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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>
Expression proteomics and histone analysis reveals extensive chromatin network changes and a role for histone tail trimming during cellular differentiation
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Convolutional Neural Networks in action
<p>https://x.com/Hamptonism/status/1794469409999786451</p>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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Network pharmacology combined with gene chip to explore the targets of sanguinarine in ovarian cancer
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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>
Cancer-specific heterogeneous networks for TREE
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Advanced neural network-based model for predicting court decisions on child custody
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Fracture data from Poissonian network
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Supplementary material for "A large-scale demonstration and sustainability evaluation of ductile-porous vascular networks for self-healing concrete"
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Dataset: AMC Networks Inc. (AMCX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Network Model with Internal Complexity Bridges Artificial Intelligence and Neuroscience
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Multiway Alignment of Twitter networks from 2019 and 2023 Finnish Parliamentary Elections
<p>This dataset includes partition identifiers obtained from retweet networks collected before the 2019 Finnish Parliamentary Elections and before the 2023 Finnish Parliamentary Elections from conversation streams related to Finnish political parties (SDP, National Coalition, Finns Party, Green Party, Left Party, Center Party) and salient topics (economic policy, social security, immigration, climate, education).</p> <p>The column headers in the two files are the same and indicate the topic of the retweet network. Each row in one file represents all the memberships of one particular Twitter account for the corresponding networks. Memberships are obtained by partitioning each retweet network with METIS algorithm into two similar-sized groups with minimal retweet activity between them. For each column, one partition includes all users marked with 0, whereas the other partition includes all users marked with 1. The order of the numbering is random. Missing values mean that the account was not found in the corresponding network.</p> <p>The dataset does not contain any identifying information or original raw data from the Twitter platform. Anonymization was achieved by randomly shuffling the order of the users so that, within each file, a row index identifies a user. Importantly, there no correspondence can be implied between users in 2019 and users in 2023: in general, the user at row 1 in the 2019 dataset is not the same as the user at row 1 in the 2023 dataset.</p>
Precision of Radiation Chemistry Networks: Playing Jenga with Kinetic Models for Liquid-Phase Electron Microscopy
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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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Exploring the Relationships Between Health Behaviours and Related Outcomes in UK University Students: a Bayesian Network Approach
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New Forest Peat Sensor Network Dataset
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River Network .tif file
<p>This dataset has the river network .tif file extracted usin 3m resolution DEM</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.