Nature Portfolio aims to enhance the reproducibility of published research through structured reporting guidelines. This section outlines specific requirements for statistical analyses, software and code availability, data availability, and detailed reporting on materials, experimental systems, and methods.
- **Statistics**: Ensure that all statistical analyses include key details such as sample size, type of measurements, statistical tests used, covariates, assumptions, and parameters. For null hypothesis testing, provide test statistics, effect sizes, and P values. For Bayesian analysis, include prior choices and Markov chain Monte Carlo settings. For complex designs, specify the appropriate level for tests and outcomes.
- **Software and Code**: Use Python 3.10 for data collection and analysis, with specific packages like openai, transformers, numpy, pandas, scipy, and statsmodels. All code must be available to editors and reviewers, preferably deposited in a community repository like GitHub.
- **Data Availability**: All manuscripts must include a data availability statement, providing access codes, unique identifiers, or web links for publicly available datasets. Describe any restrictions on data availability, especially for clinical or third-party data. Publicly available datasets are provided for various studies and corpora.
- **Materials & Experimental Systems**: Disclose the involvement of specific materials, systems, or methods in the study, such as antibodies, cell lines, animals, clinical data, plants, and novel plant genotypes.
- **Methods**: Provide details on methods used, including ChIP-seq, flow cytometry, MRI-based neuroimaging, and other relevant techniques.
- **Plants**: Report on seed stocks, novel plant genotypes, and authentication procedures, including collection details, transformation methods, and assessment of mutations and secondary effects.
For more detailed information, refer to the [nature.com/documents/nr-reporting-summary-flat.pdf](nature.com/documents/nr-reporting-summary-flat.pdf).Nature Portfolio aims to enhance the reproducibility of published research through structured reporting guidelines. This section outlines specific requirements for statistical analyses, software and code availability, data availability, and detailed reporting on materials, experimental systems, and methods.
- **Statistics**: Ensure that all statistical analyses include key details such as sample size, type of measurements, statistical tests used, covariates, assumptions, and parameters. For null hypothesis testing, provide test statistics, effect sizes, and P values. For Bayesian analysis, include prior choices and Markov chain Monte Carlo settings. For complex designs, specify the appropriate level for tests and outcomes.
- **Software and Code**: Use Python 3.10 for data collection and analysis, with specific packages like openai, transformers, numpy, pandas, scipy, and statsmodels. All code must be available to editors and reviewers, preferably deposited in a community repository like GitHub.
- **Data Availability**: All manuscripts must include a data availability statement, providing access codes, unique identifiers, or web links for publicly available datasets. Describe any restrictions on data availability, especially for clinical or third-party data. Publicly available datasets are provided for various studies and corpora.
- **Materials & Experimental Systems**: Disclose the involvement of specific materials, systems, or methods in the study, such as antibodies, cell lines, animals, clinical data, plants, and novel plant genotypes.
- **Methods**: Provide details on methods used, including ChIP-seq, flow cytometry, MRI-based neuroimaging, and other relevant techniques.
- **Plants**: Report on seed stocks, novel plant genotypes, and authentication procedures, including collection details, transformation methods, and assessment of mutations and secondary effects.
For more detailed information, refer to the [nature.com/documents/nr-reporting-summary-flat.pdf](nature.com/documents/nr-reporting-summary-flat.pdf).