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opencc-by-4.0May 2024View details →

Emergence of Compact Oligomers inside the Small-World Network of TDP-43 Condensates

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Social Network Data for Jones, The Decline and Fall of the Assyrian Court Scholar

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Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks

Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively. Filenames denote the structure of the SS profile:  ???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}   LT = elastic lithosphere thickness (km) UMV = upper mantle viscosity (1E21 Pa s) LMV = lower mantle viscosity (1E21 Pa s) i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile 96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity   The columns of the input files are as follows: LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0) units for the above are as follows: km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m,  mm/year where 'days since 2000' assumes exactly 365.25 days per year.

Temperature, water velocity and heat consumption of a heat network in southern Germany

This is a heat network dataset from the Institute of Space Propulsion (DLR) in southern Germany. The data was collected every 15 minutes from meters installed at the site of each building (September 2021 - May 2022). The records show the temperature at and after the heat supply, the water velocity and the thermal power.

openmit-licenseSep 2023View details →

BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network

This repository contains a pre-trained checkpoint for BigVSAN proposed in the paper <a href="https://arxiv.org/abs/2309.02836">BigVSAN: Enhancing GAN-based Neural Vocoders with Slicing Adversarial Network</a> by Sony.More information about BigVSAN including our code is available at <a href="https://github.com/sony/bigvsan">https://github.com/sony/bigvsan</a>.

Random Woody Crown Network (WCN) from 1 to 248 nodes

Those woody crown networks are part of data of manuscript to consider publication

Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes

Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes

Statistical Analysis of Feature-based Molecular Networking Results from Non-Targeted Metabolomics Data

This folder contains the following used for the publication:<ul><li>MASSIVE Repositories: MSV000082312 and MSV000085786. This contains the original data in both .raw and .mzxml formats.</li><li>MZmine 3 files: The feature table (SD_BeachSurvey_GapFilled_quant.csv), the associated mgf file, the batch file (.xml) used for MZmine 3 to obtain the feature table, the mgf file for SIRIUS annotations (SD_BeachSurvey_SIRIUS_fixed.mgf)</li><li>SIRIUS and CANOPUS summary files (.tsv files)</li><li>FBMN Result files</li></ul>

Dataset for Utilizing Pathway Dependency and Network Representation of Anticancer Drug Sensitivity for Drug Synergy Prediction

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Branching tidal creek networks morphology data set

Data and codes used in the paper entitled "The origin of branching in tidal creek networks".

opennotspecifiedSep 2023View details →

Fig. 3. Haplotype network for 419 in Across mountains and ocean: species delimitation and historical connectivity in Holarctic and Arctic-Alpine wolf spiders (Lycosidae, Pardosa)

Fig. 3. Haplotype network for 419 specimens of P. saltuaria species group based on mitochondrial COI data. Haplotypes are colored based on morphological identification. Each line is 1 mutation step.

opennotspecifiedSep 2010View details →

Figure 3. The minimum spanning network for the 656 in Revision of Chaetodipus arenarius (Rodentia: Heteromyidae)

Figure 3. The minimum spanning network for the 656-bp cytochrome b data set includes populations from the Baja California Peninsula. Each perpendicular hash mark across the line between adjacent haplotypes in the network represents a single-base substitution. The key for the haplotypes is shown in Table 1; some of the haplotypes are present in more than one population. The peninsular-Chaetodipus arenarius minimum spanning network has three subnetworks: Northern peninsula (a), Southern peninsula (b), and Loreto (c).

Original waveform of TanluArray portable seismic network in the Tanlu fault zone and surrounding areas

The waveform in this folder was recorded by the National Institute of Natural Hazards (NINH) (cut by catalog from 2019 and 2021). Waveform data were intercepted from the 10s before and 10s after the theoretical arrival-time of Pn. It is only used for scientific research.

openApr 2023View details →

Map of the Burgundian Connectivity within the Network of Western- and Central European Marriages between the Nobility, 1350-1550

Map of the Burgundian Connectivity within the Network of Western- and Central European Marriages between the Nobility, 1350-1550. ArcGIS (Esri). Lines indicate a marital relationship between two individuals based on their place of birth. Red lines indicate marital relationships between the Burgundian dukes and their spouses.See: Miara Fraikin and Meike Wiedemann, 'The "Burgundian Model" revisited: Using Digital Approaches to Explore the Reach of Burgundy', in Sanne Maekelberg and Krista De Jonge (eds.), Mapping the Space of the Early Modern Court in Europe. Functionality and Representation, 2023, pp.13-34.

GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction

The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.The dataset files are saved as .pkl file for the convenience of use.Paper Abstract:Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.

Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks

This dataset contains the data and scripts for the publication "<a href="https://doi.org/10.1029/2023MS003829">Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks</a>" in Journal of Advances in Modeling Earth Systems. Before going into details, here is a reminder that the NEMO runs for the training dataset are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. For the testing dataset: 'bf663' is the REPEAT1970 run and 'bi646' is the 4xCO2 run. Most of the formatting and preprocessing of the training data has been made for <a href="https://tc.copernicus.org/articles/16/4931/2022/">Burgard et al. 2022</a>. The raw and to-some-degree processed data can therefore be found here: <a href="https://doi.org/10.5281/zenodo.7308352">https://doi.org/10.5281/zenodo.7308352</a>.  The raw data for the testing dataset is from <a href="https://doi.org/10.1029/2021MS002520">Smith et al. 2021</a>, you can find it here: <a href="doi.org/10.5281/zenodo.7886986">https://doi.org/10.5281/zenodo.7886986</a> The following folders and files can be found here: =============== raw/ Some geometrical files needed for initial data formatting and masking. =============== interim/ <ul> <li>ANTARCTICA_IS_MASKS/ (from INTERIM_ANTARCTICA_IS_MASKS.zip): contains <ul> <li>masks and geometric information for the testing dataset to be included in the input file of the neural network and for the classic parameterisations.</li> <li>local bedrock and ice meridional and zonal slopes</li> </ul> </li> <li>BOXES/ (from INTERIM_BOXES.zip): contains variables needed to apply the box parameterisation for the testing dataset</li> <li>PLUMES/ (from INTERIM_PLUMES.zip): contains the variables needed to apply the plume parameterisation for the testing dataset</li> <li>SMITH_bf663/ and SMITH_bi646/ (from INTERIM_SMITH*.zip): for testing dataset, <ul> <li>corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land</li> <li>custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land</li> <li>isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid</li> <li>other_mask_vars_Ant_stereo.nc: contains other variables used for the masks</li> <li>the reference melt: 1D containing integrated melt, 2D containing melt fields, box1 containing melt near the grounding line</li> </ul> </li> <li>T_S_PROF/ (from INTERIM_T_S_PROF.zip) <ul> <li>Mean profiles used as input for traditional parameterisations</li> <li>T and S 2D fields, extrapolated from the mean profiles to the local ice draft depth (needed as input for the neural network)</li> <li>Fields of mean and standard deviation T and S for all points (needed as input for the neural network)</li> </ul> </li> <li>NN_MODELS/ (from INTERIM_NN_MODELS.zip) contains all neural networks trained for this paper (for the cross validation and over the whole dataset for testing)</li> <li>INPUT_DATA/ (from INTERIM_INPUT_DATA.zip) contains all input csv files containing the input datasets for the different training and testing iterations. Also contains the metrics to normalise the input. For the cross-validation, the input csv files are not included because they are too large. However, they can be reconstructed from the individual files for ice shelves and time blocks. The metrics to normalise the data during the cross-validation are included in EXTRAPOLATED_ISFDRAFT_CHUNKS_CV!</li> </ul> =============== processed/MELT_RATE/ Contains resulting melt rates <ul> <li>CV_ISF : Cross-validation results over ice shelves</li> <li>CV_TBLOCKS : Cross-validation results over time</li> <li>SMITH_bf663 : Neural network results for REPEAT1970</li> <li>SMITH_bf663_CLASSIC  : "Traditional" parameterisation results for REPEAT1970</li> <li>SMITH_bi646 : Neural network results for 4xCO2</li> <li>SMITH_bi646_CLASSIC: "Traditional" parameterisation results for 4xCO2</li> </ul> ===================== The explanation around the scripts can be found in README.rst with the scripts in scripts_paper_simpleNN_basal_melt.zip. Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: <a href="https://github.com/ClimateClara/https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt">https://github.com/ClimateClara/scripts_paper_simpleNN_basal_melt</a>, find the most up-to-date version of the package 'multimelt' here: <a href="https://github.com/ClimateClara/multimelt">https://github.com/ClimateClara/multimelt</a> and a version you can install via pip here: <a href="https://github.com/ClimateClara/multimelt">https://pypi.org/project/multimelt/</a> Finally, if anything is unclear, check out the "Methods" section of the paper: <a href="https://doi.org/10.1029/2023MS003829">https://doi.org/10.1029/2023MS003829</a>

Data and code for Fitzgerald et al: MDD seeded co-expression networks

Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder" <table> <tbody> <tr> <td>Generated data </td> </tr> <tr> <td>Data</td> <td>About</td> <td> </td> </tr> <tr> <td>All_GTEx_DLPFC_networks.RData</td> <td>Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex</td> <td> </td> </tr> <tr> <td>my_big_negative_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td>my_big_positive_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Generated code</td> </tr> <tr> <td>File</td> <td>About</td> <td>Related figure</td> </tr> <tr> <td>Chang_bootstrap.R</td> <td>Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states</td> <td>Fig 4G</td> </tr> <tr> <td>CMC_QC.R</td> <td>Quality control for the common mind consortium data for validation of coexpression networks</td> <td>Supp</td> </tr> <tr> <td>Cont_vs_MDD_modscores.R</td> <td>Generating module scores in snRNA-seq data</td> <td>Fig 4E</td> </tr> <tr> <td>FADS1_clustering.R</td> <td>Clustering of snRNA-seq data using genes coexpressed with FADS1</td> <td>Fig 5</td> </tr> <tr> <td>gene_analysis.sh</td> <td>Annotation of GWAS summary statistics using Hi-C data</td> <td>Fig 2A</td> </tr> <tr> <td>gene_set_analysis.sh</td> <td>GWAS enrichment analysis using MAGMA</td> <td>Fig 6C</td> </tr> <tr> <td>GTEx_coexp_networks.R</td> <td>Generating seeded coexpression networks for MDD risk genes in the GTEx dataset</td> <td>Fig 2B</td> </tr> <tr> <td>GTEx_QC_1.R</td> <td>Filtering of the GTEx dataset</td> <td>NA</td> </tr> <tr> <td>GTEx_QC_2.R</td> <td>Normalisation and regression of technical covariates from the GTEx data</td> <td>NA</td> </tr> <tr> <td>Labonte_et_al_QC.R</td> <td>Quality control, filtering and regression of technical covariates from the Labonte et al dataset</td> <td>Fig 4F</td> </tr> <tr> <td>Milo_analysis.R</td> <td>Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei</td> <td>Fig 5H</td> </tr> <tr> <td>Nagy_et_al_astro_subsetting.R</td> <td>Subsetting astrocytes from the full Nagy et al snRNA-seq dataset </td> <td>NA</td> </tr> <tr> <td>Network_analysis.R</td> <td>To generate and analyse a graph of coexpression networks</td> <td>Fig 3E</td> </tr> <tr> <td>NicheNet.R</td> <td>For a NicheNet analysis to infer patterns of cell-cell communication</td> <td>Fig 6F</td> </tr> <tr> <td>Vizium_analysis.R</td> <td>Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states</td> <td>Fig 5F</td> </tr> </tbody> </table>

MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults

Description: This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper.  Dataset Access: The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors. Data Format: The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>] We appreciate your interest in our work. [1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" Machines 11, no. 12: 1089. https://doi.org/10.3390/machines11121089

Pressure monitoring dataset and frequency domain analysis in Padua water distribution network

The dataset includes:<ul><li>the acquired pressure signal at measurement section P036 with a high sampling frequency (indicated by "fre") for 24 hours;</li><li>the frequency domain analysis of three pressure datasets within a certain range, "omega", of frequencies</li></ul>

NicheNet-v2 ligand-receptor networks: integrated information from Omnipath

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