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2,227 results for “markets”
Data and code from: Spatial selection undermines flood protection in U.S. wetland markets
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Marketing birds: The traits birdwatching tourism companies highlight in Costa Rican tour itineraries
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Data from: United States cattle market location and annual market sales estimate data
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Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance
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Stratified Reward Structures and Competition in Markets for Creative Production
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Paninvasion severity assessment of a U.S. grape pest to disrupt the global wine market
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Data from: Pollination treatment affects fruit set and modifies marketable and storable fruit quality of commercial apples
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Reinforcement learning theory reveals the cognitive requirements for solving the cleaner fish market task
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Data and Code for: Food distribution, but not market forces, predict behavioral social tolerance in rhesus macaques
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The impact of market integration on the digital divide: An analysis based on the heterogeneous effect of innovation resource synergy
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Qualitative survey instruments for a study on equity from a large-scale private-sector healthcare intervention in Ghana and Kenya: the African Health Markets for Equity (AHME) study
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Data for: Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels
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Spatial and temporal variation in the value of solar power across United States electricity markets
<p>This repository includes python scripts and input/output data associated with the following publication:</p> <p>[1] Brown, P.R.; O'Sullivan, F. "Spatial and temporal variation in the value of solar power across United States Electricity Markets". Renewable & Sustainable Energy Reviews 2019. <a href="https://doi.org/10.1016/j.rser.2019.109594">https://doi.org/10.1016/j.rser.2019.109594</a></p> <p>Please cite reference [1] for full documentation if the contents of this repository are used for subsequent work.</p> <p>Many of the scripts, data, and descriptive text in this repository are shared with the following publication:</p> <p>[2] Brown, P.R.; O'Sullivan, F. "Shaping photovoltaic array output to align with changing wholesale electricity price profiles". Applied Energy 2019, 256, 113734. <a href="https://doi.org/10.1016/j.apenergy.2019.113734">https://doi.org/10.1016/j.apenergy.2019.113734</a></p> <p>All code is in python 3 and relies on a number of dependencies that can be installed using pip or conda.</p> <p><strong>Contents</strong></p> <ul> <li>pvvm/*.py : Python module with functions for modeling PV generation and calculating PV energy revenue, capacity value, and emissions offset.</li> <li>notebooks/*.ipynb : Jupyter notebooks, including: <ul> <li>pvvm-vos-data.ipynb: Example scripts used to download and clean input LMP data, determine LMP node locations, assign nodes to capacity zones, download NSRDB input data, and reproduce some figures in [1]</li> <li>pvvm-example-generation.ipynb: Example scripts demonstrating the use of the PV generation model and a sensitivity analysis of PV generator assumptions</li> <li>pvvm-example-plots.ipynb: Example scripts demonstrating different plotting functions</li> <li>validate-pv-monthly-eia.ipynb: Scripts and plots for comparing modeled PV generation with monthly generation reported in EIA forms 860 and 923, as discussed in SI Note 3 of [1]</li> <li>validate-pv-hourly-pvdaq.ipynb: Scripts and plots for comparing modeled PV generation with hourly generation reported in NREL PVDAQ database, as discussed in SI Note 3 of [1]</li> <li>pvvm-energyvalue.ipynb: Scripts for calculating the wholesale energy market revenues of PV and reproducing some figures in [1]</li> <li>pvvm-capacityvalue.ipynb: Scripts for calculating the capacity credit and capacity revenues of PV and reproducing some figures in [1]</li> <li>pvvm-emissionsvalue.ipynb: Scripts for calculating the emissions offset of PV and reproducing some figures in [1]</li> <li>pvvm-breakeven.ipynb: Scripts for calculating the breakeven upfront cost and carbon price for PV and reproducing some figures in [1]</li> </ul> </li> <li>html/*.html : Static images of the above Jupyter notebooks for viewing without a python kernel</li> <li>data/lmp/*.gz : Day-ahead nodal locational marginal prices (LMPs) and marginal costs of energy (MCE), congestion (MCC), and losses (MCL) for CAISO, ERCOT, MISO, NYISO, and ISONE. <ul> <li>At the time of publication of this repository, permission had not been received from PJM to republish their LMP data. If permission is received in the future, a new version of this repository will be linked here with the complete dataset.</li> </ul> </li> <li>results/*.csv.gz : Simulation results associated with [1], including modeled energy revenue, capacity credit and revenue, emissions offsets, and breakeven costs for PV systems at all LMP nodes</li> </ul> <p><strong>Data notes</strong></p> <ul> <li>ISO LMP data are used with permission from the different ISOs. Adapting the MIT License (<a href="https://opensource.org/licenses/MIT">https://opensource.org/licenses/MIT</a>), "The data are provided 'as is', without warranty of any kind, express or implied, including but not limited to the warranties of merchantibility, fitness for a particular purpose and noninfringement. In no event shall the authors or sources be liable for any claim, damages or other liability, whether in an action of contract, tort or otherwise, arising from, out of or in connection with the data or other dealings with the data." Copyright and usage permissions for the LMP data are available on the ISO websites, linked below.</li> <li>ISO-specific notes on LMP data: <ul> <li>CAISO data from <a href="http://oasis.caiso.com/mrioasis/logon.do">http://oasis.caiso.com/mrioasis/logon.do</a> are used pursuant to the terms at <a href="http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse">http://www.caiso.com/Pages/PrivacyPolicy.aspx#TermsOfUse</a>.</li> <li>ERCOT data are from <a href="http://www.ercot.com/mktinfo/prices">http://www.ercot.com/mktinfo/prices</a>.</li> <li>MISO data are from <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/</a> and <a href="https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/">https://www.misoenergy.org/markets-and-operations/real-time--market-data/market-reports/market-report-archives/</a>.</li> <li>PJM data were originally downloaded from <a href="https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx">https://www.pjm.com/markets-and-operations/energy/day-ahead/lmpda.aspx</a> and <a href="https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx">https://www.pjm.com/markets-and-operations/energy/real-time/lmp.aspx</a>. At the time of this writing these data are currently hosted at <a href="https://dataminer2.pjm.com/feed/da_hrl_lmps">https://dataminer2.pjm.com/feed/da_hrl_lmps</a> and <a href="https://dataminer2.pjm.com/feed/rt_hrl_lmps">https://dataminer2.pjm.com/feed/rt_hrl_lmps</a>.</li> <li>NYISO data from <a href="http://mis.nyiso.com/public/">http://mis.nyiso.com/public/</a> are used subject to the disclaimer at <a href="https://www.nyiso.com/legal-notice">https://www.nyiso.com/legal-notice</a>.</li> <li>ISONE data are from <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-da-hourly</a> and <a href="https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final">https://www.iso-ne.com/isoexpress/web/reports/pricing/-/tree/lmps-rt-hourly-final</a>. The Material is provided on an "as is" basis. ISO New England Inc., to the fullest extent permitted by law, disclaims all warranties, either express or implied, statutory or otherwise, including but not limited to the implied warranties of merchantability, non-infringement of third parties' rights, and fitness for particular purpose. Without limiting the foregoing, ISO New England Inc. makes no representations or warranties about the accuracy, reliability, completeness, date, or timeliness of the Material. ISO New England Inc. shall have no liability to you, your employer or any other third party based on your use of or reliance on the Material.</li> </ul> </li> <li>Data workup: LMP data were downloaded directly from the ISOs using scripts similar to the pvvm.data.download_lmps() function (see below for caveats), then repackaged into single-node single-year files using the pvvm.data.nodalize() function. These single-node single-year files were then combined into the dataframes included in this repository, using the procedure shown in the pvvm-vos-data.ipynb notebook for MISO. We provide these yearly dataframes, rather than the long-form data, to minimize file size and number. These dataframes can be unpacked into the single-node files used in the analysis using the pvvm.data.copylmps() function.</li> </ul> <p><strong>Usage notes</strong></p> <ul> <li>Code is provided under the <a href="https://opensource.org/licenses/MIT">MIT License</a>, as specified in the pvvm/LICENSE file and at the top of each *.py file. </li> <li>Updates to the code, if any, will be posted in the non-static repository at <a href="https://github.com/patrickbrown4/pvvm_vos">https://github.com/patrickbrown4/pvvm_vos</a>. The code in the present repository has the following version-specific dependencies: <ul> <li>matplotlib: 3.0.3</li> <li>numpy: 1.16.2</li> <li>pandas: 0.24.2</li> <li>pvlib: 0.6.1</li> <li>scipy: 1.2.1</li> <li>tqdm: 4.31.1</li> </ul> </li> <li>To use the NSRDB download functions, you will need to modify the "settings.py" file to insert a valid NSRDB API key, which can be requested from <a href="https://developer.nrel.gov/signup/">https://developer.nrel.gov/signup/</a>. Locations can be specified by passing (latitude, longitude) floats to pvvm.data.downloadNSRDBfile(), or by passing a string googlemaps query to pvvm.io.queryNSRDBfile(). To use the googlemaps functionality, you will need to request a googlemaps API key (<a href="https://developers.google.com/maps/documentation/javascript/get-api-key">https://developers.google.com/maps/documentation/javascript/get-api-key</a>) and insert it in the "settings.py" file.</li> <li>Note that many of the ISO websites have changed in the time since the functions in the pvvm.data module were written and the LMP data used in the above papers were downloaded. As such, the pvvm.data.download_lmps() function no longer works for all ISOs and years. We provide this function to illustrate the general procedure used, and do not intend to maintain it or keep it up to date with the changing ISO websites. For up-to-date functions for accessing ISO data, the following repository (no connection to the present work) may be helpful: <a href="https://github.com/catalyst-cooperative/pudl">https://github.com/catalyst-cooperative/pudl</a>.</li> </ul> <p> </p>
European Procurement Markets as Bipartite Networks
<p><strong>EU Procurement Market Network Data</strong></p> <p>Johannes Wachs<br> January 2020<br> <strong>*In case you use this data, please reference:</strong> </p> <ul> <li>Wachs, J., Fazekas, M. & Kertész, J. Corruption risk in contracting markets: a network science perspective. Int J Data Sci Anal (2020). 10.1007/s41060-019-00204-1</li> <li>Available open access at: <a href="https://link.springer.com/article/10.1007%2Fs41060-019-00204-1">https://link.springer.com/article/10.1007%2Fs41060-019-00204-1</a></li> </ul> <p>These 234 networks represent the annual national public procurement markets of 26 European countries from 2008-2016, inclusive. Data is sourced from Tenders Electronic Daily (TED), the official procurement portal of the European Union.</p> <p>Nodes with the suffix "_i" are issuers (sometimes referred to as buyers) of public contracts, for instance public hospitals, ministries, local governments. Nodes with the suffix "_w" are winners (sometimes called suppliers) of public contracts, generally private-sector firms. Identities have been statistically deduplicated, as described in the paper. </p> <p>Each network is bipartite: edges only exist between issuers and winners. Edges represent contracting relationships and have two attributes: </p> <ul> <li>``count'' measures the volume of contracts between the issuer and winner in the given year. This attribute can be interpreted as a weight or strength of the relationship.</li> <li>``pctSingleBid'' describes the share of contracts between the issuer and winner awarded without competition, i.e. with the winner as single bidder or sole-supplier. Note: missing data on single-bidding is imputed. This is an elementary indicator of corruption risk of the contract. For more information consult the paper referenced above.</li> </ul> <p>Ids of issuers and winners are consistent across time and within countries. Node ids have been randomly generated and do not correspond to any official statistics.</p> <p>The networks are stored in the GML format. For example: they can be read in directly by Gephi or by the Python NetworkX library with the following commands (using the 2010 Portuguese market as an example):</p> <blockquote> <p> import networkx as nx<br> G=nx.read_gml('country_year_networks/PT_2010.gml')</p> </blockquote>
Online Appendix - Pricing in Integrated Heat and Power Markets
<p>Online Appendix for the letter "Pricing in Integrated Heat and Power Markets". This appendix contains the nomenclature, generation data, and table with the results of the test cases.</p>
Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers
<p>PSCC - Data Source</p> <p>Paper title: Exploring the Effects of Local Energy Markets on Electricity Retailers and Customers</p> <p>1. Wholesale price,</p> <p>2. Linear and quadratic benefit coefficients of flexible consumers, </p> <p>3. Linear and quadratic cost coefficients of micro-generators,</p> <p>4. Power capacity, minimum & maximum energy limits, initial energy level, charging & discharging efficiency of energy storages.</p>
Catch me if you can. Can human observers identify insiders in asset markets?
<p>Supplementary material</p> <p>This documentation provides information on the files and folders contained in the supplementary material. Note that all .do and .R files in the supplementary material are meant to be run directly from the folder containing them.</p> <p>1.contracts\</p> <p>1.1.contracts\RegulationContractsModPas.do</p> <p>Contains calculations for the results reported in section 3.2 and for Table 5 of the paper.</p> <p>2.globals\</p> <p>Contains data potentially used in analysis.</p> <p>3.infodata\</p> <p>Contains data potentially used in analysis.</p> <p>4.predictions\</p> <p>4.1.Predictions\Regulation_predictions.do</p> <p>Contains calculations for Tables 2-4 of the paper.</p> <p>4.2.Predictions\Regulation_predictions_DetectionProb.do</p> <p>Contains calculations for the results reported in section 3.1 of the paper.</p> <p>5.subjects\</p> <p>Contains data potentially used in analysis.</p> <p>6.timelog\</p> <p>6.1.timelog\Regulation_timelog.do</p> <p>Contains calculations for Figure 4 and Figure A1 of the paper.</p> <p>7.z-Tree\</p> <p>This folder contains the experimental software.</p> <p>7.1.z-Tree\*.zdata</p> <p>Parameter files for group assignment, market assignment, subject identifiers etc. used in the experimental software.</p> <p>7.2.z-Tree\z-Tree_Documentation.pdf</p> <p>Documentation of all variables used in the z-Tree experimental software.</p> <p>8.DetectionProbability.xlsx</p> <p>Contains data for Table 2 in the paper. Based on 4.1 Predictions\Regulation_predictions.do, section stata02.</p> <p>9.groups.zdata</p> <p>Overview of group IDs. Used In some analyses.</p> <p>10.Insider_regulation_data.do</p> <p>Stata .do file which processes the raw z-Tree output data (see section 12 of this document) in preparation for all analyses.</p> <p>11.PowerAnalysis.R</p> <p>Contains all post-hoc power analyses reported in sections 3.1 and 3.2 of the paper.</p> <p>12.PUN*_C*.xls</p> <p>Original z-Tree output files containing all experimental results. These are processed into Stata format in Insider_regulation_data.do.</p> <p>13.ResultsWithReferences.pdf</p> <p>The file “ResultsWithReferences.pdf” contains the results section of the paper, including (in blue) references to the analysis scripts described in the present document.</p>
Use of social media in the marketing of agricultural products and farmers' turnover in South-South Nigeria
<p class="CxSpFirst"><span>The study investigated the use of social media in the marketing of agricultural products and farmers turnover in South-South Nigeria. The purpose of the study was to determine the extent to which the usage of social media in the marketing of agricultural products in Nigeria can enhance efficiency and farmers' sales turnover. It employed the survey research design and data were collected with the help of a structured questionnaire. Research data were analysed using t-test and least square method. The results showed that the use of social media (Facebook, WhatsApp and Instagram) in the marketing of agricultural products enhances efficiency and turnover of farmers through a significant reduction in the cost of marketing agricultural products as well as increased awareness and the attendant increase in demand for agricultural produce.</span></p>
Learning agents in black-scholes financial markets
<p>Black-Scholes (BS) is a remarkable quotation model for European option pricing in financial markets. Option prices are calculated using an analytical formula whose main inputs are strike (at which price to exercise) and volatility. The BS framework assumes that volatility remains constant across all strikes, however, in practice it varies. How do traders come to learn these parameters? We introduce natural agent-based models, in which traders update their beliefs about the true implied volatility based on the opinions of other traders. We prove exponentially fast convergence of these opinion dynamics using techniques from control theory and leader-follower models, thus providing a resolution between theory and market practices. We allow for two different models, one with feedback and one with an unknown leader.</p>
Data from: Time to market for drugs approved in Canada between 2014-2018: an observational study
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objectives: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>This study examines the length of time between when a patent application is filed in Canada for a new drug and when it is available for patients (time to market) and various components of that time. It also looks at whether various factors explain the time between patent application to New Drug Submission and compares Canadian and American times. Drugs approved between January 1, 2014 and December 31, 2018 are examined. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Design: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Descriptive study</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Data Sources: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Websites from Health Canada, Food and Drug Administration, Merck Index, United States Patent and Trademark Office, World Health Organization and previously published articles.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Interventions: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>None</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Primary and Secondary Outcomes: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>The primary outcomes are time to market, time from patent application to New Drug Submission (pre-NDS time), review time, time from approval to availability (postapproval time) and factors that may influence the pre-NDS time. The secondary outcome is a comparison of Canadian and American review times and times between patent application and approval. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>There were 113 drugs available for analysis. The median time to market was 11.80 years (inter-quartile range (IQR) 9.40, 14.05). The component median times were pre-NDS 10.00 years (IQR 8.05, 12.80), review time 0.96 years (IQR 0.75, 1.15) and postapproval time 0.15 years (IQR 0.08, 0.28). Less than 8% of the pre-NDS time was explained by the factors that were analyzed in a multiple linear regression equation. There was no statistically significant difference between Canadian and American pre-NDS times. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusion: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>On average, once a drug reaches the market companies have a median of 8.2 years before the patent expires and generics can reach the market. Most of the time between the filing of a patent application and when a drug is marketed is determined by decisions that are largely under the control of the company.</span></span></span></span></span></span></span></span></span></span></span></p>
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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.