Synthesizer: Expediting synthesis studies from context-free data with information retrieval techniques

Lisa M. Gandy, Jordan Gumm, Benjamin Fertig, Anne Thessen, Michael J. Kennish, Sameer Chavan, Luigi Marchionni, Xiaoxin Xia, Shambhavi Shankrit, Elana J. Fertig

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Scientists have unprecedented access to a wide variety of high-quality datasets. These datasets, which are often independently curated, commonly use unstructured spreadsheets to store their data. Standardized annotations are essential to perform synthesis studies across investigators, but are often not used in practice. Therefore, accurately combining records in spreadsheets from differing studies requires tedious and error-prone human curation. These efforts result in a significant time and cost barrier to synthesis research. We propose an information retrieval inspired algorithm, Synthesize, that merges unstructured data automatically based on both column labels and values. Application of the Synthesize algorithm to cancer and ecological datasets had high accuracy (on the order of 85-100%). We further implement Synthesize in an open source web application, Synthesizer (https:// The software accepts input as spreadsheets in comma separated value (CSV) format, visualizes the merged data, and outputs the results as a new spreadsheet. Synthesizer includes an easy to use graphical user interface, which enables the user to finish combining data and obtain perfect accuracy. Future work will allow detection of units to automatically merge continuous data and application of the algorithm to other data formats, including databases.

Original languageEnglish (US)
Article numbere0175860
JournalPloS one
Issue number4
StatePublished - Apr 2017

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General


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