How do you identify duplicates? Can I get a list of the duplicates identified?

How do you identify duplicates? Can I get a list of the duplicates identified?

Duplicate detection and resolution are essential for ensuring a clean and accurate dataset in systematic reviews. EasySLR provides a robust de-duplication feature that helps identify, review, and manage duplicate records efficiently.

1. Identifying Duplicates
EasySLR will automatically scan the dataset and flag potential duplicates based on the defined criteria.
  1. Choose one or more of the following fields for duplicate identification:

    • DOI (Digital Object Identifier) – Detects articles with the same DOI.

    • Title Similarity – Flag articles with identical titles.

    • Authors – Compare articles based on the same authors.

    • Abstract – Identify articles with a high percentage of overlap in abstracts.

    • Publication Year –  Compare articles based on the same publication year.

    • Volume – Matches based on the journal volume number.

    • Issue – Matches based on the journal issue number.

    • Journal – Matches articles from the same journal name.

    • Pages – Matches based on identical page numbers.

    • PMID – Matches articles using the unique PubMed ID.

    • OpenAlex ID- Matches articles using the unique OpenAlex ID.

    • NCTID- Matches articles using the unique NCT ID.



  1. Confirming and Resolving Duplicates
    • View the list of potential duplicates identified based on the set criteria.
    • Click on "Mark All as Duplicate" to confirm all the listed articles as duplicates in a single action.
  • Click on "Start Review" to manually review each potential duplicate pair.
  • If you choose "Start Review," you will see two panes displaying the abstracts of the potentially duplicate articles. By default, the duplicate is shown on the right pane, and the original is on the left.
  • Carefully analyse the articles listed as potential duplicates and choose the appropriate action:

If you confirm that the articles are indeed duplicates, select the "Confirm Duplicate" option. The article will then be moved to the "Confirmed Duplicates" section.
If, upon review, you determine that the articles are not duplicates, select the "Not Duplicate" option. The article will be moved back to the screening stage for further processing.

If necessary, you can switch the duplicate and original articles. If the article on the left pane should be marked as the duplicate instead, click the "Mark as Duplicate" option located in the left pane. The article on the right will then be considered the original.

2. Changing Criteria for Different Results
You can modify the duplicate detection criteria and re-run the process to generate an updated list. This allows you to experiment with different combinations to refine the search and take appropriate action accordingly.

Steps to Modify Criteria and Re-run Detection:
  1. Click on the “Duplicates Detection” button on the Review Duplicates home page.
  1. Select the fields and click update.
  1. EasySLR will re-evaluate the articles based on the criteria set and will list potential duplicates.
  2. Carefully analyse the articles listed as potential duplicates and choose the appropriate action.
3. Unmarking Duplicates
If an article was incorrectly marked as a duplicate, you can undo the action.

Steps to Unmark a Duplicate:
  • Locate the article marked as a duplicate in the confirmed section.
  • Use the new Unmark Duplicate option to modify or change your previous decision.
  • Choose the applicable option to make changes in the previous decision.

You can also unmark duplicates in bulk by filtering the articles that were incorrectly marked and selecting them for unmarking.


Once unmarked, these articles will move to the Title-Abstract screening stage. 

If needed, you may adjust the duplicate detection criteria and reassess the articles accordingly.

Best Practices for De-duplication in EasySLR
  • Run de-duplication after importing all sources to ensure maximum efficiency.
  • Review duplicate suggestions carefully before confirming, especially when using abstract similarity.
  • If unsure, keep both articles  rather than confirming potential duplicates.
  • Periodically update criteria based on the nature of the dataset and project requirements.
If you need further clarification or assistance with duplicate detection and resolution, feel free to reach out to our support team.