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519 results for “Optimisation”
Data from: Compact cities or sprawling suburbs? optimising the distribution of people in cities to maximise species diversity
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Optimising bat bioacoustic surveys in human-modified neotropical landscapes
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Data from: Evaluating management strategies to optimise coral reef ecosystem services
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Optimising nature conservation outcomes for a given region-wide level of food production
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Data from: How to best threshold and validate stacked species assemblages? Community optimisation might hold the answer
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Data from: Optimising sampling of flying insects using a modified window trap
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Data from: High detectability with low impact: optimising large PIT tracking systems for cave-dwelling bats
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Data from: Optimising sample sizes for animal distribution analysis using tracking data
<p><span>1. Knowledge of the spatial distribution of populations is fundamental to management plans for any species. When tracking data are used to describe distributions, it is sometimes assumed that the reported locations of individuals delineate the spatial extent of areas used by the target population.</span></p> <p><span>2. Here, we examine existing approaches to validate this assumption, highlight caveats, and propose a new method for a more informative assessment of the number of tracked animals (i.e. sample size) necessary to identify distribution patterns. We show how this assessment can be achieved by considering the heterogeneous use of habitats by a target species using the probabilistic property of a utilisation distribution. Our methods are compiled in the R package <i>SDLfilter</i>.</span></p> <p><span>3. We illustrate and compare the protocols underlying existing and new methods using conceptual models and demonstrate an application of our approach using a large satellite tracking data-set of flatback turtles, <i>Natator depressus, </i>tagged with accurate Fastloc-GPS tags (n = 69).</span></p> <p><span>4. Our approach has applicability for the post-hoc validation of sample sizes required for the robust estimation of distribution patterns across a wide range of taxa, populations and life history stages of animals.</span></p>
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>full</strong> data bundle to be used for rigorous research. It includes 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 MIT, <strong>different licenses and terms of use</strong> apply to the various input data, which are summarised 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>Terms of Use: </strong><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>natura/*</strong></p> <ul> <li>Natura 2000 natural protection areas</li> <li><strong>Source:</strong> <a href="https://www.eea.europa.eu/data-and-maps/data/natura-10">https://www.eea.europa.eu/data-and-maps/data/natura-10</a></li> <li><strong>Terms of Use:</strong><a href="https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata"> https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata</a></li> </ul> <p><strong>gebco/GEBCO_2014_2D.nc</strong></p> <ul> <li>GEBCO bathymetric dataset</li> <li><strong>Source:</strong> <a href="https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/">https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/</a></li> <li><strong>Terms of Use: </strong><a href="https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf">https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf</a></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>Terms of Use: <br></strong></li> <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_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>Terms of Use:</strong></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>GDP_per_capita_PPP_1990_2015_v2.nc</strong></p> <ul> <li>Gross Domestic Product per capita (PPP) from years 1999 to 2015</li> <li>Rectangular cutout for European countries in PyPSA-Eur, including a 10 km buffer</li> <li>Kummu et al. "Data from: Gridded global datasets for Gross Domestic Product and Human Development Index over 1990-2015"</li> <li><strong>Source:</strong> https://doi.org/10.1038/sdata.2018.4 and associated dataset https://doi.org/10.1038/sdata.2018.4</li> </ul> <p><strong>ppp_2019_1km_Aggregated.tif</strong></p> <ul> <li>The spatial distribution of population in 2020: Estimated total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 30 arc (approximately 1km at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.</li> <li>Rectangular cutout for non-NUTS3 countries in PyPSA-Eur, i.e. MD and UA, including a 10 km buffer</li> <li>WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647</li> <li><strong>Source:</strong> https://data.humdata.org/dataset/worldpop-population-counts-for-world and https://hub.worldpop.org/geodata/summary?id=24777</li> <li><strong>License: </strong>Creative Commons Attribution 4.0 International Licens</li> </ul> <p><strong>data/bundle/era5-HDD-per-country.csv</strong></p> <p>- Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875<br>- License: CC-BY 4.0<br>- Contains country-level heating degree days in Europe for<br> 1941-2023. Used for rescaling heat demand in weather years not covered by<br> energy balance statistics.</p> <p><strong>data/bundle/era5-runoff-per-country.csv</strong></p> <p>- Link: https://gist.github.com/fneum/d99e24e19da423038fd55fe3a4ddf875<br>- License: CC-BY 4.0<br>- Contains country-level daily sum of runoff in Europe for<br> 1941-2023. Used for rescaling hydro-electricity availability in weather years<br> not covered by EIA hydro-generation statistics.</p> <p><strong>shipdensity_global.zip</strong></p> <ul> <li>Global Shipping Traffic Density</li> <li>Creative Commons Attribution 4.0</li> <li><a href="https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density">https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density</a></li> </ul> <p><strong>seawater_temperature.nc</strong></p> <ul> <li>Global Ocean Physics Reanalysis</li> <li>Seawater temperature at 5m depth</li> <li>Link: https://data.marine.copernicus.eu/product/GLOBAL_MULTIYEAR_PHY_001_030/services</li> <li>License: https://marine.copernicus.eu/user-corner/service-commitments-and-licence</li> </ul> <p><strong>hera_be_2013-03-01_to_2013-03-08.zip</strong></p> <ul> <li>Tilloy, A., Paprotny, D., Luc, F., Grimaldi, S., Goncalo, G., Hylcke, B., Lange, S., Bianchi, A. (2024): HERA: a high-resolution pan-European hydrological reanalysis (1950-2020).</li> <li>6-hourly river discharge and ambient temperature for 2019 at 1-arc minute spatial resolution. Subset to the PyPSA-Eur test cutout (2013-03-01 to 2013-03-08 for longitude 1.5 to 7 and latitude 49 to 52)</li> <li>Link: <a href="https://publications.pik-potsdam.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_29543">https://publications.pik-potsdam.de/pubman/faces/ViewItemOverviewPage.jsp?itemId=item_29543</a></li> <li>License: <a href="https://data.jrc.ec.europa.eu/licence/com_reuse">https://data.jrc.ec.europa.eu/licence/com_reuse</a></li> </ul>
Raw data and code for publication "Optimisation of surfactin yield in Bacillus using active learning and high-throughput mass spectrometry"
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The effects of multifactorial pharmacist-led intervention protocol on medication optimisation and adherence among patients with type 2 diabetes
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Modernising Optimisation in Decision Making
<p>Optimisation in decision making has broad applicability to the whole spectrum of nuclear and radiation-related policy, regulation and practice. The way it is applied has changed in recent years as society has evolved to promote an inclusive and holistic decision-making process. In the radiological protection area, the increasing challenge is to apply it in the broader context of overall risk management, broaden the stakeholder participation process, and deepen the thinking on reasonableness in optimisation. Experience in various circumstances has shown that there is a need to develop a framework in which very different aspects can be balanced to support risk-based decision-making and determine the level of tolerance of risk and uncertainty. Currently, optimisation, as one of the three protection principles of the international radiological protection system, is well defined in theory, complex in practice in a large number of situations, and increasingly based on the combination of four pillars:</p> <ul> <li>holistic, or integrated, protection where optimisation of radiological protection is applied as part of an overall optimisation of protection from all relevant hazards (sometimes referred to as an ‘all hazards approach’);</li> <li>maximising net benefit and the ‘common good’ - taking all relevant socio-economic and environmental risks and impacts, and benefits, into account so that radiological protection facilitates and enhances well-being and does not result in unintended consequences;</li> <li>involving stakeholders – with stakeholder engagement being integral to the decision-making process and key for defining acceptance or tolerance;</li> <li>the need for proportionality – by implementing a graded approach.</li> </ul> <p>As radiological protection moves towards a more holistic approach, recognising its multi-dimensional nature, there should be an even greater focus on understanding and meeting the expectations of those affected by the application of protection. Development is therefore needed along three main lines:</p> <p>(i) how policy and practice are developed in consultation with, and understood by a wider cross section of society;</p> <p>(ii) how policy and practice could be simplified;</p> <p>(iii) how to integrate consideration of other policies and practices so that radiological protection is properly integrated into the wider decision-making process and not seen as a separate "add-on" process.</p> <p>The forthcoming work of the Committee on Radiological Protection and Public Health on modernising the way optimisation is implemented should help inform the revision of the ICRP system. The ultimate goal is to facilitate sustainable and transparent decision-making, beyond the optimisation of radiological protection, in the broader perspective of individual and social well-being.</p> <p>Text on behalf the NEA CRPPH's bureau.</p>
Data for EMO2023 Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"
<p><strong>Data for Paper "Feature-based Benchmarking of Distance-based Multi/Many-objective Optimisation Problems: A Machine Learning Perspective"</strong></p> <p><br> The file <strong>dbmopp_dataset_perf.csv</strong> contains results from the 945 x 30 instances, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>instance_id</em>: instance (fold) identifier {1, ..., 30}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume</em>: hypervolume reached by the algorithm [0.0, 1.0]</li> </ul> <p> </p> <p>The file <strong>dbmopp_dataset_perf_aggregated.csv</strong> contains average results from the 945 problems, with the following columns:</p> <ul> <li><em>design_id</em>: problem identifier</li> <li><em>n_var</em>: number of variables {2, ..., 20}</li> <li><em>n_obj</em>: number of objectives {2, ..., 10}</li> <li><em>nonident_ps</em>: non-identical Pareto sets {0 (no), 1 (yes)}</li> <li><em>var_density</em>: varying density {0 (no), 1 (yes)}</li> <li><em>n_discon_ps</em>: number of disconnected Pareto sets {0, ..., 6}</li> <li><em>n_local_fronts</em>: number of local fronts {0, ..., 6}</li> <li><em>n_resist_regions</em>: number of dominance resistance regions {0, ..., 6}</li> <li><em>budget</em>: number of evaluations performed by the algorithm {5000, 10000, 30000, 50000}</li> <li><em>algo</em>: multi-objective evolutionary algorithm {NSGAII, IBEA, MOEAD, Random}</li> <li><em>hypervolume_avg</em>: average hypervolume reached by the algorithm [0.0, 1.0]</li> <li><em>best</em>: 1 if the corresponding algorithm obtains the best average hypervolume, 0 otherwise</li> </ul> <p> </p>
Data of "Mix design optimisation of self-sealing concrete containing microcapsules with polyurethane shell and water repellent cargo"
<p>Dataset for the paper "Mix design optimisation of self-sealing concrete containing microcapsules with polyurethane shell and water repellent cargo"</p>
GRIPP-2 checklist for 'Patient and public involvement in the design and protocol development for a platform randomised trial to evaluate diagnostic tests to optimise antimicrobial therapy (PROTECT)'
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Figure 1a from: Hambly A, Stedmon C (2018) FluoRAS Sensor - Online organic matter for optimising recirculating aquaculture systems. Research Ideas and Outcomes 4: e23957. https://doi.org/10.3897/rio.4.e23957
Figure 1a An example of a fluorescence EEM "fingerprint" of drinking water. Multivariate modelling of EEM datasets allows the most important areas of the EEM to be identified, and used for simplified online fluorescence sensors. - EMM viewed as a contour plot
Figure 1b from: Hambly A, Stedmon C (2018) FluoRAS Sensor - Online organic matter for optimising recirculating aquaculture systems. Research Ideas and Outcomes 4: e23957. https://doi.org/10.3897/rio.4.e23957
Figure 1b An example of a fluorescence EEM "fingerprint" of drinking water. Multivariate modelling of EEM datasets allows the most important areas of the EEM to be identified, and used for simplified online fluorescence sensors. - EMM viewed as a waterfall plot
Supplementary Data: Code, Input Data and Model data: PyPSA-Eur: An Open Optimisation Model of the European Transmission System
<p>Supplementary Data (preliminary version)</p> <p>PyPSA-Eur: An Open Optimisation Model of the European Transmission System</p> <p>Authors: J. Hörsch, F. Hofmann, D. Schlachtberger, T. Brown</p> <p>and</p> <p>The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios</p> <p>Authors: J. Hörsch, T. Brown</p> <p>The files in this record contain the scripts to build a <a href="http://pypsa.org/">PyPSA</a> model of the European Electricity System including renewable feed-in from wind, solar and hydro installations derived from reanalysis weather data satellite irradiation. The model PyPSA-Eur is described in the above publication.</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.</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>base_network.py creates the initial PyPSA network topology.</p> <p>add_electricity.py adds generators and storage units to the models, it generates the detailed resolved model described in the PyPSA-Eur paper.</p> <p>simplify_network.py removes stub ac-buses from network topology and simplifies long dc lines.</p> <p>cluster_network.py creates clustered representations of the electricity network for a given number of buses following the topology described in the "spatial scale" paper.</p> <p>prepare_network.py adds parameters like the CO2 limit and the transmission expansion volume relevant for the optimization to the model.</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><strong>Data</strong></p> <p>The input data include:</p> <ul> <li>Electricity sector data</li> <li>Topology derived from the analysis of an extract of the <a href="https://www.entsoe.eu/data/map/">ENTSO-E online map</a> using <a href="https://github.com/bdw/GridKit">GridKit</a> .</li> <li>A cost database with literature sources.</li> </ul> <p> </p>
Supplementary material 5 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
Alternative statistical model :
Supplementary material 4 from: Deiner K, Lopez J, Bourne S, Holman LE, Seymour M, Grey EK, Lacoursière-Roussel A, Li Y, Renshaw MA, Pfrender ME, Rius M, Bernatchez L, Lodge DM (2018) Optimising the detection of marine taxonomic richness using environmental DNA metabarcoding: the effects of filter material, pore size and extraction method. Metabarcoding and Metagenomics 2: e28963. https://doi.org/10.3897/mbmg.2.28963
4_NTC :
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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.