Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
31
datasets available to search
ShareScore release 0.9.0
Dataset results
31 results for “Meta Learning”
Sex-dependent discrimination learning in lizards: a meta-analysis
<p>Raw data and R code used for analysis and to create plots</p>
Delaunay data set learn2learn l2l for meta-learning and few-shot learning
<p>Delaunay data set learn2learn l2l for meta-learning and few-shot learning. We split it into 3 meta-train, meta-val and meta-test sets. </p> <p> </p> <p>For details of original data see: https://github.com/camillegontier/DELAUNAY_dataset</p>
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)
<p>Replication dataset for experiments performed in MLSys 2021 submission: "Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)"</p> <p>(currently using a placeholder to acquire link for submission, will update with dataset)</p>
The raw data from the article "KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling"
<p>The raw data from the article "KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling"</p>
30 years of terrestrial insect richness patterns across elevation: What have we learned? A global meta-analysis
Open the record for dataset details and reuse information.
Data for "Microbiome meta-analysis and cross-disease comparison enabled by the SIAMCAT machine learning toolbox"
<p>Data for the SIAMCAT manuscript</p>
Dataset for "Meta-reinforcement learning via orbitofrontal cortex"
<p>Neural and behavioral dataset for <a href="https://doi.org/10.1038/s41593-023-01485-3">"Meta-reinforcement learning via orbitofrontal cortex" published in Nature Neuroscience</a>.<br><br>The zip file includes 3 folders: "Imaging", "Inactivation", "paAIP2"</p> <p><em><strong>"Imaging"</strong></em><br>Datasets are separated into different folders based on the mouse name (e.g. RH365) and imaging plane (deeper or shallower). Each data file in .npz format contains the following variables:<br><strong>zSR_readystart</strong>: OFC neural population activity. Deconvolved calcium signals are aligned to a ready cue onset and saved in 3d tensor format ([Neuron ID] x [Frames] x [Trials]). Neural activity of 10 sec before and 14 sec after the ready cue onset is included.<br><strong>SR_fps</strong>: Frame rate of neural activity data (frame per sec).<br><strong>pre_ready_sec</strong>: Pre-ready onset time (10 sec) included in zSR_readystart.<br><strong>post_ready_sec</strong>: Post-ready onset time (14 sec) included in zSR_readystart.<br><strong>pre_ready_frame</strong>: Pre-ready onset frames included in zSR_readystart. The frame number corresponds to pre_ready_sec.<br><strong>post_ready_frame</strong>: Post-ready onset frames included in zSR_readystart. The frame number corresponds to post_ready_sec.<br><strong>Ready2Ans_frameSR</strong>: Frame number from ready cue onset to go cue. This can be used to re-align activity to go cue onset.<br><strong>Ready2Choice_frameSR</strong>: Frame number from ready cue onset to choice. This can be used to re-align activity to choice onset.<br><strong>Trials4Use</strong>: Trial numbers (begins from 0) with good imaging quality. Use only these trials for neural activity analyses.<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p><em><strong>"Inactivation"</strong></em><br>Datasets for optogenetic OFC inactivation experiments. Datasets are separated into different folders based on experiment type (Bilateral or Unilateral OFC inactivation), mouse type (e.g. ChrimsonR, tdTomato control), illumination type (e.g. iti_2sec, ready), and the mouse name (e.g. RH895). Each data file in .npz format contains the following variables:<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>Opto</strong>: 1: inactivation trials, 0: masking light only<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p><em><strong>"paAIP2"</strong></em><br>Datasets for OFC plasticity suppression experiments. Datasets are separated into different folders based on experiment type (Suppression at expert or during training), mouse type (e.g. paAIP2 or Control), and the mouse name (e.g. RH923). Each data file in .npz format contains the following variables:<br><strong>a</strong>: Actions of mice during behavioral session. 1: right choice, 2: left choice, 3: alarm trial, 4: miss trial<br><strong>R</strong>: Reward outcomes during behavioral session. 1: reward, 0: no-reward<br><strong>rprob</strong>: Reward assignment probability on right side in each trial.<br><strong>lprob</strong>: Reward assignment probability on left side in each trial.</p> <p>Details of the datasets and experimental conditions are described in the publication (<a href="https://doi.org/10.1038/s41593-023-01485-3">Hattori et al., Meta-reinforcement learning via orbitofrontal cortex, Nature Neuroscience, 2023</a>).</p> <p><br><em>There was a report that Mac users may get "extra bytes at beginning" error when trying to unzip the file. A solution to this problem was to run "zip -FFv foo.zip --out fixed.zip" where foo.zip is the path of the file downloaded from Zenodo. Then, run "unzip fixed.zip".</em></p>
Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility
<p>This upload contains 1) the used thematic maps in the study, 2) the samples to train and validate the proposed model, and 3) the samples used to predict landslide susceptibility of Fengjie County and Fuling District. The code related to the methods is available on the website: <a href="https://github.com/Young-Excavator/Meta_LSM">https://github.com/Young-Excavator/Meta_LSM</a>.</p>
LML-Learning Meta-Learning Dataset_update version
<p>The LML dataset presented in this paper contains both categorical and continuous data for 7 meta-learning parameters: age, gender, degree of illusion of competence, sleep duration, chronotype, experience of imposter phenomenon and multiple intelligence. Convenience sampling and simple random sampling method are used to structure the volunteered anonymous online survey data collection project for LML dataset creation. Survey questionnaires are set to explore adult learners' (age over 18) meta-learning parameters. The responses from the 54 survey questionnaires contains raw data from 1021 current university students from 11 universities of Bangladesh.</p> <p>Mean and standard deviation for the participant's baseline attributes are given for scale parameters, and frequency and percentage are calculated for categorical parameters. Academic curriculum, courses as well as professional training materials can be reviewed and re-designed with focusing on the diversity of learners. How the designed courses will be learned by learners along with how they will be taught is a significant point for education in any discipline. As the survey questionnaires are set for adult learners and only current university students have participated in this survey, this dataset is appropriate for study andragogy and heutagogy but pedagogy.</p> <p>Ethics statements Ethical approval (Involvement of Human Subjects) were obtained from the Biosafety, Biosecurity and Ethical Clearance Committee, Jahangirnagar University (reference no. is BBEC,JU/M 2022/01 (18)). The dataset presented in this article is open for public access. It is mandatory to follow the correct citation guidelines when using this LML dataset.</p> <p>Credit author statement Sonia Corraya: Conceptualization, Methodology, Data curation, Visualization, Writing – original draft; Professor Shamim Al Mamun: Supervision; Professor M. Shamim Kaiser: Supervision.</p> <p>This dataset belongs to Authors, Institute of Information Technology, Jahangirnagar University, savar, Dhaka-1342, Bangladesh.</p>
Learning Mechanisms for Placebo and Nocebo Studies on Somatosensory Sensations: a Systematic Review and Meta-analysis.
ClinicalTrials.gov study NCT04387851. IPD Sharing: YES. Countries: 1. Publications: 2.
Task-Informed Meta-Learning data
<p>Data accompanying the Task-Informed Meta-Learning codebase (https://github.com/nasaharvest/timl).</p>
Supplementary material 2 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
These are studies that were potentially-relevant to the meta-anlayses, but that were ultimately excluded during inclusion coding. Future researchers might find this list especially valuable.
Few-Shot Meta-Learning Applied to Whole Brain Activity Maps Improves Systems Neuropharmacology and Drug Discovery
<p>Data and Code are all included</p>
Big Five Personality Traits and Online Learning: A Meta-analysis of 40 Years' Research
Open the record for dataset details and reuse information.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
<p>Datasets and pre-trained models for NeurIPS 2020 submission "Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning".</p>
Supplementary material 1 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Coding forms
Figure 4 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 4 Egger's regression intercept.
Figure 3 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 3 Funnel plot of oral language outcomes with missing studies imputed
Figure 11 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 11 Egger's Regression for Other Academic Outcomes
Figure 2 from: Cole MW (2018) Effectiveness of peer-mediated learning for English language learners: A meta-analysis. Research Ideas and Outcomes 4: e29375. https://doi.org/10.3897/rio.4.e29375
Figure 2 Forest plot of oral language outcomes.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.