Skill UI

A comprehensive guide for analyzing and reporting longitudinal data in Developmental Psychology manuscripts, ensuring compliance with high academic standards. It emphasizes modeling developmental change (e.g., growth curves, multilevel models, SEM), establishing measurement invariance (configural, metric, scalar) before interpretation, and rigorously reporting effect sizes and confidence intervals (CIs) using JARS guidelines. It guides the distinction between confirmatory and exploratory findings.

A comprehensive guide for preparing research artifacts (code, data, prompts, models) for EACL submissions. It details the two stages: an anonymized supplement for the initial review (ARR) and a fully licensed, public release for post-acceptance. Emphasis is placed on reproducibility, responsible NLP practices, and proper documentation.

This skill guides authors on submitting original, high-quality, and reusable Earth System datasets (observations, reanalysis, model outputs) as primary publications. It assesses whether the work fits the criteria of a 'data paper,' requiring open deposition in a FAIR repository with a persistent DOI, detailed provenance documentation, and rigorous quality control. It serves as a venue-selection and manuscript re-framing tool.

Comprehensive guidelines for structuring and submitting reproducible AI/ML research papers for top-tier conferences. It details two modes: Mode 1 for theoretical/KR work (requiring full, explicit proofs in the supplement), and Mode 2 for empirical/ML work (requiring fixed seeds, cached outputs, and rigorous provenance pinning for datasets, models, and environments). The guide also covers best practices for maintaining double-blind anonymity and properly dividing content between the main body and the supplementary material.

This guide provides essential structural advice for authors writing high-impact research papers for top-tier AI conferences. It covers the 'first-page arc,' ensuring the contribution is generalizable (not model-specific), matching evidence types (proofs vs. empirical data) to claims, and maintaining narrative density over a limited page count. Perfect for researchers preparing submissions.

This guide provides a detailed strategy for managing and releasing research artifacts (code, models, data) associated with a vision paper submitted to ECCV. It covers two critical phases: the sealed, anonymous archival submission during review, and the structured public release runway post-acceptance, ensuring reproducibility and adherence to conference policies.

A comprehensive guide detailing the multi-cycle, rolling model for EDBT research submissions. It covers the entire review pipeline, from initial program committee evaluation and author-feedback exchange, through the accept/revise/reject decision, and critically, the in-cycle revise-and-resubmit stage. The guide also contrasts EDBT's open-access, three-cycle cadence with other top-tier database conferences (e.g., SIGMOD, VLDB).

A comprehensive project management guide for submitting database systems papers to EDBT. It details the complexities of the multiple-cycle rolling publication model, covering everything from initial evidence lock and cycle selection to the five-step review arc (Submission, Author Feedback, Revision, etc.). Use this to backward-plan your research timeline and manage the entire academic publishing process.

A comprehensive guide for researchers submitting NLP papers, detailing best practices for achieving high reproducibility. It instructs users to pin model versions, log data provenance, report compute costs, and adhere rigorously to the Responsible NLP checklist. Essential for ensuring academic integrity and enabling rigorous verification of ML experiments.

This guide provides detailed reporting norms for reducing and analyzing complex Earth and Planetary Science Letters (EPSL) data, specifically focusing on isotope geochemistry and geochronology. It mandates full uncertainty budgeting, proper statistical interpretation (e.g., MSWD, weighted means), and thorough model diagnostics to ensure results are scientifically defensible and reproducible.

A comprehensive guide for planning rigorous scientific studies in Earth and Planetary Sciences, covering sampling, analytical campaigns, experimental runs, and modeling. It ensures that research designs are robust, addressing critical elements like sample context, traceability to reference materials, replication levels, and falsifiability before data collection begins. Essential for high-impact scientific publishing.

A comprehensive guide for conducting advanced, methodologically rigorous data analysis for manuscripts in Entrepreneurship Theory and Practice (ETP). It covers specialized techniques such as survival analysis, handling endogeneity (IV, selection bias), advanced panel data models (Callaway–Sant'Anna), and rigorous qualitative coding schemes. The goal is to ensure that findings are theoretically robust and translate into actionable, venture-relevant insights, moving beyond simple p-value reporting.