Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PHILDB: THE TIME SERIES DATABASE WITH BUILT-IN CHANGE LOGGING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The sample dataset used in experiments have not been submitted nor cited as required by journal policy.

The language may be improved, as there are some long sentences difficult to read. For example, "...easier to integrate with (compared with proprietary systems) and they are more fitting..." Isn't it better with the included parenthesis or other phrase structure? By the way, "are more fitting" -> "fit better"?

In my humble opinion, the novel system proposed architecture is weakly explained. Several ideas to improve: explain all relevant details, maybe some figures can clarify, what about the use cases in which the log is useful (specially in research).
Experimental design: The paper could benefit from a more rigorous experimentation. Some ideas:
 - Comparing with the only other TS DB mentioned with logging (Stonebraker).
 - Would not data from table 1 be better represented in a quartile plot (with a note including mean and std)?
 - What about performing experiments on the use of the logging facility?
 - The comment about the problem with dates in InfluxDB deserves a bit of work to actually explain why InfluxDB can not be used. Being open source the code is available and in a time series database this should be something that can be answered.
 - The results about the space required by each database could be commented explaining which are the differences in the storage mechanism. is there any functionality in InfluxDB not supported in PhilDB. This may be ok. It is just that it would be good to know when each one fits better.
Validity of the findings: No comments
Additional comments: Interesting idea. It seems a good piece of software. However I think the paper needs revision to fit in a research journal.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PHILDB: THE TIME SERIES DATABASE WITH BUILT-IN CHANGE LOGGING
Review round: 2
Reviewer: 1

Output:
Basic reporting: The author has taken into account the suggestions proposed in the first review, except the style of writing. It is written as a report. Introduction should have the context of the problem, motivation, the goal of the paper and how it is organised, then, background, architecture, implementation and evaluation. In research paper sentences such as “The author’s interest is derived …” or “The need of the author is …” must be avoided, it’s better to write “Phildb aims at handling data for exploratory purposes….”
The adjective “simple” in the last sentence of the summary devalues the paper.
Evaluation section should be better organised, it would be good to add a paragraph or table where you indicate the metrics to be evaluated before starting to show results. Dataset description should be described before the experiment as well as the methodology followed; later, results and discussion. Sections 6.2,6.3 and 6.4 should be grouped in one subsection since it exposes DB setting preliminaries or moved to an addendum since their description is this section breaks the threat of the evaluation.
Experimental design: As previously said, evaluation section should be rewritten. Now the experimentation is acceptable although the evaluation of another dataset with different features would be welcome.
Validity of the findings: correct
Additional comments: I consider this database tries to fill a gap in the temporal series database market and thus this contribution is valuable. The style of writing must be enhanced.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PHILDB: THE TIME SERIES DATABASE WITH BUILT-IN CHANGE LOGGING
Review round: 2
Reviewer: 2

Output:
Basic reporting: All basic issues commented have been reasonably addressed and the paper has improved.
Experimental design: I think the experimental design is clearer, reproducible and reasonably complete now.
Validity of the findings: Experiment results are meaningful now.
Additional comments: No comments.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MONOPHY: A SIMPLE R PACKAGE TO FIND AND VISUALIZE MONOPHYLY ISSUES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article is easy to understand and well written.

My major comment is that the authors state that higher order taxa not being monophyletic might be caused by problems in the taxonomy (i.e., mis-annotation) or problems with the data (i.e., contamination).  One of the most important (but not discussed) reasons is gene tree/species tree conflict.  This could be a result of incomplete lineage sorting (ILS) or hortizontal gene transfer (HGT).  I think some discussion about this would help strengthen the paper.  For example, an intruder species might be due to error (as suggested by the authors), or it might be a real biological finding due to HGT.  The authors could then motivate MonoPhy as not only being able to identify error but also potentially HGT events.

Finally, there were a few minor corrections needed:

Page 2, Line 64:  higher taxon -> higher order taxon
Page 3, Line 89:  from by all -> from all
Experimental design: No comments
Validity of the findings: No comments
Additional comments: I have tested this software and it is easy to use.  My only suggestion is that it would be helpful if the taxonomy file allowed header information.  Thus, instead of using this command:

GetIntruderTips(solution1, taxa="Ericoideae", taxlevels=2)

One could use this command:

GetIntruderTips(solution1, taxa="Ericoideae", taxlevels='order')

As I have many different metadata files with header information beyond just taxonomy (i.e., geographic location, host organism, etc..)  I could then easily write a script that could use any of my metadata files without needing to know exactly which column number is the group I'm interested in, I could just use the column name.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MONOPHY: A SIMPLE R PACKAGE TO FIND AND VISUALIZE MONOPHYLY ISSUES
Review round: 1
Reviewer: 2

Output:
Basic reporting: In this paper, Schwery and O’Meara describe a new R package they have written, MonoPhy, for assessing taxonomic monophyly of phylogenetic trees. The package takes in phylogenetic trees with taxonomic leaf labels, and from that input makes plots and finds “intruder” taxa, which are taxa that disrupt monophyly. This should be a helpful package for researchers using R. The paper is written as a software announcement, and is generally suitable for that format.

However, I was left wanting clearer definitions of the objects under discussion. In particular, I was confused by the definitions of “intruders” and “outliers”. If I parse the definition of intruders correctly, “intruded” taxa will commonly themselves be intruders as well. For example, imagine that we have a large clade that is monophyletic with the exception of two somewhat widely spaced leaves. These two leaves will be marked as “intruders”, as should be the case, but by the definition the leaves below their MRCA will also be marked as intruders, which seems strange. Is this the case? I shouldn’t have to read the source to understand.

The defintion of outliers starts with the sentence “We thus implemented an option to specify a cutoff value, which gives the minimal proportion of tips among the descendants of a taxon’s MRCA that are actual members of that taxon.” I think this is supposed to mean “are assumed to be actual members of that taxon”? Again, it’s important to make these definitions clear.

Please describe the algorithm the code uses to find these various characteristics of the trees in the paper.

The paper does not cover relevant prior literature. In fact, I couldn’t find any reference to computational approaches to assessing concordance between a phylogeny and a taxonomy. Here are some relevant papers:

Dalevi, D., Desantis, T. Z., Fredslund, J., Andersen, G. L., Markowitz, V. M., & Hugenholtz, P. (2007). Automated group assignment in large phylogenetic trees using GRUNT: GRouping, Ungrouping, Naming Tool. BMC Bioinformatics, 8, 402.

This next paper (cited 667 times) builds on that one, developing the `tax2tree` tool:

McDonald, D., Price, M. N., Goodrich, J., Nawrocki, E. P., DeSantis, T. Z., Probst, A., … Hugenholtz, P. (2011). An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. The ISME Journal, 6(3), 610–618.

We also have written a paper in the area:

Matsen, F., & Gallagher, A. (2012). Reconciling taxonomy and phylogenetic inference: formalism and algorithms for describing discord and inferring taxonomic roots. Algorithms for Molecular Biology: AMB, 7(1), 8.

In it we address the ambiguity of intruder versus intruded (described above) is by casting it as a minimization problem, which is NP-complete but fixed-parameter tractable.
Experimental design: This paper is a software announcement rather than a research paper, so the “research question” doesn’t quite fit here.

The code is well commented, and the vignette does a nice job of showing functionality of the package. The reference manual seems complete. I could not find any sort of tests, which is unfortunate. I was surprised that the code does not handle multifurcating trees.
Validity of the findings: No Comments, as this is a software announcement.
Additional comments: My recommendation is somewhere between minor and major revisions. If it would seem helpful for me to look at the paper again, I'd be happy to do so, but I'm not going to insist.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MONOPHY: A SIMPLE R PACKAGE TO FIND AND VISUALIZE MONOPHYLY ISSUES
Review round: 1
Reviewer: 3

Output:
Basic reporting: The manuscript appears to meet all the standards for the journal.
Experimental design: The experimental design seems sound, apart from one small point. It would seem better not to report on the monophyly of taxa with one representative. The outcome is trivial, as there is no opportunity to not be monophyletic. Hence, restricting the evaluation to taxa with two more more representatives allows more meaningful results, and perhaps might save some computation. I do agree that reporting the number of taxa with a single representative is useful, but considering them as monophyletic when there is no possible way for them not to be monophyletic seems silly.
Validity of the findings: The findings seem valid.
Additional comments: Although my determination is accept as is, I think you should consider the point raised regarding evaluating and reporting monophyly for taxa with one representative.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMPUTATIONAL FRAMEWORK FOR COLOUR METRICS AND COLOUR SPACE TRANSFORMS
Review round: 1
Reviewer: 1

Output:
Basic reporting: The author presents an interesting material related to colour metrics and colour space transformation . It will be nice if he explains better the figures  5 to 8 , and  organizes better the references.
Experimental design: Nice work . However,  the inclusion of more figures in section 2 and 3 will help to illustrate the work applications, as well as more examples  in the "Example Application" section (4).
Validity of the findings: The Conclusion (section 5) deserve  be expanted.
Additional comments: How about some comparison with other usual metrics for instance those presented in: M. M. Deza and E. Deza, Encyclopedia of Distances, Springer-Verlag Berlin Heidelberg 2009


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMPUTATIONAL FRAMEWORK FOR COLOUR METRICS AND COLOUR SPACE TRANSFORMS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The word "CIEDE2000" is used as both of metric and space. Readers may confuse. CIEDE2000 is usually used as a color difference metric. You should clearly define the CIEDE2000 color space.
Experimental design: In the manuscript, it is written that new color spaces can be created. However, there is no discussion about the meaningful color spaces. It is important to derive  new color spaces from the derived framework.
Validity of the findings: The discussion of computational complexity or processing speed lacks.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A COMPUTATIONAL FRAMEWORK FOR COLOUR METRICS AND COLOUR SPACE TRANSFORMS
Review round: 1
Reviewer: 3

Output:
Basic reporting: It is unclear whether others have already implemented a similar job and a comparison between the proposed method and the other.
Experimental design: no comment
Validity of the findings: There is uncertainty regarding the strengths of the work. it is necessary to explain the innovative points.
It is not clear the robustness of the method.
there are no news from the point of view of research but only from the implementation point of view.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HOW ARE FUNCTIONALLY SIMILAR CODE CLONES SYNTACTICALLY DIFFERENT? AN EMPIRICAL STUDY AND A BENCHMARK
Review round: 1
Reviewer: 1

Output:
Basic reporting: In the Intro. it is mentioned "traditional clone detection approaches and tools are hardly able to detect functionally equivalent clones...". but the reference does not cover the recent research in the past five years. Recently, there has been several work on Semantically similar clone detection by exploiting various representation of program such as AST, PDG, bytecode etc.

This sentence in Section 1.1 is not clear: "...make a functionally similar clone really different from copy..".  A FSC clone can be the result of copy&paste and then some immediate modification and customization. this kind of clones belong to which group?
Experimental design: The tools selected for the study are fine, but the justification in Section 3.4.1 is a little bit imprecise. For example the sentence that is supporting your choice is outdated  (from 2010): "They have been described as most up-to-date implementations of token based and AST based clone detection algorithms". Or another case, "Commercial tools were not exact enough in what they detect."

Please report Number of methods in Table 3. Also, in Table 3, the size column is unnecessary.

Section 3.5.2, it is mentioned the parameters are set in a way to find more clones. However, min clone length == 6 seems too restrictive, specifically for small programs.

The most important aspect of Type-3 clone detection is the choice of similar threshold. Please, discuss them in Section 3.5.2.
Validity of the findings: "No Comments"
Additional comments: The paper reports an interesting analysis which looks into the source code of programming competitions. I liked the idea of analyzing such code repositories for clone detection research, and I believe this would be an interesting article for the community.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HOW ARE FUNCTIONALLY SIMILAR CODE CLONES SYNTACTICALLY DIFFERENT? AN EMPIRICAL STUDY AND A BENCHMARK
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper is written clearly, very easy to read. It introduces an intriguing problem of detecting functionally similar code with sufficient background, and motives well why it is needed to study the characteristics of functionally similar code that cannot be detected as syntactically similar code. What is unclear though is the definition of "functionally similar code", in comparison with the definition of "functionally the same code"; probably better to clarify what is considered "similar", e.g., same input producing same output except for output formats, etc.

The contents are mostly self-contained except for more details about the tools and parameters used.

Although the paper says all the data are available in their github project, no RUL link is provided.
Experimental design: To study functionally similar code that cannot be detected as syntactically similar code, the paper chose to use code written in different programming languages from Google Code Jam Contest as the subjects for the study, which is a good choice. Code clone detection tools that can handle different programming languages are used so that the study can be carried out for different programming languages and different tools, which is a good design for achieving better generalizability.

However in the actual study, only two languages, Java and C, and 3 clone detection tools, ConQAT, Deckard, and CCCD, are considered;  this limits the general applicability of the results, especially the hypotheses H1, H2, H4 which consider the differences between different languages and tools. May consider revising the hypotheses to be more specific with respect to the actual data used.
Validity of the findings: The paper aims to better understand the characteristics of functionally similar code that cannot be detected as syntactically similar code. With the limited dataset from Google Code Jam Contest, it categorizes the code differences between functional clone pairs into 5 categories, each of which is split into 3 levels of difference degrees. The results provide a new perspective on such differences, although the definition of "low", "medium", "high" of difference degrees are too vague and may not be easy to automate.

The benchmark containing categorized functional clones can be useful. However, the size of the benchmark seems to be too small in comparison with all the code from Google Code Jam Contest. Can the categorization of functional clones be automated, so as to make it more scalable to have larger benchmarks?
Additional comments: The paper addresses an important, but challenging problem. It provides a categorization of functionally similar code different from those categories in the literature, complementing other studies. Although the results showing the categories are interesting, it would be much better to have more insights and a more concrete future plan for designing a better functional clone detection technique. Just based on the empirical study results, it is still unclear to me how a better detection technique may be derived. So I think only the first part of the research objectives (as stated in Section 1.2: to better understand the differences...to support future research on their detection) is achieved.

Minor issues about writing:
- Page 10, "similar CVS Files" -> CSV
- Page 14, "The other categories of the pairs are very low, ideally zero." I don't understand the rationale of the sentence.
- Page 14, "A final set of clone pairs that are not detected as full clones..." Does this mean the benchmark include functional clones that can be detected partial syntactic clones? Why don't exclude partial clones too to make the benchmark "stronger"?
- Page 22, among the 3 advantages mentioned, only the second one may be considered as a contribution of this paper; may make this point clear when comparing to other studies in the literature.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: HOW ARE FUNCTIONALLY SIMILAR CODE CLONES SYNTACTICALLY DIFFERENT? AN EMPIRICAL STUDY AND A BENCHMARK
Review round: 1
Reviewer: 3

Output:
Basic reporting: See general comments.
Experimental design: See general comments.
Validity of the findings: See general comments.
Additional comments: The presented submission is almost identical to a previous submission to a conference for which I have been a reviewer. The submission was rejected at the conference and the authors received detailed and valuable feedback. However, the current submission has not considered the feedback and the only changes are the addition of a few tables and four paragraphs of text.

I appreciate the effort that the authors have invested in the paper and believe that in the end, the work can lead to publishable material after the authors have considered the comments they have received for their earlier version.

As the paper has not been significantly changed compared to the conference version, I refer the authors to the comments they have received for that version.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 1
Reviewer: 1

Output:
Basic reporting: The article is well written. There is sufficient background and introduction. The literature is well cited - of a few additional references might be in appropriate drug repositioning (21849665, 21849664), drug-drug interactions (25122524,  22422992).
Experimental design: I'm not familiar with the scope of the journal but the experimental design is well described and easy to understand.
Validity of the findings: The authors make the following assumption, which is inherently problematic. “drug X causes a distinct profile of side-effects, and this side-effect profile is typical of drugs used to treat a certain disease Y, then drug X should be evaluated for repositioning for the treatment of disease Y”. Repositioning shouldn’t be done on the basis of similar side effects, but side effects “rescuing” a disease phenotype. Side effects are a negative consequence and if one drug with a certain side effect profile is used to treat a disease, there is no evidence that another drug with a similarly bad side effect profile will be a better therapeutic. This should either be discussed in depth as a limitation of the approach or the study shouldn’t be framed as a drug repositioning study, but a way to identify drugs with similar mechanism. In addition to mining Twitter for drug information, I would suggest mining it for disease symptoms and try to link drugs and diseases based on a directional inverse association of the side effects and symptoms.
Additional comments: The authors describe a novel computational method that uses side-effect data mined from social media to show that known indications are well recovered and current trial indications can also be identified. The work relies on the assumption that Drugs with similar side-effects might share a common mechanism of action; therefore it may be possible to infer new indications based on the similarity of side-effect profiles. While the approach of mining social media in the context of drug repositioning is innovative, the computational methods applied are robust, there is an underlying flaw in the study.

In addition to the issue raised above, there several other concerns:

•	Figure 2 is a hairball and doesn’t offer much value, it would be more useful to provide the statistics on the network – how many nodes, significant edges, which is the node with the highest number of connections? What are the strongest relationships? I would also incorporate side effect information here - how many different side effects were included? Which drug has the highest number of side effects?
•	Table 1 – how were these chosen? Are these the top 10? Also should include original indication
•	It would be interesting to see what are the underlying “tweets/side effects” for each of the drug pairs.
•	How were the three candidates chosen to discuss further? Maybe list in Table 1 and expand Figure 4?
•	The datasets used for validation namely “Cortellis Clinical Trials Intelligence” is not a resource available to those who would like to replicate the findings. At least for the compounds investigated here, that data should be provided as a supplement

While the method shows encouraging results, I agree with the authors that it is more likely to play a role in drug repositioning as a component in an integrated approach, potentially with databases like SIDER and molecular predictions.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 1
Reviewer: 2

Output:
Basic reporting: The INTRODUCTION section provides a good overview of drug repurposing and the different pharmacological aspects used by different researchers, including side-effects similarities. However, in my opinion there is information that should be described in detail in this section:
•	Page 2: The authors mention that existing repositioning methods based on side-effects have used data from the SIDER database and other similar resources, but they do not reference the corresponding papers.
•	As an alternative to the lack of completeness of these traditional resources, they propose using side-effects mined from social media. However, the only limitation described by the authors is that only a small fraction of daily tweets contain reports of drug side-effects, restricting the number of drugs analysed in the study. However, text mining of side effects is still an open research area confronting several challenges such as use of idiomatic expressions, spelling errors, use of ambiguous terms, or exaggerated information that might provide false positives, among others (Leaman et al., 2010; Sampathkumar, Chen, & Luo, 2014; Segura-Bedmar, Martínez, Revert, & Moreno-Schneider, 2015).
•	In addition to this, side-effects were collected from social media during a 6 months period. In my opinion, it is probable that the number of side effects reported for a drug in social media during that period of time would be smaller than the number of side-effects collected in traditional resources − such as, for example, the Summary of Product Characteristics. If this is the case, using data from social media would not address the limitation of traditional resources – i.e., their lack of completeness and difficulty to be updated with new side-effects information.
•	(Page 6) In RESULTS AND DISCUSSION section it is asserted that “our method should provide a viable alternative to existing approaches”. However, these other approaches are not discussed in the paper. Moreover, their current limitations, and how they could be overcome by the method proposed by the authors, are not described.
•	(Page 2) The authors describe the hypothesis that “drugs sharing a significant number of side-effects might share a common mechanism of action linking side-effects with disease treatment”. However, the examples they use to illustrate this (exenatide, minoxidil and sildenafil) are not based on this hypothesis (i.e., drugs sharing a similar side-effects profile could share a common indication, which is not necessarily related to any of the side-effects), but in the identification of a potential indication directly related to one specific side effect. I would recommend to point this out, or to illustrate these examples in a different part of the text to avoid confusion.

Leaman, R., Wojtulewicz, L., Sullivan, R., Skariah, A., Yang, J., & Gonzalez, G. (2010). Towards Internet-Age Pharmacovigilance : Extracting Adverse Drug Reactions from User Posts to Health-Related Social Networks. In Association for Computational Linguistics (Ed.), Proceedings of the 2010 Workshop on Biomedical Natural Language Processing. BioNLP ’10 (pp. 117–125). Stroudsburg, PA, USA.
Sampathkumar, H., Chen, X., & Luo, B. (2014). Mining Adverse Drug Reactions from online healthcare forums using Hidden Markov Model. BMC Medical Informatics & Decision Making, 14(91), 1–18.
Segura-Bedmar, I., Martínez, P., Revert, R., & Moreno-Schneider, J. (2015). Exploring Spanish health social media for detecting drug effects. BMC Medical Informatics and Decision Making, 15(Suppl 2), S6.
Experimental design: My main concern is that the paper describing the methods followed to obtain drug side-effects from Twitter is described in a paper that is not published, but under preparation (reference [45] in the manuscript). I suggest that this document should be provided to the reviewers as supplementary material for this review. Otherwise, a better description of the methods − especially the evaluation and limitations − should be provided in this paper.
In addition to this, there is one possible bias in the experiment “RECOVERERING KNOWN INDICATIONS”, that is not discussed by the authors. Drugs belonging to the same group of drugs (e.g., statins), could have similar side-effect profiles. Therefore, it would be expected that two drugs, such as simvastatin and lovastatin, would be ranked to be similar, and therefore the main indication would be correctly “predicted” through this method. Authors should explain if this could have influenced these results, or how did they handle this problem.
Validity of the findings: The authors say that “While data sets and underlying statistical models clearly differ, these results taken together suggest that the use of side-effect data mined from social media can certainly offer comparable performance to methods using side-effect data extracted from more conventional resources” (page 6). However, this conclusion cannot be extracted from the present study. It is possible that the method described by the authors could obtain better results using side-effects data from the same resources than other researches (e.g., Ye et al.). The influence of the provenance of data in the results obtained with this new method has not been evaluated.
Common side-effects shared by drugs within the subgraphs in the three examples (oxytocin, ramelteon and meloxicam) are side-effects commonly associated to a large number of drugs. However, other authors have observed an inverse correlation between side-effects frequency and the likelihood of two drugs to share a protein target (Campillos, Kuhn, Gavin, Jensen, & Bork, 2008). In my opinion, this is an interesting observation that should be discussed in the paper.
Limitations of the study refer mainly to a paper that is not published. Therefore, a better description of the limitations should be provided.

Campillos, M., Kuhn, M., Gavin, A., Jensen, L. J., & Bork, P. (2008). Drug Target Identification Using Side-Effect Similarity All us. Science, 321(5886), 263–266.
Additional comments: All my comments are covered by the 3 areas above.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 1
Reviewer: 3

Output:
Basic reporting: The article is generally well-written but there are some typographical mistakes as well as writing style problems that I have listed in the comments for the authors.
Experimental design: The research explained in the paper is of value and useful. However, experiments and evaluations should be clarified by the authors as explained in the comments to the authors.
Validity of the findings: The data used here is Twitter data which cannot be shared (only Tweet ids can be shared). Even that, authors make no comment if they are sharing that to make their experiments repeatable by other researchers.

Evaluations explained in the paper have some ambiguity which they have to be cleared before any decision is made for the publication of this work.
Additional comments: The paper investigates the usability of social media (Twitter) for repositioning of the drugs based on their side effects. The motivation is clear and the problem itself is interesting and worth pursuing. The paper itself basically uses an existing method with limited modification to a new problem and does not propose a novel algorithm/method. Novelty is mostly the application. One main issue here is that the evaluations are weak and mostly based on anecdotes. How this system is useful in practice is something that the presented evaluations are not showing much. Below are more detailed comments:

- The paper hardly touches on the data it uses and its main issue due to the tweets nature: data quality. It is assumed that the tweets it gets (with the given criteria) are really talking about side effects. However, they could easily being misleading. Also, who did you deal with drug misspellings and the informal language of the tweets? Conclusions discuss the limitation of Twitter data but not this issue. I'd like to see this issue is clearly analyzed or discussed in this work. Also, it would help if a Materials or Data section come before or right after Methods.

- Page 2, lines 75-85: A number of references to the "recent" work in extracting adverse reactions from social media/medical forums are given. These studies are not longer any recent and in fact many more studies have been published in 2015 alone. I'd suggest authors to have a look at more advanced work than a workshop paper in 2010. You can look at a 2015 survey published in ACM computing survey for some of the later work (Text and Data Mining Techniques in Adverse Drug Reaction Detection/the social media section).

- Page 3, line 90: you cannot cite a work that is in preparation. If you want to use its material you either have to include it in this paper so it gets reviewed here or wait till your other paper gets published.

- page 3, line 113: how clinical trial is used as a feature to SVM?

- page 3: what was the training data for the SVM classifier?

- Page 3: how normalization to World Drug bank was done? This step important if done automatically as there is often large error rate is associated with it.

- page 6: Figure 1(a): How the frequencies are measures that they are all less than one? Not sure if that figure is correct given the explanation (top-n, etc). Needs to be clarified.

- In general it would be nicer to have figures or table top or bottom aligned in a page.

- Page 6, line 223: what does extracted from [32-35] means?? Why not mention exactly what here rather than ambiguously referring to others work and expect your reader to go read those work to understand yours.

- A discussion on what the performance/effectiveness values mean for the experiment on page 6-7. If we have a system that finds some correct indication but much more false ones, how is this helpful in a realistic setting. That is, what human workload would you expect to make use of this?

- Page 8: what the value of PCC tells us? What is the range and what values are good or bad?

Minor issues:
some proof-reading is required, for example:
- page 6: remove --> removing
- Page 7: know indications --> known indications

- Page 7: Figure 2 is not showing much and can be removed. The colors are also not helpful.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comments
Experimental design: No comments
Validity of the findings: No comments
Additional comments: The authors have addressed all my comments and have updated the manuscript in a way that the description of methods and results, as well as limitations of their approach, are clearly exposed. Therefore, I believe that the paper meets the PeerJ criteria and should be accepted as is.
However, I would like to point out two minor mistakes:
page 4, 151: Side effect – Side effects
page  8, 318: Using Using -- Using


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 2
Reviewer: 2

Output:
Basic reporting: The paper has improved since its original version addressing concerns mentioned the previous review. Thanks.
Experimental design: While authors have included more details on their experimental setting in the revised version, I still have a few more questions on the settings and methodology:

1- Line 149 mentions topic and volume filters. Could you please clarify in the text what are these filters. If that's already there it is not very clear.

2- CHV is very limited. Do you have any idea how much its limitation may have affected your data collection?

3- How the annotation of the tweets were done? Who annotated them and if they were annotated by multiple annotators, did you calculate inter-annotator agreement?

4- Did you randomly divide the data into training, testing and development or did you sort them based on a filter such as time? Past work has shown that it is possible to overfit a SVM classifier if tweet data is randomly divided because of the dependency of the tweets (look at:  Reading the markets: Forecasting public opinion of political candidates by news analysis, in COLING 2008, and Evaluation Methods for Statistically Dependent Text, Computational Linguistics journal 2015).
If your data is in fact divided randomly, I'd be interested to know if you get similar results by sorting them and using old tweets as training/dev and new ones as testing.
In any case, please clarify this in your manuscript for your readers.
Validity of the findings: Please see above.
Additional comments: - I still think that Figure 2 could be removed and the corresponding information being shown in the text. The caption does include interesting information, but the figure itself not.

- Normalization method has to be explained in the text the way you explained in response to my comment before. Also, mention this to the limitation of your work.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: COMPUTATIONAL DRUG REPOSITIONING BASED ON SIDE-EFFECTS MINED FROM SOCIAL MEDIA
Review round: 3
Reviewer: 1

Output:
Basic reporting: Please add a sentence to clarify how you split the data into training and testing (time based).
Experimental design: no comment
Validity of the findings: no comment
Additional comments: No comment


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PATACSDB—THE DATABASE OF POLYA TRANSLATIONAL ATTENUATORS IN CODING SEQUENCES
Review round: 1
Reviewer: 1

Output:
Basic reporting: The abstract looks concise to me.

I cannot find any figure or table for the manuscript. It would be best if the authors could include some figures and tables for user-friendly explanation. It is important as the manuscript is a database paper.
Experimental design: The experimental setting "pattern 12A allowing for one mismatch" has to be carefully supported on the manuscript. Have the authors tried other experimental setting since this setting can significantly affect the database content ?
Validity of the findings: The sentence "Given that no genomic database reports polyA tracks in coding sequences" may be arguable, please look at the following databases and state your difference to them:
http://exon.umdnj.edu/polya_db/
http://bioinfolab.miamioh.edu/polya/
http://mosas.sysu.edu.cn/utr/
https://www.google.com/webhp?q=polya%20database
Additional comments: It is very nice that the authors have released the scripts which can recreate the
database from the scratch on user's own computer.

The sentence "...is of course a bit simplistic...." is not appropriate on a scientific manuscript.

The first studies
The past studies

gathered researchers' attention
drew researchers' attention


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: PATACSDB—THE DATABASE OF POLYA TRANSLATIONAL ATTENUATORS IN CODING SEQUENCES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The paper reports generation of a database of coding region polyA sequences from 250 genomes and web service to look this up.
While databases are useful for researchers to look things up in their studies, I am not sure this justifies a scientific report as there is not much experiments or science in this report.
There was also no mention of the validation of the search results at all:should we just trust that the results by the processes reported are reliable? Are all the sequences reported in coding regions? without sequencing errors? I think it is troubling that there is no mentioning of quality examination of the results.
Experimental design: The paper reported the process of collecting the polyA sequences in coding regions. There was no mention of validity check of the results and quality control.
Validity of the findings: No way to judge.
Additional comments: It is essential to validate the results in the database. It is also important to survey the distribution of such sequences in a few model genomes.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NANOPUBLICATION BEYOND THE SCIENCES: THE PERIODO PERIOD GAZETTEER
Review round: 1
Reviewer: 1

Output:
Basic reporting: The PeerJ Computer Science policies list is quite lengthy.  I do not feel that I can determine whether the submission is in compliance or meets all of the requirements.  I will proceed with the review as if the submission meets these.
Experimental design: The manuscript describes application of a rigorous methodology to structuring and organizing data meant for humanistic study.  As such, it does not mimic or pretend to conform to scientific methods used in quantitative or qualitative research.  However the data model described in the manuscript can be valuably applied to collections scientific data. The attributes of nanopublication have great value over traditional publications in journals - either those reporting on scientific work or humanities research.  Gregory Crane has noted in numerous white papers and presentations the advantages of "recombinant" documents.  Carole Goble has spoken frequently about the need for "staged-release" of findings of scholarly workflows.  Nanopublication as used here gives a useful formalization of these traits.
Validity of the findings: The authors address, correctly in my view, some of the inherent difficulties of working with humanities data.  Spatial and temporal information associated with a given place at a given time can often not be established with accuracy.  Geographical locations of places vary -  different texts and maps move them about.  The names of the places change as well.  This is sometimes a result of the ambiguity of natural language and translation possibilities.  Temporal data is subject to the same uncertainties.  The work described in this manuscript is very important in that regard and the authors convincingly make a case that humanistic

Linked data and semantic web technologies show their promise and power in humanities research.  The authors recognize this and make a strong case for a liberal interpretation of data as used by humanists.
Additional comments: The paper elevates the discussion of linked data and humanities research by providing a clear description of the nature of humanities data and reasonabe expectations in its use.  Provenance will always be a problem.  The exact origin of assertions and factual data may be beyond one's reach or simply unknowable.  The tasks of data curation and stewardship have not been yet identified clearly or who will be responsible for them.  It may be too much to ask of the scholar/researcher for certain types of studies.  This topic deserves a separate study.  I did not see a need to include all the detail in the that section of the paper.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NANOPUBLICATION BEYOND THE SCIENCES: THE PERIODO PERIOD GAZETTEER
Review round: 1
Reviewer: 2

Output:
Basic reporting: NBS is a challenging article for the uninitiated. As a scholarly communications librarian I am not the typical PeerJ Computer Science reader. However, I really enjoyed the challenge after finding nanopub.org, perio.do, and reading Mons et al. (2011) “The value of data.” My sticky-note comments target some of the places in the article where examples would have been helpful.
Experimental design: The first paragraph of the conclusion would have provided me with a better introduction to the article.

Unfalsifiable definitions and the role of curation are hugely important issues to the humanities and social sciences. I hope they will have the opportunity to read this article.
Validity of the findings: The authors appropriately explained the benefits of nanopublications to the humanities and social sciences while not denying the areas where PeriodO doesn't exactly fit the model.

This article deserves to join the scientific literature.
Additional comments: I would have gotten more out of the first reading of this article if the explanatory information had preceded the technical details. I would entitle this article “Nanopublication Beyond the Sciences and the PeriodO Case Study” since the authors did not offer any other examples of nanopubs in the humanities.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: NANOPUBLICATION BEYOND THE SCIENCES: THE PERIODO PERIOD GAZETTEER
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors took the reviewers comments seriously and have greatly improved the readability of this article.
Experimental design: No comments.
Validity of the findings: No comments.
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A SYSTEMATIC ANALYSIS OF THE SCIENCE OF SANDBOXING
Review round: 1
Reviewer: 1

Output:
Basic reporting: This paper conducts an analysis of individual sandbox mechanisms and reports on
different approaches to sandboxing and evaluates the design space and trade-offs
of these mechanisms. This survey looks at 5 major conferences and conducts the
meta study on the papers of the last couple of years.
Experimental design: Only a small subset of conferences were evaluated. For generality, other
(top) conferences like ASPLOS, ATC, OOPSLA, PLDI must be considered as well.
Also, the authors should look at VEE, a conference, co-located with ASPLOS that
focuses on virtualization and sandboxes with a long detailed history on
sandboxing work. The dataset therefore seems limited and selective (likely to
reasonably constrain work for the authors).

In 4.2 you explain the problems of a user when applying a sandbox. I missed a
discussion from the perspectives of a system administrator and a programmer.
Those should be more involved than a regular user and have additional domain
knowledge that would be interesting.
Validity of the findings: The authors selected papers by reading through titles. Why not consider keywords
and abstract as well? Especially, in systems-related works. Also, a fulltext
search for sandbox should have been considered. Therefore, I find that the
search for related work is limited.
Additional comments: The authors use a large amount of footnotes, many of them containing shortened
links. The paper should be self-contained which it is not. Also, as a reviewer I
cannot follow shortened goo.gl links because they would leak my anonymity. I was
therefore unable to evaluate all this additional material that was referenced.

Generally, the paper is very well structured, clear, and well written.

Nit: line 359: you cannot say that you don't look at these conferences and in
the next line that you are not aware of any such paper. If you are not
evaluating these conferences then you are naturally not aware of the existence
of such a paper.

In Figure 2: names of authors, names of techniques, and names of mechanisms are
mixed together. Can you make this figure more consistent?

In Figures 3 and 4 you introduce a lot of metrics

Figures 5 and 6 are very similar (yet 5 is in much worse quality).

Typo, line 312: is anywhere the application is: remove first is
Typo, line 328: diversity to binaries to: diversity in binaries


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A SYSTEMATIC ANALYSIS OF THE SCIENCE OF SANDBOXING
Review round: 1
Reviewer: 2

Output:
Basic reporting: The article is written very clearly, following a logical structure. Sufficient motivation for the study is provided in the introduction, with the subsequent material solidly delivering on the promises made.
 Based on the insights gained from the analysis, the authors propose a concise and comprehensive definition of the term "sandbox", report that the evaluation of sandboxes can be improved, and propose the use of systematic sandbox evaluation techniques as well as work on the usability front.

Figures are clean and well drawn.  Figures 3 and 4 could be more usefully presented by combining them into a cluster column chart. The dendrograms can be improved by labelling their top branches, and by linking the leaves to the corresponding papers. Figure 7 can be improved by combining the clusters lying in the vertical axis into one cluster column chart and labelling the corresponding clusters.  The meaning of Figure 7 should be expanded in the text.

Tables are used to good effect for the clear and succinct presentation of data.  In particular, Table 6 provides an exemplary concise and informative overview of the surveyed work.

On lines 160-167 the authors describe how they selected the conferences based on quantitative study of the conferences where sandboxing papers were published. It would benefit the community and the paper's reproducibility to provide these data.

The observation that policy flexibility can be used as a usability bellwether is novel and very interesting.

The finding regarding a lack of benchmark suites to highlight a sandbox's vulnerabilities (line 476) could be added to the conclusions.
Experimental design: The article reports on the state of the art in sandboxing methods through the use of quantitative content analysis on related papers published in top security conferences. It is certainly within the journal's scope. The 13 research questions studied and the possible answers are clearly defined in Table 3.  It is however unclear how the research questions were derived. The questions seem to place considerable weight on security policies, their usability, and evaluation, while e.g. placing less focus on other possible attributes, such as portability, security, or performance.  The authors allude to this bias on lines 169-173 and 554-555.  Focusing on a specific area is fine, as long as the authors explicitly clarify (and maybe also justify) the focus.

The choice of the conferences used can be also examined and justified by reference to the CORE Conference Ranking activity <http://www.core.edu.au/index.php/conference-rankings>.

The authors should be commended for their choice of quantitative content analysis as a method, which by itself is a valuable contribution to the field. The method is well described, though the reader must reference the supplementary data in order to understand its use. A few concrete examples could help the reader's understanding.

It would be nice to provide the rationale for the criteria used for picking papers (lines 180-190).  Theoretical constructs for sandboxes could also be explicitly listed as outside the paper's scope (line 183).

The regression method used for adjusting citation ranks according to the publication year (line 289) should be explained.
Validity of the findings: The statistical techniques used are appropriate for answering the research questions, and are correctly used to support the conclusions.

The study's raw data are made available as Google documents accessible through Google shortened URLs.  Given Google's relatively poor record in the long term support of its services <http://www.lemonde.fr/pixels/visuel/2015/03/06/google-memorial-le-petit-musee-des-projets-google-abandonnes_4588392_4408996.html>, I would recommend that the authors archive their data in a repository that explicitly supports the long-term preservation of scientific data.

Another reason for preferring static policies (line 317) could be that they could be more secure, because they cannot be modified into a less secure version.

It is unclear how the authors found that performance cannot be correlated with a sandbox's adoption (lines 470-474). Given the amount of work that is being done in the community to improve the performance of sandboxes, it is important to empirically substantiate the finding.

The discussion regarding the usability of sandoxes in section 5.2 is interesting and to the point.  However, it is well known that usability is often at odds with security.  Who would be the people targeted for the usability improvements of the aspects listed in lines 492-497?  Given that these are probably experts, would they really benefit from the proposed usability improvements?  Perhaps the term "usability" is misleading, and terms such as "expressiveness", "readability", "analyzability", and "maintainability" should be used instead.

The authors mention how lacking metadata in the bibliographic databases they used hindered their work.  Could Scopus provide the authors with the data they required?

The conclusions section can be expanded to make justice to the considerable amount of work done by the authors, perhaps by summarizing their findings in actionable form.
Additional comments: The provided definition of a sandbox (line 137) can be improved by stating that it is an encapsulation mechanism *that is used to impose* a security policy on software components. This adds the use's intent, thus precluding technologies such as OS processes and virtual machines that are not explicitly used to sandbox components although they impose security policies.

On lines 117-118 the authors state that one might object to the use of "encapsulation, because it is typically used to refer to cases where the inside is protected from the outside, whereas sandboxes protect in the reverse direction.  One can overcome this problem by considering that a sandbox encapsulates the external system (which needs to be protected) from the potentially malicious component.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A SYSTEMATIC ANALYSIS OF THE SCIENCE OF SANDBOXING
Review round: 1
Reviewer: 3

Output:
Basic reporting: This paper is a detailed survey and classification of sandboxing. The authors apply a systematic approach. This paper will help the community finding the most relevant material on sandboxing as well as how the different contributions compare.
Experimental design: The systematic methodology used to select and analyze papers on sandboxing includes steps like the selection of papers, the categorization of datasets, and the analysis of the datasets.
Validity of the findings: The results are based on statistical clustering of how the papers were coded, discovered trends in the dataset, and observations when reading the papers or reviewing the data. The results include a list of research questions stated in the papers, of policies, as well as the use of validation.
The results of this paper like Table 6 will allow the community to have a clear picture of the various contributions on sandboxing.
The authors included a section on the threats to validity.
Additional comments: This paper is well structured and written. This systematic analysis is a contribution to the field allowing the community to better compare published works. The analysis is carefully conducted.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH COLLABORATION AND TOPIC TRENDS IN COMPUTER SCIENCE BASED ON TOP ACTIVE AUTHORS
Review round: 1
Reviewer: 1

Output:
Basic reporting: No Comments
Experimental design: Although it is said that for most experiments are presented only the results for a window due to space limitations, it would be a good idea showing, at least briefly, results of other windows, to see that the results are similar (as it is said in the paper).
Validity of the findings: No Comments
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH COLLABORATION AND TOPIC TRENDS IN COMPUTER SCIENCE BASED ON TOP ACTIVE AUTHORS
Review round: 1
Reviewer: 2

Output:
Basic reporting: The authors present an interesting study on collaboration based on MAS data for the area of Computer Science. For this they compare two subset of authors: UCP authors vs. average authors. In general terms the paper is correct and written in a clear fashion. Although research collaboration is a widely studied topic, the data set employed allows to analyze a large dataset at the individual level, offering interesting insights on the collaboration and productivity patterns of researchers. However, there are several aspects which need of a more through description as well as the organization of the paper is a bit confusing.

Content

- The introduction includes details about the dataset (last paragraph) even before describing it. I would not describe such details at this point and mainly focus on the objectives and specific goals the paper aims at.

- Also, research collaboration is a highly studied topic in research policy, it would be desirable for the authors to relate their introduction to this topic in order to emphasize the importance of their findings later on. In this regard, the authors characterize UCP authors as senior researchers, probably leaders of their research teams, etc. Identifying different 'types' of researchers may allow to develop different evaluation frameworks, maybe the authors could ellaborate on this in their discussion.

- In the related work section again there are important contributions in the field of scientometrics which are missing such as the classical papers of Katz, Beaver or Crane on research collaboration.

- The dataset section should be much more detailed. A more comprehensive description should be given about MAS, its coveraged, the metadata offered, its evolution (it seems to have been unofficially terminated (http://dx.doi.org/10.1108/OIR-07-2014-0169)) as well as figures on the total number of authors identified for the time frame used,  number of publications, etc. Wrapping up, a detailed description of the limitations of the data source and descriptive figures of the data set employed.

Structure

The structure of the paper is quite confusing and I believe it could easily adapt to the 'standard' sections recommended by peerj author instructions. I suggest dividing the dataset section into Methods (description of the data source and data retrieval and processing) and Data (descriptive figures on the dataset and description on the metadata). Then, the three analyses: 1) Comparison between UCP and average authors, 2) Collaboration patterns and 3) Topic analysis could all be subsection of the results section. Discussion would be a separate section and not a subsection as currently. I believe this would greatly improve the readibility of the paper.

Minor issues

As someone who didn't know about the UCP author concept I found the beginning of the paper a bit confusing. I suggest paragraph 2 could be inserted in paragraph 1 before the sentence 'The idea is that UCP authors are the core...'.
Experimental design: There are a serious of limitations on the data set that should be reported with regard to its reliability. I understand that the authors are working with a large dataset and hence, these should be minimized. In any case, they should be mentioned.

Other than that I have no further comments with regard to this section.
Validity of the findings: It would be desirable for the authors to make available their data, code, algorithms and results, to show transparency on their study and allow reproducibility.

Also the conclusions seem quite weak. I encourage the authors to reinforce their findings by relating to the wide corpus of literature existing in relation with research collaboration, profiles of researchers and their implications on research policy and evaluation
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH COLLABORATION AND TOPIC TRENDS IN COMPUTER SCIENCE BASED ON TOP ACTIVE AUTHORS
Review round: 1
Reviewer: 3

Output:
Basic reporting: The paper presents an study about the research activity in Computer Science. It analyzes the research collaboration and topic trends in that research field.

The work is not original, it was prior published in a conference (http://dl.acm.org/citation.cfm?id=2742015). The paper summited to PeerJ is identical to that one.
Experimental design: The dataset used was gathered from Microsoft Accademic Research, which is a not updated and incomplete data source. Authors also limited the analysis to 2009. Other bibliographical data sources as ISI web of Science or Scopus could have been used.
Validity of the findings: The paper presents some limitations:

- the first one is the dataset used.

- the second one, is the "uninterrupted and continuous presence" (UCP) concept. It considers authors publishing papers every year in the periods analyzed. It is an important restriction. UCP authors are not necessarily the most productive and more cited one. A study only focused in this set of authors is very biased. Authors argue incorrect address, saying things as:

"... By analyzing their activities, we can visualize the major trends of the whole community".

"... From the collaboration patterns of UCP authors, we can get more insight about the Team Research, in particular its correlation to resarch productivity".

"... UCP authors ara able to achieve more sustained research activity, much higher level of research output, and accelerated growth in research out-put, BECAUSE OF THEIR ABILITITY TO BUILD RESEARCH TEAMS" => The authors dare to say something very difficult to prove.

-  third one, Why authors use two non consecutive periods ([1988-1995] and [1998-2009]) to analyze the collaboration among UCP authors?  In this address, authors justify the rise in collaboration to the birth of the WWW. This conclusion is very light. The WWW enabled greater access to information and the possibility of more data, but this does not necessarily imply or justify an increase in collaboration. Other reasons have had to contribute to this increase.

Examples of UCP authors must be added for a better understanding.
Additional comments: The work is not original, it was prior published in a conference (http://dl.acm.org/citation.cfm?id=2742015). The paper summited to PeerJ is identical to that one.

For these reasons, I consider the paper must be rejected.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: RESEARCH COLLABORATION AND TOPIC TRENDS IN COMPUTER SCIENCE BASED ON TOP ACTIVE AUTHORS
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have responded to all the comments and made the necessary changes.
Experimental design: No comments
Validity of the findings: No comments
Additional comments: No comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A SOCRATIC EPISTEMOLOGY FOR VERBAL EMOTIONAL INTELLIGENCE
Review round: 1
Reviewer: 1

Output:
Basic reporting: This article describes a computational model for computers to learn subjective information about emotions by playing a game named EMO20Q (emotion twenty questions).

The research work is well presented and motivated. The methodology is suitable.  The English is very good and easy to read. A few English/style-related suggestions are indicated below:
- Line 143. Revise grammar: “a logical device to used to model”
- Line 215. The advice given to the reader about using “his or her episodic buffer” is not appropriate in scientific writing.
- Line 399. “there were unique 99 emotion words” -> “there were 99 unique emotion words”
Experimental design: The experiments have been carefully designed and have been reported in a clear and convincing way. I understand that carrying out experiments for evaluating this kind of techniques is costly. However, to highlight the importance of the contribution it would have been desirable (although not mandatory) to compare the proposed method to some baseline method.
Validity of the findings: The experimental results are well described. Both the discussion and conclusion sections are suitable. It would be useful to discuss some additional issues. For instance, have the authors analyzed if the approach could be generalized to identify other kinds of words that may not be emotions? It is clear that for emotion-related words it is easier to keep a controlled vocabulary and standardized questions. But how this could be adapted to other domains? On the other hand, if the domain is limited to emotions, why not taking advantage of other mechanisms, such as ad hoc ontologies describing emotions?
Additional comments: There are a few aspects that require further clarification:
1. Why have the authors decided to use a Bayesian learning model instead of other kinds of models? For this kind of problems, a decision tree may also be appropriate. A more detailed justification for their choice should be presented.
2. It is not clear how the feature engineering process is carried out. Examples of features should be included.
3. It is not clear how questions are derived. Are the standardized questions the only ones that are used or does the system learn additional questions through the human-human EMO20Q games?
4. How do the questions relate to the features?
5. Please, give further details on the process that is applied to learn new vocabulary.
6. By new vocabulary do you only mean new words describing emotions or do you also mean other terms related to emotions that might be used as part of the questions?


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: A SOCRATIC EPISTEMOLOGY FOR VERBAL EMOTIONAL INTELLIGENCE
Review round: 1
Reviewer: 2

Output:
Basic reporting: An interesting study of 20Q-games about emotion words.
The algorithm is very simple - maybe a little bit too simple for really being novel in any way - nonetheless, I found the paper an interesting read and generally a good engineering contribution on how to go about solving the proposed challenge.

The philosophical relations to the good-old Greeks is a matter of taste - I find it a little bit overly lengthy but judge it as acceptable.
Experimental design: clearly the psychological studies could have been done in a better manner - while the authors emphasize that they wanted to keep any option open, I think it would have been a better idea to restrict the emotion words to say 100 or so.

In consequence, the results are hard to interpret. Statistical tests due to the different groups are hard to conduct and thus the result remain qualitative.
Validity of the findings: The qualitative results are hard to interpret - did the system really make progress (statistical significant difference between results in table 5 and 6?).

I think one could at least compare performance of the system in the 100 subject experiments comparing, for example, performance with the first 50 subjects with those from the second 50 subjects (e.g. with respect to avg. turns in the easy and medium categories). A similar comparison could be made between the preliminary and the mechanical turk study.
Additional comments: I upload the annotated pdf - there were quite a few spelling and simple English mistakes. Please correct these. I also recommend in the future to do a final spell-check before submitting - these mistakes suggest that the manuscript has not undergone sufficient care in its preparation. Also verify the equation and explain your choices in the algorithm (as annotated in the pdf).


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SEMANTIC REPRESENTATION OF SCIENTIFIC LITERATURE: BRINGING CLAIMS, CONTRIBUTIONS AND NAMED ENTITIES ONTO THE LINKED OPEN DATA CLOUD
Review round: 1
Reviewer: 1

Output:
Basic reporting: No Comments.
Experimental design: The overall methodology is sound; however, some system components are simplistic and naive in nature, especially, the rule-based approach for rhetorical entity detection. It is not clear how expressive and accurate the set of 190 gazetteer entries and the subsequent rules are. The authors present results on a small data set with only a modest 68% F1 score, making the assessment even harder. A more detailed description of these entries and rules along with a qualitative analysis is required (e.g., what are the major categories of claim and contribution statements? what portion of these are targeted by the system? how much linguistic variation do claim and contribution statements exhibit? how much of it can the designed rules accommodate? etc.).
Validity of the findings: Without any baseline/benchmark, it is not clear how to properly judge the reported results. I suggest that the authors compare their RE and NE detection subsystems with at least one other system (some of which are mentioned by reviewer 1 in the earlier set of reviews).

In addition, the authors should also provide experimental evidence that such a system is helpful for users in some quantifiable way. Something along the line of what reviewer 2 suggested in the earlier set of reviews would be helpful.
Additional comments: Overall, a well-written paper describing a sound methodology. However, I do strongly feel that the authors should additionally carry out the above experiments and report the results. The 'major revisions' recommendation that follows is really meant to stress the importance of these experiments rather than to suggest extensive rework.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: SEMANTIC REPRESENTATION OF SCIENTIFIC LITERATURE: BRINGING CLAIMS, CONTRIBUTIONS AND NAMED ENTITIES ONTO THE LINKED OPEN DATA CLOUD
Review round: 1
Reviewer: 2

Output:
Basic reporting: No comments.
Experimental design: No comments.
Validity of the findings: No comments.
Additional comments: Typo : In the results section of the abstract "semantic queries than show" -> "semantic queries then show".

This paper lays out a framework for a valuable contribution to bridging the gaps in semantic annotation for scientific literature.  It will be interesting to see the results of your future experiments honing DBPedia Spotlight onto scientific domains so that the over-generalizations addressed in this paper do not dim the importance of this line of research.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MEASURING ONLINE SOCIAL BUBBLES
Review round: 1
Reviewer: 1

Output:
Basic reporting: No Comments
Experimental design: Generally looks fine. The question is defined and the calculation process is explained. A single issue appears where the "density measure" is identified. However this measure and its calculation is presented clearly, but the paper fails to talk about the literature of this measure and the reason that the authors have chosen this method.
Validity of the findings: No comments
Additional comments: It is recommended that authors include the literature for the diversity measure. Then they can explain why the presented method is chosen.
Also more explanation regarding the importance of the study is required. Why quantifying the filter bubble hypothesis across different information sources is important and required?


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MEASURING ONLINE SOCIAL BUBBLES
Review round: 1
Reviewer: 2

Output:
Basic reporting: The key idea proposed in this article is that the diversity of information sources we are exposed to is significantly larger through search than through social media or e-mail communications. The explanation of the intuition behind this idea as well as the development of the argument is done well in the introduction.The article presents a good introduction and suitable background to demonstrate how this work fits in with related work.
Experimental design: Some of what the authors call intuitions and assumptions are not clearly explained - why does visiting 10 articles from 10 different news domains constitute greater diversity? Does this not depend on the type of domain? There is a mention of differences in domains such as wikipedia but the notion of greater diversity as it applies here is not clear. Similarly a more careful if only brief discussion on how excluding image and video search does or does not impact search results will be useful.

The discussion on sample volume and entropy seems a little incomplete. Why restrict sample sizes and not use some normalization measure instead? Search generates more data than emails for example and that is an important factor to consider. If the sample sizes were restricted, were multiple independent experiments conducted to validate findings?
Validity of the findings: There is considerable manual filtering involved in the data used for experiments in this article. Reproducing of results is of some concern in such cases. Can the authors make the subset of the data (used as input for this article after all the processing done on the original Click Dataset) available? Figure 3(a) shows results for a month. How representative is this of the entire data?
Additional comments: No additional comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MEASURING ONLINE SOCIAL BUBBLES
Review round: 1
Reviewer: 3

Output:
Basic reporting: Axis names for diagrams should be more informative. For instance, comparing  Figure 5 data is not possible without reading the whole caption.
Experimental design: The article debates an important issue yet lacks proper debate on the significance and impacts of the matter that is being discussed. As I have commented on the paper in the attached file, the personalized source of information may not be the significant matter, as it has been always like this for people who had the preferences over them, for instance, left or right wing newspapers. What matters is the choice, whether or not an individual seeks for personalized source of news and entertainment or just is thrown at the main stream without knowing other choices exist. This should be considered as well in the introduction.
Validity of the findings: As I have commented in the PDF attached, some assumptions are questionable, such as increase in trusting the validity of news when it is reported from different sources.
Additional comments: No Comments.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: MEASURING ONLINE SOCIAL BUBBLES
Review round: 2
Reviewer: 1

Output:
Basic reporting: The authors have made modifications that improve the clarity of the article.
Experimental design: No Comments
Validity of the findings: No comments
Additional comments: Typo: The caption on Figure 3 (a) reads (b)


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: THE INTERPLAY OF PHYSICAL AND SOCIAL WELLBEING IN OLDER ADULTS: INVESTIGATING THE RELATIONSHIP BETWEEN PHYSICAL TRAINING AND SOCIAL INTERACTIONS WITH VIRTUAL SOCIAL ENVIRONMENTS
Review round: 1
Reviewer: 1

Output:
Basic reporting: •	Missing data sets and code for application. I am a novice at this particular type of review/publication, but careful reading of the review documents seems to say that the authors should have raw data available as well as the code or downloadable application that was used.

•	References good missing this one:
Sveistrup, H., et al. Experimental studies of virtual reality-delivered compared to conventional exercise programs for rehabilitation. in Cyberpsychology & behavior : the impact of the Internet, multimedia and virtual reality on behavior and society. 2003.[1]

•	A mention of a ‘Coach’ first appears on the 11th page of the paper. There should be a description of the function and role of this Coach. Is it a wizard of OZ type person? Is it driven by rules or algorithmically in some how? Does it differ in interaction and content from participant to participant?

•	More formal definitions would be helpful for:
        −	 Social Learning
        −	Social support
        −	Social facilitation
        −	Normative Influence
Experimental design: •	There is a skewed woman to man ratio (almost 3:1). However research (that I can find) seems to disprove the ‘common sence’ (please don’t blame me for pervasive sexist streriotypes) notion that femails are more responcive to social cues and reinforcement than males. So I would take a look at and possibly add these references:
      o	Ryan, R.M., et al., Intrinsic motivation and exercise adherence. International Journal of Sport Psychology, 1997. 28: p. 335-354.
      o	[3] Koivula, N., Sport Participation: Differences in Motivation and Actual. Journal of Sport Behavior, 1999. 22: p. 360.
and also discuss the 28 to 9 proportions as not invalidating the generalizability of the results across genders.

•	Really confusing second hypothesis - If I read this right the authors were measuring:
In control group how many exercised together when there was no way for the group members to know if they were exercising together versus the social group knew when there were other people exercising. This is not a good hypothesis unless we were testing for the random possibility versus the test group. If that was their purpose it makes sense but needs to be stated more clearly.

•	They might have tested for self-efficacy to remove that variable

•	They might have done some testing of the ease of use of the interface and app to make sure that was not an issue

Except for the above - the design was excellent and the consideration of possible confounding results (i.e. weighing the social groups in h2) was also good
Validity of the findings: See the discussion on gender imbalance above.
Additional comments: I was surprised in doing my literature search that there have not been more research done on this topic. The subject of virtual presence as motivation or to encourage socialization has been quite prevalent (especially in CSCW and HCI), but this is one of few studies I have read being done.

My suggestions for improvement are to:
1)	Clarify the gender issue
2)	Explain the persona and action of the coach
3)	Expand the section on the second hypothesis to prevent readers from thinking that this is either just common sense (i.e. if I can see you it is more probable that I may exercise with you) or trivial (why are they measuring random occurrences (i.e.exercising together purely chance and with no knowledge that I am doing so))


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: THE INTERPLAY OF PHYSICAL AND SOCIAL WELLBEING IN OLDER ADULTS: INVESTIGATING THE RELATIONSHIP BETWEEN PHYSICAL TRAINING AND SOCIAL INTERACTIONS WITH VIRTUAL SOCIAL ENVIRONMENTS
Review round: 1
Reviewer: 2

Output:
Basic reporting: Overall I think this is a very good paper, interesting and well laid out for the reader. The article will add to the field of knowledge in this area. The literature review is appropriate and well referenced.  The methodology seems sound, but I have some questions about the measurements used which should be answered prior to publication. I was unable to access the raw data as suggested by the criteria for the review.  There did not appear to be any files present with raw data.

Besides the Frailty Index were there any other inclusion/excusion criteria? Please put this information into your text.

I think it is important to give the background information as to whether or not the study participants (both groups) were previously experienced tablet users, if so, how much experience did they have. If not, how did you train them? Also had they participated previously in exercise programmes either on of off line?  What were their motivations to participate in this study?  Please expand a little on what the training involved, how long it took etc. Did the training involved teaching users how to use the tablet or just how to use the programme?

How much support did users from both groups need during the study? Was there any difference in the support requirement between the groups?

Were the study participants living alone or with others? Did living status have any affect on the results of the exercise participation?

How was participation measured? Was it just by signing in and agreeing to exercise or did the participant have to engage regularly with the tablet?  Were participants fitness levels measured before and after the programme to see if there was any difference in their fitness levels? How do you know that participants actually carried out the exercises and did not just interact with the tablet?

How was completeness measured? The method of measurements should be explained to the reader.

While the issues I have mentioned are minor enough and should be verified by the editor before publication, I am happy to review the changes if required.
Experimental design: No comments
Validity of the findings: Once the questions on how measurements were made and activity verified are answered and explained for the reader , then the findings should be sound.
Additional comments: In the attached pdf I have highlighted a few areas to suggest where the english may be improved but overall it is well written.  I have also entered sticky notes in places where I was confused or felt more explanation was needed for the reader. These issues should be addressed before publication. Thank you .


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 1
Reviewer: 1

Output:
Basic reporting: Most of the part is very good, suggest the "A NetLogo implementation" should be a new section.
Experimental design: No comments
Validity of the findings: The Table 12 should give more information to explain, like how to get the Statistics-wise ( for example the value of max Xi is 4.5)
Additional comments: Overall, this is a good paper can give the people a tutorial fashion who want to start his agent-based modeling research. The whole paper used most of  paragraphs to show the model's description and analysis. The analysis is the key point of this paper( the author also believe "this paper proposes a methodology on how to achieve these goals), so, the author should add one paragraph in end of the "Background" section, compare to the literature review paper, summarize the shortage of the current ABMs and point out why this work is important and significant.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 1
Reviewer: 2

Output:
Basic reporting: No Comments
Experimental design: No Comments
Validity of the findings: No Comments
Additional comments: The article, “A template model for agent-based simulations”, proposes an agent-based model named “Predator-Prey for High-Performance Computing”. Experiments using NetLogo implementation are carried out, and results are statistically analyzed.  The authors propose to set this model and the corresponding result analyzing process as a paradigm for agent-based modeling research. I think the idea is interesting, and suggest publishing this article.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 1
Reviewer: 3

Output:
Basic reporting: The reason to select predator-prey model as the template for formal description and output analysis is not discussed, although many other alternatives (StupidModel/Sugarscape/Heatbugs/Boids) are mentioned. A discussion on choosing predator-prey model needs to be conducted.
Experimental design: Line 36 “While no formal standard for ABM description exists, the ODD protocol (Overview, Design concepts, Details) is currently one of the most widely used templates…” It’s a not a convincing explanation for readers to understand why you would use ODD protocol for model conceptual description, as there does exist  popular formalisms to describe conceptual models in classic M&S area, such as DEVS.
Validity of the findings: No Comments
Additional comments: Minor problems:
1 Line 20 Lack of reference
2 Line 313 Double “can be”
3 Algorithm 2 can be discarded.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 1
Reviewer: 4

Output:
Basic reporting: No Comments
Experimental design: No Comments
Validity of the findings: No Comments
Additional comments: The paper presents a conceptual model which captures important characteristics of spatial agent-based models. The model is elegantly described and evaluated its correctness by a small example implemented in Netlogo. A comprehensive simulation output analysis theory is reviewed and illustrated by the simulation data of the example.
There are two small problems which should be revised based on my understanding:
1. In table 1 (page 4), the reproduction probability should be real numbers in [0,1], not integers. Also the variables are not very clear, for example, based on the row of Grid cells, X and Y should be the size of the whole cell space environment; but based on the row of Agents, X and Y are interpreted as the position of an agent. Please be more consistent and clear.
2. In the initialization process of page 5, the procedure describes only one agent’s initialization, please give a complete procedure for multiple agents, though the procedure is very simple.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comments
Experimental design: No comments
Validity of the findings: No comments
Additional comments: The paper is improved according to reviewers' comments from the first round. The motivation and the proposed "Predator-Prey for High-Performance Computing" model become clearer. I again suggest publishing it.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 2
Reviewer: 2

Output:
Basic reporting: No Comments
Experimental design: No Comments
Validity of the findings: No Comments
Additional comments: No Comments


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: TOWARDS A STANDARD MODEL FOR RESEARCH IN AGENT-BASED MODELING AND SIMULATION
Review round: 2
Reviewer: 3

Output:
Basic reporting: No comments.
Experimental design: No comments.
Validity of the findings: No comments.
Additional comments: Based on the first round review, the author has revised the paper accordingly, and it is much more clear and consistent.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENERATION OF HIGH ORDER GEOMETRY REPRESENTATIONS IN OCTREE MESHES
Review round: 1
Reviewer: 1

Output:
Basic reporting: No Comments
Experimental design: No Comments
Validity of the findings: No Comments
Additional comments: This paper use their open source mesh generator named Seeder to implement a robust first order method in obtain polynomial representations of geometrical objects to describe non-smooth material distributions. The seeder is introduced very well in the paper, the different experiment figure shown the good experiment results as well. Most important part is “Numerical properties”, the figure 7 and figure 8 shown the value and contribution of this paper. However, there are some place I want to point out:
1.	The equation (1) - (4) should explain as well.
2.	In the three steps: Voxelization, Probe and Conversion, what is probe is not very clear.
3.	The summary part is very weak.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENERATION OF HIGH ORDER GEOMETRY REPRESENTATIONS IN OCTREE MESHES
Review round: 1
Reviewer: 2

Output:
Basic reporting: I think the article meets all the basic standards here.
Experimental design: I think the article meets the experimental design standards.
Validity of the findings: I think the findings are valid.
Additional comments: This article proposes a numerical method to generate high order approximations of 3D surfaces. I think the proposed method and experimental results are valid.
The presentation of this paper may be further improved:
1. For the proposed algorithm, other than the English description, I suggest the authors to also describe it in formal mathematical language, that is, clearly formulate the inputs, outputs and each step of the algorithm.
2. Figure 1 is not clear. What I can see is only a white sphere, voxelization in red, and others in blue. I do not see “the thick black lines outline the elements of the actual mesh”, or “the voxelization within elements follows the octree refinement towards the sphere and is indicated by the dark blue lines.” I suggest the authors to either improve the figure or change the description.
3. Minor typos, e.g., in the description of Figure 6 “Blue is the sphere and yellow the isosurface of the color value at 0.5.” I suggest the authors to perform another round of grammar check.


Instruction: You are one of the reviewers, your task is to write a review for the article. You will be given the title of the article, the number of the round in which the article is located, and your order among the reviewers.

Input:
Title: GENERATION OF HIGH ORDER GEOMETRY REPRESENTATIONS IN OCTREE MESHES
Review round: 2
Reviewer: 1

Output:
Basic reporting: No comments
Experimental design: No comments
Validity of the findings: No comments
Additional comments: The presentation is largely improved. Most typos are fixed. I suggest publishing it.