Amplifying Data Curation Efforts to Improve the Quality of Life Science Data
In the era of data science, datasets are shared widely and used for many purposes unforeseen by the original creators of the data. In this context, defects in datasets can have far reaching consequences, spreading from dataset to dataset, and affecting the consumers of data in ways that are hard to predict or quantify. Some form of waste is often the result. For example, scientists using defective data to propose hypotheses for experimentation may waste their limited wet lab resources chasing the wrong experimental targets. Scarce drug trial resources may be used to test drugs that actually have little chance of giving a cure.
Because of the potential real world costs, database owners care about providing high quality data. Automated curation tools can be used to an extent to discover and correct some forms of defect. However, in some areas human curation, performed by highly-trained domain experts, is needed to ensure that the data represents our current interpretation of reality accurately. Human curators are expensive, and there is far more curation work to be done than there are curators available to perform it. Tools and techniques are needed to enable the full value to be obtained from the curation effort currently available.
In this paper,we explore one possible approach to maximising the value obtained from human curators, by automatically extracting information about data defects and corrections from the work that the curators do. This information is packaged in a source independent form, to allow it to be used by the owners of other databases (for which human curation effort is not available or is insufficient). This amplifies the efforts of the human curators, allowing their work to be applied to other sources, without requiring any additional effort or change in their processes or tool sets. We show that this approach can discover significant numbers of defects, which can also be found in other sources.
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