Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,227
datasets available to search
ShareScore release 0.7.1
Dataset results
2,227 results for “Market”
Techno-economic details of fixed-bottom offshore wind projects deployed in the European markets
<p>Version (with all files) - Updated version (research article is accepted).</p> <p>Publishing Date: July 10, 2022</p> <p>This dataset describes the techno-economic information of fixed-bottom offshore wind projects deployed in the North Sea region (DK, NL, BE, DE, and the UK). </p> <p>Contents: </p> <p>1) Offshore wind farm project prices and technical characteristics (farm size, turbine rated power, water depth, etc.,)</p> <p>2) Offshore wind farm capacity factor and cumulative energy generation</p> <p>3) Monopile weight </p> <p>4) Offshore wind farm installation duration </p> <p>5) UK offshore wind farms' transmission system cost</p> <p> </p>
Marketing_09/29/22
Documentation material from the Mastic pilot of the Mingei project
Historical Annual Revenue of Energy Storage on European Electricity Markets
<p>This dataset provides the optimized annual revenue for 96 generic storage technologies (12 efficiency x 8 discharge duration). It covers 17 European electricity markets for up to 16 years (Austria, AT; Belgium, BE; Switzerland, CH; Czech Republic, CZ; German, DE; Spain, ES; France, FR; Italy, IT; Ireland, IR; Netherlands, NL; Nordpool which includes Denmark, Estonia, Finland, Latvia, Lithuania, Norway, Sweden, NP; Poland, PL; Portugal, PT; Romania, RO; Slovakia, SK; United Kingdom, UK). The optimization is an adaptation of the model presented in Gaudard et al. [2013]. It assumes perfect foresight assumption and a stochastic algorithm. Therefore, the results are an approximation of the maximum rather than the absolute optimum. Further information is provided in "Gaudard L. and Madani K., Energy storage race: Has the monopoly of pumped-storage in Europe come to an end?, forthcoming". </p> <p>The following information is provided:</p> <p>Country: Code of the specific market (also the filename)</p> <p>Currency: The currency in which the results are expressed</p> <p>Discharge duration [hours]: The time required to empty at full nominal power a device that is fully charged. </p> <p>Efficiency: Ratio between the amount of discharged and charged energy during a full cycle.</p> <p>Year: From January 1st to December 31st.</p> <p>The given numbers are in euros or GBP per year and normalized to 1kWh of energy storage. This means that for a specific device, the given figures must be multiplied by the volume of energy storage (in terms of kWh). As an example, for an energy device with the efficiency of 0.95, discharge duration of 6h and volume of energy storage of 2000kWh, the revenues in 2003 in Austria would be 9.26 x 2000=18520 euros. </p> <p>For any questions or further requirements, please feel free to get in touch with the authors.</p>
Dataset - CORE-MD Post-Market Surveillance Tool
<p>WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).</p>
Dataset for "An alternative to market-oriented energy models: nexus patterns across hierarchical levels"
<p>Dataset used for the publication "Di Felice, Louisa Jane, Maddalena Ripa, and Mario Giampietro. "An alternative to market-oriented energy models: Nexus patterns across hierarchical levels." <em>Energy Policy</em> 126 (2019): 431-443.". The dataset follows the distinction across hierarchical levels as specified in the publication.</p> <p>The same dataset was also used for a case study developed for the MAGIC project, available <a href="http://magic-nexus.eu/case_study/electric-grid-catalonia-illustrations-musiasem">here</a>. </p>
Dataset of Horizon scanning to identify invasion risk of ornamental plants marketed in Spain
<p>Full dataset for the research entitled "Horizon scanning to identify invasion risk of ornamental plants marketed in Spain". We classified non-native species into six different lists based on their invasion status in Spain and elsewhere, their climatic suitability in Spain, and their potential environmental and socioeconomic impacts.</p>
Usability Testing Data for Web Application Prototype: Enhancing Efficiency and Transparency in Ghana's Rental Housing Market
<p><span>The dataset includes both quantitative and qualitative responses from participants who tested the web application prototype designed to enhance decision-making in Ghana's rental housing market. The testing focused on evaluating the user interface, ease of use, satisfaction levels, and the effectiveness of key functionalities.</span></p>
Global Drug Addiction Treatment Market 2024–2033
<h2><a title="Global Drug Addiction Treatment Market 2024&ndash;2033" href="https://www.custommarketinsights.com/report/drug-addiction-treatment-market/" target="_blank" rel="noopener">Global Drug Addiction Treatment Market </a>Size, Trends and Insights By Type (Opioid Addiction, Benzodiazepine Addiction, Barbiturate Addiction, Others), By Treatment (Therapy, Medication, Others), By Route of Administration (Oral, Parenteral, Others), By End Users (Hospitals, Specialty Clinics, Others), By Distribution Channel (Hospital Pharmacy, Retail Pharmacy, Online Pharmacies, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</h2>
Global Synthetic Ethanol Market 2024–2033
<h3><a href="https://www.custommarketinsights.com/report/synthetic-ethanol-market/" target="_blank" rel="noopener">Synthetic Ethanol Market</a> Size, Trends and Insights By Feedstock (Starch, Sugar, Cellulose Based, Others), By Application (Fuel & Fuel Additives, Industrial Solvents, Beverages, Disinfectant, Personal Care, Others), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</h3>
Global Artificial Intelligence in Banking Market 2024–2033
<p><a href="https://www.custommarketinsights.com/report/artificial-intelligence-in-banking-market/" target="_blank" rel="noopener">Artificial Intelligence in Banking Market Size</a>, Trends and Insights By Component (Service, Solution), By Application (Fraud Detection and Prevention, Transaction Monitoring, Identity Verification, Customer Service, Virtual Assistants, Automated Customer Support, Risk Management, Credit Scoring, Market Risk Analysis, Personalized Banking, Customer Recommendations, Targeted Marketing, Compliance and Regulatory Reporting, Anti-Money Laundering (AML), Know Your Customer (KYC), Others), By Technology (Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Natural Language Processing (NLP), Text Analysis, Speech Recognition, Chatbots and Virtual Assistants, Robotic Process Automation (RPA), Process Automation, Workflow Automation, Predictive Analytics, Risk Management, Customer Insights), By Enterprise Size (Large Enterprise, SMEs), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>VC investments in AI by country</strong></p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>VC Investment</strong></td> </tr> <tr> <td><strong>US</strong></td> <td><strong>54,836</strong></td> </tr> <tr> <td><strong>China</strong></td> <td><strong>18,270</strong></td> </tr> <tr> <td><strong>EU</strong></td> <td><strong>7,921</strong></td> </tr> </tbody> </table> <p>For more details <strong>DOWNLOAD FREE SAMPLE</strong> Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189</a></p>
US Smart Fleet Management Market 2024 - 2033
<p><a href="https://www.custommarketinsights.com/report/us-smart-fleet-management-market/" target="_blank" rel="noopener">US Smart Fleet Management Market</a> Size, Trends and Insights By Mode of Transport (Automotive, Rolling Stock, Marine, Others), By Hardware (Tracking, Optimization, Advanced Driver Assistance Systems (ADAS), Remote Diagnostics, Others), By Connectivity (Short Range Communication, Long Range Communication, Cloud), By Solutions (Vehicle Tracking, Fleet Optimization), and By Region - Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p>
AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)
<p>NOTE: for some reason the pptx and previews keep getting munged on this supposedly permanent arxiv, but the data is still there, unchanged, and you can see how the pptx should look in either the jpg, or the the article.</p> <p>Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern.</p> <p>As of 28 June, this work now also appears in a formal publication: Joanna J. Bryson, Helena Malikova; Is There an AI Cold War?. <em><em>Global Perspectives</em></em> 2021; 2 (1): 24803. doi: <a href="https://doi.org/10.1525/gp.2021.24803">https://doi.org/10.1525/gp.2021.24803</a></p> <p>Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author.</p> <p>Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.</p>
Dataset defining representative route network for GLOWOPT market segments
<p>For calculating the GLOWOPT representative route network, a forecast model chain was used. The model was calibrated with 2019 flight movement data (unimpeded by COVID-19) and provided forecasted aircraft movements from the year 2019 (~2020) to 2050 in 5 years intervals.</p> <p>Two formats of datasets are generated with the results of the forecast model chain, a csv file format and 4-dimensional array supported with MATLAB (.mat).</p> <p><strong>CSV Datasets</strong></p> <p>For each forecasted year a csv file is generated with the information on the origin-destination (OD) airports IATA codes, region, latitude and longitude of OD pair, representative aircraft type along with the aircraft category , the average load factor and finally, the distance between the OD pair. The airports worldwide are sub-dived into nine regions namely Africa, Asia, Caribbean, Central America, Europe, Middle East, North America, Oceania and South America. There are total of seven datasets, one for each forecasted year i.e. for years 2019 (~2020), 2025, 2030, 2035, 2040, 2045 and 2050.</p> <p><strong>Description of the data labels:</strong></p> <p><strong>Origin-</strong> Origin airport IATA code</p> <p><strong>Origin_Region-</strong> Region of the Origin Airport</p> <p><strong>Origin_Latitude-</strong> Latitude of the Origin Airport</p> <p><strong>Origin_Longitude-</strong> Longitude of the Origin Airport</p> <p><strong>Destination-</strong> Destination airport IATA code</p> <p><strong>Destination_Region-</strong> Region of the Destination Airport</p> <p><strong>Destination_Latitude-</strong> Latitude of the Destination Airport</p> <p><strong>Destination_Longitude-</strong> Longitude of the Destination Airport</p> <p><strong>AcType- </strong>Representative aircraft type</p> <p><strong>Load_Factor- </strong>Average load factor per flight</p> <p><strong>Yearly_Frequency-</strong> Total aircraft movements per annum</p> <p><strong>RefACType-</strong> Aircraft Category based on number of seats (Category 6 represents aircraft with seats 252-301 and category 7 represents aircraft with seats greater than 302.)</p> <p><strong>Distance-</strong> Great circle distance between Origin and Destination in Km.</p> <p> </p> <p><strong>MATLAB Datasets</strong></p> <p>The dataset generated with MATLAB is a 4-dimensional array with the extension *.mat. The first dimension is the region of the origin airport and subsequently the second dimensions contains the region of the destination airport. The third and fourth dimension are the aircraft category based on seat numbers and the categorized great circle distances. The information received therein is a 1X1 cell with the IATA codes of the OD pairs, frequency and great circle distance in Km.</p> <p>The 4D array is categorised such that the user can select the route segment specific to a region or a combination of regions. The range categorisation in combination with an aircraft category additionally offers the user the possibility to select routes depending on their great circle distances. The ranges are categorised to represent very short range (0-2000 km), short range (2000-6000 km), medium range (6000-10000 km) and long range (10000 – 15000 km).</p> <p><strong>Indexing based on the categorisation of the 4D array dataset</strong> - Refer to file 'Indexing_MAT_Dataset.PNG'</p> <p>For example:</p> <p>To derive the OD pairs and yearly frequency of aircraft movements for routes which originate from Europe and are destined to Asia, operated with category 6 aircraft type and are separated by distances between 10,000 to 15,000 km:</p> <p><strong>In MATLAB (Indexing based on file </strong> 'Indexing_MAT_Dataset.PNG' <strong>): </strong></p> <p><strong>Route_Network (5,2,1,4), </strong></p> <p>Description on Index:</p> <p>5 – Europe: Origin Region </p> <p>2 – Asia: Destination Region</p> <p>1– Category 6: Aircraft Type</p> <p>4 – 10000-15000 km: Range</p>
Case study result data set for Energy Economics (submitted) article "On Wholesale Electricity Prices and Market Values in a Carbon-Neutral Energy System"
<p>The data set contains wholesale power price time series data for Germany and France focussing on price setting effects in a long term low carbon European energy system context (scenario year 2050) generated with the model SCOPE SD of Fraunhofer Institute for Energy Economics and Energy System Technology IEE. The single time series are focussing on the price setting effects of different flexible technologies including both traditional and new market participants due to cross-sectoral integration.</p> <p>Unit: Euro/Megawatthour</p> <p><strong>Abbreviations:</strong></p> <ul> <li>BEV - Battery Electric Vehicles</li> <li>GER - Germany</li> <li>FRA - France</li> <li>OCGT - Open Cycle Gas Turbine</li> <li>PHEV - Plug-In Hybrid Vehicles</li> <li>RES - Renewable energy sources (here: wind and solar power)</li> <li>th. - thermal</li> </ul>
Market indexes information
<p>Information about <strong>market indexes</strong> scraped from <a href="http://google.com/finance">google.com/finance</a> on November 22st, 2022.</p> <p>The markets studied are:</p> <ul> <li>Market indexes: <ul> <li>Americas</li> <li>Europe, Middle East and Africa</li> <li>Asia Pacific</li> </ul> </li> <li>Most active</li> <li>Gainers</li> <li>Losers</li> <li>Climate leaders</li> <li>Crypto</li> <li>Currencies</li> </ul>
Trade policy announcements can increase price volatility in global food commodity markets (Replication Data)
<p>Replication data for "Trade policy announcements can increase price volatility in global food commodity markets":</p> <ul> <li>Original dataset on trade policy announcements from 2005 to 2017 for wheat and maize (corn) (details in codebook)</li> <li>Daily price ranges based on the highest and lowest price recorded for wheat and corn futures (traded at the Chicago Board of Trade, CBOT)</li> <li>Stocks-to-use data for the United States, which is compiled by the United States Department for Agriculture (USDA) and available at monthly frequency from their World Supply and Demand Estimates report</li> </ul>
Data set and classification method for low quality web traffic identification in video marketing campaigns
<p>Final outcomes of the InPreVi (AI4Media) project developed in 2022. </p> <p>1. Data set describing the statistics of the video ad marketing campaigns</p> <p>2. Script for web traffic classification</p>
S17 | KEMIMARKET | KEMI Market List
<p>This is the collection associated with list S17 KEMIMARKET on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S17 : KEMIMARKET: <strong>KEMI Market List</strong></p> <p>Provided by Stellan Fischer, KEMI including Hazard and Exposure scores, documented <a href="https://www.norman-network.com/sites/default/files/files/suspectListExchange/MarketList_Documentation_25July2017.docx">here</a>. Curated by Reza Aalizadeh, University of Athens and Emma Schymanski, UniLu. </p> <p>Nov 14 2019 update: added CSV of latest file. Nov 21 Update: fixed 3 truncated InChIKeys in CSV and InChIKey file. Feb 6, 2020: fixed SMILES issues in CSV for PubChem import. Jul 24, 2020: fixed two synonyms based on feedback from PubChem for GFMHHNOUDDHEOO-UHFFFAOYSA-N and LAUVMIDRJMQUQL-UHFFFAOYSA-N. 17 July 2022: fixed several SMILES errors in CSV, updated IK file for PubChem deposition. 18 July 2022: fixed truncated SMILES that failed PubChem standardization (LCTORFDMHNKUSG-GJHKVECASA-N). 18 Jun 2023: fixed two SMILES that failed PubChem deposition for ILENATNBFPBMHG-UHFFFAOYSA-N and NOESYZHRGYRDHS-ZYCCASTOSA-N, plus one that failed standardization (JBEHRBTVEGZLAF-UHFFFAOYSA-N).</p>
Dataset to Study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets
<p>We publish the dataset used to study Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets (as part of chapter 7 of deliverable D3.3 of the OneNet project).</p> <p>The dataset is composed by an interconnected system consisting of the IEEE 14-bus (TN) transmission network connected to two distribution networks: the Matpower systems 69-bus (DN_69), and 141-bus (DN_141). All systems topology and some parameters are based on the corresponding cases in Matpower [1]. Injections and loads of the nodes are adapted to create an anticipated imbalance in the interconnected system, resolved by flexibility. In addition, the lines’ upper limits are adjusted to create anticipated congestion in the networks. The interconnected system is fully represented in "Network_case_A_B_C.xlsx" (upward balancing need) and "Network_case_D.xlsx" (downward balancing need).<br>Upward and downward flexibility bids are randomly generated and allocated to the nodes. </p> <p>7 bids lists are available in this dataset. </p> <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 Grid-Secure Use of Distributed Flexibility in Sequential DSO-TSO Markets. 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>
Machine Learning Applications in Marketing: Literature Review and Research Agenda
<p>Currently, machine learning applications in marketing allow to optimize strategies, personalize experiences and improve decision making. However, there are still several research gaps, so the objective is to examine the research trends in the use of machine learning in marketing. A bibliometric analysis is proposed to assess the current scientific activity, following the parameters established by PRISMA-2020. Machine learning applications in marketing have experienced steady growth and increased attention in the academic community. Key references, such as Miklosik and Evans, and prominent journals, such as IEEE Access and Journal of Business Research, have been identified. A thematic evolution towards big data and digital marketing is observed, and thematic clusters such as "digital marketing", "interpretation", "prediction", and "healthcare" stand out. These findings demonstrate the continued importance and research potential of this evolving field.</p>
ScienceDex guides
Understand access before you commit
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.