Publish a Research Data Table Readers Can Verify and Cite
Give each number a visible unit, denominator and source. Build the summary from the same records you describe in the method, keep failed observations separate from negative results, and publish the table alongside its scope, version and limits. The copyable example below includes all ten fictional input records so you can check every total before adapting the structure to real research.
Copy the complete HTML table example
Save this as table-example.html and open it in a browser, or adapt the table and notes inside your CMS. It contains fictional teaching data; replace the records, captions and method together before using it for real findings.
<!doctype html> <html lang="en"> <head><meta charset="utf-8"><meta name="viewport" content="width=device-width, initial-scale=1"><title>Fictional integration-page table — teaching example</title></head> <body> <main> <h1>Do these integration pages state their plan requirements?</h1> <p><strong>Fictional teaching example, version 1.0, September 12, 2026.</strong> No real websites, customers or AI answers were measured.</p> <p id="method">Unit: one nominated integration page, one English signed-out page variant, one review window. Include public integration pages; exclude unpublished drafts and private portals. A minimum-plan or explicit all-plans statement qualifies. A saved page without that statement is No; a failed capture is not assessed. Native and Partner are supplied, mutually exclusive labels. The full fictional input ledger follows.</p> <p>10 nominated; 2 excluded; 8 eligible; 7 observed; 1 not observed.</p> <div style="overflow-x:auto" tabindex="0" role="region" aria-label="Plan requirements table"> <table aria-describedby="method calculation limits"> <caption>Plan requirements in the fictional selected pages; percentages use observed pages</caption> <thead><tr><th scope="col">Group</th><th scope="col">Eligible pages</th><th scope="col">Observed pages</th><th scope="col">Plan stated</th><th scope="col">Not stated</th><th scope="col">Not observed</th><th scope="col">Share of observed</th></tr></thead> <tbody> <tr><th scope="row">Native</th><td>5</td><td>4</td><td>3</td><td>1</td><td>1</td><td>75.0%</td></tr> <tr><th scope="row">Partner</th><td>3</td><td>3</td><td>1</td><td>2</td><td>0</td><td>33.3%</td></tr> <tr><th scope="row">Total</th><td>8</td><td>7</td><td>4</td><td>3</td><td>1</td><td>57.1%</td></tr> </tbody> </table> </div> <p id="calculation">Share = Plan stated / Observed pages × 100, rounded to one decimal. Zero observed pages means Not calculable. Totals use counts from the same protocol, not the mean of group percentages.</p> <p id="limits">This constructed example teaches reporting. It does not estimate an industry rate, compare product quality, show a trend or establish an SEO or AI-citation effect. Missing observations and selection limits remain visible.</p> <div style="overflow-x:auto" tabindex="0" role="region" aria-label="Fictional input ledger"> <table> <caption>Complete fictional input ledger; Yes and No apply only to observed pages</caption> <thead><tr><th scope="col">ID</th><th scope="col">Group</th><th scope="col">State</th><th scope="col">Plan stated</th><th scope="col">Record note</th></tr></thead> <tbody> <tr><th scope="row">I01</th><td>Native</td><td>observed</td><td>Yes</td><td>Minimum plan stated in the fictional capture.</td></tr> <tr><th scope="row">I02</th><td>Native</td><td>observed</td><td>Yes</td><td>Minimum plan stated in the fictional capture.</td></tr> <tr><th scope="row">I03</th><td>Native</td><td>observed</td><td>No</td><td>No explicit plan requirement in the fictional capture.</td></tr> <tr><th scope="row">I04</th><td>Native</td><td>observed</td><td>Yes</td><td>An explicit all-plans statement qualifies under this rule.</td></tr> <tr><th scope="row">I05</th><td>Native</td><td>failed</td><td>Not assessed</td><td>Eligible page; the fictional collection failed before content was saved.</td></tr> <tr><th scope="row">I06</th><td>Partner</td><td>observed</td><td>No</td><td>No explicit plan requirement in the fictional capture.</td></tr> <tr><th scope="row">I07</th><td>Partner</td><td>observed</td><td>Yes</td><td>Minimum plan stated in the fictional capture.</td></tr> <tr><th scope="row">I08</th><td>Partner</td><td>observed</td><td>No</td><td>No explicit plan requirement in the fictional capture.</td></tr> <tr><th scope="row">I09</th><td>Native</td><td>excluded</td><td>Not assessed</td><td>Unpublished draft; excluded by the declared public-page rule.</td></tr> <tr><th scope="row">I10</th><td>Partner</td><td>excluded</td><td>Not assessed</td><td>Private portal; excluded by the declared public-page rule.</td></tr> </tbody> </table> </div> <p>Source: <a href="https://rankecho.io/resources/fix/source-worthy-data-table">RankEcho instructional example</a>, version 1.0. All input records appear above. Preserve the fictional label when citing this example; replace the records and method together when adapting the structure to your own work. State your approved reuse terms on a real release.</p> </main> </body> </html>
What makes a data table worth citing?
It answers a useful question and lets a reader trace the answer back to evidence. For a SaaS team, that might be a narrow capability test, a documented content audit or an original study with a disclosed sample. A percentage without its count and population cannot carry the same meaning when copied into another article.
Google's helpful-content questions favor original information and clear sourcing. Use that as a reason to make your work inspectable. Do not manufacture a study, label compiled statistics as your own measurements or call a convenience sample an industry benchmark. If you only have an illustration, label it wherever the numbers appear.
What does the worked table show?
Fictional teaching example, version 1.0, September 12, 2026: a constructed list contains ten integration-page records, assigned to Native or Partner groups. The question is whether each captured page explicitly states a minimum plan or says all plans qualify. One unpublished draft and one private portal are excluded by the public-page rule. One eligible page has a failed capture; seven have supplied observations.
The unit is one nominated page in one English signed-out variant and one review window. The labels and observations are invented inputs, not captured websites, product tests, customer data or AI answers. The table illustrates a reproducible reporting decision.
| Group | Eligible pages | Observed pages | Plan stated | Not stated | Not observed | Share of observed |
|---|---|---|---|---|---|---|
| Native | 5 | 4 | 3 | 1 | 1 | 75.0% |
| Partner | 3 | 3 | 1 | 2 | 0 | 33.3% |
| Total | 8 | 7 | 4 | 3 | 1 | 57.1% |
How do you reconcile the counts?
Start with the nominated list: 10 minus 2 excluded equals 8 eligible pages. Of those, 7 are observed and 1 is not observed. The 7 observed pages split into 4 with an explicit plan statement and 3 without one. Therefore the observed share is 4/7 = 57.1%, rounded to one decimal. The failed capture remains unknown.
The Native row uses 3/4 = 75.0%; Partner uses 1/3 = 33.3%. Because these rows partition the same protocol, the total comes from 4/7. Averaging the two displayed percentages would give about 54.2% and answer a different question. Do not pool rows with incompatible units, selection rules or observation windows.
If no eligible page was observed, report Not calculable instead of 0%. A zero observed share requires at least one observed page and no qualifying statements. Keep counts visible even when a denominator is small, and do not interpret these fictional percentages as group differences in the market.
Which records produce each table cell?
The full ledger is inside the copied HTML. Yes and No are allowed only for observed pages; failed and excluded records are not assessed. The local evaluator rejects duplicate IDs, unknown states, missing notes and invalid combinations such as a failed capture marked No.
For real work, add each exact URL, capture date, saved evidence location, codebook version and reviewer decision. Define what makes two rows the same unit before collection. Repeated captures, URL aliases and syndicated pages may require different treatment depending on the question; do not silently count every file as a new observation.
| Record IDs | Contribution | Reason |
|---|---|---|
| I01, I02, I04, I07 | 4 stated; 4 observed | The supplied record contains an explicit plan or all-plans statement. |
| I03, I06, I08 | 3 not stated; 3 observed | The supplied captured content lacks that statement. |
| I05 | 1 not observed; still eligible | Collection failed before content was preserved. |
| I09, I10 | 2 excluded; outside the eligible denominator | Draft and private-portal exclusions were declared in the rule. |
What must the method note explain?
Write the method before promoting the headline number. A reader should know what was selected, what was measured and what could not be observed. Separate the data's collection window from the article's publication date. Describe any corrections or changes to the procedure.
W3C's data-publication recommendations cover provenance, quality information and version history. Keep a stable record of the original inputs and transformations so another reviewer can follow a displayed cell back to the source. If you cannot publish detailed records, explain the access limit and which parts someone else can actually reproduce.
| Field | Required decision for your own table |
|---|---|
| Question and unit | One precise question; one row-level unit and how repeats or aliases are handled. |
| Selection and dates | How the nominated list was assembled; inclusion/exclusion rules; collection window and relevant page or product context. |
| Coding rule | What counts as a positive, a negative and not assessed; how ambiguous records and reviewer disagreements are resolved. |
| Computation | Numerator, denominator, group rules, missingness and rounding; a script or an auditable formula trail. |
| Evidence and limits | Source locations, permissions and publication restrictions; the population the results describe and claims they cannot support. |
| Release identity | Responsible author, version/date, correction history, contact route and approved reuse terms. |
How should the table appear in HTML?
Use a real HTML table for values that have row and column relationships. Give it a short caption that names its subject, and put units and denominator language in the headers or an associated note. W3C explains that captions identify tables and scoped headers help associate values with headings. The copied example includes column and row scopes and connects the summary table to its method and limits.
Keep the table readable on small screens. The example wraps each table in a named, focusable scrolling region; a real site must still check keyboard use, zoom, contrast and assistive-technology behavior in its own design. Do not hide missingness or uncertainty only in a tooltip, image or collapsed note that disappears when the table is reused.
Publish useful table text in the page response and inspect the final delivered output after the CMS processes it. A PDF, screenshot or chart can complement the table, but a reader should still be able to inspect the exact values. For large datasets, pair a concise visible summary with an appropriate machine-readable file and data dictionary when you can publish them.
Does a research table need Dataset schema?
Decide from the actual material you publish. Google's Dataset guidance describes metadata for Dataset Search and includes tables and CSV files as possible datasets. Review its current required properties, access and validation instructions for a real dataset landing page. A familiar table shape alone does not validate the data or establish a research finding.
This page is an instructional article; its fictional example does not add Dataset markup. Keep any markup consistent with the visible name, description, creator, access and distribution information. Never claim a download, license or persistent identifier that you have not provided. Google also says its AI features need no special AI schema. Neither table markup nor eligibility guarantees a citation.
What should pass before you publish?
For this example, local checks recomputed the summary from the frozen inputs, retained excluded and failed records, and compared the complete copyable HTML with the reviewed artifact. A jsdom parser checked captions, header scopes, cell values and referenced note IDs; no real browser or screen reader was tested. Changed-input checks cover zero observations, real zero shares, duplicate records, invalid state/value pairs, row ordering and pooled counts. These checks establish internal consistency only; they do not validate source collection, human coding, a production CMS or search performance.
For your own release, have a reviewer trace selected cells to the saved evidence and reproduce the totals. Inspect the page after publishing, including headers, links, table structure and any download. Save the previous version and record corrections; avoid silently replacing data behind an unchanged version label.
| Check | Acceptance evidence |
|---|---|
| Arithmetic | Every group and total reconciles to the declared input records and denominator. |
| Interpretation | Headline, caption and excerpt preserve the population, dates, unit and limits. |
| Delivery | Visible HTML and any export agree; method and source pointers are reachable; mobile and keyboard review is recorded. |
| Source and reuse | Author, source locations, version, access restrictions and approved terms describe the actual release. |
| Maintenance | A responsible person, correction channel and version history support later updates. |
How do you turn the table into a useful page improvement?
Use the table where it answers the buyer's question, with a short explanation of the decision it supports. Package the page URL, approved data, method and proposed section for review. Inspect the sample fix and evaluate the paid Fix Engine for organizing a bounded page proposal with human review and manual shipment. Research design and dataset verification remain with your team.
Free audits check Perplexity and Gemini once per prompt. Paid and trialing accounts add ChatGPT, Claude, and Google AI Overviews when configured for the account, for up to 5 engines. RankEcho does not currently run Microsoft Copilot checks.
After the page is accurate and usable, use the corroboration plan for a relevant editorial handoff. Track search clicks, identified AI referrals and product actions separately in your own analytics. RankEcho does not imply a native GA4 or Search Console connection, and a later citation or conversion does not establish that the table caused it.
Frequently asked questions
No. They are constructed teaching records. Keep that label or replace the records and method with verified work that supports your stated population.
No. A failed capture leaves the content unobserved. Only a captured page can be coded positive or negative under this example's rule.
No. Zero qualifying pages among observed pages can yield 0%. With no observed pages, the share is not calculable.
A compact table needs enough method to interpret it. More complex study designs require additional documentation, data-quality review and appropriate analysis.
Sources reviewed
Provider eligibility and measurement claims below were checked against primary documentation. These records do not establish a universal selection formula, causation, or a guaranteed ranking, impression, recommendation, or citation.
6 claim-level source records
| Claim reviewed | Official source | Review record |
|---|---|---|
| Google's helpful-content guidance asks whether content provides original information, research or analysis and clear sourcing. | Google: helpful, reliable content | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · Supports publishing inspectable original work; it does not promise traffic for a table. · Confidence: High |
| W3C recommends documenting data provenance, quality, license information, version indicators and version history. | W3C: Data on the Web Best Practices | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · The publication checklist adapts these data-management recommendations; the example and arithmetic are original. · Confidence: High |
| W3C's table tutorial explains how captions identify tables and help readers understand their subject. | W3C WAI: captions and summaries | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · The copyable HTML includes captions and associated method notes; no screen-reader usability study was performed. · Confidence: High |
| W3C explains using header cells and scope to associate ambiguous or larger table cells with their headings. | W3C WAI: table headers | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · The copied example uses explicit column and row scopes. Structural checks do not establish complete accessibility conformance. · Confidence: High |
| Google's Dataset guidance describes metadata for Dataset Search and includes tables and CSV files among possible datasets. | Google: Dataset structured data | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · This teaching article does not add Dataset markup or claim Dataset Search inclusion; readers must evaluate their actual dataset and requirements. · Confidence: High |
| Google's AI features use normal Search eligibility and require no special AI schema; eligibility does not guarantee appearance. | Google: AI features and your website | Checked 2026-09-12 · Primary documentation reviewed September 12, 2026 · Publishing a clear table is a content improvement, not evidence of provider selection or business impact. · Confidence: High |