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research data supporting "Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"
<p>This repository contains all the data generated and analyzed including the starting structures, the input files, the trajectory files, the output data from cpptraj and network analyses, and in-house scripts used to prepare the network and module files shown in the paper <strong>"Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"</strong> published in <strong>Journal of Chemical Information and Modeling</strong> (DOI: 10.1021/acs.jcim.2c00101). </p>
Data from: Computing the local field potential (LFP) from integrate-and-fire network models
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
Data for Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents
<p>Results data for the manuscript "Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents".</p>
data of 5GC network fault
<p>include alarm information,network element information and performance metrics</p> <ul> <li>01.performance metrics:Performance index data such as time and index value when the digital twin network element is running;</li> <li>02.alarm information:Alarm information occured by the digital twin network element, such as the occurrence time and the alarm name;</li> <li>03.network element information:Basic information of digital twin network elements such as name, generator room;</li> </ul>
Biodiversity information retrieval across networked data sets
<p>Recording of presentation. An approach to integrate data through wrapping of various datasets stored in relational databases located on networked platforms. It is designed to overcome copyright problems in data sharing.</p>
NMR and HRMS raw data for "Metabolite annotation from knowns to unknowns through knowledge-guided multi-layer metabolic networking"
<p>Raw NMR data and HRMS data for synthesized compounds</p>
Supplementary material 3 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary material 2 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
Definitions and concepts in the context of the main paper.
Supplementary material 1 from: Smirnova L, Mergen P, Groom Q, De Wever A, Penev L, Stoev P, Pe'er I, Runnel V, Camacho A, Vincent T, Agosti D, Arvanitidis C, Bonet F, Saarenmaa H (2016) Data sharing tools adopted by the European Biodiversity Observation Network Project. Research Ideas and Outcomes 2: e9390. https://doi.org/10.3897/rio.2.e9390
List of selected tools.
Data for Evaluating the generalizability of graph neural networks for predicting collision cross section
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Data for "Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit"
<h2>1. Repository Information</h2> <p>This repository contains the data produced during the work discussed in in the paper "<a href="https://iopscience.iop.org/article/10.1088/1361-6382/ad7c14" target="_blank" rel="noopener">Towards quantum gravity with neural networks: Solving quantum Hamilton constraints of 3d Euclidean gravity in the weak coupling limit</a>". Please refer to this paper for more details on how the data was produced.</p> <p> </p> <h2>2. Citing</h2> <p>In addition to citing this repository, please also cite the paper mentioned above if you use the data. The citation is:</p> <p>Hanno Sahlmann and Waleed Sherif 2024 <em>Class. Quantum Grav.</em> <strong>41</strong> 215006</p> <p> </p> <h2>3. File Description</h2> <p>In this repository, you will find 4 general directories (here called parent directories):</p> <ol> <li>Ground Energy + Fluctuations</li> <li>Misc</li> <li>Quantum Constraint</li> <li>Volume</li> </ol> <p>Each of these directories correposnd to different data produced and discussed in the corresponding parts in the paper mentioned above (e.g. the directory "Ground Energy + Fluctuations" contains the data used in Table 1 and Table 2 in the paper while the "Volume" directory contains the data used in Section 4.3 of the paper).</p> <p>Some of these parent directories, which involve simulations solving constraints, contain within them several sub-directories (child directories) corresponding to different produced data. The raw data of the simulation can be found in a <code>.json</code> file inside the child directories.</p> <p> </p> <h2>4. Usage</h2> <h3>4.1 Raw Simulation Data</h3> <p>The <code>.json</code> files include the raw data produced during the study. These files can be easily accessed using a python script, as an example, by using:</p> <p><code>import json</code></p> <p><code>filePath = ...</code></p> <p><code>data = json.load(open(filePath))</code></p> <p>where <code>filePath</code> should hold the correct path to the local data once downloaded. Once loaded, the data is handled as a python <code>dict</code>.</p> <p> </p> <p>The dictionary will have <em>at least one</em> parent key called "Energy". The data in the "Energy" key corresponds to the data being minimised. The data in any other parent key correspond to operators which were being observed during the simulation. For example, in the data in the "Quantum Constraint" directory, some .json files will have multiple parent keys such as FG, H, HG, .... Each of these keys correspond to different operators which were observed during that simulation. Each parent key is yet another dictionary in itself. The structure of the dictionaries corresponding to any parent key are always the same and always include the keys:</p> <ul> <li>iters</li> <li>Mean</li> <li>Variance</li> <li>Sigma</li> <li>R_hat</li> <li>TauCorr</li> </ul> <p>Hence, to access the "Mean" values, you use <code>data["Energy"]["Mean"]</code> (or alternatively <code>data["FG"]["Mean"]</code> if you wish to observe the value of the F + G operator during the simulation). The data represents the values during a simulation of typically 500 iterations, hence, each of the keys mentioned above will correspond to an array of 500 items. The <code>iters</code> array includes merely the iteration number. The <code>Mean</code> array includes the value of the expectation value of the constraint at the corresponding iteration. The <code>Variance</code>, <code>Sigma</code>, <code>R_hat</code> and <code>TauCorr</code> includes the values of the variance and error in the expectation value at the given iteration as well as the split R-hat diagnostic and the time correlation also in the given iteration. </p> <p> </p> <h3>4.2 Variational State Data</h3> <p><em><strong>The files for the variational arrays are too large to be uploaded to a general repository hosting service. Therefore, they will be provided directly upon request in a direct download link. Please contact the author of the paper (Waleed Sherif, email: waleed.sherif@fau.de) for accessing the data. </strong></em></p> <p> </p> <h3>4.3 Fluctuation results</h3> <p>In some child directories, there will be a <code>.txt</code> file which includes the output of the calculation of the expectation value of some operators and their quantum fluctuations. These are only results, and not data, as the data can only be computed during the simulation.</p> <p> </p> <h2>4.4 Probabilities</h2> <p>The "Misc/Probabilities" directory contains <code>.npy</code> files which should be handled in the same manner as the variational states. These files correspond to the probability simulations conducted in section 4.4.4 in the paper.</p> <p> </p> <h2>5. Contact</h2> <p>Shall you have any unanswered questions regarding the usage of the data, please contact the author:</p> <p>Waleed Sherif</p> <p>email: waleed.sherif@fau.de</p> <p> </p> <h2>6. References</h2> <p>The data provided in this repository was produced using the <a href="https://github.com/netket" target="_blank" rel="noopener">NetKet</a>[1] package</p> <p>[1] <a href="https://doi.org/10.21468/SciPostPhysCodeb.7" target="_blank" rel="noopener">doi: 10.21468/SciPostPhysCodeb.7</a></p>
Data and Sourcecode from: Neural Network-based Occupancy Detection on the Edge
<div> <h2>Environmental Data Collected for Data-Driven Occupancy Detection</h2> <h3>Version Information</h3> <p>v1.0: Holds repository alongside the dataset inside<br>v2.0: Holds dataset in a seperate zip folder</p> <h3>Dataset Information</h3> <div>The following data is collected from LoRa sensors of two rooms for a period of three months in an office building on the ground floor in Graz, Austria:</div> <div> <ul> <li>Open status of windows/doors</li> <li>Relative humidity</li> <li>CO2 concentration</li> <li>Ambient temperature</li> <li>PIR-based motion counter</li> <li>Light level</li> <li>IR-based occupancy (only room A)</li> <li>Average/peak sound level</li> <li>Radar-based people counter (left-to-right and right-to-left; only room A; <em><strong>no</strong> trustworthy ground truth!</em>)</li> </ul> </div> <div> </div> <div>Folder Organization in occupancy-detection-dataset.zip</div> <br> <div> ├── data</div> <div> │ ├── interim <- Intermediate data of room A and B that has been transformed.</div> <div> │ └── raw <- The original, immutable sensor data dump of room A and B.</div> <br><br> <div><strong>Raw Data</strong></div> <div>Raw sensor data of room A and B consisting of six and two work places respectively. Data is gathered in an interval of five minutes.</div> <br> <div>Note:</div> <ul> <li>Timezone ist UTC+00:00.</li> <li>Column "occupancy" in df_features.csv refers to IR based occupancy sensor from Elsys ERS Eye (Possible values 0-2).</li> <li>Column "motion" in df_features.csv refers to a PIR based motion counter.</li> <li>IR-based occupancy is not measured in room B.</li> </ul> <br> <div><strong>Intermediate Data</strong></div> <div>Event-based (door and window sensors) and interval based (humidity, CO2, temperature, ....) data is synchronized to retrieve a homogenous data set.</div> <div>Window columns are merged to represent the number of open windows. Nothing else was applied to the data.</div> <br> <div><strong>Ground Truth</strong></div> <div>Image-based occupancy ground truth data is separated in a file (df_occ.csv).</div> <div>It describes the number of occupants at a certain time stamp provided from images (manually labelled).</div> <h3>References</h3> <p>Coming soon.</p> </div>
Fracture data from Poissonian network
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Analyzing Bayesian Network Data, Beta Distribution, and IT2F-Z
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ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'
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Data for "A quantum-network register assembled with optical tweezers in an optical cavity"
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Image Synthesis with a Convolutional Capsule Generative Adversarial Network- Prepared Data
<p>A set of prepared datasets for running experiments to replicate paper (see below).</p> <p>List of data:</p> <p>Training capspix2pix:</p> <ul> <li>crops256.zip - folder containing 256x256 crops from the original dataset for training capspix2pix. Images are in the "train/original" folder, and labels are in the "train/mask" folder.</li> <li>syn256_x_data_val.npy + syn256_y_data_val.npy + syn256_y_points_data_val.npy (images + labels + centrelines) - validation synthetic dataset, used while training capspix2pix for plotting</li> </ul> <p>Training u-net:</p> <ul> <li>capspix2pix_AR_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from real labels</li> <li>capspix2pix_SSM_data_train.npy + capspix2pix_AR_mask_train.npy (images + labels) - data generated from a capspix2pix model from synthetic labels</li> <li>PBAM_SSM_data_train.npy + PBAM_SSM_mask_train.npy (images + labels) - data generated from PBAM (Physics-based model) for training u-net</li> <li>pix2pix_AR_data_train.npy + pix2pix_AR_mask_train.npy (images + labels) - data generated from a pix2pix model from real labels for training u-net</li> <li>pix2pix_SSM_data_train.npy + pix2pix_SSM_mask_train.npy (images + labels) - data generated from a pix2pix model from synthetic labels for training u-net</li> <li>real_data_data_train.npy + real_data_mask_train.npy (images + labels) - augmented real dataset for training u-net</li> </ul> <p>Testing u-net:</p> <ul> <li>org64_data_test.npy + org64_mask_test.npy (images + labels) - crops from original test dataset for testing u-net</li> </ul> <p>Interpolation:</p> <ul> <li>crops256_inter_data_train.npy + crops256_inter_mask_train.npy (images + labels) - example data for interpolation</li> </ul> <p><strong>Please cite the following paper when using this dataset:</strong></p> <p>Bass, C., Dai, T., Billot, B., Arulkumaran, K., Creswell, A., Clopath, C., De Paola, V., and Bharath, A. A., 2019. “Image synthesis with a convolutional capsule generative adversarial network,” <em>Medial Imaging with Deep Learning.</em></p> <p><strong>See Github page for further instructions:</strong></p> <p>https://github.com/CherBass/CapsPix2Pix</p> <p> </p>
Predicting and containing epidemic risk using on-line friendship networks - data and code
<p>Contains code and data I used for the paper</p>
The "lastfm" data set used in the article "A comparative study of social network models: Network evolution models and nodal attribute models"
<p>This is the "lastfm" network used in the article:</p> <p>Toivonen, R., Kovanen, L., Kivelä, M., Onnela, J. P., Saramäki, J., & Kaski, K. (2009). A comparative study of social network models: Network evolution models and nodal attribute models. Social networks, 31(4), 240-254.</p> <p>doi:10.1016/j.socnet.2009.06.004</p> <p>The data set is described in the article. Please cite the original article when using this data set.</p> <p>Format of the data set is an edge list, where row in the file is an edge connecting the two nodes indicated by the two numbers separated by a whitespace. Each node number corresponds to a single account in the website.</p> <p>The original data in which this network is based on was licensed under the "Creative Commons Attribution-NonCommercial-ShareAlike 2.0 UK: England & Wales" licese, and accordinly this data set uses the same license. License available at https://creativecommons.org/licenses/by-nc/2.0/uk/</p>
Data: Imitation learning of fish and swarm behavior with Recurrent Neural Networks
<p>Data for the Master's Thesis <em>Imitation learning of fish and swarm behavior with Recurrent Neural Networks</em></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.