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.
11,506
datasets available to search
ShareScore release 0.9.0
Dataset results
11,506 results for “Dynamics”
Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
Adult survival has a stronger role than productivity in the population dynamics of European songbirds
Open the record for dataset details and reuse information.
Dataset for: Dynamically patterning X-ray beam by a femtosecond optical laser
<p>This record contains the image and plot data for the paper "Dynamically patterning X-ray beam by a femtosecond optical laser".</p>
Supporting Dataset for the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model"
<p>This archive contains the data used in the study "Influence of Floodplains and Groundwater Dynamics on the Present-Day Climate simulated by the CNRM Model" submitted to <a href="https://www.earth-system-dynamics.net/">Earth System Dynamics</a>.</p> <p> </p>
Online material to publication "Exploring the dynamical evolution of Cepheid multiplicity in star clusters and its implications for B-star multiplicity at birth"
<p>The description of the movie can be found in the file movie_description.pdf.</p> <p>The full paper can be found here: https://ui.adsabs.harvard.edu/abs/2024arXiv240907530D/abstract</p>
Supplemental Online Material for "Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution": Interactive Figures
<p>This repository contains the interactive figures accompanying the manuscript "<em>Comparing the Effects of Euclidean Distance Matching and Dynamic Time Warping in the Clustering of COVID-19 Evolution</em>". These interactive visualizations provide a dynamic exploration of some of the data and results discussed in the paper. The following interactive figures are included:</p> <ul> <li><code>Figure-1.html</code>: Interactive version of Figure 1 from the paper, showing a time series heatmap of the 14-day simple moving average of reported COVID-19 cases for each of Europe's 298 NUTS 2 regions, from 02/10/2020 - 07/12/2020.</li> <li><code>Figure-2.html</code>: Interactive version of Figure 2, which visualizes two heatmap dendrograms showcasing the pairwise distance matrices and hierarchical clustering results for (a) the Euclidean distance and (b) the Dynamic Time Warping distance.</li> <li><code>cluster_maps.html</code>: Interactive cluster maps, allowing for detailed exploration of the clusters discussed in the paper. Users can select between different distance metrics (Euclidean or Dynamic Time Warping) and adjust the number of clusters to visualize, providing greater flexibility in exploring the clustering patterns. This figure is not included as a static figure in the paper but is provided merely as supplemental online material.</li> </ul> <p>These interactive figures are best viewed using a modern web browser (e.g., Chrome, Firefox ...). For all visualizations, upon hovering over the figure, a label appears displaying the NUTS 2 region and the country it is in, providing additional geographic context.</p>
Graph neural network emulator for modeling of ice dynamics and calving in the Pine Island Glacier, Antarctica
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Pine Island Glacier, Antarctica</p> <ul> <li>ISSM_DGL_PIG2.py: Python file for training GNN models (*single.py: code for single GPU environment)</li> <li>ISSM_CNN_PIG.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results (graphs for GNNs)</li> <li>*.pkl: Datasets of the ISSM transient simulation results (grids for CNNs)</li> </ul>
A fMRI dataset in response to large number of short natural dynamic facial expression videos
<p><span>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although </span>the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a <span>small number of</span> lab-controlled video stimuli, how the human brain processes <span>natural</span> facial expressions still needs to be investigated. <span>T</span>o our knowledge<span>,</span> this type of data <span>specifically</span> <span>on </span><span>large number of </span><span>natural</span> <span>facial expression videos</span> is currently missing. <span>W</span>e describe <span>here </span>the <span>natural </span>Facial Expressions Dataset (NFED), a fMRI dataset <span>including </span>responses to 1,320 short (<span>3-second</span>) <span>natural</span> facial expression video clips. <span>These video clips is </span><span>annotated</span> <span>with three types of labels: emotion, gender, and ethnicity</span><span>, along with accompanying metadata</span>. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of <span>natural </span> <span>facial expression videos.</span></p>
APS DPP 2024 - The impact of stable modes on the merging dynamics of Kelvin- Helmholtz vortices
<p>Supplementary figures for the poster presentation</p>
Enhancing Student Academic Performance Through The Implementation of Dynamic Gamification in Educational Settings
<p><span>Dynamic gamification is a gamification strategy that uses features and game mechanics that are flexible and adaptive to engage users in a variety of tasks. Dynamic gamification, in contrast to traditional gamification, allows for real-time modification of game features in response to user behavior or other circumstances. This study aims to show whether incorporating dynamic gamification enhances students' academic achievement. Numerous factors, such as motivation, learning engagement, academic achievement, relevance, confidence, and satisfaction, will be tested in order to advance the objectives of this study. In this research, a non-probability purposive sampling strategy was used. </span><span>An online questionnaire is distributed from March 23 until May 2, 2024 which featured 477 participants from all around Indonesia. However, only 400 respondents are considered valid, as the respondents must either comprehend the concept of gamification or have prior experience using gamification. The results stated that ten out of ten hypotheses had significant effect.<span> </span><span> </span></span></p>
Dataset for "Dynamical Study of the Reaction of N+ Ions with O2 Neutrals"
<p>Dataset for "Dynamical Study of the Reaction of N+ Ions with O2 Neutrals"</p>
Dataset for "Reaction dynamics of the methoxy anion CH3O- with methyl iodide CH3I"
<p>Dataset for "Reaction dynamics of the methoxy anion CH3O- with methyl iodide CH3I"</p>
Single-molecule analysis of transcription activation: dynamics of SAGA co-activator recruitment
<p>Source data for:</p> <p><strong>Single-molecule analysis of transcription activation: dynamics of SAGA co-activator recruitment</strong></p> <p><strong> </strong></p> <p>Jongcheol Jeon<sup>1</sup>, Larry J. Friedman<sup>2</sup>, Daniel H. Zhou<sup>2</sup>, Hogyu David Seo<sup>1</sup>, Oluwatobi A. Adeleke<sup>3</sup>, Bria Graham<sup>3</sup>, Emily F. Patteson<sup>3</sup>, Jeff Gelles<sup>2</sup>*, and Stephen Buratowski<sup>1,</sup>*</p> <p> </p> <p><sup>1</sup>Department of Biological Chemistry and Molecular Pharmacology,</p> <p>Harvard Medical School, Boston, MA 02115</p> <p><sup>2</sup> Department of Biochemistry, Brandeis University, Waltham, MA 02454</p> <p><sup>3</sup> EpiCypher Inc., Durham NC 27709</p> <p> </p> <p>*Corresponding authors</p> <p>Lead author contact information:</p> <p><a href="mailto:steveb@hms.harvard.edu">steveb@hms.harvard.edu</a></p> <p> </p> <p> </p> <p>The source data for the single molecule experiments are provided as "intervals" files and "CoSMoS parameters", which can be read and manipulated using the publicly available programs "Imscroll" and "Tapqir", respectively:</p> <p><a href="https://github.com/gelles-brandeis/CoSMoS_Analysis">https://github.com/gelles-brandeis/CoSMoS_Analysis</a></p> <p><a href="https://github.com/gelles-brandeis/tapqir">https://github.com/gelles-brandeis/tapqir</a></p>
Dataset for "Imaging the ion-molecule reaction dynamics of O- + CD4"
<p>Dataset for "Imaging the ion-molecule reaction dynamics of O- + CD4"</p>
Dynamic changes in cortical Neurotrophic Factor-positive interneurons during sleep
<p>The Data provided is sufficient to replicate the analysis presented in "<strong><span>Dynamic changes in cortical </span><span>Neurotrophic Factor-positive interneurons</span><span> during sleep".</span></strong></p> <p><span>The data was collected using nVoke or nVista miniaturized microscopes (Inscopix/Brucker) and normalized by calculating dF/F0 where F0 is the mean over one imaging session. Each imaging session is normalized separately and then all sessions were concatenated (over 48h of the experiment). The cacium data then was synchronized with the EEG/EMG data, to obtain sleep stage information for the calcium data. To assure correct alignment, the raw data were timestamped when recording using TTLs.</span></p> <div> <h4>General Info on the Experiment</h4> <a href="https://github.com/christinemlmuheim/NDNF-INs-and-Sleep#general-info"></a></div> <p>Aim of the code is to analyze calcium activity of NDNF interneurons in layer 1 of V1 of mice over the 24h day and in different sleep stages. The data used to run this script contains timestamped and sleep-scored calcium recordings, aquired with nVoke or nVista systems from Inscopix/Bruker.</p> <div> <h4>Experimental Design:</h4> <a href="https://github.com/christinemlmuheim/NDNF-INs-and-Sleep#experimental-design"></a></div> <p>NDNF-cre mice were injected with a AAV-GECI, equipped with a cranial window over the injection site and EEG/EMG electrodes. For details, see the Manuscript Methods section.</p> <p>After adequate recovery time, animals were mounted with a baseplate to securely but reversibly attache the miniaturized microscope.</p> <p>Animals were recorded over 48h, whereof the first 24hrs were undisturbed (baseline, BL), followed by a 6h sleep deprivation (SD) and 18hrs recovery period. SD started at ZT0 when the lights were turned on.</p>
Resulting videos from the article: A Novel Technique for the Extraction of Dynamic Events in Extreme Ultraviolet Solar Images
<p>Videos called 'MFM PCP DMD ...' correspond to Figure 5 in the mentioned article. They show comparison of MFM, PCP, and DMD algorithms. Upper images in each year correspond to the separated background matrix, the lower images correspond to matrix of dynamic component. The videos cover 1.7 hours of observations starting at 06:00:00 UTC on 2011 June 7, 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2.<br><br>The other videos correspond to Figure 7 in the article. They compare the results from PCP algorithm applied to the 30.4 and 17.1 nm AIA bandpasses. The videos starting at 16:50:00 UTC on 2012 April 16 and 18:30:11 UTC on 2014 October 2, respectively, and cover 1.7 hours of observations.</p>
A fMRI dataset in response to large-scale short natural dynamic facial expression videos
<p>Facial expression is among the most natural methods for human beings to convey their emotional information in daily life. Although the neural mechanism of facial expression has been extensively studied employing lab-controlled images and a small number of lab-controlled video stimuli, how the human brain processes natural facial expressions still needs to be investigated. To our knowledge, this type of data specifically on large<span>-scale</span> natural facial expression videos is currently missing. We describe here the natural Facial Expressions Dataset (NFED), a fMRI dataset including responses to 1,320 short (3-second) natural facial expression video clips. These video clips is annotated with three types of labels: emotion, gender, and ethnicity, along with accompanying metadata. We validate that the dataset has good quality within and across participants and, notably, can capture temporal and spatial stimuli features. NFED provides researchers with fMRI data for understanding of the visual processing of large number of natural facial expression videos.</p>
Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA
<p>Scripts and methylation frequencies for "Expanded dynamic methylome and quantitative trait detection by long-read epigenome profiling of personal DNA" manuscript.</p>
Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics
<p>This dataset provides python implementation of the paper: Deep Learning for Structure-Preserving Universal Stable Koopman-Inspired Embeddings for Nonlinear Canonical Hamiltonian Dynamics, which is available on arXiv https://arxiv.org/abs/2308.13835.</p>
Molecular Probes for Tracking Lipid Droplet Membrane Dynamics
<p><strong><span>Abstract</span></strong></p> <p><span>Lipid droplets (LDs) and their membrane proteins play crucial roles in lipid metabolism, signaling, and information transport within cells. LDs feature a unique monolayer lipid membrane that has not been extensively studied due to the lack of suitable molecular probes that are able to distinguish this membrane from the LD lipid core. In this work, we present a three-pronged molecular probe design strategy that combines lipophilicity-based organelle targeting with microenvironment-dependent activation. As a proof-of-concept, we designed an <u>LD</u> <u>m</u>embrane labeling pro-probe called<strong> LDM</strong>, which selectively localizes around LD membranes. Upon activation by the HClO/ClO</span><sup><span>−</span></sup><span> microenvironment that surrounds LDs, <strong>LDM</strong> pro-probe undergoes a color change and releases<strong> LDM-OH</strong> probe that binds to LD membrane proteins. This localizes the probe to the LD-as</span><span>sociated protein space which is restricted to the membrane thus enabling visualization of the ring-like LD membrane. By utilizing<strong> LDM</strong>, we identified the dynamic mechanism of LD membrane contacts and their protein accumulation parameters. Furthermore, using <strong>LDM</strong> in liver cancer cells allowed us to examine the changes in LD/mitochondrial protein accumulation caused by the state of starvation these cells encounter. This led to the discovery that liver cancer cells respond to energy stress during hunger by enhancing LD-mitochondria interactions. Taken together, <strong>LDM</strong> represents the first molecular probe for imaging LD membranes in live cells, and represents an attractive tool for further investigations into the specific regulatory mechanisms and drug discovery associa</span><span>ted with LD related metabolism diseases.</span></p> <p><span> </span></p> <p><em><span>Keywords:</span></em><span> Molecular Imaging, Cancer, Lipid droplets, Super-resolution Imaging</span></p>
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.