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100 results for “Cost models”
Data for "emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves"
<p>This dataset contains codes, data, tables, andd figures (high resolution) related to the following publication: Xiong, W., K. Tanaka, P. Ciais, D. J. A. Johansson, M. Lehtveer (2022) emIAM v1.0: an emulator for Integrated Assessment Models using marginal abatement cost curves. Submitted to arXiv on 23 December 2022.</p>
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>
Light-Emitting Diode (LED) Manufacturing Cost Model
<p>Excel files containing a bottom-up cost-models for GaN-based white light-emitting diodes (LEDs). Covers the commercial origins of the technology around 2003, 2012 and 2020.</p> <p>Compiled as part of the research project <a href="https://web.archive.org/web/20220920225758/https://www.ceenrg.landecon.cam.ac.uk/research/climate-change-and-energy-policy/what-factors-drive-innovation-in-energy-technologies-the-role-of-technology-spillovers-and-government-investment">"What factors drive innovation in energy technologies? The role of technology spillovers and government investment"</a>, funded by the Alfred P. Sloan Foundation.</p> <table> <tbody> <tr> <th>File</th> <th>Content</th> <th>Comment</th> </tr> </tbody> <tbody> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2003.xlsx</a></td> <td>Cost model for 2003. Includes additional description and credits.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2012.xlsx</a></td> <td>Cost model for 2012.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/LEDCOM2003.xlsx">LEDCOM2020.xlsx</a></td> <td>Cost model for 2020.</td> <td> </td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/Cost%20Model%20Inputs.xlsx">Cost Model Inputs.xlsx</a></td> <td>Inputs for the cost model (all years).</td> <td>Includes data on electricity, clean room costs, etc.</td> </tr> <tr> <td><a href="../api/files/cd6bf7b4-fe99-48b5-b3b2-d30c76172a61/Cost%20Model%20Inputs.xlsx">LEDCOMv2.zip Inputs.xlsx</a></td> <td>Archive of the original U.S. Department of Energy cost model</td> <td>Includes descriptive documents.</td> </tr> </tbody> </table> <p>Version 2: An incorrent comment in Cell D6 in the “Global” sheet in the “LEDCOM2020.xlsx” file has been removed.</p>
Data package for modeling the journey of Colonel William Leake in the southern Mani Peninsula, Greece, using least-cost analysis
<p>Data used to model Colonel William Leake's journey in the southern Mani Peninsula, Greece, in the year 1805. Leake's journey is described in the book, <em>Travels in the Morea: Volume I </em>(Leake 1830, pp. 233-321). The data may be used to calculate least-cost paths between the places where Leake stopped, taking into consideration the contemporary path network and calculating cost in time based on Tobler's hiking function and the Modified Tobler function. A paper interpreting these data, 'Reconstructing Historical Journeys with Least-Cost Analysis: Colonel William Leake in the Mani Peninsula, Greece,' is published in <em>Journal of Archaeological Science: Reports</em> and can be accessed here: <a href="http://doi.org/10.1016/j.jasrep.2019.01.014">https://doi.org/10.1016/j.jasrep.2019.01.014</a>. The article pre-print can be accessed here: <a href="https://works.bepress.com/rebecca-seifried/11/">https://works.bepress.com/rebecca-seifried/11/</a>.</p> <p>Dr. Rebecca M. Seifried mapped the pre-modern paths as part of a PhD dissertation completed in 2016 through the Department of Anthropology at the University of Illinois at Chicago, entitled 'Community Organization and Imperial Expansion in a Rural Landscape: The Mani Peninsula, Greece (AD 1000-1821)' (<a href="http://hdl.handle.net/10027/21274">https://hdl.handle.net/10027/21274</a>). Fieldwork was conducted in 2014 and 2016 under the auspices of the 5th Ephorate of Byzantine Antiquities in Sparta and in collaboration with the Diros Project, an archaeological survey and excavation co-directed by Dr. Giorgos Papathanassopoulos and Dr. Anastasia Papathanasiou through the Ephorate of Palaeoanthropology & Speleology of Southern Greece. The remaining datasets were created in collaboration with Dr. Chelsea A.M. Gardner as part of the 'CART-ography Project: Cataloguing Ancient Routes and Travels in the Mani Peninsula,' whose goal is to catalogue the historic accounts of travelers to Mani and to model their routes throughout the peninsula.</p> <p>This research was funded by the National Science Foundation (BCS-1346694), Marie Sklodowska-Curie Actions (H2020-MSCA-IF-2016 750843), the DigitalGlobe Foundation, the National Cadastre and Mapping Agency, SA (Ktimatologio), ArchaeoLandscapes Europe, the University of Illinois at Chicago, the Society of Women Geographers, the Archaeological Institute of America, and Mount Allison University.</p>
Model-driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1440 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_null_D_0.95_E_F.mat</p> <p>Where the fields A, B, C, D, E, and F are described as follows.</p> <p>A: number of users.<br /> Considered values are: 2500, 5000, 10000, 20000, 40000.</p> <p>B: variation in the profile of the requests.<br /> Considered values are: ref, var1, var2, var3, var4, var5.<br /> ref -> reference experiment, no users are halved or doubled<br /> var1 -> users in class 1 halved, other users doubled <br /> var2 -> users in class 2 halved, other users doubled <br /> var3 -> users in class 3 halved, other users doubled <br /> var4 -> users in class 4 halved, other users doubled <br /> var5 -> users in class 5 halved, other users doubled </p> <p>C: variation of the replaceability set of the application server.<br /> Considered values are: ref, k1, k2, k3, k4, k5.<br /> ref -> reference experiment, no rates are halved or doubled<br /> k1 -> only possible substitution has rate k1 halved and other rates doubled<br /> k2 -> only possible substitution has rate k2 halved and other rates doubled <br /> k3 -> only possible substitution has rate k3 halved and other rates doubled <br /> k4 -> only possible substitution has rate k4 halved and other rates doubled <br /> k5 -> only possible substitution has rate k5 halved and other rates doubled </p> <p>D: variation of the design constraints.<br /> Considered values are: none, 4, 34, 234.<br /> none -> no components can be replicated<br /> 4 -> only the application server can be replicated<br /> 34 -> only the application server and the database server can be replicated<br /> 234 -> all the components can be replicated</p> <p>E: Optimization algorithm.<br /> Considered values are: norefactoring, replacement, reassignment, full<br /> norefactoring -> experiment with no refactorings<br /> replacement -> experiment with only replacement refactoring<br /> reassignment -> experiment with only reassignment refactoring<br /> full -> experiment with replacement and reassignment refactorings</p> <p>F: Experiment seed.<br /> Considered values are from 1 to 20</p> <p> </p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
<p><span>Knowledge of individual age can help both in-situ and ex-situ conservation programs to design more efficient and suitable management plans for targeted wildlife species. DNA methylation is one of the epigenetic aging markers that has emerged as a promising tool that can estimate age with high accuracy using only a tiny amount of biological material, which can be collected in a minimally invasive way. Here, we sequenced five targeted genetic regions and used </span><span>8–23</span><span> selected CpG sites to build age estimation models with machine learning methods </span><span>with about only $3–7 per sample</span><span>, using blood samples of seven Felidae species—ranging from small to big, and domestic to endangered species: domestic cats (<em>Felis catus</em>, 139 samples), Tsushima leopard cats (<em>Prionailurus bengalensis euptilurus</em>, 84 samples), and five<em> Panthera </em>species (96 samples). </span><span>The models built achieved satisfactory accuracy—the mean absolute error of the best models was 1.966, 1.348, and 1.552 years in domestic cats, Tsushima leopard cats, and <em>Panthera</em> spp., respectively.</span><span> Our models in domestic cats and Tsushima leopard cats were applicable to individuals regardless of health conditions, indicating the high applicability of our models to samples collected from diverse situations, e.g., rescued individuals in the context of conservation. We also showed the possibility of developing universal age estimation models for the five<em> Panthera</em> spp. using two of the five genetic regions, suggesting an even lower cost to use our models for future applications.</span></p>
Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
<p>In order to calibrate and evaluate a recently developed growth model, the Maintenance-Growth Model (MGM), for the case of growth under food restriction, empirical data for house crickets (<em>Acheta</em> <em>domesticus</em>) were collected and analysed. This data set contains data for individually reared crickets growing under two different regimes of controlled food limitation as well as data for food-limited cohorts of growing house crickets. The sets include temporal data for body mass and ingestion as well as age and size at maturation (imago emergence). The data for food-limited cohorts were collected prior to this study and parts of it have previously been analysed and presented in a publication on animal self-thinning. </p>
TURDATA: a database of low-cost air quality and remote sensing measurements for the validation of micro-scale models in the real Prague urban environments
<p><strong>README</strong></p> <p>TURDATA is a supplementary data set for the TURBAN project Prague observation campaign described in the manuscript Bauerová et al. 2024 (submitted for publication). The measurement campaign was focused on air pollution and meteorological measurement, including vertical profiles in selected part of Prague city centre called here as Legerova domain. Within this area, one professional meteorological station (MS) Prague Karlov and one reference traffic air quality monitoring (AQM) station Prague 2-Legerova (classified as traffic hotspot) are located. To gain high spatial and temporal resolution data, the supplementary measurement network was established, which consisted of:</p> <p>- 20 combined low-cost sensor (LCS) stations for monitoring of PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> and O<sub>3</sub> concentrations (using Plantower PMS7003 particle counters and Envea Cairsense electrochemical sensors) placed in different sites and different height levels AGL (higher = H, lower = L),</p> <p>- 1 mobile telescopic meteorological mast for measuring temperature, relative humidity, wind velocity and direction and air pressure (using 2D ultrasonic anemometer Gill WindSonic 60 and weather station Gill MetConnect THP),</p> <p>- 1 MTP-5-He microwave radiometer (MWR; Attex) for temperature vertical profile,</p> <p>- 1 StreamLine XR Doppler LIDAR (HALO Photonics) for wind vertical profile. </p> <p>The main Legerova campaign lasted from 30 May 2022 to 28 March 2023 with some exceptions (see <em>TURDATA_metadata.xlsx</em> with all details). Because LCSs are known for their highly variable measurement quality, before their deployment the Legerova campaign, a sufficiently long-term initial field comparative measurement of all LCSs at RM Prague 4-Libuš was carried out (lasting from 16/12/2021 to 30/5/2022). The results showed that most of the LCSs were in raw measurement differently zero-shifted against each other and against gaseous reference or aerosol optical equivalent monitors (RMs or EMs). Therefore, the Multivariate Adaptive Regression Splines (MARS) method was applied to calculate corrected LCS concentrations based on initial field comparative measurement complemented by meteorological data from MS Prague Libuš. To check the quality of raw and MARS corrected LCS concentrations at the end of the measurement campaign, the final comparative field measurement of all LCSs at Prague 4-Libuš RM station was performed.</p> <p>Therefore, in case of LCSs measurement (both raw and corrected) the important columns of location (measurement placement: RM_Prague_4-Libus and Legerova_domain) and measurement_program (Initial_comparative_measurement, Legerova_campaign and Final_comparative_measurement) were added.</p> <p>In case of PM<sub>10</sub> and PM<sub>2.5</sub> measurement the maximum raw and MARS-corrected concentrations were influenced by temporary pollution episode on 26 July 2022 around 4 a.m. and 9 p.m. (both UTC) caused by aerosol pollution transported from large forest fire in Hřensko (the northern part of the Czech Republic). </p> <p> </p> <p>TURDATA includes the following files:</p> <p>1. <strong>TURDATA_metadata_and_photos.zip</strong> containing:</p> <p>- "<em>TURDATA_metadata.xlsx</em>" with the important list of metadata about devices placement, locations parameters and measurement periods</p> <p>- Folder "<em>Photos_from_Legerova_campaign</em>" with photos from Legerova measurement campaign</p> <p>2. <strong>AQ_LCSs_raw_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> raw measured concentrations by all LCSs</p> <p>- "<em>O3_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM10_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> raw measured concentrations by all LCSs</p> <p>- "<em>PM2_5_RAW_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> raw measured concentrations by all LCSs</p> <p>- "<em>AQ_LCSs_raw_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>3. <strong>AQ_data_RM_stations_Prague_TURDATA.zip</strong> containing:</p> <p>- "<em>AQ_data_Prague_RM_stations_TURDATA_12-2021_06-2023.xlsx</em>" with air quality data measured by reference AQM stations in Prague</p> <p>- "<em>AQ_data_RM_stations_Prague_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>4. <strong>Meteo_data_Prague_MS_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_12-2021_06-2023.xlsx</em>" with meteorological data measured by professional meteorological stations in Prague</p> <p>- "<em>Meteo_data_Prague_MS_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>5. <strong>AQ_LCSs_MARS-corrected_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>NO2_COR_LCSs_TURDATA.xlsx</em>" with complete data set of NO<sub>2</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>O3_COR_LCSs_TURDATA.xlsx</em>" with complete data set of O<sub>3</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM10_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>10</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>PM2_5_COR_LCSs_TURDATA.xlsx</em>" with complete data set of PM<sub>2.5</sub> MARS-corrected concentrations for all LCSs</p> <p>- "<em>AQ_LCSs_MARS-corrected_measurement_TURDATA_readme.txt</em>" with all necessary information for correct data use and brief description of MARS correction method</p> <p>6. <strong>Meteo-mast_PVK_measurement_TURDATA.zip</strong> containing:</p> <p>- "<em>Meteo-mast_PVK_TURDATA_06-2022_06_2023.xlsx</em>“ with non-referential meteorological data measured by mobile meteo-mast</p> <p>- "<em>Meteo-mast_data_PVK_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>7. <strong>MWR_temperature_profile_TURDATA.zip</strong> containing:</p> <p>- "<em>MWR_5min_temperature_TURDATA_02-2022_03-2023.xlsx</em>" with raw temperature vertical profile measurement from microwave radiometer</p> <p>- "<em>MWR_1hour_temperature_TURDATA.xlsx</em>" with 1-hour averaged temperature vertical profile from microwave radiometer</p> <p>- "<em>MWR_1hour_TMP_gradient_TURDATA.xlsx</em>" with 1hour temperature gradient calculated from raw temperature profiles measured by microwave radiometer</p> <p>- "<em>MWR_temperature_profile_TURDATA_readme.txt</em>" with all necessary information for correct data use</p> <p>8. <strong>LIDAR_wind_profile_TURDATA.zip</strong> contains:</p> <p>- Individual folders "yyyymm“ -> "yyyymmdd"</p> <p>- Each daily folder "yyyymmdd" contains files:</p> <p>a) "<em>Processed_Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with processed WV and WS data</p> <p>b) "<em>Wind_Profile_188_yyyymmdd_hhmmss.hpl</em>" with non-processed Doppler wind profile data</p> <p>- "<em>LIDAR_wind_profile_TURADATA_readme.txt</em>" with all necessary information for correct data use</p>
Input data for the OnStove Nepal model "AAchieving Nepal's clean cooking ambitions: an open source and geospatial cost–benefit analysis"
<p>This repository includes input data to run the OnStove Nepal 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 result files and figures can be downloaded from the permanent repository <a href="https://doi.org/10.5281/zenodo.10643983">https://doi.org/10.5281/zenodo.10643983</a>.</p> <p>The "<strong>GIS_input_data/</strong>" directory includes all the geospatial datasets needed to run the model. Each dataset folder contains a Source.md file describing the dataset, source, attribution, and license. To run the model extract the data inside your "<strong>1. Data</strong>"<strong> </strong>folder in your project. </p> <p>The "<strong>Scenario_inputs/</strong>" directory includes the CSV files with the input socio- and techno-economic data for the different scenarios. Sources for the socio- and techno-economic data can be found in the <strong>supplementary material</strong> of the related publication in the link <a href="https://doi.org/10.1016/S2542-5196(24)00209-2">https://doi.org/10.1016/S2542-5196(24)00209-2</a>. To run the model extract the scenario data inside your "<strong>2. Scenario inputs</strong>"<strong> </strong>folder in your project. </p>
Global agricultural costing model
<p>The agricultural costing model is a cost calculator that includes national-scale information on costs of production in two versions: 1) ten major crops including, barley, groundnut, maize, potato, rice, sorghum, soybean, sugar beet, sunflower, and wheat, and 2) vegetables, including onion, cucumber, cabbage, carrot, spinach, lettuce, cauliflower, broad beans, pumpkin, peas, and<br>‘vegetables’ combining the aforementioned in a single group and, works in combination to FABLE calculator. The present version of the costing inventory includes five cost items corresponding to costs for the production inputs of fertiliser, pesticides, labour, machinery and fuel. The current setting of the cost calculation is based on the integration of physical requirement (volumes) per hectare for any given production input, the respective cost per unit of input and the cropland extent.</p>
Modelling input for cooking costs
<p>This dataset is meant to provide a concise way of representing all the important associated costs for the clean cooking fuels being considered during the modelling.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK"
<p>The raw data for the research "Comparing Neural Network Models Based on Macro Perspective Economic and Environmental Indicators with ARIMA Model in predicting Construction Cost Index in UK".</p> <p>Data collector: Runda Zheng</p>
Fig. 1 in LiDAR sensors in smartphones can enrich herbarium specimens with 3D models of habitat at high precision and little cost
Fig. 1. Example of a 3D point-cloud model of specimen habitat obtained with the LiDAR scanner of an iPad Pro. A, Plan view of the model with potential use cases, including annotation and extraction of general habitat characteristics; B, Side view with measurements that can be extracted from the model at centimetre precision (DBH, diameter at breast height); C, Average times needed for physical herbarium specimen collection (orange) and LiDAR scanning (purple) in the field over 20 replicates; time for scanning depends on the area scanned and the habitat.
Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
Open the record for dataset details and reuse information.
Data from: A new mechanistic model for individual growth suggests upregulated maintenance costs when food is scarce in an insect
Open the record for dataset details and reuse information.
Long-term research and hierarchical models reveal consistent fitness costs of being the last egg in a clutch
1. Maintenance of phenotypic heterogeneity in the face of strong selection is an important component of evolutionary ecology, as are the consequences of such heterogeneity. Organisms may experience diminishing returns of increased reproductive allocation as clutch or litter size increases, affecting current and residual reproductive success. Given existing uncertainty regarding trade-offs between the quantity and quality of offspring, we sought to examine the potential for diminishing returns on increased reproductive allocation in a long-lived species of goose, with a particular emphasis on the effect of position in the laying sequence on offspring quality. 2. To better understand the effects of maternal allocation on offspring survival and growth, we estimated the effects of egg size, timing of breeding, inter- and intra-annual variation, and position in the laying sequence on gosling survival and growth rates of black brent (Branta bernicla nigricans) breeding in western Alaska from 1987–2007. 3. We found that gosling growth rates and survival decreased with position in the laying sequence, regardless of clutch size. Mean egg volume of the clutch a gosling originated from had a positive effect on gosling survival (β = 0.095, 95% CRI: 0.024, 0.165), and gosling growth rates (β = 0.626, 95% CRI: 0.469, 0.738). Gosling survival (β = -0.146, 95% CRI: -0.214, -0.079) and growth rates (β = -1.286, 95% CRI: -1.435, -1.132) were negatively related to hatching date. 4. These findings indicate substantial heterogeneity in offspring quality associated with their position in the laying sequence. They also potentially suggest a trade-off mechanism for females whose total reproductive investment is governed by pre-breeding state. 20-Mar-2020
Scenario data, model source code and plotting routine for manuscript: Separating CO2 emission from removal targets comes with limited cost impacts
<p>This data archive contains REMIND model setup, results data and data analysis files for manuscript:<br><strong>Separating CO2 emission reduction from removal targets comes with limited cost impact.<br><br>plotting</strong>(directory) contains results data, manuscript specific data analysis and plotting routine scripts used to generate the figures of the manuscript.<br><strong>remind</strong>(directory) contains REMIND model source code and scenario set-up. Detailed scenario configurations are set in remind/config/scenario_config_SepMark.csv.<br><strong>remind2</strong>(directory) contains the slightly modified R-library package used for post-processing of REMIND output.<br><br>AMENDMENT<br><strong>Plots_SeparateMarkets_afterReviewProcess.Rmd</strong> After the review process, the new plotting script was added including the additional figures in the Supplementary Material. This file should replace the previous R-markdown file SepMark_essential/plotting/Plots_SeparateMarkets.Rmd.</p>
Performance Measurement Dataset of the HPC Benchmarks FASTEST, Kripke, and RELeARN for Cost-Effective Modeling Analysis with Extra-P
<p>Performance Measurement Dataset of the HPC Benchmarks FASTEST, Kripke, RELeARN for Scalability Studies with Extra-P. This data was used to analyze cost-effective modeling approaches presented in the IPDPS 2020 paper "Learning Cost-Effective Sampling Strategies for Empirical Performance Modeling".</p>
Data for "International Transport costs: New Findings from modeling additive costs"
<p>Downloading the data will provide you with a .zip file.</p> <p>These data are US imports from 1974 to 2020, by partner, transport mode and product at the 5 digit level in the "Hummels" data and by partner, transport mode, district of entry, district of unlading and product at the 10 digit level for the rest of the data.</p> <p>All the data originally come from the Census Bureau "Foreign Trade" (see https://www.census.gov/foreign-trade/index.html) and belong to the public domain.</p> <p>"Hummels_JEP _data" was downloaded from David Hummels’s website (https://www.krannert.purdue.edu/faculty/hummelsd/research/jep/data.html -- Hummels, David, "Transportation Costs and International Trade in the Second Era of Globalization", <em>Journal of Economic Perspectives</em>, Vol 21, No 3, pp 131-154. It covers 1974 to 2004.</p> <p>The other main files were bought directly from the Census Bureau ("Annual Merchandise Trade files"). They cover 1997-1999 and 2002-2020.</p> <p>Various files are included for the conversion of country codes (dist_cepii.dta, countrycodes_use.txt), product codes (HS2002_SITC2.txt) and to associate quantity units with hts codes (hts... and ..._hts_...).</p>
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International Brain Laboratory public data
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OpenNeuro
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