# Orgsci Tables Figures > Use when building the exhibits for an Organization Science manuscript — data-structure diagrams and process models for qualitative work, correlation/result tables and interaction plots for quantitative work, and simulation/network figures, all in INFORMS house style with self-explanatory titles and notes. - Skill: `thedixitjain/orgsci-tables-figures` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/orgsci-tables-figures` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/orgsci-tables-figures/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/orgsci-tables-figures --- # Tables, Figures & Exhibits (orgsci-tables-figures) ## When to trigger - Your exhibits are cluttered, off house style, or not self-explanatory - A reviewer cannot follow your qualitative data structure or process model - Your regression tables omit information readers need to evaluate the claim - A simulation or network figure does not communicate the mechanism ## House-style basics (INFORMS) - Number tables and figures **consecutively** with **self-explanatory titles and notes** — each exhibit should be legible without the running text. - Use **author-date** references in notes and captions (e.g., Norman 1977), consistent with the INFORMS author-date style. - Keep fonts to the Garamond/Times-New-Roman family the journal specifies; avoid Helvetica Narrow. Minimize footnotes. - Remember the **all-inclusive ~50-page norm**: exhibits count toward length. Heavy supplementary tables belong in the **separate anonymized appendix** that is submitted standalone for review and later posted as online supplementary material. ## Exhibits by method (the journal is eclectic — build what your design needs) - **Qualitative / inductive.** A **Gioia-style data structure** (first-order codes → second-order themes → aggregate dimensions), a **process model** diagram, and a **representative-quotation table** keyed to themes — often the paper's centerpiece, carrying the analysis's trustworthiness. - **Quantitative.** A descriptives/correlation table (reliabilities on the diagonal), nested regression tables that build the model, and **interaction plots with simple slopes**; for multilevel models, variance components and ICCs. - **Event-history / panel.** Survival curves or hazard-ratio tables with the time structure explicit. - **Computational / formal.** Figures of the qualitative pattern across parameter ranges with sensitivity panels and a parameter table; for formal models, a comparative-statics table. - **Network.** A clear sociogram or block structure with the construct (brokerage, cohesion) annotated, not a hairball. ## Quality checks - Every exhibit is anonymized (no author-identifying site names where they would breach blinding). - Notes define all abbreviations, significance conventions, units, and N. - The main-text exhibits tell the core story; the rest go to the standalone appendix. - A reader who sees only the exhibits could reconstruct the argument. ## Execution bridge (StatsPAI / Stata MCP) Generate exhibits from the fitted result, not by retyping numbers (the usual source of body-vs-appendix drift). Full map: [`execution-with-mcp`](../../../shared-resources/empirical-methods/execution-with-mcp.md). Org Science spans field studies, experiments, and computational/qualitative work; the chain below is for its empirical/causal lane — simulation and qualitative work are outside it. - **Tables:** `etable` (multi-model columns) or `did_summary_to_latex` straight from the `result_id`. - **Figures:** `plot_from_result` / `enhanced_event_study_plot` / `event_study_table` — axis units and the SE/clustering note baked in. - **Every note** names the estimator + clustering and states the effect size in interpretable units. See a full fitted-result → exhibit chain in the [JF execution walkthrough](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md). ## Anti-patterns - A data structure that lists codes but shows no path to aggregate dimensions. - Regression tables with stars but no effect sizes, CIs, or model-build logic. - A network "hairball" with no annotated structure. - Overloading the main text and blowing the ~50-page norm instead of using the appendix. ## Exhibit pass for Organization Science Treat this skill as an executable review pass, not a prose hint. First lock a level map, a mechanism paragraph, and the cover-letter contribution statement; then judge whether the current manuscript answers the venue's real reader: interdisciplinary organization reviewers who ask whether the mechanism travels across levels of analysis. - **Do the pass:** For every table or figure, state the estimand or object, sample or case base, uncertainty display, and one sentence the exhibit proves for the venue audience. - **Return a ledger:** give `claim / evidence / risk / manuscript location` rows, so the next agent can edit rather than rediscover the issue. - **Sibling guard:** compare against AMJ for empirical management framing, ASQ for organization-theory depth, Management Science for formal/quantitative operations; if a sibling owns the contribution, recommend re-routing before polishing format. - **Submission-ready gate:** do not give final advice until the pack's `resources/official-source-map.md` has been checked for upload-week rules and the manuscript has one concrete fix for the largest venue-specific risk. ## Output format ``` 【Centerpiece exhibit】data structure + process model / nested tables / sensitivity figure 【House style】numbered, self-explanatory titles+notes, author-date, no Helvetica Narrow 【Length plan】main-text exhibits vs. standalone anonymized appendix 【Anonymization】site/author identifiers handled 【Next step】orgsci-writing-style ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Organization-Science-Skills/skills/orgsci-tables-figures/SKILL.md`