Statistical Software: R-Studio Version 4.4.0
Data Analysis Tools: Excel 2016
Text Editor: Notepad
Adobe acrobat reader
Possession of a moderate level of familiarity with the utilized tools, ranging from beginner to intermediate proficiency. This includes a basic understanding of R-Studio and Microsoft Excel, enabling comprehension and analysis of the provided code and results. Additionally, familiarity with text editors and Adobe Acrobat Reader facilitated the viewing of the supplementary data graphically.
R-Studio
Statistical Software: R-Studio Version 4.4.0
Data Analysis Tools: Excel 2016
Text Editor: Notepad
Adobe Acrobat Reader
Directory Management Challenges: • Review of the paper and the OSF repository identified several critical issues with the management and organization of the review paper, figures, and data. First, the repository was not up to date, leading to inconsistencies and potential confusion. In addition, the naming convention for the R code files began with a confusing number (e.g., "2. Timeseries"), which could be misleading. • A significant problem was the lack of raw data from the original survey questions before they were cleaned. This absence, coupled with the discrepancy in question numbering between the raw survey data and the cleaned data (e.g., Q25 in the raw data vs. Q1 in the cleaned data), increases the risk of errors in the R code. Such errors are particularly difficult to detect because each question is measured on a scale of 1 to 5. • The documentation of the data could be better organized. There was also a lack of metadata for the R code, especially a ReadMe file, and the existing metadata did not meet the completeness standards set by the DataCite scheme. Essential information such as resource details, funding/support information, and affiliated institutions were missing from the OSF page. Software Management Challenges: •The correct definition of the dictionary and output path was required. The data folder was missing essential files such as Network_Orthogonal and Network_Correlated, which had to be created by uncommenting certain sections of the code. This may explain why the results of the network model script were not reproducible and differed from the results of the paper regarding the numbering of the nodes. •Some measurement information was missing from the measurement files for variables such as Ethica Items, Rawdata_post, Clean_post, and Label/Scale. • Directory management proved to be a major problem. Sometimes the output was outside the specified working directory, making it difficult to locate certain graphs or figures after the code had run. Some parts of the code that required tinkering should have been commented out much earlier but were not. This led to wasted time going through the code to find the problem, which was not a trivial task. •Variables were assigned and reassigned quite often, which was tedious to track, especially in the case of raw data to clean data. The abundance of variable assignments in the program increased its overall complexity, making it difficult to understand and monitor variable states.
•Data repository: The use of a data repository hosted on the Open Science Framework (OSF), a platform widely recognized in the field of psychology for promoting reproducibility, was an effective way for the reader to gain quick access and an overview of the project. The repository includes an identifiable DOI, which improves its traceability and citation in the scientific literature. • Metadata: Basic metadata is provided, including license information (CC-By Attribution 4.0 International), ensuring clear usage rights. The data is freely accessible and stored in open data formats such as XLSX for Excel sheets and RData for cleaned data. In addition, the R code is available and can be easily opened with R and RStudio, both free and easy-to-use tools. • User Friendliness: Most of the datasets were pre-cleaned and ready for analysis, with only two datasets requiring extraction from the script. Most of the figures and results were reproducible, demonstrating the effectiveness of the R code provided. • Detailed Documentation: The great attention to detail in the paper demonstrates a dedication to thoroughness and precision. By giving full coverage of the subject matter – COVID and mental health, a good amount of knowledge can be gleaned. Furthermore, allowing their research to be audited promotes openness and accountability in the academic or professional setting by ensuring that all essential information is available to scrutiny. This is very admirable! • Proper Categorization: The methodical classification of the material into relevant categories (pre/post assessment and the Ecological Momentary Assessment) provided a structural framework that improved accessibility and comprehension. The documentation made it easier to navigate and retrieve relevant material by using categorization concepts such as theme groups used in the supplementary materials like the code book, figures, etc. • Effective Data Representation: The appropriate depiction of data is critical to expressing complicated concepts and empirical findings in a clear manner. By using appropriate graphical, tabular, or text formats, the documentation improves the trends, patterns, and correlations, increasing the interpretive value of the material. • Inclusion of references, legends, and tables of figures: The use of references, legends, and figure tables demonstrates commitment to academic standards. The data sets and trends were more efficiently tracked and confirmed. Similarly, this improved clarity by providing contextual information to the time series and network models charted, etc. • Exploratory Nature and Transparency: The exploratory nature of the research demonstrates a dedication to intellectual inquiry and discovery-driven research. Also, the fact that their work was opened up to scrutiny is a testament to their upholding the full transparency code of conduct while also protecting the anonymity of the participants.
The paper has created the basis for future research efforts by providing a complete approach. As a result, any researcher who would like to expand or reproduce this research would have the necessary knowledge of where to start. Even though, it may not allow for full replication, it does however, provide a good enough starting point and an invaluable insight into the discussed topic.
•To improve the efficiency and clarity of data analysis, all datasets used to generate results could be included in the dataset folder. This would eliminate the need for readers to create datasets themselves, as currently required by the network model script, thereby saving time. •Additionally, improving the naming conventions of variables that represent survey questions or, preferably, using consistent question numbering across both raw and cleaned data sets would help prevent confusion and errors. •The repository also needs to be updated, especially the R code for the network model analyses. The review paper stored in the folder should also be updated, as it is currently an early draft with empty figures. Updating these items will ensure that the repository reflects the final, polished state of the research and its results. •Less variable reassignment would be beneficial in further work to avoid confusion of what variables were tracked, especially on a scale as this.
•Very detailed. •Cohesive and followed a straight-forward trend (mostly). •The naming convention was reasonable and comprehensible. •Provision of an extra file to serve as guide/legend for the reported variables
Overall, the steps taken with regards to transparency were quite detailed and in depth. Providing the code was quite useful to delve into the tools used and the thought process. All bases were covered in this respect.
•To enhance the reliability and reproducibility of the results, it is crucial to address the issues mentioned by updating the repository, standardizing naming conventions, ensuring raw data availability, aligning question numbering, improving documentation, providing complete metadata. •Coding could be improved to help auditors without knowledge of the software to be able to run things in one click without the need for tinkering. •The variable assignment while the reassignment was documented could have been renamed with better conventions to avoid confusion about what was being audited. •Some of the data used cannot be accessed due to being closed. While this was unavoidable in this case, as the data belonged to Apple Maps, it understandably cannot be accessed anymore. It would be better to use data that is available in the long term for cross-checking and audits.
This paper's documentation demonstrates accessibility and clarity and exhibits an admirable commitment to accuracy and completeness. The careful classification of resources in conjunction with efficient methods for presenting data improves the research findings' overall communication effectiveness. To further enhance the study's credibility and dependability, references, legends, and figure tables are included in accordance with academic norms. Given that code and additional materials are available for review, the exploratory nature of the research demonstrates a dedication to intellectual curiosity and transparency. Despite being thorough and well-organized, the documentation might use some polish, especially when it comes to minimizing the variable reassignment (survey questions) to prevent misunderstandings and improving code readability for greater reusability. All things considered, this work establishes a strong framework for further research projects by offering a thorough method that makes replication and growth easier.
