This table derives from the grain price spreads data. It includes the latest week of data, as well as averages and standard deviations of price spreads over the past year for each commodity, origin, and destination combination. An indicator is calculated as a ratio, where the numerator is the difference between this week's price spread and the associated average price spread, and the denominator is the associated standard deviation.

Average infections for the target concept for real-world networks in the LTM and a r value of 2, with standard deviation in brackets, and the best performing heuristic in bold.

This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data. The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record.

Average infections for the target concept for scale-free networks in the ICM with no burn-in time, and a r value of 2, with standard deviation in brackets, and the best performing heuristic in bold.

This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.

The ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.

An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that "ERA5.1 is very close to ERA5 in the lower and middle troposphere." but users of data from this period should read the technical memo 859 for further details.

Average infections for the target concept for scale-free networks in the LTM with no burn-in time, and a r value of 2, with standard deviation in brackets, and the best performing heuristic in bold.

Data for Figure 3.21 from Chapter 3 of the Working Group I (WGI) Contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6). Figure 3.21 shows the seasonal evolution of observed and simulated Arctic and Antarctic sea ice area (SIA) over 1979-2017. --------------------------------------------------- How to cite this dataset --------------------------------------------------- When citing this dataset, please include both the data citation below (under 'Citable as') and the following citation for the report component from which the figure originates: Eyring, V., N.P. Gillett, K.M. Achuta Rao, R. Barimalala, M. Barreiro Parrillo, N. Bellouin, C. Cassou, P.J. Durack, Y. Kosaka, S. McGregor, S. Min, O. Morgenstern, and Y. Sun, 2021: Human Influence on the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 423–552, doi:10.1017/9781009157896.005. --------------------------------------------------- Figure subpanels --------------------------------------------------- The figure has several subplots, but they are unidentified, so the data is stored in the parent directory. --------------------------------------------------- List of data provided --------------------------------------------------- This dataset contains Sea Ice Area anomalies over 1979-2017 relative to the 1979-2000 means from: - Observations (OSISAF, NASA Team, and Bootstrap) - Historical simulations from CMIP5 and CMIP6 multi-model means - Natural only simulations from CMIP5 and CMIP6 multi-model means --------------------------------------------------- Data provided in relation to figure --------------------------------------------------- - arctic files are used for the plots on the left side of the figure - antarctic files are used for the plots on the right side of the figure - _OBS_NASATeam files are used for the first row of the plot - _OBS_Bootstrap are used for the second row of the plot - _OBS_OSISAF are used for the third row of the plot - _ALL_CMIP5 are used in the fourth row of the plot - _ALL_CMIP6 are used in the fifth row of the plot - _NAT_CMIP5 are used in the sixth row of the plot - _NAT_CMIP6 are used in the seventh row of the plot --------------------------------------------------- Notes on reproducing the figure from the provided data --------------------------------------------------- The significance are for the grey dots, it's nan or 1 values. The data has to be overplotted to colored squares. Grey dots indicate multi-model mean anomalies stronger than inter-model spread (beyond ± 1 standard deviation). The coordinates of the data are indices, but in global attributes 'comments' of each file there are relations of indices to months, since months are the y coordinate. --------------------------------------------------- Sources of additional information --------------------------------------------------- The following weblinks are provided in the Related Documents section of this catalogue record: - Link to the report component containing the figure (Chapter 3) - Link to the Supplementary Material for Chapter 3, which contains details on the input data used in Table 3.SM.1 - Link to the code for the figure, archived on Zenodo.

This dataset contains ERA5.1 surface level analysis parameter data ensemble means over the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). The ensemble means are calculated from the ERA5.1 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.

Note, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). The main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store.

The concentrations, distributions, and stable carbon isotopes (d13C) of plant waxes carried by fluvial suspended sediments contain valuable information about terrestrial ecosystem characteristics. To properly interpret past changes recorded in sedimentary archives it is crucial to understand the sources and variability of exported plant waxes in modern systems on seasonal to inter-annual timescales. To determine such variability, we present concentrations and d13C compositions of three compound classes (n-alkanes, n-alcohols, n-alkanoic acids) in a 34-month time series of suspended sediments from the outflow of the Congo River.

We show that exported plant-dominated n-alkanes (C25-C35) represent a mixture of C3 and C4 end members, each with distinct molecular distributions, as evidenced by an 8.1 ± 0.7 per mil (±1Sigma standard deviation) spread in d13C values across chain-lengths, and weak correlations between individual homologue concentrations (r = 0.52-0.94). In contrast, plant-dominated n-alcohols (C26-C36) and n-alkanoic acids (C26-C36) exhibit stronger positive correlations (r = 0.70-0.99) between homologue concentrations and depleted d13C values (individual homologues average <= -31.3 per mil and -30.8 per mil, respectively), with lower d13C variability across chain-lengths (2.6 ± 0.6 per mil and 2.0 ± 1.1 per mil, respectively). All individual plant-wax lipids show little temporal d13C variability throughout the time-series (1 Sigma <= 0.9 per mil), indicating that their stable carbon isotopes are not a sensitive tracer for temporal changes in plant-wax source in the Congo basin on seasonal to inter-annual timescales.

Carbon-normalized concentrations and relative abundances of n-alcohols (19-58% of total plant-wax lipids) and n-alkanoic acids (26-76%) respond rapidly to seasonal changes in runoff, indicating that they are mostly derived from a recently entrained local source. In contrast, a lack of correlation with discharge and low, stable relative abundances (5-16%) indicate that n-alkanes better represent a catchment-integrated signal with minimal response to discharge seasonality. Comparison to published data on other large watersheds indicates that this phenomenon is not limited to the Congo River, and that analysis of multiple plant-wax lipid classes and chain lengths can be used to better resolve local vs. distal ecosystem structure in river catchments.

Bird collisions with aircraft pose a serious threat to human safety. However, broad-scale patterns in how bird strikes might vary through space and time have yet to be fully understood. Here, we conducted a biogeographical study of bird strikes to answer two questions: (1) Are bird strikes higher at certain times of the year in the Northern and Southern Hemispheres? and (2) Is seasonality in bird strikes more prominent in the Northern Hemisphere than in the Southern Hemisphere? We collated data on monthly bird strikes from 122 airports across the globe and used circular statistics to test for hemispherical asymmetries in the circular mean and variance in bird strikes. Results showed that annual peaks in bird strikes occurred between late summer to autumn seasons, and as a result, they occurred at opposite times of year in the northern and southern hemispheres. Results also showed that bird strikes were more seasonal in the Northern Hemisphere than the Southern Hemisphere, where strikes tended to occur more consistently throughout the year. Practical implications Overall results indicate that avian collisions with aircraft show strong biogeographic patterning, concomitant with global patterns in bird breeding seasons and migration tendencies. Methods We focused on bird strikes instead of all wildlife strikes since birds strikes have resulted in considerably more human fatalities (Avisure 2024), thus posing a larger threat to human safety than strikes with other groups. We searched the Google Web, Google Scholar, and Web of Science search engines for data on monthly bird strikes at airports using the keywords ‘bird strike’ with ‘airport’, ‘seasonal’, ‘monthly’ and ‘annual’. The language settings allowed for search results in any language. We included sources that reported bird strikes in each calendar month over a minimum of 12 months at individual airports. The data availability ranged from one year to 20 years (Table S1). Monthly data from studies conducted over multiple years were averaged for each month among years prior to analyses. Apart from online sources, data for Wellington Airport were obtained via personal communication with airport personnel. Studies that reported either yearly bird strikes for individual airports, or monthly bird strikes for entire countries did not match the spatial and/or temporal resolution of this study and hence were not included. Data on strikes with non-avian species (bats, non-volant mammals, reptiles) were also not included. We restricted our data sources to those available via web search for logistical reasons (except for the Wellington data, to which we had access prior to commencing the study). Given that time is a ‘non-linear’ variable, we used circular statistics to analyze the data (Berens 2009). In this instance, the circle is used to represent one cycle (i.e. one calendar year), and we analyze the timing of an event within this cycle (i.e. occurrences of high bird strikes, see Jammalamadaka and Sengupta 2001). Seasonality in bird strikes was assessed using the temporal scale of months of the year. Each month occupied 30° on the circular plot starting with January by convention and going clockwise. The monthly strike frequency data were visualized using circular plots called rose diagrams (circular frequency distributions). Seasons were classified generally as December-January-February being boreal winter and austral summer, March-April-May being boreal spring and austral autumn, June-July-August being boreal summer and austral winter, and September-October-November being boreal autumn and austral spring. For some airports, bird strikes were reported as number of strikes in each month, while others used ‘strike rates’ (typically strikes/10,000 flight movements) accounting for aircraft movements. We converted the strike frequencies to angles (in degrees) prior to analyses using the circular package in R to make them comparable across airports (Agostinelli and Lund 2022, R Core Team 2022). For each airport, we calculated two metrics to characterize the annual distribution of bird strikes: 1) angle of mean vector (in degrees, 0°-360°), a measure of central tendency, to identify the time of year with overall high bird strikes (referred to in this study as timing of ‘peak strikes’), and 2) circular standard deviation, a measure of dispersion of the bird strikes around the mean (as per Ting et al. 2008). These values were then used to calculate the overall angles of mean vector and circular standard deviations to identify the month with the highest bird strikes for the Northern and Southern Hemispheres. The angle of the mean vector represents the timing of peak strikes for each airport and corresponds to the direction of the vector arrow on the circular plot. Circular standard deviation represents the spread of the strikes around the mean, and is inversely related to the length of the vector (see Wright and Calderon 1995, Ting et al. 2008). A high circular standard deviation would indicate that the strikes are more dispersed around the mean, while a low circular standard deviation would indicate that strikes are less dispersed around the mean, and thus, more seasonal. To answer the first question of whether bird strikes were higher at certain times of the year, we used the angles of mean vector for each airport to calculate the overall angles of mean vector and circular standard deviations for the Northern and Southern Hemispheres. The month of peak bird strikes for each hemisphere was determined by back-calculating the angle of mean vector to the corresponding month (January for values between 0° and 30°, February between 30° and 60°, and so on). Rayleigh’s Test of Uniformity was conducted to assess if the strikes were uniformly distributed throughout the year for each hemisphere. The means of the distributions of strikes in both hemispheres were compared using Watson-William’s Test of Homogeneity of Means (see Pewsey et al. 2013). To answer the second question of whether the annual spread of bird strikes differed between hemispheres, we conducted linear regression analyses to assess the relationship of circular standard deviations at individual airports with latitude in both hemispheres. Additionally, we conducted Welch’s t-test on the circular standard deviations between hemispheres to test for overall hemispherical differences in annual dispersions of strikes. Analyses and visualizations were conducted in the R environment using circular and ggplot2 packages, respectively (Wickham 2016, Agostinelli and Lund 2022, R Core Team 2022).

This dataset has been archived and will no longer be updated as of 10/16/2024. For updated data, please refer to the ILINet State Activity Indicator Map.

Information on outpatient visits to health care providers for respiratory illness referred to as influenza-like illness (ILI) is collected through the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). ILINet consists of outpatient healthcare providers in all 50 states, Puerto Rico, the District of Columbia, and the U.S. Virgin Islands. More than 100 million patient visits were reported during the 2022-23 season. Each week, more than 3,000 outpatient health care providers around the country report to CDC the number of patient visits for ILI by age group (0-4 years, 5-24 years, 25-49 years, 50-64 years, and ≥65 years) and the total number of visits for any reason. A subset of providers also reports total visits by age group. For this system, ILI is defined as fever (temperature of 100°F [37.8°C] or greater) and a cough and/or a sore throat. Activity levels are based on the percent of outpatient visits due to ILI in a jurisdiction compared to the average percent of ILI visits that occur during weeks with little or no influenza virus circulation (non-influenza weeks) in that jurisdiction. The number of sites reporting each week is variable; therefore, baselines are adjusted each week based on which sites within each jurisdiction provide data. To perform this adjustment, provider level baseline ILI ratios are calculated for those that have a sufficient reporting history. Providers that do not have the required reporting history to calculate a provider-specific baseline are assigned the baseline ratio for their practice type. The jurisdiction level baseline is then calculated using a weighted sum of the baseline ratios for each contributing provider.

The activity levels compare the mean reported percent of visits due to ILI during the current week to the mean reported percent of visits due to ILI during non-influenza weeks. The 13 activity levels correspond to the number of standard deviations below, at, or above the mean for the current week compared with the mean during non-influenza weeks. Activity levels are classified as minimal (levels 1-3), low (levels 4-5), moderate (levels 6-7), high (levels 8-10), and very high (levels 11-13). An activity level of 1 corresponds to an ILI percentage below the mean, level 2 corresponds to an ILI percentage less than 1 standard deviation above the mean, level 3 corresponds to an ILI percentage more than 1 but less than 2 standard deviations above the mean, and so on, with an activity level of 10 corresponding to an ILI percentage 8 to 11 standard deviations above the mean. The very high levels correspond to an ILI percentage 12 to 15 standard deviations above the mean for level 11, 16 to 19 standard deviations above the mean for level 12, and 20 or more standard deviations above the mean for level 13.

Disclaimers:

The ILI Activity Indicator map reflects the intensity of ILI activity, not the extent of geographic spread of ILI, within a jurisdiction. Therefore, outbreaks occurring in a single area could cause the entire jurisdiction to display high or very high activity levels. In addition, data collected in ILINet may disproportionally represent certain populations within a jurisdiction, and therefore, may not accurately depict the full picture of respiratory illness activity for the entire jurisdiction. Differences in the data presented here by CDC and independently by some health departments likely represent differing levels of data completeness with data presented by the health department likely being more complete.

More information is available on FluView Interactive.

The goal of SODA is to reconstruct the historical physical (and eventually biogeochemical) history of the ocean since the beginning of the 20th century. As its name implies, the Simple Ocean Data Assimilation ocean/sea ice reanalysis (SODA) uses a simple architecture based on community standard codes with resolution chosen to match available data and the scales of motion that are resolvable. Agreement with direct measurements (to within observational error estimates) as well as unbiased statistics are expected. While SODA remains a university-based research project, an objective is to support potential users by providing a reliable, well-documented, source of seasonal climate time-scale ocean reanalysis to complement the atmospheric reanalyses available elsewhere (NOAA/EMC, NASA/GMAO, and ECMWF, for example). SODA3 (SODA Version 3) is the latest release of SODA. The model has been switched to GFDL MOM5/SIS1 with eddy permitting 0.25 degree by 0.25 degree by 50 level resolution (28 kilometers at the Equator down to less than 10 kilometers at polar latitudes), similar to the ocean component of the GFDL CM2.5 coupled climate model, and includes the same SIS1 active sea ice model. A number of improvements have been included in the sequential DA filter, but for many reanalyses SODA3 retains a pre-specified flow-dependent error covariance. One of the focuses for SODA3 has been to identify, quantify, and limit sources of bias. A major source of bias is in the forward model that predicts the evolution of the flow. A major (but not the only) source of model bias, in turn, is introduced through bias in the meteorological fluxes (heat, freshwater, and momentum). To address this problem SODA3 is an 'ensemble' reanalysis, the spread of whose members provides information about sensitivity to errors in surface forcing. Many of these ensemble members are driven by fluxes that have been bias-corrected.

This statistic depicts the age distribution in the United States from 2013 to 2023. In 2023, about 17.59 percent of the U.S. population fell into the 0-14 year category, 64.97 percent into the 15-64 age group and 17.43 percent of the population were over 65 years of age. The increasing population of the United States The United States of America is one of the most populated countries in the world, trailing just behind China and India. A total population count of around 320 million inhabitants and a more-or-less steady population growth over the past decade indicate that the country has steadily improved its living conditions and standards for the population. Leading healthier lifestyles and improved living conditions have resulted in a steady increase of the life expectancy at birth in the United States. Life expectancies of men and women at birth in the United States were at a record high in 2012. Furthermore, a constant fertility rate in recent years and a decrease in the death rate and infant mortality, all due to the improved standard of living and health care conditions, have helped not only the American population to increase but as a result, the share of the population younger than 15 and older than 65 years has also increased in recent years, as can be seen above.

In 2023, just over 50 percent of Americans had an annual household income that was less than 75,000 U.S. dollars. The median household income was 80,610 U.S. dollars in 2023. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Maryland, New Jersey, and Massachusetts were among the states with the highest median household income in 2020. In terms of income by race and ethnicity, the average income of Asian households was 94,903 U.S. dollars in 2020, while the median income for Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates its list of poverty levels. Preliminary estimates show that the average poverty threshold for a family of four people was 26,500 U.S. dollars in 2021, which is around 100 U.S. dollars less than the previous year. There were an estimated 37.9 million people in poverty across the United States in 2021, which was around 11.6 percent of the population. Approximately 19.5 percent of those in poverty were Black, while 8.2 percent were white.

This dataset is associated with the following publication: https://doi.org/10.5194/egusphere-2024-1735. It contains the water vapor mole fractions and bias correction magnitude (2010-2018) to the CO2 mole fractions measured at each airborne vertical profile site in the Amazon. The vertical profile data is freely available at: https://doi.pangaea.de/10.1594/PANGAEA.926834. The flags used to select the best quality data were: ALF: '...' and '..>' , TAB: '...' , TEF: '...' , SAN: '...' and '..>' , 'RBA: '...' . The water vapor was extracted from ECMWF-IFS using the STILT model (Lin et al., 2003). The water vapor mole fraction (wv_extr) is reported in % and the CO2 bias correction (bcorr) is in ppm. The bias correction has a systematic and a random component. The column bcorr_std provides the standard deviation of the bias for a given water vapor mole fraction bin of 1% (from 0 to 3%). Note that the standard deviation corresponds to the random component of the error, which is associated with the spread for each 1% bin. For the site RBA, there are 6 flights in which the water vapor correction was not applied because there was a drier installed. Those flights are not included in this dataset and the dates are (DDMMYYYY): 1. 16022018 / 2. 09032018 / 3. 06042018 / 4. 08052018 / 5. 15072018 / 6. 19092018

The “infrastructure index†describes the degree of development of physical facilities and networks in a certain area in 2010. The quality of infrastructure is an important measure of the relative adaptive capacity of a region. Regions with developed infrastructure systems are presumed to be better able to adapt to climatic stresses. Improved infrastructure may reduce transactions costs, and strengthen the links between labor and product markets. Moreover, improved infrastructure should encourage the formation of non-farm enterprises as a source of diversification in the short run and, eventually, a transition out of agriculture. The index results from the second cluster of the Principal Component Analysis preformed among 10 potential variables. The analysis identifies three dominant variables, namely “road density†, “road availability†and “infrastructure poverty†, assigning weights of 0.47, 0.36 and 0.17, respectively. Before to perform the analysis all variables were log transformed to shorten the extreme variation and then were score-standardized (converted to distribution with average of 0 and standard deviation of 1) in order to be comparable. A shapefile of road network was published by the Center for International Earth Science Information Network of Columbia University in 2013. The “road density†was computed by calculating the Kilometers of road per cell (size 0.5 arc-minute) and then running a focal statistic (radius of about 30 km to spread the effect of a transportation network in a neighborhood). The “road availability†is the road density divided by the logarithm of population. The 0.5 arc-minute grid “infrastructure poverty†is based on the average lights per pixel in 2010, which was produced by NOAA National Geophysical Data Center, divided by the logarithm of population. The original data was highly fragmented and at fine resolution may have contained fine-scale artifacts at urban edges due to data mismatch between the population and night-lights datasets. Thus focal statistics ran within 20 Km to calculate an average values and represents some of the extend influence of the infrastructure network for local people. The density and availability of road is a normally accepted indicator of infrastructure development degree. Moreover, developed road network facilitate the diffusion of rural products to large markets enhancing the income of rural population and sharing the risk of crisis among larger area. The average night light density per capita represents the diffusion of electricity among population and here is considered a proxy of diffusion of developed infrastructural network. This dataset has been produced in the framework of the “Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)†project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

Samples of manganese nodules from the south Penrhyn Basin were analyzed for a number of elemental concentrations. Chemical analyses were performed by the Australian Mineral Development Laboratories (AMDEL), with funds contributed to CCOP/SOPAC by the Australian government, using XRF. Au and Pt were analyzed for using fire assay collection and emission spectrography. The overall chemical composition of nodulesfrom the south Penrhyn Basin is fairly uniform throughout the area sampled. The total spread of data is small and generally amounts to less than t. 20% of the mean, as measured by standard deviation except in the case of elements present in lower concentrations where analytical errors become more significant.

In 2023, around 10.3 percent of U.S. private households had an annual income between 35,000 and 49,999 U.S. dollars in the United States. Income levels between 100,000 to 149,999 U.S. dollars made up the largest share of the population at 16.5 percent in 2023.

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.