Data management and best practice for plant science

Leonelli, Sabina; Davey, Robert P.; Arnaud, Elizabeth; Parry, Geraint; Bastow, Ruth

doi:10.1038/nplants.2017.86

Cited by 40 publications

(42 citation statements)

References 11 publications

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“…One example is the famous eGFP browser which provides the content of RNA-Seq datasets in a simple way to biologists. The alternative would be downloading and analysing raw RNA-Seq datasets from the Sequence Read Archive (SRA) which would Further, it has to be considered that specific fields require the integration of data of various types, formats and abundance (21) which is hard to realise by a single database and therefore requires cooperation to encourage data reuse. Here, we focus on the reuse of primary data, specifically different sequences as the data type characteristic of the life sciences and continuously producing vast amounts of information.…”

Section: Types Of Reusable Datamentioning

confidence: 99%

The Reuse of Public Datasets in the Life Sciences: Potential Risks and Rewards

Sielemann

Hafner

Pucker

2020

Preprint

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The 'big data revolution' has enabled novel types of analyses in the life sciences, facilitated by public sharing and reuse of datasets. Here, we review the prodigious potential of reusing publicly available datasets and the challenges, limitations and risks associated with it. Due to the prominence, abundance and wide distribution of sequencing results, we focus on the reuse of publicly available sequence datasets. Through selected examples of successful reuse of different data (genome, transcriptome, proteome, metabolome, phenotype and ecosystem), with their respective limitations and risks, we illustrate the enormous potential of the practice. A checklist to determine the reuse value and potential of particular dataset is also provided.

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Section: Types Of Reusable Datamentioning

confidence: 99%

The Reuse of Public Datasets in the Life Sciences: Potential Risks and Rewards

Sielemann

Hafner

Pucker

2020

Preprint

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“…Strong efforts are being made towards data interoperability and standardized data and metadata formats . Guidelines for data storage and management are actively being published with a focus on open standards . In this article we will detail methods to manage medium‐to‐large acquired datasets within the laboratory, with a focus on storing data for automated analysis.…”

Section: Standardized Data Enable Automated Processingmentioning

confidence: 99%

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

González

Evans

2019

BioEssays

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Here, a streamlined, scalable, laboratory approach is discussed that enables medium‐to‐large dataset analysis. The presented approach combines data management, artificial intelligence, containerization, cluster orchestration, and quality control in a unified analytic pipeline. The unique combination of these individual building blocks creates a new and powerful analysis approach that can readily be applied to medium‐to‐large datasets by researchers to accelerate the pace of research. The proposed framework is applied to a project that counts the number of plasmonic nanoparticles bound to peripheral blood mononuclear cells in dark‐field microscopy images. By using the techniques presented in this article, the images are automatically processed overnight, without user interaction, streamlining the path from experiment to conclusions.

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“…Data production, Images, Source Code etc). As such, COPO directly addresses the Accessible and Interoperable tenets of FAIR 14 . This has been achieved by developing wizards built into a web-based platform to allow users to describe their data without having to resort to complex spreadsheets, multiple redundant data descriptions when handling multiple research object entities, or multiple submissions to different repositories.…”

Section: Researchers Need Help To Biocuratementioning

confidence: 99%

COPO: a metadata platform for brokering FAIR data in the life sciences

Etuk

Shaw

González-Beltrán

et al. 2019

Preprint

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Scientific innovation is increasingly reliant on data and computational resources. Much of today's life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.COPO is a computational system that attempts to address some of these challenges by enabling scientists to describe their research objects (raw or processed data, publications, samples, images, etc.) using community-sanctioned metadata sets and vocabularies, and then use public or institutional repositories to share it with the wider scientific community. COPO encourages data generators to adhere to appropriate metadata standards when publishing research objects, using semantic terms to add meaning to them and specify relationships between them. This allows data consumers, be they people or machines, to find, aggregate, and analyse data which would otherwise be private or invisible. Building upon existing standards to push the state of the art in scientific data dissemination whilst minimising the burden of data publication and sharing. Availability:COPO is entirely open source and freely available on GitHub at https://github.com/collaborative-open-plant-omics . A public instance of the platform for use by the community, as well as more information, can be found at copo-project.org .

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Data management and best practice for plant science

Cited by 40 publications

References 11 publications

The Reuse of Public Datasets in the Life Sciences: Potential Risks and Rewards

The Reuse of Public Datasets in the Life Sciences: Potential Risks and Rewards

Biomedical Image Processing with Containers and Deep Learning: An Automated Analysis Pipeline

COPO: a metadata platform for brokering FAIR data in the life sciences

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