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7,505 results for “Generation”
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>
Data for Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation
<p>Numerical dataset for the data presented in Dodds et al., The direction of core solidification in asteroids: implications for dynamo generation, manuscript submitted to Icarus journal.</p>
Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control.
<p>This project contains the data obtained as a result of the Preludium Grant no 2022/45/N/ST8/01425 of the Polish National Science Centre fundings. Within the "Aeroacoustic investigations of streamwise vortex generators for boundary layer separation control" project, two main research tasks were defined:<br> 1. Implementation of porous FW-H analogy into the developed aeroacoustic code.<br> 2. Validation of the porous FW-H analogy implementation against analytical solutions for elementary sources.</p> <p>The resutls from these tasks are uploaded here. The details of the data are included in the EOP_medata_2.docx document uploaded. </p>
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.
Evaluation of the saturator efficiency of the low frost-point generator INRIM 03 Mark 1
<p>These datasets refers to the evaluation of the saturator efficiency of the low frost-point generator INRIM 03 developed at the Istituto Nazionale di Ricerca Metrologica. Alternating the inlet gas between a dry source and a moist gas source is it possible to test the capability of the generator to saturate the carrier gas (or condensate the excess water) at the corresponding saturation<br>temperature.</p> <p>This work has been carried out within the European Metrology Programme for Innovation and Research (EMPIR) Project ‘PROMETH2O—Metrology for trace water in ultra-pure process gases’.<br>This project (Grant No. 20IND06 PROMETH2O) has received funding from the EMPIR programme co-financed by the Participating States and from the European Union’s Horizon 2020 research and innovation programme.</p>
The Delayed Box: Biphenyl Bisimide Cyclophane, a Supramolecular Nano‒environment for Efficient Generation of Delayed Fluorescence
<p>Additional data to report <a title="DOI URL" href="https://doi.org/10.1021/jacs.4c07730">https://doi.org/10.1021/jacs.4c07730</a>:</p> <p>Activating delayed fluorescence emission in a dilute solution via a non-covalent approach is a formidable challenge. In this report, we propose a strategy for efficient delayed fluorescence generation in dilute solution using a non-covalent approach via supramolecularly engineered cyclophane-based nanoenvironments that provide sufficient binding strength to π-conjugated guests and that can stabilize triplet excitons by reducing vibrational dissipation and lowering the singlet–triplet energy gap for efficient delayed fluorescence emission. Toward this goal, a novel biphenyl bisimide-derived cyclophane is introduced as an electron-deficient and efficient triplet-generating host. Upon encapsulation of various carbazole-derived guests inside the nanocavity of this cyclophane, emissive charge transfer (CT) states close to the triplet energy level of the biphenyl bisimide are generated. The experimental results of host–guest studies manifest high association constants up to 10<sup>4</sup> M<sup>–1</sup> as the prerequisite for inclusion complex formation, the generation of emissive CT states, and triplet-state stabilization in a diluted solution state. By means of different carbazole guest molecules, we could realize tunable delayed fluorescence emission in this carbazole-encapsulated biphenyl bisimide cyclophane in methylcyclohexane/carbon tetrachloride solutions with a quantum yield (QY) of up to 15.6%. Crystal structure analyses and solid-state photophysical studies validate the conclusions from our solution studies and provide insights into the delayed fluorescence emission mechanism.</p>
Computational models for kaolinite nano-particles (Generations 1-3) and their comprehensive FTIR spectra
<p>The dataset contains a large number of computational models and detailed spectral comparison, fitting, and deconvolution of a large set of FTIR data for crystalline and exfoliation kaolinite, nano-kaolinite and halloysite, nano-halloysite samples.<br> The <strong>G1.xyz</strong>, <strong>G2.xyz</strong>, and <strong>G3.xyz</strong> files contain the initial structures for the first three generations of nano-kaolinite molecules.<br> The compressed folder <strong>SVP-def2TZVP.zip</strong> contains the structural information relevant for comparing and contrasting the performance a double-zeta (SVP) and triple-zeta (TZVP) basis sets.<br> The <strong>edge_protonation.zip</strong> folder guides the reader through the stepwise evaluation of various edge protonation models and shows the final converged results.<br> The <strong>full_optimization.zip</strong> folder summarizes the stationary structure calculations at various levels of theory carried out for the G2 model.<br> </p>
Reference Workloads for Traffic Generation
<p>Collection of traffic generator profiles, implemented for the needs of Superfluidity 5G project.</p> <p>Used in the context of the performance characterization (WP4) and validation (WP7) activities.</p>
Data for: Generation of sanitation system options for urban planning considering novel technologies
<p>This data has been used (1) to quantify the appropriateness of a set of sanitation technologies for a small town (Katarnyia) in Nepal and (2) to generate sanitation system options from the appropriate technologies as an input into strategic sanitation planning using a structured decision making process. For (1), the appropriateness is quantified based on a set of criteria, also called screening criteria. These criteria include technical, socio-demographic, climatic, and institutional aspects and are quantified using uncertainty functions in order to account for the quality and quantity of available input information.</p> <p>The data contains raw data as well as modelling results. The raw data is a compilation of information collected from literature, information collected through a household survey in the small town, field observations. They are all used to describe the screening criteria for the studied sanitation technologies and the small town. Results include: (1) the outcome of the technology appropriateness assessment (technology appropriateness scores); and (2) the sanitation system options (all possible sanitation systems built from the appropriate technologies, and a smaller set of divers and highly appropriate sanitation system options as an input into decision-making).</p>
Predictive models for off-target binding profiles generation
<p>Models for predicting off-target binding, built with Conformal Prediction, and the <a href="http://cpsign-docs.genettasoft.com">CPSign software</a>. The dataset is part of an upcoming publication (Manuscript in preparation), which will provide more details.</p> <p>The dataset is a GZipped Tar archive, with the models as Java Archive (JAR) files. For every JAR-file, there is also a corresponding audit log, with the extension ".audit.json", produced by the workflow software (<a href="http://scipipe.org">SciPipe</a>) used to train the models. This audit file contains all the shell commands used in the workflow that produced the models.</p>
Visualization of guided elastic waves generated by SHPFP transducers
<p>DualSH-PFP_measurement.avi: velocity magnitude generated by the Dual SHPFP and measured by Laser Doppler vibrometry</p> <p>SH-PFP_measurement.avi: velocity magnitude generated by the original SHPFP and measured by Laser Doppler vibrometry</p> <p>DualSHPFP_simulation.avi: circumferential component of the velocity generated by the Dual SHPFP and determined by simulation</p> <p>SHPFP_simulation.avi: circumferential component of the velocity generated by the original SHPFP and determined by simulation</p> <p>All simulations and measurements are performed at a center frequency of 80 kHz.</p>
Evaluation Data of the Implementation of the Approach for Automatic Test Generation for Information-Flow Properties
<p>This data set contains the programs for which the automatic test generation approach of the KeY theorem prover was used to automatically generate noninterference tests.</p> <p>The approach is described in <a href="http://dx.doi.org/10.1145/3297280.3297500 ">http://dx.doi.org/10.1145/3297280.3297500 </a></p> <p>DATA<br> ---------<br> The data folder contains the secure and insecure programs which were evaluated and the tests which were generated for them.</p> <p>Each program is in the folder "program" and is written in Java and specified in an extended version of the JML specification language. Check out <a href="http://dx.doi.org/10.5445/IR/1000046878">http://dx.doi.org/10.5445/IR/1000046878</a> for a reference on the used specification language.</p> <p>For each example we provide the tests that were generated. For the insecure examples we provide the tests generated with each of the two options of our approach. The tests generated with the option for searching for counterexamples is in the folder "WithPost" of each insecure example.</p> <p> </p>
Generation of synthetic, realistic vehicular traces for three access highways of Quito using SUMO
<p>These files present the maps of three access highways of Quito simulated in SUMO. The contributions are.</p> <ul> <li>Careful validation of the imported maps from OpenStreetMaps (imported in July 2019) including time intervals in traffic lights, location of traffic lights, suppression on non-existing junctions, edges, etc.</li> <li>Simulation of realistic number of vehicles for each road considerings the statistics from traffic authority of Quito</li> <li>Configuration of the 5 generation tools provided in the SUMO package. We used all the meaningful configuration for each tool to obtain synthetic realistic vehicular traces</li> </ul>
From the hot carrier solar cell to the intermediate band solar cell, passing through the multiple-exciton generation solar cell and then back to the hot carrier solar cell: the Dance of the Electro-chemical Potentials
<p>Presentation titled "From the hot carrier solar cell to the intermediate band solar cell, passing through the multiple-exciton generation solar cell and then back to the hot carrier solar cell: the Dance of the Electro-chemical Potentials" given by Antonio Marti at the 36th European PV Solar Energy Conference and Exhibition in Marseille, in September 2019.</p>
Data for: Strong bottom currents in large, deep Lake Geneva generated by higher vertical-mode Poincaré waves
<p>Combining entire summer season current and temperature observations and 3D numerical modeling, we demonstrate that previously undetected vertical mode-two and vertical mode-three Poincaré waves in 309-meter deep Lake Geneva (Switzerland/France) generate strong bottom-boundary layer currents at 300-m depth. The data include measurements from moored Acoustic Doppler Current Profilers (ADCPs), vertical thermistor lines, and the corresponding 3D modeling results. The three-dimensional model used in this study is based on the MIT General Circulation Model (MITgcm, <a href="http://mitgcm.org/">http://mitgcm.org/</a>, <a href="https://doi.org/10.1029/96JC02775">https://doi.org/10.1029/96JC02775</a>). The main MITgcm model configuration files are available online at <a href="https://doi.org/10.5281/zenodo.13144189">https://doi.org/10.5281/zenodo.13144189</a>.</p> <p>The related scientific publication can be found at <a href="https://doi.org/10.1038/s43247-024-01653-8">https://doi.org/10.1038/s43247-024-01653-8</a></p>
Outputs of the next generation sea ice model (neXtSIM) for winter 2006 - 2007 saved for comparison with RGPS.
<p>NeXtSIM was run from 1 December 2006 to 15 April 2007 with the following parameters:</p> <p><code>[mesh]</code><br><code>filename=small_arctic_10km.msh</code></p> <p><code>[simul]</code><br><code>duration=150</code><br><code>time_init=2006-11-15</code><br><code>timestep=900</code></p> <p><code>[dynamics]</code><br><code>compression_factor=13800</code><br><code>C_lab=2675000</code><br><code>nu0=0.301</code><br><code>tan_phi=0.624</code><br><code>substeps=90</code><br><code>time_relaxation_damage=15</code><br><code>use_temperature_dependent_healing=true</code></p> <p><code>[output]</code><br><code>exporter_path=/cluster/work/users/akorosov/music/sa10free_mat00</code><br><code>output_per_day=4</code><br><code>variables=M_VT</code><br><code>variables=Concentration</code><br><code>variables=Thickness</code></p> <p><code>[setup]</code><br><code>atmosphere-type=era5</code><br><code>ice-type=topaz_osisaf_icesat</code><br><code>ocean-type=topaz</code><br><code>bathymetry-type=etopo</code><br><code>dynamics-type=bbm</code></p> <p><code>[thermo]</code><br><code>diffusivity_sss=0</code><br><code>diffusivity_sst=0</code><br><code>h_young_max=0.3</code><br><code>newice_type=1</code><br><code>hnull=0.5</code></p> <p><code>[debugging]</code><br><code>check_fields_fast=false</code></p> <p>The outputs (binary snapshots at every 3 hours) were then merged with RGPS data from the same period using this notebook:</p> <p>https://github.com/nansencenter/music_nextsim_tuning_paper/blob/main/02_process_nextsim.ipynb</p> <p> </p>
Generated Metastatic CT 3D Femurs with lesions segmentation 1/3
<p>This file regroups the 5675 synthetic femurs used and described in the paper: "Enhanced segmentation of femoral bone metastasis in CT scans of patients using synthetic data generation with 3D diffusion models" Saillard et al. </p> <p><strong>Three repositories are needed </strong>to form the full archive. When all are downloaded, they can be deflated with the following bash command : </p> <pre><code>cat Generated_3DCT_Metastatic_Femurs.tar.gz* | tar faxv -</code></pre> <p> </p> <p>This is the first part (1/3)</p> <p>part 2/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824177" target="_blank" rel="noopener"> 10.5281/zenodo.13824177 </a></p> <p>part 3/3: <a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.13824179" target="_blank" rel="noopener"> 10.5281/zenodo.13824179 </a></p> <p> </p> <p>Generated CT Scans are organized in two directories: one for files generated with DDPM this one) and one without DDPM.</p> <p>├── Generated_3DCT_Metastatic_Femurs_with_DDPM <br>│ ├── img<br>│ ├── lbl<br>│ └── msk</p> <p>└── Generated_3DCT_Metastatic_Femurs_NO_DDPM<br> ├── img<br> ├── lbl<br> └── msk</p> <p>The content of each directory is as follows: </p> <ul> <li>img : the generated 3D CT scan (nii.gz format) from the 26 healthy femurs</li> <li>lbl : the lesions segmentation (nii.gz format) of the generated 3D CT scan</li> <li>msk : the segmentations of the 26 healthy femurs (nii.gz)</li> </ul> <p>A given filename corresponds to in img and lbl directories :</p> <ul> <li>./lbl/MEK03les0MEK14.nii.gz is the lesions segmentation of ./img/MEK03les0MEK14.nii.gz.</li> <li>./lbl/MEK03les197MEK28.nii.gz is the lesions segmentation of ./img/MEK03les197MEK28.nii.gz</li> </ul> <p>But in msk, the corresponding femur mask for these examples is ./msk/MEK03.nii.gz</p> <p> </p>
More social species live longer, have longer generation times, and longer reproductive windows
<p>Data and scripts required to reproduce the results of the manuscript "More social species live longer, have longer generation times, and longer reproductive windows"</p>
From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers
<p>This dataset contains the raw data from the evaluation instruments and accompanies the manuscript: "From Ridge 2 Reef: An Interdisciplinary Model for Training the Next Generation of Environmental Problem Solvers". It contains all trainee and advisor interviews from 2018 - 2022, as well as a select few partner interviews. It also contains pre and post-annual trainee survey data and the codebook to decipher the survey data. Rubric criteria and scores are included for trainees enrolled in the R2R Communication Skills course. The R script contains the statistical analyses reported in the manuscript and code used to generate figures.</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.