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.
48
datasets available to search
ShareScore release 0.7.1
Dataset results
48 results for “analytic modelling”
Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine
<p><i><strong>"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"</strong></i></p><p><i>CHILECON 2023 - </i><a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/"><i>https://site.ieee.org/chilesur/ieee-chilecon-2023/</i></a><i> </i></p><p>---</p><p>En el marco del trabajo de referencia, los autores ponemos a disposición de los lectores la base de datos utilizada para el proceso de toma de decisión multicriterio para la selección del software y del MCU de una maquina CNC. </p><p>En el repositorio podrán encontrar los datos referentes a los criterios, subcriterios, indicadores, datos, fuentes de los datos extraídos, política de decisión, cálculos de las evaluaciones de los modelos AHP aplicados y el análisis de sensibilidad de estos. Además, podrán encontrar las gráficas utilizadas en el estudio en la mejor calidad posible. </p><p>El material fue puesto a disposición de todos los interesados para fines académicos y científicos. </p><p>Atte. </p><p>Los autores. </p><p>---</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Data associated to: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors
<p>Data associated to the manuscript entitled: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Dataset: Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation
<p>Data corresponding to the figures of the manuscript "Analytical Physical Model for Electrolyte Gated Organic Field Effect Transistors in the Helmholtz Approximation" by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
GALAKSIENN: the G.A.S semi-analytical model associtated library
<p>The GALAKSIENN library contains data sets generated by the G.A.S. semi-analytical model of galaxy formation and evolution. The G.A.S. model is fully described in a set of three Astronomy & Astrophysics papers: G.A.S. I: A prescription for turbulence-regulated star formation and its impact on galaxy properties; G.A.S. II: Dust extinction in galaxies, luminosity functions and InfraRed Excess and G.A.S. III: The panchromatic view of galaxies, Stellar/dust continua and main gas emission lines. The library stores mock galaxy catalogues and ascii tables (stellar mass functions, luminosity functions, number counts ...).</p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Artifact Description/Artifact Evaluation/Computational Artifact for paper, entitled Analytic Roofline Modeling and Energy Analysis of the LULESH Proxy Application on Multi-Core Clusters
We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper. The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Analytical kinetic model of native tandem promoters in E. coli
<p><span>Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in <i>E. coli</i>. We also studied the sequence (distance between transcription start sites '<i>d<sub>TSS</sub>'</i>,<i> </i>pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and <i>d<sub>TSS</sub> </i>are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when <i>d<sub>TSS</sub> </i>≤ 35 bp) and RNAP occupancy of the downstream promoter (when <i>d<sub>TSS</sub> </i>> 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of <i>d<sub>TSS</sub> i</i>s implemented by a continuous step function, fit to <i>in vivo</i> data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of <i>d<sub>TSS</sub></i>. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.</span></p>
Analytical modeling of an hybrid power module based on diamond and SiC devices
<p>This dataset contains the raw data used for the publication (available here : <a href="https://doi.org/10.1016/j.diamond.2022.108936">10.1016/j.diamond.2022.108936</a> ).</p> <p><strong>Analytical modeling of an hybrid power module based on diamond and SiC devices</strong></p> <p>Marine Couret, Anne Castelan, Nazareno Donato, Florin Udrea, Julien Pernot, Nicolas Rouger</p> <p>Detailed descriptions for each file can be found in "Dataset_Description.docx".</p>
Equivariant analytical mapping of first principles Hamiltonians to accurate and transferable materials models
<p>Supporting data for <a href="https://arxiv.org/abs/2111.13736">https://arxiv.org/abs/2111.13736</a>.</p> <p>ACEhamiltonians.jl code</p> <p>This is an archived copy of the ACEhamiltonians.jl code to accompany the paper <a href="https://arxiv.org/abs/2111.13736">arXiv:2111.13736</a>.</p> <p>See <a href="https://github.com/ACEsuit/ACEhamiltoniansExamples">https://github.com/ACEsuit/ACEhamiltoniansExamples</a> for examples of how to use this code.</p> <p>The code is written in <a href="https://julialang.org/">Julia</a> and requires v1.6 or later. To install the Julia depenendencies:</p> <pre><code><code>$ cd ACEhamiltonians.jl $ julia julia> import Pkg julia> Pkg.activate(".") julia> Pkg.instantiate() </code></code></pre> <p>The scripts <code>test/plots.jl</code>, <code>test/fcc-to-bcc.jl</code> and <code>test/vacancy.jl</code> which produce all the plots in the paper can then run as, e.g.</p> <pre><code><code>julia --project=. test/plots.jl </code></code></pre> <p>Training data</p> <p>The <code>training_data</code> folder contains the atomic structure, Hamiltonian and overlap matrices stored in HDF5 format with the following schema:</p> <ul> <li>Data Group : <strong>aitb/</strong></li> <li>Datasets : <ul> <li><strong>H</strong> : Real-space Hamiltonian Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>S</strong> : Real-space Overlap Matrix. Type: Float64. Shape: Tensor(# of TB Cells, # of Rows, # of Columns)</li> <li><strong>energy</strong> : Energy. Unit: eV. Type: Float64. Shape: Scalar</li> <li><strong>freeenergy</strong> : Free Energy. Unit: eV. Shape: Scalar</li> <li><strong>unitcell</strong> : Unit cell vectors. Type: Float64. Shape: Matrix(3,3)</li> <li><strong>positions</strong> : Atom positions. Type: Float64. Shape: Array(3)</li> <li><strong>forces</strong> : (Optional, if available) Forces. Type: Float64. Shape: Array(3)</li> <li><strong>metadata</strong> : JSON String including dictionary of information of FHIaims calculation (k-points, basis sets), TB Cells, Cutoff, Orbital definitions.,</li> </ul> </li> </ul> <p>The molecular dynamics and FHI-aims parameters are described in the manuscript.</p> <p>On-site models</p> <p>The <code>onsite_models_ord2</code> folder contains our correlation order 2 models for the on site blocks of the Hamiltonian, in a JSON format readable by the <a href="https://github.com/acesuit/ACE.jl">ACE.jl</a> and <a href="https://github.com/ACEsuit/ACEhamiltonians.jl">ACEhamiltonians.jl</a> Julia packages. There are separate files for the Hamiltonian (<code>*_H.json</code>) and overlap (<code>*_S.json</code>) models. The JSON files also contain training and test sets and associated errors as plotted in Figure 3 in our manuscript.</p> <p>Models have a unique identifier (UUID) which is a hash of the input parameters and training data. The mapping from (order, max_degree) to UUID is as follows:</p> <pre><code><code>(2,4) - 13427527590286463256 (2,5) - 10538156191357510769 (2,6) - 1646489440533135164 (2,7) - 12130775482127724115 (2,8) - 12487060958610974041 (2,9) - 2653067664384673997 (2,10) - 1143382251563115664 (2,11) - 4564001820340015372 (2,12) - 9474261500251782658 </code></code></pre> <p>Off-site models</p> <p>The <code>offsite_models_ord1</code> and <code>offsite_models_ord2</code> folders contain our order 1 and order 2 offsite models for Hamiltonian and overlap matrices. The mapping from (H_order, H_max_degree) + (S_order, S_max_degree) to UUID is as follows:</p> <pre><code><code>(1,6) + (1,8) - 7014526518680934587 (1,7) + (1,9) - 8594416159488562244 (1,8) + (1,10) - 10204186688118368371 (1,9) + (1,11) - 13078304848585360574 (1,10)+ (1,12) - 14750835312950641338 (1,11)+ (1,13) - 9883802224093245794 (1,12)+ (1,14) - 3907899412408606585 (1,13)+ (1,15) - 201683837542179657 (1,14)+ (1,16) - 277744202775070779 (2,6) + (1,8) - 4699475053563592071 (2,7) + (1,9) - 489637409713831432 (2,8) + (1,10) - 18034631670613263469 (2,9) + (1,11) - 720654516759450160 (2,10)+ (1,12) - 15214900801060024044 (2,11)+ (1,13) - 13798832597295943078 (2,12)+ (1,14) - 13162803789413134473 </code></code></pre> <p>FCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 5311732756869418284 2 7 13030014632886405308 2 8 5820099621734447846 2 9 10161014511878227635 2 10 11298425190201843107 2 11 9932031839231628354 2 12 9447261873515969583 </code></code></pre> <p>Optimised FCC model <code>16110190062237887798</code></p> <p>BCC only</p> <p>Onsite models:</p> <pre><code><code>2 6 8949023800586845770 2 7 8045797268444730200 2 8 6919809282139600809 2 9 9935027806122780319 2 10 6376963380608532713 2 11 5001375576268070883 2 12 9678585765722197901 </code></code></pre> <p>Optimised BCC model <code>10293566074413000591</code></p> <p>FCC+BCC optimised models</p> <p>Onsite models</p> <pre><code><code>2 6 2154760103892646619 2 7 6450474921309693835 2 8 14227277988574899288 2 9 476820595195218567 2 10 5364136683220082110 2 11 14619519825012606580 2 12 14181614899005838824 </code></code></pre> <p>Offsite FCC+BCC optimised model - <code>4570230078043807257</code></p> <p>Model errors</p> <p>The <code>model_errors</code> directory contains summarised model errors for the training and testing errors for the models listed above.</p> <p>Reference data</p> <p>Reference electronic structure data computed for the BCC and FCC crystals, along the Bain path and for the relaxed vacancy is stored in the <code>reference_data</code> folder. The Hamiltonian and overlap matrices are stored as compressed binary HDF5 files. The format and metadata can be viewed with the <code>h5dump</code> utility, or read in using the supplied Julia code (or indeed from other languages).</p> <p>Predicted data</p> <p>The <code>predicted_data/FCC</code> and <code>predicted_data/BCC</code> folders contain HDF5 files with the results of all model predictions shown in the manuscript on the FCC and BCC crystal structures. <code>predicted_data/FCC-to-BCC</code> contains the results of predictions along the Bain path with the optimized model described in the manuscript and <code>predicted_data/vacancy</code> contains the vacancy calculations.</p>
Analytical kinetic model of native tandem promoters in E. coli
Open the record for dataset details and reuse information.
The observed data used in paper titled "An analytic method for calculating parameters of the van Genuchten model for soil water retention curve"
<p>In the file, the 46 soil samples from UNSODA were used to test the proposed method of estimating the parameters of VG model, including the physical and hydraulic properties data, PSD, SWRC, saturated hydraulic conductivity, porosity and saturated water content. Also, this data file includes some process data and results data, <em>D</em><sub><em>i</em>, psd</sub>, predicted <em>K<sub>s</sub></em> , the data relate to <em>a </em>and <em>n </em>and sensitivity analysis.</p>
Analytical model of delay mechanism calculations
<p>The first excel file provides the code based on the analytical equations of Gaume and Puzrin (2020) to evaluate the delay for slab avalanche release and produce the figures of the paper. The second file provides the deflection curves obtained based on MPM simulations (Figure 5).</p>
Analytical model for collisional impurity transport in tokamaks at arbitrary collisionality
<p>Database of NEO simulations used to develop a set of formulae for an analytical model for collisional impurity transport in tokamaks.</p>
Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models
<p>Dataset of the paper "Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models" published on Energies [1].</p> <p>[1] Lin, M., & Porté-Agel, F. (2019). Large-eddy simulation of yawed wind-turbine wakes: comparisons with wind tunnel measurements and analytical wake models. <em>Energies</em>, <em>12</em>(23), 4574.</p>
Dataset - An Analytical Hierarchy Process (AHP) model for mapping expectations
<p>This dataset contains detailed information about an Analytical Hierarchy Process (AHP) model developed in order to map actors' perceptions about their contributions to a gender policy. </p>
Data supplement for: Agreement of analytical and simulation-based estimates of the required land depth in climate models
<p>Many current-generation climate models have land components that are too shallow. Under climate change conditions, the long-term warming trend at the surface propagates deeper into the ground than the commonly used 3-10m. Shallow models alter the terrestrial heat storage and distribution of temperatures in the subsurface, influencing the simulated land-atmosphere interactions. Previous studies focusing on annual timescales suggest that deeper models are required to match subsurface-temperature observations and the classic analytical heat conduction solution. However, for a systematic investigation of land-model deepening in the frame of anthropogenic climate change, the classic analytical solution is inaccurate because it does not mimic the timescale and amplitude of the simulated warming trend. This study intends to bridge the gap between analytical and simulation-based estimates of the subsurface thermodynamic state by adapting the classic analytical framework to mimic long-term anthropogenic warming. The analysis shows that a land-model depth of at least 170m is recommended for a proper simulation of the post-1850 ground climate, which differs up to 30% from the estimate of the classic approach. Compared to previous studies, this provides an accurate estimate of the required land model depth for long-term climate-change simulations and indicates the relative bias in insufficiently deep land models.</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.