Skill UI

This guide provides authors with best practices for structuring scientific papers, specifically focusing on the division of content between the main body (The Letter) and the Supplemental Material (SM). It emphasizes the 'stand-alone' rule, ensuring the core argument remains fully understandable without consulting the SM, while effectively organizing detailed derivations, extended datasets, and secondary controls in the appendix.

A comprehensive guide on preparing and structuring evidence packages for recommender system research, especially for academic submissions. It details how to ensure that all ranking claims, off-policy estimates, and evaluation metrics are end-to-end reproducible by providing artifacts like datasets, split scripts, model checkpoints, and propensity logs in an anonymous, verifiable repository.

A comprehensive guide detailing best practices for achieving high reproducibility in recommender system research. It covers critical areas such as dataset version pinning, defining symmetric tuning protocols, selecting full-ranking metrics, handling non-determinism via multiple seeds, and structuring rigorous divergence analysis for academic publications.

A comprehensive guide detailing how to make systematic reviews and meta-analyses fully auditable and reproducible for academic publishing. It emphasizes adhering to standards like PRISMA and MARS, quantifying inter-coder reliability (e.g., Kappa/ICC), and sharing all open materials, including search strings, coding datasets, and analysis code (via OSF/DOI).

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 guide details the comprehensive policy for preparing research replication packages for academic journals, especially in finance. It covers structuring code, handling proprietary data (pseudo-datasets, logs), drafting Data Availability Statements, and ensuring full traceability of results for successful publication.

A comprehensive guide for authors in data systems and related fields on achieving rigorous, verifiable reproducibility for academic papers. It mandates detailing the provenance of every claimed result by pinning five critical layers: code version, configuration parameters, dataset source, workload characteristics, and hardware specifications. Best practices include reporting distributions (e.g., percentiles, variance) and maintaining a detailed 'repro debt ledger' to withstand intense peer review.

This comprehensive guide outlines best practices for creating and submitting supplementary materials for top-tier academic conferences. It addresses the critical challenges of maintaining strict anonymity, adhering to page limits, and structuring extended proofs, code links, and datasets across multi-round, double-anonymous review cycles.

This guide provides deep insights into the requirements for publishing in top-tier sociological journals. It emphasizes shifting the focus from mere econometric policy evaluation or variable listing to profound sociological problem consciousness and theoretical dialogue. It guides authors on structuring articles that engage with core social mechanisms, whether using large-scale quantitative datasets or rich qualitative ethnographic material.

A comprehensive guide for researchers submitting academic papers, detailing the standards required for scientific reproducibility. It covers fixing experimental environments (toolchains, datasets, hardware), differentiating result sources (simulation vs. physical measurement), statistical reporting best practices, and providing clear guidelines for code and data availability statements.

A comprehensive guide detailing how to package and present research artifacts—including code, hardware designs, and datasets—for major academic conferences. It outlines best practices for both initial review-time evidence and long-term public reuse, ensuring reproducibility and maximizing the chances of adoption by future builders.

This guide covers the mandatory Open Science appendix for high-level security research. It instructs authors on enumerating all artifacts (code, datasets, configurations), documenting data withholding decisions, and implementing engineering controls (like environment pinning and version tracking) to ensure that published results are independently and reliably reproducible.