post berkeley-schema-fy24 merge issue: review `FileTypeEnum` composition and correlation with other `DataObject` slots/relationships

[turbomam]

• Can a DataObject that is the output of any process use any FileTypeEnum in it's data_object_type slot?
• do the different permissible values come from different axes of differentiation?
• should we use an is_a hierarchy within the PVs?
• should we re-normalize all of the permissible values to lower_snake_case (would require a corresponding data migration and changes to code that would describe future DataObjects)

As one example: what are the advantages and disadvantages of generality or specificity in

• https://github.com/microbiomedata/berkeley-schema-fy24/pull/256

The same question might apply to other PVs in this enumeration.

low priority for now (in my opinion)

cc @mslarae13 @brynnz22

see also the following label (although we might want to remove it at some point)

• https://github.com/microbiomedata/nmdc-schema/issues?q=+label%3AFileTypeEnum

for example, we could use a link like this, instead of a lable (berkeley-schema-fy24 in this case)

• https://github.com/microbiomedata/berkeley-schema-fy24/pulls?q=is%3Apr+filetypeenum

[turbomam]

Claude finds these different axes of differentiation or concerns in FileTypeEnum:

1. Data Type / Analysis Method:

   • Metagenome data
   • Metabolomics data (FT ICR-MS, GC-MS)
   • Metaproteomics data
   • Assembly data
   • Annotation data (various types)
   • Read-based analysis
   • Taxonomic classification (GOTTCHA2, Kraken2, Centrifuge)

2. Processing Stage:

   • Raw data
   • Filtered data
   • Error-corrected data
   • Assembled data
   • Annotated data

3. File Format:

   • FASTQ
   • BAM
   • FASTA
   • GFF
   • JSON
   • TSV
   • PDF
   • HTML

4. Sequencing Read Type:

   • Raw Read 1 (forward)
   • Raw Read 2 (reverse)
   • Interleaved paired-end

5. Quality Control Stage:

   • QC Statistics
   • QC non-rRNA reads

6. Biological Entity Focus:

   • Protein-related
   • Peptide-related
   • RNA-related (rRNA, tRNA, etc.)
   • Gene-related

7. Output Type:

   • Report files
   • Statistical files
   • Plot files (heatmap, barplot, Krona plot)
   • Binning results

8. Annotation Type:

   • Structural annotation
   • Functional annotation
   • Various specific annotation types (e.g., TIGRFam, CRT, Genemark, etc.)

9. Compression Status:

   • Compressed files (e.g., zip files for bins)
   • Uncompressed files

10. Workflow Stage:

    • Intermediate files
    • Final output files
    • Workflow statistics