Merge 2 Projects

## Overview

Merge 2 Projects is a dataset for object detection tasks - it contains Stop TurnRight PkFs annotations for 1,220 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).

Phase 2 Merge + Combine Overgrowth And All Damage

## Overview

Phase 2 Merge + Combine Overgrowth And All Damage is a dataset for object detection tasks - it contains Instances 5pnE House Attributes TcoT 3Gds HxDq annotations for 892 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).

ORACLES_Merge_Data are pre-generated aircraft merge data files created utilizing data collected during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 – October 27, 2016, August 1, 2017 – September 4, 2017 and September 21, 2018 – October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.Southern Africa produces almost one-third of the Earth’s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 – October 27, 2016; August 1, 2017 – September 4, 2017; September 21, 2018 – October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.

Dataset

This dataset was created by Shaleen Bhartiya

Released under MIT

Contents

merge-with-keys-2

This dataset was generated using a phospho starter pack. This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot and RLDS.

NIS 2002-2011 Within Year Merge

• Each year of the NIS has a Core, Hospital and Severity file: File Level ID Core discharge KEY, HOSPID Hospital hospital HOSPID Severity discharge KEY, HOSPID

1. The 2 dischrage-level files will trimmed down to desired variables and then merged by KEY and saved into a temporary SAS dataset.
2. The hospital file will be trimmed and then merged into the core-severity and saved into a permanent SAS dataset with following notation: NIS_YYYY
3. Working directory cleared after every year since very large datasets.

Testing Merge 2

## Overview

Testing Merge 2 is a dataset for object detection tasks - it contains Fish X0CN 7IfR annotations for 1,069 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).

ACTIVATE_Merge_Data is the pre-generated merge data files created from data collected onboard the HU-25 Falcon aircraft during the ACTIVATE project. ACTIVATE was a 5-year NASA Earth-Venture Sub-Orbital (EVS-3) field campaign. Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project was a five-year project that provides important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studied the atmosphere over the western North Atlantic and sampled its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air was primarily used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements were also onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic occurred through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy was implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.Marine boundary layer clouds play a critical role in Earth’s energy balance and water cycle. These clouds cover more than 45% of the ocean surface and exert a net cooling effect. The Aerosol Cloud meTeorology Interactions oVer the western Atlantic Experiment (ACTIVATE) project is a five-year project (January 2019-December 2023) that will provide important globally-relevant data about changes in marine boundary layer cloud systems, atmospheric aerosols and multiple feedbacks that warm or cool the climate. ACTIVATE studies the atmosphere over the western North Atlantic and samples its broad range of aerosol, cloud and meteorological conditions using two aircraft, the UC-12 King Air and HU-25 Falcon. The UC-12 King Air will primarily be used for remote sensing measurements while the HU-25 Falcon will contain a comprehensive instrument payload for detailed in-situ measurements of aerosol, cloud properties, and atmospheric state. A few trace gas measurements will also be onboard the HU-25 Falcon for the measurements of pollution traces, which will contribute to airmass classification analysis. A total of 150 coordinated flights over the western North Atlantic are planned through 6 deployments from 2020-2022. The ACTIVATE science observing strategy intensively targets the shallow cumulus cloud regime and aims to collect sufficient statistics over a broad range of aerosol and weather conditions which enables robust characterization of aerosol-cloud-meteorology interactions. This strategy is implemented by two nominal flight patterns: Statistical Survey and Process Study. The statistical survey pattern involves close coordination between the remote sensing and in-situ aircraft to conduct near coincident sampling at and below cloud base as well as above and within cloud top. The process study pattern involves extensive vertical profiling to characterize the target cloud and surrounding aerosol and meteorological conditions.

ORACLES_Merge_Data are pre-generated aircraft merge data files created utilizing data collected during the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) campaign. These measurements were collected from August 19, 2016 – October 27, 2016, August 1, 2017 – September 4, 2017 and September 21, 2018 – October 27, 2018. ORACLES provides multi-year airborne observations over the complete vertical column of key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments on the planet. The P-3 Orion aircraft was utilized as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds and was supplemented by ER-2 remote sensing during the 2016 campaign. Data collection for this product is complete.Southern Africa produces almost one-third of the Earth’s biomass burning aerosol particles. The ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) experiment was a five year investigation with three intensive observation periods (August 19, 2016 – October 27, 2016; August 1, 2017 – September 4, 2017; September 21, 2018 – October 27, 2018) and was designed to study key processes that determine the climate impacts of African biomass burning aerosols. ORACLES provided multi-year airborne observations over the complete vertical column of the key parameters that drive aerosol-cloud interactions in the southeast Atlantic, an area with some of the largest inter-model differences in aerosol forcing assessments. These inter-model differences in aerosol and cloud distributions, as well as their combined climatic effects in the SE Atlantic are partly due to the persistence of aerosols above clouds. The varying separation of cloud and aerosol layers sampled during ORACLES allow for a process-oriented understanding of how variations in radiative heating profiles impact cloud properties, which is expected to improve model simulations for other remote regions experience long-range aerosol transport above clouds. ORACLES utilized two NASA aircraft, the P-3 and ER-2. The P-3 was used as a low-flying platform for simultaneous in situ and remote sensing measurements of aerosols and clouds in all three campaigns, supplemented by ER-2 remote sensing in 2016. ER-2 observations will be used to enhance satellite-based remote sensing by resolving variability within a particular scene, and by guiding the development of new and improved remote sensing techniques.

In this exercise, we'll merge the details of students from two datasets, namely student.csv and marks.csv. The student dataset contains columns such as Age, Gender, Grade, and Employed. The marks.csv dataset contains columns such as Mark and City. The Student_id column is common between the two datasets. Follow these steps to complete this exercise

SEAC4RS_Merge_Data are pre-generated merge data files collected during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. This product contains merged data products collected from instruments onboard the DC-8 and ER-2 aircrafts. Data collection for this product is complete.Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.

SEAC4RS_Merge_Data are pre-generated merge data files collected during the Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEA4CRS) airborne field study. This product contains merged data products collected from instruments onboard the DC-8 and ER-2 aircrafts. Data collection for this product is complete.Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) airborne field study was conducted in August and September of 2013. The field operation was based in Houston, Texas. The primary SEAC4RS science objectives are: to determine how pollutant emissions are redistributed via deep convection throughout the troposphere; to determine the evolution of gases and aerosols in deep convective outflow and the implications for UT/LS chemistry; to identify the influences and feedbacks of aerosol particles from anthropogenic pollution and biomass burning on meteorology and climate through changes in the atmospheric heat budget (i.e., semi-direct effect) or through microphysical changes in clouds (i.e., indirect effects); and lastly, to serve as a calibration and validation test bed for future satellite instruments and missions.The airborne observational data were collected from three aircraft platforms: the NASA DC-8, ER-2, and SPEC LearJet. Both the NASA DC-8 and ER-2 aircraft were instrumented for comprehensive in-situ and remote sensing measurements of the trace gas, aerosol properties, and cloud properties. In addition, radiative fluxes and meteorological parameters were also recorded. The NASA DC-8 was mostly responsible for tropospheric sampling, while the NASA ER-2 was operating in the lower stratospheric regime. The SPEC LearJet was dedicated to in-situ cloud characterizations. To accomplish the science objectives, the flight plans were designed to investigate the influence of biomass burning and pollution, their temporal evolution, and ultimately, impacts on meteorological processes which can, in turn, feedback on regional air quality. With respect to meteorological feedbacks, the opportunity to examine the impact of polluting aerosols on cloud properties and dynamics was of particular interest.

PocketDoc/Dans-Assistantmaxx-Opus-Merge-2 dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

Final Merge 2

## Overview

Final Merge 2 is a dataset for object detection tasks - it contains Ball Team1 Team2 HrWL annotations for 825 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).

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)}

Dataset Card for Evaluation run of zelk12/MT-Merge2-gemma-2-9B

Dataset automatically created during the evaluation run of model zelk12/MT-Merge2-gemma-2-9B The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 1 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results. An… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/zelk12_MT-Merge2-gemma-2-9B-details.

This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data products have been created for seven different aggregation intervals (1 second, 10 seconds, and 5 instrument-specific intervals). In the case of data obtained over longer time intervals (e.g., flask data), the merge files provide (weighted) averages to match the sampling intervals. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Combine: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 2(0.24%) households where the householder is under 25 years old, 329(39.73%) households with a householder aged between 25 and 44 years, 298(35.99%) households with a householder aged between 45 and 64 years, and 199(24.03%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of Combine, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Combine median household income by age. You can refer the same here

Dataset Card for Evaluation run of ymcki/gemma-2-2b-ORPO-jpn-it-abliterated-18-merge

Dataset automatically created during the evaluation run of model ymcki/gemma-2-2b-ORPO-jpn-it-abliterated-18-merge The dataset is composed of 38 configuration(s), each one corresponding to one of the evaluated task. The dataset has been created from 4 run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is… See the full description on the dataset page: https://huggingface.co/datasets/open-llm-leaderboard/ymcki_gemma-2-2b-ORPO-jpn-it-abliterated-18-merge-details.