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363 results for “Price”
Food riots and food prices in the Eastern Mediterranean (Bilād al-Shām) in the 19th and 20th centuries: a data set
<p>This is an archival release to document the state of the data set for this research project before it got severely derailed by the Covid-19 pandemic and the explosion in Beirut on 4 August 2020. Please consult the readme for a detailed description of the contents and workflows.</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Crop Prices and Deforestation in the Tropics
<p>We provide the stata files that allow to reproduce the results presented in the paper <br> "Crop Prices and Deforestation in the Tropics" by Nicolas Berman, Mathieu Couttenier, Antoine Leblois and Raphaël Soubeyran.</p> <p>The replication folder contains different files:<br> 1- *.dta file: database<br> 2- *.do file: do-file containing the codes to replicate the results (figures and tables)<br> 3- * Ancillary data: <br> .csv file: data needed to produce a map of the initial forest cover (in 2000). <br> .dta additional files to run sensitivity analysis</p> <p>Simply change the path to files (on line 25 of the replication_code.do file) to re-run the analysis:<br> ** Change pathway to load and save the data<br> global dir ".../Replication_files_BCLS_2022"</p> <p><br> Stata 17 was used for this work.</p>
Spain fuel prices
<p>This dataset provides insight into:</p> <ul> <li>All fuel types available at each petrol station</li> <li>Covers all petrol stations of Spain</li> <li>Informs fuel price evolution along wk46, 2022</li> </ul>
Ryanair: All december flight departures from Spain and their average prices
<p>In this dataset, we collected the average prices of all flight departures in Spain projected in December by Ryanair. Although we are uploading only raw data, this dataset can be useful to study the connectivity of Spain, and its accessibility by the people (comparing frequencies and prices in different airports).</p> <p>We searched for the December period to focus on the relative fluctuations of prices in holidays versus the rest of the month. Also, the prices may change between holidays lengths in days, and the day of departure. Specifically, we searched for 2 to 4 days of vacation, and departures from Thursday, Friday and Saturday. We only searched for the average prices of two adults because Ryanair prices are averages per person.</p> <p>All in all, this dataset has 11 attributes and 9794 rows.</p>
Renting prices in Spain
<p>This dataset contains in a .csv format the data about the houses in rent in the cities of Madrid, Barcelona, Valencia, Sevilla, Bilbao and Tenerife, at the time of the 22nd November 2022. The source code to obtain the data can be found in <a href="https://github.com/adrianvallsc/webscraping_housing">https://github.com/adrianvallsc/webscraping_housing</a></p>
High frequency (tick data) of historical FOREX prices
<p>Price tick data for the most liquid Forex assets (AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, USDJPY). The period covered 09 March 2020 to 07, September 2022. </p>
Metrics As Scores Dataset: Price, Weight, and Other Properties of Over 1,200 Ideal-Cut and Best-Clarity Diamonds
<p>This dataset is a subset of the original diamonds dataset with more than 54,000 diamonds. It was reduced to only contain diamonds of the best cut (ideal) and clarity (IF). The group is now given by the colors from J (worst) to D (best). This dataset comes from the R-package ggplot2 (Wickham 2016). For each color, we can examine the following attributes (<strong>features</strong>) of each diamond:</p> <ul> <li><em>Carat</em>: Weight of the diamond</li> <li><em>Depth</em>: Total depth percentage</li> <li><em>Price</em>: Price in US dollars [discrete]</li> <li><em>Table</em>: Width of top of diamond relative to widest point</li> <li><em>X</em>: Length in mm</li> <li><em>Y</em>: Width in mm</li> <li><em>Z</em>: Depth in mm</li> </ul> <p>It has a total of 7 Colors (<strong>groups</strong>): <em>D</em>, <em>E</em>, <em>F</em>, <em>G</em>, <em>H</em>, <em>I</em>, and <em>J</em>. The best color is <em>D</em> and the worst color is <em>J</em>. This dataset was created to analyze whether there are differences between the colors.</p>
Trade policy announcements can increase price volatility in global food commodity markets (Replication Data)
<p>Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":</p> <ul> <li>Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)</li> <li>Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)</li> <li>Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report</li> </ul>
EVIDENT H2020 - Average Price Bias Dataset
<p>The average price bias choice quasi-experiment is designed to elicit consumers’ perceptions about different pricing schemes. The experiment aims to correlate the findings with participants’ characteristics, potential behavioural biases and the participants’ financial and environmental literacy levels.</p> <p>The experiment consists of five discrete key sections: 1) a section about participant’s demographic data, 2) a small set of questions related to behavioural biases, 3) a set of questions related to financial literacy, 4) a section with questions related to environmental literacy and 5) the choice experiment about price perceptions.</p> <p>Section 5 presents a hypothetical scenario about the participant’s yearly energy consumption and several pricing tariff options. The participant has to choose a pricing tariff they think is the most cost-effective. There are six (1-6) broader cases, each including four subcategories (a-d). The further the case is from the beginning, the more complicated it is.</p> <p>The implementation of the experiment is as follows:</p> <p>Step 1. The participant first receives the following message: “Assuming that your yearly energy consumption is exactly 6,000 kWh, which one of the following tariffs would you choose as the most cost-effective?”</p> <p>Step 2. Each participant will be asked to participate in only 2 cases (all subcategories of each case are included). A case will be randomly chosen from cases 1-3 (simple case) and a second random choice will be made from cases 4-6 (complex case). Thus, all participants will answer a simple and a complicated set of questions.</p> <p>Step 3. The participant receives the first set of choices.</p> <p>If the participant answers correctly, he receives the next subcategory's choice set. If he answers false, he gets the next set of choices within the same subcategory. Thus, as soon the participant answers correctly, he skips the following set of choices and moves to the next subcategory. For a participant answering correctly, this will be a short survey. However, for someone answering wrong, the survey will last longer.</p> <p>More information can be found on the public deliverables of the EVIDENT project <a href="https://evident-h2020.eu/deliverables/">https://evident-h2020.eu/deliverables/</a>. More specifically, the experiment's theoretical framework and motivation are described in deliverable <strong>D1.2</strong> <a href="https://evident-h2020.eu/wp-content/uploads/2021/12/EVIDENT_D1.2_Assessing_behavioural_biases_and_financial_literacy.pdf">Assessing behavioural biases and financial literacy</a>, in section 5 while the final design is reported in <strong>D3.2</strong> <a href="http://evident-h2020.eu/wp-content/uploads/2023/01/EVIDENT_D3.2_Implementation-of-preparatory-actions-for-RCT-surveys-and-serious-game.pdf">Implementation of preparatory actions for RCT, surveys and serious game</a>.</p>
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Dataset for "A new method for identifying weather-induced power system stress using shadow prices"
<p>These are data accompanying "A new method for identifying weather-induced power system stress using shadow prices". They consist of</p> <ul> <li>solved network files (generated with <a href="https://github.com/PyPSA/pypsa-eur/">PyPSA-Eur</a>, here v0.6.1), used for the analysis,</li> <li>necessary data to reproduce the figures in the paper and supplementary material.</li> </ul> <p>The optimised network files are of the form `workflow_data/results/stressful-weather/optimum/{weather_year}_181_90m_c1.25_Co2L0.0-1H.nc` (for weather_years in {1980,...,2019}). Unsolved ones can be found in `workflow_data/networks/...`.</p> <p>The filenames in `plot_data/` indicate which figure the data are associated to (e.g. `plot_data/fig_1_hourly_costs.csv` contains the hourly electricity costs during the winter of all networks and is necessary for Figure 1). We also added weather data for all system-defining events (mean surface level pressure, 10m wind speed anomaly, 2m temperature anomaly) in .nc files.</p> <p>Find more information about how to use these data and how they were generated in the README of the GitHub repository: <a href="https://github.com/koen-vg/stressful-weather/tree/v0">https://github.com/koen-vg/stressful-weather/tree/v0</a>.</p>
Bitcoin Historical Prices Dataset
<p>The following dataset contains the attributes:</p> <ol> <li>Date: Specific date to be observed for the corresponding price.</li> <li>Open: The opening price for the day</li> <li>High: The maximum price it has touched for the day</li> <li>Low: The minimum price it has touched for the day</li> <li>Close: The closing price for the day</li> <li>percent_change_24h: Percentage change for the last 24hours</li> <li>Volume: Volume of Bitcoin traded at the date</li> <li>Market Cap: Market Value of traded Bitcoin</li> </ol>
Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885
<p>Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885</p>
New Zealand emission unit (NZU) monthly prices 2010 to date
<p>This data and R code repository provides a reproducible public domain data series of mean monthly spot prices of the New Zealand emission unit (or "NZU"), the domestic emission unit in the New Zealand emissions trading scheme (https://en.wikipedia.org/wiki/New_Zealand_Emissions_Trading_Scheme/). </p>
Data for sensitivity analysis of Hübler, M., M. Wiese, M. Braun and J. Damster (2023): The distributional effects of CO2 pricing at home and at the border on German income groups
<p>This dataset contains the output files used in the sensitivity analysis of the computable general equilibrium (CGE) model developed in Hübler et al. (2023). Each folder is labeled with the respective set of sector-level elasticity of substitution parameters considered in the analysis: elasticities between domestically produced versus imported goods (esubd), Armington elasticities (esubm) and elasticities between production factors (esubva).</p> <p>For each set of parameters, we generate 1000 random draws from a +-10 % interval around each of the sector-specific elasticities, resulting in 1000 sets of sectoral parameter values. Each .xlsx output file located in a dedicated subfolder corresponds to a model run with a specific set of parameter values. In addition, we conduct sensitivity analyses of two individual parameters, namely the CO2 target (CO2factor) considered in our policy scenarios and the elasticity of substitution in consumption (esub_cons).</p> <p>The sensitivity analysis is carried out using the <a href="https://snakemake.readthedocs.io/en/stable/">Snakeflow</a> workflow management system, and R code for generating parameter spaces and processing the output files is available on <a href="https://github.com/mariuslbraun/climate-trade-distribution-sensitivity">GitHub</a>.</p>
Detailed information on cost and sale prices on Polish pig market in the period 2017-mid2022
<p>The database contains detailed information on costs and sale prices of piglets and finishers on Polish martket in the period January 2017 - July 2022. The prices of cereals used for feeding are taken from the average monthly data of the Ministry of Agriculture and the daily stock exchange quotations of Agrolok. The remaining operational costs (veterinary costs, utilities, labor, and transport) were assumed at a constant average level established on the basis of the reference methodological publication of the Danish research and development organization called “Seges Innovation” (2022) and a manual on pig farming (Pawłowski, 2020). The same sources were used to determine the optimal feeding model, which is important for calculating feed costs. Assumptions for calculating feed cost, the cost of falls, labor costs in piglet and finisher production, as well as piglet transportation costs are presented in the excel sheet.</p>
Amazon EC2 Spot Price History: 2014--2015, 2017--2023
<p>Spot price history for Amazon EC2 instances for various regions collected from 2014 to 2015, and 2017 to 2023.</p>
RTX 3060 and RTX3060 ti price tracking dataset
<p><strong>El dataset contiene los siguientes campos:</strong></p> <ol> <li> <p><strong>Identificador “id”: dato cualitativo nominal que identifica a un único producto en la web de amazon. Podría utilizarse en una fase de procesado de datos para agrupar los productos y calcular tendencias de precio.</strong></p> </li> <li> <p><strong>Nombre “name”: dato cualitativo nominal que indica el nombre del producto. En los nombres de las tarjetas gráficas se suelen especificar datos de esta como el montador, el tipo y la cantidad de memoria y las interfaces. En una fase de procesado de los datos se podría extraer esta información buscando patrones en los nombres. En el ejemplo del apartado de representación gráfica podemos ver que la tarjeta es montada por Zotac Gaming, tiene 12Gb de memoria GDDR6 y tiene una interfaz HDMI 2.1 y 3 interfaces DisplayPort 1.4a.</strong></p> </li> <li> <p><strong>Enlace “link”: dato cualitativo nominal. Dirección web de donde se han extraído los datos.</strong></p> </li> <li> <p><strong>Modelo “model”: dato categorico. Indica el modelo de la tarjeta gráfica. Con las búsquedas que estamos haciendo este campo vale “rtx 3060” o “rtx 3060 ti”</strong></p> </li> <li> <p><strong>Valoración “valoration”: dato cuantitativo. Valoración del producto.</strong></p> </li> <li> <p><strong>Valoraciones “valorations”: dato cuantitativo. Cantidad de valoraciones que se han hecho del producto. Sirve sobre todo para tomar en consideración la valoración media del producto.</strong></p> </li> <li> <p><strong>Precio “price”: dato cuantitativo. Precio de la oferta.</strong></p> </li> <li> <p><strong>Coste de envío “shippingCosts”: dato cuantitativo. Coste de envío de la oferta.</strong></p> </li> <li> <p><strong>Vendedor “seller”: dato cualitativo nominal. Vendedor de la oferta</strong></p> </li> <li> <p><strong>Fecha de obtención del registro “date”. Tiempo en que los datos son capturados.</strong></p> </li> </ol>
Practica 1 Web Scraping Oil Price Data
<p>Dataset about the prices of the oil and its products with and without taxes across the years1.0</p>
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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)
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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.