OmniDocExtracting Research Data from Papers for Reviews
Learn how to collect data points from research papers for a systematic review, export tables, and keep coding a manual, researcher-led step.
Data extraction in research is the slow part of a systematic review. You open dozens of papers, hunt for the sample size, the outcome measures and the effect estimates, then copy each value into a shared form. The work gets harder when the papers arrive in mixed formats: some are scanned, some are written in other languages, and some split a single table across two pages.
This guide walks through a practical workflow for that job. You will set up your collection in a Zotero-connected workspace, use OmniDoc to translate and export tables, and keep the coding itself under your team's control. The tools handle the reading and the copying. Your protocol and your judgment handle the decisions.
How do you extract research data for a review? The short answer
Write your extraction protocol first, gather the papers in Zotero, and use OmniDoc to translate and export the tables you need. Then have your team code each value by hand against the protocol, checking it on the source page.
| Your situation | What to do | Why |
|---|---|---|
| Your papers are already in Zotero | Open them in the Zotero-connected literature workspace in the OmniDoc desktop client to find and translate them | Keeps references and PDFs in one place |
| The PDF text can be selected | Export the tables to Excel or Word from the PDF editor, then check each cell | Saves retyping and reduces copy errors |
| The PDF is a scan | Run OCR in the PDF editor before you read or export it | Text must be searchable before you can copy values |
| The paper is in another language | Translate with bilingual output so each sentence sits next to the original | You can confirm numbers and terms against the source |
| You are unsure what counts as a data point | Define it in the protocol before you start coding | Coding is a researcher decision, not a tool output |
The scenario: a 30-paper review with mixed sources
Picture a small team of three researchers running a review with 30 included papers. Most PDFs came from database searches. A handful are scanned copies of older journal issues, and two are in German. The team keeps a shared spreadsheet form with columns for study design, population, sample size, intervention, outcomes, effect sizes and risk-of-bias items.
Each researcher has to fill in the same fields with the same meaning, and every value needs a page number so a colleague can check it. The team gets stuck in three places: tables that break across pages, values that appear only in figure captions, and units that change from one paper to the next. The goal is a completed sheet where every value traces back to a page.
The workflow
- Write the extraction protocol. List every field, define its units, and note where in a paper it usually appears, such as the methods section, a results table or a supplementary file. Agree on how to handle missing values and conflicting numbers.
- Gather the papers in Zotero. Open them through the Zotero-connected literature workspace in the OmniDoc desktop client. You can use it to find PDFs for records that lack a file. You can download the desktop client from download.omnidoc.top.
- Sort the PDFs by readability. Open each file and try selecting a line of body text. If the text highlights, it is searchable. If it does not, mark the file as scanned and run OCR on it in the PDF editor.
- Read and translate the non-English papers. Translate each one with bilingual output, so each translated passage sits beside the original. Read the methods and results against the source before you trust any figure.
- Export the tables. For tables that contain real text, export from the PDF editor to Word or Excel. For tables that do not export cleanly, copy the values from the page by hand.
- Code by hand. Enter each value into the shared form along with its page number. Have two reviewers code each paper independently, then reconcile differences against the source.
- Compare versions. When a preprint and a published version both appear in your set, use the PDF editor's compare feature on the two files. Record which version you coded and why.
- Archive the evidence. Keep the source PDF, any exported file and the final coded row together, labeled with the same reference key from Zotero. Anyone auditing the review can then trace each value.
Where it can go wrong
- Exported columns shift or merge. Notice this when values land under the wrong headers or when a row has too few cells. Fix it by comparing the export with the page image and correcting the cell by hand.
- OCR misreads numbers. Digits like 1 and 7, decimal points and minus signs are easy to lose. Check every number from an included paper against the page image before it goes into the sheet.
- Translation changes meaning. Machine translation can alter technical terms, units or negations. Keep bilingual output on for key passages and verify important content against the original text.
- Duplicate papers slip in. A preprint and a journal article can both reach the sheet and double-count a study. Use the compare feature and remove the duplicate with a note.
- Values get coded without a full read. A value copied from an export or a summary does not count until a reviewer has seen it in context. Make this a written rule in your protocol.
🎯 Which approach fits you?
If you are a solo student on a small review
Keep one spreadsheet, one Zotero library and a short protocol. Check each exported table against the page, and reread a random few papers after you finish to catch slips in your own coding.
If you are on a team with a shared form
Assign each reviewer the same protocol and have two people code every paper. Use the compare feature for versions of a paper, and log every disagreement with the page where you resolved it.
If your sources are mainly scanned
Run OCR on each scanned file before anything else, then check numbers against the image. Expect more manual copying than with digital PDFs, and budget time for it in your plan.
If many of your sources are in other languages
Translate with bilingual output and read the original methods section for every study that affects a coded field. Note in your protocol which language versions you used to code each paper.
❓ FAQ
1. What are some examples of data extraction tools for a literature review?
Common options include shared spreadsheet forms, reference managers that store PDFs and notes, dedicated review-management software and PDF editors that can export tables. Pick based on how your team needs to share and audit the coded data.
2. Can AI do data extraction for a systematic review?
AI can help you read and translate papers faster, but it should not decide what a value is. Reviewers still need to check each coded value against the source and record how they made each decision. Follow your journal's and your protocol's rules on reporting AI use.
3. How do I find a free data extraction tool for a literature review?
Look past the price to the limits. Check file size caps, whether exports keep table structure, whether watermarks appear, how your files are stored and whether the tool keeps your data private. Test each candidate on one of your real papers before you commit.
4. Do I need OCR before extracting data from a scanned paper?
Yes, if you want to select text or export tables. Run OCR first, then check every number against the scanned image, because OCR errors are most likely in digits and symbols.
📝 Summary
Good data extraction in research depends on a clear protocol, a tidy set of PDFs and a coding step that a person verifies against the source. OmniDoc can help with collecting papers, translating them and exporting tables. The decisions about what to code, and whether each value is right, remain with your review team.
