Comments (26)
OK thanks for the review here @Kevin-Mattheus-Moerman!
@cMadan - this is now accepted into JOSS, your DOI is: http://dx.doi.org/10.21105/joss.00031
from joss-reviews.
from joss-reviews.
For the paper PDF, references after the first one aren't displayed quite right. Would using a semicolon between them (rather than a comma) be the fix?
@cMadan - yep, I've fixed this in cMadan/prism#1 and updated the PDF in the review issue.
from joss-reviews.
This opens a bigger question for me—as JOSS editor—, which is: Should we be publishing packages that rely on proprietary programming and execution environments?
A quick look at the README for this submission immediately struck me with: "requires three first-party toolboxes: (1) Curve Fitting Toolbox; (2) Statistics and Machine Learning Toolbox; (3) Signal Processing Toolbox"
Are these paid MATLAB Toolboxes?
Should we have a conversation about that on another Issue or in the Google Group?
from joss-reviews.
This opens a bigger question for me—as JOSS editor—, which is: Should we be publishing packages that rely on proprietary programming and execution environments?
Yes I think these should be acceptable with the caveat that it should to be possible for a reviewer to address the following review points:
[ ] Installation: Does installation proceed as outlined in the documentation?
[ ] Functionality: Have the functional claims of the software been confirmed?
[ ] Performance: Have any performance claims of the software been confirmed?Should we have a conversation about that on another Issue or in the Google Group?
Let's have this conversation in an issue. Are you happy to start it?
from joss-reviews.
@cMadan - changing the Markdown to:
...quadratic regression are often used (e.g., @SoweEtal2003; @HogsEtal2013; @MadaKens2016), it has been shown that...
results in this output:
Does that look better? I'm not a pandoc expert I'm afraid.
from joss-reviews.
/ cc @openjournals/joss-reviewers - would anyone be willing to review this submission?
If you would like to review this submission then please comment on this thread so that others know you're doing a review (so as not to duplicate effort). Something as simple as :hand: I am reviewing this
will suffice.
Reviewer instructions
- Please work through the checklist at the start of this issue.
- If you need any further guidance/clarification take a look at the reviewer guidelines here http://joss.theoj.org/about#reviewer_guidelines
- Please make a publication recommendation at the end of your review
Any questions, please ask for help by commenting on this issue!
from joss-reviews.
For the paper PDF, references after the first one aren't displayed quite right. Would using a semicolon between them (rather than a comma) be the fix?
from joss-reviews.
I have reviewed this submission in terms of MATLAB implementation and installation. I have also verified the demo and benchmark work as described. However I am not an expert in the technical topic the software addresses. Would it be possible to provide a more graphical display of the functionality and technical claims of the software, i.e. plot graphs and figures visualizing the nature of the input data, the processing and the output results? Perhaps if the functionality can be described more thoroughly and graphically in this manor users new to this field may be able to grasp functionality better.
I tested this submission using MATLAB R2016b PreRelease.
I can confirm installation (of the software and the third party SparseBayes V2 toolbox) and implementation proceeded as described in the README.md.
I was able to install the software and run the demo.m.
Comments on paper:
Check spelling for “lregularization” in paper
Fix spelling error “which camn be obtained” in paper
Does the figure need a caption/description, like in the README.md?
Check the references. Is the author missing for the reference: “Sparse Bayesian Learning and the Relevance Vector Machine”
In README.md check spelling for: “regression lead to”. Should this be “leads”?
from joss-reviews.
@Kevin-Mattheus-Moerman, thanks for the review! I've fixed the typos and missing figure caption (was in paper.md, but not PDF). Reference is correct (is same as previous, 'line' notation is produced when PDF is compiled).
In demo.m, I have now added some scatter plots for evaluating the predictions.
@arfon, the subsequent references still look incorrect in the PDF. I've also made a few changes, so maybe you could re-compile the PDF?
@labarba, yes, these are paid MATLAB toolboxes, though (most) educational institutions that have MATLAB licences would also have these toolboxes. More broadly, I think any restriction on dependencies would preclude many MATLAB-based projects from being published in JOSS.
from joss-reviews.
@cMadan - I've recompiled the paper here. Could you take a look and see if it's formatted better now?
from joss-reviews.
@arfon - much better! The only remaining issue is this "(e.g., (Sowell et al. 2003; Hogstrom et al. 2013; Madan and Kensinger 2016))". I'm not sure how I should change the md to make that turn out better. Effectively want something equivalent to "\cite<e.g.,>{REFS}".
from joss-reviews.
I think that instead of the logo an image of a result would be much illustrative.
from joss-reviews.
@nicoguaro - I've just added example output.
@arfon - that's sufficiently better, thanks! I really appreciate the help! I've updated paper.md on the repo.
from joss-reviews.
@arfon - that's sufficiently better, thanks! I really appreciate the help! I've updated paper.md on the repo.
OK great. Here's the updated paper: 10.21105.joss.00031.pdf
from joss-reviews.
@arfon - that looks great, thanks!
from joss-reviews.
Thanks for implementing the changes and for adding basic visualizations of results in demo.m. For the paper it seems that the reference with the title: "Sparse Bayesian Learning and the Relevance Vector Machine" misses an author field. Should this be Michael E. Tipping?
I've now ticked all the boxes above. Once the reference for the paper is verified I can recommend this paper for publication in JOSS.
from joss-reviews.
There is as you know an ongoing discussion on the inclusion of software relying on proprietary programming environments. It may be that a final decision on your submission will be made after that discussion has been concluded. Apologies for the delay.
from joss-reviews.
Even though I'm not an editor I think that the output of the discussion should not influence the publication of this paper. This paper was submitted before the discussion even started, if it would have started before maybe the author would have not submitted.
from joss-reviews.
@Kevin-Mattheus-Moerman - The author is Tipping, but it is being displayed 'properly'. The PDF is compiled from markdown+bib by @arfon's code, and is based on an IEEE convention. See here: http://tex.stackexchange.com/questions/29381/is-it-normal-for-bibtex-to-replace-similar-author-names-with
from joss-reviews.
@arfon - I think all of the reviewer comments have been addressed, and the paper has been recommended for publication.
from joss-reviews.
OK thanks @cMadan. I just need you to update this thread with the archive DOI (Zenodo/figshare etc) for the software before I can complete the submission.
from joss-reviews.
@arfon - the doi is 10.5281/zenodo.56821. I don't think I can edit the initial comment from @whedon.
from joss-reviews.
I just noticed that under the second figure, it says "Figure 1" (that isn't in the markdown itself). I made a change to paper.md that hopefully fixes this.
from joss-reviews.
@cMadan - could you take a look now and if this is better? https://github.com/openjournals/joss-papers/blob/master/joss.00031/10.21105.joss.00031.pdf
from joss-reviews.
@arfon - great, thank you!
from joss-reviews.
Related Issues (20)
- [REVIEW]: TextDescriptives: A Python package for calculating a large variety of metrics from text HOT 9
- [PRE REVIEW]: Regl-Scatterplot: A Scalable Interactive JavaScript-based Scatter Plot Library HOT 13
- [REVIEW]: pysr3: A Python Package for Sparse Relaxed Regularized Regression HOT 7
- [PRE REVIEW]: PeleLMeX: An AMR Low Mach Number Reactive Flow Simulation Code Without Level Sub-cycling HOT 17
- [PRE REVIEW]: IssyOsborne: transIent Squeeze and Starvation in elastohYdrodynamic - Oscillating Surfaces sOlving Reynolds oNe-dimensional Equation HOT 10
- [PRE REVIEW]: SCALib: A Side-Channel Analysis Library HOT 19
- [PRE REVIEW]: Hypercomplex: abstract & fast header-only C++ template library for lattice-based cryptosystems in high-dimensional algebras HOT 21
- [REVIEW]: QuaCa: an open-source library for fast calculations of steady-state quantum friction HOT 9
- [REVIEW]: RxInfer: A Julia package for reactive real-time Bayesian inference HOT 15
- [REVIEW]: XGI: A Python package for higher-order interaction networks HOT 65
- JOSS REVIEW- Performance -> Reading saved sites HOT 1
- [PRE REVIEW]: Melissa: coordinating large-scale ensemble runs for deep learning and sensitivity analyses HOT 25
- [REVIEW]: ctbench: Compiler-assisted benchmarking for the study of C++ metaprogram compile times HOT 5
- [PRE REVIEW]: `repytah`: An Open-Source Python Package That Builds Aligned Hierarchies for Sequential Data Streams HOT 35
- [PRE REVIEW]: Torch PESQ - a PyTorch implementation of the Perceptual Evaluation of Speech Quality HOT 7
- [PRE REVIEW]: Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data HOT 22
- [PRE REVIEW]: Quasi-Monte Carlo Methods in Python HOT 10
- [PRE REVIEW]: `SuperNOVA`: Semi-Parametric Identification and Estimation of Interaction and Effect Modification in Mixed Exposures using Stochastic Interventions in `R` HOT 12
- [PRE REVIEW]: normflows: A PyTorch Package for Normalizing Flows HOT 6
- [PRE REVIEW]: PDSim: A Shiny App for Polynomial Diffusion Model Simulation and Estimation HOT 7
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from joss-reviews.