@article{askin2025DeduplicationMethods,
	title = {Deduplication methods for literature citations in systematic evidence reviews: practical insights to guide decision-making},
	volume = {25},
	issn = {1387-3741, 1572-9400},
	shorttitle = {Deduplication methods for literature citations in systematic evidence reviews},
	url = {https://link.springer.com/10.1007/s10742-024-00335-4},
	doi = {10.1007/s10742-024-00335-4},
	abstract = {When deciding on an appropriate software and/or methodology for deduplication of retrieved literature citations in systematic evidence synthesis, several factors ought to be considered, including the cost of a deduplication software, familiarity with its use, and the time necessary to conduct deduplication using a particular method. To guide researchers with and without knowledge of systematic evidence synthesis methods in making informed decisions, we sought to compare different commonly used deduplication software/methodologies—Covidence, Rayyan, the Automated Systematic Search Deduplicator, the Systematic Review Accelerator (SRA) Deduplicator, Deduklick, and the Bramer method in Endnote—from the perspectives of an experienced information specialist and a biomedical researcher. We summarized the cost, time, and effort required from each deduplication software/method and reported our findings descriptively. The findings suggest a properly balanced consideration between cost-effectiveness, ease of application of a method, context and researcher’s needs when deciding on deduplication method for systematic evidence synthesis.},
	language = {en},
	number = {2},
	urldate = {2025-10-03},
	journal = {Health Services and Outcomes Research Methodology},
	author = {Askin, Nicole and Okoli, George N.},
	month = jun,
	year = {2025},
	note = {0 citations (Crossref/DOI) [2024-09-11]},
	keywords = {\_annoté\_FF},
	pages = {214--221},
}

When deciding on an appropriate software and/or methodology for deduplication of retrieved literature citations in systematic evidence synthesis, several factors ought to be considered, including the cost of a deduplication software, familiarity with its use, and the time necessary to conduct deduplication using a particular method. To guide researchers with and without knowledge of systematic evidence synthesis methods in making informed decisions, we sought to compare different commonly used deduplication software/methodologies—Covidence, Rayyan, the Automated Systematic Search Deduplicator, the Systematic Review Accelerator (SRA) Deduplicator, Deduklick, and the Bramer method in Endnote—from the perspectives of an experienced information specialist and a biomedical researcher. We summarized the cost, time, and effort required from each deduplication software/method and reported our findings descriptively. The findings suggest a properly balanced consideration between cost-effectiveness, ease of application of a method, context and researcher’s needs when deciding on deduplication method for systematic evidence synthesis.

@article{premji2025SameSame,
	title = {Same, same, but different: {A} method to harmonise and deduplicate study records from {WHO} {ICTRP} and {ClinicalTrials}.gov prior to screening},
	volume = {16},
	copyright = {https://creativecommons.org/licenses/by-nd/4.0},
	issn = {1759-2879, 1759-2887},
	shorttitle = {Same, same, but different},
	url = {https://www.cambridge.org/core/product/identifier/S1759287925000201/type/journal_article},
	doi = {10.1017/rsm.2025.20},
	abstract = {Abstract
            Trials registry records represent a challenge in deduplication compared to deduplicating studies reported in journals and exported from bibliographic databases such as MEDLINE. We demonstrate why this is the case and propose a method to deduplicate registry records from the WHO International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov (CTG) specifically in the reference management tool EndNote (desktop version). We believe that our method is not only more efficient but that it will minimise the risk of registry records being incorrectly removed as duplicates in automated deduplication. The method has seven steps and is detailed in this tutorial as a step-by-step guide.},
	language = {en},
	number = {4},
	urldate = {2025-10-03},
	journal = {Research Synthesis Methods},
	author = {Premji, Zahra and Cooper, Chris},
	month = jul,
	year = {2025},
	note = {Pinned\_Collections: I55CQYBG},
	keywords = {\_annoté\_FF},
	pages = {587--600},
}

Abstract Trials registry records represent a challenge in deduplication compared to deduplicating studies reported in journals and exported from bibliographic databases such as MEDLINE. We demonstrate why this is the case and propose a method to deduplicate registry records from the WHO International Clinical Trials Registry Platform (ICTRP) and ClinicalTrials.gov (CTG) specifically in the reference management tool EndNote (desktop version). We believe that our method is not only more efficient but that it will minimise the risk of registry records being incorrectly removed as duplicates in automated deduplication. The method has seven steps and is detailed in this tutorial as a step-by-step guide.

@misc{mita2025HowUse,
	title = {How {I} use {Zotero} + {OpenRefine} + {QuickStatements} to create {Scholia} profiles from {Wikidata}},
	url = {https://librarian.aedileworks.com/2025/08/01/how-i-use-zotero-openrefine-quickstatements-to-create-scholia-profiles-from-wikidata/#:~:text=%C2%A74},
	abstract = {Let’s make scholarly profiles for our colleagues. Together.},
	language = {en},
	urldate = {2025-10-01},
	journal = {Librarian of Things},
	author = {Mita, Williams},
	month = aug,
	year = {2025},
	keywords = {zfrancophone\_wikidata},
}

Let’s make scholarly profiles for our colleagues. Together.

@misc{koutsiana2025TalkingWikidata,
	title = {Talking {Wikidata}: {Communication} patterns and their impact on community engagement in collaborative knowledge graphs},
	shorttitle = {Talking {Wikidata}},
	url = {http://arxiv.org/abs/2407.18278},
	doi = {10.48550/arXiv.2407.18278},
	abstract = {We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.},
	language = {en},
	urldate = {2025-09-26},
	publisher = {arXiv},
	author = {Koutsiana, Elisavet and Reklos, Ioannis and Alghamdi, Kholoud Saad and Jain, Nitisha and Meroño-Peñuela, Albert and Simperl, Elena},
	month = feb,
	year = {2025},
	note = {arXiv:2407.18278 [cs]
version: 2},
	keywords = {zfrancophone\_wikidata},
}

We study collaboration patterns of Wikidata, one of the world's largest open source collaborative knowledge graph (KG) communities. Collaborative KG communities, play a key role in structuring machine-readable knowledge to support AI systems like conversational agents. However, these communities face challenges related to long-term member engagement, as a small subset of contributors often is responsible for the majority of contributions and decision-making. While prior research has explored contributors' roles and lifespans, discussions within collaborative KG communities remain understudied. To fill this gap, we investigated the behavioural patterns of contributors and factors affecting their communication and participation. We analysed all the discussions on Wikidata using a mixed methods approach, including statistical tests, network analysis, and text and graph embedding representations. Our findings reveal that the interactions between Wikidata editors form a small world network, resilient to dropouts and inclusive, where both the network topology and discussion content influence the continuity of conversations. Furthermore, the account age of Wikidata members and their conversations are significant factors in their long-term engagement with the project. Our observations and recommendations can benefit the Wikidata and semantic web communities, providing guidance on how to improve collaborative environments for sustainability, growth, and quality.

@misc{2025EncyclopediaGalactica,
	title = {Encyclopedia {Galactica}},
	copyright = {Creative Commons Attribution-ShareAlike License},
	url = {https://fr.wikipedia.org/w/index.php?title=Encyclopedia_Galactica&oldid=225084148},
	abstract = {L'Encyclopedia Galactica est une encyclopédie fictive élaborée par une civilisation étendue à l'ensemble de la Voie lactée et contenant la totalité du savoir accumulé par une société d'un billiard d'individus durant des milliers d'années d'histoire. Son nom rappelle à la fois la volonté d'exhaustivité et la connotation impérialiste de l'Encyclopædia Britannica existant dans la réalité. Elle a été imaginée par Isaac Asimov dans son œuvre majeure, le cycle de Fondation.},
	language = {fr},
	urldate = {2025-09-24},
	journal = {Wikipédia},
	publisher = {Wikimedia},
	month = apr,
	year = {2025},
	note = {Page Version ID: 225084148},
	keywords = {zfrancophone\_wikidata},
}

L'Encyclopedia Galactica est une encyclopédie fictive élaborée par une civilisation étendue à l'ensemble de la Voie lactée et contenant la totalité du savoir accumulé par une société d'un billiard d'individus durant des milliers d'années d'histoire. Son nom rappelle à la fois la volonté d'exhaustivité et la connotation impérialiste de l'Encyclopædia Britannica existant dans la réalité. Elle a été imaginée par Isaac Asimov dans son œuvre majeure, le cycle de Fondation.

@misc{collectif2024Zotero7,
	type = {Billet},
	title = {Zotero 7: {Zotero}, remanié},
	issn = {2108-6664},
	shorttitle = {Zotero 7},
	url = {https://zotero.hypotheses.org/5027},
	doi = {10.58079/126ku},
	abstract = {Vendredi 9 août, l'équipe de Zotero annonçait la sortie officielle de Zotero 7 sur son blog: https://www.zotero.org/blog/zotero-7/. Nous avons traduit ce billet en français.},
	language = {fr-FR},
	urldate = {2026-01-23},
	journal = {Le blog Zotero francophone},
	author = {Collectif},
	month = aug,
	year = {2024},
	doi = {10.58079/126ku},
}

Vendredi 9 août, l'équipe de Zotero annonçait la sortie officielle de Zotero 7 sur son blog: https://www.zotero.org/blog/zotero-7/. Nous avons traduit ce billet en français.

@article{mckeown2024ConsiderationsConducting,
	title = {Considerations for conducting systematic reviews: {A} follow‐up study to evaluate the performance of various automated methods for reference de‐duplication},
	volume = {15},
	copyright = {© 2024 The Author(s). Research Synthesis Methods published by John Wiley \& Sons Ltd.},
	issn = {1759-2879, 1759-2887},
	shorttitle = {Considerations for conducting systematic reviews},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/jrsm.1736},
	doi = {10.1002/jrsm.1736},
	abstract = {Abstract
            Searching multiple resources to locate eligible studies for research syntheses can result in hundreds to thousands of duplicate references that should be removed before the screening process for efficiency. Research investigating the performance of automated methods for deduplicating references via reference managers and systematic review software programs can become quickly outdated as new versions and programs become available. This follow‐up study examined the performance of default de‐duplication algorithms in EndNote 20, EndNote online classic, ProQuest RefWorks, Deduklick, and Systematic Review Accelerator's new Deduplicator tool. On most accounts, systematic review software programs outperformed reference managers when deduplicating references. While cost and the need for institutional access may restrict researchers from being able to utilize some automated methods for deduplicating references, Systematic Review Accelerator's Deduplicator tool is free to use and demonstrated the highest accuracy and sensitivity, while also offering user‐mediation of detected duplicates to improve specificity. Researchers conducting syntheses should take automated de‐duplication performance, and methods for improving and optimizing their use, into consideration to help prevent the unintentional removal of eligible studies and potential introduction of bias to syntheses. Researchers should also be transparent about their de‐duplication process to help readers critically appraise their synthesis methods, and to comply with the PRISMA‐S extension for reporting literature searches in systematic reviews.},
	language = {en},
	number = {6},
	urldate = {2025-10-03},
	journal = {Research Synthesis Methods},
	author = {McKeown, Sandra and Mir, Zuhaib M.},
	month = nov,
	year = {2024},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jrsm.1736
Pinned\_Collections: I55CQYBG},
	keywords = {\_annoté\_FF},
	pages = {896--904},
}

Abstract Searching multiple resources to locate eligible studies for research syntheses can result in hundreds to thousands of duplicate references that should be removed before the screening process for efficiency. Research investigating the performance of automated methods for deduplicating references via reference managers and systematic review software programs can become quickly outdated as new versions and programs become available. This follow‐up study examined the performance of default de‐duplication algorithms in EndNote 20, EndNote online classic, ProQuest RefWorks, Deduklick, and Systematic Review Accelerator's new Deduplicator tool. On most accounts, systematic review software programs outperformed reference managers when deduplicating references. While cost and the need for institutional access may restrict researchers from being able to utilize some automated methods for deduplicating references, Systematic Review Accelerator's Deduplicator tool is free to use and demonstrated the highest accuracy and sensitivity, while also offering user‐mediation of detected duplicates to improve specificity. Researchers conducting syntheses should take automated de‐duplication performance, and methods for improving and optimizing their use, into consideration to help prevent the unintentional removal of eligible studies and potential introduction of bias to syntheses. Researchers should also be transparent about their de‐duplication process to help readers critically appraise their synthesis methods, and to comply with the PRISMA‐S extension for reporting literature searches in systematic reviews.

@article{youngberg2024OurNeighbor,
	title = {Our {Neighbor} {Zotero}: {UMass} {Amherst} {Libraries}’ {Chosen} {Reference} {Manager}},
	volume = {36},
	issn = {1941-126X, 1941-1278},
	shorttitle = {Our {Neighbor} {Zotero}},
	url = {https://www.tandfonline.com/doi/full/10.1080/1941126X.2024.2417124},
	doi = {10.1080/1941126X.2024.2417124},
	language = {en},
	number = {4},
	urldate = {2025-10-03},
	journal = {Journal of Electronic Resources Librarianship},
	publisher = {Routledge},
	author = {Youngberg, Margaret and Radik, Melanie and Toole, Eric},
	month = oct,
	year = {2024},
	note = {\_eprint: https://doi.org/10.1080/1941126X.2024.2417124
Pinned\_Collections: library},
	keywords = {\_annoté\_FF},
	pages = {325--330},
}

@article{affengruber2024ExplorationAvailable,
	title = {An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review},
	volume = {24},
	issn = {1471-2288},
	shorttitle = {An exploration of available methods and tools to improve the efficiency of systematic review production},
	url = {https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-024-02320-4},
	doi = {10.1186/s12874-024-02320-4},
	abstract = {Systematic reviews (SRs) are time-consuming and labor-intensive to perform. With the growing number of scientific publications, the SR development process becomes even more laborious. This is problematic because timely SR evidence is essential for decision-making in evidence-based healthcare and policymaking. Numerous methods and tools that accelerate SR development have recently emerged. To date, no scoping review has been conducted to provide a comprehensive summary of methods and ready-to-use tools to improve efficiency in SR production.},
	language = {en},
	number = {1},
	urldate = {2025-10-03},
	journal = {BMC Medical Research Methodology},
	author = {Affengruber, Lisa and Van Der Maten, Miriam M. and Spiero, Isa and Nussbaumer-Streit, Barbara and Mahmić-Kaknjo, Mersiha and Ellen, Moriah E. and Goossen, Käthe and Kantorova, Lucia and Hooft, Lotty and Riva, Nicoletta and Poulentzas, Georgios and Lalagkas, Panagiotis Nikolaos and Silva, Anabela G. and Sassano, Michele and Sfetcu, Raluca and Marqués, María E. and Friessova, Tereza and Baladia, Eduard and Pezzullo, Angelo Maria and Martinez, Patricia and Gartlehner, Gerald and Spijker, René},
	month = sep,
	year = {2024},
	keywords = {\_annoté\_FF},
	pages = {210},
}

Systematic reviews (SRs) are time-consuming and labor-intensive to perform. With the growing number of scientific publications, the SR development process becomes even more laborious. This is problematic because timely SR evidence is essential for decision-making in evidence-based healthcare and policymaking. Numerous methods and tools that accelerate SR development have recently emerged. To date, no scoping review has been conducted to provide a comprehensive summary of methods and ready-to-use tools to improve efficiency in SR production.

@article{forbes2024AutomationDuplicate,
	title = {Automation of duplicate record detection for systematic reviews: {Deduplicator}},
	volume = {13},
	issn = {2046-4053},
	shorttitle = {Automation of duplicate record detection for systematic reviews},
	url = {https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-024-02619-9},
	doi = {10.1186/s13643-024-02619-9},
	abstract = {To describe the algorithm and investigate the efficacy of a novel systematic review automation tool “the Deduplicator” to remove duplicate records from a multi-database systematic review search.},
	language = {en},
	number = {1},
	urldate = {2025-10-03},
	journal = {Systematic Reviews},
	author = {Forbes, Connor and Greenwood, Hannah and Carter, Matt and Clark, Justin},
	month = aug,
	year = {2024},
	note = {Pinned\_Collections: I55CQYBG},
	keywords = {\_annoté\_FF},
	pages = {206},
}

To describe the algorithm and investigate the efficacy of a novel systematic review automation tool “the Deduplicator” to remove duplicate records from a multi-database systematic review search.

@article{fulbright2024FindingFull,
	title = {Finding full texts in bulk: a comparison of {EndNote} 20 versus {Zotero} 6 using the {University} of {York}’s subscriptions},
	volume = {112},
	copyright = {https://creativecommons.org/licenses/by/4.0},
	issn = {1558-9439, 1536-5050},
	shorttitle = {Finding full texts in bulk},
	url = {http://jmla.pitt.edu/ojs/jmla/article/view/1880},
	doi = {10.5195/jmla.2024.1880},
	abstract = {Objective: To understand the performance of EndNote 20 and Zotero 6’s full text retrieval features.
Methods: Using the University of York’s subscriptions, we tested and compared EndNote and Zotero’s full text retrieval. 1,000 records from four evidence synthesis projects were tested for the number of: full texts retrieved; available full texts retrieved; unique full texts (found by one program only); and differences in versions of full texts for the same record. We also tested the time taken and accuracy of retrieved full texts. One dataset was tested multiple times to confirm if the number of full texts retrieved was consistent. We also investigated the available full texts missed by EndNote or Zotero by: reference type; whether full texts were available open access or via subscription; and the content provider.
Results: EndNote retrieved 47\% of available full texts versus 52\% by Zotero. Zotero was faster by 2 minutes 15 seconds. Each program found unique full texts. There were differences in full text versions retrieved between programs. For both programs, 99\% of the retrieved full texts were accurate. Zotero was less consistent in the number of full texts it retrieved.
Conclusion: EndNote and Zotero do not find all available full texts. Users should not assume full texts are correct; are the version of record; or that records without full texts cannot be retrieved manually. Repeating the full text retrieval process multiple times could yield additional full texts. Users with access to EndNote and Zotero could use both for full text retrieval.},
	language = {en},
	number = {3},
	urldate = {2025-10-03},
	journal = {Journal of the Medical Library Association},
	author = {Fulbright, Helen and Evans, Connor},
	month = jul,
	year = {2024},
	note = {Number: 3
Pinned\_Collections: library},
	keywords = {\_annoté\_FF},
	pages = {214--224},
}

Objective: To understand the performance of EndNote 20 and Zotero 6’s full text retrieval features. Methods: Using the University of York’s subscriptions, we tested and compared EndNote and Zotero’s full text retrieval. 1,000 records from four evidence synthesis projects were tested for the number of: full texts retrieved; available full texts retrieved; unique full texts (found by one program only); and differences in versions of full texts for the same record. We also tested the time taken and accuracy of retrieved full texts. One dataset was tested multiple times to confirm if the number of full texts retrieved was consistent. We also investigated the available full texts missed by EndNote or Zotero by: reference type; whether full texts were available open access or via subscription; and the content provider. Results: EndNote retrieved 47% of available full texts versus 52% by Zotero. Zotero was faster by 2 minutes 15 seconds. Each program found unique full texts. There were differences in full text versions retrieved between programs. For both programs, 99% of the retrieved full texts were accurate. Zotero was less consistent in the number of full texts it retrieved. Conclusion: EndNote and Zotero do not find all available full texts. Users should not assume full texts are correct; are the version of record; or that records without full texts cannot be retrieved manually. Repeating the full text retrieval process multiple times could yield additional full texts. Users with access to EndNote and Zotero could use both for full text retrieval.

@article{zhang2024ChoosingRight,
	title = {Choosing the {Right} {Tool} for the {Job}: {Screening} {Tools} for {Systematic} {Reviews} in {Education}},
	volume = {17},
	issn = {1934-5747, 1934-5739},
	shorttitle = {Choosing the {Right} {Tool} for the {Job}},
	url = {https://www.tandfonline.com/doi/full/10.1080/19345747.2023.2209079},
	doi = {10.1080/19345747.2023.2209079},
	abstract = {In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience.},
	language = {en},
	number = {3},
	urldate = {2025-10-03},
	journal = {Journal of Research on Educational Effectiveness},
	author = {Zhang, Qiyang and Neitzel, Amanda},
	month = jul,
	year = {2024},
	pages = {513--539},
}

In recent years, the rapid development of artificial intelligence has enabled the launch of many new screening tools. This review aims to facilitate screening tool selection through a systematic narrative review and feature analysis. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight studies reported screening tools. More research is needed to understand the reasons behind this phenomenon. After consulting various sources, 26 available screening tools in the market were found. Among them, we identified and evaluated 12 screening tools for educational reviewers and ranked them in descending order of feature score: Covidence (1), DistillerSR (2, tied), EPPI-Reviewer (2, tied), CADIMA (4), Swift-Active (5), Rayyan (6, tied), SysRev (6, tied), Abstrackr (8, tied), ReLiS (8, tied), RevMan (8, tied), ASReview (11), and Excel (12). In the discussion, we provide insights into the promise and bias in tools' machine learning algorithms. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience.

@article{janka2024HighPrecision,
	title = {High precision but variable recall – comparing the performance of five deduplication tools},
	volume = {20},
	copyright = {Copyright (c) 2024 Heidrun Ilonka Janka},
	issn = {1841-0715},
	url = {https://ojs.eahil.eu/JEAHIL/article/view/607},
	doi = {10.32384/jeahil20607},
	abstract = {Deduplication methods for multiple database searches conducted for evidence syntheses differ in terms of time invested, accuracy, and comprehensiveness of identified duplicates. Deduplication tools can significantly contribute to a more efficient conduct of the search task in evidence syntheses. Widely-used tools for deduplication include reference management software (e.g. EndNote), built-in deduplication features in systematic review software (e.g. Covidence, Rayyan), and automated deduplication tools (e.g. Deduklick, SRA Deduplicator). Newer tools leverage machine learning algorithms crafted by information specialists, that encompass natural language normalization and rule-based approaches. We investigated five frequently used automated and semi-automated deduplication tools regarding their performance, core features and time efficiency in comparison to manual deduplication in EndNote using six datasets.},
	language = {en},
	number = {1},
	urldate = {2025-10-02},
	journal = {Journal of EAHIL},
	author = {Janka, Heidrun and Metzendorf, Maria-Inti},
	month = mar,
	year = {2024},
	keywords = {\_annoté\_FF},
	pages = {12--17},
}

Deduplication methods for multiple database searches conducted for evidence syntheses differ in terms of time invested, accuracy, and comprehensiveness of identified duplicates. Deduplication tools can significantly contribute to a more efficient conduct of the search task in evidence syntheses. Widely-used tools for deduplication include reference management software (e.g. EndNote), built-in deduplication features in systematic review software (e.g. Covidence, Rayyan), and automated deduplication tools (e.g. Deduklick, SRA Deduplicator). Newer tools leverage machine learning algorithms crafted by information specialists, that encompass natural language normalization and rule-based approaches. We investigated five frequently used automated and semi-automated deduplication tools regarding their performance, core features and time efficiency in comparison to manual deduplication in EndNote using six datasets.

@misc{janka2023EvaluationPerformance,
	address = {London},
	type = {Poster},
	title = {Evaluation of the performance of five deduplication tools},
	url = {https://abstracts.cochrane.org/2023-london/evaluation-performance-five-deduplication-tools},
	urldate = {2024-10-10},
	author = {Janka, H and Bongaerts, B and Franco, JVA and Escobar Liquitay, CM and Metzendorf, MI},
	year = {2023},
	keywords = {\_annoté\_FF, linter/error},
}

@article{hair2023AutomatedSystematic,
	title = {The {Automated} {Systematic} {Search} {Deduplicator} ({ASySD}): a rapid, open-source, interoperable tool to remove duplicate citations in biomedical systematic reviews},
	volume = {21},
	issn = {1741-7007},
	shorttitle = {The {Automated} {Systematic} {Search} {Deduplicator} ({ASySD})},
	url = {https://bmcbiol.biomedcentral.com/articles/10.1186/s12915-023-01686-z},
	doi = {10.1186/s12915-023-01686-z},
	abstract = {Abstract
            
              Background
              Researchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews.
            
            
              Methods
              We evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1845–79,880 citations). We compared the performance of ASySD with EndNote’s automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM).
            
            
              Results
              ASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of {\textgreater} 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset.
            
            
              Conclusions
              For duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.},
	language = {en},
	number = {1},
	urldate = {2025-10-03},
	journal = {BMC Biology},
	author = {Hair, Kaitlyn and Bahor, Zsanett and Macleod, Malcolm and Liao, Jing and Sena, Emily S.},
	month = sep,
	year = {2023},
	keywords = {\_annoté\_FF},
	pages = {189},
}

Abstract Background Researchers performing high-quality systematic reviews search across multiple databases to identify relevant evidence. However, the same publication is often retrieved from several databases. Identifying and removing such duplicates (“deduplication”) can be extremely time-consuming, but failure to remove these citations can lead to the wrongful inclusion of duplicate data. Many existing tools are not sensitive enough, lack interoperability with other tools, are not freely accessible, or are difficult to use without programming knowledge. Here, we report the performance of our Automated Systematic Search Deduplicator (ASySD), a novel tool to perform automated deduplication of systematic searches for biomedical reviews. Methods We evaluated ASySD’s performance on 5 unseen biomedical systematic search datasets of various sizes (1845–79,880 citations). We compared the performance of ASySD with EndNote’s automated deduplication option and with the Systematic Review Assistant Deduplication Module (SRA-DM). Results ASySD identified more duplicates than either SRA-DM or EndNote, with a sensitivity in different datasets of 0.95 to 0.99. The false-positive rate was comparable to human performance, with a specificity of \textgreater 0.99. The tool took less than 1 h to identify and remove duplicates within each dataset. Conclusions For duplicate removal in biomedical systematic reviews, ASySD is a highly sensitive, reliable, and time-saving tool. It is open source and freely available online as both an R package and a user-friendly web application.

@article{borissov2022ReducingSystematic,
	title = {Reducing systematic review burden using {Deduklick}: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research},
	volume = {11},
	issn = {2046-4053},
	shorttitle = {Reducing systematic review burden using {Deduklick}},
	url = {https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-022-02045-9},
	doi = {10.1186/s13643-022-02045-9},
	abstract = {Abstract
            
              Background
              Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related to manual deduplication, we developed an automated, efficient, and rapid artificial intelligence-based algorithm named Deduklick. Deduklick combines natural language processing algorithms with a set of rules created by expert information specialists.
            
            
              Methods
              Deduklick’s deduplication uses a multistep algorithm of data normalization, calculates a similarity score, and identifies unique and duplicate references based on metadata fields, such as title, authors, journal, DOI, year, issue, volume, and page number range. We measured and compared Deduklick’s capacity to accurately detect duplicates with the information specialists’ standard, manual duplicate removal process using EndNote on eight existing heterogeneous datasets. Using a sensitivity analysis, we manually cross-compared the efficiency and noise of both methods.
            
            
              Discussion
              Deduklick achieved average recall of 99.51\%, average precision of 100.00\%, and average F1 score of 99.75\%. In contrast, the manual deduplication process achieved average recall of 88.65\%, average precision of 99.95\%, and average F1 score of 91.98\%. Deduklick achieved equal to higher expert-level performance on duplicate removal. It also preserved high metadata quality and drastically reduced time spent on analysis. Deduklick represents an efficient, transparent, ergonomic, and time-saving solution for identifying and removing duplicates in SRs searches. Deduklick could therefore simplify SRs production and represent important advantages for scientists, including saving time, increasing accuracy, reducing costs, and contributing to quality SRs.},
	language = {en},
	number = {1},
	urldate = {2025-10-03},
	journal = {Systematic Reviews},
	author = {Borissov, Nikolay and Haas, Quentin and Minder, Beatrice and Kopp-Heim, Doris and Von Gernler, Marc and Janka, Heidrun and Teodoro, Douglas and Amini, Poorya},
	month = aug,
	year = {2022},
	keywords = {\_annoté\_FF},
	pages = {172},
}

Abstract Background Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues related to manual deduplication, we developed an automated, efficient, and rapid artificial intelligence-based algorithm named Deduklick. Deduklick combines natural language processing algorithms with a set of rules created by expert information specialists. Methods Deduklick’s deduplication uses a multistep algorithm of data normalization, calculates a similarity score, and identifies unique and duplicate references based on metadata fields, such as title, authors, journal, DOI, year, issue, volume, and page number range. We measured and compared Deduklick’s capacity to accurately detect duplicates with the information specialists’ standard, manual duplicate removal process using EndNote on eight existing heterogeneous datasets. Using a sensitivity analysis, we manually cross-compared the efficiency and noise of both methods. Discussion Deduklick achieved average recall of 99.51%, average precision of 100.00%, and average F1 score of 99.75%. In contrast, the manual deduplication process achieved average recall of 88.65%, average precision of 99.95%, and average F1 score of 91.98%. Deduklick achieved equal to higher expert-level performance on duplicate removal. It also preserved high metadata quality and drastically reduced time spent on analysis. Deduklick represents an efficient, transparent, ergonomic, and time-saving solution for identifying and removing duplicates in SRs searches. Deduklick could therefore simplify SRs production and represent important advantages for scientists, including saving time, increasing accuracy, reducing costs, and contributing to quality SRs.

@article{cowie2022WebBasedSoftware,
	title = {Web-{Based} {Software} {Tools} for {Systematic} {Literature} {Review} in {Medicine}: {Systematic} {Search} and {Feature} {Analysis}},
	volume = {10},
	issn = {2291-9694},
	shorttitle = {Web-{Based} {Software} {Tools} for {Systematic} {Literature} {Review} in {Medicine}},
	url = {https://medinform.jmir.org/2022/5/e33219},
	doi = {10.2196/33219},
	abstract = {Background
              Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices.
            
            
              Objective
              This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools.
            
            
              Methods
              We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements.
            
            
              Results
              Of the 53 SR tools found, 55\% (29/53) were excluded, leaving 45\% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46\%. Giotto Compliance (27/30, 90\%), DistillerSR (26/30, 87\%), and Nested Knowledge (26/30, 87\%) support the most features, followed by EPPI-Reviewer Web (25/30, 83\%), LitStream (23/30, 77\%), JBI SUMARI (21/30, 70\%), and SRDB.PRO (VTS Software) (21/30, 70\%). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42\%) support direct search, only 7 (29\%) offer dual extraction, and only 13 (54\%) offer living/updatable reviews.
            
            
              Conclusions
              DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.},
	language = {en},
	number = {5},
	urldate = {2025-10-03},
	journal = {JMIR Medical Informatics},
	author = {Cowie, Kathryn and Rahmatullah, Asad and Hardy, Nicole and Holub, Karl and Kallmes, Kevin},
	month = may,
	year = {2022},
	keywords = {\_annoté\_FF},
	pages = {e33219},
}

Background Systematic reviews (SRs) are central to evaluating therapies but have high costs in terms of both time and money. Many software tools exist to assist with SRs, but most tools do not support the full process, and transparency and replicability of SR depend on performing and presenting evidence according to established best practices. Objective This study aims to provide a basis for comparing and selecting between web-based software tools that support SR, by conducting a feature-by-feature comparison of SR tools. Methods We searched for SR tools by reviewing any such tool listed in the SR Toolbox, previous reviews of SR tools, and qualitative Google searching. We included all SR tools that were currently functional and required no coding, and excluded reference managers, desktop applications, and statistical software. The list of features to assess was populated by combining all features assessed in 4 previous reviews of SR tools; we also added 5 features (manual addition, screening automation, dual extraction, living review, and public outputs) that were independently noted as best practices or enhancements of transparency and replicability. Then, 2 reviewers assigned binary present or absent assessments to all SR tools with respect to all features, and a third reviewer adjudicated all disagreements. Results Of the 53 SR tools found, 55% (29/53) were excluded, leaving 45% (24/53) for assessment. In total, 30 features were assessed across 6 classes, and the interobserver agreement was 86.46%. Giotto Compliance (27/30, 90%), DistillerSR (26/30, 87%), and Nested Knowledge (26/30, 87%) support the most features, followed by EPPI-Reviewer Web (25/30, 83%), LitStream (23/30, 77%), JBI SUMARI (21/30, 70%), and SRDB.PRO (VTS Software) (21/30, 70%). Fewer than half of all the features assessed are supported by 7 tools: RobotAnalyst (National Centre for Text Mining), SRDR (Agency for Healthcare Research and Quality), SyRF (Systematic Review Facility), Data Abstraction Assistant (Center for Evidence Synthesis in Health), SR Accelerator (Institute for Evidence-Based Healthcare), RobotReviewer (RobotReviewer), and COVID-NMA (COVID-NMA). Notably, of the 24 tools, only 10 (42%) support direct search, only 7 (29%) offer dual extraction, and only 13 (54%) offer living/updatable reviews. Conclusions DistillerSR, Nested Knowledge, and EPPI-Reviewer Web each offer a high density of SR-focused web-based tools. By transparent comparison and discussion regarding SR tool functionality, the medical community can both choose among existing software offerings and note the areas of growth needed, most notably in the support of living reviews.

@article{guimaraes2022DeduplicatingRecords,
	title = {Deduplicating records in systematic reviews: there are free, accurate automated ways to do so},
	volume = {152},
	issn = {08954356},
	shorttitle = {Deduplicating records in systematic reviews},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0895435622002566},
	doi = {10.1016/j.jclinepi.2022.10.009},
	language = {en},
	urldate = {2025-10-03},
	journal = {Journal of Clinical Epidemiology},
	publisher = {Elsevier},
	author = {Guimarães, Nathalia Sernizon and Ferreira, Andrêa J.F. and Ribeiro Silva, Rita De Cássia and De Paula, Adelzon Assis and Lisboa, Cinthia Soares and Magno, Laio and Ichiara, Maria Yury and Barreto, Maurício Lima},
	month = dec,
	year = {2022},
	note = {Pinned\_Collections: I55CQYBG},
	keywords = {\_annoté\_FF},
	pages = {110--115},
}

@misc{martinolli2022UserPmartinolli,
	title = {User:{Pmartinolli}/{Tutoriel} chercheur},
	url = {https://www.wikidata.org/wiki/User:Pmartinolli/Tutoriel_chercheur},
	language = {fr},
	urldate = {2025-06-18},
	journal = {Wikidata},
	author = {Martinolli, Pascal},
	year = {2022},
	keywords = {zfrancophone\_wikidata},
}

@article{mckeown2021ConsiderationsConducting,
	title = {Considerations for conducting systematic reviews: evaluating the performance of different methods for de-duplicating references},
	volume = {10},
	issn = {2046-4053},
	shorttitle = {Considerations for conducting systematic reviews},
	url = {https://systematicreviewsjournal.biomedcentral.com/articles/10.1186/s13643-021-01583-y},
	doi = {10.1186/s13643-021-01583-y},
	abstract = {Abstract
            
              Background
              Systematic reviews involve searching multiple bibliographic databases to identify eligible studies. As this type of evidence synthesis is increasingly pursued, the use of various electronic platforms can help researchers improve the efficiency and quality of their research. We examined the accuracy and efficiency of commonly used electronic methods for flagging and removing duplicate references during this process.
            
            
              Methods
              A heterogeneous sample of references was obtained by conducting a similar topical search in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases. References were de-duplicated via manual abstraction to create a benchmark set. The default settings were then used in Ovid multifile search, EndNote desktop, Mendeley, Zotero, Covidence, and Rayyan to de-duplicate the sample of references independently. Using the benchmark set as reference, the number of false-negative and false-positive duplicate references for each method was identified, and accuracy, sensitivity, and specificity were determined.
            
            
              Results
              We found that the most accurate methods for identifying duplicate references were Ovid, Covidence, and Rayyan. Ovid and Covidence possessed the highest specificity for identifying duplicate references, while Rayyan demonstrated the highest sensitivity.
            
            
              Conclusion
              This study reveals the strengths and weaknesses of commonly used de-duplication methods and provides strategies for improving their performance to avoid unintentionally removing eligible studies and introducing bias into systematic reviews. Along with availability, ease-of-use, functionality, and capability, these findings are important to consider when researchers are selecting database platforms and supporting software programs for conducting systematic reviews.},
	language = {en},
	number = {1},
	urldate = {2025-10-03},
	journal = {Systematic Reviews},
	publisher = {Springer Science and Business Media LLC},
	author = {McKeown, Sandra and Mir, Zuhaib M.},
	month = dec,
	year = {2021},
	keywords = {\_annoté\_FF},
	pages = {38},
}

Abstract Background Systematic reviews involve searching multiple bibliographic databases to identify eligible studies. As this type of evidence synthesis is increasingly pursued, the use of various electronic platforms can help researchers improve the efficiency and quality of their research. We examined the accuracy and efficiency of commonly used electronic methods for flagging and removing duplicate references during this process. Methods A heterogeneous sample of references was obtained by conducting a similar topical search in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and PsycINFO databases. References were de-duplicated via manual abstraction to create a benchmark set. The default settings were then used in Ovid multifile search, EndNote desktop, Mendeley, Zotero, Covidence, and Rayyan to de-duplicate the sample of references independently. Using the benchmark set as reference, the number of false-negative and false-positive duplicate references for each method was identified, and accuracy, sensitivity, and specificity were determined. Results We found that the most accurate methods for identifying duplicate references were Ovid, Covidence, and Rayyan. Ovid and Covidence possessed the highest specificity for identifying duplicate references, while Rayyan demonstrated the highest sensitivity. Conclusion This study reveals the strengths and weaknesses of commonly used de-duplication methods and provides strategies for improving their performance to avoid unintentionally removing eligible studies and introducing bias into systematic reviews. Along with availability, ease-of-use, functionality, and capability, these findings are important to consider when researchers are selecting database platforms and supporting software programs for conducting systematic reviews.

@article{zhang2021MethodologicalReview,
	title = {Methodological review: {A} systematic narrative review of screening tools for conducting systematic reviews in educational research},
	shorttitle = {Choosing the right tool for the job},
	url = {https://www.semanticscholar.org/paper/f77287cdee6d58df0234fb2a98e9b99640850bda},
	doi = {10.31219/osf.io/efs2n},
	abstract = {In recent years, the increasing influence of evidence-based research and the rapid development of artificial intelligence have enabled the launch of many new reference screening software tools. Due to a dearth of research comparing different screening tools in educational research, researchers often choose the most convenient rather than the most suitable screening tool. This review aims to provide assistance for screening tool selection through a systematic narrative review and feature analysis of these tools’ functions and privacy policies. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight (4.19\%) studies reported screening tools. To locate available screening tools in the market, we consulted various sources and found 24 tools. Through citation search, we identified eight screening tools used by educational reviewers and ranked them in descending order of feature score: EPPI-Reviewer (tie), DistillerSR (tie), Covidence, Rayyan, Abstrackr, ASReview, RevMan, and Excel. For practitioners’ convenience, we concluded the paper with a decision tree to assist educational systematic reviewers in identifying suitable tools. This paper represents the first effort to provide educational researchers with guidance on how to navigate screening tools. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience.},
	language = {en-us},
	urldate = {2025-09-30},
	author = {Zhang, Qiyang and Neitzel, Amanda},
	month = dec,
	year = {2021},
}

In recent years, the increasing influence of evidence-based research and the rapid development of artificial intelligence have enabled the launch of many new reference screening software tools. Due to a dearth of research comparing different screening tools in educational research, researchers often choose the most convenient rather than the most suitable screening tool. This review aims to provide assistance for screening tool selection through a systematic narrative review and feature analysis of these tools’ functions and privacy policies. The current adoption rate of transparent tool reporting is low: by screening 191 studies published in the Review of Educational Research since 2015, we found that only eight (4.19%) studies reported screening tools. To locate available screening tools in the market, we consulted various sources and found 24 tools. Through citation search, we identified eight screening tools used by educational reviewers and ranked them in descending order of feature score: EPPI-Reviewer (tie), DistillerSR (tie), Covidence, Rayyan, Abstrackr, ASReview, RevMan, and Excel. For practitioners’ convenience, we concluded the paper with a decision tree to assist educational systematic reviewers in identifying suitable tools. This paper represents the first effort to provide educational researchers with guidance on how to navigate screening tools. Our results encourage researchers to report their tool usage in publications and select tools based on suitability instead of convenience.

@article{clark2020FullSystematic,
	title = {A full systematic review was completed in 2 weeks using automation tools: a case study},
	volume = {121},
	issn = {08954356},
	shorttitle = {A full systematic review was completed in 2 weeks using automation tools},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S089543561930719X},
	doi = {10.1016/j.jclinepi.2020.01.008},
	abstract = {Abstract Background and Objectives Systematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR. Study Design and Setting We systematically reviewed evidence of the impact of increased fluid intake, on urinary tract infection (UTI) recurrence, in individuals at risk for UTIs. The review was conducted by experienced systematic reviewers with complementary skills (two researcher clinicians, an information specialist, and an epidemiologist), using Systematic Review Automation tools, and blocked off time for the duration of the project. The outcomes were time to complete the SR, time to complete individual SR tasks, facilitators and barriers to progress, and peer reviewer feedback on the SR manuscript. Times to completion were analyzed quantitatively (minutes and calendar days); facilitators and barriers were mapped onto the Theoretical Domains Framework; and peer reviewer feedback was analyzed quantitatively and narratively. Results The SR was completed in 61 person-hours (9 workdays; 12 calendar days); accepted version of the manuscript required 71 person-hours. Individual SR tasks ranged from 16 person-minutes (deduplication of search results) to 461 person-minutes (data extraction). The least time-consuming SR tasks were obtaining full-texts, searches, citation analysis, data synthesis, and deduplication. The most time-consuming tasks were data extraction, write-up, abstract screening, full-text screening, and risk of bias. Facilitators and barriers mapped onto the following domains: knowledge; skills; memory, attention, and decision process; environmental context and resources; and technology and infrastructure. Two sets of peer reviewer feedback were received on the manuscript: the first included 34 comments requesting changes, 17 changes were made, requiring 173 person-minutes; the second requested 13 changes, and eight were made, requiring 121 person-minutes. Conclusion A small and experienced systematic reviewer team using Systematic Review Automation tools who have protected time to focus solely on the SR can complete a moderately sized SR in 2 weeks.},
	language = {en},
	number = {NA},
	urldate = {2025-10-03},
	journal = {Journal of Clinical Epidemiology},
	publisher = {Elsevier BV},
	author = {Clark, Justin and Glasziou, Paul and Del Mar, Chris and Bannach-Brown, Alexandra and Stehlik, Paulina and Scott, Anna Mae},
	month = may,
	year = {2020},
	keywords = {\_annoté\_FF},
	pages = {81--90},
}

Abstract Background and Objectives Systematic reviews (SRs) are time and resource intensive, requiring approximately 1 year from protocol registration to submission for publication. Our aim was to describe the process, facilitators, and barriers to completing the first 2-week full SR. Study Design and Setting We systematically reviewed evidence of the impact of increased fluid intake, on urinary tract infection (UTI) recurrence, in individuals at risk for UTIs. The review was conducted by experienced systematic reviewers with complementary skills (two researcher clinicians, an information specialist, and an epidemiologist), using Systematic Review Automation tools, and blocked off time for the duration of the project. The outcomes were time to complete the SR, time to complete individual SR tasks, facilitators and barriers to progress, and peer reviewer feedback on the SR manuscript. Times to completion were analyzed quantitatively (minutes and calendar days); facilitators and barriers were mapped onto the Theoretical Domains Framework; and peer reviewer feedback was analyzed quantitatively and narratively. Results The SR was completed in 61 person-hours (9 workdays; 12 calendar days); accepted version of the manuscript required 71 person-hours. Individual SR tasks ranged from 16 person-minutes (deduplication of search results) to 461 person-minutes (data extraction). The least time-consuming SR tasks were obtaining full-texts, searches, citation analysis, data synthesis, and deduplication. The most time-consuming tasks were data extraction, write-up, abstract screening, full-text screening, and risk of bias. Facilitators and barriers mapped onto the following domains: knowledge; skills; memory, attention, and decision process; environmental context and resources; and technology and infrastructure. Two sets of peer reviewer feedback were received on the manuscript: the first included 34 comments requesting changes, 17 changes were made, requiring 173 person-minutes; the second requested 13 changes, and eight were made, requiring 121 person-minutes. Conclusion A small and experienced systematic reviewer team using Systematic Review Automation tools who have protected time to focus solely on the SR can complete a moderately sized SR in 2 weeks.

@techreport{collegeeuropeetinternational2019IdentifiantsOuverts,
	type = {Note d’orientation},
	title = {Des identifiants ouverts pour la science ouverte},
	url = {https://hal-lara.archives-ouvertes.fr/hal-03640303},
	doi = {10.52949/22},
	abstract = {Le document définit dans un premier chapitre ce que sont les identifiants et les registres, leur rôle et leur importance, les attentes en matière de science ouverte. Dans un second chapitre, le comité pour la science ouverte expose son programme d’actions à l’échelle nationale pour les identifiants des publications scientifiques et des données de la recherche.},
	language = {fr},
	urldate = {2025-09-27},
	institution = {Comité pour la science ouverte},
	author = {{Collège Europe et International}},
	month = jul,
	year = {2019},
	keywords = {zfrancophone\_wikidata},
	pages = {15},
}

Le document définit dans un premier chapitre ce que sont les identifiants et les registres, leur rôle et leur importance, les attentes en matière de science ouverte. Dans un second chapitre, le comité pour la science ouverte expose son programme d’actions à l’échelle nationale pour les identifiants des publications scientifiques et des données de la recherche.

@article{bramer2018ReferenceChecking,
	title = {Reference checking for systematic reviews using {Endnote}},
	volume = {106},
	copyright = {http://creativecommons.org/licenses/by/4.0},
	issn = {1558-9439, 1536-5050},
	url = {http://jmla.pitt.edu/ojs/jmla/article/view/489},
	doi = {10.5195/jmla.2018.489},
	abstract = {In searches for systematic reviews, it is recommended that authors review references from the reference lists of retrieved relevant reviews for possible additional, relevant references. This process can be time consuming, since there often is overlap between the references lists and the lists contain references that were already retrieved in the initial searches. The author proposes a method in which EndNote is used in combination with the Scopus or Web of Science databases to semi-automatically download these references into an existing EndNote library.},
	number = {4},
	urldate = {2025-10-03},
	journal = {Journal of the Medical Library Association},
	publisher = {Medical Library Association},
	author = {Bramer, Wichor M.},
	month = oct,
	year = {2018},
	pages = {542--546},
}

In searches for systematic reviews, it is recommended that authors review references from the reference lists of retrieved relevant reviews for possible additional, relevant references. This process can be time consuming, since there often is overlap between the references lists and the lists contain references that were already retrieved in the initial searches. The author proposes a method in which EndNote is used in combination with the Scopus or Web of Science databases to semi-automatically download these references into an existing EndNote library.

@misc{2017WikidataZotero,
	title = {Wikidata:{Zotero}},
	url = {https://www.wikidata.org/wiki/Wikidata:Zotero},
	language = {en},
	urldate = {2025-06-18},
	journal = {Wikidata},
	year = {2017},
	keywords = {zfrancophone\_wikidata},
}

@article{bramer2016DeduplicationDatabase,
	title = {De-duplication of database search results for systematic reviews in {EndNote}},
	volume = {104},
	copyright = {http://creativecommons.org/licenses/by/4.0},
	issn = {1558-9439, 1536-5050},
	url = {http://jmla.pitt.edu/ojs/jmla/article/view/24},
	doi = {10.5195/jmla.2016.24},
	abstract = {When conducting exhaustive searches for systematic reviews, information professionals search multiple databases with overlapping content. They typically remove duplicate records to reduce the reviewers’ workload associated with screening titles and abstracts; sometimes the reviewers remove the duplicates.This article describes a de-duplication method.},
	language = {en},
	number = {3},
	urldate = {2025-10-03},
	journal = {Journal of the Medical Library Association},
	author = {Bramer, Wichor M. and Giustini, Dean and De Jonge, Gerdien B. and Holland, Leslie and Bekhuis, Tanja},
	month = sep,
	year = {2016},
	note = {58 citations (Crossref/DOI) [2024-09-11]
Number: 3},
	pages = {240--242},
}

When conducting exhaustive searches for systematic reviews, information professionals search multiple databases with overlapping content. They typically remove duplicate records to reduce the reviewers’ workload associated with screening titles and abstracts; sometimes the reviewers remove the duplicates.This article describes a de-duplication method.

@misc{bibliothequenationaledefrance2015TransitionBibliographique,
	title = {Transition bibliographique : des catalogues vers le web de données},
	url = {https://www.transition-bibliographique.fr/},
	abstract = {Exposer les catalogues des bibliothèques dans le web de données, c'est l’objectif du programme Transition bibliographique lancé en 2015 par Abes et BnF.},
	language = {fr},
	urldate = {2025-09-24},
	journal = {Transition bibliographique},
	author = {{Bibliothèque nationale de France} and {Agence Bibliographique de l’Enseignement Supérieur}},
	year = {2015},
	keywords = {zfrancophone\_wikidata},
}

Exposer les catalogues des bibliothèques dans le web de données, c'est l’objectif du programme Transition bibliographique lancé en 2015 par Abes et BnF.

@article{grant2009TypologyReviews,
	title = {A typology of reviews: an analysis of 14 review types and associated methodologies},
	volume = {26},
	issn = {1471-1834, 1471-1842},
	shorttitle = {A typology of reviews},
	url = {https://onlinelibrary.wiley.com/doi/10.1111/j.1471-1842.2009.00848.x},
	doi = {10.1111/j.1471-1842.2009.00848.x},
	abstract = {Abstract
            
              Background and objectives: 
              The expansion of evidence‐based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains.
            
            
              Methods: 
              Following scoping searches, an examination was made of the vocabulary associated with the literature of review and synthesis (literary warrant). A simple analytical framework—Search, AppraisaL, Synthesis and Analysis (SALSA)—was used to examine the main review types.
            
            
              Results: 
              Fourteen review types and associated methodologies were analysed against the SALSA framework, illustrating the inputs and processes of each review type. A description of the key characteristics is given, together with perceived strengths and weaknesses. A limited number of review types are currently utilized within the health information domain.
            
            
              Conclusions: 
              Few review types possess prescribed and explicit methodologies and many fall short of being mutually exclusive. Notwithstanding such limitations, this typology provides a valuable reference point for those commissioning, conducting, supporting or interpreting reviews, both within health information and the wider health care domain.},
	language = {en},
	number = {2},
	urldate = {2025-10-03},
	journal = {Health Information \& Libraries Journal},
	author = {Grant, Maria J. and Booth, Andrew},
	month = jun,
	year = {2009},
	note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1471-1842.2009.00848.x},
	pages = {91--108},
}

Abstract Background and objectives:  The expansion of evidence‐based practice across sectors has lead to an increasing variety of review types. However, the diversity of terminology used means that the full potential of these review types may be lost amongst a confusion of indistinct and misapplied terms. The objective of this study is to provide descriptive insight into the most common types of reviews, with illustrative examples from health and health information domains. Methods:  Following scoping searches, an examination was made of the vocabulary associated with the literature of review and synthesis (literary warrant). A simple analytical framework—Search, AppraisaL, Synthesis and Analysis (SALSA)—was used to examine the main review types. Results:  Fourteen review types and associated methodologies were analysed against the SALSA framework, illustrating the inputs and processes of each review type. A description of the key characteristics is given, together with perceived strengths and weaknesses. A limited number of review types are currently utilized within the health information domain. Conclusions:  Few review types possess prescribed and explicit methodologies and many fall short of being mutually exclusive. Notwithstanding such limitations, this typology provides a valuable reference point for those commissioning, conducting, supporting or interpreting reviews, both within health information and the wider health care domain.