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From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions

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Abstract

The scientific literature is important source of experimental and chemical structure data. Very often this data has been harvested into smaller or bigger data collections leaving the data quality and curation issues on shoulders of users. The current research presents a systematic and reproducible workflow for collecting series of data points from scientific literature and assembling a database that is suitable for the purposes of high quality modelling and decision support. The quality assurance aspect of the workflow is concerned with the curation of both chemical structures and associated toxicity values at (1) single data point level and (2) collection of data points level. The assembly of a database employs a novel “timeline” approach. The workflow is implemented as a software solution and its applicability is demonstrated on the example of the Tetrahymena pyriformis acute aquatic toxicity endpoint. A literature collection of 86 primary publications for T. pyriformis was found to contain 2,072 chemical compounds and 2,498 unique toxicity values, which divide into 2,440 numerical and 58 textual values. Every chemical compound was assigned to a preferred toxicity value. Examples for most common chemical and toxicological data curation scenarios are discussed.

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Notes

  1. Two chemical naming systems are used through the manuscript: (i) Standardized chemical names, i.e. Preferred IUPAC Name (PIN) and (ii) original chemical names from referenced publications. They are distinguished by the font: PIN is in font Courier+Italic and original names in font Courier.

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Acknowledgments

Estonian Science Foundation (Grant 7709) and Estonian Ministry for Education and Research (Grant SF0140031Bs09) for financial support. Authors are grateful to Prof. T.W. Schultz (University of Tennessee) for his assistance in resolving selected data points. Authors are thankful to Dr. Sulev Sild (University of Tartu, Estonia) for the discussion at final stages of manuscript preparation.

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Authors and Affiliations

  1. Institute of Chemistry, University of Tartu, Ravila 14A, 50411, Tartu, Estonia

    Villu Ruusmann & Uko Maran

Authors
  1. Villu Ruusmann
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  2. Uko Maran
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Corresponding author

Correspondence to Uko Maran.

Additional information

Disclaimer on fair use conditions. The data set which accompanies current publication should be regarded as a derivative work or the earlier publications of Prof. T.W. Schultz (University of Tennessee) and his co-workers. Further users are expected to properly credit and reference the most significant of them.

Disclaimer on undiscovered and new data. Authors did their best effort to find all possible T. pyriformis acute aquatic toxicity primary publications. Despite of that it can occur that something is missed or new data becomes available. In either case we are most grateful for references to such potential primary publications.

Electronic supplementary material

Below is the link to the electronic supplementary material.

10822_2013_9664_MOESM1_ESM.doc

Supplementary Material includes Tables with examples for data curation with respective detailed discussion and fully curated dataset. (DOC 612 kb)

Supplementary material 2 (XLS 737 kb)

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Ruusmann, V., Maran, U. From data point timelines to a well curated data set, data mining of experimental data and chemical structure data from scientific articles, problems and possible solutions. J Comput Aided Mol Des 27, 583–603 (2013). https://doi.org/10.1007/s10822-013-9664-4

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  • DOI: https://doi.org/10.1007/s10822-013-9664-4

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