[PRE REVIEW]: PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks

[editorialbot]

Submitting author: @MicroPhen (Stephan Noack)
Repository: https://github.com/JuBiotech/peak-performance/
Branch with paper.md (empty if default branch): peak-performance-paper
Version: v0.7.0
Editor: @csoneson
Reviewers: @Adafede, @lazear
Managing EiC: Kevin M. Moerman

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Thanks for submitting your paper to JOSS @MicroPhen. Currently, there isn't a JOSS editor assigned to your paper.

@MicroPhen if you have any suggestions for potential reviewers then please mention them here in this thread (without tagging them with an @). You can search the list of people that have already agreed to review and may be suitable for this submission.

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[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.27 s (182.5 files/s, 476210.6 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
SVG                             10              1            128         108888
Python                          10            366           1162           2309
Markdown                         7            233              0            921
YAML                             9             25             30            224
TeX                              1             16              0            197
Jupyter Notebook                 5              0          12948            193
TOML                             1             10              1             49
DOS Batch                        1              8              1             26
reStructuredText                 4             25             32             22
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                            49            688          14309         112838
-------------------------------------------------------------------------------

Commit count by author:

   343	j.niesser
    35	Michael Osthege
    32	Osthege, Michael
    25	Jochen Nießer
     5	dependabot[bot]

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 5787

✅ The paper includes a Statement of need section

[editorialbot]

License info:

🟡 License found: GNU Affero General Public License v3.0 (Check here for OSI approval)

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

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[Kevin-Mattheus-Moerman]

@editorialbot check references

[Kevin-Mattheus-Moerman]

@editorialbot check references

[Kevin-Mattheus-Moerman]

@editorialbot invite @csoneson as editor

[editorialbot]

Invitation to edit this submission sent!

[csoneson]

@editorialbot assign @csoneson as editor

[editorialbot]

Assigned! @csoneson is now the editor

[csoneson]

👋🏻 @MicroPhen - I will handle your paper, and will start by looking for a couple of suitable reviewers. Before that, however, I would like to ask you to significantly cut down on the length of your paper - it's currently almost 6,000 words, and the target is 250-1,000 (see the JOSS guidelines for more details). Once this is done, please let me know here (you can generate a new proof by typing @editorialbot generate pdf in a comment in this issue). Thanks!

[Y0dler]

Hello @csoneson, thanks for handling our paper.

Regarding the page number, I must admit we apparently overlooked that part. The problem is just that we realized during an earlier attempt to publish this content that a) most people in our primary target audience of biologists and chemists, who usually deal with chromatographic peak data, have very little to no experience with Bayesian statistics and b) we have to explain the model structure in order to set ourselves apart from other instances where Bayesian statistics were employed in one way or another to peak recognition or related topics. We will do our best to shorten the paper and shift some sections from the manuscript to the documentation but it would be very difficult to arrive at < 1000 words and still address the initially mentioned problems.
Is this an immovable upper limit or is it just the case that 6000 words are too far off from the target?

[csoneson]

Hi @Y0dler - 6,000 words is definitely too far from the target, we can accept a bit over 1,000 words but I think the goal should be to get reasonably close. As mentioned in the guidelines, JOSS papers are not intended to be "regular" research papers, and longer descriptions of models etc may fit better in the documentation. Thanks!

[Y0dler]

Hello again, @csoneson, I removed many sections and trimmed the remaining ones down to arrive at 1693 words (counting from the beginning of summary to the end of conclusion). Is this acceptable?

[csoneson]

@editorialbot generate pdf

[csoneson]

@editorialbot check repository

[editorialbot]

Software report:

github.com/AlDanial/cloc v 1.90  T=0.27 s (181.5 files/s, 472768.5 lines/s)
-------------------------------------------------------------------------------
Language                     files          blank        comment           code
-------------------------------------------------------------------------------
SVG                             10              1            128         108888
Python                          10            366           1162           2309
Markdown                         7            185              0            747
YAML                             9             25             30            224
TeX                              1             16              0            197
Jupyter Notebook                 5              0          12948            193
TOML                             1             10              1             49
DOS Batch                        1              8              1             26
reStructuredText                 4             25             32             22
make                             1              4              7              9
-------------------------------------------------------------------------------
SUM:                            49            640          14309         112664
-------------------------------------------------------------------------------

Commit count by author:

   343	j.niesser
    35	Michael Osthege
    32	Jochen Nießer
    32	Osthege, Michael
     5	dependabot[bot]

[editorialbot]

Paper file info:

📄 Wordcount for paper.md is 1955

✅ The paper includes a Statement of need section

[editorialbot]

License info:

🟡 License found: GNU Affero General Public License v3.0 (Check here for OSI approval)

[editorialbot]

👉📄 Download article proof 📄 View article proof on GitHub 📄 👈

[editorialbot]

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⚠️ Note to editors: If these papers look like they might be a good match, click through to the review issue for that paper and invite one or more of the authors before considering asking the reviewers of these papers to review again for JOSS.

[csoneson]

Hi @Y0dler - yes, I think we can leave it like this for now. I will start by looking for a couple of suitable reviewers - if you have suggestions (e.g. from the list linked in the first post in this issue), feel free to let me know.

[Y0dler]

Hello @csoneson, I'm glad to hear we're moving on for now :)

Regarding reviewer suggestions, we would put forward Prof. Aljoscha Wahl from the FAU Erlangen-Nürnberg and Virgile Andreani from Boston University.

[csoneson]

👋🏻 @Armavica, @Adafede, @bittremieux - would you be interested in reviewing this submission for the Journal of Open Source Software (JOSS)?

PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks
#7141

The checklist-based review is carried out on GitHub, more details can be found here.

Thanks in advance!

[bittremieux]

With a full teaching schedule and several conference travels in the next few weeks, unfortunately I can't dedicate the necessary time to this at the moment.

[Adafede]

Hi,
Similar to Wout, next weeks are rather busy. For when would you expect the first reviews?

[csoneson]

@Adafede - we aim to have comments for the authors within 2-4 weeks after the review has started.

[Adafede]

Seems doable, you can count on me! 👍

[csoneson]

Perfect, thanks @Adafede! I will assign you now, and open the actual review issue (where you will have your checklist etc) as soon as we have secured one more reviewer.

[csoneson]

@editorialbot add @Adafede as reviewer

[editorialbot]

@Adafede added to the reviewers list!

[csoneson]

👋🏻 @lazear, @hechth - would one of you be interested in reviewing this submission for JOSS?

PeakPerformance - A tool for Bayesian inference-based fitting of LC-MS/MS peaks
#7141

Thanks in advance!

[lazear]

I am willing to review it

[csoneson]

Great, thank you @lazear! I will assign you and open the review issue

[csoneson]

@editorialbot add @lazear as reviewer

[editorialbot]

@lazear added to the reviewers list!

[csoneson]

@editorialbot start review

[editorialbot]

OK, I've started the review over in #7313.