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.
17
datasets available to search
ShareScore release 0.9.0
Dataset results
17 results for “training events”
Dataset for training Dev-ResNet for detection of developmental events in Lymnaea stagnalis
<p>Dataset for training <a href="https://github.com/EmbryoPhenomics/dev-resnet">Dev-ResNet</a> for detection of developmental events in<em> Lymnaea stagnalis. </em></p> <p> </p> <p>The following naming scheme is used for the individual GIF files:</p> <p>Example: "./video/20C_A_B1_315hr.gif"</p> <ul> <li><strong>"./video/"</strong> --> Source folder.</li> <li><strong>"20C" </strong> --> Treatment, here incubation temperature.</li> <li><strong>"A"</strong> --> Egg mass label.</li> <li><strong>"B1" </strong> --> Individual embryo label.</li> <li><strong>"315hr" </strong>--> Hours since first cell division.</li> </ul> <p> </p>
Elements of Style in Reproducible Workflow Creation and Analysis: An INCLUDE Training Event
<p><a href="https://github.com/NIH-NICHD/Elements-of-Style-Workflow-Creation-Maintenance/blob/main/README.md">Elements of Style Workflow Creation and Maintenance</a>: An INCLUDE Training Event</p> <p>The <a href="https://includedcc.org/">INCLUDE Data Hub</a> is a new resource that securely hosts human clinical, genomic, transcriptomic, proteomic, and other data providing a wealth of opportunities to study conditions that affect individuals with Down syndrome. Today, the approach to answering new scientific questions with these data often uses cloud-based methods accessible through web browsers.</p> <p>During a three-hour virtual training, users learn the know-how to ask scientific questions with these data using cloud platforms and workflows. Users will learn how to build and share processes that assure reproducibility, repurposablility regardless of the computational environment. While many things are possible, the user will be oriented to approaching their work in a modular, testable fashion. </p>
Discover the Data Archiving Guide (DAG) - a training event for new(ish) staff members [Workshop recording]
<p>The CESSDA Data Archiving Guide (https://dag.cessda.eu/) is a new resource developed by CESSDA and is designed to provide employees at data archives and repositories with an understanding of the work a data archive performs. The information in the DAG was collected by experts from CESSDA social science data archives reflecting the procedures and policies at their local archives. While the context of these archives varies — in size, the underlying technical architecture or in the specific services provided to researchers — the DAG focuses on common ground and is a useful tool for professionals new to data archiving or those who are knowledgeable in one domain and now seek to broaden their expertise.<br> <br> The full-day workshop was targeted mainly for new employees in data archives; people generally interested in the DAG were welcome as well.<br> <br> This workshop focused specifically on the Chapters Pre-Ingest, Ingest and FAIR with an additional excursion into the glossary to deepen participant's knowledge in a playful way.</p> <p>The video is also available on the <a href="https://www.youtube.com/watch?v=yzPzVK5UZKE">CESSDA Training YouTube Channel</a>.</p>
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository includes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p> <p>The .wav file are compressed in the file: <a href="../api/records/11220741/draft/files/wind-noise-detection-main.7z/content" target="_blank" rel="noopener noreferrer">wind-noise-detection-main.7z</a>/data/208_file_recordings_paper_wind.tar</p> <p> </p> <p> </p>
Assessment of the Effectiveness of Continuous Labour Support by a Trained Companion of Choice on Events of Labor and Maternal Satisfaction in the Bugisu Sub Region, Uganda
ClinicalTrials.gov study NCT04771325. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: ASSET: analysis of sequences of synchronous events in massively parallel spike trains
With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.
Trained Machine Learning Model to Detect Surface Events from Seismograms
Open the record for dataset details and reuse information.
Effects of Mindfulness Training on Memories of Personal Past Events
ClinicalTrials.gov study NCT05121116. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: ASSET: analysis of sequences of synchronous events in massively parallel spike trains
Open the record for dataset details and reuse information.
Physicians in Training and Critical Care Nurses Performance in Medical Code Events: Effect of Simulation-Based Training
ClinicalTrials.gov study NCT02707185. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Neurofeedback Training of Metacognition in Subjects With Alcohol Use Disorder. Disorder Induces Neural Modifications: an Event-related Potentials Study
ClinicalTrials.gov study NCT05913518. IPD Sharing: NO. Countries: 1. Publications: 0.
Animal-assisted Resilience Training for Children With Stressful Life Events or Autism Spectrum Disorder
ClinicalTrials.gov study NCT06025539. IPD Sharing: NO. Countries: 1. Publications: 0.
Simulation Team Training and Critical Event Checklist Use for Optimizing the Management of Critical Perioperative Events in an Ambulatory Surgical Facility
ClinicalTrials.gov study NCT01808001. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Transcriptional events in human skeletal muscle at the outset of concentric resistance exercise training
GEO Series GSE45426. Homo sapiens. 45 samples. Type: Expression profiling by array.
Windy events detection in big bioacoustics datasets using a pre-trained Convolutional Neural Network
<p>This repository icludes the code and all relevant files used throughout our study. These encompass everything from the initial sheets of the whole acoustic dataset utilised for selecting the annotated dataset to the notebook (.ipynb) and the recordings employed in training the model.</p>
Promoting Positive Emotions in Adolescents Using Positive Events Training: An Indicated Approach
ClinicalTrials.gov study NCT07288541. IPD Sharing: NO. Countries: 0. Publications: 0.
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.