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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>
Conversational Networks For Automatic Online Moderation
<p><strong>Description. </strong>This repository contains several datasets of conversational networks, extracted from the chat messages exchanged by players of the <a href="https://www.spaceorigin.fr/">SpaceOrigin</a> MMORPG. Each graph represents a specific conversation, and belongs to one of two classes: <em>Abusive</em> (1) or <em>Non-abusive</em> (0). Vertices represent users, and edges represent the fact that the connected users exchanged message during the considered time period. Edges are weighted and directed: weights represent the intensity of the message exchanges, and directions represent who sent messages to whom.</p> <p>We provide two types of graphs: unsigned and signed. Unsigned graphs were extracted using the method described in paper [1], below. Version 1.0 of this dataset contain only a part of the conversations, subsampled to get balanced classes. Version 1.1 is extended to contain all available conversations, and there are much more <em>Non-abusive</em> than <em>Abusive</em> conversations. Signed graphs were extracted later, using the method described in publication [9] below. Each edge is described by an additional sign, that indicates the polarity of the messages exchanged by two users; friendly (positive) vs. hostile (negative). </p> <p>These datasets were used to train a classifier into automatically recognizing abusive messages. See the below papers for more details. The repository also contains some figures that appear in these papers.</p> <p><strong>Publications. </strong>The following papers used the unsigned version of the conversational networks. The extraction method is described in paper [1].</p> <ul> <li>[1] É. Papégnies, V. Labatut, R. Dufour & G. Linarès, “Conversational Networks for Automatic Online Moderation,” <em>IEEE Transactions on Computational Social Systems </em>6(1):38–55, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01999546">hal-01999546</a>⟩ DOI: <a href="https://doi.org/10.1109/TCSS.2018.2887240">10.1109/tcss.2018.2887240</a></li> <li>[2] É. Papegnies, R. Dufour, V. Labatut & G. Linarès. “Détection de messages abusifs au moyen de réseaux conversationnels,” in <em>8ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et informatiques</em> (MARAMI), 2017. ⟨<a href="https://hal.science/hal-01614279">hal-01614279</a>⟩</li> <li>[3] É. Papegnies, V. Labatut, R. Dufour, & G. Linares. “Graph-based Features for Automatic Online Abuse Detection,” in <em>International Conference on Statistical Language and Speech Processing</em> (SLSP), Springer, Lecture Notes in Computer Science 10583:70-81, 2017. ⟨<a href="https://hal.science/hal-01571639/">hal-01571639</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-68456-7_6">10.1007/978-3-319-68456-7_6</a></li> <li>[4] N. Cécillon. “Exploration de descripteurs de plongements de graphes pour la détection de messages abusifs,” MSc Thesis, <em>Université d'Avignon, </em>2019. ⟨<a href="https://dumas.ccsd.cnrs.fr/dumas-04073337">dumas-04073337</a>⟩</li> <li>[5] N. Cécillon, V. Labatut, R. Dufour, and G. Linarès, “Abusive Language Detection in Online Conversations by Combining Content- and Graph-based Features,” in <em>International Workshop on Modeling and Mining Socia-Media Driven Complex Networks</em>, Frontiers in Big Data 2:8, 2019. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02130205">hal-02130205</a>⟩ DOI: <a href="http://doi.org/10.3389/fdata.2019.00008">10.3389/fdata.2019.00008</a></li> <li>[6] N. Cécillon, V. Labatut, R. Dufour, & G. Linarès. “Tuning Graph2vec with Node Labels for Abuse Detection in Online Conversations,” in <em>11ème Conférence sur les modèles et l'analyse de réseaux : approches mathématiques et</em> informatiques (MARAMI), 2020. <a href="https://hal.science/hal-02993571" target="_blank" rel="noopener">⟨hal-02993571⟩</a> <a href="http://ceur-ws.org/Vol-2750/paper8.pdf">Official Page</a></li> <li>[7] N. Cécillon, V. Labatut, R. Dufour & G. Linarès. “Graph embeddings for Abusive Language Detection,” <em>Springer Nature Computer Science</em> 2:37,<em> </em>2021<em>. </em>⟨<a href="https://hal.science/hal-03042171/">hal-03042171</a>⟩ DOI: <a href="http://doi.org/10.1007/s42979-020-00413-7">10.1007/s42979-020-00413-7</a></li> <li>[8] N. Cécillon, R. Dufour & V. Labatut. “Approche multimodale par plongements de texte et de graphes pour la détection de messages abusifs,” <em>Traitement Automatique des Langues</em><em> </em>62:13-38, 2021. ⟨<a href="https://hal.science/hal-03527016">hal-03527016</a>⟩ <a href="https://aclanthology.org/2021.tal-2.2/">Official Page</a></li> </ul> <p>The following publications use the signed version of the graphs. The modified extraction method is described in publication [9].</p> <ul> <li>[9] N. Cécillon. “Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection,” PhD Thesis, <em>Université d'Avignon, </em>2024. ⟨<a href="https://theses.hal.science/tel-04441308">tel-04441308</a>⟩</li> </ul> <p><strong>Funding. </strong>Part of this work was funded by a grant from the <em>Provence-Alpes-Côte-d'Azur</em> region (PACA, France) and the <em>Nectar de Code</em> company.</p> <p><strong>Citation. </strong>If you use this dataset, please cite paper [1] for the unsigned networks:</p> <p><br><code>@Article{Papegnies2019,</code><br><code> author = {Papegnies, Étienne and Labatut, Vincent and Dufour, Richard and Linarès, Georges},</code><br><code> title = {Conversational Networks for Automatic Online Moderation},</code><br><code> journal = {IEEE Transactions on Computational Social Systems},</code><br><code> year = {2019},</code><br><code> volume = {6},</code><br><code> number = {1},</code><br><code> pages = {38-55},</code><br><code> doi = {10.1109/TCSS.2018.2887240},</code><br><code>}</code></p> <p>and [9] for the signed ones:</p> <p><br><code>@PhdThesis{Cecillon2024,</code><br><code> author = {Cécillon, Noé},</code><br><code> title = {Combining Graph and Text to Model Conversations: An Application to Online Abuse Detection},</code><br><code> school = {Université d'Avignon},</code><br><code> year = {2024},</code><br><code> type = {PhD Thesis},</code><br><code> address = {Avignon, FR},</code><br><code> url = {https://theses.fr/2024AVIG0100},</code><br><code>}</code></p>
Chromium Conversations
<p>This dataset was released as part of the following publication.</p> <ul> <li>Benjamin S. Meyers, Nuthan Munaiah, Emily Prud'hommeaux, Andrew Meneely, Cecilia O. Alm, Josephine Wolff, and Pradeep Murukannaiah. <strong>A Dataset for Identifying Actionable Feedback in Collaborative Software Development.</strong> Proceedings of the 2018 Meeting for the Association for Computational Linguistics (ACL). Melbourne, Australia. http://www.aclweb.org/anthology/P18-2021</li> </ul> <p><strong>Files:</strong></p> <pre><code>chromium_conversations.csv</code></pre> <p>This is the full dataset containing over 1.5 million comments posted by developers reviewing proposed code changes. The dataset also includes the values we calculated for all nine linguistic features (described in Section 4 of the paper cited above).</p> <pre><code>chromium_conversations_annotations.csv</code></pre> <p>This dataset is a subset of the <strong>chromium_conversations.csv</strong> dataset. It contains the data used in the classification experiment outlined in Section 5 of the paper cited above (2,994 comments automatically identified as acted-upon and 800 comments manually identified as not (known-to-be) acted-upon).</p> <p><strong>CSV Fields:</strong></p> <ul> <li><strong>Organizational:</strong> <ul> <li><em>review_id:<strong> </strong></em>Unique identifier of a code review in the Chromium project. The URL https://codereview.chromium.org/<review_id> may be used to access the review online</li> <li><em>patchset_id:</em> Unique identifier of a code review patchset (i.e., collection of changes to the source code) associated with a review</li> <li><em>patch_id:</em> Unique identifier of a code review patch (i.e., individual change to the source code) associated with a patchset</li> <li><em>file_path: </em>The path to the file being modified in the patch</li> <li><em>line_number: </em>The line number in the file at which the comment was posted</li> <li><em>posted_timestamp: </em>The timestamp indicating when the comment was posted</li> <li><em>author_email:</em> The (de-identified) author of the comment</li> <li><em>author_type: </em>The role of the author (i.e., reviewer or developer)</li> </ul> </li> <li><strong>Natural Language:</strong> <ul> <li><em>text:</em> The raw natural language text of the code review comment</li> </ul> </li> <li><strong>Linguistic Metrics:</strong> <ul> <li><em>yngve: </em>The maximum Yngve score of sentences in the code review comment</li> <li><em>frazier: </em>The maximum Frazier score of sentences in the code review comment</li> <li><em>pdensity: </em>The Propositional Density score of the code review comment</li> <li><em>cdensity:</em> The Content Density score of the code review comment</li> <li><em>pct_neg_tokens: </em>Ratio (percentage) of total number of tokens in negative sentences to the total number of tokens in all sentences in the code review comment</li> <li><em>pct_neu_tokens: </em>Ratio (percentage) of total number of tokens in neutral sentences to the total number of tokens in all sentences in the code review comment</li> <li><em>pct_pos_tokens: </em>Ratio (percentage) of total number of tokens in positive sentences to the total number of tokens in all sentences in the code review comment</li> <li><em>pct_nne_tokens: </em>Ratio (percentage) of total number of tokens in non-neutral sentences to the total number of tokens in all sentences in the code review comment</li> <li><em>min_politeness:</em> Minimum of the politeness of sentences in the code review comment</li> <li><em>max_politeness: </em>Maximum of the politeness of sentences in the code review comment</li> <li><em>min_formality: </em>Minimum of the formality of sentences in the code review comment</li> <li><em>max_formality: </em>Maximum of the formality of sentences in the code review comment</li> <li><em>num_tokens: </em>Total number of tokens in the code review comment</li> <li><em>num_sentences: </em>Total number of sentences in the code review comment</li> <li><em>has_doxastic: </em>Binary indicator of presence of a sentence with doxastic uncertainty in the code review comment</li> <li><em>has_epistemic: </em>Binary indicator of presence of a sentence with epistemic uncertainty in the code review comment</li> <li><em>has_conditional: </em>Binary indicator of presence of a sentence with conditional uncertainty in the code review comment</li> <li><em>has_investigative: </em>Binary indicator of presence of a sentence with investigative uncertainty in the code review comment</li> <li><em>has_uncertainty: </em>Binary indicator of presence of a sentence with any uncertainty in the code review comment</li> </ul> </li> <li><strong>Classification:</strong> <ul> <li><em>comment_type: </em>Manual annotation of the type of code review comment between acted-upon and not (known to be) acted-upon</li> </ul> </li> </ul>
Security Bug Conversations
<p>This dataset will be released as part of the following publication.</p> <ul> <li>Benjamin S. Meyers, Nuthan Munaiah, Andrew Meneely, and Emily Prud'hommeaux. <strong>Pragmatic Characteristics of Security Conversation: An Exploratory Linguistic Analysis. </strong><em>Forthcoming.</em><strong> </strong>Proceedings of the 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE 2019). Montréal, QC, Canada.</li> </ul> <p><strong>Files:</strong></p> <pre><code>security_bug_conversations.csv</code></pre> <p>The full dataset containing over 2.1 million comments posted by developers discussing bugs in the Chromium project. The dataset also includes the values we calculated for the five pragmatic features (described in Section 3 of the paper cited above).</p> <p><strong>CSV Fields:</strong></p> <ul> <li><strong>Organizational:</strong> <ul> <li><em>Bug ID:</em> Unique identifier of a bug discussion in the Chromium project. The URL https://bugs.chromium.org/p/chromium/issues/detail?id=<Bug ID> may be used to access the bug online</li> <li><em>Comment ID:</em> Unique identifier of a comment in a bug discussion</li> </ul> </li> <li><strong>Classification:</strong> <ul> <li><em>Is Security:</em> Binary indicator of whether or not a comment is part of a bug that is about security</li> </ul> </li> <li><strong>Natural Language:</strong> <ul> <li><em>Comment Text:</em> The raw natural language text of the bug comment</li> </ul> </li> <li><strong>Linguistic Metrics:</strong> <ul> <li><em>Min. Formality:</em> Minimum of the formality of sentences in the bug comment</li> <li><em>Max. Formality:</em> Maximum of the formality of sentences in the bug comment</li> <li><em>Max. Informativeness:</em> Maximum of the informativeness of sentences in the bug comment</li> <li><em>Max. Implicature:</em> Maximum of the implicature of sentences in the bug comment</li> <li><em>Min. Politeness:</em> Minimum of the politeness of sentences in the bug comment</li> <li><em>Max. Politeness:</em> Maximum of the politeness of sentences in the bug comment</li> <li>Number of Tokens</li> <li>Number of Sentences</li> <li><em>Has Doxastic Uncertainty:</em> Binary indicator of presence of a sentence with doxastic uncertainty in the bug comment</li> <li><em>Has Epistemic Uncertainty:</em> Binary indicator of presence of a sentence with epistemic uncertainty in the bug comment</li> <li><em>Has Conditional Uncertainty:</em> Binary indicator of presence of a sentence with conditional uncertainty in the bug comment</li> <li><em>Has Investigational Uncertainty:</em> Binary indicator of presence of a sentence with investigational uncertainty in the bug comment</li> <li><em>Has Uncertainty:</em> Binary indicator of presence of a sentence with any uncertainty in the bug comment</li> </ul> </li> </ul>
Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses
<p>This repository contains the supplementary file for our study "Automated Classification of Conversation Valence and Arousal using Autonomic Nervous System Responses". The MS Excel file contains all physiological features (individual features and synchrony features) for all valid dyads and all intervals together with self-report ratings of the conversation (Self-Assessment Manikin) and personality trait data (CES-D, BFNES, QCAE). Synchrony features were calculated using code from a previous Zenodo submission (https://zenodo.org/record/7140829).</p>
Replication Package for the paper "Conversing with business process-aware Large Language Models: the BPLLM framework"
<p>Replication Package for the research paper "<em>Conversing with business process-aware Large Language Models: the BPLLM framework</em>".</p> <p>The package includes the process models, the questions (and expected answers), the results of the qualitative evaluation, and the Hugging Face links to the fine-tuned versions of Llama 3.1 8B employed in the quantitative evaluation of the framework.</p> <p>In particular, the process models are:</p> <ul> <li>The natural language Directly-follows graph (DFG) of the Food Delivery process: <em>food_delivery_activities.txt</em> for the definition of the activities and <em>food_delivery_flow.txt</em> for the sequence flow.</li> <li>The BPMN model of the Food Delivery, E-commerce, and Reimbursement processes: <em>ecommerce.bpmn</em>, <em>food_delivery.bpmn</em>, and <em>reimbursement.bpmn</em>.</li> </ul> <p>The datasets with the questions and the expected answers are:</p> <ul> <li><em>1_questions_answers_not_refined_for_DFG.csv</em> ;</li> <li><em>1.1_questions_answers_refined_for_DFG.csv</em> ;</li> <li><em>2_questions_answers_not_refined.csv</em> ;</li> <li><em>3_questions_answers_refined.csv</em> ;</li> <li><em>4_questions_answers_different_processes.csv</em> ;</li> <li><em>5_questions_answers_similar_processes.csv</em> ;</li> <li><em>6_questions_answers_refined_ft.csv</em> .</li> </ul> <p>The complete results of the qualitative evaluation are contained in the file <em>qualitative_experiments_results.pdf</em>.</p> <p>The Hugging Face links to the fine-tuned versions of Llama 3.1 8B are reported in <em>hf_links_finetuned_models.pdf</em>.</p>
Facile synthesis of CuxS electrocatalysts for CO2 conversion into formate and study of relations between Cu and S with the selectivity
<p>Datasets for figures provided in the manuscript main text.</p>
Webis-Conversational-Query-Reformulations-21
<p>The dataset contains 2694 messages from 284 participants (from 5 countries), collected as conversational queries and reformulations for 4 search domains (arguments, books, news, trips).</p>
On energy transfer of parametric resonance for wave energy conversion
<p>Parametric resonance has been observed, both numerically and experimentally, in various studies of wave energy converters (WECs). A large motion in heave induces a periodic variation in the metacentric height of a WEC body, and, consequently, causes a harmonic variation in pitch/roll restoring coefficients, which can parametrically excite the pitch/roll modes. Current studies have a specific focus to determine the occurrence conditions of parametric resonance, by detecting the boundaries between stable and unstable regions in the parameter space. In the literature, some studies aim to make use of parametric resonance for improving power capture. In contrast, some studies try to suppress the effect of parametric resonance, as it reduces power capture efficiency. However, how energy transfers from one mode to another is not fully understood. This study aims to analyse the energy transfer between heave and pitch/roll modes when parametric resonance occurs. A generic cylindrical point absorber is studied as a WEC floater to considering non-linear wave-structure interaction, including non-linear Froude-Krylov and viscous forces. A heave-pitch-roll three-degree-of-freedom model is derived for numerical study of the energy transfer between different operation modes.</p>
Supplemental data for: Increased mutation and gene conversion within human segmental duplications
<p>Data used for figure generation and analysis in: <strong>Increased mutation and gene conversion within human segmental duplications</strong></p> <ol> <li>new-assemblies.zip contains all the new assemblies added in this work beyond the HPRC assemblies (Clint PTR, CHM1, HG00514, NA12878, HG03125). All other assemblies used in this analysis are available through the HPRC: <a href="https://github.com/human-pangenomics/HPP_Year1_Assemblies/blob/main/assembly_index/Year1_assemblies_v2_genbank.index">assembly_index/Year1_assemblies_v2_genbank.index</a>.</li> <li>all-sample.vcf is a vcf file with all the variant calls used in this analysis. </li> <li>alignments.zip contains all the syntenic alignments used for analysis. </li> <li>data.zip contains annotation data and other information used in analysis and figure making. </li> <li>Online tables 1-4 (Online-tables.xlsx)</li> </ol> <p>Code used in figure making and analysis is on <a href="https://github.com/mrvollger/sd-divergence-and-igc-figures">GitHub</a>.</p> <p>Snakemake pipelines used in the analysis are also on GitHub:</p> <ul> <li>Assembly alignment and IGC calling: https://github.com/mrvollger/asm-to-reference-alignment</li> <li>Variant calling from assembly alignments: https://github.com/mrvollger/sd-divergence</li> <li>Analysis of the triplet content of SNVs: https://github.com/mrvollger/mutyper_workflow</li> </ul>
Data for "Impact of Charge Conversion on NV-Center Relaxometry"
<p>Here, data sets as plotted in the preprint "Impact of Charge Conversion on NV-Center Relaxometry" (v2) are uploaded.</p> <p>The zip file "Data_v2" contains a folder for each figure in the preprint (named after the figure).</p> <p>Each folder contains the data for all graphs in each figure (e.g. (a) and (b) of figure 3) in separate csv files and, if necessary, a text document file "README", in which additional information can be found. If a fit function is plotted in a graph, the folder contains another separate csv file in which the fit data and fit function are contained.</p>
Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland
<p>Data to replicate the publication "Let's talk about COVID-19 vaccination: relevance of conversations about COVID-19 vaccination and information sources on vaccination intention in Switzerland?". This publication examines how public information sources and conversations about COVID-19 are associated with COVID-19 vaccination intention. Multivariable logistic regression and mediation analysis using generalized structural equation modeling were applied.</p>
Brain activity during reciprocal social interaction investigated using conversational robots as control condition
Open the record for dataset details and reuse information.
Stance in Replies and Quotes (SRQ): A New Dataset For Learning Stance inTwitter Conversations
<p>Automated ways to extract stance (denying vs. supporting opinions) from conversations on social media are essential to advance opinion mining research. Recently, there is a renewed excitement in the field as we see new models attempting to improve the state-of-the-art. However, for training and evaluating the models, the datasets used are often small. Additionally, these small datasets have uneven class distributions, i.e., only a tiny fraction of the examples in the dataset have favoring and denying stance, and most other examples have no clear stance. Moreover, the existing datasets do not distinguish between the different types of conversations on social media (e.g., replying vs. quoting on Twitter). Because of this, models trained on one event do not generalize to other events. </p> <p>In the presented work, we create a new dataset by labeling stance in responses to posts on Twitter (both replies and quotes) on controversial issues. To the best of our knowledge, this is currently the largest human-labeled stance dataset for Twitter conversations with over 5200 stance labels. More importantly, we designed a tweet collection methodology that favours the selection of denial-type responses. This class is expected to be more useful in the identification of rumours and determining antagonistic relationships between users. </p>
Dataset for sound source localization with 101 Blinky sound-to-light conversion sensors
<p>Blinkies are sound-to-light conversion devices that can be used to monitor the sound level over large areas. The data from the sensors is harvested using a video camera. This dataset contains seven videos that were recorded in the gymnastical hall of Tokyo Metropolitan University, Hino Campus on July 3rd 2018. In the video, 101 Blinkies are spread on the ground of the gymnastic hall. A bluetooth speaker mounted on a remote controlled car runs between the Blinkies, causing them to change intensity. The file `pyramic_json` is a JSON format file containing all the meta-data necessary such as sensor locations, room dimensions, and segmentation information.</p> <p>This dataset was used to demonstrate sound source localization in the paper "Blinkies: Open source sound-to-light conversion sensors for large-scale acoustic sensing and applications" by Robin Scheibler and Nobutaka Ono (to appear).</p>
Dataset for paper "Automatically Identifying Archival-worthy, Software-related Slack Conversations"
<p>This dataset consists of 2000 conversations from 5 programming related Q&A channels, hosted on Slack, and accompanies the paper "Automatically Identifying Archival-worthy, Software-related Slack Conversations". In addition to the text of the conversations, each conversation has been annotated as either archival worthy or not. Our definition of archival-worthiness is:</p> <p><em>"If a conversation contains information that could be useful to other users, whether in the Slack channel or elsewhere, then it should be archived. These conversations have no determinate length and no need for objectivity. A conversation should be archived based on the availability and ease of identifying information that could help a person to gain useful software-related knowledge."</em></p> <p><strong>Data Origin: </strong>Numerous public Slack chat channels (<a href="https://slack.com/">https://slack.com/</a>) have recently become available that are focused on specific software engineering-related discussion topics, e.g., Python Development (<a href="https://pyslackers.com/web/slack">https://pyslackers.com/web/slack</a>). The data reflects a portion of the conversations on public channels related to Python, Clojure, Elm and Racket programming.</p> <p><strong>Data Pre-Processing:</strong> To protect privacy, we replace usernames with fake names, and replace absolute times with relative times (in seconds). The conversations are disentangled from the overall chat stream with each unique <em>thread </em>in the dataset specifying a conversation in the channel. Archival-worthy conversations are marked with 1, while non-archival-worthy with 0.</p>
Data associated with Lark et al. 2020: U.S. cropland conversion (2008-16)
<p>Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found <strong><a href="https://zenodo.org/record/3905556#.XvLXQ21Kipo">here</a></strong>.</p> <ul> <li>Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. <em>Nature Communications </em>(In review)</li> </ul> <p>Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:</p> <ul> <li><em><strong>mtr</strong></em> = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period: <ol> <li>"<em>stable non-cropland</em>" -- areas of consistent non-cropland throughout the duration of the study period.</li> <li>"<em>stable cropland</em>" -- areas of consistent cropland throughout the duration of the study period.</li> <li>"<em>cropland expansion</em>" -- areas converted to crop production between 2008 and 2016.</li> <li>"<em>cropland abandonment</em>" -- areas converted away from crop production between 2008 and 2016.</li> <li>"<em>intermittent cropland/confusion</em>" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion. </li> </ol> </li> <li><em><strong>ytc</strong></em> = "year to cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.</li> <li><em><strong>yfc</strong></em> = "year from cropland"; Indicates the year in which pixels with an <em>mtr</em> classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season. </li> <li><em><strong>bfc</strong></em> = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fc</strong></em> = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>bfnc</strong></em> = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> <li><em><strong>fnc</strong></em> = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table<strong> <a href="https://developers.google.com/earth-engine/datasets/catalog/USDA_NASS_CDL#bands">here</a>.</strong></li> </ul> <p>Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (<em>mtr</em> = 3) and those of stable croplands (<em>mtr </em>= 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).</p> <ul> <li><strong>corn_relDiff_local.tif </strong>= local yield differential (%) of corn grain.</li> <li><strong>corn_relDiff_national.tif</strong> = national yield differential (%) of corn grain.</li> <li><strong>soy_relDiff_local.tif</strong> = local yield differential (%) of soybeans.</li> <li><strong>soy_relDiff_national.tif</strong> = national yield differential (%) of soybeans.</li> <li><strong>wheat_relDiff_local.tif</strong> = local yield differential (%) of wheat.</li> <li><strong>wheat_relDiff_national.tif</strong> = national yield differential (%) of wheat.</li> </ul>
German Lombard conversation recordings
<p>These sound files contain conversations of three actors (one female and two male speakers) sitting in a cafeteria, simulating a daily life conversation of three students about six different topics in German. Lombard speech, where speaking in a noisy environment leads to an elevated amplitude and frequencies [1], was generated by providing the speakers with headphones with background noise during the recordings.</p> <p>These recordings can be used as speech material in hearing studies, for example. The recordings were used in [2].</p> <p>Files</p> <p>Lombard_conversations_script.pdf: Script of the conversations</p> <p>Wav-files (three speakers in three channels):</p> <p>bg_story_1.wav: Studying for exams</p> <p>bg_story_2.wav: Leisure activities and appointment at a restaurant</p> <p>bg_story_3.wav: Semester break plans and jobs</p> <p>bg_story_4.wav: Sports</p> <p>bg_story_5.wav: Pets</p> <p>bg_story_6.wav: Weather and appointment for a bike tour</p> <p>*.license: License abbreviation and author information, for use in the acoustic simulation tool TASCAR [3]</p> <p>two_channel_playback.m: Matlab/Octave script to play back the .wav files on two channels</p> <p>two_channel_playback.tsc: TASCAR [3] example scene playing back the .wav files on two channels</p> <p><br> </p> <p>Methods</p> <p>In preparation of the recordings, Sennheiser HDA200 headphones were calibrated with a Brüel & Kjær 4153 artificial ear and a 92 dB calibrator. A sound file containing a background noise recorded in a cafeteria was then calibrated to 75 dB. For the recordings, the background noise was played back by the Sennheiser HDA200 headphones which the speakers were wearing. Additionally, each speaker was equipped with a cardioid microphone and a script of the conversations. Then, the speakers spoke the conversations by improvising the text of the script, and the speech was recorded by the microphones. From the resulting recordings, distracting sounds such as crosstalk from the other speakers, page-flipping or verbal slips and errors were cut out and the three channels were combined and saved in sound files.</p> <p><br> </p> <p>References</p> <p>[1] Junqua, Jean‐Claude. "The Lombard reflex and its role on human listeners and automatic speech recognizers." The Journal of the Acoustical Society of America 93.1 (1993): 510-524.</p> <p>[2] Hendrikse, MME, Eichler, T Grimm, G and Hohmann, V. (in preparation) Interaction of hearing aids with self-motion and the influence of hearing impairment</p> <p>[3] Grimm, Giso; Luberadzka, Joanna; Hohmann, Volker. A Toolbox for Rendering Virtual Acoustic Environments in the Context of Audiology. Acta Acustica united with Acustica, Volume 105, Number 3, May/June 2019, pp. 566-578(13), doi:10.3813/AAA.919337</p>
Conversion of measurements of tree ring gains: Canada, Africa, Mexico, South America from RWL- files to JSON format.
<p>The International Tree Rings Data Bank (ITRDB) is the most comprehensive tree growth database (https://www1.ncdc.noaa.gov/pub/data/paleo/treering).</p> <p>Shoudong Zhao, et al. (2019, 2018) analyzes the representativity of dendrochronological data (ITRDB) and proposes a corrected database with error indications. One of the bottlenecks of data use (ITRDB) is that the data is loaded as a collection of separate files in the Tucson positional format.</p> <p>The purpose of our data presentation is to change the Tucson data format to JSON format and combine the separate files into one.</p> <p>We convert the initial data for the <strong>Canada</strong>, <strong>Africa</strong>, <strong>Mexico</strong> and <strong>Southamerica</strong> rwl-files into Json format of data on tree growth in four files: <strong>canada.json</strong>, <strong>africa.json</strong>, <strong>mexico.json</strong> and <strong>southamerica.json</strong>. The data was converted using the R programming language and the dplR program library Bunn, A. (2008)</p> <p>The experience of developing the structure of dendroclimatic data in JSON format is described in the works of Kachaev A. (2016, 2017, 2020).</p> <p>Description of the structure of JSON data format is attached in the files ReadMe.pdf</p> <p> </p> <p>References</p> <p>Bunn, A. G. (2008). A dendrochronology program library in R (dplR). Dendrochronologia, 26, 115-124. https://doi.org/10.1016/j.dendro.2008.01.002</p> <p>Kachaev, Alexander (2020), "Compact dataset of dendrochronological data of pri-mary metric characteristics of tree rings of Asia.", Mendeley Data, V1, doi: 10.17632 / p9zhpmzgtk.1</p> <p>Kachaev A. V. (2017) Model for describing the structure of dendroclimatic data In the collection: Regional problems of remote sensing of the Earth Materials of the IV international scientific conference. Siberian Federal University, Institute of Space and Information Technologies. p. 120-122. (Russia)</p> <p>Kachaev A. V. (2016) NOSQL Approach for Development of Dendroclimatic Data Bank. In the collection: Regional problems of remote sensing of the Earth. Materials of the III International Scientific Conference. p. 89-91. (Russia)</p> <p>Shoudong Zhao, et al. (2019). The International Tree-Ring Data Bank (ITRDB) revisited: Data availability and global ecological representativity. Journal of Biogeography, 46 (2), 355-368. doi: 10.1111 / jbi.13488</p> <p>Zhao, Shoudong et al. (2018), Data from: The International Tree-Ring Data Bank (ITRDB) revisited: data availability and global ecological representativity, Dryad, Dataset, https://doi.org/10.5061/dryad.kh0qh06</p>
Conversion of fluoride and chloride catalized by SAM-dependent fluorinase in Nocardia brasiliensis
<p>Data sets show the following reactions:</p> <p>- Fluorinase catalized conversion of fluoride and SAM to 5'-FDA and L-methionine (Explanation file: Figure 2).</p> <p>- Fluorinase catalyzed conversion of chloride and SAM to 5’-ClDA and L-methionine in the presence of L-amino acid oxidase (Explanation file: Figure 3).</p>
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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.