consent added

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+# Consent
+
+Before we begin, please read the following consent form carefully and thereafter complete the section below.
+
+## Purpose of the Study
+Taking valuable feedback and assessment from sighted community is crucial as a forst evaluation step for our generated text descriptions. The sole purpose of the study is to evaluate our text description for UpSet plots.
+
+## Tasks
+The study contains three visualization content (Only Visualization 1, Textual description of a Visualization, Visualization and Textual Description of a Visualization). For each visualization content, we ask you 13/ 14 questions regarding understanding the content. The questions
+contain various types such as single-choice, paragraph, long text, likert scale, etc. Each participant should take 50-60 minutes to complete the questionnaire.
+
+Each participant in this study is assigned a random ID for analyses. As such, your participation will remain anonymous, and your responses will not be used to identify you.
+
+## Record Keeping and Confidentiality
+
+Records of your participation in this study will be confidential as permitted by law. However, the study investigators, the sponsor or its designee, and, under certain circumstances, the University of Utah Institutional Review Board (IRB) will be able to inspect and access this data.
+
+Any publication or presentation of the data will not identify you. The data collected in this study is only used for the present study and not for future research.
+
+## Cost/Payment
+You will be compensated $15.00/hr for participating via Prolific.
+
+## Contact
+For more information about this research or about the rights of research participants, or in case of research-related injury, contact: Ishrat Jahan Eliza [[email protected]]([email protected]). Contact the Institutional Review Board (IRB) if you have questions regarding your rights as a research participant. Also, contact the IRB if you have questions, complaints, or concerns which you do
+not feel you can discuss with the investigator. The University of Utah IRB may be reached by phone at (801) 581-3655 or by e-mail at [[email protected]]([email protected]). You may also contact the Research Participant Advocate (RPA) by phone at (801) 581-3803 or by email at
+[[email protected]]([email protected]). Your participation in this research is voluntary. Your refusal to participate will not result in any penalty to you or any loss of benefits to which you may otherwise be entitled. You may decide to stop participating in the research at any time without penalty or loss of other benefits. The project investigators retain the right to cancel or postpone the experimental procedures at any time they see fit.
+
+**By accepting below,** you acknowledge that you have been informed about and consent to be a participant in the
+study described above. Make sure that your questions are answered to your satisfaction before
+accepting. You will receive a copy of this consent agreement.

public/Upset-Alttext-User-Survey/assets/introduction.md

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-# UpSet Introduction
+# Introduction
 
-Welcome to our study. In this study, we ask you questions about our generated text description for UpSet Plots. Before delving into the question-answer session, this page serves you to introduce with what UpSet plot is, why is it used, how you can interpret an UpSet plot.
-
-The major challenge in understanding relationships between sets is the combinatorial explosion of the number of set intersections if the number of sets exceeds a trivial threshold. The most common set visualization approach – Venn Diagrams – doesn't scale beyond three or four sets. **UpSet, in contrast, is well suited for the quantitative analysis of data with more than three sets.** 
-
-
-![A simple UpSet Example](./assets/upsetr.png)
-
-UpSet visualizes set intersections in a matrix layout. The matrix layout enables the effective representation of associated data, such as the number of elements in the intersections.
-
-
-
-<!-- ## When should you use UpSet?
-
-**UpSet works best for set data with more than three and less than about 30 sets**.
-
-**UpSet is well suited for analyzing distributions and properties of many items**. Items are abstracted away as “counts”, though attributes of the items can be visualized in integrated or adjacenct plots. If you want to see individual items in your set, you should probably go with a [Euler Diagram](https://de.wikipedia.org/wiki/Datei:British_Isles_Euler_diagram_15.svg).
-
-**UpSet shines when you want to look at all combinations of how sets intersect.**. If you want to look at pairwise intersections between sets, some sort of co-occurence matrix might be a better choice. 
-
-Also take a look at the [Nature Methods Points of View article](https://www.nature.com/articles/nmeth.3033) discussing these trade-offs.  -->
-
-
-## UpSet Explained
-
-UpSet plots the intersections of a set as a matrix, as shown in the following figure. Each column corresponds to a set, and bar charts on top show the size of the set. Each row corresponds to a possible intersection: the filled-in cells show which set is part of an intersection. Also notice the lines connecting the filled-in cells: they show in which direction you should read the plot: 
-
-<img style="width: 350px; height: 400px" class="centered-image" src="./assets/concept_1_matrix.svg" alt="Explaining the matrix approach in UpSet.">
-
-Here you can see examples of how these intersections correspond to the segments in a Venn diagram. The first row in the figure is completely empty – it corresponds to all the elements that are in none of the sets. The green (third) row corresponds to the elements that are only in set B, (not in A or C). The orange (fifth) row represents elements that are shared by sets A and B, but not with C. Finally, the last (violet) row represents the elements shared between all sets. 
-
-<img style="height: 400px; width: 490.5px" class="centered-image" src="./assets/concept_2_intersections.svg" alt="Explaining the intersections in UpSet">
-
-This layout is great because we can plot the size of the intersections (the “cardinality”) as bar charts right next ot the matrix, as you can see in the following example: 
-
-<img style="height: 400px; width: 531.8px" class="centered-image" src="./assets/concept_3_cardinality.svg" alt="Plotting intersection sizes with bars in UpSet.">
-
-This makes the size of intersections easy to compare. 
-
-The matrix is also very useful because it can be sorted in various ways. A common way is to sort by the cardinality (size), as shown in the following figure, but it's also possible to sort by degree, or sets, or any other desired sorting. 
-
-<img style="height: 400px; width: 298.4px" class="centered-image" src="./assets/concept_4_sorting.svg" alt="Sorting by cardinality in UpSet">
-
-Finally, UpSet works just as well horizontally or vertically. Vertical layouts are better for interactive UpSet plots that can be scrolled, while horizontal layouts are best for figures in papers. 
-
-<img style="height: 250px; width: 340.3px" class="centered-image" src="./assets/concept_5_horizontal.svg" alt="Horizontal layout in UpSet">
-
-
-These are the basiscs of UpSet! There's a lot more than you can do with UpSet plots, such as visualize attributes of the intersections, or group intersections. Look at the [upset.multinet.app](https://upset.multinet.app/) for details.
-
-
-<!-- ## Interpreting UpSet Plots
-
-UpSet Plots are generally easy to read. There is one important caveat though: **you should be careful about interpreting data where the size of the sets is very different.**  Look at the following example: 
-
-![UpSet and unequal set sizes.](./assets/unequal_set_size.png)
-
-Here' we're looking at movie genres, and it looks like the 2-set combination of “Drama” and “Comedy” is the largest two-set intersection. While this is a correct obervation it seems odd: dramas and comedy don't seem to go together all that well. What we're seeing here is an effect of the large size of the “Drama” and “Comedy” sets. Compared to the “Children“ and “Documentary” sets, those two sets are huge. To understand this, it's important to also look at the set sizes, and hence **no upset plot should omit the visualization of set sizes**. The above example shows another metric that can be used to interprete this: the “Deviation” (orange and blue bars) that indicate how much an intersection deviates from the expected size if we assumed that set membership were random. We see that the comedy-drama intersection is actually much smaller than it should be if the data were random.
-
-
-## UpSet vs. Venn Diagrams
-
-
-Venn diagrams are not suitable to visualize intersections of more than three or four sets. The figure below shows an example of a six-set venn diagram [published in Nature](https://www.nature.com/nature/journal/v488/n7410/full/nature11241.html) that shows the relationship between the banana's genome and the genome of five other species by visualizing which genes are shared between the plant species.
-
-![The six set banana venn diagram.](./assets/banana.png)
-
-While this figure looks fun, it is not a useful visualization. Try to extract any information from it. It's really hard to trace which intersection involves which sets. It's not obvious which is the biggest intersection from the visualization – you have to read the labels one by one. 
-
-You might ask, how does the banana venn diagram look in UpSet? Here you go:
-![UpSet showing the banana data.](./assets/upsetr-banana.png)
-
-It is a little hard to read because the figure is rather small. But we can simply remove the small intersections, and we get a nice plot that shows us the main features of the data:
-
-![UpSet showing the bana data with small intersections removed. ](./assets/upsetr-banana_clipped.png)
-
-Notice how easy it is to see trends: the vast majority of genes is shared between all plants, as highlighted in the next figure:
-
-![UpSet showing the banana data with highlight on largest intersection, which includes all sets.](./assets/upset_genome_top.png)
-
-Similarily, the first three species (Oryza_sativa, Sorghum_bicolor, and Brachypodium_distachyon) seem to be highly related, as all of them are part of the top-three intersections. In contrast, the sixth species (Phoenix dactylifera) seems to be most different from the others, as it only again is part of the sixth-largest intersection.
-
-![UpSet showing the banana data with highlight on the first three sets, and on the intersection of the date with the rest.](./assets/upset_genome_top-3.png)
-
-Such an analysis is almost impossible with a Venn diagram!  -->
-
-
-<!-- ## Frequently Asked Questions
-
-- _How can I create high-resolution UpSet plots for a paper or other publication?_  
-
-    There are three options:
-
-    - If you prefer to use the interactive web-based version you can print an interactive UpSet plot to a PDF and edit the PDF with a vector editing software such as Adobe Illustrator.
-    - You can create an exportable figure to generate a plot using a programming language such as R or Python. 
-    - You can create a static figure using, e.g., the R-Shiny versions of Upset. 
-
-    To explore all of these options, please refer to the [implementations page](/implementations/).
-
-- _Can I show attributes of the intersections?_ 
-
-    Yes, [most implementations](/implementations/) support visualizing attributes in some way.
-
-- _Can I export the elements in a particular intersection?_
-
-    Yes, but to our knowledge, only the interactive [UpSet 2](/upset/#upset2) version supports this.  -->
+Welcome to our study. In this study, we ask you questions about our generated text description for UpSet Plots. UpSet plot is a data visualization technique. Data visualization techniques serve as a visual language that conveys intricate patterns, trends, and relationships within data. With their increasing popularity, it’s also necessary that the visualizations reach a wide range of people. Our research aims to make data visualizations more accessible to communities so that people with visual impairments such as low vision, residual vision, and blind users do not miss any valuable information from the visualization. To serve this purpose, we have generated textual-natural-language rich textual descriptions. Before delving into the question-answer session, we will introduce you with what UpSet plot is, why is it used, how you can interpret an UpSet plot.