SlideShare a Scribd company logo
1 of 17
NSF DATA POLICIES:
A VERY BRIEF
INTRODUCTION


                     Fe b ru a ry 29, 2012
OVERVIEW

1. Introduction & Context
2. NSF Policies
3. Research support @ IUPUI
WHY THE LIBRARY?

Trusted member of the institution
Organizational structure lends itself to
 collaboration with researchers
Existing expertise in making available and
 preserving information
   Program of Digital Scholarship
Existing infrastructure


   Preservation, curation, and access
UL DATA SERVICES PROGRAM

 Services
   Workshops
   Individual consultations
   Data repository


 Resources
   Guide to NSF Data Management Plan Requirement
   Website
       Sample NSF DMP from other institutions
       Tools
       Guidance from institutions like the ICPSR and Digital Curation Centre (UK)
       Significant publications discussing data management and curation
       Open datasets and data repositories
CONTEXT OF THE NSF DATA POLICIES

 Driver – greater impact of research dollars
 Context = scholarly communications
 Encouraging two separate types of activities
   Data management & curation
   Data sharing
 Scholarly impact: greater exposure, facilitates
  reproducibility, facilitates new discoveries via
  secondary analysis/data re-use, fosters productive
  collaborations, leads to new computational techniques
 Planning ahead 5, 50, 100 years – preservation,
  persistent access
   If you can’t find it, it doesn’t exist
POLICY ON DISSEMINATION & SHARING [1]

 …promptly prepare and submit for publication, with authorship
  that accurately reflects the contributions of those involved, all
  significant findings from work conducted under NSF grants

 …expected to share with other researchers, at no more than
  incremental cost and within a reasonable time, the primary
  data, samples, physical collections and other supporting
  materials created or gathered in the course of work under NSF
  grants…expected to encourage and facilitate such sharing.
  Privileged or confidential information should be released only in
  a form that protects the privacy of individuals and subjects
  involved. General adjustments and, where essential, exceptions
  to this sharing expectation may be specified by the funding NSF
  Program or Division/Office for a particular field or discipline…
POLICY ON DISSEMINATION & SHARING [2]

 Investigators and grantees are encouraged to share software and
  inventions created under the grant or otherwise make them or
  their products widely available and usable .

 NSF normally allows grantees to retain principal legal rights to
  intellectual property developed under NSF grants to provide
  incentives for development and dissemination of inventions,
  software and publications that can enhance their usefulness,
  accessibility and upkeep. Such incentives do not, however,
  reduce the responsibility that investigators and organizations
  have as members of the scientific and engineering community,
  to make results, data and collections available to other
  researchers.
POLICY ON DISSEMINATION & SHARING [3]

 NSF program management will implement these policies for
  dissemination and sharing of research results, in ways
  appropriate to field and circumstances, through the proposal
  review process; through award negotiations and conditions; and
  through appropriate support and incentives for data cleanup,
  documentation, dissemination, storage and the like.
NSF DMP REQUIREMENT

 the types of data, samples, physical collections, software,
  curriculum materials, and other materials to be produced in the
  course of the project;
 the standards to be used for data and metadata format and
  content (where existing standards are absent or deemed
  inadequate, this should be documented along with any proposed
  solutions or remedies);
 policies for access and sharing including provisions for
  appropriate protection of privacy, confidentiality, security,
  intellectual property, or other rights or requirements;
 policies and provisions for re-use, re-distribution, and the
  production of derivatives; and
 plans for archiving data, samples, and other research products,
  and for preservation of access to them .
          http://www.nsf.gov/pubs/policydocs/pappguide/nsf11001/gpg_2.jsp#dmp
NSF DMP: OVERVIEW

 Should reflect
   Awareness of data management and curation in your discipline
   Feasible plan to utilize available cyberinfrastructure


 Throughout the DMP, try to
   Explain the rationale for your choices
   Identify roles for data management and curation activities


 Implementation costs of the DMP CAN be included in
  direct costs
DMP: DATA, STANDARDS, & METADATA

Utilize standards common within your discipline/community
 Data & standards
   Characterize the data to be generated or used
   How will these characteristics impact storage, management, and
    processing?
   What is the backup and security plan?
   Describe data & project documentation
 Metadata & standards
   Will your data be self-explanatory or understandable in isolation?
   Types of metadata
     Descriptive (for findability, context, etc.)
     Structural (for things like geospatial files)
     Administrative (for preservation)
DMP: ACCESS & SHARING

 How and when will data be made available?

 What is the process for gaining access?

 Ethical or legal issues such as privacy, confidentiality,
  security, intellectual property, or other rights?

 Limits or conditions placed on sharing for political,
  commercial, or patent reasons?
DMP: RE-USE, DISTRIBUTION, ETC.

 Policies & permissions
   Will permission restrictions be necessary?
   What rights will you retain before data is made available?
   Is there an embargo period?


 Re-use
   Who is likely to be interested in this data?
   How might you anticipate this data being used?
   What value might the data have for these people?
DMP: LONG-TERM PRESERVATION

 Researcher ’s role
   Selection of data for preservation
       How long do you think the data will be useful?
       What data will be preserved for the long -term?
   Transformations necessary to prepare data for preservation?
       data cleaning, de-identification, etc.
   Contextual information to make the data reusable
       metadata, documentation, references, reports, manuscripts, grant proposal,
        etc.
 Data repository ’s role
     Links to published materials and other outcomes?
     Use of persistent citation?
     Procedures for preservation and back-up?
     Access mechanisms
RESEARCH @ IUPUI

P ro gram o f D i g i ta l Sc h o l a rshi p: htt p :/ / ul ib.i upui .e du/di gi tal sc hol arshi p
C e nte r fo r Re s e a rc h & L e a r n ing: htt p :/ /c rl.i upui .e du /
OVC R : htt p ://res earc h.iupu i.e du/ deve lop me nt/
O ff i c e o f Aca d e m i c Affa i rs : htt p :/ /www. acade mi caffai rs.i upui .e du
I nte ll e ct ual P ro p e r ty Po l i c y : htt ps :/ /www. i ndi ana.e du/~ vpfaa /
a ca de m i cgui de /i ndex .php /Pol i cy_ I - 1 1


Re s e a rch Fi l e Syste m : htt p :// pt i.i u.e du/storage / rfs
Sc h o l arl y D ata Arc h i ve : htt p :/ /pti .iu.e du/sto rage /sda
Re s e a rch Te c h n o l ogie s , U I TS: htt p :/ / u its .iu.e du/ page /ave l
C o re Se r vi c e s , U I TS: htt p :/ /pti.i u.e du/c s
Sc h o l arl y C y b e r i nf rast ruc ture , U I TS: htt p :/ /ui ts.i u.e du/ page /am e e


I U Wa re : htt ps :/ / i uware .i u.e d u
I U a nyWare : htt ps :/ / iuanyware .i u.e du/vpn/ in dex.htm l
Stat M ath : htt p :// www. i ndiana.e du/ ~ statm ath/
Stat i sti cs C o n s u lti ng C e nte r : htt p :/ / www. math.i upui.e du /asc i /
CONTACT INFORMATION

Heather Coates
Digital Scholarship & Data Management Librarian
University Library

Email: hcoates@iupui.edu
Phone: 317-278-7125
Web: http://ulib.iupui.edu/digitalscholarship/dataservices
UPCOMING WORKSHOP

Meeting the NSF Data Management Plan
Requirement: What you need to know

March 7, 2012 @ 2:00pm, UL 1116

Register online at:
http://events.iupui.edu/event/?event_id=6064

More Related Content

What's hot

Jeff Haywood - Research Integrity: Institutional Responsibility
Jeff Haywood - Research Integrity: Institutional ResponsibilityJeff Haywood - Research Integrity: Institutional Responsibility
Jeff Haywood - Research Integrity: Institutional Responsibility
Jisc
 
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
Sherry Lake
 

What's hot (20)

Publishing perspectives on data management & future directions
Publishing perspectives on data management & future directionsPublishing perspectives on data management & future directions
Publishing perspectives on data management & future directions
 
Creating dmp
Creating dmpCreating dmp
Creating dmp
 
Jeff Haywood - Research Integrity: Institutional Responsibility
Jeff Haywood - Research Integrity: Institutional ResponsibilityJeff Haywood - Research Integrity: Institutional Responsibility
Jeff Haywood - Research Integrity: Institutional Responsibility
 
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...
 
Introduction to DATS v2.2 - NIH May 2017
Introduction to DATS v2.2 - NIH May 2017Introduction to DATS v2.2 - NIH May 2017
Introduction to DATS v2.2 - NIH May 2017
 
INCLUSION OF DATA ARCHIVES IN DATA MANAGEMENT PLAN
INCLUSION OF DATA ARCHIVES IN DATA MANAGEMENT PLANINCLUSION OF DATA ARCHIVES IN DATA MANAGEMENT PLAN
INCLUSION OF DATA ARCHIVES IN DATA MANAGEMENT PLAN
 
Preparing Your Research Material for the Future - 2014-06-09 - Humanities Div...
Preparing Your Research Material for the Future - 2014-06-09 - Humanities Div...Preparing Your Research Material for the Future - 2014-06-09 - Humanities Div...
Preparing Your Research Material for the Future - 2014-06-09 - Humanities Div...
 
Preparing Your Research Data for the Future - 2014-02-17 - Social Sciences Di...
Preparing Your Research Data for the Future - 2014-02-17 - Social Sciences Di...Preparing Your Research Data for the Future - 2014-02-17 - Social Sciences Di...
Preparing Your Research Data for the Future - 2014-02-17 - Social Sciences Di...
 
RDAP14: Collaboration and tension between institutions and units providing da...
RDAP14: Collaboration and tension between institutions and units providing da...RDAP14: Collaboration and tension between institutions and units providing da...
RDAP14: Collaboration and tension between institutions and units providing da...
 
Preparing Your Research Material for the Future - 2015-02-23 - Humanities Div...
Preparing Your Research Material for the Future - 2015-02-23 - Humanities Div...Preparing Your Research Material for the Future - 2015-02-23 - Humanities Div...
Preparing Your Research Material for the Future - 2015-02-23 - Humanities Div...
 
Introduction to Research Data Management - 2016-02-03 - MPLS Division, Univer...
Introduction to Research Data Management - 2016-02-03 - MPLS Division, Univer...Introduction to Research Data Management - 2016-02-03 - MPLS Division, Univer...
Introduction to Research Data Management - 2016-02-03 - MPLS Division, Univer...
 
Using a Case Study to Teach Data Management to Librarians
Using a Case Study to Teach Data Management to LibrariansUsing a Case Study to Teach Data Management to Librarians
Using a Case Study to Teach Data Management to Librarians
 
Why managedata
Why managedataWhy managedata
Why managedata
 
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
NISO Virtual Conference Scientific Data Management: Caring for Your Instituti...
 
Introduction to Research Data Management - 2014-02-26 - Mathematical, Physica...
Introduction to Research Data Management - 2014-02-26 - Mathematical, Physica...Introduction to Research Data Management - 2014-02-26 - Mathematical, Physica...
Introduction to Research Data Management - 2014-02-26 - Mathematical, Physica...
 
Course Research Data Management
Course Research Data ManagementCourse Research Data Management
Course Research Data Management
 
Data management plan template
Data management plan templateData management plan template
Data management plan template
 
Dc101 oxford sj_16062010
Dc101 oxford sj_16062010Dc101 oxford sj_16062010
Dc101 oxford sj_16062010
 
Research Lifecycles and RDM
Research Lifecycles and RDMResearch Lifecycles and RDM
Research Lifecycles and RDM
 
Data citation - new AGU guidelines
Data citation - new AGU guidelinesData citation - new AGU guidelines
Data citation - new AGU guidelines
 

Similar to NSF Data Policies webcast February 29, 2012

Data management plans
Data management plansData management plans
Data management plans
Brad Houston
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
Brad Houston
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
Brad Houston
 

Similar to NSF Data Policies webcast February 29, 2012 (20)

Praetzellis "Data Management Planning and Tools"
Praetzellis "Data Management Planning and Tools"Praetzellis "Data Management Planning and Tools"
Praetzellis "Data Management Planning and Tools"
 
Digital curation for postgraduate students
Digital curation for postgraduate studentsDigital curation for postgraduate students
Digital curation for postgraduate students
 
Data management plans
Data management plansData management plans
Data management plans
 
Digital Curation 101 - Taster
Digital Curation 101 - TasterDigital Curation 101 - Taster
Digital Curation 101 - Taster
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
 
Data management plans (dmp) for nsf
Data management plans (dmp) for nsfData management plans (dmp) for nsf
Data management plans (dmp) for nsf
 
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
 
You down with dmp yeah you know me!
You down with dmp  yeah you know me!You down with dmp  yeah you know me!
You down with dmp yeah you know me!
 
Meeting the NSF DMP Requirement: March 7, 2012
Meeting the NSF DMP Requirement: March 7, 2012Meeting the NSF DMP Requirement: March 7, 2012
Meeting the NSF DMP Requirement: March 7, 2012
 
Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...
Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...
Ands ttt2 perth_accelerate your data skills training_ top tips for topics and...
 
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
 
Introduction to RDM for Geoscience PhD Students
Introduction to RDM for Geoscience PhD StudentsIntroduction to RDM for Geoscience PhD Students
Introduction to RDM for Geoscience PhD Students
 
Managing data throughout the research lifecycle
Managing data throughout the research lifecycleManaging data throughout the research lifecycle
Managing data throughout the research lifecycle
 
DMP health sciences
DMP health sciencesDMP health sciences
DMP health sciences
 
Introduction to research data management
Introduction to research data managementIntroduction to research data management
Introduction to research data management
 
NIH Grants and Data: New Rules Coming in 2023
NIH Grants and Data: New Rules Coming in 2023NIH Grants and Data: New Rules Coming in 2023
NIH Grants and Data: New Rules Coming in 2023
 
EPSRC research data expectations and research software management
EPSRC research data expectations and research software managementEPSRC research data expectations and research software management
EPSRC research data expectations and research software management
 
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
 
Using a behavioral framework to understand researchers data management practi...
Using a behavioral framework to understand researchers data management practi...Using a behavioral framework to understand researchers data management practi...
Using a behavioral framework to understand researchers data management practi...
 

More from IUPUI

Building the Future of Research Together
Building the Future of Research TogetherBuilding the Future of Research Together
Building the Future of Research Together
IUPUI
 

More from IUPUI (20)

Altmetrics 101 - Altmetrics in Libraries
Altmetrics 101 - Altmetrics in LibrariesAltmetrics 101 - Altmetrics in Libraries
Altmetrics 101 - Altmetrics in Libraries
 
Gather evidence to demonstrate the impact of your research
Gather evidence to demonstrate the impact of your researchGather evidence to demonstrate the impact of your research
Gather evidence to demonstrate the impact of your research
 
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
 
Case studies for open science
Case studies for open scienceCase studies for open science
Case studies for open science
 
Midwest Medical Library Association 2015 Big Data Panel
Midwest Medical Library Association 2015 Big Data PanelMidwest Medical Library Association 2015 Big Data Panel
Midwest Medical Library Association 2015 Big Data Panel
 
Gathering Evidence to Demonstrate Impact
Gathering Evidence to Demonstrate ImpactGathering Evidence to Demonstrate Impact
Gathering Evidence to Demonstrate Impact
 
Citation & altmetrics - a comparison
Citation & altmetrics - a comparisonCitation & altmetrics - a comparison
Citation & altmetrics - a comparison
 
Altmetrics for Team Science
Altmetrics for Team ScienceAltmetrics for Team Science
Altmetrics for Team Science
 
Ensuring data quality
Ensuring data qualityEnsuring data quality
Ensuring data quality
 
Preventing data loss
Preventing data lossPreventing data loss
Preventing data loss
 
Practical Data Management Plans
Practical Data Management PlansPractical Data Management Plans
Practical Data Management Plans
 
Teaching data management in a lab environment (IASSIST 2014)
Teaching data management in a lab environment (IASSIST 2014)Teaching data management in a lab environment (IASSIST 2014)
Teaching data management in a lab environment (IASSIST 2014)
 
Building the Future of Research Together
Building the Future of Research TogetherBuilding the Future of Research Together
Building the Future of Research Together
 
NIH Data Sharing Plan Workshop - Handout
NIH Data Sharing Plan Workshop - HandoutNIH Data Sharing Plan Workshop - Handout
NIH Data Sharing Plan Workshop - Handout
 
NIH Data Sharing Plan Workshop - Slides
NIH Data Sharing Plan Workshop - SlidesNIH Data Sharing Plan Workshop - Slides
NIH Data Sharing Plan Workshop - Slides
 
Data Management Lab: Session 4 Slides
Data Management Lab: Session 4 SlidesData Management Lab: Session 4 Slides
Data Management Lab: Session 4 Slides
 
Data Management Lab: Session 4 Review Outline
Data Management Lab: Session 4 Review OutlineData Management Lab: Session 4 Review Outline
Data Management Lab: Session 4 Review Outline
 
Data Management Lab: Session 3 Slides
Data Management Lab: Session 3 SlidesData Management Lab: Session 3 Slides
Data Management Lab: Session 3 Slides
 
Data Management Lab: Session 3 Data Review Checklist
Data Management Lab: Session 3 Data Review ChecklistData Management Lab: Session 3 Data Review Checklist
Data Management Lab: Session 3 Data Review Checklist
 
Data Management Lab: Session 3 Data Entry Best Practices
Data Management Lab: Session 3 Data Entry Best PracticesData Management Lab: Session 3 Data Entry Best Practices
Data Management Lab: Session 3 Data Entry Best Practices
 

Recently uploaded

The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
heathfieldcps1
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
Krashi Coaching
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
CaitlinCummins3
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
中 央社
 

Recently uploaded (20)

BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
 
The basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptxThe basics of sentences session 4pptx.pptx
The basics of sentences session 4pptx.pptx
 
Major project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategiesMajor project report on Tata Motors and its marketing strategies
Major project report on Tata Motors and its marketing strategies
 
philosophy and it's principles based on the life
philosophy and it's principles based on the lifephilosophy and it's principles based on the life
philosophy and it's principles based on the life
 
Including Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdfIncluding Mental Health Support in Project Delivery, 14 May.pdf
Including Mental Health Support in Project Delivery, 14 May.pdf
 
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
MSc Ag Genetics & Plant Breeding: Insights from Previous Year JNKVV Entrance ...
 
SURVEY I created for uni project research
SURVEY I created for uni project researchSURVEY I created for uni project research
SURVEY I created for uni project research
 
demyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptxdemyelinated disorder: multiple sclerosis.pptx
demyelinated disorder: multiple sclerosis.pptx
 
An Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge AppAn Overview of the Odoo 17 Knowledge App
An Overview of the Odoo 17 Knowledge App
 
IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.IPL Online Quiz by Pragya; Question Set.
IPL Online Quiz by Pragya; Question Set.
 
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽會考英聽
 
Dementia (Alzheimer & vasular dementia).
Dementia (Alzheimer & vasular dementia).Dementia (Alzheimer & vasular dementia).
Dementia (Alzheimer & vasular dementia).
 
PSYPACT- Practicing Over State Lines May 2024.pptx
PSYPACT- Practicing Over State Lines May 2024.pptxPSYPACT- Practicing Over State Lines May 2024.pptx
PSYPACT- Practicing Over State Lines May 2024.pptx
 
e-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopale-Sealing at EADTU by Kamakshi Rajagopal
e-Sealing at EADTU by Kamakshi Rajagopal
 
Sternal Fractures & Dislocations - EMGuidewire Radiology Reading Room
Sternal Fractures & Dislocations - EMGuidewire Radiology Reading RoomSternal Fractures & Dislocations - EMGuidewire Radiology Reading Room
Sternal Fractures & Dislocations - EMGuidewire Radiology Reading Room
 
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
 
Graduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptxGraduate Outcomes Presentation Slides - English (v3).pptx
Graduate Outcomes Presentation Slides - English (v3).pptx
 
8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management8 Tips for Effective Working Capital Management
8 Tips for Effective Working Capital Management
 
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
ĐỀ THAM KHẢO KÌ THI TUYỂN SINH VÀO LỚP 10 MÔN TIẾNG ANH FORM 50 CÂU TRẮC NGHI...
 
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...When Quality Assurance Meets Innovation in Higher Education - Report launch w...
When Quality Assurance Meets Innovation in Higher Education - Report launch w...
 

NSF Data Policies webcast February 29, 2012

  • 1. NSF DATA POLICIES: A VERY BRIEF INTRODUCTION Fe b ru a ry 29, 2012
  • 2. OVERVIEW 1. Introduction & Context 2. NSF Policies 3. Research support @ IUPUI
  • 3. WHY THE LIBRARY? Trusted member of the institution Organizational structure lends itself to collaboration with researchers Existing expertise in making available and preserving information  Program of Digital Scholarship Existing infrastructure Preservation, curation, and access
  • 4. UL DATA SERVICES PROGRAM  Services  Workshops  Individual consultations  Data repository  Resources  Guide to NSF Data Management Plan Requirement  Website  Sample NSF DMP from other institutions  Tools  Guidance from institutions like the ICPSR and Digital Curation Centre (UK)  Significant publications discussing data management and curation  Open datasets and data repositories
  • 5. CONTEXT OF THE NSF DATA POLICIES  Driver – greater impact of research dollars  Context = scholarly communications  Encouraging two separate types of activities  Data management & curation  Data sharing  Scholarly impact: greater exposure, facilitates reproducibility, facilitates new discoveries via secondary analysis/data re-use, fosters productive collaborations, leads to new computational techniques  Planning ahead 5, 50, 100 years – preservation, persistent access  If you can’t find it, it doesn’t exist
  • 6. POLICY ON DISSEMINATION & SHARING [1]  …promptly prepare and submit for publication, with authorship that accurately reflects the contributions of those involved, all significant findings from work conducted under NSF grants  …expected to share with other researchers, at no more than incremental cost and within a reasonable time, the primary data, samples, physical collections and other supporting materials created or gathered in the course of work under NSF grants…expected to encourage and facilitate such sharing. Privileged or confidential information should be released only in a form that protects the privacy of individuals and subjects involved. General adjustments and, where essential, exceptions to this sharing expectation may be specified by the funding NSF Program or Division/Office for a particular field or discipline…
  • 7. POLICY ON DISSEMINATION & SHARING [2]  Investigators and grantees are encouraged to share software and inventions created under the grant or otherwise make them or their products widely available and usable .  NSF normally allows grantees to retain principal legal rights to intellectual property developed under NSF grants to provide incentives for development and dissemination of inventions, software and publications that can enhance their usefulness, accessibility and upkeep. Such incentives do not, however, reduce the responsibility that investigators and organizations have as members of the scientific and engineering community, to make results, data and collections available to other researchers.
  • 8. POLICY ON DISSEMINATION & SHARING [3]  NSF program management will implement these policies for dissemination and sharing of research results, in ways appropriate to field and circumstances, through the proposal review process; through award negotiations and conditions; and through appropriate support and incentives for data cleanup, documentation, dissemination, storage and the like.
  • 9. NSF DMP REQUIREMENT  the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project;  the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies);  policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements;  policies and provisions for re-use, re-distribution, and the production of derivatives; and  plans for archiving data, samples, and other research products, and for preservation of access to them . http://www.nsf.gov/pubs/policydocs/pappguide/nsf11001/gpg_2.jsp#dmp
  • 10. NSF DMP: OVERVIEW  Should reflect  Awareness of data management and curation in your discipline  Feasible plan to utilize available cyberinfrastructure  Throughout the DMP, try to  Explain the rationale for your choices  Identify roles for data management and curation activities  Implementation costs of the DMP CAN be included in direct costs
  • 11. DMP: DATA, STANDARDS, & METADATA Utilize standards common within your discipline/community  Data & standards  Characterize the data to be generated or used  How will these characteristics impact storage, management, and processing?  What is the backup and security plan?  Describe data & project documentation  Metadata & standards  Will your data be self-explanatory or understandable in isolation?  Types of metadata  Descriptive (for findability, context, etc.)  Structural (for things like geospatial files)  Administrative (for preservation)
  • 12. DMP: ACCESS & SHARING  How and when will data be made available?  What is the process for gaining access?  Ethical or legal issues such as privacy, confidentiality, security, intellectual property, or other rights?  Limits or conditions placed on sharing for political, commercial, or patent reasons?
  • 13. DMP: RE-USE, DISTRIBUTION, ETC.  Policies & permissions  Will permission restrictions be necessary?  What rights will you retain before data is made available?  Is there an embargo period?  Re-use  Who is likely to be interested in this data?  How might you anticipate this data being used?  What value might the data have for these people?
  • 14. DMP: LONG-TERM PRESERVATION  Researcher ’s role  Selection of data for preservation  How long do you think the data will be useful?  What data will be preserved for the long -term?  Transformations necessary to prepare data for preservation?  data cleaning, de-identification, etc.  Contextual information to make the data reusable  metadata, documentation, references, reports, manuscripts, grant proposal, etc.  Data repository ’s role  Links to published materials and other outcomes?  Use of persistent citation?  Procedures for preservation and back-up?  Access mechanisms
  • 15. RESEARCH @ IUPUI P ro gram o f D i g i ta l Sc h o l a rshi p: htt p :/ / ul ib.i upui .e du/di gi tal sc hol arshi p C e nte r fo r Re s e a rc h & L e a r n ing: htt p :/ /c rl.i upui .e du / OVC R : htt p ://res earc h.iupu i.e du/ deve lop me nt/ O ff i c e o f Aca d e m i c Affa i rs : htt p :/ /www. acade mi caffai rs.i upui .e du I nte ll e ct ual P ro p e r ty Po l i c y : htt ps :/ /www. i ndi ana.e du/~ vpfaa / a ca de m i cgui de /i ndex .php /Pol i cy_ I - 1 1 Re s e a rch Fi l e Syste m : htt p :// pt i.i u.e du/storage / rfs Sc h o l arl y D ata Arc h i ve : htt p :/ /pti .iu.e du/sto rage /sda Re s e a rch Te c h n o l ogie s , U I TS: htt p :/ / u its .iu.e du/ page /ave l C o re Se r vi c e s , U I TS: htt p :/ /pti.i u.e du/c s Sc h o l arl y C y b e r i nf rast ruc ture , U I TS: htt p :/ /ui ts.i u.e du/ page /am e e I U Wa re : htt ps :/ / i uware .i u.e d u I U a nyWare : htt ps :/ / iuanyware .i u.e du/vpn/ in dex.htm l Stat M ath : htt p :// www. i ndiana.e du/ ~ statm ath/ Stat i sti cs C o n s u lti ng C e nte r : htt p :/ / www. math.i upui.e du /asc i /
  • 16. CONTACT INFORMATION Heather Coates Digital Scholarship & Data Management Librarian University Library Email: hcoates@iupui.edu Phone: 317-278-7125 Web: http://ulib.iupui.edu/digitalscholarship/dataservices
  • 17. UPCOMING WORKSHOP Meeting the NSF Data Management Plan Requirement: What you need to know March 7, 2012 @ 2:00pm, UL 1116 Register online at: http://events.iupui.edu/event/?event_id=6064