U.S. Department of the InteriorU.S. Geological Survey

Scientific Information Management at the U.S. Geological Survey: Issues, Challenges, and a Collaborative Approach to Identifying and Applying Solutions

David L. Govoni and Thomas M. GuntherUSGS Geospatial Information Office

Geoinformatics 2006May 12, 2006

Geospatial Information Office (GIO)Science Information and Education Office

Responsibilities: Publishing policy and coordination

Libraries and Information Centers

Web infrastructure and content policy

Product Warehouse and distribution

Education and outreach

Scientific information management

Geospatial Information Office (GIO)Science Information and Education Office

Accomplished in partnership with USGS science and administrative programs through a combination of: Governance

Consultation

Facilitation

Collaborative development

Goal is to enable and support an “Integrated Information Environment” for the USGS

Integrated Information Environment (IIE)

Problems, problems … everywhere

Common issues identified from discussions with scientists and others across USGS disciplines: Search and discovery (especially by place and topic)

Database access and integration

Interoperability of tools and processes

Advanced visualization, modeling, other tools

Archive and preservation

Compliance with mandates: Security, science quality, publishing, records

management, accessibility, …

The solution? Good news … bad news

Lots of talent, innovation, and motivation, but: Widely scattered geographically and organizationally Many local efforts unknown to others in USGS Duplicative or overlapping in purpose, capabilities Built on multiple platforms in multiple languages Some good, some not so good Some potentially scalable, some not “Costly” to organization as a whole

So how do we …

Increase awareness? Identify “best of breed”? Accelerate diffusion? Provide support? Institutionalize?

Communities of Practice (CoPs)

What is a “Community of Practice”?

Communities of Practice are groups of people who share a concern or a passion for something they do and learn how to do it better through the process of collective learning as they interact regularly. CoPs are: Problem driven

Self-organizing, voluntary, and motivated

Not constrained by position in formal organizations

Not formally chartered or accountable through management chains as for teams Modified after

Etienne Wenger(www.ewenger.com)

USGS Scientific Information Management (SIM) Workshop

Three day Scientific Information Management Workshop, March 2006

150+ people representing all USGS regions and both science and administrative programs

Other DOI bureaus, other public and private-sector organizations also participated

Explicit focus on intersection of SIM and CoPs

SIM Workshop

Three parts: Overviews of problems and approaches to SIM both

inside and outside of the USGS

Introduction to “Community of Practice” concept as a framework for collective learning and collaborative problem solving

Breakouts designed to simultaneously: Identify key issues and needs Explore and encourage the formation of CoPs to develop

solutions

Potential communities

Data/information management Field data for small research projects Large time series data sets Scientific data from monitoring programs

Classification and discovery Metadata Knowledge organization systems

Delivery Digital libraries Portals and frameworks

Potential communities

Interoperability and integration Database networks

Preservation and long-term access Archiving of scientific data and information

Preservation of physical collections

Knowledge management Knowledge capture

Emerging workforce

Outcomes

At least 9 of 12 potential communities agreed to continue on as “formal” CoPs

Other potential communities proposed, e.g., Open access

Open source software

Search

Program management

Management commitment to support creation of bureau-wide infrastructure to enable current and future CoPs

USGS Communities Network

Common gateway to all known USGS CoPs Framework of shared collaborative services and tools

available to support interested communities: Discussion forums Document management Digital library and bibliography management News and Events calendar Wikis and annotation RSS feeds …

Initially USGS-only but eventually available to external collaborators and partners

Workshop evaluation

Reviews positive: Met or exceeded expectations: 89%

Change practices as result: 33%

Participate in communities: 72%

Learned new tools or approaches: 50%

Make valuable new contacts: 90%

Suggests broad interest and appeal of communities approach

(based on ~50% survey response)

What was learned

One size won’t always fit all, but … Many issues are common to all USGS disciplines

Local approaches may be broadly applicable, scalable, and cost-effective for the USGS as a whole

Those “in the trenches” know best: Cannot implement top-down SIM

solutions

Solutions can come from (and be managed from) anywhere

What was learned … a digression

SIM needs to be considered from two distinct, but intimately related perspectives: “Information life-cycle” or Producer perspective

Course of data and information from initial acquisition to final disposition

Consumer perspective How data and information is used to accomplish tasks

Producer perspective

Fieldwork(in situ, in vitro,

in silico)

Analysis, synthesis& interpretation

Preparation & distribution

(via any medium)

Preservation & archiving

refers to

Direct & remote observation, monitoring &

recording

Laboratory experiments,

modeling, visualization

Publications, data, talks, seminars, models, libraries

refers to refers to refers to

includes includes includes

Collect Analyze Publish Preserve

Records management,

data rescue, physical sample preservation

includes

Consumer perspective

Resource discovery(search, browse,

mine)

Acquisition(contact, determine

restrictions, access, & download, etc.)

Evaluation(assess relevance,

quality, significance, suitability)

Use for/Integration into studies, models,

visualizations, experiments, etc.

refers to

Catalogs; controlled vocabularies,

ontologies; geospatial, topical & preservation

metadata

Documented exchange formats &

protocols; administrative

& legal metadata

Contextual information, e.g., documentation,

reports; quality & functionality metadata

refers to refers to refers to

depends on depends on depends on

Find Get Understand Use

Documenteddata schema, service

models, protocols, etc.

depends on

“Metainformation” is critical to both

Broadly defined here to encompass both “classic” metadata and “contextual information” (rules, assumptions, ontologies, schema, documentation, etc.) that impart deeper understanding or facilitate use

Metainformation: Critical to our ability to conduct integrated studies

Critical to maintaining long-term access

Should be, but very often is not, formally captured and preserved all along the information life-cycle

(End of digression)

What was learned … SIM is not easy

Despite advances in technology, many tasks: Remain time-consuming

Require significant involvement by scientists (sometimes at the expense of their science)

Lack incentives to “do the right thing”

Volume outpacing resources Legacy data may already be beyond saving

SIM is not an option

Good stewardship of data, information, physical artifacts, and associated metainformation is an obligation of the research community: As a matter of self interest (e.g., as precondition for

being viewed as a “trusted source”)

Data and information is of little value if it cannot be found or delivered in a timely or usable condition

Reproducibility of results – a hallmark of the scientific method – may impaired or impossible without it

Meeting the challenges … There is hope!

Communities of practice, if encouraged and supported, offer several benefits: Strength in numbers:

Multiple perspectives and insights brought to bear on problems

Yield better solutions, faster

Organizational adaptability: Ability to coalesce rapidly around

issues driven by changing technologies, research needs, or other challenges without time-consuming organizational realignments

There is hope!

Cost-effectiveness: Fewer development “stovepipes” Less likely to “reinvent the wheel” Useful knowledge, tools, and techniques are rapidly

distributed throughout the organization Standardization, interoperability more likely

Collaborative learning: Participation increases knowledge and skills of all participants Overall organizational competence is enhanced Knowledge is more likely to be preserved for the next

generation

Thank you. Questions?

Dave Govoni(dgovoni@usgs.gov)

Tom Gunther(tgunther@usgs.gov)