Data Management

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Present

Kurt Anderson, Nick Barry, Dan Brooke, Grant Calder (session chair), Paul Cormack, Ian Dobbie, Andrew Frenandez, Jonathan Howe, Alan Kidger, Alex Laude, Heng Hng, Peter Humphreys, Darren Robinson, Mark Scott, Anne Vaahtokari, Simon Walker, Rolly Wiegand.

Chair: Grant Calder (JIC)

Existing Facility Data Management

Microscopy data needs to be stored as it is a legal requirement for the grant and is necessary for queries on data validation (Kurt Anderson’s experience).

There is an uneven level of IT support available to microscopy facilities with some institutions giving no IT support. The lack of support forces these facility managers with limit resources to supply an image storage solution for their facility. This greatly increases the burden on the facility manager having a negative impact on level of storage (backup / archiving) and inhibits the addition of complex data management packages to their storage system. A good example of a well constructed image storage solution was Ian Dobbie’s system in Oxford runs a multi tiered storage solution (HP expensive licence) and OMERO data management, which he demonstrated live during the meeting.

Funding

Funding for data storage and management is difficult to acquire as PIs are either reluctant to write this into grant (although most grant applications have a section for this purpose) or will spend the money on something else. It was a common view that money allocated for data storage should be automatically removed from a grant, to prevent it being spent on something else. How, this issue is dealt with greatly varies between institutions.

“As volume of data increases the need for a data management system becomes essential” Ian Dobbie.

OMERO

OMERO is an open source data management system which can deal with most imaging modalities. There are very few imaging facilities that are actively using OMERO, with several facilities still testing the system. Perkin-Elmer version of OMERO called “Columbus” has 50 installations in the U.S. showing that the system is slowly becoming adopted.

The view from facility managers who are actively using the OMERO system is that the biggest problem they encounter is from the reluctance of their microscopy users to use data management system like OMERO. It was pointed out that in digital pathology users seem to have a better mindset and except image management as standard. It was thought that microscopy users need to be properly educated / encouraged so that they adopt these systems as standard practise. Perkin-Elmer’s experience with installing “Columbus” is that good training standards are vital focusing on what the user requires (not all the functions) and business rules also need to be enforced (carrot and stick approach). The questioned was then raised that how a facility manager enforces data management rules? Ian Dobbie tries to encourage users by not charging for OMERO storage space.

There was question on can OMERO connecting to other data management systems? The answer was yes it should be possible as OMERO is designed to interact with other system and several ways of getting information out of OMERO.

There was great deal of interest in OMERO being able to interact with other image analysis programs both open source and commercial e.g. imageJ, Huygens etc. This was thought to greatly strength the adoption of OMERO as a standard data management package.

OMERO development requests

-Reduce the frequency of new versions, to update to the new version this requires a lot of effort and frequently breaks the previous storage system.

-Encourage new users with simpler training material would be very useful.

-Encourage new users with a simplified interface (skin) as this would buffer first time users from the complexity of the system.

-Ensure easy OMERO interaction with familiar image analysis programs that users are more comfortable with using like imageJ (the user does not need to learn a new software package).

Super Resolution

With new super resolution modalities evolving facilities in the future will face the need to save large raw data sets e.g. stochastic reconstruction techniques can collect up to 20,000 images. Should facilities store all the raw data which has a large cost implication? It was generally felt that that it was necessary to keep all the original data for both reanalysis (new algorithms) and validating experimental results. With image compression techniques and cheaper mass data storage becoming available it was thought that this would not be a problem.