Press Release

MeCAT has an official press release: ANSTO and Australian Synchrotron choose Aussie software

IT Blog Awards 2010: Individual IT Professional Male

Brian Kelly’s UK Web Focus Blog has been nominated for the IT Blog Awards 2010: Individual IT Professional Male.  In his post, Brian quotes and makes reference to his Blog Policies.  My purpose in starting this blog was similar to Brian’s with an obvious focus on my current project, MeCAT, so I’ve taken the liberty of adapting some of Brian’s points:

• The contents of the blog will primarily address issues related to the MeCAT project and its work on Tardis, including issues around data and metadata management in the research sector.
• The blog will also provide a test bed for experiments and for testing new services and provide access to discussions about the experiment.
• The blog will provide an opportunity for me to ‘think out loud“: i.e. describe speculative ideas, thoughts which may occur to me, etc. which may be of interest to others or for which I would welcome feedback.
• The blog will seek to both disseminate information and encourage discussion and debate.
• The blog will be used as an open notebook, so that ideas, thoughts and opinions can be shared with others.

Thanks Brian for articulating what had been a rather fuzzy set of ideas!

Modeling Experimental Data – basic description

The schema used in Tadis is based on the Core Scientific Metadata Model (CSMD) developed for the ICAT project.

At the simplest level, the experimental data is simply a collection of files (Datafiles), which are grouped in to Datasets, which are grouped in to Experiments:

Tardis High-level data model

(Please note that the schema is only partially shown in the diagram above)

At the top level, Tardis stores a flat list of Experiments.   Each Experiment contains one or more Datasets, and each Dataset contains one or more Datafiles.

At each level, Experiment, Dataset and Datafile, user defined parameters may be added, grouped in to Parameter Sets.

Tardis doesn’t impose any interpretation on what is considered an Experiment or Dataset.   Examples of how datasets may be grouped are: by sample, by instrument settings, or as a time sequence, e.g. artificially aging a material and investigating the effects.

In the last post I listed two metadata hierarchies: 1) The Core, Discipline and Project hierarchy from the University of Southampton, and 2) the Core, Instrument and Science hierarchy from STFC.  The core metadata schema is hard-coded in Tardis.  The Instrument, Science and Project schema’s can all be implemented using Parameter Sets.

Metadata strategy

The University of Southampton data management project has proposed a three-level metadata strategy, see their blog entry “Metadata strategy“:

1. Project
2. Discipline
3. Core

Tardis is based on the Core Scientific Metadata model (CSMD) developed within the Science & Technology Facilities Council (STFC).  One metadata hierarchy they’ve adopted is (turned upside down to match Southampton’s):

1. Science Specific
2. Instrument Specific
3. Core

(This reminds me of Robert Pirsig’s Intellectual Scalpel)

We’re extending Tardis for use within the Australian Synchrotron and ANSTO, where the STFC model is more appropriate.  However, institutional use of Tardis may also be project based.

Tardis supports configurable schemas (parameter sets) at  the experiment, dataset and datafile level.  Appropriate use of the configurable schema should allow us to handle both models, or a combined model.