Why Do We Need Data Management?

Digital research, especially biological, creates larges amounts of complex data

• 3 Billion base-pairs in the 23 human chromosome pairs
• 20,000+ humangenes
• 60,000+ human protein variants
• measurement of the expression patterns of all these requires many files for each element of the biology central dogma

• Data complexity
  • huge numbers of files, particularly counting the meta-data relating data-to-data
  • large storage capacity needed, either for individual files or collectively

• This data varies in format and type
  • raw text
  • delimited text
  • binary (not human readable)
  • often extreme differences in storage requirements or limitations
• Without a management plan:
  • protecting, sharing, and even locating data can be a challenge
• With a plan:
  • researchers can focus on their areas of expertise
  • management policies can be automated
  • scientific replication, open-access, and cross-collections can be created, curated, and maintained

Example:

• Given 100 similarly named files in a directory file0.dat - file100.dat
  • DISCUSS: what can we guess about this data?
    • very little
• Over these modules, we’ll discuss data organization best practices for improved computational efficiency, performance, and security.