Merger

## Overview

Merger is a dataset for object detection tasks - it contains Merge Test Gloves All Ppe annotations for 4,580 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This data set contains NSF/NCAR GV HIAPER 1 Minute Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 30 June 2012. These are updated merges from the NASA DC3 archive that were made available 13 June 2014. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrg60-gV_merge_YYYYMMdd_R5_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

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.

Merging (in Table R) data published on https://www.data.gouv.fr/fr/datasets/ventes-de-pesticides-par-departement/, and joining two other sources of information associated with MAs: — uses: https://www.data.gouv.fr/fr/datasets/usages-des-produits-phytosanitaires/ — information on the “Biocontrol” status of the product, from document DGAL/SDQSPV/2020-784 published on 18/12/2020 at https://agriculture.gouv.fr/quest-ce-que-le-biocontrole

All the initial files (.csv transformed into.txt), the R code used to merge data and different output files are collected in a zip. enter image description here NB: 1) “YASCUB” for {year,AMM,Substance_active,Classification,Usage,Statut_“BioConttrol”}, substances not on the DGAL/SDQSPV list being coded NA. 2) The file of biocontrol products shall be cleaned from the duplicates generated by the marketing authorisations leading to several trade names.
3) The BNVD_BioC_DY3 table and the output file BNVD_BioC_DY3.txt contain the fields {Code_Region,Region,Dept,Code_Dept,Anne,Usage,Classification,Type_BioC,Quantite_substance)}

KORUSAQ_Merge_Data are pre-generated merge data files combining various products collected during the KORUS-AQ field campaign. This collection features pre-generated merge files for the DC-8 aircraft. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.

SPACs, or special purpose acquisition companies, are public companies which raise money and then aim to merge with a private company, thus taking the target company public in the process. The value of SPAC merger deals which had been announced between 2019 and February 2021, but not yet completed, amounted to 51.4 billion U.S. dollars. However, the completed deals accounted for only one third of the deal volume, yet were worth almost half of the aggregate value of 99.3 billion U.S. dollars.

The global PDF merge software market is experiencing robust growth, driven by the increasing adoption of digital documents across personal and enterprise sectors. The market's expansion is fueled by the rising need for efficient document management, collaboration tools, and streamlined workflows. Businesses of all sizes rely on PDF merging for tasks ranging from creating comprehensive reports and presentations to consolidating contracts and legal documents. The seamless integration of PDF merge functionality within broader productivity suites and cloud-based platforms further enhances market appeal. While the precise market size in 2025 is unavailable, considering a conservative estimate for a market with a projected CAGR (let's assume 10% for illustration purposes, though this would need verification against actual data), and given the prevalence of PDF usage, a reasonable estimate for the 2025 market size could be in the range of $500 million USD. This figure accounts for both consumer and enterprise segments, with the latter likely commanding a larger share due to higher software spending. The market is segmented by operating system (iOS and Android) and application type (personal and enterprise). The enterprise segment is projected to grow faster due to increased demand for advanced features like security and integration with enterprise resource planning (ERP) systems. The prevalence of mobile devices and cloud-based services is pushing the adoption of mobile-friendly PDF merge solutions. Key restraints include the availability of free or low-cost alternatives, along with the learning curve associated with some advanced software. However, the overall market trajectory indicates sustained growth, with the increasing complexity of document management and the growing preference for digital workflows fueling demand for sophisticated PDF merging tools. Competition is fierce, with established players like Adobe and newer entrants constantly innovating to capture market share. The continued rise in remote work and digital transformation initiatives across industries will significantly impact future market growth.

KORUSAQ_Merge_Data are pre-generated merge data files combining various products collected during the KORUS-AQ field campaign. This collection features pre-generated merge files for the DC-8 aircraft. Data collection for this product is complete.The KORUS-AQ field study was conducted in South Korea during May-June, 2016. The study was jointly sponsored by NASA and Korea’s National Institute of Environmental Research (NIER). The primary objectives were to investigate the factors controlling air quality in Korea (e.g., local emissions, chemical processes, and transboundary transport) and to assess future air quality observing strategies incorporating geostationary satellite observations. To achieve these science objectives, KORUS-AQ adopted a highly coordinated sampling strategy involved surface and airborne measurements including both in-situ and remote sensing instruments.Surface observations provided details on ground-level air quality conditions while airborne sampling provided an assessment of conditions aloft relevant to satellite observations and necessary to understand the role of emissions, chemistry, and dynamics in determining air quality outcomes. The sampling region covers the South Korean peninsula and surrounding waters with a primary focus on the Seoul Metropolitan Area. Airborne sampling was primarily conducted from near surface to about 8 km with extensive profiling to characterize the vertical distribution of pollutants and their precursors. The airborne observational data were collected from three aircraft platforms: the NASA DC-8, NASA B-200, and Hanseo King Air. Surface measurements were conducted from 16 ground sites and 2 ships: R/V Onnuri and R/V Jang Mok.The major data products collected from both the ground and air include in-situ measurements of trace gases (e.g., ozone, reactive nitrogen species, carbon monoxide and dioxide, methane, non-methane and oxygenated hydrocarbon species), aerosols (e.g., microphysical and optical properties and chemical composition), active remote sensing of ozone and aerosols, and passive remote sensing of NO2, CH2O, and O3 column densities. These data products support research focused on examining the impact of photochemistry and transport on ozone and aerosols, evaluating emissions inventories, and assessing the potential use of satellite observations in air quality studies.

The largest automotive merger or acquisition in 2019 was the merger between two auto manufacturers, Fiat Chrysler Automobiles and Peugeot SA, worth 30.1 billion U.S. dollars. The merger is set to create the fourth largest vehicle manufacturing group in the world.

This data set contains NASA DC-8 1 Second Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 22 June 2012. These merges are an updated version that were provided by NASA. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. No "grand merge" has been provided for the 1-second data on the DC8 aircraft due to its prohibitive size (~1.5GB). In most cases, downloading the individual merge files for each day and simply concatenating them should suffice. This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments. For more information on the updates to this dataset, please see the readme file.

These records are internal office searches of land transactions over a parcel/s of land undertaken to establish the status of registered estates or interests affecting the parcel/s of land that is the subject of a Merger Application lodged at Lands. The reference to the Merger Application known as a Dealing Number is written on each page. Such searches are no longer created separately, but form part of the documentation of the Merger Application series itself.

Papers are arranged by the Dealing Number written on the top of each page prefixed by the words Merger Application Number.

These internal records were initiated at the request of a Lands Examining Officer as part of the investigation process to a Merger Application. A Merger Application is a request to merge or combine two separate estates for the same piece of land now in the ownership of the same person(s). A Merger occurs when a greater estate and a lesser estate in the same land both vest in the same person in the same right and without any intervening estates. The estates and the lesser estate is merged and extinguished and the notation of the lesser estate is removed from the land title.

IRPC Public Company Limited, a Thai integrated petroleum and petrochemical company, is likely to merge with PTT Global Chemical Public Company Limited, a leading producer and distributor of petrochemical products in Thailand. The planned merger would be spearheaded by PTT Group, a majority shareholder in both companies, who tried to merge the two companies two years ago but did not succeed due to legal battles with IRPC’s founder, Prachai Leophairatana. Now, with the ousting of Leophairatana from the Board of Directors (BOD) of IRPC in February this year, the legal hurdle appears to be over and PTT can go ahead with its strategy to create a flagship integrated petrochemical and refining company. The merger will help both companies to achieve cost synergies and reduce the overall operational cost. The merger will also allow the combined company to achieve leadership in several product markets in the country. IRPC shares have already witnessed an increase in prices in anticipation of the merger. Read More

This data set contains NASA DC-8 SAGAAERO Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 22 June 2012. These merge files were updated by NASA. The data have been merged to SAGAAero file timeline. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrgSAGAAero-dc8_merge_YYYYMMdd_R*_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

This data set contains DLR Falcon 1 Minute Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 29 May 2012 through 14 June 2012. These merges were created using data in the NASA DC3 archive as of September 25, 2013. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrg06-falcon_merge_YYYYMMdd_R2_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This data set is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and data set comments.

Scripts used for analysis of V1 and V2 Datasets.seurat_v1.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, PCA analysis, clustering, tSNE visualization. Used for v1 datasets. merge_seurat.R - merge two or more seurat objects into one seurat object. Perform linear regression to remove batch effects from separate objects. Used for v1 datasets. subcluster_seurat_v1.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA. Used for v1 datasets.seurat_v2.R - initialize seurat object from 10X Genomics cellranger outputs. Includes filtering, normalization, regression, variable gene identification, and PCA analysis. Used for v2 datasets. clustering_markers_v2.R - clustering and tSNE visualization for v2 datasets. subcluster_seurat_v2.R - subcluster clusters of interest from Seurat object. Determine variable genes, perform regression and PCA analysis. Used for v2 datasets.seurat_object_analysis_v1_and_v2.R - downstream analysis and plotting functions for seurat object created by seurat_v1.R or seurat_v2.R. merge_clusters.R - merge clusters that do not meet gene threshold. Used for both v1 and v2 datasets. prepare_for_monocle_v1.R - subcluster cells of interest and perform linear regression, but not scaling in order to input normalized, regressed values into monocle with monocle_seurat_input_v1.R monocle_seurat_input_v1.R - monocle script using seurat batch corrected values as input for v1 merged timecourse datasets. monocle_lineage_trace.R - monocle script using nUMI as input for v2 lineage traced dataset. monocle_object_analysis.R - downstream analysis for monocle object - BEAM and plotting. CCA_merging_v2.R - script for merging v2 endocrine datasets with canonical correlation analysis and determining the number of CCs to include in downstream analysis. CCA_alignment_v2.R - script for downstream alignment, clustering, tSNE visualization, and differential gene expression analysis.

Exelon Corporation (Exelon) has entered into a definitive merger agreement with Constellation Energy (Constellation Energy) to combine the two companies in a stock-for-stock exchange transaction valued at $7,900m. The resulting company will retain the Exelon name and will be headquartered in Chicago. Exelon’s Power team and Constellation Energy’s retail and wholesale businesses will be amalgamated under the Constellation Energy brand and be headquartered in Baltimore. Both the companies’ renewable businesses will also be headquartered in Baltimore. The three utilities of Exelon- BGE, Commonwealth Edison Company (ComEd) and PECO Energy Company (PECO) will remain as independent organizations. The agreement brings the two large companies together, creating a platform for growth and delivering stakeholder benefits. The new amalgamated company will bring a clean power fleet and competitive prices to millions of customers. The most important factor behind the merger of these two companies is the creation of a new company that will evolve as the number one energy provider in the US with market capitalization of $34 billion. Read More

What is the relationship between environment and democracy? The framework of cultural evolution suggests that societal development is an adaptation to ecological threats. Pertinent theories assume that democracy emerges as societies adapt to ecological factors such as higher economic wealth, lower pathogen threats, less demanding climates, and fewer natural disasters. However, previous research confused within-country processes with between-country processes and erroneously interpreted between-country findings as if they generalize to within-country mechanisms. In this article, we analyze a time-series cross-sectional dataset to study the dynamic relationship between environment and democracy (1949-2016), accounting for previous misconceptions in levels of analysis. By separating within-country processes from between-country processes, we find that the relationship between environment and democracy not only differs by countries but also depends on the level of analysis. Economic wealth predicts increasing levels of democracy in between-country comparisons, but within-country comparisons show that democracy declines as countries become wealthier over time. This relationship is only prevalent among historically wealthy countries but not among historically poor countries, whose wealth also increased over time. By contrast, pathogen prevalence predicts lower levels of democracy in both between-country and within-country comparisons. Our longitudinal analyses identifying temporal precedence reveal that not only reductions in pathogen prevalence drive future democracy, but also democracy reduces future pathogen prevalence and increases future wealth. These nuanced results contrast with previous analyses using narrow, cross-sectional data. As a whole, our findings illuminate the dynamic process by which environment and democracy shape each other.

Methods Our Time-Series Cross-Sectional data combine various online databases. Country names were first identified and matched using R-package “countrycode” (Arel-Bundock, Enevoldsen, & Yetman, 2018) before all datasets were merged. Occasionally, we modified unidentified country names to be consistent across datasets. We then transformed “wide” data into “long” data and merged them using R’s Tidyverse framework (Wickham, 2014). Our analysis begins with the year 1949, which was occasioned by the fact that one of the key time-variant level-1 variables, pathogen prevalence was only available from 1949 on. See our Supplemental Material for all data, Stata syntax, R-markdown for visualization, supplemental analyses and detailed results (available at https://osf.io/drt8j/).

PowerStone Metals and Libra Lithium Corp. merge to form Libra Energy Materials Inc., promising enhanced lithium exploration in Ontario and Quebec.