This dataset compiles most of the data from the "BIO" database for the Seychelles islands. It has three main objectives: 1.To share a nationally agreed taxonomic index of all species recorded in the country 2.To associate with that checklist key data on the conservation value of these species or their invasiveness in Seychelles. This includes a National Red List using IUCN threat statuses assessed at the National level. 3.To share all data available on the distribution of these species, including occurrences with exact coordinates except for species considered sensitive (which are provided here without exact coordinates, and for which the complete data are shared separately in a private GBIF dataset shared only with chosen conservation actors in Seychelles). In the first version, and in the short term, this dataset is restricted to plants, but in future the plan is to extend it to all taxonomic groups. In addition, because the species listed, and their taxonomic and conservation statuses need to be reviewed and discussed with the local scientific community in Seychelles, we use this dataset publication as an opportunity to strengthen partnerships in Seychelles. The pre-published first version was presented to stakeholders and discussed. We agreed to form a Key Biodiversity Areas (KBA) National Coordination Group (NCG) (see list of contacts in the section "Associated Parties"), which is the collective author of this dataset and whose participants are involved in verifying and improving the dataset. This dataset will therefore serve as an open source repository for a formal KBA review in Seychelles. The group will eventually take part to and be complemented by a GBIF National Node which is being developed simultaneously.

This dataset is accompanied by 3 R scripts available online (https://github.com/bsenterre/seychecklist): •The first shows how the BIO database manager converted the BIO data into text files ready for upload in the IPT •The second downloads the dataset from GBIF and compile it into an enriched format that is used for a Shiny app •The third creates a Shiny app that allows users to explore the data and to verify the status of Key Biodiversity Areas based on the distribution of species triggers. The app also provides users with a nationally agreed checklist of species of the Seychelles along with their conservation value or invasion status. These scripts provide therefore a fully transparent approach to identifying KBAs, where the data is open source and where the data analysis and synthesis are also explicit and open source.

To prepare this dataset, we have reviewed the various standards available with GBIF through the main 'cores' and extensions (http://rs.gbif.org/extension/gbif/1.0/). Based on that review, considering the content of our BIO database and differences between our taxonomic backbone and GBIF backbone, we have decided to prepare a dataset using the Taxon Core and the following extensions (i.e. a checklist based on occurrences): •Occurrence: for the core of the BIO database •Species Distribution: for biogeography aspects, of native range (endemic to what) •Species Profiles: for basic ecology (marine, freshwater, terrestrial), basic invasion ecology at species level (isInvasive) and basic functional biology. •Vernacular names (although still in development) •Alternative identifiers: to link to GBIF IDs and to IUCN Red List IDs

Complementary data are spread over the following other datasets: •seysensitive: a private dataset providing the exact geographic coordinates for sensitive taxa (sharing their occurrenceID with the obscured duplicate found in the current seychecklist dataset) •seynotinchecklist: an occurrence dataset containing all species occurrence data from the BIO database which are not linked to a species name listed in the current seychecklist dataset (https://www.gbif.org/dataset/99ccf1cc-03e3-4bd4-8a78-50d46dee8cb7) •seyvegplot: dataset compiling vegetation plots, with eventID linking to the seychecklist dataset (https://www.gbif.org/dataset/4fc42f17-eaeb-4296-949d-34b8414eb1c1) •ecosystemology: a dataset providing an index of ecosystem types, their names and synonymies (https://www.gbif.org/dataset/f513fe98-b1c3-45ee-8e14-7f2a5b7890bf). The ID of each individual stand (ecosystem occurrence) is referred to in the seychecklist dataset using the field eventRemarks (while locationID is used to store the code for the location of the stand).

About this dataset

Context

The dataset tabulates the population of Cedar Key by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Cedar Key across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of female population, with 55.16% of total population being female. 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.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Cedar Key is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Cedar Key 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 Cedar Key Population by Race & Ethnicity. You can refer the same here

Background:
The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:

• to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will require
• to provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)
• to collect information on previously neglected topics, such as fathers' involvement in children's care and development
• to focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may be
• to emphasise intergenerational links including those back to the parents' own childhood
• to investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when available

Additional objectives subsequently included for MCS were:

• to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)
• to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of England

Further information about the MCS can be found on the Centre for Longitudinal Studies web pages.

The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website.

The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old. End User Licence versions of MCS studies:
The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.

Sub-sample studies:
Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).


How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:
For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.

Secure Access datasets:
Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).

Secure Access versions of the MCS include:

• detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627
  
• detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)
• linked education administrative datasets for Key Stages 1, 2, 4 and 5 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)
  
• linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)
• linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)
  
• linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302
• linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;
• Banded Distances to English Grammar Schools for MCS5 held under SN 8394
• linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030
• linked Health Administrative Datasets (SAIL) for Wales held under SN 9310
  
• linked Hospital of Birth data held under SN 5724.

The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).

Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page).

The Millennium Cohort Study: Linked Health Administrative Data (Scottish Medical Records), Scottish Birth Records, 2000-2002: Secure Access includes data files from the NHS Digital Hospital Episode Statistics database for those cohort members who provided consent to health data linkage in the Age 50 sweep, and had ever lived in Scotland. The Scottish Medical Records database contains information about all hospital admissions in Scotland. This study concerns the Scottish Birth Records.

Other datasets are available from the Scottish Medical Records database, these include:

• Child Health Reviews (CHR) held under SN 8709
• Prescribing Information System (PIS) held under SN 8710
• Scottish Immunisation and Recall System

Abstract copyright UK Data Service and data collection copyright owner.Next Steps (also known as the Longitudinal Study of Young People in England (LSYPE1)) is a major longitudinal cohort study following a nationally representative group of around 16,000 who were in Year 9 attending state and independent schools in England in 2004, a cohort born in 1989-90.The first seven sweeps of the study were conducted annually (2004-2010) when the study was funded and managed by the Department for Education (DfE). The study mainly focused on the educational and early labour market experiences of young people.In 2015 Next Steps was restarted, under the management of the Centre for Longitudinal Studies (CLS) at the UCL Faculty of Education and Society (IOE) and funded by the Economic and Social Research Council. The Next Steps Age 25 survey was aimed at increasing the understanding of the lives of young adults growing up today and the transitions out of education and into early adult life.The Next Steps Age 32 Survey took place between April 2022 and September 2023 and is the ninth sweep of the study. The Age 32 Survey aimed to provide data for research and policy on the lives of this generation of adults in their early 30s. This sweep also collected information on many wider aspects of cohort members' lives including health and wellbeing, politics and social participation, identity and attitudes as well as capturing personality, resilience, working memory and financial literacy.Next Steps survey data is also linked to the National Pupil Database (NPD), the Hospital Episode Statistics (HES), the Individualised Learner Records (ILR) and the Student Loans Company (SLC).There are now two separate studies that began under the LSYPE programme. The second study, Our Future (LSYPE2) (available at the UK Data Service under GN 2000110), began in 2013 and will track a sample of over 13,000 young people annually from ages 13/14 through to age 20.Further information about Next Steps may be found on the CLS website.Secure Access datasets:Secure Access versions of Next Steps have more restrictive access conditions than Safeguarded versions available under the standard End User Licence (see 'Access' section).Secure Access versions of the Next Steps include:sensitive variables from the questionnaire data for Sweeps 1-9. These are available under Secure Access SN 8656. National Pupil Database (NPD) linked data at Key Stages 2, 3, 4 and 5, England. These are available under SN 7104.Linked Individualised Learner Records learner and learning aims datasets for academic years 2005 to 2014, England. These are available under SN 8577.detailed geographic indicators for Sweep 1 and Sweep 8 (2001 Census Boundaries) - available under SN 8189 and geographic indicators for Sweep 8 (2011 Census Boundaries) - available under SN 8190. The Sweep 1 geography file was previously held under SN 7104.Linked Health Administrative Datasets (Hospital Episode Statistics) for years 1998-2017 held under SN 8681.Linked Student Loans Company Records for years 2007-2021 held under SN 8848.When researchers are approved/accredited to access a Secure Access version of Next Steps, the Safeguarded (EUL) version of the study - Next Steps: Sweeps 1-9, 2004-2023 (SN 5545) - will be automatically provided alongside. The Student Loans Company (SLC) is a non-profit making government-owned organisation that administers loans and grants to students in colleges and universities in the UK. The Next Steps: Linked Administrative Datasets (Student Loans Company Records), 2007 - 2021: Secure Access includes data on higher education loans for those Next Steps participant who provided consent to SLC linkage in the age 25 sweep. The matched SLC data contains information about participant's applications for student finance, payment transactions posted to participant's accounts, repayment details and overseas assessment details.

About this dataset

Context

The dataset tabulates the Keysville 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 Keysville 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 Keysville was 307, a 4.42% increase year-by-year from 2021. Previously, in 2021, Keysville population was 294, a decline of 2.33% compared to a population of 301 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Keysville increased by 130. In this period, the peak population was 344 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. 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 Keysville is shown in this column.
• Year on Year Change: This column displays the change in Keysville 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 Keysville Population by Year. You can refer the same here

A high-resolution three-year dataset supporting rooftop photovoltaics (PV) generation analytics https://doi.org/10.5061/dryad.m37pvmd99 ## General Information 1. Description: This dataset includes measured photovoltaic (PV) power generation data and on-site weather data collected from 60 grid-connected rooftop PV stations in Hong Kong over a three-year period (2021-2023). The PV power generation data was collected at 5-minute intervals. The meteorological data was collected at 1-minute intervals from an on-site weather station. The metadata was represented using Brick schema was developed, which simplifies the data comprehension and the development of smart analytics applications. 2. Date of data collection: 2021-01-01 to 2023-12-31 3. Geographic location of data collection: Sai Kung District, Hong Kong, China(22.3363°N 114.2634°E) 4. Funding sources that supported the collection of the data: The data collection was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. C6003-22Y), and by the National Key R&D Program of China (2023YFC3807100). ## Description of the data and file structure The open-sourced dataset is divided into two categories: time-series data and metadata. Longitudinal PV generation and meteorological data are provided in .csv format, while metadata of the data measurements is represented by the Brick model in .ttl format. 1. Time-series dataset folder: This folder contains two subfolders, Meteorological dataset and PV generation dataset. The Meteorological dataset subfolder contains seven sub-subfolders, each of which contains three .csv files corresponding to time series data at one-minute resolution for the years 2021, 2022, and 2023. The PV generation dataset subfolder contains two sub-subfolders: PV stations with panel level optimizer and PV stations without panel level optimizer. The former contains two sub-subfolders: Inverter level dataset and Site level dataset, with 44 and 37 .csv files respectively. The latter contains one sub-subfolder: Site level dataset, with 23 .csv files. 2. Metadata folder: PV generation system metadata.ttl is the Brick model of the dataset, which represents the location, equipment, and temporal information for PV systems. 3. The time-series data was pre-processed by replacing missing values with "NA" and resampling the data to ensure temporal consistency. 4. Relationship between files, if important: Each .csv file under the time series dataset is uniquely associated with a specific PV station or inverter name in the Brick model. 5. Additional related data collected that was not included in the current data package: N/A 6. Are there multiple versions of the dataset? No ## Methodological Information 1. Data collection: For stations without panel level optimizers (comprising 23 stations, accounting for 38.3% of the total), the data was individually measured and transferred by the inverter. For stations equipped with panel level optimizers (comprising 37 stations, accounting for 61.7% of the total), the PV generation data was measured and transferred by both the inverter and the panel level optimizer. Meteorological data is collected from the weather station located on the eastern side of the campus. The station comprises a 10-meter-high automatic weather tower and an outdoor plinth area that housing 6 samplers that measure meteorological data at 1-minute intervals. 2. Data transmission and storage: The collected PV generation data was transferred to wireless gateway using dedicated Wi-Fi. Meteorological data is initially sensed, transmitted, and stored in the data logger using RS232 or RS485 communication protocols, and subsequently transferred to the server via LAN cables. All streams of PV generation and meteorological data were extracted and consolidated into a centralized database. 3. Instrument- or software-specific information needed to interpret the data: The time-series data is stored in Comma-Separated Values (CSV) format, which can be further processed and analyzed using various software tools, such as Microsoft Excel or code-based interpreters. The Brick model is available in Turtle (TTL) format, which can be interpreted using the Brick TTL Viewer (https://viewer.brickschema.org/). 4. Environmental/experimental conditions: The data were collected from 60 grid-connected rooftop PV stations and 1 weather station from a subtropical university campus under real operating conditions. 5. PV system management: The rooftop solar power project is managed by the HKUST Sustainability/Net-Zero Office (https://sust.hkust.edu.hk), and was initiated in December 2020. The PV stations and sensors undergo regular maintenance as per the specifications outlined by the Hong Kong Electrical and Mechanical Services Department (EMSD) to ensure their proper functioning. ## Sharing/Access in...

About this dataset

Context

The dataset tabulates the Keysville 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 Keysville. The dataset can be utilized to understand the population distribution of Keysville by age. For example, using this dataset, we can identify the largest age group in Keysville.

Key observations

The largest age group in Keysville, GA was for the group of age 50 to 54 years years with a population of 52 (14.44%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Keysville, GA was the Under 5 years years with a population of 2 (0.56%). 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 Keysville is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Keysville 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 Keysville Population by Age. You can refer the same here

The ISOTOPE database stores compiled age and isotopic data from a range of published and unpublished (GA and non-GA) sources. This internal database is only publicly accessible through the webservices given as links on this page. This data compilation includes sample and bibliographic links. The data structure currently supports summary ages (e.g., U-Pb and Ar/Ar) through the INTERPRETED_AGES tables, as well as extended system-specific tables for Sm-Nd, Pb-Pb, Lu-Hf and O- isotopes. The data structure is designed to be extensible to adapt to evolving requirements for the storage of isotopic data. ISOTOPE and the data holdings were initially developed as part of the Exploring for the Future (EFTF) program. During development of ISOTOPE, some key considerations in compiling and storing diverse, multi-purpose isotopic datasets were developed: 1) Improved sample characterisation and bibliographic links. Often, the usefulness of an isotopic dataset is limited by the metadata available for the parent sample. Better harvesting of fundamental sample data (and better integration with related national datasets such as Australian Geological Provinces and the Australian Stratigraphic Units Database) simplifies the process of filtering an isotopic data compilation using spatial, geological and bibliographic criteria, as well as facilitating ‘audits’ targeting missing isotopic data. 2) Generalised, extensible structures for isotopic data. The need for system-specific tables for isotopic analyses does not preclude the development of generalised data-structures that reflect universal relationships. GA has modelled relational tables linking system-specific Sessions, Analyses, and interpreted data-Groups, which has proven adequate for all of the Isotopic Atlas layers developed thus far. 3) Dual delivery of ‘derived’ isotopic data. In some systems, it is critical to capture the published data (i.e. isotopic measurements and derived values, as presented by the original author) and generate an additional set of derived values from the same measurements, calculated using a single set of reference parameters (e.g. decay constant, depleted-mantle values, etc.) that permit ‘normalised’ portrayal of the data compilation-wide. 4) Flexibility in data delivery mode. In radiogenic isotope geochronology (e.g. U-Pb, Ar-Ar), careful compilation and attribution of ‘interpreted ages’ can meet the needs of much of the user-base, even without an explicit link to the constituent analyses. In contrast, isotope geochemistry (especially microbeam-based methods such as Lu-Hf via laser ablation) is usually focused on the individual measurements, without which interpreted ‘sample-averages’ have limited value. Data delivery should reflect key differences of this kind.

The data sets provide an overview of selected data on waterworks registered with the Norwegian Food Safety Authority. The information has been reported by the waterworks through application processing or other reporting to the Norwegian Food Safety Authority. Drinking water regulations require, among other things, annual reporting.The Norwegian Food Safety Authority has created a separate form service for such reporting. The data sets include public or private waterworks that supply 50 people or more. In addition, all municipal owned businesses with their own water supply are included regardless of size. The data sets also contain decommissioned facilities. This is done for those who wish to view historical data, i.e. data for previous years or earlier. There are data sets for the following supervisory objects: 1. Water supply system. It also includes analysis of drinking water. 2. Transport system 3. Treatment facility 4. Entry point. It also includes analysis of the water source. Below you will find datasets for: 2. Transport system. In addition, there is a file (information.txt) that provides an overview of when the extracts were produced and how many lines there are in the individual files. The withdrawals are done weekly. Furthermore, for the data sets water supply system, transport system and intake point it is possible to see historical data on what is included in the annual reporting. To make use of that information, the file must be linked to the “moder” file.to get names and other static information. These files have the _reporting ending in the file name. Description of the data fields (i.e. metadata) in the individual data sets appears in separate files. These are available in pdf format. If you double-click the csv file and it opens directly in excel, then you will not get the æøå. To see the character set correctly in Excel, you must: & start Excel and a new spreadsheet & select data and then from text, press Import & select separator data and file origin 65001: Unicode (UTF-8) and tick of My Data have headings and press Next &remove tab as separator and select semicolon as separator, press next & otherwise, complete the data sets can be imported into a separate database and compiled as desired. There are link keys in the files that make it possible to link the files together. The waterworks are responsible for the quality of the datasets. — Purpose: Make information on the supply of drinking water available to the public. The data sets provide an overview of selected data on waterworks registered with the Norwegian Food Safety Authority. The information has been reported by the waterworks through application processing or other reporting to the Norwegian Food Safety Authority. Drinking water regulations require, among other things, annual reporting. The Norwegian Food Safety Authority has created a separate form service for such reporting. The data sets include public or private waterworks that supply 50 people or more. In addition, all municipal owned businesses with their own water supply are included regardless of size. The data sets also contain decommissioned facilities. This is done for those who wish to view historical data, i.e. data for previous years or earlier. There are data sets for the following supervisory objects: 1. Water supply system.It also includes analysis of drinking water. 2. Transport system 3. Treatment facility 4. Entry point. It also includes analysis of the water source. Below you will find datasets for: 2. Transport system. In addition, there is a file (information.txt) that provides an overview of when the extracts were produced and how many lines there are in the individual files. The withdrawals are done weekly. Furthermore, for the data sets water supply system, transport system and intake point it is possible to see historical data on what is included in the annual reporting. To make use of that information, the file must be linked to the “moder” file. to get names and other static information. These files have the _reporting ending in the file name. Description of the data fields (i.e. metadata) in the individual data sets appears in separate files. These are available in pdf format. If you double-click the csv file and it opens directly in excel, then you will not get the æøå. To see the character set correctly in Excel, you must: & start Excel and a new spreadsheet & select data and then from text, press Import & select separator data and file origin 65001: Unicode (UTF-8) and tick of My Data have headings and press Next & remove tab as separator and select semicolon as separator, press next & otherwise, complete the data sets can be imported into a separate database and compiled as desired. There are link keys in the files that make it possible to link the files together.The waterworks are responsible for the quality of the datasets.

— Purpose: Make information on the supply of drinking water available to the public.

About this dataset

Context

The dataset presents the median household income across different racial categories in Key Colony Beach. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Key Colony Beach population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 93.14% of the total residents in Key Colony Beach. Notably, the median household income for White households is $105,571. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $105,571.

Content

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

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Key Colony Beach.
• Median household income: Median household income, adjusting for inflation, presented in 2023-inflation-adjusted dollars

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 Key Colony Beach median household income by race. You can refer the same here

About this dataset

Context

The dataset tabulates the Keysville Hispanic or Latino population. It includes the distribution of the Hispanic or Latino population, of Keysville, by their ancestries, as identified by the Census Bureau. The dataset can be utilized to understand the origin of the Hispanic or Latino population of Keysville.

Key observations

Among the Hispanic population in Keysville, regardless of the race, the largest group is of Mexican origin, with a population of 7 (77.78% of the total Hispanic population).

Content

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

Origin for Hispanic or Latino population include:

• Mexican
• Puerto Rican
• Cuban
• Other Hispanic or Latino

Variables / Data Columns

• Origin: This column displays the origin for Hispanic or Latino population for the Keysville
• Population: The population of the specific origin for Hispanic or Latino population in the Keysville is shown in this column.
• % of Total Hispanic Population: This column displays the percentage distribution of each Hispanic origin as a proportion of Keysville total Hispanic or Latino 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 Keysville Population by Race & Ethnicity. You can refer the same here

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Keysville, VA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 2,103, while the mean income for the highest quintile (20% of households with the highest income) is 160,452. This indicates that the top earners earn 76 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 308,955, which is 192.55% higher compared to the highest quintile, and 14691.16% higher compared to the lowest quintile.

Content

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

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

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 Keysville median household income. You can refer the same here

In the dataset ´GLES Cross-Section 2013-2021, Sensitive Regional Data´, the recoded or deleted variables of the GLES Cross-Section Scientific Use Files, which refer to the respondents’ place of residence, are made available for research purposes. The basis for the assignment of the small-scale regional units are the addresses of the respondents. After geocoding, i.e. the calculation of geocoordinates based on the addresses, the point coordinates were linked to regional units (e.g. INSPIRE grid cells, municipality and district ids, postal codes). The regional variables of this dataset can be linked to the survey data of the pre- and post-election cross-sections of the GLES.

This data set contains the following sensitive regional variables (both ids and, if applying, names): - 3-digit key for the adminsitrative governmental district (Regierungsbezirk) (since 2013) - 3-digit key for spatial planning region (since 2013) - 5-digit key for (city-) districts (since 2013) - 9-digit key for municipalities (since 2021) - 8-digit general municipality key (AGS) (since 2013) - 12-digit regional key (Regionalschlüssel) (since 2021) - Zip code (since 2013) - Constituencies (since 2013) - NUTS-3 code (since 2013) - INSPIRE ID (1km) (since 2013) - municipality size (since 2013) - BIK type of municipality (since 2013)

This sensitive data is subject to a special access restriction and can only be used within the scope of an on-site use in the Secure Data Center in Cologne. Further information and contact persons can be found on our website: https://www.gesis.org/en/secdc

In order to take into account changes in the territorial status of the regional units (e. g. district reforms, municipality incorporations), the regional variables are offered as time-harmonized variables as of December 31, 2015 in addition to the status as of January 1 of the year of survey.

If you want to use the regional variables to add additional context characteristics (regional attributes such as unemployment rate or election turnout, for example), you have to send us this data before your visit. In addition, we require a reference and documentation (description of variables) of the data. Note that this context data may be as sensitive as the regional variables if direct assignment is possible. Due to data protection it is problematic if individual characteristics can be assigned to specific regional units – and therefore ultimately to the individual respondents – even without the ALLBUS dataset by means of a table of correspondence. Accordingly, the publication of (descriptive) analysis results based on such contextual data is only possible in a coarsened form.

Please contact the GLES User Service first and send us the filled GLES regional data form (see ´Data & Documents´), specifying exactly which GLES datasets and regional variables you need. Contact: gles@gesis.org

As soon as you have clarified with the GLES user service which exact regional features are to be made available for on-site use, the data use agreement for the use of the data at a guest workstation in our Secure Data Center (Safe Room) in Cologne will be sent to you. Please specify all data sets you need, i.e. both the ´GLES Sensitive Regional Data (ZA6828)´ and the Scientific Use Files to which the regional variables are to be assigned. Furthermore, under ´Specific variables´, please name all the regional variables you need (see GLES regional data form).

About this dataset

Context

The dataset tabulates the population of Keysville by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Keysville across both sexes and to determine which sex constitutes the majority.

Key observations

There is a majority of male population, with 60.3% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Keysville is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Keysville 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 Keysville Population by Race & Ethnicity. You can refer the same here

This is a copy of another layer - see original source: https://www.arcgis.com/home/item.html?id=e02b85c0ea784ce7bd8add7ae3d293d0OverviewThe national fire history perimeter data layer of conglomerated Agency Authoratative perimeters was developed in support of the WFDSS application and wildfire decision support for the 2021 fire season. The layer encompasses the final fire perimeter datasets of the USDA Forest Service, US Department of Interior Bureau of Land Management, Bureau of Indian Affairs, Fish and Wildlife Service, and National Park Service, the Alaska Interagency Fire Center, CalFire, and WFIGS History. Perimeters are included thru the 2021 fire season. Requirements for fire perimeter inclusion, such as minimum acreage requirements, are set by the contributing agencies. WFIGS, NPS and CALFIRE data now include Prescribed Burns. Data InputSeveral data sources were used in the development of this layer:Alaska fire history USDA FS Regional Fire History Data BLM Fire Planning and Fuels National Park Service - Includes Prescribed Burns Fish and Wildlife ServiceBureau of Indian AffairsCalFire FRAS - Includes Prescribed BurnsWFIGS - BLM & BIA and other S&LData LimitationsFire perimeter data are often collected at the local level, and fire management agencies have differing guidelines for submitting fire perimeter data. Often data are collected by agencies only once annually. If you do not see your fire perimeters in this layer, they were not present in the sources used to create the layer at the time the data were submitted. A companion service for perimeters entered into the WFDSS application is also available, if a perimeter is found in the WFDSS service that is missing in this Agency Authoratative service or a perimeter is missing in both services, please contact the appropriate agency Fire GIS Contact listed in the table below.AttributesThis dataset implements the NWCG Wildland Fire Perimeters (polygon) data standard.https://www.nwcg.gov/sites/default/files/stds/WildlandFirePerimeters_definition.pdfIRWINID - Primary key for linking to the IRWIN Incident dataset. The origin of this GUID is the wildland fire locations point data layer. (This unique identifier may NOT replace the GeometryID core attribute)INCIDENT - The name assigned to an incident; assigned by responsible land management unit. (IRWIN required). Officially recorded name.FIRE_YEAR (Alias) - Calendar year in which the fire started. Example: 2013. Value is of type integer (FIRE_YEAR_INT).AGENCY - Agency assigned for this fire - should be based on jurisdiction at origin.SOURCE - System/agency source of record from which the perimeter came.DATE_CUR - The last edit, update, or other valid date of this GIS Record. Example: mm/dd/yyyy.MAP_METHOD - Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality.GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; Digitized-Topo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; OtherGIS_ACRES - GIS calculated acres within the fire perimeter. Not adjusted for unburned areas within the fire perimeter. Total should include 1 decimal place. (ArcGIS: Precision=10; Scale=1). Example: 23.9UNQE_FIRE_ - Unique fire identifier is the Year-Unit Identifier-Local Incident Identifier (yyyy-SSXXX-xxxxxx). SS = State Code or International Code, XXX or XXXX = A code assigned to an organizational unit, xxxxx = Alphanumeric with hyphens or periods. The unit identifier portion corresponds to the POINT OF ORIGIN RESPONSIBLE AGENCY UNIT IDENTIFIER (POOResonsibleUnit) from the responsible unit’s corresponding fire report. Example: 2013-CORMP-000001LOCAL_NUM - Local incident identifier (dispatch number). A number or code that uniquely identifies an incident for a particular local fire management organization within a particular calendar year. Field is string to allow for leading zeros when the local incident identifier is less than 6 characters. (IRWIN required). Example: 123456.UNIT_ID - NWCG Unit Identifier of landowner/jurisdictional agency unit at the point of origin of a fire. (NFIRS ID should be used only when no NWCG Unit Identifier exists). Example: CORMPCOMMENTS - Additional information describing the feature. Free Text.FEATURE_CA - Type of wildland fire polygon: Wildfire (represents final fire perimeter or last daily fire perimeter available) or Prescribed Fire or UnknownGEO_ID - Primary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature. Globally Unique Identifier (GUID).Cross-Walk from sources (GeoID) and other processing notesAK: GEOID = OBJECT ID of provided file geodatabase (4580 Records thru 2021), other federal sources for AK data removed. CA: GEOID = OBJECT ID of downloaded file geodatabase (12776 Records, federal fires removed, includes RX)FWS: GEOID = OBJECTID of service download combined history 2005-2021 (2052 Records). Handful of WFIGS (11) fires added that were not in FWS record.BIA: GEOID = "FireID" 2017/2018 data (416 records) provided or WFDSS PID (415 records). An additional 917 fires from WFIGS were added, GEOID=GLOBALID in source.NPS: GEOID = EVENT ID (IRWINID or FRM_ID from FOD), 29,943 records includes RX.BLM: GEOID = GUID from BLM FPER and GLOBALID from WFIGS. Date Current = best available modify_date, create_date, fire_cntrl_dt or fire_dscvr_dt to reduce the number of 9999 entries in FireYear. Source FPER (25,389 features), WFIGS (5357 features)USFS: GEOID=GLOBALID in source, 46,574 features. Also fixed Date Current to best available date from perimeterdatetime, revdate, discoverydatetime, dbsourcedate to reduce number of 1899 entries in FireYear.Relevant Websites and ReferencesAlaska Fire Service: https://afs.ak.blm.gov/CALFIRE: https://frap.fire.ca.gov/mapping/gis-dataBIA - data prior to 2017 from WFDSS, 2017-2018 Agency Provided, 2019 and after WFIGSBLM: https://gis.blm.gov/arcgis/rest/services/fire/BLM_Natl_FirePerimeter/MapServerNPS: New data set provided from NPS Fire & Aviation GIS. cross checked against WFIGS for any missing perimetersFWS -https://services.arcgis.com/QVENGdaPbd4LUkLV/arcgis/rest/services/USFWS_Wildfire_History_gdb/FeatureServerUSFS - https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_FireOccurrenceAndPerimeter_01/MapServerAgency Fire GIS ContactsRD&A Data ManagerVACANTSusan McClendonWFM RD&A GIS Specialist208-258-4244send emailJill KuenziUSFS-NIFC208.387.5283send email Joseph KafkaBIA-NIFC208.387.5572send emailCameron TongierUSFWS-NIFC208.387.5712send emailSkip EdelNPS-NIFC303.969.2947send emailJulie OsterkampBLM-NIFC208.258.0083send email Jennifer L. Jenkins Alaska Fire Service 907.356.5587 send emailLayers

About this dataset

Context

The dataset tabulates the data for the Key Colony Beach, FL population pyramid, which represents the Key Colony Beach population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey 5-Year estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Key Colony Beach, FL, is 2.9.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Key Colony Beach, FL, is 136.3.
• Total dependency ratio for Key Colony Beach, FL is 139.2.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Key Colony Beach, FL is 0.7.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 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 for the Key Colony Beach population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Key Colony Beach for the selected age group is shown in the following column.
• Population (Female): The female population in the Key Colony Beach for the selected age group is shown in the following column.
• Total Population: The total population of the Key Colony Beach for the selected age group is shown in the following column.

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 Key Colony Beach Population by Age. You can refer the same here

Abstract copyright UK Data Service and data collection copyright owner.Background:The Millennium Cohort Study (MCS) is a large-scale, multi-purpose longitudinal dataset providing information about babies born at the beginning of the 21st century, their progress through life, and the families who are bringing them up, for the four countries of the United Kingdom. The original objectives of the first MCS survey, as laid down in the proposal to the Economic and Social Research Council (ESRC) in March 2000, were:to chart the initial conditions of social, economic and health advantages and disadvantages facing children born at the start of the 21st century, capturing information that the research community of the future will requireto provide a basis for comparing patterns of development with the preceding cohorts (the National Child Development Study, held at the UK Data Archive under GN 33004, and the 1970 Birth Cohort Study, held under GN 33229)to collect information on previously neglected topics, such as fathers' involvement in children's care and developmentto focus on parents as the most immediate elements of the children's 'background', charting their experience as mothers and fathers of newborn babies in the year 2000, recording how they (and any other children in the family) adapted to the newcomer, and what their aspirations for her/his future may beto emphasise intergenerational links including those back to the parents' own childhoodto investigate the wider social ecology of the family, including social networks, civic engagement and community facilities and services, splicing in geo-coded data when availableAdditional objectives subsequently included for MCS were:to provide control cases for the national evaluation of Sure Start (a government programme intended to alleviate child poverty and social exclusion)to provide samples of adequate size to analyse and compare the smaller countries of the United Kingdom, and include disadvantaged areas of EnglandFurther information about the MCS can be found on the Centre for Longitudinal Studies web pages.The content of MCS studies, including questions, topics and variables can be explored via the CLOSER Discovery website. The first sweep (MCS1) interviewed both mothers and (where resident) fathers (or father-figures) of infants included in the sample when the babies were nine months old, and the second sweep (MCS2) was carried out with the same respondents when the children were three years of age. The third sweep (MCS3) was conducted in 2006, when the children were aged five years old, the fourth sweep (MCS4) in 2008, when they were seven years old, the fifth sweep (MCS5) in 2012-2013, when they were eleven years old, the sixth sweep (MCS6) in 2015, when they were fourteen years old, and the seventh sweep (MCS7) in 2018, when they were seventeen years old.End User Licence versions of MCS studies:The End User Licence (EUL) versions of MCS1, MCS2, MCS3, MCS4, MCS5, MCS6 and MCS7 are held under UK Data Archive SNs 4683, 5350, 5795, 6411, 7464, 8156 and 8682 respectively. The longitudinal family file is held under SN 8172.Sub-sample studies:Some studies based on sub-samples of MCS have also been conducted, including a study of MCS respondent mothers who had received assisted fertility treatment, conducted in 2003 (see EUL SN 5559). Also, birth registration and maternity hospital episodes for the MCS respondents are held as a separate dataset (see EUL SN 5614).Release of Sweeps 1 to 4 to Long Format (Summer 2020)To support longitudinal research and make it easier to compare data from different time points, all data from across all sweeps is now in a consistent format. The update affects the data from sweeps 1 to 4 (from 9 months to 7 years), which are updated from the old/wide to a new/long format to match the format of data of sweeps 5 and 6 (age 11 and 14 sweeps). The old/wide formatted datasets contained one row per family with multiple variables for different respondents. The new/long formatted datasets contain one row per respondent (per parent or per cohort member) for each MCS family. Additional updates have been made to all sweeps to harmonise variable labels and enhance anonymisation. How to access genetic and/or bio-medical sample data from a range of longitudinal surveys:For information on how to access biomedical data from MCS that are not held at the UKDS, see the CLS Genetic data and biological samples webpage.Secure Access datasets:Secure Access versions of the MCS have more restrictive access conditions than versions available under the standard End User Licence or Special Licence (see 'Access data' tab above).Secure Access versions of the MCS include:detailed sensitive variables not available under EUL. These have been grouped thematically and are held under SN 8753 (socio-economic, accommodation and occupational data), SN 8754 (self-reported health, behaviour and fertility), SN 8755 (demographics, language and religion) and SN 8756 (exact participation dates). These files replace previously available studies held under SNs 8456 and 8622-8627detailed geographical identifier files which are grouped by sweep held under SN 7758 (MCS1), SN 7759 (MCS2), SN 7760 (MCS3), SN 7761 (MCS4), SN 7762 (MCS5 2001 Census Boundaries), SN 7763 (MCS5 2011 Census Boundaries), SN 8231 (MCS6 2001 Census Boundaries), SN 8232 (MCS6 2011 Census Boundaries), SN 8757 (MCS7), SN 8758 (MCS7 2001 Census Boundaries) and SN 8759 (MCS7 2011 Census Boundaries). These files replace previously available files grouped by geography SN 7049 (Ward level), SN 7050 (Lower Super Output Area level), and SN 7051 (Output Area level)linked education administrative datasets for Key Stages 1, 2 and 4 held under SN 8481 (England). This replaces previously available datasets for Key Stage 1 (SN 6862) and Key Stage 2 (SN 7712)linked education administrative datasets for Key Stage 1 held under SN 7414 (Scotland)linked education administrative dataset for Key Stages 1, 2, 3 and 4 under SN 9085 (Wales)linked NHS Patient Episode Database for Wales (PEDW) for MCS1 – MCS5 held under SN 8302linked Scottish Medical Records data held under SNs 8709, 8710, 8711, 8712, 8713 and 8714;Banded Distances to English Grammar Schools for MCS5 held under SN 8394linked Health Administrative Datasets (Hospital Episode Statistics) for England for years 2000-2019 held under SN 9030linked Hospital of Birth data held under SN 5724.The linked education administrative datasets held under SNs 8481,7414 and 9085 may be ordered alongside the MCS detailed geographical identifier files only if sufficient justification is provided in the application. Users are also only allowed access to either 2001 or 2011 of Geographical Identifiers Census Boundaries studies. So for MCS5 either SN 7762 (2001 Census Boundaries) or SN 7763 (2011 Census Boundaries), for the MCS6 users are only allowed either SN 8231 (2001 Census Boundaries) or SN 8232 (2011 Census Boundaries); and the same applies for MCS7 so either SN 8758 (2001 Census Boundaries) or SN 8759 (2011 Census Boundaries).Researchers applying for access to the Secure Access MCS datasets should indicate on their ESRC Accredited Researcher application form the EUL dataset(s) that they also wish to access (selected from the MCS Series Access web page). Further objectives for MCS2 were as follows: to chart continuity and change since the age of nine months in the child's family and parenting environmentto chart the child's transitions and adaptations to settings and relationships outside the immediate home and familyto assess key aspects of the child's physical, cognitive, social and emotional developmentto maximise longitudinal potential for predicting and explaining future developmentto 'recapture' information not collected at the first sweep May 2017: The longitudinal family file is now available separately under SN 8172.Latest edition information:For the twelfth edition (February 2024), some edits and improvements have been applied to the following data files:mcs2_cm_cognitive_assessmentThe section of variables relating to interviewer observations has been removed from this dataset and will be made available under Secure Access.mcs2_parent_interviewA number of variables have been removed due to low frequency responses and will be made available under Secure Access. Two truncated SOC code variables and two recoded variables have been added to the dataset.mcs2_parent_cm_interviewTwo recoded variables have been added to the dataset.mcs2_hhgridFor this dataset and those listed above, the total number of cases has changed due to data updates. For sample size please check the longitudinal family file (available under SN 9172).mcs2_family_interview file has been removed from this edition and will be redeposited at a later date.

About this dataset

Context

The dataset tabulates the Key Colony Beach population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Key Colony Beach. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 65 years and above with a poulation of 424 (56.99% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Key Colony Beach population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Key Colony Beach is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Key Colony Beach is shown in the following column.

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 Key Colony Beach Population by Age. You can refer the same here

Radiogenic isotopes decay at known rates and can be used to interpret ages for minerals, rocks and geologic processes. Different isotopic systems provide information related to different time periods and geologic processes, systems include: U-Pb and Ar/Ar, Sm-Nd, Pb-Pb, Lu-Hf, Rb-Sr and Re-Os isotopes. The GEOCHRON database stores full analytical U-Pb age data from Geoscience Australia's (GA) Sensitive High Resolution Ion Micro-Probe (SHRIMP) program. The ISOTOPE database is designed to expand GA's ability to deliver isotopic datasets, and stores compiled age and isotopic data from a range of published and unpublished (GA and non-GA) sources. OZCHRON is a depreciated predecessor to GEOCHRON and ISOTOPE, the information once available in OZCHRON is in the process of migration to the two current databases.

The ISOTOPE compilation includes sample and bibliographic links through the A, FGDM, and GEOREF databases. The data structure currently supports summary ages (e.g., U-Pb and Ar/Ar) through the INTERPRETED_AGES tables, as well as extended system-specific tables for Sm-Nd, Pb-Pb, Lu-Hf and O- isotopes. The data structure is designed to be extensible to adapt to evolving requirements for the storage of isotopic data. ISOTOPE and the data holdings were initially developed as part of the Exploring for the Future (EFTF) program - particularly to support the delivery of an Isotopic Atlas of Australia.

During development of ISOTOPE, some key considerations in compiling and storing diverse, multi-purpose isotopic datasets were developed:

1) Improved sample characterisation and bibliographic links. Often, the usefulness of an isotopic dataset is limited by the metadata available for the parent sample. Better harvesting of fundamental sample data (and better integration with related national datasets such as Australian Geological Provinces and the Australian Stratigraphic Units Database) simplifies the process of filtering an isotopic data compilation using spatial, geological and bibliographic criteria, as well as facilitating 'audits' targeting missing isotopic data.

2) Generalised, extensible structures for isotopic data. The need for system-specific tables for isotopic analyses does not preclude the development of generalised data-structures that reflect universal relationships. GA has modelled relational tables linking system-specific Sessions, Analyses, and interpreted data-Groups, which has proven adequate for all of the Isotopic Atlas layers developed thus far.

3) Dual delivery of 'derived' isotopic data. In some systems, it is critical to capture the published data (i.e. isotopic measurements and derived values, as presented by the original author) and generate an additional set of derived values from the same measurements, calculated using a single set of reference parameters (e.g. decay constant, depleted-mantle values, etc.) that permit 'normalised' portrayal of the data compilation-wide.

4) Flexibility in data delivery mode. In radiogenic isotope geochronology (e.g. U-Pb, Ar-Ar), careful compilation and attribution of 'interpreted ages' can meet the needs of much of the user-base, even without an explicit link to the constituent analyses. In contrast, isotope geochemistry (especially microbeam-based methods such as Lu-Hf via laser ablation) is usually focused on the individual measurements, without which interpreted 'sample-averages' have limited value. Data delivery should reflect key differences of this kind.

Value: Used to provide ages and isotope geochemistry data for minerals, rocks and geologic processes.

Scope: Australian jurisdictions and international collaborative programs involving Geoscience Australia

In 2017, the NESP Marine Biodiversity Hub committed to developing field manuals for selected marine sampling platforms to ensure that data collected at different times and places across Australia are directly comparable. Ultimately, 136 individuals from 53 organisations contributed to the Field Manuals for Marine Sampling in Australian Waters released in 2018 (Version 1) and 2020 (Version 2). These field manuals are underpinned by a highly collaborative and iterative process, involving extensive community consultation and review and can thus be considered best practices. In this report, we aim to compile the outcomes of these marine sampling best practices. These outcomes are then integrated into an impact assessment based on the CSIRO Impact Framework. Due to the short period in which the best practices have existed, impact cannot yet be fully assessed, but we lay the foundations to facilitate such an assessment in the future. Overall, the marine sampling best practices are spreading nationally and internationally, as evidenced by uptake and adoption, including by industry (e.g. Woodside) and developing countries (e.g. St Lucia). Australia and the Unites States represent countries with the most downloads, and highest uptake seems to be for the survey design, benthic BRUV, pelagic BRUV, and multibeam manuals. In addition, the best practices have received community endorsement, with recommendations from key national and international organisations (e.g. Parks Australia, Global Ocean Observing System (for the BRUV manual), National Offshore Petroleum Safety and Environmental Management Authority). We anticipate several social, economic, and environmental impacts of the best practices to be measurable in 5-10 years after the release of the best practices (i.e. after 2025). For any single survey, the impact of these best practices may be small, but there is much stronger impact when considering a national perspective, as combined multiple datasets from multiple surveys allow us to see the bigger spatial and temporal picture. In this case, standardised datasets can be combined without the fear of confounding between method-of-observation and ecological signal. Thus, a series of compatible surveys are needed before they can be usefully combined, and the true impact of these best practices will not be felt for years, or maybe even decades. Ultimately, the measures of outcome and impact described in this report will help strengthen the links between marine observing communities and policymaking communities by ensuring that timely and fit-for-purpose information is generated for evidence-based decisions. Citation: Przeslawski R, Foster S, Gibbons B, Langlois T, Monk J (2021). Impact and Outcomes of Marine Sampling Best Practices. Report to the National Environmental Science Program, Marine Biodiversity Hub. Geoscience Australia.