Phytoplankton data collected by the IBSS staff during a cruise in the Black Sea in May 1957. This dataset contains abundance (individuals per liter) and biomass (mg/m³) data for phytoplankton taxa. No additional metadata is available.

This table contains the numbers of passengers (excluding crew members) arriving by cruise ship on Bonaire. The figures published here are entirely based on available registers, i.e. the accuracy of these figures depends on the quality of the registers and Statistics Netherlands has conducted numerous plausibility checks on these registers, for example by establishing mutual links among these registers.

Data available from: January 2012

Status of the figures: The figures for 2012 to 2021 are final. The figures for 2022 to 2024 are provisional.

Changes as of January 21, 2025: - The previously published figures for 2021 have been made final. - From 2012 to 2021, the final quarterly figures have been added. - From 2022 to 2024, the provisional figures for the months, quarters and years have been added.

When will new figures be published? The new figures will be published within three months after expiration of the period available.

Date/Time is given in UTC. Certainty of identification: definite if observer clearly identified the species. The number of individuals is binned according to the options given in the data acquisition software: 1, 2, 3, less equal 5, less equal 10, greater than 10, greater than 20. More precise values may exist, due to observer comments. See doi:10.1594/PANGAEA.326643 and doi:10.1594/PANGAEA.586852 for weather condition and doi:10.1594/PANGAEA.666238 for the sea surface oceanography.

Zooplankton data collected by the IBSS staff on the R/V Knipovich in the Black Sea along the Yalta - Sukhumi transect, at the diurnal station 8, 50 km from Sukhumi on 20th April 1950. This dataset contains abundance data (individuals per liter) and biomass data (mg/m³) for zooplankton taxa. Also stage and size are registered for some species. No additional metadata is available.

The dataset comprises 13558 individuals from 169 species of fishes, sharks, rays and sea snakes observed at around Southern Great Barrier Reef Marine Park Seabed Project Cruise 4 using 229 baited …Show full descriptionThe dataset comprises 13558 individuals from 169 species of fishes, sharks, rays and sea snakes observed at around Southern Great Barrier Reef Marine Park Seabed Project Cruise 4 using 229 baited remote underwater video stations (BRUVS(TM)). 1879 images were captured from these cameras. Approximately 3500 of the best images from all BRUVS (TM) projects are stored in a reference library. Data recorded concern: - classification of the habitat in the field of view (topography, sediments, benthos) - the identity of fish and CAABCODES - their time of arrival - their behaviour (8 categories, including feeding on the bait) - their maturity (adult or juvenile) - their relative abundance (as MaxN = the maximum number visible at one time, or distinguishable at different times as separate individuals e.g. much larger/smaller, male/female) - the time elapsed before MaxN and feeding occurs. A custom interface has been developed by AIMS staff, using Microsoft Access, for reading and analysis of BRUVS(TM) tapes.

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 Uninsured ED Visits - This indicator shows the percentage of persons without health (medical) insurance who seek care through the Emergency Department. People without health insurance are more likely to be in poor health than the insured. Lack of health insurance can result in increased visits to the emergency department and decreased routine care visits with a primary care provider.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Ship Bottom. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Ship Bottom, the median income for all workers aged 15 years and older, regardless of work hours, was $74,583 for males and $41,176 for females.

These income figures highlight a substantial gender-based income gap in Ship Bottom. Women, regardless of work hours, earn 55 cents for each dollar earned by men. This significant gender pay gap, approximately 45%, underscores concerning gender-based income inequality in the borough of Ship Bottom.

- Full-time workers, aged 15 years and older: In Ship Bottom, among full-time, year-round workers aged 15 years and older, males earned a median income of $122,000, while females earned $110,938, resulting in a 9% gender pay gap among full-time workers. This illustrates that women earn 91 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the borough of Ship Bottom.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Ship Bottom.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 Ship Bottom median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Ship Bottom. The dataset can be utilized to gain insights into gender-based income distribution within the Ship Bottom population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Ship Bottom, among individuals aged 15 years and older with income, there were 448 men and 434 women in the workforce. Among them, 174 men were engaged in full-time, year-round employment, while 97 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, none fell within the income range of under $24,999, while 5.15% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 61.49% of men in full-time roles earned incomes exceeding $100,000, while 54.64% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

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:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar 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 Ship Bottom median household income by race. You can refer the same here

Context

There's a story behind every dataset and here's your opportunity to share yours.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

These datasets support the conclusions in the journal article entitled " Population vulnerability of residents, employees, and cruise-ship passengers to tsunami hazards in complex seismic regions: a case study of the U.S. Virgin Islands" as described in the abstract below: Reducing the potential for loss of life from tsunamis is challenging on islands located in complex seismic regions given the multiple sources that surround islands, differences among islands in the amount of time to evacuate before wave arrival, and the high number of residents, employees, and tourists in tsunami-hazard zones. We examine variations in population vulnerability in island communities to multiple tsunami threats and use the United States territory of the U.S. Virgin Islands (USVI), including St. Thomas, St. John, and St. Croix islands, as our case study. We estimate the tsunami-hazard exposure of residents, employees, and cruise-ship passengers on vessels docking at USVI marine facilities, as well as model pedestrian travel times out of inundation zones for 13 credible tsunami scenarios. Results indicate that the threat to life safety in USVI posed by tsunamis is not equal among the three islands, both in terms of the magnitude of people in hazard zones and the amount of time available to evacuate for the various scenarios. The number of employees and cruise-ship passengers in tsunami-hazard zones is orders of magnitude higher than the number of residents, suggesting that risk assessments that only account for residents are under-estimating threats to life safety from tsunamis. Finally, reducing departure delays has a greater impact than increasing pedestrian travel speeds on reducing the number of people that may have insufficient time to evacuate hazard zones before wave arrival.

This dataset contains MOCNESS (Multiple Opening/Closing Net Environmental Sensing System) profile data collected on board the R/V F.G. Walton Smith during Natural Resource Damage Assessment (NRDA) Plankton Survey Walton Smith 3 (WS3) in the Gulf of Mexico from 2010-09-26 to 2010-10-02. This survey (WS3, chief scientist: Malinda Sutor) was part of a series of NRDA cruises conducted in 2010 and 2011 to evaluate the distribution and densities of ichthyoplankton and other zooplankton in northern Gulf of Mexico waters potentially affected by the Deepwater Horizon Oil Spill (DWHOS). The dataset includes the original profile data in three formats (.RAW, .TAB, .PRO) for individual MOCNESS tows, as well combined and summary data (all tows) for the full cruise. Datasets include sampling event data (time, longitude, latitude, salinity, temperature, depth, fluorescence, velocity, and volume).This dataset corresponds with the MOCNESS Ichthyoplankton data found at DOI: R6.x815.000:0010. cdm_altitude_proxy=depth_range cdm_data_type=TrajectoryProfile cdm_profile_variables=time cdm_trajectory_variables=trajectory, latitude, longitude comment=1-m^2 Multiple Opening/Closing Net Environmental Sensing System (MOCNESS) outfitted with 9 .333mm nets. comment1=MOCNESS data (RAW, PRO, and TAB files) were acquired from the Deepwater Horizon Plankton Assessment Archive (DWHPAA) and NCEI, and are provided here in their original form for reference. MOC All and MOC summary files were generated by the dataset authors to collate and summarize the MOCNESS environmental data after additional processing and QC. The MOC Deployment ID file is included for organizational reference and does not contain environmental data. The dataset authors are curating the deep-pelagic plankton data and associated environmental data for analysis, and were not part of the sample collection effort. comment2=Data provided in this dataset are generated from the Natural Resource Damage Assessment Deepwater Horizon Oil Spill Plankton Processing Plan. Stations sampled are on the Southeast Area Monitoring and Assessment Program (SEAMAP) Gulf of Mexico grid. contributor_email=carley.zapfe@usm.edu, verena.wang@usm.edu contributor_institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences contributor_name=Carley Zapfe, Verena Wang contributor_role=Research Technician, postdoctoral Research Associate contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=https://hernandezfishecologylab.com/people-2/carley-zapfe/, https://hernandezfishecologylab.com/people-2/dr-verena-wang/ Conventions=CF-1.6, ACDD-1.3, IOOS-1.2, COARDS Country=USA cruise_name=WS3_MOC date_metadata_modified=2021-04-01T14:55:23Z Easternmost_Easting=-88.9242 featureType=TrajectoryProfile geospatial_bounds=Points ((27.5 -90,28.5 -87,28 -88,28 -89,28.5 -89,28 -89.5,28.71 -88.42,29.16 -88.02,28.6092 -87.741)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=27.9839 geospatial_lat_min=27.9601 geospatial_lat_resolution=3.1151832460739434E-5 geospatial_lat_units=degrees_north geospatial_lon_max=-88.9242 geospatial_lon_min=-88.9413 geospatial_lon_resolution=-2.238219895293437E-5 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=-0.0041884816753926775 geospatial_vertical_units=dbar history=2021-04-01T14:54:16Z id=WS4_MOC infoUrl=https://data.gulfresearchinitiative.org/data/R6.x815.000:0006 institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences instrument=MOCNESS instrument_vocabulary=GCMD Science Keywords Version 9.1.5 keywords_vocabulary=GCMD Science Keywords metadata_link=https://data.gulfresearchinitiative.org/data/R6.x815.000:0006 naming_authority=edu.usm Northernmost_Northing=27.9839 platform=RV_Walton_Smith platform_name=RV_Walton_Smith platform_vocabulary=https://mmisw.org/ont/ioos/platform processing_level=Geophysical units from raw data program=The Gulf of Mexico Research Initiative (GOMRI) project=Deep-Pelagic Plankton Communities of the Northern Gulf of Mexico: Trophic Ecology, Assemblage Dynamics, and Connectivity with the Upper Ocean references=data.gomri.org sea_name=Gulf of Mexico source=in situ measuremnts sourceUrl=(local files) Southernmost_Northing=27.9601 standard_name_vocabulary=CF Standard Name Table v72 station_name=B083_D3_S subsetVariables=trajectory, depth_range, signal_strength, platform, instrument, crs time_coverage_duration=P00Y0M00DT00H51M08S time_coverage_end=2011-04-30T14:53:42Z time_coverage_start=2011-04-30T14:02:34Z Westernmost_Easting=-88.9413

Analysis of ‘COVID-19 dataset in Japan’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/lisphilar/covid19-dataset-in-japan on 28 January 2022.

--- Dataset description provided by original source is as follows ---

1. Context

This is a COVID-19 dataset in Japan. This does not include the cases in Diamond Princess cruise ship (Yokohama city, Kanagawa prefecture) and Costa Atlantica cruise ship (Nagasaki city, Nagasaki prefecture). - Total number of cases in Japan - The number of vaccinated people (New/experimental) - The number of cases at prefecture level - Metadata of each prefecture

Note: Lisphilar (author) uploads the same files to https://github.com/lisphilar/covid19-sir/tree/master/data

This dataset can be retrieved with CovsirPhy (Python library).

pip install covsirphy --upgrade

import covsirphy as cs
data_loader = cs.DataLoader()
japan_data = data_loader.japan()
# The number of cases (Total/each province)
clean_df = japan_data.cleaned()
# Metadata
meta_df = japan_data.meta()

Please refer to CovsirPhy Documentation: Japan-specific dataset.

Note: Before analysing the data, please refer to Kaggle notebook: EDA of Japan dataset and COVID-19: Government/JHU data in Japan. The detailed explanation of the build process is discussed in Steps to build the dataset in Japan. If you find errors or have any questions, feel free to create a discussion topic.

1.1 Total number of cases in Japan

covid_jpn_total.csv Cumulative number of cases: - PCR-tested / PCR-tested and positive - with symptoms (to 08May2020) / without symptoms (to 08May2020) / unknown (to 08May2020) - discharged - fatal

The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with mild symptoms (to 08May2020) / severe symptoms / unknown (to 08May2020) - requiring hospitalization, but waiting in hotels or at home (to 08May2020)

In primary source, some variables were removed on 09May2020. Values are NA in this dataset from 09May2020.

Manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)

The number of vaccinated people: - Vaccinated_1st: the number of vaccinated persons for the first time on the date - Vaccinated_2nd: the number of vaccinated persons with the second dose on the date - Vaccinated_3rd: the number of vaccinated persons with the third dose on the date

Data sources for vaccination: - To 09Apr2021: 厚生労働省 HP 新型コロナワクチンの接種実績(in Japanese) - 首相官邸 新型コロナワクチンについて - From 10APr2021: Twitter: 首相官邸（新型コロナワクチン情報）

1.2 The number of cases at prefecture level

covid_jpn_prefecture.csv Cumulative number of cases: - PCR-tested / PCR-tested and positive - discharged - fatal

The number of cases: - requiring hospitalization (from 09May2020) - hospitalized with severe symptoms (from 09May2020)

Using pdf-excel converter, manually collected the data from Ministry of Health, Labour and Welfare HP:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English)

Note: covid_jpn_prefecture.groupby("Date").sum() does not match covid_jpn_total. When you analyse total data in Japan, please use covid_jpn_total data.

1.3 Metadata of each prefecture

covid_jpn_metadata.csv - Population (Total, Male, Female): 厚生労働省 厚生統計要覧（2017年度）第１－５表 - Area (Total, Habitable): Wikipedia 都道府県の面積一覧 (2015)

• Hospital_bed: With the primary data of 厚生労働省 感染症指定医療機関の指定状況（平成31年4月1日現在）, 厚生労働省 第二種感染症指定医療機関の指定状況（平成31年4月1日現在）, 厚生労働省 医療施設動態調査（令和２年１月末概数）, 厚生労働省 感染症指定医療機関について and secondary data of COVID-19 Japan 都道府県別 感染症病床数,

  • Specific: Hospital beds of medical institutions designated for specific infectious diseases
  • Type-I: Hospital beds of medical institutions designated for type I infectious diseases
  • Type-II: Hospital beds of medical institutions designated for type II infectious diseases
  • Tuberculosis: Hospital beds of medical institutions designated for tuberculosis (outpatient care)
  • Care: long term care bed of hospitals
  • Total: Beds of all hospitals

• Clinic_bed: With the primary data of 医療施設動態調査（令和２年１月末概数） ,

  • Care: long term care beds of clinics
  • Total: Beds of all clinics

• Location: Data is from LinkData 都道府県庁所在地 (Public Domain) (secondary data).

  • Latitude
  • Longitude

• Admin

  • Capital: Prefectural capital city. Data is from LinkData 都道府県庁所在地 (Public Domain) (secondary data).
  • Region: Region name. Data is from WIkipedia (secondary data). "Kyushu-Okinawa region" was separated to "Kyushu" and "Okinawa" by this datasets' author.
  • Num: Prefecture code (JIS X 0401: Hokkaido=1,...Okinawa=47). Data is from 国土交通省 GIS HP Pref code. cf. (not source) Japan VIsitor: Japan Prefectures Map.

2. Acknowledgements

To create this dataset, edited and transformed data of the following sites was used.

厚生労働省 Ministry of Health, Labour and Welfare, Japan:
厚生労働省 HP (in Japanese)
Ministry of Health, Labour and Welfare HP (in English) 厚生労働省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)

国土交通省 Ministry of Land, Infrastructure, Transport and Tourism, Japan: 国土交通省 HP (in Japanese) 国土交通省 HP (in English) 国土交通省 HP 利用規約・リンク・著作権等 CC BY 4.0 (in Japanese)

Code for Japan / COVID-19 Japan: Code for Japan COVID-19 Japan Dashboard (CC BY 4.0) COVID-19 Japan 都道府県別 感染症病床数 (CC BY)

Wikipedia: Wikipedia

LinkData: LinkData (Public Domain)

Inspiration

1. Changes in number of cases over time
2. Percentage of patients without symptoms / mild or severe symptoms
3. What to do next to prevent outbreak

License and how to cite

Kindly cite this dataset under CC BY-4.0 license as follows. - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, GitHub repository, https://github.com/lisphilar/covid19-sir/data/japan, or - Hirokazu Takaya (2020-2022), COVID-19 dataset in Japan, Kaggle Dataset, https://www.kaggle.com/lisphilar/covid19-dataset-in-japan

--- Original source retains full ownership of the source dataset ---

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Units As in all probability sample surveys, it is important that each sampling unit in the surveyed population has a known, non-zero probability of selection. To achieve this, there has to be an appropriate list, or sampling frame of the primary sampling units (PSUs).The universe defined for the GLSS 5 is the population living within private households in Ghana. The institutional population (such as schools, hospitals etc), which represents a very small percentage in the 2000 Population and Housing Census (PHC), is excluded from the frame for the GLSS 5.

The Ghana Statistical Service (GSS) maintains a complete list of census EAs, together with their respective population and number of households as well as maps, with well defined boundaries, of the EAs. . This information was used as the sampling frame for the GLSS 5. Specifically, the EAs were defined as the primary sampling units (PSUs), while the households within each EA constituted the secondary sampling units (SSUs).

Stratification In order to take advantage of possible gains in precision and reliability of the survey estimates from stratification, the EAs were first stratified into the ten administrative regions. Within each region, the EAs were further sub-divided according to their rural and urban areas of location. The EAs were also classified according to ecological zones and inclusion of Accra (GAMA) so that the survey results could be presented according to the three ecological zones, namely 1) Coastal, 2) Forest, and 3) Northern Savannah, and for Accra.

Sample size and allocation The number and allocation of sample EAs for the GLSS 5 depend on the type of estimates to be obtained from the survey and the corresponding precision required. It was decided to select a total sample of around 8000 households nationwide.

To ensure adequate numbers of complete interviews that will allow for reliable estimates at the various domains of interest, the GLSS 5 sample was designed to ensure that at least 400 households were selected from each region.

A two-stage stratified random sampling design was adopted. Initially, a total sample of 550 EAs was considered at the first stage of sampling, followed by a fixed take of 15 households per EA. The distribution of the selected EAs into the ten regions or strata was based on proportionate allocation using the population.

For example, the number of selected EAs allocated to the Western Region was obtained as: 1924577/18912079*550 = 56

Under this sampling scheme, it was observed that the 400 households minimum requirement per region could be achieved in all the regions but not the Upper West Region. The proportionate allocation formula assigned only 17 EAs out of the 550 EAs nationwide and selecting 15 households per EA would have yielded only 255 households for the region. In order to surmount this problem, two options were considered: retaining the 17 EAs in the Upper West Region and increasing the number of selected households per EA from 15 to about 25, or increasing the number of selected EAs in the region from 17 to 27 and retaining the second stage sample of 15 households per EA.

The second option was adopted in view of the fact that it was more likely to provide smaller sampling errors for the separate domains of analysis. Based on this, the number of EAs in Upper East and the Upper West were adjusted from 27 and 17 to 40 and 34 respectively, bringing the total number of EAs to 580 and the number of households to 8,700.

A complete household listing exercise was carried out between May and June 2005 in all the selected EAs to provide the sampling frame for the second stage selection of households. At the second stage of sampling, a fixed number of 15 households per EA was selected in all the regions. In addition, five households per EA were selected as replacement samples.The overall sample size therefore came to 8,700 households nationwide.

Mode of data collection

Face-to-face [f2f]

The data was collected during the Nansen Legacy joint cruise (JC2-2, cruise number: 2021710) from 24.08 - 25.09.2021 onboard the research vessel RV Kronprins Haakon, along a transect in the Arctic Basin from 81N to 87N. The dataset contains abundance and biomass of sea-ice meiofauna (> 20 µm) including large protists such as foraminifers and ciliates and metazoans eggs. Sea-ice meiofauna were identified and counted with stereo microscopy and result are given as Individuals per liter (cells/L) and Individuals per square meter (Ind/m2). Biomass (μg C Ind.-1) was only determined for alive individuals according to carbon contents for ice meiofauna taxa based on literature summarized in Ehrlich et al. (2021, doi: https://doi.org/10.1525/elementa.2020.00169), and carbon values for foraminifers were provided by de Freitas et al. (2021) for benthic species (Bolivinia sp.: 0.00875 μg C Ind.-1; Elphidium sp.: 0.126 μg C Ind.-1, https://doi.org/10.2113/gsjfr.51.4.249) and Anglada-Ortiz et al. (submitted to PiO) for the pelagic species Neogloboquadrina pachyderma (0.0013 μg C Ind.-1).

This is historical data. The update frequency has been set to "Static Data" and is here for historic value. Updated on 8/14/2024 High School Graduation Rate - This indicator shows the percentage of students who graduate high school in four years. Completion of high school is one of the strongest predictors of health in later life. People who graduate from high school are more likely to have better health outcomes, regularly visit doctors, and live longer than those without high school diplomas. Link to Data Details

Early Prenatal Care - This indicator shows the percentage of pregnant women who receive prenatal care beginning in the first trimester. Inadequate prenatal care services have been linked to higher rates of infant mortality, low birth weight and pre-term deliveries. While Maryland as a whole ranks better than the National average and the Healthy People 2020 Target, disparities still exist. Due to the change in methodology for collecting information on the Maryland birth certificate, data collected in 2010 and after are not comparable to data collected in earlier years. Link to Data Details

The POSEIDON cruise POS298/2 was carried out by the Institute of Oceanography of the University of Hamburg. Members of the University of Venice and the Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, Trieste were participating in the cruise. The project was aimed at gaining a deeper knowledge on the water mass transformations occurring in the southern Adriatic and western Ionian Sea. To obtain this result CTD profiles, lADCP profiles and water samples for oxygen and salinity were taken and analysed. The cruise had several objectives: 1. Identifying the routes and characteristics of the fraction of deep water in the Ionian Sea which was generated in the Adriatic Sea. 2. Quantifying the mixing of the deep water generated in the Adriatic Sea with the ambient water masses on its way southward. 3. Estimating the importance of the deep water generated in the Adriatic Sea for the ventilation of the eastern Mediterranean Sea. Hydrographic observations in the Adriatic- and Mediterranean Sea during Poseidon cruise 298 from 1. - 28. May 2003.

Abstract

Survey based Harmonized Indicators (SHIP) files are harmonized data files from household surveys that are conducted by countries in Africa. To ensure the quality and transparency of the data, it is critical to document the procedures of compiling consumption aggregation and other indicators so that the results can be duplicated with ease. This process enables consistency and continuity that make temporal and cross-country comparisons consistent and more reliable.

Four harmonized data files are prepared for each survey to generate a set of harmonized variables that have the same variable names. Invariably, in each survey, questions are asked in a slightly different way, which poses challenges on consistent definition of harmonized variables. The harmonized household survey data present the best available variables with harmonized definitions, but not identical variables. The four harmonized data files are

a) Individual level file (Labor force indicators in a separate file): This file has information on basic characteristics of individuals such as age and sex, literacy, education, health, anthropometry and child survival. b) Labor force file: This file has information on labor force including employment/unemployment, earnings, sectors of employment, etc. c) Household level file: This file has information on household expenditure, household head characteristics (age and sex, level of education, employment), housing amenities, assets, and access to infrastructure and services. d) Household Expenditure file: This file has consumption/expenditure aggregates by consumption groups according to Purpose (COICOP) of Household Consumption of the UN.

Geographic coverage

National

Analysis unit

• Individual level for datasets with suffix _I and _L
• Household level for datasets with suffix _H and _E

Universe

The survey covered all de jure household members (usual residents).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Frame The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) was used as a frame to select EAs from the rural part of the country. On the other hand, the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC), was used as a frame in order to select sample enumeration areas for the urban HICE survey. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. This list was, thus, used as a frame in order to select households from sample EAs.

Sample Design For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of eight regional states and two administrative councils (Addis Ababa and Dire Dawa) of the country, except Gambella region. Each region was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Twelve households per sample EA were selected as a Second Stage Sampling Unit (SSU) to which the survey questionnaire were administered.

Category II:- Major urban centers:- In this category all regional capitals (except Gambella region) and four additional urban centers having higher population sizes as compared to other urban centers were included. Each urban center in this category was considered as a reporting level. However, each sub-city of Addis Ababa was considered to be a domain (reporting levels). Since there is a high variation in the standards of living of the residents of these urban centers (that may have a significant impact on the final results of the survey), each urban center was further stratified into the following three sub-strata. Sub-stratum 1:- Households having a relatively high standards of living Sub-stratum 2:- Households having a relatively medium standards of living and Sub-stratum 3:- Households having a relatively low standards of living. The category has a total of 14 reporting levels. A stratified two-stage cluster sample design was also adopted in this instance. The primary sampling units were EAs of each urban center. Allocation of sample EAs of a reporting level among the above mentioned strata were accomplished in proportion to the number of EAs each stratum consists of. Sixteen households from each sample EA were inally selected as a Secondary Sampling Unit (SSU).

Category III: - Other urban centers: - Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella region a domain of "other urban centers" is formed for each region. Consequently, 7 reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three-stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Sixteen households from each EA were lastly selected at the third stage and the survey questionnaires administered for all of them.

Mode of data collection

Face-to-face [f2f]

This dataset contains MOCNESS (Multiple Opening/Closing Net Environmental Sensing System) profile data collected on board the M/V Nick Skansi during Natural Resource Damage Assessment (NRDA) Plankton Survey Nick Skansi 9 (NS9) in the Gulf of Mexico from 2011-04-18 to 2011-06-26. This survey (NS9, chief scientists: Lora Pride, Sandra Arismendez, Jean de Marignac, James J. Pierson) was part of a series of NRDA cruises conducted in 2010 and 2011 to evaluate the distribution and densities of ichthyoplankton and other zooplankton in northern Gulf of Mexico waters potentially affected by the Deepwater Horizon Oil Spill (DWHOS). The dataset includes the original profile data in three formats (.RAW, .TAB, .PRO) for individual MOCNESS tows, as well as combined and summary data (all tows) for the full cruise. Datasets include sampling event data (e.g., time, latitude, longitude, depth, volume filtered). This dataset corresponds with the MOCNESS Ichthyoplankton dataset available under GRIIDC Unique Dataset Identifier (UDI) R6.x815.000:0027 (DOI: 10.7266/n7-1v14-fc70). cdm_data_type=Trajectory cdm_trajectory_variables=trajectory comment=1-m^2 Multiple Opening/Closing Net Environmental Sensing System (MOCNESS) outfitted with 9 .333mm nets. comment1=MOCNESS data (RAW, PRO, and TAB files) were acquired from the Deepwater Horizon Plankton Assessment Archive (DWHPAA) and NCEI, and are provided here in their original form for reference. MOC All and MOC summary files were generated by the dataset authors to collate and summarize the MOCNESS environmental data after additional processing and QC. The MOC Deployment ID file is included for organizational reference and does not contain environmental data. The dataset authors are curating the deep-pelagic plankton data and associated environmental data for analysis, and were not part of the sample collection effort. comment2=Data provided in this dataset are generated from the Natural Resource Damage Assessment Deepwater Horizon Oil Spill Plankton Processing Plan. Stations sampled are on the Southeast Area Monitoring and Assessment Program (SEAMAP) Gulf of Mexico grid. contributor_country=USA contributor_email=carley.zapfe@usm.edu, verena.wang@usm.edu contributor_institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences contributor_name=Carley Zapfe, Verena Wang contributor_role=Research Technician, postdoctoral Research Associate contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=https://hernandezfishecologylab.com/people-2/carley-zapfe/, https://hernandezfishecologylab.com/people-2/dr-verena-wang/ Conventions=CF-1.6, ACDD-1.3, IOOS-1.2, COARDS Country=USA cruise_name=NS9_MOC date_metadata_modified=2021-04-30T20:48:02Z Easternmost_Easting=-85.3278 featureType=Trajectory geospatial_bounds=Points ((27.5 -92.5, 27 -92, 27 -91, 27.5 -90.5, 27 -90, 27.5 -89.5, 27 -89, 27.5 -89, 28 -89)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=28.9703 geospatial_lat_min=26.8393 geospatial_lat_resolution=-7.232472600902286E-6 geospatial_lat_units=degrees_north geospatial_lon_max=-85.3278 geospatial_lon_min=-92.5574 geospatial_lon_resolution=-4.279091347894152E-6 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=-0.004852330937351521 geospatial_vertical_units=dbar history=2021-04-30T19:54:13Z id=NS9_MOC infoUrl=https://data.gulfresearchinitiative.org/data/R6.x815.000:0030 institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences instrument=MOCNESS instrument_vocabulary=GCMD Science Keywords Version 9.1.5 keywords_vocabulary=GCMD Science Keywords metadata_link=https://data.gulfresearchinitiative.org/data/R6.x815.000:0030 naming_authority=edu.usm Northernmost_Northing=28.9703 platform=MV_Nick_Skansi platform_name=MV_Nick_Skansi platform_vocabulary=https://mmisw.org/ont/ioos/platform processing_level=Geophysical units from raw data program=The Gulf of Mexico Research Initiative (GOMRI) project=Deep-Pelagic Plankton Communities of the Northern Gulf of Mexico: Trophic Ecology, Assemblage Dynamics, and Connectivity with the Upper Ocean references=data.gomri.org sea_name=Gulf of Mexico sourceUrl=(local files) Southernmost_Northing=26.8393 standard_name_vocabulary=CF Standard Name Table v72 subsetVariables=trajectory, cruiseName, cruiseNumber, depth_range, platform, instrument, crs time_coverage_duration=P00Y2M08DT15H02M40S time_coverage_end=2011-06-26T03:05:00Z time_coverage_start=2011-04-18T00:06:36Z Westernmost_Easting=-92.5574

This dataset contains MOCNESS (Multiple Opening/Closing Net Environmental Sensing System) profile data collected on board the M/V Nick Skansi during Natural Resource Damage Assessment (NRDA) Plankton Survey Nick Skansi PC3 (NSPC3) in the Gulf of Mexico from 2010-09-25 to 2010-10-03. This survey (NSPC3, chief scientist: Tara Bardi) was part of a series of NRDA cruises conducted in 2010 and 2011 to evaluate the distribution and densities of ichthyoplankton and other zooplankton in northern Gulf of Mexico waters potentially affected by the Deepwater Horizon Oil Spill (DWHOS). The dataset includes the original profile data in three formats (.RAW, .TAB, .PRO) for individual MOCNESS tows, as well as combined and summary data (all tows) for the full cruise. The dataset includes profile data collected (e.g. time, longitude, latitude, salinity, temperature, depth, fluorescence, velocity, and volume). This dataset corresponds with the MOCNESS Ichthyoplankton dataset available under GRIIDC Unique Dataset Identifier (UDI) R6.x815.000:0025 (DOI: 10.7266/n7-7fxg-2980). cdm_altitude_proxy=depth_range cdm_data_type=TrajectoryProfile cdm_profile_variables=time cdm_trajectory_variables=trajectory, latitude, longitude comment=1-m^2 Multiple Opening/Closing Net Environmental Sensing System (MOCNESS) outfitted with 9 .333mm nets. comment1=MOCNESS data (RAW, PRO, and TAB files) were acquired from the Deepwater Horizon Plankton Assessment Archive (DWHPAA) and NCEI, and are provided here in their original form for reference. MOC All and MOC summary files were generated by the dataset authors to collate and summarize the MOCNESS environmental data after additional processing and QC. The MOC Deployment ID file is included for organizational reference and does not contain environmental data. The dataset authors are curating the deep-pelagic plankton data and associated environmental data for analysis, and were not part of the sample collection effort. comment2=Data provided in this dataset are generated from the Natural Resource Damage Assessment Deepwater Horizon Oil Spill Plankton Processing Plan. Stations sampled are on the Southeast Area Monitoring and Assessment Program (SEAMAP) Gulf of Mexico grid. contributor_email=carley.zapfe@usm.edu, verena.wang@usm.edu contributor_institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences contributor_name=Carley Zapfe, Verena Wang contributor_role=Research Technician, postdoctoral Research Associate contributor_role_vocabulary=https://vocab.nerc.ac.uk/collection/G04/current/ contributor_url=https://hernandezfishecologylab.com/people-2/carley-zapfe/, https://hernandezfishecologylab.com/people-2/dr-verena-wang/ Conventions=CF-1.6, ACDD-1.3, IOOS-1.2, COARDS Country=USA cruise_name=NSPC3_MOC date_metadata_modified=2021-04-01T15:03:53Z Easternmost_Easting=-90.9741 featureType=TrajectoryProfile geospatial_bounds=Points ((27.5 -92.5, 27 -92, 27 -91, 27.5 -90.5, 27 -90, 27.5 -89.5, 27 -89, 27.5 -89, 28 -89)) geospatial_bounds_crs=EPSG:4326 geospatial_bounds_vertical_crs=EPSG:5831 geospatial_lat_max=27.0243 geospatial_lat_min=26.8519 geospatial_lat_resolution=-3.8442659526996854E-5 geospatial_lat_units=degrees_north geospatial_lon_max=-90.9741 geospatial_lon_min=-91.0087 geospatial_lon_resolution=7.608210620257728E-6 geospatial_lon_units=degrees_east geospatial_vertical_positive=down geospatial_vertical_resolution=-4.6854082998662314E-4 geospatial_vertical_units=dbar history=2021-04-01T15:02:57Z id=NSPC3_MOC infoUrl=https://data.gulfresearchinitiative.org/data/R6.x815.000:0028 institution=University of Southern Mississippi / Gulf Coast Research Laboratory, University of Southern Mississippi / Department of Coastal Sciences instrument=MOCNESS instrument_vocabulary=GCMD Science Keywords Version 9.1.5 keywords_vocabulary=GCMD Science Keywords metadata_link=https://data.gulfresearchinitiative.org/data/R6.x815.000:0028 naming_authority=edu.usm Northernmost_Northing=27.0243 platform=MV_Nick_Skansi platform_name=MV_Nick_Skansi platform_vocabulary=https://mmisw.org/ont/ioos/platform processing_level=Geophysical units from raw data program=The Gulf of Mexico Research Initiative (GOMRI) project=Deep-Pelagic Plankton Communities of the Northern Gulf of Mexico: Trophic Ecology, Assemblage Dynamics, and Connectivity with the Upper Ocean references=data.gomri.org sea_name=Gulf of Mexico source=in situ measuremnts sourceUrl=(local files) Southernmost_Northing=26.8519 standard_name_vocabulary=CF Standard Name Table v72 station_name=SW8_D_D subsetVariables=trajectory, depth_range, signal_strength, platform, instrument, crs time_coverage_duration=P00Y0M00DT05H10M09S time_coverage_end=2010-09-28T14:37:38Z time_coverage_start=2010-09-28T09:27:29Z Westernmost_Easting=-91.0087