Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
An overview of archiving of clinical studies and data. By RISHI MAHESHWARI , JSS COLLEGE OF PHARMACY , OOTY
For students in V PharmD this topic has been prepared.
Clinical Data Management: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Everything related to CDM. Importance of CDM, Flow Activities in Clinical Trials, Data Management Plan, Database Designing, Data Management tools, Essential Characters of the database, Standard Global Dictionaries, Data Review and Validation, Query Generation, Database Lock, Technology in CDM, and Professionals of CDM.
Clinical Data Management Plan_Katalyst HLSKatalyst HLS
Introduction to Data Management Plan in Clinical Data Management in Clinical Trials of Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
An overview of archiving of clinical studies and data. By RISHI MAHESHWARI , JSS COLLEGE OF PHARMACY , OOTY
For students in V PharmD this topic has been prepared.
Clinical Data Management: Best Practices and Key ConsiderationsClinosolIndia
Clinical data management (CDM) is a critical component of clinical research, involving the collection, processing, and analysis of data generated during clinical trials. Implementing best practices and considering key considerations is essential for ensuring data quality, integrity, and regulatory compliance. Here are some important considerations and best practices in clinical data management:
Data Standardization: Standardizing data collection and documentation across study sites is crucial for ensuring consistency and facilitating data analysis. Develop standardized data collection forms, case report forms (CRFs), and electronic data capture (EDC) systems that capture relevant data elements in a consistent manner.
Data Validation and Quality Control: Implement robust data validation procedures to ensure the accuracy and completeness of collected data. Conduct thorough quality control checks, including data validation checks, range checks, and consistency checks, to identify and resolve data discrepancies or errors.
Data Security and Privacy: Ensure data security and protect participant privacy by implementing appropriate measures such as data encryption, secure data transfer protocols, access controls, and adherence to applicable data protection regulations like GDPR or HIPAA.
Data Monitoring and Cleaning: Regularly monitor data collection processes to identify and address data discrepancies, missing data, or outliers. Implement data cleaning procedures to identify and resolve data errors, inconsistencies, and outliers that may impact the integrity and reliability of the study data.
Data Traceability and Audit Trail: Maintain a comprehensive audit trail that captures all changes and activities related to data entry, data modifications, and data review. This ensures data traceability and facilitates data validation and regulatory inspections.
Standard Operating Procedures (SOPs): Develop and adhere to well-defined SOPs for data management activities. SOPs should cover all aspects of data collection, processing, validation, cleaning, and archiving, ensuring consistency and adherence to regulatory requirements.
Everything related to CDM. Importance of CDM, Flow Activities in Clinical Trials, Data Management Plan, Database Designing, Data Management tools, Essential Characters of the database, Standard Global Dictionaries, Data Review and Validation, Query Generation, Database Lock, Technology in CDM, and Professionals of CDM.
FOMAT Medical Research is a site research network specializes in developing clinical. We offer a wide range of solutions for Sponsors, Clinical Contract Organizations (CROs), and Sites throughout the Americas. Visit here- https://www.fomatmedical.com
Explaining the importance of a database lock in clinical researchTrialJoin
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
How to Comply with Grants: Writing Data Management Plans and Providing Public...Margaret Henderson
Brown Bag Lunch presentation for researchers on how to comply with DMP and public access sections on grants, as required by the OSTP memo of 2013. Note: Many slides are included for reference. The actual presentation only touched on sections relevant to attendees.
Summary of the requirements for compliance with the new public access plans from US federal agencies under the Office of Science and Technology Memo. This talk was presented to the Research Administration & Compliance group at VCU.
Many thanks to Rebecca Reznik-Zellen for the HHS slides that were developed for the eScience Symposium.
Thanks to Amanda Lea Whitmire for her one memo to rule them all slide.
FOMAT Medical Research is a site research network specializes in developing clinical. We offer a wide range of solutions for Sponsors, Clinical Contract Organizations (CROs), and Sites throughout the Americas. Visit here- https://www.fomatmedical.com
Explaining the importance of a database lock in clinical researchTrialJoin
One of the most crucial aspects of research is clinical data management or CDM. Proper CDM will generate results with excellent quality, integrity, and reliability. Quality data is essential in order to support the final conclusions of a certain study.
The person responsible for this area of research is called a clinical data manager. This job position can be filled by a PI, a study coordinator, or a CRA. No matter who fills this position at your site, data management has to be done promptly and correctly in order to generate the best results. Aside from all the other reasons why data management is so important, it’s also what determines the future IP (investigational product) development.
Introduction to Aggregate Reporting in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
Reconciliation and Literature Review and Signal Detection_Katalyst HLSKatalyst HLS
Introduction Reconciliation and Literature Review and Signal Detection in Drug Safety & Pharmacovigilance in Pharmaceuticals, Bio-Pharmaceuticals, Medical Devices, Cosmeceuticals and Foods.
Contact:
"Katalyst Healthcares & Life Sciences"
South Plainfield, NJ, USA
info@KatalystHLS.com
How to Comply with Grants: Writing Data Management Plans and Providing Public...Margaret Henderson
Brown Bag Lunch presentation for researchers on how to comply with DMP and public access sections on grants, as required by the OSTP memo of 2013. Note: Many slides are included for reference. The actual presentation only touched on sections relevant to attendees.
Summary of the requirements for compliance with the new public access plans from US federal agencies under the Office of Science and Technology Memo. This talk was presented to the Research Administration & Compliance group at VCU.
Many thanks to Rebecca Reznik-Zellen for the HHS slides that were developed for the eScience Symposium.
Thanks to Amanda Lea Whitmire for her one memo to rule them all slide.
Inroads into Data: Getting Involved in Data at Your InstitutionMargaret Henderson
Every institution creates and uses data for many reasons. Data needs to be collected, described, stored, organized, retrieved, and shared, all things that librarians can help with. But how do you get started when there are many types of data and a range of services that can be offered? I will cover how to leverage the skills librarians already have to work with data and suggest some areas of data and service to get you started.
Compliance: Data Management Plans and Public Access to DataMargaret Henderson
Presented at The 8th Annual University of Massachusetts and New England Area Librarian e-Science Symposium, Wednesday, April 6, 2016
University of Massachusetts Medical School
This slide deck is an overview of some of the main points of the federal department plans created in response to the OSTP Memo that requires public access to papers and data produced with government funds. Specifically, this covers HHS, DOD, DOE, NASA, and NSF responses. We created this just in case a speaker didn't show and though it might be useful to others. You are welcome to use any or all of the presentation as you see fit.
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.
Research Data Management: Part 1, Principles & ResponsibilitiesAmyLN
This two-part course is a collaboration between CU Libraries/Information Services and the Office of Research Compliance & Training. The purpose of this course is to familiarize you with the various aspects of research data management (RDM)
Part 1: Why RDM is both recommended and required
What research data are
Who is responsible for RDM
Part 2:
When RDM activities occur
How you can carry out RDM activities
dkNET Webinar: Creating and Sustaining a FAIR Biomedical Data Ecosystem 10/09...dkNET
Abstract
In this presentation, Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health, will share the NIH’s vision for a modernized, integrated FAIR biomedical data ecosystem and the strategic roadmap that NIH is following to achieve this vision. Dr. Gregurick will highlight projects being implemented by team members across the NIH’s 27 institutes and centers and will ways that industry, academia, and other communities can help NIH enable a FAIR data ecosystem. Finally, she will weave in how this strategy is being leveraged to address the COVID-19 pandemic.
Presenter: Susan Gregurick, Ph.D., Associate Director of Data Science and Director, Office of Data Science Strategy at the National Institutes of Health
dkNET Webinar Information: https://dknet.org/about/webinar
Data sharing promotes many goals of the NIH research endeavor. It is particularly important for unique data that cannot be readily replicated. Data sharing allows scientists to expedite the translation of research results into knowledge, products, and procedures to improve human health. Do you know what a data sharing plan should include? Are you aware of common practices and standards for data sharing? Do you know what services are available to help share your data responsibly? This workshop will begin to address these questions. Q&A will follow the presentation. Anyone interested in or planning to apply for NIH funding should attend. Note: The NIH data-sharing policy applies to applicants seeking $500,000 or more in direct costs in any year of the proposed research.
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
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.
Overview and library support for data management/sharingrds-wayne-edu
Presented as part of the 16Jan2014 Professional & Academic Development (PAD) Seminar on "Developing a Data Management Plan and Ensuring Secure Data Access", Wayne State University - Division of Research.
January 23, 2017
The Fifth Annual Health Law Year in P/Review symposium featured leading experts discussing major developments during 2016 and what to watch out for in 2017. The discussion at this day-long event covered hot topics in such areas as health policy under the new administration, regulatory issues in clinical research, law at the end-of-life, patient rights and advocacy, pharmaceutical policy, reproductive health, and public health law.
The Fifth Annual Health Law Year in P/Review was sponsored by the Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, Harvard Health Publications at Harvard Medical School, Health Affairs, the Hastings Center, the Program On Regulation, Therapeutics, And Law (PORTAL) in the Division of Pharmacoepidemiology and Pharmacoeconomics at Brigham and Women’s Hospital, and the Center for Bioethics at Harvard Medical School, with support from the Oswald DeN. Cammann Fund.
Learn more on our website: http://petrieflom.law.harvard.edu/events/details/5th-annual-health-law-year-in-p-review
Preparing Health Sciences Students for Real World Information Gathering Using...Margaret Henderson
Paper presented at the Medical Library Association annual meeting in Chicago, 2019. Focuses on using critical pedagogy to help students learn how to find real world information to help with their work or assignments.
There are many online and in-person courses available for librarians to learn about research data management, data analysis, and visualization, but after you have taken a course, how do you go about applying what you have learned? While it is possible to just start offering classes and consultations, your service will have a better chance of becoming relevant if you consider stakeholders and review your institutional environment. This lecture will give you some ideas to get started with data services at your institution.
There is more to RDM services than the technical skills necessary for data management. Soft skills and non-technical skills are very important when setting up RDM services, and continue to be important to the sustainability of services. Reference skills, relationship building, negotiation, listening, facilitating access to de-centralized resources, policy knowledge and assessment, are all important to the success of a service. Margaret Henderson will discuss these skills and show you how to start RDM services, even if you don’t feel confident about your statistical skills or knowledge of R.
Paper presented at the 2012 MLA Quad Chapter meeting in Baltimore, MD, Oct. 13-16. Discusses i2b2 and how it could be used in medical education. And suggests other data if i2b2 not available in your hospital.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...
Research Data Management for Clinical Trials and Quality Improvement
1. Research Data Management for
Clinical Trials and
Quality Improvement
Margaret Henderson
Director, Research Data Management
mehenderson@vcu.edu
@mehlibrarian
2. Don’t be a disaster story!
http://www.centreforwelfarereform.org/news/major-breaktn-pace-trial/00296.html
6. Librarians can:
• Help with policies.
• Help with resources.
• Help with data management planning.
• Help reduce administrative burden.
Report of the College of Arts and Sciences Committee on Streamlining Research Administration.
https://blogs.cornell.edu/deanoffaculty/files/2016/11/Streamlining-Administration-1x4k5pj.pdf
9. Research Data: Recorded information, regardless of form or the media on which it
may be recorded, which constitute the original observations and methods of a study
and the analyses of these original data that are necessary for reconstruction and
evaluation of the Report(s) of a study made by one or more Investigators. Research
Data also includes all such recorded information gathered in anticipation of a Report.
Research Data differ among disciplines. The term may include but is not limited to
technical information, computer software, laboratory and other notebooks, printouts,
worksheets, other media, survey, memoranda, evaluations, notes, databases, clinical
case history records, study protocols, statistics, findings, conclusions, samples, physical
collections, other supporting materials created or gathered in the course of the
Research, Tangible Research Property, unique Research resources such as synthetic
compounds, organisms, cell lines, viruses, cell products, cloned DNA as well as genetic
sequences and mapping information, crystallographic coordinates, plants, animals and
spectroscopic data, and other compilations formed by selecting and assembling
preexisting materials in a unique way. The term does not include information
incidental to research administration such as financial, administrative, cost or pricing,
or management information.
http://www.policy.vcu.edu/sites/default/files/Research%20Data%20Ownership%2C%20Retention%2C%20Access%20and%20Securty.pdf
10. Ownership
“Principal Investigator has primary stewardship of
Research Data on behalf of the University. In this
capacity the Principal Investigator (PI) is responsible for
data collection, recording, storage, access, and
retention in keeping with this policy and best practices
in the PI’s discipline.”
13. NIH Public Access Policy
SEC. 218. The Director of the National Institutes of Health shall require that all
investigators funded by the NIH submit or have submitted for them to the
National Library of Medicine’s PubMed Central an electronic version of their
final peer-reviewed manuscripts upon acceptance for publication, to be
made publicly available no later than 12 months after the official date of
publication: Provided, That the NIH shall implement the public access policy
in a manner consistent with copyright law.
https://publicaccess.nih.gov/
14. NIH Data Sharing Policy
“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. “
http://grants.nih.gov/grants/policy/data_sharing/data_sharing_guidance.htm
15. NIH Genomic Data Sharing Policy
“Policy for Sharing of Data Obtained in NIH Supported or Conducted Genome-
Wide Association Studies (GWAS) (effective January 2015)
• “For the purposes of this policy, a genome-wide association study is
defined as any study of genetic variation across the entire human genome
that is designed to identify genetic associations with observable traits
(such as blood pressure or weight), or the presence or absence of a
disease or condition.”
• Applies to all NIH-funded research that generates large-scale human or
non-human genomic data, as well as the use of those data for subsequent
research.
• Requires “Genomic Data Sharing Plan”.
• Allows for expenses in project budget.
• Requires public availability of data in a “timely manner.”
• Recommends NIH-funded or third-party repositories for deposition.
http://grants.nih.gov/grants/guide/notice-files/NOT-OD-07-088.html
16. NSF Policies
NSF Data Sharing Policy
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. See Award & Administration Guide
(AAG) Chapter VI.D.4. http://www.nsf.gov/bfa/dias/policy/dmp.jsp
NSF Data Management Plan Requirements
Proposals submitted or due on or after January 18, 2011, must include a
supplementary document of no more than two pages labeled “Data
Management Plan”. This supplementary document should describe how the
proposal will conform to NSF policy on the dissemination and sharing of
research results. See Grant Proposal Guide (GPG) Chapter II.C.2.j for full
policy implementation. https://www.nsf.gov/eng/general/dmp.jsp
Slide courtesy of Amanda Whitmire
17. OSTP Memorandum
Increasing Access to the Results of Federally Funded
Scientific Research -February 22, 2013
“ensuring that, … the direct results of federally funded
scientific research are made available to and useful for
the public, industry, and the scientific community. Such
results include peer-reviewed publications and digital
data.”
https://www.whitehouse.gov/blog/2013/02/22/expanding-public-access-results-federally-funded-research
18. NIH
Publications
• Peer-reviewed scientific articles
• Deposit of final peer-reviewed
manuscript into PMC
• Upon acceptance, with maximum
12-month embargo
• Include appropriate costs in
proposals
• Reporting through eRA Commons
and My NCBI
• Withholding of funds
Data
• Unclassified digital scientific
research data
• Submission of DMP; deposit of
data into appropriate, existing,
publicly accessible repositories,
including NIH data repositories
• Upon acceptance for publication
(will explore)
• Include appropriate costs in
proposals
• Utilize existing reporting
structures
• “enforcement actions” including
withholding of funds
Plan for Increasing Access to Scientific Publications and Digital Scientific Data from NIH Funded Scientific Research
19. FDA
Publications
• Peer-reviewed scientific
articles
• Deposit of final peer-reviewed
manuscript into PMC
• With maximum 12-month
embargo*
• Include appropriate costs in
proposals
• Utilize existing reporting
structures
• Termination of contract or
grant; withholding of funds
Data
• Digitally formatted scientific
data resulting from
unclassified research
• Submission of DMP; deposit of
data into discipline-specific
repositories
• Upon acceptance for
publication
• Include appropriate costs in
proposals
• Utilize existing reporting
structures
• Termination of contract or
grant; withholding of funds
Plan to Increase Access to Results of FDA-Funded Scientific Research
20. See SPARC for full information: http://researchsharing.sparcopen.org/
24. NIH Clinical Trials Policy
• All NIH-funded trials need to be registered at
ClinicalTrials.gov (not later than 21 days after
enrollment of first participant)
• Summary results need to be submitted for
public posting.
• Need to submit a plan for the dissemination of
trial information, and how it will meet policy
requirements.
25. Any Other Applicable Guidelines
• SQUIRE (Standards for Quality Improvement
Reporting Excellence) Guidelines – revised
doi:10.1136/bmjqs-2015-004411 and explanation
doi:10.1136/bmjqs-2015-004480
• FDA Guidance Documents
http://www.fda.gov/ScienceResearch/SpecialTopics/
RunningClinicalTrials/GuidancesInformationSheetsan
dNotices/default.htm
26. • NIH/FDA/HHS
– Rigor and Reproducibility
– Office of Clinical Research and Bioethics Policy
– Clinical Trials Research Policy
– ClinicalTrials.gov
– Final Rule Clinical Trials Registration and Results
Information Submission
• ICMJE
– Clinical Trial Registration
– Sharing Clinical Trial Data pdf (Proposed)
29. Public Access to Peer Reviewed Articles
Check Author’s Rights – From DOD:
• Will be advised to work with the publisher before
any publication rights are transferred to ensure that
all conditions of the projected DoD public access
policy can be met.
• Will be advised not to sign any agreements with
publishers that do not allow the author to comply
with the projected DoD public access initiative.
30. Data Management Plans
• All agencies will require a data
management plan.
• “Not all data need to be shared or
preserved. The costs and benefits of doing
so should be considered in data
management planning.” DOE third principle
http://science.energy.gov/funding-opportunities/digital-data-management/
• DOE and NSF have indicated they will review
and evaluate DMPs
31. Data Sharing
•Digitally formatted data arising from unclassified, publicly
releasable research and programs.
•Decentralized approach to data storage.
•Allow for inclusion of costs for data management and access.
•Will establish a system to enable the identification, attribution,
(federated) storage, and access of digital data.
From NASA FAQ
•“First of all, be reassured that we are not going to force you to
reveal your precious proprietary data prior to publication. No
personal, proprietary or ITAR data is included.”
http://science.nasa.gov/researchers/sara/faqs/dmp-faq-roses/
43. Don’t Forget a Reference Interview
Image from https://www.juniorlibraryguild.com/news/article.dT/10-scary-librarian-super-powers
A good text on interviewing: Ross, Catherine Sheldrick, Kirsti Nilsen, and Marie L. Radford. 2009. Conducting the Reference Interview: A how-to-do-it Manual for
Librarians. 2nd ed. New York: Neal-Schuman Publishers
44. What
Describe
Reuse
Preserve
Data types, samples, software, other materials.
Standards, metadata, if applicable. Readme or Data Dictionary
Method for sharing or making data public.
Note any restrictions or licenses for reuse.
How long and where data will be kept.
Who Name of data owner or steward who is responsible for data .
Share
45. Who Name of data owner or steward who is responsible for data .
46. What Data types, samples, software, other materials.
And how they will be secured.
50. Data Types to Share
What does the grant ask for?
• NIH - Final Research Data - Recorded factual
material commonly accepted in the scientific
community as necessary to document and
support research findings. (spreadsheets, images,
scans of written notes if applicable, etc.)
• OSTP - Digitally formatted data arising from
unclassified, publicly releasable research and
programs.
53. Ways to Share Data
Upload to a repository; general, subject, or
institutional repository (IR).
• DataVerse http://dataverse.org/
• Dryad http://datadryad.org/
• figshare http://figshare.com/
• Open Science Framework https://osf.io/
• Zenodo https://zenodo.org/
54. Supplemental file with journal article or link to
the upload.
• Be sure to check the contract.
• Will the data be available to the public as per
funder or policy requirements?
• Will the rights conflict with institutional ownership
of the data?
57. Sensitive Data Access
• Researchers must request access to database,
explaining research and providing IRB
approval forms, e.g. registry
or
• Data must be deidentified or anonymized in
some way before being made publicly
available. (see http://www.hhs.gov/hipaa/for-
professionals/privacy/special-topics/de-identification/index.html )
58. Reuse What can be done with your data? Licenses can help.
59.
60. License Data to Encourage Reuse
• Creative Commons licenses
https://creativecommons.org/licenses/
or use license chooser
https://creativecommons.org/choose/
• Open Data Commons
http://opendatacommons.org/
• Pantone Principles
http://pantonprinciples.org/
62. Preserve
• How long must the data be kept?
– Minimum 5 years after publication or final grant
report.
– Check grant and policies.
• What is the long-term value of the data? Hint:
Ask an archivist in the subject area.
63. Don’t Forget Print
• Set a schedule to scan lab notebooks and other print
materials (makes for a good back up and easier to share
data within group).
• Print original should have similar security to digital data (i.e.
good, secure storage and labelling of files).
64. Summary
• Learn local policies
• Learn federal and other external policies
• Assess available resources – local and external
• Find out what they need (reference interview)
• Help with plan
• Connect researchers to what/who they need
65. More Information
• For a general research data management overview you can
view my webinar for NN/LM Southeastern/Atlantic Region:
https://nnlm.gov/sea/newsletter/2015/10/beyond-the-sea-
webinar-november-18-inroads-into-data-getting-
involved-in-data-at-your-institution/
• Or a previous talk on compliance with Hillary Miller at the
eScience Symposium 2016:
http://escholarship.umassmed.edu/escience_symposium
/2016/program/8/
Recommended in 2004 by House Appropriations Committee, they recommended 6 month embargo, voluntary 2005 (per Peter Suber); mandatory requirement 2008; funding withheld 2013.
This policy took effect earlier (2003) than public access but it is limited to larger grants so it isn’t as well known. Policy requires Data Sharing Plan to describe how final research data will be shared, or explain why data sharing is not possible.
•Applies to any projects funded by NIH over 500K since 2003
As data sharing becomes the norm, there will be more an more policies to make sure privacy and other ethical concerns are taken into account.
Genomic Data Sharing Policy expands on previously implemented, long-standing policies to make data it funds publicly available in a timely manner.
Genome wide association studies (GWAS) had data sharing policies initially implemented in 2007.
No details on what should be in the genomic data sharing plan.
“... Whole genome information, when combined with clinical and other phenotype data, offers the potential for increased understanding of basic biological processes affecting human health, improvement in the prediction of disease and patient care, and ultimately the realization of the promise of personalized medicine. In addition, rapid advances in understanding the patterns of human genetic variation and maturing high-throughput, cost-effective methods for genotyping are providing powerful research tools for identifying genetic variants that contribute to health and disease.”
data sharing plan and data management plan are different. Existing NIH policies establish expectations for data sharing (2007 FDA Amendment Act requiring applicable clinical trials to go to clinicaltrials.gov; 2003 NIH Data Sharing Policy; 2002 NIH Intramural Policy on large Database Sharing; 2014 NIH Genomic Data Sharing Policy; Grants Policy Statement requiring final progress reports to describe sharable data). DMP is modification to 2003 NIH Data Sharing Policy. Note that some funding mechanisms (training grants) may be exempted.
deposit to existing repositories “before considering other means of making data available.”
NIH will develop guidance for key elements to be included in a DMP; determining which data should be prioritized for preservation (6b); finding acceptable repositories not funded by NIH (8b);
NIH will expand its database of existing repositories for example NIH Data Science – The Commons http://datascience.nih.gov/commons (as per Philip Bourne)
SPARC notes emphasis on roles and responsiblities in policy, as well as scope - which defines what they consider to be data.
Also, check any rights when data is attached to a publication by the journal, or using journal recommended repository.