Find research datasets worth reusing

opencc-by-4.0Jul 2020View details →

Trophic cascade driven by behavioural fine-tuning as naïve prey rapidly adjust to a novel predator

The arrival of novel predators can trigger trophic cascades driven by shifts in prey numbers. Predators also elicit behavioural change in prey populations, via phenotypic plasticity and/or rapid evolution, and such changes may also contribute to trophic cascades. Here we document rapid demographic and behavioural changes in populations of a prey species (grassland melomys Melomys burtoni, a granivorous rodent) following the introduction of a novel marsupial predator (northern quoll Dasyurus hallucatus). Within months of quolls appearing, populations of melomys exhibited reduced survival and population declines relative to control populations. Quoll-invaded populations (n = 4) were also significantly shyer than nearby, quoll-free populations (n = 3) of conspecifics. This rapid but generalised response to a novel threat was replaced over the following two years with more threat-specific antipredator behaviours (i.e. predator-scent aversion). Predator-exposed populations, however, remained more neophobic than predator-free populations throughout the study. These behavioural responses manifested rapidly in changed rates of seed predation by melomys across treatments. Quoll-invaded melomys populations exhibited lower per-capita seed take rates, and rapidly developed an avoidance of seeds associated with quoll scent, with discrimination playing out over a spatial scale of tens of metres. Presumably the significant and novel predation pressure induced by quolls drove melomys populations to fine-tune behavioural responses to be more predator-specific through time. These behavioural shifts could reflect individual plasticity (phenotypic flexibility) in behaviour or may be adaptive shifts from natural selection imposed by quoll predation. Our study provides a rare insight into the rapid ecological and behavioural shifts enacted by prey to mitigate the impacts of a novel predator and shows that trophic cascades can be strongly influenced by behavioural as well as numerical responses.

opencc-by-4.0Sep 2024View details →

scGPT: End-to-End Protocol for Fine-tuned Retina Cell Type Annotation

<h1>Abstract</h1> Single-cell research faces challenges in accurately annotating cell types at high resolution, especially when dealing with large-scale datasets and rare cell populations. To address this, foundation models like scGPT offer flexible, scalable solutions by leveraging transformer-based architectures. This protocol provides a comprehensive guide to fine-tuning scGPT for cell-type classification in single-cell RNA sequencing (scRNA-seq) data. We demonstrate how to fine-tune scGPT on a custom retina dataset, highlighting the model’s efficiency in handling complex data and improving annotation accuracy achieving 99.5% F1-score. This protocol automates key steps, including data preprocessing, model fine-tuning, and evaluation. This protocol enables researchers to efficiently deploy scGPT for their own datasets. The provided tools, including a command-line script and Jupyter Notebook, simplify the customization and exploration of the model, proposing an accessible workflow for users with minimal Python and Linux knowledge. The protocol offers an off-the-shell solution of high-precision cell-type annotation using scGPT for researchers with intermediate bioinformatics.

opencc-by-4.0Jun 2024View details →

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> 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. <h2>Intended Use</h2> 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). <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> 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): <ul> <li>id: 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>topic: 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>subtopic: 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>persona: description of the persona used to simulate a user</li> <li>opening_question: the first question asked by the user to start a conversation with the AI-assistant</li> <li>messages: 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>role: the role of the speaker, either <code>user</code> or <code>assistant</code></li> <li>content: 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> Important See the full list of topics and subtopics covered below. <h3>Metadata</h3> 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> <h3>Generation Method</h3> We used a method inspired from Ultrachat dataset. Especially, we implemented our own version of Human-Model interaction from Sector I: Questions about the World of their paper: 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. <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> All instances in the dataset are in english <h3>Size</h3> 901 synthetically-generated dialogue <h2>USAGE AND GUIDELINES</h2> <h3>License</h3> AstroChat © 2024 by Patrick Fleith is licensed under Creative Commons Attribution 4.0 International <h4>Restrictions</h4> No restriction. Please provide the correct attribution following the license terms. <h4>Citation</h4> Patrick Fleith, AstroChat – A Dataset of synthetically generated conversations for LLM supervised fine-tuning in the domain of Space Mission Engineering and Astronautics, (2024). <h4>Update Frequency</h4> Will be updated based on feedbacks. I am also looking for contributors. Help me create more datasets for Space Engineering LLMs :) <h4>Have a feedback or spot an error?</h4> Use the community discussion tab directly on the huggingface AstroChat dataset page. <h4>Contact Information</h4> Reach me here on the community tab or on LinkedIn (Patrick Fleith) with a Note. <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> topic: [ Space Law ] subtopics: <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> topic: [ Space Business ] subtopics: <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> topic: [ Space Missions Operations ] subtopics: <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> topic: [ Human Spaceflight ] subtopics: <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> topic: [ Space Environment ] subtopics: <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> topic: [ Space Propulsion Systems ] subtopics: <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> topic: [ Space Processes ] subtopics: <ul> <li>Trade Studies</li> <li>Margins, Coningencies, Reserves</li> <li>Systems Engineering</li> <li>Quality Assurance</li> </ul> topic: [ Ground Systems ] subtopics: <ul> <li>Ground Stations</li> <li>Ground Support Equipments</li> <li>Control Centers</li> <li>Tracking Systems</li> <li>AntennasGround Systems Engineering</li> </ul> topic: [ Planetary Rovers ] subtopics: <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> topic: [ Planetary Science and Exploration ] subtopics: <ul> <li>Astrobiology</li> <li>Exoplanets</li> <li>AsteroidsJupiter</li> <li>Saturn</li> <li>Search for Extraterrestrial Life</li> </ul> topic: [ Structures ] subtopics: <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> topic: [ Mechanisms ] subtopics: <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> topic: [ Materials ] subtopics: <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> topic: [ Entry Descent and Landing (EDL) ] subtopics: <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> topic: [ Reliability, Availability, Maintainability, and Safety (RAMS) ] subtopics: <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> topic: [ Orbital Mechanics ] subtopics: <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> topic: [ Launchers, Launches, Launch Operations ] subtopics: <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> topic: [ Attitude Determination and Control ] subtopics: <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> topic: [ Payload S/S and Optics ] subtopics: <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> topic: [ Power System ] subtopics: <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> topic: [ Thermal Control ] subtopics: <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> topic: [ Command and Data System ] subtopics: <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> topic: [ Telecommunication ] subtopics: <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>

opencc-by-nc-sa-4.0Apr 2023View details →

Virtual audiovisual scenes for hearing device fine-tuning

The virtual audiovisual scenes in this upload were developed to be used during hearing device fine-tuning. By letting patients try out the settings of their hearing devices in a number of different daily-life situations, they can experience what it sounds like in different situations. The audiologist can then ask specific questions and make further adjustments to the settings. This may result in a better fit. The development of  the virtual audiovisual scenes is documented in a paper presented at Forum Acusticum (Hendrikse, Dingemanse, Grimm, Hohmann, & Goedegebure, 2023). The development and evaluation of the fine-tuning procedure using these virtual audiovisual scenes will be the focus of future work, and the publication will be added to the related identifiers. This document provides a description of the virtual audiovisual scenes, software & hardware requirements, installation and usage instructions, and information about the licensing. For further information, contact Maartje Hendrikse (<a href="mailto:m.hendrikse@erasmusmc.nl">research.audiologie@erasmusmc.nl</a>).

opencc-by-4.0May 2023View details →

Fine-Tuning of Colloidal Polymer Crystals by Molecular Simulation

Data archive corresponding to the manuscript "Fine-Tuning of Colloidal Polymer Crystals by Molecular Simulation" by M. Herranz et al., Phys. Rev. E 107,  064605 (2023); DOI: 10.1103/PhysRevE.107.064605 Please see README.txt for instructions on how to access and read the files from the crystallographic analysis based on the CCE norm descriptor. All snapshots have been generated and successively analyzed by the Simu-D software.

opencc-by-4.0Jun 2021View details →

Supplementary Data: "Dark matter, fine-tuning and mu(g-2) in the pMSSM" (arxiv 2104.03245)

The data consists of the output files of all the runs that were done. The files are organized in iterations, according to the iteration they were produced in. The map name within one iteration is labeled by the "Barbieri-Giudice fine-tuning measure"_"Electroweak fine-tuning measure"_"time stamp".  The files that are contained include: <ul> <li> spheno_in.dat, spheno_out.dat, feynhiggs14.dat, slhafile.dat, slhafileGUT.dat, susyhit.dat: in and outputs of the spectrum generators that were used. The slhafile.dat is obtained from softsusy with inputs defined at the SUSY scale, fed to FeynHiggs (produces the feynhiggs14.dat file) and then fed to SUSYHIT to produce susyhit.dat. The slhafileGUT.dat is the same spectrum but the input parameters are run to the GUT scale (defined by the scale where the unification of the coupling constants happens).The susyhit.dat file is used as input for prospino, micromegas, SUSY-AI, the fine-tuning calculation, superiso, gm2calc and ddcalc. The spheno files are used to cross-check our spectra, but not used in the results of the paper; </li> <li> DDcalcv2.out: output of the DDcalc program; </li> <li> Micromegas5_2_1.out, micro.out: micromegas output (version 5.2.1 and version 5.0.8); </li> <li> Prospino.dat: output containing the cross sections for chi1pm chi1pm, chi1pm, neu2, and slepton slepton production; </li> <li> SUSY_AI.dat: output from SUSY-AI; </li> <li> ft_contri.dat: electro-weak fine-tuning calculation, contains the number and the source of the dominant contribution; </li> <li> gm2_out.dat: output of GM2Calc; </li> <li> output_superiso, output_superiso_true: command-line output (output_superiso_true) and the slha-file (output_superiso). </li> </ul>   The data used in our plots is stored in a CSV file (datagm2_right_omegah2_only.csv). This contains all the files that have the right omegah^2.  This contains: <ul> <li>dir_name: name of saving directory;</li> <li>bsmumu_spheno,btaunu_spheno,bsgamma_spheno,gm2_spheno: calculations done by Spheno of Br(b_s -> mu+ mu-), Br(b->tau nu), Br(b->s \gamma), g-2_mu;</li> <li>flag_prospino.dat_valid,flag_gm2_out.dat_valid,flag_ft_contri.out_valid,flag_DDcalcv2.out_valid,flag_susyhit.dat_valid,flag_spheno_out.dat_valid,flag_micromegas5_2_1.out_valid,flag_output_superiso_true_valid,flag_SUSY_AI.dat_valid: flags to check whether the files were present in the output;</li> <li>flag_LSP: flag to check whether the LSP is the neutralino;</li> <li>sigmachan_5,sigmaSIn_5,sigmaSIp_5,sigmaSDn_5,sigmaSDp_5,sigmav_5,omegachan_5,sigmacontri_5,omegacontri_5,omegah_mic5,omega_chan_label,sigma_chan_label: micromegas 5.2.1 output of the dominant annihilation channel for sigmav, sigmaSIn, sigmaSIp, sigmaSDn, sigmaSDp, sigmav, dominant annihilation channel for omegah^2, contribution of the dominant annihilation channel for sigmav, contribution of the dominant annihilation channel for omegah^2, omegah^2, label for omegah dominant annihilation channel (used for plotting), label for sigmav dominant annihilation channel (used for plotting);</li> <li>pval_DarkSide,pval_XENON,pval_PICO,pval_CRESST: p-values for DarkSide, XENON, PICO, CRESST;</li> <li>gm2_mic5,dmunu_mic5,bsgnlo_mic5,dtaunu_mic5,bsmumu_mic5,btaunu_mic5: output of micromegas 5.2.1 for g-2, D->munu, b->s gamma, D->tau nu, B_s -> mu mu, B->tau nu;</li> <li>zdec_neutralinos_5: output of micromegas 5.2.1 for branching fraction of the invisible decay of the Z boson to neutralinos;</li> <li>dtaunu_iso,bsmumu_iso,btaunu_iso,bsmumuuntag_iso,bsgamma_iso,dmunu_iso,gm2_iso: the low-energy observables as calculated by SuperIso;</li> <li>susyaistopexcl,susyai8pval,susyaistoppval,susyaiewexcl,susyai13excl,susyaiewpval,susyai8excl,susyai13pval: p-values for the SUSY-AI exclusions;</li> <li>n12,n13,n11,n23,n14,n24,n31,n32,n21,n34,n22,n33,n41,n43,u11,u12,v22,v21,u21,v12,u22,n44,n42,v11: neutralino (n) and chargino (u,v) components of the mixing matrices;</li> <li>alpha_higgs: higgs mixing angle;</li> <li>mgluino,md2,mu1,md1,mu2,mc2,mc1,msneutrinotau,mH,mstop2,msbottom2,msbottom1,mstop1,mslepton1,mHpm,mDM,mn2,mn3,mn4,mstau1,mstau2,,msneutrinoe,mA0,mslepton2: masses of the pMSSM spectrum;</li> <li>MeL,M2,AB,M1,M3,MeR,Mq3L,MuR,MuL,SUSYSCALE,AT,MdR,ATAU,MbR,Ml3R,MA,MtR,tanb,mu,Ml3L: input parameters of the pMSSM spectrum;</li> <li>mhiggsFeyn: mass of the SM-like higgs boson as computed by FeynHiggs;</li> <li>FTEW: value for electro-weak fine-tuning;</li> <li>PandaX (2017),DarkSide 50,omegah_ddcalc,XENON1T (2018),DarkSide 20k,DARWIN,LZ,PICO-500,PICO-60 (2017): p-value outputs of DDcalc;</li> <li>gm2calc,gm2calc_unc: GM2Calc calculation for g-2 and the uncertainty;</li> <li>xsec_lRlR,xsec_c1pn2,xsec_lLlL,xsec_c1c1,xsec_c1mn2,xsec_stau1stau1: cross sections for slepton_R sleptonR, slepton_L, slepton_L, chi1pm chi1mp, chi1pm neu2, stau_1 stau_1;</li> <li>excl: number to determine whether the channel is excluded and for what reason (> 0.5 = excluded, < 0.5 = not excluded, 0.4 = sensitive to PICO-500, 0.26 = sensitive to LZ, 0.13 = sensitive to Darwin, 0.0 = not sensitive to any proposed DMDD future experiment). </li> </ul>

opencc-by-4.0Nov 2024View details →

scGeneAI bone marrow fine-tune dataset

<div>The input bone marrow fine-tune dataset used in the full-size examples in scGenAI is uploaded here</div> <div> <div>   </div> </div>

opencc-by-4.0Oct 2019View details →

Transfer fine-tuned BERT models by paraphrases

Transfer fine-tuned BERT models by phrasal paraphrases.  <ul> <li>transferFT_bert-base-uncased.pkl bases on the bert-base-uncased model</li> <li>transferFT_bert-large-uncased.pkl bases on the bert-large-uncased model</li> </ul> For usage, please refer to our GitHub page. <a href="https://github.com/yukiar/TransferFT">https://github.com/yukiar/TransferFT</a> For details of these models, please refer to our paper. Yuki Arase and Junichi Tsujii. 2019. Transfer Fine-Tuning: A BERT Case Study. in Proc. of Conference on Empirical Methods in Natural Language Processing (EMNLP 2019). <a href="https://arxiv.org/abs/1909.00931">https://arxiv.org/abs/1909.00931</a>

opencc-by-4.0Aug 2021View details →

Biomedical Data-to-Text Generation via Fine-Tuning Transformers

Biomedical Dataset (”BioLeaflets”) for the paper "Biomedical Data2Text Generation via fine-tuning transformers" (INLG'21)

opencc-by-4.0Sep 2021View details →

Flow Cytometry data from: "The EMT transcription factor Zeb1 is essential for HSPC differentiation that acts synergistically with Zeb2 in fine-tuning hematopoietic lineage fidelity"

Abstract: The Zeb2 transcription factor has been demonstrated to play important roles in hematopoiesis and leukemic transformation. Zeb1 is a close family member of Zeb2 but has remained more enigmatic concerning its roles in hematopoiesis. Here we show using conditional loss of function approaches and bone marrow reconstitution experiments that Zeb1 plays cell autonomous role in hematopoietic lineage differentiation, particularly as a positive regulator of monocyte development in addition to its previously reported important role in T-cell differentiation. Analysis of existing single cell RNAseq data of early hematopoiesis has revealed distinctive expression differences between Zeb1 and Zeb2 in HSPC differentiation with Zeb2 being more highly and broadly expressed that Zeb1 except at a key transition point (ST-HSCàMPP1) whereby Zeb1 appears to be the dominantly expressed family member. Inducible deletion of both Zeb1 and Zeb2 using a tamoxifen inducible Cre-mediated approach leads to acute bone marrow failure at this transition point with increased long-term and shortterm hematopoietic stem cell numbers and an accompanying decrease in all hematopoietic lineage differentiation. Bioinformatics analysis of RNAseq data has revealed that Zeb2 acts predominantly as a transcriptional repressor involved in restraining mature hematopoietic lineage gene expression programs from being expressed too early in hematopoietic stem and progenitor cells (HSPCs). Zeb1 appears to fine tune this repressive role during hematopoiesis to ensure hematopoietic lineage fidelity. Analysis of ROSA26 locus based transgenic models has revealed that Zeb1 as well as Zeb2 overexpression within the hematopoietic system can drive extramedullary hematopoiesis/splenomegaly and enhanced monocyte development. Finally, deletion of Zeb2 alone or Zeb1/2 together was found to enhance survival in secondary MLL-AF9 AML models attesting to the oncogenic role of Zeb1/2 in AML.   Flow cytometric and Hematocrit analysis methods:   Cells were stained with antibodies listed in the provided Supplemental Table (Antibodies.xlsx) according to the  manufacturer guidelines. Flow cytometric analyses were performed on the LSRII and Fortessa  X-20 cytometer (BD Biosciences) and the results were analysed by FACSDiva or FlowJo software (BD Biosciences). Cells for MLL-AF9 experiments and RNA-seq were stained and  sorted on Influx or FACSAria Fusion sorters (BD Biosciences) at AMREP Flow Cytometry  Core Facility and FlowCore, Monash University.  Submandibular blood samples were collected into EDTA-coated tubes, and hematology parameters were measured using a HemaVet 950FS automated blood analysis machine (Drew Scientific).

opencc-by-4.0Oct 2021View details →

Dataset for "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"

Dataset covering the DFT simulations performed for the journal article  "Fine-Tuning A Robust Metal–Organic Framework Towards Enhanced Clean Energy Gas Storage"

edi40/100

Fine-tuned phenotypes: Tadpole plasticity under 16 combinations of predators and competitors.

It is now well appreciated that most organisms can alter their phenotypes when faced with environmental variation. Decades of empirical investigations have documented hundreds of examples of phenotypic plasticity, yet most studies have focused on the presence or absence of a single environmental factor. As a result, we know little about how organisms respond to gradients of environmental factors (i.e., threshold responses vs. continuous responses), nor do we understand how organisms respond to combinations of environmental variables. I examined how larval wood frogs (Rana sylvatica) altered their behavior, morphology, and growth in response to combined gradients of predation and competition. Increased predation risk induced lower activity, deeper tails, and shorter bodies, which collectively caused slower growth. Increased competition caused slower growth which induced higher activity, shallower tails, and longer bodies. For both environmental gradients, the responses were frequently continuous rather than threshold responses. Moreover, predation and competition had interactive effects. Responses to predators were always larger under low competition than under high competition. Responses to competition were larger under low predation risk when predation and competition induced traits in the same direction, but larger under high predation risk when predation and competition induced traits in opposite directions. The results demonstrate that responses to phenotypically plastic traits can be fine-tuned to a wide variety of environmental combinations.

openCC (other)Jun 2024View details →

opencc-by-4.0Dec 2023View details →

Dataset for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study"

# Data and code for "Effect of benzothiadiazole-based π-spacers on fine-tuning of optoelectronic properties of oligothiophene-core donor materials for efficient organic solar cells: a DFT study."## Contents* data-{type}/*: reproducible data* job.job : example slurm script ## Description of the dataThe data are organized in subdirectories *data-{type}/{system}/* corresponding to the considered molecules and simulation type:* data-gs: Ground state calculations* data-td: TD-DFT calculationsThe contents of each subdirectory are:* data-gs/{system}/structure.xyz: physical atomic structure* data-td/{system}/td-dft/td_uvvis.txt: photoabsorption spectrumThe spectrum plots in the article correspond to the first (x values) and second (y values) columns of the spectrum files. ## Reproduction of the dataThe data were produced using Gaussian version g16.A.01The calculation of the data of a system consists of the following steps:1. Ground-state (gs) calculation:   * Prepare the input file for the gs by adjusting the parameters of the ground state calculations:     * "# opt b3lyp/6-311+g(d,p) scrf=(smd,solvent=chloroform) geom=connectivity empiricaldispersion=gd3bj out=wfn"      * out = wfn keyword to create a wfn file of the ground state that will be used for EDD and RDG investigations   * Submit the job.job file for the gs calculation as appropriate for the particular input file of the system   * The optimized sturctures are visualised using GaussView2. Time-propagation calculation:   * Requires finished ground-state calculation   * Set up the TD-DFT calculation parameters as necessary:     * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density out=wfn"      * density out = wfn keywords to create a wfn file of the excited state that will be used for EDD investigation   * Submit the job.job file for TD-DFT calculation as appropriate for the particular system   * The photoabsoption specta are visualised using GaussView3. RDG calculation:  * Put the .wfn file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 3.23.1 in the manual4. DOS calculation:  * The dos curves are plotted starting from the .fchk of the ground state geometry, select the atoms index  corresponding to the diffrents subpart of the studied molecules (donor, acceptor, pi-spacer)  * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the structure in Section 4.10.1 in the manual    * the output generates .chk file which is transformed to .fchk file : formchk .chk .fch5. TDM calculations:  * Requires finished ground-state calculation  * Set up the TD-DFT calculaton parameters as necessary    * "# td=(nstates=6) wb97xd/6-311+g(d,p) scrf=(smd,solvent=chloroform) guess=read density transition=1 iop(6/8=3) out=wfn"  * Put the .fchk file of the gs calculation in the command window of the open source Multiwfn software and follow the sturcture in Section 4.18.8 in the manual6. EDD calculation:  * Edd plots are plotted based on the es.wfn and gs.wfn following Section 4.18.1 in the manual

dryad36/100

Supplementary material data for: Unstable environmental conditions constrain the fine-tune between opsin sensitivity and underwater light in an Amazon forest stream fish

Visual adaptations can stem from variations in amino acid composition, chromophore utilization, and differential opsin gene expression levels, enabling individuals to adjust their light sensitivity to environmental lighting conditions. In stable environments, adaptations often involve amino acid substitutions, whereas in unstable conditions, differential gene expression may be a more relevant mechanism. Amazon forest streams present diverse underwater lighting conditions and experience short-term water colour fluctuations. In these environments, it is less likely for genetic and amino acid sequences to undergo modifications that tailor opsin proteins to the prevailing lighting conditions, particularly in species having several copies of the same gene. The sailfin tetra, Crenuchus spilurus, inhabits black and clear water Amazon forest streams. The long wavelength sensitivity (LWS) is an important component for foraging and courtship. Here, we investigated LWS opsin genes in the sailfin tetra. Three copies of LWS1 and two copies of LWS2 genes were found. The maximum absorbance wavelength (λmax) estimated from the amino acid sequences of LWS1 genes exhibited variation among the different copies. In contrast, the copies of LWS2 genes showed identical expected λmax values. Although the amino acid positions affecting λmax varied among LWS genes, they remained consistent among populations living in different water colours. The relative expression levels of LWS genes differed between gene copies. While not formally tested, our results suggest that in fluctuating environments, visual adaptations may primarily stem from alterations in gene expression profiles and/or chromophore usage rather than precise genetic tuning of protein light sensitivity to environmental lighting conditions.

opencc-zeroJan 2024View details →

opencc-by-4.0May 2024View details →

PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data

Describes the taxonomy used in the paper "PRICER: Leveraging Few-Shot Learning with Fine-Tuned Large Language Models for Unstructured Economic Data", presented at the Second Workshop on Semantic Technologies and Deep Learning Models for Scientific, Technical and Legal Data at the Extended Semantic Web Conference (ESWC) 2024.

dryad36/100

How small deviations in kinematics and body form dictate muscle performances in the finely tuned avian downstroke

Avian takeoff requires peak pectoralis muscle power to generate sufficient aerodynamic force during the downstroke. Subsequently the much smaller supracoracoideus recovers the wing during the upstroke. How the pectoralis work loop is tuned to power flight is unclear. We integrate wingbeat-resolved muscle, kinematic and aerodynamic recordings in vivo with a new mathematical model to disentangle how the pectoralis muscle overcomes wing inertia and generates aerodynamic force during takeoff in doves. Doves reduce the incidence of their wing mid-downstroke to efficiently generate aerodynamic force, resulting in an aerodynamic power dip, that allows transferring excess pectoralis power into tensioning the supracoracoideus tendon to assist the upstroke—improving the pectoralis work loop efficiency simultaneously. Integrating extant bird data, our model shows how the pectoralis of birds with faster wingtip speed need to generate proportionally more power. Finally, birds with disproportionally larger wing inertia need to activate the pectoralis earlier to tune their downstroke.

opencc-zeroSep 2023View details →

openNov 2024View details →

Processed Sentinel 1, Sentinel 2 and Copernicus Emergency Management Service data for fine tuning and predicting flood extent with IBM's granite-geospatial-uki-flood-detection model

This dataset contains processed Sentinel 1 Sentinel 2 imagery together with flood event labels extracted from the Copernicus Emergency Management Service. It has been assembled to demonstrate fine tuning and inference of flood event segmentation using granite geospatial foundation models developed by IBM Research. Please see <a href="https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection">https://huggingface.co/ibm-granite/granite-geospatial-uki-flooddetection</a> for more information on models and use. Sentinel-1 The European Space Agency. 2014. Sentinel-1 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-1">https://sentinel.esa.int/web/sentinel/missions/sentinel1</a>. Accessed: 2024-11-25. Sentinel-2 The European Space Agency. 2015. Sentinel-2 Mission. <a href="https://sentinel.esa.int/web/sentinel/copernicus/sentinel-2">https://sentinel.esa.int/web/sentinel/missions/sentinel2</a>. Accessed: 2024-11-25. Copernicus Emergency Management Service <a href="https://emergency.copernicus.eu/mapping/list-of-activations-rapid">https://emergency.copernicus.eu/mapping/list-of-activations-rapid</a>. Accessed: 2024-11-25.  Attribution Contains modified Copernicus Sentinel data [2019-2024] Contains modified Copernicus Service information [2019-2023]

opencc-by-4.0Oct 2021View details →

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)