This document summarizes data management and sharing policies in the US and Canada. It outlines that major US funding agencies like the NSF and NIH require data management plans and sharing of results. Canadian agencies like CIHR and SSHRC also have data archiving policies. Other groups developing policies include domain-specific professional organizations and journals. Library organizations in both countries are working to help institutions support these policies through initiatives like the ARL/DLF E-Science Institute.
The document summarizes the DMPTool, an online tool that helps researchers create data management plans. It provides a step-by-step wizard to generate DMPs. The tool aims to 1) provide a simple way for researchers to create DMPs required by funding agencies and 2) provide institution-specific resources to help manage data. It is accessed through institutional login and provides customized help text, links, and answers. Usage has grown significantly since launch. Future work includes adding funders, functionality, and integrating with other systems to help coordinate data management.
RDAP14: Building a data management and curation program on a shoestring budgetASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
Margaret Henderson
Director, Research Data Management
Virginia Commonwealth University
Trailblazing in the Wilderness of Data ManagementStephanie Wright
The document discusses trailblazing in research data management. It defines key terms like data, data management, and big data. It outlines why various stakeholders like funding agencies, universities, researchers, and libraries are venturing into research data management. It reviews assessments of data management needs conducted at various universities, examples of existing research data management programs, and available tools and resources. Finally, it discusses how institutions can blaze their own trail in research data management by identifying needs, partners, priorities, and potential services and policies to develop.
"Undergrad ecologists aren't learning data management" - ESA 2013Carly Strasser
Presentation for Ecological Society of America 2013 Meeting in Minneapolis, MN on 6 August 2013. Results published in Ecosphere doi: 10.1890/ES12-00139.1
RDAP14: It’s a Real World: Developing Preservation Policy for DryadASIS&T
This document discusses Dryad's process of developing a formal preservation policy. It provides context on Dryad as a digital repository and the benefits of data preservation. It then outlines Dryad's needs for a policy, the development process, key elements of the final policy, lessons learned in creating the policy, and open questions. The policy development involved input from a working group and staff over 18 months to balance ideals with practical realities.
The document summarizes the DMPTool, an online tool that helps researchers create data management plans. It provides a step-by-step wizard to generate DMPs. The tool aims to 1) provide a simple way for researchers to create DMPs required by funding agencies and 2) provide institution-specific resources to help manage data. It is accessed through institutional login and provides customized help text, links, and answers. Usage has grown significantly since launch. Future work includes adding funders, functionality, and integrating with other systems to help coordinate data management.
RDAP14: Building a data management and curation program on a shoestring budgetASIS&T
Research Data Access and Preservation Summit, 2014
San Diego, CA
Margaret Henderson
Director, Research Data Management
Virginia Commonwealth University
Trailblazing in the Wilderness of Data ManagementStephanie Wright
The document discusses trailblazing in research data management. It defines key terms like data, data management, and big data. It outlines why various stakeholders like funding agencies, universities, researchers, and libraries are venturing into research data management. It reviews assessments of data management needs conducted at various universities, examples of existing research data management programs, and available tools and resources. Finally, it discusses how institutions can blaze their own trail in research data management by identifying needs, partners, priorities, and potential services and policies to develop.
"Undergrad ecologists aren't learning data management" - ESA 2013Carly Strasser
Presentation for Ecological Society of America 2013 Meeting in Minneapolis, MN on 6 August 2013. Results published in Ecosphere doi: 10.1890/ES12-00139.1
RDAP14: It’s a Real World: Developing Preservation Policy for DryadASIS&T
This document discusses Dryad's process of developing a formal preservation policy. It provides context on Dryad as a digital repository and the benefits of data preservation. It then outlines Dryad's needs for a policy, the development process, key elements of the final policy, lessons learned in creating the policy, and open questions. The policy development involved input from a working group and staff over 18 months to balance ideals with practical realities.
The document discusses the need for an ecosystem to better manage research data through its entire lifecycle, from creation to publication to sharing and reuse. It proposes that libraries can play a key role in this ecosystem by providing services like curation repositories, identifiers, metadata, and tools to help researchers publish, share, and get credit for their data. The goal is to improve data discovery, access, attribution, and incentivize data sharing to make research data as integral to the scholarly record as journal articles.
NISO Webinar on data curation services at the CDLCarly Strasser
"Building communities and Services in Support of Data-Intensive Research". Webinar on 18 Sept 2013 for the NISO Webinar Series. This was part 2 of 2 for Data Curation
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Research Data Service at the University of EdinburghRobin Rice
The University of Edinburgh provides research data management services and resources to support researchers through the entire data lifecycle. These include tools for creating data management plans, storing and sharing research data securely, and preserving data in the long term. The Research Data Service aims to help researchers comply with open science principles and data policies through a range of training programs, online guidance, and technical infrastructure. It has developed a multi-year roadmap and maturity model to continuously improve services based on researchers' needs and priorities like relationship building, communication skills, and consultation.
NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Enabling transparency and efficiency in the research landscape
Dr. Melissa Haendel, Associate Professor, Ontology Development Group, OHSU Library, Department of Medical Informatics and Epidemiology, Oregon Health & Science University
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Presentation by Lisa Federer (UCLA) on 16 July 2013 as part of the IMLS-sponsored DMPTool Webinar Series.
Description: This webinar will discuss the special needs of health sciences researchers and help you learn how to talk to researchers in the health and medical fields about their data management needs. We will cover NIH Data Sharing Policy and how to write a data management plan that meets NIH’s requirements. After viewing this webinar, participants will understand: who is required to submit a plan; specific information that should be included in a plan; how to use the DMPTool to write an NIH-specific DMP; and where to find additional resources for help.
This presentation was provided by Scott Warren and Anne Rauh of Syracuse University during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Lightning Talk, Konkiel: Bootstrapping Library Data Management Services for E...ASIS&T
This document discusses how libraries can provide data management services to support epidemiology research. It describes the characteristics of epidemiology data, including its sensitive nature and complexity. It outlines some of the needs of epidemiology researchers, such as secure storage, training, and tools for data sharing and citation. The document proposes several library services to address these needs, such as repositories for long-term preservation of epidemiology data with access controls, training in data management and standards, and assigning persistent identifiers to data. Finally, it provides examples of resources on related topics like informed consent workflows and disciplinary metadata standards.
This presentation was provided by Peggy Layne, Andi Ogier, and Ginny Pannabecker of Virginia Tech during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
This presentation was provided by Muhammad Javed of Cornell University during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Research data management free online courses, publisher policiesNikesh Narayanan
The document provides information on free online courses and resources for research data management as well as policies from various publishers. It lists links to courses on Coursera, the Research Data Management Librarian Academy, Elsevier Researcher Academy, and FutureLearn. It also provides a link to EDDAT training and outlines the data policies of publishers including Elsevier, Wiley, Springer Nature, Taylor and Francis, Emerald, PLOS ONE, and Nature.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
This presentation was provided by Julie Goldman of Harvard University, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This presentation was delivered by Gloria Gonzalez of Zepheira during the NISO Virtual Conference, BIBFRAME & Real World Applications of Linked Bibliographic Data, held on June 15, 2016.
RDAP14: OSTP Panel NIH’s Update Public Access ASIS&T
Research Data Access & Preservation Summit
March 26-28, 2014
San Diego, CA
Panel: Funding agency responses to federal requirements for public access to research results
Dr. Neil M. Thakur, National Institutes of Health, Special Assistant to the Deputy Director for Extramural Research
This presentation was provided by William Cross, Madison Sullivan, and Eka Grguric of NCSU during the Aug 10 NISO-NASIG webinar, How Libraries Use, Support and Can Implement Researcher Identifiers.
In order to be reused, research data must be discoverable.
The EPSRC Research Data Expectations* requires research organisations to maintain a data catalogue to record metadata about research data generated by EPSRC-funded research projects.
Universities are increasingly making research data assets available through repositories or other data portals.
The requirement for a UK research data discovery service has grown as universities become more involved in RDM and capacity develops.
Michigan State University campus policy, resources and best practices for research data management offered by the MSU Libraries Research Data Management Guidance service. http://www.lib.msu.edu/rdmg/
The document discusses the need for an ecosystem to better manage research data through its entire lifecycle, from creation to publication to sharing and reuse. It proposes that libraries can play a key role in this ecosystem by providing services like curation repositories, identifiers, metadata, and tools to help researchers publish, share, and get credit for their data. The goal is to improve data discovery, access, attribution, and incentivize data sharing to make research data as integral to the scholarly record as journal articles.
NISO Webinar on data curation services at the CDLCarly Strasser
"Building communities and Services in Support of Data-Intensive Research". Webinar on 18 Sept 2013 for the NISO Webinar Series. This was part 2 of 2 for Data Curation
This presentation was provided by Jan Fransen of the University of Minnesota - Twin Cities during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Research Data Service at the University of EdinburghRobin Rice
The University of Edinburgh provides research data management services and resources to support researchers through the entire data lifecycle. These include tools for creating data management plans, storing and sharing research data securely, and preserving data in the long term. The Research Data Service aims to help researchers comply with open science principles and data policies through a range of training programs, online guidance, and technical infrastructure. It has developed a multi-year roadmap and maturity model to continuously improve services based on researchers' needs and priorities like relationship building, communication skills, and consultation.
NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Enabling transparency and efficiency in the research landscape
Dr. Melissa Haendel, Associate Professor, Ontology Development Group, OHSU Library, Department of Medical Informatics and Epidemiology, Oregon Health & Science University
This presentation was provided by Libbie Stephenson, UCLA Social Science Data Archive, during a NISO Virtual Conference on the topic of data curation, held on Wednesday, August 31, 2016
February 18 2014 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Capacity Building: Leveraging existing library networks to take on research data
Heidi Imker, Director of the Research Data Service, University of Illinois at Urbana-Champaign
Presentation by Lisa Federer (UCLA) on 16 July 2013 as part of the IMLS-sponsored DMPTool Webinar Series.
Description: This webinar will discuss the special needs of health sciences researchers and help you learn how to talk to researchers in the health and medical fields about their data management needs. We will cover NIH Data Sharing Policy and how to write a data management plan that meets NIH’s requirements. After viewing this webinar, participants will understand: who is required to submit a plan; specific information that should be included in a plan; how to use the DMPTool to write an NIH-specific DMP; and where to find additional resources for help.
This presentation was provided by Scott Warren and Anne Rauh of Syracuse University during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Lightning Talk, Konkiel: Bootstrapping Library Data Management Services for E...ASIS&T
This document discusses how libraries can provide data management services to support epidemiology research. It describes the characteristics of epidemiology data, including its sensitive nature and complexity. It outlines some of the needs of epidemiology researchers, such as secure storage, training, and tools for data sharing and citation. The document proposes several library services to address these needs, such as repositories for long-term preservation of epidemiology data with access controls, training in data management and standards, and assigning persistent identifiers to data. Finally, it provides examples of resources on related topics like informed consent workflows and disciplinary metadata standards.
This presentation was provided by Peggy Layne, Andi Ogier, and Ginny Pannabecker of Virginia Tech during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
This presentation was provided by Muhammad Javed of Cornell University during the NISO virtual conference, Research Information Systems: The Connections Enabling Collaboration, held on August 16, 2017.
Research data management free online courses, publisher policiesNikesh Narayanan
The document provides information on free online courses and resources for research data management as well as policies from various publishers. It lists links to courses on Coursera, the Research Data Management Librarian Academy, Elsevier Researcher Academy, and FutureLearn. It also provides a link to EDDAT training and outlines the data policies of publishers including Elsevier, Wiley, Springer Nature, Taylor and Francis, Emerald, PLOS ONE, and Nature.
February 18 2015 NISO Virtual Conference Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Building Best Practices in Research Data Management: Tisch Library’s Initiatives
Regina F. Raboin, Science Research and Instruction Librarian/ Data Management Services Group Coordinator, Tisch Library, Tufts University
This presentation was provided by Julie Goldman of Harvard University, during part two of the NISO two-part webinar "Building Data Science Skills: Strategic Support for the Work, Part Two," which was held on March 18, 2020.
This presentation was delivered by Gloria Gonzalez of Zepheira during the NISO Virtual Conference, BIBFRAME & Real World Applications of Linked Bibliographic Data, held on June 15, 2016.
RDAP14: OSTP Panel NIH’s Update Public Access ASIS&T
Research Data Access & Preservation Summit
March 26-28, 2014
San Diego, CA
Panel: Funding agency responses to federal requirements for public access to research results
Dr. Neil M. Thakur, National Institutes of Health, Special Assistant to the Deputy Director for Extramural Research
This presentation was provided by William Cross, Madison Sullivan, and Eka Grguric of NCSU during the Aug 10 NISO-NASIG webinar, How Libraries Use, Support and Can Implement Researcher Identifiers.
In order to be reused, research data must be discoverable.
The EPSRC Research Data Expectations* requires research organisations to maintain a data catalogue to record metadata about research data generated by EPSRC-funded research projects.
Universities are increasingly making research data assets available through repositories or other data portals.
The requirement for a UK research data discovery service has grown as universities become more involved in RDM and capacity develops.
Michigan State University campus policy, resources and best practices for research data management offered by the MSU Libraries Research Data Management Guidance service. http://www.lib.msu.edu/rdmg/
This document provides an overview and agenda for a research data management workshop. The agenda includes introductions, background on funder data management policies, fundamentals of data management practices like documentation, file organization, and storage, and resources for the data lifecycle. The workshop aims to educate researchers on best practices for managing research data in response to changing funder requirements and data sharing landscapes.
The Consortia Advancing Standards in Research Administration Information (CASRAI) is an international non-profit dedicated to reducing the administrative burden on researchers and improving business intelligence capacity of research institutions and funders.
Presentation for Northwestern University's first Computational Research Day, April 22, 2014. http://www.it.northwestern.edu/research/about/campus-events/research-day/agenda.html . By Cunera Buys, e-Science Librarian, and Claire Stewart, Director, Center for Scholarly Communication and Digital Curation and Head, Digital Collections
The Center for Research Libraries (CRL) facilitates several collaborative library programs in the United States. These include print archiving networks that coordinate the distributed archiving of print collections among libraries. CRL also coordinates various collection development programs where libraries cooperatively purchase and develop collections in specific subject domains. Some examples mentioned are collaborative print archives for law, agriculture, and government document collections. CRL also coordinates several global resource partnerships to provide digital access and preservation of collections in news, law, agriculture, and science/technology areas.
Library resources and services for grant developmentrds-wayne-edu
This document discusses library resources and services to support grant development, specifically regarding data management and sharing requirements of major funders like NIH and NSF. It provides an overview of mandates from these agencies requiring data management plans and sharing of research data. The WSU Library System online guide for research data services is introduced, which provides tools, templates and guidance on data management policies and repositories. A case study example is presented of a consultation provided to a researcher on developing a strong data sharing plan for an NIH proposal.
Data Literacy: Creating and Managing Reserach Datacunera
This document discusses best practices for creating and managing research data. It covers defining data, the importance of data management, developing a data management plan, file naming conventions, metadata, data sharing and preservation. Key points include making a data management plan addressing types of data, standards, access and sharing policies; using descriptive file names with dates; storing multiple versions of data; and including metadata to explain the data. Resources for data management support are provided.
This document discusses research data management (RDM). It defines research data and describes the RDM lifecycle. Key aspects of RDM include creating data management plans, documenting and organizing data, and ensuring long-term preservation and sharing of data. The document outlines best practices for RDM, such as using appropriate file formats and metadata standards. It also discusses challenges around sensitive data and guidelines for data sharing and citation. The roles libraries can play in supporting RDM are identified, such as developing RDM policies, training researchers, and setting up data repositories.
Presentation to the UM Library Emergent Research SeriesSEAD
SEAD is a 5-year project funded by the NSF to develop cyberinfrastructure for sustainable data preservation and access. It is a partnership between the universities of Michigan, Indiana, and Illinois. SEAD aims to serve researchers in sustainability science who work in small teams and have diverse data needs. It provides active curation tools, collaboration spaces, and interfaces that integrate data, publications, and people. Data can be deposited to university repositories through the SEAD Virtual Archive for long-term preservation and discovery. Lessons show more support is needed to bridge data production and long-term infrastructure. Future plans include expanding the user community and repository options.
Understanding ICPSR - An Orientation and Tours of ICPSR Data Services and Edu...ICPSR
This is ICPSR's core workshop deck designed to introduce, remind, and refresh your knowledge of ICPSR. It contains four "tours" or sub-presentations describing ICPSR's general reason for being, it's social and behavioral research data complete with search strategies, its training, educational, and instructional resources, and its data management and curation services, data repository options, and support resources (content and budget estimates) for those writing grant proposals.
Alain Frey Research Data for universities and information producersIncisive_Events
Research data is growing exponentially but is disparate and challenging to understand fully. Universities face challenges in managing research data to meet funding and standards requirements. Thomson Reuters launched the Data Citation Index to make research data discoverable, accessible, and citable by bringing important data from diverse repositories into one searchable index. This addresses the need for a single access point for quality research data across disciplines and locations.
Steven McEachern - ADA, DDI (metadata standard) and the Data LifecycleSteve Androulakis
Dr. McEachern is Director of the Australian Data Archive at the Australian National University, and has research interests in data management and archiving, community and social attitude surveys, new data collection methods, and reproducible research methods.
This talk was given for the Monthly Tech Talks event hosted by Australian data infrastructure groups ANDS, NeCTAR, RDS and others.
The document discusses research data management services provided by MSU Libraries. It provides an overview of their services which include training, consultation, and guidance on developing data management plans. Requirements for data management and sharing are becoming more common from major funders like NSF, NIH, NASA and others to maximize access, reuse and transparency of research results. The libraries help researchers and faculty understand and comply with these policies and best practices for managing, preserving and sharing research data.
The document provides an overview of research data management (RDM) and the RDM services that Lancaster University plans to offer. It discusses that RDM involves maintaining and preserving digital research data throughout its lifecycle. It also notes that funder requirements and policies are driving universities to improve RDM practices to ensure long-term access and reuse of research data. Lancaster University plans to offer storage, advocate for RDM, provide training and support, help with data management plans, and collaborate with other universities and groups like N8 on RDM issues.
Overview and library support for data management/sharingrds-wayne-edu
This document provides an overview of library support for data management and sharing at Wayne State University. It discusses the data sharing mandates from US government funders like NIH and NSF, as well as other reasons for managing and sharing research data. The library provides resources to help researchers with all stages of the data lifecycle, from identifying existing datasets and writing data management plans, to collecting, analyzing and sharing data according to funder requirements. Services include data interviews, repository recommendations, and assistance drafting data sharing plans. Future work includes developing a checklist of WSU policies related to research data.
Research Data Management Guidance overviewAaron Collie
The document discusses research data management services provided by MSU Libraries. It provides an overview of their services which include training, consultation, and guidance on developing data management plans. Requirements for data management and sharing are becoming more common from major funders like NSF, NIH, NASA and others to maximize access, reuse and transparency of research results. The libraries help researchers and faculty understand best practices to properly store, share and preserve research data in accordance with university and funder policies.
This document provides a summary of the Libra 2.x project to replace the University of Virginia's institutional repository software. It describes the approval of the project in November 2014 due to limitations of the original software. An analysis in 2015 recommended adopting Penn State's Sufia platform for open access and ETD repositories and Harvard's Dataverse for a data repository. The document outlines subsequent work by a working group on usability testing, policies, configuration, and technical setup for the Libra Data repository, which went live in January 2016.
Virginia Data Management Bootcamp: Building the Research Data Community of Pr...Sherry Lake
This document summarizes the Virginia Data Management Bootcamp, a collaborative data education initiative held annually since 2013 among several Virginia universities. It provides details on the planning, logistics, content, and assessments of the bootcamp. According to participant feedback, the hands-on sessions were most useful but some topics could have been covered in more depth. Organizers aim to expand participation to more institutions and offer additional workshops throughout the year, as well as biennial large-scale collaborations and other collaborative efforts to support the growing Virginia data management community of practice.
1. The document discusses best practices for managing research data over the data life cycle, from collection through sharing and archiving. It provides tips for organizing, documenting, and storing data in sustainable file formats and naming conventions. Following best practices helps ensure usability, reproducibility, and long-term access to research data.
2. Specific best practices covered include using consistent organization, standardized naming and formats, descriptive filenames, quality assurance, scripting for processing, documenting file contents, and choosing open file formats. The document also addresses data security, backup, and storage considerations.
3. Managing data properly is important for reuse and sharing data with others now or in the future. Scripting helps capture data workflows for reproducibility.
Using a Case Study to Teach Data Management to LibrariansSherry Lake
This document outlines the agenda and learning objectives for a workshop on research data management for libraries. The workshop uses a case study approach and hands-on activities to teach librarians best practices for data collection, organization, documentation, backup/storage, and sharing/preservation. The goal is to prepare librarians to teach researchers about data management and illustrate opportunities for library involvement in the area. Based on a survey after the workshop, most attendees felt their expectations were met or exceeded, and they found the hands-on case study activities and practical tips to be most useful.
Documentation and Metdata - VA DM BootcampSherry Lake
This document discusses documentation and metadata for research data. It begins with an overview of why documentation is important at different stages of the research data lifecycle from collection through archiving. Key elements to document include how the data was created, its content and structure, who created and maintains it, and how it can be accessed and cited. The document then discusses common documentation formats like readmes, data dictionaries, and codebooks. It also introduces metadata as structured information that describes resources and explains common metadata standards and tools for creating structured metadata files. Exercises guide creating documentation in these formats for a weather dataset example.
This document discusses creating a data management plan. It explains that a data management plan is a comprehensive plan for managing research data throughout a project's lifecycle and briefly describing how data will be shared per a funder's policy. It provides an overview of key elements to include in a plan such as file formats, organization, sharing, and preservation. The document also reviews funder requirements and available tools to create plans, noting they can be tailored to different funders' guidelines.
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.
This document summarizes a workshop on preparing data management plans. It discusses what data management entails, why it is important, and what components are typically included in a data management plan. Key points covered include an overview of the data lifecycle and how plans help ensure research can be replicated, preserved, and shared. The workshop also demonstrates how to create a data management plan using the DMPTool, a online tool that guides users through the process.
This document summarizes recent federal mandates requiring open access to publications and data resulting from federally funded scientific research. It discusses a 2013 White House memo requiring federal agencies spending over $100 million annually on research to develop public access plans. It also outlines policies from agencies like NIH, NSF, and NOAA requiring data management plans and sharing of published results and supporting data. Stakeholder responses to these mandates like the CHORUS publishing initiative and the SHARE academic consortium proposal are also summarized.
DMPTool2 demo for DMPTool-DMPonline Workshop IDCC 2014Sherry Lake
The document discusses updates and improvements to the DMPTool and DMPonline platforms. It notes that DMPTool was originally released in 2011 and was self-funded. DMPonline, funded by additional sources, includes expanded functionality for researchers and administrators, including collaborative plan creation, review capabilities, and institutional templates. Upcoming improvements include co-ownership of plans, expanded administrative roles, and self-service admin functions. The speaker encourages using the platforms' resources to advance data stewardship education.
This document provides information about developing a data management plan for grant proposals. It discusses the goals of the workshop which are to learn about data management planning, available resources, develop a draft plan, and receive feedback. It then covers what good data management involves, who requires data management plans, examples of requirements from agencies like NSF, and parts of a generic data management plan. Finally, it discusses resources available for creating plans like the DMPTool.
This document provides information about a webinar on environmental scanning to identify important stakeholders on campus for data management. Participants must call in for audio and can ask questions in the chat. The webinar will cover goals of environmental scanning, doing a scan based on a data management plan, resources, conducting an institutional scan, and supporting the research lifecycle through collaborations and partnerships.
The document summarizes the Data Management Planning Tool (DMPTool), an online tool that helps researchers create data management plans. It discusses the goals of providing a simple way for researchers to create plans for their funders and offering institution-specific resources. The summary describes the tool's increasing participation from universities and future plans to improve functionality, sustainability, and community involvement through grants and an open source model.
The document discusses the importance of managing research data. It notes that data management saves time, makes long-term data preservation easier, and supports sharing data with others. Data sharing is now required by most major funding agencies and academic journals. The document provides examples of problems caused by poor data management practices and outlines the key components of a data management plan, such as describing the data, file formats, sharing and archiving policies, and responsibilities. Researchers are encouraged to seek help from scientific consulting services for creating data management plans.
This document discusses re-tooling library staff and resources to support research data management. It describes the Scientific Data Consulting Group model developed at the University of Virginia Library, which involved conducting stakeholder analysis, prioritizing data interviews and preparing data management plans. It also outlines models from other universities, such as Purdue and Johns Hopkins, and discusses training librarians through workshops and data interviews. The document emphasizes that investment in staff and services is critical to providing effective research data management support.
Data management involves organizing and storing large amounts of information from various online sources. Links to data management resources include a Zotero group for sharing citations and references, as well as a site exploring the use of social networks for collaborating on data sets. Effective data management allows researchers to locate, access, and analyze information from different online locations and platforms.
This presentation discusses managing research data through the data life cycle. It begins with an overview of the research life cycle and embedding the data life cycle within it. Key aspects of data management are then covered, including why manage data, ethical and legal issues, requirements for data sharing and retention, and creating a data management plan. The rest of the presentation delves into each stage of the data life cycle, providing best practices for data collection, organization, security, storage, documentation, processing, analysis, and long-term preservation or sharing. File formats, metadata, repositories, and bibliographic resources are also addressed.
This document outlines best practices for creating research data. [1] It recommends using consistent data organization with standardized formats and descriptive file names. [2] Researchers should perform quality assurance checks and use scripted programs to analyze data while keeping notes. [3] All aspects of data collection and analysis should be thoroughly documented. Following these practices will improve data usability, sharing, and reproducibility.
Funder requirements for Data Management PlansSherry Lake
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.
This document summarizes a workshop on data management. It outlines the typical research lifecycle including proposal planning, project start up, data collection, analysis, sharing, and end of project. It discusses support for researchers within areas like data mining, curation, and preservation. It also discusses support from outside through infrastructure, policy, and best practices. Finally, it identifies 9 key skills gaps for librarians in advising researchers on data management tasks.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
Physiology and chemistry of skin and pigmentation, hairs, scalp, lips and nail, Cleansing cream, Lotions, Face powders, Face packs, Lipsticks, Bath products, soaps and baby product,
Preparation and standardization of the following : Tonic, Bleaches, Dentifrices and Mouth washes & Tooth Pastes, Cosmetics for Nails.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Assessment and Planning in Educational technology.pptxKavitha Krishnan
In an education system, it is understood that assessment is only for the students, but on the other hand, the Assessment of teachers is also an important aspect of the education system that ensures teachers are providing high-quality instruction to students. The assessment process can be used to provide feedback and support for professional development, to inform decisions about teacher retention or promotion, or to evaluate teacher effectiveness for accountability purposes.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
International FDP on Fundamentals of Research in Social Sciences
at Integral University, Lucknow, 06.06.2024
By Dr. Vinod Kumar Kanvaria
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
RPMS TEMPLATE FOR SCHOOL YEAR 2023-2024 FOR TEACHER 1 TO TEACHER 3
Lake us-canada policesupdate
1. UPDATE ON DATA MANAGEMENT
(AND SHARING) POLICES
US AND CANADA
Sherry Lake
University of Virginia
iConference 2013
February 12, 2013
2. Outline
• U.S. Funders
• Canadian Funders
• Other Policies
– Domain specific professional groups
– Journals
• Organizational Support
– Institutions
3. 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.
4. US Funding Agencies
• Require a Data Management Plan (DMP)
– National Science Foundation
– National Institutes of Health
– National Oceanographic and Atmospheric Research
(NOAA)
– Institute of Museum and Library Services (IMLS)
– National Endowment of Humanities – office of digital
humanities (NEH)
• Require Sharing of Results
– NASA
– NEH – Preservation & Access
This list is not inclusive.
5. Research Data Polices in Canada
• Canada Institutes of Health Research (CIHR)
– Policy on Access to Research Outputs
• Social Sciences and Humanities Research
Council of Canada (SSHRC)
– Research Data Archiving Policy
• Natural Sciences and Engineering Research
Council of Canada (NSERC)
– Implementing some policies through specific
projects
6. Research Data Canada
• Working group to address problems on
access & preservation of data from Canadian
Research
• Challenges to be addressed collectively from
a national perspective with the participation
of all parts of the research
community, including researchers themselves
who create and use this data.
• Supported by National Research Council
Canada
7. Other Data Policies (Examples)
US
• American Psychology Association (APA)
Canada
• McGill University
• Fisheries & Oceans Canada (one example of a
government department data policy)
Journals
• Nature Publishing Group
8. Library Organizations
US
– Association of Research Libraries (ARL)
– ASERL (Association of Southeastern Research
Libraries)
– Council on Library and Information Resources
(CLIR)
Canada
– Canadian Association of Research Libraries
(CARL)
9. ARL/DLF E-Science Institute
Goal: to help academic and research libraries develop
a strategic agenda for e-research support, with a
particular focus on the sciences
• Initial cohort July 2011: 70 institutions
• Sept-Dec. 2012 offered in partnership with
DuraSpace
• additional 22 institutions
• http://duraspace.org/esi-course-description
10. ASERL/SURA
Association of Southeastern Research Libraries Southeastern
Universities Research Association
Developed model language for their member
universities to consider when drafting policy to
support data management policies at their
institutions
http://www.aserl.org/wp-content/uploads/2013/01/ASERL-
SURA_Model_Language_RDM_Policy_Language_FINAL.pdf
12. CLIR: Digital Library Federation
• Data Curation Post Doctoral Program
Creating a professional career path for data specialists
aligned with the discipline but grounded with data
curation community.
– Science and Social Sciences
• Purdue - Scientific Data Management
– Medieval Studies
• Funded by Mellon Foundation
• http://www.clir.org/fellowships/postdoc/applicants/dc-
medieval
Organizations: Libraries and institutionsWhat are libraries doing to support the growing data management polices and requirements?
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).
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 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”. Other agencies require sharing, but do not explicitly require a DMP as part of a proposal – NASA, NEH access & preservationNEH Sustainability of project deliverables and datasets – long term preservationDissemination – sharingNew NSF as of Jan. 2013 – Bio Sketch can include products of research
Government of Canada’s principal funders of research and scholarship in the higher education sectorCIHR, NSERC, SSHRC working together to improve access to publicly-funded research: Consultation with relevant stakeholders Recognition of disciplinary differences Exploring international best practices, standards and policies Considerations for policy development include infrastructure requirements and monitoring Canada Funding Agencies Policiesdeveloping a shared approach for improving access to publicly funded research in keeping with internationally recognized best practices, standards and policies for funding and conducting research.CIHR – Open Access policy As of January 1, 2013, CIHR-funded researchers will be required to make their peer-reviewed publications accessible at no cost within 12 months of publication – at the latest. grant recipients to retain original data sets arising from CIHR-funded research for a minimum of five years after the end of the grant.SSHR - SSHRC has adopted a policy to facilitate making data that has been collected with the help of SSHRC funds available to other researchers.
Research Data Strategy Working Group – Data SummitCanadian National Collaborative Data Infrastructure Project: CARL and others. National Research CouncilResearch Data CanadaThe research process generates huge amounts of data that are an important part of Canada's scholarly record and contribute to a worldwide body of knowledge. There are, however, no nationally adopted standards or policies governing how this data is collected, catalogued, or preserved. As a result, this data is often inaccessible by other researchers or structured in such a way that it cannot be fully exploited for other uses. This means researchers are missing out on opportunities to re-analyze or re-evaluate the data in the context of new knowledge and essentially losing the chance to get additional value from the data.challenges of implementing a system to collect, preserve, and facilitate and control access to research data are substantial,
APAHere’s what APA's ethics code states:8.14 Sharing Research Data for Verification(a) After research results are published, psychologists do not withhold the data on which their conclusions are based from other competent professionals who seek to verify the substantive claims through reanalysis and who intend to use such data only for that purpose, provided that the confidentiality of the participants can be protected and unless legal rights concerning proprietary data preclude their release. Canada:According to a 2009 report, Research Data: Unseen Opportunities – CARL awareness toolkitMcGill was the only university that had data policyData must be organized in a manner that allows ready verification....Subject to exceptions based on a duty of confidentiality and the laws respecting intellectual property andaccess to information, after data are published, they must be made available to any party presenting a reasonable request to examine them. In cases where there is a disagreement between the researcher and the person requesting the data, the matter shall be referred to the Office of the Vice Principal Research for resolution...(a)ll original data must be retained for a reasonable length of time. A period of at least five years from the date of publication is recommended Fisheries:responsibility of Science and Oceans managers to ensure that data collectors under their management submit their data as well as data collected under contract to or partnership with other agencies, to the appropriate data centre in a timely fashion. Nature Publishing GroupA condition of publication in a Nature journal is that authors are required to make materials, data and associated protocols promptly available to others without undue qualifications.
ARL –”Key issue” Evolving E-Research/ E-Science Guide for Research Libraries – NSF Data Sharing Polices (specifically, but good in formation for any DM policy) google group for data sharing support groupCARL has data management sub committeesurvey of existing Canadian initiatives related to the management of researcher-generated datadevelop "best practice" models or frameworks for the management of researcher-generated data to be used by individual CARL members liaise with the related initiatives such as the ARL Task Force on e-Science, the granting councils, and disciplinary-based initiatives
E-Science Institute is designed to help academic and research libraries develop a strategic agenda for e-research support, with a particular focus on the sciences.series of interactive modules that take small teams of individuals from academic institutions through a dynamic learning process to strengthen and advance their strategy for supporting computational scientific research. The coursework begins with a series of exercises for teams to complete at their institutions, and culminates with an in-person workshop. Local institution assignments help staff establish a high level understanding of research support background needs and issues.Participate in teams: library admin, data or e-science librarian, non-library personCheck the URL here for future institutes.
The Association of Southeastern Research Libraries (ASERL) and the Southeastern Universities Research Association (SURA) have endorsed language to assist their member institutions in drafting sound policies to govern the uses and management of research data generated by university faculty and staff. ASERL and the Southeastern Universities Research Association (SURA) have jointly endorsed model language university data management policies. ASERL and SURA strongly encourage their members to consider this language to guide development of data management activities policies at their institutions. Few institutions have research data policy. So many differences on how institutions support research (no OSP or VPR office). Most about admin data if have any policies at all. Some have tweaked current admin data policy to “force” DM of research data to fit in existing policies.
Notice the “lack” of policies list here.Many institutions have policies for institutional data (student, HR and budget data), but not research.Wisconsin’s data stewardship policy, establishes University (faculty, staff, researchers, postDoc, grad, undergrads) involved in the design, conduct or reporting of research at or under the auspices of UW-Madison, and it shall apply to all research projects on which those individuals work, regardless of the source of funding for the project) policy to assure that research data are appropriately maintained, archived for a reasonable period of time, and available for review and use under the appropriate circumstances.
Opportunities for continued support and educationCLIR is an independent, nonprofit organization that forges strategies to enhance research, teaching, and learning environments in collaboration with libraries, cultural institutions, and communities of higher learning.Review of applications started Jan. 1 2013Through these fellowships, CLIR seeks to raise awareness and build capacity for sound data management practice throughout the academy.n its pilot cycle, 2012-2014, CLIR is co-hosting six fellows with its partner institutions: Indiana University, Lehigh University, McMaster University, Purdue University, The University of California Los Angeles, and The University of Michigan. The fellowships offered at these institutions are relevant to a wide variety of disciplines in the sciences and social sciences.Who May ApplyRecent Ph.D.s from any social science or science discipline are encouraged to apply, so long as they meet the eligibility criteria for the Postdoctoral Fellowship Program.2013 Host Institutions and Position DescriptionsFellows will be placed at research institutions throughout the United States and Canada. The following institutions are offering fellowships for the 2013-2015 cycle.Arizona State University, California Digital Library, Michigan State University, University of Alberta Libraries, University of California Davis, University of Colorado NSIDC