Data Charting in a Scoping Review: Template, Examples and JBI Best Practices

What is data charting in a scoping review?

A scoping review systematically identifies and maps the evidence on a topic, often across many kinds of sources, to show what exists, how it has been studied and where the gaps are, rather than to judge whether something works (Peters et al., 2020). Charting the data is how that map gets built. In Arksey and O'Malley's (2005) framework, this is the fifth stage, after identifying the question, searching, and selecting sources: you review each included source and record the same set of details in a structured form.

The terminology varies. Arksey and O'Malley (2005), Levac et al. (2010), and the PRISMA-ScR reporting checklist (Tricco et al., 2018) call the stage data charting. Current JBI guidance uses data extraction for consistency with other evidence syntheses, while noting that scoping reviews extract at a higher, more descriptive level than systematic reviews do (Pollock et al., 2023). Either term is acceptable; state which framework you follow and use its language consistently.

New to scoping reviews? If you are new to scoping reviews, start with our step-by-step guide to how to do a scoping review, and check that a scoping review is the right design with our comparison of systematic reviews, meta-analyses and scoping reviews.

Data charting vs data extraction in a systematic review

Charting looks similar to systematic review data extraction, but its purpose differs, which changes what you record.

How scoping review charting differs from systematic review extraction

Pollock et al. (2023) urge caution about charting the results of included studies, such as effect sizes or qualitative themes. Because scoping reviews do not appraise study quality or pool results, presenting those findings invites readers to draw conclusions about effectiveness that the review cannot support. If your question needs results like these, a systematic review is usually the better design.

What to chart: building your data charting form

Chart only what helps answer your review questions (Pollock et al., 2023). The simplest way to decide is to start from the PCC elements that define your eligibility criteria and add the items your objectives require: methods if you are mapping how a topic has been studied, definitions if you are clarifying a concept, intervention details if you are mapping what has been tried.

Scoping review data charting template

The fields below cover most scoping reviews. Copy them into a spreadsheet, one row per source and one column per field, then add the items specific to your questions. The example values come from a fictional review of digital mental health tools for university students.

Data charting form template with example entries

Alongside the form, write a short guidance sheet that defines every field, gives examples and says what to enter when information is missing (for example, "not reported"). Pollock et al. (2023) recommend this so that every reviewer charts in the same way. You can build both quickly with our free data extraction form builder.

Chart against a framework: Frameworks make charting precise. If you are mapping how interventions are described, for example, chart each item of a reporting checklist as fully, partly or not reported for every source, which turns a vague impression into countable data (Pollock et al., 2023).

How to chart the data: step by step

1. Plan charting in your protocol. Describe the charting approach and include a draft charting form that goes beyond the PCC headings. JBI's protocol guidance asks for a planned, topic-specific form and a description of how the data will be analysed and presented for each review question (Peters et al., 2022).

2. Write the guidance sheet that defines each field.

3. Pilot the form. Each reviewer charts two or three sources of every type you have included (primary studies, reviews, guidelines and so on), then the team compares results. Ask whether anything is missing, redundant or unclear, and how long each source took, so you can plan the remaining time (Pollock et al., 2023).

4. Agree the final form in a team meeting and update the guidance sheet.

5. Chart independently. Best practice is two reviewers charting every source independently and resolving differences by discussion. If that is not feasible, one reviewer can chart while a second checks all or a sample of the entries (Pollock et al., 2023). Levac et al. (2010) also recommend that the team chart iteratively and meet early to discuss uncertainties.

6. Contact authors when key information is missing or unclear, and note it.

7. Update the form if you need to. Charting is iterative in a scoping review: you may discover useful items you did not anticipate. Add them, chart them for all sources, and report the change and the reason as a deviation from the protocol (Pollock et al., 2023).

Practical guides for nursing and midwifery teams stress the same points: rigour and transparency at the charting stage are what separate a credible scoping review from a loosely organised literature review (Pollock et al., 2021).

Analysing charted data: counts and basic qualitative content analysis

Scoping reviews are descriptive, so the analysis should be too. JBI guidance recommends frequency counts, tables and figures and, where appropriate, basic qualitative content analysis (Aromataris et al., 2024; Pollock et al., 2023). Plan the analysis for each review question in your protocol, in more detail than "descriptive statistics and a narrative summary" (Peters et al., 2022).

Frequency counts

Most charted items become counts and percentages: how many sources come from each country or year, how many used each design, how many measured each outcome. These need only a spreadsheet.

Basic qualitative content analysis

When a question asks you to identify characteristics, factors or definitions- for example, barriers and facilitators, or how a concept is defined, you may need to group text into categories. Pollock et al. (2023) describe basic qualitative content analysis in three phases: preparation (decide in the protocol whether you need it and whether to work inductively or deductively), organising (code and categorise the charted text), and reporting. Two approaches are possible:

• Inductive: read the charted text, code it openly, then build a coding framework of categories with definitions, which two reviewers apply and reconcile. Useful when little is known or when the review aims to inform a conceptual framework.

• Deductive: chart text directly into categories from an existing framework chosen in advance, noting anything that does not fit. If the framework turns out to fit poorly, you can switch to an inductive approach and report the change.

What a scoping review should not do is reinterpret the evidence with thematic synthesis, meta-aggregation or meta-analysis. Those methods answer questions about experiences or effectiveness and belong in systematic reviews (Pollock et al., 2023). This is different from reflexive thematic analysis of your own interview data.

Watch for double counting: If you include reviews as well as primary studies, the same study can be counted twice: once on its own and again inside a review. State how you handled overlap and which primary studies each included review contains (Pollock et al., 2023).

Presenting the results: tables, charts and evidence maps

Results are usually presented as a table of source characteristics plus figures that summarise the main patterns, each accompanied by a narrative that explains what the figure shows (Pollock et al., 2023). Large tables can go in a supplementary file, with the main text reporting summaries.

Four common ways to present charted data, using hypothetical results from 55 sources.

• Characteristics table: one row per source with citation, country, design, population and concept. Often the first table in the results.

• Bar or line charts: sources by year, country or design.

• Waffle or pie charts: the mix of source types.

• Geographic maps or heat maps showing where evidence was produced.

• Evidence and gap maps: a grid of, for example, interventions by outcomes, with the number of sources in each cell, which shows at a glance where evidence clusters and where it is missing (White et al., 2020). Build one from your charted data with our evidence gap map generator.

Writing the results section

A clear scoping review results section usually follows this order: the PRISMA-ScR flow diagram and number of sources included; the characteristics of the sources (year, country, design); then one subsection for each review question, each with its table or figure and a descriptive summary. Describe what the evidence covers and where it is thin, and avoid language that implies effectiveness, such as "the intervention improved anxiety".

Reporting data charting with PRISMA-ScR

PRISMA-ScR is the reporting guideline for scoping reviews (Tricco et al., 2018). Several items relate directly to charting: item 10 (the data charting process), item 11 (the data items charted), item 14 (how the charted data were summarised) and items 17, 18, 20 and 21, which cover the sources and their results; Pollock et al. (2023) list these as the items to check when writing up charting, analysis and presentation. Include the completed checklist, with page numbers, as a supplementary file, and our free PRISMA-ScR checklist can generate it.

Example methods paragraph (illustrative)

Data were charted using a standardised form developed from the review's population, concept and context and piloted by two reviewers on three sources of each evidence type. Two reviewers then charted all sources independently, resolving discrepancies by discussion with a third reviewer. Charted items included country, source type, population characteristics, intervention details, setting and outcomes measured. Two items (delivery format and cost) were added during charting and charted for all sources, a deviation from the protocol. Data were summarised as frequencies and percentages and, for barriers and facilitators, through deductive basic qualitative content analysis.

Software for charting scoping review data

Common tools for charting, analysing and presenting scoping review data

Pollock et al. (2023) list spreadsheets, qualitative analysis software and visualisation tools as suitable options; the best choice is the one your team already knows. Whatever you use, keep the charting file versioned so the final dataset matches what you report.

Common data charting mistakes

• Charting everything. Every extra field costs time across every source; chart to your questions.

• A form with only the PCC headings. Protocols need a detailed, topic-specific form (Peters et al., 2022).

• Skipping the pilot, which leads to inconsistent entries that are hard to count.

• Charting effect sizes and drawing conclusions about effectiveness without appraisal or synthesis.

• Using thematic synthesis or other interpretive methods instead of descriptive counts and basic content analysis.

• Undocumented changes to the form. Report additions as protocol deviations.

• Double counting studies that appear both individually and inside included reviews.

• Figures without a narrative, or a narrative that goes beyond what was charted.

Khalil et al. (2021) note that many of the difficulties scoping reviewers face, from protocol to publication, come from unclear methods; a well-documented charting stage solves several of them at once.

Getting help with your scoping review

If you would like experienced reviewers to help design your charting form, act as a second charting reviewer, analyse the charted data or build your evidence map, see our scoping review service. We follow JBI methods and report to PRISMA-ScR, and you keep ownership of your question and conclusions.