This document discusses funder requirements for data management and sharing. It notes that major funders like the National Science Foundation (NSF) and National Institutes of Health (NIH) require applicants to submit a data management plan. These plans describe how research data will be organized, preserved, and shared. The document provides details on what funders expect to see in a data management plan, including a description of the data, metadata standards, data access and sharing policies, and plans for long-term data preservation. It also lists other funders that require applicants to have a data management or sharing plan.
Overview of the UKRDDS pilot project at Univwersity of Edinburgh employing PhD interns to validate metadata about research data created by University of Edinburgh researchers and held in local RDM services solutions. This was presented at IASSIST in June 2016, Bergen, Norway.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Overview of the UKRDDS pilot project at Univwersity of Edinburgh employing PhD interns to validate metadata about research data created by University of Edinburgh researchers and held in local RDM services solutions. This was presented at IASSIST in June 2016, Bergen, Norway.
An introduction to Research Data Management and Data Management Planning for research managers and administrators. The presentation was given at the Open University on 18th July 2013.
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Have you implemented a Data Management Plan (DMP) tool at your institution or are you currently involved in discussions to implement one? Would you like to connect with others who are involved in implementing DMPs? Then this webinar is for you!
This webinar will bring together those involved in planning or implementing DMP to exchange information and explore ideas around DMP.
>>>>>>>>>>>>>>>>>>>>>>>>
Kathryn Unsworth and Natasha Simons lead the conversation by starting off with a few thoughts on:
-- a wrap up of the DMP Birds of a Feather session at eResearch Australasia (Oct 2016)
-- DMPs v2
-- discussion around DMPs as Thing 15 in the 23 (Research Data) Things program
-- and some thought provoking ideas.
This section WILL be recorded.
Then open up for discussion - NOT recorded.
We will also be looking to gauge interest in the formation of a DMP Community of Practice in Australia.
>>>>>>>>>>>>>>>>>>>>>>>>
Background:
Significant advocacy and technical enterprise have been directed towards the development and use of DMP tools. However, the agents and motivations driving DMP use differ, presenting use cases to explore and questions to be answered:
-- Why implement a DMP tool?
-- Does DMP use align with an agent’s motivations and more importantly with intended outcomes?
-- What are the expected outcomes?
-- Is there a one-size-fits-all DMP?
-- Is best practice for researchers an aim or a hoped-for by product?
>>>>>>>>>>>>>>>>>>>>>>>>
More info about DMPs: http://www.ands.org.au/working-with-data/data-management/data-management-plans
Australian DMP examples: https://projects.ands.org.au/policy.php
>>>>>>>>>>>>>>>>>>>>>>>>
Contact:
Kathryn.Unsworth@ands.org.au
Natasha.Simons@ands.org.au
Presentation given by Sarah Jones at a seminar run by LSHTM on 6th November 2012. http://www.lshtm.ac.uk/newsevents/events/2012/11/developing-data-management-expertise-in-research---half-day-event
Management of research data specifically for Engineering and Physical Science. Delivered by Stuart Macdonald at the "Support for Enhancing Research Impact" meeting at the University of Edinburgh on 22 June 2016.
Research Data Management: An Introductory Webinar from OpenAIRE and EUDATTony Ross-Hellauer
OpenAIRE and EUDAT co-present this webinar which aims to introduce researchers and others to the concept of research data management (RDM). As well as presenting the benefits of taking an active approach to research data management – including increased speed and ease of access, efficiency (fund once, reuse many times), and improved quality and transparency of research – the webinar will advise on strategies for successful RDM, resources to help manage data effectively, choosing where to store and deposit data, the EC H2020 Open Data Pilot and the basics of data management, stewardship and archiving.
Webinar recording available: http://www.instantpresenter.com/eifl/EB57D6888147
Presentation given to EC project officers as part of workshops run by the FOSTER (foster open science) project. The presentation covers the Horizon 2020 open data pilot.
Have you implemented a Data Management Plan (DMP) tool at your institution or are you currently involved in discussions to implement one? Would you like to connect with others who are involved in implementing DMPs? Then this webinar is for you!
This webinar will bring together those involved in planning or implementing DMP to exchange information and explore ideas around DMP.
>>>>>>>>>>>>>>>>>>>>>>>>
Kathryn Unsworth and Natasha Simons lead the conversation by starting off with a few thoughts on:
-- a wrap up of the DMP Birds of a Feather session at eResearch Australasia (Oct 2016)
-- DMPs v2
-- discussion around DMPs as Thing 15 in the 23 (Research Data) Things program
-- and some thought provoking ideas.
This section WILL be recorded.
Then open up for discussion - NOT recorded.
We will also be looking to gauge interest in the formation of a DMP Community of Practice in Australia.
>>>>>>>>>>>>>>>>>>>>>>>>
Background:
Significant advocacy and technical enterprise have been directed towards the development and use of DMP tools. However, the agents and motivations driving DMP use differ, presenting use cases to explore and questions to be answered:
-- Why implement a DMP tool?
-- Does DMP use align with an agent’s motivations and more importantly with intended outcomes?
-- What are the expected outcomes?
-- Is there a one-size-fits-all DMP?
-- Is best practice for researchers an aim or a hoped-for by product?
>>>>>>>>>>>>>>>>>>>>>>>>
More info about DMPs: http://www.ands.org.au/working-with-data/data-management/data-management-plans
Australian DMP examples: https://projects.ands.org.au/policy.php
>>>>>>>>>>>>>>>>>>>>>>>>
Contact:
Kathryn.Unsworth@ands.org.au
Natasha.Simons@ands.org.au
This slideshow was used in an Introduction to Research Data Management course taught for the Mathematical, Physical and Life Sciences Division, University of Oxford, on 2015-02-09. It provides an overview of some key issues, looking at both day-to-day data management, and longer term issues, including sharing, and curation.
Meeting Federal Research Requirements for Data Management Plans, Public Acces...ICPSR
These slides cover evolving federal research requirements for sharing scientific data. Provided are updates on federal agency responses to the 2013 OSTP memo, guidance on data management plans, resources for data management and curation training for staff/researchers, and tips for evaluating public data-sharing services. ICPSR's public data-sharing service, openICPSR, is also presented. Recording of this presentation is here: https://www.youtube.com/watch?v=2_erMkASSv4&feature=youtu.be
This slideshow was used in a Research Data Management Planning course taught at IT Services, University of Oxford, on 2015-02-18 and 2015-05-13. It provides an overview of the elements of a data management plan, plus an introduction to some tools that can be used to build one.
5 Reasons Why Healthcare Data is Unique and Difficult to MeasureHealth Catalyst
Healthcare data is not linear. It is a complex, diverse beast unlike the data of any other industry. There are five ways in particular that make healthcare data unique:
1. Much of the data is in multiple places.
2. The data is structured and unstructured.
3. It has inconsistent and variable definitions; evidence-based practice and new research is coming out every day. 4. The data is complex.
5. Changing regulatory requirements.
The answer for this unpredictability and complexity is the agility of a late-binding Data Warehouse.
EUDAT & OpenAIRE Webinar: How to write a Data Management Plan - July 7, 2016|...EUDAT
| www.eudat.eu | 1st Session: July 7, 2016.
In this webinar, Sarah Jones (DCC) and Marjan Grootveld (DANS) talked through the aspects that Horizon 2020 requires from a DMP. They discussed examples from real DMPs and also touched upon the Software Management Plan, which for some projects can be a sensible addition
Federal Funder Mandates for Open Access Brown Bag
UVa OA Week Presentation
Library data management experts Sherry Lake and Andrea Denton will lead a discussion of current and upcoming mandates for making the results of federally-funded research open to the public. Bring your questions about NIH, NEH, NSF, DOE, and other funders.
Managing and Sharing Research Data - Workshop at UiO - December 04, 2017Michel Heeremans
These slides were presented during a workshop on Research Data Management, given at the University of Oslo, Department of Geosciences on December 04, 2017
This presentation was provided by Maria Praetzellis of California Digital Library, during the NISO hot topic virtual conference "Effective Data Management," which was held on September 29, 2021.
Presenter(s): Jeffrey Mortimore.
As federal funding requirements continue to evolve and more publishers are requiring open data sharing as a condition of publication, academic libraries have an important role to play supporting campus researchers’ data management needs. This session explores in detail the National Science Foundation’s current data management requirements, giving special attention to data planning as part of the NSF’s grant application process.
Meeting the NSF DMP Requirement June 13, 2012IUPUI
June 13 version of the IUPUI workshop Meeting the NSF Data Management Plan Requirement: What you need to know. This workshop is co-sponsored by the Office of the Vice Chancellor for Research and the University Library.
Supplementary presentation slides from a lecture on digital preservation given at the University of the West of England (UWE) as part of the MSc in Library and Library Management, University of the West of England, Frenchay Campus, Bristol, March 10, 2010
Introduction to DMPTool2. Originally released in 2011, the DMPTool provides a free step-by-step wizard, detailed guidance, and links to general and institutional resources to walk a researcher through the process of generating a comprehensive data management plan tailored to specific funder requirements.
This webinar will demonstrate some of the original features of the tool, as well as the new features in DMPTool2, which include institutional customizations and researcher collaborations.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
2. US Funding Agencies
Requirement
The Office of Management and Budget
(OMB) Circular A-110 provides the federal
administrative requirements for grants and
agreements with institutions of higher
education, hospitals and other non-profit
organizations.
In1999, revised to provide public access
under some circumstances to research data
through the Freedom of Information Act
(FOIA).
Funding agencies have implemented the
OMB requirement in various ways.
3. Who is Requiring Data
Sharing?
National Science Foundation (NSF)
National Institute of Health (NIH) – for awards
asking for $500,000 or more (since 2003)
NIH Public Access Mandate (for publications)
National Endowment for the Humanities (NEH)
Office of Digital Humanities – New Grant
Program Digital Humanities Implementation
Grants
4. What is a Data Management
Plan?
A comprehensive plan of how you will
manage your research data throughout
the lifecycle of your research project
OR
Briefdescription of how you will comply
with funder’s data sharing policy
Reviewed as part of a grant application
5. NSF Data Archiving and
Sharing Policy Prior to 2011
To advance science by encouraging data
sharing among researchers:
Data obtained with federal funds be
accessible to the general public
Grantees must develop and submit
specific plans to share materials collected
with NSF support, except where this is
inappropriate or impossible
6. Dissemination & Sharing of
Research Results
“Investigators are 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. Grantees are
expected to encourage and facilitate such
sharing.”
National Science Foundation: Award &
Administration Guide (AAG) Chapter VI.D.4
7. Scientists Seeking NSF Funding Will Soon Be
Required to Submit Data Management Plans
NSF Press Release 10-077
On or around October 2010:
Require that all proposals include a data
management plan in the form of a two-
page supplementary document
Change in the implementation of NSF’s
data sharing policy
Specifics forthcoming
8. What Will a Data
Management Plan Look Like?
“Long-Lived Digital Data Collections: Enabling Research
and Education in the 21st Century.” National Science
Board, September 2005.
“To Stand the Test of Time: Long-term Stewardship of Digital
Data Sets in Science and Engineering.” Report to National
Science Foundation from Association of Research Libraries
(ARL) Workshop, September 2006.
“Harnessing the Power of Digital Data for Science and
Society.” Report of the Interagency Working Group on
Digital Data to the Committee on Science of the National
Science and Technology Council, January 2009.
9. Plan for Data Management &
Sharing of the Products of Research
As of January 18, 2011:
“Proposals must include a supplementary
document of no more than two pages labeled
“Data Management Plan”. This supplement
should describe how the proposal will conform to
NSF policy on the dissemination and sharing of
research results, and may include…...”
NSF: Grant Proposal Guide (GPG) Chapter II.C.2.j
10. Parts of a (Generic) NSF Data
Management Plan
I. Products of the Research: The types of data, samples, physical
collections, software, curriculum materials, and other materials
to be produced in the course of the project.
II. Data Formats: 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).
III. Access to Data and Data Sharing Practices and Policies:
Policies for access and sharing including provisions for
appropriate protection of
privacy, confidentiality, security, intellectual property, or other
rights or requirements.
IV. Policies for Re-Use, Re-Distribution, and Production of
Derivatives.
V. Archiving of Data: Plans for archiving data, samples, and other
research products, and for preservation of access to them.
Grant Proposal Guide (GPG) Chapter II.C.2.j
11. Requirements by Directorate, Office,
Division, Program, or other NSF Units
Mathematical and Physical
Directorate-wide Guidance Sciences Directorate (MPS)
Biological Sciences Directorate (BIO) Division of Astronomical Sciences
Computer & Information Sciences & Division of Chemistry
Engineering (CISE) Division of Materials Research
Education & Human Resources Division of Mathematical Sciences
Directorate (EHR)
Division of Physics
Engineering Directorate (ENG)
Social, Behavioral and Economic
Sciences Directorate (SBE)
Geological Sciences Directorate
(GEO)
Division of Earth Sciences
Division of Ocean Sciences
Atmospheric & Geospace Sciences
12. Which NSF requirement to
use?
Which Guideline Should I follow?
First: follow the requirements laid out in the
specific solicitation, if any.
Second: follow the guidelines published by the
appropriate NSF directorate and/or division. If
there is a conflict, the latter takes precedence.
Third: follow the more general guidelines.
Interdisciplinary Proposals
Use guidelines appropriate to the lead program
(if there are specific guidelines)
13. Parts of a Data Management Plan
1. The types of data and other information
Types of data produced
Relationship to existing data
How/when/where will the data be captured
or created?
How will the data be processed?
Quality assurance & quality control measures
Security: version control, backing up
Who will be responsible for data
management during/after project?
14. Parts of a Data Management Plan
2. Data & Metadata Standards
Identify the formats of data files created over
the course of the project
What metadata are needed to make the
data meaningful?
How will you create or capture these
metadata?
Why have you chosen particular standards
and approaches for metadata?
15. Parts of a Data Management Plan
3. Policies for access and sharing
4. Policies for re-use & re-distribution
Are you under any obligation to share data?
How, when, & where will you make the data
available?
What is the process for gaining access to the data?
Who owns the copyright and/or intellectual
property?
Will you retain rights before opening data to wider
use? How long?
Embargo periods for political/commercial/patent
reasons?
Ethical and privacy issues?
Who are the foreseeable data users?
How should your data be cited?
16. Parts of a Data Management Plan
5. plans for archiving and preservation
What data will be preserved for the long
term? For how long?
Where will data be preserved?
What data transformations need to occur
before preservation?
What metadata will be submitted alongside
the datasets?
Who will be responsible for preparing data for
preservation? Who will be the main contact
person for the archived data?
17. What is a Data Management
Plan?
A comprehensive plan of how you will
manage your research data throughout
the lifecycle of your research project
OR
Briefdescription of how you will comply
with funder’s data sharing policy
Reviewed as part of a grant application
18. Who Else is Requiring a Data
Management or Sharing Plan?
Institute of Museum and Library Services
(IMLS)
Gordon and Betty Moore Foundation
Data Sharing Philosophy and Plan (since
2008)
Joint Fire Science Program
National Oceanic and Atmospheric
Administration (NOAA)
19. Questions? Discussion?
Sherry Lake
Senior Scientific Data Consultant, UVA Library
shlake@virginia.edu
Twitter: shlakeuva
Web:
http://www.lib.virginia.edu/brown/data
Editor's Notes
Requirement of Sharing data started in 1999. In recent years several national scientific organizations have issued statements and policies underscoring the need for prompt archiving of data and funding agencies have started to require that the data they fund be deposited in a public archive. The requirement of Dissemination & Sharing of Research Results has been in the NSF Grant Policy Manual since 2002.Even though this “sharing” requirement was in the Admin Guide, there had been little if any enforcement. There was only a “check box” in the Fast Lane system. (might want to ask if this is true?, had they noticed it, had they asked researcher anything about it, or just checked the box).
NSF isn’t the only funding agency requiring a data management plan for data to be shared NEH announced on June 22, 2011 applies to Grants deadline Jan. 24, 2012 NIH 2003 Data Sharing Policy:In NIH's view, all data should be considered for data sharing. Data should be made as widely and freely available as possible while safeguarding the privacy of participants, and protecting confidential and proprietary data. To facilitate data sharing, investigators submitting a research application requesting $500,000 or more of direct costs in any single year to NIH on or after October 1, 2003 are expected to include a plan for sharing final research data for research purposes, or state why data sharing is not possible. The NIH Public Access Policy ensures that the public has access to the published results of NIH funded research. It requires scientists to submit final peer-reviewed journal manuscripts that arise from NIH funds to the digital archive PubMed Central upon acceptance for publication. To help advance science and improve human health, the Policy requires that these papers are accessible to the public on PubMed Central no later than 12 months after publication.
This policy has been in the Grant Policy Manual since 2002.Little or no enforcement, no more than a “Checkbox” in the grant submission system
This policy has been in the Grant Policy Manual since 2002.Little or no enforcement, no more than a “Checkbox” in the grant submission system
May 10, 2010 announcement: each discipline has its own culture about data-sharing, and said that NSF wants to avoid a one-size-fits-all approach to the issue. But for all disciplines, the data management plans will be subject to peer review, and the new approach will allow flexibility at the directorate and division levels to tailor implementation as appropriate. This is a change in the implementation of NSF's long-standing policy (Grant Policy Manual since 2002) that requires grantees to share their data within a reasonable length of time, so long as the cost is modest. making sure that any data obtained with federal funds be accessible to the general public.
The research community will be informed of the specifics of the anticipated changes and the agency's expectations for the data management plans.With no guidelines from NSF, how were we going to support our researchers when Oct. comes around?Looked through piles of NSF, gov. papers to get a hint as to what could be required.Long-lived:In this report we have asserted that NSF should have a coherent and thoughtful digital data collection strategy. The same is true for the individual or teams of researchers who will author and curate data. They need to have a strategy for dealing with data from their inception to their demise, or at least the foreseeable future. We define a data management plan to be a plan that describes the data that will be authored as well as how the data will be managed and made accessible throughout its lifetime.---- SPECIFIED CONTENTS of planTest of Time:NSF should require inclusion of DMP in proposal submission. Several key elements were identified.IWGDDThis report provides a strategy to ensure that digital scientific data can be reliably preserved for maximum use in catalyzing progress in science and society. AGENCIES: to promote data management planning process – includes preparing a data management plan for proposals. Nice list of elements to be considered, with guidance/definitions.Uva started creating a DMP guide with the IWGDD elements. Our 1st “template”.
In October 2010, the Grant Proposal Guide was updated with the following: As of January 18, 2011, all new NSF proposals are required to include a data management plan: Describes how the researcher will adhere to the NSF Sharing PolicyUploaded as 2-page supplemental document in FastLane labeled as “Data Management Plan”Formally peer-reviewed, and will require status updates in all progress reports Broad guidelines, but directorates may have specific guidelines for their community Important.... The policy was not starting until Jan. 2011!!! This is NOT a all encompassing Data Management Plan on how the researcher will manage his research throughout the project, ONLY how the researcher will manage data to “share”, per the policy on “Dissemination & Sharing of Research Results”. Implementation will be flexible within NSF divisions. In many of the answers to questions, the FAQ includes “will be determined by the community of interest through the process of peer review and program management”
DMP should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results (see AAG Chapter VI.D.4), and may include: A valid Data Management Plan may include only the statement that no detailed plan is needed, as long as the statement is accompanied by a clear justification. These are the parts from the Generic guidelines.
The 1st bullet in the handout is the link for the NSF page on Data Management Plan Guidelines for Divsions/Directorates. Here’s a list of which one have guidelines: Left-side lists Directorate-wide guidanceRight-side lists the divisions under the directorates with Division Specific guidance Not all directorates/divisions have guidelines. If guidance specific to the program is not available, Use the more general guidelines in the Grant Proposal Guide.
With differing guidelines, which one should you use? Guidelines should be followed in this order:First, follow the requirements laid out in the specific solicitation, if any. These can generally be found in a section entitled "Proposal Preparation Instructions." Contact the program officer with any questions. Second, follow the guidelines published by the appropriate NSF directorate and/or division. Not all directorates and divisions have published data management guidelines; check the NSF's page on Dissemination and Sharing of Research Results for updates (1st link in handout) Third, follow the more general guidelines in the Grant Proposal Guide.
Describe means by which you will provide access to data and applicable time frame.Describe means for preserving data, if different from above.How long should the data be kept?
As we will see, the NSF is really concerned with managing data in order to share it. Currently, it is not interested researchers providing a more comprehensive Data Management Plan though out the research life cycle. We recommend initiating a more comprehensive Data Management Plan to see the full benefits of managing your data. But to comply with NSF mandate, all you need is a 2-page description what data you have and how you will share it.
IMLS as of March 2011 for Projects that Develop digital data