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363 results for “Price”
Price et al, 2005
PRICE, J. J., JOHNSON, K. P., BUSH, S. E., & CLAYTON, D. H. (2005). Phylogenetic relationships of the Papuan Swiftlet Aerodramus papuensis and implications for the evolution of avian echolocation. Ibis, 147(4), 790–796. <p></p>https://doi.org/10.1111/j.1474-919X.2005.00467.x<p></p>
Emissions-weighted Carbon Price
<div>This note describes the data and methods used to calculate the emissions-weighted carbon price (ECP), a sector or economy-wide average price on CO<sub>2</sub> emissions. A major benefit is that it provides a methodology to measure sector- or economy-level average prices consistently across jurisdictions. To the best of our knowledge, the ECP data constitute the first centralized and systematic assessment providing a consistent description of carbon prices that simultaneously includes price information disaggregated at the sector(-fuel) level, extends back to 1990 to include price information for the earliest carbon <span>tax</span><span>pricing</span> policies, and accounts for as many sector(-fuel) exemptions as accurately possible. The methodology and data currently available allow to readily expand the calculation to new national or subnational jurisdictions, should some of their emissions become subject to a carbon pricing mechanism, as well as to new greenhouse gases.</div> <div> </div> <div>It is calculated for 46 national and 31 subnational jurisdictions (13 Canadian provinces and territories, 11 US states, and 7 Chinese provinces) over 1990–2022. The average World CO<sub>2</sub> price is also calculated. For national jurisdictions, the emissions-weighted price accounts for the prices arising from carbon pricing instruments introduced in their respective subnational jurisdictions. For instance, the emissions-weighted price for the United States includes the prices arising from state-level carbon pricing mechanisms.</div>
Paul Price edit and charts - WEM Agriculture_2022_WEM_EPA (2024) PRP EDIT (with some WAM analysis added).xlsx
<p>This is an edited version of the 2024 EPA Ireland GHG emissions inventory Excel workbook for Agriculture under the With Existing Measures emissions scenario (uploaded in original form as doi: <span>10.5281/zenodo.13941591). This version also includes data from the corresponding EPA agriculture workbook for the With Existing Measures emissions scenario (uploaded in original form as doi: <span>10.5281/zenodo.13941662).</span></span><br><br>Additions by Paul Price are found on the tab "<strong>3.A and 3.B CH4</strong>": in rows 147–160 are Historic Total EF+MM (CH4 kt/Yr) timeseries for the different animal type and in total, giving the total for each of methane in kilotonnes from Enteric Fermentation and Manure Management combined. Then in rows 186-192 you can see the added calculation, giving the percentage of the total from each animal type. A related chart shows the historic and projected (WEM and WAM) outputs from these calculations.</p>
Dataset Questionnaire Driving Repeat Purchases and E-WOM: How Price, Reputation, Hedonic Appeal, and Social Interaction Shape Consumer Behavior in Indonesia's E-Commerce Smartphone Market
<p>The following dataset is a dataset from a study that investigated price advantage, reputation, hedonic effort, and social interaction influence customer satisfaction, which in turn impacts repurchase intention and e-WOM (electronic word-of-mouth).</p>
PriceCharting product price data
<p>PriceCharting product price data (the three price columns next to the product name when entering the category) in CSV format</p>
Dataset on prices for streaming media services, 2008-2019
<p>Terje Colbjørnsen, Alan Hui and Benedikte Solstad are the authors of the journal article "What do you pay for all you can eat? Pricing practices and strategies in streaming media services", to be published in <em>Journal of media business studies</em> 2021. <a href="http://dx.doi.org/10.1080/16522354.2021.1949568">http://dx.doi.org/10.1080/16522354.2021.1949568</a></p> <p>We prepared this spreadsheet to support that article. The data was collected 1. March 2019 - 30. April 2019. The spreadsheet contains and analyses streaming service price data, 2008-2019. We hope this spreadsheet will assist you and other researchers.</p>
How distorted food prices discourage a healthy diet
<p>Public policy making for the prevention of diet-related disease is impeded by a lack of evidence on whether poor diets are a matter of personal responsibility or a choice set narrowed by environmental conditions. An important element of the environment is market imperfections in food retail that distort prices. We use a rich dataset on quantities and prices of food purchases in the United States and a structural model of dietary choices to examine variation in diets across households that have different levels of income and live in different neighborhoods. We find that price distortions account for one-third of the gap between the recommended and actual intake of fruits and vegetables. A feasible fiscal intervention that remedies these distortions makes all consumers better off.</p>
AWS Spot Price History
<h1>AWS Spot Price History</h1> <p>This dataset tracks historical prices for AWS spot prices across all regions. It is updated automatically on the 1st of each month to contain data from the previous month.</p> <h1>Data format</h1> <p>Each month of data is stored as a ZStandard-compressed <code>.tsv.zst</code> file.</p> <p>The data format matches that returned by AWS's <code>describe-spot-instance-prices</code>, with the exception that availability zones have been replaced by their global ID. For instance, here are some example lines from one capture:</p> <p><code>euc1-az2 i4i.8xlarge Linux/UNIX 1.231800 2023-02-28T23:59:57+00:00</code><br><code>euc1-az3 r5b.8xlarge Red Hat Enterprise Linux 0.749600 2023-02-28T23:59:58+00:00</code><br><code>euc1-az3 r5b.8xlarge SUSE Linux 0.744600 2023-02-28T23:59:58+00:00</code><br><code>euc1-az3 r5b.8xlarge Linux/UNIX 0.619600 2023-02-28T23:59:58+00:00</code><br><code>euc1-az3 m5n.4xlarge Red Hat Enterprise Linux 0.476000 2023-02-28T23:59:59+00:00</code><br><code>euc1-az2 m5n.4xlarge Red Hat Enterprise Linux 0.492000 2023-02-28T23:59:59+00:00</code><br><code>euc1-az3 m5n.4xlarge SUSE Linux 0.471000 2023-02-28T23:59:59+00:00</code><br><code>euc1-az2 m5n.4xlarge SUSE Linux 0.487000 2023-02-28T23:59:59+00:00</code><br><code>euc1-az3 m5n.4xlarge Linux/UNIX 0.346000 2023-02-28T23:59:59+00:00</code><br><code>euc1-az2 m5n.4xlarge Linux/UNIX 0.362000 2023-02-28T23:59:59+00:00</code></p> <p>When fetching spot instance pricing from AWS, results contain some prices from the previous month so that the price is known at the start of the month. These prices are adjusted in this dataset to be at the exact start of the month UTC:</p> <p><code>euw3-az2 g4dn.4xlarge Linux/UNIX 0.558600 2023-01-01T00:00:00+00:00</code></p> <p>For data from 2023-01 and before, this data was fetched more than one month at a time. This should have no negative impact unless, for example, an instance type was retired before the month began (and there should therefore be no price). These older files also only contain default regions. Data from 2023-02 and later contains all regions, including opt-in regions.</p> <h1>Using data</h1> <p>You can process each month individually. If you need the entire data stream at once, you can cat all files to <code>zst</code> together:</p> <p><code>cat prices/*/*.tsv.zst | zstd -d</code></p>
FPCA - From mobile app-based crowdsourcing to crowd-trusted food price estimates in Nigeria: pre-processing and post-sampling strategy for optimal statistical inference
<p>Timely and reliable monitoring of commodity food prices is an essential requirement for the assessment of market and food security risks and the establishment of early warning systems, especially in developing economies. However, data from regional or national systems for tracking changes of food prices in sub-Saharan Africa lacks the temporal or spatial richness and is often insufficient to inform targeted interventions. In addition to limited opportunity for [near-]real-time assessment of food prices, various stages in the commodity supply chain are mostly unrepresented, thereby limiting insights on stage-related price evolution. Yet, governments and market stakeholders rely on commodity price data to make decisions on appropriate interventions or commodity-focused investments. Recent rapid technological development indicates that digital devices and connectivity services are becoming affordable for many, including in remote areas of developing economies. This offers a great opportunity both for the harvesting of price data (via new data collection methodologies, such as crowdsourcing/crowdsensing — i.e. citizen-generated data — using mobile apps/devices), and for disseminating it (via web dashboards or other means) to provide real-time data that can support decisions at various levels and related policy-making processes. However, market information that aims at improving the functioning of markets and supply chains requires a continuous data flow as well as quality, accessibility and trust. More data does not necessarily translate into better information. Citizen-based data-generation systems are often confronted by challenges related to data quality and citizen participation, which may be further complicated by the volume of data generated compared to traditional approaches. Following the food price hikes during the first noughties of the 21st century, the European Commission's Joint Research Centre (JRC) started working on innovative methodologies for real-time food price data collection and analysis in developing countries. The work carried out so far includes a pilot initiative to crowdsource data from selected markets across several African countries, two workshops (with relevant stakeholders and experts), and the development of a spatial statistical quality methodology to facilitate the best possible exploitation of geo-located data. Based on the latter, the JRC designed the Food Price Crowdsourcing Africa (FPCA) project and implemented it within two states in Northern Nigeria. The FPCA is a credible methodology, based on the voluntary provision of data by a crowd (people living in urban, suburban, and rural areas) using a mobile app, leveraging monetary and non-monetary incentives to enhance contribution, which makes it possible to collect, analyse and validate, and disseminate staple food price data in real time across market segments. The granularity and high frequency of the crowdsourcing data open the door to real-time space-time analysis, which can be essential for policy and decision making and rapid response on specific geographic regions. <a href="https://datam.jrc.ec.europa.eu/datam/perm/news/870?rdr=1666109837893">Link to the project</a></p>
PERTEMUAN 7_10 nOVEMBER 2022_MODEL KESEIMBANGAN :CAPITAL ASSETS PRICING MODEL DAN ARBITRAGE PRICING THEORY_AIMR_JAM 18.00-21.30
<p><strong>Capaian Pembelajaran Mata Kuliah (CPM</strong></p> <p>Mahasiswa mampu Memahami:Model-model Keseimbangan: Capital Assets Pricing Model dan Arbitrage Pricing Theory</p> <p><strong>Indikator Pembelajaran</strong></p> <p>Model-model Keseimbangan: Capital Assets Pricing Model</p> <p>Arbitrage Pricing Theory</p>
FCBarcelona Ticket Prices
<p>This dataset contains the prices for the different sections of FCBarcelona (Men's football, women's football, basket, handball, roller hockey, futsal and youth men's football) for the 2022-23 season (scraped from its website the 22 november 2022)</p>
Data from: Two for the price of one: eDNA metabarcoding reveals temporal and spatial variability of mussel and fish co-distributions in Michigan riverine systems
<p>Freshwater mussels (family Unionidae) are among the world's most endangered taxa, with almost 75% of North American taxa classified as a species of concern, threatened, or endangered. Despite the critical importance of comprehensive distributional data for the conservation of unionids and fishes, these data are often lacking because of the labor and resources associated with traditional survey methods. During their larval stage, unionid mussels use various fish species as obligate hosts, making native fish species vital to unionid persistence and an understanding of host distribution similarly important. Here, we utilized an eDNA metabarcoding approach to evaluate patterns of co-distribution of unionid mussels and fishes along ~362 km of the densely sampled Grand River network as well as the outlets of 19 tributaries along the eastern shore of Lake Michigan, USA. We detected a total of 21 mussel and 40 fish taxa, with distinctive composition of both mussel and fish assemblages across tributaries and differences in fish taxa between sampling periods. Notably, we detected more mussel taxa within the Grand River watershed than at the outlets of all 20 rivers combined. Within the Grand River network, two fish taxa (<em>Pylodictus</em> <em>olivaris</em> and <em>Cyprinella</em>) were found more frequently in areas of high mussel diversity, and three fish taxa more frequently in areas of low mussel diversity (<em>Umbra</em>, Leuciscidae, and <em>Etheostoma</em>). There was little difference between eDNA detections of mussels from samples collected in June versus August, but we detected significantly more fish taxa in August compared to June. Taken together, our findings demonstrate the value of eDNA metabarcoding for evaluating co-distribution of ecologically connected taxa. The use of eDNA as a tool for determining distributions of mussels and their obligate hosts may facilitate conservation efforts for these imperiled taxa.</p>
Crypto Price Monitoring Dataset for On-chain Derivatives Research
<p># Crypto Price Monitoring Repository</p> <p>This repository contains two CSV data files that were created to support the research titled "Price Arbitrage for DeFi Derivatives." This research is to be presented at the IEEE International Conference on Blockchain and Cryptocurrencies, taking place on 5th May 2023 in Dubai, UAE. The data files include monitoring prices for various crypto assets from several sources. The data files are structured with five columns, providing information about the symbol, unified symbol, time, price, and source of the price.</p> <p>## Data Files</p> <p>There are two CSV data files in this repository (one for each date):</p> <p>1. `Pricemon_results_2022_11_01.csv`<br> 2. `Pricemon_results_2022_11_08.csv`</p> <p>## Data Format</p> <p>Both data files have the same format and structure, with the following five columns:</p> <p>1. `symbol`: The trading symbol for the crypto asset (e.g., BTC, ETH).<br> 2. `unified_symbol`: A standardized symbol used across different platforms.<br> 3. `time`: Timestamp for when the price data was recorded (in UTC format).<br> 4. `price`: The price of the crypto asset at the given time (in USD).<br> 5. `source`: The name of the price source for the data.</p> <p>## Price Sources</p> <p>The `source` column in the data files refers to the provider of the price data for each record. The sources include:</p> <p>- `chainlink`: Chainlink Price Oracle<br> - `mycellium`: Built-in oracle of the Mycellium platform<br> - `bitfinex`: Bitfinex cryptocurrency exchange<br> - `ftx`: FTX cryptocurrency exchange<br> - `binance`: Binance cryptocurrency exchange</p> <p>## Usage</p> <p>You can use these data files for various purposes, such as analyzing price discrepancies across different sources, identifying trends, or developing trading algorithms. To use the data, simply import the CSV files into your preferred data processing or analysis tool.</p> <p>### Example</p> <p>Here's an example of how you can read and display the data using Python and the pandas library:</p> <p> import pandas as pd<br> <br> # Read the data from CSV file<br> data = pd.read_csv('Pricemon_results_2022_11_01.csv')<br> <br> # Display the first 5 rows of the data<br> print(data.head())` </p> <p> </p> <p>## Acknowledgements</p> <p>These datasets were recorded and supported by <a href="https://datamint.ai">Datamint</a> company (value-added on-chain data provider) and its team.</p> <p><br> ## Contributing</p> <p>If you have any suggestions or find any issues with the data, please feel free to contact authors.</p>
Case study result data set for Energy Systems (submitted) article "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe"
<p>The data set contains result data for the European system in a long term climate-neutral European energy system (scenario year 2050) as described in the publication "Influence of hydrogen import prices on hydropower systems in climate-neutral Europe". The results have been generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. </p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>CCGT - Combined Cycle Gas Turbine</li> <li>CHP - Combined heat and power</li> <li>con - consumption</li> <li>gen - generation</li> <li>HighCLEQ - High import prices / clustered-equivalent hydropower units</li> <li>HighEQ - High import prices / equivalent hydropower units</li> <li>LowCLEQ - Low import prices / clustered-equivalent hydropower units</li> <li>LowEQ - Low import prices / equivalent hydropower units</li> <li>MedCLEQ - Medium import prices / clustered-equivalent hydropower units</li> <li>MedEQ - Medium import prices / equivalent hydropower units</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>PS - Pumped Storage</li> <li>w/ - with</li> <li>w/o - without</li> <li>yr - year</li> </ul>
Price and characteristics of flats for rent in Catalonia
<p>Important note: simulated dataset for carrying out an academic project.</p> <p>The dataset contains the list of rental flats in Catalonia present on the website of a real estate agency as of April 10, 2023, with the location, price, square meters and relevant characteristics of the flat.</p>
Exploratory Analysis of Top 50 Companies in Indian Stock Market: A Time Series Analysis of Historical Stock Prices
<p>The dataset consists of 'open, close, high, low, close, adj close and volume' columns for the top 50 Indian Companies and the dataset been fetched from YahooFinance for over 20 years. </p>
Data from: Predicting regional carbon price in China based on multi-factor HKELM by combining secondary decomposition and ensemble learning
<p class="MsoNormal"><span>Accurately predicting carbon price is crucial for risk avoidance in the carbon financial market. In light of the complex characteristics of the regional carbon price in China, this paper proposes a model to forecast carbon price based on the multi-factor hybrid kernel-based extreme learning machine (HKELM) by combining secondary decomposition and ensemble learning. Variational mode decomposition (VMD) is first used to decompose the carbon price into several modes, and range entropy is then used to reconstruct these modes. The multi-factor HKELM optimized by the sparrow search algorithm is used to forecast the reconstructed subsequences, where the main external factors innovatively selected by maximum information coefficient and historical time-series data on carbon prices are both considered as input variables to the forecasting model. Following this, the improved complete ensemble-based empirical mode decomposition with adaptive noise and range entropy are respectively used to decompose and reconstruct the residual term generated by VMD. Finally, the nonlinear ensemble learning method is introduced to determine the predictions of residual term and final carbon price. In the empirical analysis of Guangzhou market, the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 0.1716, 0.1218 and 0.0026, respectively. The proposed model outperforms other comparative models in predicting accuracy. The work here extends the research on forecasting theory and methods of predicting the carbon price.</span></p>
Enhancing stock price data analysis through variants of principal component analysis
<p>The dataset used in the research titled "Enhancing stock price data analysis through variants of principal component analysis". It includes the daily closing prices of top 100 stocks in S&P500 from 29th March 2020 to 28th March 2023.</p>
Is the sentiment priced? Evidence from the Korean stock market
<p>SAS files.</p> <p>1) dtset_ks.KOSPI </p> <p>2) dtset_kq: KOSDAQ</p> <p>Data description: This datasets are the common stock of non-financial companies traded on KOSPI(dtset_ks) and KOSDAQ(dtset_kq), including delisted stocks. KOSPI comprises primarily of large, established firms, whereas KOSDAQ comprises young, entrepreneurial firms. The spans from February 2000 to June 2022 and is based on the stock excess return. This dataset utilizes data from FnGuide and the yields of CD (91-day) provided by the Bank of Korea as a risk-free rate. </p> <p>Please look at the paper to see specific variables. </p>
Case study result data set for the submitted article "Implications of hydrogen import prices for the German energy system in a model-comparison experiment"
<p>The data set contains result data for the German energy system in a long term scenario (scenario year 2045) as described in the publication "Implications of hydrogen import prices for the German energy system in a model-comparison experiment". The results have been generated with the models REMod of Fraunhofer Institute for Solar Energy Systems ISE, Enertile of Fraunhofer Institute for Systems and Innovation Research ISI, and SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE.</p><p><strong>Abbreviations:</strong></p><ul><li>BEV - Battery Electric Vehicles</li><li>CC - Combined Cycle</li><li>CCGT - Combined Cycle Gas Turbine</li><li>CHP - Combined heat and power</li><li>CO2 - Carbon dioxide</li><li>con - consumption</li><li>FC - Fuel Cell</li><li>FCEV - Fuel Cell Electric Vehicle</li><li>gen - generation</li><li>H2 - Hydrogen</li><li>HT - High temperature</li><li>ICE - Internal Combustion Engine</li><li>LDV - Light-Duty Vehicle</li><li>LT - Low temperature</li><li>med - medium</li><li>OC - Open Cycle</li><li>OCGT - Open Cycle Gas Turbine</li><li>PHEV - Plug-In Hybrid Vehicles</li><li>PS - Pumped Storage</li><li>PV - Photovoltaics</li><li>ST - Steam turbine</li><li>SynFuel - Synthetic fuel</li><li>w/ - with</li><li>w/o - without</li><li>yr - year</li></ul>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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