Track the MY DONA AMELIA II in real-time with AIS data. TRADLINX provides live vessel position, speed, and course updates. Search by MMSI: 319228100, IMO: 1006271

Applications of modern methods for analyzing data with missing values, based primarily on multiple imputation, have in the last half-decade become common in American politics and political behavior. Scholars in these fields have thus increasingly avoided the biases and inefficiencies caused by ad hoc methods like listwise deletion and best guess imputation. However, researchers in much of comparative politics and international relations, and others with similar data, have been unable to do the same because the best available imputation methods work poorly with the time-series cross-section data structures common in these fields. We attempt to rectify this situation. First, we build a multiple i mputation model that allows smooth time trends, shifts across cross-sectional units, and correlations over time and space, resulting in far more accurate imputations. Second, we build nonignorable missingness models by enabling analysts to incorporate knowledge from area studies experts via priors on individual missing cell values, rather than on difficult-to-interpret model parameters. Third, since these tasks could not be accomplished within existing imputation algorithms, in that they cannot handle as many variables as needed even in the simpler cross-sectional data for which they were designed, we also develop a new algorithm that substantially expands the range of computationally feasible data types and sizes for which multiple imputation can be used. These developments also made it possible to implement the methods introduced here in freely available open source software that is considerably more reliable than existing strategies. These developments also made it possible to implement the methods introduced here in freely available open source software, Amelia II: A Program for Missing Data, that is considerably more reliable than existing strategies. See also: Missing Data

Dataset Card for AMELIA - Argument Mining Evaluation on Legal documents in ItAlian: A CALAMITA Challenge

This dataset consists of argumentative components extracted from 225 Italian decisions on Value Added Tax, annotated to identify and categorize argumentative text. The proposed tasks consists of three classifications, in the context of argument mining in the legal domain. The objective of the first task is to classify each argumentative component as premise or conclusion, while… See the full description on the dataset page: https://huggingface.co/datasets/nlp-unibo/AMELIA.

This dataset provides information about the number of properties, residents, and average property values for Amelia Avenue cross streets in Deland, FL.

Dataset Overview

The Amelia10 dataset provides air traffic position reports for 10 U.S. airports, including the following airports:

KBOS (Boston Logan International Airport) KDCA (Washington National Airport) KEWR (Newark Liberty International Airport) KJFK (John F. Kennedy International Airport) KLAX (Los Angeles International Airport) KMDW (Chicago Midway International Airport) KMSY (Louis Armstrong New Orleans International Airport) KSEA (Seattle-Tacoma International Airport)… See the full description on the dataset page: https://huggingface.co/datasets/AmeliaCMU/Amelia-10.

This project supports the deployment and realtime data delivery of autonomous underwater gliders in the coastal ocean to better resolve and understand essential ocean features and processes that contribute to hurricane intensification or weakening prior to making landfall. This is a partnership between NOAA Ocean and Atmospheric Research (OAR) through the Atlantic Oceanographic and Meteorological Laboratory (AOML) and Integrated Ocean Observing System (IOOS) regional associations such as MARACOOS, SECOORA, CariCOOS and institutions including the University of Puerto Rico, University of the Virgin Islands, Skidaway Institute of Oceanography, University of Delaware, Virginia Institute of Marine Science - William & Mary, and Rutgers University. The goal of the project is to provide realtime data for ocean model validation and assimilation throughout hurricane season. This project is supported by the Disaster Recovery Act. The glider was deployed out of Virginia Beach, VA over the mid-shelf region of the southern Mid-Atlantic Bight and will transect offshore to the shelf break 75 km south of Norfolk Canyon. The glider will then head northward toward Norfolk Canyon, then toward an inshore point at about 35 meters depth in between Norfolk and Washingont Canyons, then head toward the shelfbreak north of Washington Canyon. The triangle pattern between Norfolk Canyon south, 35 m isobath, and Washington Canyon north will then repeat. This real-time dataset contains CTD measurements from a RBRlegato3 inductive CTD.

About this dataset

Context

The dataset illustrates the median household income in Amelia County, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Amelia County decreased by $5,580 (8.25%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 5 years and declined for 6 years.

https://i.neilsberg.com/ch/amelia-county-va-median-household-income-trend.jpeg" alt="Amelia County, VA median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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 Amelia County median household income. You can refer the same here

DREAM is an initiative that allows researchers to assess how well their methods or approaches can describe and predict networks of interacting molecules [1]. Each year, recently acquired datasets are released to predictors ahead of publication. Researchers typically have about three months to predict the masked data or network of interactions, using any predictive method. Predictions are assessed prior to an annual conference where the best predictions are unveiled and discussed. Here we present the strategy we used to make a winning prediction for the DREAM3 phosphoproteomics challenge. We used Amelia II, a multiple imputation software method developed by Gary King, James Honaker and Matthew Blackwell[2] in the context of social sciences to predict the 476 out of 4624 measurements that had been masked for the challenge. To chose the best possible multiple imputation parameters to apply for the challenge, we evaluated how transforming the data and varying the imputation parameters affected the ability to predict additionally masked data. We discuss the accuracy of our findings and show that multiple imputations applied to this dataset is a powerful method to accurately estimate the missing data. We postulate that multiple imputations methods might become an integral part of experimental design as a mean to achieve cost savings in experimental design or to increase the quantity of samples that could be handled for a given cost.

Realtime Earth Satellite object tracking and orbit data for LEMUR-2-SAM-AMELIA. NORAD Identifier: 42781.

This dataset is about books. It has 2 rows and is filtered where the author is Amelia Fielden. It features 7 columns including author, publication date, language, and book publisher.

This is a test mission for the Northwest Passage Project. This glider will be deployed in the southern Mid-Atlantic Bight. The goal of the mission is to conduct CTD and DO Optode cross-calibration with a recently calibrated MARACOOS glider Sylvia in the MARACOOS domain. It will provide additional hydrographic data in the Mid-Atlantic region even though its CTD has not been recently calibrated. The glider is scheduled to be deployed on Tuesday, May 1, 2018 and recovered on Monday May 14, 2018. The glider will be deployed near the 35 m isobath at mid-shelf off Wachepreague VA and fly offshore toward Washington Canyon. It will conduct virtual mooring profiling flight at the head of Washington Canyon down to 350 m for about a week, then head back inshore toward Wachepreague VA for recovery.

This dataset is about book series. It has 1 row and is filtered where the authors is Amelia Chia. It features 2 columns including publication dates.

This dataset provides information about the number of properties, residents, and average property values for Amelia National Parkway cross streets in Fernandina Beach, FL.

Included here are four Stata datasets where missing values have been imputed multiple times using Amelia II software (m=100, random number seed=902). Stata .do and output files are included. File names indicate the corresponding tables in the article: Tables 1-3; Tables 4-6; and supplemental online tables.

About this dataset

Context

The dataset tabulates the Amelia population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Amelia. The dataset can be utilized to understand the population distribution of Amelia by age. For example, using this dataset, we can identify the largest age group in Amelia.

Key observations

The largest age group in Amelia, OH was for the group of age 25 to 29 years years with a population of 1,160 (9.29%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Amelia, OH was the 85 years and over years with a population of 30 (0.24%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Amelia is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Amelia total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Amelia Population by Age. You can refer the same here

Critical minerals are essential for the energy transition; to meet sustainability goals, production of these resources must significantly increase within the coming decades. For example, demand for lithium, an element essential for battery technologies found in many electronics and vehicles, is anticipated to increase by more than ten-fold by 2050 compared to current production. Critical minerals occur in Amelia County, Virginia, however, considerable uncertainty remains regarding their location, amount, and types of commodities present. In fulfilling its mission to provide information on mineral resources of the national domain, the U.S. Geological Survey aims to synthesize and disseminate high quality data on mineral sites. Ensuring the accuracy of extracted information involves digitizing and georeferencing historic maps of the area that contain locations of mines and prospects. These maps, compared with records in the Mineral Resources Data System (MRDS; Schweitzer, 2019), wer ...

This dataset is about books. It has 1 row and is filtered where the book is Amelia & the animals. It features 7 columns including author, publication date, language, and book publisher.

This dataset provides information about the number of properties, residents, and average property values for Amelia Concourse cross streets in Fernandina Beach, FL.

About this dataset

Context

The dataset tabulates the Amelia County population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Amelia County across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Amelia County was 13,455, a 0.88% increase year-by-year from 2021. Previously, in 2021, Amelia County population was 13,337, an increase of 0.52% compared to a population of 13,268 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Amelia County increased by 2,000. In this period, the peak population was 13,455 in the year 2022. The numbers suggest that the population has not reached its peak yet and is showing a trend of further growth. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Amelia County is shown in this column.
• Year on Year Change: This column displays the change in Amelia County population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Amelia County Population by Year. You can refer the same here

This dataset provides information about the number of properties, residents, and average property values for Amelia Drive cross streets in Mobile, AL.