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Data Management Tools

Data Management Tools. David Wallom. YOUR DATA DOES NOT BELONG TO YOU! IT BELONGS TO YOUR EMPLOYING INSTITUTION!. Publish. Expose. Analyse. Collaborate. Experiment. The RDM Lifecycle. Slide borrowed with permission from Anthony Beitz , Monash University. Presented at OR 2012, Edinburgh.

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Data Management Tools

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  1. Data Management Tools David Wallom

  2. YOUR DATA DOES NOT BELONG TO YOU!IT BELONGS TO YOUR EMPLOYING INSTITUTION!

  3. Publish Expose Analyse Collaborate Experiment The RDM Lifecycle Slide borrowed with permission from Anthony Beitz, Monash University. Presented at OR 2012, Edinburgh Expose Publish Analyse Collaborate Experiment Design Conceive

  4. The RDM Lifecycle Slide borrowed with permission from Anthony Beitz, Monash University. Presented at OR 2012, Edinburgh Design Conceive

  5. Conceive and Design • Collaborative • Multiple Investigators, multiple institutions • Process driven • RCUK, EC, a.n.other funder all have processes that must be followed • Time limited • Calls come with deadlines and everyone leaves it until the last moment…

  6. Tools • Shared document development tools • E.g. Google Docs, Office 365 • Shared document/project management tools • Sharepoint – locally managed and normally connected to an Exchange system • Online • Teambox • Projecturf • Apollo • Basecamp • Huddle • Data Management Planning • DMPOnline

  7. Analyse Collaborate Experiment The RDM Lifecycle Analyse Collaborate Experiment

  8. Backup • What data you need to back-up? • Criteria? • How many versions you should retain? • Current, raw, processed? • How often you intend to backup? • After every change, daily, weekly, monthly? • How many copies you should retain and their location? • 2, 3, onsite, offsite? • How you intend storing backup data? • Media, online, cloud? • What software you will use to manage backups? • Operating system based, 3rd party, criteria to choose?

  9. Tools • Desktop Drive (USB) • RO Media • Online • ‘Institutional Dropbox’ • SkyDrive • iCloud • Dropbox • Humyo • Memopal • Mozy • ZumoDrive

  10. Data Security • The available skills and expertise required to ensure an adequate level of data security; • Risk assessment to determine the value of data • the level of confidentiality required • applicable statutory requirements • impact of unauthorised access to, or loss of, the data • steps required to provide appropriate data protection. • The prevention of unauthorised and malicious access to buildings and rooms where computers and other devices holding data may be housed. • How access to data is managed, authorised and logged. • How data is protected from loss or damage, for example by regular backups, implementing version control and installing anti-malware software. • The means to access data from both within Oxford and from outside the Oxford network; and the transmission of data from one computer to another (e.g. via email, ftp, Web server). • The storage and encryption of data taken offsite (whether, for example, on an external drive, laptop, mobile device). • The process to verify the deletion of confidential data (for example, when equipment is re-deployed or in line with a project's exit strategy)

  11. Active Research Data Storage • DataStage • DataVerse • NeuroHub/Hub http://www.dcc.ac.uk/resources/external/category/active-data-storage

  12. Workflow and Lab Books • LabTrove • cRUNCH • NeuroHub/Hub http://www.dcc.ac.uk/resources/external/category/workflow-and-lab-notebook-management

  13. Publish Expose The RDM Lifecycle Slide borrowed with permission from Anthony Beitz, Monash University. Presented at OR 2012, Edinburgh Expose Publish

  14. Sharing Your Data • Enables that data to be validated and tested, improving the scientific record. • Meets funding body requirements obliging award holders to share their data to avoid duplication of effort and to cut costs. • Is in the public interest, where research data has been publicly funded. • Can facilitate its rediscovery and its preservation as technology becomes obsolete. • Means that data can be reused for scientific and educational purposes.

  15. When should I consider not sharing my data? • There may be occasions when you should consider not sharing your data: • If your research data is potentially commercially valuable or exploitable by your employer. • If there are ethical issues, legal issues, time constraints and other issues which could limit data sharing opportunities, • If there are conditions of confidentiality (eg. through industrial sponsors) attached to the funding of your research. • Often, sensitive and confidential data can be shared ethically if informed consent for data sharing has been given, or by anonymising research data. 

  16. Community Data Repositories

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