Research Data Management - for a Researcher
by areff 2000
1. Store your data
1.1. Cloud
1.1.1. eg Cloudstor
1.1.2. eg Dropbox
1.1.3. eg Figshare
1.1.4. eg data.mendeley.com
1.1.4.1. Who owns data?
1.1.4.2. Can you add a licence to the data?
1.1.5. eg Zenodo
1.2. Discipline Repository
1.2.1. eg listed at re3data.org
1.3. Network
1.4. Local
1.5. Needs: size
1.6. Needs:collaboration
1.7. Needs: reliability
1.8. Needs: data explained/described
2. Publish your data
2.1. Licence
2.1.1. ANDS recommends CC-BY
2.1.2. AUSGOAL Suite of Licences
2.2. DOI
2.3. Talk data
2.3.1. Cite data in publications
2.3.2. Store your data in public view
3. Your local RDM support
3.1. Library
3.2. IT Office
3.3. Research / Ethics Office
3.3.1. Training in RDM
3.4. Research Supervisor
4. Plan for your Data
4.1. Data Mgmt Plan template
5. Title: Value, Innovation, Profit
5.1. Sub: More than something new...
5.2. Abstract: Value is a new and (potentially) better was for policy-makers to understand innovation. A value approach to innovation sheds some new light on 'winning the future'. This article will outline such an approach and test its usefulness against five innovation policy reports, to assess the value of a value approach.
6. Explain/Describe your data
6.1. DOI; Digital ID
6.1.1. Author
6.1.2. Title
6.1.3. Subject
6.1.4. Permanent Identifier
6.1.5. Publisher
6.2. What's your vocab?
6.2.1. Connect to Discipline Stds
7. Connect your data
7.1. Literature
7.2. Researcher
7.2.1. eg ORCID ID
7.3. Grant
7.3.1. Project proposals
7.3.2. Project reports
7.3.2.1. eg Annual
7.4. Subject
7.5. Institution
7.6. Publications
7.7. Instruments
7.7.1. eg myTardis
7.8. Ethics
7.9. Significance statement
7.9.1. Problem
7.9.2. Methodology
7.9.3. How is your research different? better? important?