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Data science for the scientific life cycle
8 references·0 citing·36 second hop
Data science for the scientific life cycle (2019)Data science for the scientific life cycleAutomating Sciences: Philosophical and Social Dimensions (2018)Automating Sciences: Philosop…Algorithmic Bias (2016)Algorithmic BiasFive selfish reasons to work reproducibly (2015)Five selfish reasons to work …Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention (2014)Publication and other reporti…Data‐driven hypotheses (2012)Data‐driven hypothesesReproducible Research in Computational Science (2011)Reproducible Research in Comp…Towards Robot Scientists for autonomous scientific discovery (2010)Towards Robot Scientists for …Robust and Efficient Designs for the Michaelis–Menten Model (2003)Robust and Efficient Designs …Locally Optimal Designs for Estimating Parameters (1953)Experimental designs for estimating the kinetic parameters for enzyme-catalysed reactions (1979)Estimating Michaelis-Menten Parameters: Bias, Variance and Experimental Design (1982)Support points of locally optimal designs for nonlinear models with two parameters (2009)Optimal designs for the emax, log-linear and exponential models (2010)Minimax optimal designs via particle swarm optimization methods (2014)Scientific Discovery: Computational Explorations of the Creative Processes (1987)Functional genomic hypothesis generation and experimentation by a robot scientist (2004)The Automation of Science (2009)Design of experiments‐based high‐throughput strategy for development and optimization of efficient cell disruption protocols (2016)Computational Scientific Discovery (2017)Globus automation services: Research process automation across the space–time continuum (2023)The file drawer problem and tolerance for null results. (1979)The case for motivated reasoning. (1990)Trim and Fill: A Simple Funnel‐Plot–Based Method of Testing and Adjusting for Publication Bias in Meta‐Analysis (2000)Top 10 Replicated Findings From Behavioral Genetics (2016)CONSORT-SPI 2018 Explanation and Elaboration: guidance for reporting social and psychological intervention trials (2018)Sample size evolution in neuroimaging research: An evaluation of highly-cited studies (1990–2012) and of latest practices (2017–2018) in high-impact journals (2020)Promoting an open research culture (2015)Tools and techniques for computational reproducibility (2016)Packaging Data Analytical Work Reproducibly Using R (and Friends) (2017)Science as behaviour: Using a behaviour change approach to increase uptake of open science (2019)Creating clear and informative image-based figures for scientific publications (2021)Fairness through awareness (2012)Big Data's Disparate Impact (2014)Certifying and Removing Disparate Impact (2015)FA*IR (2017)An Overview of End-to-End Entity Resolution for Big Data (2020)Climate Change, Environmental Disasters, and Health Inequities: The Underlying Role of Structural Inequalities (2022)I.—COMPUTING MACHINERY AND INTELLIGENCE (1950)The Structure of Scientific Revolutions (1963)The Structure of Scientific Revolutions (1963)Scientometrics for management of science: collaboration and knowledge structures and complexities in an interdisciplinary research project (2021)Increasing value and reducing waste in research design, conduct, and analysis (2014)Reproducibility in Science (2014)An introduction to Docker for reproducible research (2015)
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References

  • 2011Reproducible Research in Computational Science1,446 cited
  • 2014Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention471 cited
  • 2016Algorithmic Bias334 cited
  • 2010Towards Robot Scientists for autonomous scientific discovery190 cited
  • 2015Five selfish reasons to work reproducibly108 cited
  • 2003Robust and Efficient Designs for the Michaelis–Menten Model82 cited
  • 2012Data‐driven hypotheses35 cited
  • 2018Automating Sciences: Philosophical and Social Dimensions28 cited

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