[FIX] Remove requirement for equal {participants x sessions} across pipelines/assessments

Changes proposed in this pull request:

Removed unnecessary check for equal {participants x sessions} across pipelines (for imaging CSVs) or assessments (for phenotypic CSVs)
- When pivoting the long-format input to wide, pandas will simply create empty cells for any combinations that do not exist in the original data, which we think is fine for now
~Switched to using a method for reshaping the long input data to wide (pd.pivot -> pd.pivot_table) that has finer control over the aggregation method when there are duplicates, and values used to fill missing values in the resulting pivoted table~
- EDIT: Sticking with pd.pivot due to known problems with pd.pivot_table silently dropping NaNs
Added input check for duplicate entries for a given participant-session and pipeline/assessment combination (i.e., when there is >1 value for a participant-session for a specific pipeline or phenotypic measure)
- When this happens, error out for now (pivot_table could theoretically handle this by keeping only the first occurrence in the resulting pivoted table, but this is not intuitive and may mean relevant data is lost - rather, we probably want to flag and tell users to fix duplicate observations)

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