Essentials 4 Data Support: a fine course in FAIR Data Support
•Télécharger en tant que PPTX, PDF•
1 j'aime•132 vues
Introduction of Essentials 4 Data Support and how the FAIR principles are incorporated in the course
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
Similaire à Essentials 4 Data Support: a fine course in FAIR Data Support
Similaire à Essentials 4 Data Support: a fine course in FAIR Data Support (20)
Dernier
Dernier (20)
Essentials 4 Data Support: a fine course in FAIR Data Support
- 1. Essentials 4 Data Support: A fine course in FAIR Data Support Ellen Verbakel, 4TU.Centre for Research Data
- 2. Content • E4DS, FO/BO • Explain FAIR • Explain how FAIR is incorporated in the course
- 3. Essentials 4 Data Support • Essentials 4 Data Support is an introductory course for those people* who (want to) support researchers in storing, managing, archiving and sharing their research data. • Librarians, • IT Staff, • Policy Makers/Advisors, • Researchers
- 4. • Essentials 4 Data Support is a product of Research Data Netherlands • E4DS started at 4TU.Centre for Research Data, 2011
- 5. Topics • Definitions • Planning phase • Research phase • User Phase • Legislation and policy • Data Support University of Bath. Project Research360, 2011
- 6. Three ways to take the course 1. Online only 2. Online only with user profile
- 7. 3. Full course (6 weeks) - online content - 2 face to face days: fellow course participants coaches experts in the field - private forum: assignments and discussions - certificate Study load is about 50 hrs in total.
- 9. but now and than also in house training, upon request • School of Applied Science, Utrecht • National Forum for Research Data Management, Denmark • Medical-technical organization in the Netherlands • Planning: School of Applied Science, Utrecht
- 10. Danmark
- 11. Competencies Skillfully handles ICT Shows entrepeneurship Sees from the whole Consulting skills Co-operative skills
- 13. Data supporter in the institution
- 14. FAIR
- 15. FAIR, 2016 “There is an urgent need to improve the infrastructure supporting the reuse of scholarly data.” “ … FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.“ Source: Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Nature, Scientific Data 3:160018 doi: 10.1038/sdata.2016.18 (2016)
- 17. Findable • (meta) data are assigned globally unique and persistent identifiers • Data are described with rich metadata • Metadata clearly and explicitly include the identifier of the data it describes • (meta)data are registered or indexed in a searchable resource
- 18. Accessible • (meta)data are retrievable by their identifier using a standardized communication protocol. • The protocol is open, free and universally implementable • The protocol allows for an authentication and authorization when required • Metadata should be accessible even when the data is no longer available
- 19. Interoperable • (meta)data use a formal, accessible, shared and broadly applicable language for knowledge representation • (meta)data use vocabularies that follow the FAIR principles • (meta)data include qualified references to other (meta)data.
- 20. Reusable • meta(data) are richly described with a plurality of accurate and relevant attributes • (meta)data are released with a clear and accessible data usage license. • (meta)data are associated with detailed provenance • (meta)data meet domain-relevant community standards
- 21. F in E4DS Module: Research Phase
- 22. Data documentation is describing the characteristics of a dataset - Research process - Data itself - Changes of dataset in time - ‘Metadata is a love note to the future’ (source: UK Higher Education Research Data Management (RDM) Survey, http://t.co/J80ySXEsf5)
- 23. A in E4DS Module: User Phase Persistent identifiers: a unique label that is linked to a digital object. So, the object can always be found, even if the name and place change.
- 24. I in E4DS • Common formats • Machine-readable!
- 25. • The history of digital storageprovides a wonderful insight into the limitations of information carriers. If software/hardware is no longer used, data can become unreadable. In order to prevent this, it is vital to choose an open format: that is a software format that is not attached to a certain software supplier (proprietary software).
- 26. R in E4DS Module: User phase Metadata in data archives: Apply schedules and standards to link the metadata to other files and automatically search through them, which increases the familiarity of the data. Many communities have their own schedules
- 27. Module: Legislation and policy Information on: • Licensing agreements • Privacy issues • Ownership of data
- 28. Conclusion Fair is a ‘code of conduct’ for researchers E4DS is a course for supporters E4DS educates supporters so that researchers can be FAIR!
Notes de l'éditeur
- 1. Explain E4DS, FO/BO 2. Explain FAIR 3. Every letter explain how they are incorporated in the course, examples!! https://danielskatzblog.wordpress.com/2017/06/22/fair-is-not-fair-enough/ FAIR is not really fair enough. First, note that FAIR doesn’t actually require the objects (e.g., data, software) to be openly available. Second, FAIR doesn’t include the idea of credit. But: recognizing and using the expertise of individuals is how large communities function effectively.
- Plaatje van inhoudsopgave
- Research life cycle and what supporters can do to support researchers in managing their data This article describes a research data management course for support staff such as librarians and IT staff. The authors, who coach the participants, introduce the three course formats and describe the training in more detail. In the last years over 170 persons have participated in this training. It combines a wealth of online information with face-to-face meetings. The aim of the course is to support the participants in strengthening various skills and acquiring knowledge so they feel confident to support, advise and train researchers. Interaction among the students is embedded in the structure of the training, because we regard it as a valuable instrument to develop a professional network. Recently the course has taken on a new challenge: in addition to the regular courses, a couple of in house trainings has been delivered on request. The paper ends with a description of the key group assignments for such compact trainings. Source: IFLA paper Plaatje van homepage cursus
- Plaatje RDNL, 3 logo’s
- We now have the research life cycle as Starting Point of the training. What does the data supporter should know, needs to know of RDM in the various phases of the research: before, during and after the research. The researcher is starting point.
- Plaatje hand met 3 vingers Online only (without registration) You have access to the chapters in the learning environment and can study them in whatever way that suits you and meets your learning needs. You will not have access to most assignments and quizzes. Online only with user profile When you register, you will be able to comment on the texts (through Post Comments) and start and engage in discussions on forum 4 all. You will also see which sections you have already studied. As in option 1, you will not have access to most assignments and quizzes. Full course (online + face 2 face with certificate) The full course starts with a course day during which you will meet your fellow course participants, your instructors and a number of experts in the field. The instructors will also explain the structure of the online part of the course. Until the second course day (about six weeks later) you will have time to study the content of the online learning environment. Among other things, you will be asked to do a current topic assignment, create a data management plan, reflect on your learning process, comment on a case about the legal framework for research data and end with an assignment of your choice. A private forum (visible to you, your fellow course participants and the instructors) is used for sending in assignments and to post discussions. The second course day focuses on presentations by the course participants and an evaluation. In addition, a number of experts will talk about their own experiences and you will receive a certificate – provided you have completed all assignments.
- Foto van Denemarken
- Competency Explanation Skillfully handles ICT Efficiently uses available information technology. Shows entrepeneurship Aims to improve data services in response to changing needs in the field. Keeps an eye on trends which emerge in the profession, knows where knowledge is available (networks) and disseminates important information to key people in the organisation. Regularly inquires into perceived needs in the field, e.g. by using questionnaires, interviews or focus groups. Actively contributes to developments in the field by visiting or contributing to training sessions, conferences etc. Sees from the whole Acknowledges that data are only part of the scientific lifecycle and is aware of the significance research data have for carrying out scientific research. Sees data- and information services as part of larger whole in which decisions are made. Consulting skills Can handle questions skillfully. Knows when to give advice and when to refer question about data management to a dedicated expert (e.g. questions about data formats, data documentation, storage, data citation (persistent identifiers), writing a data management plan (DMP), intellectual property and funder requirements). Can emphatize with customer perceptions. Asks for feedback on his consulting skills and adjust his behaviour accordingly. Co-operative skills Examines how collaboration with others (employees, researchers, institutions) may enhance service provision. Acknowledges the necessity of a forum where data supporters can communicate and stand up together to make a fist when it comes to important themes like data policies, copyright and information-infrastructure. Takes responsibility for his contribution to these partnerships.
- You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
- Bron: Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016). These priniciples are now well known in ‘data land’ Every body sees the need of having a basic standard on how data can/should be made available for others. Nowadays it is more and more important, not just archiving the data, but archiving them in a way others can benefit from them: reuse! Plaatje (screenshot) van artikel
- FAIR principles are Incorporated in H2020 How FAIR is 4TU.Centre of Research Data?
- Findable: Data and metadata are easy to find by both humans and computers. Machine readable metadata is essential for automatic discovery of relevant datasets and services, and for this reason are essential to the FAIRification process.
- Accessible: Limitations on the use of data, and protocols for querying or copying data are made explicit for both humans and machines.
- Interoperable: The computer can interpret the data, so that they can be automatically combined with other data. There is a historical trend in computer science toward increased interoperation (for example, between different hardware designs, operating systems, programming languages, and communication protocols). Data interoperability can be seen as the ragged edge of this long-term trend. However, data interoperation is a non-trivial problem and the “I” will require the most creative effort in making FAIR Data.
- Reusable: Data and metadata are sufficiently well described for both humans and computers, so that they can be replicated or combined in future research.
- Data Documentation we focus on the metadata and what rich metadata means; in the module Citing Data and Data Impact, Persistent Identifiers are explained. F Metadata; look why we think it is important to have this, what does E4DS says on this topic regarding the FAIR principles? Persistent identifier: http://datasupport.researchdata.nl/en/start-de-cursus/iv-gebruiksfase/data-citeren/ Metadata: http://datasupport.researchdata.nl/en/start-de-cursus/iv-gebruiksfase/data-archiveren/metadata/ http://datasupport.researchdata.nl/en/start-de-cursus/iii-onderzoeksfase/datadocumentatie/
- 1. Res. Proc. : how the data are collected 2. How much, format, software and so on 3. The editing, versions
- Protocol is open Metadata are always open
- Licences meta)data are associated with detailed provenance Various disciplines apply their own metadata schedules and standards (see box). Which metadata fields are mandatory or desirable may vary per file.