DwC mappings: Removed ':/list/' root (full version: '::[@xmlns:dcterms=http://purl.org/dc/terms/]/list/') from map spreadsheets to simplify the boilerplate in each file. Since intermediate DwC XML files no longer need to be produced for automated tests, these roots are not needed.

inputs/import.stats.xls: Updated with stats from latest import

inputs/import.stats.xls: Moved independent-import data to separate tab so that it wouldn't get moved to the side whenever a new column of simultaneous-import data is inserted. It is also no longer updated, because all column-based imports are now done simultaneously.

Use strings.ustr() or strings.urepr() everywhere that columns are stringified, in order to support column names with non-ASCII characters (such as in the Madidi data)

strings.py: concat(): Convert args to raw (non-Unicode) strings first, so that multi-byte Unicode sequences are considered by # of bytes instead of # of chars. This is necessary because PostgreSQL truncates identifiers by # of bytes instead of # of chars, so that identifiers will actually be less than 63 chars long when some chars were multi-byte.

strings.py: ustr(): Call str() method manually like urepr() to avoid Unicode errors when the returning string is non-ASCII

strings.py: Added urepr() and use it in repr_no_u(), to better support repr() return values with non-ASCII characters. Avoiding repr() also provides a more complete stack trace in the case of such errors.

schemas/vegbien.sql: plantobservation: plantobservation_aggregateoccurrence_count_1() trigger: Don't raise an error if existing count was >1, because there are in fact datasets (notably SALVIAS) where input records for individual stems may themselves contain aggregate data (such as plant and stem counts). For this data, we have an anomalous condition where an aggregateoccurrence has count >1 but contains one plantobservation, due to the plant/stem count being included in the first stem's record. (See <https://projects.nceas.ucsb.edu/nceas/projects/bien/wiki/SALVIAS_issues#Data-interpretation-issues> for more info on this problem.) Note that our desired 1:1 relationship between aggregateoccurrence and plantobservation is still guaranteed by a constraint, but the anomalous data may still cause irregularities later on in the analysis.

sql_io.py: put_table(): Ignoring all rows on unrecoverable errors: Also support the case where has_joins == True, by setting it to False so that the no-joins case is effectively used

inputs/import.stats.xls: Moved Simultaneously above Independently because that is how we are now running the imports