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    Data Literacy

    Data Literacy

    - How to Make Your Experiments Robust and Reproducible

    Af
    Bog E-bog
    • Format
    • Bog, paperback
    • Engelsk

    Beskrivelse

    Data Literacy: How to Make Your Experiments Robust and Reproducible provides an overview of basic concepts and skills in handling data, which are common to diverse areas of science. Readers will get a good grasp of the steps involved in carrying out a scientific study and will understand some of the factors that make a study robust and reproducible.The book covers several major modules such as experimental design, data cleansing and preparation, statistical analysis, data management, and reporting. No specialized knowledge of statistics or computer programming is needed to fully understand the concepts presented. This book is a valuable source for biomedical and health sciences graduate students andresearchers, in general, who are interested in handling data to make their research reproducibleand more efficient.

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    Detaljer
    • SprogEngelsk
    • Sidetal282
    • Udgivelsesdato11-09-2017
    • ISBN139780128113066
    • Forlag Academic Press Inc
    • FormatPaperback
    Størrelse og vægt
  • Vægt590 g
    • coffee cup img
    10 cm
    book img
    19,1 cm
    23,5 cm

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    Metadata Placebo Power Copyright Data mining Neural networks Ontology. Replication Terminology Indexing Calibration Central limit theorem Workflow Hypothesis Decision Trees Validity Semantic Web Binomial distribution Proxy Monty Hall problem Bias Data pubmed Ordinal Likelihood Statistical Significance Findings Linear regression Random error Margin of error Metaanalysis Missing data Blocking Conclusion Paradigms Conditional probability Ratio K-means Clustering Multiple Regression T-test Threshold Research program Ranks Logistic regression Data Transformation Permutation tests Stratification Accuracy Outcome measures Principal component analysis Mann-Whitney U Test Degrees of freedom Normal distribution Median Interval Nominal Rule» RDF Altmetrics Data Cleansing Outliers Precision Robust Censored Data Balancing Generalizability Systematic review Overfitting Randomization Factorial design Deep learning: Data Provenance Version control Systematic error Sample Effect size Bin Unsupervised learning Supervised learning Prior probability Creative Commons Research question Blinding Open Access Outlier Mean Null hypothesis Pearson correlation coefficient Data Normalization Narrative review Exponential Decay 95% confidence region 10-fold cross-validation arXiv A priori probability Best-fit line Bonferroni method ARRIVE guidelines Assumption controls bioRxiv Bayes' Bayesian inference Benjamini-Hochberg method Chi-square distribution Coefficient of variation Confounds Background variable CONSORT guidelines Corresponding author Ceiling effect Conservative test Cumulative distribution Electronic laboratory notebook (ELN)Senior author categorical Exact test Factorial ANOVA External reference standard Gold access Green access Data fishing Derived measure Digital object identifier (DOI)�Glue� Dummy variables Intersystem controls Institutional Review Board (IRB)Metadata Hypergeometric distribution Lily-pad science Inferred measure F-distribution File drawer problem Linear correlation Geomean Percentiles p-hacking Post hoc power analysis Impact factor (IF)Medical Subject Headings (MeSH)MEDLINE Prospective power estimation Quasinormal Post hoc analysis PLoS One Implicit information Relative citation ratio (RCR)Repeated measures quantiles Power law decay Primary measure HARKing reproducible Sampling Distribution Statistically significant difference Support vector machines (SVM)Topic modeling Simpson's Paradox Strong inference Imputed Data Standard deviation (SD)Standard error of the mean (SEM)Standard form Sign Test Type II error z-scores Sum of squares (SS)Variance Wilcoxon signed-rank test Logit transformation Prosecutor's Fallacy Posterior probability Programmatic science Pseudorandomization PubMed Central Paired ANOVA Type I Error Quartiles Two-way ANOVA
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