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https://ori.hhs.gov/ori-intro
https://ori.hhs.gov/images/ddblock/data.pdf 2016 MGH Javier Irazoqui, PhD
Community values
HONESTY
Convey information truthfully and honoring commitments
ACCURACY
Report findings precisely and taking care to avoid errors
EFFICIENCY
Use resources wisely and avoiding waste
OBJECTIVITY
Let the facts speak for themselves; avoid improper bias
2016 MGH Javier Irazoqui, PhD
Government Office of Research Integrity (DHHS)
Before starting a new scientific research project, the PI and research
team must address issues related to data management, including:
Data Ownership
Data Collection
Data Storage
Data Protection
Data Retention
Data Analysis
Data Sharing
Data Reporting
2016 MGH Javier Irazoqui, PhD
What are DATA?
Any information or observations that are
associated with a particular project
Includes experimental specimens,
technologies, and products related to the
inquiry
2016 MGH Javier Irazoqui, PhD
Data Ownership Refers to the control and rights over the data, as well as data
management and use
The Sponsoring Institution
The Funding Agency
The Principal Investigator
2016 MGH Javier Irazoqui, PhD
Data Ownership Refers to the control and rights over the data, as well as data
management and use
The Sponsoring Institution
Maintains ownership of a project’s
data as long as the PI is employed
by that institution
Controls funding, thus is responsible
for ensuring responsibility and
ethics
The PI is granted stewardship over
the project data, subject to
institutional review
The Funding Agency
The Principal Investigator
2016 MGH Javier Irazoqui, PhD
Data Ownership Refers to the control and rights over the data, as well as data
management and use
The Sponsoring Institution
The Funding Agency
Federal government, foundations,
industry
Often have specific stipulations for
how data are retained and
disseminated
The Principal Investigator
2016 MGH Javier Irazoqui, PhD
Data Ownership Refers to the control and rights over the data, as well as data
management and use
The Sponsoring Institution
The Funding Agency
The Principal Investigator
Is the steward of a project’s data
May retain some ownership of the
data
Sometimes are allowed to take
their research and its data if they
move
2016 MGH Javier Irazoqui, PhD
Subjects as
stakeholders
Individuals who suggest research
ideas and/or participate in the
research
Informed consent imposes
limitations on future use
It is important to consider study
participants’ beneficence and
dignity
2016 MGH Javier Irazoqui, PhD
Data Collection Provides the information necessary to develop and justify
research
What information is recorded
How that information is recorded
How a research project isdesigned
AIM: uphold the integrity of theproject, the institution, and theresearchers
Collect reliable and valid data
Accurately analyze and assesswork by researchers
Independent replication
Provides justification to sponsors
2016 MGH Javier Irazoqui, PhD
Collecting reliable data Collection is reliable when consistent and comprehensive
Data collection guidelines and
methodologies should be
developed before the research
begins
Thorough training of team
members
Well-planned and systematic data
collection
Thorough data collection enables
team members to answer any
question about a project
Purpose of research
Methodologies chosen
Methodology implementation
How data were collected and
analyzed
Unexpected results or significant
errors
Implications and future directions
2016 MGH Javier Irazoqui, PhD
Collecting valid data Record keeping is essential to ensure the validity of the data
Good science is precise andhonest
Record keeping
Records should accuratelyrepresent the progress of a project
Should answer: WHAT, HOW, WHYdata were collected or amended
Records should be durable andaccessible
Records should be safe fromtampering or falsification
Smaller projects are oftenrecorded in bound notebooks
Errors should be marked anddated, never erased
Include notes that described whatactually occurred, what worked ordidn’t
Entries should be chronologicaland consistent
Indelible pen (not pencil)
Record ANYTHING that seemsRELEVANT to the project, its data,and the project’s standards
2016 MGH Javier Irazoqui, PhD
Collecting valid data Record keeping is essential to ensure the validity of the data
Electronic records
There are a large number of
programs that allow researchers to
enter, store, and audit research
data
Security of records is a significant
concern
Most projects use a combination of
handwritten/electronic records
Policies and Procedures
Should be aware of all the
guidelines that apply to the project
Human and animal subject
regulations
Hazardous materials
Controlled biological agents
2016 MGH Javier Irazoqui, PhD
Minimal data to record
Date and Time
Names and roles of any team
members who worked with the
data
Materials, instruments, software
used
ID numbers to indicate subject
and/or session
Data from the experiment and
any pertinent observations about
the collection of data
2016 MGH Javier Irazoqui, PhD
Data Storage Safeguards your research, allows future access
Safeguards research investment
Allows future access to explain or
augment subsequent research
Other researchers must be able to
evaluate or use the results of your
research
Can be used to establish
precedence in the event of
publication of similar data
Can protect subjects and
researchers in the event of legal
allegations
Enough data should be stored so that
a project can be reconstructed with
ease
All primary data related to a
publication must be saved (5 years as
per HMS, best forever)
Electronic data:
Thorough documentation
Storage format that is easily adaptable
Rapid access
Low cost
Archives
Removability
Backup system2016 MGH Javier Irazoqui, PhD
Data Protection Best way to protect data is by limiting access
Protection from physical damage andtampering, loss, theft
Pis decide who is authorized to accessand manage data
Notebooks and questionnaires should belocked
Encoded identifiers to protect identity
Hacking and theft are concerns withdigital data
Protect access to data
IDs and passwords
Centralized access
Limit admin rights
Protect your system
Anti-virus
Updates
Firewall
Protect data integrity
Record original creation date and time
Encryption, signatures, watermarking tokeep track of changes
Regular backups, hard and soft copies
Ensure proper destruction
2016 MGH Javier Irazoqui, PhD
Data Retention Sponsor Institutions and funding agencies have their
requirements
USDHHS requires data be retained
for at least 3 years after the
funding period ends
Once minimum is met, PI must
decide
Data must be thoroughly and
completely destroyed when
disposed of
Electronic data must be
irretrievable
https://hms.harvard.edu/about-hms/integrity-academic-
medicine/hms-policy/faculty-policies-integrity-
science/guidelines-investigators-scientific-research
2016 MGH Javier Irazoqui, PhD
Data Analysis The form of analysis must be appropriate for the project’s
needs
The way raw data are chosen,
evaluated, and expressed
To translate data into meaningful
information, it must be managed
and analyzed appropriately
Guidelines and objectives should
be determined before a project
begins
All team members should
understand the data analysis plan
and be able to interpret results
2016 MGH Javier Irazoqui, PhD
Data Analysis It is important to avoid potential pitfalls that can invalidate or
lessen the integrity of the data
Methods of analysis
Researchers should work within the
accepted standards
Deviations must be justified
Awareness of the abilities and
limitations of a chosen method of
analysis
Usage of data
Include or exclude outliers
Missing or incomplete data
Appropriate alteration or
amendment
Data display and organization
Responsible analysis accurately
represents what occurred, but
does not overstate the importance
2016 MGH Javier Irazoqui, PhD
Data Analysis It is important to avoid potential pitfalls that can invalidate or
lessen the integrity of the data
Intentional falsification or
fabrication
Forging: inventing data or
experiments never performed
Cooking: retaining only those
results that “fit” the hypothesis
Trimming: unreasonable smoothingof irregularities to make the data
look more accurate and precise
Appropriate data amendment or
exclusion
Instrument malfunctions
Loss or change in subjects or
specimens
Interruptions or deviations in
procedure
2016 MGH Javier Irazoqui, PhD
Data Sharing and Reporting The way in which research is represented to the scientific
community and the general public
Data are expected to be shared
and reported
Acknowledge a study’s
implications
Contribute to a field of study
Stimulate new ideas
Before publication, often no
obligation to share preliminary
data (even discouraged)
Can benefit from feedback from
peers (but can be stolen)
After publication, any information
related to the project should be
considered open data
Other researchers may request
raw data or miscellaneous
information
Various guidelines and restrictions
may apply
Government-sponsored research
or research related to biological
agents may be subject to
legislation (Patriot Act, Freedom of
Information Act)
2016 MGH Javier Irazoqui, PhD
Data Sharing and Reporting The way in which research is represented to the scientific
community and the general public
NIH policy:
“The NIH expects and supports the timely
release and sharing of final research data from NIH-supported studies for use by other
researchers”
http://grants.nih.gov/grants /guide/notice-files/NOT-OD-03-032.html2016 MGH Javier Irazoqui, PhD
Research Misconduct
The Office of Science and
Technology Policy (OSTP)
in the Executive Office of the
President adopted a Federal
Policy on Research Misconduct in
2000
OSTP Policy defines “research
misconduct” as “fabrication,
falsification, or plagiarism in
proposing, performing, or
reviewing research, or in reporting research results”
2016 MGH Javier Irazoqui, PhD
Misconduct
Fabrication is making up data orresults and recording or reportingthem.
Falsification is manipulating researchmaterials, equipment, or processes,or changing or omitting data orresults such that the research is notaccurately represented in theresearch record.
Plagiarism is the appropriation ofanother person’s ideas, processes,results, or words without givingappropriate credit.
Research misconduct does notinclude differences of opinion.
2016 MGH Javier Irazoqui, PhD
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15
© 2004 Rockefeller University Press
Gross manipulation of blots.
2016 MGH Javier Irazoqui, PhD
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15 © 2004 Rockefeller University Press
Gross manipulation of blots.
2016 MGH Javier Irazoqui, PhD
Manipulation of blots: brightness
and contrast adjustments.
© 2004 Rockefeller University Press
Manipulation of blots: brightness and contrast adjustments. (A) Adjusting the intensity of a single band (arrow). B) Adjustments of contrast. Images 1, 2, and 3 show sequentially more severe adjustments of contrast. Although the adjustment from 1 to 2 is acceptable because it does not obscure any of the bands, the adjustment from 2 to 3 is unacceptable because several bands are eliminated. Cutting out a strip of a blot with the contrast adjusted provides the false impression of a very clean result (image 4 was derived from a heavily adjusted version of the left lane of image 1). For a more detailed discussion of “gel slicing and dicing,” see Nature Cell Biology editorial (2).
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15 2016 MGH Javier Irazoqui, PhD
Manipulation of blots: cleaning up
background.
© 2004 Rockefeller University Press
Manipulation of blots: cleaning up background. The Photoshop “Rubber Stamp” tool has been used in the manipulated image to clean up the background in the original data. Close inspection of the image reveals a repeating pattern in the left lane of the manipulated image, indicating that such a tool has been used.
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15 2016 MGH Javier Irazoqui, PhD
Misrepresentation of immunogold
data.
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15
© 2004 Rockefeller University Press
Misrepresentation of immunogold data. The gold particles, which were actually present in the original (left), have been enhanced in the manipulated image (right). Note also that the background dot in the original data has been removed in the manipulated image.
2016 MGH Javier Irazoqui, PhD
Misrepresentation of image data.
© 2004 Rockefeller University Press
Misrepresentation of image data. Cells from various fields have been juxtaposed in a single image, giving the impression that they were present in the same microscope field. A manipulated panel is shown at the top. The same panel, with the contrast adjusted by us to reveal the manipulation, is shown at the bottom.
Mike Rossner, and Kenneth M. Yamada J Cell Biol
2004;166:11-15 2016 MGH Javier Irazoqui, PhD
Other Data Management Issues
Keep original digital or analog
data exactly as they were
acquired
Record instrument settings
Some journal reviewers or editors
request access to such primary
data to ensure accuracy
Selective acquisition of data by
adjusting settings on the
instrument
Selecting and reporting a veryunusual result as representative
Hiding negative results that maycontradict your conclusions
Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15 2016 MGH Javier Irazoqui, PhD
Philosophy of data manipulation
“For every adjustment that you make to a
digital image, it is important to ask yourself,
“Is the image that results from this
adjustment still an accurate
representation of the original data?” If the
answer to this question is “no,” your
actions may be construed as
misconduct.”
Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15 2016 MGH Javier Irazoqui, PhD