Dataset

This dataset was created by Sanjana Murthy

Released under CC BY-NC-SA 4.0

Contents

This data contains Stacked Column Chart.

Ask Ubuntu temporal network

https://snap.stanford.edu/data/sx-askubuntu.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Ask Ubuntu (http://askubuntu.com/). There are three different types of
interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph sx-askubuntu-a2q) user u commented on user v's question at time t (in the graph
sx-askubuntu-c2q) user u commented on user v's answer at time t (in the
graph sx-askubuntu-c2a)

The graph sx-askubuntu contains the union of these graphs. These graphs
were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-askubuntu)
Nodes 159,316
Temporal Edges 964,437
Edges in static graph 596,933
Time span 2613 days

Dataset statistics (sx-askubuntu-a2q)
Nodes 137,517
Temporal Edges 280,102
Edges in static graph 262,106
Time span 2613 days

Dataset statistics (sx-askubuntu-c2q)
Nodes 79,155
Temporal Edges 327,513
Edges in static graph 198,852
Time span 2047 days

Dataset statistics (sx-askubuntu-c2a)
Nodes 75,555
Temporal Edges 356,822
Edges in static graph 178,210
Time span 2418 days

Source (citation)
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal Networks." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017.

Files
File Description
sx-askubuntu.txt.gz All interactions
sx-askubuntu-a2q.txt.gz Answers to questions
sx-askubuntu-c2q.txt.gz Comments to questions
sx-askubuntu-c2a.txt.gz Comments to answers

Data format

SRC DST UNIXTS                             

where edges are separated by a new line and

SRC: id of the source node (a user)                  
TGT: id of the target node (a user)                  
UNIXTS: Unix timestamp (seconds since the epoch)            
               ...

Notebook to create stacked area plot showing cluster data contributions over time, cumulatively

This bar chart displays companies by stack using the aggregation count in Seattle. The data is about companies.

This bar chart displays companies by stack using the aggregation count in Leganés. The data is about companies.

Each stack plot is a one week time series of the solar wind full resolution rate. Each horizontal trace is one minute of data. There are 1440 sweeps per day, and 7 days are shown per stack plot. As the solar wind speed changes, the location of the peak in ESA Step (corresponding to an E/Q setting) changes. Sometimes seen to the right of the prominent proton peak is a smaller profile representing solar wind helium (He+2).

This pie chart displays companies per stack using the aggregation count. The data is filtered where the industry is Broadline Retail. The data is about companies.

United States - Producer Price Index by Industry: Industrial Truck, Trailer, and Stacker Manufacturing: Industrial Trucks, Tractors, Mobile Straddle Carriers and Cranes, and Automatic Stacking Machines was 336.95300 Index Dec 1979=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Industrial Truck, Trailer, and Stacker Manufacturing: Industrial Trucks, Tractors, Mobile Straddle Carriers and Cranes, and Automatic Stacking Machines reached a record high of 336.95300 in July of 2025 and a record low of 39.70000 in January of 1965. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Industrial Truck, Trailer, and Stacker Manufacturing: Industrial Trucks, Tractors, Mobile Straddle Carriers and Cranes, and Automatic Stacking Machines - last updated from the United States Federal Reserve on November of 2025.

Blockchain data dashboard: Chart GroupBy + Stacking QA – Many series in many chart types

Blockchain data query: Stacked Bar Chart by Hour

Ceramic building material quantification data by context type for Illus. 5.21.

Location, type, and access information for electric vehicle charging stations in San Mateo County

Traffic counts data for NJ DOT. The data sets includes short term counts (48 hours volumes) and continuous data.

Updated daily, Monday through Friday. International Passenger Count excluding flights from Canada. May 2012 counts included for comparison.

The dataset includes demographic information setting forth the number of filings made by business entities with the Department of State’s Division of Corporations. Such filings are categorized by type and filer.

Blockchain data query: Dex stacked chart

Dataset

This dataset was created by Fahad Rehman

Released under Attribution 4.0 International (CC BY 4.0)

Contents

Commerce Dashboard Measures - Fiscal Year Part 2

This pie chart displays companies per stack using the aggregation count in Galion. The data is about companies.

dataset repo: https://github.com/dorianprill/dataset-bicycle-geometry

The data set contains more than 6400 observations of the following 30 variables:

columns = [
  'URL',
  'Brand',
  'Model',
  'Year',
  'Category',
  'Motorized',
  'Frame Size',
  'Frame Config',
  'Wheel Size',
  'Reach',
  'Stack',
  'STR',
  'Front Center',
  'Head Tube Angle',
  'Seat Tube Angle Effective',
  'Seat Tube Angle Real',
  'Top Tube Length',
  'Top Tube Length Horizontal',
  'Head Tube Length',
  'Seat Tube Length',
  'Standover Height',
  'Chainstay Length',
  'Wheelbase',
  'Bottom Bracket Offset',
  'Bottom Bracket Height',
  'Fork Installation Height',
  'Fork Offset',
  'Fork Trail',
  'Suspension Travel (rear)',
  'Suspension Travel (front)',
]

Multiple variants may be recorded for each model. Variants depend mostly on Frame Size, Frame Config, Wheel Size, Suspension Travel (rear), Suspension Travel (front).

Most of the columns are self-explanatory if you are into bikes. There may be many nulls in the numeric columns since different manfuacturers may use a slightly different set of values and some values are normally only stated for a certain category of bikes.
Some of these values can be computed with simple geometry.

The URL column contains the URL of the page from which the data was extracted. The last number in the URL is the database ID of the bike.

Ideas

• Plot a facet grid of scatterplots for stack/reach vs other variables of a certain class of bikes (e.g. Mountain).
• Plot the median head tube angle of a certain class of bikes (e.g. > 140 mm rear travel MTBs) as a line chart over the years.
• Plot histograms of some geometry values for a certain class of bikes (e.g. <= 130 mm rear travel MTBs) over the years.
• Manufacturer sizing is very arbitrary. Develop a model for sizing based on variables stack and reach. Find a relationship using a scatterplot and some regression model
• Try to predict the category of a bike given only a limited set of geometry values.
• Try to predict the model year given only a limited set of geometry values.
• Try to predict the suspension travel given only a limited set of geometry values.

Errata

The API currently has no category for electric bikes. It has a variable has_motor but it is always false. This is probably a bug in the API or not recorded in the database (yet).
I have included it in the data set as Motorized for future-proofing but you can safely drop it for now.