SAGE-Dataset: Dataset for Knowledge Graph based Recommendation Generation Algorithms

This data viewer provides access to a variety of geospatial data layers that can be used to help implement the Department of Interior's Integrated Rangeland Fire Management Strategy (IRFMS) which "sets forth enhanced policies and strategies for preventing suppressing rangeland fire and for restoring sagebrush landscapes impact by fire across the West".Multiple government agencies, including the Bureau of Land Management (BLM), US Geological Survey (USGS) and US Fish and Wildlife Service (USFWS), and partners are collectively contributing data layers to this viewer. These data are related to sagebrush ecosystems, fire and invasive species, and the Greater Sage-Grouse (GRSG). Point of Contact: Bureau of Land Management, National Operations Center, Fire Resource Data Liaison (BLM_OC_Fire_Geospatial@blm.gov)

g3btb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V6 data product. It contains pixel group transmission profiles for a single solar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. Data collection for this product is ongoing. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.

We present five hierarchical demarcations of greater sage-grouse population structure, representing the spatial structure of populations which can exist due to differences in dispersal abilities, landscape configurations, and mating behavior. These demarcations represent Thiessen polygons of graph constructs (least-cost path [LCP] minimum spanning trees [MST; LCP-MST]) representing greater sage-grouse population structure. Because the graphs included locational information of sage-grouse breeding sites, we have provided polygons of the population structure. We also present two results using graph analytics representing node/connectivity importance based on our population structure. Understanding wildlife population structure and connectivity can help managers identify conservation strategies, as structure can facilitate the study of population changes and habitat connectivity can provide information on dispersal and biodiversity. We developed an approach to define hierarchical population structure (in other words, demarcation of subpopulations) using graph theory (in other words, connectivity) from an amalgamation of biological inferences encompassing dispersal capabilities based on movements and genetic flow, seasonal habitat conditions, and functional processes (for example, selection of habitat at multiple scales) affecting movements. We applied our approach to greater sage-grouse (Centrocercus urophasianus), an upland gamebird species of conservation concern in western United States. We defined sage-grouse population structure by creating a cost surface, informed from functional processes of habitat characteristics to account for the resistance of inter-patch movements, and developing least-cost paths between breeding habitat sites (leks). The least-cost paths were combined into a multi-path graph construct for which we then used information on potential connectivity (dispersal distances) and functional connectivity (permeability of fragmented landscapes based on selection preferences) to decompose the graph into structures of subpopulations.

Abstract

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content

Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in all ten administrative regions in Ghana. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older were selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

Ghana used a stratified, multistage cluster design. The sample was stratified by administrative region (Ashanti, Brong Ahafo, Central, Eastern, Greater Accra, Northern, Upper East, Upper West, Volta and Western) and type of locality (urban/rural) resulting in 20 strata and is nationally representative. The Census Enumerated Areas (CEA) of the 2000 Population and Housing Census was used as the sampling frame. A sample of 251 EAs was selected as the primary sampling units (PSU). One of the selected PSUs was not used. This was because the EA which was expected to be located at Korle Bu Teaching hospital cccould not be traced. The number of EAs to be selected from each strata was based on proportional allocation (determined by the number of EAs in each strata specified on the census frame). EAs were then selected from each stratum with probability proportional to size; the measure of size being the number of individuals aged 50 years or more in the EA. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: (1) WHS/SAGE Wave 0 follow-up households with one or more members aged 50 years or more; (2) New households with one or more members aged 50 years or more; (3) WHS/SAGE Wave 0 follow-up households which did not include any members aged 50 years or more, but included residents aged 18-49; and, (4) New households which did not include any members aged 50 years or more, but included residents aged 18-49.

Twenty-four households were randomly selected from each selected EA. All WHS/SAGE Wave 0 follow-up 50-plus households were eligible for the household interview (one household respondent was selected). Twenty such households were selected. If this target number was not reached, then the balance was selected using systematic sampling from the new 50-plus households. All 50+ members of the household were eligible for the individual interview (multiple individual interviews possible in these households).

Stages of selection Strata: Region, Locality=20 PSU: EAs=235 surveyed SSU: Households=5269 surveyed TSU: Individual=5573 surveyed

Sampling deviation

One of the 251 selected PSUs was not used. This was because the EA which was expected to be located at Korle Bu Teaching hospital cccould not be traced.

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into three local languanges: Akan, Ga and Twi. All SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=86% Cooperation rate=98%

Individual: Response rate=80% Cooperation rate=92%

Abstract

Purpose: The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Innovation, Information, Evidence and Research Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 1 (2007/10) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa. Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions and Vignettes 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilization 6000 Social Cohesion 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 19 of the 28 states in India which covers 96% of the population. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households.

Kind of data

Sample survey data [ssd]

Sampling procedure

World Health Survey Sampling India has 28 states and seven union territories. 19 of the 28 states were included in the design representing 96% of the population. India used a stratified multistage cluster sample design. Six states were selected in accordance with their geographic location and level of development. Strata were defined by the 6 states:(Assam, Karnataka, Maharashtra, Rajasthan, Uttar Pradesh and West Bengal), and locality (urban or rural). There are 12 strata in total. The 2000 Census demarcation was used as the sampling frame. Two stage and three stage sampling was adopted in rural and urban areas, respectively. In rural areas PSUs(villages) were selected probability proportional to size. The measure of size being the 2001 Census population in the village. SSUs (households) were selected using systematic sampling. TSUs (individuals) were selected using Kish tables. In urban areas, PSUs(city wards) were selected probability proportional to size. SSUs(census enumeration blocks), two were randomly selected from each PSU. TSU (households) were selected using systematic sampling. QSU (individuals) were selected as in rural areas. A sample of 379 EAs was selected as the primary sampling units(PSU).

SAGE Sampling The SAGE sample was pre-determined as all PSUs and households selected for the WHS/SAGE Wave 0 survey were included. Exceptions are three PSUs in Assam which were replaced as they were inaccessible due to flooding. And a further six PSUs were omitted for which the household roster information was not available. In each selected EA, a listing of the households was conducted to classify each household into the following mutually exclusive categories: 1)Households with a WHS/SAGE Wave 0 respondent aged 50-plus: all members aged 50-plus including the WHS/SAGE Wave 0 respondent were eligible for the individual interview. 2)Households with a WHS/SAGE Wave 0 respondent aged 47-49: all members aged 50-plus including the WHS/SAGE Wave 0 respondent aged 47-49 was eligible for the individual interview. 3)Households with a WHS/SAGE Wave 0 female respondent aged 18-46: all females members aged 18-49 including the WHS/SAGE Wave 0 female respondent aged 18-46 were eligible for the individual interview. 4)Households with a WHS/SAGE Wave 0 male respondent aged 18-46: three households were selected using systematic sampling and one male aged 18-49 was eligible for the individual interview. In the households not selected, all members aged 50-plus were eligible for the individual interview.

Stages of selection Strata: State, Locality=12 PSU: EAs=375 surveyed SSU: Households=10424 surveyed TSU: Individual=12198 surveyed

Mode of data collection

Face-to-face [f2f] PAPI

Research instrument

The questionnaires were based on the WHS Model Questionnaire with some modification and many new additions. A household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to households that had a death in the last 24 months. An Individual questionniare was administered to eligible respondents identified from the household roster. A Proxy questionnaire was administered to individual respondents who had cognitive limitations. A Womans Questionnaire was administered to all females aged 18-49 years identified from the household roster. The questionnaires were developed in English and were piloted as part of the SAGE pretest in 2005. All documents were translated into Hindi, Assamese, Kanada and Marathi. SAGE generic questionnaires are available as external resources.

Cleaning operations

Data editing took place at a number of stages including: (1) office editing and coding (2) during data entry (3) structural checking of the CSPro files (4) range and consistency secondary edits in Stata

Response rate

Household Response rate=88% Cooperation rate=92%

Individual: Response rate=68% Cooperation rate=92%

This shapefile represents habitat suitability categories (High, Moderate, Low, and Non-Habitat) derived from a composite, continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada and northeastern California during the winter season, and is a surrogate for habitat conditions during periods of cold and snow. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap (Petersen 2008, SageStitch (Comer et al. 2002, LANDFIRE (Landfire 2010), and the CA Fire and Resource Assessment Program (CFRAP 2006). The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished; based on the methods of Homer et al. 2014, Xian et al. 2015 ) and conifer (primarily pinyon-juniper, P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014); additional telemetry location data from field sites in 2014 were added to the dataset. The dataset was then split according calendar date into three seasons (spring, summer, winter). Winter included telemetry locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) and using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell during the spring season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. HABITAT CATEGORIZATION: Using the same ecoregion boundaries described above, the habitat classification dataset (an independent data set comprising 10% of the total telemetry location sample) was split into locations falling within respective north and south regions. HSI values from the composite and relativized statewide HSI surface were then extracted to each classification dataset location within the north and south region. The distribution of these values were used to identify class break values corresponding to 0.5 (high), 1.0 (moderate), and 1.5 (low) standard deviations (SD) from the mean HSI. These class breaks were used to classify the HSI surface into four discrete categories of habitat suitability: High, Moderate, Low, and Non-Habitat. In terms of percentiles, High habitat comprised greater than 30.9 % of the HSI values, Moderate comprised 15 – 30.9%, Low comprised 6.7 – 15%, and Non-Habitat comprised less than 6.7%.The classified north and south regions were then clipped by the boundary layer and mosaicked to create a statewide categorical surface for habitat selection . Each habitat suitability category was converted to a vector output where gaps within polygons less than 1.2 million square meters were eliminated, polygons within 500 meters of each other were connected to create corridors and polygons less than 1.2 million square meters in one category were incorporated to the adjacent category. The final step was to mask major roads that were buffered by 50m (Census, 2014), lakes (Peterson, 2008) and urban areas, and place those masked areas into the non-habitat category. The existing urban layer (Census 2010) was not sufficient for our needs because it excluded towns with a population lower than 1,500. Hence, we masked smaller towns (populations of 100 to 1500) and development with Census Block polygons (Census 2015) that had at least 50% urban development within their boundaries when viewed with reference imagery (ArcGIS World Imagery Service Layer). REFERENCES: California Forest and Resource Assessment Program (CFRAP). 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Census 2010. TIGER/Line Shapefiles. Urban Areas [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2014. TIGER/Line Shapefiles. Roads [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Census 2015. TIGER/Line Shapefiles. Blocks [Geospatial data.] U.S. Census Bureau, Washington D.C., https://www.census.gov/geo/maps-data/data/tiger-line.html Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis Xian, G., Homer, C., Rigge, M., Shi, H., and Meyer, D. 2015. Characterization of shrubland ecosystem components as continuous fields in the northwest United States. Remote Sensing of Environment 168:286-300. NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

Seriation and PoissonC were applied to a simulated SAGE data set containing three expression patterns and increasing amount of noise tags. The dataset is described in more detail in the text and in Table S1. TP (True Positives) include tags that were correctly classified as belonging to the correct expression group (expression pattern 1, 2, or 3 or noise) by assigning them to the cluster (PoissonC) or contig (seriation) containing other members of the expression group. FP (False Positives) include noise tags that have been erroneously assigned to a cluster or contig with tags that conform to the expression pattern 1, 2, or 3.*The false positive rate is significantly higher for the PoissonC algorithm than it is for seriation mostly due to the erroneous assignment of noise tags to an expression pattern (p

Data quality notice: The SAGE III/ISS team recommends against using data from events 2024030913SS, 2024030915SS, and 2024030917SS. These events were affected by line-of-sight blockage from a docked spacecraft which undermined the data quality. Typically, such events are withheld by a quality assurance process. g3btb_53 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 1B Solar Event Transmission Data (Native) V053 data product. It contains pixel group transmission profiles for a single solar event. Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, the Stratospheric Aerosol and Gas Experiment III on the International Space Station (SAGE III/ISS), the second instrument from the SAGE III project, is externally mounted on the International Space Station (ISS). This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth’s atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III/ISS includes other key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, nitrogen dioxide, nitrogen trioxide, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.

The Sage Group reported GBP0.2 in EPS Earnings Per Share for its fiscal semester ending in March of 2025. Data for The Sage Group | SGE - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last June in 2025.

This dataset tracks annual distribution of students across grade levels in Sage & Sand Academy

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Abstract

The multi-country Study on Global Ageing and Adult Health (SAGE) is run by the World Health Organization's Multi-Country Studies unit in the Health Systems and Innovation Cluster. SAGE is part of the unit's Longitudinal Study Programme which is compiling longitudinal data on the health and well-being of adult populations, and the ageing process, through primary data collection and secondary data analysis. SAGE baseline data (Wave 0, 2002/3) was collected as part of WHO's World Health Survey http://www.who.int/healthinfo/survey/en/index.html (WHS). SAGE Wave 2 (2014/15) provides a comprehensive data set on the health and well-being of adults in six low and middle-income countries: China, Ghana, India, Mexico, Russian Federation and South Africa.

Objectives: To obtain reliable, valid and comparable health, health-related and well-being data over a range of key domains for adult and older adult populations in nationally representative samples To examine patterns and dynamics of age-related changes in health and well-being using longitudinal follow-up of a cohort as they age, and to investigate socio-economic consequences of these health changes To supplement and cross-validate self-reported measures of health and the anchoring vignette approach to improving comparability of self-reported measures, through measured performance tests for selected health domains To collect health examination and biomarker data that improves reliability of morbidity and risk factor data and to objectively monitor the effect of interventions

Additional Objectives: To generate large cohorts of older adult populations and comparison cohorts of younger populations for following-up intermediate outcomes, monitoring trends, examining transitions and life events, and addressing relationships between determinants and health, well-being and health-related outcomes To develop a mechanism to link survey data to demographic surveillance site data To build linkages with other national and multi-country ageing studies To improve the methodologies to enhance the reliability and validity of health outcomes and determinants data To provide a public-access information base to engage all stakeholders, including national policy makers and health systems planners, in planning and decision-making processes about the health and well-being of older adults

Methods: SAGE's first full round of data collection included both follow-up and new respondents in most participating countries. The goal of the sampling design was to obtain a nationally representative cohort of persons aged 50 years and older, with a smaller cohort of persons aged 18 to 49 for comparison purposes. In the older households, all persons aged 50+ years (for example, spouses and siblings) were invited to participate. Proxy respondents were identified for respondents who were unable to respond for themselves. Standardized SAGE survey instruments were used in all countries consisting of five main parts: 1) household questionnaire; 2) individual questionnaire; 3) proxy questionnaire; 4) verbal autopsy questionnaire; and, 5) appendices including showcards. A VAQ was completed for deaths in the household over the last 24 months. The procedures for including country-specific adaptations to the standardized questionnaire and translations into local languages from English follow those developed by and used for the World Health Survey.

Content

Household questionnaire 0000 Coversheet 0100 Sampling Information 0200 Geocoding and GPS Information 0300 Recontact Information 0350 Contact Record 0400 Household Roster 0450 Kish Tables and Household Consent 0500 Housing 0600 Household and Family Support Networks and Transfers 0700 Assets and Household Income 0800 Household Expenditures 0900 Interviewer Observations

Verbal Autopsy questionnaire Section 1: Information on the Deceased and Date/Place of Death Section 1A7: Vital Registration and Certification Section 2: Information on the Respondent Section 3A: Medical History Associated with Final Illness Section 3B: General Signs and Symptoms Associated with Final Illness Section 3E: History of Injuries/Accidents Section 3G: Health Service Utilization Section 4: Background Section 5A: Interviewer Observations

Individual questionnaire 1000 Socio-Demographic Characteristics 1500 Work History and Benefits 2000 Health State Descriptions 2500 Anthropometrics, Performance Tests and Biomarkers 3000 Risk Factors and Preventive Health Behaviours 4000 Chronic Conditions and Health Services Coverage 5000 Health Care Utilisation 6000 Social Networks 7000 Subjective Well-Being and Quality of Life (WHOQoL-8 and Day Reconstruction Method) 8000 Impact of Caregiving 9000 Interviewer Assessment

Proxy Questionnaire Section1 Respondent Characteristics and IQ CODE Section2 Health State Descriptions Section4 Chronic Conditions and Health Services Coverage Section5 Health Care Utilisation

Geographic coverage

National coverage

Analysis unit

households and individuals

Universe

The household section of the survey covered all households in 31 of the 32 federal states in Mexico. Colima was excluded. Institutionalised populations are excluded. The individual section covered all persons aged 18 years and older residing within individual households. As the focus of SAGE is older adults, a much larger sample of respondents aged 50 years and older was selected with a smaller comparative sample of respondents aged 18-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

In Mexico strata were defined by locality (metropolitan, urban, rural). All 211 PSUs selected for wave 1 were included in the wave 2 sample. A sub-sample of 211 PSUs was selected from the 797 WHS PSUs for the wave 1 sample. The Basic Geo-Statistical Areas (AGEB) defined by the National Institute of Statistics (INEGI) constitutes a PSU. PSUs were selected probability proportional to three factors: a) (WHS/SAGE Wave 0 50plus): number of WHS/SAGE Wave 0 50-plus interviewed at the PSU, b) (State Population): population of the state to which the PSU belongs, c) (WHS/SAGE Wave 0 PSU at county): number of PSUs selected from the county to which the PSU belongs for the WHS/SAGE Wave 0; The first and third factors were included to reduce geographic dispersion. Factor two affords states with larger populations a greater chance of selection.

All WHS/SAGE Wave 0 individuals aged 50 years or older in the selected rural or urban PSUs and a random sample 90% of individuals aged 50 years or older in metropolitan PSUs who had been interviewed for the WHS/SAGE Wave 0 were included in the SAGE Wave 1 ''primary'' sample. The remaining 10% of WHS/SAGE Wave 0 individuals aged 50 years or older in metropolitan areas were then allocated as a ''replacement'' sample for individuals who could not be contacted or did not consent to participate in SAGE Wave 1. A systematic sample of 1000 WHS/SAGE Wave 0 individuals aged 18-49 across all selected PSUs was selected as the ''primary'' sample and 500 as a ''replacement'' sample.

This selection process resulted in a sample which had an over-representation of individuals from metropolitan strata; therefore, it was decided to increase the number of individuals aged 50 years or older from rural and urban strata. This was achieved by including individuals who had not been part of WHS/SAGE Wave 0 (which became a ''supplementary'' sample), although the household in which they lived included an individual from WHS/SAGE Wave 0. All individuals aged 50 or over were included from rural and urban ''18-49 households'' (that is, where an individual aged 18-49 was included in WHS/SAGE Wave 0) as part of the ''primary supplementary'' sample. A systematic random sample of individuals aged 50 years or older was then obtained from urban and rural households where an individual had already been selected as part of the 50 years and older or 18-49 samples. These individuals then formed part of the ''primary supplementary'' sample and the remainder (that is, those not systematically selected) were allocated to the ''replacement supplementary'' sample. Thus, all individuals aged 50 years or older who lived in households in urban and rural PSUs obtained for SAGE Wave 1 were selected as either a primary or replacement participant. A final ''replacement'' sample for the 50 and over age group was obtained from a systematic sample of all individuals aged 50 or over from households which included the individuals already selected for either the 50 and over or 18-49. This sampling strategy also provided participants who had not been included in WHS/SAGE Wave 0, but lived in a household where an individual had been part of WHS/SAGE Wave 0 (that is, the ''supplementary'' sample), in addition to follow-up of individuals who had been included in the WHS/SAGE Wave 0 sample.

Strata: Locality = 3 PSU: AGEBs = 211 SSU: Households = 6549 surveyed TSU: Individual = 6342 surveyed

Mode of data collection

Face-to-face [f2f], CAPI

Research instrument

The questionnaires were based on the SAGE Wave 1 Questionnaires with some modification and new additions, except for verbal autopsy. SAGE Wave 2 used the 2012 version of the WHO Verbal Autopsy Questionnare. SAGE Wave 1 used an adapted version of the Sample Vital Registration iwth Verbal Autopsy (SAVVY) questionnaire. A Household questionnaire was administered to all households eligible for the study. A Verbal Autopsy questionnaire was administered to 50 plus households only. In follow-up 50 plus household if the death occured since the last wave of the study and in a new 50 plus household if the death occurred in the the

minhhienhptv25104/sage dataset hosted on Hugging Face and contributed by the HF Datasets community

This raster represents a continuous surface of sage-grouse habitat suitability index (HSI) values for Nevada. HSIs were calculated for spring, summer, and winter sage-grouse seasons, and then multiplied together to create this composite dataset. Summary of steps to create Habitat Categories: HABITAT SUITABILITY INDEX: The HSI was derived from a generalized linear mixed model (specified by binomial distribution and created using ArcGIS 10.2.2) that contrasted data from multiple environmental factors at used sites (telemetry locations) and available sites (random locations). Predictor variables for the model represented vegetation communities at multiple spatial scales, water resources, habitat configuration, urbanization, roads, elevation, ruggedness, and slope. Vegetation data was derived from various mapping products, which included NV SynthMap, SageStitch, LANDFIRE, and the CA Fire and Resource Assessment Program. The analysis was updated to include high resolution percent cover within 30 x 30 m pixels for Sagebrush, non-sagebrush, herbaceous vegetation, and bare ground (C. Homer, unpublished) and conifer (P. Coates, unpublished). The pool of telemetry data included the same data from 1998 - 2013 used by Coates et al. (2014) as well as additional telemetry location data from field sites in 2014. The dataset was then split according to calendar date into three seasons (spring, summer, winter). Spring included telemetry locations (n = 14,058) from mid-March to June; summer included locations (n = 11,743) from July to mid-October; winter included locations (n = 4862) from November to March. All age and sex classes of marked grouse were used in the analysis. Sufficient data (i.e., a minimum of 100 locations from at least 20 marked Sage-grouse) for modeling existed in 10 subregions for spring and summer, and seven subregions in winter, using all age and sex classes of marked grouse. It is important to note that although this map is composed of HSI values derived from the seasonal data, it does not explicitly represent habitat suitability for reproductive females (i.e., nesting and with broods). Insufficient data were available to allow for estimation of this habitat type for all seasons throughout the study area extent. A Resource Selection Function (RSF) was calculated for each subregion using R software (v 3.13) using generalized linear models to derive model-averaged parameter estimates for each covariate across a set of additive models. Subregional RSFs were transformed into Habitat Suitability Indices, and averaged together to produce an overall statewide HSI whereby a relative probability of occurrence was calculated for each raster cell for each season. In order to account for discrepancies in HSI values caused by varying ecoregions within Nevada, the HSI was divided into north and south extents using a slightly modified flood region boundary (Mason 1999) that was designed to represent respective mesic and xeric regions of the state. North and south HSI rasters were each relativized according to their maximum value to rescale between zero and one, then mosaicked once more into a state-wide extent. The three seasonal HSIs were then multiplied to create a composite annual HSI. REFERENCES: California Forest and Resource Assessment Program. 2006. Statewide Land Use / Land Cover Mosaic. [Geospatial data.] California Department of Forestry and Fire Protection, http://frap.cdf.ca.gov/data/frapgisdata-sw-rangeland-assessment_data.php Coates, P.S., Casazza, M.L., Brussee, B.E., Ricca, M.A., Gustafson, K.B., Overton, C.T., Sanchez-Chopitea, E., Kroger, T., Mauch, K., Niell, L., Howe, K., Gardner, S., Espinosa, S., and Delehanty, D.J. 2014, Spatially explicit modeling of greater sage-grouse (Centrocercus urophasianus) habitat in Nevada and northeastern California—A decision-support tool for management: U.S. Geological Survey Open-File Report 2014-1163, 83 p., http://dx.doi.org/10.3133/ofr20141163. ISSN 2331-1258 (online) Comer, P., Kagen, J., Heiner, M., and Tobalske, C. 2002. Current distribution of sagebrush and associated vegetation in the western United States (excluding NM). [Geospatial data.] Interagency Sagebrush Working Group, http://sagemap.wr.usgs.gov Homer, C.G., Aldridge, C.L., Meyer, D.K., and Schell, S.J. 2014. Multi-Scale Remote Sensing Sagebrush Characterization with Regression Trees over Wyoming, USA; Laying a Foundation for Monitoring. International Journal of Applied Earth Observation and Geoinformation 14, Elsevier, US. LANDFIRE. 2010. 1.2.0 Existing Vegetation Type Layer. [Geospatial data.] U.S. Department of the Interior, Geological Survey, http://landfire.cr.usgs.gov/viewer/ Mason, R.R. 1999. The National Flood-Frequency Program—Methods For Estimating Flood Magnitude And Frequency In Rural Areas In Nevada U.S. Geological Survey Fact Sheet 123-98 September, 1999, Prepared by Robert R. Mason, Jr. and Kernell G. Ries III, of the U.S. Geological Survey; and Jeffrey N. King and Wilbert O. Thomas, Jr., of Michael Baker, Jr., Inc. http://pubs.usgs.gov/fs/fs-123-98/ Peterson, E. B. 2008. A Synthesis of Vegetation Maps for Nevada (Initiating a 'Living' Vegetation Map). Documentation and geospatial data, Nevada Natural Heritage Program, Carson City, Nevada, http://www.heritage.nv.gov/gis NOTE: This file does not include habitat areas for the Bi-State management area and the spatial extent is modified in comparison to Coates et al. 2014

The Sage Group reported GBP255M in EBIT for its fiscal semester ending in March of 2025. Data for The Sage Group | SGE - Ebit including historical, tables and charts were last updated by Trading Economics this last June in 2025.

The revenue of The Sage Group with headquarters in the United Kingdom amounted to 2.18 billion British pounds in 2023. The reported fiscal year ends on September 30.Compared to the earliest depicted value from 2020 this is a total increase by approximately 0.28 billion British pounds. The trend from 2020 to 2023 shows ,however, that this increase did not happen continuously.

g3blspb_6 is the Stratospheric Aerosol and Gas Experiment III (SAGE III) on the International Space Station (ISS) (SAGE III/ISS) Level 2 Lunar Event Species Profiles (Native) V06 data product. It contains all the species products for a single lunar event. SAGE III was Launched on February 19, 2017 on a SpaceX Falcon 9 from Kennedy Space Center, SAGE III-ISS is the second instrument from the SAGE III project, externally mounted on the ISS. This ISS-based instrument uses a technique known as occultation, which involves looking at the light from the Sun or Moon as it passes through Earth's atmosphere at the edge, or limb, of the planet to provide long-term monitoring of ozone vertical profiles of the stratosphere and mesosphere. The data provided by SAGE III-ISS includes key components of atmospheric composition and their long-term variability, focusing on the study of aerosols, chlorine dioxide, clouds, nitrogen dioxide, nitrogen trioxide, pressure and temperature, and water vapor. SAGE data has historically been used by the World Meteorological Organization to inform their periodic assessments of ozone depletion. These new observations from the International Space Station will continue the SAGE team's contributions to ongoing scientific understanding of the Earth's atmosphere.