Find research datasets worth reusing

opencc-by-4.0Nov 2023View details →

Dataset: An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine

"An Analytic Hierarchy Process-Based Multicriteria Model for Component Selection in a Computational Numerical Control (CNC) Machine"CHILECON 2023 - <a href="https://site.ieee.org/chilesur/ieee-chilecon-2023/">https://site.ieee.org/chilesur/ieee-chilecon-2023/</a> ---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. 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. El material fue puesto a disposición de todos los interesados para fines académicos y científicos. Atte. Los autores. ---

opengpl-3.0-or-laterApr 2024View details →

Unlocking the power of computer modelling and simulation across the life sciences product lifecycle

Unlocking the Power of Computer Modelling and Simulation Across the Life Sciences Product Lifecycle In an era where technology continuously reshapes the boundaries of research and development, the field of life sciences stands at the cusp of a transformative shift. The potent combination of computer modelling and simulation has begun to unlock unprecedented opportunities across the product lifecycle in life sciences, promising to revolutionize everything from medicinal product development to clinical research. Let's delve into how these technological advancements are paving the way for groundbreaking progress in medicine and healthcare. The Fusion of Technology and Life Sciences In Silico Methods: A New Frontier in Medicine The term 'in silico' refers to computer simulations used in the study of biological and chemical processes. The video highlights the growing importance of in silico methods in the life sciences sector, particularly in the United Kingdom. These methods allow for the virtual testing of new medicinal products, significantly reducing the need for costly and time-consuming physical trials. Bridging the Gap with Computational Modeling Computational modeling is another key aspect discussed in the presentation. It involves the use of computer algorithms and mathematical models to simulate real-world medical data. This approach enables researchers to predict how medicinal products will behave in various scenarios, including their interaction with different types of patient data. As a result, computational modeling is instrumental in enhancing the precision of clinical research and improving medical equitability by considering a broader range of patient profiles. The Impact on Clinical Research and Patient Care Enhancing Precision and Efficiency One of the most notable benefits of integrating computer modelling and simulation into the life sciences is the enhanced precision and efficiency it brings to clinical research. By leveraging real-world medical data, researchers can obtain more accurate predictions about the efficacy and safety of new medicinal products. This not only accelerates the development process but also ensures that treatments are more tailored to individual patient needs. Promoting Medical Equitability The video underscores the role of these technologies in promoting medical equitability. Through the use of patient data simulations, it becomes possible to account for a wider array of genetic, environmental, and lifestyle factors that influence health outcomes. This inclusive approach ensures that the benefits of medical advancements are accessible to a diverse population, addressing disparities in healthcare access and treatment efficacy. Conclusion: The Future is Now The integration of computer modelling and simulation in the life sciences heralds a new era of medical research and patient care. As we continue to explore the potential of these technologies, it's clear that they hold the key to unlocking more efficient, precise, and equitable healthcare solutions. The journey towards fully realizing this potential is just beginning, but the promise it holds is immense. As we stand on the brink of this technological revolution, one thing is certain: the future of medicine and healthcare is being shaped here and now, and it's brighter than ever.

opencc-by-4.0Sep 2021View details →

3D magnetotelluric modeling using high-order tetrahedral Nédélec elementson massively parallel computing platforms

Accompanying data to journal article <blockquote> Castillo-Reyes, O., Modesto, D., Queralt, P., Marcuello, A., Ledo, J., Amor-Martin, A., de la Puente, J., García-Castillo, L.E. (2021) 3D magnetotelluric modeling using high-order tetrahedral Nédélec elements on massively parallel computing platforms. Computers & Geosciences, vol.(160): 105030 DOI: 10.1016/j.cageo.2021.105030. ISSN 0098-3004, Elsevier. </blockquote>

opencc-by-4.0Sep 2022View details →

Dataset for the publication "The TACS Model: Understanding Teachers' Adoption of Computer Science Pedagogical Content in Primary School"

This dataset contains the quantitative teacher data used to analyse an in service teacher training program for Computer Science that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. Approximately 180 teachers from the the 5th and 6th grade in primary school (ages 9-11) participated in 3 days of training sessions. At the end of each training session, teachers were asked to fill in a web-based questionnaire providing information relating to their perception of the training sessions and adoption of the computer science activities. The surveys were analysed from three perspectives which are detailed in the corresponding article (the professional development program's perspective, the activities' perspective, the teacher's perspective). The present repository thus contains three csv files, one per analysis. A README is included and provides additional information regarding : - the requirements for re-use.  - the survey instrument used - the specific content of the 3 csv files

opencc-by-4.0Nov 2020View details →

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted).

Model data for Sequential Dynamics of Stearoyl-CoA Desaturase /Ligand Binding and Unbinding Mechanism: A Computational Study by Petroff et al. (submitted). This folder contains the files needed to start each of the models described in the paper. The files were created using MOE 2020 software made by Chemical Computing Group and run on NAMD2. The models identifiers in the paper correspond to the following terms in the code: Substrate: "13_5_coa" Product: "13_5_coa_desat_fe3" Apoprotein: "13_5_no_ligand" Saturated Lipid: "13_5_nocoa" Desaturated Lipid: "13_5_nocoa_desat_fe3" CoA model: "13_5_coa_nolipid" Substrate-waterbox model: "13_5_coa_waterbox" Saturated Lipid-waterbox: "13_5_nocoa_waterbox"

opencc-by-4.0Aug 2023View details →

Phlorest phylogeny derived from Chacon & List 2015 'Improved computational models of sound change shed light on the history of the Tukanoan languages'

Cite the source of the dataset as: <blockquote> Chacon TC, List J-M (2015) Improved computational models of sound change shed light on the history of the Tukanoan languages. Journal of Language Relationship, 3:177–203. </blockquote>

opencc-by-4.0Aug 2017View details →

Multiscale continuum figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling"

Accessible versions of selected figures from Tratnyek et al. (2017) "In silico environmental chemical science: Properties and processes from statistical and computational modelling" Environ. Sci. Processes Impacts 19(3): 188-202. DOI: 10.1039/C7EM00053G. The Abstract Art figure shows a classification of variables for predictive/diagnostic models used in silico environmental chemical science, in terms of system scales and variable types. Figure 3 shows a continuum of system scales encompassing the whole scope of predictive/diagnostic modelling for in silico environmental chemical sciences, juxtaposing earth and biological scales. The published version of Figure 3 is tall, for two-column page-layouts, but a wide version of Figure 3 is provided for landscape oriented formats. The 300 dpi versions of each figure should be adequate resolution for most purposes, and therefore are recommended. The large versions of the figures may take significant time to download, but may be useful for high resolution applications. This work is from the perspectives/review paper at the beginning of a themed issue on "Quantitative Structure-Activity Relationships (QSARs) and Computational Chemistry Methods in the Environmental Chemical Sciences", published in the March 2017 issue of the Royal Society of Chemistry journal Environmental Sciences: Process and Impacts. The whole collection of papers can be accessed at rsc.li/qsars.

opencc-by-4.0Mar 2018View details →

Computational models for kaolinite nano-particles (Generations 1-3) and their comprehensive FTIR spectra

The dataset contains a large number of computational models and detailed spectral comparison, fitting, and deconvolution of a large set of FTIR data for crystalline and exfoliation kaolinite, nano-kaolinite and halloysite, nano-halloysite samples. The G1.xyz, G2.xyz, and G3.xyz files contain the initial structures for the first three generations of nano-kaolinite molecules. The compressed folder SVP-def2TZVP.zip contains the structural information relevant for comparing and contrasting the performance a double-zeta (SVP) and triple-zeta (TZVP) basis sets. The edge_protonation.zip folder guides the reader through the stepwise evaluation of various edge protonation models and shows the final converged results. The full_optimization.zip folder summarizes the stationary structure calculations at various levels of theory carried out for the G2 model.

opencc-by-4.0Nov 2018View details →

DeepPredSpeech: computational models of predictive speech coding based on deep learning

This dataset contains all data, source code, pre-trained computational predictive models and experimental results related to:   Hueber T., Tatulli E., Girin L., Schwatz, J-L "How predictive can be predictions in the neurocognitive processing of auditory and audiovisual speech? A deep learning study." (<a href="https://doi.org/10.1101/471581">biorXiv preprint https://doi.org/10.1101/471581</a>).  <ul> <li>Raw data are extracted from the publicly available database NTCD-TIMIT (10.5281/zenodo.260228).  <ul> <li>Audio recordings are available in the audio_clean/ directory</li> <li>Post-processed lip image sequences are available in the lips_roi/ directory (67x67 pixels, 8bits, obtained by lossless inverse DCT-2D transform from the DCT feature available in the original repository of NTCD-TIMIT)</li> <li>Phonetic segmentation (extracted from NTCD-TIMIT original zenodo repository) is available in the HTK MLF file volunteer_labelfiles.mlf</li> </ul> </li> <li>Audio features (MFCC-spectrogram and log-spectrogram) are available in the mfcc_16k/ and fft_16k/ directories. </li> <li>Models (audio-only, video-only and audiovisual, based on deep feed-forward neural networks and/or convolutional neural network, in .h5 format, trained with Keras 2.0 toolkit) and data normalization parameters (in .dat scikit-learn format) are available in models_mfcc/ and models_logspectro/ directories</li> <li>Predicted and target (ground truth) MFCC-spectro (resp. log-spectro) for the test databases (1909 sentences), and for the different values of $\tau_p$ or $\tau_f$ are available in pred_testdb_mfccspectro/ (resp. pred_testdb_logspectro/) directory</li> </ul> Source code for extracting audio features, training and evaluating the models is available on GitHub https://github.com/thueber/DeepPredSpeech/ All directories have been zipped before upload. Feel free to contact me for more details. Thomas Hueber, Ph. D., CNRS research fellow, GIPSA-lab, Grenoble, France, thomas.hueber@gipsa-lab.fr

opencc-by-4.0Nov 2021View details →

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).

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). 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> The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki

opencc-by-4.0Jun 2021View details →

Electrochemical and Spectroscopic Data supported by Computational Models for Exploring the Metal- and Ligand-Based Oxidation of Mackinawite Nanoparticles

Supporting information to our study, where under anaerobic conditions, ferrous iron reacts with sulfide producing FeS precipitate, which can then undergo a temperature, redox potential, and pH dependent maturation process resulting in the formation of oxidized mineral phases such as gregite or pyrite. The dataset provide information about the chemical speciation of iron-sulfide by cyclic voltammetry, Raman and X-ray absorption spectroscopic techniques. Nanoparticulate FeS was found to get oxidized to a Fe3+ containing FeS phase at -0.5 V vs. Ag/AgCl (pH = 7) and in a concomitant oxidation step, polysulfides are proposed to give a material described as Fe2+(1−3x)Fe3+(2x)S2-(1-y)(Sn2-)y. The thermodynamic differences between ligand- and metal-based oxidation processes from density functional theory can be used to describe one- and two-electron electronic and structural transformations. These findings together point to the existence of a previously unknown, metastable FeS phase located between FeS and greigite (Fe2+Fe3+2S2-4) along a metal oxidation path, and Fe2+S2- and pyrite (Fe2+S22-) along a ligand oxidation path, respectively.

opencc-by-4.0Jul 2021View details →

Modelling of excitation propagation on computer models of insoles colonised by fungal mycelium. Videos and potential difference recordings.

We used an artistic image of the mycelium network projected onto a $364 \times 985$ nodes grid.  The original image $M=(m_{ij})_{1 \leq j \leq n_i, 1 \leq j \leq n_j}$, $m_{ij} \in \{ r_{ij}, g_{ij}, b_{ij} \}$, where $n_i=364$ and $n_j=985$, and $1 \leq r, g, b \leq 255$, was converted to a conductive matrix $C=(m_{ij})_{1 \leq i,j \leq n}$ derived from the image as follows: $m_{ij}=1$  if $r_{ij}>170$, $g_{ij}>170$ and $b_{ij}<200$; a dilution operation was applied to $C$.  FitzHugh-Nagumo (FHN) equations is a qualitative approximation of the Hodgkin-Huxley model of electrical activity of living cells: \begin{eqnarray} \frac{\partial v}{\partial t} & = & c_1 u (u-a) (1-u) - c_2 u v + I + D_u \nabla^2 \\ \frac{\partial v}{\partial t} & = & b (u - v), \end{eqnarray} where $u$ is a value of a trans-membrane potential, $v$ a variable accountable for a total slow ionic current, or a recovery variable responsible for a slow negative feedback, $I$ {is} a value of an external stimulation current. The current through intra-cellular spaces is approximated by $D_u \nabla^2$, where $D_u$ is a conductance. The term $D_u \nabla^2 u$ governs a passive spread of the current. The terms $c_2 u (u-a) (1-u)$ and $b (u - v)$ describe the ionic currents. The term $u (u-a) (1-u)$ has two stable fixed points $u=0$ and $u=1$ and one unstable point $u=a$, where $a$ is a threshold of an excitation. We integrated the system using the Euler method with the five-node Laplace operator, a time step $\Delta t=0.015$ and a grid point spacing $\Delta x = 2$, while other parameters were $D_u=1$, $a=0.13$, $b=0.013$, $c_1=0.26$. We controlled excitability of the medium by varying $c_2$ from 0.05 (fully excitable) to 0.015 (non excitable). Boundaries are considered to be impermeable: $\partial u/\partial \mathbf{n}=0$, where $\mathbf{n}$ is a vector normal to the boundary.  To record dynamics of excitation in the network, as if in laboratory experiments, we simulated electrodes by calculating a potential $p^t_x$ at an electrode location $x$ as $p_x = \sum_{y: |x-y|<2} (u_x - v_x)$. Configuration of electrodes $1, \cdots, 16$ is shown in Fig.~\ref{fig:mycelium}c.  Time-lapse snapshots provided in the paper were recorded at every 100\textsuperscript{th} time step, and we display sites with $u >0.04$; videos and figures were produced by saving a frame of the simulation every 100\textsuperscript{th} step of the numerical integration and assembling the saved frames into the video with a play rate of 30 fps.  Insole_01: Excitation started at electrode E2 Insole_10: Excitation started at electrode E1 Insole_11: Excitation started at electrodes E1 and E2

opencc-by-4.0Oct 2022View details →

Computed surface and chemical potentials, expansion coefficients, structures, models and results for the PMFPredictor Toolkit

The PMFPredictor toolkit enables the prediction of the potentials of mean force describing the interaction between a surface and a small molecule in aqueous solution, which would otherwise be obtained from lengthy metadynamics simulations. This repository contains files to enable the operation of the toolkit, with source code available at https://github.com/ijrouse/PMFPredictor-Toolkit and corresponding to release v0.5-alpha. In PMFPredictor-Repository.zip we provide supplementary data necessary for the operation of the PMFPredictor Toolkit including: <ul> <li>Structures of surfaces ("Structures/Surfaces") and chemicals ("Structures/Chemicals") in a united tabulated (.csv) format, listing x/y/z co-ordinates, atom IDs, mass (in amu), charge (in elementary units), Lennard Jones 6-12 parameters: sigma (in nm) and epsilon (in kJ/mol).</li> <li>Interaction potentials of surfaces ("SurfacePotentials") and chemicals ("ChemicalPotentials") with probe atoms and molecules in tabulated format with distances relative to reference points in nm and energies in kJ/mol. Also included in these folders are the potentials with the molecular probes in individual files.</li> <li>Hypergeometric expansion coefficients of the interaction potentials ("Datasets/SurfacePotentialCoefficientsNoise-1-oct12.csv" and "Datasets/ChemicalPotentialCoefficients-oct10.csv") in tabulated form, corresponding to potentials with units of nm for distance and kJ/mol for energy. Descriptions of the headers are provided in DatasetHeaderDescription.txt, included in the archive.</li> <li>Trained TensorFlow models for the prediction of potentials of mean force from HG interaction coefficients, suitable for loading via the Keras backend.</li> <li>PMFs generated for a range of surfaces and chemicals as output from the trained model, in both text format and figures showing comparisons to training PMFs where available. PMFs are supplied as tabulated data with comma separated values of distance in nm and interaction energies in kJ/mol.</li> <li>Adsorption energies in kJ/mol evaluated at T=300K extracted from all PMFs and compared to the values obtained from known PMFs where available.</li> </ul> The surface_pmfpredictor.zip  archive contains PMFs selected for the operation of the UnitedAtom software package for the calculation of protein-nanoparticle interactions. This data is included in the main repository file and provided separately to avoid the download of unnecessary data if only the final PMFs are required. As with the main set, these are provided in tabulated form with distance [nm], energy [kJ/mol] pairs. This repository also contains the sets of figures illustrating these PMFs for each surface. Both archives contain further information on the contents, including descriptions of the surfaces and chemicals for which PMFs are computed. We also supply the training data used to build the model in a separate archive, PMFPredictor-TrainingData.zip, along with a text file containing descriptions of all headers in this file. This training data is quite large when uncompressed, c.a. 7 Gb, hence its exclusion from the main archive. If you use results from this repository please cite the following paper in addition to the repository itself: I. Rouse, V. Lobaskin, Machine-learning based prediction of small molecule -- surface interaction potentials,      arXiv:2211.07999 https://arxiv.org/abs/2211.07999

opencc-by-4.0Apr 2023View details →

A computationally efficient statistically downscaled 100 m resolution Greenland product from the regional climate model MAR: accompanying dataset

Dataset containing surface temperature and surface mass balance datasets generated from the MAR regional climate model over Greenland over two test areas using statistical downscaling tools from 6 km to 100m. The abstract of the accompanying submitted paper follows:    The Greenland Ice Sheet (GrIS) has been contributing directly to sea level rise and this contribution is projected to accelerate over next decades. A crucial tool for studying the evolution surface mass loss (e.g., surface mass balance, SMB) consists of regional climate models (RCMs) which can provide current estimates and future projections of sea level rise associated with such losses. However, one of the main limitations of RCMs is the relatively coarse horizontal spatial resolution at which outputs are currently generated. Here, we report results concerning the statistical downscaling of the SMB modeled by the Modèle Atmosphérique Régional (MAR) RCM from the original spatial resolution of 6 km to 100 m building on the relationship between elevation and mass losses in Greenland. To this goal, we developed a geospatial framework that allows the parallelization of the downscaling process, a crucial aspect to increase the computational efficiency of the algorithm. The results obtained in the case of the SMB, assessed through the comparison of the modeled outputs with in-situ SMB measurements, show a considerable improvement in the case of the downscaled product with respect to the original, coarse output. In the case of the downscaled MAR product, the coefficient of determination (R2) increases from 0.868 for the original MAR output to 0.935 for the downscaled product. Moreover, the value of the slope and intercept of the linear regression fitting modeled and measured SMB values shifts from 0.865 for the original MAR to 1.015 for the downscaled product in the case of the intercept and from the value -235mm (original) to -57 mm (downscaled) in the case of the slope, considerably improving upon results previously published in the literature.

openapache2.0Jun 2023View details →

Supplementary Material: A method for the estimation of a motor unit innervation zone center position evaluated with a computational sEMG model

This repository contains supplementary data for the journal paper: <blockquote> Mechtenberg M and Schneider A (2023) A method for the estimation of a motor unit innervation zone center position  evaluated with a computational sEMG model. Front. Neurorobot. 17:1179224. doi:  10.3389/fnbot.2023.1179224 </blockquote> It contains the configuration files for the simulator used in that publication [1]. These configuration files are to be found in the archive EMG_model_configs.zip. The files IP_tracking_opt_res.json<a href="https://zenodo.org/api/files/d21f2990-1849-40d8-90de-674fb0965938/IP_tracking_opt_res.json"> </a>and IP_tracking_opt_res.pkl contain the same information but in different file formats. In these files the results of the optimization described in the corresponding paper are stored. For each optimization condition the optimal parameters for the innervation point tracking algorithm are stored, as well as the error score for all calculated parameter combinations.   [1] Mechtenberg, Malte. (2023). UAS-Embedded-Systems-Biomechatronics/EMG-concentrated-current-sources: v0.2.1 (v0.2.1). Zenodo. https://doi.org/10.5281/zenodo.7995152

opencc-by-4.0Aug 2020View details →

PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets

This repository contains the data accompanying the PLOS Computational Biology paper "Biophysically detailed mathematical models of multiscale cardiac active mechanics", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni. It contains the following datasets: <ul> <li>steady_state.csv: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li>isometric_twitches.csv: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li>force_velocity_relationship.csv: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li>fast_transient_response.csv: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> CSV headers refer to the following variables (and measure units): <ul> <li>Ca (μM): intracellular calcium concentration.</li> <li>SL (μm): sarcomere length.</li> <li>active_tension (kPa): active tension.</li> <li>Delta_L (nm/hs): step length.</li> <li>velocity (hs/s): shortening velocity.</li> <li>time (s): time.</li> </ul>

opencc-by-4.0Feb 2020View details →

Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism

This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)

opencc-by-4.0Jun 2020View details →

Computational modelling of metal soap formation in historical oil paintings: the influence of fatty acid concentration and nucleus geometry on the induced chemo-mechanical damage.

Metal soap formation is one of the most wide-spread degradation mechanisms observed in historical oil paintings, affecting works of art from museum collections worldwide. Metal soaps develop from a chemical reaction between metal ions present in the pigments and saturated fatty acids, which are released by the oil binder. The presence of large metal soap crystals inside paint layers or at the paint surface can be detrimental for the visual appearance of artworks. Moreover, metal soaps can possibly trigger mechanical damage, ultimately resulting in flaking of the paint. This paper departs from a recently proposed computational model to predict chemo-mechanical degradation in historical oil paintings, as presented in Eumelen et al. (J Mech Phys Solids 132:103683, 2019). The model describes metal soap formation and growth, which are phenomena that are driven by the diffusion of saturated fatty acids and proceed by a nucleation process from a crystalline nucleus of small size. This results into a chemically-induced strain in the paint, which may promote crack nucleation and propagation. The proposed model is here used to investigate the effects of saturated fatty acid concentration and initial nucleus geometry on the amount of chemo-mechanical damage generated. Numerical simulations show that both factors have a marginal influence on the growth rate of the metal soap crystal, but play a significant role on the extent of fracture induced in the paint.

opencc-by-4.0Mar 2016View details →

Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow

L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008). The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard.  Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery.  After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard. In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008). Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning. The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock. What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of  shoots of peach trees (see references). Description of files Simulation 1: L-PEACH-d over three years of growth. Simulation 2: Detailed growth of leaves and fruit using L-PEACH. Simulation 3: Pruning L-PEACH-d to a v-system. Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h. Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h. Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.