Currently I am a PAL in the IMOMESIC project (Integrating Modelling of Metabolism and Signalling towards an Application in Liver Cancer) which is a part of the ERASysAPP (ERA-Net for Systems Biology Applications).
Before that I already gathered some experience as a PAL in the The Virtual Liver Network (VLN) project. A motivation and advantage to be a PAL is that you obtain a comprehensive overview of the different needs and possibilities in terms of data management in a project. By this you can actively take part in setting useful standards and mediate between the different users and involved actors and support efficient and successful scientific work.
I am working as an experimentalist in Systems Biology in cancer research context. Coming from an engineering background my work is focused on method development and improvement of experimental workflows to increase sample through put and data quality in proteomics (Mass spectrometry, Protein Array). Since volume, speed and diversity of our data in this area is rapidly increasing there is a growing interest for experimentalists and our modeling partners to find efficient and sustainable ways to store, process, share and find these valuable data.
Another focus of my work therefore is data management. In our computational based proteomics and systems biology approaches we see a great potential and benefits in integrating data management tools and databases in the workflow starting from experimental design to data analysis and visualization. Two nice examples from our past and recent efforts in this direction I would like to mention are Excemplify and openBIS:
The user friendly tool Excemplify is a flexible template based solution, parsing and managing data in spreadsheets for experimentalists (http://www.ncbi.nlm.nih.gov/pubmed/23549603). It was developed together with the group of Scientific Databases and Visualization (SDBV) from the Heidelberg Institute for Theoretical Studies.
Recently we are also starting to use openBIS (https://sis.id.ethz.ch/software/openbis.html) an open, distributed system for managing biological information for our MS and image data.