We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on People's Climate March in 2014.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2014-09-19 00:46:19
End: 2014-09-22 06:56:25

Ref: E. Omodei, M. De Domenico, A. Arenas. - Characterizing interactions in online social networks during exceptional events.. Front. Phys. 3, 59 (2015)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on Cannes Film Festival in 2013.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-05-06 07:23:49
End: 2013-06-03 05:48:26

Ref: E. Omodei, M. De Domenico, A. Arenas. - Characterizing interactions in online social networks during exceptional events.. Front. Phys. 3, 59 (2015)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on 2013 World Championships in Athletics.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-08-05 11:25:46
End: 2013-08-19 14:35:21

Ref: E. Omodei, M. De Domenico, A. Arenas. - Characterizing interactions in online social networks during exceptional events.. Front. Phys. 3, 59 (2015)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on 50th aniversary of Marthin Luther King's speech "I have a dream..." in 2013.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-08-25 15:41:36
End: 2013-09-02 10:16:21

Ref: E. Omodei, M. De Domenico, A. Arenas. - Characterizing interactions in online social networks during exceptional events.. Front. Phys. 3, 59 (2015)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on a visit to Israel by US President Barack Obama in 2013
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-03-19 16:56:29
End: 2013-04-03 23:24:34

Ref: E. Omodei, M. De Domenico, A. Arenas. - Characterizing interactions in online social networks during exceptional events.. Front. Phys. 3, 59 (2015)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on Uefa Champions League final in 2016.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2016-05-27
End: 2016-06-01

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on NBA finals in 2015.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2015-06-09
End: 2015-06-21

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on the Gravitational Waves Discovery in 2016.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2016-02-10
End: 2016-02-16

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on the final of Sanremo Music Festival in 2016.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2016-02-13
End: 2016-02-13

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on the Paris Attacks in 2015.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2015-11-13
End: 2015-11-15

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on the Pope Francis Conclave in 2013.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-02-25
End: 2013-03-19

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

We consider different types of social relationships amoung users, obtained from Twitter during an exceptional event. In this specific dataset we focused on the Boston Bombing Attacks in 2013.
The multiplex network used in the paper makes use of 3 layers, corresponding to retweet, mentions and replies observed between:
Start: 2013-04-15
End: 2013-04-22

Ref: M. De Domenico, E. G. Altmann. - Unraveling the Origin of Social Bursts in Collective Attention.. Scientific Reports 10, 4629 (2020)

Files format: layerID nodeID nodeID weight

Data was collected in 2013 from the official website of Transport for London ( https://www.tfl.gov.uk/) and manually cross-checked.

Nodes are train stations in London and edges encode existing routes between stations. Underground, Overground and DLR stations are considered (see https://www.tfl.gov.uk/ for further details). The multiplex network used in the paper makes use of three layers corresponding to:

1. The aggregation to a single weighted graph of the networks of stations corresponding to each underground line (e.g., District, Circle, etc)
2. The network of stations connected by Overground
3. The network of stations connected by DLR

The multilayer network is composed by thirty-seven different layers each one corresponding to a different airline operating in Europe.

Ref: Alessio Cardillo, Jesús Gómez-Gardenes, Massimiliano Zanin, Miguel Romance, David Papo, Francisco del Pozo and Stefano Boccaletti - Emergence of network features from multiplexity. Scientific Reports 3, Article number: 1344 doi:10.1038/srep01344

See the official web page for further details.

The multiplex social network consists of five kinds of online and offline relationships (Facebook, Leisure, Work, Co-authorship, Lunch) between the employees of Computer Science department at Aarhus.

Ref: Matteo Magnani, Barbora Micenkova, Luca Rossi - Combinatorial Analysis of Multiple Networks. arXiv:1303.4986 (2013)

See the official web page for further details.

Data collected by Coleman, Katz and Menzel on medical innovation, considering physicians in four towns in Illinois, Peoria, Bloomington, Quincy and Galesburg.
They were concerned with the impact of network ties on the physicians' adoption of a new drug, tetracycline. Three sociometric matrices (layers) were generated, based on the following questions:

1. When you need information or advice about questions of therapy where do you usually turn?
2. And who are the three or four physicians with whom you most often find yourself discussing cases or therapy in the course of an ordinary week -- last week for instance?
3. Would you tell me the first names of your three friends whom you see most often socially?

Interactions in a tailor shop in Zambia (then Northern Rhodesia) over a period of ten months.
Layers represent two different types of interaction, recorded at two different times (seven months apart) over a period of one month. TI1 and TI2 record the "instrumental" (work- and assistance-related) interactions at the two times; TS1 and TS2 the "sociational" (friendship, socioemotional) interactions.
The data are particularly interesting since an abortive strike occurred after the first set of observations, and a successful strike took place after the second.

Ref: Kapferer B. (1972) - "Strategy and transaction in an African factory".

The multiplex social network consists of 3 kinds of relationships (Advice, Friendship and "Reports to") between managers of a high-tech company.

Ref: D. Krackhardt - "Cognitive social structures". Social Networks (1987), 9, 104-134

The multiplex social network consists of 3 kinds of (Co-work, Friendship and Advice) between partners and associates of a corporate law partnership.

Ref:Emmanuel Lazega - "The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership". Oxford University Press (2001)
Ref: Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robins, and Mark S. Handcock - "New specifications for exponential random graph models". Sociological Methodology (2006), 99-153.

The multiplex social network consists of 2 layers (marriage alliances and business relationships) describing florentine families in the Renaissance.

Ref: JF Padgett, CK Ansell - "Robust Action and the Rise of the Medici, 1400-1434". American journal of sociology, 1259-1319 (1993)

The data were collected by Vickers from 29 seventh grade students in a school in Victoria, Australia. Students were asked to nominate their classmates on a number of relations including the following three (layers):

1. Who do you get on with in the class?
2. Who are your best friends in the class?
3. Who would you prefer to work with?

Caenorhabditis elegans connectome, where the multiplex consists of layers corresponding to different synaptic junctions: electric ("ElectrJ"), chemical monadic ("MonoSyn"), and polyadic ("PolySyn").
The multiplex network used in the paper makes use of three layers corresponding to:

1. Electric ("ElectrJ")
2. Chemical Monadic ("MonoSyn")
3. Chemical Polyadic ("PolySyn")

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns arabidopsis thaliana.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Additive genetic interaction defined by inequality
4. Suppressive genetic interaction defined by inequality
5. Synthetic genetic interaction defined by inequality
6. Association
7. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Bos Linnaeus.
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Association
3. Direct interaction
4. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Candida Albicans.
The multiplex network used in the paper makes use of the following layers:

1. Synthetic genetic interaction defined by inequality
2. Direct interaction
3. Suppressive genetic interaction defined by inequality
4. Additive genetic interaction defined by inequality
5. Physical association
6. Association
7. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Caenorhabditis Elegans.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Additive genetic interaction defined by inequality
4. Suppressive genetic interaction defined by inequality
5. Association
6. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014).
The present folder concerns danio rerio.
The multiplex network used in the paper makes use of the following layers:

1. Association
2. Suppressive genetic interaction defined by inequality
3. Direct interaction
4. Additive genetic interaction defined by inequality
5. Physical association

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Drosophila Melanogaster.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Suppressive genetic interaction defined by inequality
3. Additive genetic interaction defined by inequality
4. Physical association
5. Colocalization
6. Association
7. Synthetic genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Gallus Gallus.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Synthetic genetic interaction defined by inequality
4. Colocalization
5. Additive genetic interaction defined by inequality
6. Association

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014).
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Direct interaction
3. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns homo sapiens.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Suppressive genetic interaction defined by inequality
4. Association
5. Colocalization
6. Additive genetic interaction defined by inequality
7. Synthetic genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns human herpes virus 4.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Association
4. Colocalization

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns human HIV type 4.
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Direct interaction
3. Colocalization
4. Association
5. Suppressive genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Mus Musculus.
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Association
3. Direct interaction
4. Colocalization
5. Additive genetic interaction defined by inequality
6. Synthetic genetic interaction defined by inequality
7. Suppressive genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns homo sapiens.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Association
3. Physical association

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Plasmodium Falciparum.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Physical association
3. Association

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Rattus Norvegicus.
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Direct interaction
3. Colocalization
4. Association
5. Additive genetic interaction defined by inequality
6. Suppressive genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Saccharomyces Cerevisiae.
The multiplex network used in the paper makes use of the following layers:

1. Physical association
2. Suppressive genetic interaction defined by inequality
3. Direct interaction
4. Synthetic genetic interaction defined by inequality
5. Association
6. Colocalization
7. Additive genetic interaction defined by inequality

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns Saccharomyces Pombe.
The multiplex network used in the paper makes use of the following layers:

1. Direct interaction
2. Colocalization
3. Physical association
4. Suppressive genetic interaction defined by inequality
5. Synthetic genetic interaction defined by inequality
6. Additive genetic interaction defined by inequality
7. Association

We consider different types of genetic interactions for organisms in the Biological General Repository for Interaction Datasets (BioGRID, thebiogrid.org), a public database that archives and disseminates genetic and protein interaction data from humans and model organisms. BioGRID currently includes more than 720,000 interactions that have been curated from both high-throughput data sets and individual focused studies using over 41,000 publications in the primary literature. We use BioGRID 3.2.108 (updated 1 Jan 2014). The present folder concerns xenopus laevis.
The multiplex network used in the paper makes use of the following layers:

1. Association
2. Direct interaction
3. Physical association
4. Colocalization
5. Suppressive genetic interaction defined by inequality

Multiplex in which the layers correspond to interaction networks of genes in Saccharomyces cerevisiae (which was obtained via a synthetic genetic-array methodology) and correlation-based networks in which genes with similar interaction profiles are connected to each other. Positive and negative interactions, as well as positive and negative correlations, are considered.
The multiplex network used in the paper makes use of the following layers:

1. Positive interactions
2. Negative interactions
3. Positive correlations
4. Negative correlations

The multiplex consists of layers corresponding to different arXiv categories. To restrict the analysis to a well-defined topic of research, we only included papers with "networks" in the title or abstract up to May 2014.
The multiplex network used in the paper makes use of 13 layers corresponding to:

1. physics.soc-ph
2. physics.data-an
3. physics.bio-ph
4. math-ph
5. math.OC
6. cond-mat.dis-nn
7. cond-mat.stat-mech
8. q-bio.MN
9. q-bio
10. q-bio.BM
11. nlin.AO
12. cs.SI
13. cs.CV

The multiplex consists of layers corresponding to different working tasks within the Pierre Auger Collaboration. We considered all submissions between 2010 and 2012 and assigned each report to 16 layers according to its keywords and its content, with manual disambiguation to avoid spurious results from an automated process.
The multiplex network used in the paper makes use of 16 layers corresponding to:

1. Neutrinos
2. Detector
3. Enhancements
4. Anisotropy
5. Point-source
6. Mass-composition
7. Horizontal
8. Hybrid-reconstruction
9. Spectrum
10. Photons
11. Atmospheric
12. SD-reconstruction
13. Hadronic-interactions
14. Exotics
15. Magnetic
16. Astrophysical-scenarios

We consider different types of trade relationships amoung countries, obtained from FAO (Food and Agriculture Organization of the United Nations). The worldwide food import/export network is an economic network in which layers represent products, nodes are countries and edges at each layer represent import/export relationships of a specific food product among countries. We collected the data from FAO and built the multilayer network corresponding to trading in 2010.
The multiplex network used in the paper is a subset of the full dataset, which consists of 364 layers.

Ref: M. De Domenico, V. Nicosia, A. Arenas, and V. Latora - "Structural reducibility of multilayer networks" - Nature Communications 2015 6, 6864

We consider different types of microbial communities across the human body, building an interaction network for each of 18 distinct human body sites. We have elaborated the resulting network from the original work by Ding and Schloss.
The multiplex network used in the paper consists of 18 layers.

Ref 1: T. Ding and P. Schloss - "Dynamics and associations of microbial community types across the human body" - Nature 2014 509, 357–360
Ref 2: M. De Domenico and J. Biamonte - “Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison” - Physical Review X 2016 6, 041062