 So the recording is now going on, please. And hopefully you can all see my screen. Yes. Great. All right, so hello, everyone. I am Elibaboula Boulou. I work for Athena Research and Innovation Centre in Greece and one of my roles there is unresponsible for a product that is dealing with research data management and specifically data management planning, which is called ARGOS. So this is one of the things that we will be talking about today. Before we will see also what are the steps involved in the research data management lifecycle. This is not me here that you see. This is an avatar that we have created just to feel better when we talk about research data management and plans because some people get very frightened about these terms. Okay, so let's see. This is another one of my presentation. As I said, we will be going through the different steps of research data management lifecycle. We will see what the DMP is, Data Management Plan, sort for DMP. What is ARGOS? The service that is provided by Opener for writing data management plans and also we will have a demo on the data management plan that we have created for Chist Era in ARGOS. So starting with the research data management life cycles, let's see who is involved in them. Who is involved in the creation of data in the preservation of data, in the analysis and curation and so on, all the different steps. And why we need them? Because for researchers, for example, we result with better data following the research data management practices and more qualitative data, more quality of data. We have best practices and standards that we can follow that will help us apply the third principles that also Emma was mentioning in the previous modules of the course. We can comply with the policies that are developed for research data management and we can gain credits from citations when data are cited from our peers in different repositories and in different venues and platforms. So the research funding organizations that are involved in data management, they can, this helps them to monitor their research and to avoid duplication of research that's been done. They can better control their funds and they increase research excellence from having better quality of the research of the data and the research overall that they fund. Plus it's easier for them to identify what are the areas and that can formulate innovative solutions and innovative products. For service providers, services data management means that they apply and they also develop standards and best practices and they find what are the gaps and what are the tools that can support and can close those gaps in the data management life cycle in support of researchers practices and they are more targeted to supporting the data intensive activities through their services. Research performed organizations, it's similarly to research funding organizations. This helps them to identify and increase their research excellence. It helps them to communicate a research better since it's findable, it's accessible and all these aspects. They can also monitor the research and they can see through that, they can understand what are the trends and where they can probably, you know, what are the areas that they need to focus on supporting better, probably, if it's identified that it's needed. And all these activities are targeted are central, let's say to researchers. Researchers are at the center of research data management activities. While we're talking about research data management, I think this was also evident in Emma's presentation, but just to recap again, it's three different things that we would like to achieve, it's repeatability, replicability and reproducibility. And I took this very nice table from, you can find it in this paper, Incentivizing Reproducibility, from ICM. And it really, it helps in a very good way and clear way to understand what are the differences and clarify amongst us why we're doing it. Cause at the end we want everything to be reproducible. But sometimes we confuse reproducibility with repeatability and reproducibility. So what this means is when something is repeatable, means that I can, I that I'm a researcher and working in a group can repeat this result in my practice with the same and get the same results. When something is replicable, it means that the different groups outside of my team can repeat the research and have the same evaluation, same results. And when something is reproducible, it means that a different group outside of my team can repeat the same results. My team can take them and use them and produce different, different results through let's say applying different methods more in a different, based on the different disciplines. So this means that we can have multidisciplinary research and not only sharing research among us, common research communities. And by doing that and to do that, we have open and fair principles. And you see something that is open can or cannot be fair and the other way around. So these are the differences and the commonalities where they intersect were the two different areas intersect but Emma has covered everything. So it's just a reminder. And where we can see those principles, we see them in the research sector. They are applied nowadays in the research sector meaning that all big funders like Chistera have developed and adopted policy for research data management and researchers can follow this policy and comply and also produce actually achieve open fair and also repeatability, replicability and reproducibility of research. Also we see that in academia, some of some big universities have already adopted policies for research data management. And this is a requirement for the PhD students to get in most of the cases to get their PhD. And also we see that this data management and open and fair are actually at the center of the European Open Science Cloud which is currently realized. And not only it drives its architecture and how the European Open Science Cloud looks from the backend, but also it drives researchers practices as it includes services that support all the aforementioned open fair and so on. And overall the objective is to have open and fair digital objects, so not only data, but other digital objects such as code, such as research results like workflows, like publications or everything that it's in a digital format and research outputs that are in digital format to be open and fair. So you might have seen around how a research data management life site looked like. It shows, oops, yes. It might look like this. So you might see also, I'm sorry, just something. If you want to ask something, please add your questions in the Q&A and we can answer them later on or you can interrupt me, I don't mind. It's better if we have a discussion better than only presentation. So at any point please interrupt or add your comments. Yes, so about the research data management life cycles, you might have seen this is a very common one with the bubbles of the circles and it shows how activities, how a workflow within our research shows the data management workflow in a research activity. So we start from data creation. We go, so we create and collect our data. We go to data processing. So we process our data. We'll talk about that in a few minutes. We analyze our data. We preserve our data. We give access to our data if and how we are doing it. It's the restrictions we'll see. And we can then reuse and select how we can reuse or how others can reuse our data. Similar is the following, research data management life cycle, but it splits some of the steps into different Yes, it splits some of the activities into different steps. So we have plan, we have create process, analyze, preserve, serve and reuse. This is again follows the same logic and has the same output at the end. But it's described with a different way. We see that we have data, sorry, data management plan, collection, description, analysis. We have the storage where we store our data, how long we have the data retention period, how we promote the data and how we search and reuse our data. And we move on to more complex graphs. This is that one. Again, this same logic and the same output produces the same output. We start by conceptualizing, having an idea, we then create or receive data, we then select which are the data that we want to continue with, ingesting them, preserving them, storing there somewhere, providing access, transforming and so on. But this also shows other concurrent practices, practices not only attached to researchers, but also to repository managers and to data storage, like the curation of the data or the preservation of the data. So they all mean the same. And they all have the same practices for researchers to follow. This is from the Open Sense and Research Initiative. And it's a different, again, life cycle which doesn't affect anything. And how different the graph or the interpretation of the different steps can be, they don't affect the quality of the data or the steps and the best practices that are attached to them. So we start from the hypothesis. In the hypothesis, we consider what are the financial requirements? So we start calculating the costs. We break down, like in our research, we have working groups, sorry, work packages. And in the work packages, we start understanding what are the activities that we're going to undertake and what we'll need and cost them. Produce a list of costs based on them. So that's in the hypothesis. Data collection, we clarify the user's rights and we assure that we give credit to citations. So we collect data and we have to see if there are any, and if there's a copyright attached to it, we should clean that. And if there are citations are provided, from the data owners up to site, the data that we have collected and so on. Similarly to how we are doing with publications, with the references. In the processing, we make use of open source software and open interfaces. So to avoid mostly increasing the cost by getting more services or software with which we process our data. We store data and results. We make use of service infrastructure. We attach a persistent identifier to our results. We attach the script metadata to our results, publish metadata with an open license. All this you've seen also in the previous course, but here you are viewing it from the perspective of a research data management lifecycle and not of the fair principles perspective. But all these are, if you were with us in the previous two courses, you can already, I think, identify which element goes where in the research data lifecycle where we're talking about open and fair. Which is very useful if you feel that you can already do that. Yes, you can tell the script metadata and publish the data in the open license. Then long-term preservation of the activities that ensure that you have preserved your data in a correct way following those practices is with use of services that safeguard the presentation and integrity of materials and with having standard metadata, describing your data with metadata standards, basically. And publication distribution. You publish your metadata with an open license. So we have to attach a sign a license to our data. We can have open evaluation on our data and publication and ensure that there are links between the publication and the data and the methods and all the different elements and make use of institutional repositories for that. For reuse, we again, clear citations and ensure the accumulation of credits by doing that by referencing other people's data. So reuse data. This is something that I would like you to answer. What do you perceive as a reuse data and what are the different things in the reusability that you are called to address when we're talking about reusability? I will give you a hint. It's two different things when we're talking about the reused data that you have to have in mind. And I don't know if anyone would like to say out loud what they perceive when hearing about reuse data. I don't know if there is anything wrong or if there is anything I'm trying to say. No, there is no, but nothing written in the charter they're in the question and answer, but if they want, okay, that now it's coming. So Stephen is saying reuse data is a good thing. Great. But what do you mean by a reuse data? In scientific sense. Maybe we can also allow you to speak if you want. Let us know if you want to open your mic, you can raise your hand and I can give you the permission to, okay. So Stephen, if you want, I am asking you to open your mic. Let me just, there you are. So I think we can hear you now. Okay, very good. Yeah, I was just thinking that if we have a study done with some set of data and then there's another guy coming and doing another study on that data. So we have comparability, right? So that sense I thought is good. And also as I think it was on the slides. So if I make the data available then others can, I know you had these three terms, but basically can do the study and confirm the results maybe in different environment. Exactly. So you're correct. These are the two things that we'll see now. And thank you very much for your answer for the interaction. We have existing data that we know that they are somewhere. They have a DOI possibly they are in a repository. It doesn't matter if it's our data that we have from a different project that we were involved or it's others data, but they exist and they are described somewhere and we are reusing them. So how I will be reusing those data is very important. And the steps like what I have to have in mind during this, when I'm dealing with existing data that I am reusing is to check copyright, make sure that I don't violate and copyright, perform copyright clearance. We have some tools we will see in Friday how you can do copyright clearance following some steps. We check the license. We check, you know, showing how we can probably we can remix should we reuse them, but share them in which way we can share them again, probably with the same license or not, all these different aspects that need to be taken into consideration for that part. For the other part, again, correct. We have new data that we are now producing or collecting that they are derived from our current activities. They are not described somewhere else and we there have to have in mind how others can reuse our data. So we have to make sure that we assign the licenses based on how we would like others to reuse our data to attach any conditions to let them know how they can access the data and so on. So these are the two different aspects that I want to highlight and it's very important to understand because we will see that later in the template during the demo. Yes, and that's why I wanted to highlight that. So we'll quickly go through each step and see in more details what is needed in steps. So for planning, during the planning process, we first have to cost how versus data management activities, all addresses data management activities. And I've added here a guide that was developed in OpenAir in Digital Creation Center. And we can have a look later when I'll share the slides with everyone. You can see that there are two different things that you have to consider when costing versus data management. The services that you might have to acquire or you might have to use in order, for example, to store your data or to transcribe probably the data depending on the domain that you are in. But also how you will preserve your data, maybe you will need to have an encryption mechanism that you don't have and you need to acquire. These are costs and these are significant costs. But also the other set of costs is people and work that is required to perform all these activities. Will you have a dedicated data manager, for example? You have to consider that from the beginning, from the planning and during costing of the risk data management. Will you have a data manager that will be dedicated for this project? Will you have people like, I don't know, will you have students working for for some different bits here like data ensuring data collection, ensuring the documentation, for example, because this also affects costs. There's also, for IP in particular, there's also this great tool, IP cost tool, which shows the costs during submission in different countries or what are the costs during submission of a patent and other procedural costs attached. Yes, so basically we're going through the whole, through each of the different activities, preparing data, collecting data, documenting data and so on. And we try to calculate accordingly. So then during the creation and collection of data, we have to ensure that we also have the right metadata in place for discovery for others, but also for interoperability. You see here that this is the minimum with doubling core set with minimum metadata required which this can secure how our metadata, how our data and metadata records are exchanged between platforms, compatible platforms. And this is a more, you know, extensively I would say with more data, reaching the data is from the open there guidelines. And here we see that this also enriches the information exchange on different entities and different aspects. We can have general all domain specific metadata standards, which we will see with your demo because there are different needs for information exchange between the research communities, other things required. And even from, even within the same research community, there are different needs based on the exact area that is being researched. Plus we have metadata for MPIDs for other things. During the process, we see that the process is the face, the operational phase during which raw data is being manipulated to result in meaningful information. And during this process, we clean and tidy our data. For example, we might be, we might be able to do a lot of research for example, we might be performing a piece of here. I don't know if you know what we're gonna find. It's a very good tool where we can, it identifies all the variables that have been maybe miswritten or they have mistakes. They have some, some missing information in some columns and so on. So it identifies all the different errors and the different ways that the same variable is expressed and it tries to combine them and then merge them in order to then we get the clean version of our data and use them in the best way possible. And there, yes, during this process, of course, we might perform an anonymization. Again, it depends on the domain that we are and the data that we have in the domain that we are working in. We might have to do an anonymization, perform an anonymization of our data. This is a tool to do that. We'll see that in Friday. Yes, for others not to be able to identify and cross-reference the specific entities and variables found in our data. So yeah, there's some of the process ingestion, aggregation, analysis, classification, metadata enrichment, this can be done during this step. Then during the analyzation of our data, this is the part where we start producing outputs and prepare for sharing our data. So we use different methods and tools and software for that. We might be using notebooks or end-to-end code scripts for statistics. We might be using RStudio, for example, Python, Matlab, again, different software or we might be using our own scripts to analyze data. For preservation, there is a, what we need to have in mind is, and be mindful about is the backups of our data. And this helps with performing, let's say in formal risk assessment ourselves. I don't know how many of you know the three to one backup rule, but it says that you need to have at least three copies of your data to at least be stored in two different types of storage. And one of these types of storage to be not connected with the internet, be an off-site storage. And I would like, okay, I will make the question afterwards. And here, yes, we see that we need to be aware of the frequency when we backup our data. What is the storage that we are using? What are the methods? Like this is a method, but also if we're using an external provider, they might have backup plans. So we have to check that as well. And for preservation, again, backup is different from preservation. This is for backup and for preservation, we use services as we saw with Emma, like services like trustworthy repositories found in Re3Data where we store our data for the long-term. And here is a question. What is the difference between backups and preservation? What would you say? You can again use the chat or question and answer or raise your hand and I will let you speak. Okay, maybe it would have been easier if I used the mentee, but I like to listen to people. Yeah, but the comments are coming. So Ann is saying, is that relates to the format in which we store the data? Backup is exact copy. True. Backup is the exact copy. If it relates to the format, it relates to the format, yes, but I don't have that in mind for now. So let's exclude the format for now. What are the differences apart from the format? So I will keep that because it's true. Backup is the exact copy of the current version of our data and preservation has a copy. Is it something else? Preserve means guarantee that the data will stay over time. Exactly. Because, yes, thank you. Exactly. Because they are relocated to a different store, different storage facility where they are preserved in the long-term while back up is ensures the current version now and it doesn't necessarily mean that it is linked with the long-term facilities. Thank you very much. That was nice. Okay. Next. During preservation again, we see that the format as very nicely who was, it was unmentioned. We need to have in mind the format, but also the PIDs, the persistent identifiers, which we allow others to find our data with and access our data in the long-term, access mainly. And there are different kinds of PIDs. There are PIDs for digital objects like DOIs, which is the, you know, it's used in the world community, in the world open science community. It's the Handel registry. Some of you might have come across that. It's ARC and it's other that are used in some domains, but DOI and Handel are the more popular, let's say, and they're most used. For researchers, we have PIDs for researchers. So orchids, I don't know how many of you are aware of orchids and have already an orchid, but this helps to have all your, be distinguished from other researchers that might have the same name and surname as you. And also with, and this distinguished, this aspect that distinguishes you from them is held in the PID in the persistent identifier that you get. For your publications in your data, your profile, and there's also for funders, there's a funder registry which also assigns PIDs to funders. Plus there are other activities like research activities where people, research projects where people can also assign PIDs to them. So there are different standards and protocols in place for PIDs. Was there a question? Or kit is a not good solution that did not keep track of your publications. That's an argument. Let's have a debate, but at the end for that. Let's keep it. Emma will make sure that I don't forget about that. And for PIDs, yes, moving on from preservation, having preserved our data and having backed up and preserved our data, we then are called to, to take the road of open, as mentioned, or closed or mediated access. So we have to let other researchers know how we are sharing our data. And in the sharing of data, we mean that researchers, other researchers need to know about the naming conventions that we have used. So they understand exactly how they can reuse our data. And this means what are, you know, different letters, characters, abbreviations that we have used in the files, in the folders, but also, but also in our code, how we, how we might have the type, how we have the types of variables, prefix, and examples like providing examples for that. The means of sharing, how we are sharing them is important. Do we, do we share them via Google Drive, for example, which is very debatable. And which is, yes, but it can be done by a commercial cloud, but it's not a best practice. Do we share it via cloud infrastructure for research, like B2SER? So we use a specific, specific service for that. Do we serve it via FTP servers? Do we serve via USB drives? What, what is exactly the means that we're using? And how we create the links after sharing them without the research outputs and during module two, I think it was, we saw how these links can be created. Oops, sorry. And then during access of the data, we have to, again, know, let people know how they can access our data if there are any embargoes and embargoes that apply if there are restrictions like restrictions so they cannot have access. If there are access controls in place, like what are the restrictions, for example, do we, can we immediately provide access or do we have to pay attention to other policies? And see what are the conditions that might restrict parts of the access. And here you can also see a very, very nice table from the UK data service where you see going from open to control data, open safeguard control data, what are the different levels and conditions, what are the different levels of access and conditions that differentiate them. So for open data, for example, we have the requirement is that we have a suitable, that is suitable, data are suitable for fully anonymized data or data with agreement to publish personal details. They are accessible without user registration and they can be accessed because they have an open license in place. And on the contrary, control data, they are to detail confidential or sensitive to download so they can be downloaded. They are accessible to authenticated users only, using secure remote access or secure on-site form and it requires user accreditation and registration for training and approval by data access committee. So we understand different levels and the in-between barriers to accessing data. For data reuse, we have the license tool. Licensing is a very good tool for that as it provides the conditions under which other people can reuse our data. This is the standardized and machine readable, sorry, licensing schema of creative commons. Most of you are already aware of that and we touched upon that in the previous module. Licenses helps us understand how we can how we can attribute, acknowledge the work of others and if it's required to do so when we are using their data and when others are reusing their data if they should attribute, acknowledge our work, how to serve them, if it's non-commercial, non-derivative, so I'm not going to go into much detail because we saw that. And also citations are part of the step of data reuse because if there are open citations in particular, in particular we get more credits when we are reusing data and it specifies, we need to specify the data citation. Is it something? Okay. So moving on to DMPs, as we we revisited some of the concepts that we saw in module one and two, but on a different perspective, on the perspective of versus data management life cycle. So we'll see how this all boils down to DMPs, data management plans. And what is a DMP? I'm not sure how many of you have heard, have heard probably all of you, but I don't know how many of you have all actually used a tool or actually created from scratch a data management plan. I would like to know later. So data management plan is a document. It's a text document essentially. It's a deliverable in the context of a project and it's a living document meaning that we have to provide the DMP, the data management plan, this document in the beginning of the research and then as research follows and evolves, we might have more data, we might have changes and modifications applied to the data. These we go and fix in the data management plan, we update it. And that's why it's called living document because it's open to updates and it's open to new versions of our data. And we can describe the evolution of our data on the document. Yes, we describe all the different steps that are involved in this data management including costs and including the different people that have been involved in performing data management activities. What is not a DMP is it's not a research assessment method. It is not. You will not be, your research won't be assessed by your DMP. What data does it cover? I mean, we pretty much, again, this is a revision, scientific data. So a data management plan covers scientific data and metadata, but only those that we have used and we have used and are useful to others to verify and validate the conclusions that we have written in paper. So they are linked to our conclusions in this paper, but also we have, I say it here, we also include other raw or structured data and metadata and we provide guidelines for how they can be reproduced from others because we might, we might didn't have a time or the orientation of the project was not relevant for those data, but other disciplines might find them useful. So we include also other types of metadata that are not directly linked to publications. And what data it covers, those that do not infringe copyright, of course, because we cannot use them and they, that they are a non-personal and or sensitive in content and meaning not those that we can already anonymize or pseudononymize and get away with, but those that we cannot. So for these two in particular, we set access restrictions and we describe them in the data management plan and restrictions in access are described in advance in the data management plan. It's good if we have that at the very primary and early stage of our data management plan. So the statement for that. When is it delivered? It's delivered at the proposal stage. It's encouraged, but it's not necessary that you do that. It's a bonus if you do when you are submitting a proposal to have just one page of how I'm planning to manage my data, what kind of data I'm planning to create, collect. I foresee that I will create, collect standards that I have in mind using and to who this will be available and where I will preserve them. Very basic information just to showcase that I am aware of all the different processes involved and I will follow them and I will follow the best practices and produce quality data. But it's mandatory during the project stage and Donald Ahmed based on the policy that you have at which stage will choose era affiliated institutions where the receiving funding will be asked to give you the DMP. So for the next calls or starting from the call that we will open in a few days, the DMP would have to be submitted within three months of the project start. Why did we say three months? That's because it coincides with the deadline for providing the consortium agreement as well. So we have decided to somehow align all the deadlines to make it simple for the project. I think in the future the tendency is to have the DMP submitted before the beginning of the project since we believe that it's part of the project planning. So in fact, one could even in principle ask for a DMP even with the project submission. Because I think it's part of the thought about how the data of the project is going to be managed at least as a planning phase. But for the moment, for the next call it will be within three months. For the call 2019 that has been evaluated and for which project will start soon, we simply ask the DMP to be submitted within the first year without strict restrictions. Okay. Good to know. Thank you. Yes. So during the project stage three months or first year as Ahmad mentioned you can submit the first version and then keep updating and make revisions until the end of the project where you have to submit the final version the more coherent and more complete. So who needs DMPs? I think it's linked to what we've already mentioned in the beginning, organization needs DMPs to track R&D outputs and then incurring bindings to them to identify the consumed and the implications and to facilitate research and development via data discovery and sharing. So basically to facilitate the research development. Funders to track direct and direct products and impact funding and know where exactly to allocate resources which areas need to be allocated more resources or less and to identify or refine strategies with respect to research and data production sharing and so on in order to again further and accelerate science to produce within their framework. For researchers they need the DMPs to facilitate and even enforce data referencing and also to again help and contribute not help, contribute to reproducibility and reusability of data which accelerate science and of course open science needs DMPs to promote fairness of data and interdisciplinary research. Who is involved in DMPs? Everyone is involved in DMPs from funders that define the rules from organizations and policy offices that define policies from projects managers that apply the policies and align to that align their project policies to the organizational or funding policies the data managers that manage the data management plan and the researchers who manage their data and they describe their data sets and are attributed by data sets. So overall the value is that what we see is that it increases data management plans increase quality of research and therefore outputs these as organizations and integrity as researchers to ensure that research it ensures that research outputs including data in particular findable by everyone available to people consumable and exploitable by others and to avoid duplications of same research concepts and ensure that research evolves to understand strengths and weaknesses example what discipline has more results if data are described based on the requirements and so on this helps us understand we understand how research data management activities are perceived from the community and we can provide we can act timely act on them and provide solutions to ensure research integrity and excellence of researchers Is there a question? Yes it's about the sharing of the presentation but yes we provide we will provide a link to you where you will find all the information there is also a raised hand that I see now Manuel who also is writing that duplication of research designs so maybe Manuel do you want to Yeah maybe we can have it I can open your mic so you can share with us your thought We can hear now Hello the question is about the previous slide about duplication of research to avoid duplications of same research concept I think is where most people is doing more or less repeating experiments in this kind of things that lead to new data so this kind of this line opens the way to copyright to introduce some copyright in the design of the research experiments so I think this is kind of maybe controversial issue so if I cannot duplicate research designs life is quite different from now so this is my concern in this slide what do you think I will mute now I will mute my microphone yes to avoid duplications of same research concepts maybe I didn't write it correctly we don't mean that you are not allowed or we try to limit let's say your possibilities to make perform the same research method and follow the same paths again it's not that it's more on the aspect that we see that many times we see that very similar projects receive funding let's say for example for the same to have the same result the problem is that precisely in some areas of science mostly in medicine and bioscience this duplication some people doing or many people doing the same thing is very important because it's the way to ratify to confirm or falsify the assertions and in this situation of pandemic that we are living I think that we must be very very conscious of this value of multiple assessment of the scientific tool so I think this to do the same thing can be quite controversial thank you it's different duplicating and it's different as I understand what you are saying it's reproducing and repeating so it goes more into replicability rather than duplicating which has different implications so I don't know if Emma wants to I was thinking that these has to deal with authorship also and also of how to say do not duplicate the funding for the same research does it make sense to Manuel so one thing is replicating extending reproducing as you said in some specific context and one other is asking for money to do something that has already been done and of course this varies this is a fair argument because it varies to different disciplines and to different okay what is this we have created that's me responding to yes I was responding to a user that was asking for where they can find material there is a thin line between duplication and replicability and repeatability which has positive impact and this is what we want I guess this is more also from the funder point of view avoid refunding the same research I don't know if Manuel you want to add anything no, not just to raise my concern one of the things is copywriting the research design so if somebody has the power to say that this design is mine and no other people can do the same research design this is like a meta copyright of data well it's probably not the place now to discuss that commenting on my concern I think that the funder the European Commission they have to have this thing very clear thank you very much for your answer thank you for adding your view to the conversation no, it's important it's true that it's a very thin line moving on to Argos we'll see how we can create a DMP in ARC we'll see what Argos stands for and how it works and then we'll see in practice how it works Argos is the data management planning tool of OpenAir it's based on open source software the open DMP software which was co-developed with UDAT and OpenAir and it's again open source it's configurable and extensible so people can take them and install it in their organisation and configure different services on top of that integrate different services and also it's extensible if you have an interest in data management planning in particular you can push your features that you might have in our GitLab page and we can update and create new versions of the software based on your feedback and your community contributions and based on that software we took it and we integrated all the services that OpenAir has the underlying services and we did Argos which is accessible if you go to argos.openair.eu and this is what a DMP life cycle looks like in Argos it starts with creating our DMP so we create our DMP with Argos it goes to a draft status so our DMP is in draft status we can delete it we can further modify it and add more things on top once we are ready we have to choose if we want to validate it and when we are ready to validate it because we have to validate it first step of validation means that the system checks that all the input that we have added in the system is all the mandated fields that we have added in the system are correct so they are true there is input for these mandated fields and similarly for the false it ignores like those that are not mandatory fields it doesn't matter if they have been complete or not and once this validation has taken place the DMP is again in its draft mode but it can then further move to finalization and to a more stable let's say status which is the finalized status and by doing that you cannot go back and delete it you have a stable version that we are working on and once you finalize your DMP then you have two different options either to keep it and publish it in our data in our data collection or to issue a DOI by publishing it in the nodal you have integrated in the nodal so it's easy after you finish writing your DMP to click on the button and upload the deposit version of your DMP in the nodal and by doing that you get a DOI automatically and then your DMP gets also in the environment of the nodal and it can be further shared with other people it can be it has a versioning it uses the versioning mechanism of the nodal so if you update it let's say that you are publishing a DMP in the first three months of the project of course it's not the final version of the DMP so things change, you modify you update specific aspects and then you publish it again the republication of your DMP will be a new version of the first DMP so we support a versioning of your DMPs and that they exist as living documents in our platform as well yes this is what it is and the key features that it has is which makes it different from other tools is that it helps differentiate DMPs from data sets so DMPs can have more than one description of data sets meaning that you will see that Argus has two different editors, a DMP editor and a data set editor the DMP editor holds all the metadata and all the data, all the information about a project all the basic metadata that has to do with the scope of why the DMP is created who is the author of the DMP what is the project that the DMP is created for the grant ID that is created for again the links between the grant ID and so on these are the basic metadata and it also has the data set metadata and all of these information that has to do with open and fair principles that have been applied on the data like what kind of metadata standards have been used in which repository I can find the data set and so on so the DMP has all this information but the flexibility that is provided in Argus is with a data set editor which you can use at any time and add new data sets in existing DMPs so this helps with reusing your descriptions of your data sets within the Argus environment because you might be reusing the data for a different project for a different project and it's easier for you to copy the description and paste it on the new DMP that you're creating for that project so it helps with the usability of the descriptions as well and the purpose of the descriptions as well plus I will not go through that again plus it's another thing that is important is that you can describe as many data sets as you want from choosing the data set editor at any time and describe them and describe not have all the information at the same place so make it easier for you and for the other researchers to understand exactly what type of data is described here and what is the exact metadata standard that I will need and it has been used to describe this data and I will need to understand it and what is the data repository that this specific data is in so I can go and download it and use it in my own research rather than having all of this information together like all of the different data types together and it makes it difficult to repurpose and reproduce which is the correct mapping of information between data types and metadata repositories and so on a DMP can contain more than one templates but this I don't think you will need for Chistera projects but maybe you do meaning that when you create when you first create a data management plan you can select up to what infinite actually infinite templates to use per DMP and this is very handy because you might be working for a very big project like I don't know like an international funding project that receives funding from many resources and some of those funders require you to write the DMP like the NIH the the NIH let's say in the US the European Commission here and these are big funders they want you to create two different DMPs let's say you can from the beginning select those two templates and continue working with them and you can easily select Opener and EOS resources we use API as I'm sure that you're familiar with what the rest API is based on you know that this is Chistera is an ICT oriented consortium so we use APIs to facilitate completion of DMPs and it's easier for you to select something and also it's easier for us to to help you make the links with other outputs later and we use Opener and EOS API for that you will see in a minute key features other key features is that it supports collaborative writing so you can manage workload with your colleagues you can add as many people as you want to work with you and you can divide you know tasks now we have a few like let's say that I created a DMP and I want dataset one to be described by john data set 2 to be described by Maria and so on so you can you can manage workload like this class supports argos supports exports in json format and we integrate a world common standard for that, the RDA common standard for DMPs, which really helps in the sense that it allows you to use RDA compatible platforms. Sorry. Sorry. It allows to use other RDA, even on the occasion that you're using different platform, which is RDA compatible, you can, in a seamless way, and without losing any vital information, you can download your DMP and upload it in another RDA compatible platform, and don't miss any vital information, continue work, as you would do. For example, when you're working in a text editor, like let's say I work deliverable, I have a deliverable that I'm working on my OneDrive, and then I download it and I upload it on, I don't know, like a Google Drive or something, Microsoft, I don't know. So in, I don't, you see that if I do that, I don't, I don't miss any vital information. So this is the concept that is applied for DMP tools, and we have also, that we have also followed here. And it also provides, I said that we integrate it's an auto for publishing DMPs, so we provide DOIs, we mint DOIs from Zanodo, so your outputs can get cited and can also be findable and accessible at any time. Yes, we also provide versioning, so do I see something clicking here? Elie, yes, there is, Manuel wants to ask a question. Manuel, you can just, yes, you can open it. Yeah, the thing is, well, we have experience with publishing and getting plagiarism detection rates and these kind of things using authenticate or other things. So the question is, if DMP becomes a published document with DOI, then maybe, well, I don't know, if Argos will integrate this plagiarism detection, or at some point somebody will be applying this plagiarism detection tools. The problem is that to avoid plagiarism, you must be changing things. So you must be writing the same thing in very different ways. So this will be, I feel, that will be a kind of source of confusion in the future. For me, it can be very, because for the same experiment or the same data management process, you will be obliged to do very, very different writings in order to avoid plagiarism. So I think these kind of very basic tools of research must be clearly assumed as a standard and then out of this plagiarism detection craziness. So my question is, if Argos will be integrating these kind of tools or not, because I think it's quite feasible. The thing is that DMPs do not fully care about the text that you have written, like a publication cares in that aspect. But it focuses more on whether you have addressed specific aspects of the research data management life cycle by when writing your data management plan. And this cannot be plagiarism, cannot undergo plagiarism because it differs. Sometimes, yes, it's the same, like some people, it's true that we, most majority of the people that are having reposters, for example, they use data site standard. This is not, if we trained Argos to see this as plagiarism, it will have negative effects. This is not what we want here. We just want to see the processes that have been followed and how open and fair has been applied during the project. Okay, thank you all for your answer. But for the publication, yes, it's different. Yeah, it's also when you publish your DMP, if you are giving an open access to the document, you can also, you have to apply a license. And so if someone will, how to say, use parts of your DMP, can cite it through the TOI. But it's more of a technical document. So as Ali was saying, if you are listing the same standards, this does not fall into the plagiarism, rather coping parts of the text of the document would, but yes, it's like every other piece of, every other papers. Okay, thank you. I like this interaction because it really helps to avoid, you know, this better understand the concepts. Okay, so let's see how Argos is enhanced and why, what's the value behind it. So here you can see, I don't remember if you talked about that Emma, but here you can see the research graph, the opener research graph, which is a scientific knowledge graph with including all open access, all available open access information from mostly from Europe, but also from different other regions of the world who collaborate with opener. And we see there and we can identify all the, we can explore all the wealth of information contained in that graph and we can create entities and these are the entities currently available in the graph. You have the research outputs that are in open access, organizations linked and links entities and links linked to organizations linked to communities, to projects, funding, funding, meaning grant ideas, funders, but also what are the sources that help with enhancing those products, what types of products are they, publication, data software and different other categorizations. And here in this graph and this wealth of information, we have worked with research, oops, with research graph to also create a DMP entity, so it's easier for us to exploit DMPs and understand how they evolve in time and find all, find again, as I mentioned in previous slides, what are the areas that we have to act on so that we better support research practice and researchers, individual researchers and researchers communities as well. So by creating these entities, we also try to create links with the projects and the data again for the same reason to understand and receive data management trends and practices. And then we also integrate other services that the opener has won't go into much detail, so that like explore, for example, we work with them so that we make DMPs, expose DMPs and make them searchable. When monitor, we see how many DMPs are included in the graph, we see the usage of the DMPs by others and develop, we use APIs to, as I said, make, facilitate the writing process for researchers, we use provide where we try to make links with repository managers and inform them, send them notifications when someone mentions in their DMP, yes, when someone from creating an August DMP mentions that I'm planning to deposit my data in this repository, the notifications are sent to monitor repository managers so that they are aware and they can plan accordingly. Yes, we use a nodal, this helps us publish our DMPs in an open fair manner. And we also, we are very lucky to be collaborating with people that work in opener, like these are national open access desks, nods, they are called nods, in more than 35, 36, I don't know, I've lost count in many countries in Europe and their role is to support open science and research data management in their countries in many ways, technical information and educational and so on. And by having them, and actually through their contribution, we were able to translate the Argos in many languages and some you won't be able to see because it's still in a draft in a moment, but we are now in, yeah, we have translated Argos in 10 different languages and this hopefully, because we realize that it's easier for some researchers to see and read and understand, comprehend better, they comprehend better things that are expressed in their language. And also we integrate the open science primers and all the different resources. So open areas operates for more than 10 years now. And you can imagine how much resource, yes, how many resources they have, the network has produced. So we integrate this resource as well to make the process easier for researchers. And having said that, I think it's the time to have the demo. Let's see, I won't use the production now because we don't have, we are currently testing the just error template in Argos. So we have that only in our beta environment, the test environment, but you will be able in, by the end of the year, I think, yes, that's the plan by the end of the year. So after Christmas, log in to Argos.com and you will be able to see and test the Argos, the just error template in Argos. So what I want to do now is go to the developer, which is our developer platform and show you how this looks in practice and create a DNP with you. Here is the first page, the home page. You can see, you can learn a few things. Ali, we still your presentation. Okay, because yeah, because you're sharing the other screen probably. Okay, but now I don't see, oh, user. Okay, this is that one. You see now that? Yes, now we see it. Oh, yeah, sorry. Okay, good, sorry. All right. So yes, this is the home page. You can get informed about resources, the roadmap, you can contact us from here. Then you can have a look at what are the features and what is there for different stakeholders, different users, types of users that Argos targets. And if you want to use Argos in your own organization, this is the code branding section for you. You can choose there are two ways to log in, start your DNP or login. Let's say that I want to log in. And as you can see, there are many ways to log in via academia, by academia, or commercial providers, social media providers, actually, sorry. For which, if you see in our statement here, terms of service, you can understand how we are using those data. Basically, the only thing that we are doing for these providers is, sorry, get the email and nothing else, email and name. Okay, so I was, oh, I logged in again. Okay, logged out again. So here I'm logged in, I'm not logged in. And now I choose which way I want to log in. And let's say I want to use I will use Google because I know that it will be easier. But you can use any other option. Okay. And you cannot, I see also, sorry, I'm reading the chat as well. You cannot use your email directly. No, we do not have sign up process, but we have signed in only. And you use the email that you have used for this provider that you will select. This is not a good privacy practice. Yes, but we do not provide, there is a, there is a, let's see, there are specific conditions of how developers can use those providers. And the only thing that we are getting is name and email. Okay. Let's say, so this is how it looks like. So as you can see here, you can see the personal usage. I have created lots of DMPs, 38. In my DMPs, I have 33 data sets that I'm describing. And the grants for the DMPs are 30 grants. So this is my, yes, so this is my dashboard. I would like, so if you are a new user, you see zeros here because you will be a new user and you will have a tool guide option. So to guide you to how to use the tool. But as I said, I've already used it. So you're viewing what someone that has created at least one DMP views. And this is the dashboard. I'm in the dashboard. I can see in my dashboard, I can see the data sets that I have created. I have described, sorry, the DMPs that are storing those data sets and the data set descriptions and all my activity can be viewed in my dashboard. I can select only to view my DMPs from my DMPs. I can view only my data sets from my data sets. And I can also view what is publicly out there. So what other people have done for me to get inspiration and, yes, consult before I, or during I'm writing my data management plan. So these are the public DMPs and these are the public data set descriptions. So let's say that I want to now add my, create my data management plan. I click on start new DMP from here. And I have the option to either import the file, which is according to, it's a JSON file that is following the RDA standard, or I can start a wizard from scratch. Since I don't have something ready as JSON, and I'm starting this from scratch, I will click start wizard. Okay. And these are the basic metadata that I mentioned in my presentation that it wants me to add. Title of DMP would be the description. So you provide a title that is not just a data management plan. It has few, few information that can, you know, but can separate your data management plan from others. It's easier for finding your data management plan afterwards. Description. Let's say I am creating project X creates this DMP or other that I don't know what's there, the purpose behind the creation of DMP. Language. What's the language that my DMP is using? So it will be English. What is the visibility? So here I get to choose how I want it to be shared in Argos. If it's going to be public, it will be under public DMPs here if I click public. And if I keep restricted, which is the default, when you start your DMP, it means that you and all the people that you will share your DMP with have access for now. This can be changed at any time. So when we're ready, we can publicize it. Researchers who are the people that are contributors in this, people that the data sets are associated with and people that are co-authors of this DMP. So let's say that it's me and it's also like if I don't find it, I can search by Orchid ID, but if someone doesn't have an Orchid, I can insert manually. Let's say, and these are the people that are working for the DMP, the organizations, let's say, who are the partners. I can select, we take this information, this is the API opener that we're using, and I can select all the different partners. Let's say that I want this too. And here I see that I am the main contact. So whoever creates the whole DMP first is the manager, the DMP manager, meaning that he will be the person to be contacted afterwards in revisions and after the project ends. So next, after I have added this basically the data, I get to describe the funding organizations that I'm creating the DMP for. I can search Chistera. I can have Chistera. I have added Chistera manually because it's not in opener yet, but this will change in two or three months. So you will be able to find it in the list. The grants that are the grants of Chistera that I want to describe and create the DMP for. So this is an example, and I guess that's something that it's not for all projects, it's only for projects that receive multiple grants. So let's say that my project doesn't receive multiple grants, so I'm moving on to the next. Then I'm still in the DMP editor, so I get to provide a license of how I would like others to reuse my DMP. Let's say that I don't have any issues, so I want to have creative comments for it. So this is how others and selected employee to describe my data. There is a whole collection of templates that we have here, and I want Chistera. This is Chistera project. And let's start. So now you see that it's the same environment, but with different colors. This means that I'm in the data set, so I'm now describing the data sets itself. I will provide the data set name, so it's good if the title of the data set correlates with the exact title of the data set that I will upload later somewhere so that we don't confuse. It's easier for us to find it as researchers to find our own data set and understand where the description is. Let's say that I want to describe data set one. Now that's the name of the data set that I want to describe. I'm providing a brief description of why I'm creating the data set, and I can add tags for this data set, what tags you would like to add. And again, I can select the template that I want and start my process. So Chistera template, let me, sorry, I have a laptop which is tiny, so I have to zoom out. Okay, I think it's better for you. So the Chistera template has, let's see, for now has seven sections, data description and collection, documentation and data quality, storage and backup during the research process, legal and ethical requirements, codes of contact, data sharing and long-term preservation, data management responsibilities and resources and reusable data. Do I see something here? Yes, if you are using, sorry, I'm also looking at my Chistera templates are not present at the moment. Is it normal? Will it be updated? Yes, because now I'm using the test environment. Once we finalize everything and we have a stable version of this Chistera template, everything will be up on artist.opener.eu and this will happen by end of the year. So in this, after your vacation, your Christmas vacation, you will be able to find it yourself and play around. Okay, so in the data description, these are the things that, let's see, okay. So in the data description and collection, these are the two options that you, the two questions. So first is explain which methodologies or software will be used if new data are collected or produced. We use the EOSC API here. So let's say that I want to use this service, for example, to collect my data and methodologies is, I don't know, this creation of methodologies. And I have to explain how data provenance will be documented, like data provenance will be provided by the next tool, I don't know if it's tied to a tool or what how you're going to do that. But data, for example, what kind of formats and volumes you will describe is sample or specimen data, observational, experimental simulation, derived, reference or canonical or other. Let's say that for that particular dataset, it's a 3D models that I am describing. And give details on the data format. Again, I can choose from those, from those options. And I choose models, 3D statistical, since this is the type I'm dealing with. Then I can justify the use of certain formats, like why I have, I have used this particular format. I don't know how many of you or if any of you actually is working on that area with 3D models. But as in many cases, there are closed and open formats. So let's say that some closed formats are STL or OBJ, and I try to avoid them and prefer open formats. So GLB, let's say, we'd be preferred, because it's open, for example, and provides give details. Similarly, I can write that I have described, that I am describing data that are in data, that are in a closed format. But I can let everyone know which data these are. Give details on the, sorry, the give details on the volumes of the data. So this dataset, how this dataset translates to bytes, let's say that it's, I don't know, let's say that it's very big terabyte. And that's that I have successfully answered all questions from the first, from the first section. Moving on, the first section was about data description and collection. Moving on to documentation and data piloting, I have to indicate how metadata, if I have used any metadata, what metadata these are, if I use any standards. And one of the questions is to indicate which metadata will be provided to help others identify and discover the data, who will be structural, administrative, descriptive. I will add descriptive here, just to describe, I will use my data to describe my dataset. So indicative, indicate which metadata standard. So which is the standard that I will use to describe the data with. Let's say that I want a common information model, I will use all the fields that are included in this model. And if I cannot find it, of course, I can always add things manually. Then I have to indicate how the data will be organized during the project, meaning if there are any conventions, version controls for those tracks, so that I communicate to people that will be potentially reusing my data, how they can read them, how they can understand them. Like let's say, for example, that I have a type of, sorry, variables, and I have the prefix. Then I have the, this depends, of course, on the conventions, the type of conventions that you're using, it's code, if it's convention, if we're talking about files and how you have, how you have, how you're dealing with files, for files, for example, we can say that it will have the structure will be a month, sorry, year, a underscore, something like this, so that people understand how to search your datasets and how to read them. Consider what other documentation is needed to enable reuse. This may include information on the methodology used to collect the data, analytical and procedural information, definitions of variables, units of measures, and so on. So if something else is needed for people to, in this documentation, you should also add it here. Consider how this information will be captured and where it will be recorded. I have to, this is, for example, in the database with links to each item, a readme text file, file headers, code books, or lab notebooks, so I can find, for example, oh no, notebooks, I can find, if there are any, let's say that I want to use pan notebook and how this information will be captured and where it will be recorded, it will be recorded in the pan notebook and how the information, this information, so I can add details on my answer, right? What data quality control measures will be used? Explain how the consistency and quality of data collection will be controlled and documented. I get to choose, and I can choose from one of these pre-defined answers, or add my own here and also specify my selection. Let's say that I want to, I will use peer review, so I will have someone peer review my data, and I will have a sample measurements, so this will validate the quality of my data. So I can specify how this is going to be, and now I've finished the second, which is about documentation and data quality. So moving on to storage and backup during the research process, how will data and metadata be stored and backed up during the research? Here I have to describe where the data will be stored and backed up during research activities, how often the backup will be performed. I can select from a list of, from a list of services, let's say I want to use storage. Let's say that my institution uses the AGI and the online storage, and this is where I also store my data, and so backup is tied with this storage facility as well. Frequency I have to check with the service, what are the policies of the service, so let's say that it's per month, and if I cannot find it or if I want to specify things, I can also add more information here. How will data protection, like here for example, we could say what is the strategy that we are using? The team uses the 321 backup rule and so on, as we saw during the presentation. How will data security and protection of sensitive data be taken care of during the research? So we're talking about sensitive data and data security and protection, explain how the data will be recovered in the event of an incident, compiled by all backups stored internally. I explain who will have access to the data during the research and how access to data is controlled, especially collaborative partnerships. Let's say that access is ensured during the project is open, let's say all partners become open once project ends. Access is controlled through I don't know X mechanism encryption, passwords. Describe the main risks and how these will be managed. Again, what are the risks if they are complementary risks and explain which institutional data protection policies are in place here. I have to check the institutional data protection policies and add the URL. So I have added the URL here and I'm finished. I've completed the fourth section. Fifth section has to do with legal and ethical requirements and codes of contact. If I'm dealing with personal data, for example, is personal data processed? How will compliance with legislation on personal data end on security be ensured? Am I processing data? Yes or no? No data. Explain whether there is a managed access procedure in place for authorized use of personal data, so explain the process. Then we have how will other legal issues such as intellectual property rights and ownership be managed? What legislation is applicable? It's about data ownership and applicability. Who will be the owner of the data? As we saw in different modules, the owner is the affiliated organization that has developed the database. So let's say that it's I think that Athena Research Center has done and I can select also if there's a researcher who owns the data, which is very... It might not be the case. I can select a researcher and I can specify my answer if I want. I cannot more. Explain what access conditions will apply to the data. Will it be open, mediated, embargoed, closed? Let's say that it's mediated for this dataset of 3D models and I can explain why and how this is mediated. Will the data be openly accessible or will there be access conditions? I can again select if it's going to be openly accessible or if there are access conditions that apply. In case of intellectual property rights, again, if there are any intellectual property rights affected, yes or no. Third party data descriptions again indicate whether there are any restrictions on the reuse of third party data, no restrictions. Next is about ethical issues. What ethical issues and cause of contact are there and how will they be taken into account? Let's say that I have commercial interests surrounding patents and why and how they will be taken into account and how you will act. That's the fifth section completed about ethical issues. Moving on to the sixth, the next section. How and when will data be shared? Are there responsibilities? Are there possible restrictions, data sharing or embargo reasons? Explain how the data will be discoverable and shared. Let's say that they will be deposed for repository. Outline depend for data preservation. Data will be preserved for as long, for example, for as long as the repository offers as options, things like that. From our side, the team will retain, for example, data for 10 years. Explain when the data will be made available and let's say that they will be made available next year, in one year from now and explain why it will be one year after. I will skip it because I see this is a bug and I will move on to that one. Will exclusive views of the data be claimed? Yes or no? Indicate whether data sharing will be postponed or restricted? Again, data sharing will be postponed or restricted and why will they be restricted? Because indicate who will be able to use the data. Researchers, research communities, for example, or all the different people. Is it necessary to restrict access to certain communities or to apply data sharing agreement? Yes or no? If you have to restrict it and if you have to apply and sign an agreement with people, how will data for preservation be selected and where data will be preserved for long term? Indicate what data must be retained or destroyed for contractual or regulatory purposes and here I can find the exact data that I'm talking about but probably after before the project ends, once I have a dataset and if we use Zenodo, so whatever is currently in Zenodo, sorry, what is currently in Opener? What is currently Opener in Opener Explorer? If you search datasets here, can you still see the new tab? Can you still see my new tab here? I see the research outcomes. Is that what you want to show? Open access include publication? Okay, now I see. Yes, yes, yes, yes, okay. The research outcomes, yes. So what is indexed here? If I click I want only research data, then I can search for models and get a dataset. These are all datasets, get a dataset. But I can do it from Margos. I don't have to go to Opener. This is what I'm trying to make the links now to do. So whatever you see here, it's here as well. So 3D model, let's say, I don't know, let's take this dataset that this can be destroyed for contractual reasons because someone won't want to reuse it. Indicate how it will be decided, what data to keep, data to be kept here. Describe the data that we preserved long term. What are those data? We're talking about 3D models here, so this will be the same. Explain the foreseeable research uses and users for the data. How the data can be, how you foresee that the data are used. Indicate where the data will be deposited. So you can search from a repository that's from the 17,000 plus repositories that are in Opener, like let's say I want to establish repository proposed, yes or no. These questions should have been on top, but okay. Indicate how the data will be shared, will there be any repository or another mechanism or request handed directly? Indicate whether potential users need specific tools to access and reuse data. And here I can find access tools, let's say that I will need, I don't know, something to access. If I don't find it, I cannot add a tool. Or if there are no tools needed to access and reuse my data, then I can add no tools needed to access the data. Okay. How will the application of the unique and persistent identifier to its dataset be ensured? Explain how the data might be reused in other contexts. To share information, to make informed decisions, to develop a product. And indicate whether a persistent identifier for the data will be pursued. Yes, I will use a persistent identifier and I will use DOI because the repository that I selected uses DOI. Okay, let me close this so you don't get confused. I'm moving on to the seventh section which is who is responsible for data management, outline the role and responsibilities for data management and stewardship. I can select from, find people with their DOIs and also write that work on data analysis that this person has worked on data analysis. I can add more people here. If I don't find people, I can add Emma, for example. So I can add the people and their role in data management here. Is it a collaborative project? Yes, no. If it is, I have to explain the coordination of data management possibilities across partners. Let's say that partner X is dealing with data processing. Partner is developing scripts to analyze the data and so on. So things like that indicate who is responsible for implementing the DMPN for ensuring it is reviewed and if necessary revised. This should be the same name as it was back in the contact. So as with the person that is creating the DMP from scratch. So in this case, it's me. What resources are dedicated to data management and ensuring that data will be fair? Explain how the necessary sources are costed. So what are the, how I'm dealing with costs? Am I using a national infrastructure to compensate costs? Am I using institutional infrastructure? Do I have a grant that I can use for the infrastructure to get more services or build even developed infrastructure from scratch? Do I collaborate with other projects? So I can select multiple and indicate whether additional resources will be needed to prefer data for the positor to meet any charges for data repositories. Yes and no again. And at the end, what is the total cost? Let's say it's $2,200. But I will need for data management. And this I see should have been its third in the row. But for some reason it appeared here, but we can fix it because it's nothing stable at the moment. So we can fix it and we will have it correct in Argos. How will it, it's about reusable data. How will existing data be reused? And how I'm using other people, other people's data, the resources data to follow up research on a specific topic and to develop new products. Let's say I can provide the URL of the DMP or reuse data where the DMP is described. Where can reuse data be found? I can search for repositories again that are in the app that are in opener. And let's say that I want, I will deposit them there. And if I can't find it, I can add the repository. And I can select which are the data sets, exactly the data sets that I'm reusing, read the models, let's say that I'm reusing this data set. Or another one again, I can provide the URL. If it's not here, I can provide the URL and state any constraints on reuse of existing data. And if we say there isn't if reuse of existing data have been considered but discarded. So this is, this is the concept and these are the thematic areas that Chistera has selected for the DMP template. You see that it has to do with how you are handling your data within the project, how you plan to share and preserve the data afterwards, how you allocate resources for data management, who is responsible for doing what in this, in the data management planning workflow. What is, if there are used data, what are, are there any restrictions to use it, what are the specific data that we use, and so on. So once you have created it, you can save, save and close and save it and add new. I can save it, save and close it, say. And then I can view my, again I can review, I can go through the steps and review what I've done and add new data sets at any point that I want, if I have new data. And then if I go to my home or, sorry, my DMPs, I can see that this is the DMP that I have created. It's on my dashboard. I can view that I'm the owner. I can see if there is a different version of that already available, if I'm revising it, edit, clone, delete my DMP, see what is the grant associated to this DMP, see who are the researchers working on it, what is the description of the DMP, what the project is and how it's created, and what are the data sets used, how many and what kind of data sets. I can then finalize it, export it or start new version. And I can invite people to work with me. Okay, let's say that I have his name here, so I can invite people to join me in writing this DMP. I can export it, as I mentioned, PDF, document, XML, RDA, JSON, let's say that I want the PDF. Oh, it takes longer with the PDF. Let's say that I want the document. Oh, you won't be able to see it now, or are you new, sir? So now, okay. So now, this is the plan. Oops, hello. Yes, yes, we don't see your screen anymore. Now? Now we see a document, a world document. Exactly, data management plan information, yes. Okay, so this is the data management plan, I can see what I have created in a world document, I can change, I cannot headers, I can do whatever I want, I cannot logo, you know, restructure things as I would like. I can even now remove, let's say, things and write them in a more concrete way in paragraphs and in sentences. And this is how it looks like. Is there other questions and answers? Oh, not again. Okay, let me see. Okay, and then I can also finalize, let's say that I'm now ready to share it with, to deposit it, I can finalize it. I have to cross check this information that it's the DMP, which is there. Yes, the description is correct. This data set, usually here, you will see, if you have more than one data set, which it's the case that you will have, you can even here select, if you have a data set with personal data, it's your sensitive data that you don't want to publish and you want to outside from this bundle, then you cannot select it. So what you select, you will publish as well. So if you don't select, you don't publish. It's very handy for handling sensitive information and metadata as well, and submit. So I'm submitting it and I'm depositing this to Zenodo. Yes, this I want to deposit. Yes, because I am in, okay, I am in the beta version and I don't have the right to deposit. But if you go to argus.obnet.u, click deposit, then you will be redirected to Zenodo login page. And if you login with your, so here you will see a popup with Zenodo when you click deposit and add your credentials and publish when you click publish, it will be automatically there. You don't have to do anything else. Yes, I can always start new version if I want. And I can edit the new version and move from the there. I really wanted to, I would like to, I would have liked actually because I see that the time is, we are short on time. I would have liked you to also go through one section and you know, let us know what are the challenges that you faced because this is, this is ongoing. This is not stable at the moment, as I said, this template. So we can, we have time to further improve it. So your input would be actually valuable. If there are things that you feel that you don't fully understand, we can provide more guidance on them on the template itself before the question or after the question. And I understand that we don't have the time and we don't have the breakout session facility that could help with that. But during this, during this, maybe, maybe, you know, what maybe we have a module for, maybe I can take 20 minutes from that module and we can do it on Friday. I think that would be good. I can do that then. But until then, is there is something that is not clear? It's the first question. And the second question is, would you need support? Do you feel that you need support on which, which exact areas from the, from the demo as, as I, as you show? Heli, while they think and write possible questions about these, we should remember to ask to the question to the argument that was about the orchid. Because we have it, yes, we, we can use this a few minutes, giving some time to the people to write any questions or comments in the chat or question and answer. Yes, to discuss with the manual. Sorry, I don't see if manual is still online. Yes, he is. Manual was arguing that orchid is not a good solution because they do not keep track of your publication. And Zenodo instead is perfect for that. So you can maybe comment on these. Oh, sorry, sorry. Zenodo is very good for archiving the data. But the orchid is just, it's empty. It's nothing because they don't, they only give you a number, but they don't check anything. They don't track anything. I think a Google scholar is much better. It's a much more, sorry, but I trust a Google scholar. I don't trust syncing with Google into any service. I have a Google account as everybody, but almost everybody. But I think for orchid, I don't think why the institutions are enforcing this registration that is for nothing. It's useless. I don't know. For me, it's a mystery. So, okay, let's see. So Zenodo is the repository where you upload, deposit your papers, your data, all the outputs, right? And you also go to search for them, right? Or other people's projects. It doesn't necessarily bind those outputs with the authors. So it doesn't create those links with, it does because it uses orchid, to be honest. So if you apply your orchid, it will create the links. But orchid is something different. It's a different tool than Zenodo and Google scholar. Absolutely. I know, I know, I know. I have uploaded a lot of things to Zenodo and I also have my orchid account. And the orchid account is useless. It's useless. I think you have to distinguish two things. I think Dimitris has this. I will open your mic, Dimitris. And I think just to respond to Manuel, one thing is your, how to say the platform, the orchid platform where you go and you put your details about your institution and so on. And one other thing is the use that we can make by building services on the fact that people have a single ID, which is what, for example, OpenAir is doing because, for example, in the OpenAir graph, we do use your orchid ID to find you and to track your results. So you need to distinguish what is available on the orchid website. I think actually I think I have two orchid accounts because I started with one email address and then I think my institution changed the suffix. So I have two accounts in orchid. And it's useless, absolutely useless. It's for nothing. I don't know why. It's crazy. It's crazy. No, but it's okay. So when you say it's useless because you refer to the platform where you go. The main thing of orchid is to keep track of the works of people who have some kind of warranty or something like that. And well, if you have to introduce all your publications and everything, well, when you are young, maybe you have two or three papers, it's okay. At my age, it's not okay. So I expect the service to keep track of my publications are more important that they check if I am trying to introduce something that is not mine. And they do nothing. They do nothing. It's crazy. But because they are not the ones. So it's so orchid. It's crazy. It's a crazy scheme. It's crazy. It's absolutely empty. I don't know why everybody in Spain, they are using orchid as a kind of reference. But it's useless. At my orchid account, I only have two papers because I don't know. You can check. You have to act on that because you click on add references. I don't remember how it's called now. Maybe do you see my new tab now? Yeah, you must add. But I can add a lot of references that are not mine. So they have to check. And they don't. They do nothing. They do nothing. It's crazy. They collaborate. I think it's not a topic of this presentation. So sorry for raising this issue. No, no problem. So maybe Dimitris wants to add something about this. Hello, I am Dimitris D'Acuniotis. I am a researcher in National Technical University of Athens. And I work on a sister project called DruidNet. And we are funding from the 2018 call of DruidNet. So let me express first my opinion on this discussion about orchid and the other tools. I agree with Manuel that orchid is not very convenient because you have to add manually your publication. I don't know if then the references are automatically presented. But also I use also Google Scholar. But I think that Google Scholar now misses some references. So you have a rough view of your visibility, but it's not the exact view. Because from my personal experience, Google Scholar sent me, sent once alert to me that a paper, my paper is referenced by another paper, but it was never counted on my references. Or I know that there are papers that have cited my work, but they are not count on, they are not shown in Google Scholar metrics. But I just check it from other tools like Research Gate or something like this. We are here. We like also Zenodo because we are part of Fed4Fire project, which is a large Horizon 2020 project. And we give a lot of money to external experimenters to use our facilities for experimentation in ICT technologies. And we encourage them to upload their data from their experiment to Zenodo platform in order to be reproducible and visible for many other experimenters from industry and research. And I would like to ask also something else. You mentioned before that we have to create a data management plant before the end of the first year, am I right or not? Ahmad told us about the correct period when this is stated in the policy. So you can also view that from the policy of Chisterra. But it's in three months, I think, and one year, right? Yeah, exactly. So far for the past call, it's within the first year, but then from the next call on, so for next year, that will be three months. From the project start. We were founded on the call of 2018. It's just three years ago, but our project started on May of 2020 this year. Okay, so you don't actually, for the call 2018, you don't have to. You have no such requirements. I think that was on the good old days. I have to check again, but I think you don't have any obligations of DMP and data sharing. But of course you can do, obviously. So if you want, I would personally strongly encourage you to try out the Argos tool and submit it and keep updating the DMP. So the tools are available and will be available. Okay, thank you very much. Because we had described a data management plan on our proposal and we will create a data management plan, but we didn't know if there is a strict deadline for this. So we are willing to use Argos and it seems a very helpful tool to create a data management plan, especially for us, which are not very, we are not experts on this. So I hope that Argos will help us to build something meaningful. Yeah, hopefully, I'm sure. Okay. Thank you very much. Thank you. Because this is again a trial period, this next two, three weeks, you can let us know if you find anything missing or if you need more guidance. And overall, we are here to support you nevertheless. Okay, thank you very much. I see the Q&A orchid can be populated in connection with several services, problems, etc. Yes. So this was what I was trying to say before we actually, the orchid that gives you the number and then the population and the tracking can be done by other services. Like as I said, OpenAir is applying mining for looking at the orchid IDs and other platforms allow you to search for specific orchid IDs like Zenoro, for example. And so this is the, it's what you can build on the tool that is interesting, not the platform itself. But you can populate it also manually and also by downloading references from other sources. So it's linked, for example, to... But as I understand, orchid is a private enterprise. No, it's an initiative. It's not something like it's sustained by, I don't know, a severe springer. No, no, no, no, no, no. This is not the other ways we want to include that in fair prison. Yes. No, no, no, no, it's not a private. It's actually, it's an initiative that has been supported by libraries. So libraries fund all the different repositories to get the DOIs and integrate them in the system. So this is, yes, the DOI, sorry, the orchids, I'm sorry. So orchids is part of this open and fair ecosystem. And it's one way to assign PIDs for researchers, for individuals. It's like, you know, our identity in the scientific world and it's unique and it's persistent. So it means that we can, we will be able to find and access it in the long term. So plus it helps to distinguish us, our work from similarly named authors, which is class. But yes, orchid is in this ecosystem. And sustained by the community. Okay, thank you. Okay, so we have, yeah, we have other, other comments about these orchid can provide easy access to many manuscripts, platforms. Yes, it depends. Yes, it basically depends also on the services that are built on it. So for example, now they're trying to assign PIDs also to organizations. It's a huge initiative. It's actually a non-for-profit organization, but it's an initiative of the community. So I don't see any other questions in the chat or in the question and answer. So okay, I will say that we will have an exercise so that you get the chance to use Argos on Friday. So I have noted that since we didn't have the times and the means to do that now. But yeah, other than that, thank you very much. Yes, thank you. I also inform you that I sent to all of those that were registered an email with the link to the page where you can find all the materials so far shared. So I believe Ali will put her presentation. I think Ali is in order, right? Yes, yes. Yes. And then we will link it in the page so you have all the material and the recordings as well. So thank you very much. And if you have any question before the next lesson, so please drop us a message. Thank you. Great, thank you.