Mus Musculus metadata, mismatch with column names

### Context

I'm analyzing the Mus Musculus normalized expression compendium (downloaded on March 10, 2020, re-downloaded and confirmed md5sums today, 3/19/2020). Because there are some characters in the TSV column names (e.g., spaces) that cause problems in my downstream processing, I wanted to link each column name with an entry in the metadata file so that I could assign each sample a unique alphanumeric identifier.

### Problem or idea

The number of samples (columns) in the MUS_MUSCULUS.tsv dataset itself is 228,708, but the number of samples indicated in both the metadata tsv & json files is 279,781. When I tried to match the column names to the values in the "refinebio_accession_code" field in the metadata tsv, I could not identify metadata records for 168 of the column names. Likewise, comparing the refinebio_accession_code values from the metadata tsv revealed 51,241 samples in the metadata that could not be matched to a column name.

### Solution or next step

Given that 228,708 (# columns in normalized expression data) + 51,241 (# metadata entries unmatched in data) - 168 (# column names unmatched in metadata) = 279,781 (# entries in metadata), I wanted to ask whether there were 168 samples that were dropped from the metadata files and 51,241 samples that were dropped from the dataset (either intentionally or inadvertently)? If you could advise which set of samples you'd recommend using in analyses, I'd be very grateful. Thank you!

Start by comparing the 228,708 columns in MUS_MUSCULUS.tsv with refinebio_accession_code values in the metadata TSV and JSON files. Reproduce the unmatched counts and trace how the expression dataset and metadata were generated. Done means explaining whether the 168 and 51,241 discrepancies are intentional or accidental and documenting which sample set analyses should use.

Written by the indexing model from the issue text.