Escape Excel
Escape Excel prevents Microsoft Excel's automatic conversion of text strings by escaping problematic fields in text files to preserve data integrity for downstream scientific analysis.
Key Features:
- Detection of date-like strings: Identifies text strings that resemble dates which Excel might convert to date formats.
- Detection of time-like strings: Identifies text strings that resemble times which Excel might convert to time values.
- Detection of numbers with leading zeroes: Identifies numeric strings with leading zeroes that Excel might truncate or alter.
- Detection of long numeric and alphanumeric identifiers: Identifies long identifiers that Excel might truncate or convert to scientific notation.
- Escaping to preserve textual format: Escapes identified problematic strings so they remain as text upon import into Microsoft Excel.
Scientific Applications:
- Prevention of import-induced data corruption: Ensures text files retain original string values when imported into Microsoft Excel, reducing corruption risk.
- Preservation of numeric identifiers: Maintains leading zeroes and exact numeric codes required for accurate downstream analyses.
- Retention of long identifiers: Prevents truncation or conversion of long numeric and alphanumeric identifiers that are critical in datasets.
- Improved reliability for large datasets and publications: Reduces the likelihood of Excel-induced errors in large datasets used for analyses and publication.
Methodology:
Scans text files to identify date-like strings, time-like strings, numbers with leading zeroes, and long numeric or alphanumeric identifiers, and escapes those strings to preserve their textual format on import into Microsoft Excel.
Topics
Details
- Tool Type:
- command-line tool, web application
- Operating Systems:
- Linux, Mac
- Programming Languages:
- Perl
- Added:
- 6/19/2018
- Last Updated:
- 12/10/2018
Operations
Publications
Welsh EA, Stewart PA, Kuenzi BM, Eschrich JA. Escape Excel: A tool for preventing gene symbol and accession conversion errors. PLOS ONE. 2017;12(9):e0185207. doi:10.1371/journal.pone.0185207. PMID:28953918. PMCID:PMC5617173.
Funding: - National Cancer Institute: F99 CA212456, P30-CA076292-18