Value, but high costs in post-deposition data curation

Discoverability of sequence data in primary data archives is proportional to the richness of contextual information associated with the data. Here, we describe an exercise in the improvement of contextual information surrounding sample records associated with metagenomics sequence reads available in the European Nucleotide Archive. We outline the annotation process and summarize findings of this effort aimed at increasing usability of publicly available environmental data. Furthermore, we emphasize the benefits of such an exercise and detail its costs. We conclude that such a third party annotation approach is expensive and has value as an element of curation, but should form only part of a more sustainable submitter-driven approach

Value, but high costs in post-deposition data Curation

© The Author(s) 2016. Published by Oxford University Press. Discoverability of sequence data in primary data archives is proportional to the richness of contextual information associated with the data. Here, we describe an exercise in the improvement of contextual information surrounding sample records associated with metagenomics sequence reads available in the European Nucleotide Archive. We outline the annotation process and summarize findings of this effort aimed at increasing usability of publicly available environmental data. Furthermore, we emphasize the benefits of such an exercise and detail its costs. We conclude that such a third party annotation approach is expensive and has value as an element of curation, but should form only part of a more sustainable submitter-driven approach

Biocuration: Distilling data into knowledge

Data, including information generated from them by processing and analysis, are an asset with measurable value. The assets that biological research funding produces are the data generated, the information derived from these data, and, ultimately, the discoveries and knowledge these lead to. From the time when Henry Oldenburg published the first scientific journal in 1665 (Proceedings of the Royal Society) to the founding of the United States National Library of Medicine in 1879 to the present, there has been a sustained drive to improve how researchers can record and discover what is known. Researchers' experimental work builds upon years and (collectively) billions of dollars' worth of earlier work. Today, researchers are generating data at ever-faster rates because of advances in instrumentation and technology, coupled with decreases in production costs. Unfortunately, the ability of researchers to manage and disseminate their results has not kept pace, so their work cannot achieve its maximal impact. Strides have recently been made, but more awareness is needed of the essential role that biological data resources, including biocuration, play in maintaining and linking this ever-growing flood of data and information. The aim of this paper is to describe the nature of data as an asset, the role biocurators play in increasing its value, and consistent, practical means to measure effectiveness that can guide planning and justify costs in biological research information resources' development and management

Taxonomy and the Production of Semantic Phenotypes

Preprint of chapter appearing in "Studies on the Semantic Web: Volume 33: Application of Semantic Technology in Biodiversity Science"Taxonomists produce a myriad of phenotypic descriptions. Traditionally these are provided in terse (telegraphic) natural language. As seen in parallel within other fields of biology researchers are exploring ways to formalize parts of the taxonomic process so that aspects of it are more computational in nature. The currently used data formalizations, mechanisms for persisting data, applications, and computing approaches related to the production of semantic descriptions (phenotypes) are reviewed, they, and their adopters are limited in number. In order to move forward we step back and characterize taxonomists with respect to their typical workflow and tendencies. We then use these characteristics as a basis for exploring how we might create software that taxonomists will find intuitive within their cur- rent workflows, providing interface examples as thought experiments.NSF - DBI-1356381NSF 0956049https://deepblue.lib.umich.edu/bitstream/2027.42/148811/1/yoder_proof.pdfDescription of yoder_proof.pdf : Proof of book chapte

Best practice data life cycle approaches for the life sciences

Throughout history, the life sciences have been revolutionised by technological advances; in our era this is manifested by advances in instrumentation for data generation, and consequently researchers now routinely handle large amounts of heterogeneous data in digital formats. The simultaneous transitions towards biology as a data science and towards a ‘life cycle’ view of research data pose new challenges. Researchers face a bewildering landscape of data management requirements, recommendations and regulations, without necessarily being able to access data management training or possessing a clear understanding of practical approaches that can assist in data management in their particular research domain. Here we provide an overview of best practice data life cycle approaches for researchers in the life sciences/bioinformatics space with a particular focus on ‘omics’ datasets and computer-based data processing and analysis. We discuss the different stages of the data life cycle and provide practical suggestions for useful tools and resources to improve data management practices.Philippa C. Griffin, Jyoti Khadake, Kate S. LeMay, Suzanna E. Lewis, Sandra Orchard ... Nathan S. Watson-Haigh ... et al