# Resources

**DATA SETS**

**DATA SETS**

**Development of ****C. elegans**

In case you use the following data for your publications, please cite the following article as source for the data:

Varier S, Kaiser M (2011) Neural development features: Spatio-temporal development of the *C. elegans* neuronal network. *PLoS Computational Biology* 7:e1001044 (PDF)

**Development of**

*In case you use the following data for your publications, please cite the following article as source for the data:*

**C. elegans**

Varier S, Kaiser M (2011) Neural development features: Spatio-temporal development of the

*C. elegans*neuronal network.

*PLoS Computational Biology*7:e1001044 (PDF)

**C. elegans**** neuronal birth times.** This zip file contains a comma-seperated list (.csv) and a Matlab file (.mat) with the names of 279 neurones and their times of birth. Birth Time is in minutes obtained from Sulston *et al.* 1977 and 1983.

**Neuronal and cortical networks**

In case you use the following data for your publications, please cite the following articles as source for the data:

Kaiser M, Hilgetag CC (2006) Non-Optimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems. *PLoS Computational Biology* 2:e95 (PDF)

Kötter R (2004) Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database. *Neuroinformatics* 2:127-144.

Choe Y, McCormick BH, Koh W (2004) Network connectivity analysis on the temporally augmented C. elegans web: A pilot study. *Society of Neuroscience Abstracts* 30:921.9.

**Neuronal and cortical networks**

In case you use the following data for your publications, please cite the following articles as source for the data:

Kaiser M, Hilgetag CC (2006) Non-Optimal Component Placement, but Short Processing Paths, due to Long-Distance Projections in Neural Systems.

*PLoS Computational Biology*2:e95 (PDF)

Kötter R (2004) Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database.

*Neuroinformatics*2:127-144.

Choe Y, McCormick BH, Koh W (2004) Network connectivity analysis on the temporally augmented C. elegans web: A pilot study.

*Society of Neuroscience Abstracts*30:921.9.

**Macaque cortical connectivity** network within one hemisphere. The files include the adjacency *matrix*, the *labels* of the cortical regions, and the spatial *positions* of the regions as three-dimensional coordinates (the unit is mm).

**C. elegans**** local network of 131 frontal neurons**. The files include the adjacency *matrix*, the *labels*of the neurons, and the spatial *positions* of the neurons as two-dimensional coordinates (unit is mm).

**C. elegans**** global network of 277 neurons**. The files include the adjacency *matrix*, the *labels* of the neurons, and the spatial *positions* of the neurons as two-dimensional coordinates (unit is mm).

**Artificial networks**

**Artificial networks**

Contains Matlab file with the **German highway system** network (raw data from Autobahn-Informations-System, AIS from www.bast.de). It includes the adjacency matrix (value 1 if two locations are directly connected by a highway) and the labels of all 1,168 nodes. Please cite the following source for the data: Kaiser M., and Hilgetag C.-C. (2004) Spatial growth of real-world networks. Physical Review E 69:036103. Further information on how the network was derived can be found in that article (PDF).

**Flight connections for the top 500 airports**, based on total passenger volume, worldwide. The existence of flight connections between airports is based on flights within one year from 1 July 2007 to 30 June 2008. The zip file include the matrix of connections between airports as well as a list of airport codes for each network node (csv files). It also includes a Matlab .mat file with the same information. Please cite the following source for the data: Marcelino J. and Kaiser M. (2012) Critical paths in a metapopulation model of H1N1: Efficiently delaying influenza spreading through flight cancellation. PLoS Currents Influenza. Further information on how the network was derived can be found in that article (PDF).

**PROGRAM CODE**

**PROGRAM CODE**

Matlab/Octave code for simulating activity and stimulation of brain tissue.

Environment for simulating tissue development.

Matlab/Octave code for detecting **singular node motifs** (characteristic nodes of a network). Documentation of the routines can be found here. If you use these algorithms, please cite

Echtermeyer C, Rodriguez F, Costa FdL, Kaiser M (2011). Automatic network fingerprinting through singular node motifs. PLoS ONE 6(1):e15765 (PDF) and

Costa LdF, Rodrigues FA, Hilgetag CC, Kaiser M (2009). Beyond the average: detecting global singular nodes from local features in complex networks. Europhysics Letters 87:18008 (PDF).

Matlab code for **generating hierarchical networks**, simulating activity spreading, and calculating the proporation of simulation runs with limited sustained activity (LSA). These routines were used for the article Marcus Kaiser and Claus Hilgetag (2010). Optimal hierarchical modular topologies for producing limited sustained activation of neural networks. Frontiers in Neuroinformatics (PDF)

Adapa is a free reusable **tool to run parallel applications on multiple computing platforms**, while being flexible enough to allow its users to use any familiar programming languages that they are acquainted with. The tool has been applied to the computation of correlation networks of multi-electrode array (MEA) recordings, see Pedro Ribeiro, Jennifer Simonotto, Marcus Kaiser and Fernando Silva (2009). Parallel calculation of multi-electrode array correlation networks. Journal of Neuroscience Methods (PDF)

Matlab script for spatial networks generated by **spatial growth**. For details on the algorithm, look at Kaiser M., and Hilgetag C.-C. (2004) Spatial growth of real-world networks. Physical Review E 69:036103 (PDF)

Matlab scripts for (1) the clustering coefficient definitions C1, C2, and C’, (2) the indirect definition **disconnectedness D**, and (3) the **inverse generation of small-world networks** starting with a random network instead of a regular network and therefore generating a higher percentage of leaf and isolated nodes. For details on the algorithms, see Kaiser M. (2008) Mean clustering coefficients – The role of isolated nodes and leafs on clustering measures for small-world networks. New Journal of Physics 10:083042 (PDF)