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2,727 results for “Cost”
Numerical simulations of AZO/ZnGeO/Cu2O solar cells: Impact of the germanium composition of the buffer layer and the use of low cost fabrication on the photovoltaic performances
<p>The dataset contains the results of the numerical simulations of AZO/ZnGeO/Cu2O solar cell models.</p> <p>The physical parameters of the model are chosen with special care to match literature experimental measurements or are interpolated using the values from binary metal oxides in the case of the new ZnGeO compound. The solar cell structure includes an interface and a defective layer at the ZnGeO/Cu2O heterojunction.</p> <p>The AZO/ZnGeO/Cu2O model results reproduce the photovoltaic characteristics of experimental devices presented by Minami et al. (Applied Physics Express 9, 052301 (2016) DOI:10.7567/APEX.9.052301)</p> <p>The dataset also includes results using models with different germanium compositions for the ZnGeO buffer layer.</p> <p>Other solar cell simulation results are presented to model the impact of low cost fabrication processes, such as spray pyrolysis, by varying the thickness, doping concentration, carrier mobilities and defect concentration of the Cu2O absorber layer as well as the germanium composition of the buffer layer.</p>
plan4res public dataset for case study 3 "Cost of RES integration and impact of climate change for the European Electricity System in a future world with high shares of renewable energy sources"
<p>The objective of the plan4res project is to provide a well-structured and highly modular modelling framework to enable consistent insights into the different needs of future energy system. Three case studies will highlight the potentials of this framework by dealing with different aspects of a future energy systems.<br> Case study 3 will focus on cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources. Ist overall objectives are to identify the Cost of RES integration and impact of climate change for the European electricity system in a future world with high shares of renewable energy sources will be the main focus of case study 3.<br> The present dataset contains all the public data built for this case study.</p> <p>The related documentation is included in plan4res deliverable D4.5 </p> <pre>https://doi.org/10.5281/zenodo.3785010</pre>
OptiSpot: Minimizing Application Deployment Cost using Spot Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1800 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_D_E_F_G.mat</p> <p>Where the fields A, B, C, D, E, F, and G are described as follows.</p> <p>A: number of users.<br /> Considered values are: 1000, 2000, 5000, 10000.</p> <p>B: maximum response time in milliseconds.<br /> Considered values are: 60, 80, 100, 200.</p> <p>C: overbid time cap in hours.<br /> Considered values are: 5, 20, 80, 0 (note: 0 is a code used to express infinite hours).</p> <p>D: Amazon region.<br /> Considered values are: us-east, eu-west.</p> <p>E: Operating system.<br /> Considerede values are: Windows, Linux.</p> <p>F: Optimization algorithm.<br /> Considered values are: heuristic (which is OptiSpot), fmincon.</p> <p>G: Experiment seed.<br /> Considered values are from 1 to 30</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 associated to "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis"
<p>Data for replication of main results in "The Direct Cost of Contaminated Brownfield Sites on Real Estate in France: A Quasi-Exhaustive Hedonic Price Analysis". The folder "data_estim" contains all necessary data to replicate all estimations in the article (see the R code "codes_cbs-cost") with three .csv files: dvf_estim.csv, dvfbasol_estim.csv and cell200_simulation.csv. The variable names in these files are as follow:</p><p> </p><p>Identifier Variables:</p><p>- IDMUTATION: identifier for each transacted property</p><p>- comm_code: identifier for each commune defined in 2021</p><p>- admin_code: identifier for urban areas defined in 2021</p><p>- iris2014_code: identifier for each neighborhood defined in 2014</p><p>- cell200_code: identifier for each 200-meters gredded cells</p><p>- dvf_x: longitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- dvf_y: latitude of each transacted property (EPSG: 2154, Lambert-93, RGF93)</p><p>- basol_code: identifier for each CBS (only reported in dvfbasol_estim.csv)</p><p>- anneemut: year of transaction for each property</p><p> </p><p>Dependent Variable:</p><p>- pm2: price in euro per square meter of transacted properties</p><p> </p><p>Interest Variables:</p><p>- areaha_basol250: area in hectare of CBS between 0 and 250 meters from transacted property</p><p>- areaha_basol500: area in hectare of CBS between 250 and 500 meters from transacted property</p><p>- areaha_basol1000: area in hectare of CBS between 500 and 1000 meters from transacted property</p><p>- areaha_basol2000: area in hectare of CBS between 1000 and 2000 meters from transacted property</p><p>- areaha_basol3000: area in hectare of CBS between 2000 and 3000 meters from transacted property</p><p>- area250_indpro: area in hectare of CBS with industrial manufacturing activities between 0 and 250 meters from transacted property</p><p>- area500_indpro: area in hectare of CBS with industrial manufacturing activities between 250 and 500 meters from transacted property</p><p>- area1000_indpro: area in hectare of CBS with industrial manufacturing activities between 500 and 1000 meters from transacted property</p><p>- area2000_indpro: area in hectare of CBS with industrial manufacturing activities between 1000 and 2000 meters from transacted property</p><p>- area3000_indpro: area in hectare of CBS with industrial manufacturing activities between 2000 and 3000 meters from transacted property</p><p>- area250_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 0 and 250 meters from transacted property</p><p>- area500_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 250 and 500 meters from transacted property</p><p>- area1000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 500 and 1000 meters from transacted property</p><p>- area2000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 1000 and 2000 meters from transacted property</p><p>- area3000_indoth: area in hectare of CBS with industrial non-manufacturing activities (extractive) between 2000 and 3000 meters from transacted property</p><p>- area250_othact: area in hectare of CBS with other or unknown activities between 0 and 250 meters from transacted property</p><p>- area500_othact: area in hectare of CBS with other or unknown activities between 250 and 500 meters from transacted property</p><p>- area1000_othact: area in hectare of CBS with other or unknown activities between 500 and 1000 meters from transacted property</p><p>- area2000_othact: area in hectare of CBS with other or unknown activities between 1000 and 2000 meters from transacted property</p><p>- area3000_othact: area in hectare of CBS with other or unknown activities between 2000 and 3000 meters from transacted property</p><p>- areaha_specific250: area in hectare of CBS specific to a unique CBS between 0 and 250 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific500: area in hectare of CBS specific to a unique CBS between 250 and 500 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific1000: area in hectare of CBS specific to a unique CBS between 500 and 1000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p>- areaha_specific2000: area in hectare of CBS specific to a unique CBS between 1000 and 2000 meters from transacted property (only reported in dvfbasol_estim.csv)</p><p> </p><p>Robustness Variables:</p><p>- pm2mean_iris: average transaction price per square meter of neighborhood IRIS</p><p>- shpoorhouse: share in percentage of poor households </p><p>- dvfschool_nb250: number of schools within 250 meters of property</p><p>- dvfschool_nb500: number of schools within 500 meters of property</p><p>- dvfschool_nb1000: number of schools within 1000 meters of property</p><p>- dvfschool_nb2000: number of schools within 2000 meters of property</p><p>- dvfschool_nb3000: number of schools within 3000 meters of property</p><p>- dvfroad_nb250: number of road connections within 250 meters of property</p><p>- dvfroad_nb500: number of road connections within 500 meters of property</p><p>- dvfroad_nb1000: number of road connections within 1000 meters of property</p><p>- dvfroad_nb2000: number of road connections within 2000 meters of property</p><p>- dvfroad_nb3000: number of road connections within 30000 meters of property</p><p>- dvfrail_nb250: number of railway stations within 250 meters of property</p><p>- dvfrail_nb500: number of railway stations within 500 meters of property</p><p>- dvfrail_nb1000: number of railway stations within 1000 meters of property</p><p>- dvfrail_nb2000: number of railway stations within 2000 meters of property</p><p>- dvfrail_nb3000: number of railway stations within 3000 meters of property</p><p> </p><p>Control Variables:</p><p>- center_dist: distance in kilometers of transacted property from urban area center</p><p>- sterr: surface area in square meter of parcel of each property</p><p>- sbati: surface area in square meter of building surfaces</p><p>- vente_cla: transaction through a classical process (binary variable)</p><p>- vente_adj: transaction through adjudicated process (binary variable)</p><p>- vente_ech: transaction through special exchange process (binary variable)</p><p>- vente_exp: transaction through expropriation process (binary variable)</p><p>- vente_efa: transaction before completion (binary variable)</p><p>- nblocmai: number of houses in each transaction</p><p>- nblocapt: number of apartments in each transaction</p><p>- nblocdep: number of building dependencies in each transaction</p><p>- nblocact: number of properties for commercial purpose in each transaction</p><p>- nbapt1pp: number of apartment with 1 room in each transaction</p><p>- nbapt2pp: number of apartment with 2 rooms in each transaction</p><p>- nbapt3pp: number of apartment with 3 rooms in each transaction</p><p>- nbapt4pp: number of apartment with 4 rooms in each transaction</p><p>- nbapt5pp: number of apartment with 5 and more rooms in each transaction</p><p>- nbmai1pp: number of house with 1 room in each transaction</p><p>- nbmai2pp: number of house with 2 rooms in each transaction</p><p>- nbmai3pp: number of house with 3 rooms in each transaction</p><p>- nbmai4pp: number of house with 4 rooms in each transaction</p><p>- nbmai5pp: number of house with 5 and more rooms in each transaction</p><p>- pm2mean_comm: average transaction price in euro per square meter of commune</p><p>- dvfmonument_nb500: number of historical monuments between 0 and 500 meters from transacted property</p><p>- dvfmonument_nb1000: number of historical monuments between 500 and 1000 meters from transacted property</p><p>- dvfmonument_nb2000: number of historical monuments between 1000 and 2000 meters from transacted property</p><p>- dvfindus_nb500: number of active industrial sites between 0 and 500 meters from transacted property</p><p>- dvfindus_nb1000: number of active industrial sites between 500 and 1000 meters from transacted property</p><p>- dvfindus_nb2000: number of active industrial sites between 1000 and 2000 meters from transacted property</p><p>- sh_apt: share of apartments in neighborhood IRIS</p><p>- sh_1945: share in percentage of properties with a building age before 1945</p><p>- sh_1970: share in percentage of properties with a building age before 1970</p><p>- sh_1990: share in percentage of properties with a building age before 1990</p><p>- sh_ap90: share in percentage of properties with a building age between 1990 and 2015</p><p>- sh_2015: share in percentage of properties with a building age after 2015</p><p>- clc1000_urbanhousing: share in percentage of land within 1000 meters of transacted properties with housing</p><p>- clc1000_urbanpark: share in percentage of land within 1000 meters of transacted properties with urban parks</p><p>- clc1000_recreation: share in percentage of land within 1000 meters of transacted properties with recreative activities</p><p>- clc1000_industrial: share in percentage of land within 1000 meters of transacted properties with industrial activities</p><p>- clc1000_transport: share in percentage of land within 1000 meters of transacted properties with transport infrastructures</p><p>- clc1000_nature: share in percentage of land within 1000 meters of transacted properties with natural land use</p><p>- clc1000_agr: share in percentage of land within 1000 meters of transacted properties with agricultural land use</p><p>- clc1000_forest: share in percentage of land within 1000 meters of transacted properties with forest</p><p>- clc1000_water: share in percentage of land within 1000 meters of transacted properties with water</p><p> </p><p> </p>
Data: Cutting the costs of coastal protection by integrating vegetation in flood defences.
<p>File: levee_crest_height_reduction_per_country_version_July2021.nc<br>Fields: (1) Crest height reduction m per km along the populated coastline susceptible to flooding (return period = 100 years)<br> (2) Crest height reduction cost saving per country in million USD<sub>2005</sub> PPP along the populated coastline susceptible to flooding (return period = 100 years)<br> (3) Cost savings as percentage of GDP<sub>2005</sub> along the urban populated coastline susceptible to flooding (return period = 100 years)</p> <p>File: transectdata_version_July2021.nc<br> Transectdata of vegetated transects within the study area.<br>Fields: <br>(1) rps = return period <br>(2) fid = id of the transects<br>(3) centroids = coordinates of the transects<br>(4) inun = (1) in area susceptible to flooding<br>(5) urban = (1) in urban area, (0) not in urban area<br>(6) veg_width = derived coastal vegetation belt width along the foreshore<br>(7) veg_type = derived coastal vegetation type along the foreshore (1: salt marshes, 2: mangroves)<br>(8) hsig = Offshore significant wave heights (multiple return periods) corresponding to the transects<br>(9) wave period = Offshore peak wave period (multiple return periods) corresponding to the transects<br>(10) surge = Extreme water level combination of surge and tide (m +MSL) (multiple return periods)<br>(11) veg_z0 = elevation at the start of the vegetated zone (m +MSL)<br>(12) hrms_end_noveg = root mean square wave height at the end of the foreshore (without vegetation) (multiple return periods)<br>(13) hrms_endveg = root mean square wave height at the end of the foreshore (with vegetation) (multiple return periods) <br>(14) pdens_15km = population density derived using buffer of 15 kilometre radius</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>
Data and Code for: Real-Time Pricing and the Cost of Clean Power
<p>Solar and wind power are now cheaper than fossil fuels but are intermittent. The extra supply-side variability implies growing benefits of using real-time retail pricing (RTP). We evaluate the potential gains of RTP using a model that jointly solves investment, supply, storage, and demand to obtain a chronologically detailed dynamic equilibrium for the island of Oahu, Hawai'i. We find that RTP reduces costs in high-renewable systems by roughly 6 to 12 times as much as in fossil systems holding demand assumptions fixed, markedly lowering the cost of clean energy integration.</p>
HEATDesalination - Case-study lowest-cost optimisation results
<p>Optimisation results for the lowest lifetime cost system consisting of solar photovoltaic (PV), hybrid photovoltaic-thermal (PV-T) and solar-thermal collectors alongside battery and hot-water storage systems for meeting the electrical and thermal (hot-water) needs of three multi-effect distillation (MED) plants.</p> <p>The updated results are from optimisations runs carried out in response to peer-review comments.</p>
Data of paper "Global supply chains amplify economic costs of future extreme heat risk"
<p>This is the database of articles "Global supply chains amplify economic costs of future extreme heat risk". The database contains the number of deaths caused by future heat waves in regions around the world under different SSP scenarios (e.g. SSP119, SSP245, SSP585), as well as global health losses, labor losses, and indirect losses as a percentage of regional or sectoral value added under different SSP scenarios. The regions of the database are aggregated using the GTAP 141 aggregating schema.</p>
Design files for a low-cost high-resolution imaging device for hyphae in soil
<p>This dataset contains the stereolithography (STL) files for the 3D-printed and cut parts of a low-cost high-resolution imaging device for hyphae in soil called <em>Hyphascope</em>. The design of <em>Hyphascope</em> was adopted from the 3D printer i3 MK3S+ by Prusa Research, with a digital microscope camera (DMC; 600× magnification) replacing the filament extruder. Repeated imaging of a soil profile with the imaging device enables researchers to observe and quantify changes in the amount, distribution, and morphology of hyphae.</p> <p>The parts were created and modified using <a href="https://www.freecad.org">FreeCAD</a> (version 0.20). STL files for the original parts are distributed under the Creative Commons Attribution 4.0 International License, STL files for the remixed parts under the GNU General Public License v2.0. For a detailed description on how to prepare and assemble the parts see <a href="https://doi.org/10.17504/protocols.io.bp2l6xo3zlqe/v1">this protocol on protocols.io</a>. For information on the development, limitations, and expected outcomes of the protocol, see <a href="https://doi.org/10.1371/journal.pone.0318083">this article</a> published in PLOS ONE.</p> <p> </p> <div> <h2>STL files of 3D-printed parts</h2> <h3>Original parts</h3> </div> <div> <div> <ul> <li><em>dmc-attachment.stl</em></li> <li> <div><em>dmc-attachment-gear.stl</em></div> </li> <li><em>dmc-attachment-gear-wider.stl</em> (optional part)<em><br></em></li> <li><em>dmc-holder-back.stl</em></li> <li><em>dmc-holder-front.stl</em></li> <li><em>f-axis-motor-gear.stl</em></li> <li><em>f-axis-spring-end.stl</em></li> <li><em>f-axis-tighteners.stl</em></li> <li><em>frame-foot-inserts.stl</em></li> <li> <div><em>frame-foot-left.stl</em></div> </li> <li> <div><em>frame-foot-right.stl</em></div> </li> <li> <div><em>frame-hat.stl</em></div> </li> <li> <div><em>frame-hat-insert.stl</em></div> </li> </ul> </div> <h3>Remixed parts originally designed by Prusa Research</h3> <p><em>The five parts below are <strong>remi</strong></em><strong><em>xed from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts">i3 MK3S+ printable parts</a> </em></strong><em>and </em><strong><em>re-distributed under the <a href="http://www.gnu.org/licenses/old-licenses/gpl-2.0.html">GNU General Public License v2.0</a></em></strong><em>.</em></p> </div> <ul> <li> <div><em>dmc-carriage-back.stl</em> (Remix of <em>x-carriage-back.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</div> </li> <li><em>dmc-carriage-front.stl </em>(Remix of <em>x-carriage.stl</em>; the design was largely modified to fit the DMC including changes to the shape and screw hole placement; the inserts for the linear bearings have the most resemblence to the original part.)</li> <li><em>x-end-idler-mod.stl</em> (Remix of <em>x-end-idler.stl</em>; the height was increased by 20 mm.)</li> <li><em>x-end-motor-mod.stl</em> (Remix of <em>x-end-motor.stl</em>; the height was increased by 20 mm and the counterbores of the three motor screws were moved to the opposite side.)</li> <li><em>z-axis-top-mod.stl </em>(Remix of <em>z-axis-top.stl</em>; 14.8 mm-long spacers were added.)</li> </ul> <h3>Parts designed by Prusa Research</h3> <ul> <li> <div><em>z-axis-bottom.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> <li> <div><em>z-screw-cover.stl</em> (available from <a href="https://www.printables.com/model/57217-i3-mk3s-printable-parts" target="_blank" rel="noopener">Printables</a>)</div> </li> </ul> <p> </p> <div> <h2>STL files of cut parts</h2> <h3>Original parts</h3> </div> <ul> <li><em>box-bottom.stl</em></li> <li><em>box-hook.stl</em></li> <li> <div><em>box-lid.stl</em></div> </li> <li> <div><em>box-lid-frame.stl</em></div> </li> <li><em>box-lid-valve-base.stl</em></li> <li><em>box-wall.stl</em></li> <li><em>box-wall-cables.stl</em></li> <li> <div><em>frame.stl</em></div> </li> </ul> <p> </p>
Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment - Measurements and Locations
<p>Many wetlands in East Africa are farmed and wetland reservoirs are used for irrigation, livestock, and fishing. Water quality and agriculture have a mutual influence on each other. Turbidity is a principal indicator of water quality and can be used for, otherwise, unmonitored water sources. Low-cost turbidity sensors improve in situ coverage and enable community engagement. The availability of high spatial resolution satellite images from the Sentinel-2 multispectral instrument and of bio-optical models, such as the Case 2 Regional CoastColor (C2RCC) processor, has fostered turbidity modeling. However, these models need local adjustment, and the quality of low-cost sensor measurements is debated. We tested the combination of both technologies to monitor turbidity in small wetland reservoirs in Kenya. We sampled ten reservoirs with low-cost sensors and a turbidimeter during five Sentinel-2 overpasses. Low-cost sensor calibration resulted in an R² of 0.71. The models using the C2RCC C2X-COMPLEX (C2XC) neural nets with turbidimeter measurements (R² = 0.83) and with low-cost measurements (R² = 0.62) performed better than the turbidimeter-based C2X model. The C2XC models showed similar patterns for a one-year time series, particularly around the turbidity limit set by Kenyan authorities. This shows that both the data from the commercial turbidimeter and the low-cost sensor setup, despite sensor uncertainties, could be used to validate the applicability of C2RCC in the study area, select the better-performing neural nets, and adapt the model to the study site. We conclude that combined monitoring with low-cost sensors and remote sensing can support wetland and water management while strengthening community-centered approaches.</p> <p>The provided dataset includes a point shapefile with the studied reservoirs in central Kenya and a data table with the sampling date (Sentinel-2 overpass plus/minus one day), low-cost sensor setup number, reservoir ID, sampling location within the reservoir, the voltage measurements of the three respective low-cost sensor heads for sensor setups A and B, the averaged voltage, and the turbidimeter measured turbidity value in nephelometric turbidity units (NTU).</p> <p>The study is available in (please cite):</p> <div> <div>Steinbach, S., Rienow, A., Chege, M.W., Dedring, N., Kipkemboi, W., Thiong’o, B.K., Zwart, S.J., Nelson, A., 2024. Low-Cost Sensors and Multitemporal Remote Sensing for Operational Turbidity Monitoring in an East African Wetland Environment. <em>IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing</em> <em>17</em>, 8490–8508. <a href="https://doi.org/10.1109/JSTARS.2024.3381756">https://doi.org/10.1109/JSTARS.2024.3381756</a></div> </div> <p>This research was supported in part by the German Federal Ministry of Education and Research (BMBF) through the Project “Participatory Approach to Environmental Conservation of the Muringato Catchment Area for Sustainable Management and Enhanced Ecosystem Health” (CITGI4Muringato) under Grant Agreement No. 01DG20022.</p>
Scan4CFU: Low-cost, open-source bacterial colony tracking over large areas and extended incubation times
<p>A hallmark of bacterial populations cultured <em>in vitro</em> is their homogeneity of growth, where the majority of cells display identical growth rate, cell size and content. Recent insights, however, have revealed that even cells growing in exponential growth phase can be heterogeneous with respect to variables typically used to measure cell growth. Bacterial heterogeneity has important implications for how bacteria respond to environmental stresses, such as antibiotics. The phenomenon of antimicrobial persistence, for example, has been linked to a small subpopulation of cells that have entered into a state of dormancy where antibiotics are no longer effective. While methods have been developed for identifying individual non-growing cells in bacterial cultures, there has been less attention paid to how these cells may influence growth in colonies on a solid surface. In response, we have developed a low-cost, open-source platform to perform automated image capture and image analysis of bacterial colony growth on multiple nutrient agar plates simultaneously. The descriptions of the hardware and software are included, along with details about the temperature-controlled growth chamber, high-resolution scanner, and graphical interface to extract and plot the colony lag time and growth kinetics. Experiments were conducted using a wild type strain of <em>Escherichia coli </em>K12 to demonstrate the feasibility and operation of our setup. By automated tracking of bacterial growth kinetics in colonies, the system holds the potential to reveal new insights into understanding the impact of microbial heterogeneity on antibiotic resistance and persistence. </p>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
Raw data and code for "Addressing gaps in small-scale fisheries: a low-cost tracking system"
<p>This repository contains the raw data and code to reproduce results and plots presented in: "Addressing gaps in small-scale fisheries: a low-cost tracking system". The release contains:</p> <ul> <li>ssf_function.R. The R function developed for the analysis</li> <li>ssf_workflow.R. The R scripts to reproduce the analysis and the results.</li> <li>gps_data.csv. Raw data used in the paper</li> </ul>
Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution - Dataset
<p>This repository contains the data associated with the paper: Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution.</p> <p>Bulot, F.M.J.; Russell, H.S.; Rezaei, M.; Johnson, M.S.; Ossont, S.J.J.; Morris, A.K.R.; Basford, P.J.; Easton, N.H.C.; Foster, G.L.; Loxham, M.; Cox, S.J. Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution. <em>Sensors</em> <strong>2020</strong>, <em>20</em>, 2219.</p> <p><a href="https://doi.org/10.3390/s20082219">https://doi.org/10.3390/s20082219</a> </p> <p>It contains:</p> <p>- DHT22.csv measurements from the DHT22 humidity and temperature sensor</p> <p>- dusttrak.csv measurements from the DustTrak</p> <p>- ops.csv measurements from the OPS TSI 3330</p> <p>- sensors.csv measurement from the low-cost PM sensors</p> <p>- sensors_blank.csv measurements from the low-cost PM sensors during the blank test</p> <p> </p> <p>sensors_blank.csv contains the following variables:</p> <ul> <li>Bin1 to Bin15: particle numbers for different bin sizes reported by the Alphasense OPCR1, as defined by its user's manual available here https://www.alphasense.com/products/optical-particle-counter/</li> <li>SamplingPeriod: sampling period of the Alphasense OPCR1 in seconds</li> <li>SFR: sampling flow rate of the Alphasense OPCR1 in ml/s</li> <li>PM1, PM25, PM4, PM10: PM concentrations reported by the sensors in ug/m3.</li> <li>gr03um to gr100um: particle number concentrations for different bin sizes for the Plantower PMS5003, in particle per 100ml, as defined by its user's manual https://aqicn.org/air/view/sensor/spec/pms5003-manual_v2-3</li> <li>n05 to n10: particle number concentrations for different bin sizes for the Sensirion SPS30, in particles per cm3, as defined by its user's manual: https://www.sensirion.com/fileadmin/user_upload/customers/sensirion/Dokumente/9.6_Particulate_Matter/Datasheets/Sensirion_PM_Sensors_Datasheet_SPS30.pdf</li> <li>humidity and temperature: relative humidity (%) and temperature (Celsius) recorded by the SHT35 sensors</li> <li>sensor: sensor identifier</li> <li>site: name of the air quality monitor containing the sensors</li> <li>exp: name of the experiment considered</li> <li>source: source of PM used</li> <li>variation: peak or stable concentration</li> <li>date: date and time of the experiment</li> </ul>
Supporting Dataset for the Analysis on TSO-DSOs Cooperation and Stable Cost Allocation for the Joint Procurement of Flexibility (Network and Bid List)
<p>The data provides supporting material for the two case studies in Chapter 5 of CoordiNet D6.2 (the deliverable is available at <a href="https://coordinet-project.eu/publications/deliverables">https://coordinet-project.eu/publications/deliverables</a>) and the two case studies in paper on TSO-DSO cooperation (available at <a href="https://arxiv.org/abs/2111.12830">https://arxiv.org/abs/2111.12830</a>).</p> <p>The dataset is cooresponding to two case studies. In the first case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). The interface flow limit is TPmax. In the second case study, the interconnected system consists of the IEEE 14-bus (TN) transmission network connected to three Matpower systems 18-bus distribution networks, who are named as DN_1, DN_2, DN_3. </p> <p>All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line. The interconnected system is fully represented in "Network_XXX.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_XXX);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 50 to 55. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected.. The generated orderbook is presented in "OrderbookTN_XXX.xlsx" (transmission system) and "OrderbookDN_XXX.xlsx" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_XXX) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
French domestic bilateral transport costs between districts c. 1789
<p>Downloading the data will provide you with a .zip file.</p> <p>The data include bilateral transport costs between French districts around 1789, as computed for the paper See Daudin, G. (2010). Domestic Trade and Market Size in Late-Eighteenth-Century France. <em>The Journal of Economic History,</em> 70(3), 716-743. doi:10.1017/S0022050710000598</p>
Analysis scripts for the evaluation of a low-cost high-throughput plant phenotyping system
<p>Data analyses to complement "Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system" (DOI: 10.5281/zenodo.5725224). "README_SetupAndAnalyses.pdf" contains instructions for setting up the high-throughput phenotyping (HTP) system and analyzing the resulting image datasets. The analyses are split into two parts. First, the automatically acquired HTP and manually acquired (DSLR) images are processed using the Python script labeled "finalGreennessAnalyses.py". The csv file labeled "labelTable.csv" is used to rename the DSLR images in terms of the date acquired and experimental conditions and must be included for the Python script to process the DSLR images. The output of the Python script includes "greennessGoProTable.txt" containing tab-delimited data regarding foliar size and greenness for each HTP image and "greennessDSLRTable.txt" containing tab-delimited data regarding foliar size and greenness for each DSLR image. The second step of the analyses includes inferential statistics (e.g., correlations and linear mixed effects modeling) and is based on the R script labeled "ghGoProAndDSLR_toPublish2.R". The csv file labeled "parAllBenches.csv" includes average solar daily light integral (solar DLI) data that were used as part of the linear mixed effects models in R.</p>
RINEX files from low-cost GNSS receivers in Wrocław, Poland; January - March, 2021
<p>Daily RINEX files with multi-GNSS (GPS, GLONASS, Galileo) observations at 30 sec. interval obtained with low-cost GNSS receiver u-blox ZED-F9P and u-blox patch antennas (except BX02 - ArduSimple survey antenna). Time period (depending on stations): 27.02.2021 - 28.03.2021.</p>
COST-ARKWORK Societal Challenges Survey Dataset
<p>A survey was relating to the societal role of archaeology and the impact of societal challenges on archaeological practices is based on views collected from the members of COST Action Archaeological Practices and Knowledge Work in the Digital Environment (<a href="http://www.arkwork.eu/">www.arkwork.eu</a>). The purpose of this survey is to collect views from COST-ARKWORK members on the relation of contemporary societal challenges and archaeological practices. The survey was administered online using Survey & Report tool (survey form, Fig. 1) hosted by Swedish University Computer Network (Sunet). The survey was open from May 8, 2020 until June1, 2020. </p> <p> </p> <p>The participants of the network consist of over 200 experts of archaeological practices from a broad range of disciplinary backgrounds from archaeology to information science, museum studies, computer science and business studies, including researchers and practitioners from 30 European countries. 50 members of the network participated in the survey. The views of the Action participants were collected using an online survey with two open ended questions: 1. “From your perspective, what is the role and value of archaeology in helping to solve contemporary societal problems?”; and, 2. “What societal changes and challenges are likely to affect archaeology and archaeological practices the most during the next five years?”. In addition, the respondents were asked to indicate whether they identified themselves as being an archaeologist or not. 64% (32/50) of the experts identified themselves as archaeologists while the rest described themselves as non-archaeologists. The experts shared their opinions as individuals, not as representants of specific institutions or countries.</p>
ScienceDex guides
Understand access before you commit
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.