About this dataset

Context

The dataset tabulates the data for the Science Hill, KY population pyramid, which represents the Science Hill population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Science Hill, KY, is 39.3.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Science Hill, KY, is 28.7.
• Total dependency ratio for Science Hill, KY is 68.0.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Science Hill, KY is 3.5.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 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 Science Hill population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Science Hill for the selected age group is shown in the following column.
• Population (Female): The female population in the Science Hill for the selected age group is shown in the following column.
• Total Population: The total population of the Science Hill 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 Science Hill Population by Age. You can refer the same here

The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Research Programmes, NIHR Centres of Excellence and Facilities, plus the NIHR Academy. NIHR awards from all NIHR Research Programmes and the NIHR Academy that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion criteriaNIHR awards are categorised as public health awards if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health awards across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR awards categorised as public health awards from NIHR Research Programmes and the NIHR Academy. This dataset does not currently include public health awards or projects funded by any of the three NIHR Research Schools or any of the NIHR Centres of Excellence and Facilities. Therefore, awards from the NIHR Schools for Public Health, Primary Care and Social Care, NIHR Public Health Policy Research Unit and the NIHR Health Protection Research Units do not feature in this curated portfolio.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. This caveat applies to all data within the dataset irrespective of the funding NIHR Research Programme or NIHR Academy award.Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further informationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows: NIHR School for Public Health here. NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here. NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here. NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

Description:

👉 Download the dataset here

Welcome to the Oceanic_Life-Dataset, an extraordinary collection of 7,990 high-resolution images that reveal the vast and diverse beauty of the ocean’s inhabitants. This meticulously curated dataset highlights the vibrant and intricate life forms thriving beneath the waves, from vibrant corals to swift fish, stealthy octopuses, and apex predators like sharks. The Oceanic_Life-Dataset offers a comprehensive visual resource that allows researchers, educators, and enthusiasts to dive deep into the ocean’s mysterious ecosystems.

Download Dataset

Key Features:

Expansive Image Database: With a vast array of 7,990 professionally curated images, this dataset presents a detailed exploration of marine life, providing a broad perspective on the species populating our oceans.

Diverse Marine Species: This dataset captures an impressive variety of aquatic creatures, from the delicate corals forming vibrant underwater landscapes to graceful fish, majestic sharks, intricate cephalopods, and other remarkable ocean inhabitants. It encapsulates the stunning biodiversity of marine ecosystems in unprecedented detail.

Exceptional Image Quality: Each image has been selected with care to ensure optimal clarity and high-definition visual accuracy. Whether you’re studying marine biology or conducting research on marine habitats, these visuals provide the intricate details necessary for an in-depth analysis of marine species.

Broad Taxonomic Coverage: Spanning a wide range of oceanic life, the dataset includes images of various marine species, from tropical coral reefs to deep-sea organisms. It serves as a critical resource for biodiversity research, enabling the study of species interactions, population dynamics, and ecological roles.

Ideal for Research, Education, and Conservation:

This dataset is designed to be a powerful tool for scientific research, educational purposes, and conservation efforts. Researchers can leverage this data for machine learning models aimed at identifying species, while educators can use the images to engage students in marine biology lessons. Conservationists can also use the dataset to bring awareness to the rich diversity found in our oceans and the importance of protecting it.

Enhanced Conservation Efforts: By visually capturing the beauty and complexity of marine life, the Oceanic_Life-Dataset encourages a deeper appreciation of the underwater world. It can serve as a strong foundation for campaigns that promote marine conservation, sustainability, and environmental stewardship.

Enriched Ecological Insights: Researchers can use this dataset to explore ecological relationships, study the impact of human activity on oceanic species, and develop data-driven solutions to preserve fragile marine ecosystems. The diversity within the dataset makes it suitable for AI-based research, image classification, and the development of conservation strategies.

Applications:

Marine Biology & Ecology: This dataset supports the study of ocean ecosystems, species distribution, and habitat interactions.

Artificial Intelligence & Machine Learning: Ideal for training computer vision models to recognize and categorize marine species.

Environmental Monitoring: A valuable resource for assessing the health of marine ecosystems and identifying changes in biodiversity due to climate change or pollution.

Education & Outreach: Engages audiences of all ages by providing captivating visuals that highlight the need for ocean conservation.

This dataset is sourced from Kaggle.

The "Forest Proximate People" (FPP) dataset is one of the data layers contributing to the development of indicator #13, “number of forest-dependent people in extreme poverty,” of the Collaborative Partnership on Forests (CPF) Global Core Set of forest-related indicators (GCS). The FPP dataset provides an estimate of the number of people living in or within 5 kilometers of forests (forest-proximate people) for the year 2019 with a spatial resolution of 100 meters at a global level.

For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L. Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: A new methodology and global estimates. Background Paper to The State of the World’s Forests 2022 report. Rome, FAO.

Contact points:

Maintainer: Leticia Pina

Maintainer: Sarah E., Castle

Data lineage:

The FPP data are generated using Google Earth Engine. Forests are defined by the Copernicus Global Land Cover (CGLC) (Buchhorn et al. 2020) classification system’s definition of forests: tree cover ranging from 15-100%, with or without understory of shrubs and grassland, and including both open and closed forests. Any area classified as forest sized ≥ 1 ha in 2019 was included in this definition. Population density was defined by the WorldPop global population data for 2019 (WorldPop 2018). High density urban populations were excluded from the analysis. High density urban areas were defined as any contiguous area with a total population (using 2019 WorldPop data for population) of at least 50,000 people and comprised of pixels all of which met at least one of two criteria: either the pixel a) had at least 1,500 people per square km, or b) was classified as “built-up” land use by the CGLC dataset (where “built-up” was defined as land covered by buildings and other manmade structures) (Dijkstra et al. 2020). Using these datasets, any rural people living in or within 5 kilometers of forests in 2019 were classified as forest proximate people. Euclidean distance was used as the measure to create a 5-kilometer buffer zone around each forest cover pixel. The scripts for generating the forest-proximate people and the rural-urban datasets using different parameters or for different years are published and available to users. For more detail, such as the theory behind this indicator and the definition of parameters, and to cite this data, see: Newton, P., Castle, S.E., Kinzer, A.T., Miller, D.C., Oldekop, J.A., Linhares-Juvenal, T., Pina, L., Madrid, M., & de Lamo, J. 2022. The number of forest- and tree-proximate people: a new methodology and global estimates. Background Paper to The State of the World’s Forests 2022. Rome, FAO.

References:

Buchhorn, M., Smets, B., Bertels, L., De Roo, B., Lesiv, M., Tsendbazar, N.E., Herold, M., Fritz, S., 2020. Copernicus Global Land Service: Land Cover 100m: collection 3 epoch 2019. Globe.

Dijkstra, L., Florczyk, A.J., Freire, S., Kemper, T., Melchiorri, M., Pesaresi, M. and Schiavina, M., 2020. Applying the degree of urbanisation to the globe: A new harmonised definition reveals a different picture of global urbanisation. Journal of Urban Economics, p.103312.

WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University, 2018. Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00645

Online resources:

GEE asset for "Forest proximate people - 5km cutoff distance"

This dataset contains results from the Aurora Australis Voyage 7 (KROCK) 1992-93, related to mesoscale distribution of krill and zooplankton communities in Prydz Bay in relation to physical and biological oceanographic parameters. There were five objectives of this project: to define the distribution patterns and abundance of krill in the krill dominated continental shelf area of the Prydz Bay region; to define the krill population structure within this area and the distribution pattern of developmental stages, especially spawning females; to define the distribution patterns and composition of the other two principal communities, neritic and oceanic, which border the krill dominated community; to specifically determine the zooplankton composition within the main feeding area of Adelie Penguins from Bechervaise Island monitoring site, Mawson; to record and analyse various physical and biological processes, eg. salinity, temperature, ice and phytoplankton, to determine how these parameters affect the observed distribution patterns. Surveys of krill and other zooplankton were taken in Prydz Bay, Antarctica between January and February 1993. At each station, rectangular midwater trawls and CTDs/bottle casts were made. During the program, echosounders and echointegrators were operating to provide krill abundance and distribution data, in addition to that from the RMT trawls. Initial analysis has shown that Euphausia crystallorophias dominates the neritic community on the shelf, while Euphausia superba was found not to occur in high abundance in the central Prydz Bay area between 70 and 78 degrees East. This dataset is a subset of the full cruise.

This indicator is no longer maintained, and is considered OBSOLETE.

INDICATOR DEFINITION The number of Antarctic species that are protected under an international agreement.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: RESPONSE

RATIONALE FOR INDICATOR SELECTION As a response-type indicator, the number of species under some type of protection scheme reflects the level of management associated with protecting the Antarctic environment. It may also reflect the number of Antarctic species that require heightened protection (see Analysis).

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctica and Southern Ocean including sub-Antarctic Islands.

Frequency: Each of the international conventions or protocols relating to the protection of species is searched annually for those species that could be classed in any way Antarctic.

Measurement technique: The species and the convention or protocol is noted and added to the indicator database. An inclusive definition of the Antarctic is used. This definition does not correspond to the Antarctic Treaty area sensu stricto, CCAMLR zone or for example Antarctic Convergence.

Once the search has been completed and the data entered into the database, an automatic report produces a single figure - the indicator.

LINKS TO OTHER INDICATORS

DATA DESCRIPTION The data describes the conservation status accorded to most Southern Ocean species by various international (and some Australian) treaties, protocols and agreements. Links to conservation bodies and agencies (eg IUCN, BLI) are given with the database, as are the texts justifying the conservation status accorded each species.

Information was obtained from the ANARE Health Register. See Metadata record entitled ANARE Health Register.

INDICATOR DEFINITION Human population in stations and ships expressed in person-days.

TYPE OF INDICATOR There are three types of indicators used in this report: 1.Describes the CONDITION of important elements of a system; 2.Show the extent of the major PRESSURES exerted on a system; 3.Determine RESPONSES to either condition or changes in the condition of a system.

This indicator is one of: PRESSURE

RATIONALE FOR INDICATOR SELECTION It is generally accepted that the potential impact on the natural environment is proportional to the human population. This is the 'human footprint'. Human activities can cause disruption in physical, chemical and biological systems. As stated by the Australian Bureau of Statistics (1996): 'To understand the human impact on the Australian environment, it is necessary to know how many people live here, and how they are distributed across the continent.'

This indicator reveals where the greatest direct pressures related to size of the human population (e.g. fuel usage, sewerage and other waste generation etc) occur.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctic and sub-Antarctic stations and ANARE ships travelling to and from these stations.

Frequency: Monthly figures reported annually.

Measurement technique: The Polar Medicine Branch collects data on all expeditioner movements. These data are entered into the Health Register and updated as personnel arrive on or leave a station.

RESEARCH ISSUES Now that this figure is available, research is required to ascertain the quantitive relationships of station and ship population to other indicators such as fuel usage and waste generation. This measure may be able to deliver a quantitative estimate of human pressure on the Antarctic environment.

LINKS TO OTHER INDICATORS SOE Indicator 47 - Number and nature of incidents resulting in environmental impact SOE Indicator 49 - Medical consultations per 1000 person years SOE Indicator 50 - Effluent monitoring - Volume of coastal discharge from Australian stations SOE Indicator 51 - Effluent monitoring - Biological oxygen demand SOE Indicator 52 - Effluent monitoring - Suspended solids content SOE Indicator 53 - Recycled and quarantine waste returned to Australia SOE Indicator 54 - Amount of waste incinerated at Australian Stations SOE Indicator 56 - Monthly fuel usage of the generator sets and boilers SOE Indicator 57 - Monthly total of fuel used by station incinerators SOE Indicator 58 - Monthly total of fuel used by station vehicles SOE Indicator 59 - Monthly electricity usage SOE Indicator 60 - Total helicopter hours SOE Indicator 61 - Total potable water consumption

The fields in this dataset are: Location Date Population (person-days) Illness Rate (per 1000 person years) Injury Rate (per 1000 person years)

This dataset was originally set up as a "State of the Environment" indicator - however, that application no longer functions at the Australian Antarctic Data Centre, so the data have been extracted and attached to this original metadata record for the indicator.

Information was obtained from the ANARE Health Register. See Metadata record entitled ANARE Health Register.

INDICATOR DEFINITION Human population in stations and ships expressed in person-days.

RATIONALE FOR INDICATOR SELECTION It is generally accepted that the potential impact on the natural environment is proportional to the human population. This is the 'human footprint'. Human activities can cause disruption in physical, chemical and biological systems. As stated by the Australian Bureau of Statistics (1996): 'To understand the human impact on the Australian environment, it is necessary to know how many people live here, and how they are distributed across the continent.'

This indicator reveals where the greatest direct pressures related to size of the human population (e.g. fuel usage, sewerage and other waste generation etc) occur.

DESIGN AND STRATEGY FOR INDICATOR MONITORING PROGRAM Spatial scale: Antarctic and sub-Antarctic stations and ANARE ships travelling to and from these stations.

Frequency: Monthly figures reported annually.

Measurement technique: The Polar Medicine Branch collects data on all expeditioner movements. These data are entered into the Health Register and updated as personnel arrive on or leave a station.

RESEARCH ISSUES Now that this figure is available, research is required to ascertain the quantitative relationships of station and ship population to other indicators such as fuel usage and waste generation. This measure may be able to deliver a quantitative estimate of human pressure on the Antarctic environment.

The fields in this dataset are: Location Date Population (person-days) Illness Rate (per 1000 person years) Injury Rate (per 1000 person years)

File List TempTrait_001.txt (MD5: 6b02c81d8d48ab10e7a57b9b5370df46)

Description

      Environmental temperature has strong and systematic effects on biological processes at all levels of organization, ranging from cells to ecosystems. The large temporal and spatial variation in earth’s temperature creates a complex thermal landscape within which life evolves and operates. Here, we present a data set on how diverse biological rates and times respond to temperature, which we hope will aid in the search for general mechanisms of thermal dependence. For nearly a century, intraspecific studies (within single species’ populations) of thermal responses have been conducted on a wide range of organismal traits. Comparative studies of these data are essential for elucidating mechanisms underlying thermal response curves. However, such comparative intraspecific studies have been limited because of a lack of a comprehensive database that organizes these data with consistent units and trait definitions. Here, we present a database of 2352 thermal responses for 220 traits for microbes, plants, and animals compiled from 270 published sources. This represents the most diverse and comprehensive thermal response data set ever compiled. The traits in this database span levels of biological organization from internal physiology to species interactions, and were measured in marine, freshwater, and terrestrial habitats for 411 species. Although we include some physiological rates, most data are for ecological traits, which we define here to mean any organismal trait that directly determines interactions between individuals within or between species. We hope that publication of our data set will encourage others to compile complementary data sets, especially on individual physiology and life history traits. Intraspecific and interspecific (across species’ populations) analyses of our data set should provide new insights into generalities and deviations in the thermal dependence of biological traits, and thus how biological systems, from cells to ecosystems, respond to temperature change. Such insights are essential for understanding how natural biological systems function, and for how life is responding to Earth’s complex and rapidly changing thermal landscape.


      Key words: database; ecoinformatics; ecology; environmental driver; evolution; interspecific; intraspecific; species; thermal response; temperature; trait.

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset and its metadata …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. The Collaborative Australian Protected Areas Database (CAPAD) 2010 provides both spatial and text information about government, Indigenous and privately protected areas for continental Australia. State and Territory conservation agencies supplied data current for various dates between June 2010 and January 2011. This is the eighth version of the database, with previous versions released in 1997, 1999, 2000, 2002, 2004, 2006 and 2008. CAPAD provides a snapshot of protected areas that meet the IUCN definition of a protected area: "A protected area is an area of land and/or sea especially dedicated to the protection and maintenance of biological diversity, and of natural and associated cultural resources, and managed through legal or other effective means" (IUCN 1994). The department publishes a summary of the CAPAD data biannually on its website at http://www.environment.gov.au/parks/nrs/science/capad/index.html. Purpose This version of CAPAD 2010 is for USE BY NON-COMMERCIAL USERS ONLY. It contains all data supplied by Victoria and South Australian Forests data. It is available for non-commercial use by agreeing to the license conditions. If commercial use is sought an approach needs to be made to the individual data suppliers. CAPAD 2010 - restricted spatial data is available for password protected download from the Discover Information Geographically (DIG) website: http://www.environment.gov.au/metadataexplorer/explorer.jsp. See metadata document CAPAD2010restrictedMetadata.htm stored with the data for a list of the Main attributes in the database. Dataset History The Collaborative Australian Protected Areas Database (CAPAD) 2010 provides both spatial and text information about government, Indigenous, private and jointly managed protected areas for continental Australia. State and Territory conservation agencies supplied data current for various dates between July 2010 and January 2011. This is the seventh version of the database, with previous versions released in 1997, 1999, 2000, 2002, 2004, 2006 and 2008. CAPAD provides a snapshot of protected areas that meet the IUCN definition of a protected area. Dataset Citation "Department of Sustainability, Environment, Water, Population and Communities" (2013) Collaborative Australian Protected Areas Database (CAPAD) 2010 - External Restricted. Bioregional Assessment Source Dataset. Viewed 11 December 2018, http://data.bioregionalassessments.gov.au/dataset/47312aee-722e-4c6e-bef8-9e439480503e.