1. Data Management Chi-Bin Chien Dept. Neurobiology & Anatomy chi-bin@neuro.utah.edu a developmental biologist/ex-physicist's perspective emphasis on image data general principles applicable to other disciplines

2. A data-centric view of the scientific process Data collection Data recording Data analysis Data presentation (talks, publication) Data archiving Data ownership What are ethical and intellectual issues? What are the pitfalls?

3. Data management issues Data collection Data recording Data analysis Data presentation Data archiving Data ownership what results to include? how to record your data? how do you know there's an effect? quantification, image processing how to keep it? how long? intellectual property issues

4. Data management pitfalls (the Dark Side) Data collection Data recording Data analysis Data presentation Data archiving Data ownership poor experimental design, data theft sloppiness conscious/unconscious bias image manipulation, misrepresentation data loss, irreproducibility, theft lawsuits and other unpleasantness

5. Data recording Who's it for? you your colleagues reviewers and the wider community Why does it matter? you will need to redo the experiment you will someday write a paper with this data guarding against fire, earthquake, or theft establishing intellectual property rights

6. Data recording methods Acceptable bound lab notebook looseleaf notebook (?) computer hard disk (??) data CDs or DVDs Unacceptable Post-Its pieces of lab tape loose photos in a drawer in your head

7. Data collection and analysis How do you deal with poor experimental methods? How do you deal with outliers? What can you leave out when analyzing/publishing? single points? single trials? whole experiments? When is a result believable?

8. Good data management often = good science good experimental technique (and well-written methods) appropriate negative and positive controls reproducing experimental results blinded scoring careful quantification statistics peer review For good science you need to: (1) guard against fooling yourself (2) don't fool others (3) convince everyone that you've done (1) and (2)

9. Data presentation: 20th vs. 21st century misconduct 20th century selective inclusion of experiments shopping for statistical tests selective display of microscopic fields mislabeling of experimental results darkroom dodging and burning 21st century Adobe Photoshop and the Rubber Stamp tool!

10. Image processing: JCB's policy "No specific feature within an image may be enhanced, obscured, moved, removed, or introduced. The grouping of images from different parts of the same gel, or from different gels, fields, or exposures must be made explicit by the arrangement of the figure (e.g., using dividing lines) and in the text of the figure legend. Adjustments of brightness, contrast, or color balance are acceptable if they are applied to the whole image and as long as they do not obscure or eliminate any information present in the original. Nonlinear adjustments (e.g., changes to gamma settings) must be disclosed in the figure legend." counterexamples: from Rossner and Yamada, 2004

11. Image manipulation: gels (1) from Rossner and Yamada, 2004 X

12. Image manipulation: gels (2) from Rossner and Yamada, 2004 mislabeling or "reuse" of images is not acceptable!

13. Image manipulation: gels (3) you must know what you're doing and be careful when adjusting brightness and contrast. from Rossner and Yamada, 2004

14. Image manipulation: EM selective enhancement/removal of features is unacceptable take a better picture or get better staining! what about dust at the edge of the field of view? from Rossner and Yamada, 2004

15. Image manipulation: micrographs readers assume that an image is a single microscopic field; don't make a "hidden montage" from Rossner and Yamada, 2004

16. Data archiving How to store? notebooks CDs, hard disks (watch out for software and hardware obsolescence) reagents: freezers, stock centers slide boxes or tissue sample storage keep backups How long to store? at least several years

17. Data ownership Who has a claim? funding sources: Federal government foundations private companies the principal investigator the individual experimenter (you)

18. References Rossner & Yamada (2004) "What's in a picture? The temptation of image manipulation" J Cell Biol. 166:11-15. ANAT 7790 "Special Techniques in Microscopy: Light Microscopy and Digital Imaging" Chi-Bin Chien, Chris Rodesch spring 2007