 Okay, we have time for a few questions. No questions at the moment? Maybe they'll come at the end when we heard all three speakers. Thank you, Robin, for your talk. Moving on to Ellen. Okay. Good. I'll introduce our next speaker. Ellen Verbagel. She will be talking about 3TU Data Centrum and Data Intelligence for Librarians support to staff and researchers on data management. Now, Ellen Verbagel is a Data Librarian at 3TU Data Centrum, which is in the Netherlands. She was a co-developer of the Data Intelligence for Librarians Training, and she is co-author of the White Paper Data Professionals in the Netherlands, a report of the working group Expertise, a group of surf Netherlands. In March 2012, she organized a workshop for research supporters in the Netherlands. They talked two days on how to support the researcher, the scientist, and data management, and she's going to tell us more about this. Ellen, the floor is yours. Now you can hear me. Okay. Hello, everybody. You told me, I told you, I'm Ellen Verbagel from 3TU Data Centrum, and I will tell you about the Data Intelligence Training for Library staff we developed. I will start with an introduction to the 3TU Data Centrum. Then I will tell you about how we developed the training and the content of the training. And I will end with some experiences of researchers who deposited their data in the 3TU Data Centrum. The 3TU Data Centrum consists of data archive, data labs, and data services. It was launched in 2008. It is an initiative of the three libraries of the three technical universities in the Netherlands, University of Delft, University Twente, and University of Eindhoven. The 3TU Data Centrum is a trusted long-term storage facility for scientific and academic research data. This year, January, we received the Data Seal of Approval. The data archive is the storage facility for completed valuable data sets from completed technical scientific studies. The data lab provides an electronic working environment that can be used both for the work processes and for the storage of dynamic data. That environment consists of a set of tools on the researcher's desktop. Our most important task is the provision of advice on the organization of the data lab. With the data services, we stimulate improvements in data management, providing services in licensing, standards, research and retrieval, and, of course, the training. We see the training as a part of our data services. The data intelligence for librarians is developed for the support staff, not for the researchers. It also fits very well in the TU Delft library mission to be a partner in research. The development of the training consisted of three phases. Breakout sessions were organized with the information specialists of the three libraries. And during these sessions, the librarians expressed a great need for more knowledge and ICT skills before they would feel capable of establishing data services. Also the soft skills, like collaboration skills, interview techniques, and how to inspire researchers who need it. Then we designed and built the course. It is designed and built as a competence-based modular course, combining online and face-to-face tuition at the so-called blended learning. From the inventory of learning goals, seven core competencies were defined for a data library. It's working, okay. The data librarian should know about ICT. He should know about the library. He should have advisory skills, collaboration skills. And apart from acquiring knowledge, sharing knowledge and experiences is a key component of the course. Students can combine knowledge, skills, and attitude and put this into practice. The course has four modules. And between the modules, students have a period of three to four weeks. In that period, they can prepare the assignments for the next module. A Google Plus group is created so the students can exchange information and interesting news. In this way, a community can be built. In the first module, the Current Topics module, the students will gain understanding about current developments in the working field of data librarians who support researchers in their research data management. The students are advised to see what terms they should follow on Twitter, who to follow, and so on. They should subscribe to some RSS feeds, read blogs, and see what's going on in the world of data management. In the second module, the students learn about research data management in general. So they learn about formats, metadata, data policy, and the data lifecycle. In the third module is more technique. It explains what digital objects are, how data are to be cited, how research data are transformed to different formats during different phases in its lifecycle. How to search for data and how to enhance publications. Students practice searching for existing data sets and describing and uploading a data set. In the last module, students write an acquisition plan. They present their plan to follow students and give feedback on each other's plans. They try the advisory skills in role plays and finally put the acquisition plan into practice by actually carrying out the first steps described in their own plans. In this final module, theory, skills, and attitude come together. After we knew how to design the course, we developed and wrote the training materials. For each module, a team of three to four experts on the topic came together for two to four sessions of one or two hours. The content was written in between the sessions. This design was fine tuned with the feedback of the knowledge experts. Texts were reviewed by the experts and then rewritten. Two coaches were selected for their tactical skills following the belief that being an inspiring coach requires different skills than being a knowledge expert. The design of the course places much emphasis on learning by trying things yourself without putting too much trust in an expert. In this way, group work is stimulated. Even though the course was initially designed for Dutch participants, the course website was translated into English in order to provide a possible source of inspiration for universities or other institutions abroad. Here you see some examples of the images we use in the website. After two rounds of the course, we have made some evaluation for the students and the main findings is that the participants appreciate the discussions during the sessions where they meet each other. They like the teachers we invite them to talk to the students and they like very much the images I show you. But they wanted to hear more about writing a data management plan. They want to know more about how to set up a front office back office model. Most of the participants didn't like Google Plus. After that, we concluded that the development of the course was a leap into the unknown because no similar course existed until then. We had a pragmatic approach of just getting started and that has been paid off. Participants appreciate the course as a whole but have indicated some shortcomings. So now we are improving. Last year, the course was given by a three-tier data centre and dance together. One of the teachers, one of the coaches is from dance and other learning materials are expanded to other disciplines. Our next step is to design a new flexible and dynamic learning environment to make the course more interactive, more collaborative and more constructive. And we'll do this together with dance. It's now a joint project of us because since May 8th we have the research data in the Netherlands and we work together with dance but not only in the training but we organize also the Dutch data prize. Now I will give you some examples of researchers who deposited their data in the 3DU data centre and one example is the IDRA data set. IDRA is a data which generates large time series of numerical data from the radar. This data set is consequently updated. It started in 2009 and 3DU data centre advised in the data archiving process and the creation of the metadata. And now because of that IDRA is much more visible to the scientific community. If you can read it. These are the data from the CRIOTEM microscope and it generates large data sets and they were stored in the 3DU data centre. These sets are linked to an article published in Science and the author is convinced that people may use the same data sets for things we were not looking for. They are generating new signs with the same data. Like most of our data sets these data sets have been assigned a digital object identifier, a door which makes these data easily found and sightable. The author wants the data sets to be found and encourages reuse. So we help them. That's the Majorana Fermanon. It's a bizarre particle. Because it represents its own antiparticle and it can also be used to build quantum computers. And the 3DU data centre ingested this data although not in the preferred format. And the nano research team strongly believes in openness of the scientific process. And these data are companies paper in their science magazine. And we have people involved here. Niko Potters, Marina Nordegraaf, Madeline Smale. And if you have some questions here you can follow us on Twitter at data librarians or visit our homepage. Thank you. Yes. Okay. Are there any questions? Questions for the end of this time? Yes. I think I have a question more for Robin. Thank you both for your talks. I think you're targeting a technical group of libraries. Do you have any advice for libraries who have a larger discipline focus within their researchers? And should any data management planning involve more different disciplines? And deal with many different researchers Robin in developing your data tools for the librarians you were dealing with. Did they have to have discipline knowledge when they were outreaching to faculties? Well I guess that's one of the things that made the librarians nervous because at the University of Edinburgh we don't have strict subject librarians who might have a master's degree in a particular discipline. So they may be assigned to a particular school because they have a background where they may not and in some cases they're doing outreach with more than one school. So they are kind of generalists. And for us again our comfort zone is with social science data especially quantitative data and with the research data management initiative we've had to build the repository for all types of research and also get used to talking to all kinds of researchers and try and make our policies so that it doesn't really quite depend on us understanding the data so much entirely. And the mantra a lot of it's kind of almost computer literacy to some people. So again that helps build the confidence and that may not reach all kinds of researchers especially if they're working on you know UNIX machines in a team but anyone kind of working alone at their desk hopefully it would appeal to them. How deep is that computer literacy? What learning curve is involved? Is it pretty? No I think no it's more the long lines of awareness raising and just getting comfortable. We have what I didn't say about mantras it has data handling practicals in four kinds of software environments but the librarians didn't work through those. Thank you. Okay any other questions? Question for Alan I was intrigued a very small point you described at the end of things that your librarians did like and didn't like about the course and generally it's very positive but you mentioned one of the negative things was their dislike of Google Plus I was intrigued because I can understand that that idea of trying to create a community between the students I think is an important thing to do and I understand that's why you did it so is it specifically the technology that they didn't like with Google Plus or did they just not want you to make a community of them? Do you think there's an alternative? No they didn't like Google Plus because at the first meeting they all had to make this group and then to invite the others and that was just the way of thinking the other way around and most of the students thought they make a group and then get to it but not to make their own group it's another way of thinking and that was difficult but they like the network of components of the group of that course I was one of the students in the second training about Google Plus Google Plus was before they released this community feature so there was not a real group functionality in Google Plus then so it just didn't work for the objective that maybe better created some other group system but Google Plus with communities I don't know that's the main thing it was problematic but everybody had to create their own circles I just discovered after the course that I just forgot two people that was the problem More questions? Thank you Ellen