Data Merge Up 2

## Overview

Data Merge Up 2 is a dataset for semantic segmentation tasks - it contains Cardboard H3Yh annotations for 3,453 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).

Context

This dataset compiles player performance data from the NFL Combine, sourced from Pro Football Reference. The dataset spans from 2010 to 2023, featuring various attributes showcasing the physical and athletic prowess of NFL prospects during their combine assessments.

Example Projects

1. Draft Prediction Model: Build a machine learning model to predict the likelihood of a player getting drafted based on their combine performance.
2. Position-Specific Analysis: Analyze and compare combine metrics across different player positions. Identify trends and differences in physical attributes and performance.
3. Draft Round Prediction: Build a model to predict the likely round in which a player will be drafted based on their combine performance.

CAMP2Ex_Merge_Data are pre-generated aircraft merge data files created utilizing data collected during the Clouds, Aerosol and Monsoon Processes-Philippines Experiment (CAMP2Ex) NASA field study. Data collection for this product is complete. CAMP2Ex was a NASA field study, with three main science objectives: aerosol effect on cloud microphysical and optical properties, aerosol and cloud influence on radiation as well as radiative feedback, and meteorology effect on aerosol distribution and aerosol-cloud interactions. Research on these three main objectives requires a comprehensive characterization of aerosol, cloud, and precipitation properties, as well as the associated meteorological and radiative parameters. Trace gas tracers are also needed for airmass type analysis to characterize the role of anthropogenic and natural aerosols. To deliver these observations, CAMP2Ex utilized a combination of remote sensing and in-situ measurements. NASA’s P-3B aircraft was equipped with a suite of in-situ instruments to conduct measurements of aerosol and cloud properties, trace gases, meteorological parameters, and radiative fluxes. The P-3B was also equipped passive remote sensors (i.e. lidar, polarimeter, radar, and radiometers). A second aircraft, the SPEC Learjet 35A, was primarily dedicated to measuring detailed cloud microphysical properties. The sampling strategy designed for CAMP2Ex coordinated flight plans for both aircraft to maximize the science return. The P-3B was used primarily to conduct remote sensing measurements of cloud and precipitation structure and aerosol layers and vertical profiles of atmospheric state variable, while the Learjet flew below the P-3B to obtain the detailed cloud microphysical properties. During the 2019 field deployment in the vicinity of the Philippines, completed from August 20-October 10, the P-3B conducted 19 science flights and the SPEC Learjet conducted 11 flights. Ground-based aerosol observations were also recorded in 2018 and 2019. CAMP2Ex was completed in partnership with Philippine research and operational weather communities. Measurements completed during CAMP2EX provide a 4-D observational view of the environment of the Philippines and its neighboring waters in terms of microphysical, hydrological, dynamical, thermodynamical and radiative properties of the environment, targeting the environment of shallow cumulus and cumulus congestus clouds.

Data Merge 3

## Overview

Data Merge 3 is a dataset for object detection tasks - it contains Colonies annotations for 262 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).

ARISE_Merge_Data_1 is the Arctic Radiation - IceBridge Sea & Ice Experiment (ARISE) 2014 pre-generated aircraft (C-130) merge data files. This product is a result of a joint effort of the Radiation Sciences, Cryospheric Sciences and Airborne Sciences programs of the Earth Science Division in NASA's Science Mission Directorate in Washington. Data collection is complete.ARISE was NASA's first Arctic airborne campaign designed to take simultaneous measurements of ice, clouds and the levels of incoming and outgoing radiation, the balance of which determined the degree of climate warming. Over the past few decades, an increase in global temperatures led to decreased Arctic summer sea ice. Typically, Arctic sea ice reflects sunlight from the Earth. However, a loss of sea ice means there is more open water to absorb heat from the sun, enhancing warming in the region. More open water can also cause the release of more moisture into the atmosphere. This additional moisture could affect cloud formation and the exchange of heat from Earth’s surface to space. Conducted during the peak of summer ice melt (August 28, 2014-October 1, 2014), ARISE was designed to study and collect data on thinning sea ice, measure cloud and atmospheric properties in the Arctic, and to address questions about the relationship between retreating sea ice and the Arctic climate. During the campaign, instruments on NASA’s C-130 aircraft conducted measurements of spectral and broadband radiative flux profiles, quantified surface characteristics, cloud properties, and atmospheric state parameters under a variety of Arctic atmospheric and surface conditions (e.g. open water, sea ice, and land ice). When possible, C-130 flights were coordinated to fly under satellite overpasses. The primary aerial focus of ARISE was over Arctic sea ice and open water, with minor coverage over Greenland land ice. Through these efforts, the ARISE field campaign helped improve cloud and sea ice computer modeling in the Arctic.

This data set contains DLR Falcon 1 Second 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-mrg01-falcon_merge_YYYYMMdd_R1_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 NASA DC-8 10 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 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. 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-mrg10-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. For the latest information on the updates to this dataset, please see the readme file.

ACCLIP_Merge_WB57-Aircraft_Data is the pre-generated merge files created from a variety of in-situ instrumentation collecting measurements onboard the WB-57 aircraft during the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP). Data collection for this product is complete.ACCLIP is an international, multi-organizational suborbital campaign that aims to study aerosols and chemical transport that is associated with the Asian Summer Monsoon (ASM) in the Western Pacific region from 15 July 2022 to 31 August 2022. The ASM is the largest meteorological pattern in the Northern Hemisphere (NH) during the summer and is associated with persistent convection and large anticyclonic flow patterns in the upper troposphere and lower stratosphere (UTLS). This leads to significant enhancements in the UTLS of trace species that originate from pollution or biomass burning. Convection connected to the ASM occurs over South, Southeast, and East Asia, a region with complex and rapidly changing emissions due to its high population density and economic growth. Pollution that reaches the UTLS from this region can have significant effects on the climate and chemistry of the atmosphere, making it important to have an accurate representation and understanding of ASM transport, chemical, and microphysical processes for chemistry-climate models to characterize these interactions and for predicting future impacts on climate.The ACCLIP campaign is conducted by the National Aeronautics and Space Administration (NASA) and the National Center for Atmospheric Research (NCAR) with the primary goal of investigating the impacts of Asian gas and aerosol emissions on global chemistry and climate. The NASA WB-57 and NCAR G-V aircraft are outfitted with state-of-the-art sensors to accomplish this. ACCLIP seeks to address four scientific objectives related to its main goal. The first is to investigate the transport pathways of ASM uplifted air from inside of the anticyclone to the global UTLS. Another objective is to sample the chemical content of air processed in the ASM in order to quantify the role of the ASM in transporting chemically active species and short-lived climate forcing agents to the UTLS to determine their impact on stratospheric ozone chemistry and global climate. Third, information is obtained on aerosol size, mass, and chemical composition that is necessary for determining the radiative effects of the ASM to constrain models of aerosol formation and for contrasting the organic-rich ASM UTLS aerosol population with that of the background aerosols. Last, ACCLIP seeks to measure the water vapor distribution associated with the monsoon dynamical structure to evaluate transport across the tropopause and determine the role of the ASM in water vapor transport in the stratosphere.

NAAMES_Merge_Data is the North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) pre-generated aircraft merge data files created using data collected during the NAAMES campaign. NAAMES was a NASA funded Earth-Venture Suborbital (EVS) mission with 4 deployments occurring from 2015-2018. Data collection is complete.The NASA North Atlantic Aerosols and Marine Ecosystems Study (NAAMES) project was the first NASA Earth Venture – Suborbital mission focused on studying the coupled ocean ecosystem and atmosphere. NAAMES utilizes a combination of ship-based, airborne, autonomous sensor, and remote sensing measurements that directly link ocean ecosystem processes, emissions of ocean-generated aerosols and precursor gases, and subsequent atmospheric evolution and processing. Four deployments coincide with the seasonal cycle of phytoplankton in the North Atlantic Ocean: the Winter Transition (November 5 – December 2, 2015), the Bloom Climax (May 11 – June 5, 2016), the Deceleration Phase (August 30 – September 24, 2017), and the Acceleration Phase (March 20 – April 13, 2018). Ship-based measurements were conducted from the Woods Hole Oceanographic Institution Research Vessel Atlantis in the middle of the North Atlantic Ocean, while airborne measurements were conducted on a NASA Wallops Flight Facility C-130 Hercules that was based at St. John's International Airport, Newfoundland, Canada. Data products in the ASDC archive focus on the NAAMES atmospheric aerosol, cloud, and trace gas data from the ship and aircraft, as well as related satellite and model data subsets. While a few ocean-remote sensing data products (e.g., from the high-spectral resolution lidar) are also included in the ASDC archive, most ocean data products reside in a companion archive at SeaBass.

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 NSF/NCAR GV HIAPER 10 Second Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 30 June 2012. These are updatd merges available through the NASA DC3 archive as of 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-mrg10-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.

URL: https://geoscience.data.qld.gov.au/dataset/ds000017

Compilation of open file gravity observations made by exploration companies, state and federal regional surveys.

The possibility that two data sets may have different underlying phylogenetic histories (such as gene trees that deviate from species trees) has become an important argument against combining data in phylogenetic analysis. However, two data sets sampled for a large number of taxa may differ in only part of their histories. This is a realistic scenario and one in which the relative advantages of combined, separate, and consensus analysis become much less clear. I suggest a simple methodology for dealing with this situation that involves (1) partitioning the available data to maximize detection of different histories, (2) performing separate analyses of the data sets, and (3) combining the data but considering questionable or unresolved those parts of the combined tree that are strongly contested in the separate analyses (and which therefore may have different histories), until a majority of unlinked data sets supports one resolution over another. In support of this methodology, computer simulations suggest that (1) the accuracy of combined analysis at recovering the true species phylogeny may exceed that of either of two separately analyzed data sets under some conditions, particularly when the mismatch between phylogenetic histories is small and the estimates of the underlying histories are imperfect (few characters and/or high homoplasy), and (2) combined analysis provides a poor estimate of the species tree in areas of the phylogenies with different histories but an improved estimate in regions that share the same history. Thus, when there is a localized mismatch between the histories of two data sets, separate, consensus, and combined analysis may all give unsatisfactory results in certain parts of the phylogeny. Similarly, approaches that allow data combination only after a global test of heterogeneity will suffer from the potential failings of either separate or combined analysis, depending on the outcome of the test. Excision of conflicting taxa is also problematic in that it may obfuscate the position of conflicting taxa within a larger tree, even when their placement is congruent between data sets. Application of the proposed methodology to molecular and morphological data sets for Sceloporus lizards is discussed.

Combined Data

## Overview

Combined Data is a dataset for object detection tasks - it contains Cervid Species annotations for 326 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 [MIT license](https://creativecommons.org/licenses/MIT).

(Includes MeSH 2023 and 2024 changes) NOTE - There are no Merges for 2025 MeSH. The MeSH 2025 Update - Merge Report describes cases where two or more terms have merged into a single term. The term(s) being merged into another term become(s) an Entry Term. Merges can be between Descriptors and Supplementary Concept Records (SCRs), between Descriptors, or between SCRs. This report includes MeSH changes from previous years, starting from 2023.

This data set contains raw data and parsed data of all experiments [1] run for the PhD thesis. They were generated using lab (see https://doi.org/10.5281/zenodo.399255).

The raw data files (sievers-phd2017-raw-data-part*.tar.gz) contain a subdirectory for each experiment, each containing a subdirectory for each planner run of the experiment, distributed over the directories runs-*. For each run, there are the input PDDL files, domain.pddl and problem.pddl, the compressed output as generated by the translator component of Fast Downward (output.sas.xz), the run log file "run.log" (stdout), possibly also a run error file "run.err" (stderr), and the run script "run" used to start the experiment. The latter cannot be directly used, however, because the directory containing source code and build (compiled object files) have been removed for space reasons. The code is publicly available under https://doi.org/10.5281/zenodo.1163381. The (lab) scripts for parsing run.log are also available in the main directory of each experiment. All other scripts and a corresponding lab version are available on request.

For each raw data experiment, the parsed data file (sievers-phd2017-parsed-data.tar.gz) also contains a directory of the same name, with "-eval" appended. It contains a single file called "properties" that combines all of the experiment's parsed data (which can be and was generated from the raw data using lab and the parser scripts). They are in the json format and can be used for easy manipulation of the data. The directories with the prefix "paper-" and "talk-" are combinations of other directories (using the "fetch" mechanism of lab). It is recommended to use these, because due to technical errors, the original eval directories do not contain all runs of all planners (to be more precise: they contain all runs, but a subset of the planner have not been started in these experiments for technical errors and thus considered not solving the task). The missing ones have been run separately, see the directories with "missing-runs" in their name. This is also the reason some of these directories ("paper-", "talk-") contain files named "old-properties" and "fixed-properties" besides the actual "properties". "old-properties" are those with missing/faulty runs, "fixed-properties" are as "old-properties", however with the data of faulty runs removed, and "properties" are as "fixed-properties", however with the addition of the fixed missing runs (in fact, these always contain all fixed missing runs of all experiments, for technical reasons).

The file sievers-phd2017-parsed-data-all-and-random-merge-strategies.tar.gz contains parsed data of earlier experiments (see [1]), for which no raw data has been archived. The directories contain properties files in the json format.

[1] except raw data for the parsed data "sota-symba-spmas-eval" (which in the meantime was added to a separate data set available under https://doi.org/10.5281/zenodo.1189912) and all re-used experiments from the paper "An Analysis of Merge Strategies for Merge-and-Shrink Heuristics" (Silvan Sievers, Martin Wehrle and Malte Helmert, ICAPS 2016), for which the raw data was too large to be archived.

This data set contains NSF/NCAR GV HIAPER 1 Second 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 on 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. No "grand merge" has been provided for the 1-second data on the GV aircraft due to its prohibitive size. 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.

Dataset for "Combining in vitro and in silico New Approach Methods to investigate type 3 iodothyronine deiodinase chemical inhibition across species". This dataset is associated with the following publication: Mayasich, S., M. Goldsmith, K. Mattingly, and C. Lalone. Combining In Vitro and In Silico New Approach Methods to Investigate Type 3 Iodothyronine Deiodinase Chemical Inhibition Across Species. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 42(5): 1032-1048, (2023).

This dataset contains the official The Learning Agency Lab - PII Data Detection trian dataset (train.json) and the version of external data from [Notebook](https://www.kaggle.com/code/valentinwerner/fix-punctuation-tokenization-external-dataset (moredata_dataset_fixed.json and pii_dataset_fixed.json).

Dataset

This dataset was created by Hritik Jaiswal

Contents