We collected data on almost the complete population of the merger control decisions by the Directorate-General Competition’s (DG COMP) of the European Commission. We started the data collection with the first year of common European merger control, 1990, and included all years up to 2014. This amounts to 25 years of data on European merger control. With regard to the scope of the decisions, we collected data in all cases where a legal decision document exists. This includes all cases settled in the first phase of an investigation (Art. 6(1)(a), 6(1)(b), 6(1)(c) and 6(2)) and all cases decided in the second phase of an investigation (Art. 8(1), 8(2), and 8(3)). Note that this also includes all cases settled under a ‘simplified procedure’, provided that a legal decision document exists. Furthermore, we also intended to collect data on cases that were either referred back to member states by DG COMP or aborted by the merging parties. While we have collected some data on such cases, data on these cases is not always available. Therefore, we cannot guarantee that the final dataset covers all of these cases. The level of observation is not a particular merger case but a particular product/geographic market combination concerned by a merger. In total, the final dataset contains 5,196 DG COMP merger decisions. For each of this decision, we record a number of observations equal to the number of product/geographic markets identified in the specific transaction. Hence, the total dataset contains 31,451 observations.

To get high quality singers:

First we have to create a Google sheet. Name it as Project 3. then we have to create 23 sheets. name it from 1992 to 2014. now go to the website and copy the link. then by using importhtml function import the data to all the sheets from 1992 to 2014. create a sheet name it as merged data and copy the data from second row from all the 23 sheets and paste it in merged data. create the column names as Rank, Artist, Title, Year. we will get 2300 rows. now create a new google sheet name it as prolific-1. to get unique artist use unique function. and to get frequency use countif function. And sort them in descending order. now plot the bar. before we made with frequency now we make it with score. create a column score in merged data and use 101-rank function to get the scores. now create a google sheet as prolific-2. use artist and score columns. now use unique function to get the data of artists. for score use arrayfunction(). now sort the data and plot the bar

This dataset contains simulations of a model of two human drivers interacting in a top-down-view merging scenario. This merging scenario is a simplified version of highway merging. In this scenario, two vehicles approach a pre-defined merge point. In a previous experiment, we asked two participants to stick to their initial velocity yet avoid collisions. The data in this dataset contains model simulations that describe this human interactive behaviour during driving.

Data used by scripts applied for analyzing merging tools.

Ion transport through electrolytes critically impacts the performance of batteries and other devices. Many frameworks used to model ion transport assume hydrodynamic mechanisms and focus on maximizing conductivity by minimizing viscosity. However, solid-state electrolytes illustrate that non-hydrodynamic ion transport can define device performance. Increasingly, selective transport mechanisms, such as hopping, are proposed for concentrated electrolytes. However, viscosity–conductivity scaling relationships in ionic liquids are often analyzed with hydrodynamic models. We report data-centric analyses of hydrodynamic transport models of viscosity–conductivity scaling in ionic liquids by merging three databases to bridge physical properties and computational descriptors. With this expansive database, we constrained scaling analyses using ion sizes defined from simulated volumes, as opposed to estimating sizes from activity coefficients. Remarkably, we find that many ionic liquids exhibit positive deviations from the Nernst–Einstein model, implying ions move faster than hydrodynamics should allow. We verify these findings using microrheology and conductivity experiments. We further show that machine learning tools can improve predictions of conductivity from molecular properties, including predictions from solely computational features. Our findings reveal that many ionic liquids exhibit super-hydrodynamic viscosity–conductivity scaling, suggesting mechanisms of correlated ion motion, which could be harnessed to enhance electrochemical device performance.

This data set includes information on all public merger enforcement actions brought by the Federal Trade Commission from fiscal year 1996 to fiscal year 2019. The data set contains descriptive information on the characteristics of each matter as well as URLs to the individual case pages that are published on the FTC's website and that contain public documents pertaining to each case.

The sample SAS and Stata code provided here is intended for use with certain datasets in the National Neighborhood Data Archive (NaNDA). NaNDA (https://www.openicpsr.org/openicpsr/nanda) contains some datasets that measure neighborhood context at the ZIP Code Tabulation Area (ZCTA) level. They are intended for use with survey or other individual-level data containing ZIP codes. Because ZIP codes do not exactly match ZIP code tabulation areas, a crosswalk is required to use ZIP-code-level geocoded datasets with ZCTA-level datasets from NaNDA. A ZIP-code-to-ZCTA crosswalk was previously available on the UDS Mapper website, which is no longer active. An archived copy of the ZIP-code-to-ZCTA crosswalk file has been included here. Sample SAS and Stata code are provided for merging the UDS mapper crosswalk with NaNDA datasets.

This dataset was recorded in a top-down-view driving simulator where two participants solved a merging conflict. This merging scenario is a simplified version of highway merging. In this scenario, two vehicles approach a pre-defined merge point. Two participants were asked to stick to their initial velocity, yet avoid collisions. The data was (and can be) used to analyze human interaction behavior during driving.

Detailed data on credit union merger activity over the last 3 years from the most recent quarter-end.

We present the data used in "DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains". In this paper, we test domain adaptation techniques, such as Maximum Mean Discrepancy (MMD) and adversarial training with Domain Adversarial Neural Networks (DANNs) for cross-domain studies of merging galaxies. Domain adaptation is performed between two simulated datasets of various levels of observational realism (simulation-to-simulation experiments), and between simulated data and observed telescope images (simulation-to-real experiments). For more details about the datasets please see the paper mentioned above.

Simulation-to-Simulation Experiments

Data used to study distant merging galaxies using simulated images from the Illustris-1 cosmological simulation at redshift z=2. The images are 75x75 pixels with three filters applied that mimic Hubble Space Telescope (HST) observations (ACS F814W,NC F356W, WFC3 F160W) with added point-spread function (PSF) and with or without observational noise.

Source Domain

 Images: SimSim_SOURCE_X_Illustris2_pristine.npy


 Labels: SimSim_SOURCE_y_Illustris2_pristine.npy

Target Domain

Images: SimSim_TARGET_X_Illustris2_noisy.npy


Labels: SimSim_TARGET_y_Illustris2_noisy.npy

Simulation-to-Real Experiments

Data used to study nearby merging galaxies using simulated Illustris-1 images at redshift z=0 and observed Sloan Digital Sky Survey (SDSS) images from the Galaxy Zoo project. All images have three filters. SDSS images have (g,r,i) filters, while simulated Illustris images also mimic the same three SDSS filters with added effects of dust, PSF and observational noise.

Source Domain

Images: SimReal_SOURCE_X_Illustris0.npy


Labels: SimReal_SOURCE_y_Illustris0.npy

Target Domain

Images: SimReal_TARGET_X_postmergers_SDSS.npy


Labels: SimReal_TARGET_y_postmergers_SDSS.npy

Data associated with the paper: G. Roncoroni, E. Forte, I. Santin, M. Pipan, (2023), Deep Learning based multi-frequency GPR data merging, Geophysics, DOI: Data are associated to code presented in https://github.com/Giacomo-Roncoroni/merging_GPR/

Motivated by the recent increase in bank mergers, this paper examines the performance of German cooperative banks that merged between 2014 and 2019. We are particularly interested in whether elevated merger rates are due to bank inefficiencies or to challenging policy measures such as low-for-long interest rates. The results indicate that banks that perform relatively worse before and during the low interest environment exhibit a greater probability of becoming a target during this period. Consolidation generally occurs among low performing banks where large and well-capitalized banks merge with their small and inefficient peers. Ultimately, our results attribute the increased number of mergers to inefficiencies in the banking industry, as banks that exited the market were inefficient prior to the adverse low interest rate environment.

Data Fusion Solutions Market Outlook

The global data fusion solutions market size is anticipated to grow significantly from USD 10.2 billion in 2023 to USD 25.7 billion by 2032, with a compound annual growth rate (CAGR) of 11.2% during the forecast period. This robust growth is primarily driven by the increasing demand for real-time data analysis, the integration of advanced technologies such as AI and machine learning, and the rising need for comprehensive data management solutions across various industries.

One of the primary growth factors for the data fusion solutions market is the exponential increase in data generation and the subsequent need for effective data management and analysis tools. As businesses and government entities increasingly rely on data-driven decision-making, the ability to amalgamate diverse data sources into a coherent and actionable format becomes crucial. Technologies like IoT, AI, and machine learning are further augmenting this demand by enabling more sophisticated data fusion capabilities, thereby providing deeper insights and fostering innovation across sectors.

Another significant driver is the growing complexity and diversity of data types that organizations need to manage. Traditional data management systems are often inadequate for handling the vast volumes and varieties of data generated today. Data fusion solutions, which integrate data from multiple sources to produce more accurate and comprehensive information, are becoming essential. This is particularly true in industries such as healthcare, defense, and transportation, where timely and accurate data integration can lead to better outcomes and operational efficiencies.

The third major growth factor is the critical role of data fusion in enhancing security and surveillance systems. In the defense and surveillance sector, for example, data fusion technologies are employed to combine inputs from various sensors, cameras, and other sources to provide a complete situational awareness picture. This capability is not only vital for national security but also for public safety, traffic management, and disaster response. The growing investments in smart cities and intelligent transportation systems are further propelling the demand for advanced data fusion solutions.

Regionally, North America is expected to dominate the data fusion solutions market throughout the forecast period. This can be attributed to the high adoption rate of advanced technologies, significant investments in R&D, and the presence of major market players in the region. Europe and Asia Pacific are also anticipated to witness substantial growth, driven by technological advancements, increasing government initiatives, and the rapid expansion of industries such as healthcare, transportation, and defense in these regions.

Component Analysis

The data fusion solutions market is segmented by components into software, hardware, and services. The software segment is expected to hold the largest market share, driven by the increasing demand for advanced data analytics and management tools. These software solutions are versatile and can be tailored to meet the specific needs of various industries, thereby enhancing their appeal. Moreover, the integration of AI and machine learning technologies into data fusion software is providing more sophisticated and accurate data analysis capabilities, which is further fuelling market growth.

Hardware components, although not as dominant as software, still play a crucial role in the data fusion ecosystem. The hardware segment includes sensors, data storage devices, and processing units that are essential for collecting, storing, and analyzing vast amounts of data. Advances in sensor technology and the increasing deployment of IoT devices are driving the demand for more robust and high-performance hardware solutions. Additionally, the development of edge computing technologies is enhancing the capability of hardware to process data closer to the source, thereby reducing latency and improving real-time decision-making.

The services segment encompasses various support services such as consulting, implementation, and maintenance, which are vital for the successful deployment and operation of data fusion solutions. As businesses increasingly invest in data fusion technologies, the demand for specialized services to ensure seamless integration and optimal performance

During a survey conducted among TV marketers in the United States and released in May 2023, the main challenge of merging linear and digital data was identified by 53 percent of respondents with the lack of common metrics across channels. The creation of a holistic framework for planning and measurement was mentioned by 41 percent of respondents, while 40 percent cited data-sharing restrictions by walled gardens.

This dataset contains the data necessary to reproduce the analyses by Mocking et al. Scripts for reproducing our analyses are available at: https://github.com/AUMC-HEMA/imputation-manuscript

COMBINE is a taxonomically integrated database of mammal traits obtained from published sources, in which we maximized trait number and coverage without compromising data quality. COMBINE contains information on 54 traits for 6,234 extant and recently extinct mammal species, including information on morphology, reproduction, diet, biogeography, life-habit, phenology, behavior, home range and density. Additionally, we calculated other relevant traits such as habitat and altitudinal breadths for all species and dispersal for terrestrial non-volant species. All data are compatible with the taxonomies of the IUCN Red List v. 2020-2 and PHYLACINE v. 1.2. Missing data were adequately flagged and imputed for non-biogeographical traits with 20% or more data available. We obtained full data sets for 21 traits such as female maturity, litter size, maximum longevity, trophic level and dispersal, providing imputation performance statistics for all. This data set will be especially useful for those interested in including species’ traits in large-scale ecological and conservation analyses.

The data show how different types of rocks resist the flow of electrical currents across Ireland. The rock types can then be mapped. The data show the magnetic field strength of different rock types across Ireland. The rock types can then be mapped.The data show the intensity of gamma rays released by Uranium, Thorium and Potassium in different soils and rocks in Ireland. Different soils and rock types can then be mapped. The data were collected between 2005 and 2021.This report summarizes the main operations from the latest A8 and A9 surveys and discusses the processing of the acquired data and its merging with pre-existing datasets to produce seamless merged geophysical datasets. The A6 Block (west Cork) has a small overlap with A9 and is included in the merging of the current data. It is anticipated, however, that a better constrained merge of A6 will be possible after completion of subsequent survey blocks, which will provide more substantial overlap with A6. Several surveys were merged to create this dataset. (1) Tellus Northern Ireland 2005-2006(2) Cavan-Monaghan, 2006(3) Tellus Border, 2011-2012(4) Tellus North Midlands, 2014-2015(5) Block A1, 2015(6) Block A2, 2016(7) Waterford, 2016(8) Block A3, 2017(9) Block A4, 2017(10) Block A5, 2018-2019(11) Block A6, 2018-2019(12) Block A7, 2019(13) Block A8 2020-2021(14) Block A9 2021The Tellus project is a national survey which collects geochemical and geophysical data across Ireland. It allows us to study the chemical and physical properties of our soil, rocks and water. It is managed by the Geological Survey Ireland.

Numerically-generated gravitational waveforms for binary neutron stars.

We analyze a large merger in the Swedish market for analgesics (painkillers). The merging firms raised prices by 40 percent, and some outsiders raised prices by more than 10 percent. We confront these changes with predictions from a merger simulation model. With basic supply side assumptions, the models correctly or moderately underpredict the merging firms' price increase. However, they predict a larger price increase for the smaller firm, which was not the case in practice, and they underpredict the outsiders' responses. We consider several supply side explanations: a plausible cost increase after the merger and the possibility of partial collusion.