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.
41
datasets available to search
ShareScore release 0.7.1
Dataset results
41 results for “collective dynamics”
Mathematical model results for: Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration
<p>Collective cell migration plays an essential role in vertebrate development, yet the extent to which dynamically changing microenvironments influence this phenomenon remains unclear. Observations of the distribution of the extracellular matrix (ECM) component fibronectin during the migration of loosely connected neural crest cells (NCCs) lead us to hypothesize that NCC remodeling of an initially punctate ECM creates a scaffold for trailing cells, enabling them to form robust and coherent stream patterns. We evaluate this idea in a theoretical setting by developing an agent-based model that incorporates reciprocal interactions between NCCs and their ECM. ECM remodeling, haptotaxis, contact guidance, and cell-cell repulsion are sufficient for cells to establish streams in silico, however additional mechanisms, such as chemotaxis, are required to consistently guide cells along the correct target corridor. Further investigations of the model imply that contact guidance and differential cell-cell repulsion between leader and follower cells are key contributors to robust collective cell migration by preventing stream breakage. Global sensitivity analysis and simulated underexpression/overexpression experiments suggest that long-distance migration without jamming is most likely to occur when leading cells specialize in creating ECM fibers, and trailing cells specialize in responding to environmental cues by upregulating mechanisms such as contact guidance. This dataset contains summary statistics, movies, parameter values, and photos obtained from individual realizations of the mathematical model.</p>
Mathematical model results for: Dynamic fibronectin assembly and remodeling by leader neural crest cells prevents jamming in collective cell migration
Open the record for dataset details and reuse information.
Data from: Collective dynamical regimes predict invasion success and impacts in microbial communities
Open the record for dataset details and reuse information.
Collective magnetic dynamics in artificial spin ice probed by AC susceptibility
<p>A collection of data presented and analysed in a manuscript under the same title and author list, posted on arXiv.</p>
Collective dynamics and pair-distribution function of active Brownian ellipsoids
<p>Supplemental data for the following manuscript: Stephan Bröker, Michael te Vrugt, Raphael Wittkowski,</p> <p>"Collective dynamics and pair-distribution function of active Brownian ellipsoids"</p>
Including population and environmental dynamic heterogeneities in continuum models of collective behaviour with applications to locust foraging and group structure Data and Code
<p>This dataset includes all data used for the creation of "Including dynamic population and environmental heterogeneity in continuum models of collective behaviour with applications to locust foraging and group structure" as well as a snapshot of the code used.<br><br>Each zip should be unzippable and the code should operate with only the contents of the zip file.</p>
A dynamic multicellularity emerges for collective invasion within opisthokonta
<p>Raw data used for the quantification of the Fonticula Collective behaviours : https://github.com/apicco/Fonticula_collective_invasion</p>
MDD-Molecular Dynamics Dataset: Collection of protein-ligand complex simulations
<p>Dataset is part of the paper: https://chemrxiv.org/engage/chemrxiv/article-details/664c73f6418a5379b0de8152.</p> <p>This dataset consists of molecular dynamics (MD) simulations of 862 unique protein-ligand complexes, covering a wide range of protein families and diverse chemical classes of ligands. It is derived from publicly available repositories and represents the largest single source of MD simulations to date.</p> <p>All protein-ligand complexes included in the dataset were prepared following a standardized protocol. Missing atoms in the protein structures were added using the PDBFixer tool. The protein targets were parameterized using the AMBER99SB-ILDN force field, while ligands were parameterized with the ANTECHAMBER module within the ACPYPE tool. Ligand partial charges were determined to match the quantum-mechanically generated electrostatic potential via the Restrained Electrostatic Potential (RESP) method, and the remaining parameters were set using the GAFF2 force field. The molecular dynamics simulations were performed using GROMACS. The simulations were configured in a cubic simulation box with periodic boundary conditions and employed a TIP3P water model within an electrostatically neutral environment. The simulation protocol included an initial minimization cycle, followed by temperature equilibration in the NVT ensemble and pressure equilibration in the NPT ensemble. Production simulations were conducted over a period of 200 ns, with a timestep of 100 ps.</p> <p>Constructing a large, representative set of MD simulations poses challenges due to the high computational costs and complexities associated with preparing molecular systems. Moreover, given the limited number of suitable training examples (complexes) and the large volume of MD data from each simulation, careful filtering and feature selection are crucial. This dataset is valuable for exploring how molecular dynamics simulation data can be integrated with protein-ligand binding affinity prediction tasks, an essential component of in silico drug discovery pipelines. MD simulations, in particular, offer a dynamic view by illustrating the temporal interactions within protein-ligand complexes, potentially providing additional insights for affinity and specificity estimates.</p>
Plasticity in Collective Decision-Making for Robots: Creating Global Reference Frames, Detecting Dynamic Environments, and Preventing Lock-ins
<p>Swarm robots operate as autonomous agents and a swarm as a whole gets autonomous by its capability of collective decision-making.<br> Despite intensive research on models of<br> collective decision-making, the implementation in multi-robot systems is still challenging.<br> Here, we advance the state of the art by introducing more plasticity to the decision-making process and by increasing the task difficulty.<br> Most studies on large-scale multi-robot decision-making are limited to one instance of an iterated exploration-dissemination phase followed by successful and permanent convergence.<br> We investigate a dynamic environment that requires constant collective monitoring of option qualities.<br> Once a significant change in qualities is detected by the swarm, it has to collectively reconsider its previous decision accordingly.<br> This is only possible by preventing lock-ins, a global consensus state of no return.<br> In addition, we introduce a task of increased difficulty as the robots must locate themselves to assess the quality of an option.<br> Using local communication, swarm robots propagate hop-count information throughout the swarm to form a global reference frame.<br> We successfully validate our implementation in many swarm robot experiments concerning robustness to disruptions of the reference frame, scalability, and adaptivity to a dynamic environment.</p>
Collection of data and Matlab-codes for analyzing water dynamic at the Zugspitze.
<p>This collection includes the raw data and the Matlab codes used for the analyses of the article: "Decadal in-situ hydrological observations and empirical modeling of pressure head in a high-alpine, fractured calcareous rock slope" by Scandroglio et al. 2024 [Link].<br>Explanations are included in the file METADATA.docx. See the paper for further details.</p> <p>Documentation and code by Riccardo Scandroglio (<a href="mailto:johannes.leinauer@tum.de">r.scandroglio@tum.de</a>)<br>Technische Universität München, Arcisstr. 21, 80333 München, Germany</p> <p>For updating the analysis: <br>- Water discharges are collected by the Environmental Research Station Schneefernerhaus (contact: Dr. Till Rehm).<br>- Data from the German Weather Service can be downloaded from the official server: <a href="https://cdc.dwd.de/portal/">https://cdc.dwd.de/portal/</a>.<br>- Snow data for SNOWPACK can be obtained from the Bavarian Avalanche Center (contact: Dr. Thomas Feistl).</p> <p>This study was supported by the AlpSenseRely project, funded by the Bavarian State Ministry of the Environment and Consumer Protection (TUS01UFS-76976), and by the Hydro-PF project, funded by the TUM International Graduate School of Science and Technology IGSSE (Team 12.9). </p>
Data for "Learning Collective Cell Migratory Dynamics from a Static Snapshot with Graph Neural Networks"
<p>This dataset contains snapshots of cell monolayers, represented as graphs, along with their corresponding average displacement measurements.</p>
A Monitoring System Based on the Multifactorial Dynamic Perfusion Index to Predict and Prevent the Onset of Postoperative Acute Kidney Injury After Cardiac Surgery, Based on a Dynamic Collection of He
ClinicalTrials.gov study NCT06786416. IPD Sharing: NO. Countries: 1. Publications: 16.
Static and Dynamic Data Collection
<p>Contains the JSON files with the collected data and the results of the analysis done.</p>
Data from: In the mood: the dynamics of collective sentiments on Twitter
We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly, we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example, they use positive sentiment more often and negative sentiment less often. Secondly, we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable with those obtained from our empirical dataset.
Рис. 4. Àинамика гибеΛи гусениц коΛьчатого шеΛкопряΔа, собранных в районе УПН, соΔержащихся в Λабораторных усΛовиях. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата осмотра в Λаборатории. В правом верхнем угΛу графика: Δата сбора гусениц в районе УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 4. Dynamics of death of the Lackey moth caterpillars collected at the SOM and kept in the laboratory, 2019. Vertical: number of deaths (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. Upper right corner: collection date. — death from NPV; — death from bacteriosis in Lackey Moth (Malacosoma Neustria L., Lasiocampidae, Lepidoptera) Population During The Eruptive Phase
Рис. 4. Àинамика гибеΛи гусениц коΛьчатого шеΛкопряΔа, собранных в районе УПН, соΔержащихся в Λабораторных усΛовиях. ВертикаΛьно: коΛичество погибших гусениц (% от чисΛа собранных за весь периоΔ иссΛеΔований в 2019 г. гусениц). ГоризонтаΛьно: Δата осмотра в Λаборатории. В правом верхнем угΛу графика: Δата сбора гусениц в районе УПН. — гибеΛь от вируса яΔерного поΛиэΔроза; — гибеΛь от бактериоза Fig. 4. Dynamics of death of the Lackey moth caterpillars collected at the SOM and kept in the laboratory, 2019. Vertical: number of deaths (percentage from the total number of caterpillars collected in 2019 (578 caterpillars)); horizontal: dates of laboratory controls. Upper right corner: collection date. — death from NPV; — death from bacteriosis
Supplementary material 1 from: Woodburn M, Droege G, Grant S, Groom Q, Jones J, Trekels M, Vincent S, Webbink K (2021) A Data Standard for Dynamic Collection Descriptions. Biodiversity Information Science and Standards 5: e73902. https://doi.org/10.3897/biss.5.73902
Provisional list of classes.
Data files for "Comprehensive Wastewater Sequencing Reveals Community and Variant Dynamics of the Collective Human Virome"
<p>Files required to run analyses and generate charts for R notebooks at: <a href="https://github.com/cmmr/TX_wastewater_virome">https://github.com/cmmr/TX_wastewater_virome</a></p> <p> </p> <p> </p>
Data from: In the mood: the dynamics of collective sentiments on Twitter
Open the record for dataset details and reuse information.
Computed Tomography (CT) and Lung Function Data Collection for Computational Fluid Dynamics (CFD) in Chronic Obstructive Pulmonary Disease (COPD) Patients
ClinicalTrials.gov study NCT00966459. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data Collection Study for the Spectrum Dynamics Multi-purpose CZT SPECT Camera
ClinicalTrials.gov study NCT03438123. IPD Sharing: NO. Countries: 1. 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.