Visualization - Science topic

One of the most applicable visualizer environment for OpenSees software is the OSLite software. You can use it to see the modeled structure in OpenSees. Also you are able to run analysis by this software.

The environment is like the attached image.

Use the Fisher criterion or the method of variance analysis (ANOVA)

Python is relatively more useful but R is also good

Dinesh Kumar Duraisamy

The antipodes of twisted fibrils cannot be determined using TEM and CEM. You will not distinguish between the left and right hands with your eyes if their morphology from above and below is the same. Also, you can't tell which antipode you see if you don't see another side by side. Alpha and beta glucose will look like white crystals under the microscope.

Hi Saif, thanks for your quick and elaborate response! However, what I meant was that I wanted to plot the Pearson's coefficient values in a meaningful way, NOT the transformed data since those numbers are just arbitrary values to obtain a normalised population. The Pearson's scale would range from 1 (full colocalisation/dependency) to 0 (no correlation) to -1 (mutual exclusivity), whereas the Fisher transformed values have no meaning. I think I will just plot the spread of all values without a mean, and manually add the mean, p-value etc. to keep the intuitive visual and provide all relevant parameters.

This is not a question, but some sort of advertising/publicity plug.

Delete/Ignore

Shaolin Lü,yes the dataset is open.I had analyzed the high-rated GPS data from SANT GPS station of Chile, with respect to the 57th, 58th, and 59th day (Julian days) of 2010, which was related to the great Chile Earthquake 2010.

You can use Google Classroom.

Each software has a specific way of reading the .pdb file...

Dear Merve Özcan ,

VESTA can be used in Windows, but please note that to visualize Optimised structure, Xcrysden should be used.

Please let me know, if further help is required.

--

Best wishes

Vipul

Thank you, Ahmed Khalid Ahmed Asinger, for the explanation.

After conducting your data analysis in SPSS for your thesis, there are several data visualization tools you can consider to present your findings effectively. Here are some popular options:

Consider the complexity of your data, the type of visualization you want to create, and your comfort level with different tools when selecting the most suitable option. Additionally, it's important to ensure that the chosen tool aligns with any specific guidelines or requirements set by your institution or intended publication platform.

Remember that the purpose of data visualization is to effectively communicate your findings, so choose tools and techniques that best represent and highlight the key insights from your analysis.

Hi Ruben,

I was also inspired by the research to add some clusters to my visualized lda model but I did not succeed although I tried different ways of visualizing by LDAvis or pyLDAvis.

I wonder if you found a way that could share with me?

Thank you!

Look for spines (ARC protein deposits) at locations where synchronous activation of neurons AND fine fibres of branched axons of other neurons are co-located, such as in the cerebral cortex.

I would not expect to see them where associative memory formation is not considered likely.

1. UCSF Chimera: UCSF Chimera is a powerful molecular visualization tool that is available for free download. It has a user-friendly interface and a wide range of features, including the ability to visualize and analyze protein structures.

2. Jmol: Jmol is a free, open-source molecular visualization tool that is designed for the display of molecular structures in 3D. It has a user-friendly interface and a range of features, including the ability to mark specific amino acids and customize the display of the protein.

3. Yasara: Yasara is a molecular modeling and simulation software that includes a user-friendly molecular visualization tool. It has a range of features, including the ability to mark specific amino acids and customize the display of the protein.

Pretty old thread, but for everyone how faces this problem:

As long as there is some sort of equivalent or similar element available from the ABAQUS library you can use a work around using these as dummy variables together with a UMAT.

More detailed explanation can be found here:

Hi, this is ChatGPT Answer, I have not tried but I hope it would work

To include self-citations only in VOSViewer or Biblioshiny, you can follow these steps:

Note that the exact steps may vary slightly depending on the version and settings of VOSViewer or Biblioshiny that you are using.

Abderrahmane Boudribila Thank you

I use wordart https://wordart.com/

Variations on this question have been asked here several times. The basic conclusion is that all of the major qualitative data analysis programs (ATLAST.ti, Dedoose, MAXQDA, and NVivo) do essentially the same thing, but there are meaningful differences in how they implement these procedures.

The best thing to do is examine the training materials that these programs make available at their websites to determine which one feels most comfortable to you.

Thank you for your answers, really appreciated.

I want to visualise multiple clinical variables ( temperature , blood pressure, sugar… ) for multiple patients, recorded in multiple clinical visits by time.

sure. Deleted.

Anelia Kurteva Thank you for your advice. Thanks?(?ω?)?

But I just want to visualize RDF triples, and I don't want to use neo4j to store RDF triples.

My data is stored in RDF Databases and queried by SPARQL Endpoint.

Thank you sir for the suggestion.

try this one:

all the best

fred

Hello everyone.

First, Heena, please have a look at my advisor's ResearchGate profile. His name is James Liburdy and you can find him via my RG profile. He used to do flow vis of air jets in a wind tunnel. Just go through his publications and they will give you a sense of how to get started with air flow vis.

Second, because this thread is about flow visualization, I would like to let everybody know that we've started a Flow visualization Stack exchange forum. We are building a community currently. Once we have 60 people we will be allowed to proceed to a private beta version of the forum. Please join us if you are interested in flow vis and have questions to ask. Here's the link: https://area51.stackexchange.com/proposals/127312/flow-visualization?referrer=NTJlZjIyYzI3Zjk4N2I1NDZmMTJhZDUxMTViODcwMWUyNTM4OTI1YTU1OTYxN2ZkNDcwY2U2ZWI5NmU2OTY5OGhTtnNa4jEjFBFgB4o_K-u-LTdCqWX8yd8vul6HHcUb0

Update: The company replied and said they will remove my publication from their references. We will see if they do it and if they will remove the other papers with trichloroethanol.

If you want to use Python tools, you could use a combination of BioPython (https://biopython.org/wiki/Phylo) for conversion from nexus to newick tree format and use ete3 (http://etetoolkit.org/) for visualization.

Approximately 50 particles in static condition.

I totally agree that (good) illustrations are essential to draw in those who are unfamiliar with the work, especially those who are less scientifically inclined.

Thank you. Subhankar Banerjee

Thank you.

Hi Syed, if you decide to contact them & they are interested, you can email me deborah.hilton@gmail.com maybe I can help with analysis in Excel or reviewing questionnaire, but they maybe too busy with their orders etc. Kind Regards.

The following link is also very useful: https://link.springer.com/article/10.1007/BF02537208#:~:text=A%20rapid%20unidimensional%20thin%2Dlayer,and%20cholesterol%20are%20thereby%20achieved.

You can research the business side of sports, if you are interested - optimizing training/preparation costs; maximizing events' revenue, ecx.

no sir

Thank you Yulia Panina, I did more tests and the results are better (no tail and green ring around the red droplet), thus it seems to be matter of sample preparation.

Probably if pH is too high, the FCF dye loses its property.

Many thanks for your interest.

Best wishes,

Filippo

Hi Somaya A.Amer. With an inverted microscope, you would have to invert your slide as well. If you have a coverslip covering your cells, this should face your objective lens. If your cells are fluorescent, you have to make sure your acquisition parameters are correct (excitation laser wavelength, emission range, gain settings, etc).

Dears;

I'm having the same issue. Did you manage to find a solution? Is it possible for you to share it with me?

Dear Ravi L. Hadimani ,

Look the link, maybe useful.

Regards,

Shafagat

hi.,

kindly refer this link:

Best wishes..

OK, if you decide to use chemcraft with the output that includes the keyword pop=full and gfinput, the surfaces are visualized as follows:

Tools > Orbitals > Render molecular orbitals > select the orbitals you want

and ready!

Nitin Sharma, Try this

library(rpart)

library(randomForest)

library(tidyr)

library(ggplot2)

set.seed(42)

r <- rpart(Species~., data = iris)

for(i in 1:10) {

f <- randomForest(Species~.,data = iris, ntree = i)

r_pred = predict(r, iris, type = 'class')}

f

errors <- data.frame(f[["err.rate"]])

errors$ntree <- seq(1,10, by = 1)

error_df <- gather(errors, "Species", "Value", 1:4)

ggplot(error_df, aes(x = Species, y = Value, color = as.factor(ntree)))+

geom_point()+

labs(col = "ntree")

1) Always set seeds when working with random functions, this allows you to reproduce numbers. If you run your code multiple times, you will get different results each time.

2) Error rates can be fetched from the rf-formula object (f) by extracting it with [["err.rate]]. Open the f-object in the viewer to see the other information you can extract.

3) The rest of the code will gather your data in a long format, which makes it easier to plot, and code to plot it as a scatterplot with colorcoding for each ntree-run. Code can be expanded on to show values etc.

Hope this helps

Best !!

AN

After setting your brightness and contrast click on the "set" button and then tick the first of the two check boxes "propagate to all other n channel images". This should apply the settings to the whole stack (but also to any other images that are open at the same time)

Dear Vrushali Lanjewar thank you so much.......

Can you say more about what you mean by qualitative analysis?

Samir el Shawish showed me the way.Here is his profile:

You can do it by creating a display group for each element set. When adding all of them, the feature edges will separate each element set.

Thanks!

Try

ShinyGO

webgestat

funrich

David

Dear Ana,

Check that if your model fully supports these outputs. Make sure that the analysis type, material properties, and element type are defined in a way to produce these outputs.

Dear Mrs. Matusiak

Virtual and augmented reality research in the architecture field reveals a wide range of potential applications for systems to assist designers, laypeople, and decision-makers in the architectural design process. There is an urgent need to reconsider how we use the set of collaborative technologies to move toward a more sustainable world. e a new vision of virtual reality (VR) as a discipline-agnostic platform for interdisciplinary integration of allied design, social, and environmental disciplines to address emerging challenges across the building sectors. This contribution is built in the following steps. First, we situate VR technologies within the evolving digital landscape and the underlying tensions in built-environment practices. Second, we characterize the challenges that have arisen as a result of using them to address challenges, illuminating our point with leading examples. Third, we conceptualize VR configurations and investigate underlying assumptions for their application across disciplinary scenarios. Fourth, we propose a vision of VR as a platform that can be used across disciplines.

Hello Onur,

Your can use the Climate Data Toolbox (https://github.com/chadagreene/CDT) with its funcionalities described here:

In PyMOL you can use A > align > all to this option to align all the open structures to a particular structure in a single step.

For multiple structure alignment you can also use VMD MultiSeq (https://www.ks.uiuc.edu/Training/Tutorials/vmd/tutorial-html/node7.html).

Seo Boil

These links seem to be useful, have a look:

1. https://www.youtube.com/watch?v=OKt4T18yJNI

2. https://www.comsol.com/video/creating-2d-models-from-3d-geometries-in-comsol-multiphysics

3. https://groups.google.com/g/dream3d-users/c/v4waOeEDuss

4. https://www.eng-tips.com/viewthread.cfm?qid=450782

يمكن سؤال المتخصص

Sharmila Jayasena

The UV transilluminator functions by emitting high quantities of UV light through the viewing surface. A UV transilluminator is most commonly used to see DNA and protein agarose and polyacrylamide gels following electrophoresis.

The transilluminator with a 312nm wavelength is appropriate for normal gel documentation needs. Transilluminators with a wavelength of 254nm induce greater DNA damage and are better suited for other purposes such as crosslinking. Because 254nm bulbs may be seen through the filter, they are inappropriate for gel documentation.

Dear Rajesh Ramesh

Using transmission electron microscopy, one method of estimating the SFE is to measure the distance between the partial dislocations (TEM). sf = 2Ghcp-fcc + 2.

The size of the stacking fault region (distance between the partial dislocations) is thus defined by a balance between the repulsive elastic contact between the partial dislocations and the energy required to generate the stacking fault between them, referred to as the SFE.

Kind Regards

@Qamar Ul Islam thank you for taking the time to answer!

However, I don't think that this answer resolves my query. Actually, by using VMD, I wish to make movies of the MD simulation outputs (i.e., after loading the trajectory .xtc files to the main system .gro files), wherein the two peptides are represented in distinct colours for ease of visualisation. I hope this explains my question better.

Personally, some times I use Microsoft office words. You have the option to arrange them in your pleasure. Also, you can select a wife range of colors and fonts.

If you are using AutoDock/Vina for docking, you can use the prepare_ligand.py or prepare_ligand4.py scripts. These scripts can be found within the installation directory of AutoDockTools (../MGLToolsPckgs/AutoDockTools/Utilities24/). PyRx probably use the same for batch processing of ligands. You can try PyRx as well.

Dear Jesus Granados , since I do not know which type of model you are dealing with, please excuse if my answer does not fit your needs. Put one dimensionality reduction algorithm which usually works very well for data viz and is thus worth checking out is the t-SNE algorithm.

Here is an explanation:

and here are some pieces of code which implement it in Python:

Anyway, good with your project!

David M Jameson Thank you very much, I definitely consider all of these issues. Thank you, sir.

Abdelilah Bouragba try the following:

1) Make sure your UMAT subroutine does not generate NaN values (typical mistakes causing this are 1/0 or log(0) ).

2) Make sure you define field output for the stage and variables you want to look at (e.g. PEEQ or mises stress).

Other than that I cannot help you any further.

I use Paraview which IMHO is the best simulation visualization software out there

sure

If your are interested in good visualizations I recommend you to see this application: TerriStory.fr

Dear Shreyashi Basak

These links might be useful, have a look:

1. https://www.mathworks.com/help/stats/prob.normaldistribution.random.html

2. https://github.com/SebastianRiedel/bingham/blob/master/matlab/tutorials/bingham_tutorial.m

3. https://www.researchgate.net/publication/324605982_Simulation_and_Visualization_of_Spherical_Distributions

Kind Regards

Dear Edward Obianagha

Kind Regards

I very strongly advocate against bar plots (as Kristina suggested).

You should be aware that the expression of GAPDH might not be constant across cell lines. You should check this. I suggest using a couple of several reference genes so that differences between cell lines will average out (maybe not completely but at least as good as possible).

Since you are interested in an expression pattern accross cell lines, there is no need to choose any "reference line". Show the dCt values. I also do not agree with John in this point, who said you should "Pick a cell line that you wish to be your 'reference' line". Chosing one such line (without very good reason) means that every error and bias from this line will affect (unneccesarily!) all the other measurements. Instead of chosing one you may use all of them. This is described below in 2) and 3).

Having data from many genes in many cell lines, this is usually best presented in heat maps (the dCt is coded by color). Rows and columns may be clustered to get genes with similar activity profiles together as well as to get celllines with similar expression profiles together.

There are different options you may consider to highlight the information relevant to your problem:

1) The heat map can show the dCt values "as is", so one will see the "raw expression score" in all the measurements. Since dCt values are interval scale and the choise of the reference gene is arbitrary, these absolute values are arbitrary as well and may not be of primary interest.

2) You can subtract the mean dCt (across all cell lines) for each gene. The resulting ddCt values will be gene-wise centered at 0. Positive values indicate "above average", negative values indicate a "below average" expression (average relative to the cell lines you analysed). Make sure to chose a diverging color scale with a neutral color for 0. This may be a more relevant information, retaining the infromation of how much different (in log2 scale) the expression is between the cell lines.

3) You can z-transform the dCt values by gene (across cell lines). The picture will be similar to 2), but now one unit difference does not mean a log2 difference but a standard deviation difference. This will make patterns more comparable between genes that show a large variance with large difference to genes with small variance and small differences. It shows more what a t-test "sees".

There might be more options. Think of what is relevant for you to be shown. The third option is the one most frequently used, but it need not always be the best option (I suppose most authors use it just because others used it and because they don't really think of other options). I have seen authors using 2^dCt with heatmaps, what is clearly suboptimal, as it distorts the symmetry and leads to biased perception of biological differences (as always when 2^dCt is used, so better never ever use it, anyway).

Just one example for illustration:

try python libraries

BioStructMap

rstools

ssbio

SC mapping remains a good method of visualisation especially in traditional activities characterised by stability and repetitiveness of practices. Also, each modern or traditional activity is characterised by specificities and particularities that are perfectly captured by SC mapping.

Dear Pratik Sarkar

These links might be useful, have a look:

1. https://chemistry.stackexchange.com/questions/33932/generating-neat-orbitals-surfaces-from-molden-wfn-files

2. https://discuss.avogadro.cc/t/orbital-density-plot-in-avogadro/191

3. https://answers.uillinois.edu/scs/page.php?id=103413

Kind Regards