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.
1,271
datasets available to search
ShareScore release 0.9.0
Dataset results
1,271 results for “Data Flow”
Data for the manuscript "Microscopic Description of Basal Stress Generated by Granular Free-surface Flows"
<p>Data and Code for the manuscript "Microscopic Description of Basal Stress Generated by Granular Free-surface Flows"</p> <p>The following documents are necessary for reproducing the DEM case indicated in the main article.</p> <p>LIGGGHTS download insttuctions and operation manual can be found in this web site</p> <p>https://www.cfdem.com/media/DEM/docu/Manual.html<br> </p>
Data_ Effects of Structured 3D Electrodes on the Performance of Redox Flow Batteries
<p>The folder contains the processed data of figures 3, 5, 7, 8, 9, S4, S5, S6, S7, S8, S9, S10, S11 and S12 that appear in:</p> <p>R. De Wolf, M. De Rop, and J. Hereijgers, “Effects of Structured 3D Electrodes on the Performance of Redox Flow Batteries,” <em>ChemElectroChem</em>, vol. 202200640, 2022, doi: 10.1002/celc.202200640.</p>
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Aircraft Proximity Maps Based on Data-Driven Flow Modeling
With the forecast increase in air traffic demand over the next decades, it is imperative to develop tools to provide traffic flow managers with the information required to support decision making. In particular, decision-support tools for traffic flow management should aid in limiting controller workload and complexity, while supporting increases in air traffic throughput. While many decision-support tools exist for short-term traffic planning, few have addressed the strategic needs for medium- and long-term planning for time horizons greater than 30 minutes. This paper seeks to address this gap through the introduction of 3D aircraft proximity maps that evaluate the future probability of presence of at least one or two aircraft at any given point of the airspace. Three types of proximity maps are presented: presence maps that indicate the local density of traffic; conflict maps that determine locations and probabilities of potential conflicts; and outliers maps that evaluate the probability of conflict due to aircraft not belonging to dominant traffic patterns. These maps provide traffic flow managers with information relating to the complexity and difficulty of managing an airspace. The intended purpose of the maps is to anticipate how aircraft flows will interact, and how outliers impact the dominant traffic flow for a given time period. This formulation is able to predict which "critical" regions may be subject to conflicts between aircraft, thereby requiring careful monitoring. These probabilities are computed using a generative aircraft flow model. Time-varying flow characteristics, such as geometrical configuration, speed, and probability density function of aircraft spatial distribution within the flow, are determined from archived Enhanced Traffic Management System data, using a tailored clustering algorithm. Aircraft not belonging to flows are identified as outliers.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Turbulence Models: Data from Other Experiments: Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers
Shock Wave / Turbulent Boundary Layer Flows at High Mach Numbers. This web page provides data from experiments that may be useful for the validation of turbulence models. This resource is expected to grow gradually over time. All data herein arepublicly available.
Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows
<p>The dataset shown in the following manuscript is made.</p> <p>Development of a Data Assimilation Method Using Vibration Equation for Large-Eddy Simulations of Turbulent Boundary Layer Flows</p>
Data for Hierarchical Controls of Mixed Sediment Transport in a Series of Unsteady Flows
<p>The dataset contain three files namely, cs_all, fluxes and case study data. </p> <p>'cs_all' contains the cross-section data presented in the associated manuscript for all simulation cases. This data was used in the following figures in the associated manuscript: Figures 6-10, 15C</p> <p>'fluxes' contains the flux data for the flux surface mentioned in the associated manuscript. This data was used in the following figures in the associated manuscript: Figures 1,2.</p> <p>'case study data' contains the rainfall, wind speed and backscatter data in different sheets. The bed grain size distributions are also given for the floodplain. This data was used in the following figures in the associated manuscript: Figures 11G, 14, 15A</p>
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Compressible Flow Cases from 1980-81 Data Library
This grouping contains the compressible-flow cases from the 1980-81 Data Library.
Collaborative Testing of Turbulence Models: Incompressible Flow Cases from 1980-81 Data Library
This grouping contains the incompressible-flow cases from the 1980-81 Data Library.
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.