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2,227 results for “Market”
Market Power / Import demand elasticity faced by an exporter at 6-digit HS level from Solleder (2020)
<p><strong>Description</strong></p> <p>This dataset contains the market power of exporters at the country level for more than 4000 6-digit HS codes (HS 1992 / H0) from Solleder (2020). Market power is proxied by the inverse of the import demand elasticity faced by the exporting country. Elasticities are estimated following the method developed by Kee et al. (2008). For more information, please refer to Solleder (2020).</p> <p>The <em>dta </em>file can be opened with STATA 14 or above. The <em>csv</em> file is a comma-separated value file. The separator is ',', and the first row is variable names. The content is the same in both files. Variables are:</p> <ul> <li><em>exporter</em>: ISO 3166 3-character country codes, string; </li> <li><em>commoditycode</em>: product 6-digit HS codes in HS revision 1992 (H0), string;</li> <li><em>epsilon</em>: import demand elasticity faced by the exporter, numeric;</li> <li><em>epsilon_se</em>: standard error of <em>epsilon</em>, numeric;</li> <li><em>marketpower</em>: market power, inverse of the absolute value of the import demand elasticity faced by the exporter, numeric.</li> </ul> <p> </p> <p><strong>Reference</strong></p> <div> <div>Kee H.L., A. Nicita, M. Olarreaga 2008 'Import demand elasticities and trade distortions' Rev. Econ. Stat., 90 (4), pp. 666-682</div> <div> </div> <div>Solleder J.M. 2020 'Market power and export taxes' European Economic Review, Volume 125, 103425, ISSN 0014-2921, <a href="https://doi.org/10.1016/j.euroecorev.2020.103425">https://doi.org/10.1016/j.euroecorev.2020.103425</a>.</div> </div> <p> </p>
Systematic Literature Review on Tourism Marketing in the Metaverse
<p>The aim of this research is to investigate tourist marketing within the embryonic context of the metaverse in order to comprehend the building blocks and the primary technologies employed in the sector. For this purpose, a systematic literature review is conducted. The references are extracted in January 2023. The data in this document correspond to the articles finally included in the systematic literature review after the article screening phase.</p> <p>Keywords: tourism marketing, metaverse, technologies, building blocks, SLR (Systematic Literature Review), PRISMA</p> <p> </p>
Gross domestic product at market prices
<p>Gross domestic product at market prices</p> <p>Dataset created from the Eurostat dataset [<a href="http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=tec00001&lang=en">tec00001</a>] using linear approximation, backcasting and forecasting missing values. The information gain is a 33% increased non-missing matrix for regression or machine learning models.<br> </p>
H2020 Platone German Demonstrator Use Case 1 Market Data
<p>This dataset contains settings of the Local Energy Management System, that have been set via a Graphical User Interface (GUI). The dataset contain follwowing data:</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Timestamp</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Use Case</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_ID</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Option</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Priority</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_Submission_Time</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_Start_Date</p> <p>H2020_Platone_GER_Market__ALF-C_GUI_UC_End_Date</p> <p> </p> <p> </p>
Short Food Supply Chains Business and Marketing Models Categorisation
<p>The present dataset contains information about the categorisation of Business Models for Short Food Supply Chains, in the context of the <a href="http://agrobridges.eu">agroBRIDGES project</a> (Horizon 2020, GA No. 101000788). Regional information about existing and new business models for Short Food Supply Chains were collected from 12 regions and countries of focus for the project (Beacon Regions) through desk research, interviews and co-creation workshops.</p> <p>The datasets collected information provided by Beacon Region Leaders in the co-creation workshops about models applied and newly or potentially developed ones and available in the literature, to categorise them into stylized models with specific and distinguishable features and attributes, enabling a systematic approach in the development of the sustainability assessment framework. An initial ranking of local business models was provided at a regional / country level. </p> <p>Through analysis of the aggregate ranking results of business models at local level, a final list of business models for SFSCs was derived. Five different Business Model types have been addressed and developed ( i. Community Supported Agriculture - CSA, ii. Face to face trade, iii. Online Food Trade, iv. Local Food Trade, and v. Improved Logistics). The final categorization list provided valuable insights in order to represent all regional contexts and eventually offer value-added information about the main factors for developing a successful business model that can enhance market success in the long term; as well as to strengthen farmer´s strategy that eventually enhances their position within the whole value chain and improve their customer experience.</p> <p>The Value Proposition Canvas and Business Model Canvas was defined for each of the most common SFSC represented in each of the targeted regions.</p> <p><br> The dataset contains:<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation_2021.12.01_v1 [zip file]: </strong>The initial list of business models collected from the co-creation workshops<br> • <strong>agroBRIDGES_SFSCs-BMModels-Categorisation-Tool_2021.12.01_v1 [.xlsx file]: </strong>Internal Assessment tool for Business models regional data collection: A data collection tool was built in-house and shared to all Beacon Region Leaders in order to enable them to fill it regarding their assumptions and conclusions after the co-creation participants discussed and presented their insights from regional activities.<br> • <strong>agroBRIDGES_SFSCs-BModels-CategorisationResults_2021.12.01_v1 [.xlsx file]:</strong> SFSCs BM type results in each of the regions.<br> • <strong>agroBRIDGES_BusinessModels_Canvas_2021.12.01_v1 [zip file]: </strong>The Business Model and Value Proposition Canvas of the 5 BM categories</p>
Ethnic Discrimination in the Swiss Labour Market
<p>Discrimination of ethnic minorities in hiring decisions in the labour market has become a common phenomenon. Across OECD countries ethnic minority applicants have to write approximately 50% more applications to be invited for a job interview compared to an equally qualified majority applicant. Little is known about the extent of discrimination in the Swiss Labour Market, since the last correspondence test conducted in Switzerland is more than 15 years old and targets only a very specific sector of the labour market - the transition from school to apprenticeships.<br> To fill this gap in knowledge on discrimination in the Swiss labour market, a new correspondence test was conducted. It measures discrimination in various occupations, against different ethnic minority groups, including candidates with German or French backgrounds, and looks at the differences and similarities between the German and French speaking regions in Switzerland.</p>
Exploring Housing Affordability in Illinois: An In-Depth Study of the State's Real Estate Market
<p>“Exploring Housing Affordability in Illinois: An In-Depth Study of the State’s Real Estate Market” focuses on the Illinois housing market from 2013 to 2022, mainly targeting housing affordability. Housing has been a cornerstone of stability in anyone’s life throughout history. Yet today, housing affordability has emerged as a critical societal issue impacting numerous individuals and families statewide. This study aims to get an overview of the trends of Illinois housing affordability over time across different counties in Illinois. It involves a comprehensive analysis of median home value and median incomes across Illinois counties, using data from two authoritative sources: the Census Bureau and Zillow. By providing insights, we can analyze and study the hidden factors that influence housing affordability over time and forecast future trends.</p>
Geospatial Modelling of Australia's National Electricity Market - Dataset
<p>This dataset contains information relating to the topology of Australia's largest electricity transmission network, along with details pertaining to the technical and economic characteristics of generators operating within this grid. Information has been compiled from publicly available datasets released by the Australian Energy Market Operator (AEMO) [1, 2] and Geoscience Australia (GA) [3, 4, 5]. Potential applications include the development of economic dispatch, power-flow, and unit commitment models.</p> <p>The network is comprised of 912 nodes, 1406 AC edges, and three HVDC links. Information regarding forward and reverse power-flow limits for two AC interconnectors is also provided. Latitude and longitude coordinates are given for each node, with the network based off of geospatial datasets obtained from GA [3, 4, 5]. Signals for electricity demand at each node were derived using regional load profiles in combination with population data obtained from the Australian Bureau of Statistics (ABS) [6]. Allocation methods outlined in [7, 8] were used to disaggregate regional load profiles according to the geospatial distribution of Australia's population. Construction of the generator dataset involved compiling information obtained from AEMO's Market Management System Data Model (MMSDM) [1] and National Transmission Network Development Plan (NTNDP) [2] datasets. Historic generator dispatch signals were also obtained from AEMO [1], allowing the output of market models to be compared with realised outcomes.</p> <p>For further information regarding the contents of each csv file please refer to <code>dataset_summary.pdf</code>. Jupyter Notebooks at [9] contain the Python code necessary to reproduce these datasets.</p> <p><strong>Version history:</strong></p> <p><strong>v1.3 - Documentation update:</strong></p> <ul> <li>The document summarising datasets, <code>dataset_summary.pdf</code>, has been updated.</li> </ul> <p><strong>v1.2 - Startup cost correction:</strong></p> <ul> <li>Startup cost column labels in <code>generators.csv</code> were mistakenly switched (specifically, SU_COST_WARM and SU_COST_HOT). This has now been corrected.</li> </ul> <p><strong>v1.1 - Transmission line parameters and demand allocation update</strong></p> <ul> <li><strong>Transmission lines: </strong>Line resistance and shunt susceptance values have been updated. Transmission line lengths and voltages have also been added to <code>network_edges.csv</code>. AC interconnector information is split over two files to better capture aggregate flow limits defined over the New South Wales - Victoria interconnector. Connection points for these interconnectors are described in <code>network_ac_interconnector_links.csv</code>, while <code>network_ac_interconnector_flow_limits.csv</code> contains aggregate forward and reverse power flow limits.</li> <li><strong>Demand allocation: </strong>The algorithm used to approximate demand at different nodes has been updated. The new method constructs a Voronoi tessellation based on network nodes. These cells are then overlapped with geospatial ABS population data, which are used to estimate the number of people served by each node.</li> </ul> <p><strong>v1.0 - First release</strong></p>
CATCO2NVERS Market Assessment Questionnaire Results
<p><span>This dataset contains the results of a market assessment questionnaire conducted as part of the CATCO2NVERS project, focused on exploring the potential markets, competitors, customer segments, and state-of-the-art trends for the Key Exploitable Results (KERs) of the project. The survey was completed by various project partners and provides valuable insights into geographical markets, customer segments, and industry trends relevant to the commercialization of the KERs. The data also includes information on potential competitors and the current landscape of the targeted sectors.</span></p>
Dataset with Risk estimates of major currency pairs on the Forex market
<p>This dataset includes Value at Risk (VaR) and Expected Shortfall (ES) estimations of the major currency pairs on the Forex market. Notably, it provides daily VaR and ES estimates for the AUDUSD, EURCAD, EURCHF, EURUSD, GBPUSD, and USDJPY FX assets for January 2021 to September 2022. The reported risk estimates were calculated by various parametric and non-parametric models, including Variance-Covariance (VS), Historical Simulation (HS), Monte Carlo (MC), and Garch(1,1) at both 95% and 99% confidence levels. To enable model evaluation, the last column of each CSV file, named pnl, provides the actual daily returns of the FX asset.</p> <p>The data and code used to create this dataset are available at <a href="https://doi.org/10.5281/zenodo.7411148">Zenodo</a> and <a href="https://marketplace.infinitech-h2020.eu/assets/portfolio-value-at-risk-estimation">INFINITECH Marketplace</a>, respectively.</p>
Flexibility market results
<p>The SLO_ACTIVATION_DATA dataset includes data about requested activation energy, delivered activation energy and price of delivered energy (monthly aggregates). The data set in CIM XML contains flexibility market results of Slovenian pilot in OneNet project. It is intended to enable o<span><span>bserving effectiveness </span><span>of flexibility services activation and ratio between requested and activated flexibility.</span></span><span> </span></p> <p>More about the Slovenian demo in the One Net deliverable 10.4 (<a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">OneNet_D10.4_V1.0.pdf (onenet-project.eu)</a>)</p>
Activation market document
<p>CIM ESMP XML document that is used for the flexibility service activation (DSO sends this document to the aggregator).</p> <p>The data set in CIM XML format is for the activation of the flexibility service for DSO, as it gives an example of the activation signal.</p> <div> <p>More about the Slovenian demo in the One Net deliverable 10.4 (<a href="https://www.onenet-project.eu//wp-content/uploads/2023/10/OneNet_D10.4_V1.0.pdf">OneNet_D10.4_V1.0.pdf (onenet-project.eu)</a>)</p> </div> <p>XSD is compliant with ActivationMarket_Document defined by ENTSO-E (<a href="https://eepublicdownloads.entsoe.eu/clean-documents/EDI/Library/cim_based/schema/Activation_document_UML_model_and_schema_v1.2.pdf">Activation document uml model and schema (entsoe.eu)</a>).</p> <p> </p>
Case study result data set for Energy Economics article "Demystifying market clearing and price setting effects in low-carbon energy systems"
<p>The data set contains country-specific power generation and consumption time series data for the European energy system, including both traditional and new market participants due to cross-sectoral integration.</p> <p>Country codes: ALPHA-3<br> Unit: Megawatt (electric) (interval average values, i.e. MWh/h)</p> <p><strong>Generation technology types</strong></p> <ul> <li>batteryStorage (Li-Ion)</li> <li>conventionalHydro (aggregated for different equivalent hydropower systems)</li> <li>natural_gas_CC_COND (Combined Cycle Gas Turbine)</li> <li>natural_gas_CC_EXCOND (Combined Cycle Gas Turbine as extraction condensing CHP plant for district heating)</li> <li>natural_gas_GT_COND (Open-Cycle Gas Turbine)</li> <li>natural_gas_GT_EXCOND (Open-Cycle Gas Turbine as extraction condensing CHP plant for industry)</li> <li>offshoreWind (aggregated for different LCOE and IEC wind turbine classes)</li> <li>offshoreWindExplicit (offshore wind generation considered for offshore grid investments in the North Seas area, aggregated for different LCOE classes)</li> <li>onshoreWind (solar PV, aggregated for different LCOE classes)</li> <li>other (geothermal, waste)</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> <li>solar (solar PV, aggregated for different LCOE classes)</li> <li>uran_ST_COND (steam turbine condensing power plant)</li> </ul> <p><strong>Consumption technology types</strong></p> <ul> <li>BEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>PHEV (Battery Electric Vehicles, aggregated for different market segments)</li> <li>airConditioning</li> <li>batteryStorage (Li-Ion)</li> <li>conventionalLoad</li> <li>heatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridHeatPump (aggregated for different combinations of building, e.g. residential and non-residential, and technology, e.g. air-source, ground-source, types)</li> <li>hybridTruck (Hybrid Overhead-Line truck)</li> <li>largeScaleDirectResistiveHeating (Centralised CHP systems)</li> <li>natural_gas_CC_EXCOND_electrodeHeater</li> <li>natural_gas_CC_EXCOND_heatpumpHeater</li> <li>natural_gas_GT_EXCOND_electrodeHeater</li> <li>natural_gas_GT_EXCOND_heatpumpHeater</li> <li>powerToGas</li> <li>pumpedHydro (aggregated for different equivalent hydropower systems)</li> </ul>
Quality assessment of biomass pellets available on the market: Example from Poland
<p><strong>Submitted data was used to write an article</strong>: Drobniak, A., Jelonek, Z., Mastalerz, M., Jelonek, I., Widziewicz-Rzońca, K., Quality assessment of biomass pellets available on the market: Example from Poland. Environmental Science and Pollution Research. https://doi.org/10.1007/s11356-024-33452-1</p> <p> </p> <p><strong>Funding acknowledgments</strong>: The project is co-financed by the Polish National Agency for Academic Exchange within the Polish Returns Programme (BPN/PPO/2021/1/00005/DEC/1), the National Science Center, Poland (2022/01/1/ST10/00024), and the research activities co-financed by the funds granted under the Research Excellence Initiative of the University of Silesia in Katowice, Poland. </p> <p> </p> <p><strong>Article Abstract</strong>: This study evaluates the quality of 30 biomass pellets sold for residential use in Poland. It provides data on their physical, chemical, and petrographic properties and compares them to existing standards and the information provided by the fuel producers. The results reveal considerable variations in the quality of the pellets and show that some of the purchased samples are not within the DINplus and/or ENplus certification thresholds. Among all 30 purchased samples, only one passes the quality thresholds set by the PL-US BIO, a newly established quality certification in Poland that combines quality assessment following DINplus with optical microscopy analysis. The primary issues causing a decrease in pellet quality include elevated ash and fines content, compromised mechanical durability, too low ash melting temperature, and additions of undesired additions like bark, inorganic matter, and petroleum products. Our research highlights the need for improved fuel quality control measures, and transparent and accurate product labeling, as well as the need for a comprehensive and publicly available national database of solid biomass fuel producers and fuels sold. These are essential steps toward increasing customers’ awareness and trust, encouraging them to embrace biomass fuels as reliable and sustainable sources of energy.</p> <p> </p>
US Material Handling Equipment Market 2024 to 2033
<p><strong>Reports Description</strong></p> <p>According to current market research conducted by the CMI Team, the global <a href="https://www.custommarketinsights.com/report/us-material-handling-equipment-market/" target="_blank" rel="noopener"><strong>US Material Handling Equipment Market</strong></a> is expected to record a CAGR of <strong>7.89%</strong> from 2024 to 2033. In 2024, the market size is projected to reach a valuation of USD <strong>42.37 Billion</strong>. By 2033, the valuation is anticipated to reach USD <strong>83.92 Billion</strong><strong>.</strong></p> <p>Dynamic and evolving quickly, driven by technical improvement, the U.S. Material Handling Equipment market has many changes. This contains an assortment of devices composed of forklifts, conveyors, automated storage and retrieval systems, and robots to improve handling processes concerning materials in industries. The main trends shaping this space include automation and robotization, energy efficiency with a sustainability focus on solutions, and an increased level of e-commerce and distribution channels.</p> <p>DOWNLOAD FREE SAMPLE Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=59218</a></p>
Free WiFi to monitor flow in Hanoian traditional markets
<p>Despite being the main source of fresh, convenient, and affordable food for 80% of Hanoi’s population, food flows within traditional markets remain largely invisible due to a lack of tracing systems and environmental conditions which make traditional tracking approaches challenging.</p> <p>By providing free internet to a series of wholesalers and markets in the Cau Giay and Dong Anh districts of Hanoi, Vietnam, this project will put in place the first pieces of tracking system that will characterize and monitor food flows between traders, retailers, and consumers.</p> <p>Research has found that 10-40% of traditional market food is contaminated with microbes or parasites which cause foodborne illnesses. As shoppers become increasingly concerned about food safety and large-scale retailers that can offer food safety certification expand rapidly, this project aims to equip traditional market actors with data that could prevent their marginalization through urban policy decisions that may favor organized retailers, as well as improve the safety of traditional market goods.</p> <p>The collected food flow data will allow for improved linkages among key traditional market actors and help identify better policy and planning options for improving distribution channels in ways that benefits under-resourced communities.</p> <p>To implement the project, the Alliance of Bioversity International and CIAT and the General Statistics Office (GSO) of Vietnam survey actors and track space and time data points on all devices within the range of the WiFi routers and signal amplifiers, whether connected to the internet or not.</p> <p>The pilot system ran on three layers of data:</p> <p><strong>Layer One</strong></p> <p>Every smartphone has a unique media access control (MAC) address that the WiFi routers installed in the markers use to identify how many MAC addresses visit the markets over time, how many return to the market and how often, and how markets differ on these metrics. This data is collected even if the smartphone is not connected to the WiFi network.</p> <p><strong>Layer Two</strong></p> <p>When a smartphone user connects to the free WiFi, they are prompted to answer a series of questions depending on their user type (vendor, customer, etc.). For example, a user that identifies as a vendor is asked questions regarding sales of specific commodities which will allow for sales to be characterized across time and space.</p> <p><strong>Layer Three</strong></p> <p>To validate findings in Layer One and Two, in-person surveys were conducted with vegetable, pork and rice sellers in five traditional markets in Hanoi</p> <ul> <li><strong>mac</strong>: An anonymized version of the MAC. All the MAC address were anonymized through a SHA-3 256 hashing function. The hashed mac ensure anonymity while is consistent across all markets and during the whole period of the analysis. We can therefore ensure that a given mac found in two different dataset will correspond to the same phone.</li> <li><strong>market</strong>: The name of the market where the phone was seen</li> <li><strong>role</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>gender</strong>: Self-identified role if the user connected to the wifi and filled-out the layer 2 form</li> <li><strong>median_first_seen: </strong>The median time when the user is first seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user entered the market)</li> <li><strong>median_last_seen: </strong>The median time when the user is last seen in the markets (in minutes starting at 0 from midnight) (e.g. the time the user left the market)</li> <li><strong>average_time_day: </strong>The average number of time the user visited the market. A period of time of at least 2 hours between two consecutive observation of the user in the market is needed to be counted as a different visit.</li> <li><strong>average_duration_day: </strong>The average duration spent on the market daily.</li> <li><strong>average_day_week: </strong>The average number of visits per week.</li> <li><strong>average_total_day_seen: </strong>The total number of days a user was seen on the market.</li> <li><strong>total_durantion: </strong>Total duration spent by a single user on the market.</li> </ul>
Dataset to Study TSO-DSO Coordination Market Models for Flexibility Procurement to Balancing and Congestion Management
<p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to three distribution networks: the Matpower systems 18-bus (DN_18), 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Base demand is adapted from the case, while base generation profiles are added to all nodes. All distribution systems are balanced, and the transmission system is imbalanced. Thermal limits of the lines are adapted in order to create congestion in the systems. Each distribution system is connected to the transmission system through one line, which has capacity of 1.0. The interconnected system is fully represented in "Network.xlsx", in which:</p> <ul> <li>System: transmission (TN) or distribution (DN_18, DN_69, DN_141);</li> <li>LineID: ID of the lines;</li> <li>BusNumber: number of the nodes within the systems. This parameter is used to define the lines (from/to);</li> <li>BaseDemand and BaseSupply: base active demand and generation of each node;</li> <li>ConnectedDN: distribution system to which the transmission system node is connected to. If blank, the node is not connected to any distribution system. Only for the transmission system;</li> <li>InterfaceCapacity: thermal limit of the interface between the transmission and distribution systems;</li> <li>ThermalLimit: thermal limit of the transmission/distribution systems lines. For distribution systems, a value of 10 indicates that the line has no limit; </li> <li>SFTN: shift factor matrix of the transmission system. Capture the change in the active power flow over a line due to a change in injection or offtake at a node;</li> <li>BaseReactiveDemand and BaseReactiveSupply: base reactive demand and generation at each node. Only for distribution systems;</li> <li>VoltageLB and VoltageUB: lower and upper limits for the magnitude squared of the voltage in each distribution system node. Only for distribution systems;</li> <li>ConnectedTN: identify if the distribution node is connected or not to the transmission system. Only for distribution systems;</li> <li>ResistanceR: resistence of the distribution system lines. Only for distribution systems;</li> <li>ReactanceX: reactance of the distribution system lines. Only for distribution systems.</li> </ul> <p>Flexibility bids are randomly generated in the different nodes. For downward flexibility bids, the prices are drawn from the uniform distribution in the range 10 to 15, and for upward flexibility bids, they are drawn from the range 45 to 50. The bids maximum quantities are generated according to the base demand or supply of the node from which they are connected. A minimum value for the quantity is imposed as 0.01. The generated orderbook is presented in "OrderbookTN" (transmission system) and "OrderbookDN" (distribution systems):</p> <ul> <li>OrderID: the ID of the order, to make each order unique;</li> <li>System: the system (TN, DN_18, DN_69, DN_141) from which the order is offered;</li> <li>BusNumber: the node from which the order is offered;</li> <li>FlexibilitySense: UPWARD for increase in generation or decrease in demand; DOWNWARD for increase in demand or decrease in generation;</li> <li>Price: the submitted order price;</li> <li>Quantity: the maximum quantities of the order.</li> </ul> <p>Source of the systems' topology:</p> <p>[1] R. D. Zimmerman, C. E. Murillo-Sanchez, and R. J. Thomas, “Mat-power: Steady-state operations, planning, and analysis tools for power systems research and education,” IEEE Transactions on power systems, vol. 26, no. 1, pp. 12–19, 2010.</p> <p>Please notice that this dataset does not replace the information provided by Matpower related to the aforementioned systems. It rather uses those systems topology and some of their parameters to build a case study to investigate TSO-DSO coordination market models for the procurement of flexibility. For the full description of these systems, please visit: <a href="https://matpower.org/">MATPOWER – Free, open-source tools for electric power system simulation and optimization</a>.</p>
ID market related datasets for HLU9 of the CROSSBOW project
<p>Datasets for HLU9 of the CROSSBOW project organised by countries individually. What is included in the dataset?</p> <ol> <li><strong>Cross-Zonal Capacities</strong> - unformatted dataset published as received from the TSOs <ul> <li>CZC data for the years 2018 & 2019 were gathered in HLU9. The CZC values were for the timeframe before the ID market took place. Parameters included: Date & Time, CZC value to and from the delivering LFC area. CZC data was collected for borders of TSOs from the CROSSBOW project consortium<strong>.</strong></li> </ul> </li> <li><strong>ID market data - </strong>unformatted dataset published as received from the TSOs <ul> <li>ID related data for the years 2018 & 2019 were gathered in HLU9. The parameters that were included in the data were: Date & Time, Volume Weighted Average Price, Traded volume, Location… ID market related data was collected for countries of CROSSBOW TSOs, where such data was available.</li> </ul> </li> </ol>
Iphones publicados en la página web de Back Market el 25/03/2022 extraídos con Web Scraping
<p>El dataset recoge todos los Iphone en venta a la página web de Back Market el día 25/03/2022 a las 19:56, correspondiendo a una extracción con Web Scraping de esta plataforma online, que se dedica a vender productos reacondicionados.</p> <p>El conjunto de datos incluye todos los Iphone disponibles en la tienda con sus características principales: nombre del producto (<em>Producte</em>), su capacidad en Gigas (<em>Capacitat</em>), su color (<em>Color</em>), si es libre de operador (<em>Operador</em>), el precio del producto (<em>Preu</em>), la puntuación del producto (<em>Puntuacio</em>), la empresa que ha reacondicionado el producto (<em>Reacondicionador</em>), desde donde se envía el producto (<em>Origen_enviament</em>), los meses de garantía (<em>Garantia</em>) y la url de cada producto (<em>Url</em>). </p> <p> </p>
Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions.
<p>This dataset contains the raw experimental data and the analysis script for the paper Merl, R., Stöckl, T., Palan, S., 2022. "Insider trading regulation and shorting constraints. Evaluating the joint effects of two market interventions", Journal of Banking and Finance 106490, https://doi.org/10.1016/j.jbankfin.2022.106490.</p> <p>Instructions:</p> <p>1. Unpack all files into one folder.<br> 2. Open R version 4.1.2 and set the working directory to the folder with all the files.<br> 3. Run Script.R.</p> <p>In case the SPTools package is not available from GitHub anymore, you can also find it included in this dataset so you can install it from here.</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.