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363 results for “Price”
Library of price and performance data of domestic and commercial technologies for low-carbon energy systems
<p>This library consists of extensive price and performance data of commercially available technologies for low-carbon energy systems on the UK market, including domestic and commercial applications. All information is obtained from published manufacturer datasheets and pricelists. The library contains useful information for energy-system and technology modellers in their efforts to capture the techno-economic characteristics of different technology options, minimise uncertainties and suggest reliable system- and technology-design strategies.</p> <p>The data analysis shows how technology characteristics vary with component choice, technology size and to capture the spread of values found between different suppliers, which can be used to determine uncertainty bounds on key performance indicators. Fitting techniques can be used to determine relationships arising from the collected data, infer values for which data are not available and quantify the related variability in technology characteristics.</p> <p>This work was conducted by members of the <a href="https://www.imperial.ac.uk/clean-energy-processes/">Clean Energy Processes (CEP) Laboratory</a> and is part of Project 2 of the <a href="https://www.imperial.ac.uk/energy-futures-lab/idles/">Integrated Development of Low-Carbon Energy Systems (IDLES)</a> project. IDLES brings together researchers across Imperial College London and partner organisations and companies to provide the evidence needed to facilitate a cost-effective and secure transition to a low-carbon future. The overarching aim of Project 2 is: (i) to characterise current and new/future technologies in terms of cost and performance to provide evidence for whole-energy system modelling; and (ii) to extend the capabilities of whole-energy-system models so that they can, apart from optimising energy network infrastructures, provide information to manufacturers about the optimal choice of materials, components and the design of key technologies.</p> <p>The library will be updated regularly as more data regarding existing and new technologies are collected.</p>
Day ahead market power prices (EU)
<p>Day ahead power prices for Europe different systems. Hourly data.</p>
Market prices in Tigray (March 2020 – March 2022)
<p>In northern Ethiopia, Tigray continues to be blockaded. The 2021 crop yield was just 25-50 percent of what it would be in a regular year. At the beginning of March 2022, colleagues and friends in Tigray provided us with average statistics on the cost of living in Tigray (especially food). Farmlands had been poorly ploughed and planted lately or not at all due to military targeting of farming activities. Hence, every crop has gotten more costly. As a result of distress or desperation sales, the price of live animals has dropped dramatically. Coffee, firewood, gasoline, and transportation have also seen significant price rises. Extreme shortages of food and energy supplies drive up inflation, but the fact that there are very limited amounts of cash in circulation curbs it.</p>
Rent prices
<pre>El dataset contiene los siguientes campos: * ID Número identificativo de la vivienda. * Precio (€/mes): Precio de la vivienda en euros mensuales. * Tipo: Tipo de vivienda. * Teléfono: Número de teléfono del anunciante. * Ciudad: Ciudad donde se encuentra el inmueble. * Dirección: Calle donde se encuentra el inmueble. * Barrio: Barrio donde se encuentra la vivienda. * Habitaciones: Número de habitaciones. * Baños: Número de baños. * Superficie (m2): Superficie de la vivienda en metros cuadrados. * Planta: Planta donde se encuentra la vivienda. * Ascensor: La vivienda dispone de ascensor. * Terraza: La vivienda dispone de terraza. * Parking: La vivienda dispone de parking. * Calefacción: La vivienda dispone de calefacción. * Aire: La vivienda dispone de aire acondicionado. * Balcón: La vivienda dispone de balcón. * Precio del m2 (€/m2): Relación entre el precio y la superficie de la vivienda en euros por metro cuadrado.</pre>
MTG Price Set
<p>Dataset containing core and expansion sets for MTG pulled from Scryfall on 2022-04-11. It includes a summary of each card and the price in EUR, USD and TIX.</p>
CROSSBOW HLU3-UC1-TC1 & HLU3-UC3-TC1 AM Market prices in the demonstration period
<p>CROSSBOW RES-CC continuously participates in the CROSSBOW AM ID market. The dataset comprises some of the market results that are used in the demonstrations. Fields are:</p> <ul> <li>Time slot (hourly)</li> <li>BSP (data is aggregated by hour and BSP)</li> <li>Energy forecasted</li> <li>Energy price in ID market</li> </ul>
A gridded dataset on population densities, real estate prices, transport and land use inside 192 worldwide urban areas
<p>This dataset provides, on a systematic basis, gridded population densities, rents, real estate prices, and transport times (both in<br> public transport and private car) in 192 cities across the world.</p>
Figure 1. Pomerantzia benhami Price, 1974 in First record of the family Pomerantziidae (Acari: Trombidiformes) from Middle East, with recording of two species for the first time from Asia
Figure 1. Pomerantzia benhami Price, 1974 – Dorsal (left) and ventral (right) view of body and dorsolateral view of the chelicerae (center).
Assessing predictive performance of supervised machine learning algorithms for a diamond pricing model
<p>The diamond is 58 times harder than any other mineral in the world, and its elegance as a jewel has long been appreciated. Forecasting diamond prices is challenging due to nonlinearity in important features such as carat, cut, clarity, table, and depth. Against this backdrop, the study conducted a comparative analysis of the performance of multiple supervised machine learning models (regressors and classifiers) in predicting diamond prices. Eight supervised machine learning algorithms were evaluated in this work including Multiple Linear Regression, Linear Discriminant Analysis, eXtreme Gradient Boosting, Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosted Regression and Classification Trees, and Multi-Layer Perceptron. The analysis is based on data preprocessing, exploratory data analysis (EDA), training the aforementioned models, assessing their accuracy, and interpreting their results. Based on the performance metrics values and analysis, it was discovered that eXtreme Gradient Boosting was the most optimal algorithm in both classification and regression, with a R<sup>2</sup> score of 97.45% and an Accuracy value of 74.28%. As a result, eXtreme Gradient Boosting was recommended as the optimal regressor and classifier for forecasting the price of a diamond specimen.</p>
Fig. 3 Neopolystoma scorpioides n in Tracking platyhelminth parasite diversity from freshwater turtles in French Guiana: First report of Neopolystoma Price, 1939 (Monogenea: Polystomatidae) with the description of three new species
Fig. 3 Neopolystoma scorpioides n. sp. Hohotupe. a Ventnah vies. b testis of hohotupe. c cenitah spines. d haptonah sucken shosinc a ninc of skehetah ehements. e mancinah hookhets. Abbreviations: ec, ecc; cb, cenitah buhb; hp, hapton; ic, intestinah caecum; mo, mouth; ov, ovanu; ph, phanunx; su, sucken; te, testis; va, vacina; vd, vas defenens; vi, vitehhania. Scale-bars: a, 500 μm; b, 100 μm; c, 10 μm; d, 100 μm; e, 10 μm
Fig. 1 Neopolystoma cayensis n in Tracking platyhelminth parasite diversity from freshwater turtles in French Guiana: First report of Neopolystoma Price, 1939 (Monogenea: Polystomatidae) with the description of three new species
Fig. 1 Neopolystoma cayensis n. sp. Hohotupe. a Ventnah vies. b Testis. c Genitah spines. d Haptonah sucken shosinc a ninc of skehetah ehements. e Mancinah hookhets. Abbreviations: ec, ecc; cb, cenitah buhb; hp, hapton; ic, intestinah caecum; mo, mouth; ov, ovanu; ph, phanunx; su, sucken; te, testis; va, vacina; vd, vas defenens; vi, vitehhania. Scale-bars: a, 500 μm; b, 500 μm; c, 10 μm; d, 100 μm; e, 10 μm
Fig. 4 in Tracking platyhelminth parasite diversity from freshwater turtles in French Guiana: First report of Neopolystoma Price, 1939 (Monogenea: Polystomatidae) with the description of three new species
Fig. 4 Bauesian tnee infenned fnom the anahusis of foun concatenated cenes. Numbens at nodes connespond to Bauesian postenion pnobabihities. Abbreviations: C. sacs, conjunctivah sacs; P. cavitu, phanunceah cavitu
Fig. 2 Neopolystoma guianensis n in Tracking platyhelminth parasite diversity from freshwater turtles in French Guiana: First report of Neopolystoma Price, 1939 (Monogenea: Polystomatidae) with the description of three new species
Fig. 2 Neopolystoma guianensis n. sp. Hohotupe. a, Ventnah vies. b, testis. c cenitah spines. d haptonah sucken shosinc a ninc of skehetah ehements. e mancinah hookhets. Abbreviations: ec, ecc; cb, cenitah buhb; hp, hapton; ic, intestinah caecum; mo, mouth; ov, ovanu; ph, phanunx; su, sucken; te, testis; va, vacina; vd, vas defenens; vi, vitehhania. Scale-bars: a, 1,000 μm; b, 100 μm; c, 10 μm; d, 100 μm; e, 10 μm
LAB4SUPPLY- WEEKLY PRICES TOMATO IN SPAIN
<p>Weekly prices received by the producer and paid by the consumer for the period 2012-2022. Product: tomato</p>
LAB4SUPPLY- WEEKLY PRICES FIG IN SPAIN
<p>Weekly prices received by the producer and paid by the consumer for the period 2012-2022. Product: fig</p>
Dataset: T. Rowe Price Group, Inc. (TROW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Script and Data Repository - "Future food prices will become less sensitive to agricultural market prices and mitigation costs" Chen et al.
<p>MAgPIE Model outputs and scripts for analysis of markups, based on MarkupsChen package version 1.2 available here: https://github.com/caviddhen/MarkupsChen/releases/tag/v1.2</p> <p> </p> <p> </p>
Datasets for explanation of physical interpretation for locational marginal prices
<p>Matlab/Octave functions that extract the following information</p> <p>Initial: </p> <p>[rus] Исходная схема IEEE-30 в формате MATPOWER [1]. По сравнению с case30 cкорректированы ограничения на переток мощности в линиях.</p> <p>[eng] Initial case IEEE-30 in MATPOWER format [1]. With comparison with case30 line rates were corrected</p> <p>Base:</p> <p>[rus] Результаты оптимизации в MATPOWER со следующими настройками:</p> <p>[eng] Optimization results in MATPOWER with following options:</p> <p>mpopt = mpoption('opf.ac.solver','MIPS','out.all', outall,...<br> 'opf.flow_lim','P');<br> <br> [mpc, exitflagac]=runopf(mpc,mpopt);</p> <p>coeffs:</p> <p>[rus] Коэффициенты режимной компоненты, компонент, обусловленных сетевыми ограничениями и ограничениями по напряжению. Отклики в задачах расчета установившегося режима и оптимизации установившегося режима.</p> <p>[eng] LMP weights of power flow, transmission constraints, and voltage constraints components. Derivatives in power flow and optimal power flow problems.</p> <p>Model_1204:</p> <p>[rus] Результаты оптимизации в MATPOWER с заменой заявки второго генератора на 1204</p> <p>[eng] Optimization results in MATPOWER with new offer price 1204 for second generator.</p> <p>Model_775:</p> <p>[rus] Результаты оптимизации в MATPOWER с заменой заявки генератора 27 на 775</p> <p>[eng] Optimization results in MATPOWER with new offer price 775 for generator 27.</p>
Data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities
<p>This is the first release of the data and R code used in Delory et al (2019) The exotic species Senecio inaequidens pays the price for arriving late in temperate European grassland communities.</p>
Pangasius-economic performance and price
<p>Data used in Primefish project include production volume, farm gate price and export volume & value by countries.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.