Skill UI

This comprehensive guide outlines the standardized procedures and required repositories for ensuring data and code availability in scientific publications, particularly in molecular biology. It details where to deposit various data types (e.g., sequencing, structures, proteomics) in approved archives (GEO, PDB, PRIDE, Zenodo), provides the mandatory standardized statement format, and emphasizes the need to obtain accessions/DOIs before submission to ensure reproducibility.

Provides comprehensive guidelines for meeting high-level scientific publication standards, detailing the mandatory deposition of raw data (sequences, structures, datasets) and analysis code into approved public repositories. Learn how to obtain persistent identifiers (DOI/Accession) and ensure the reproducibility and integrity of your research findings for journals like PNAS.

This guide outlines the mandatory standards for open science publication, detailing how researchers must deposit all data, code, and raw materials (images, sequences) in public, archived repositories (like Zenodo, GEO, PDB). It emphasizes the need for DOIs/accession numbers and prohibits relying solely on "available on request" statements for primary findings.

This guide outlines the rigorous requirements for preparing a reproducible package for Quantitative Economics (QE) journal submissions. It details the strict standards set by the Econometric Society Data Editor, covering raw data, code, documentation, and handling complex data exemptions to ensure the submission passes the pre-acceptance reproducibility check (DCAS compliant).

Mandatory package structure for submitting accepted empirical papers to the Quarterly Journal of Economics (QJE). It ensures complete research reproducibility by organizing raw data, derived files, and analysis code. This package guides authors in creating a comprehensive, self-contained deposit that allows any investigator to regenerate every figure, table, and result published in the manuscript, adhering to strict academic data-availability policies.

This guide details the strategic placement of supplementary evidence (e.g., tuning grids, ablation studies, code) for academic submissions, particularly in conference formats like RecSys. It clarifies the difference between material belonging in the main body, the page-counted appendix, and the anonymous repository, ensuring that critical findings are visible and adhere to strict page budget constraints.

This guide establishes the rigorous policy for submitting computational and empirical papers to academic journals. It mandates that authors provide sufficient data, source code, and detailed documentation (including execution order, random seeds, and OS used) to ensure that research findings can be fully reproduced by others, adhering to global standards like Option C.

This guide details how to assemble a submission-ready replication package for top academic journals, such as The Review of Economic Studies (REStud). It covers the necessary repository structure, mandatory README sections, and coding best practices (e.g., master scripts, relative paths, version pinning) to ensure that the data and code can be fully reproduced and verified by journal editors before publication.

This comprehensive guide outlines the rigorous standards for creating publication-grade tables and figures for top-tier academic journals, such as The Review of Economic Studies. It emphasizes the principles of elegance, economy, and self-containment. Authors must ensure that every exhibit carries one single, clear result, presents interpretable magnitudes (not just stars), and is generated programmatically from code to guarantee reproducibility and consistency.

Provides comprehensive guidelines for creating publication-grade, self-contained tables and figures for top finance journals, such as The Review of Financial Studies (RFS). It covers best practices for rigorous statistical reporting, emphasizing standard error reporting, economic magnitude, and ensuring that all presented results are fully reproducible from the source code.

A comprehensive guide for authors submitting manuscripts to the RAND Journal of Economics (RJE). It details how to build a robust, reproducible replication package, focusing on creating a master script, pinning the computational environment, and properly documenting restricted datasets. It specifically addresses RJE's unique policy, which does not mandate formal data or code deposits, while ensuring academic rigor is maintained for reviewers.

This skill bridges theoretical econometric designs (DiD, IV, RDD, SCM, DML) to concrete, audited statistical estimates. It orchestrates the necessary tool calls to fit models, generate comprehensive diagnostics, and report reliable findings, ensuring that every academic claim is backed by executable, traceable code. Essential for advanced causal inference research.