Considerations for Research Data Management
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1 Considerations for Research Data Management Andrew Dean - OCF adean@ocf.co.uk Wednesday 3 rd December 2014
2 What is an RDM solution? Research Data Management A method of effectively managing data to support research including: Active Data used on a day-to-day basis Long-term archive of published static data Not just storage! Management, Ingest, Backup, Protocol conversion etc.. The RDM solution is a service which should compliment researchers data management plan which outlines how researcher will handle their data both during research, and after the project is completed.
3 Why do you need an RDM Solution/Strategy? Compliance with Research Councils (e.g. EPSRC) Research organisations will ensure that appropriately structured metadata describing the research data they hold is published (normally within 12 months of the data being generated) and made freely accessible on the internet; in each case the metadata must be sufficient to allow others to understand what research data exists, why, when and how it was generated, and how to access it research data is securely preserved for a minimum of 10 years from the date that any researcher privileged access period expires or, if others have accessed the data, from last date on which access to the data was requested by a third party Research organisations will ensure that effective data curation is provided throughout the full data lifecycle, with data curation and data lifecycle being as defined by the Digital Curation Centre. Minimise Management and hardware costs Maximise the value you are getting from the data you already have
4
5 You could But how will you: Ensure compliance/enforce rules? Efficiently ingest & manage metadata Find a document or project in 10 years time? (esp with incomplete information) Retrieve data in a reasonable amount of time What if lots of retrieval requests were received? And what if SLA s were introduced? Manage multiple copies in multiple locations Get value from the data you are have to store?
6 Main Components of an RDM Active Research Store Ingest Archive (+ supporting services) Archive to achieve: Scalability Compliance Find Documents Retrieve Data Manage Multiple Copies Get value
7 Things to consider Define the users and their requirements. Understand existing data retention expectations and consider future requirements Strongly consider working with commercial, or commercially supported software the data you are storing has huge value! Consider how you could migrate away from the software stack if you wanted (or needed) to. (open standards xml for metadata for example) (same for open source - what if the project becomes orphaned) Take your predicted requirements (capacity/performance/number of users etc) and x10 would the system you are considering still be viable at this scale? Think of this as an opportunity Creating a service containing all of the University s research data, in an accessible manner, could be an incredible resource - how could I maximise the value I get out it?
8 Data Analytics By combining comprehensive metadata and big data exploration you could: Metadata exploration: Find projects using google like search Gain insight visually on the research data across the University Find existing or complimentary data-sets already produced to support your research (cross-departmental) More focused analytics & visualisation e.g. search the RDM for specific kinds of images (brain scans/pictures of polar bears) and look for themes/trends in metadata or images Further exploration on data found from metadata: Find new markets and new research opportunities by combining your own data with publicly available data Providing a centralised resource for advanced visual analytics (including combining structured & unstructured data) Hack the City type projects And I m sure you all have any other ideas
9 Why talk to OCF? Proven experience in architecting and delivering Petascale Environments Excellent partner network in disk and tape hardware as well as storage management software to allow us to provide multi-vendor best of breed solutions Big-data expertise We have Big Data scientists to help you get value out of the data you have
10 Thank you! Any Questions?
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