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.
982
datasets available to search
ShareScore release 0.7.1
Dataset results
982 results for “Interface”
command line interface practical - 2022 bioinformatics summer school
<p>Files and folders for the comman line interface practical of the 2022 bioinformatics summer school. Second release</p>
A structural database of chain-chain and domain-domain interfaces of proteins
<p>Library of protein-protein and domain-domain interfaces from the protein data bank. The data also contains the structural clusters of protein-protein and domain-domain interfaces.</p>
The dataset for the mechanical parameters of the ultraviolet adhesive polymer-inorganic interfaces
<p>We perform molecular dynamics (MD) simulation with full-atom representation to investigate the mechanical properties of interfaces between polymers, including seven ultraviolet (UV) adhesive polymers and other common polymers, and inorganic substrates (Si, SiO<sub>2</sub>, ZrO<sub>2</sub>). The interfacial mechanical parameters such as strength and energy release rate in the cohesive zone models (CZMs) are calculated from the MD simulations. The typical traction separation and shear deformation are applied to the polymer-inorganic interface. Different interfacial crosslink densities of the polymer-inorganic interfaces are also considered. The dataset provided here can be used as the input for failure prediction and design optimization by the finite element analysis (FEA), for example, layered polymer-inorganic composites used in electronic device packages.</p>
Isolated Ballistic Non-Abelian Interface Channel
<p>Dataset for B. Dutta et al., "Isolated Ballistic Non-Abelian Interface Channel".</p> <p>The following datafiles are used for the figures in main text and supplementary materials.</p>
Data used in article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces'
<p>Data underlying the article 'Tuning Charge Carrier Dynamics and Surface Passivation in Organolead Halide Perovskites with Capping Ligands and Metal Oxide Interfaces' published in Advanced Optical Materials.</p>
Data from: A geometric VOF method for interface flow simulations
<p>This set is the original data used in the article titled "A Geometric VOF Method for Interface Flow Simulations". In this paper, a novel numerical technique designed for interface flow simulations using the Volume of Fluid (VOF) method on arbitrary unstructured meshes has been introduced. The method is called SimPLIC, which seamlessly integrates Piecewise Linear Interface Calculation (PLIC) and Simpson's rule. The main focus of the proposed method is to compute the volume of the primary phase that moves across a mesh face within a single time step. This is achieved by reconstructing the interface and assessing how the submerged face area evolves over time. Simpson's rule is employed to integrate the time evolution of this submerged face area, ensuring an accurate estimation of the volume of the transported primary phase. The method's robustness was validated by solving a spherical interface advection problem in a non-uniform three-dimensional flow across unstructured meshes with diverse cell types and dimensions. Key metrics such as volume conservation, shape retention, friction boundedness and solving efficiency were meticulously monitored and juxtaposed. Numerical outcomes underscored the precision and adequacy of the PLIC-VOF technique when complemented with Simpson's rule in advecting the interface. Furthermore, the SimPLIC method has been integrated into OpenFOAM v2312 as an unofficial extension and is now accessible to the community.</p>
Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface
<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> <p>We ran simulations for four different systems:</p> <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> <p>For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis.</p> <p>This data repository consists of<br> (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units<br> (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units<br> (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs)<br>Units are also clarified in each file's header.</p> <p>The simulation units can be converted to SI units via:</p> <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul> <p> </p> <p>Details for (1) and (2):<br>Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files:</p> <p><strong>Ree:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>Rg:</strong></p> <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> <p><strong>resDist:</strong></p> <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> <p>distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14] </p> <p> </p> <p>Details for (3):<br>The folder '<strong>droplet</strong>' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files:</p> <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>
Dataset: Interface, Inc. (TILE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
WT, Delta and Omicron RBDs Adsorption onto Hydrophobic, Hydrophilic Surfaces and Biological Interfaces
<p>Simulations (trajectories) and analysis of the 3 VoCs RBDs of the SARS-CoV-2.</p> <p>For more information go to this article: https://doi.org/10.1021/acs.jcim.4c00460</p>
Fig. 1 in Generalists at the interface: Nematode transmission between wild and domestic ungulates
Fig. 1. Correlation between degree (vertical axis) and number of references (horizontal axis) for nematode parasites of wild ungulate species (black dots). Blue line is fitted linear model, and gray area shows standard error.
Investigation of TMA2SnI4/GaN interface
Open the record for dataset details and reuse information.
Figure 6. Interface of FFE program-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network
<p>Points, Features Extraction and save all input information for the classifier (Features, Ethnic group,<br> Gender and emotion). Figure 6 shows the interface of FFE program.</p>
Figure 6. Three interfaces available on the mobile device
<p>The device provides two important facilities: multimedia facilities (it allows recording, processing and playing audio samples) as well as graphic facilities (it provides a friendly and accessible interface). In Figure 6 is illustrated the main page of the application that is implemented on the mobile device (a), as well as two types of exercises; (b) the child is required to identify whether a sound is present in a word (which is indicated by an image); and (c), the child is required to choose a word from a group of paronyms.</p>
BRAIN Journal-Computational Intelligence in a Human Brain Model-Figure 2. Processes and Interfaces of the SAH Human Brain Model
<p>The proposed SAH Human Brain Model starts assigning the main attributes to the “heavy pieces” (king, queen, rooks, bishops, knights) and assigning to pawns the interfaces as an advanced guard. The interface represents senses and processed human actions (equilibrium, movements, and speech) and it results from the brain activity (see Figure 2). </p>
The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 4. The artefact during the initial structuring stage
<p>The IT developer elaborates the detailed structure of the artefact, while considering several aspects:</p> <p>— The resources that are necessary to the artefact in order to accomplish its mission;</p> <p>— The artefact’s resistance to the changes regarding the functional requirements;</p> <p>— The artefact’s resistance to the technological changes;</p> <p>— The reasonably priced integration of the artefact in the structure of the host system;</p> <p>— The assurance of a reasonable reusability coefficient of the artefact during the struc- turing process of other artefacts;</p> <p>— The flexibility of the relations that exist among the components of the artefact;</p> <p>— The flexibility of the artefact’s connections with the host system.</p> <p> </p>
Figure 3. The artefact as it exists as a black box-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts
<p>Consequently, the artefact as it exists as a black box can be represented according to the representation in Figure 3. It can be noticed that the artefact as it exists as a black box begins to interact with the environment. Two main categories of interfaces may be utilized by any artefact in order to interact with the environment: — Human Computer Interfaces (HCI); — Shared Resource Interfaces (SRI).</p>
Figure 1. Visual and synthetic representation of the modelling process in the software industry-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts
<p>The experience that is accumulated regarding the modelling paradigms in the software engineering is impressive. Thus, the software engineering recognizes modelling paradigms like object orientation, aspect orientation, component orientation, service orientation, agent orientation. In one form or another, these paradigms prove their ex- cellence in certain types of IT projects. At the same time, these paradigms reveal their objective limits when they are used to engineer the real world software systems. Every modelling paradigm represents, in fact, a modality to represent the real world using a specific formal framework. The specificity of the formal framework is defined from both a syntactic and semantic perspective. The formal syntactic framework of a paradigm refers to the concepts that are used by the paradigm in order to represent the real world, but also to the recommended principles that allow for these concepts to interact in a correct and efficient manner. Both the concepts and the principles benefit from a formal representation that ultimately favours communication as a secondary modelling lever inside the IT projects. Every syntactic artefact of a paradigm can be associated with a certain real world semantics, which it abstracts. As a consequence, considering that the real world continuously enhances its semantic potential, the syntactic constructs that are favoured by the paradigm may become problematic.</p>
The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 2. The UML representation of the artefact as it exists as a metaphor
<p>The accumulation of energy that exists in each ingenious metaphor is progressively released, thus contributing to the transformation of a theoretical promise into effective reality. The artefact successively goes through several maturation stages, as the creator is preoccupied with obtaining an as precise and as close as possible description of the artefact as it exists as a metaphor. The completion of these successive stages is achieved through a methodic abstraction process, while leaving open the possibility to innovate and targeting three main objectives: — broadening the abstraction scope; — adding new details; — detecting and eliminating abstraction errors.</p>
Water Interface Sediment Experiment (WISE) data set produced at the CIEM flume, Hydralab IV
<p>The present work was developed in the framework of the HYDRALAB IV as part of the WISE Joint Research Activity. The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona.</p> <p>The data set here presented aims to observe the simultaneous and collocated profiles, of water and sediment flow and the associated bed-dynamics and particle features. The experiments considered have a flume bed configuration which starts with a concrete flat part while the study area is a 1/15 constant sandy slope. The granular beach consisted of commercial well-sorted sand with a medium sediment size d50=0.25 mm. The water depth at the toe of the wave maker is 2.5 m for all tested conditions.</p> <p>Different waves conditions were tested Erosive (Hs=0.47 m and Tp=3.7s) and Accretive (Hs=0.32 m and Tp=4.7s; Hs=0.27 m and Tp=5.3s) while collecting data of velocity, suspended sediment concentration and profile evolution.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Cáceres, I. and Sánchez-Arcilla, A., 2015. Erosive and Accretive mobile bed experiments in large scale tests, Coastal Sediments 2015, San Diego, USA.</p> <p>Eichentopf, S., Cáceres, I. and Alsina, J.M., 2018. Breaker bar morphodynamics under erosive and accretive wave conditions in large-scale experiments. Coastal Engineering, Vol. 138, 36-48.</p> <p>Sánchez-Arcilla, A. and Cáceres, I., 2018. An analysis of nearshore profile and bar development under large scale erosive and accretive waves. Journal of Hydraulic Research, Vol. 56(2), 231-244.</p> <p> </p>
DBv5 features and codified complexes used in BIPSPI: a method for the prediction of partner-specific protein–protein interfaces
<p>Features and protein complexes codified for DBv5 BIPSPI paper</p> <p>(BIPSPI: a method for the prediction of partner-specific protein–protein interfaces</p> <p><a>Ruben Sanchez-Garcia</a> <a>C O S Sorzano</a> <a>J M Carazo</a> <a>Joan Segura</a></p> <p><em>Bioinformatics</em>, bty647, <a href="https://doi.org/10.1093/bioinformatics/bty647">https://doi.org/10.1093/bioinformatics/bty647</a>)</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.