Panel 6 - Legal and Social Implications of Shared Collections

Panel Questions:

• For your domain, what are the legal requirements related to data management (Sarbanes-Oxley, IRB, HIPAA, IP)?
• What implications does sharing of data impose on data management systems?
• Can legal requirements be expressed as policies that automate enforcement?
• What are the issues (legal, proprietary, accessibility) behind internationally shared collections?

Ann Zimmerman, University of Michigan [PowerPoint]

opendata.si.umich.edu

• why is it hard to share data ?
• i-school master programs
• paper co-authorship for data providers
• data papers !

Noshir Contractor, Northwestern University [PowerPoint]

slides worth a look - some keywords

• self-interest
• social and resource exchange
• mutual interest and collective action
• theory of contagion
• theory of balance
• homophily
• proximity
• co-evolution

sharing data and knowledge can expose researcher to avalanche of requests for more information - competent jerks vs lovable fools

Melissa Cragin [PowerPoint]

• small science research and data sharing issues - slide well worth looking at - different perspective to big science

Panel 5 - Developing Assessment Criteria

Panel Questions:

• Who defines the required assessment criteria?
• How is a consensus reached on the required assessment criteria?
• Can an international standard form the basis for assessment criteria?
• Are assessment criteria dependent upon the organizing community?

Mark Conrad, NARA LCDRG and ISO MOIMS-rac [PowerPoint]

NARA's ERA - 163 high-level requirements -some beyond current state-of-the-art

NCAST - Nara's technology research group

• expand scope of advanced research
• bring new technologies and methods into NARA
• identify needed expertise and assist NARA to secure it

Who defines required assessment criteria ?

• Acts - Federal, Presidential, FOI
• Communities - president, congress, courts, other fed agencies, public
• archival principles eg arrangement, provenance

Jane Greenberg, Dryad [PowerPoint] (UNC Metadata Research Centre)

• one-stop deposition/access for data objects supporting published research in evolutionary biology
• data package metadata includes (1) article metadata (2) datafile metadata eg genbank object, treebase object
• hierarchy of goals - preservation, discovery, sharing, synthesis
• HIVE - vocabulary services to researchers (Helping Interdisciplinary Vocabulary Engineering) - uses SKOS

John Graybeal, Ocean Observatories Initiative

Steve Richard, Arizona Geological Society [PowerPoint]

Panel 4 - DataNet Federation

Panel Questions:

• How challenges are addressed by your data management infrastructure?
• What are the current practices for data management, curation, preservation?
• What are the interoperability mechanisms for federating registries, ontologies, and processing models (workflows)?
• "What do you see to be the biggest challenges and opportunities with respect to evolution of a DataNet Federation?"
• "What is your vision for a DataNet Federation?"

Bill Michener, DataOne - see pilot catalog for earth observations

http://prototype.dataone.org/pilotcatalog/

• priority - user-friendly tools for researchers eg automated metadata acquisition tools
• more investment needed in documenting researcher requirements for tools

Sayeed Choudhury, Data Conservancy [PowerPoint]

Reagan Moore, DataNet Federation Consortiuim [PowerPoint]

Policy-based data environments:

• Purpose - reason a collection is assembled
• Properties - attributes needed to ensure the purpose
• Policies - control for ensuring maintenance of properties
• Procedures - functions that implement the policies
• State information - results of applying the procedures
• Assessment criteria - validation that state information conforms to the desired purpose
• Federation - controlled sharing of logical name spaces

Goal - Generic Infrastructure

• Manage all stages of the data life cycle
• Data organization
• Data processing pipelines
• Collection creation
• Data sharing
• Data publication
• Data preservation
• Create reference collection against which future information and knowledge is compared
• Each stage uses similar storage, arrangement, description, and access mechanisms

Panel 3 - Large-Scale Data Management Challenges (bottom-up drivers for infrastructure):

Panel Questions:

• What technical solutions exist that meet your academic project requirements?
• What technical solutions exist that meet corporate requirements?
• What requirements are unique to the academic environment?
• What requirements are unique to the corporate environment?
• Are there common approaches for managing large-scale collections?

John Orcutt, Ocean Observatories Initiative [home page]

• linking observational data and users
• uses message-based technology - this enables better enforcement of policy
• uses only cloud services
• has three components (1) deep sea nodes (2) coastal nodes (3) regional cable network
• 25 year operational lifetime
• transformative
• policy-based data systems
• rich data and metadata practices
• scalable architecture
• both geospatial and temporal enabled

Ed Kearns, National Climatic Data Center [PowerPoint presented by Kenneth Galluppi]

Laurin Herr, Cinegrid [PowerPoint]

• media convergence motivates CineGrid
• media technology is driven by 3 sectors - entertainment/media/the arts - science/medicine/education/research - military/intelligence/security/police
• 35mm is still the standard but digital films may never see this format
• hollywood studios now facing decisions about what to throw away - eg out-takes
• use RDF framework for objects
• use iRODS because of programmable microservices

Philip Maechling, Southern California Earthquake Center [PowerPoint]

• data is both observational and from simulations
• the majority of data is from simulations
• 2-way inter-dependence of data and models
• computer-generated models should always have associated verified and validated results and code must be included
• metadata strategies - tools for converting wikis, labels and entries into the metadata store
• advocates practical metadata standard - should be just enough to go from the data to the results

Panel 2 - Promoting Re-use of Scientific Data Collections

Panel Questions:

• How do you handle organization of collections today?
• What are the biggest issues with building collections for new communities?
• When new communities access existing data collections, what new access capabilities are required?
• What level of description is required to meet expectations of new communities?
• Is long-term sustainability enabled through repurposing of collections?
• Are their other driving purposes behind promoting re-use of collections?
• Which institutions can be approached for sustaining re-purposed collections?

Peter Wittenburg, Max Planck Society, Institute for Pyscholinguistics [PowerPoint]

Sudha Ram, iPlant Collaborative [Paper]

• building cyberinfrastructure to support the solution of grand challenges in plant biology
• they are a virtual organisation
• their long term focus is on sustainable food supply
• their challenge is to build an iPlant tree-of-life (IPTOL) - a single tree of evolutionary relationships for all green plants
• methodology: Genotype to Phenotype i.e. construct a methodology whereby an investigator, given the genomic and environmental information about a given plant, can predict it’s characteristics
• modeling is still in it’s infancy compared to other disciplines
• Strong focus on data integration, not simulation: Plant science is truly data driven.
• no single ontology - sequences, gene expressions, whole plant data, environmental and climate data - therefore Data Integration is Key
• issues regarding data integration, provenance, and quality are key enabling technologies
• observes the language barrier between the bio community and the computing/CI community is far higher than any other discipline

Roy Williams, International Virtual Observatory Alliance [PowerPoint]

John Harrison, SHAMAN [PowerPoint Talk, Responses to questions]

Jonathan Crabtree, Odum Social Science Institute [PowerPoint]

Panel 1 Data Life Cycle Management

Panel Questions:

• Which phases of the data life cycle are managed by your repository?
• How do data management requirements differ across the data life cycle?
• What systems do you use to support the data life cycle?
• Can you generalize the mechanisms used to migrate data between different phases of the data life cycle?

Mark McFarland, Texas Digital Repository {PowerPoint}

• TDL has 19 member institutions and use DSpace/Manakin.
• currently moving contents from inside the TDL to a distributed datastore using iRODS.
• still to build a federated discovery layer
• are only just starting to deal with datasets
• collaboration with Mexican research infrastructure

Erin O'Meara, electronic records archivist, Carolina Digital Repository [PowerPoint]

• mission to ensure UNC digital scholarly output is safe and accessible far into the future
• use Fedora as middleware and iRODS for data grid
• content model uses PREMIS/METS/MODS
• custodial shift after objects are deposited
• they apply an OAIS model throughout the architecture
• noted that the data management effort increases during SIP preparation
• their challenge is to develop an approach to active at-risk data - potential approach is precustodial intervention
• CDR Demo

Stacy Kowalczyk, Indiana University, Scholarly Data Services [PowerPoint]

• have a new "Data Publishing service" combining their IUScholarWorks institutional repository and existing Scholarly Data System (was called Massive Data Storage System - MDSS)
• provide data management support and data access
• data will have a persistent URL so it can be linked to publications
• Preservation – bit level
• Use Dspace and for small datasets DSpace bitstreams - for large datasets DSpace records link to stable URLs in MDSS
• facilitate the process for submitters via the DSpace Configurable Submission system
• facilitate the data collection manager’s process via steps in the DSpace workflow system
• engage researchers during projects not at the end
• use immediate benefits to drive automatic capture and 'volunteering’ of metadata
• reduce costs by re-engineering curation processes to leverage this rich metadata and volunteered effort
• Diagram of Data Curation Lifecycle Elements

Cynthia Ghering, Michigan State University [Paper]

• very decentralised environment
• there is NO institutional repository
• Primary Investigator is responsible for curation of their data (chair and dean have oversight)
• they have a digital curation planning project to support investigators

Michael Wright, National Science Digital Library [Paper]

• michael wright was unable to present
• NSDL since 2008 is now a network of repositories for science education materials and not research materials and they have actually de-accessioned much of the research material

Summit Introduction

Desired outcomes were:

• Identification of exemplar data management systems
• Identification of opportunities for federation across institutional repositories
• Identification of interoperability mechanisms at technology, social, and legal levels
• Promotion of collaborations between the identified parties
• Roadmap for the research agenda needed to federate systems.

Interesting References

SHAMAN - Sustaining Heritage Access through Multivalent ArchiviNg

(Securing communication with the future)

http://shaman-ip.eu/

Under a mid term vision, SHAMAN will design and progressive implement large-scale European-wide collections with innovative access services that support communities of practice in the creation, interpretation and use of cultural and scientific content, including multi-format and multi-source digital objects. They will be combined with robust and scalable environments which include semantic-based search capabilities and essential digital preservation features.

For the longer term, SHAMAN will develop radically new approaches to Digital Preservation, such as those inspired by human capacity to deal with information and knowledge, providing a sound basis and instruments for unleashing the potential of advanced ICT to automatically act on high volumes and dynamic and volatile digital content, guaranteeing its preservation, keeping track of its evolving semantics and usage context and safeguarding its integrity, authenticity and long term accessibility over time.

Archivist on Board - including an archivist in the research team

http://www.qualitative-research.net/index.php/fqs/article/view/1022

[abstract] In this article, we demonstrate the advantages of including an archivist as a member of the research team based on our experience with two multi-site, multi-method, primarily qualitative projects. The Principal Investigator, committed to the principles of data sharing and preservation, recruited a data archivist at the inception of the projects. Several issues arose that are not typically encountered in a research project: investigators needed to agree to the principles of data preservation and sharing—concepts that are not typically discussed a prior; the research ethics application and approval had to incorporate the conditions of preservation and sharing; and we needed a comprehensive plan for preservation that would ensure the creation of high-quality data products worthy of deposition. This comprehensive plan required that we identify the standards of archiving, incorporating within the data management plan an appropriate inventory list and a design for tagged fields and a corresponding Document Type Definition (DTD) used in the mark-up of textual data. A plan for creating access to the data for secondary analysis was also developed. The conditions of use, cataloguing records, and citation guide are all part of preparing the data for access. Finally, the challenges of this approach are summarized.

Recommendations for independent scholarly publication of data sets

Jonathan Rees, Science Commons white paper Creative Commons, March 2010

http://sciencecommons.org/wp-content/uploads/datapaperpaper.pdf

Data Archiving and Networked Services (DANS)

http://www.dans.knaw.nl/en

Since its establishment in 2005, DANS has been storing and making research data in the arts and humanities and social sciences permanently accessible. To this end DANS itself develops permanent archiving services, stimulates others to follow suit, works closely with data managers to ensure as much data as possible is made freely available for use in scientific research.