Responsible Conduct of Research · 6/9/2016 · Responsible Conduct of Research DATA INTEGRITY AND...

Responsible

Conduct of

Research DATA INTEGRITY AND MANAGEMENT

2016 MGH Javier Irazoqui, PhD

Sources

https://ori.hhs.gov/ori-intro

https://ori.hhs.gov/images/ddblock/data.pdf 2016 MGH Javier Irazoqui, PhD

Examples NOT

Source: ORI.HHS.GOV 2016 MGH Javier Irazoqui, PhD

Guidelines

Institutional

Governmental

Community

Laboratory


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


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


What are DATA?

Any information or observations that are

associated with a particular project

Includes experimental specimens,

technologies, and products related to the

inquiry





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



management and use


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




management and use


The Funding Agency

Federal government, foundations,

industry

Often have specific stipulations for

how data are retained and

disseminated




management and use


The Funding Agency


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


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


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


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


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


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


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


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


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


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


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


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


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)


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”


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.


Editorials address misconduct


Mike Rossner, and Kenneth M. Yamada J Cell Biol

2004;166:11-15

© 2004 Rockefeller University Press

Gross manipulation of blots.



2004;166:11-15 © 2004 Rockefeller University Press

Gross manipulation of blots.


Manipulation of blots: brightness

and contrast adjustments.


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).


2004;166:11-15 2016 MGH Javier Irazoqui, PhD

Manipulation of blots: cleaning up

background.


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.



Misrepresentation of immunogold

data.


2004;166:11-15


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.


Misrepresentation of image data.


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.



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

A matter of trust


Happy researching!


Date post:	17-Aug-2020
Category:	Documents
Upload:	others
View:	1 times
Download:	0 times

Responsible Conduct of Research · 6/9/2016 · Responsible Conduct of Research DATA INTEGRITY AND...

Documents