This dataset provides values for RETIREMENT AGE MEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key health statistics from a variety of sources to provide a look at global health and population trends. It includes information on nutrition, reproductive health, education, immunization, and diseases from over 200 countries.

Update Frequency: Biannual

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://datacatalog.worldbank.org/dataset/health-nutrition-and-population-statistics

https://cloud.google.com/bigquery/public-data/world-bank-hnp

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Citation: The World Bank: Health Nutrition and Population Statistics

Banner Photo by @till_indeman from Unplash.

Inspiration

What’s the average age of first marriages for females around the world?

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

This dataset provides values for RETIREMENT AGE WOMEN reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

In 2023, the median age of the population of the United States was 39.2 years. While this may seem quite young, the median age in 1960 was even younger, at 29.5 years. The aging population in the United States means that society is going to have to find a way to adapt to the larger numbers of older people. Everything from Social Security to employment to the age of retirement will have to change if the population is expected to age more while having fewer children. The world is getting older It’s not only the United States that is facing this particular demographic dilemma. In 1950, the global median age was 23.6 years. This number is projected to increase to 41.9 years by the year 2100. This means that not only the U.S., but the rest of the world will also have to find ways to adapt to the aging population.

Albania Population: Average: Age 35 to 39 data was reported at 179.750 Person th in 2022. This records an increase from the previous number of 177.710 Person th for 2021. Albania Population: Average: Age 35 to 39 data is updated yearly, averaging 176.315 Person th from Dec 2001 (Median) to 2022, with 22 observations. The data reached an all-time high of 216.905 Person th in 2001 and a record low of 161.921 Person th in 2015. Albania Population: Average: Age 35 to 39 data remains active status in CEIC and is reported by Institute of Statistics. The data is categorized under Global Database’s Albania – Table AL.G001: Population: by Gender and Age Group.

Photoplethysmograph (PPG) is a physiological signal used to describe the volumetric change of blood flow in peripherals with heart beats. A hardware configuration is employed to capture PPG signals from a number of persons using an IoT sensor. This dataset contains PPG signals from 35 healthy persons , with 50 to 60 PPG signal for each one. The age range of participants is 10-75 years, with an average age of 28.4 years. Each PPG signal contains 300 samples (6 seconds recording) with 50 sample/second sampling rate. The dataset is split into two files: one for training the ANN which contains 1374 PPG signal (about 66% of complete dataset), and the other file to test the ANN which contains 700 PPG signal (about 34% of complete dataset).

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

As of April 2024, around 16.5 percent of global active Instagram users were men between the ages of 18 and 24 years. More than half of the global Instagram population worldwide was aged 34 years or younger.

              Teens and social media

              As one of the biggest social networks worldwide, Instagram is especially popular with teenagers. As of fall 2020, the photo-sharing app ranked third in terms of preferred social network among teenagers in the United States, second to Snapchat and TikTok. Instagram was one of the most influential advertising channels among female Gen Z users when making purchasing decisions. Teens report feeling more confident, popular, and better about themselves when using social media, and less lonely, depressed and anxious.
              Social media can have negative effects on teens, which is also much more pronounced on those with low emotional well-being. It was found that 35 percent of teenagers with low social-emotional well-being reported to have experienced cyber bullying when using social media, while in comparison only five percent of teenagers with high social-emotional well-being stated the same. As such, social media can have a big impact on already fragile states of mind.

As of April 2024, almost 32 percent of global Instagram audiences were aged between 18 and 24 years, and 30.6 percent of users were aged between 25 and 34 years. Overall, 16 percent of users belonged to the 35 to 44 year age group.

              Instagram users

              With roughly one billion monthly active users, Instagram belongs to the most popular social networks worldwide. The social photo sharing app is especially popular in India and in the United States, which have respectively 362.9 million and 169.7 million Instagram users each.

              Instagram features

              One of the most popular features of Instagram is Stories. Users can post photos and videos to their Stories stream and the content is live for others to view for 24 hours before it disappears. In January 2019, the company reported that there were 500 million daily active Instagram Stories users. Instagram Stories directly competes with Snapchat, another photo sharing app that initially became famous due to it’s “vanishing photos” feature.
              As of the second quarter of 2021, Snapchat had 293 million daily active users.

Most of the world’s mountain glaciers have been retreating for more than a century in response to climate change. Accurate, spatially explicit information on glacier retreat is pivotal to study the consequences of ice loss on both abiotic and biotic components of the environment. Here, we present a spatially explicit dataset showing positions of glacier fronts since the Little Ice Age (LIA) maxima. The dataset is based on multiple historical archival records including topographical maps; repeated photographs, paintings and aerial or satellite images with supplement of geochronology and our own field data. We provide ESRI shapefiles showing 728 past positions of 93 glacier fronts from all continents, except Antarctica, covering the period between the Little Ice Age maxima and the present. On average, the time series span the past 190 years. From 2 to 46 past positions per glacier are depicted (on average: 7.8). Past positions of the glaciers have been obtained mostly on the basis of the literature, provided in a separate file, complemented with information obtained from topographical maps, historical, aerial or satellite pictures, and with our own field data, dating the position of geomorphological elements in the landscape on the basis of measurements taken in the field, signals and marks reporting the ancient position of the glacier front, and additional approaches for dating older moraines (lichenometry, dendrochronology, radiocarbon chronology).NOTES TO THE DATABASE:Database structure:glacier: glacier nameGLIMS id: glacier id, according to the Global Land Ice Measurements from Space (GLIMS)dating: calculated (or estimated) dating for a given line. source: source followed to draw the lines. Notes to fields of the database:GLIMS id: the database version is glims_db_20200630 (downloaded on February 3rd 2021). Exceptions i) Maladeta: the glacier is not mapped in the GLIMS db, but it appears in the online viewer (https://www.glims.org/maps/glims); ii) Qamanaarsuup Sermia and Popocatepetl: the glacier is not mapped neither in the GLIMS db nor in the online viewer.dating: in the cases a reference is cited in this field, it refers to the source we followed to estimate the age of the moraine ridge / position, sometimes by analogy with surrounding glaciers (cf. main text).source: specifically, we used:1) Articles / theses / maps: one or more figures from a given source were georeferenced, and the lines were redrawn following the original maps;2) Satellite / orthophotogrammetric data: the glacier profile in the specific year was drawn interpreting the satellite / aerial images provided by the sources (i.e., Esri ArcGIS World Imagery, GN orthophotogrammetry, Google Earth, IGN orthophotogrammetry, Regional orthophotogrammetry - Lombardia, Regional orthophotogrammetry - Vallee d Aoste / Valle d Aosta);3) Databases: lines were used as provided by the sources (i.e., GlaRiskAlp, GLIMS, OpenData Trentino);4) unpublished data / field marks: the identification of the moraine / position occurred in the field or using sources not yet published.The complete description of methodologies has been published on this paper:Marta, S., R. S. Azzoni, D. Fugazza, Levan Tielidze, P. Chand, K. Sieron, P. Almond, R. Ambrosini, F. Anthelme, P. A. Gazitúa, R. Bhambri, A. Bonin, M. Caccianiga, S. Cauvy-Fraunié, J. L. C. Lievano, J. Clague, J. A. C. Rapre, O. Dangles, P. Deline, A. Eger, R. C. Encarnación, S. Erokhin, A. Franzetti, L. Gielly, Fabrizio Gili, M. Gobbi, A. Guerrieri, S. Hågvar, N. Khedim, R. Kinyanjui, E. Messager, M. A. Morales-Martínez, G. Peyre, F. Pittino, J. Poulenard, R. Seppi, M. C. Sharma, N. Urseitova, B. P. Weissling, Y. Yang, V. Zaginaev, A. Zimmer, G. A. Diolaiuti, A. Rabatel, and G. F. Ficetola. 2021. The Retreat of Mountain Glaciers since the Little Ice Age: a Spatially Explicit Database. Data 6:10.3390/data6100107. https://www.mdpi.com/2306-5729/6/10/107#When referring to this dataset, please cite the Marta et al. 2021 paper.

This dataset tracks the average applied tariff rates in both industrial and developing countries. Data is averaged for the years 1981-2005. Figures for 2005 have been estimated. Notes: All tariff rates are based on unweighted averages for all goods in ad valorem rates, or applied rates, or MFN rates whichever data is available in a longer period. Tariff data is primarily based on UNCTAD TRAINS database and then used WTO IDB data for gap filling if possible. Data in 1980s is taken from other source.** Tariff data in these countries came from IMF Global Monitoring Tariff file in 2004 which might include other duties or charges. Country codes are based on the classifications by income in WDI 2006, where 1 = low income, 2 = middle income, 3 = high incone non-OECDs, and 4 = high income OECD countries. Sources: UNCTAD TRAINS database (through WITS); WTO IDB database (through WITS); WTO IDB CD ROMs, various years and Trade Policy Review -- Country Reports in various issues, 1990-2005; UNCTAD Handbook of Trade Control Measures of Developing Countries -- Supplement 1987 and Directory of Import Regimes 1994; World Bank Trade Policy Reform in Developing Countries since 1985, WB Discussion Paper #267, 1994 and World Development Indicators, 1998-2006; The Uruguay Round: Statistics on Tariffs Concessions Given and Received, 1996; OECD Indicators of Tariff and Non-Tariff Trade Barriers, 1996 and 2000; and IMF Global Monitoring Tariff data file 2004. Data source: http://go.worldbank.org/LGOXFTV550 Access Date: October 17, 2007

The 2007 World Bank Group Entrepreneurship Survey measures entrepreneurial activity in 84 developing and industrial countries over the period 2003-2005. The database includes cross-country, time-series data on the number of total and newly registered businesses, collected directly from Registrar of Companies around the world. In its second year, this survey incorporates improvements in methodology, and expanded participation from countries covered, allowing for greater cross-border compatibility of data compared with the 2006 survey. This joint effort by the IFC SME Department and the World Bank Developing Research Group is the most comprehensive dataset on cross-country firm entry data available today. This database The World Bank Group Entrepreneurship Dataaset presents data collected primarily from country business registries using the first annual World Bank Group Questionnaire on Entrepreneurship (alternative sources were tax authorities, finance ministries, and national statistics offices). For more information on the author of the database, Leora Klapper, visit: http://go.worldbank.org/DK5AHCQSO0. This data was access at the preceeding link, on October 11, 2007. Please visit the link for more information in regards to this dataset.

This dataset includes 663 predictor grids used for k-NN global prediction of seafloor total organic carbon.

663 predictor grids available in netCDF4 HDF5 file format. Grids are cell-centered sized 4320 x 2160. File names adhere to the naming conventions discussed below. The naming structure is partioned by underscores and periods in the following order: interface to which the gridded values refer to, quantity of values contained within the grid, units and reference values/units (e.g. meters below sea level), data source, statistic calculated (if applicable), grid pitch, and file extension.

Possible interfaces from the top – down:

SS – Sea surface – atmosphere interface (may also be average of the entire water column)

SF – Seafloor – water interface (may also be denoted by GL)

GL – Ground level (e.g. bottom of pure liquid, top of dirt)

SC – Sediment – crust interface (e.g. sediment above, igneous/metamorphic below)

CM – Crust – mantle interface (e.g. Mohorovicic discontinuity)

Appropriate reference naming marker (bold), original data source, and date of last access:

Becker

Becker, J. J., Wood, W. T., & Martin, K. M. (2014). Global crustal heat flow using random decision forest prediction, Abstract NG31A-3788 presented at 2014 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 06/23/2015.

CRUST1

Pasyanos, M.E., Masters, G., Laske, G. & Ma, Z. (2012). LITHO1.0 - An Updated Crust and Lithospheric Model of the Earth Developed Using Multiple Data Constraints, Abstract T11D-09 presented at 2012 Fall Meeting, AGU, San Francisco, California, U.S.A. Last access: 07/01/2014.

CRUST1_NOAA

As the NOAA sediment thickness database is globally not complete, data gaps in the NOAA grid with this have been supplemented by the CRUST1 sediment thickness (see above citation).

Whittaker, J., Goncharov, A., Williams, S., Müller, R. D., & Leitchenkov, G. (2013) Global sediment thickness dataset updated for the Australian-Antarctic Southern Ocean, Geochemistry, Geophysics, Geosystems. https://doi.org/10.1002/ggge.2018. Last access: 09/02/2018.

GVP

Global Volcanism Program (2013) Volcanoes of the World. In E. Venzke (ed.). (Vol. 4.7.3). Smithsonian Institution. https://doi.org/10.5479/si.GVP.VOTW4-2013. Last access: 09/22/2014.

ETOPO2v2

National Geophysical Data Center (2006). 2-minute Gridded Global Relief Data (ETOPO2) v2. National Geophysical Data Center, NOAA. DOI: 10.7289/V5J1012Q. Last access: 02/06/2013.

PLATES

Coffin, M.F., Gahagan, L.M., & Lawver, L.A. (1998). Present-day Plate Boundary Digital Data Compilation. University of Texas Institute for Geophysics Technical Report (No. 174, pp. 5). Last access: 09/15/2014.

ONRL

Ludwig,W., Amiotte-Suchet, P., & Probst, J. L. (2011). ISLSCP II Global River Fluxes of Carbon and Sediments to the Oceans. In F. G. Hall, G. Collatz, B. Meeson, S. Los, E. Brown de Colstoun, and D. Landis (Eds.), ISLSCP Initiative II Collection. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, U.S.A. http://dx.doi.org/10.3334/ORNLDAAC/1028. Last Access: 02/15/2015.

Muller

Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust, Geochemistry, Geophysics, Geosystems, 9(4), Q04006. https://doi.org/10.1029/2007GC001743. Last accessed: 07/19/2011.

Woa13x

Boyer, T.P., Antonov, J. I., Baranova, O. K., Coleman, C., Garcia, H. E., Grodsky, A., et al. (2013) World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), NOAA Atlas NESDIS 72, Technical Ed. Silver Spring, MD. http://doi.org/10.7289/V5NZ85MT. Last Access: 09/18/2014.

KIM

Kim, S.S. & Wessel, P. (2011). New global seamount census from the altimetry-derived gravity data, Geophysical Journal International, 186, 615-631. https://doi.org/10.1111/j.1365-246X.2011.05076.x. Last access: 09/22/2014.

HYCOM

The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data.Last access: 03/19/2014.

NCEDC

NCEDC (2016). Northern California Earthquake Data Center. UC Berkeley Seismological Laboratory. Dataset. doi:10.7932/NCEDC. Last access: 09/21/2014.

Wei2010

Wei, C.-L., Rowe, G. T., Escobar-Briones, E., Boetius, A., Soltwedel, T., Caley, M. J., et al.(2010). Global patterns and predictions of seafloor biomass using random forests. PLoS ONE,5(12), e15323. https://doi.org/10.1371/journal.pone.0015323 Last access: 06/20/2016.

NGA_egm2008

Pavlis, N.K., Holmes, S. A., Kenyon, S. C., & Factor, J. K. (2008). The EGM2008 Global Gravitational Model, Abstract 2008AGUFM.G22A..01P presented at the 2008 General Assembly of the European Geosciences Union, Vienna, Austria. Last access: 07/10/2014.

WAVEWATCH3

The 1/12 deg global HYCOM+NCODA Ocean Reanalysis was funded by the U.S. Navy and the Modeling and Simulation Coordination Office. Computer time was made available by the DoD High Performance Computing Modernization Program. The output is publicly available at https://hycom.org/publications/acknowledgements/ocean-reanalysis-data. Last access: 03/19/2014.

Updated global seafloor porosity grid using our k-nearest neighbors algorithm using 5 nearest neighbors. Observed data used for prediction from Martin et al. (2015).

Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. Geophysical Research Letters, 42(24), 10640. https://doi.org/10.1002/2015GL065279

Other grids which have been generated by empirical means are latitude (and derivatives), longitude (and derivatives), Coriolis, coast_is_1.0, and the random noise grids.

Units referenced are as follows:

KGM3 - kilogram per cubic meter
MS - meters per second
KM - kilometer
M_ASL - meters above sea level (i.e. meters referenced to sea level)
MWM2 - milliwatt per square meter
TGCYR - terragram of carbon per year
TGYR - terragram per year
MA - megaannum
M - meters
MGCM2 - milligram of carbon per square meter
DEG - degree
S - seconds

Statistics grids are calculated within a given radius (e.g. 10km, 50km, 125km, 250km, 500km, 1000km) of the respective cell-centered value. The statistics grids include mean (.men), average absolute deviation from the mean (.aad), and the common logarithm (.log) of the absolute value of the mean (.mlg). Additionally, some grids are a weighted count for given radii (e.g. seamounts) where weight is a cosine taper from the center of the grid cell.

The grid pitch for this dataset is uniformly at 5-arc minute denoted by “.5m”. Additionally, the extension used (netCDF4) is denoted by “.nc”.

This dataset contains zooarchaeological data relevant to cattle, sheep/goat, and pigs from lowland northern Italy, and an associated R analysis and visualisation script. This dataset supports the journal article:A. Trentacoste, A. Nieto-Espinet, S. Guimarães Chiarelli, and Valenzuela-Lamas. (2023). Systems change: Investigating climatic and environmental impacts on livestock production in lowland Italy between the Bronze Age and Late Antiquity (c. 1700 BC - AD 700). Quaternary International 662–663:26-36. https://doi.org/10.1016/j.quaint.2022.11.005

The majority of the data were collected under the auspices of the ERC-Starting Grant ZooMWest – Zooarchaeology and Mobility in the Western Mediterranean: Husbandry production from the Late Bronze Age to the Late Antiquity (award number 716298), funded by the European Research Council Agency (ERCEA) under the direction of Sílvia Valenzuela-Lamas (2017–2022). This work built on previous data collection undertaken for Trentacoste's (2014) PhD thesis. The dataset was also expanded with support from a Gerda Henkel Stifling Scholarship (AZ 44/F/20) awarded to A. Trentacoste.

The chronological timespan of the dataset is between the Middle Bronze Age and Late Antiquity (c. 1700 BC - AD 700). For details on the methodology underlying the creation of the dataset see Trentacoste et al. (2018) and Trentacoste et al. (2021).

Zooarchaeological data were collected from published sources (see references file), with the exception of some data for the sites of Spina, Vidulis and Aquileia. Metadata for these sites were available in the published literature, but individual data were collected from the archive papers of Italian zooarchaeologist Alfredo Reidel (1925–2014). We are grateful to Francesco Boschin (Università degli Studi di Siena) for access to the archive.

The dataset includes:

Raw biometric data for post-cranial bones for cattle, sheep/goat, pigs, and wild boar on a specimen level. Measurement abbreviations follow Von den Driesch (1976) and Davis (1996; only humerus HT and HTC). File: NItaly_Livestock_Metric_Data.csv

NISP (Number of Identified Specimens) data for site phases with over 100 identified cattle/sheep/goat/pig specimens. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv

Location coordinates and information on environmental context: mean, min, and/or max values for a 5km radius for sites with NISP data. Elevation information was taken from Shuttle Radar Topography Mission (SRTM) terrain data from the U.S. Geological Survey (90m resolution; Jarvis et al., 2008). Precipitation data were from World Clim 2.1 (average monthly climate data for 1970–2000, 30 arc-sec; Fick and Hijmans, 2017), and solar irradiance data were from Global Solar Atlas 2.0 (9 arc-sec; developed and operated by Solargis s.r.o. on behalf of the World Bank Group, utilizing Solargis data, with funding provided by the Energy Sector Management Assistance Program (ESMAP); https://globalsolaratlas.info). Soil characteristics were derived from LUCAS topsoil data (500m; Ballabio et al., 2016): clay, silt, sand, and coarse fragments content (%), bulk density, and Available Water Capacity (AWC) for the topsoil fine earth fraction. [This is a duplicate of the Supplementary Table 1 included with the journal article.] File: Supp01_Site_NISP_Landscape_Data.csv

Bibliographic information for each assemblage with indication of whether NISP and/or biometric data was used. File: NItaly_Livestock_References.csv

R script file for the analyses and visualisations in the above journal article. File: NItaly_Livestock_SysChange_Script.R

If you re-use this data, please cite this dataset and the associated journal articles as relevant.

This dataset displays data from the 2005 Census of Japan. It displays population by age, selected age ranges, percentages of age ranges, average average, and median age in the selected prefectures in Japan. Only 30 of the 47 prectures were displayed in the data source. There are also 2 other datasets that break this data up by male and female figures. This data comes from Japan's Ministry of Internal Affairs and Communication's Statistics Bureau.

The dataset is based on ASHRAE Global Thermal Comfort Database II (version 2.01), which includes sets of objective indoor and outdoor climatic observations with accompanying “right-here-right-now” subjective evaluations by the building occupants. Data rows lacking information for any of the selected variables from the original dataset were excluded. Consequently, the dataset contains 10,618 data rows. Description of individual variables in the dataset are as follows:

· Tair: Air temperature measured in the occupied zone (°C) ranging from 13.4 to 45.3 with 10,618 data points.

· Vair: Air speed in the occupied zone (m/s) ranging from 0 to 4.71.

· Relative Humidity (RH): Relative humidity in the occupied zone (%) ranging from 14.5 to 88.8.

· SET: Standard Effective Temperature in Celsius (°C) ranging from 10.93 to 38.94.

· CLO: Intrinsic clothing ensemble insulation of the occupant (clo) ranging from 0.23 to 2.87.

· MET: Average metabolic rate of the occupant (met) ranging from 0.7 to 3.5.

· Age: Age of the occupants ranging from 16 to 95 years.

· Sex: Sex of the occupants, categorized as Male or Female.

· Tout: Outdoor monthly average temperature during the field study (°C) ranging from 5.3 to 38.1.

· Season: Season during which the study was conducted, categorized as Spring, Summer, Autumn, or Winter.

· Building Operation Mode: Can be Air Conditioned (air, radiant, etc., with no operable windows), Naturally Ventilated (no mechanical cooling, with operable windows), or Mixed Mode (mechanical cooling and operable windows, with concurrent, changeover, or zoned control).

· Building type: Type of building where the study was conducted, categorized as Classroom, Office, or Senior Center.

· Thermal Sensation Vote (TSV): ASHRAE Thermal Sensation Vote of the occupant, ranging from -3 (cold) to +3 (hot).

Motor Vehicles Registration: Estimated Average Age: Motor Cycles: Tasmania data was reported at 14.300 Year in 2021. This records an increase from the previous number of 13.832 Year for 2020. Motor Vehicles Registration: Estimated Average Age: Motor Cycles: Tasmania data is updated yearly, averaging 10.700 Year from Jan 2001 (Median) to 2021, with 21 observations. The data reached an all-time high of 14.300 Year in 2021 and a record low of 10.000 Year in 2009. Motor Vehicles Registration: Estimated Average Age: Motor Cycles: Tasmania data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.TA004: Motor Vehicles Registration: Estimated Average Age: by Type and State (Discontinued).

An important step in the analysis of magnetic resonance imaging (MRI) data for neuroimaging is the automated segmentation of white matter hyperintensities (WMHs). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel disease and Alzheimer’s disease (AD). In order to achieve high spatial resolution in each of the three voxel dimensions, clinical MRI protocols are evolving to a three-dimensional (3D) FLAIR-weighted acquisition. The current study details the deployment of deep learning tools to enable automated WMH segmentation and characterization from 3D FLAIR-weighted images acquired as part of a national AD imaging initiative. Based on data from the ongoing Norwegian Disease Dementia Initiation (DDI) multicenter study, two 3D models—one off-the-shelf from the NVIDIA nnU-Net framework and the other internally developed—were trained, validated, and tested. A third cutting-edge Deep Bayesian network model (HyperMapp3r) was implemented without any de-novo tuning to serve as a comparison architecture. The 2.5D in-house developed and 3D nnU-Net models were trained and validated in-house across five national collection sites among 441 participants from the DDI study, of whom 194 were men and whose average age was (64.91 +/- 9.32) years. Both an external dataset with 29 cases from a global collaborator and a held-out subset of the internal data from the 441 participants were used to test all three models. These test sets were evaluated independently. The ground truth human-in-the-loop segmentation was compared against five established WMH performance metrics. The 3D nnU-Net had the highest performance out of the three tested networks, outperforming both the internally developed 2.5D model and the SOTA Deep Bayesian network with an average dice similarity coefficient score of 0.76 +/- 0.16. Our findings demonstrate that WMH segmentation models can achieve high performance when trained exclusively on FLAIR input volumes that are 3D volumetric acquisitions. Single image input models are desirable for ease of deployment, as reflected in the current embedded clinical research project. The 3D nnU-Net had the highest performance, which suggests a way forward for our need to automate WMH segmentation while also evaluating performance metrics during on-going data collection and model retraining.

Motor Vehicles Registration: Estimated Average Age: Campervans: Tasmania data was reported at 22.800 Year in 2021. This records an increase from the previous number of 22.348 Year for 2020. Motor Vehicles Registration: Estimated Average Age: Campervans: Tasmania data is updated yearly, averaging 20.500 Year from Jan 2001 (Median) to 2021, with 21 observations. The data reached an all-time high of 22.800 Year in 2021 and a record low of 19.500 Year in 2001. Motor Vehicles Registration: Estimated Average Age: Campervans: Tasmania data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.TA004: Motor Vehicles Registration: Estimated Average Age: by Type and State (Discontinued).