Updates are useful, I have added some comments too. Really this is about a system, that combines multiple techniques and idioms. There's lots that's good in here, but the more you can focus on one of the documented methods the easier it will be to reflect and make claims. Tight integration is proposed as important, but actually you didn't achieve this and there isn't really any evidence that the tight integration was necessary (is there?). Instead, it seems that loose integration as actually pretty effective and enabled you to understand a complex parameter space! This may be worth thinking about! Hopefully the comments help you think that through and line things up a bit. This is very rich and very useful!  And impressive! I'm sure it will make a nice systems paper on its own!

jason dykes

Clear, but can you frame this as a User Story please?
Is this adequate?
Please work on this!

As an epidemiological modeller simulating the spread of Covid
I would like to explore the relationships between factors in agent-based models and simulated time-series
so that I can select and optimize models to match real World observations

Sounds OK _ as long as you are happy (you took my uninformed suggestion directly!) Feel free to revise and refine!

The one you suggested was identical to the earlier description we wrote in this box, so worked perfectly.

Slingsby.

Can you make reference to a few more solutions that informed your design and solution?
This would be a great way to connect the scientific community to the VIS literature. See below also! Which published work, including your own, influenced the design?

Typo - "SLINGSBY" needs a N

This is the weakest section - if you can add images of examples, with descriptions, this would be helpful to readers.

Actually - ignore this - you do it nicely below! Sorry!

I hope you can find some examples of this approach being used elsewhere?

Can you add references in the markdown format shown here? https://observablehq.com/@jsndyks/rampvis-idiom-template#cell-27

OK, so you are trying to find a combination of algorithms, visual designs and interactions that address the User Story. You don't really say which existing solution you are using, but useful that you link to candidate methods that you can incorporate. Fine!

There is a nice paper in one of the notebooks on levels of integration ("Interactive Visual Steering – Rapid Visual Prototyping of a Common Rail Injection System" Matkovic et al). Does figure 1 help here, can you use this to identify the level of interactive steering?
Just a thought!

"In our study" sounds as though this is a paper, rather than a description of what "Integrated Algorithmic Tools for Visual Analytics" is and where it comes from. See the Matkovic et al paper for ideas.

The picture is pretty, but I don't know what it shows from the description. Can you be more explicit and specific.

Do the 6 classes have names? 
How are the 160 (10x16) output spaces ordered?
Why not use partially opaque symbols so that we can see density?
What does a blue outlying dot in a red cluster (very salient) mean?

There is a bit of an inconsistency here in that section 6 does not describe the Integrated Algorithmic Tools for Visual Analytics. There is no tool described here (is there?). It seems to describe a visualization of a clustering algorithm in output space. So, this 'description and use' section really addresses the user story ...

As an epidemiological modeller simulating the spread of Covid
I would like to see how sampled model input parameters map to output spaces
so that I can understand the relationships between factors in agent-based models and simulated time-series

I can't really figure out what the output spaces are from the description - help! 😬😉

This is a nice explanation - much better, great!
The semi-opaque dote work well - try smaller more transparent dots to see more!
The thing that is missing is a description of the layout used for the 16*10 grid. I still don't understand this. What do row and column mean? How are they ordered?

Could you say something about what the different structures mean - we are trying to persuade people of the use of this! 
 - straight line [4,1] [15,3]
 - linear patterns [7,4] [5,1]
 - mixed colours [3,3] [3,10]
 - outliers [7,6] [2,3] [12,10] [4,6]
 - coloured outliers [8,8] [7,3] [6,1] [10,4]
This would really help!

As we have 160 configuration sets, it is a straight way to organise them into 16X10 or 10X16. But once the interaction is added, we will consider how we could show few figures each time and make it more interactive.

Sure - but there are 10! ways to arrange the columns (3,628,800) and 6! (720) ways to arrange the rows.
So which of these 2.6 billion arrangements did you use?
And why choose that one?
 What do X and Y or R and C mean in the small multiple layout?!
🤔

OK - please add direct links to the data if you can.

Can you explain what the figure is trying to show and how? Colours?

Much better - much clearer description - thanks!

CT : Size image by putting it in a separate block with:

await FileAttachment("image@1.png").image({ width: 600 })

"more insightful analysis about the relationship between input and output spaces is achieved.  "
So - can you tell us what you found in Section 6?

"It is easily to identify which parameter settings generate more variations in its simulation and which settings generate more similar outputs. These findings provide better configurations as well as find out which causal factors are important to drive the changes in the output space."

OK - sounds good, but you have to show us which these are and what this means in section 6!

Again, "more" is relative. More than what? ;)
"Better"is relative - better than what ;)

I really like this:
"Our approach supports the identification of which parameter settings generate more variations in its simulation and which settings generate more similar outputs"

If your user story is about "the identification of which parameter settings generate more variations in its simulation and which settings generate more similar outputs" then this is great!
And if you can really clearly and definitively describe "which parameter settings generate more variations in its simulation and which settings generate more similar outputs" in section 6 (and you get pretty close already!) this will work very well. Which are they - and how do the graphics and the tight integration between vis and analytics help with that?

So only partial integration?
what was the reason? Time, Technology, Data Complexity ... let us know!

This is a slight problem as the idiom is about tight integration - and you don't have tight integration!
Maybe you have shown that loose integration works well (as a first step in a pandemic, when time and resources are scarce and needs are unknown)?
Can you reflect on this?

Agree with it. Yes, it is not a tight integration at the current stage.

"More" is relative - more powerful than what?
The more you can focus on the user story - the need you are trying to address, the better!
use this as your benchmark, even if your evidence is solely reflective.
How well did the methods you used work in the context of the user story once you had used them?

Reduce image size with 
await FileAttachment("tool (2).png").image({width:600})

It would be great to get full names here!