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519 results for “Optimisation”
Head Of Michelangelo's David, Optimised
This model is a repost of data released under the CC0 dedication by SMK - Statens Museum for Kunst: https://www.myminifactory.com/object/3d-print-head-of-michelangelo-s-david-52645 > Digitised using an Artec Eva. > >This is a scan from a cast of the original David by Michelangelo, a masterpiece of Renaissance art. The cropped bust was crafted in 1899 (Beckett number 756). Optimised using https://rapidcompact.com/ to reduce facecount and generate normal and occlusion maps. Source: Objaverse 1.0 / Sketchfab
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Fig. 7 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images
Fig. 7 Confusion matrix of predictions by YOLOv4 models. YOLO,You Only Look Once (model)
Data for publication of "Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes"
<p>Implementation of Gaussian process regression-based Bayesian optimisation (G-BO) using the emcee package (<a href="https://emcee.readthedocs.io/en/stable/" rel="nofollow">https://emcee.readthedocs.io/en/stable/</a>).</p> <p>For more information about the implementation of G-BO in optimising the Weather Research and Forecasting (WRF) model parameters, please refer to the paper - <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">Gaussian process regression-based Bayesian optimisation (G-BO) of model parameters - a WRF model case study of southeast Australia heat extremes</a>.</p> <p><code>G-BO_script.ipynb</code> implements the GPR-based Bayesian optimisation using the Affine Invariant Markov chain Monte Carlo (MCMC) Ensemble sampler.</p> <ul> <li><strong>QMC_sobol_samples</strong>: This file contains the 128 parameter samples across the parameter space of three sensitive parameters utilizing the Quasi Monte-Carlo (QMC) Sobol sequence design.</li> <li><strong>nmae_all_128_ens_T_Rh</strong>: This file contains the normalised mean absolute error (NMAE) values of temperature (T) and relative humidity (Rh) of the 128 parameter sample WRF simulations. For more details, please refer to <a href="https://essopenarchive.org/doi/full/10.22541/essoar.171292045.52489731" rel="nofollow">this link</a>.</li> </ul>
Adaptive cross-country optimisation strategies in thermal soaring birds
<p>This dataset contains published and unpublished high-frequency GPS data of 12 soaring birds.</p> <p>Published data are collected from: </p> <p>Griffon Vulture:</p> <p>Harel R, Nathan R. 2018. Data from: The characteristic time scale of perceived information for decision-making: departure from thermal columns in soaring birds. Movebank Data Repository. https://www.doi.org/10.5441/001/1.46t5141d</p> <p>Harel R, Nathan R. 2018. The characteristic time scale of perceived information for decision-making: departure from thermal columns in soaring birds. Funct Ecol. 32(8):2065-2072. https://doi.org/10.1111/1365-2435.13136</p> <p>Verreaux's eagle</p> <p>Murgatroyd, Megan, Theoni Photopoulou, Les G. Underhill, Willem Bouten, and Arjun Amar. 2018. Where eagles soar: Fine‐resolution tracking reveals the spatiotemporal use of differential soaring modes in a large raptor. Ecology and Evolution 8, no. 13: 6788-6799. https://doi.org/10.1002/ece3.4189.</p> <p>Northern Bald Ibis</p> <p>Voelkl B. and Fritz J. 2017. Relation between travel strategy and social organization of migrating birds with special consideration of formation flight in the northern bald ibis. Phil. Trans. R. Soc. B37220160235. http://doi.org/10.1098/rstb.2016.0235</p> <p>Steppe Eagle</p> <p>Reynolds Kate V., Thomas Adrian L. R. and Taylor Graham K. 2014. Wing tucks are a response to atmospheric turbulence in the soaring flight of the steppe eagle Aquila nipalensisJ. R. Soc. Interface.1120140645. http://doi.org/10.1098/rsif.2014.0645</p> <p>Reynolds, K. 2015. “Soaring and Gust Response in the Steppe Eagle.” PhD thesis, University of Oxford.</p> <p>Lesser Kestrel</p> <p>Hernández-Pliego J., Rodríguez C., Bustamante J. 2015. Why Do Kestrels Soar? PLoS ONE 10(12): e0145402. https://doi.org/10.1371/journal.pone.0145402</p> <p>https://datarepository.movebank.org/entities/datapackage/225f9132-72f9-4727-bf0f-cb27fbdc6240</p> <p>The rest of the dataset is collected by Olivier Duriez, Andrea Flack and Mate Nagy. </p> <p> </p>
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
Optimising C2C12 muscle myoblast transfection with polyethylenimine (PEI) or Lipofectamine-2000
<p>Optimisation of DNA transfection of the C2C12 muscle myoblast cell line, comparing use of polyethylenimine (PEI) and Lipofectamine-2000 with varying treatment conditions. </p>
Example results for use-cases of DAVOS toolkit (dependability assessment, verification, optimisation and selection of hardware models)
<p>This dataset exemplifies the results that can be obtained for several basic experimentation scenarios by means of DAVOS toolkit, available under MIT licence at https://github.com/IlyaTuzov/DAVOS</p> <p>Particular experimentation scenarios are:</p> <p>- Dependability assessment (LEON3 processor core)<br> - Dependability benchmarking of implementation alternatives (MC8051 processor core)<br> - Dependability-aware design space exploration (when implementing PIC core by Xilinx ISE toolkit)</p> <p><br> <strong>Installation steps.</strong></p> <p>1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p>– search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html<br> </p>
Space rocks and optimising scanning electron channelling contrast Dataset
<p>Data bundle for "Space rocks and optimising scanning electron channelling contrast"<br> Ben Britton, Daniel Goran, Vivian Tong<br> Accepted for publication in Materials Characterization<br> https://doi.org/10.1016/j.matchar.2018.06.001</p> <p>Also available as an ArXiv preprint:<br> https://arxiv.org/abs/1804.08754</p> <p>Contact Dr Ben Britton - e: b.bbritton@imperial.ac.uk; t: @bmatb</p> <p>Contents:</p> <p>Figures - high resolution images of the figures that were included in the paper.<br> Rot_series - the full rotation series, including the rotation stacked as a gif.<br> DD_series - the full detector retraction series, including these stacked as a gif.<br> DTilt_series - the full detector retraction series, including these stacked as a gif.<br> Raw_Images - is a full folder of the images with cropped and full ROI (which stack) for each of the images, including the results from the EBSP fits.</p> <p>To request the EBSD data, please contact Dr Britton directly.</p>
Code and data for "Optimising habitat management for amphibians: from simple models to complex decisions"
<p>The zip archive contains code and data to reproduce the analysis contained in the following manuscript:</p> <p>Scroggie, M.P., Preece, K., Nicholson, E., McCarthy, M.A., Parris, K.M. and Heard, G.W. Optimising habitat management for amphibians: from simple models to complex decisions.</p> <p>Included in the archive is the source code of two R packages (METAPOP, and METAPOPPLAN), which must first be installed, along with their various dependencies which include Rcpp, RcppArmadillo, sp, spdep and rgeos. As package METAPOPPLAN contains C++ code, installation requires the presence of the appropriate C++ compilers and other software development tools. These should be available or easily installable on Linux or other Unix based systems, but Microsoft Windows users must first install the appropriate version of Rtools, which can be downloaded from https://cran.r-project.org/bin/windows/Rtools/.</p> <p>With all appropriate packages installed, the analysis can be replicated by running the included Makefile.</p> <p>Total execution time will be quite long, due to the large number of simulations that must be run. On my Windows system, with 12 cores and 4GB of RAM, execution took approximately 10 days. The code will run much faster if the various management scenarios included in the analysis are executed in parallel. This can be done by executing make with a -j argument specifying the number of cores to utilise. For example, if your system has 12 cores, invoke *make* as follows:</p> <p>make -j 12</p> <p>Overall execution time will scale roughly with the number of available cores, up to a maximum of 24 (the total number of management scenarios).<br> </p>
Flow diagram depicting an overview of the proposed layer optimisation methodology for Li-ion pouch cells
<p>Flow diagram depicting an overview of the proposed layer optimisation methodology for Li-ion pouch cells.</p>
Optimisation of HTT-HAP40 purification using heparin affinity chromatography (2019/01/14)
<p><strong>Project</strong> - Huntingtin structure-function open lab notebook. </p> <p><strong>Experiment</strong> - Optimisation of HTT-HAP40 purification using heparin affinity chromatography.</p> <p><strong>Aims</strong> - The current protocol for HTT and HTT-HAP40 purification I am using requires a long incubation of clarified cell lysate with FLAG resin. To potentially improve yields and sample quality, it would perhaps be beneficial to have a quick heparin resin purification step prior to FLAG binding which may also remove contaminating nucleic acid material. To test this hypothesis, small-scale purification of Q23 HTT-HAP40 samples in different buffer systems will be carried out using heparin and FLAG affinity chromatography. </p>
Optimisation of HTT-HAP40 purification using heparin affinity chromatography - 2019/01/29
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Optimisation of HTT-HAP40 purification using heparin affinity chromatography</p> <p><strong>Date completed:­ </strong>2019/01/29</p> <p><strong>Rationale: </strong>The current protocol for HTT and HTT-HAP40 purification I am using requires a long incubation of clarified cell lysate with FLAG resin. To potentially improve yields and sample quality, it would perhaps be beneficial to have a quick heparin resin purification step prior to FLAG binding which may also remove contaminating nucleic acid material. To test this hypothesis, small-scale purification of Q23 HTT-HAP40 samples in different buffer systems were conducted using heparin and FLAG affinity chromatography which showed the sample bound heparin resin – see <a href="https://zenodo.org/record/2553669">https://zenodo.org/record/2553669</a>. Now this need to be scaled up and tested more stringently. </p>
Reliable Many-to-Many Routing in Wireless Sensor Networks Using Ant Colony Optimisation
<p>Results files for testing of ACO protocol for many to many routing in wireless sensor networks. </p>
Optimisation of HTT-HAP40 purification using heparin affinity chromatography - 2019/04/02
<p><strong>Project: </strong>Biophysical investigation of purified HTT protein samples</p> <p><strong>Experiment: </strong>Optimisation of HTT-HAP40 purification using heparin affinity chromatography</p> <p><strong>Date completed:­ </strong>2019/04/02</p> <p><strong>Rationale: </strong>Previous attempts to generate a much purer and homogenous HTT-HAP40 sample showed that the complex can bind heparin resin. In addition, I will incorporate helpful suggestions from scientists at the CHDI Palm Springs meeting full-length HTT research breakout group i.e. ATP wash to remove HSP proteins. The experiment will include 3 affinity chromatography steps with FLAG, heparin and NiNTA resin and finally a gel filtration step. </p>
Optimisation of viability assay for DIPG patient-derived cell lines
<p>Evaluation of the efficacy of M4K compounds in DIPG patient-derived cell lines is essential before any promising compounds can be further tested in mouse xenograft models. This approach can aid in narrowing down clinical compound candidates and reduce the time, resources and animal sacrifice needed downstream.</p> <p>A robust and efficient readout for the changes in the viability of the DIPG cells needs to be established before it can be used to evaluate the M4K compounds. In addition, the amount of cells to be seeded at the beginning of the experiment has to be optimised to avoid overcrowding and starvation of the cells after extended culture times. Overcrowding and starvation will lead to increased cell death and prevent accurate estimation of the potency of M4K compounds (EC50).</p>
Sensor data from Almeria and Barcelona for the implementation and optimisation of INCOVER's irrigation system (FINoT controller and scheduler).
<p>The purpose of the data is to help local irrigation communities, city's landscape gardeners and others that perform irrigation activities in INCOVER’s Demo Sites 1 and 2 to define site-specific thresholds that deficit irrigation can be achieved and set limits under which the automated irrigation profile can operate by optimising water consumption. These sensor values are associated with the sensor technology exploited by FINT in INCOVER (FDR). Moreover, sensor streams can also help technology modellers in the area of IoT to get an example of syntactic formulation of data services that are based on IoT networked devices.</p>
Surrogate Model Optimisation of a 'micro core' PWR fuel assembly arrangement using deep learning models - Figures
<p>Figures for Physior 2020 paper</p>
Tutorial Data Bundle for PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p><strong>PyPSA-Eur</strong> is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at <a href="https://github.com/PyPSA/PyPSA-eur">https://github.com/PyPSA/PyPSA-eur</a>.</p> <p><strong>It contains</strong> alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.</p> <p><strong>Not all data dependencies</strong> are shipped with the <a href="https://github.com/PyPSA/PyPSA-eur">code repository</a>, since git is not suited for handling large changing files. Instead we provide separate <strong>data bundles</strong> to be downloaded and extracted as noted in the <a href="https://pypsa-eur.readthedocs.io/en/latest/installation.html">documentation</a>.</p> <p>This is the <strong>lightweight</strong> data bundle to be used for the <a href="https://pypsa-eur.readthedocs.io/en/latest/tutorial.html">PyPSA-Eur tutorial</a>. It excludes large bathymetry and natural protection area datasets.</p> <p>While the <a href="https://github.com/PyPSA/PyPSA-eur">code</a> in PyPSA-Eur is released as free software under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GPLv3</a>, <strong>different licenses and terms of use</strong> apply to the various input data, which are summarised and linked below:</p> <p><strong>corine/*</strong></p> <ul> <li>CORINE Land Cover (CLC) database</li> <li><strong>Source:</strong> <a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:</p> <ul> <li> <p>When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.</p> </li> <li> <p>Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.</p> </li> <li> <p>Where that data or information has been adapted or modified, the user shall clearly state this.</p> </li> <li> <p>The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.</p> </li> </ul> </blockquote> <ul> <li><a href="https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata">https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata</a></li> </ul> <p><strong>eez/*</strong></p> <ul> <li>World exclusive economic zones (EEZ)</li> <li><strong>Source:</strong> <a href="http://www.marineregions.org/sources.php#unioneezcountry">http://www.marineregions.org/sources.php#unioneezcountry</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.</p> </blockquote> <ul> <li><a href="http://www.marineregions.org/disclaimer.php">http://www.marineregions.org/disclaimer.php</a></li> </ul> <p><strong>naturalearth/*</strong></p> <ul> <li>World country shapes</li> <li><strong>Source:</strong> <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/">https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.</p> <p>No permission is needed to use Natural Earth. Crediting the authors is unnecessary.</p> </blockquote> <ul> <li><a href="http://www.naturalearthdata.com/about/terms-of-use/">http://www.naturalearthdata.com/about/terms-of-use/</a></li> </ul> <p><strong>NUTS_2013_60M_SH/*</strong></p> <ul> <li>Europe NUTS3 regions</li> <li><strong>Source:</strong> <a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:</p> <ol> <li> <p>The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").</p> </li> <li> <p>The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.</p> </li> </ol> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units">https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units</a></li> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>ch_cantons.csv</strong></p> <ul> <li>Mapping between Swiss Cantons and NUTS3 regions</li> <li><strong>Source:</strong> <a href="https://en.wikipedia.org/wiki/Data_codes_for_Switzerland">https://en.wikipedia.org/wiki/Data_codes_for_Switzerland</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Creative Commons Attribution-ShareAlike 3.0 Unported License</p> </blockquote> <ul> <li><a href="https://en.wikipedia.org/wiki/Data_codes_for_Switzerland">https://en.wikipedia.org/wiki/Data_codes_for_Switzerland</a></li> </ul> <p><strong>EIA_hydro_generation_2000_2014.csv</strong></p> <ul> <li>Hydroelectricity generation per country and year</li> <li><strong>Source:</strong> <a href="https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016">https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."</p> </blockquote> <ul> <li><a href="https://www.eia.gov/about/copyrights_reuse.php">https://www.eia.gov/about/copyrights_reuse.php</a></li> </ul> <p><strong>hydro_capacities.csv</strong></p> <p>Hydroelectricity generation and storage capacities</p> <ul> <li><strong>Source:</strong> <ul> <li> <p>A. Kies, K. Chattopadhyay, L. von Bremen, E. Lorenz, D. Heinemann, RESTORE 2050 Work Package Report D12: Simulation of renewable feed-in for power system studies., Tech. rep., RESTORE 2050 (2016).</p> </li> <li> <p>B. Pfluger, F. Sensfuß, G. Schubert, J. Leisentritt, Tangible ways towards climate protection in the European Union (EU Long-term scenarios 2050), Fraunhofer ISI. <a href="https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/2011/Final_Report_EU-Long-term-scenarios-2050.pdf">https://www.isi.fraunhofer.de/content/dam/isi/dokumente/ccx/2011/Final_Report_EU-Long-term-scenarios-2050.pdf</a></p> </li> </ul> </li> </ul> <p><strong>je-e-21.03.02.xls</strong></p> <ul> <li>Population and GDP data for Swiss Cantons</li> <li><strong>Source:</strong> <a href="https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html">https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.</p> <p>Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.</p> <p>Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given. Anyone who intends on using statistical results for commercial purposes or gain must obtain an authorisation pursuant to Art. 13 of the Fee Ordinance and is liable to pay an indemnity. Please contact the FSO for this purpose.</p> </blockquote> <ul> <li><a href="https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html">https://www.bfs.admin.ch/bfs/en/home/fso/swiss-federal-statistical-office/terms-of-use.html</a></li> <li><a href="https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html">https://www.bfs.admin.ch/bfs/de/home/bfs/oeffentliche-statistik/copyright.html</a></li> </ul> <p><strong>nama_10r_3gdp.tsv.gz</strong></p> <ul> <li>Gross domestic product (GDP) by NUTS3 region</li> <li><strong>Source:</strong> <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=">http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided that:</p> <ul> <li> <p>the source is indicated as Eurostat;</p> </li> <li> <p>when re-use involves modifications to the data or text, this must be stated clearly to the end user of the information.</p> </li> </ul> <p>Exceptions</p> <ul> <li> <p>The permission granted above does not extend to any material whose copyright is identified as belonging to a third-party, such as photos or illustrations from copyright holders other than the European Union. In these circumstances, authorisation must be obtained from the relevant copyright holder(s).</p> </li> <li> <p>Logos and trademarks are excluded from the above mentioned general permission, except if they are redistributed as an integral part of a Eurostat publication and if the publication is redistributed unchanged.</p> </li> <li> <p>When reuse involves translations of publications or modifications to the data or text, this must be stated clearly to the end user of the information. A disclaimer regarding the non-responsibility of Eurostat shall be included.</p> </li> </ul> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>nama_10r_3popgdp.tsv.gz</strong></p> <ul> <li>Population by NUTS3 region</li> <li><strong>Source:</strong> <a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en">http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Eurostat has a policy of encouraging free re-use of its data, both for non-commercial and commercial purposes. All statistical data, metadata, content of web pages or other dissemination tools, official publications and other documents published on its website, with the exceptions listed below, can be reused without any payment or written licence provided that:</p> <ul> <li> <p>the source is indicated as Eurostat;</p> </li> <li> <p>when re-use involves modifications to the data or text, this must be stated clearly to the end user of the information.</p> </li> </ul> <p>Exceptions</p> <ul> <li> <p>The permission granted above does not extend to any material whose copyright is identified as belonging to a third-party, such as photos or illustrations from copyright holders other than the European Union. In these circumstances, authorisation must be obtained from the relevant copyright holder(s).</p> </li> <li> <p>Logos and trademarks are excluded from the above mentioned general permission, except if they are redistributed as an integral part of a Eurostat publication and if the publication is redistributed unchanged.</p> </li> <li> <p>When reuse involves translations of publications or modifications to the data or text, this must be stated clearly to the end user of the information. A disclaimer regarding the non-responsibility of Eurostat shall be included.</p> </li> </ul> </blockquote> <ul> <li><a href="https://ec.europa.eu/eurostat/about/policies/copyright">https://ec.europa.eu/eurostat/about/policies/copyright</a></li> </ul> <p><strong>time_series_60min_singleindex_filtered.csv</strong></p> <ul> <li>ENTSO-E hourly per-country load profiles</li> <li><strong>Source:</strong> <a href="https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv">https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv</a></li> <li><strong>Extract from Terms of Use:</strong></li> </ul> <blockquote> <p>Attribution in Chicago author-date style should be given as follows: "Open Power System Data. 2019. Data Package Time series. Version 2019-06-05. <a href="https://doi.org/10.25832/time_series/2019-06-05">https://doi.org/10.25832/time_series/2019-06-05</a>. (Primary data from various sources, for a complete list see URL)."</p> </blockquote> <ul> <li><a href="https://data.open-power-system-data.org/time_series/2019-06-05/README.md">https://data.open-power-system-data.org/time_series/2019-06-05/README.md</a></li> </ul>
Script and data from: The best of two worlds: toward large-scale monitoring of biodiversity combining metabarcoding and optimised parataxonomic validation.
<h2>Description</h2> <div> <p>Zenodo linked to : Penel, B., Meynard, C.N., Benoit, L., Bourdonné, A., Clamens, A., Soldati, L., Migeon, A., Chapuis, M.-P., Piry, S., Kergoat, G. and Haran, J. (2025), The best of two worlds: toward large-scale monitoring of biodiversity combining COI metabarcoding and optimized parataxonomic validation. Ecography, 2025: e07699. <a href="https://doi.org/10.1111/ecog.07699">https://doi.org/10.1111/ecog.07699</a></p> <div> <div> <div> <div> <p><strong>Publication abstract </strong></p> </div> </div> </div> <p>In a context of unprecedented insect decline, it is critical to have reliable monitoring tools to measure species diversity and their dynamic at large-scales. High-throughput DNA-based identification methods, and particularly metabarcoding, were proposed as an effective way to reach this aim. However, these identification methods are subject to multiple technical limitations, resulting in unavoidable false-positive and false-negative species detection. Moreover, metabarcoding does not allow a reliable estimation of species abundance in a given sample, which is key to document and detect population declines or range shifts at large scales. To overcome these obstacles, we propose here a Human-Assisted Molecular Identification (HAMI) approach, a framework based on a combination of metabarcoding and image-based parataxonomic validation of outputs and recording of abundance. We assessed the advantages of using HAMI over the exclusive use of a metabarcoding approach by examining 492 mixed beetle samples from a biodiversity monitoring initiative conducted throughout France. On average, 23% of the species are missed when relying exclusively on metabarcoding, this percent being consistently higher in species-rich samples. Importantly, on average, 20% of the species identified by molecular-only approaches correspond to false positives linked to cross-sample contaminations or mis-identified barcode sequences in databases. The combination of molecular methodologies and parataxonomic validation in HAMI significantly reduces the intrinsic biases of metabarcoding and recovers reliable abundance data. This approach also enables users to engage in a virtuous circle of database improvement through the identification of specimens associated with missing or incorrectly assigned barcodes. As such, HAMI fills an important gap in the toolbox available for fast and reliable biodiversity monitoring at large scales.</p> <div> <h4><strong>File description: </strong></h4> <h4>MiSeq raw sequences of the COI barcode from 492 Coleoptera field samples :</h4> <div>The Raw_sequencage_data ZIP directory contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each Coleoptera field samples in duplicate using the MiSeq platform GenSeq (ISEM - University of Montpellier)</div> <div> </div> <div>The HAMI_data_script_results_R zip directory contains Rmarkdown script files (.Rmd and .html) and associated data used to analyse the systemic errors of the metabarcoding approach (N= 492 Coleoptera field samples).</div> <div> </div> <div>The HAMI_pipeline zip directory contains all the codes associated with the HAMI pipeline, as well as a ReadMe file and a test data set.</div> <div> </div> <div>The Residual_chimera.zip directory contains lists of MOTUs associated to residual chimeric sequences that were not filtered using FROGS pipeline but secondarily detected with the <em>de novo</em> approach implemented in HAMI pipeline with ‘isBimeraDenovo’ R function from DADA2 v1.28.0. It contains two distinct files according to the two sequencing runs.</div> <div> </div> <div>The NUMTS_filtered.zip directory contains lists of MOTUs that were excluded of the final dataset according to the NUMTS filtering. File xxx_pseudogene_f1_deteled.csv corresponds to MOTUs that were excluded according to the first filtrering step based on DNA sequencing. File xxx_pseudogene_f2_deteled.csv corresponds to merged MOTUs that were excluded according to the second filter based on occurrence and percentage of identity. This folder contains files for the two sequencing runs.</div> </div> </div> </div>
ScienceDex guides
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