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Dear Confocal Microscopy Listers,

We are looking at setting up an Image repository Database here at the University of Auckland in conjunction with our Centre for eResearch (CeR). In addition to the detailed image and instrument metadata, we are also trying to capture more of the sematic and ontological metadata associated with the images. The objective in capturing this rich metadata is to make the Image DB a searchable resource.
(“A Bioimage Data Integration and Publication Platform: The Image Data Resource. Williams et al. Nat Methods. 2017 Aug;14(8)” has been extremely helpful and informative in guiding us) 

Moreover, such a well-annotated image DB can act as a useful source for training datasets in machine learning efforts down the line.
There appears to be a fine balance between capturing enough metadata versus capturing too much as to make it too arduous or too granular as to be counterproductive. 

In addition, there is also the question of how to collect this metadata. Having to enter lots of fields that need to be entered manually will be counterproductive. So we are also thinking of ideas on how to capture the metadata in the easiest way possible.
We are setting up a discussion group among our researchers here to try and synthesis a balanced list of metadata fields that will prove useful (now and in future). I was hoping to also reach out to the (fantastic!) contributors to the Confocal Microscopy list to ‘crowd source’ ideas and suggestions about this. 

I’ll briefly list out the types of fields we have currently considered and I would appreciate any suggestions about these and others we may consider.

Image Metadata
•	Unique image ID
•	Dimensional info (x/y/z pixel size, wavelength, time, bitrate etc)
•	Positional info (ROIs etc)
•	File info (compression, file formats etc)

Equipment Metadata
•	Make /model/ Version
•	Instrument setting
•	Hardware configuration (filters, objectives, NA)

User Metadata
•	User/PI/Dept
•	Linked Grant/Project/Papers

Experimental Metadata
•	Project ID
•	Experiment type (disease vs control, knockdown vs control etc)
•	Cell/Tissue type
•	Fixation, thickness
•	Antibodies used? Concentrations

Semantic/Ontological Metadata
•	Research area ontology
•	Phenotype Ontology
•	Image quality score (1-10 user scored)
•	Image processing steps
•	Linked Segmentation masks 

Thank you

Yours sincerely,

Praju Vikas Anekal. Ph.D.
Biomed Imaging Microscopist/BioImage Analyst, Biomedical Imaging Research Unit.
Faculty of Medical and Health Sciences, The University of Auckland.
E-Mail : [log in to unmask] , Ext : 87831