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A. Dunst Network and Table Dialectics of Liberation.zip

CSV- file for and PNG image of social network for the "Congress on the Dialectics of Liberation" in London, July 15-30, 1967

CIANNA V1.0 release example network models

Set of pre-trained artificial network models to be used with CIANNA examples associated with the V-1.0 release of the framework. See related works for more information, or go to https://github.com/Deyht/CIANNA for the latest version of the related framework. The provided files are in binary format and are only useful for loading with CIANNA.

Datasets collected for Masked adversarial neural network for cell type deconvolution in spatial transcriptomics

Open the record for dataset details and reuse information.

Optimal network sizes for most robust Turing patterns

Dataset for Figures 3 and 4.

opencc-by-4.0Jun 2024View details →

Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation

Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation" This upload contains <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> used for the paper as pickle files and weights of trained models as zip files. Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training. Code for reading in the data sets, preprocessing and state estimation is available in the linked repository. To replicate the results of the paper follow these steps: <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> Version 1.1: Added data and model weights for the IEEE33 grid to improve comparability. For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states.  The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder. Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000 to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).

opencc-by-4.0Feb 2018View details →

Network images of TMM pathways

Contains images of version 0.1 TMM pathways for 68 iterations.

opencc-by-4.0Feb 2018View details →

Network .xgmml files for TMM pathways

Contains Cytoscape compatible .xgmml files of TMM pathways for 68 iterations.

opencc-by-4.0Jun 2018View details →

Extending the gene regulatory network model for B cell differentiation

Scripts used for modelling the gene regulatory network of B cell differentiation

opencc-by-4.0Aug 2018View details →

Dataset for network simulator comparison

Dataset for network simulator comparison

opencc-by-4.0Jan 2019View details →

Multi_Level_Biological_Networks

In our work, we collected biological entities and their relations, and put them into these networks. The below description is in our manuscript. As soon as the manuscript is published, we will present the access information in this page. --- The molecule type consisted of genes, and the function type consisted of GO terms. The phenotypic type consisted of diseases that are MeSH terms in disease categories, and the concepts in some UMLS concept types: Congenital Abnormality, Acquired Abnormality, Finding, Pathologic Function, Disease or Syndrome, Mental or Behavioral Dysfunction, Cell or Molecular Dysfunction, Sign or Symptom, Anatomical Abnormality, and Neoplastic Process. Those UMLS concept types contained a considerable number of the MeSH terms in disease categories. 18 types of biological relations were defined with their pre-defined relation types and their original resources. The 18 types are presented in the Supplementary Material (Supplementary Table S2). From the CODA, 13 types of relations for this study were defined by combining the six original resources and the eleven relation types that were pre-defined in the CODA. The six original resources were BioGRID41, RegNetwork, TRANSFAC42, EndoNet43, KEGG, GO, and PhenoGO44, which were released until 2016. The six pre-defined types in CODA were: Undirected Link, Directed Link, Positive Increase, Positive Decrease, Negative Increase, and Negative Decrease. Moreover, their reversal types were also made. The Undirected Link means a non-directional association, and the Positive Decrease means that the activity or the amount of a receiver decreases as the activity or the amount of an actor increases19.  From the UMLS (version 2016AA), four types of relations were defined for the present study. They were defined only by their original resources: MedlinePlus45, MTHMST46, NCI47, and OMIM. Although the UMLS has more than 200 resources, only the four resources provided a considerable number of relations among the nodes that we considered. The pre-defined types of relations in the four resources were too many for us to properly categorize them. Therefore, we ignored them when defining the types of relations for this study. The MEDLINE (version 2017) provides the co-occurrence frequency of two MeSH terms. It says how many times two MeSH terms have been attached to the same biomedical literature. Only one type of relations was defined for our research. Among functions or phenotypes, if two nodes had any co-occurrence frequency, we determined there is a co-occurrence relation between them. We tried to separate them according to their frequency. However, the performance, AUROC values, in the application was best when the co-occurrence relations were not separated.

Figure 2 from: Kelly-Quinn M, Bruen M, Carlsson J, Gurnell A, Jarvie H, Piggott J (2019) Managing the small stream network for improved water quality, biodiversity and ecosystem services protection (SSNet). Research Ideas and Outcomes 5: e33400. https://doi.org/10.3897/rio.5.e33400

Figure 2 Schema illustrating the eight work packages in SSNet and their linkages. Note: stakeholder engagement occurs throughout the project particularly in relation to the recommendations relating to the management of the small stream network.

Figure 4 from: Smith R (2019) The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks. Research Ideas and Outcomes 5: e33510. https://doi.org/10.3897/rio.5.e33510

Figure 4 Screenshots from QGIS showing the two Marxan outputs displayed in CLUZ. The map on the left shows the best portfolio of planning units selected by Marxan, while the map on the right shows the selection frequency score of each planning unit based on running Marxan ten times, with planning units in red being selected in every one of the ten Marxan runs.

Figure 3 from: Smith R (2019) The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks. Research Ideas and Outcomes 5: e33510. https://doi.org/10.3897/rio.5.e33510

Figure 3 Screenshot of QGIS showing the planning units layer, CLUZ target table and Change Status panel. The Change Status Panel was used to change the status of the patch of planning units in the northwest of the planning region from Conserved to Earmarked. This updated the target table, which shows that adding this new patch to the protected area network would meet the target for rock faces and would contribute towards targets for another five conservation features.

Figure 1 from: Smith R (2019) The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks. Research Ideas and Outcomes 5: e33510. https://doi.org/10.3897/rio.5.e33510

Figure 1 Screenshot of QGIS showing the planning units layer and CLUZ target table. The CLUZ target table provides details on all the conservation features, including the PC_target field showing the "gap" features that are currently under-represented.

Figure 2 from: Smith R (2019) The CLUZ plugin for QGIS: designing conservation area systems and other ecological networks. Research Ideas and Outcomes 5: e33510. https://doi.org/10.3897/rio.5.e33510

Figure 2 Screenshots of QGIS showing on the left, a CLUZ distribution map of one of the conservation features, and on the right a map of the number of conservation features found in each planning unit.

Figure 8b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521

Figure 8b "Point Pattern Edition" features. - Information that is displayed (marks of the point pattern, if available, as defined by the user) when an event is clicked

Figure 5a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521

Figure 5a Example of use of the SimplifyLinearNetwork function. - A road network introduced as input in which there is an excess of road segments and vertex

Figure 1 from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521

Figure 1 Workflow that describes all the steps that could be carried out in order to perform a spatial analysis on a point pattern that lies on a linear network. Some of these steps which lead to the final statistical analysis may be skipped but, at least, all of them should be considered. The blocks pointing the steps of the process include some of the R packages that would allow to successfully achieve each of them.

Figure 3b from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521

Figure 3b "Network Edition" example of use (I). - Network resulting from clicking on "Rebuild linear network" in the situation of a

Figure 2a from: Briz-Redón Á (2019) SpNetPrep: An R package using Shiny to facilitate spatial statistics on road networks. Research Ideas and Outcomes 5: e33521. https://doi.org/10.3897/rio.5.e33521

Figure 2a "Network Edition" features. - Overview of the "Network Edition" section of the SpNetPrep application