Review Comments Research Articles

Enhancing Ammonia Synthesis and CO Hydrogenation to Methanol via Nonthermal Plasma Catalysis: A Focus on Catalyst Pore Structure and Active Site Design

The increasing energy demand, climate change and sustainable development necessitate the transition from fossil fuels to renewable sources (such feedstock and energy). Green hydrogen is one of the key components in sustainable energies/fuels being crucial for reducing carbon emissions and achieving carbon neutrality. However, hydrogen storage and transportation are challenging, and chemical hydrogen storage reactions such as ammonia synthesis and CO2 hydrogenation (to methanol) are considered promising solutions. Nonthermal plasmas (NTPs) are partially ionized gases with various energetic species, which are produced under ambient conditions being able to facilitate efficient chemical reactions under mild conditions. Importantly, NTP technologies can theoretically utilize green electricity produced by renewable energies (such as solar and wind) showing significant low-carbon potential. Previous studies have shown the catalysts design has an important impact on the performance of catalytic reactions under NTP conditions, and hence this aspect deserves attention. Here this mini review comments on the application of NTP catalytic technologies in ammonia synthesis and CO2 hydrogenation to methanol, with a special focus on catalyst pore structure and active site design. The critical summary and perspective can serve as the most current snapshot of the relevant research fields in NTP technologies, helping their further development.

ACoRA – A Platform for Automating Code Review Tasks

Background: Modern Code Reviews (MCR) are frequently adopted when assuring code and design quality in continuous integration and deployment projects. Although tiresome, they serve a secondary purpose of learning about the software product. Aim: Our objective is to design and evaluate a support tool to help software developers focus on the most important code fragments to review and provide them with suggestions on what should be reviewed in this code. Method: We used design science research to develop and evaluate a tool for automating code reviews by providing recommendations for code reviewers. The tool is based on Transformer-based machine learning models for natural language processing, applied to both programming language code (patch content) and the review comments. We evaluate both the ability of the language model to match similar lines and the ability to correctly indicate the nature of the potential problems encoded in a set of categories. We evaluated the tool on two open-source projects and one industry project. Results: The proposed tool was able to correctly annotate (only true positives) 35%–41% and partially correctly annotate 76%–84% of code fragments to be reviewed with labels corresponding to different aspects of code the reviewer should focus on. Conclusion: By comparing our study to similar solutions, we conclude that indicating lines to be reviewed and suggesting the nature of the potential problems in the code allows us to achieve higher accuracy than suggesting entire changes in the code considered in other studies. Also, we have found that the differences depend more on the consistency of commenting rather than on the ability of the model to find similar lines.

Why do consumers purchase energy-efficient kitchen appliance? An analysis based on online review comments.

Kitchen appliances constitute a significant portion of residential energy consumption. Promoting consumer purchases of high-energy-efficient kitchen appliances can effectively facilitate the transition to a low-carbon society. However, the role of energy saving in the current purchasing behavior of Chinese consumers regarding kitchen appliances remains unclear. Based on user-generated content from online e-commerce platforms (N=59,908), this study investigates whether Chinese consumers consider energy efficiency and its influencing factors when purchasing kitchen appliances through natural language processing and structural equation modeling techniques. The study findings reveal that: (1): Perceived energy saving is an important consideration for consumers to adopt kitchen appliances. (2): Prices benefits increase consumers' willingness to purchase energy-efficient appliances. (3): Consumers prioritize energy saving in the purchase of large kitchen appliances. (4): the effect of perceived energy saving on intention to purchase kitchen appliances is much stronger among high energy-efficient appliances. The results of this study propose specific policy recommendations to ensure the continuity of current demand-side energy efficiency policies related to kitchen appliances, utilize pricing mechanisms to incentivize consumers to purchase high-energy-efficient appliances, emphasize the importance of energy consumption of different types of kitchen appliance in environmental education, and establish stricter energy efficiency standards for kitchen appliances.

Explaining Explanations: An Empirical Study of Explanations in Code Reviews

Code reviews are central for software quality assurance. Ideally, reviewers should explain their feedback to enable authors of code changes to understand the feedback and act accordingly. Different developers might need different explanations in different contexts. Therefore, assisting this process first requires understanding the types of explanations reviewers usually provide. The goal of this paper is to study the types of explanations used in code reviews and explore the potential of Large Language Models (LLMs), specifically ChatGPT, in generating these specific types. We extracted 793 code review comments from Gerrit and manually labeled them based on whether they contained a suggestion, an explanation, or both. Our analysis shows that 42% of comments only include suggestions without explanations. We categorized the explanations into seven distinct types including rule or principle, similar examples, and future implications. When measuring their prevalence, we observed that some explanations are used differently by novice and experienced reviewers. Our manual evaluation shows that, when the explanation type is specified, ChatGPT can correctly generate the explanation in 88 out of 90 cases. This foundational work highlights the potential for future automation in code reviews, which can assist developers in sharing and obtaining different types of explanations as needed, thereby reducing back-and-forth communication.

Which review aspect has a greater impact on the duration of open peer review in multiple rounds? —Evidence from Nature Communications

PurposePeer review plays a crucial role in scientific writing and the publishing process, assessing the quality of research work. As the volume of paper submissions increases, peer review becomes increasingly burdensome, highlighting the importance of studying the duration of peer review. This study aims to explore the correlation between review aspect sentiment and the duration of peer review as well as the differences in this relationship across different disciplines and review rounds. Thus helping authors make targeted revisions and optimizations to their papers while reducing the duration of peer review, which enables authors’ research findings to reach the academic community and public domain more rapidly.Design/methodology/approachThe study employs a two-step approach to understand the impact of review aspects on the duration of peer review. First, it extracts fine-grained aspects from peer review comments and uses sentiment classification models to classify the sentiment of each review aspect. Then, it conducts a correlation analysis between review aspect sentiment and the duration of peer review. Additionally, the study calculates sentiment scores for various review rounds to explore the differences in the impact of review aspect sentiment on the duration of peer review across different review rounds.FindingsThe study found that there is a weak but significant negative correlation between the sentiment of the review and the duration of peer review. Specifically, the aspect clusters, such as Evaluation & Result and Impact & Research Value, exhibit a relatively stronger correlation with the duration of peer review. Additionally, the correlation between review aspect sentiments and the duration of peer review varies significantly in different review rounds.Originality/valueThe significance of this study lies in connecting peer review comments text with the peer review process. By analyzing the correlation between review aspects and the duration of peer review, it identifies aspects that have a greater impact on the duration of peer review. This helps improve the efficiency of peer review from the perspectives of authors, reviewers and editors. Thus alleviating the burden of peer review and accelerating academic exchange and knowledge dissemination.

Analysing Quality Metrics and Automated Scoring of Code Reviews

Code reviews are an important part of the software development process, and there is a wide variety of approaches used to perform them. While it is generally agreed that code reviews are beneficial and result in higher-quality software, there has been little work investigating best practices and approaches, exploring which factors impact code review quality. Our approach firstly analyses current best practices and procedures for undertaking code reviews, along with an examination of metrics often used to analyse a review’s quality and current offerings for automated code review assessment. A maximum of one thousand code review comments per project were mined from GitHub pull requests across seven open-source projects which have previously been analysed in similar studies. Several identified metrics are tested across these projects using Python’s Natural Language Toolkit, including stop word ratio, overall sentiment, and detection of code snippets through the GitHub markdown language. Comparisons are drawn with regards to each project’s culture and the language used in the code review process, with pros and cons for each. The results show that the stop word ratio remained consistent across all projects, with only one project exceeding an average of 30%, and that the percentage of positive comments across the projects was broadly similar also. The suitability of these metrics is also discussed with regards to the creation of a scoring framework and development of an automated code review analysis tool. We conclude that the software written is an effective method of comparing practices and cultures across projects and can provide benefits by promoting a positive review culture within an organisation. However, rudimentary sentiment analysis and detection of GitHub code snippets may not be sufficient to assess a code review’s overall usefulness, as many terms that are important to include in a programmer’s lexicon such as ‘error’ and ‘fail’ deem a code review to be negative. Code snippets that are included outside of the markdown language are also ignored from analysis. Recommendations for future work are suggested, including the development of a more robust sentiment analysis system that can include detection of emotion such as frustration, and the creation of a programming dictionary to exclude programming terms from sentiment analysis.

The fate of manuscripts rejected by the Turkish Journal of Physical Medicine and Rehabilitation

Objectives: The aim of this study was to evaluate the fate of the articles after they were rejected from the Turkish Journal of Physical Medicine and Rehabilitation (Turk J Phys Med Rehab). Materials and methods: Between January 2016 and December 2021, rejected manuscripts by the Turk J Phys Med Rehab were retrospectively analyzed and whether these rejected articles were published in another journal was identified. For the manuscripts published elsewhere, article type, change in the article name, and the number and order of authors were noted. The index of the new journal, the impact factor for SCI-E journals and journal quartile were recorded. Whether the journal was a national journal/international journal, a specialty or non-specialty journal, and whether the impact factors were higher, lower, or the same as Turk J Phys Med Rehab were evaluated. Results: Totally, 76% of 1,051 rejected articles were accepted for publication in another journal, after an average of 13.73 months. The name of the article, the order of the authors, and the number of the authors remained unchanged in 71.4%, 79.3%, and 80.8% of the articles, respectively. A total of 69.9% of the journals were non-specialty journals and 61.8% were general international medical journals. In addition, 32.6% of the journals were included in the SCI-E, and 70.9% of the articles in SCI-E were included in the Q4 and Q3 scope. The impact factor with 51.9% were lower or the same with the Turk J Phys Med Rehab. Conclusion: Our study results showed that a high percentage of the articles rejected by the Turk J Phys Med Rehab found a place in another journal later, and that non-specialty journals that accept general articles were more prominent in the selection of journal. The fact that an article rejected from a journal can be corrected and amended in accordance with valuable reviewer comments by improving its academic quality and seeking success in other journals may be promising for researchers who submit their articles to journals.

Using AI‐supported peer review to enhance feedback literacy: An investigation of students' revision of feedback on peers' essays

AbstractAs a vital learning activity in second language (L2) writing classrooms, peer feedback plays a crucial role in improving students' writing skills. However, student reviewers face challenges in providing impactful feedback on peers' essays. Low‐quality peer reviews emerge as a persistent problem, adversely affecting the learning effect of peer feedback. To enhance students' peer feedback provision, this study introduces EvaluMate, an AI‐supported peer review system, which incorporates a chatbot named Eva, designed to evaluate and provide feedback on student reviewers' comments on peers' essays. Forty‐four Chinese undergraduate students engaged with EvaluMate, utilising its features to generate feedback on peers' English argumentative essays. Chat log data capturing the students' interactions with the chatbot were collected, including the comments they wrote on peer essays and the feedback offered by the chatbot on their comments. The results indicate that the integration of AI supervision improved the quality of students' peer reviews. Students employed various strategies during their comment revision in response to AI feedback, such as introducing new points, adding details, and providing illustrative examples, which helped improve their comment quality. These findings shed light on the benefits of AI‐supported peer review systems in empowering students to provide more valuable feedback on peers' written work. Practitioner notesWhat is already known about this topic Scholars have extensively investigated diverse pedagogical strategies to enhance students' peer feedback provision skills in second language (L2) writing classrooms. Artificial intelligence (AI) technologies have been utilised to monitor and evaluate the peer feedback generated by student reviewers. AI‐enabled peer feedback evaluation tools have demonstrated the ability to provide valid assessments of student reviewers' peer feedback. What this paper adds In the context of L2 writing, there is a lack of bespoke AI‐enabled peer feedback evaluation tools. To address this gap, we have developed an AI‐supported peer review system, EvaluMate, which incorporates a large language model‐based chatbot named Eva. Eva is designed to provide feedback on L2 students' comments on their peers' writing. While previous studies have primarily focused on assessing the validity of AI‐enabled peer feedback evaluation tools, little is known about how students incorporate AI support into improving their peer review comments. To bridge this gap, our study examines not only whether using the system (EvaluMate) can enhance the quality of L2 students' peer review comments but also how students respond to Eva's feedback when revising their comments. Implications for practice and/or policy The development of the AI‐supported peer review system (EvaluMate) introduces an innovative pedagogical approach for L2 writing teachers to train and enhance their students' peer feedback provision skills. Integrating AI supervision into L2 students' peer feedback generation improves the quality of comments provided by student reviewers on their peers' writing. Students employ various strategies when revising their comments in response to Eva's feedback, and these strategies result in varying degrees of improvement in comment quality. L2 writing teachers can teach effective revision strategies to their students.

Part I: examining the broken history of manual therapy across professions. A survey-based analysis

ABSTRACT The historical development of manual therapy is an area of ongoing debate impacting clinical practice, education, and practice regulations. Primary professions utilizing manual therapy include chiropractic, manual medicine, osteopathy, and physiotherapy. A survey was developed to explore perceptions, experiences, and opinions across professions, and was disseminated globally. It was completed by 194 individuals. Results demonstrated this topic is of significance with over 80% reporting that knowledge of historical development informs professional identity. Of the respondents, 64% had over 20 years professional experience. Student participation was low (<1%). Over 95% acknowledged an ancient basis for manual therapy, with 67% emphasizing bonesetter contributions. North America was reported as the primary area for the development of modern manual therapies by all except physiotherapy, which identified Northern Europe. Osteopathy’s impact on current practice was recognized, though each profession ranked its own impact highest. Of respondents, 85% agreed there was conflict between professions over history. Thematic elements identified a shift for respondents from their initial education to a more nuanced understanding of the history over time, and an appreciation that there is not one profession that owns or developed manual therapy. Practice limitations were identified, as 19% of respondents reported limitations due to inaccurate historical understanding. This study highlights a lack of historical knowledge and its potential benefits for practice, education, regulation and interprofessional relations if recaptured. (the abstract was rewritten per reviewer comments to reformat)

What matters most? A sustainable or a conventional hotel experience? The interaction effect on guest satisfaction: A mixed-methods approach

This study examined the interaction effect of sustainable and conventional hotel experience on satisfaction in sustainable hotels. Using a hierarchical linear model, Study 1 analysed 60,725 Tripadvisor review comments from the ten highest-rated sustainable hotels in the USA, and Study 2 conducted a ‘2 × 2’ online experiment. Results empirically confirmed the positive impact of sustainable and conventional hotel experiences on satisfaction. They also demonstrated that how the sustainable and conventional hotel experiences interact can result in varying satisfaction levels. Specifically, a rank order in generating satisfaction was identified, with guests being more satisfied with highly sustainable, low conventional experiences than with low sustainable, high conventional ones. This study contributes to sustainable tourism literature by theoretically and empirically testing how and why the interaction effects on satisfaction occur in sustainable hotels. It provides practical implications for hotels on how to increase satisfaction by strategically integrating sustainable practices with conventional services.

Using the Review Process to Professionally Develop Auditors

SUMMARY In this article, we provide a practitioner summary of our paper “Coaching today’s auditors: What causes reviewers to adopt a more developmental approach?” (Andiola, Brazel, Downey, and Schaefer 2024). In our study, we examine workpaper review by supervising audit seniors as a quality control mechanism intended to detect audit preparer (staff) errors in the short term and professionally develop preparers in the long term. We find that reviewers adopt a more developmental approach when reviewing workpapers completed by local preparers. We also observe that, regardless of office affiliation, reviewers are apt to adopt a more developmental approach when preparers are likely to recur. Our results also indicate that reviewers who adopt a more developmental approach exhibit greater coaching in their actual review comments and are also more likely to detect preparers’ errors. We summarize our key findings and discuss practical implications and actionable suggestions for audit practitioners. Data Availability: Contact the authors. JEL Classifications: L2; M40; M42; M51.

Egyptian pediatric kidney transplantation: highlights on post-transplant follow-up and management of complications by the Egyptian Pediatric Clinical Practice Guidelines Committee (EPG) Nephrology Group

BackgroundPediatric kidney transplantation is unique when compared to adults. They need optimum post-transplant care to achieve longer graft and patient survival. Infections, rejection, recurrence, malignancy, bone problems, hypertension, and surgical complications are the most important challenges that require prevention, early detection, and management. Chronic allograft nephropathy needs different approaches based on its related causes, allograft pathology, graft function, and availability of re-transplantation.Aim/objectivesThis national adapted guideline aims to frame evidence-based post-transplant recommendations adopted or adapted from EB source guidelines to optimize post-transplant patient care.MethodsOur guideline is evidence based adapted from four national and international source guidelines with permissions [KDIGO 2020, RA/BTS 2022–2018, EAU 2018] that were appraised with AGREE II instrument tool. We followed the “adapted ADAPTE” CPG formal adaptation methodology that consists of 3 phases and 24 steps and tools. Virtual monthly meetings all through the year 2023 were activated for reviewing and validation of final adaptation of evidence-based guideline draft, considering all comments of external reviewers. This guideline was registered in the practice guideline registration for transparency (PREPARE), number: PREPARE-2023CN364.ResultsRecommendation statements in guidelines stand for results in articles. Discussion of important recommendations and the rationale behind selection of adopted statements and tailoring of others to suit our local facilities, expertise, and renal native disease profile was presented in the text with reasons and references.ConclusionThe provided guidelines aim to improve graft and patient outcome through prevention and early management of complications and suggest research areas lacking validated research recommendations.

10.K. Skills building seminar: Practical and methodological guidance for conducting online qualitative surveys in public health

Abstract Online qualitative surveys are surveys which prioritise qualitative (open text) questions in data collection. Researchers have stated that these types surveys have rich potential for qualitative researchers. They are able to collect rapid insights into new or emerging areas of health, and are an effective way of reaching populations who may not otherwise engage in qualitative interview studies or focus groups. Although these types of surveys are increasingly used by public health and health promotion researchers, there is very limited discussion in the research literature to guide researchers about the practical and methodological aspects of such surveys. This creates some challenges for qualitative researchers - as well as funders, and peer reviewers - in assessing the rigour and robustness of these studies. Using data from a range of online qualitative surveys that we have conducted with youth and adults relating to gambling, alcohol, climate change, and the commercial determinants of health, we describe the processes associated with developing, implementing, interpreting, and writing up online qualitative survey data. In this practical and interactive multimedia workshop and developing on the work of Braun, Clarke and colleagues, with a focus on Big Q interpretivist qualitative approaches. We will provide: 1. Practical examples, and lessons learned from our own studies about survey design, question development, the importance of rigorous piloting strategies, the use of novel techniques to prompt detailed responses from participants, and the decisions that are made about data cleaning. 2. Examples of the types of responses that can be expected in different types of surveys, and how decisions are made about the quality of responses to questions. 3. Guidance about how to analyse the volume of qualitative data that is generated from such surveys, including developing robust mechanisms to ensure trustworthiness of decision making and data interpretation. 4. Tips based on our role as journal editors about how to present qualitative survey data, and how to respond to challenging reviewer comments. Participants will be provided with a booklet containing survey templates, question examples, tips and examples of how to respond to reviewer comments. Key messages • Online qualitative surveys have rich potential for collecting insights about emerging public health issues. • This workshop will provide practical and methodological guidance for researchers who are interested in using these surveys. Speakers/Panelists Hannah Pitt Deakin University, Burwood, Australia Simone McCarthy Deakin University, Armadale, Australia Samantha Thomas Deakin University, Geelong, Australia

Leveraging large language models for daily tourist demand forecasting

ABSTRACT Large Language Models have attracted the attention of tourism researchers, and many discussions have been published in leading tourism journals. However, little research has been conducted on how to use Large Language Models in tourism research. In this paper, we propose a new tourist arrival forecasting model based on the Large Language Model. The Large Language Model is used to extract and produce the satisfaction scores from the review comments by the tourists. The generated satisfaction scores are incorporated into the tourist arrival forecasting model to take full advantage of the extracted information from the review comments. We have applied the Large Language Model based forecasting model to predict the tourist arrival in Macao. Experiment results show that the proposed model has produced forecasts with improved forecasting accuracy.

„Covidowa” dylogia Ewy Głażewskiej i Małgorzaty Karwatowskiej: „Maska w »czasach zarazy«. Covidowe wizerunki masek – typologie i funkcje” (Wydawnictwo UMCS, Lublin 2021, ss. 206) oraz „Humor w »czasach zarazy«” (Wydawnictwo UMCS, Lublin 2023, ss. 265)

The review comments on two books on COVID-19 – constituting a kind of whole. In both monographs, the authors, scientists from Lublin, offered an in-depth description and interesting analyzes of selected aspects of the modern pandemic. From the dominant perspectives (linguistic and cultural studies), the multidimensionality of both the mask phenomenon and the category of humour was examined. The studies highlight the richness of their manifestations and functions. The most important values of the books are the attractiveness and topicality of the topics covered.

Sentiment Analysis of the MK Decision Trial of the Result of the 2024 President and Vice President General Election on Social Media X Using the Support Vector Machine Method

Support Vector Machine (SVM) is a method of machine learning often used in classification and regression issues, especially in the classification of commentary reviews on social media such as Twitter. The Constitutional Court (MK) has the authority to resolve disputes resulting from the general election, including the 2024 presidential election. As an institution that maintains fairness and transparency in the democratic process, the Constitutional Court's decisions are often at the center of public attention and debate, especially on social media. In the 2024 general election, various allegations of fraud led to protests from several parties who felt aggrieved. The final and binding Constitutional Court's decision is expected to resolve the conflict that arises, but it often does not satisfy all parties, causing political and social tensions. This conflict can be reflected through public opinion expressed on social media, such as Twitter, where various responses and sentiments to the decision are essential analysis materials. This Research uses the Support Vector Machine (SVM) algorithm with a dataset of 1383 review comments divided by an 80:20 ratio for training and testing. The system was implemented using the Python programming language, with evaluations showing the highest accuracy at 61.00%, precision at 61.00%, and recall at 62.00%. This study aims to analyze public sentiment regarding the Constitutional Court's decision using the SVM method and identify the tendency of public opinion as positive, negative, or neutral. Through this study, it is expected that a deeper understanding of the public's perception of the Constitutional Court's decision is obtained. In addition, this Research is likely to contribute to developing sentiment analysis methods in the future and provide a basis for recommendations for the Constitutional Court in handling election result disputes better.

Defining, measuring and managing healthcare quality using unstructured physician review comments

PurposePhysician review websites (PRW) and Medicare requirements are pressing administrators to measure, monitor and improve healthcare service delivery. Healthcare service attributes linked to patient satisfaction have received increased attention. Text analysis provides an alternative methodology to capture contemporaneous data on service delivery attributes. A Kano analysis based on these service attributes can help administrators prioritize service delivery and ultimately improve patient satisfaction.Design/methodology/approachHealthcare service attributes were defined from 4,000+ comments on a PRW using latent content text analysis. The resulting 15 attributes were validated by medical professionals using a q-sort methodology and analyzed using a Kano methodology.FindingsThe 15 attributes cover three domains of healthcare service – clinic operations, competency and care. The Kano analysis yields a hierarchy, or pyramid, of healthcare service attributes: (1) must-be’s: establish service operational capabilities and benchmark peer performance; (2) satisfiers: establish and increase trust through: (a) clinical competence, (b) careful management of young patients and (c) delivery of appropriate care and treatment (3) delighters: use service-dominant logic to provide patient-centered care.Originality/valueThis research bridges the gap between the “what” and “how” that is frequently missing in text analysis of online reviews. We provide a methodology coupled with a Kano analysis, a widely used quality improvement tool, which results in a hierarchy of service attributes that can guide administrators and researchers.

Investigation of research quality and transparency in neurosurgery through the utilization of open science practices.

Neurosurgical research is a rapidly evolving field, with numerous studies continuously published. As the body of research grows, upholding high-quality standards becomes increasingly essential. Open science practices offer tools to ensure quality and transparency. However, the prevalence of these practices remains unclear. This study investigated the extent to which neurosurgical publications have implemented open science practices. Five open science practices (preprint, equator guidelines, published peer review comments, preregistration, and open accessibility to data and methods) were measured from five top-ranked neurosurgical journals (Neurosurgery, Journal of Neurosurgery, World Neurosurgery, Neurosurgical Review, and Acta Neurochirurgica), according to Google Scholar. One hundred fifty articles were randomly sampled from January 1, 2022, to December 31, 2023. Two reviewers analyzed these articles for their utilization of open science practices. A third reviewer settled disagreements. One journal required (20%) and three journals (60%) recommended utilizing EQUATOR guidelines. Three journals (60%) allowed preprints, and all five journals (100%) recommended or required preregistration of clinical trials, but only two (40%) recommended preregistration for systematic reviews (Fig.1). All five journals (100%) recommended or required methods to be publicly available, but none (0%) published peer-review comments. Neurosurgical Review utilized the most open science practices, with a mean utilization of 1.4 open science practices per publication versus 0.9 across the other four journals (p < 0.001). Moreover, Neurosurgical Review significantly utilized more open science practices versus Journal of Neurosurgery (p < 0.05) and World Neurosurgery (p < 0.05). Both randomized controlled trials (p < 0.001) and systematic reviews (p < 0.001) significantly utilized more open science practices compared to observational studies. Despite advocacy from neurosurgical journals, the adoption of open science practices still needs improvement. Implementing incentives and clearer requirements may prove beneficial. Promoting these practices is crucial to enhancing research quality in neurosurgery.

Employee Ratings and Reviews Data from Glassdoor

ABSTRACT This paper presents the employee ratings and reviews data from Glassdoor and the R codes used to collect, clean, and organize the data. We collect three types of information for each Glassdoor review: review metrics, content, and reviewer information. We also calculate some commonly used textual metrics, such as sentiment, readability, the number of uncertainty words, etc. The datasets include necessary identifiers that can connect to other financial data sources. All the variables and metrics are provided at the review level, which enables researchers to aggregate the data from different levels and angles. As a demonstrative example, we use a simple word list to measure how often employees mention COVID-19 in review comments. The R codes provided as an RStudio project are self-contained and can also be modified and applied to other data sources of interest.

Cultural distance, gender and praise in peer review

ABSTRACT Background: Understanding review comments holds significant importance within the realm of scientific discourse. This study aims to conduct an empirical analysis of factors associated with praise in peer review.Methods: The study involved manual labeling of “praise” in 952 review comments drawn from 301 articles published in the British Medical Journal, followed by regression analysis.Results: The study reveals that authors tend to receive longer praise when they share a cultural proximity with the reviewers. Additionally, it is observed that female reviewers are more inclined to provide praiseConclusions: In summary, these discoveries contribute valuable insights for the development of a constructive peer review process and the establishment of a more inclusive research culture.