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Lightweight Distributed Provenance Model for Complex Real–world Environments
12 references·1 citing·47 second hop
Lightweight Distributed Provenance Model for Complex Real–world Environments (2022)Lightweight Distributed Provenance Model …Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles (2021)Machine Learning Pipelines: P…Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv (2019)Sharing interoperable workflo…Enabling precision medicine via standard communication of HTS provenance, analysis, and results (2018)Enabling precision medicine v…A Templating System to Generate Provenance (2017)A Templating System to Genera…Big data provenance: Challenges, state of the art and opportunities (2015)Big data provenance: Challeng…Time to do something about reproducibility (2014)Time to do something about re…Reproducibility in Science (2014)Reproducibility in ScienceIncreasing value and reducing waste in research design, conduct, and analysis (2014)Increasing value and reducing…Implementing interoperable provenance in biomedical research (2013)Implementing interoperable pr…Provenance for MapReduce-based data-intensive workflows (2011)Provenance for MapReduce-base…Believe it or not: how much can we rely on published data on potential drug targets? (2011)Believe it or not: how much c…The Open Provenance Model core specification (v1.1) (2010)The Open Provenance Model cor…Provenance of specimen and data – A prerequisite for AI development in computational pathology (2023)Provenance of specimen and da…Empirical Evidence of Design-Related Bias in Studies of Diagnostic Tests (1999)Why Most Discovered True Associations Are Inflated (2008)The COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and how to select an outcome measurement instrument (2016)The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta‐analyses (2016)“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021)An open investigation of the reproducibility of cancer biology research (2014)Policy: NIH plans to enhance reproducibility (2014)A living document: reincarnating the research article (2015)Can cancer researchers accurately judge whether preclinical reports will reproduce? (2017)The challenges of replication (2017)Scientific workflow management and the Kepler system (2005)Provenance Collection Support in the Kepler Scientific Workflow System (2006)Big Data Provenance: State-Of-The-Art Analysis and Emerging Research Challenges (2016)A survey of simulation provenance systems: modeling, capturing, querying, visualization, and advanced utilization (2018)A Big Data Provenance Model for Data Security Supervision Based on PROV-DM Model (2020)Why Most Published Research Findings Are False (2005)Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015 (2018)A survey of data provenance in e-science (2005)SPADE: Support for Provenance Auditing in Distributed Environments (2012)AiiDA: automated interactive infrastructure and database for computational science (2015)A survey on provenance: What for? What form? What from? (2017)Confirmation Bias: A Ubiquitous Phenomenon in Many Guises (1998)Reproducible Research in Computational Science (2011)Reporting guidelines for health care simulation research: extensions to the CONSORT and STROBE statements (2016)Redefine statistical significance (2017)The Power of Bias in Economics Research (2017)Computational solutions to large-scale data management and analysis (2010)Big data and its technical challenges (2014)A systematic review of provenance systems (2018)Big and open linked data analytics ecosystem: Theoretical background and essential elements (2018)Big data quality framework: a holistic approach to continuous quality management (2021)Lineage retrieval for scientific data processing: a survey (2005)Information accountability (2008)Capturing and querying fine-grained provenance of preprocessing pipelines in data science (2020)Data Provenance (2020)DfAnalyzer: Runtime dataflow analysis tool for Computational Science and Engineering applications (2020)The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update (2016)What does research reproducibility mean? (2016)Bioinformatics tools developed to support BioCompute Objects (2021)The role of metadata in reproducible computational research (2021)Communicating regulatory high-throughput sequencing data using BioCompute Objects (2022)Investigating reproducibility and tracking provenance – A genomic workflow case study (2017)Nextflow enables reproducible computational workflows (2017)Singularity: Scientific containers for mobility of compute (2017)Bioconda: sustainable and comprehensive software distribution for the life sciences (2018)Containers for computational reproducibility (2023)Applying the FAIR Principles to computational workflows (2025)
this work references cited by second hop

References

  • 2011Believe it or not: how much can we rely on published data on potential drug targets?1,885 cited
  • 2014Increasing value and reducing waste in research design, conduct, and analysis1,597 cited
  • 2014Reproducibility in Science1,031 cited
  • 2010The Open Provenance Model core specification (v1.1)706 cited
  • 2015Big data provenance: Challenges, state of the art and opportunities100 cited
  • 2019Sharing interoperable workflow provenance: A review of best practices and their practical application in CWLProv81 cited
  • 2017A Templating System to Generate Provenance52 cited
  • 2014Time to do something about reproducibility44 cited
  • 2011Provenance for MapReduce-based data-intensive workflows39 cited
  • 2018Enabling precision medicine via standard communication of HTS provenance, analysis, and results37 cited
  • 2013Implementing interoperable provenance in biomedical research33 cited
  • 2021Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles31 cited

Cited by

  • 2023Provenance of specimen and data – A prerequisite for AI development in computational pathology9 cited

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