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96
datasets available to search
ShareScore release 0.7.1
Dataset results
96 results for “Reinforcement Learning”
Modulation of Reinforcement Learning
ClinicalTrials.gov study NCT01712464. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Refinement and Adaption of Reinforcement Learning to Personalize Behavioral Messaging for Healthy Habits
ClinicalTrials.gov study NCT05742685. IPD Sharing: NO. Countries: 1. Publications: 0.
Effects of Oxytocin on Reinforcement Learning and Interoception
ClinicalTrials.gov study NCT05245708. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Pulsed tVNS Protocol and Reinforcement Learning
ClinicalTrials.gov study NCT06205108. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Piloting a Reinforcement Learning Tool for Individually Tailoring Just-in-time Adaptive Interventions
ClinicalTrials.gov study NCT05751993. IPD Sharing: NO. Countries: 1. Publications: 0.
Tuberculosis - Learning the Effect of Parasites and Reinforcing Diets
ClinicalTrials.gov study NCT05048485. IPD Sharing: UNDECIDED. Countries: 2. Publications: 0.
Using Reinforcement Learning to Personalize Electronic Health Record Tools to Facilitate Deprescribing
ClinicalTrials.gov study NCT06660979. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Reinforcement Learned Automated Anesthesia Systems During Painless Abortion
ClinicalTrials.gov study NCT07132151. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effects of Oxytocin on Reinforcement Learning
ClinicalTrials.gov study NCT03846271. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Vocal learning via social reinforcement by infant marmoset monkeys
Open the record for dataset details and reuse information.
Reinforcement Learning-Based Test Case Generation
Open the record for dataset details and reuse information.
Reinforcement Learning for Warfarin Dosing
ClinicalTrials.gov study NCT03962400. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset for Automated Unit Test Generation via Chain of Thought Prompt and Reinforcement Learning
<p>This is the replication package including three types datasets: training dataset with CoT prompts, reward dataset for training reward model, rl dataset for optimizing policy model. The training dataset includes filter_test_cot_rule_50k.csv, filter_train_cot_rule_50k.csv, and filter_valid_cot_rule_50k.csv. These three datasets includes multiple fields (i.e., src_fm, intention, plan, elaboration, gpt_test, src_fm_cot_gpt, target, src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,idx,rule_cot,rule_cot_nlp,combine_cot,src_fm_rule_cot_nlp,src_fm_cot_nlp_gpt,gpt_cot_filter,src_fm_plan_intention). The reward dataset includes test_athena.json, train_athena.json, and valid_athena.json three files. The rl dataset includes three files: filter_test_cot_gpt_rl.csv, filter_train_cot_gpt_rl.csv, filter_valid_cot_gpt_rl.csv. These files include mulitple fields: src_fm,intention,plan,elaboration,gpt_test,src_fm_cot_gpt,target,src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,gpt_cot_filter.</p>
Dataset for offline training of a self-driving telescope with Reinforcement Learning
<p>Go to https://zenodo.org/doi/10.5281/zenodo.8388482 for the updated version!!</p> <p>The dataset was generated through simulation and used during the offline training and the comparison of algorithms while targeting the self-driving telescope implementation with Deep Reinforcement Learning (RL).<br> </p>
Datasets for "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning"
<p>Go to https://zenodo.org/doi/10.5281/zenodo.8388482 for the updated version!!</p> <p> </p> <p>These datasets are the official data of the submission "Self-Driving Telescopes: Autonomous Scheduling of Astronomical Observation Campaigns with Reinforcement Learning", anonymous for the double-blind submission system.<br>A README.md file will contain all the information related to the datasets and a metadata.json file will contain their metadata according to the Schema.org format.</p>
Mutation Testing of Deep Reinforcement Learning Based on Real Faults
<p>Trained agents to be used in the replication package of the paper "<em>Mutation Testing of Deep Reinforcement Learning Based on Real Faults</em>" submitted to the International Conference on Software Engineering (ICST) 2023. Authors are set to anonymous for the peer-review process.</p>
ScienceDex guides
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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.