Skill UI

Provides comprehensive guidelines for constructing self-contained, highly legible tables and figures for population and development research manuscripts. It emphasizes that exhibits must communicate not only raw demographic patterns but also their broader development and policy significance to a wide, non-specialist readership (e.g., economists, environmental scholars). Key principles include showing uncertainty, using appropriate specialized graphics (e.g., population pyramids, survival curves), and maintaining data reproducibility.

This guide outlines the rigorous methodology for developing theoretical arguments in psychological review papers. Instead of presenting raw data, the focus is on deriving specific, falsifiable predictions from a model's core assumptions. Techniques involve confronting these predictions against existing published evidence and rigorously comparing them head-to-head with rival theoretical models, ensuring logical and quantitative soundness.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

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 best practices for structuring supplementary materials for scientific conferences, ensuring the main paper remains self-contained. It covers transforming raw data, failure logs, and robotic videos into credible evidence annexes. Key areas include anonymization of media, maintaining archive hygiene, and structuring appendices to deepen belief without being mandatory for understanding the core claims.

This guide outlines essential strategies for ensuring that complex physical or scientific measurements are fully reproducible by external parties, long after the original testbed is decommissioned. It mandates capturing detailed provenance data, including energy measurement methods, hardware specifications, firmware toolchains, environmental conditions, and ground-truth protocols, thus transforming raw data into verifiable, sharable scientific artifacts.

A comprehensive guide for authors submitting technical papers to top-tier conferences, detailing how to effectively partition content between the main paper body and supplementary artifacts. It emphasizes the critical rule that all decision-making evidence (e.g., headline results, key performance metrics) must reside within the limited page budget of the main submission, ensuring the work is judgeable even without accessing raw data.

A comprehensive guide for constructing non-empirical, theoretical figures for sociological theory manuscripts. It details how to design conceptual exhibits—including mechanism diagrams, process models, typologies, and concept maps—that carry theoretical work. Crucially, it emphasizes that these figures must illustrate mechanisms or classifications, and must never include raw data, regression tables, or empirical results.

This guide provides rigorous guidelines for integrating various forms of empirical evidence—including quantitative data, qualitative case studies, and historical comparisons—into advanced social science manuscripts. It emphasizes that evidence must actively support or test the core proposition, requiring authors to translate raw findings into conceptual insights and mechanisms, rather than simply presenting methods or data.

This comprehensive guide outlines the rigorous standards required for achieving experimental reproducibility in systems research papers (e.g., VLDB). It instructs authors on disclosing critical context, including hardware specifications, software configurations, data provenance, workload patterns, and handling measurement variance. Adhering to these guidelines ensures that all performance claims are fully traceable from raw data to published figures, meeting the highest standards of academic review.

A comprehensive guide for ensuring the scientific reproducibility of research papers that rely on dynamic web data, such as web crawls, platform APIs, or live system traffic. It defines three reproducibility regimes (Frozen, Decaying, Unreplayable) and provides detailed instructions on documenting data decay, temporal splits, environment configurations, and computational seeds to maintain the rigor of published findings.