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An open future for ecological and evolutionary data?

ABSTRACT: As part of BioMed Central's open science mission, we are pleased to announce that two of our journals have integrated with the open data repository Dryad. Authors submitting their research to either BMC Ecology or BMC Evolutionary Biology will now have the opportunity to deposit their data directly into the Dryad archive and will receive a permanent, citable link to their dataset. Although this does not affect any of our current data deposition policies at these journals, we hope to encourage a more widespread adoption of open data sharing in the fields of ecology and evolutionary biology by facilitating this process for our authors. We also take this opportunity to discuss some of the wider issues that may concern researchers when making their data openly available. Although we offer a number of positive examples from different fields of biology, we also recognise that reticence to data sharing still exists, and that change must be driven from within research communities in order to create future science that is fit for purpose in the digital age. This editorial was published jointly in both BMC Ecology and BMC Evolutionary Biology.

SUBMITTER: Kenall A

PROVIDER: S-EPMC3992165 | biostudies-literature | 2014 Apr

REPOSITORIES: biostudies-literature

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An open future for ecological and evolutionary data?

Kenall Amye A Harold Simon S Foote Christopher C

BMC ecology 20140402

As part of BioMed Central's open science mission, we are pleased to announce that two of our journals have integrated with the open data repository Dryad. Authors submitting their research to either BMC Ecology or BMC Evolutionary Biology will now have the opportunity to deposit their data directly into the Dryad archive and will receive a permanent, citable link to their dataset. Although this does not affect any of our current data deposition policies at these journals, we hope to encourage a ...[more]

PMID: 24690219

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Project description:Chemical cues are arguably the most fundamental means of animal communication and play an important role in mate choice and kin recognition. Consequently, there is growing interest in the use of gas chromatography (GC) to investigate the chemical basis of eco-evolutionary interactions. Both GC-MS (mass spectrometry) and FID (flame ionization detection) are commonly used to characterise the chemical composition of biological samples such as skin swabs. The resulting chromatograms comprise peaks that are separated according to their retention times and which represent different substances. Across chromatograms of different samples, homologous substances are expected to elute at similar retention times. However, random and often unavoidable experimental variation introduces noise, making the alignment of homologous peaks challenging, particularly with GC-FID data where mass spectral data are lacking. Here we present GCalignR, a user-friendly R package for aligning GC-FID data based on retention times. The package was developed specifically for ecological and evolutionary studies that seek to investigate similarity patterns across multiple and often highly variable biological samples, for example representing different sexes, age classes or reproductive stages. The package also implements dynamic visualisations to facilitate inspection and fine-tuning of the resulting alignments and can be integrated within a broader workflow in R to facilitate downstream multivariate analyses. We demonstrate an example workflow using empirical data from Antarctic fur seals and explore the impact of user-defined parameter values by calculating alignment error rates for multiple datasets. The resulting alignments had low error rates for most of the explored parameter space and we could also show that GCalignR performed equally well or better than other available software. We hope that GCalignR will help to simplify the processing of chemical datasets and improve the standardization and reproducibility of chemical analyses in studies of animal chemical communication and related fields.

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An open future for ecological and evolutionary data?

Publications

An open future for ecological and evolutionary data?

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