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363 results for “Price”
Promotional Scheduling Software Price For Supermarkets
<p>Supermarket chains and independent grocers use Demo Wizard <a href="http://https//www.demo-wizard.com/pricing.html">promotional Scheduling Software</a> Price to maximize utilization of their floor space for in store demos and improve their customer experience.</p>
PC Componentes prices
<p>This Dataset contains the prices of the Spanish website www.pccomponentes.com which sales PC parts, in this case we retrieved the information about the prices, name, brand, and category in which they have classified the components in their website.</p>
Does Investor Risk Perception Drive Asset Prices in Markets? Experimental Evidence.
<p>Extract the .zip-file into one folder (sub-folders for the raw data will be created). Then run the GIMS_DataAnalysis.R for the main results, GIMS_DataAnalysis_Return.R for the RETURN results, GIMS_DataAnalysis_2Assets.R for the EXPERIENCE results, and GIMS_DataAnalysis_2Markets.R for the 2MARKETS results.</p>
Conjunto de dados Modelo de Regressão Aplicado à Previsão de Preços SPOT de Energia Elétrica (Dataset Regression Model Applied to Electric Energy SPOT Price Forecasting)
<p>Esse conjunto de dados utilizou DataSets de duas fontes distintas: CCEE e ONS. Como são órgãos públicos os dados são acurados, transparentes, confiáveis e de boa qualidade. Os dados de entrada possuem as variáveis que são utilizadas no modelo atual do PLD, já citado. São elas: as datas, o armazenamento de água, a ENA, a expectativa de ENA para a próxima semana e a carga.</p> <p>As datas são dados diários entre janeiro de 2013 e janeiro de 2017. O armazenamento de água é dado por submercado e apresentado em porcentagem da capacidade máxima. A ENA e a expectativa dela para a semana seguinte são apresentadas em porcentagem a partir das chuvas realizadas convertidas em MWmédio pelas previsões feitas utilizando dados históricos (1932-2007). A carga está em MWmédio. E o PLD em R$/MWh.</p> <p>Os soma dos dados dos quatro submercados (SE/CO, SU, NE, NO) de cada dado nos fornece a informação do Sistema Nacional Interligado (SIN).</p> <p>As variáveis de carga, armazenamento e ENA foram retiradas do histórico de operações do site da ONS, disponibilizados para download em ‘csv’. E o PLD do site da CCEE, disponibilizados em ‘xls’.</p> <p>Foram mesclados a partir das datas formando o arquivo de entrada para o modelo utilizado nos experimento</p> <p> </p> <p>Metadados / Metadata</p> <p>Storage of water: percentage of storage of water by submarket.<br> ENA and expectative of ENA: presented by percentage of previsions of rains that happen converted on MWmedium by previsions did with a historic (1932-2007). Data are by submarket.<br> Charge: charge by submarket give on MWmedium.<br> PLD: give on R$/MWh<br> The sum of each variable is the data of the total system.</p> <p> </p> <p> </p>
Determinants of Airbnb prices in European cities: A spatial econometrics approach (Supplementary Material)
<p>This repository contains supplementary materials for the article:</p> <p><strong>Determinants of Airbnb prices in European cities: A spatial econometrics approach</strong></p> <p><strong>(</strong>DOI<strong>: </strong><a href="https://doi.org/10.1016/j.tourman.2021.104319">https://doi.org/10.1016/j.tourman.2021.104319</a>)</p> <p>The materials include the used datasets and Python scripts for spatial regression models.</p> <p><strong>Datasets</strong></p> <p>For each city two files are provided: data for weekday and weekend offers</p> <p>The columns are as following:</p> <ul> <li>realSum: the full price of accommodation for two people and two nights in EUR</li> <li>room_type: the type of the accommodation </li> <li>room_shared: dummy variable for shared rooms</li> <li>room_private: dummy variable for private rooms</li> <li>person_capacity: the maximum number of guests </li> <li>host_is_superhost: dummy variable for superhost status</li> <li>multi: dummy variable if the listing belongs to hosts with 2-4 offers</li> <li>biz: dummy variable if the listing belongs to hosts with more than 4 offers</li> <li>cleanliness_rating: cleanliness rating</li> <li>guest_satisfaction_overall: overall rating of the listing</li> <li>bedrooms: number of bedrooms (0 for studios)</li> <li>dist: distance from city centre in km</li> <li>metro_dist: distance from nearest metro station in km</li> <li>attr_index: attraction index of the listing location</li> <li>attr_index_norm: normalised attraction index (0-100)</li> <li>rest_index: restaurant index of the listing location</li> <li>attr_index_norm: normalised restaurant index (0-100)</li> <li>lng: longitude of the listing location</li> <li>lat: latitude of the listing location</li> </ul> <p><strong>Programming Scripts</strong></p> <p>In this repository you will find a script for spatial regressions in Python using PySAL (models_robust.py).</p> <p>The codes cover the following regression models:</p> <ul> <li>OLS</li> <li>SLX (lagged_x)</li> <li>SAR (lagged_y)</li> <li>SDM (lagged_x_y)</li> <li>SEM (lagged_e)</li> <li>SDEM (lagged_e_x)</li> </ul> <p>Main parameters:</p> <ul> <li>cities - list of cities from the dataset to be included in the analysis</li> <li>Robust=False: calculate the OLS, SLX, SAR and SDM regressions with W (weight matrix) based on 10 closest neighbours</li> <li>Robust=True: calculate all regression models with different specifications of W</li> <li>direct_indirect=True: calculate the direct and indirect effects (based on Golgher, A. B., & Voss, P. R. (2016). How to Interpret the Coefficients of Spatial Models: Spillovers, Direct and Indirect Effects. Spatial Demography (Vol. 4). https://doi.org/10.1007/s40980-015-0016-y)</li> </ul> <p>Key functions:</p> <ul> <li>create_weights - defines the W specification</li> <li>write_stats - calculates's Moran's I and Geary's C</li> <li>direct - calculates the direct effect of the variable</li> <li>indirect - calculates the indirect effect</li> <li>coord - sets the coordinate refence system (CRS) appropriate to the analysed city</li> <li>total_results calculates the regressions</li> <li>the coordinates are projected from GPS (epsg:4326) to the local CRS (km_lat, km_lon)</li> <li>all regressions are saved as formatted txt table</li> <li>the results can be also saved as csv table</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Data from: The price of insurance: costs and benefits of worker production in a facultatively social bee
Kin selection theory is foundational in helping to explain the evolution of sociality; however, the degree to which indirect fitness benefits may underlie helping behavior in species of early stage sociality has received relatively little empirical attention. Facultatively social bees, which demonstrate multiple forms of social organization, provide prime systems in which to empirically test hypotheses regarding the evolutionary origins of sociality. The subsocial small carpenter bee, Ceratina calcarata, may establish a social nest by manipulating brood provisions to rear a worker daughter, which then assists in critical late-season alloparental care. Here, we combine nest demographic and behavioral data with genetic relatedness estimates to calculate the relative inclusive fitness of both subsocial and social reproductive strategies in C. calcarata. Social mothers benefit from improved likelihood of brood survivorship and have higher fitness than subsocial mothers. Worker daughters have low indirect fitness on average, and will not produce their own offspring. Among-sibling relatedness is significantly higher in social nests than subsocial nests, though mothers of either reproductive strategy may mate multiply. Though this study corroborates the ultimate role of indirect fitness and assured fitness returns in the evolution of social traits, it also offers additional support for maternal manipulation as the proximate mechanism underlying evolutionary transitions in early stage insect societies.
Data from: Symbiotic immuno-suppression: is disease susceptibility the price of bleaching resistance?
Accelerating anthropogenic climate change threatens to destroy coral reefs worldwide through the processes of bleaching and disease. These major contributors to coral mortality are both closely linked with thermal stress intensified by anthropogenic climate change. Disease outbreaks typically follow bleaching events, but a direct positive linkage between bleaching and disease has been debated. By tracking 152 individual coral ramets through the 2014 mass bleaching in a South Florida coral restoration nursery, we revealed a highly significant negative correlation between bleaching and disease in the Caribbean staghorn coral, Acropora cervicornis. To explain these results, we propose a mechanism for transient immunological protection through coral bleaching: Removal of Symbiodinium during bleaching may also temporarily eliminate suppressive symbiont modulation of host immunological function. We contextualize this hypothesis within an ecological perspective in order to generate testable predictions for future investigation.
Data From: Analysing social media forums to discover potential causes of phasic shifts in cryptocurrency price series
<p>The recent extreme volatility in cryptocurrency prices occurred in the setting of popular social media forums devoted to the discussion of cryptocurrencies. We develop a framework that discovers potential causes of phasic shifts in the price movement captured by social media discussions. This draws on principles developed in healthcare epidemiology where, similarly, only observational data are available. Such causes may have a major, one-off effect or recurring effects on the trend in the price series. We find a one-off effect of regulatory bans on bitcoin, the repeated effects of rival innovations on ether and the influence of technical traders, captured through discussion of market price, on both cryptocurrencies. The results for Bitcoin differ from Ethereum, which is consistent with the observed differences in the timing of the highest price and the price phases. This framework could be applied to a wide range of cryptocurrency price series where there exists a relevant social media text source. Identified causes with a recurring effect may have value in predictive modelling, whilst one-off causes may provide insight into unpredictable black swan events that can have a major impact on a system.</p>
Photonics4All - Video Interview with Nobel Price Winner Shuji Nakamura
<p>Interview with Nobel Price winner Shuji Nakamura by Ulrich Trog from Photonics Austria.</p>
Product prices of the Mercadona supermarket in Spain (November 2023)
<p>The prices of all products that are available online on November 13 on the Mercadona supermarket website in Barcelona, Spain. Since the prices of Mercadona products are supposedly unified in all regions, we assume central Barcelona as a reference point for all of Spain. Mercadona online shop: <a href="https://tienda.mercadona.es/categories">https://tienda.mercadona.es/categories.</a></p><p>The dataset consists of the following columns: classification, category; sub_category; product_name; product_format; product_price; product_unit.</p><ul><li><strong>classification</strong>: Product classification can be understood as a higher and more general level than the category. E.g Marisco y pescado</li><li><strong>category</strong>: The product category. E.g Pescado fresco</li><li><strong>sub_category</strong>: The subcategory of the product. E.g Salmón</li><li><strong>product_name</strong>: The name of the product. E.g Filete de salmón, Salmón sin aletas y sin escamas</li><li><strong>product_format</strong>: The format that the product presents. E.g Bandeja 400 g aprox., Pieza 2,61 kg aprox., etc.</li><li><strong>product_price</strong>: The price of the product. E.g 9,58 €</li><li><strong>product_unit</strong>: The unit of the product. E.g /ud, /pack</li></ul>
Record of the temporal evolution of the price of Decathlon sports footwear by gender and brand
<p>This dataset includes simulated data for a potential project of analysis of data for prices of sports shoes classified by brand and gender collected from a website at different dates.</p>
Additional resources for "Day ahead electricity price forecasting with neural networks - one or multiple outputs?"
Open the record for dataset details and reuse information.
Data repository for study "Stabilizing international wheat prices through international cooperation after the Russian invasion of Ukraine"
<p>This repository contains both the input and output data associated with the study. The data are crucial for running and understanding the results generated by the <a href="https://gitlab.pik-potsdam.de/twist/twist-global-model/-/tree/ukraine"><em>TWIST</em></a> model and the <a href="https://github.com/mjpuma/FSC-WorldModelers/tree/ukraine"><em>FSC</em></a> models.</p> <h2>Directory Structure and Data Description</h2> <h3>Input Data</h3> <h4>Directory: <code>fsc</code></h4> <p>Contains files necessary to run the <em>FSC</em> model:</p> <ul> <li><code>wheat_export_restriction_*.csv</code>: National export restrictions for various scenarios.</li> <li><code>wheat_total_production_decline_*.csv</code>: National production reductions for different scenarios.</li> </ul> <h4>Directory: <code>twist</code></h4> <p>Includes files required for the <em>TWIST</em> model simulations:</p> <ul> <li><code>psd_wheat_*_world_1961to2031.csv</code>: Historical and projected global wheat data for production, consumption and stocks based on <a href="https://apps.fas.usda.gov/psdonline/app/index.html#/app/downloads">USDA-PSD</a> data</li> <li><code>World_country_codes.csv</code>: World code reference.</li> <li><code>US_BLS_ConsumerPriceIndex_Annual_1960to2019.csv</code>: Annual Consumer Price Index data from the US BLS.</li> <li><code>monthlyNominalGrainPricesWB_WheatUSHRW_1960to2022.csv</code>: Monthly nominal observed wheat prices.</li> <li>Scenario-specific files (<code>world_export_restrictions_*.csv</code>, <code>world_import_strategy_*.csv</code>, <code>world_production_anomaly_*.csv</code>): Global export restrictions, import strategies, and production changes for respective scenarios.</li> </ul> <h3>Output Data</h3> <h4>Directory: <code>fsc</code></h4> <ul> <li><code>raw</code>: Contains the raw output data from the <em>FSC</em> model for all scenarios.</li> <li><code>processed</code>: Includes datasets of national impaired supply relative to baseline supply and baseline domestic reserves, with results summarized per scenario.</li> </ul> <h4>Directory: <code>twist</code></h4> <ul> <li>Contains the raw output data from the <em>TWIST</em> model.</li> </ul> <h2>Contact Information</h2> <p>For inquiries, please contact Dr. <a href="https://orcid.org/0000-0002-8698-1246">Kilian Kuhla</a> at kilian.kuhla@pik-potsdam.de</p>
Price discounts on low energy dense foods on food intake and health status
<p>The objective of this study was to observe the effects of a multi-level (30%, 15%, and 0%) randomized discount on fruits, vegetables, and non-caloric beverages on changes in dietary intake. This randomized controlled trial (RCT) comprised an 8-week baseline, a 32-week intervention, and a 16-week follow-up. 24-hour dietary recalls were conducted during the baseline period and before the intervention midpoint. In-person clinical measures were analyzed from Week 8 (end of baseline) and 24 (midpoint). This report is from an interim analysis up to the intervention period midpoint at Week 24, as the study is still ongoing. Participants with BMIs of 24.5-50 kg/m<sup>2</sup> and ages 18-70 years old who were the primary household shoppers were recruited from several New York City supermarkets, starting in September 2018. Of these, we analyzed 20 in the 30% discount group, 25 in the 15% discount group, and 19 in the 0% discount group. The 30% discount group reported greater intake of vegetables (+98.4 g ± 48.9 SD, <em>P </em>= 0.049) and diet soda (+63.3 g ± 29.3, <em>P</em> = 0.035) relative to the baseline period, compared to the 0% discount group. The clinical measures including body weight remained unchanged. The participants who experienced the COVID-19 pandemic had a marginal increase in body weight of 1.5 kg, P = 0.053. In conclusion, we observed a significant increase in intake of vegetables and diet soda in the 30% discount group relative to the 0% discount group.</p>
Testing nectar price effect on bumblebee feeding by automatized computer-controlled laboratory platform
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Google Pixel 7 Pro prices evolution at eBay (Dec 2024)
<p>Aquest conjunt de dades conté informació extreta de eBay sobre productes de segona mà relacionats amb el terme de cerca "pixel7pro". Les dades han estat raspades dels resultats de cerca de eBay i estan organitzades en un format tabular amb les següents columnes:</p> <ol> <li><strong>Title</strong>: El títol del producte llistat a eBay.</li> <li><strong>Price</strong>: El preu del producte, que inclou el cost de l'article i l'enviament.</li> <li><strong>Link</strong>: L'enllaç a la pàgina del producte a eBay.</li> <li><strong>Image URL</strong>: La URL de la imatge principal del producte.</li> <li><strong>Description</strong>: Descripció del producte proporcionada al llistat (si està disponible).</li> <li><strong>Seller</strong>: Informació sobre el venedor del producte, incloent el nom o l'estat (per exemple, "venedor verificat").</li> <li><strong>Location</strong>: Ubicació geogràfica de l'article, indicant si és internacional o local.</li> <li><strong>Bids</strong>: Nombre de pujades realitzades (per a articles en subhasta).</li> <li><strong>Time Left</strong>: Temps restant per tal que acabi la subhasta, si és aplicable.</li> <li><strong>Seller Rating</strong>: Valoració del venedor basada en les ressenyes dels usuaris (si està disponible).</li> <li><strong>Shipping Info</strong>: Informació sobre l'enviament, com "Enviament gratuït" o els costos associats.</li> <li><strong>Query Date</strong>: Data en què es va realitzar la consulta i el raspatge de les dades, en format <code>YYYY-MM-DD</code>.</li> </ol> <h3>Notes:</h3> <ul> <li>El conjunt de dades inclou només articles que es troben en subhasta ("Auction") i que han estat venuts o que estan complets.</li> <li>Els articles es filtren per excloure termes prohibits com "damaged" (danyat) o "refurbished" (rehabilitat), entre d'altres, per centrar-se en productes específics.</li> <li>La data de la consulta proporciona un context temporal, indicant quan es va scrapejar cada conjunt de dades.</li> </ul> <p>Aquest conjunt de dades pot ser útil per a l'anàlisi de preus, venedors, condicions dels articles i tendències en la venda de productes de segona mà a eBay.</p>
Results Data for "Price Formation Without Fuel Costs: The Interaction of Elastic Demand with Storage Bidding"
<h2>Abstract</h2> <p>Studies on electricity market design for high shares of wind and solar often predict the breakdown of energy-only markets, citing a lack of fuel costs to set prices. Issues include prolonged zero prices, politically unacceptable scarcity prices, price collapses from minor capacity changes, low market value cost recovery, revenue variability across different weather years, and challenges in long-term storage operation. These issues arise from modeling with perfectly inelastic demand. Introducing even a small amount of short-term elasticity (-5%) significantly mitigates these problems. A simplified model with wind, solar, batteries, and hydrogen storage shows that demand elasticity and storage opportunity costs stabilize pricing, smoothing the price duration curve, reducing zero-price hours from 90% to 30%, and ensuring price stability across capacities and weather years. Green hydrogen-derived fuels replace fossil fuels as backup. The long-term model matches the short-term model prices with identical capacities, guiding storage bidding strategies for short-term operations. A model trained on 35 years of weather data and tested on another 35 years demonstrates the energy-only market's potential in future dispatch and investment coordination.</p> <h2>Data Sources</h2> <p>- <strong>Solar and Wind Time Series (1950-2020):</strong> <a href="https://doi.org/10.17864/1947.000321">Bloomfield and Brayshaw (2021)</a><br>- <strong>Techno-Economic Assumptions:</strong><a href="https://github.com/PyPSA/technology-data/tree/v0.8.1"> technology-data (v0.8.1)</a>, <a href="https://ens.dk/en/our-services/technology-catalogues">Danish Energy Agency</a><br>- <strong>Demand Elasticity Assumptions:</strong> <a href="https://doi.org/10.1016/j.eneco.2024.107652">Hirth et al. (2024)</a>, <a href="https://www.ewi.uni-koeln.de/en/publications/on-the-functional-form-of-short-term-electricity-demand-response-insights-from-high-price-years-in-germany-2/">Arnold (2023)</a></p> <h2>Installation</h2> <p>Use <code>conda</code> environment manager:</p> <p><br><code>conda update conda</code><br><code>conda env create -f workflow/envs/environment.fixed.yaml</code><br><code>conda activate price-formation</code></p> <p><strong>Main Dependencies:</strong></p> <ul> <li>pypsa (v0.27.1)</li> <li>linopy (v0.3.8)</li> <li>snakemake (v8.5)</li> <li>gurobi (v11.0.2)</li> </ul> <h2>Run</h2> <p>From the root of the repository:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda</code></p> <p>Or with a specific scenario configuration file:</p> <p><br><code>snakemake -call --use-conda --conda-frontend conda --configfile config/config.foo.yaml</code></p> <h2>Cluster</h2> <p>On an HPC cluster, run:</p> <p><br><code>snakemake -call --profile slurm --use-conda --conda-frontend conda</code></p> <h2>Compress Results</h2> <p>Use <code>tar</code> to compress results (excluding the report directory):</p> <p><br><code>tar -cJf price-formation-results.tar.xz \</code><br><code> config data figures results resources workflow \</code><br><code> .gitignore .pre-commit-config.yaml .syncignore-receive \</code><br><code> .syncignore-receive CITATION.cff LICENSE matplotlibrc README.md</code></p> <h2>Licenses</h2> <p>The code in this repository is MIT licensed.</p> <p>The data in this repository is CC-BY-4.0 licensed.</p> <h2>Amendments</h2> <p>In <code>v0.2.0</code>, we added the file <code>revision-1-amendments.tar.xz</code>, which includes additional results for sensitivity runs with cross-elastic terms.</p>
Fuel tax loss in a world of electric mobility: A window of opportunity for congestion pricing
<p>The excel file "Data_Documentation" provides estimations of the passenger car stocks in Germany and the two states Berlin-Brandenburg until 2030 under a scenario of a dynamic diffusion of electric vehicles to the market. From the car stocks, the energy tax revenues are also estimated. The Tableau package book provides data and visualizations of congestion pricing simulation results. This is an updated version of its previous one. The corrections are only the names of the sheets in the files. The data itselft stays the same.</p>
Spanish energy supply prices
<p>This dataset collects information about energy supply prices by days and hours since June 1st 2021, when prices were changed by the government, which also carried to an inflation of the prices. This dataset pretends to be a reference when studying this inflation, by comparing, for example, hour prices with the performance of energy suppliers' stations, determining whether this inflation is justified or not.</p> <p>Dataset structure goes as follows:</p> <ul> <li><strong>Tarifa</strong>. There are two tax types identified by a three-letter code: "Península, Baleares y Canarias" (pbc) and "Ceuta y Melilla" (cym).</li> <li><strong>Fecha</strong>. Date of the extracted data in DD-MM-YYYY format.</li> <li><strong>Hora</strong>. Hour of the extracted data (int type from 0 to 23).</li> <li><strong>Precio total</strong>. Total tax price in kWh/€.</li> <li><strong>Mercado diario e intradiario</strong>. Price breakdown corresponding to diary and intraday markets in kWh/€.</li> <li><strong>Servicios de ajuste</strong>. Price breakdown corresponding to adjust services in kWh/€.</li> <li><strong>Financiación OS</strong>. Price breakdown corresponding to OS financing in kWh/€.</li> <li><strong>Financiación OM</strong>. Price breakdown corresponding to OM financing in kWh/€.</li> <li><strong>Coste Comercialización variable</strong>. Price breakdown corresponding to variable commercialization cost in kWh/€.</li> <li><strong>Peajes y cargos</strong>. Price breakdown corresponding to tolls and charges in kWh/€.</li> <li><strong>Pago por capacidad. </strong>Price breakdown corresponding to capacity payment in kWh/€.</li> <li><strong>Excedente o deficit subastas renovables</strong>. Price breakdown corresponding to surplus or deficit of renewable energy auctions in kWh/€.</li> <li><strong>Servicio de interrumpibilidad</strong>. Price breakdown corresponding to interruptibility service in kWh/€.</li> </ul>
Oil Prices - Support File for Data Manipulation Starter Data Kits
<p>This file can be used to manipulate the oil process data for the Starter Data Kits. </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)
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.