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Improving ecological data science with workflow management software
13 references·0 citing·48 second hop
Improving ecological data science with workflow management software (2023)Improving ecological data science with wo…The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing (2021)The targets R package: a dyna…tarchetypes: Archetypes for Targets (2021)tarchetypes: Archetypes for T…Sustainable data analysis with Snakemake (2021)Sustainable data analysis wit…The drake R package: a pipeline toolkit for reproducibility and high-performance computing (2018)The drake R package: a pipeli…Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions (2018)Situating Ecology as a Big-Da…Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators (2017)Unmet needs for analyzing bio…A review of bioinformatic pipeline frameworks (2016)A review of bioinformatic pip…Ten Simple Rules for Reproducible Computational Research (2013)Ten Simple Rules for Reproduc…A survey of the practice of computational science (2011)A survey of the practice of c…Data-intensive Science: A New Paradigm for Biodiversity Studies (2009)Data-intensive Science: A New…Workflows and e-Science: An overview of workflow system features and capabilities (2008)Workflows and e-Science: An o…Scientific workflow design for mere mortals (2008)Scientific workflow design fo…Kepler: An Extensible System for Design and Execution of Scientific Workflows (2004)Kepler: An Extensible System …A survey of data provenance in e-science (2005)Scientific workflow management and the Kepler system (2005)Taverna: lessons in creating a workflow environment for the life sciences (2005)Scientific Workflows: Business as Usual? (2009)An algebraic approach for data-centric scientific workflows (2011)A survey on provenance: What for? What form? What from? (2017)Galaxy: A platform for interactive large-scale genome analysis (2005)Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010)Believe it or not: how much can we rely on published data on potential drug targets? (2011)Reproducibility in Science (2014)Good enough practices in scientific computing (2017)Preferred reporting items for systematic reviews and meta‐analyses in ecology and evolutionary biology: a PRISMA extension (2021)A modeling and execution environment for distributed scientific workflows (2004)An introduction to Docker for reproducible research (2015)KBase: The United States Department of Energy Systems Biology Knowledgebase (2018)Chimera: a virtual data system for representing, querying, and automating data derivation (2002)Taverna: a tool for the composition and enactment of bioinformatics workflows (2004)Geospatial Cyberinfrastructure: Past, present and future (2010)A characterization of workflow management systems for extreme-scale applications (2017)Reproducibility in Scientific Computing (2018)The Structure of Scientific Revolutions (1963)New Visions for Addressing Sustainability (2003)Big Data, new epistemologies and paradigm shifts (2014)The ethics of smart cities and urban science (2016)The SAGE Handbook of Social Media Research Methods (2016)Snakemake—a scalable bioinformatics workflow engine (2012)The Taverna workflow suite: designing and executing workflows of Web Services on the desktop, web or in the cloud (2013)snakePipes: facilitating flexible, scalable and integrative epigenomic analysis (2019)The nf-core framework for community-curated bioinformatics pipelines (2020)Reproducible, scalable, and shareable analysis pipelines with bioinformatics workflow managers (2021)Big data: The next frontier for innovation, competition, and productivity (2011)Big Data: A Survey (2014)Jupyter Notebooks – a publishing format for reproducible computational workflows (2016)MoveApps: a serverless no-code analysis platform for animal tracking data (2022)‘Small Data’ for big insights in ecology (2023)Big data applications: overview, challenges and future (2024)Assessment of Course-Based Undergraduate Research Experiences: A Meeting Report (2014)Big Data: Astronomical or Genomical? (2015)Our path to better science in less time using open data science tools (2017)Reproducible image handling and analysis (2021)clustermq enables efficient parallelization of genomic analyses (2019)Cross-national analyses require additional controls to account for the non-independence of nations (2023)The five pillars of computational reproducibility: bioinformatics and beyond (2023)Same data, different analysts: variation in effect sizes due to analytical decisions in ecology and evolutionary biology (2025)1,500 scientists lift the lid on reproducibility (2016)Nextflow enables reproducible computational workflows (2017)The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update (2018)Applying the FAIR Principles to computational workflows (2025)
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References

  • 2013Ten Simple Rules for Reproducible Computational Research935 cited
  • 2008Workflows and e-Science: An overview of workflow system features and capabilities836 cited
  • 2021Sustainable data analysis with Snakemake785 cited
  • 2004Kepler: An Extensible System for Design and Execution of Scientific Workflows745 cited
  • 2009Data-intensive Science: A New Paradigm for Biodiversity Studies369 cited
  • 2018Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions331 cited
  • 2016A review of bioinformatic pipeline frameworks250 cited
  • 2021The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing242 cited
  • 2008Scientific workflow design for mere mortals168 cited
  • 2017Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators156 cited
  • 2011A survey of the practice of computational science113 cited
  • 2018The drake R package: a pipeline toolkit for reproducibility and high-performance computing62 cited
  • 2021tarchetypes: Archetypes for Targets11 cited

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