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Slides from a presentation given at a Digital Curation Centre workshop, Cardiff University, 14 May 2013
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Developing
institutional RDM
services
Michael Day
Digital Curation Centre (DCC)
UKOLN, University of Bath
DCC Workshop, Cardiff University 14 May 2013
Session outline
Managing active data Storage options
Long-term retention of data Selection criteria
Data repositories
Finding and citing data Data registries and metadata
Presentation based on: Sarah Jones, Graham Pryor and Angus Whyte, How to Develop Research Data Management Services – a guide for HEIs (DCC, 2013):
http://www.dcc.ac.uk/resources/how-guides/how-develop-rdm-services
Some slides reused from RDMRose training materials:
http://rdmrose.group.shef.ac.uk/
Managing active data: key tasks
Researchers: Have a duty to ensure that research data is stored securely and backed-up on a
regular basis
Have choices (e.g. network drives, laptops, external storage devices, online / cloud-based storage)
Need to take data security seriously
This should be considered as part of the data management planning process
Institutions: Need to be constantly review data holdings and RDM practices in order to
evaluate whether current storage infrastructures are sufficient
May need to make a case for investing in the provision of additional data storage capability
Need procedures for the allocation and management of storage
Need to be flexible, taking account of a diverse range of research contexts and data storage requirements
Research data storage
Trend for some HEIs to enhance the capacity of research data storage facilities Extending capacity of existing filestores (e.g. Bath)
Exploring secure cloud storage
Utilising High Performance Computing facilities
Managing storage University of Bristol (data.bris) – registered researchers (data
stewards) are allocated 5TB storage to manage, e.g. deciding how long data should be kept, who has access, etc.
http://data.blogs.ilrt.org
Options for managing active data
Cloud storage options There may be benefits in terms of costs and expertise
There may also be risks (e.g. loss of control, jurisdictional issues)
Janet Brokerage - promoting the use of cloud and off-site data centre facilities
Academic dropbox-like services Dropbox is often used for sharing and synching data between
machines, but institutions are keen to retain control
Systems developed in-house Typically developed with an disciplinary focus, e.g. BRISSkit
(biomedicine)
Selecting data for retention
RCUK, Common Principles on Data Policy (2011): “Data with acknowledged long-term value should be preserved and remain
accessible and usable for future research”
http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx
Institutions will need to establish clear criteria to guide decisions on what should be kept It will not be possible to retain everything
Carefully considered selection processes are essential to help prioritise that data that has long-term value
Institutional selection processes will need to take account of: Data that institutions are legally obliged to retain (or destroy), e.g. for contractual
or regulatory reasons
Different disciplinary practices (e.g., some disciplines will have mature data sharing infrastructures and will already deposit data with third party services)
Researcher sensitivities about losing control of data (deposit agreements)
Developing guidance on selection
Establishing guidelines, processes and good practice for data selection and deposit can be one of the more challenging aspects of an RDM service There is a need for buy-in from researchers
There is a need for clarity on what kinds of data are within the remit of an institutional RDM service
There may be a need to apply different levels of curation, e.g. depending on the perceived value of the data accepted
DCC selection categories
DCC How to Select and Appraise Research Data for Curation (Whyte and Wilson, 2010) proposes seven main criteria: Relevance to mission
Scientific or historic value
Uniqueness
Potential for redistribution
Non-replicability
Economic case
Full documentation
http://www.dcc.ac.uk/resources/how-guides/appraise-select-data
Data repositories
Focusing on how data will be preserved and made available for others Main options:
Developing an institutional data repository Building, where possible, on existing systems, e.g. IR, CRIS,
etc.
Essex Research Data demo: http://researchdata.essex.ac.uk/
Liaising with external research data repositories (or data centres)
Often subject based, some UK data centres supported by funding bodies
Providing researchers with information on external services
RCUK Common Principles
RCUK, Common Principles on Data Policy (2011):
“To enable research data to be discoverable and effectively re-used by others, sufficient metadata should be recorded and made openly available to enable other researchers to understand the research and re-use potential of the data. Published results should always include information on how to access the supporting data”
Also EPSRC Principle 6
http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx
EPSRC Expectation V
“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. Where the research data referred to in the metadata is a digital object it is expected that the metadata will include use of a robust digital object identifier (For example as available through the DataCite organisation - http://datacite.org).”
http://www.epsrc.ac.uk/about/standards/researchdata/Pages/expectations.aspx
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e
Some questions to consider
What metadata is required to adequately record datasets? What is “sufficient metadata” for discovery and re-use?
Does any of this metadata already exist? If so, where might it be found?
If not, how can the appropriate metadata be generated or captured?
Will there be a need to share this metadata, e.g. with third-party discovery services? National data services? If so, what standards exist to support metadata sharing?
Examples: UKOLN Scoping Study
Scientific Data Application Profile Scoping Study (UKOLN, 2009) Building on work undertaken on the Scholarly Works Application Profile
(SWAP)
Analysed the metadata used by UK data centres and repositories, selected domain models (e.g. DDI, CCLRC Metadata Model, CIDOC CRM)
Concluded that: Simple Dublin Core (e.g., as mandated by OAI-PMH) would be insufficient
There was sufficient convergence between the different schemas to suggest that a generic metadata profile could be constructed
A generic metadata profile would benefit interdisciplinary research and institution based services (e.g. IRs)
http://www.ukoln.ac.uk/projects/sdapss/
Examples: DataCite metadata (1)
DataCite:
Organisation aiming to facilitate easier access to (and citation of) research data, e.g. through the use of persistent identifiers (DOIs)
DataCite Metadata Schema (currently v. 2.2, 2011) defines core metadata properties
Broadly based on Dublin Core concepts
http://schema.datacite.org
Examples: DataCite metadata (2)
Mandatory Properties: Identifier
Creator
Title
Publisher
PublicationYear
Administrative Metadata LastMetadataUpdate
MetadataVersionNumber
Optional Properties: Subject
Contributor
Date
Language
ResourceType
AlternateIdentifier
RelatedIdentifier
Size
Format
Version
Rights
Description
Examples: University of Oxford
The DaMaRO project at the University of Oxford is developing a metadata schema for its DataFinder (Rumsey, 2012).
A three-tier metadata approach: Mandatory minimal metadata to enable basic discovery, such as
Creator, Title, Publisher, Date, Location, Access terms & conditions
Mandatory contextual metadata (mostly administrative and partly based on EPSRC expectations), such as Funding Agency, Grant Number, Last access request date, Project Information, Data Generation Process, Why the data was generated, Date (range) of data collection, Reasons for embargoes
Optional metadata (including discipline-specific metadata) to enable reuse, such as machine settings and the experimental conditions under which the data were gathered
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e
Examples: University of Essex
RDE Metadata Profile for EPrints
Based on DataCite, INSPIRE, DDI 2.1 and DataShare
Mixture of generic schema and standards specific to
social science data
http://data-
archive.ac.uk/media/375386/rde_eprints_metadatapr
ofile.pdf
Seems to be convergence on layered approach
Some practical questions (1)
Technical choices for institutions: Developing new institutional services, e.g. the
approach taken by ANDS: http://www.ands.org.au/guides/metadata-stores-solutions.html Defining metadata stores by their coverage, the granularity of
data that they describe, and the specialisation of their descriptions (e.g. collection-level, object level, local, institutional, national and discipline-specific)
Building upon existing infrastructures, e.g.: Institutional Repositories
CRIS (e.g. Pure, Symplectic, Converis)
Some practical questions (2)
Research Information Management interaction? There is interest in what RIM standards like CERIF can offer RDM (e.g.
potentially richer metadata structures for linking research outputs with organisational groupings and funding streams, some level of buy-in from funding bodies), but implementation
CERIF for Datasets (C4D): http://cerif4datasets.wordpress.com
We need to think about how metadata can be shared with: Discipline-based repositories and data centres
Emerging national (and international) discovery infrastructures Australian National Data Service
Uses RIF-CS schema (based on ISO 2146:2010) as a data interchange format
Jisc and DCC are currently exploring the options for collating metadata about research data at national level
Data Citation
Issues include (Ball & Duke, 2011a and b):
At what granularity should data be made citeable?
How to credit each contributor in a dataset that is
assembled from very many contributions?
Where in a research paper should a data citation be
given (e.g. a paper describing a dataset versus
subsequent papers using it)?
What to do with frequently updated data?
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e
DataCite
DataCite (http://www.datacite.org) is a not-for-profit organisation that aims to promote and support the sharing of research data
They are developing an infrastructure that supports methods of data citation, discovery, and access
They are currently leveraging the DOI (Digital Object Identifier) infrastructure, which is also used for research articles
They can provide DOIs for datasets
DataCite DOIs have to resolve to a public landing page with information about the dataset and a direct link to it
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e
DataCite
Basic form:
Creator (PublicationYear): Title. Publisher. Identifier
Version and ResourceType are optional extra elements
For citation purposes, DataCite recommends that DOI names are displayed as linkable, permanent URLs
More info in DataCite (2011)
University of Poppleton (2011): Precipitation measurements 1905-2010 taken at Western Bank weather station. Meteorological service, The University of Poppleton. http://dx.doi.org/10.1594/UoP.MS.298
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e
References
Ball, A., (2009). Scientific Data Application Profile Scoping Study Report. Bath: UKOLN, University of Bath. Retrieved from: http://www.ukoln.ac.uk/projects/sdapss/
Ball, A., & Duke, M. (2011a). Data Citation and Linking. DCC Briefing Papers. Edinburgh: Digital Curation Centre. Retrieved from http://www.dcc.ac.uk/resources/briefing-papers/introduction-curation/data-citation-and-linking
Ball, A., & Duke, M. (2011b). How to Cite Datasets and Link to Publications. DCC How-To Guides. Edinburgh: Digital Curation Centre. Retrieved from http://www.dcc.ac.uk/resources/how-guides/cite-datasets
DataCite (2011). DataCite Metadata Schema for the Publication and Citation of Research Data. Version 2.2. London: DataCite. Retrieved from http://schema.datacite.org/meta/kernel-2.2/doc/DataCite-MetadataKernel_v2.2.pdf. doi:10.5438/0005
Rumsey, S. (2012). Just enough metadata: Metadata for research datasets in institutional data repositories [PowerPoint presentation]. Oxford: The University of Oxford. Retrieved from http://damaro.oucs.ox.ac.uk/docs/Just%20enough%20metadata%20v3-1.pdf
May-13
Learning material produced by RDMRose
http://www.sheffield.ac.uk/is/research/projects/rdmros
e