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Prompt

RAIVEN

ChatGPT

Gemini

Claude

s1

Create a volume rendering of the skull CT data from head.vti.

s2

Display a semi-transparent volume from head.vti with an overlayed isosurface highlighting the bone boundary within the tissue, use color palette turbo.

s3

Generate three layered, differently colored isosurfaces from head.vti at low, medium, and high intensity thresholds to represent skin, outer bone, and inner table.

s4

Display axial, sagittal, and coronal triplanar slices of head.vti in one view with an overlayed isosurface to provide 3D surface context.

s5

Show a volume rendering from head.vti in one view and an oblique slice rotated at an arbitrary non-axis-aligned angle in a second view, both using color palette inferno.

s6

Use divcurl_{t}.vti to create a 3×3 grid of LIC visualizations across timesteps t = 4 to 12, using the 2D vector field defined by vx and vy.

s7

Set up three slice views (XY, YZ, XZ) and a fourth 3D view combining volume rendering and triplanar slices from head.vti, synchronizing slice positions and the transfer function.

s8

Produce an LIC visualization from divcurl_19.vti using vx and vy to reveal flow patterns.

s9

Arrange three views from divcurl_15.vti: an LIC of vx and vy, a slice of div, and a slice of curl.

c71

From head.vti, generate a volume rendering of the CT data, and then use head_sample.csv to produce a layered distribution plot showing a histogram of intensity with a KDE curve.

c72

Create seperate three orthogonal slice views in the XY, XZ, and YZ planes from head.vti, link their slice positions together, and also generate a histogram of "intensity" using head_sample.csv.

c73

Render the CT isosurface in pink from head.vti, then summarize head_sample.csv using a histogram of "intensity" colored pink.

c74

Using head.vti, assemble three linked slice views (XY, XZ, YZ) along with a 3D view that combines volume rendering and triplanar slices, making sure the slice positions and transfer function remain synchronized across all four views, and also include a histogram of intensity from head_sample.csv.

c75

Produce a volume rendering from head.vti, and then visualize the statistics in head_region_stats.csv as a parallel coordinates chart including mean, std, median, q25, q75, min, and max.

c76

With divcurl_10.vti, compute an LIC visualization based on vx and vy, and alongside it create a histogram of div with an overlaid KDE using divcurl_sample.csv.

c77

Generate an LIC view from divcurl_10.vti using vx and vy. Also represent divcurl_binned_2d.csv as two heatmaps of x_bin vs. y_bin, one colored by div_mean and one colored by curl_mean, both using color palette inferno.

c78

Using divcurl_15.vti, display four views consisting of an LIC using vx and vy, a slice of div, and a slice of curl, and a histogram of curl from divcurl_sample.csv.

c79

Make a scatterplot of div versus curl generated from divcurl_sample.csv. Also, show two slice views from divcurl_10.vti, one visualizing div and the other curl.

c80

Load divcurl_0.vti, divcurl_10.vti, and divcurl_19.vti to create three LIC visualizations based on vx and vy, and use divcurl_stats.csv to build a time-series chart plotting curl_mean and div_mean against t.

c81

Create a LIC from divcurl_5.vti using vx and vy, then generate a ridgeline plot from divcurl_stats.csv with x = curl_mean and y = t.

c82

Begin with an LIC visualization from divcurl_10.vti computed from vx and vy, then construct two heatmaps from divcurl_binned_2d.csv using x_bin versus y_bin , one colored by curl_mean and the other by div_mean. Also, add a histogram of curl_mean.

c83

Build an LIC view using vx and vy from divcurl_10.vti, and also make a KDE curve for curl based on data from divcurl_sample.csv.

c84

From taylorgreen_7.vti, make a volume of "vort" and also layer streamlines using vx = "ux", vy = "uy", and vz = "uz". Then use tg_stats.csv to create a multi-line chart of vort_mean, vort_q25 and vort_q75 over t.

c85

Construct two layered isosurfaces of vort from taylorgreen_9.vti. Then make a layered histogram and KDE of vort generated from tg_sample.csv.

c86

Make an XY planar slice of vort from taylorgreen_9.vti and use tg_stats.csv to create a multi-line plot of vort_mean, vort_q25, and vort_q75 over t.

c87

Visualize ux, uy, and uz from taylorgreen_9.vti as three separate volume renderings, each with a unique color palette, and use tg_t9_binned.csv to create a heatmap where ux_mean and uy_mean define the axes and count controls the color scale.

c88

Overlay streamline sets computed using vx = ux, vy = uy, and vz = uz in taylorgreen_6.vti and taylorgreen_8.vti within a single view using a different color per timestep, and then construct a line chart from tg_stats.csv plotting vort_mean and critq_mean against t.

c89

Display four views of the Taylor-Green vortex from taylorgreen_9.vti and tg_sample.csv: (1) a volume rendering of "vort" with an XY slice using "turbo", (2) the same XY slice of "vort" using "turbo", (3) a histogram of "vort" in green with an overlaid orange KDE chart from tg_sample.csv, and (4) an XY slice of "pp" using "magma". Link only the slice positions between the slice views so scrolling one updates the other. Do not link transfer functions.

c90

Display a volume rendering of vort from taylorgreen_9.vti together with three linked XY planar slice views of ux, uy, and uz, ensure slice positions and transfer functions are synchronized across the views, and include a multi-line chart of vort_q25, vort_q50, and vort_q75 over t from tg_stats.csv.

c91

Render vort as a volume from taylorgreen_4.vti and use tg_stats.csv to create a boxplot of vort_mean by timestep t using the fields vort_min, vort_q25, vort_q50, vort_q75, and vort_max.

c92

Compute thick, green streamlines using vx = ux, vy = uy, and vz = uz in taylorgreen_9.vti and produce a point chart from tg_t9_binned.csv plotting vort_mean against critq_mean.

c93

Create a volume for Sec61-beta using green color palette, a volume for Membrane using orange color palette, and a volume for DNA using purple color palette, all of which are seperate views but use cells.vti. Also add a layered histogram of Sec61b, Membrane, and DNA in a single chart using cells_channel_sample.csv.

c94

Make layered isosurfaces using seg_Sec61-beta with color = teal, seg_Membrane with color = pink, and seg_DNA with color = green from cells.vti and construct a bar chart from cells_seg_stats.csv showing voxel_count that is colored by seg_combo.

c95

Generate an XY slice view of Sec61-beta and a linked XY slice of Membrane from cells.vti and accompany it with a scatterplot of Sec61b versus Membrane, sized by DNA, created from cells_channel_sample.csv.

c96

Make three layered isosurfaces from cells.vti, one using Sec61-beta and colored pink, another using Membrane and colored orange, and one using DNA colored purple, also summarize segmentation proportions with a pie chart built from cells_seg_stats.csv using seg_combo for slices sized by pct.

c97

Create three linked XY slice views for Sec61-beta, Membrane, and DNA from cells.vti with synchronized slice positions, and add a hexbin plot of Sec61b versus Membrane using cells_channel_sample.csv.

c98

Visualize Sec61-beta as a blue colored volume layered over red colored isosurface of Sec61-beta from cells.vti and overlay KDE over histogram of Sec61b_intensity grouped by seg_label using cells_per_seg_sample.csv.

c99

Overlay isosurface representations of seg_Sec61-beta color = orange, seg_Membrane color = purple, and seg_DNA color = teal from cells.vti and present a boxplot of Sec61b_intensity grouped by seg_label based on cells_per_seg_sample.csv.

i31

Build a bubble chart from countries_latest.csv plotting "gdp_per_capita (2023)" versus "life_expectancy (2023)", sized by "population (2023)" and colored by "continent", with a linked histogram of "gdp_per_capita (2023)" that filters when brushing the bubbles. Add a multiline chart from continents_timeseries.csv tracking "gdp_pc" over "year" with lines colored by "continent".

i32

Show the demographic transition: scatter "gdp_per_capita (2023)" versus "fertility_rate (2023)" from countries_latest.csv, sized by "population (2023)" and colored by "continent", paired with a multiline chart from continents_timeseries.csv plotting "fertility" over "year" with lines colored by "continent".

i33

Produce a ridgeline plot of "median_age (2023)" grouped by "region" from countries_latest.csv.

i34

Lay out a parallel-coordinates plot from countries_latest.csv spanning "gdp_per_capita (2023)", "life_expectancy (2023)", "population (2023)", "median_age (2023)", and "fertility_rate (2023)" with lines colored by "continent", linked to a scatter of "gdp_per_capita (2023)" versus "life_expectancy (2023)" colored by "continent". Brushing lines in the parallel coordinates should highlight the corresponding points in the scatter.

i35

Display "gdp_per_capita (2023)" from countries_latest.csv four ways side by side: a histogram, a KDE plot, a boxplot grouped by "continent", and a violin plot grouped by "continent".

i36

Create a stacked bar chart from countries_timeseries.csv with "year" on the x-axis and "pop" aggregated by "continent", next to a pie chart of "population (2023)" from countries_latest.csv grouped by "continent". Use consistent continent colors across both views.

i37

Plot population trajectories from regions_timeseries.csv as a multiline chart with "year" on the x-axis, "pop" on the y-axis, and lines colored by "region".

i38

Layer a line of "global_average" life expectancy over a shaded band spanning "global_min" to "global_max" from world_life_exp.csv, with "year" on the x-axis.

i39

Explore the climate-prosperity paradox: scatter "gdp_per_capita (2023)" versus "ghg_per_capita (2023)" colored by "continent" from countries_latest.csv, add a choropleth colored by "ghg_per_capita (2023)" using country geometries from world_iso3.geojson and a red colorscale, and include a bar chart from continents_latest.csv with "continent" on the x-axis and "ghg_per_capita (2023)" on the y-axis, colored by "continent".

i40

Pair a scatter of "gdp_per_capita (2023)" versus "internet_pct (2023)" colored by "continent" from countries_latest.csv with a choropleth colored by "internet_pct (2023)" using country geometries from world_iso3.geojson.

i41

Chart the urbanization wave: a multiline of "urban_pct" over "year" colored by "continent" from continents_timeseries.csv, plus a choropleth colored by "urban_pct (2023)" using country geometries from world_iso3.geojson and countries_latest.csv.

i42

Scatter "health_exp_per_capita (2023)" versus "life_expectancy (2023)" from countries_latest.csv as bubbles sized by "population (2023)" and colored by "continent", with a companion violin plot of "life_expectancy (2023)" grouped by "continent".

i43

Map renewable energy: a choropleth colored by "renewable_energy_pct (2012)" using country geometries from world_iso3.geojson and countries_latest.csv, alongside a stacked bar chart from region_energy.csv with "region" on the x-axis, "value" on the y-axis, and stacked by "category" (renewable, nuclear, fossil).

i44

Visualize regional trade intensity from trade_edges.csv as a heatmap with "source" on the x-axis, "target" on the y-axis, and cells colored by "trade_pct".

i45

Construct a chord diagram from trade_edges.csv connecting "source" to "target", sized by "trade_val_kusd", to reveal which inter-regional trade relationships are balanced and which are lopsided.

i46

Assemble a three-view trade dashboard: a sankey from trade_edges.csv with "source" flowing to "target" sized by "trade_val_kusd", a choropleth colored by "trade_pct_gdp (2023)" using countries_latest.csv and world_iso3.geojson, and a bar chart with "target" on the x-axis and summed "trade_val_kusd" on the y-axis, colored by "target" region.

i47

Show the main highways of global commerce as a sankey diagram from trade_edges.csv, with "source" flowing to "target" sized by "trade_val_kusd".

i48

Contrast trade volume with economic weight: a chord diagram from trade_edges.csv connecting "source" to "target" sized by "trade_val_kusd", paired with a choropleth colored by "gdp_per_capita (2023)" using countries_latest.csv and world_iso3.geojson.

i49

Map the world's neighborhood network: a force-directed graph from region_border_network.json with nodes colored by "group" and edges connecting bordering pairs, alongside a choropleth colored by "num_neighbors" using world_iso3.geojson with a point layer at "capital_lat", "capital_lon" where the points are sized by "capital_population (2025)" from countries_latest.csv and colored by "region".

i50

Present borders two ways: (1) A force-directed graph from region_border_network.json with region nodes colored by "group" and edges weighted by "value". (2) An adjacency heatmap from region_border_edges.csv with "region_a" on one axis, "region_b" on the other, and cells colored by "distance_km". Selecting a region node in the graph should highlight its row and column in the heatmap.

i51

Build a force-directed graph of border connections using region_border_network.json with region nodes colored by "group".

i52

Make a force-directed border graph from region_border_network.json with nodes colored by "group", a choropleth colored by "num_neighbors" from countries_latest.csv and world_iso3.geojson with a hexbin layer aggregating "capital_lat" using the "turbo" color scheme and "capital_lon", and a bar chart of "num_neighbors" from region_latest.csv.

i53

Render two views of stellar density from gaia.csv: a heatmap with "ra" on the x-axis and "dec" on the y-axis, and a hexbin plot of the same "ra" versus "dec" coordinates.

i54

Generate a Hertzsprung-Russell diagram from gaia.csv as a heatmap with "bp_rp" on the x-axis and "phot_g_mean_mag" on the y-axis.

i55

Wire up a four-view linked dashboard from gaia.csv: a heatmap of "ra" versus "dec" (sky map), a heatmap of "bp_rp" versus "phot_g_mean_mag" (HR diagram), a histogram of "phot_g_mean_mag", and a histogram of "parallax". Brushing in any view should filter the others.

i56

Link three views from gaia.csv: a heatmap of "bp_rp" versus "phot_g_mean_mag" (HR diagram), a histogram of "parallax", and a KDE of "phot_g_mean_mag". Brushing the parallax histogram to select nearby stars should update the HR diagram and the magnitude KDE.

i57

Tile seven choropleth views from countries_latest.csv, one per trade-region GeoJSON file (EAP.geojson, ECA.geojson, LAC.geojson, MENA.geojson, NAM.geojson, SAS.geojson, SSA.geojson), each colored by "gdp_per_capita (2023)". Use different colorscales for each choropleth.

i58

Stack three geographic layers in a single view from countries_latest.csv and world_iso3.geojson: a choropleth colored by "gdp_per_capita (2023)", a hexbin layer aggregating "capital_lat" and "capital_lon", and a point layer at "capital_lat", "capital_lon" sized by "capital_population (2025)" and colored by "continent".

i59

Display "life_expectancy (2023)" from countries_latest.csv four ways grouped by "region": a boxplot, a ridgeline plot (region on y axis, life expectancy on x axis), a violin plot, and a KDE plot, all sharing a consistent region color mapping.

i60

Set up four linked views from countries_latest.csv: a scatter of "gdp_per_capita (2023)" versus "life_expectancy (2023)" colored by "continent", a stacked histogram of "gdp_per_capita (2023)" colored by "continent", a boxplot of "life_expectancy (2023)" grouped by "continent", and a parallel-coordinates plot of "gdp_per_capita (2023)", "life_expectancy (2023)", "median_age (2023)", and "fertility_rate (2023)" colored by "continent". Brushing any view should filter the others.

i61

Create a stacked bar chart from region_energy.csv with "region" on the x-axis, "value" on the y-axis, and bars stacked by "category" (renewable, nuclear, fossil), using distinct colors per energy type.

i62

Make a brushable scatter of "gdp_per_capita (2023)" versus "life_expectancy (2023)" colored by "continent" from countries_latest.csv and connect it to three separate histograms, showing distributions of "gdp_per_capita (2023)", "life_expectancy (2023)", and "median_age (2023)".

i63

Show three linked scatter plots from countries_latest.csv, all sized by "population (2023)" and colored by "continent": (1) "gdp_per_capita (2023)" versus "life_expectancy (2023)", (2) "fertility_rate (2023)" versus "median_age (2023)", (3) "ghg_per_capita (2023)" versus "internet_pct (2023)".

i64

Create a parallel-coordinates plot from countries_latest.csv spanning "population (2023)", "gdp_per_capita (2023)", "life_expectancy (2023)", "median_age (2023)", "fertility_rate (2023)", "health_exp_per_capita (2023)", "trade_pct_gdp (2023)", "ghg_per_capita (2023)", "capital_population (2025)" and "num_neighbors" with each line representing a country and lines colored by "region". Make the plot brushable.

i65

Plot capital cities from countries_latest.csv as bubbles at y= "capital_lat", x= "capital_lon" sized by "capital_population (2025)" and colored by "continent", linked to a histogram of "capital_population (2025)".

i66

Track deforestation: a choropleth colored by "forest_area_pct (2023)" using the "Greens" color scheme with country geometries from world_iso3.geojson and countries_latest.csv, alongside a bar chart from trade_region_latest.csv with "trade_region" on the x-axis and "forest_area_pct (2023)" on the y-axis.

i67

Create a heatmap of "health_exp_per_capita (2023)" versus "median_age (2023)" from countries_latest.csv with cells colored by count, linked to a histogram of "life_expectancy (2023)". Brushing bins in the heatmap should filter the histogram.

i68

Plot a bubble chart from countries_latest.csv with "literacy_pct (2022)" versus "life_expectancy (2023)", sized by "population (2023)" and colored by "continent".

i69

Compare emissions and wealth: a scatter of "gdp_per_capita (2023)" versus "ghg_per_capita (2023)" colored by "continent" from countries_latest.csv, followed by two choropleths using world_iso3.geojson — one colored by "ghg_per_capita (2023)" using a red sequential color scale, one colored by "gdp_per_capita (2023)" using a blue sequential color scale.

i70

Create a parallel-coordinates plot from countries_latest.csv spanning "gdp_per_capita (2023)", "life_expectancy (2023)", "median_age (2023)", "fertility_rate (2023)", and "ghg_per_capita (2023)" with lines colored by "continent", linked to two KDE charts: one of "gdp_per_capita (2023)" in blue and one of "life_expectancy (2023)" in orange. Brushing the parallel coordinates should filter both KDE charts.

s10

Use divcurl_0.vti, divcurl_10.vti, divcurl_15,vti, and divcurl_19.vti to create four side-by-side LIC views, one per timestep, each computed from the velocity vector with vx = "vx" and vy = "vy".

s11

Provide two slice views from divcurl_15.vti comparing velocity components by setting the scalar field to vy in one and vx in the other.

s12

Compute streamlines from taylorgreen_9.vti using ux, uy, and uz, with seeds in the bounds [0, 64, 0, 64, 0, 0].

s13

Make a volume of "vort" and also streamlines of ux, uy, and uz layered over volume from taylorgreen_9.vti.

s14

Use dataset taylorgreen_9.vti to make a volume of "vort" with streamlines of ux, uy, and uz layered over volume as well as a linked oblique slice.

s15

Make streamline from taylorgreen_9.vti using variables vx = ux, vy = uy, and vz = uz.

s16

Generate three nested isosurfaces of vort from taylorgreen_9.vti at progressively higher thresholds.

s17

Extract an oblique slice through the ux field in taylorgreen_9.vti that is rotated off-center and "Rdylbu" color scheme.

s18

Use divcurl_0.vti, divcurl_10.vti, and divcurl_19.vti to create a 3×3 grid across timesteps, with LIC generated from the 2D vector field using vx and vy, and slices of div and curl.

s19

Overlay streamlines from taylorgreen_6.vti and taylorgreen_8.vti in a single scene using vx = "ux", vy = "uy", and vz = "uz", distinguishing timesteps with different colors.

s20

Using taylorGreen_t_9.vti, create two coordinated views of "vort": a slice in the XY plane at mid-depth and a 3D view combining a volume rendering of "vort" with an isosurface of "vort" and the XY slice. Link the slice position along Z so moving the 2D slice updates the cutting plane in the 3D view.

s21

Display two side-by-side streamline visualizations from taylorgreen_9.vti using vy = "ux", vy = "uy", and vz = "uz", one in red with thin streamlines and one in blue with thick streamlines.

s22

Show two separate isosurface views of vort at the same threshold from taylorgreen_6.vti and taylorgreen_8.vti to compare turbulence decay.

s23

Arrange a four-view grid from taylorgreen_9.vti with volume renderings of ux, uy, uz, and vort, each using different color palette.

s24

Create a view with an XY slice of the fluorescence field "Sec61-beta" from cells.vti in grayscale. Add a second linked view that shows the same "Sec61-beta" slice in grayscale overlaid with isosurfaces of "seg_Sec61-beta" in yellow, "seg_Membrane" in dark blue, and "seg_DNA" in light blue.

s25

Create three views each showing an isosurface from cells.vti, one using seg_Sec61-beta, one using seg_Membrane, and one using seg_DNA, each should be semi-transparent and a unique color.

s26

Combine a volume rendering of Sec61-beta with an isosurface from seg_Membrane in a single view from cells.vti.

s27

Layer volume renderings of Sec61-beta, Membrane, and DNA with isosurfaces from seg_Sec61-beta, seg_Membrane, and seg_DNA in one composite view from cells.vti. All volumes should use same color palette, while all isosurfaces should be distinct colors.

s28

Using cells.vti, create three slice (XY) views using color palette plasma: one for "Sec61-beta", one for "Membrane", and one for "DNA", and link their slice positions so scrolling in any view updates the other two.

s29

Display a volume rendering of Sec61-beta in one view and triplanar slices of the same field in another from cells.vti.

s30

cells.vti contains fluorescence channels ("Sec61-beta", "Membrane", "DNA") and segmentation channels ("seg_Sec61-beta", "seg_Membrane", "seg_DNA"). Create four views: (1) an XY slice of the fluorescence channel "Membrane" in grayscale, (2) a YZ slice of "Membrane" in grayscale, (3) an XZ slice of "Membrane" in grayscale, and (4) a combined view showing triplanar slices of "Membrane" in grayscale overlaid with isosurfaces of "seg_Sec61-beta" in yellow, "seg_Membrane" in dark blue, and "seg_DNA" in light blue. Link the slice positions across all four views so scrolling in any slice view updates the others and the triplanar.

c100

Display layered volumes of Sec61-beta with color = pink, Membrane with color= orange, and DNA with color = purple from cells.vti, generate a scatterplot of Sec61b versus Membrane sixed by DNA which is linked to a histogram of DNA using cells_channel_sample.csv, and conclude with a pie chart of seg_combo proportions sized by pct from cells_seg_stats.csv.