Towards real-world monitoring scenarios: An improved point prediction method for crowd counting based on contrastive learning

Rundong Cao; Jiazhong Yu; Ziwei Liu; Qinghua Liang

doi:10.1371/journal.pone.0327397

Peer Review History

Original SubmissionNovember 14, 2024
Decision Letter - Yawen Lu, Editor Dear Dr. Cao, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript by Mar 15 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Yawen Lu, Ph.D Academic Editor PLOS ONE Journal requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf Additional Editor Comments : Dear Dr. Cao, PONE-D-24-51986 I am writing to you regarding the above referenced manuscript that you submitted to Plos One. Based on the enclosed reviews, I am pleased to inform you that this manuscript is recommended for Major Revision for publication in the journal. Please carefully address the reviewers' comments and suggestions regarding network efficiency, used data, organization, explanation, and literature reviews, etc., to improve the quality of the manuscript in the revised submission. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? Reviewer #1: Partly Reviewer #2: Partly ******** 2. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #1: Yes Reviewer #2: No ****** 3. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #1: Yes Reviewer #2: No ****** Reviewer #1: 1. The point prediction method mentioned in the paper has enhanced detection in both high- and low-density regions, but adaptability in complex scenarios is still a concern. Is the introduction of more training data or the use of data augmentation techniques considered to improve the robustness of the model? For example, training the model with different lighting conditions, weather changes, or different viewpoints may make it perform more robustly in real-world applications. 2. How to further improve the detection accuracy in dense crowd scenes is an important research direction. The multi-scale feature fusion module mentioned in the paper is already a good attempt, but is it possible to explore other detection methods or optimize the existing framework? For example, it is possible to consider combining other deep learning models (e.g., the Transformer architecture) to enhance the feature extraction capability, or to introduce self-supervised learning methods to further improve the performance of the model. 3. The model mentioned in the paper directly predicts header points during inference, which may affect real-time performance. Is it possible to optimize the computational efficiency of the model for real-time monitoring while maintaining accuracy? 4. It is mentioned in the paper that the method can be applied to the counting of other objects, such as vehicles and animals, in the future. Are there any plans to conduct relevant experiments to verify the effectiveness of the method in counting different objects? In addition, is it considered to combine the method with behavioral analysis to provide a more comprehensive monitoring solution? Reviewer #2: This paper presents a novel approach to crowd counting using contrastive learning and point-based detection. While the work shows promise and addresses important challenges in crowd counting, several major issues need to be addressed. Strengths: - The paper addresses important real-world challenges in crowd counting - dealing with complex backgrounds and multi-scale targets. - The authors provide comprehensive experiments across several datasets to validate the proposed method's effectiveness. - The experimental results show the approach's state-of-the-art performance. Suggestions: - The current experiments and analysis do not sufficiently demonstrate the contribution of contrastive learning. Please provide more thorough validation and ablation studies. - The paper organization needs significant improvement. Important technical details are scattered and sometimes unclear. - Since the paper focuses on a real-world problem, the computational complexity analysis and comparison should be considered. - Please provide more detailed explanations and annotations for the figures (e.g., figure 1, 3, 6) - Current literature reviews missed some recent works on local-global feature fusion and point/feature matching in visual learning, including [Transflow: Transformer as flow learner, 2023][Fusioncount: Efficient crowd counting via multiscale feature fusion, 2022][Lightglue: Local feature matching at light speed, 2023][Superthermal: Matching thermal as visible through thermal feature exploration, 2021], etc. ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy Reviewer #1: No Reviewer #2: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org . Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0327397.r001
Revision 1
19 Feb 2025 Author Response Dear Editors and Reviewers: Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Towards Real-world Monitoring Scenarios: An Improved Point Prediction Method for Crowd Counting Based on Contrastive Learning” (No.: PONE-D-24-51986). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Reviewers' comments: Reviewer's Responses to Questions 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Partly Reviewer #2: Partly The authors’ answer: We sincerely appreciate the reviewers for their acknowledgment of our paper. We have made every effort to improve the manuscript and have implemented several changes. 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: No The authors’ answer: We sincerely appreciate the reviewers for their recognition of our paper. We have made every effort to improve the manuscript and have implemented several changes. 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes The authors’ answer: We sincerely appreciate the reviewers for acknowledging our work. 4.Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: No The authors’ answer: Thanks for your suggestion. We have made every effort to improve the manuscript and have implemented several changes. These modifications will not affect the content or structure of the paper. 5. Review Comments to the Author Reviewer #1: 1.The point prediction method mentioned in the paper has enhanced detection in both high- and low-density regions, but adaptability in complex scenarios is still a concern. Is the introduction of more training data or the use of data augmentation techniques considered to improve the robustness of the model? For example, training the model with different lighting conditions, weather changes, or different viewpoints may make it perform more robustly in real-world applications. The authors’ answer: We have used more training data and data augmentation techniques in practical applications, but this part was not included in the paper to maintain consistency with other methods. Considering the reviewer's suggestion, we have added this part, which is highlighted in red in the Introduction section. 2.How to further improve the detection accuracy in dense crowd scenes is an important research direction. The multi-scale feature fusion module mentioned in the paper is already a good attempt, but is it possible to explore other detection methods or optimize the existing framework? For example, it is possible to consider combining other deep learning models (e.g., the Transformer architecture) to enhance the feature extraction capability, or to introduce self-supervised learning methods to further improve the performance of the model. The authors’ answer: It is possible to explore other detection methods or optimize the existing framework. Considering the reviewer's suggestion, we have added a discussion on this issue , which is highlighted in red in the Conclusion section. 3. The model mentioned in the paper directly predicts header points during inference, which may affect real-time performance. Is it possible to optimize the computational efficiency of the model for real-time monitoring while maintaining accuracy? The authors’ answer: The model we designed in the paper has already considered computational efficiency of the model for real-time monitoring. Our network is faster than other deep learning models such as Transformers. Besides, our method consumes less GPU memory, which makes practical applications more advantageous. 4.It is mentioned in the paper that the method can be applied to the counting of other objects, such as vehicles and animals, in the future. Are there any plans to conduct relevant experiments to verify the effectiveness of the method in counting different objects? In addition, is it considered to combine the method with behavioral analysis to provide a more comprehensive monitoring solution? The authors’ answer: Thanks for your suggestion. We have plans to conduct relevant experiments for counting different objects and combine the method with behavioral analysis in the future. However, we will not do the experiments in this paper due to the limitations of research conditions and article length. Reviewer #2: This paper presents a novel approach to crowd counting using contrastive learning and point-based detection. While the work shows promise and addresses important challenges in crowd counting, several major issues need to be addressed. - The current experiments and analysis do not sufficiently demonstrate the contribution of contrastive learning. Please provide more thorough validation and ablation studies. The authors’ answer: Thank you for your suggestion. We have already conducted an ablation study on the use of contrastive loss to demonstrate the contribution of contrastive learning. And we provide more thorough validation and ablation studies which is highlighted in red in the Qualitative Study on Contrastive learning part. - The paper organization needs significant improvement. Important technical details are scattered and sometimes unclear. The authors’ answer: Thanks for your suggestion. We have revised the paper organization highlighted in red in the Our Method section. - Since the paper focuses on a real-world problem, the computational complexity analysis and comparison should be considered. The authors’ answer: Thanks for your suggestion. We have added the computational complexity analysis and compared with other methods. This addition is marked in red in the Experiments section. - Please provide more detailed explanations and annotations for the figures (e.g., figure 1, 3, 6) The authors’ answer: We have already provided explanations and annotations for the figures. Considering the reviewer's suggestion, we have added more explanations highlighted in red for the figures including figure 1, 3, 6. - Current literature reviews missed some recent works on local-global feature fusion and point/feature matching in visual learning, including [Transflow: Transformer as flow learner, 2023][Fusioncount: Efficient crowd counting via multiscale feature fusion, 2022][Lightglue: Local feature matching at light speed, 2023][Superthermal: Matching thermal as visible through thermal feature exploration, 2021], etc. The authors’ answer: Thanks for your suggestion. We have revised the literature reviews highlighted in red in the Introduction and Related Work section. Attachments Attachment Submitted filename: Response to Reviewers.docx https://doi.org/10.1371/journal.pone.0327397.r002
Decision Letter - Ayesha Maqbool, Editor Dear Dr. Cao, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Your manuscript presents a well-structured analysis, but a few areas require minor revisions for improved clarity and consistency. Please submit your revised manuscript by Jul 20 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org . When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'. An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols . We look forward to receiving your revised manuscript. Kind regards, Ayesha Maqbool, PhD Academic Editor PLOS ONE Journal Requirements: Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice. [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author Reviewer #2: (No Response) Reviewer #3: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions??> Reviewer #2: Partly Reviewer #3: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? -->?> Reviewer #2: No Reviewer #3: (No Response) ****** 4. Have the authors made all data underlying the findings in their manuscript fully available??> The PLOS Data policy Reviewer #2: Yes Reviewer #3: (No Response) ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English??> Reviewer #2: No Reviewer #3: (No Response) ****** Reviewer #2: This paper presents a novel approach to crowd counting using contrastive learning and point-based detection. The topic is interesting and meaningful in real-world. However, some of my concerns have not been addressed. 1. Some captions for the figures are too simple, the authors should provide an indepth analysis for each figure. For example, while the Fig. 6 illustrates the impact of different patch numbers on MAE, the caption should further explain key trends, such as why the MAE fluctuates after a certain threshold and how this finding supports the proposed method. 2. In Figure 1, the point-based detection results are really hard to recognize. The authors may consider changing the point color. 3. Some sections could be better structured for clarity, particularly in the explanation of contrastive learning. 4. I recommend a thorough revision of the writing to improve readability. Reviewer #3: Thank you for taking the time to consider and thoughtfully address my comments. I appreciate your attention to detail and your commitment to incorporating constructive feedback to improve the overall quality of the work ****** what does this mean? ). If published, this will include your full peer review and any attached files. If you choose “no”, your identity will remain anonymous but your review may still be made public. Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy Reviewer #2: No Reviewer #3: No ******** [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.] While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/ . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org https://doi.org/10.1371/journal.pone.0327397.r003
Revision 2
11 Jun 2025 Author Response Dear Editors and Reviewers: Thank you for your letter and for the reviewers’ comments concerning our manuscript entitled “Towards Real-world Monitoring Scenarios: An Improved Point Prediction Method for Crowd Counting Based on Contrastive Learning” (No.: PONE-D-24-51986R1). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have carefully studied the comments and made the necessary corrections, hoping that they will meet with your approval. Reviewers' comments: Reviewer's Responses to Questions 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #2: (No Response) Reviewer #3: All comments have been addressed The authors’ answer: We sincerely appreciate the reviewers for their acknowledgment of our paper. 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #2: Partly Reviewer #3: The authors’ answer: We sincerely appreciate the reviewers for their acknowledgment of our paper. We have made every effort to improve the manuscript and have implemented several changes. 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: No Reviewer #3: The authors’ answer: We sincerely appreciate the reviewers for their recognition of our paper. We have made every effort to improve the manuscript and have implemented several changes. 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #2: Yes Reviewer #3: The authors’ answer: We sincerely appreciate the reviewers for acknowledging our work. 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #2: No Reviewer #3: The authors’ answer: Thanks for your suggestion. We have made every effort to improve the manuscript and have implemented several changes. These modifications will not affect the content or structure of the paper. 6. Review Comments to the Author Reviewer #2: 1. Some captions for the figures are too simple, the authors should provide an indepth analysis for each figure. For example, while the Fig. 6 illustrates the impact of different patch numbers on MAE, the caption should further explain key trends, such as why the MAE fluctuates after a certain threshold and how this finding supports the proposed method. The authors’ answer: Thanks for your suggestion. We have revised the illustration, highlighted in red, for explaining key trends in MAE of different patch numbers in the Ablation Experiments section. 2. In Figure 1, the point-based detection results are really hard to recognize. The authors may consider changing the point color. The authors’ answer: Thanks for your suggestion. We have changed the point color and point size to make the point-based results easier to recognize. 3. Some sections could be better structured for clarity, particularly in the explanation of contrastive learning. The authors’ answer: Thanks for your suggestion. We have modified the paper structure highlighted in red in the Our Method section. In the section on contrastive learning network design and loss function design, we elucidate the methodology of leveraging contrastive learning to enhance the performance of crowd counting. 4. I recommend a thorough revision of the writing to improve readability. The authors’ answer: Thanks for your suggestion. We have made every effort to improve the manuscript and hope to meet with approval. Reviewer #3: Thank you for taking the time to consider and thoughtfully address my comments. I appreciate your attention to detail and your commitment to incorporating constructive feedback to improve the overall quality of the work The authors’ answer: We sincerely appreciate your acknowledgment of our paper. Attachments Attachment Submitted filename: Response_to_Reviewers_auresp_2.docx https://doi.org/10.1371/journal.pone.0327397.r004
Decision Letter - Ayesha Maqbool, Editor Towards Real-world Monitoring Scenarios: An Improved Point Prediction Method for Crowd Counting Based on Contrastive Learning PONE-D-24-51986R2 Dear Dr. Cao, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Ayesha Maqbool, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: https://doi.org/10.1371/journal.pone.0327397.r005
Formally Accepted
Acceptance Letter - Ayesha Maqbool, Editor PONE-D-24-51986R2 PLOS ONE Dear Dr. Cao, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset You will receive further instructions from the production team, including instructions on how to review your proof when it is ready. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few days to review your paper and let you know the next and final steps. Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. If we can help with anything else, please email us at customercare@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Ayesha Maqbool Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0327397.r006

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .