity on the web and your data sets are forever linked to each other. Then the data set  is cited in a paper with a specific digital object identifier with a specific UI and this citation  is tracked in an article publication and so on and so forth. So you actually know that this data set  is already being used by other people who are perhaps building extra analysis, data reparposes  and other things. And of course this opens up the possibility for data reuse because this  data set then can generate an additional data set perhaps with some extra dimensions that are  complementary to the first data collection and this can be analyzed in another paper,  in another paper, in another paper and it has already acquired like four or five citations by  now which means that the credit is given really what it's due and this is a mechanism of recognition  to you as a researcher which in turn will of course also make sure that you will stick to  this type of approach and you will stick to this type of data publishing and verification of your  data. Sometimes I have heard this, I don't want to share my data because I'm afraid people will  find mistakes and here with data of course you need to read also code. I don't want to open up  the source code that I used for my analysis because I'm afraid people will think I'm stupid and people  will think I made mistakes. Human mistakes, sincere, honest mistakes are okay. I'm getting a little bit,  I feel a little bit stupid having to say this because sometimes I think about shouldn't this  be natural, shouldn't this be obvious to everybody but unfortunately I don't think it is. It's okay  to be human and it's okay to make honest mistakes. That's why the peer community is there, that's why  other researchers are there to tell you hey I think this is a mistake, perhaps we should  look at it and make it better. Actually the reason you should open up your data is also for other  people to make sure that they can correct you because making mistakes is absolutely fine and  possible. Which I also believe goes back to the fact that we should make it okay to be slower,  to go a bit slower. I'm a big fan of this low science movement that has come and  that has emerged in the last few years especially now after the pandemic or I would say in the  pandemic still era where we know that what we do when we are within traditional academic  path and careers it's way too much. Researchers are expected to excel and to be able to take care  of everything that's on their plate, produce as much as they can while I actually believe  that science needs to slow down a little bit. So maybe if we started recognizing and giving  credit to researchers for all that even the research outputs that they produce also this  space would change and the future of academia would look a bit brighter. And last but not least  I want to close and I think I'm good on my time the last five minutes stop on why I advocate for  open data, why I think it's so important and what's the value of it even outside of the academic  bubble and I want to start with a message to all of you and my message here is that sometimes  when I read about research discoveries or when I read about articles that talk about society and  science or even when I see on televisions people citizens that are being interviewed about science  and we have seen a lot of that especially in the last year because you know COVID-19 and then  the vaccination and the fear of the vaccination and I could go on for hours unfortunately.  Sometimes I believe that science seems to have lost its connection with society and to the needs  of society but this connection of course is crucial to address the challenges that we need to address  and to build better realities and this connection is made of dialogue and collaboration and when I  look at science and society I believe the one of the first things that we really need to start  investing more is the availability of data and digital products. So this synergy where science  really goes back working with and for society it's a synergy that can happen  with collaboration and transparency. So open access in terms of open access publishing of  research papers of research articles is absolutely crucial because the citizen has all the rights  to have access to the scientific literature which is being paid with their taxes and on top of that  the availability of every digital product that it's produced on both sides. On one side within for  example governmental institutions and on another side within research institutions. So the first  study that I want to talk to you about it's a story that it's very very close to my heart  and it narrates a campaign that was launched by Ondeta which is an Italian association that promotes  the opening the publishing of government public data so of which I am an associate together with  transparency international Italia and Action 8 also with Italian chapter and basically this  campaign that you can find on datipenicommune.it it translates into English with the data common  goods and it basically what it does it asks the Italian government to open up all the data around  the pandemic the epidemiological crisis and to release this data in a fair  according to the fair principles. Why is this so crucial? This is not because we want to have this  data because God knows what are we going to do with that. This is so crucial because public trust  in this type of data it's absolutely key to ensure that there is first a correct risk perception  which in terms is followed by proper behavior from the citizens and these two things together  are absolutely fundamental to make sure that we get out of this situation as fast as we can  and actually to have had this data one year ago would have made a huge difference according to me  and the sharing and the accessing of this data is absolutely key to build this public trust  and the last but not least point of this campaign and all these conversations around public data  an opening of this data that he has produced is that when people tell you I want my data  go to this website you will find a list of all the PDF files and even some interactive dashboards  those are not data and they absolutely certainly are not fair data so we also need to start  educating educating and educating institutional roles about what data means and especially what  is human and machine-readable data so if you're curious this is something that as I say is very  close to my heart and I would like to have a look at if you want and the second story which  really comes back to the fact that data are not PDF, PDF are not data it's a story of a civic hacking  if you want or citizen science in the region where I was born and where I grew up in Sicily in the  south of Italy which talks about how a group of activists opened up Sicily as implemented a system  that automatically scrapes all the PDFs that are released on the official site of the region in  Sicily to make sure that all the information contained in these PDFs is no longer tracked  but it's made available in data sets that are in an open format in a machine-readable format  that then can be used for example to make a map like this in this specific case we talk about the  PDF where the region the institutional office of the region would list all the cities that were in  red code COVID-19 specific lockdown measurements in place there was a delay in the publication of  these documents but the most important thing is that these documents were not even accessible  with people with specific disabilities so this is really not okay and to make this data actually  available opens up an entire list of possibilities in terms of understanding what's going on  and how to live better fundamentally and the last thing that I want to mention to you before I  conclude is the story about the fair toolkit for the life science industry so I also would like to  point this out because as I said I'm an independent researcher, a civic hacker but I am  also a person that works within the data industry and there the most relevant benefit  of having fair data is really the reusability beyond the denation and primary purpose when  people tell me I don't care about fair because I cannot make my data open I really try to convince  them of the opposite fair data is not about opening data it's okay if you cannot open  your data not every data can be opened up the goal of fair is really to be capable of reuse  your own data beyond what you had conceived in the first place so it's really a benefit for  every organization out there I recommend you to have a look at all the use cases all the stories  that are reported on the fair toolkit on the epistoy alliance because they're really beautiful  and the other thing it's also that when you have linked metadata on top of data even if because of  the life cycle the data has been deleted the linked metadata is still there and it's still a  permanent scientific record which will stay forever and that has really really great value  so with this I conclude what I wanted to talk about I think I did a good time  we'll open data change the world we'll fair data change the world we'll make a better reality  no I think that's still up to us that's up to us as a bunch of you know human beings  hopefully good human beings can we do that through data through data that it's open and  that it's fair and it's accessible and reusable I think the answer is yes I'm pretty sure we can  and with this I thank you very much for your attention and I look forward to the session  with our Q&A thank you
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Allow me to start by saying that I'm extremely happy to be opening the digital research data of the Geneva workshop on innovation and scholarly communication. This entirely online edition of 2021. I would rather be somewhere with you all talking about research data, open science and all the things that I'm very passionate about. But hopefully this will be the last time we'll have to do this via the computer. So I'll be talking today to you about data, how data have changed the way that we do science and that we publish and hopefully that we evaluate the science as well in the last few years. Now, if there is a message that I would like you to take home or wherever you are to take with you at the end of this presentation, I would break down this message in three parts. I would definitely say that we cannot talk about research the way we did years and years ago. So we now conduct research in a much different way than 10 or 20 years ago. So the way we talk about it also needs to be different. The other thing is that given that this is the workshop on innovation and scholarly communication, we can only innovate once we feel free to experiment. And I believe that this is absolutely crucial for the challenges ahead of the entire scientific process. And then the last part of this message, which is also a little bit the pillar, the theme of this edition of the workshop, is the future of open science. The future of open science, I believe, is already happening. Could it be happening faster? I think so. But if I look back at the last five or six years, a lot of things have happened and the future is already present. Speaking of open science, of course, I cannot start talking about research data if I don't put them underneath the big umbrella of open science. I could be talking for hours about open science, people who have listened to me talk before know that that would be absolutely the case. A lot of things can be said. But the thing here that I want to highlight is that the open science movement really fights and encourages for the sharing of research output beyond the contents of what we call an article or a published paper. So I will say to this as, let's say, the starting point for the rest of this presentation. And what I would like to address with you during the next 25 or 30-minute stop is that the data needs to be started, treated like first-class citizens. And hopefully, in a few minutes, you will understand what I mean with that. I will also talk a little bit about why there are so many barriers to data sharing and what can we actually do to overcome this. But I will already have to tell you that I don't have a solution for everything. And last but not least, because I'm not a traditional researcher, of course, I'm an independent researcher affiliated with ICTORE, the Institute for Globally Distributed Open Research and Education. And I'm also a professional data scientist full-time in a profit organization in industry. I also would like to talk to you about the potential and the benefits of sharing data outside of the academic bubble, so really within the society. But let's start with the first point. Science has certainly evolved to the last centuries, perhaps even the last thousands of years. If it's true that a long time ago, we would be talking about science from an empirical point of view, driven by experiments. A hundred years ago, we finally were capable of drawing most of the laws that are now regulating almost all the theory that we know about science. In the last few decades, thanks to a lot of computational resources, we've also been able to look at science from a simulation point of view, using computational science. And in the last few years, we have definitely assisted into a completely new paradigm of doing science, which some people would say that it's driven by data. I prefer to talk about science that it's informed by data. I'm a data scientist. I collect and analyze. I have to interpret a lot of data every day. I don't like to think that this data will drive the business that I work for and with, but better than we inform the decisions that we're going to make. Of course, the fact that we collect all this data and that we are now capable of doing what it's called, science 2.0 or eScience also means that we have to face new challenges. And these new challenges are, first and foremost, that it's very difficult to actually produce data that is not only human, but also machine-readable, and then to use this data as a substrate for knowledge discovery. Because let's be honest, we don't produce data just for the sake of it. We produce data because we want to understand how the world works. Of course, reproducibility also comes with it and all the issues that we know about that. We'll go to that in a moment. And research assessment, given that we're now producing more data, analyzing more data, and we're also expanding somehow the portfolio of the digital skills that every researcher needs to have these days. Also, research assessment should be changed and shifting likewise in the same direction. Let's talk, let's think for a moment. We really need to spend too much time here about the research method. This would be, of course, slightly different depending on the discipline that you're studying or that you're researching in. But generally, we start from hypothesis, we design our study, then we have to run the study, which mostly means collecting some sort of data, which we then analyze. And finally, we write a report to tell the world and to tell all our peers and our colleagues at different universities and research institutions, whatever it is that we have found. This also means that research produces much more than just a PDF. And sometimes we tend to forget about this. So what I have sketched here is really, I like to think about the final paper where we write our findings as the paper that does the advertising for our research. And I don't mean this necessarily in a bad way. We need to have a mean to tell society and to tell our peers that, you know, we did an experiment, we did a study, and these are the conclusions and these are our doubts and the next questions that we have. So we need to tell the story, absolutely. But the science, the research behind the story is an entire package that it's not only made of this narrative, this text, but it's also made of all the data, the code, the version, every digital object that we are capable of producing. Of course, I say digital objects because to share physical object, it's something different. And so when we talk about the data in this context, I really refer to every single unit of digital information that we produce in our research cycle, as I have just highlighted. This also means that we have a certain reproducibility spectrum that goes really to very, very, very minimum to almost 100%, depending on how much we share what we work on and how well, of course. And just to give you a little bit of background here, but I'm pretty sure everybody is by now familiar with the concept of reproducibility is really the minimum standard for research validity. But as a minute means that if we take the same data sets and we rerun the same analysis, we should be able to reproduce the study, the workflow, and this workflow should give us the same results. And really, if you think about it, if you can't replicate an experiment, how are you going to trust its results? Right? So we have all these objects, they are all scattered, but somehow all these objects that belong together should be linked to each other and should also be linked to other objects so that they can be discovered on the web and so that they can produce this substrate on which we can enable the knowledge discovery and everything that we need to progress science and to progress discoveries. But how do we do that? It's rather complicated. We came up with this beautiful concept of the fair principles, which really provide guidance for data stewardship. Now, I'm not going to go into details, especially given that the rest of the talks you left during the session on digital research data are going to look at the fair principles from different angles and across different research disciplines. The only thing that I want to mention here is that their goal is really to assist the discovery and the reuse of all these objects through the web. We talk about the fundable, accessible, interoperable, and reusable. That doesn't necessarily mean open data. The fair principles come in degrees. This is something for me very important and I'm sure you will hear about this later during the day. And they are completely diagnostic of technical implementation. So they're out there. Somebody has come up with this beautiful idea and they are really a nice set of principles to make sure that we can achieve the discovery and the reuse of all these research objects through the web. But if we go back now for a moment to the research cycle or the research method, if you prefer that I quickly highlighted before, between the step three and four, when we run the study, we collect our data and we analyze the data. Of course, this is a little bit simple, the way I'm depicting it. If you were to zoom just inside that part, we would actually see that the data have a life cycle on their own. So we collect them, we process them, then we go through data analysis, then we need to make sure that we publish the data and we enable the access of this data. In the long term, data also needs to be preserved so that if you use this can trigger new research ideas, which turns can become more research data planning and design and the cycle comes on and so on and so forth. So you might already start to think that getting into this all entire big world of research data also means that we need to have to deal with very different specific aspects and a lot of skills are needed in terms of what does it mean to preserve a dataset, what does it mean to access it, and even the data analysis, how do I make sure that it's then reproducible, I need to be able to code, I need to be able to do versioning control. So it's really not that fair and that's something that I would like, not that fair, I mean not that simple, and that's something that I would like to keep in mind because I will go back to this in a moment. And this brings me to why I believe that data sharing is still not so common and the uptake is not as we would like it to be. So there are some barriers for sure, but as I said a little bit at the beginning of this presentation, I don't ever suggest all of them, but I would definitely like to stress and to point out what the possible solutions are already out there and so really to try to have a positive spin on the subject. Mostly when I talk about data sharing, especially within universities and research institutions, some of the things that always come back to me are that, well first it requires additional skills and it is correct. Sometimes people say that, wow you're already so very busy, we have to do so many things, we really don't have time, I mean it's time we need to take away from other things and we don't have the time, we don't have the time actually. It's not rewarding them or perhaps it's not rewarding enough. And the last but not least reason I believe it's really that we tend to hold ourselves higher standard than we tend to hold other people and I'm going to go to that in a moment. Let's start with this case. So I said before that if we want to make sure that the dataset is fair or that the dataset can be, it's not only free, open on the web but can actually be accessed so that it's accessible and that somebody can reuse it, we need to be able to check all sorts of items on a checklist. Starting from data management plan, then thinking about annotating the metadata, reporting all the tools that we have used. So we could really talk about a fair journey where we start from our data, we annotate our code, we also show and share whenever possible the tools that we used and in the end we are capable of saying look these are my results, if you take this nice data package you can reuse it as you wish, as you like, with this specific open license for example. In practice this might not be so simple to do, so easy and it's a bit what happens every time that we have a new innovation that is going to be diffused in the field. So with the innovation diffusion you always have that at the very beginning you need to kind of overcome a sort of technical obstacles or limitations because not everybody simply has the skills or knows how to do specific things and that's what I believe investing in infrastructures and in tools and in communities for good practice is crucial to help everybody through their own fair journey. In this sense I cannot avoid of course to bring to you this fantastic project, the Fair Cookbook that really aims at turning fair into practice. It's completely open source, developed really following collaborative methods, was created by researchers and by data managers professionals. Right now it's targeted at life scientists but I believe it's easily extendable to other disciplines as well and it's really like a cookbook with recipes that you would follow the same way you would follow recipes to make a specific dish, a specific meal. So it really puts things together to tell you how is it that you actually can be successful in your fair journey or as they say the fairification journey because as I said a little bit before fair really comes in degrees. Having a data set or your work fair there is no zero and a hundred percent, even 20 percent is good, even five percent is good. So this fair cookbook it's really full of recipes, example recipes on the four principles FA, I and R as you see on this slide and basically the idea is that it makes it too easy not to do. So every time somebody now will tell you I don't care about fair because it requires too much skills and I'm really not good at it, you can tell them look out to the community, look up online, there are a lot of resources and I am a hundred percent sure there would be some resource that even if it's not fitting your use case a hundred percent it goes very close by and so you can do half of the job ready for free, you can get it done ready for free. So you will see recipes for example about unique persistent identifiers which are crucial to make sure that the people can actually find your data set. How do they access it, what does mean that it transfers data with specific protocols, how to make sure the data is machine readable so interoperable between different systems, what does it mean to take care of metadata notation or controlled vocabularies or ontologies but not only theoretically but even practically with specific use cases and specific examples and of course last but not least usability. So we talk there about what does it mean to release a data set with an open science, with an open license, sorry, what are the uses that are permitted and so on and so forth. So really make it just too easy not to do it, there is no excuse. The other aspect that I believe we really need to start focusing more is the aspect of investment. I know it's not very elegant to talk about money and some people don't want to talk about money but I believe it's crucial and we are now at a stage where we need to choose how we spend our money and with our I mostly mean the public money. So I guess you have all read and if not please do read it, the report from the European Commission of a couple of years ago that investigated what is the cost, the estimation of the cost of not having fair research data. This cost has been estimated in 10 billion euros each year just in the academic sector with plus and super plus of 16 billion euro in the lost innovation opportunities. So it's a lot of money that we're wasting if we really do not have fair research data and so there is this idea, somebody that is a month that is pushing for investing research funds in ensuring the data reusable and I must say that I absolutely agree with it. It's still a shame that we care so much about transformative agreements and now to make a specific company in the publishing sector richer and richer and more profiting from public money and we still don't care enough about research data usability and fairness. So it's irresponsible to support research but not data stewardship and what does it mean? This means that most of the obstacles in my personal opinion right now are really of a cultural type. I believe that TU D'Aft is one of the few universities out there at least in the European scenario that it's really showing the way to go when it comes to research data management. I think for a moment about that cycle that I showed you about the data, you collect the data, you analyze them, you need to make sure that you publish them, you need to make sure that they are reusable, you need to make sure that the data stay there in the long term, so long-term preservation and then you need to make sure that this data can be reused by other people which in turn will generate new idea, you need to make sure that you tell people how you did share the data and how did you run specific analysis, I mean you need to do a lot of things and I really believe that expecting the researchers, every researcher this day to know everything there is to know about digital skills and about data preservation and data management is absolutely crazy. I would say it the way I think it's crazy. What I think needs to happen for a for a fair data world to become a reality is not only to make it easy, so to really establish these tools and infrastructure and good practices and communities that are built on these good practices like I showed a little bit before with the Fair Cook book, but first and foremost I think we need to invest in people. So every research institute, every university these days should have data stewards and data managers on campus which will across different faculties help the researchers address their data management needs. They will also need to have software developers because to some extent some faculty is more than others, there is a need of coding tasks, a need of moving into software development and skilled professionals also need to be able to assist the researchers with this. Of course open champions, so if you have people that are already engaging in open research practices, in open research practices around digital research data these champions are there and are super crucial. Their voice is very important because they can teach, they can support and they can inspire other researchers in embracing the same research practices. And of course the head of the research data services it all goes there, it goes back to people who believe in people and people who believe that specific budget and specific funding needs to be invested in making sure that these human resources are available and they're there to help the community grow. I want to focus for a moment on the open champions. Of course open champions you would think immediately that how do you become a champion of open data or fair data? Well first because you think it's good, maybe because the technical barrier for you is not so high to overcome, maybe because you feel the responsibility for your data to be open as much as possible but definitely to be fair. Of course it would also be nice if you felt that you were assessed because of these open research practices and that you were credited and that you were rewarded. So what still needs to happen is really a shift where we start value reproducibility but also transparency and open research practices as much as we have always valued the number of papers that we publish and unfortunately the input factor over journals where we publish those papers which is now becoming less important I would say in the common conversations but still of course present. So it's really time for this culture shift to change because it's only with the systematic system, systematic practices of research assessment and rewarding metrics that will incentivize researchers to shift towards a higher data reproducibility and sharing. The other thing of course it's the data publishing. This is something that where definitely a lot of work has been invested in the last few years. What's the idea here? The idea is that the credit needs to be given where the credit is due. We are too much used into thinking about the currency in science as a PDF but it's a bit what I say at the very beginning, right? We produce when we do research so much more than the final story, why should we receive credit just for the PDF? Only for the final part. There is an entire process, an entire list of struggles we had to go through to produce the paper and those should be recognized as well. So ideally what would happen and this is already happening somehow in some journals that allow specific publication of data, you identify yourself with an orchid of course with a digital identifier as an open researcher. You also have a persistent identifier associated with the data set that you have produced. You produce this data set and you deposit it with its own digital identifier and now from this moment on forever the two objects, you as a digital object in the sense of your digital identity on the web and your data sets are forever linked to each other. Then the data set is cited in a paper with a specific digital object identifier with a specific UI and this citation is tracked in an article publication and so on and so forth. So you actually know that this data set is already being used by other people who are perhaps building extra analysis, data reparposes and other things. And of course this opens up the possibility for data reuse because this data set then can generate an additional data set perhaps with some extra dimensions that are complementary to the first data collection and this can be analyzed in another paper, in another paper, in another paper and it has already acquired like four or five citations by now which means that the credit is given really what it's due and this is a mechanism of recognition to you as a researcher which in turn will of course also make sure that you will stick to this type of approach and you will stick to this type of data publishing and verification of your data. Sometimes I have heard this, I don't want to share my data because I'm afraid people will find mistakes and here with data of course you need to read also code. I don't want to open up the source code that I used for my analysis because I'm afraid people will think I'm stupid and people will think I made mistakes. Human mistakes, sincere, honest mistakes are okay. I'm getting a little bit, I feel a little bit stupid having to say this because sometimes I think about shouldn't this be natural, shouldn't this be obvious to everybody but unfortunately I don't think it is. It's okay to be human and it's okay to make honest mistakes. That's why the peer community is there, that's why other researchers are there to tell you hey I think this is a mistake, perhaps we should look at it and make it better. Actually the reason you should open up your data is also for other people to make sure that they can correct you because making mistakes is absolutely fine and possible. Which I also believe goes back to the fact that we should make it okay to be slower, to go a bit slower. I'm a big fan of this low science movement that has come and that has emerged in the last few years especially now after the pandemic or I would say in the pandemic still era where we know that what we do when we are within traditional academic path and careers it's way too much. Researchers are expected to excel and to be able to take care of everything that's on their plate, produce as much as they can while I actually believe that science needs to slow down a little bit. So maybe if we started recognizing and giving credit to researchers for all that even the research outputs that they produce also this space would change and the future of academia would look a bit brighter. And last but not least I want to close and I think I'm good on my time the last five minutes stop on why I advocate for open data, why I think it's so important and what's the value of it even outside of the academic bubble and I want to start with a message to all of you and my message here is that sometimes when I read about research discoveries or when I read about articles that talk about society and science or even when I see on televisions people citizens that are being interviewed about science and we have seen a lot of that especially in the last year because you know COVID-19 and then the vaccination and the fear of the vaccination and I could go on for hours unfortunately. Sometimes I believe that science seems to have lost its connection with society and to the needs of society but this connection of course is crucial to address the challenges that we need to address and to build better realities and this connection is made of dialogue and collaboration and when I look at science and society I believe the one of the first things that we really need to start investing more is the availability of data and digital products. So this synergy where science really goes back working with and for society it's a synergy that can happen with collaboration and transparency. So open access in terms of open access publishing of research papers of research articles is absolutely crucial because the citizen has all the rights to have access to the scientific literature which is being paid with their taxes and on top of that the availability of every digital product that it's produced on both sides. On one side within for example governmental institutions and on another side within research institutions. So the first study that I want to talk to you about it's a story that it's very very close to my heart and it narrates a campaign that was launched by Ondeta which is an Italian association that promotes the opening the publishing of government public data so of which I am an associate together with transparency international Italia and Action 8 also with Italian chapter and basically this campaign that you can find on datipenicommune.it it translates into English with the data common goods and it basically what it does it asks the Italian government to open up all the data around the pandemic the epidemiological crisis and to release this data in a fair according to the fair principles. Why is this so crucial? This is not because we want to have this data because God knows what are we going to do with that. This is so crucial because public trust in this type of data it's absolutely key to ensure that there is first a correct risk perception which in terms is followed by proper behavior from the citizens and these two things together are absolutely fundamental to make sure that we get out of this situation as fast as we can and actually to have had this data one year ago would have made a huge difference according to me and the sharing and the accessing of this data is absolutely key to build this public trust and the last but not least point of this campaign and all these conversations around public data an opening of this data that he has produced is that when people tell you I want my data go to this website you will find a list of all the PDF files and even some interactive dashboards those are not data and they absolutely certainly are not fair data so we also need to start educating educating and educating institutional roles about what data means and especially what is human and machine-readable data so if you're curious this is something that as I say is very close to my heart and I would like to have a look at if you want and the second story which really comes back to the fact that data are not PDF, PDF are not data it's a story of a civic hacking if you want or citizen science in the region where I was born and where I grew up in Sicily in the south of Italy which talks about how a group of activists opened up Sicily as implemented a system that automatically scrapes all the PDFs that are released on the official site of the region in Sicily to make sure that all the information contained in these PDFs is no longer tracked but it's made available in data sets that are in an open format in a machine-readable format that then can be used for example to make a map like this in this specific case we talk about the PDF where the region the institutional office of the region would list all the cities that were in red code COVID-19 specific lockdown measurements in place there was a delay in the publication of these documents but the most important thing is that these documents were not even accessible with people with specific disabilities so this is really not okay and to make this data actually available opens up an entire list of possibilities in terms of understanding what's going on and how to live better fundamentally and the last thing that I want to mention to you before I conclude is the story about the fair toolkit for the life science industry so I also would like to point this out because as I said I'm an independent researcher, a civic hacker but I am also a person that works within the data industry and there the most relevant benefit of having fair data is really the reusability beyond the denation and primary purpose when people tell me I don't care about fair because I cannot make my data open I really try to convince them of the opposite fair data is not about opening data it's okay if you cannot open your data not every data can be opened up the goal of fair is really to be capable of reuse your own data beyond what you had conceived in the first place so it's really a benefit for every organization out there I recommend you to have a look at all the use cases all the stories that are reported on the fair toolkit on the epistoy alliance because they're really beautiful and the other thing it's also that when you have linked metadata on top of data even if because of the life cycle the data has been deleted the linked metadata is still there and it's still a permanent scientific record which will stay forever and that has really really great value so with this I conclude what I wanted to talk about I think I did a good time we'll open data change the world we'll fair data change the world we'll make a better reality no I think that's still up to us that's up to us as a bunch of you know human beings hopefully good human beings can we do that through data through data that it's open and that it's fair and it's accessible and reusable I think the answer is yes I'm pretty sure we can and with this I thank you very much for your attention and I look forward to the session with our Q&A thank you
treated like first-class citizens. And hopefully, with that. I will also talk a little bit about point. Science has certainly evolved to the last computational resources, we've also been able to said a little bit at the beginning of this dataset is fair or that the dataset can be, it's disciplines as well and it's really like a reusable, you need to make sure that the data and infrastructure and good practices and rewarded. So what still needs to happen is really journals that allow specific publication of data, specific UI and this citation is tracked in an we do when we are within traditional academic look at science and society I believe the one of access publishing of research papers of research contained in these PDFs is no longer tracked but