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485 results for “cooking”
MCR LTER: Coral Reef: Modeling the effects of selectively fishing key functional groups of herbivores on coral resilience; data for Cook et al., 2023 Ecosphere
These data and code were generated in support of the manuscript: Cook DT, Schmitt RJ, Holbrook SJ, and HV Moeller, Ecosphere. To investigate the impacts of selectively harvesting functional groups of herbivorous fishes on coral resilience, we used a dynamic model that is grounded by the coral reef system in Moorea, French Polynesia. Our model simulates the fraction of a reef occupied through time by classes of key benthic spaceholders (coral, two stages of macroalgae, and turf). Benthic and fishing dynamics are linked through the harvesting of two functional groups of herbivorous fishes. We utilize data collected on the abundance of fishes on the reef and in the catch in Moorea, French Polynesia to inform our model and to empirically explore patterns of fishing selectivity. These data and code were published in Ecosphere and were a part of the thesis of D. Cook (2023). This manuscript uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2023).
Auxiliary Euro-Calliope datasets: Spatio-temporal data representing national cooking demand and electric vehicle characteristic profiles in Europe
<p>Output generated by the <a href="https://github.com/RAMP-project/">RAMP engine</a> for use in the <a href="https://github.com/calliope-project/sector-coupled-euro-calliope">Sector-Coupled Euro-Calliope model</a>. The three datasets in this repository are described briefly here and in more detail in the accompanying README files. Each dataset has an hourly temporal resolution spanning the years 2000 - 2018 (inclusive) and a national spatial resolution spanning 26* - 28** countries in Europe. All datasets are dimensionless; only the profile shapes are used in Euro-Calliope.</p> <ul> <li>Cooking energy demand profiles (<em>ramp-cooking-profiles</em>): Profiles of heat energy demand for cooking in buildings in Europe, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP">RAMP model</a> [1]. These profiles are used to distribute annual cooking energy demand in the Euro-Calliope workflow. This dataset covers 28 European countries**.</li> <li>Electric vehicle plug-in profiles (<em>ramp-ev-plugin-profiles</em>): Profiles of the percentage of parked electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are used in Euro-Calliope to define the maximum number of electric vehicles that could be plugged in and therefore available to be charged at any given time, assuming controlled (or "smart") charging. This dataset covers 26 European countries*.</li> <li>Electric vehicle energy consumption profiles (<em>ramp-ev-consumption-profiles</em>): Profiles of the electricity consumption of electric vehicles, stochastically generated using the <a href="https://github.com/RAMP-project/RAMP-mobility">RAMP-Mobility model</a> [2]. These profiles are aggregated in Euro-Calliope to provide a required percentage of total vehicle electricity demand that must be met in each month. This dataset covers 26 European countries*.</li> </ul> <p>* AUT, BEL, CHE, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, HRV, HUN, IRL, ITA, LTU, LUX, LVA, NLD, NOR, POL, PRT, ROU, SVK, SVN, SWE</p> <p>** (*) + BGR, SRB</p> <p>*** ALB, MKD, GRC, CYP, BIH, MNE, ISL</p> <p>[1] Lombardi, Francesco, Sergio Balderrama, Sylvain Quoilin, and Emanuela Colombo. 2019. ‘Generating High-Resolution Multi-Energy Load Profiles for Remote Areas with an Open-Source Stochastic Model’. <em>Energy</em> 177 (June): 433–44. https://doi.org/10.1016/j.energy.2019.04.097.</p> <p>[2] Mangipinto, Andrea, Francesco Lombardi, Francesco Davide Sanvito, Matija Pavičević, Sylvain Quoilin, and Emanuela Colombo. 2022. ‘Impact of Mass-Scale Deployment of Electric Vehicles and Benefits of Smart Charging across All European Countries’. <em>Applied Energy</em> 312 (April): 118676. https://doi.org/10.1016/j.apenergy.2022.118676.</p>
Exploring the total cost of whole fresh, fresh-cut and pre-cooked vegetables
<p>Abstract. Purpose: The food industry should evolve towards new business models which take into account the damage cost in decision making, considering the impact that its products generate on the natural and human environment. Hence, the present study aims to calculate the damage cost caused by the production of whole fresh (as average of potatoes, aubergines, and broccoli), and processed vegetables (fresh-cut and pre-cooked). Methods: The environmental life cycle approach was carried out per kilogram of assessed products (from cradle to the entrance of the market). The foreground Life Cycle Inventory was obtained from engineering procurement and construction projects of the whole fresh and processed vegetables industries. The Ecoinvent 3.8 and Agribalyse 3.0.1 databases were used for the background inventory. The ReCiPe 2016 method was used with a hierarchical perspective, evaluating eighteen midpoint categories as well as the endpoint categories (human health, ecosystems, and resources). The monetisation of these environmental impacts was then calculated using the endpoint monetisation factors developed by Ponsioen et al. (Monetisa- tion of sustainability impacts of food production and consumption. Wageningen Economic Research, Wageningen, 2020) for each product. It should be noted that this study does not include a comparative assessment. This study does not intend to compare the results for the three vegetable groups. Results and discussion: The damage costs were 0.16 €/kg for whole fresh vegetables, 0.37 €/kg for fresh-cut vegetables and 0.41 €/kg for pre-cooked vegetables. The agricultural production stage contributed most to these total damage costs due to the impact produced on land use and global warming in midpoint categories and human health and ecosystems in endpoint categories. In addition, the damage cost due to fossil resource scarcity (midpoint) and resource scarcity (endpoint) was mainly caused by the plastic packaging of fresh-cut and pre-cooked vegetables. The total cost was 1.02 €/kg for whole fresh vegetables, 2.99 €/kg for fresh-cut vegetables, and 3.43 €/kg for pre-cooked vegetables. Conclusions: These results suggest that some efforts should be made to reduce both environmental impacts and damage costs. For instance, to improve agricultural production, special attention should be paid to fertilisation and water consumption. Additionally, new packaging options should be explored as well as the inclusion of renewable sources in the electricity grid, and finally, on transporting the finished products to the market, by using trucks that run on cleaner fuels.</p>
Tree Health Conditions (mortality, damage, disease, bark beetles) in Fuel Reduction Treatments Located Near Communities in Interior Alaska and the Cook Inlet Region of Alaska - Observations from July-August 2023
This dataset contains tree-, transect-, and site-level observations of forest stands at sites that received a fuel reduction treatment. Tree-level observations include species, diameter, living status, damage, disease, and bark beetle presence. Transect-level observations include level of coarse woody debris and bark beetle presence. Sites are categorized by region (recent/ongoing spruce beetle oubreak or endemic spruce beetle population levels) and treatment type (hand-thinned or mechanincally felled and masticated). These observations are from July-August 2023. Sites are located near communities in Interior Alaska and the Cook Inlet Region.
Results from the OnStove Nepal model "Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes all result datasets and figures from the <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">OnStove Nepal</a> model presented in the paper "<strong>Achieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis</strong>" DOI: <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>.</p> <p>The code and automated workflow to run the model can be found in the Github repository <a href="https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal">https://github.com/Open-Source-Spatial-Clean-Cooking-Tool/OnStove-Nepal</a>. All model input data can be downloaded from the permanent repository at<em> </em><a href="https://doi.org/10.5281/zenodo.10641858">10.5281/zenodo.10641858</a>.</p> <h2>Folder structure</h2> <p>The folder structure consists of a <strong>Procedded GIS Data </strong>folder containing all GIS processed data. These are the outputs from the <strong>DataProcessor.ipynb </strong>script and the raw GIS input data files found in the input data repository.</p> <p>A folder for <strong>each scenario</strong> results. Within each scenario folder, there are:</p> <ul> <li>A <strong>model.pkl </strong>and a <strong>results.pkl </strong>files. These are a calibrated OnStove model with the scenario inputs and a complete results model file of the scenario respectively. Both of these files can be read and explored using the OnStove tool. </li> <li>A <strong>summary.csv </strong>file with the summary results of the scenario for each technology.</li> <li>A <strong>Subsidies_scenario_name.csv </strong>file showing the required total subsidies per technology of the scenario.</li> <li>Image files in pdf format for: <ul> <li>The baseline technologies used in the country (<strong>current_shares.pdf</strong>),</li> <li>The spatial mix of technologies providing the maximum net-benefits throughout the country (<strong>max_benefit_tech.pdf</strong>), </li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The bar plot of max benefit technology shares (<strong>tech_split.pdf</strong>),</li> <li>The max benefit technologies distribution over relative wealth in the country (<strong>tech_histogram.pdf</strong>),</li> </ul> </li> <li>A <strong>Rasters </strong>folder with raster files of different result maps in .tif format.</li> </ul> <p>Inside the <strong>MCA </strong>folder, all results from the prioritization analysis are found, including:</p> <ul> <li>The prioritized spatial technology mix to achieve the goals of the country (<strong>Prioritized_hh.pdf</strong>),</li> <li>The biogas cookstoves relative wealth distribution index (<strong>Biogas_index.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves relative wealth distribution index (<strong>Biomass_ICS_T3_index.pdf</strong>),</li> <li>The electrical cookstoves relative wealth distribution index (<strong>Electricity_index.pdf</strong>),</li> <li>The biogas cookstoves priority map (<strong>Biogas_priority_areas.pdf</strong>),</li> <li>The biomass ICS T3 cookstoves priority map (<strong>Biomass_ICS_T3_priority_areas.pdf</strong>),</li> <li>The electrical cookstoves priority map (<strong>Electricity_priority_areas.pdf</strong>),</li> <li>The total costs and benefits of the transition per technology (<strong>costs_benefits.pdf</strong>),</li> <li>The prioritized technology shares distribution over relative wealth in the country (<strong>tech_histogram_prioritized.pdf</strong>),</li> <li>A <strong>Subsidies_prioritized.csv </strong>file showing the required total subsidies per technology,</li> <li>A <strong>mca.pkl </strong>file with the MCA model that can be manipulated using the OnStove tool,</li> <li>A <strong>access_results.txt </strong>file with the current and after prioritization clean cooking access shares in the country.</li> </ul> <p>A <strong>main_plot.pdf </strong>and a <strong>prioritized_plot.pdf </strong>files showing the compiled results for all scenarios and prioritized scenario respectively.</p> <h2>License</h2> <p>All datasets are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a> (CC BY 4.0).</p>
PTR-ToF-MS data from cooking experiments in Healthy Energy-efficient Urban Home Ventilation
<pre>The dataset contains high-resolution PTR-Tof MS data from preparing meals consisting of fried salmon and vegetables in SINTEFs ventilation laboratory. <br>The data are organized in csv files containing concatenated results of ppb-values. PTR-ToF-MS grouped by month, m/z-valuens in column names. Relatable to the list of experiments. See readme file for details and 10.1016/j.buildenv.2024.111743 for description</pre>
3D Archaeological Greek Pottery: MDDS-2017-0299, Attic red-figure trefoil oinochoe, Class N, The Cook Class
<p><strong>This 3D dataset is related to the publication</strong>:</p> <ul> <li>Moitinho de Almeida, V. (2023). "<a href="https://www.researchgate.net/publication/353038967_Contributions_of_3D_digital_methods_and_techniques_to_the_study_of_ancient_pottery">Contributions of 3D digital methods and techniques to the study of ancient pottery</a>". In <em>Myths, Gods, and Heroes. Greek vase collections in Portugal / Mitos, Deuses e Heróis. As coleções de vasos gregos em Portugal</em>. R. Morais, R. Centeno, D. Ferreira (eds.). Câmara Municipal de Santa Maria da Feira - Museu Convento dos Lóios; Reitoria da Universidade do Porto; Faculdade de Letras da Universidade do Porto; Imprensa da Universidade de Coimbra. Pp.269-291. (ISBN: 978-989-8183-25-5)</li> </ul> <p>3D processed dataset for object 2017-0299, from the Museu de Arqueologia D. Diogo de Sousa (MDDS) in Braga, Portugal. <strong>CC BY-NC-SA 4.0 license</strong>.</p> <p>2017-0299 is an Attic red-figure trefoil <em>oinochoe</em>, Class N, The Cook Class, dating from c. 500-475 BCE and unknown provenance (Fundação Buehler Brockhaus / Museu D. Diogo de Sousa, 2020; Morais et al., 2021).</p> <p><strong>Aims</strong>: 3D digital documentation; morphological characterization; technological and functional analysis of archaeological Greek pottery.</p> <p><strong>Data acquisition</strong>: at the Museu de História Natural e da Ciência da Universidade do Porto (MHNC-UP), with a portable non-contact structured white light scanner, Breukmann smartSCAN3D-HE, equipped with stereo colour cameras at 250 mm FOV. Additional metadata included in associated spreadsheet.<br><strong>Data processing</strong>: 29 scans aligned and merged. MDDS-2017-0299_3D01.ply: non-manifold edges, self-intersections, small components, and small tunnels in the mesh automatically fixed, noise data removed; orientation and position normalised. MDDS-2017-0299_3D01-holesFilled.ply: holes filled for calculation of material density, filling volume, and centre of mass. Mesh is not watertight (inner surface not digitised due to occlusion). Additional metadata included in associated spreadsheet.</p> <p>Access to the 2017-0299 was granted by the MDDS.</p> <p>When citing this material: please include the original inventory ID (2017-0299) reference to the physical object.</p>
Datasets supporting the publication "Technical Note: in-situ measurements and modelling of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Milsom et al.
<p>Supporting experimental and modelling data for the manuscript entitled "Technical Note: Modelling and in-situ measurements of the oxidation kinetics in films of a cooking aerosol proxy using a Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)" by Adam Milsom et al. 2023. </p> <p>Includes raw QCM-D data with the numbers at the beginning of the files corresponding to the experiment numbers in the manuscript. </p> <p>Normalised Raman peak area data for modelling and model ensemble outputs, including uptake coefficients. </p>
Figure 18 in Expanded concept and revised taxonomy of the milliped family Xystodesmidae Cook, 1895 (Polydesmida: Leptodesmidea: Xystodesmoidea): incorporations of Euryuridae Pocock, 1909 and Eurymerodesmidae Causey, 1951, taxon revivals/proposals/ transferrals, and a distributional update
Figure 18. The East-Nearctic Region of Xystodesmidae. The eastern- and northernmost dots denote localities of the two Appalachian species of Rhysodesmus; the dot in Texas within the black line signifies the northernmost locality of R. texicolens (Chamberlin), San Diego, Duval Co. The arrow indicates Bald Head Island, NC, occupied by Apheloria (Xystodesminae: Apheloriini).
Figure 1 in Expanded concept and revised taxonomy of the milliped family Xystodesmidae Cook, 1895 (Polydesmida: Leptodesmidea: Xystodesmoidea): incorporations of Euryuridae Pocock, 1909 and Eurymerodesmidae Causey, 1951, taxon revivals/proposals/ transferrals, and a distributional update
Figure 1. Distribution of Euryurinae plotted against the maximal extent of the Cretaceous Western Inland Sea. Euryurina (Euryurini, red), Eurymerodesmina (Eurymerodesmini, black), Nannariina (Eurymerodesmini, green). The red line, Euryurina, in eastern North Carolina and southcentral Virginia connects disparate peripheral localities; the area of greatest euryurinan concentration is circumscribed by the red dots.
Figures 8–15 in Expanded concept and revised taxonomy of the milliped family Xystodesmidae Cook, 1895 (Polydesmida: Leptodesmidea: Xystodesmoidea): incorporations of Euryuridae Pocock, 1909 and Eurymerodesmidae Causey, 1951, taxon revivals/proposals/ transferrals, and a distributional update
Figures 8–15. Eurymerodesmini gonopodal tel-/acropodites; 8–11, Nannariina. 8) telopodite of Nannaria cayugae Chamberlin, Tompkins Co., NY. 9) acropodite of the same. 10) telopodite of Mimuloria castanea (McNeill), Monroe Co., IN. 11) the same of M. d. dilatata Hennen and Shelley, Marshall Co., TN. 12–15, Eurymerodesmina. 12) Eurymerodesmus varius louisianae Chamberlin, Natchitoches Par., LA. 13) acropodite of a second individual from the same locality. 14) the same, Columbia Co., AR. 15) E. v. varius (McNeill), Escambia Co., FL. Figures 8–9 reprinted from Chamberlin (1949) with permission from the Biological Society of Washington. Figures 10–11 reprinted from Hennen and Shelley (2015) with permission of the Center for Systematic Entomology. Figures 12–15 reprinted from Shelley (1990a) with permission of the American Entomological Society.
Figure 29 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 29. Doratodesmus grandifoliatus (Zhang in Zhang & Wang, 1993), ♀ from Ma Fa Tiao Dong Cave; A, texture of pro- and metazona; B, bisegmented seta; C, leg and sternum, caudal view; D, claw. − Scale bars: A, 0.05 mm, B, 0.002 mm, C, 0.1 mm & D, 0.02 mm.
Figure 26 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 26. Eutrichodesmus incisus sp. n., ♂ paratype from Tiao Shuz Dong Cave; A, leg 9, caudal view; B & C, right gonopod, mesal and lateral views, respectively (dp = distofemoral process). − Scale bar: A, 0.2 mm, B, C, 0.1 mm.
Figure 25 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 25. Eutrichodesmus incisus sp. n., ♂ paratype from Liao Jing Ling Cave; A, gonopods, ventral view; B, distal parts of gonopods, ventral. − Scale bars: A, 0.1 mm & B, 0.05 mm.
Figure 20 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 20. Eutrichodesmus similis sp. n., ♂ paratype from Gui Dong 2 Cave; A, paratergum with ozopore, lateral view; B, leg; C, gonopods, ventral view; D, gonopod apex, ventral view. − Scale bars: A-C, 0.1 mm & D, 0.05 mm.
Figure 28 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 28. Doratodesmus grandifoliatus (Zhang in Zhang & Wang, 1993), ♀ from Ma Fa Tiao Dong Cave; A, telson, ventrolateral view; B, cross-section of a midbody segment, caudal view; C, head, front view; D, distal part of antenna; E, paratergum with ozopore, lateral view; F, tegument, sublateral view. − Scale bars: A, E, 0.1 mm, B, C, 0.2 mm, D, 0.05 mm & F, 0.01 mm.
Figure 23 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 23. Eutrichodesmus incisus sp. n., ♀ paratype from Tiao Shuz Dong Cave; A, posterior part of body, dorsal view; B, anterior part of body, subventral view; C, midbody segments, ventral view; D, telson, ventral view; E, cross-section of a midbody segment, caudal view; F, paratergum with ozopore, lateral view. − Scale bars: A-C, E, 0.2 mm, D, 0.1 mm & F, 0.05 mm.
Figure 8 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 8. Eutrichodesmus armatocaudatus sp. n., ♂ paratype from cave 4; A & B, right gonopod, mesal and lateral views, respectively (micropilosity not shown; dp = distofemoral process). − Scale bar: 0.2 mm.
Figure 30 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 30. Doratodesmus grandifoliatus (Zhang in Zhang & Wang, 1993), ♂ from Ma Fa Tiao Dong Cave; A, leg 9, caudal view; B & C, right gonopod, lateral and mesal views, respectively (sl = solenomere; lo = lateral lobe). − Scale bar: A, 0.2 mm, B, C, 0.1 mm.
Figure 7 in Review of the millipede family Haplodesmidae Cook, 1895, with descriptions of some new or poorly-known species (Diplopoda, Polydesmida)
Figure 7. Eutrichodesmus armatocaudatus sp. n., ♂ paratype from cave 4; A, body segments 6 and 7, ventral view; B, gonopods, ventral view; C, same, ventral view; D, gonopod apices. − Scale bars: A, 0.5 mm, B, 0.2 mm, C, 0.05 mm & D, 0.02 mm.
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Allen Brain Atlas
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