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2,212 results for “space”
Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation
<p>This data archive provides simulated hourly heating and cooling building energy demand for current and future RCP85 climate for 8 representative cities for a single-family and small office building archetype.</p> <p>The data forms part of the following publication:</p> <p><em>Eggimann S.; Fiorentini M. (2024): Transferring energy signatures across space and time to assess their viability for rapid urban energy demand estimation. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2024.114348</em></p> <p><strong>Attributes</strong></p> <ul> <li>ID_origin: City ID of source city</li> <li>ID_destination: City ID of target city</li> <li>Signature_Cooling: Cooling demand determined by the signature approach</li> <li>Model_Cooling: Cooling demand determined by EnergyPlus</li> <li>Absolute_Diff: Absolute difference</li> <li>Percentage_Diff: Relative difference</li> <li>Daily_Tout: Average daily dry-bulb ambient temperature</li> </ul> <p><strong>Instruction</strong></p> <p>To obtain the simulation and energy signature-based results, it is required to filter the dataset and set the source ID to the destination ID. The city IDs are provided in the file city_table_ID.</p> <p><strong>Source</strong></p> <p>The archetypes are provided by the Office of Energy Efficiency & Renewable Energy: https://www.energycodes.gov/prototype-building-models</p>
1 million cMSSM parameter space points with low-energy predictions from SPheno and MicrOMEGAs
<p>This dataset was produced and used in the paper <a href="https://arxiv.org/abs/2405.18471">Symbolically Regressing Beyond the Standard Model Physics</a>. The code used to generate and to analyse these data can be found <a href="https://gitlab.com/miguel.romao/symbolic-regression-bsm">here</a>.</p> <p>The dataset specifications:</p> <ul> <li>Randomly sampled 1 million points of the cMSSM parameter space and respective low-energy observables.</li> <li>Low-energy observables computed using using `SPheno` and `MicrOMEGAs`. <ul> <li>Only points that produced `SPheno` output and neutral LSP are processed by `MicrOMEGAs`.</li> <li>The dataset includes all points, even if they are "unphysical", i.e. points without `SPheno` output or neutral LSP. In the paper, this was used to train a classifier to filter out "unphysical" points.</li> </ul> </li> <li>The columns are <ul> <li>'m0', 'm12', 'A0', 'tanb': the four physical parameters of the theory sampled in the priori <ul> <li>'m0': [0, 10] TeV</li> <li>'m12': [0, 10] TeV</li> <li>'A0': [-60,60] TeV</li> <li>'tanb': [1.5,50]</li> <li>The sign of the 'mu' parameter was fixed to positive (+1)</li> </ul> </li> <li>'idx': an utility identifier used during generation, can/should be ignored</li> <li>Flattened `SPheno` outputs. These are obtained by reading the resulting slha spectrum file outputted by SPheno and flatten the blocks. For example from the 'MINPAR' block, the key-value pairs are given by the columns 'MINPAR_1', 'MINPAR_2', 'MINPAR_3', 'MINPAR_4', 'MINPAR_5', and likewise for all blocks in the slha file.</li> <li>`MicrOMEGAs` outputs. These inlcude: 'dm_Omega', 'dm_spin', 'dm_candidate`, `mo_output`, `dm_c_{bino,wino,higgsino1,higgsino2}`, which are, respectively: dark matter relic density value, dark matter candidate spin, dark matter candidate, the whole `MicrOMEGAs` output, and the coefficient of `{bino,wino,higgsino1,higgsino2}` components of the dark matter state.</li> </ul> </li> </ul> <p>Versions:</p> <ul> <li>SPheno 4.0.5, with a patch to output a warning when the LSP is charged. This version can be found <a href="https://gitlab.com/lip_ml/blackboxbsm">here</a>.</li> <li>MicrOMEGAs 5.3.41, with the MSSM model adapted for low-scale slha inputs.</li> </ul> <p>The datasets are provided in <a href="https://parquet.apache.org/">Apache `parquet`</a> format. In order to read them using `pandas`, an installation with the optional flag `[parquet]` should be used. Alternatively, one can use <a href="https://arrow.apache.org/docs/python/index.html">`pyarrow`</a>.</p> <p> </p>
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>
Model Zoo Dataset Samples for Scalable Weight Space Learning
<p>This dataset contains small versions of model zoo datasets for our ICML 2024 paper "Towards Scalable and Versatile Weight Space Learning". These datasets are intended for testing and rapid pipeline evaluation of the code in the <a title="https://github.com/HSG-AIML/SANE" href="https://github.com/HSG-AIML/SANE">corresponding </a><a href="https://github.com/HSG-AIML/SANE">repository</a>. For full model zoos, please see <a href="modelzoos.cc">modelzoos.cc</a>.</p>
RTI Experiment Simulation assuming Convective and Diffusive Interstitial Transport in the Brain: Concentration over time (and space)
<p>Simulation of interstitial transport in the brain assuming convective and diffusive transport with perivascular efflux routes. The movie shows the transient concentration of TMA ions in a real-time iontophoresis (RTI) experiment, where a small molecular probe is applied to brain tissue at a known rate and its concentration measured over time a a point 100-200um away, here 150 um. RTI experiments are used to characterize the properties of interstitial tissue to determine its void volume and tortuosity, 0.18 and 1.85 for the condition shown here. In this simulation, a model of combined diffusion and convection (superficial velocity=50 um/min) is applied to fit experimental data and range. (Convection assumes Darcy's Law with a hydraulic conductivity of 2x10<sup>-6</sup> cm<sup>2</sup> mmHg s<sup>-1</sup> and pressure difference of 2.15 mmHg). The model domain is a cube 750 um on a side with 8 penetrating arterioles and 8 penetrating venules. The first and third columns from the left are venules and the second and fourth are arterioles, with convective flow from arteriole to venule. As transport of molecules in the perivascular space is known to be faster than in the interstitium, the concentration is assumed to be c=0 at the vascular walls. The solute (TMA) must pass through a perivascular wall with lower diffusivity than the interstitium to leave the domain through a vascular wall (D<sub>wall</sub>=5%D<sub>interstitium</sub>). Although it is difficult to see in the movie, both the presence of convection and the perivascular efflux routes cause range(variability) in the measured concentration curves for different source and detection point combinations that is consistent with experimental data--see additional posted data. Computations performed using FEniCS, movie made using Paraview. </p>
Dataset for "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study"
<p>This archive corresponds to the source code, raw data, and results described in the article "Reflectance spectra of seven lunar swirls examined by statistical methods: A space weathering study" by Chrbolková et al. (2019) published in Icarus journal. See AA_README.txt for more information.</p>
Parameter space of baryogenesis in the NuMSM
<p>Datasets for the updated version of the paper ''Parameter space of baryogenesis in the $\ν$MSM", https://arxiv.org/abs/1808.10833 </p> <ul> <li>BAU_*.dat parameter sets leading to the $[Y_B^{obs}/2, 2\cdot Y_B^{obs}]$</li> <li>Full_*.dat 800 000 parameter sets leading to various values of Y_B. Note that for very small values of Y_B numerical errors might be sizeable.</li> <li>upper and lower bounds of the region in the ${U}^2 - M$ plane where $Y_B\ge Y_B^{obs}$.</li> <li>Comparison with the benchmark points of ref <a href="http://arxiv.org/abs/arXiv:1711.08469">1711.08469</a></li> </ul> <p>In all file titles NH stands for the normal hierarchy of active neutrino masses, whereas IH stands for the inverted.</p> <p>In BAU_*.dat and Full_*.dat all data points are given in terms of the Casas-Ibarra parametrization. Mixing elements can be obtained using the formulae from Appendix A of the paper. Note that we use the oscillation data from <a href="http://www.nu-fit.org/">NuFIT 3.2 (2018), www.nu-fit.org</a>.</p> <p> </p> <p> </p>
Exploring chemical space in the search for improved Azoheteroarene-based photoswitches
<p>In the quest for improved photo switches, azoheteroarenes have emerged as a potential alternative to azobenzene. However, to date the number and types of these species that have subjected to study is insufficient to provide an in-depth understanding of the photochemical effects brought about by different substituents. Here, we computationally screen the optical properties and thermal stabilities of 512 azoheteroarenes that consist of eight different N-containing heteroarenes combined with 64 substitution patterns. The most promising compounds are identified and their properties rationalized based on the nature of the azoheteroarene core and the location and type of substitution patterns.</p>
Dataset of Kantorovich-Rubinstein-Wasserstein Polytopes of Metric Spaces on up to 6 Points
<p>We present a complete list of all combinatorial types of generic Kantorovich-Rubinstein-Wasserstein (KRW) polytopes associated with metric spaces on up to 6 points that are generic in the sense of Gordon and Petrov, see [1]. These polytopes and their properties are described in detail in [2].</p> <p>The catalog of KRW polytopes was computed using certain regular triangulations of the full root polytope, see Section 4 in [2]. These regular triangulations were enumerated up to symmetry by Jörg Rambau using the new <em>topcom</em> package described in [3].</p> <p>The provided data comes in three parts.</p> <ul> <li>The files ending in ".result" contain the original <em>topcom</em> output including the specific regular triangulations of the root polytope.</li> <li>There are <em>julia</em> files that contain these triangulations ("triangulations_x.jl"), one triangulation per line.</li> <li>There is an <em>OSCAR</em> script ("read_triangulations.jl") that reads these triangulations and produces sample metrics associated with each of these triangulations. </li> </ul> <h3>References:</h3> <p>[1] J. Gordon and F. Petrov: Combinatorics of the Lipschitz polytope, 2017, Arnold Math. J. <em>3.2.</em></p> <p>[2] E. Delucchi, L. Kühne, and L. Mühlherr: <em>Combinatorial invariants of finite metric spaces and the Wasserstein arrangement</em>, 2024, in preparation.</p> <p>[3] J. Rambau: <em>Symmetric lexicographic subset reverse search for the enumeration of circuits, cocircuits, and triangulations up to symmetry, </em>2023, <a href="https://www.wm.uni-bayreuth.de/de/team/rambau_joerg/TOPCOM/SymLexSubsetRS-2.pdf" target="_blank" rel="noopener">preprint</a>.</p>
Data from: Cubicle design and dairy cow rising and lying down behaviours in free stalls with insufficient lunge space
<p>Original data from: "Cubicle design and dairy cow rising and lying down behaviours in free-stalls with insufficient lunge space" <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.animal.2024.101314" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.animal.2024.101314</span></span></a></p>
SFS-A68-16: A dataset for the segmentation of space functions in apartment buildings
<p> </p> <p>SFS-A68-16: "A dataset for the segmentation of space functions in apartment buildings"</p> <p>Authors: "Amir Ziaee, Georg Suter, Mihael Barada, Laura Keiblinger"</p> <p>Copyright: "Design Computing Group TU Wien, 2023"</p> <p>Credits: "Design Computing Group TU Wien"</p> <p>License: "GNU GENERAL PUBLIC LICENSE Version 3"</p> <p>Version: "1.0.3"</p> <p>Maintainer: "Amir Ziaee"</p> <p>Email: "amir.ziaee@tuwien.ac.at"</p> <p>Url: <a href="https://github.com/A2Amir/SFS-A68-16">https://github.com/A2Amir/SFS-A68-16</a></p> <p>Description: "We present the <strong>SFS-A68-16</strong> dataset for space function segmentation in apartment buildings. The dataset consists of 16 multi-viewpoint space layout input and corresponding ground truth images for 68 floor plans of apartment buildings designed or built between 1952 and 2019. It addresses a limitation of the SFS-A68 dataset (version 1.0.1) we created in our previous work, which consists of single-viewpoint projection images only. Each pixel in a ground truth image of the SFS-A68-16 dataset is assigned to a space function class. Space function classes in apartment buildings that are classified by a space function segmentation network are shown below under ground truth classes. We have identified 22 space function classes for the apartment buildings in our dataset. Each element in an input image of the SFS-A68-16 dataset is colored according to a unique class color (below, under input classes). Space elements, such as doors and furnishing elements, are contextual features in input images that may help determine the function of a space. To measure whether excluding space elements in input images affects the accuracy of a space function segmentation network, we create a new dataset, <strong>SFS-A68-16-SEE</strong>, where space elements are excluded in input images of the SFS-A68-16-SEE dataset. The defined class hierarchy of the dataset with the unique RGB color code of each class can be seen below."</p> <p> </p> <p><strong>Input classes</strong></p> <p>[Root]</p> <p>├──[Space]</p> <p>│ ├── (102, 102, 122)[InternalSpace]</p> <p>│ └── (161, 162, 155)[ExternalSpace]</p> <p>└──[SpaceElement]</p> <p> ├── [SpaceContainedElement]</p> <p> │ ├── [CirculationElement]</p> <p> │ │ ├── (230, 184, 175)[FlightOfStairs]</p> <p> │ │ └── (107, 74, 101)[Landing]</p> <p> │ ├── [FurnishingElement]</p> <p> │ │ ├── (0, 191, 255)[KitchenElement]</p> <p> │ │ └── (70, 130, 180)[SanitaryElement]</p> <p> │ └── [EquipmentElement]</p> <p> │ └── [HomeAppliance]</p> <p> │ └── (159, 140, 81)[TextileCareAppliance]</p> <p> └── [SpaceEnclosingElement]</p> <p> ├── (109, 189, 110)[Opening]</p> <p> ├── (0, 250, 154)[Partition]</p> <p> ├── (255, 215, 0)[Window]</p> <p> └── [Door]</p> <p> ├── (200, 255, 0)[InternalDoor]</p> <p> ├── (72, 112, 39)[UnitDoor]</p> <p> ├── (187, 244, 154)[ElevatorDoor]</p> <p> ├── (47, 79, 79)[BalconyDoor]</p> <p> └── (195, 210, 192)[SideEntrance]</p> <p> </p> <p><strong>Ground truth classes</strong></p> <p>[Space]</p> <p>├── [ResidentialSpace]</p> <p>│ ├── [CommunalSpace]</p> <p>│ │ ├── (255, 218, 185)[DiningRoom]</p> <p>│ │ ├── (166, 206, 227)[FamilyRoom]</p> <p>│ │ └── (255, 0, 0)[LivingRoom]</p> <p>│ └── [PrivateSpace]</p> <p>│ ├── (0, 255, 0)[Bedroom]</p> <p>│ │ ├── (0, 128, 128)[MasterBedroom]</p> <p>│ │ └── (0, 128, 255)[BoxRoom]</p> <p>│ └── (160, 82, 45)[HomeOffice]</p> <p>├── [ServiceSpace]</p> <p>│ ├── (255, 192, 203)[Shaft]</p> <p>│ ├── (245, 245, 220)[StorageRoom]</p> <p>│ │ └── (0, 206, 209)[WalkInCloset]</p> <p>│ └── [SanitarySpace]</p> <p>│ ├── (128, 0, 0)[Bathroom]</p> <p>│ ├── (75, 0, 130)[Toilet]</p> <p>│ ├── (255, 255, 0)[Kitchen]</p> <p>│ └── (0, 128, 0)[LaundryRoom]</p> <p>├── [CirculationSpace]</p> <p>│ ├── [VerticalCirculationSpace]</p> <p>│ │ ├── (0, 0, 128)[Elevator]</p> <p>│ │ └── (0, 0, 255)[Stairway]</p> <p>│ └── [HorizontalCirculationSpace]</p> <p>│ ├── (255, 0, 255)[Entrance]</p> <p>│ └── (255, 100, 0)[Hallway]</p> <p>│ ├── (255, 165, 0)[MainHallway]</p> <p>│ └── (0, 255, 255)[InternalHallway]</p> <p>└── [ExternalSpace]</p> <p> ├── (128, 128, 0)[AccessBalcony]</p> <p> └── (225, 138, 96)[Loggia]</p>
Datasets for Ultra High-Capacity Band and Space Division Multiplexing Backbone EONs
<p>The datasets have been generated for the paper titled "Ultra High-Capacity Band and Space Division Multiplexing Backbone EONs: Multi-core vs. Multi-fiber." </p>
VCM Dataset for the Classification of Resident Space Objects
<p>The Vector Covariance Message (VCM) data comprise 22,303 RSOs over a period of six months (9/1/2022-2/28/2023). VCM data consist of Resident Space Objects (RSOs) ephemerides from a high-precision special perturbations orbit propagator and estimator using tracking observations. VCMs are issued by the US Space Force (USSF) Space Command (USSPACECOM) and were provided through an Orbital Data Request (ODR) the authors submitted to the 18th Space Defense Squadron (18th SDS). </p> <p>The dataset is organized into subfolders, each containing VCMs for a specific satellite. Filenames correspond to the satellite's NORAD ID (North American Aerospace Defense Catalog Number). A readme file provides details about the VCM content and format. Note that the full covariance matrix has been excluded for public release, whereas the standard deviation of error in satellite's position and velocity is provided.</p> <p>The VCM data have been used in the following work, "Early Classification of Space Objects based on Astrometric Time Series Data", presented at the 25th Advanced Maui Optical and Space Surveillance Technologies Conference (AMOS) in Maui, Hawaii, United States.</p>
Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units
<p>These are the original data used to make figures for the manuscript titled "Polyubiquitin ligand-induced phase transitions are optimized by spacing between ubiquitin units" by Sarasi Galagedera et al.</p> <p> </p>
Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics
<p>Simulation data for "Characteristics of Wave-Particle Power Transfer as a Function of Electron Pitch Angle in Nonlinear Frequency Chirping" which will be submitted to Journal of Geophysical Research: Space Physics.</p> <p>Including the simulation input parameter file and the necessary output data to plot each figure in the article. </p>
Data set accompanying the research article "Complete representation of action space and value in all striatal pathways"
<p>GCaMP6s calcium imaging data set recorded from freely behaving mice performing open field and 2-choice decision-making tasks using miniscopes. Mice were implanted in the right dorsomedial striatum and three types of output neurons were genetically targeted using transgenic Cre-lines. The data set comprises single-cell spatial filters and calcium activity traces extracted using CaImAn (https://github.com/flatironinstitute/CaImAn) as well as behavioral event logs and tracking coordinates. For more details please refer to the article "Complete representation of action space and value in all striatal pathways" published by the data sets' authors. Analysis code can be found at https://doi.org/10.5281/zenodo.5034618.</p>
Global topsoil SOC stock from 1981 to 2018 estimated by combining process-based model and space-for-time digital soil mapping
<p>This dataset include the topsoil (0-30cm) soil organic carbon (SOC) stocks in mineral soils under major land classes (forest, grassland, shrub land, savannas, cropland, cropland/natural vegetation mosaic, and sparely vegetated land) from 1981 to 2018. The long-time series of SOC stocks were estimated by using a space-for-time digital soil mapping (DSMst) model where the RothC-simulated SOC stocks were incorporated as one of the dynamic covariates of the DSMst model.</p> <p>The detail information on the products were given below:</p> <p>Name: DSMst-RothC 5-km global topsoil SOC stock products</p> <p>Period: 1981-2018</p> <p>Spatial resolution: 0.041666667 degree</p> <p>Temporal resolution: 1 year</p> <p>CRS: geographic latitude/longitude (EPSG:4326 - WGS 84 – Geographic)</p> <p>Extent: -180°, -90°: 180°, 90°</p> <p>Data format: GeoTIFF</p> <p>Compression: LZW</p> <p>Data type: Float32</p> <p>Unit: t C ha<sup>-1</sup></p>
Dataset for "Beating 1 Sievert: Optimal Radiation Shielding of Astronauts on a Mission to Mars" publication in Space Weather journal
<p>Datasets in .fig Matlab format and figures in .jpg format published in Space Weather journal</p> <p>effectiveDoseRF.mat contains the effective dose "response functions" and an example (how2useDoseResponceFunctions.m) of how to use them to assess GCR dose.</p>
Research Beyond the Lab, Spring Term 2022, Global Health Engineering, ETH Zurich. Raw data and analysis-ready derived data on waste management in public spaces in Zurich, Switzerland.
<p>This repository contains all raw and derived data produced as part of the <a href="https://rbtl-fs22.github.io/website/">ETH Zurich course "Research Beyond the Lab: Open Science and Research Methods for a Global Engineer" (151-8102-00L)</a> offered in spring term 2022.</p> <p>Students were assigned teams of four to conduct a collaborative research project broadly addressing the theme of “Trash in the Public Spaces of Zurich” in collaboration with <a href="https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling.html">Entsorgung & Recycling Zürich (ERZ)</a>, the waste management department at Stadt Zürich.</p> <p>Research methods and design are taught in the first half of the course. Surveys and a waste characterisation study are then designed based on the research questions students have developed in their respective teams. The collected raw data is used in the course to teach principles of research data management, tidy data structures, reproducible research with R & RStudio, and collaboration and version control with Git & GitHub.</p>
Data for: The structure of evolutionary model space for proteins across the tree of life
<p>Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</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.