Responsible Conduct of Research

Transcription

1 Responsible Conduct of Research DATA INTEGRITY AND MANAGEMENT

2 Sources

3 Examples NOT Source: ORI.HHS.GOV

4 Guidelines Institutional Governmental Community Laboratory

5 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

6 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

7 What are DATA? Any information or observations that are associated with a particular project Includes experimental specimens, technologies, and products related to the inquiry

8

9

10

11 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

12 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

13 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

14 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

15 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

16 Data Collection Provides the information necessary to develop and justify research What information is recorded How that information is recorded How a research project is designed AIM: uphold the integrity of the project, the institution, and the researchers Collect reliable and valid data Accurately analyze and assess work by researchers Independent replication Provides justification to sponsors

17 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

18 Collecting valid data Record keeping is essential to ensure the validity of the data Good science is precise and honest Smaller projects are often recorded in bound notebooks Record keeping Records should accurately represent the progress of a project Should answer: WHAT, HOW, WHY data were collected or amended Records should be durable and accessible Errors should be marked and dated, never erased Include notes that described what actually occurred, what worked or didn t Entries should be chronological and consistent Indelible pen (not pencil) Records should be safe from tampering or falsification Record ANYTHING that seems RELEVANT to the project, its data, and the project s standards

19 Collecting valid data Record keeping is essential to ensure the validity of the data Electronic records Policies and Procedures 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 Should be aware of all the guidelines that apply to the project Human and animal subject regulations Hazardous materials Controlled biological agents

20 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

21 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 system

22 Data Protection Best way to protect data is by limiting access Protection from physical damage and tampering, loss, theft Pis decide who is authorized to access and manage data Notebooks and questionnaires should be locked Encoded identifiers to protect identity Hacking and theft are concerns with digital 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 to keep track of changes Regular backups, hard and soft copies Ensure proper destruction

23 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

24 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

25 Data Analysis It is important to avoid potential pitfalls that can invalidate or lessen the integrity of the data Methods of analysis Usage of data Researchers should work within the accepted standards Deviations must be justified Awareness of the abilities and limitations of a chosen method of analysis 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

26 Data Analysis It is important to avoid potential pitfalls that can invalidate or lessen the integrity of the data Intentional falsification or fabrication Appropriate data amendment or exclusion Forging: inventing data or experiments never performed Cooking: retaining only those results that fit the hypothesis Trimming: unreasonable smoothing of irregularities to make the data look more accurate and precise Instrument malfunctions Loss or change in subjects or specimens Interruptions or deviations in procedure

27 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)

28 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 /guide/notice-files/not-od html

29 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

30 Misconduct Fabrication is making up data or results and recording or reporting them. Falsification is manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record. Plagiarism is the appropriation of another person s ideas, processes, results, or words without giving appropriate credit. Research misconduct does not include differences of opinion.

31 Editorials address misconduct

32 Gross manipulation of blots Rockefeller University Press Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

33 Gross manipulation of blots Rockefeller University Press Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

34 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) Rockefeller University Press Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

35 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 Rockefeller University Press Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

36 Misrepresentation of immunogold data. 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. Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166: Rockefeller University Press

37 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 Rockefeller University Press Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

38 Other Data Management Issues Keep original digital or analog data exactly as they were acquired Selective acquisition of data by adjusting settings on the instrument Record instrument settings Some journal reviewers or editors request access to such primary data to ensure accuracy Selecting and reporting a very unusual result as representative Hiding negative results that may contradict your conclusions Mike Rossner, and Kenneth M. Yamada J Cell Biol 2004;166:11-15

39 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

40 A matter of trust

41 Happy researching!