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.
464
datasets available to search
ShareScore release 0.9.0
Dataset results
464 results for “model structure”
Chemical and Structural In-Situ Characterization of Model Electrocatalysts by Combined Infrared Spectroscopy and Surface X-Ray Diffraction
<p>Raw and treated data</p>
Electronic structure calculations for model furylfulgides
<p>This dataset is intended as an attachment to the Response to the Reviewers for manuscript NJ-ART-10-2023-004752, submitted to the <em>New Journal of Chemistry.</em></p> <p><strong>Contents:</strong></p> <p><strong>1. Vertical_excitation_spectra</strong> - This section of the dataset is related to the response to question 5 from Reviewer 1. It contains input and output files from the calculations of the excitation spectra of the Eα and the C isomers of the truncated furylfulgide (compound 2). </p> <p><strong>2. DFTB_SSR_based_NAMD_simulations</strong> - This section of the dataset is related to the response to question 9 from Reviewer 1. It contains the results of nonadiabatic molecular dynamics (NAMD) simulations of the photorelaxation process of furylfulgide tBu-1, carried out at the DFTB/SSR level of theory. The individual trajectories are located in the subdirectories run_0001, run_0002, etc. </p> <p>• wrapper.cpp is the source code for the interface to DFTB+. At each time step of the simulation, the interface calls DFTB+ for the calculation of state energies, gradients, and the S1-S0 nonadiabatic coupling vector. It then parses the output files from DFTB+, and propagates the time evolution of the system.</p> <p>• The files initial_coordinates.xyz and initial_velocities specify the initial positions (in units of Angstrom) and velocities (in atomic units) of the nuclei.</p> <p>• The file control specifies the number of NAMD steps, the classical time step (in atomic units), the time step for the integration of the electronic time-dependent Schrödinger equation, the number of states, the initially occupied state, and the initial wavefunction coefficients</p> <p>• trajectory.xyz is the nuclear trajectory</p> <p>• energy.csv contains the energy of state S0, the energy of state S1, the energy of the occupied state, and the total energy during the simulation</p> <p>• coefficients.csv contains the wavefunction coefficients and the state populations during the trajectory</p>
Nasal organoids as optimal models for studying structure and function of primary nasal epithelial cell cultures - DIA-MS Dataset
Open the record for dataset details and reuse information.
RNA 3D structural models used to train, test and validate lociPARSE
<p>This repository contains all the training, validation and test decoy sets to train and evalaute lociPARSE. It also contains training and benchmarks set-2 decoys from ARES.</p>
Structural models associated with computational study of PolB2 and Pol V mutasomes
<p>This data set contains several collections of 3D models of protein complexes predicted using AlphaFold-Multimer or AlphaFold3. Detailed information regarding the composition of protein complexes and the confidence scores of models is available in the Model_data.xls file.</p> <div> </div>
Electronic structure properties of the SmartNanoTox data set (nanomaterials) for the use of meta models assesing cytotoxicity
<p>Important set of electronic structure properties data on the SmartNanoTox dataset consisting of large molecular systems representing coated materials. The data were used to study lung inflammation within a service offered through EU Horizon 2020 NanoCommons project.</p>
Supplemental Dataset for paper: Methane ebullition from subtropical peat: testing an ebullition model reveals the importance of pore structure
<p>A dataset requested for publication of Journal Article, "<strong>Methane ebullition from subtropical peat: testing an ebullition model reveals the importance of pore structure"</strong></p>
Modeled cellulase structure
<p>Cellulase structure based on PDB code 7CEL, used for Galaxy molecular dynamics tutorial.</p>
Smoothed basin velocity structure model of the Kanto Sedimentary Basin (Takemura et al., 2015)
<p><strong>Model description</strong></p> <p>We constructed sedimentary <em>S</em>-wave velocity structure model of the Northern Kanto region using 190 local <em>S</em>-wave velocity structures. In the uploaded files of Takemura2015_V0..dat and Takemura2015_Alpha.dat, longitudes, latitudes and values of <span class="math-tex">\(V_0\)</span> (and <span class="math-tex">\(\alpha\)</span>) at 190 local points are listed. Our model of the sedimentary basin was described by two parameters of a simple velocity gradient function (Ravve and Koren 2006). The depth gradient function is described as</p> <p><span class="math-tex">\(V_S(z)=V_0+\Delta V\left[1-\exp\left(-\frac{\alpha z}{\Delta V}\right)\right]\)</span></p> <p>where <span class="math-tex">\(V_0\)</span> is the <em>S</em>-wave velocity at the surface (<em>z</em> =&thinsp;0), <span class="math-tex">\(\Delta V\)</span> is <em>S</em>-wave velocity of the bedrock (3.2 km/s), and <span class="math-tex">\(\alpha\)</span> is the positive constant that determines the velocity-depth gradient.</p> <p><strong>3D sedimentary model construction </strong></p> <p>After applying GMT surface to each parameter, spatial variations of both two parameters were obtained. Using obtained spatial distributions of <span class="math-tex">\(V_0\)</span> and <span class="math-tex">\(\alpha\)</span> and the depth gradient function, 3D sedimentary velocity structure of the Northern Kanto region could be constructed. </p> <p><strong>Citation</strong></p> <p>Takemura, S., Akatsu, M., Masuda, K., Kajikawa, K., & Yoshimoto, K., (2015), Long-period ground motions in a laterally inhomogeneous large sedimentary basin: observations and model simulations of long-period surface waves in the northern Kanto Basin, Japan, Earth, Planets and Space, 67:33, <a href="https://doi.org/10.1186/s40623-015-0201-7">doi:10.1186/s40623-015-0201-7</a></p>
Figure 2. Proposed general secondary structure model for the ITS1–5.8S rDNA–ITS2 in A revised taxonomy and phylogeny of opalinids (Stramenopiles: Opalinata) inferred from the analysis of complete nuclear ribosomal DNA genes
Figure 2. Proposed general secondary structure model for the ITS1–5.8S rDNA–ITS2–LSU rDNA of Opalinida* The expansion segments (ES#L) containing helices (in red) where there are important differences between genera are annotated. Colour code: yellow* ITS1 region; blue* 5.8S rRNA; magenta* ITS2 region; grey* LSU rRNA.
Supplemental Material for 'Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space' and DeepUrfold
<p>Data provided for the paper Draizen, EJ, Veretnik, S, Mura, C, and Bourne, PE. "Deep Generative Models of Protein Structure Uncover Distant Relationships Across a Continuous Fold Space." <em>Nature Communications</em>, Aug. 2024.</p> <div> </div> <p> </p>
Replication Package: Anomaly detection via runtime monitoring data for structural equation modeling
<p>This replication package contains the following information:</p> <ul> <li><strong>Data extraction from literature & interviews: </strong><em>Generation Structural & Measurement Model via literature and interviews.xlsx</em> - here you can find the mapping of the extracted phrases to inductively summarise information regarding the structural and measurement models.</li> <li><strong>Dataset</strong> of runtime monitoring data extracted from TrainTicket via EvoMaster: <br> <ul> <li><em>TrainTicket faults classification.xlxs:</em> Describes the datasets and their faults, in which microservice the fault is injected for better explainability of the obtained results</li> <li><em>IndicatorDescriptionbasedonAnomalyDetectionToolsInterviews.xlsx:</em> description and mapping of selected indicators to the defined parameters from <a href="https://arxiv.org/abs/2408.07816" target="_blank" rel="noopener">previous work </a></li> <li>Unfortunately, the size of the datasets generated via EvoMaster and their injected faults are too big to upload here, thus, they will be available here: <a href="https://uibkacat-my.sharepoint.com/:f:/g/personal/monika_steidl_uibk_ac_at/EjLMt8SYWwtJtp2YuSaqavcBKJoCQ3b5H_l_OY0ifbVRCA?e=fatKyD" target="_blank" rel="noopener">Datasets with injected anomalies</a><br> <ul> <li>the error description can be found <a href="https://github.com/FudanSELab/train-ticket/wiki/Fault-Description" target="_blank" rel="noopener">here</a></li> <li>the datasets are named ts-error-<em>indicatorOfError</em>-reset.zip because the databases are getting reset so that no anomalies are introduced with wrong database entries</li> </ul> </li> </ul> </li> <li><strong>Code</strong> for handling and transforming data to extract indicators describing the whole system's and microservices' behavior from the collected runtime monitoring data collected from TrainTicket:<br> <ul> <li><a href="https://github.com/moniSt13/ConTest-Parsing" target="_blank" rel="noopener">link to the Github repository</a></li> </ul> </li> <li><strong>reports</strong> regarding the established PLS-SEM model using previously handled and transformed runtime monitoring data. Please be aware that opening the reports can leas to out of memory due to their size: <ul> <li><em>Assessment of Measurement Model: MeasurementModel_TrainTicket_erorcleaned.zip & MeasurementModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em> </li> <li><em>Assessment of Structural Model: StructuralModel_TrainTicket_errorcleaned.zip & StructuralModel_Bootstrap_ALL_TrainTicket_errorcleaned.zip</em></li> </ul> </li> </ul> <p><br><br>---------------------------------------</p> <p><em>Future work </em>not elaborated in the associated paper due to space restrictions:</p> <ul> <li><strong>reports regarding F5 error</strong>: PLS-SEM model results without interpretation and further mediating effects between microservices included: F5_error.zip</li> </ul> <p> </p> <p> </p> <p> </p>
Probabilistic Model Checking for Temporal Logics in Weighted Structures - Experiments
<p>Supplementary material for the thesis</p> <p>Probabilistic Model Checking for Temporal Logics in Weighted Structures</p> <ul> <li>data set for the experiments</li> <li>results of the experiments</li> <li>evaluation scripts</li> </ul>
Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures
<p>This is the training data for the paper "Symmetric Positive Definite Convolutional Network for Surrogate Modeling and Optimization of Modular Structures"</p>
Structural Model of OSM-OSMRβ-gp130 Ternary Complex Reveals the Pathways of Allosteric Communication in OSM Signaling
Open the record for dataset details and reuse information.
Data table 3 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
Open the record for dataset details and reuse information.
The comparison of structured abstracts generated by the ChatGPT language model with the author's original abstracts derived from research papers
<p>The study utilized publications in the field of information science, both in Polish and English, which appeared in the journal <em>Zagadnienia Informacji Naukowej – Studia Informacyjne</em> during the 2022-2023 period. A total of 10 research papers were selected – 5 in Polish (PL1-PL5) and 5 in English (EN1-EN5).</p>
Research Data for "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts"
<p>This is the research data supporting figures and tables for the paper "Evaluating the electronic structure and stability of epitaxially grown Sr-doped LaFeO3 perovskite alkaline O2 evolution model electrocatalysts" appearing in <em>RSC Applied Interfaces </em>under DOI <a title="Link to landing page via DOI" href="https://doi.org/10.1039/D4LF00260A">https://doi.org/10.1039/D4LF00260A</a>.</p>
Research Data for "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective"
<p>These datasets support main text and supplementary figures and tables of the paper "Electronic structure and stability of the active surface phase of NixCo3-xO4 spinel alkaline O2 evolution electrocatalysts: from an epitaxial model catalyst perspective" appearing in <em>ACS Applied Energy Materials </em>under DOI: <a href="https://doi.org/10.1021/acsaem.4c01688">https://doi.org/10.1021/acsaem.4c01688 </a></p>
Supporting Data for "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of the revised submission of Timothy M. Merlis, Ilai Guendelman, Kai-Yuan Cheng, Lucas Harris, Yan-Ting Chen, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, and Stephan Fueglistaler (2024): "The Vertical Structure of Tropical Temperature Change in Global Storm-Resolving Model Simulations of Climate Change".</p> <p> </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.