SlideShare una empresa de Scribd logo
1 de 27
Descargar para leer sin conexión
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. Gunther
USGS Geospatial Information Office

Geoinformatics 2006
May 12, 2006


U.S. Department of the Interior
U.S. Geological Survey
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
  -   Knowledge management services
  -   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?
 One approach: 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
Scientific Information Management at the U.S. Geological Survey
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

  Those “in the trenches” know best:
  -   Cannot implement top-down SIM
      solutions
  -   Solutions can come from (and be
      managed from) anywhere
 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
Perspectives on SIM … 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


     refers to              refers to             refers to              refers to


    Fieldwork                                  Preparation &
                       Analysis, synthesis                            Preservation &
 (in situ, in vitro,                             distribution
                        & interpretation                                 archiving
     in silico)                              (via any medium)


     includes               includes              includes               includes

Direct & remote            Laboratory                                    Records
                                             Publications, data,
 observation,             experiments,                                management,
                                              talks, seminars,
 monitoring &              modeling,                               data rescue, physical
                                              models, libraries
   recording              visualization                            sample preservation
Consumer perspective
“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
Perspectives on SIM




            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
         are brought to bear on problems
         Yield better solutions, faster
  -   Organizational adaptability:
         Can 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

  -   Collective 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)

Más contenido relacionado

La actualidad más candente

Why manage research data?
Why manage research data?Why manage research data?
Why manage research data?Graham Pryor
 
Research Data Management for Librarians at Oxford Brookes
Research Data Management for Librarians at Oxford BrookesResearch Data Management for Librarians at Oxford Brookes
Research Data Management for Librarians at Oxford BrookesMarieke Guy
 
Research Data Management, Challenges and Tools - Per Öster
Research Data Management, Challenges and Tools - Per Öster Research Data Management, Challenges and Tools - Per Öster
Research Data Management, Challenges and Tools - Per Öster LEARN Project
 
The Thinking Behind Big Data at the NIH
The Thinking Behind Big Data at the NIHThe Thinking Behind Big Data at the NIH
The Thinking Behind Big Data at the NIHPhilip Bourne
 
Digital Curation 101: Preserve
Digital Curation 101: PreserveDigital Curation 101: Preserve
Digital Curation 101: PreserveMichael Day
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality Paul Courtney
 
DCC 101: Preservation
DCC 101: PreservationDCC 101: Preservation
DCC 101: PreservationMichael Day
 
Managing data responsibly to enable research interity
Managing data responsibly to enable research interityManaging data responsibly to enable research interity
Managing data responsibly to enable research interityIUPUI
 
Incentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout
Incentivizing data sharing: a "bottom up" perspective/Louise BezuidenhoutIncentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout
Incentivizing data sharing: a "bottom up" perspective/Louise BezuidenhoutAfrican Open Science Platform
 
Research data management and the Digital Curation Centre
Research data management and the Digital Curation CentreResearch data management and the Digital Curation Centre
Research data management and the Digital Curation CentreMartin Donnelly
 
Paul Jeffreys - Research Integrity: Institutional Responsibility
Paul Jeffreys - Research Integrity: Institutional ResponsibilityPaul Jeffreys - Research Integrity: Institutional Responsibility
Paul Jeffreys - Research Integrity: Institutional ResponsibilityJisc
 
Open Access and Institution Repositories
Open Access and Institution RepositoriesOpen Access and Institution Repositories
Open Access and Institution Repositoriesiaaldafrika
 
Meeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthMeeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthPhilip Bourne
 
Mind the Gap: Reflections on Data Policies and Practice
Mind the Gap: Reflections on Data Policies and PracticeMind the Gap: Reflections on Data Policies and Practice
Mind the Gap: Reflections on Data Policies and PracticeLizLyon
 
Supporting Libraries in Leading the Way in Research Data Management
Supporting Libraries in Leading the Way in Research Data ManagementSupporting Libraries in Leading the Way in Research Data Management
Supporting Libraries in Leading the Way in Research Data ManagementMarieke Guy
 

La actualidad más candente (20)

Why manage research data?
Why manage research data?Why manage research data?
Why manage research data?
 
Research Data Management for Librarians at Oxford Brookes
Research Data Management for Librarians at Oxford BrookesResearch Data Management for Librarians at Oxford Brookes
Research Data Management for Librarians at Oxford Brookes
 
Research Data Management, Challenges and Tools - Per Öster
Research Data Management, Challenges and Tools - Per Öster Research Data Management, Challenges and Tools - Per Öster
Research Data Management, Challenges and Tools - Per Öster
 
The Thinking Behind Big Data at the NIH
The Thinking Behind Big Data at the NIHThe Thinking Behind Big Data at the NIH
The Thinking Behind Big Data at the NIH
 
Digital Curation 101: Preserve
Digital Curation 101: PreserveDigital Curation 101: Preserve
Digital Curation 101: Preserve
 
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and RealityA VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
A VIVO VIEW OF CANCER RESEARCH: Dream, Vision and Reality
 
DCC 101: Preservation
DCC 101: PreservationDCC 101: Preservation
DCC 101: Preservation
 
Knowledge manageability
Knowledge manageability Knowledge manageability
Knowledge manageability
 
Introduction to Research Data Management
Introduction to Research Data ManagementIntroduction to Research Data Management
Introduction to Research Data Management
 
Managing data responsibly to enable research interity
Managing data responsibly to enable research interityManaging data responsibly to enable research interity
Managing data responsibly to enable research interity
 
Incentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout
Incentivizing data sharing: a "bottom up" perspective/Louise BezuidenhoutIncentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout
Incentivizing data sharing: a "bottom up" perspective/Louise Bezuidenhout
 
Research data management and the Digital Curation Centre
Research data management and the Digital Curation CentreResearch data management and the Digital Curation Centre
Research data management and the Digital Curation Centre
 
Paul Jeffreys - Research Integrity: Institutional Responsibility
Paul Jeffreys - Research Integrity: Institutional ResponsibilityPaul Jeffreys - Research Integrity: Institutional Responsibility
Paul Jeffreys - Research Integrity: Institutional Responsibility
 
Open Access and Institution Repositories
Open Access and Institution RepositoriesOpen Access and Institution Repositories
Open Access and Institution Repositories
 
Meeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human HealthMeeting the Computational Challenges Associated with Human Health
Meeting the Computational Challenges Associated with Human Health
 
Open Science Governance and Regulation/Simon Hodson
Open Science Governance and Regulation/Simon HodsonOpen Science Governance and Regulation/Simon Hodson
Open Science Governance and Regulation/Simon Hodson
 
Mind the Gap: Reflections on Data Policies and Practice
Mind the Gap: Reflections on Data Policies and PracticeMind the Gap: Reflections on Data Policies and Practice
Mind the Gap: Reflections on Data Policies and Practice
 
Preparing Your Research Data for the Future - 2015-03-02 - University of Oxfo...
Preparing Your Research Data for the Future - 2015-03-02 - University of Oxfo...Preparing Your Research Data for the Future - 2015-03-02 - University of Oxfo...
Preparing Your Research Data for the Future - 2015-03-02 - University of Oxfo...
 
Supporting Libraries in Leading the Way in Research Data Management
Supporting Libraries in Leading the Way in Research Data ManagementSupporting Libraries in Leading the Way in Research Data Management
Supporting Libraries in Leading the Way in Research Data Management
 
A Fast-Changing World Needs Agile Policies
A Fast-Changing World Needs Agile Policies A Fast-Changing World Needs Agile Policies
A Fast-Changing World Needs Agile Policies
 

Destacado

How you can get the best out of your next survey questionnaire
How you can get the best out of your next survey questionnaireHow you can get the best out of your next survey questionnaire
How you can get the best out of your next survey questionnaireKeith Meadows
 
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring Instrument
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring InstrumentNunes Instruments, Coimbator, Agricultural and Electrical Measuring Instrument
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring InstrumentIndiaMART InterMESH Limited
 
Survey of Design Features in Selected Scientific Journals and Federal Technic...
Survey of Design Features in Selected Scientific Journals and Federal Technic...Survey of Design Features in Selected Scientific Journals and Federal Technic...
Survey of Design Features in Selected Scientific Journals and Federal Technic...wookyluvr
 
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...Raj Jagzap
 
Quantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakQuantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakHafiza Abas
 
Survey & Questionnaire Design in Applied Marketing Research
Survey & Questionnaire Design in Applied Marketing ResearchSurvey & Questionnaire Design in Applied Marketing Research
Survey & Questionnaire Design in Applied Marketing ResearchKelly Page
 
Survey question and questionnaire design slideshare 022113 dmf
Survey question and questionnaire design slideshare 022113 dmfSurvey question and questionnaire design slideshare 022113 dmf
Survey question and questionnaire design slideshare 022113 dmfDavid Filiberto
 
Research Methods in Psychology
Research Methods in PsychologyResearch Methods in Psychology
Research Methods in PsychologyJames Neill
 
Business plan vs Lean Canvas
Business plan vs Lean CanvasBusiness plan vs Lean Canvas
Business plan vs Lean CanvasAsh Maurya
 
Introduction to Survey Research
Introduction to Survey ResearchIntroduction to Survey Research
Introduction to Survey ResearchJames Neill
 
How to enter and analyze questionnaire (survey) data in SPSS
How to enter and analyze questionnaire (survey) data in SPSSHow to enter and analyze questionnaire (survey) data in SPSS
How to enter and analyze questionnaire (survey) data in SPSSquantitative_specialists
 

Destacado (13)

PROXIFIERS
PROXIFIERSPROXIFIERS
PROXIFIERS
 
Questionnaire survey
Questionnaire surveyQuestionnaire survey
Questionnaire survey
 
How you can get the best out of your next survey questionnaire
How you can get the best out of your next survey questionnaireHow you can get the best out of your next survey questionnaire
How you can get the best out of your next survey questionnaire
 
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring Instrument
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring InstrumentNunes Instruments, Coimbator, Agricultural and Electrical Measuring Instrument
Nunes Instruments, Coimbator, Agricultural and Electrical Measuring Instrument
 
Survey of Design Features in Selected Scientific Journals and Federal Technic...
Survey of Design Features in Selected Scientific Journals and Federal Technic...Survey of Design Features in Selected Scientific Journals and Federal Technic...
Survey of Design Features in Selected Scientific Journals and Federal Technic...
 
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...
FINDING POINT FOR BORE WELL USING SCIENTIFIC & DIVINING METHOD, BED ROCK INVE...
 
Quantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarakQuantitative search and_qualitative_research by mubarak
Quantitative search and_qualitative_research by mubarak
 
Survey & Questionnaire Design in Applied Marketing Research
Survey & Questionnaire Design in Applied Marketing ResearchSurvey & Questionnaire Design in Applied Marketing Research
Survey & Questionnaire Design in Applied Marketing Research
 
Survey question and questionnaire design slideshare 022113 dmf
Survey question and questionnaire design slideshare 022113 dmfSurvey question and questionnaire design slideshare 022113 dmf
Survey question and questionnaire design slideshare 022113 dmf
 
Research Methods in Psychology
Research Methods in PsychologyResearch Methods in Psychology
Research Methods in Psychology
 
Business plan vs Lean Canvas
Business plan vs Lean CanvasBusiness plan vs Lean Canvas
Business plan vs Lean Canvas
 
Introduction to Survey Research
Introduction to Survey ResearchIntroduction to Survey Research
Introduction to Survey Research
 
How to enter and analyze questionnaire (survey) data in SPSS
How to enter and analyze questionnaire (survey) data in SPSSHow to enter and analyze questionnaire (survey) data in SPSS
How to enter and analyze questionnaire (survey) data in SPSS
 

Similar a Scientific Information Management at the U.S. Geological Survey

Australia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityAustralia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityTERN Australia
 
Practical Research Data Management: tools and approaches, pre- and post-award
Practical Research Data Management:  tools and approaches, pre- and post-awardPractical Research Data Management:  tools and approaches, pre- and post-award
Practical Research Data Management: tools and approaches, pre- and post-awardMartin Donnelly
 
Research Data Management
Research Data ManagementResearch Data Management
Research Data ManagementJamie Bisset
 
My FAIR share of the work - Diamond Light Source - Dec 2018
My FAIR share of the work - Diamond Light Source - Dec 2018My FAIR share of the work - Diamond Light Source - Dec 2018
My FAIR share of the work - Diamond Light Source - Dec 2018Susanna-Assunta Sansone
 
Workshop intro090314
Workshop intro090314Workshop intro090314
Workshop intro090314Philip Bourne
 
Developing Policy in the 21st Century: Working Smarter, not Harder
Developing Policy in the 21st Century: Working Smarter, not HarderDeveloping Policy in the 21st Century: Working Smarter, not Harder
Developing Policy in the 21st Century: Working Smarter, not HarderIntegrated Knowledge Services
 
Meeting the NSF DMP Requirement June 13, 2012
Meeting the NSF DMP Requirement June 13, 2012Meeting the NSF DMP Requirement June 13, 2012
Meeting the NSF DMP Requirement June 13, 2012IUPUI
 
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...TERN Australia
 
Creating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationCreating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationHistoric Environment Scotland
 
Creating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationCreating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationEDINA, University of Edinburgh
 
How to overcome obstacles to data publication: Issues, requirements, and good...
How to overcome obstacles to data publication: Issues, requirements, and good...How to overcome obstacles to data publication: Issues, requirements, and good...
How to overcome obstacles to data publication: Issues, requirements, and good...ariadnenetwork
 
Introduction to digital curation
Introduction to digital curationIntroduction to digital curation
Introduction to digital curationMichael Day
 
Scalable Learning Analytics and Interoperability – an assessment of potential...
Scalable Learning Analytics and Interoperability – an assessment of potential...Scalable Learning Analytics and Interoperability – an assessment of potential...
Scalable Learning Analytics and Interoperability – an assessment of potential...LACE Project
 
RDM and DMP intro
RDM and DMP introRDM and DMP intro
RDM and DMP introSarah Jones
 
Research data lifecycle diagram
Research data lifecycle diagramResearch data lifecycle diagram
Research data lifecycle diagramSteven Cracknell
 

Similar a Scientific Information Management at the U.S. Geological Survey (20)

20130222 kaptur training_goldsmiths
20130222 kaptur training_goldsmiths20130222 kaptur training_goldsmiths
20130222 kaptur training_goldsmiths
 
Digital Curation 101 - Taster
Digital Curation 101 - TasterDigital Curation 101 - Taster
Digital Curation 101 - Taster
 
Australia's Environmental Predictive Capability
Australia's Environmental Predictive CapabilityAustralia's Environmental Predictive Capability
Australia's Environmental Predictive Capability
 
Practical Research Data Management: tools and approaches, pre- and post-award
Practical Research Data Management:  tools and approaches, pre- and post-awardPractical Research Data Management:  tools and approaches, pre- and post-award
Practical Research Data Management: tools and approaches, pre- and post-award
 
Intro to RDM
Intro to RDMIntro to RDM
Intro to RDM
 
Research Data Management
Research Data ManagementResearch Data Management
Research Data Management
 
Research data life cycle
Research data life cycleResearch data life cycle
Research data life cycle
 
My FAIR share of the work - Diamond Light Source - Dec 2018
My FAIR share of the work - Diamond Light Source - Dec 2018My FAIR share of the work - Diamond Light Source - Dec 2018
My FAIR share of the work - Diamond Light Source - Dec 2018
 
Workshop intro090314
Workshop intro090314Workshop intro090314
Workshop intro090314
 
Developing Policy in the 21st Century: Working Smarter, not Harder
Developing Policy in the 21st Century: Working Smarter, not HarderDeveloping Policy in the 21st Century: Working Smarter, not Harder
Developing Policy in the 21st Century: Working Smarter, not Harder
 
Meeting the NSF DMP Requirement June 13, 2012
Meeting the NSF DMP Requirement June 13, 2012Meeting the NSF DMP Requirement June 13, 2012
Meeting the NSF DMP Requirement June 13, 2012
 
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...
Stuart Phinn and Andy Lowe_TERN's national ecosystem data infrastructure is d...
 
Creating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationCreating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant Application
 
Creating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant ApplicationCreating a Data Management Plan for your Grant Application
Creating a Data Management Plan for your Grant Application
 
How to overcome obstacles to data publication: Issues, requirements, and good...
How to overcome obstacles to data publication: Issues, requirements, and good...How to overcome obstacles to data publication: Issues, requirements, and good...
How to overcome obstacles to data publication: Issues, requirements, and good...
 
Holmes "Institutional Infrastructure for Data Sharing"
Holmes "Institutional Infrastructure for Data Sharing"Holmes "Institutional Infrastructure for Data Sharing"
Holmes "Institutional Infrastructure for Data Sharing"
 
Introduction to digital curation
Introduction to digital curationIntroduction to digital curation
Introduction to digital curation
 
Scalable Learning Analytics and Interoperability – an assessment of potential...
Scalable Learning Analytics and Interoperability – an assessment of potential...Scalable Learning Analytics and Interoperability – an assessment of potential...
Scalable Learning Analytics and Interoperability – an assessment of potential...
 
RDM and DMP intro
RDM and DMP introRDM and DMP intro
RDM and DMP intro
 
Research data lifecycle diagram
Research data lifecycle diagramResearch data lifecycle diagram
Research data lifecycle diagram
 

Último

Mphasis - Schwab Newsletter PDF - Sample 8707
Mphasis - Schwab Newsletter PDF - Sample 8707Mphasis - Schwab Newsletter PDF - Sample 8707
Mphasis - Schwab Newsletter PDF - Sample 8707harshan90
 
2024.03 Strategic Resources Presentation
2024.03 Strategic Resources Presentation2024.03 Strategic Resources Presentation
2024.03 Strategic Resources PresentationAdnet Communications
 
The Power Laws of Bitcoin: How can an S-curve be a power law?
The Power Laws of Bitcoin: How can an S-curve be a power law?The Power Laws of Bitcoin: How can an S-curve be a power law?
The Power Laws of Bitcoin: How can an S-curve be a power law?Stephen Perrenod
 
Monthly Market Risk Update: March 2024 [SlideShare]
Monthly Market Risk Update: March 2024 [SlideShare]Monthly Market Risk Update: March 2024 [SlideShare]
Monthly Market Risk Update: March 2024 [SlideShare]Commonwealth
 
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.KumarJayaraman3
 
Work and Pensions report into UK corporate DB funding
Work and Pensions report into UK corporate DB fundingWork and Pensions report into UK corporate DB funding
Work and Pensions report into UK corporate DB fundingHenry Tapper
 
Introduction to Entrepreneurship and Characteristics of an Entrepreneur
Introduction to Entrepreneurship and Characteristics of an EntrepreneurIntroduction to Entrepreneurship and Characteristics of an Entrepreneur
Introduction to Entrepreneurship and Characteristics of an Entrepreneurabcisahunter
 
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGecko
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGeckoRWA Report 2024: Rise of Real-World Assets in Crypto | CoinGecko
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGeckoCoinGecko
 
India Economic Survey Complete for the year of 2022 to 2023
India Economic Survey Complete for the year of 2022 to 2023India Economic Survey Complete for the year of 2022 to 2023
India Economic Survey Complete for the year of 2022 to 2023SkillCircle
 
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptx
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptxSlideshare - ONS Economic Forum Slidepack - 18 March 2024.pptx
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptxOffice for National Statistics
 
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTES
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTESACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTES
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTESKumarJayaraman3
 
What Key Factors Should Risk Officers Consider When Using Generative AI
What Key Factors Should Risk Officers Consider When Using Generative AIWhat Key Factors Should Risk Officers Consider When Using Generative AI
What Key Factors Should Risk Officers Consider When Using Generative AI360factors
 
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...Matthews Bantsijang
 
Stock Market Brief Deck for 3/22/2024.pdf
Stock Market Brief Deck for 3/22/2024.pdfStock Market Brief Deck for 3/22/2024.pdf
Stock Market Brief Deck for 3/22/2024.pdfMichael Silva
 
The unequal battle of inflation and the appropriate sustainable solution | Eu...
The unequal battle of inflation and the appropriate sustainable solution | Eu...The unequal battle of inflation and the appropriate sustainable solution | Eu...
The unequal battle of inflation and the appropriate sustainable solution | Eu...Antonis Zairis
 
Taipei, A Hidden Jewel in East Asia - PR Strategy for Tourism
Taipei, A Hidden Jewel in East Asia - PR Strategy for TourismTaipei, A Hidden Jewel in East Asia - PR Strategy for Tourism
Taipei, A Hidden Jewel in East Asia - PR Strategy for TourismBrian Lin
 
20240315 _E-Invoicing Digiteal. .pptx
20240315 _E-Invoicing Digiteal.    .pptx20240315 _E-Invoicing Digiteal.    .pptx
20240315 _E-Invoicing Digiteal. .pptxFinTech Belgium
 
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdf
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdfLundin Gold March 2024 Corporate Presentation - PDAC v1.pdf
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdfAdnet Communications
 

Último (20)

Mphasis - Schwab Newsletter PDF - Sample 8707
Mphasis - Schwab Newsletter PDF - Sample 8707Mphasis - Schwab Newsletter PDF - Sample 8707
Mphasis - Schwab Newsletter PDF - Sample 8707
 
2024.03 Strategic Resources Presentation
2024.03 Strategic Resources Presentation2024.03 Strategic Resources Presentation
2024.03 Strategic Resources Presentation
 
The Power Laws of Bitcoin: How can an S-curve be a power law?
The Power Laws of Bitcoin: How can an S-curve be a power law?The Power Laws of Bitcoin: How can an S-curve be a power law?
The Power Laws of Bitcoin: How can an S-curve be a power law?
 
Monthly Market Risk Update: March 2024 [SlideShare]
Monthly Market Risk Update: March 2024 [SlideShare]Monthly Market Risk Update: March 2024 [SlideShare]
Monthly Market Risk Update: March 2024 [SlideShare]
 
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.
ACCOUNTING FOR BUSINESS.II DEPARTMENTAL ACCOUNTS.
 
New Monthly Enterprises Survey. Issue 21. (01.2024) Ukrainian Business in War...
New Monthly Enterprises Survey. Issue 21. (01.2024) Ukrainian Business in War...New Monthly Enterprises Survey. Issue 21. (01.2024) Ukrainian Business in War...
New Monthly Enterprises Survey. Issue 21. (01.2024) Ukrainian Business in War...
 
Work and Pensions report into UK corporate DB funding
Work and Pensions report into UK corporate DB fundingWork and Pensions report into UK corporate DB funding
Work and Pensions report into UK corporate DB funding
 
Introduction to Entrepreneurship and Characteristics of an Entrepreneur
Introduction to Entrepreneurship and Characteristics of an EntrepreneurIntroduction to Entrepreneurship and Characteristics of an Entrepreneur
Introduction to Entrepreneurship and Characteristics of an Entrepreneur
 
Effects & Policies Of Bank Consolidation
Effects & Policies Of Bank ConsolidationEffects & Policies Of Bank Consolidation
Effects & Policies Of Bank Consolidation
 
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGecko
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGeckoRWA Report 2024: Rise of Real-World Assets in Crypto | CoinGecko
RWA Report 2024: Rise of Real-World Assets in Crypto | CoinGecko
 
India Economic Survey Complete for the year of 2022 to 2023
India Economic Survey Complete for the year of 2022 to 2023India Economic Survey Complete for the year of 2022 to 2023
India Economic Survey Complete for the year of 2022 to 2023
 
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptx
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptxSlideshare - ONS Economic Forum Slidepack - 18 March 2024.pptx
Slideshare - ONS Economic Forum Slidepack - 18 March 2024.pptx
 
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTES
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTESACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTES
ACCOUNTING FOR BUSINESS.II BRANCH ACCOUNTS NOTES
 
What Key Factors Should Risk Officers Consider When Using Generative AI
What Key Factors Should Risk Officers Consider When Using Generative AIWhat Key Factors Should Risk Officers Consider When Using Generative AI
What Key Factors Should Risk Officers Consider When Using Generative AI
 
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...
Remembering my Totem _Unity is Strength_ growing in Bophuthatswana_Matthews B...
 
Stock Market Brief Deck for 3/22/2024.pdf
Stock Market Brief Deck for 3/22/2024.pdfStock Market Brief Deck for 3/22/2024.pdf
Stock Market Brief Deck for 3/22/2024.pdf
 
The unequal battle of inflation and the appropriate sustainable solution | Eu...
The unequal battle of inflation and the appropriate sustainable solution | Eu...The unequal battle of inflation and the appropriate sustainable solution | Eu...
The unequal battle of inflation and the appropriate sustainable solution | Eu...
 
Taipei, A Hidden Jewel in East Asia - PR Strategy for Tourism
Taipei, A Hidden Jewel in East Asia - PR Strategy for TourismTaipei, A Hidden Jewel in East Asia - PR Strategy for Tourism
Taipei, A Hidden Jewel in East Asia - PR Strategy for Tourism
 
20240315 _E-Invoicing Digiteal. .pptx
20240315 _E-Invoicing Digiteal.    .pptx20240315 _E-Invoicing Digiteal.    .pptx
20240315 _E-Invoicing Digiteal. .pptx
 
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdf
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdfLundin Gold March 2024 Corporate Presentation - PDAC v1.pdf
Lundin Gold March 2024 Corporate Presentation - PDAC v1.pdf
 

Scientific Information Management at the U.S. Geological Survey

  • 1. 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. Gunther USGS Geospatial Information Office Geoinformatics 2006 May 12, 2006 U.S. Department of the Interior U.S. Geological Survey
  • 2. 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 - Knowledge management services - Scientific information management
  • 3. 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
  • 5. 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, …
  • 6. 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
  • 7. So how do we … Increase awareness? Identify “best of breed”? Accelerate diffusion? Provide support? Institutionalize? One approach: Communities of Practice (CoPs)
  • 8. 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)
  • 9. 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
  • 10. 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
  • 11. 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
  • 12. 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
  • 13. 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
  • 15. 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
  • 16. 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)
  • 17. What was learned Those “in the trenches” know best: - Cannot implement top-down SIM solutions - Solutions can come from (and be managed from) anywhere 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
  • 18. Perspectives on SIM … 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
  • 19. Producer perspective refers to refers to refers to refers to Fieldwork Preparation & Analysis, synthesis Preservation & (in situ, in vitro, distribution & interpretation archiving in silico) (via any medium) includes includes includes includes Direct & remote Laboratory Records Publications, data, observation, experiments, management, talks, seminars, monitoring & modeling, data rescue, physical models, libraries recording visualization sample preservation
  • 21. “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
  • 22. Perspectives on SIM End of digression
  • 23. 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
  • 24. 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
  • 25. Meeting the challenges … There is hope! Communities of practice, if encouraged and supported, offer several benefits: - Strength in numbers: Multiple perspectives and insights are brought to bear on problems Yield better solutions, faster - Organizational adaptability: Can coalesce rapidly around issues driven by changing technologies, research needs, or other challenges without time-consuming organizational realignments
  • 26. 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 - Collective 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
  • 27. Thank you. … Questions? Dave Govoni (dgovoni@usgs.gov) Tom Gunther (tgunther@usgs.gov)