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NYC-data
README.md
SyntheticTrajectoriesVariableOrders.csv
US_flights.ngram
US_flights_gt.json
US_flights_train.ngram
US_flights_validate.ngram
air2015_1_paths.net
air2015_2_paths.net
air2015_3_paths.net
air2015_4_paths.net
custom_template.html
haggle_gt.json
lotr_chapters.json
lotr_characters.json
manufacturing_email_gt.json
ninetriangles.net
temporal_clusters.state
temporal_clusters.tedges
temporal_networks.db
toy_paths.ngram
toy_states.net
tube.edges
tube_gt.json
tube_od.csv
tube_paths_train.ngram
wikipedia_clickstreams.ngram
wikipedia_clickstreams_gt.json
wikipedia_clickstreams_train.ngram
wikipedia_clickstreams_validate.ngram

README.md

Sources, descriptions, and citations for data sets

For your convenience, in this repository we provide preprocessed (excerpts) from temporal network data. All data files have been created based on publicly and freely available data sets. In the following, we give detailed source and citation information for these data sets:

Datafile Description Citation Weblink
US_flights.ngram Contains flight itineraries of American Airlines passengers between US airports. This is an excerpt from the Airline Origin and Destination Survey (DB1B). I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, In Nature Communications, Vol. 5, Article 5024, September 24, 2014 link
tube.edges Contains direct links between stations in the London Tube Metro Network. This data file was generated based on publicly available Wikipedia data. I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, In Nature Communications, Vol. 5, Article 5024, September 24, 2014 N/A
tube_od.csv Contains the volume of passengers between origin and destination stations in the London Tube Metro Network. This data set is an excerpt from the Rolling Origin Destination Survey available from the OpenData page of Transport for London I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer. Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks, In Nature Communications, Vol. 5, Article 5024, September 24, 2014 link
temporal_clusters.tedges Synthetically generated data set N/A N/A
toy_paths.ngram Toy example generated in unit 1.2 N/A N/A
SyntheticTrajectoriesVariableOrders.csv Synthetic data for task 3.1 link link
NYC-data New York City trajectories data for task 3.2 N/A link

In addition to these datafiles, the SQLite database file temporal_networks.db contains dynamic social networks stored in the following tables:

Table name Description Citation Weblink
haggle social contacts measures by wireless devices A Chaintreau, P Hui, J Crowcroft, C Diot, R Gass, J Scott. Impact of human mobility on opportunistic forwarding algorithms. IEEE Trans. on Mobile Computing, 6(6):606--620, 2007. link
manufacturing_email E-Mail exchanges in Polish manufacturing company, prepared and published by Radoslaw Michalski R Michalski, S Palus, P Kazienko: Matching Organizational Structure and Social Network Extracted from Email Communication, Lecture Notes in Business Information Processing LNBIP, vol. 87, pp. 197-206, Springer, Berlin Heidelberg (2011) link
sociopatterns_ho Temporal network of contacts between patients and health-care workers in a hospital, collected via the SocioPatterns project P Vanhems et al.: Estimating Potential Infection Transmission Routes in Hospital Wards Using Wearable Proximity Sensors, PLoS ONE 8(9): e73970 (2013) link
sociopatterns_primary school Temporal network of contacts between children in a primary school, collected via the SocioPatterns project J Stehle et al. High-Resolution Measurements of Face-to-Face Contact Patterns in a Primary School, In PLOS ONE 6(8): e23176 (2011) link
lotr Character co-occurrence within same sentence in the text of the Lord of the Rings Trilogy self-generated N/A

Accesssing data files

Assuming that you have started Visual Studio Code in the root of the tutorial directory, files in this folder can be referenced from python code via the relative path data/FILENAME. If you start a jupyter notebook within the code folder, you will have to use the relative path ../data/FILENAME.