 Good morning everyone. Welcome to our webinar on Love Data Management which we've organized as part of Love Data Week. In this webinar we'll talk to you about our expert tour guide on data management that was developed by the CISDA working group on training during the course of 2017. As speakers we have Ellen Lenards from Dans in Holland, Ulf Jacobsen from S&D in Sweden, Gun Inger Lise from SD in Norway, part of the team who helped develop this module. As part of this webinar which will last about 35-40 minutes and will be followed by a session of questions and answers for you, we'll first give you a brief introduction on what is CISDA and in particular the CISDA Eric and the CISDA training working group. Ellen will then introduce you to the tour guides, the project and the future plans we have. Ulf will give you a taste of one of the chapters in particular data management planning and how the module can help you with that and finally Gunn will give you an overview of the entire content of the tour guide and how you can use it for self-study and for training. CISDA is a consortium of European social science data archives which has been in existence for many decades. It has in the course of last year become a European research infrastructure consortium so now called the CISDA Eric. It is a network of social science data archives throughout Europe and provides large scale integrated and sustainable data services to the social sciences. The main aim is to provide infrastructure data services, data archives to encourage high quality research in the social sciences in particular with the focus on sharing data making data available for reuse to the research communities and there's also a focus on teaching and learning in the social sciences. Within CISDA there are three important pillars, technology, trust and training. We are the group that looks at training, both training for researchers and training of trainers, train the trainer sessions staff within the various service providers. In 2016 we started a training working group across all the CISDA archives in the various countries and up until now we've had a focus within that group on data discovery in particular looking at finding and accessing data across Europe and on research data management where we train researchers in good data practices to make data findable, understandable, sustainably accessible and reusable and it is within this remit that the module that we're presenting here the expert tour guide has been developed. I now hand over to Ellen to explain to us what the project did in 2017. Yeah thank you Fjelle. Good morning to you all and it's quite surprising to see that there are a lot of trainers here that are not linked to the social sciences although the expert tour guide was designed specifically for this domain so I hope this is still of interest to you and now try to move one slide but it doesn't seem to. So why assess that online module on RDM? There are actually three reasons and as it happened the working group training found out that many CISDA partners already organized workshops on research data management for researchers and other partners would like to know more about these workshops. So what content to use, what exercises to use, so the training group decided to share the training practices a little bit more. Then research has also approached the CISDA partners more and more with questions around data management plans that are required by local funders but also by the European Rise in 2020 project. And of course there are already quite some research data management guidance is available and I think many of you might know them and this was also the case two years ago but we still thought that the domain and European specific guide would really be useful for the researchers and the RDM trainers to use. Now I will be working on the module the results of an open air and fair data expert group survey on the Horizon 2020 plate for data management plans showed that a more domain specific guidance is actually needed and requested for by researchers but also by data supporters. So we thought we are right on track with the CISDA online module here. Now for the online module we decided well during the year that it would not be a course but more like a guide so that researchers could also use it for self-study. When you go to the CISDA Eric website and you enter the module you will be able to access everything that is available. At the same time this guide is also as part of the CISDA Eric website also local workshops can use it for blended learning so they can refer to the online guide before the workshop starts or using the online guide while the workshop is organized. The timeline of this one-year project to just quickly go through it and just to mention that it was actually a one-year project and not much more so we had really a lot to do during this year and first we we already kicked off before the year so in December and then to assemble all kinds of ideas of the overall structure what would be the theme of the guide what sources did we already have what would be our story. This was already when you brainstorm it all seems that it seems logical it seems that you're going somewhere but in general from January to April it appears that we have developed a lot of content ideas and there was a still confusion about the chapters about would we create a course or a guide and so on so in May 2017 we had a workshop all the partners that were involved in the project and we were there and we tried to finalize the target audience and also the chapters outline now after that because well face-to-face workshop for that purpose is really good. We had some hard work on the content and when the content when there was a draft implementation online we had a feedback period for researchers, data management specialist and trainers and at the end of course when the feedback was received we could improve our content create a starter package for trainers and launch the website so that's in quick but I will yeah just just to mention it in the May workshop we created a project statement and they were very important because we finalized target audience we said we would address the early career social sciences researchers and we would not be like mantra or essential for data support because we targeted researchers but also because we would want it to have European perspectives in the online module. Now the content of the chapters is following the research data lifecycle so if you look at the titles of the chapters that Goon will go in more detail on the content of the chapters later on but the structure that we decided upon was this. We had several authors per chapter they are in the online version they are mentioned also so you know who wrote the specific chapters and we had a pool of people providing feedback and testing the online module in October. Now there are some elements that reoccur in several chapters now to start with the last one adaptive DMP that's the one that occurs in every chapter so that is really the central theme throughout because we realized that there was a lot of requests around data management planning and of course we'll go into that in a minute. Then we had expert tips in several chapters the European diversity so what is the specific situation in a specific country and the perspective of looking at research data management for a qualitative or quantitative data perspective. So to give some examples from the online module expert tips for example on how fair are your data or how to handle consent these are just some examples now for European diversity this is also an example you see data management requirements in Europe so we have already the situation described for quite a lot of countries and of course we are looking for more but also in the chapter on legal issues of course we point at differences between the countries. Then some examples on the quantitative and qualitative data perspective and these are always shown with their specific images so did you know when going through the module what is more from a quantitative data perspective not as more from a qualitative data perspective. Now the user feedback that we received in October and we received from 30 people in total these are mainly employees from the partner so the social science data archives but also researchers that they asked to have a closer look at the module. Now we had some very good feedback but also lots and lots of feedback of what we could change to improve the module and be a thing working on that in the last part of the year. Now while we were creating those we we were improving the model we also created two outlines for possible workshops one a one-day workshop on the introduction to research data management for social science researchers and one day content specific on ethical and legal consideration research data management. So these outlines use the online module refer to the online model and have additional exercises if they were available. So what we want to do in the future is to add more exercises to for trainers to use and also to sorry I was there was something popping up in my screen. So more exercises we will add a slide sets per the chapter for the trainers to use with images that are also in the online module so it can really link to the online model in a nicer way and of course there will be a evaluation form and so on. So if you are interested in such a starter package for trainers then please contact us. Then in December we had the launch of the expert tour guide you can see us all smiling of the hard work and then of course we had some plans for this year and what you will notice later on that the discovery chapter is still missing and this will be added at the end of this year and we will improve the start package as also mentioned in the previous slide and we will of course improve the content further on that means corrections of the English improve the content based on the feedback that we already had but we didn't have the time to implement and will be several events train the trainer event in April this is for the SESTA trainers but if you're interested let us know because we might have other opportunities where you can go and of course there will be local workshops by the SESTA data archives and using the module during this year and based on their experiences the online model will also be improved of course. Now I would like to hand over to Ulf who will say more about data management planning. So yes hello good morning everyone Ulf here. I'm going to talk a bit about the benefits of having a data management plan and what data management is. First of all I think it's important to understand the benefits of data management you have to understand the concept of data management what it implies and it is about how to handle organize structure and store research data throughout the research project. It also takes into account technical organizational structural like a selective and sustainability aspects. I'm thinking about this will help the researcher to keep the data collected and or used within the project very tidy unsee usable and safe while at the same time ensuring the longevity of the data. There is also clear structure if you have that in the data it's going to be easier to manage the data throughout the project which will make it easier to handle the data that are collected during the project but also avoid time consuming work afterwards. It also might involve some additional work of course I mean you have to write the data management plan but that extra time will pay off in the end and later on in the project as well. So in order to simplify the work on data management we think that the data management plan can be and should be created early in the research process. So the question is then what is the data management plan? It is a formal document that will provide a framework for how to handle the data and the material during and after the project. The content of the data management plan is designed in accordance with a specific research project. So depending on what you are looking into you will change the contents of the data management plan and I would suggest that and it's also my experience after talking to several researchers that looking into the checklist or kind of checklist early in the process will prevent later problems and you can also sort out problems in forehand. A couple of added values for this I mean it's first of all looking through a data management plan checklist makes you aware of possible problems as I said before so you can work around them. It also keeps all your questions rounding managing the data in one place if you have it in one document everyone can turn to that document and look into it and it's readily available for others rather than just vaguely remembered and I mean everyone knows it's simple to forget things. It also helps you calculate how much money that will be required for managing the research data during the project and that's something people tend to forget to think about. A data management plan also allows you to think through beforehand how to provide a data set to a data repository which is as fair as possible with fair principles. I'm not going into those now. Another benefit another value of this is that as a researcher you're actually showing your institution the funders your partners that you take data management seriously if you have a data management plan and that you're willing to show that you're dealing with research funds and research participants in a very responsible way. So why should you write a data management plan? Having control over how the data it is managed during the research process it becomes easier for others to understand the material. It also enables further research after the project has ended. Data should be openly available so that the research results can be verified and also it prevents a necessary data collection. If someone else can use reuse your data they don't have to collect the same information one more time. So how do you do then? I suggest that you could start with a DMP checklist. We have one here at the SESTEX module. Each chapter has a DMP section and each section has corresponding questions to that chapter and in the first chapter there's a reference list a PDF document that you can download with all these questions collected in one place and I think that's a very good start to look into this read through it and work from there. And that's for me and now I hand over to hopefully to Gunn. Yes I'm waiting for... Okay. Okay we are here. So thank you Ulf and good morning everyone. So Ulf just outlined chapter one of the online module explaining the benefits of good data management. But how do you actually manage your data in a responsible way? The remaining chapters of the online module basically provides best practices, tips and tricks to help you to get started in managing your data. The content can be used both for self-study and for training and in the following we'll just have a very quick look at the content of each chapter so you know what you expect then. In the chapter on organizing and documenting this chapter basically deals with ways to make it easy for yourself and to others to retrieve relevant files and information efficiently later on. Three main topics are covered. The first one pertains to structuring your files and organizing variables because in a research project different data files typically have different internal structures and often the data files have specific relations to each other so a careful structuring of your data will make it easier for you to navigate in your total data material. Among other things this chapter covers how to actually design qualitative or quantitative data files. Second this chapter has useful advice on conventions to name files in a logical way and as well as advice on how to organize files into folders that will make sense for you also like in three years from now and the third topic of this chapter has to do with documentation and metadata because the basic message is you have to make sure to document what you do when you do it this way you'll avoid having to try to remember towards the end of your project what you did why you did it and what those cryptic file abbreviations actually mean. Good routines for documentation will save you a lot of time in the long run especially for still unexperienced researchers though it's not so easy to foresee what documentation you should actually give priority to therefore this chapter provides practical checklists for relevant things to document separating among other things between project level documentation as you can see now in that example screenshot and data level documentation. Next the process chapter covers how to prepare your data files for analysis and data sharing this is important because throughout the different phases of your project your data files will typically be edited numerous times and during that process it's crucial to make sure that nothing is lost unintentionally so in this chapter you basically learn how to provide strategies to minimize errors during the processes of data entry and data coding and as you can see on this screenshot on the lower right all the lists that you find can easily be expanded to provide more detailed information. The chapter when storing data deals with how to protect your data against accidental loss and against unauthorized manipulation to this end there are three keywords to consider with regard to data management planning storage backup and security when collecting sensitive personal data this is of course particularly important as an example of storage information in this chapter you'll find a systematic overview of different storage solutions along with some pros and cons for each solution this will help you to determine which solutions are relevant and adequate for your needs. This chapter will also familiarize you with common backup strategies this is important in the case it should it happen that you lose data accidentally for instance through human error or because your laptop was stolen or if there is a hardware failure regarding security you should be able to decide when and how to protect your data against unauthorized access for instance using strong passwords and encryption and you can easily navigate and click on whichever of these topics that you need. The protect part of the tour guide gives you important information advice and practical help to deal with legal and ethical considerations that you will need in order to create shareable data. First of all it clarifies the different legal requirements of member states and the impact of the upcoming general data protection regulation. Furthermore people often think that it's impossible to share personal data however this chapter provides concrete information and examples and how to share personal data. As everywhere else in this online guide all such examples may be particularly useful when using this tour guide for training. For example this chapter explains with examples how to obtain informed consent as you can see on that screenshot. Also you will find advice and concrete examples of how to anonymize your data. Finally an often overlooked question concerns copyright and ownership concerns copyright and ownership I mean who actually owns your research data. The answer to that question will have practical bearings on what you are allowed to do with them and therefore clearing ownership and copyright issues is an essential part of data management planning. The chapter on publishing covers important issues that you need to consider to make sure that research data are easily sightable, visible, findable and accessible under proper conditions. Among other things you'll need to make a conscious choice on which data to actually publish. Second the chapter provides an overview and useful tips on how to select a data repository that will fit your needs considering both your needs right now and also in the future. Third licensing your data is extremely important to make your data actually reusable. Regulating data access is a powerful tool to share even data that cannot be shared openly and I'd like to mention in that context that SESTA archives strive to make research data accessible as openly as possible while at the same time protecting from inappropriate access. And also this chapter covers ways to promote your research data publication. Finally as Ellen already mentioned there will be a final chapter on data discovery which will appear later on in 2018.