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358 results for “dataset generation”
LLM generated Python Compiler Test Dataset
<p>This dataset is generated by integrating Large Language Models (LLMs) with AFL++ fuzzing to enhance compiler testing for CPython. It includes original Python test scripts created by LLMs such as Mistral 7B, Codellama 7B, and Gemma 7B, targeted at various compiler functionalities. These scripts were subjected to fuzzing, resulting in a rich collection of test cases that tests potential vulnerabilities. An optional minimization process with AFL-cmin refined the dataset, ensuring it focuses on test cases that significantly contribute to code coverage and bug discovery. This dataset serves as a valuable resource for improving compiler design and testing efficiency, supporting further research and development in AI-driven software testing methods.</p> <p>please see references for citations of software used in this development</p>
Trypanosoma Epitope Dataset: Valid Epitopes and Randomly Generated Peptides with Biochemical Metrics and AI-Generated Scores
<p>This dataset contains information about valid linear B-Cell epitopes from the Trypanosoma genus, as well as randomly generated peptides. It includes biochemical metrics generated by the EpiBuilder-1.0 tool and scores generated by the BepiPred-3.0 software. The data was originally collected from the IEDB and UniProtKB platforms and has been processed and enhanced with these informations for researchers interested in understanding the molecular interactions between Trypanosoma protozoans and the immune system.</p>
Historical Reconstruction Dataset of Hourly Expected On-Shore Wind Generation in Japan
<h2>Description</h2> <p>This is a historical reconstruction dataset of hourly expected wind generation based on dynamically downscaled atmospheric reanalysis for assessing the spatio-temporal impact of on-shore wind in Japan.</p> <p>The dataset consists of a set of <a href="https://www.unidata.ucar.edu/software/netcdf/">netCDF</a> files with yearly archives of reconstruction results from 1958 to 2012; hourly expected on-shore wind power potential in Japan with a spatial resolution of approximately 5 km mesh has been reconstructed from the numerical weather model reanalysis results. The expected per-unit output values at each location were calibrated using a nonparametric machine learning model that learns statistical relationships between spatial/meteorological features of target locations and actual wind farm outputs.</p> <p>A convenient way to handle this dataset would be to use a tool for manipulating netCDF files, such as <a href="https://code.mpimet.mpg.de/projects/cdo">CDO: Climate Data Operators</a>.</p> <h2>Associated Publication</h2> <ul> <li>Yu Fujimoto, Masamichi Ohba, Yujiro Tanno, Daisuke Nohara, Yuki Kanno, Akihisa Kaneko, Yasuhiro Hayashi, Yuki Itoda, and Wataru Wayama, "Historical Reconstruction Dataset of Hourly Expected Wind Generation Based on Dynamically Downscaled Atmospheric Reanalysis for Assessing Spatio-Temporal Impact of On-Shore Wind in Japan", <em>Big Earth Data</em>, doi: 10.1080/20964471.2024.2374044 </li> </ul> <h2>Version history</h2> <ul> <li>Ver. 1.0: Released.</li> <li>Ver. 1.1: The preprocessing of the source information used for dataset preparation has changed.</li> <li>Ver. 1.2: The hyperparameter tuning scheme for the post-processing model has changed.</li> </ul>
AstroChat - A Dataset of synthetically generated conversations for LLM supervised fine-tuning in the domain of Space Mission Engineering and Astronautics
<h1>AstroChat Dataset Description</h1> <h2>Purpose and Scope</h2> <p>The AstroChat dataset is a collection of 901 dialogues, synthetically generated, tailored to the specific domain of Astronautics / Space Mission Engineering. This dataset will be frequently updated following feedback from the community. If you would like to contribute, please reach out in the community discussion.</p> <h2>Intended Use</h2> <p>The dataset is intended to be used for supervised fine-tuning of chat LLMs (Large Language Models). Due to its currently limited size, you should use a pre-trained instruct model and ideally augment the AstroChat dataset with other datasets in the area of (Science Technology, Engineering and Math).</p> <h2>DATASET DESCRIPTION</h2> <h3>Access</h3> <ul> <li>Manual download from Hugging face hub: <a href="https://huggingface.co/datasets/patrickfleith/Astro-Ultrachat" rel="nofollow">https://huggingface.co/datasets/patrickfleith/AstroChat</a></li> <li>Or with python:</li> </ul> <pre><code>from datasets import load_dataset dataset = load_dataset("patrickfleith/AstroChat") </code></pre> <h3>Structure</h3> <p>901 generated conversations between a simulated user and AI-assistant (more on the generation method below). Each instance is made of the following field (column):</p> <ul> <li><strong>id</strong>: a unique identifier to refer to this specific conversation. Useeful for traceability purposes, especially for further processing task or merge with other datasets.</li> <li><strong>topic</strong>: a topic within the domain of Astronautics / Space Mission Engineering. This field is useful to filter the dataset by topic, or to create a topic-based split.</li> <li><strong>subtopic</strong>: a subtopic of the topic. For instance in the topic of <code>Propulsion</code>, there are subtopics like <code>Injector Design</code>, <code>Combustion Instability</code>, <code>Electric Propulsion</code>, <code>Chemical Propulsion</code>, etc.</li> <li><strong>persona</strong>: description of the persona used to simulate a user</li> <li><strong>opening_question</strong>: the first question asked by the user to start a conversation with the AI-assistant</li> <li><strong>messages</strong>: the whole conversation messages between the user and the AI assistant in already nicely formatted for rapid use with the transformers library. A list of messages where each message is a dictionary with the following fields: <ul> <li><strong>role</strong>: the role of the speaker, either <code>user</code> or <code>assistant</code></li> <li><strong>content</strong>: the message content. For the assistant, it is the answer to the user's question. For the user, it is the question asked to the assistant.</li> </ul> </li> </ul> <p><strong>Important</strong> See the full list of topics and subtopics covered below.</p> <h3>Metadata</h3> <p>Dataset is version controlled and commits history is available here: <a href="https://huggingface.co/datasets/patrickfleith/Astro-Ultrachat/commits/main" rel="nofollow">https://huggingface.co/datasets/patrickfleith/AstroChat/commits/main</a></p> <h3>Generation Method</h3> <p>We used a method inspired from Ultrachat dataset. Especially, we implemented our own version of Human-Model interaction from <strong>Sector I: Questions about the World</strong> of their paper:</p> <p><em>Ding, N., Chen, Y., Xu, B., Qin, Y., Zheng, Z., Hu, S., ... & Zhou, B. (2023). Enhancing chat language models by scaling high-quality instructional conversations. arXiv preprint arXiv:2305.14233.</em></p> <h4>Step-by-step description</h4> <ul> <li>Defined a set of user persona</li> <li>Defined a set of topics/ disciplines within the domain of Astronautics / Space Mission Engineering</li> <li>For each topics, we defined a set of subtopics to narrow down the conversation to more specific and niche conversations (see below the full list)</li> <li>For each subtopic we generate a set of opening questions that the user could ask to start a conversation (see below the full list)</li> <li>We then distil the knowledge of an strong Chat Model (in our case ChatGPT through then api with <code>gpt-4-turbo</code> model) to generate the answers to the opening questions</li> <li>We simulate follow-up questions from the user to the assistant, and the assistant's answers to these questions which builds up the messages.</li> </ul> <h3>Future work and contributions appreciated</h3> <ul> <li>Distil knowledge from more models (Anthropic, Mixtral, GPT-4o, etc...)</li> <li>Implement more creativity in the opening questions and follow-up questions</li> <li>Filter-out questions and conversations which are too similar</li> <li>Ask topic and subtopic expert to validate the generated conversations to have a sense on how reliable is the overall dataset</li> </ul> <h3>Languages</h3> <p>All instances in the dataset are in english</p> <h3>Size</h3> <p>901 synthetically-generated dialogue</p> <h2>USAGE AND GUIDELINES</h2> <h3>License</h3> <p>AstroChat © 2024 by Patrick Fleith is licensed under Creative Commons Attribution 4.0 International</p> <h4>Restrictions</h4> <p>No restriction. Please provide the correct attribution following the license terms.</p> <h4>Citation</h4> <p><em>Patrick Fleith, AstroChat – A Dataset of synthetically generated conversations for LLM supervised fine-tuning in the domain of Space Mission Engineering and Astronautics, (2024).</em></p> <h4>Update Frequency</h4> <p>Will be updated based on feedbacks. I am also looking for contributors. Help me create more datasets for Space Engineering LLMs :)</p> <h4>Have a feedback or spot an error?</h4> <p>Use the community discussion tab directly on the huggingface AstroChat dataset page.</p> <h4>Contact Information</h4> <p>Reach me here on the community tab or on LinkedIn (Patrick Fleith) with a Note.</p> <h3>Number of conversation per topic category</h3> <pre><code>Space Propulsion Systems 135 Human Spaceflight 50 Entry Descent and Landing (EDL) 45 Mechanisms 45 Planetary Rovers 45 Attitude Determination and Control 45 Telecommunication 41 Space Business 40 Structures 40 Materials 40 Launchers, Launches, Launch Operations 36 Power System 35 Payload S/S and Optics 35 Reliability, Availability, Maintainability, and Safety (RAMS) 35 Space Missions Operations 31 Space Environment 30 Command and Data System 30 Orbital Mechanics 30 Space Law 26 Ground Systems 25 Thermal Control 25 Space Processes 20 Planetary Science and Exploration 17 </code></pre> <h3>Topics and subtopics covered</h3> <p>topic: [ Space Law ]</p> <p>subtopics:</p> <ul> <li>Space Law Basics</li> <li>1998 ISS agreement</li> <li>Outer Sppace Treaty</li> <li>Geostationary Orbit Regulations</li> <li>Space Traffic Management</li> <li>French Space Law</li> </ul> <p>topic: [ Space Business ]</p> <p>subtopics:</p> <ul> <li>New Space</li> <li>Satellite Insurance</li> <li>Financing Space Project (in EU)</li> <li>Commercial Satellite Launch Services</li> <li>Space Tourism</li> <li>Business Models for Space Stations</li> <li>Public-private Partnerships</li> <li>Economic Impact of Space Technologies</li> </ul> <p>topic: [ Space Missions Operations ]</p> <p>subtopics:</p> <ul> <li>Flight control team</li> <li>Flight Dynamics</li> <li>Procedure Preparation and Validation</li> <li>Mission Planning</li> <li>Extravehicular Activities (EVAs)</li> <li>Collision Avoidance Manoeuvres</li> <li>Mission Termination and De-Orbit Strategies</li> </ul> <p>topic: [ Human Spaceflight ]</p> <p>subtopics:</p> <ul> <li>Astronaut Selection</li> <li>Astronaut Training</li> <li>research experiments onboard of the ISS</li> <li>Human Mission to Mars Design</li> <li>Environmental Control and Life Support Systems</li> <li>Moon Surface Habitats</li> <li>Microgravity effects</li> <li>Space Suit Design and Operation</li> <li>Space Medicine</li> <li>Space Food</li> </ul> <p>topic: [ Space Environment ]</p> <p>subtopics:</p> <ul> <li>Micrometeorites</li> <li>Space Radiation</li> <li>Solar Cycle</li> <li>Spacecraft Hardening</li> <li>Space Environment Effects on Satellites</li> <li>Magneto-sphere and Radiation Belt</li> </ul> <p>topic: [ Space Propulsion Systems ]</p> <p>subtopics:</p> <ul> <li>Liquid Rocket Engines</li> <li>Solid Rocket Motors</li> <li>Hybrid Rocket Engines</li> <li>Staging and Ignition Systems</li> <li>Propellant Feed Systems</li> <li>Nozzle Designs</li> <li>Thermodynamics</li> <li>Turbopumps and/or Combustion Chambers</li> <li>Specific Impulse and Thrust-to-Weight Ratios</li> <li>Chemical Monopropellant Technologies</li> <li>Chemical Bipropellant Systems</li> <li>Nuclear Thermal Propulsion</li> <li>Fuel Handling and Storage</li> <li>Nuclear Propulsion Thermal Neutron Absorbers</li> <li>Nuclear Propulsion Heat Exchangers</li> <li>Green Propellants</li> <li>Bipropellant Injector Design</li> <li>Electric Ion Thrusters</li> <li>Hall Effect Thrusters</li> <li>Electrothermal Thrusters</li> <li>Grid and Cathode Technologies</li> <li>Aerospike Engines</li> <li>Variable Specific Impulse Magnetoplasma Rocket (VASIMR)</li> <li>Bipropellant Mixing Ratios and Combustion</li> <li>Cryogenic Propellant Handling</li> <li>Oxydizer and Fuel Combinations</li> <li>Long-term Impacts of Propellant Residues in the Atmosphere</li> <li>Propellant Tank Pressurization</li> </ul> <p>topic: [ Space Processes ]</p> <p>subtopics:</p> <ul> <li>Trade Studies</li> <li>Margins, Coningencies, Reserves</li> <li>Systems Engineering</li> <li>Quality Assurance</li> </ul> <p>topic: [ Ground Systems ]</p> <p>subtopics:</p> <ul> <li>Ground Stations</li> <li>Ground Support Equipments</li> <li>Control Centers</li> <li>Tracking Systems</li> <li>AntennasGround Systems Engineering</li> </ul> <p>topic: [ Planetary Rovers ]</p> <p>subtopics:</p> <ul> <li>Mars Rovers</li> <li>Lunar Rovers</li> <li>Rover Instrumentation</li> <li>Rover Power Systems</li> <li>Rover Thermal Control</li> <li>Rover Autonomy</li> <li>Wheels Design</li> <li>Legged Rovers</li> <li>Hazard Avoidance</li> </ul> <p>topic: [ Planetary Science and Exploration ]</p> <p>subtopics:</p> <ul> <li>Astrobiology</li> <li>Exoplanets</li> <li>AsteroidsJupiter</li> <li>Saturn</li> <li>Search for Extraterrestrial Life</li> </ul> <p>topic: [ Structures ]</p> <p>subtopics:</p> <ul> <li>Structural Design and Analysis</li> <li>Load Path Determination</li> <li>Vibration and Acoustic Testing</li> <li>Thermal Protection Systems</li> <li>Composite Structures</li> <li>Joining Techniques (e.g., Welding, Bolting, Bonding)</li> <li>Manufacturing Tolerances and Quality Control</li> <li>Deployable Structures (e.g., Antennas, Solar Arrays)</li> </ul> <p>topic: [ Mechanisms ]</p> <p>subtopics:</p> <ul> <li>Actuators and Dampers</li> <li>Gimbals and Bearings</li> <li>Latch and Release Devices</li> <li>Hinges and Deployment Systems</li> <li>Robotic Arms and Tools</li> <li>Valves and Fluid Control Systems</li> <li>Thermal Expansion Joints</li> <li>Drive Systems and Motors</li> <li>Reliability and Lifetime Analysis</li> </ul> <p>topic: [ Materials ]</p> <p>subtopics:</p> <ul> <li>Composite Materials</li> <li>Metals and Alloys</li> <li>Polymers and Plastics</li> <li>Nano-materials</li> <li>Radiation Shielding Materials</li> <li>Thermal Insulation Materials</li> <li>Corrosion and Oxidation Resistance</li> <li>Material Testing and Characterization</li> </ul> <p>topic: [ Entry Descent and Landing (EDL) ]</p> <p>subtopics:</p> <ul> <li>Aerodynamics and Aeroheating</li> <li>Powered Descent</li> <li>Landing Gear and Systems</li> <li>Heat Shield Design and Materials</li> <li>Hazard Avoidance</li> <li>Surface Interaction (Airbags, Crushable Structures)</li> <li>Entry, Descent, and Landing Sequencing</li> <li>EDL on Mars</li> <li>Parachute Systems Design</li> </ul> <p>topic: [ Reliability, Availability, Maintainability, and Safety (RAMS) ]</p> <p>subtopics:</p> <ul> <li>System Reliability Modeling</li> <li>Failure Modes, Effects, and Criticality Analysis (FMECA)</li> <li>Risk Assessment and Management</li> <li>Safety-Critical Systems Design</li> <li>Availability Modeling and Prediction</li> <li>Lifecycle Cost and Duration Analysis</li> <li>Hazardous Material Handling</li> </ul> <p>topic: [ Orbital Mechanics ]</p> <p>subtopics:</p> <ul> <li>Interplanetary Trajectories</li> <li>Gravity Assist Maneuvers</li> <li>Orbit Determination and Propagation</li> <li>Space Situational Awareness and Debris Tracking</li> <li>Mission Design and Analysis Tools</li> <li>Orbit Decay and Re-entry Predictions</li> </ul> <p>topic: [ Launchers, Launches, Launch Operations ]</p> <p>subtopics:</p> <ul> <li>Launcher Types (e.g., expendable, reusable)</li> <li>Launch Vehicles</li> <li>Launch Sites and Infrastructure</li> <li>Countdown Procedures and Sequencing</li> <li>Launch Window Determination and Trajectory Analysis</li> <li>Ground and Launch Crew Training</li> <li>Payload Integration and Fairing Design</li> <li>Environmental and Weather Constraints</li> </ul> <p>topic: [ Attitude Determination and Control ]</p> <p>subtopics:</p> <ul> <li>Sensors for Attitude Determination (e.g., Gyroscopes, Star Trackers)</li> <li>Actuators for Attitude Control (e.g., Reaction Wheels, Thrusters)</li> <li>Control Algorithms (e.g., PID, Kalman Filter)</li> <li>Momentum Exchange Devices</li> <li>Attitude Dynamics Modeling</li> <li>On-Orbit Attitude Reconfiguration</li> <li>Fault Detection and Response Strategies</li> <li>Sun and Earth Sensors</li> <li>Magnetic Torquers and Gravity Gradient Stabilization</li> </ul> <p>topic: [ Payload S/S and Optics ]</p> <p>subtopics:</p> <ul> <li>Payload Design and Integration</li> <li>Spectral Imaging and Multi-spectral Sensors</li> <li>Infrared and Ultraviolet Optics</li> <li>Calibration and Validation of Optical Systems</li> <li>Image Processing and Data Analysis</li> <li>Thermal Control for Sensitive Optics</li> <li>Data Downlink and Communication Interfaces</li> </ul> <p>topic: [ Power System ]</p> <p>subtopics:</p> <ul> <li>Solar Panels and Arrays</li> <li>Battery Types and Management Systems (e.g., Li-ion, NiMH)</li> <li>Energy Storage Technologies</li> <li>Fault Protection and Isolation</li> <li>Harness and Cabling</li> <li>Alternative Power Sources (e.g., RTGs, Fuel Cells)</li> <li>Power Budgeting and Load Analysis</li> </ul> <p>topic: [ Thermal Control ]</p> <p>subtopics:</p> <ul> <li>Active Thermal Control Systems (e.g., Heat Pumps, Louvers)</li> <li>Environmental Testing and Validation</li> <li>Heating and Cooling Hardware</li> <li>Thermal Protection for Entry, Descent, and Landing</li> <li>Cryogenic Thermal Management</li> </ul> <p>topic: [ Command and Data System ]</p> <p>subtopics:</p> <ul> <li>Onboard Computers and Processing Units</li> <li>Software Architecture and Middleware</li> <li>Command Link and Telemetry Systems</li> <li>Interface and Bus Systems (e.g., MIL-STD-1553, SpaceWire)</li> <li>Real-Time Operating Systems (RTOS)</li> <li>Security Measures and Encryption</li> </ul> <p>topic: [ Telecommunication ]</p> <p>subtopics:</p> <ul> <li>Antenna Systems (e.g., Parabolic, Phased Array)</li> <li>Communication Transponders</li> <li>Frequency Bands and Spectrum Management</li> <li>Signal Modulation and Demodulation Techniques</li> <li>Inter-Satellite Links and Data Relays</li> <li>Error Detection and Correction</li> <li>Space Communication Protocols</li> <li>RF and Microwave Components</li> <li>Deep Space Communications</li> </ul>
Task 3 Dataset for Dreaming of Electrical Waves: Generative Modeling of Cardiac Excitation Waves using Diffusion Models
Open the record for dataset details and reuse information.
Dataset of "Advancing Logic Circuits with Halide Perovskite Memristors for Next-Generation Digital Systems"
<p><span>This dataset supports the article "Advancing Logic Circuits with Halide Perovskite Memristors for Next-Generation Digital Systems" </span></p> <p> </p> <p><span>Raw data for the article "<span>Advancing Logic Circuits with Halide Perovskite Memristors for Next-Generation Digital Systems</span>". For further details see the readme.txt file.</span></p>
A global inventory of solar photovoltaic generating units - dataset
<p>This is the data repository accompanying Kruitwagen, L., Story, K., Friedrich, J., Byers, L., Skillman, S., & Hepburn, C. (2021) A global inventory of photovoltaic solar generating units, <strong>Nature, </strong><em>forthcoming</em>. This repository contains the training, cross-validation, test, and predicted data set as described in the publication. The contents of this repository are briefly summarised here, see the publication for further details.</p> <p><strong>Repository contents:</strong></p> <p><em>trn_tiles.geojson: </em>18,570 rectangular areas-of-interest used for sampling training patch data.</p> <p><em>trn_polygons.geojson: </em>36,882 polygons obtained from OSM in 2017 used to label training patches.</p> <p><em>cv_tiles.geojson: </em>560 rectangular areas-of-interest used for sampling cross-validation data seeded from <a href="https://www.wri.org/research/global-database-power-plants">WRI GPPDB</a></p> <p><em>cv_polygons.geojson: </em>6,281 polygons corresponding to all PV solar generating units present in cv_tiles.geojson at the end of 2018.</p> <p><em>test_tiles.geojson: </em>122 rectangular regions-of-interest used for building the test set.</p> <p><em>test_polygons.geojson: </em>7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles.geojson at the end of 2018.</p> <p><em>predicted_polygons.geojson: </em>68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018.</p>
Dataset of 20 energy prosumers with flexibility data, distributed generation and energy storage
<p>The dataset has 20 prosumers, each with three appliances to provide flexibility for DR events, two PV generation resources, and an energy storage system. The values represent a day using 15 minutes reading periods. All the values are expressed in W, and the matrixes were created as [ time_period x info].</p> <p> </p> <p>We would be grateful if you could acknowledge the use of this dataset in your publications. Please use the Zenodo publication to cite this work.</p>
kac_drumset: A Dataset Generator for Arbitrarily Shaped Drums
<p>This publication documents the various datasets generated using the kac_drumset codebase. The aims of kac_drumset is to provide a robust framework for the generation and analysis of arbitrarily shaped drums. The source code for this project is available here: <a href="https://github.com/lewiswolf/kac_drumset">https://github.com/lewiswolf/kac_drumset</a>.</p> <p><strong>Background</strong></p> <p>Arbitrarily shaped drums are a strange family of percussion instruments and a wholly meta-physical construction in this contemporary setting. These percussive instruments possess a number of interesting musical characteristics resulting from their particular geometric designs. As it currently stands, these instruments remain largely unexplored throughout musical practice, as they were originally devised as a collection of hypothetical mathematical objects. These datasets serve to sonify these objects so as to explore these conceptual constructions in the audio domain.</p> <p><strong>Usage</strong></p> <p>To use these datasets, first install kac_drumset:</p> <pre><code class="language-bash">pip install "git+https://github.com/lewiswolf/kac_drumset.git#egg=kac_drumset"</code></pre> <p>And then in python:</p> <pre><code class="language-python">from kac_drumset import ( # methods loadDataset, transformDataset, # classes TorchDataset, ) dataset: TorchDataset = transformDataset( # load a dataset (any folder containing a metadata.json) loadDataset('absolute/path/to/data'), # alter the dataset representation, either as an end2end, fft or mel. {'output_type': 'end2end'}, ) # use the dataset for i in range(dataset.__len__()): x, y = dataset.__getitem__(i) ...</code></pre> <p>For more details on using kac_drumset, see <a href="https://github.com/lewiswolf/kac_drumset/blob/master/readme.md">the project's documentation</a>.</p> <p><strong>2000 Convex Polygonal Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated convex polygon. The audio for each sample was generated using a two-dimensional physical model of a drum. Each sample is one second long and decays linearly.</p> <p>Contained in this dataset are ten different sizes of drums - 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5, 0.6 - each of which is a measure of the longest vertex of each drum in meters. There are 40 different drums sampled for each size. Each drum is sampled five times, first by being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the vertices of each polygon, normalised to the unit interval, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Circular Drums of Varying Size</strong></p> <p>Each sample in this dataset corresponds to a randomly generated circular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters. Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size of each drum and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p> <p><strong>5000 Rectangular Drums of Varying Dimension</strong></p> <p>Each sample in this dataset corresponds to a randomly generated rectangular drum. The audio for each sample was generated using additive synthesis, inferred using a closed form solution to the two dimensional wave equation. Each sample is one second long and decays exponentially.</p> <p>Contained in this dataset are 1000 different drums, each determined by a randomly generated size (0.1, 2.0) in meters and aspect ratio (0.25, 4.0). Each drum is sampled five times, first being struck in the geometric centroid, and then by being struck four more times in random locations. This dataset is labelled with the size and aspect ratio of each drum, and the strike location of each sample.</p> <p>The audio is sampled at 48khz, and the default representation is raw audio. Each sample is stored in the metadata.json, alongside being made available audibly as a 24-bit .wav and graphically as a .png.</p>
Dataset of "Exposure to airborne SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures."
<p>The authors highly appreciate being contacted if the data is to be used for any purpose.</p> <p>The following data set was used in the study entitled "<strong>Exposure to airborne </strong><strong>SARS-CoV-2 in four hospital wards and ICUs of Cyprus. A detailed study accounting for day-to-day operations and aerosol generating procedures.</strong>" and published in <em>Heliyon</em> Journal.</p> <p>This study characterized the transmission dynamics of airborne SARS-CoV-2 in normal and intensive care units. The data were collected over the period of 2020. In total, 165 and 62 air and environmental samples, respectively, were collected in four COVID-19 wards and ICUs in Cyprus and analyzed by RT-PCR. The comparison between RT-PCR and an alternative method for SARS-CoV-2 detection in air that provides comparable results but is less cumbersome and time demanding, is also given in the tab "Comparison with BELD".</p> <p>The data from sampling airborne SARS-CoV-2 using a MOUDI impactor are not included in this document but can be found in the supplement of the relevant publication.</p> <p>Please refer to the manuscript and its supplementary material for more information about how the data was collected. </p> <p> </p>
Datasets generated by rurAllure project - promotion of rural museums and heritage sites in the vicinity of European pilgrimage routes
<p>These datasets have been generated as part of rurAllure project (funded by the European Union’s Horizon 2020 Research and Innovation programme under grant agreement no 101004887). Main goal of rurAllure is the promotion of rural museums and heritage sites in the vicinity of European pilgrimage routes: https://rurallure.eu/project/about/</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 2
<p>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al.</p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. param_fixing.zip - self-explanatory (Figure 4 & 5); contains an explanatory note for this part (experiment_details.txt), and the file containing Km values fetched from the BRENDA database (Km_database.csv).</p> <p>2. scripts.zip - scripts to generate figure 2-5 on toy data</p>
Supplementary datasets for the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" - Part 1
<p><strong>Supplementary files containing datasets needed to reproduce the results of the manuscript "Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states" by S. Choudhury et al (https://doi.org/10.1101/2023.02.21.529387).</strong></p> <p>The code to use with these data and reproduce the manuscript results is available at https://github.com/EPFL-LCSB/renaissance and https://gitlab.com/EPFL-LCSB/renaissance. The execution of parts of this code is dependent on the SkimPy toolbox (https://github.com/EPFL-LCSB/skimpy). Refer to the readme files on the RENAISSANCE code repositories for more details.</p> <p>The dataset contains the following files:</p> <p>1. models.zip - contains thermodynamically curated steady-state and nonlinear kinetic models of <em>E. coli </em>metabolism used in this study. Also contains the samples of steady-state metabolite concentrations and metabolic fluxes used in the study presented in Figure 3 (steady-state samples used for preparing Figures 2 and 4).</p> <p>2. renaissance_incidence_results.zip - self-explanatory (Figure 2a and 2b)</p> <p>3. ODE_solutions.zip - self-explanatory (Figure 2c)</p> <p>4. bioreactor_simulations1-3.zip - self-explanatory (Figure 2d)</p> <p>5. steady_state_analysis.zip - RENAISSANCE results obtained for each of the steady states (Figure 3a)</p> <p>6. subspace_analysis.zip - RENAISSANCE results presented in Figure 3b-g</p> <p><strong>The remaining datasets are published in the following links</strong></p> <p><em> - https://doi.org/10.5281/zenodo.7930084</em></p> <p><em> - https://doi.org/10.5281/zenodo.10391802</em></p>
Pertubation Profiles Dataset used for "Convection-generated gravity waves in the tropical lower stratosphere from Aeolus wind profiling and ERA5 reanalysis"
<p>These are the perturbation profiles, from 5km to 29.5km, with a 500m grid. In the study, we picked up the data between tropopause-1km to 22km, which was then squared, smoothed, and averaged into one value. We used a 14 points moving average for the smoothing.</p> <p>The data is from 2018-09 to 2022-09, based on the Aeolus L2B Rayleigh clear wind, using only quality flag 1 data.</p> <p>Please email me at mathieu.ratynski@estaca.eu if you're interested in the 100m resolution version, used in the final version of the manuscript.</p>
Dataset and Simulation Files for article "Bright and Vivid Diffractive-Plasmonic Reflective Filters for Color Generation"
<p>This work was supported by Ministério da Ciência Tecnologia, Inovações e Comunicações, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil, Finance, Code 001, National Counsel of Technological and Scientific Development, and São Paulo Research Foundation (Fapesp) through grants 2018/15580-6, 2018/15577-5, 2016/18308-0, 2012/ 17610-3, and 2012/17765-7. Part of the results presented in this work were obtained through Project 4716-11, funded by Samsung Eletrônica da Amazônia Ltda., under the Brazilian Informatics Law 8.248/91. The authors thank the Center for Semiconductor Components and Nanotechnologies for the nanofabrication infrastructure.</p>
datasets for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons"
<p>Supporting information for "VLF Transmitters and Lightning Generated Whistlers 2: Diffusion of Radiation Belt Electrons," submitted to Journal of Geophysical Research Space Physics. Diffusion coefficients for selected values of L and energy due to Very Low Frequency (VLF) transmitters and lightning generated whistlers (LGW), as well as Da0a0 and energy drag rates |dE/dt|/E from Coulomb collisions. Also provided are precipitation lifetimes, which include Da0a0 from plasmaspheric hiss but do not account for energy drag. Calculations are presented for high and low-density plasmasphere models, for all four combinations of ducted or nonducted VLF and LGW waves.</p>
Dataset for "Fmmgen: Automatic Code Generation of Operators for Cartesian Fast Multipole and Barnes-Hut Methods"
<p>This repository provides the dataset, build and run scripts for the paper "Fmmgen: Automatic Code Generation of Cartesian Fast Multipole and Barnes-Hut Operators", by Ryan Alexander Pepper and Hans Fangohr.</p> <p><br> <strong>Organisation</strong><br> The repository is organised as follows:</p> <p><strong><em>sim-scripts</em></strong></p> <p>sim-scripts/ contains the source code, build and run scripts for running the FMM calculations described in the paper. To reproduce the results from the paper, you need as prerequisites:<br> * An installation and license of the Intel Compiler (Parallel Studio 2019 Update 3 was used for the paper).<br> * An installation of the GNU compiler suite.<br> * GNU Make<br> * Python 3.6<br> * A copy of fmmgen v1.0 (available at https://github.com/rpep/fmmgen or https://zenodo.org/record/3842591)<br> * An installation of Fidimag v3.0 (available at http://github.com/computationalmodelling/fidimag or http://dx.doi.org/10.5281/zenodo.3841935)</p> <p>With these prerequisites, simply run from the sim-scripts directory:<br> ```<br> # To build the executables<br> make build<br> # To run the studies<br> make run<br> ```</p> <p>The four scripts are:<br> * run-harmonic-cse-comparison.sh - Runs the Fast Multipole Method for 50000 Coulomb particles, varying the order of expansion, and evaluating the performance benefits of various optimisation strategies introduced in the code generation stage.</p> <p>* run-scaling-comparison.sh - Runs comparisons between the Barnes-Hut and Fast Multipole Methods for different expansion orders and values of theta, the 'opening angle' parameter.</p> <p>* run-error-comparison.sh - Runs the FMM and Barnes-Hut calculations, saving the fields and performing the direct calculation, allowing evaluation of the errors for the two methods.</p> <p>* run-fidimag-tests.sh - Runs the Fidimag scaling tests for a series of magnetic dipoles placed on a lattice.</p> <p><strong><em>results</em></strong></p> <p>results contains the output data from the sim-scripts scripts. Note: running the scripts will overwrite this data!</p> <p><br> <strong><em>figure-scripts</em></strong></p> <p>This contains Python scripts needed to reproduce the figures from the paper. These generated figures are included in the repository for convenience. To run these scripts, you require:</p> <p>* Python >= 3.6<br> * Matplotlib >= 3.1.1<br> * Seaborn >= 0.9.1<br> * NumPy >= 1.17.1</p> <p><strong><em>figures</em></strong></p> <p>This contains the output figures included in the paper.<br> </p>
Training dataset: Generation of a spectral library from HEK-Ecoli Spike-in mass spectrometry data
<p>The five raw files serve as a concise but meaningful training data set in the Galaxy training network (https://galaxyproject.github.io/training-material/).</p> <p>HEK and E.coli cell pellets were lysed with 5 % SDS, 50 mM triethylammonium bicarbonate (TEAB), pH 7.55. The obtained protein extracts were reduced by adding f.c. 5 mM TCEP and alkylated by the addition of f.c. 10 mM iodacetamide. Protein digestion and purification was performed on S-Trap columns. To ensure protein binding to the S-Trap columns, samples were acidified to a final concentration of 1.2 % phosphoric acid (~ pH 2). Six times the sample volume S-Trap buffer (90% aqueous methanol containing a final concentration of 100 mM TEAB, pH 7.1) was added to the samples which were then loaded on the columns and washed with S-Trap buffer. Protein digestion was performed with trypsin and LysC for one hour at 47 °C. Peptides were eluted in three steps with (1) 50 mM TEAB, (2) 0.2 % aqueous formic acid and (3) 50 % acetonitrile containing 0.2 % formic acid. Eluted peptides of HEK and E.coli were mixed in the following ratios (amount in µg):</p> <p>Sample HEK E.coli MS method<br> Sample1 2.5 0.00 DDA<br> Sample2 2.5 0.05 DDA<br> Sample3 2.5 0.15 DDA<br> Sample4 2.5 0.40 DDA<br> Sample5 2.5 0.80 DDA</p> <p>Additionally, iRT peptides were added and 1µg of each samples was measured with a Q-Exactive Plus mass spectrometer. Besides the five raw files, we uploaded two fasta files that serve as human and ecoli protein sequence databases, an transition list for the iRT peptides as well as an experimental design for the MaxQuant search.<br> Additionally, we uploaded the Galaxy MaxQuant training result files: protein groups, peptides, mqpar, msms, evidence and PTXQC.</p>
Dataset: On the Properties of Next Generation Wireless Backhaul
<p>This dataset contains all the data used in the Paper: "On the Properties of Next Generation Wireless Backhaul" sent to Transaction of Network Science and Engineering.</p> <p>It is divided into three main archives:</p> <ul> <li>The first archive, called <strong>geodata.zip</strong>, contains the Data Surface Model (DSM) and the topographical maps used to generate the intervisibility graphs. These maps have been aggregated from different sources and they are all released under a CC-BY-SA 4.0 license. It is divided into two folders: 'dsm' and 'topo_maps': <ul> <li> 'dsm' contains the Data Surface Model (DSM) of the nine areas used in our research. Each area is saved in a separate .tif file and can be used directly from the tool. The projection system is EPSG:3003.</li> <li>'topo_mapsì contains the PostGIS dump of the tables containing the topographical maps of the cities. In order to use them, you have to import those in PostGIS and connect our tool to the PostGIS DB.<br> The CTR files (technical region maps) are divided by region, while the openstreetmap file (osm.tar) contains the whole Italian peninsula. All the geographical data are projected in the reference system EPSG:3003. The CTR of Campania is unavailable due to licensing incompatibilities.</li> </ul> </li> <li>The second archive, called <strong>visibility_graphs.zip</strong>, contains the visibility graph we have generated using our tool. It is released as a CC-BY-SA 4.0 license. It is divided into folders, one for each area of the analysis. Each folder contains two files: <ul> <li>best_p.csv : contains the ids of the nodes associated with their coordinates on a cartesian projection. The projection is EPSG:3003</li> <li>intervisibility.adj : contains the intervisibility graph represented as an adjacency matrix. It can be read by common libraries such as networkx or igraph. The intervisibility has been calculated 2m above the building.</li> </ul> </li> <li>The third folder, called <strong>network_topologies.zip</strong>, contains the network topologies we have computed using a State-of-the-Art algorithm from Polese et Al. The folder is divided into two folders: '30gNB' and '60gNB', corresponding to the density of base stations for square km. It s further divided in 9 folders (one for each area), then divided on the basis of the model we use for the visibility graph. You can find more information about these models in section V of the paper. In this last folder we have two sets of 50 files, which are the intervisibility graph of that set of Base Stations and the topology produced. This dataset has been released under a CC-BY-SA 4.0 license.</li> </ul> <p>All the results can be replicated using our code which will be released with an open-source license as soon as the article is published.</p>
Supporting dataset for manuscript: "Higher rate of tuberculosis in second generation migrants compared to native residents in a metropolitan setting in Western Europe" (PLoS ONE)
<p>This is the supporting datafile for the manuscript entitled "Higher rate of tuberculosis in second generation migrants compared to native residents in a metropolitan setting in Western Europe" (Marx et al., PLoS ONE). The dataset includes anonymized, routinely collected notification data (variables labeled as "nd") for 314 individuals and anonymized survey data (i.e. data obtained through interviews; variables labeled as "sd") for a subset of 154 individuals. The data are published open-access, in accordance with the PLoS ONE data policy (2014).</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.