Current and emerging scientific data curation practices

Current and Emerging Scientific Data Curation Practices
Michael Day, Digital Curation Centre UKOLN, University of Bath [email_address]
4th Summer School on preservation in digital libraries, Tirrenia, Italy, 12 June 2008

Presentation outline:
Some definitions
Reasons for curating research data
Some specific issues:
Scale and complexity, diversity of social contexts, costs
Types of research data collection
Roles and responsibilities
Potential for collaboration
Some open questions

Definitions – research data (1)
What is research data?
An extremely broad category of material
“ ... any information that can be stored in digital form, including text, numbers, images, video or movies, audio, software, algorithms, equations, animations, models, simulations, etc.” (National Science Board, Long-lived digital data collections , 2005)
In practice, it can mean almost anything

Definitions – digital curation (1)
DCC definition:
“ ... maintaining and adding value to a trusted body of digital information for current and future use; specifically, we mean the active management and appraisal of data over the life-cycle of scholarly and scientific materials” (http://www.dcc.ac.uk/)

Definitions – digital curation (2)
Main themes:
Curation is seen as an ongoing process, e.g. the active management of data over time
It is also about adding-value through things like community annotation
Life-cycles are important, long-term stewardship not always necessary
Not identical to digital preservation

Why curate research data? (1)
Part of the normal research process:
The need for others to validate and replicate research
In some disciplines, supporting data is routinely made available to reviewers and linked from journal papers
Principles of sharing and openness are firmly embedded in some disciplines

Extrinsic and intrinsic value;
High investment in research
Data can be very expensive to capture and analyse
Data is impossible to recreate once lost
Observational data (by definition) is irreplaceable
Current generations of instruments can gather more data than can be analysed

The potential for creating 'new' knowledge from existing data:
Re-use, re-analysis, data mining
Annotation, e.g. in molecular biology astronomy
Combining datasets in innovative ways, e.g. mapping biodiversity data onto ecological GIS
“ Science 2.0”

It is increasingly a requirement of some research funding bodies
Some have quite mature data retention policies (not necessarily for permanent retention)
Increasing expectation of access to data from publicly-funded research
OECD Principles and guidelines for access to research data from public funding (2007)

Institutional asset management:
Universities and other research organisations invest very large sums of money into research activities
Research data is a key output of this activity
It is, therefore, an institutional asset that needs stewardship

Promoting the institution, research group or individual:
Re-use helps promote visibility and 'impact'
Institutions become acknowledged 'centres of competence'

Scale and complexity (1)
Scale (1):
The “digital deluge”
e-Science
New generations of instruments
Computer simulation
Mny terabytes generated per day, petabyte scale computing (and growing)

Scale (2):
Problems of scale are particularly acute in traditional 'big-science' disciplines:
Particle physics (e.g., Large Hadron Collider)
Astronomy (sky surveys, etc)
Also increasingly important in:
Bioinformatics, crystallography, engineering design, and many others
May be cheaper just to generate the data again, e.g. for gene sequencing

Complexity (1)
Research data is extremely diverse - not really a single category of material
tabular data, images, GIS, etc.
raw machine output vs, derived data
varying levels of structure (XML, legacy formats, etc.)
many different standards
Research data is not homogeneous
No one-size-fits-all approach possible

Complexity (2):
Even wider range of social contexts in which data is used (and shared)
DCC SCARP project has been exploring disciplinary factors in curation practice
Practice even within single disciplines is very fragmented
Case studies ongoing
Big-science archives, medical and social sciences, architecutre and engineering, biological images

Diversity of contexts
Research cultures
Data practices vary widely, even within a single discipline
Gene sequence data is typically deposited in public databases
In proteomics sharing is not so widespread; partly driven by lack of standards, but also about who has exploitation rights
Role of commercial interests
Pharmaceuticals, architecture and engineering, geological prospecting

Costs
Recent JISC study (2008):
Focused on the institution level
Some findings:
The complex service requirements for curating research data means that institutions are setting-up federated approaches to repository development
Currently ingest costs are much higher than long-term storage and preservation costs
Start-up (and R&D) costs are high, but there can be economies of scale

Research data collections (1)
A typology (1):
From National Science Board report Long-lived digital data collections (2005)
Research data collections – the products of one or more focused research projects
Resource or community data collections – collections that emerge to serve particular subject sub-disciplines
Reference data collections – serve a broader and more diverse set of user communities

A typology (2)
Research data collections – the products of one or more focused research projects
Extremely diverse
Have small user communities
Inconsistent standardisation
Typically no funding available to support the collection beyond the project funding cycle

A typology (3)
Resource or community data collections – collections that emerge to serve particular subject sub-disciplines
Often establish community-level standards
In many cases supported by funding bodies or particular research institutions

A typology (4)
Reference data collections – collections that serve a broader and more diverse set of user communities
conformance to robust, well-established standards essential
Expensive (time and money)
Budget typically comes from multiple sources, expectation that collections will persist

Data at risk
Data in “research data collections” is most at risk
A modern version of the “file-drawer problem”
Data stored on personal hard-drives or on media; largely undocumented (c.f DAF)
Particular challenge when the data creator has retired or moved to another institution
Data creators not aways aware of its value
The reward structure of science is not helpful

Collections can evolve:
For example, Protein Data Bank (PDB)
Launched 1971, small-scale, focused on a limited set of biological structures
Now is the main source of experimental structural information on biological macromolecules
How do we recognise research data collections that have the potential to evolve into reference collections?

Roles and responsibilities (1)
Long-lived data collections (NSB)
Data authors
Data managers
Data scientists
Data users
Funding agencies
Dealing with data (JISC)
Scientist
Institution
Data centre
User
Funder
Publisher

Scientists
Initial creation and use of data
Expectation of first use and in gaining appropriate credit and recognition
Responsible for:
Managing data for life of project
For using standards (where possible)
For complying with data policies
For making the data available in a form that can (easily?) be used by others

Institutions:
Role less clear
Institutional policies may require short-term management of data
Advocacy and training
Some institutions are developing repository services
Are rarely currently used for research data
Federated approaches maintain disciplinary involvement

Data centres
Undertakes curation and provides access
Responsible for:
Selection and ingest
Participating in the development of standards
Protecting the rights of data creators
Supporting ingest and metadata capture
Supporting re-use (tools and services)
Training

Users:
Users of third-party data
Responsible for:
Adhering to any licenses and restrictions on use
Acknowledging data creators and curators
Managing any derived data
Provide feedback to scientists and data centres

Funding bodies:
Acting at policy level
Responsible for:
Considering wider policy perspectives
Developing policies in co-operation with other stakeholders
Monitoring and enforcing data policies
Support for long-term data management
Support for data curation

Curation infrastructures (1)
Focus on the generic:
Need for a balance between:
The 'bottom-up' discipline-based drivers that promote the generation of research data
The policy level, looking to make cost effective investment in curation
When building Infrastructures, focus on the generic
Storage systems and middleware
Identifying the needs of the wider community

Curation infrastructures (2)
The need for collaboration:
Need for 'deep-infrastructure' recognised as far back as 1996 by the Task Force on Archiving of Digital Information
Digital preservation involves the "grander problem of organizing ourselves over time and as a society ... [to manoeuvre] effectively in a digital landscape" (p. 7)

Collaboration on curation (1)
Collaboration in science:
Collaboration is deeply embedded in some (but not all) research cultures
Research collaboration is a well-established phenomenon that has been studied by sociologists of science (and others)
The nature of collaboration differs markedly between academic disciplines

Scientific collaboration types:
Informal social networks
Helps to define disciplinary norms and interpretational paradigms
Formalised, semi-permanent organisations
Traditionally most common in "big-science" domains
The growth of e-science has emphasised the collaborative nature of research

Implications for curation;
Collaborative data curation facilities might emerge first in sub-disciplines that have a more participatory collaboration pattern or otherwise have a strong emphasis on data sharing
Need for more systematic research into this across all research domains
Building on DCC SCARP

Collaboration is:
Currently focused at disciplinary or sub-disciplinary levels
It is embedded within the workflows of particular research communities (e.g., genomics, crystallography, astronomy)
Takes advantage of the specialised knowledge available within particular "designated communities"

Collaboration and standards:
Common standards emerge where there is a recognised need for data sharing
The existence of common standards make data centres and repositories viable

Interdisciplinary collaboration;
Previously little demand for collaboration on data curation across disciplinary borders
But the fundamentally collaborative nature of e-research should make us challenge this:
A need to pool resources and expertise
A need for supporting infrastructures

Need for strategic alliances
National initiatives, e.g. DPC, NDIIPP, nestor
European Alliance for Permanent Access

Open questions (1)
The role of institutions
Universities are setting up repositories
Rhetoric suggests that they aim to manage all research outputs (i.e. including data)
In practice, they currently mostly deal with research papers
What is the role of the institution with regard to research data?
Do they have the trust of researchers?

Open questions (2)
Are generic approaches possible?
There is a tension between the diversity and complexity of research data and the need for generic solutions
Promoting data sharing between disciplines
Interoperability

Open questions (3)
Data can only exist as part of wider research contexts
They are referenced in papers and other forms of research communication, in project documentation and archives
Linked from project Web pages, etc.
How do we ensure that curated data remains integrated within this scholarly web?
How do we make the links persistent?

Summing-up
Size and diversity
Research data is extremely diverse
No one-size-fits-all solution
Scale is a growing problem
Infrastructures:
Many data curation services already exist – good practice
Need to integrate these (and institutional initiatives) at the policy level

Further reading
Neil Beagrie, Jullia Chruszcz, and Brian Lavoie, Keeping research data safe: a cost model and guidance for UK universities (JISC, 2008)
Liz Lyon, Dealing with data; roles, rights, responsibilities and relationships (JISC, 2007)
National Science Board, Long-lived digital data collections: enabling research and education in the 21st century (NSF, 2005)

Exercise

Exercise (1)
4 Scenarios:
A research team in 2028 is evaluating a particular set of content for use in a particular project (Web content, multimedia, images, dataset)
Ask questions about what they would need to know to interpret the content correctly
Evaluate the relative importance of: content, context, appearance, structure, behaviour

Exercise (2)
Scenario 1: A research project in 2028 is trying to explore how the first generation Internet was used by European political parties in the 1990s to promote citizen participation in policy formation. The investigators know that a large amount of Web material from this period is held by an organisation called the Internet Archive, and they have begun to use data mining tools to explore the extent of their holdings. What will they need to know about the collection in order to be able to do their work properly?

Exercise (3)
Scenario 2: Art curators in 2028 are trying to put together an exhibition of digital art in a public gallery. They have found that a university art department retains a collection of digital art resources (chiefly multimedia) produced by their undergraduate students between 2000-2005, some of which have gone on to become extremely important figures in the art establishment. When evaluating the collection for use in the exhibition, what would they consider to be the most important object characteristics?

Exercise (4)
Scenario 3: Healthcare researchers in 2028 are trying to trace the historical incidence certain lung abnormalities and have access to a massive database of medical images (X-rays) that they intend to submit to the most up to date content-based image retrieval techniques. The database is made up of imaging output from more than one hospital and the researchers are worried that certain parameters essential to their research (e.g., the age and sex of patient, imaging dates, etc.) may be missing. What else need they know about the database before they can start running their search algorithms?

Exercise (5)
Scenario 4: A research project in 2028 is trying to find links between climate records and biological species diversity in south-west England. The principal investigator has found a promising dataset of geographically-relevant biodiversity information in a local history museum. What more does she need to know about this dataset before she can get her team to try to integrate this dataset (and others like it) with historical climate models?

Acknowledgements The Digital Curation Centre is funded by the JISC and the UK Research Councils' e-Science Core Programme. http://www.dcc.ac.uk/ UKOLN is funded by the Museums, Libraries and Archives Council, the Joint Information Systems Committee (JISC) of the UK higher and further education funding councils, as well as by project funding from the JISC, the European Union, and other sources. UKOLN also receives support from the University of Bath, where it is based. http://www.ukoln.ac.uk/

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Current and emerging scientific data curation practices

by Michael Day

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