1. Simon Briggs Department of Clinical Pharmacology University of Oxford 13 th June 2008 Data management – A researchers prospective

2. My Background & Experiences Background in biotechnology, and PhD in Chemistry; PhD write up troubled by inconsistent methods for filing and organising data; Took many months to sift through the data and make sense of it; No guidance on managing my data; Member of an applied translational science group generating large quantities of data.

3. Why We Need to Manage Our Data Grant awarding bodies now require a statement of data handing and more importantly sharing of the data produced during the duration of the project; Repetition of experiments cost money and time; Finding data when a researcher has left is very time consuming and often appears incomplete.

4. Project Concept and idea Data produced by researcher Data manipulated by researcher Raw data archived Manipulated data stored in researchers computer Manipulated data and methodology written up in lab notebook Manipulated data written up as published manuscript Grant funded and project started The process

5. Project Concept and idea Data produced by researcher Data manipulated by researcher Raw data archived Manipulated data stored in researchers computer Manipulated data and methodology written up in lab notebook Grant funded and project started The process Manipulated data written up as published manuscript

6. Data types

10. Metadata

11. Project Concept and idea Data produced by researcher Data manipulated by researcher Raw data archived Manipulated data stored in researchers computer Manipulated data and methodology written up in lab notebook Grant funded and project started The process Manipulated data written up as published manuscript

13. Data manipulation Statistical analysis; Graphical manipulation – image files; Calibration and standardization data;

14. Project Concept and idea Data produced by researcher Data manipulated by researcher Raw data archived Manipulated data stored in researchers computer Manipulated data and methodology written up in lab notebook Grant funded and project started The process Manipulated data written up as published manuscript

15. “Think of a secure "Google" behind your firewall, with the ability to search across all your internal data repositories … including the ability to search for unstructured files (like image files) … as well as across your internal databases. And having the ability to index your files to the individual word in your PowerPoint, Excel, Word, PDF files and database files with an automated schedule.” www.ardenno.com Ardenno

16. Apache Tomcat Web Application; MySQL database; Relational database; Automatic backup and centralisation of data; Indexing of metadata contained within documents; Add additional metadata tags to documents. Ardenno

21. Difficulties experienced Multiple data formats; Disparate data types – chemical, biological & physical types; No access to metadata contained within files due to proprietary data formats; Lack of integration of electronic records of the experimental procedures; People don’t like to share the information on their computers; Visualising and analysis of all the data for a single experiment is often best done on a big desk with lots of pieces of paper.

22. Collation of data needs to include… Methodology/experimental details; Multiple data formats; Multiple interpretations of manipulated data; Relationship to other experiments; Related publications; Forthcoming publications; Accessibility.

23. What else… Needs to be implemented above the level of researcher; Incentives to the PI’s; The PI must gain something from the process for it to be implemented successfully;