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1,163 results for “demonstrators”
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration data for the python package applefy.
<p>Demonstration data for the python package applefy. </p> <p>30_data: Contains the NACO L' dataset of Beta Pic (planet removed) as used in the user documentation</p> <p>70_results: Contains the results of the user documentation tutorials</p> <p>laplace_lookup_tables.csv: Contains the lookup table for the LaplaceBootstrapTest</p>
Video demonstrating dynamic calibration procedure
<p>Video recording shows dynamic calibration procedure, developed in the frameworks of ComTraForce project. The calibration was performed at the facilities of RISE Research Institutes of Sweden.</p>
Video demonstrating continuous calibration procedure
<p>Video recording shows continuous calibration procedure, developed in the frameworks of ComTraForce project. Authors would like to acknowledge SincoTec Test Systems GmbH for providing calibration facilities and explicitly Head of Measurement Department Mike Udhe for providing technical support.</p>
Internet of Laboratories - Live Demonstration
<p>A video recording of a live demonstration of an experiment that has been performed during the JRC workshop "Supporting a glocal energy transition: from local energy communities to global simulation networks". It is the result of a long-term collaboration with our research partners: RWTH Aachen and the members of the ENET RT-lab in the ENSIEL consortium (Politecnico di Torino, Università di Genova, Politecnico di Bari and Università di Napoli Federico II).</p>
Compilation of AI4DI Demonstrator Videos
<p>This video presents the demonstrators of the project's AI4DI technologies, across the different industry domains</p>
Experimental Demonstration of In-Memory Computing in a Ferrofluid System
<p>Measurements that demonstrate the feasibility of in-memory computing using an amorphous ferrofluid system (in a liquid aggregation state). This work poses the basis for the exploitation of a colloid as both an in-memory computing device and as a full-electric liquid computer thanks to its fluidity and the reported complex dynamics, via probing read-out and programming ports.</p>
TRINITY DC_3.5.1 final demonstration results
<p>This data set represents final demonstration results for the Demo Case 3.5.1 "Regional Adequacy Assessment (RAA) in the SEE region" of the TRINITY project. It contains following files:</p> <p>- PG.csv contains values of unit cost for engagement of particular generation types for different areas that are part of optimization process;</p> <p>- PT.csv contains values of unit cost for transaction on borders;</p> <p>- RAA_algorithm_results-Test1-CB_settings.xlsx contains results of RAA algorithm using CB settings;</p> <p>- RAA_algorithm_results-Test2-TY_settings.xlsx contains results of RAA algorithm using TY settings.</p> <p>Last two files contain information about values: ERIC [MW], TIC [MW], TIF [MW], Loop flow [%], Cost [€], Cost/TIC [€/MW], Optimal adequacy transaction [MW] and Net adequacy exchange [MW] for 9 areas (AL, BA, BG, GR, ME, MK, HR, RO and RS) and 331 timestamps (of Week 9 and Week 10 of 2021). </p> <p>All information about RAA algorithm and terms used in this data set could be found in document <em>D4.4 T-SENTINEL TOOLSET v2</em>, while more conclusions about DC 3.5.1 final demonstration could be found in document <em>D7.4 TRINITY demonstration activities</em><em>.</em></p>
Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator
<p>Drift velocity and longitudinal diffusion for the manuscript:</p> <p>Electron transport measurements in liquid xenon with Xenoscope, a large-scale DARWIN demonstrator</p> <p>https://arxiv.org/abs/2303.13963</p> <p> </p>
Majorana Demonstrator Data Release for AI/ML Applications
<p>The enclosed data release consists of a subset of the 228Th calibration data from the Majorana Demonstrator<br> experiment. Each Majorana event is accompanied by raw Germanium detector waveforms, pulse shape discrimina-<br> tion cuts, and calibrated final energies, all shared in an HDF5 file format along with relevant metadata. This release<br> is specifically designed to support the training and testing of Artificial Intelligence and Machine Learning (AI/ML)<br> algorithms upon our data. Please read the following ArXiV posting before using this dataset: https://arxiv.org/abs/2308.10856. Please direct questions about the material provided within this release to liaobo77@ucsd.edu (A. Li).</p>
H2020 Platone German Demonstrator - Use Case Setting Data
<p>This dataset belongs to the German demonstrator of the H2020 Platone project (WP5). The dataset contain information that have been set for Use Cases (UCs) parameterization during the project phase. UC have been parameterized along a grafical user interface (GUI) named "Use Case Selector". Each parameterized UC triggered has been logged in the dataset.</p> <p><strong>Background - Field Test Setup</strong></p> <p>The field test setup consists of a Low Voltage (LV) community with 450 kW installed generation capacity. The power exchange between the LV grid and Medium Voltage (MV) grid takes place along a single point of common coupling (PCC). i.e., a secondary substation that includes a transformer with senors on the LV busbar, to measure the net power exchange. The community consists of 89 households, 450kW of installed PV generation capacity, a Community Battery Energy Storage (CBES) connected to the LV busbar with 300 kW and 850 kWh capacity. </p> <p><strong>Definition of data:</strong></p> <p><strong>RequID</strong> - Request ID - Identifier for each triggered UC</p> <p><strong>Alert</strong> - Indicates, whether UC has been executed successfull ( " "and " true" indacates successfull implementation by Energy Management System (EMS); "false" indicates that UC has not been implemented by EMS)</p> <p><strong>Submission -</strong> timestamp of UC submission<strong> </strong></p> <p><strong>Note -</strong> Annotations entered by UseCase operator</p> <p><strong>Priority -</strong> Defines UC priority set by operator (priority: 1 - high , 2 - medium, 3 - low, 4 - very low) (only relevant for UC 2)</p> <p><strong>Status - </strong>Indicates the status of the UC (closed - UC has been executed, cancelled, UC has been has been canceled before or during application)</p> <p><strong>Start</strong> - Point of time set for the beginning of UC</p> <p><strong>End</strong> - Point of time set for the end of UC</p> <p><strong>Type</strong>- Triggered Type of UC (1 - "Virtual Islanding of LV community" (UC 1); 2 - "Coordination of Flex Request" (UC 2); 3 - "Energy Import in Bulk" (UC 3); 4 - "Bulk-based Energy Export" (UC 4)</p> <p><strong>Subtype</strong> - 0 - Rule-Based Operation Mode with 15-minutes control cycles of battery (CBES in the field) ;1 - Day-ahead forecast-based control; 2.0 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC within 24h period ; 21 - Schedule-based operation mode with optimization applied to a day-ahead forecast (optimization target: minimization of power exchanges at MV/LV PCC and achieving a requested State of Charge (of CBES) at the end of UC_End;</p> <p><strong>bulkStart</strong> - Point of time of start of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulkEnd</strong> - Point of time of end of energy bulk import or export (only relevant for UC 3 and 4)</p> <p><strong>bulk Energy</strong> - Amount of energy triggered to be imported or exported as bulk (only relevant for UC 3 and 4)</p> <p><strong>Final_SOF </strong>- State of Charge (SOC) of CBES that should be achieved at end of UC (End)</p> <p><strong>FlexDemand </strong>- Requested power exchange that should be achieved at MV/LV PCC (Only relevant for UC 2)</p> <p><strong>Ptcb - </strong>Measured CBES charging power at poin of time of UC submission </p> <p><strong>Ptei </strong>- Measured power exchange at PCC at point of time of UC submission </p> <p><strong>SoC </strong>- State of Charge of CBES at point of time of UC submission </p> <p><strong>SoE</strong> - State of Energyof CBES at point of time of UC submission </p> <p><strong>ActiveSet </strong>- State of Energyof CBES at point of time of UC submission </p> <p><strong>maxSoC </strong>- Maximum SoC set for CBES at point of time of UC submission </p> <p><strong>minSoc - </strong>MinimumSoC set for CBES at point of time of UC submission </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300.</p>
Dataset for demonstration of quantitative label-free imaging with phase and polarization
<p>The QLIPP_Reconstruction_Resources_20x.zip file contains raw images of mouse brain slice and anisotropic glass target acquired with QLIPP. The file also contains the configuration files to reconstruct the phase, retardance, and orientation from this data with the recOrder pipeline. The tutorial slides for using this dataset for reconstruction can be found here (10.5281/zenodo.5135889).</p> <p> </p> <p>v1.1.0: Upload two zip files for automated testing of recOrder and waveOrder repositories.</p> <p>v1.2.0: add pycromanager dataset for testing the reader and converter in waveOrder.</p> <p>v1.3.0: reduce the size of recOrder test dataset</p> <p>v 1.4.0: add datasets for new data schema defined for recOrder 0.4.0 </p> <p>v1.5.0: add a dataset that shows images of an embryo </p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 3rd quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 2nd quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
H2020 Platone Italian Demonstrator Use Case 1-2 Market 1st quarter 2022
<p>areti_market_flexibility_TSO_requestes</p> <p>areti_market_flexibility_DSO_requestes</p> <p>areti_market_flexibility_Aggregator_bids</p> <p>areti_market_flexibility_settlement</p> <p>areti_market_flexibility_outcomes</p> <p>- TSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>Grid Area</li> </ul> <p>- DSO flexibility requests:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes, Grid Area</li> </ul> <p>- Aggregator bids:</p> <ul> <li>Starting Time</li> <li>Duration</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> <li>Volumes</li> <li>PoDs List</li> </ul> <p>- Settlement data:</p> <ul> <li>Pod</li> <li>Requested Active Power</li> <li>Measured Active Power</li> <li>Requested Reactive Power</li> <li>Measured Reactive Power</li> </ul> <p>- Market Outcomes:</p> <ul> <li>Market Outcome Id</li> <li>Market Type</li> <li>Market Session</li> <li>Flexibility Service Type</li> </ul> <p>Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation.</p> <p>(Useful link to consult Italian UC: <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-1-voltage-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=ptAsS52VBberHmZqIzYEXZs1PQrXQ6TDz6mNK%2FNWnk0%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/</a>; <a href="https://eur01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fsmart-grid-use-cases.github.io%2Fdocs%2Fusecases%2Fplatone%2Fuc-it-2-congestion-management%2F&data=04%7C01%7Cfabio.bastianelli%40mail-bip.com%7C0bb5a42aac0c49cb876708d9f2c89510%7Cbb1a63ebeb09471aa00537b07792a5b5%7C0%7C0%7C637807765572787908%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=xWNOqSeS5JxDoBEWZ4aB63gLmsnTA8YGGfCOoLjo1eo%3D&reserved=0">https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/</a>, <a href="https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf">https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf</a>)</p>
Machine Demonstration: mechanical weed control in soybeans
<p>This video was provided by the EU funded project Legumes Translated, which supports the production and use of grain legumes in Europe. On the project website <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.legumestranslated.eu">https://www.legumestranslated.eu</a> you will find more information and practical guidelines on the production and use of grain legumes. More Info: «Mechanical weed control in organic soy cultivation - how and when to use which machine?» <a href="https://www.youtube.com/redirect?v=Bm_5JluTpc0&event=video_description&redir_token=Sf_vP1RmyENuQtEbJbo_0YXcxpN8MTU3ODQxNTk2OUAxNTc4MzI5NTY5&q=https%3A%2F%2Fwww.bioattualita.ch%2Fcoltura%2Fa">https://www.bioattualita.ch/coltura/a</a>... Weed control is one of the main factors of economic success in organic soybean production. This video presents the following machines for mechanical weed control: 1. Weeding between and in the rows MATER Macc Unica-F Einböck Chopstar Garford Robocrop Schmotzer 2. Machines working row-independent Treffler TS 620/3M Einböck Aerostar-Rotation Carre Rotanet</p>
Data from: Fluorescent biomarkers demonstrate prospects for spreadable vaccines to control disease transmission in wild bats
Vaccines that autonomously transfer among individuals have been proposed as a strategy to control infectious diseases within wildlife populations. However, understanding rates of spread and epidemiological efficacy in real world systems remain elusive. Here, we investigated whether topical vaccines that transfer among bats through social contacts can control vampire bat rabies, a medically and economically important zoonosis in Latin America. Field experiments in 3 Peruvian bat colonies which used fluorescent biomarkers as a proxy for the bat-to-bat transfer and ingestion of an oral vaccine revealed that vaccine transfer would increase population-level immunity up to 2.6 times beyond the same effort using conventional, non-spreadable vaccines. Mathematical models demonstrated that observed levels of vaccine transfer would reduce the probability, size, and duration of rabies outbreaks, even at low, but realistically achievable levels of vaccine application. Models further predicted that existing vaccines provide substantial advantages over culling bats, the policy currently implemented in North, Central, and South America. Linking field studies with biomarkers to mathematical models can inform how spreadable vaccines may combat pathogens of health and conservation concern prior to costly investments in vaccine design and testing.
BIONIC Full BSN Demonstrator
<p>This video demonstrates the real time processing capabilities of the BIONIC system in a lab environment. </p> <p> </p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>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).</p> <p>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.</p> <p>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).</p> <p>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.</p> <p>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.</p> <p>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).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
ScienceDex guides
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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.