This repository contains a collection of data about 454 value chains from 23 rural European areas of 16 countries. This data is obtained through a semi-automatic workflow that transforms raw textual data from an unstructured MS Excel sheet into semantic knowledge graphs.In particular, the repository contains:MS Excel sheet containing different value chains details provided by MOuntain Valorisation through INterconnectedness and Green growth (MOVING) European project;454 CSV files containing events, titles, entities and coordinates of narratives of each value chain, obtained by pre-processing the MS Excel sheet454 Web Ontology Language (OWL) files. This collection of files is the result of the semi-automatic workflow, and is organized as a semantic knowledge graph of narratives, where each narrative is a sub-graph explaining one among the 454 value chains and its territory aspects. The knowledge graph is based on the Narrative Ontology, an ontology developed by Institute of Information Science and Technologies (ISTI-CNR) as an extension of CIDOC CRM, FRBRoo, and OWL Time.Two CSV files that compile all the possible available information extracted from 454 Web Ontology Language (OWL) files.GeoPackage files with the geographic coordinates related to the narratives.The HTML files that show all the different SPARQL and GeoSPARQL queries.The HTML files that show the story maps about the 454 value chains.An image showing how the various components of the dataset interact with each other.

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#face2face

Summary data of each city's contribution to reduction measures of greenhouse gas emissions in the County.

Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy.

For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html

Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

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)            
               ...

Data by city showing energy contribution to greenhouse gas emissions in the County. This data is part of the Regionally Integrated Climate Action Planning Suite (RICAPS) program.

Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy.

For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html

A graph, G=(V,E), consists of a non empty finite set V of elements called vertices and a set E of pairs of elements of V called edges. The number of vertices N=|G|=|V| is the order of the graph. If (x,y) is an edge of E, we say that x and y (or y and x) are adjacent and this is usually written x --> y. It is also said that x and y are the endvertices of the edge (x,y). The degree of a vertex δ(x) is the number of vertices adjacent to x. The degree of G is Δ=max_{x ∈ V} δ(x). A graph is regular of degree Δ or Δ - regular if the degree of all vertices equal Δ. The distance between two vertices x and y, d(x,y) , is the number of edges of a shortest path between x and y , and its maximum value over all pair of vertices, D=max_{x, y ∈ V}d(x,y) , is the diameter of the graph. A (Δ,D) graph is a graph with maximum degree Δ and diameter at most D. The order of a graph with degree Δ, Δ > 2), of diameter D is easily seen to be bounded by

1 + Δ + Δ (Δ-1) + ...+ Δ (Δ-1) D-1 = (Δ (Δ-1)D -2) / (Δ-2) = N(Δ, D)

Hoffman and Singleton introduced the concept of Moore graphs, after Edward Forrest Moore, as graphs attaining this value, known as Moore bound. They also showed that, for D ≥ 2 and Δ ≥ 3, Moore graphs exist for D=2 and Δ =3,7 , and (perhaps) 57. In this context, it is of great interest to find graphs which for a given maximum diameter and maximum degree have a number of vertices as close as possible to the Moore bound.

Download the .zip package, unpack it and open in a browser the file
table_degree_diameter.html or the file taula_delta_d.html.

The table on that page presents the state of the art, as of September 2025, for the largest known (Δ, D)-graphs. Entries in boldface are optimal. Click on a position to view more information about that entry, including graph construction details, the Moore bound, author, references, and more. Entries with a border include a SageMath script to compute their relevant properties. Adjacency lists are available for most graphs with fewer than 20,000 vertices. By clicking on entry (8,3) = 253, you can access a ZIP file containing the programs used to obtain the results for this graph, as well as for the graphs (3,5), (6,8), (7,6), (7,7), (8,5), (9,4), (10,4), (10,5), (11,5), (12,5), (13,5), (14,5), and (15,5) -- all found by the author in 2024. The C program used is the same as the one that found the entry (8,3) in 1994, with minor modifications to the output. Journal publications associated with this data: F. Comellas. Table of large graphs with given degree and diameter. arXiv:2406.18994 [math.CO]. doi: 10.48550/arXiv.2406.18994 F. Comellas. New results on the degree-diameter problem for undirected graphs. Electron. J. Graph Theory Appl. 13 (1) (2025), 211-215. doi:10.5614/ejgta.2025.13.1.14.

These dynamic graphs are derived from the "CAMELS-FR dataset". A html file is provided for each catchment, where dynamic plots of hydroclimatic time series are displayed. The files are available in a few languages.

The Technical appendix can be found under: https://www.kaggle.com/datasets/agatasko/tech-appendix. List of supplements: plots:a. 01_TwiBot_20_histograms.htmlb. 02_Bitcoin_OTC_histograms.htmlc. 03_Bitcoin_Alpha_histograms.htmld. 04_TwiBot_20_dimensionality.htmle. 05_Bitcoin_OTC_dimensionality.htmlf. 06_Bitcoin_Alpha_dimensionality.htmltables:a. 01_TwiBot_20_statistics.csvb. 02_Bitcoin_OTC_statistics.csvc. 03_Bitcoin_Alpha_statistics.csvd. 04_TwiBot_20_results.csve. 05_Bitcoin_OTC_results.csvf. 06_Bitcoin_Alpha_results.csvg. 07_TwiBot_20_compression_results.csvh. 08_Bitcoin_OTC_compression_results.csvi. 09_Bitcoin_Alpha_compression_results.csv plots: a. 01_TwiBot_20_histograms.html b. 02_Bitcoin_OTC_histograms.html c. 03_Bitcoin_Alpha_histograms.html d. 04_TwiBot_20_dimensionality.html e. 05_Bitcoin_OTC_dimensionality.html f. 06_Bitcoin_Alpha_dimensionality.html tables: a. 01_TwiBot_20_statistics.csv b. 02_Bitcoin_OTC_statistics.csv c. 03_Bitcoin_Alpha_statistics.csv d. 04_TwiBot_20_results.csv e. 05_Bitcoin_OTC_results.csv f. 06_Bitcoin_Alpha_results.csv g. 07_TwiBot_20_compression_results.csv h. 08_Bitcoin_OTC_compression_results.csv i. 09_Bitcoin_Alpha_compression_results.csv (ZIP)

Network was collected by crawling Amazon website. It is based on Customers Who Bought This Item Also Bought feature of the Amazon website. If a product i is frequently co-purchased with product j, the graph contains a directed edge from i to j.

The data was collected by crawling Amazon website and contains product metadata and review information about 548,552 different products (Books, music CDs, DVDs and VHS video tapes).

For each product the following information is available:

Title Salesrank List of similar products (that get co-purchased with the current product) Detailed product categorization Product reviews: time, customer, rating, number of votes, number of people that found the review helpful

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#amazon

United States - Producer Price Index by Industry: Fabricated Structural Metal Manufacturing: Fabricated Structural Metal Bar Joists and Concrete Reinforcing Bars was 348.15700 Index Jun 1982=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Fabricated Structural Metal Manufacturing: Fabricated Structural Metal Bar Joists and Concrete Reinforcing Bars reached a record high of 370.29500 in January of 2024 and a record low of 28.40000 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: Fabricated Structural Metal Manufacturing: Fabricated Structural Metal Bar Joists and Concrete Reinforcing Bars - last updated from the United States Federal Reserve on November of 2025.

• Hyperlinks between subreddits on Reddit
• Embeddings of users and subreddits
• Textual requests for pizza with outcome labels
• Resubmitted content on reddit.com
• Images sharing common metadata on Flickr

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects. http://snap.stanford.edu/data/index.html#onlinecoms

United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Stainless (DISCONTINUED) was 96.20000 Index Dec 2010=100 in December of 2017, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Stainless (DISCONTINUED) reached a record high of 103.80000 in July of 2014 and a record low of 79.80000 in January of 2016. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Stainless (DISCONTINUED) - last updated from the United States Federal Reserve on December of 2025.

United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Carbon (DISCONTINUED) was 117.30000 Index Dec 2010=100 in December of 2017, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Carbon (DISCONTINUED) reached a record high of 119.00000 in October of 2017 and a record low of 100.00000 in December of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Commodity for Metals and Metal Products: Cold Finished Steel Bars and Bar Shapes, Carbon (DISCONTINUED) - last updated from the United States Federal Reserve on November of 2025.

United States - Producer Price Index by Industry: Rolled Steel Shape Manufacturing: Cold Finished Steel Bars and Bar Shapes, Made from Purchased Steel was 249.97800 Index Jun 1982=100 in August of 2025, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Rolled Steel Shape Manufacturing: Cold Finished Steel Bars and Bar Shapes, Made from Purchased Steel reached a record high of 298.07300 in May of 2022 and a record low of 30.80000 in February of 1967. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Rolled Steel Shape Manufacturing: Cold Finished Steel Bars and Bar Shapes, Made from Purchased Steel - last updated from the United States Federal Reserve on November of 2025.

Bar chart review and ratings distribution for The School of UX (Pixel Takeaway Limited), provided by Trustpilot.

Orkut social network and ground-truth communities

https://snap.stanford.edu/data/com-Orkut.html

Dataset information

Orkut (http://www.orkut.com/) is a free on-line social network where users form friendship each other. Orkut also allows users form a group which
other members can then join. We consider such user-defined groups as
ground-truth communities. We provide the Orkut friendship social network
and ground-truth communities. This data is provided by Alan Mislove et al. (http://socialnetworks.mpi-sws.org/data-imc2007.html)

We regard each connected component in a group as a separate ground-truth
community. We remove the ground-truth communities which have less than 3
nodes. We also provide the top 5,000 communities with highest quality
which are described in our paper (http://arxiv.org/abs/1205.6233). As for
the network, we provide the largest connected component.

Dataset statistics
Nodes 3,072,441
Edges 117,185,083
Nodes in largest WCC 3072441 (1.000)
Edges in largest WCC 117185083 (1.000)
Nodes in largest SCC 3072441 (1.000)
Edges in largest SCC 117185083 (1.000)
Average clustering coefficient 0.1666
Number of triangles 627584181
Fraction of closed triangles 0.01414
Diameter (longest shortest path) 9
90-percentile effective diameter 4.8

Source (citation)
J. Yang and J. Leskovec. Defining and Evaluating Network Communities based on Ground-truth. ICDM, 2012. http://arxiv.org/abs/1205.6233

Files
File Description
com-orkut.ungraph.txt.gz Undirected Orkut network
com-orkut.all.cmty.txt.gz Orkut communities
com-orkut.top5000.cmty.txt.gz Orkut communities (Top 5,000)

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The graph in the SNAP data set is 1-based, with nodes numbered 1 to
3,072,626.

In the SuiteSparse Matrix Collection, Problem.A is the undirected
Orkut network, a matrix of size n-by-n with n=3,072,441, which is
the number of unique user id's appearing in any edge.

Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set. A(i,j)=1 if person nodeid(i) is friends with person nodeid(j). The
node id's are the same as the SNAP data set (1-based).

C = Problem.aux.Communities_all is a sparse matrix of size n by 15,301,901 which represents the same number communities in the com-orkut.all.cmty.txt file. The kth line in that file defines the kth community, and is the
column C(:,k), where where C(i,k)=1 if person nodeid(i) is in the kth
community. Row C(i,:) and row/column i of the A matrix thus refer to the
same person, nodeid(i).

Ctop = Problem.aux.Communities_to...

Data by city showing energy contribution to greenhouse gas emissions in the County. This data is part of the Regionally Integrated Climate Action Planning Suite (RICAPS) program.

Each city in San Mateo County has the opportunity to develop its own Climate Action Plan (CAP) using tools developed by C/CAG in conjunction with DNV KEMA https://www.dnvgl.com/ and Hara. http://www.verisae.com/default.aspx. This project was funded by grants from the Bay Area Air Quality Management District (BAAQMD) and Pacific Gas and Electric Company (PG&E). Climate Action Plans developed from these tools will meet BAAQMD's California Environmental Quality Act (CEQA) guidelines for a Qualified Greenhouse Gas Reduction Strategy.

For more information, please see the RICAPS site: http://www.smcenergywatch.com/progress_report.html

AG-Monien Graph Collection, Ralf Diekmann and Robert Preis http://www2.cs.uni-paderborn.de/fachbereich/AG/monien/RESEARCH/PART/graphs.html

A collection of test graphs from various sources. Many of the graphs include XY or XYZ coordinates. This set also includes some graphs from the Harwell-Boeing collection, the NASA matrices, and some random matrices which are not included here in the AG-Monien/ group of the UF Collection. In addition, two graphs already appear in other groups:

AG-Monien/big : same as Nasa/barth5, Pothen/barth5 (not included here) AG-Monien/cage_3_11 : same as Pajek/GD98_c (included here)

The AG-Monien/GRID subset is not included. It contains square grids that are already well-represented in the UF Collection.

These graphs appear in this set, as individual graphs, all with XY or XYZ coordinates:

AG-Monien/3elt
AG-Monien/3elt_dual
AG-Monien/airfoil1
AG-Monien/airfoil1_dual
AG-Monien/big_dual
AG-Monien/crack
AG-Monien/crack_dual
AG-Monien/grid1
AG-Monien/grid1_dual
AG-Monien/grid2
AG-Monien/grid2_dual
AG-Monien/netz4504
AG-Monien/netz4504_dual
AG-Monien/ukerbe1
AG-Monien/ukerbe1_dual
AG-Monien/whitaker3
AG-Monien/whitaker3_dual
AG-Monien/brack2
AG-Monien/wave
AG-Monien/diag
AG-Monien/L
AG-Monien/L-9
AG-Monien/stufe
AG-Monien/stufe-10
AG-Monien/biplane-9
AG-Monien/shock-9

Note that L-9, stufe-10, biplane-9 and shock-9 were L.9, stufe.10, etc, in the AG-Monien set. The UF Collection does not permit "." in the matrix name.

Six more problem sets are included as sequences, each sequence being a single problem instance in the UF Collection:

AG-Monien/bfly: 10 butterfly graphs 3..12 AG-Monien/cage: 45 cage graphs 3..12 AG-Monien/cca: 10 cube-connected cycle graphs, no wrap AG-Monien/ccc: 10 cube-connected cycle graphs, with wrap AG-Monien/debr: 18 De Bruijn graphs AG-Monien/se: 13 shuffle-exchange graphs

The primary graph (Problem.A) in each sequence is the last graph in the sequence. In the Matrix Market and Rutherford-Boeing formats, the filenames will differ from the names given below, because in the UF Collection, the file name gives the place of a graph in its sequence. The correspondence with the original graph names is given below.

Graphs in the bfly sequence:

 1 : BFLY3    :   24 nodes   48 edges   96 nonzeros
 2 : BFLY4    :   64 nodes   128 edges   256 nonzeros
 3 : BFLY5    :   160 nodes   320 edges   640 nonzeros
 4 : BFLY6    :   384 nodes   768 edges  1536 nonzeros
 5 : BFLY7    :   896 nodes  1792 edges  3584 nonzeros
 6 : BFLY8    :  2048 nodes  4096 edges  8192 nonzeros
 7 : BFLY9    :  4608 nodes  9216 edges  18432 nonzeros
 8 : BFLY10    :  10240 nodes  20480 edges  40960 nonzeros
 9 : BFLY11    :  22528 nodes  45056 edges  90112 nonzeros
10 : BFLY12    :  49152 nodes  98304 edges 196608 nonzeros

Graphs in the cage sequence:

 1 : cage_3_5   :   10 nodes   15 edges   30 nonzeros
 2 : cage_3_6   :   14 nodes   21 edges   42 nonzeros
 3 : cage_3_7   :   24 nodes   36 edges   72 nonzeros
 4 : cage_3_8   :   30 nodes   45 edges   90 nonzeros
 5 : cage_3_9.1  :   58 nodes   87 edges   174 nonzeros
 6 : cage_3_9.2  :   58 nodes   87 edges   174 nonzeros
 7 : cage_3_9.3  :   58 nodes   87 edges   174 nonzeros
 8 : cage_3_9.4  :   58 nodes   87 edges   174 nonzeros
 9 : cage_3_9.5  :   58 nodes   87 edges   174 nonzeros
10 : cage_3_9.6  :   58 nodes   87 edges   174 nonzeros
11 : cage_3_9.7  :   58 nodes   87 edges   174 nonzeros
12 : cage_3_9.8  :   58 nodes   87 edges   174 nonzeros
13 : cage_3_9.9  :   58 nodes   87 edges   174 nonzeros
14 : cage_3_9.10 :   58 nodes   87 edges   174 nonzeros
15 : cage_3_9.11 :   58 nodes   87 edges   174 nonzeros
16 : cage_3_9.12 :   58 nodes   87 edges   174 nonzeros
17 : cage_3_9.13 :   58 nodes   87 edges   174 nonzeros
18 : cage_3_9.14 :   58 nodes   87 edges   174 nonzeros
19 : cage_3_9.15 :   58 nodes   87 edges   174 nonzeros
20 : cage_3_9.16 :   58 nodes   87 edges   174 nonzeros
21 : cage_3_9.17 :   58 nodes   87 edges   174 nonzeros
22 : cage_3_9.18 :   58 nodes   87 edges   174 nonzeros
23 : cage_3_10.1 :   70 nodes   105 edges   210 nonzeros
24 : cage_3_10.2 :   70 nodes   105 edges   210 nonzeros
25 : cage_3_10.3 :   70 nodes   105 edges   210 nonzeros
26 : cage_3_11  :   112 nodes   168 edges   336 nonzeros
27 : cage_3...

A stacked bar graph showing student perceptions of an assistive technology focused design project.Files included:* PNG of graph* HTML interactive graph* Python code to recreate the graph