Sea surface temperature (SST) plays an important role in a number of ecological processes and can vary over a wide range of time scales, from daily to decadal changes. SST influences primary production, species migration patterns, and coral health. If temperatures are anomalously warm for extended periods of time, drastic changes in the surrounding ecosystem can result, including harmful effects such as coral bleaching. This layer represents the standard deviation of SST (degrees Celsius) of the weekly time series from 2000-2013. Three SST datasets were combined to provide continuous coverage from 1985-2013. The concatenation applies bias adjustment derived from linear regression to the overlap periods of datasets, with the final representation matching the 0.05-degree (~5-km) near real-time SST product. First, a weekly composite, gap-filled SST dataset from the NOAA Pathfinder v5.2 SST 1/24-degree (~4-km), daily dataset (a NOAA Climate Data Record) for each location was produced following Heron et al. (2010) for January 1985 to December 2012. Next, weekly composite SST data from the NOAA/NESDIS/STAR Blended SST 0.1-degree (~11-km), daily dataset was produced for February 2009 to October 2013. Finally, a weekly composite SST dataset from the NOAA/NESDIS/STAR Blended SST 0.05-degree (~5-km), daily dataset was produced for March 2012 to December 2013. The standard deviation of the long-term mean SST was calculated by taking the standard deviation over all weekly data from 2000-2013 for each pixel.

Mean and Median Weekly and Annual Earnings

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days (monthly means are available around the 6th of each month). In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 monthly mean data on single levels from 1940 to present".

Licensed under: Creative Commons Attribution 4.0 Category: Government Views: 18 Openness rating: Dataset Actions View showcases with this dataset Contact dataset owner Median and Mean Earnings

Gridded files of daily mean temperature in the Netherlands. Based on 33 -35 automatic weather stations of the KNMI.

This dataset contains daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 revision 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) ocean and sea-ice state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of the 1-degree global configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g.,research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

Various climate variables summary for all 15 subregions based on Bureau of Meteorology Australian Water Availability Project (BAWAP) climate grids. Including

1. Time series mean annual BAWAP rainfall from 1900 - 2012.

2. Long term average BAWAP rainfall and Penman Potentail Evapotranspiration (PET) from Jan 1981 - Dec 2012 for each month

3. Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P (precipitation); (ii) Penman ETp; (iii) Tavg (average temperature); (iv) Tmax (maximum temperature); (v) Tmin (minimum temperature); (vi) VPD (Vapour Pressure Deficit); (vii) Rn (net radiation); and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend.

4. Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009).

As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

There are 4 csv files here:

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset History

Dataset was created from various BAWAP source data, including Monthly BAWAP rainfall, Tmax, Tmin, VPD, etc, and other source data including monthly Penman PET, Correlation coefficient data. Data were extracted from national datasets for the GLO subregion.

BAWAP_P_annual_BA_SYB_GLO.csv

Desc: Time series mean annual BAWAP rainfall from 1900 - 2012.

Source data: annual BILO rainfall

P_PET_monthly_BA_SYB_GLO.csv

long term average BAWAP rainfall and Penman PET from 198101 - 201212 for each month

Climatology_Trend_BA_SYB_GLO.csv

Values calculated over the years 1981 - 2012 (inclusive), for 17 time periods (i.e., annual, 4 seasons and 12 months) for the following 8 meteorological variables: (i) BAWAP_P; (ii) Penman ETp; (iii) Tavg; (iv) Tmax; (v) Tmin; (vi) VPD; (vii) Rn; and (viii) Wind speed. For each of the 17 time periods for each of the 8 meteorological variables have calculated the: (a) average; (b) maximum; (c) minimum; (d) average plus standard deviation (stddev); (e) average minus stddev; (f) stddev; and (g) trend

Risbey_Remote_Rainfall_Drivers_Corr_Coeffs_BA_NSB_GLO.csv

Correlation coefficients (-1 to 1) between rainfall and 4 remote rainfall drivers between 1957-2006 for the four seasons. The data and methodology are described in Risbey et al. (2009). As described in the Risbey et al. (2009) paper, the rainfall was from 0.05 degree gridded data described in Jeffrey et al. (2001 - known as the SILO datasets); sea surface temperature was from the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST) on a 1 degree grid. BLK=Blocking; DMI=Dipole Mode Index; SAM=Southern Annular Mode; SOI=Southern Oscillation Index; DJF=December, January, February; MAM=March, April, May; JJA=June, July, August; SON=September, October, November. The analysis is a summary of Fig. 15 of Risbey et al. (2009).

Dataset Citation

Bioregional Assessment Programme (2014) GLO climate data stats summary. Bioregional Assessment Derived Dataset. Viewed 18 July 2018, http://data.bioregionalassessments.gov.au/dataset/afed85e0-7819-493d-a847-ec00a318e657.

Dataset Ancestors

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From BILO Gridded Climate Data: Daily Climate Data for each year from 1900 to 2012

• Derived From Bioregional Assessment areas v01

• Derived From Bioregional Assessment areas v02

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

About this dataset

Context

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

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 17,180, while the mean income for the highest quintile (20% of households with the highest income) is 331,800. This indicates that the top earners earn 19 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 586,341, which is 176.72% higher compared to the highest quintile, and 3412.93% higher compared to the lowest quintile.

Content

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

Income Levels:

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

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Denver median household income. You can refer the same here

Mean price paid for residential property in England and Wales, by property type and administrative geographies. Annual data.

About this dataset

Context

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

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 21,522, while the mean income for the highest quintile (20% of households with the highest income) is 120,945. This indicates that the top earners earn 6 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 161,618, which is 133.63% higher compared to the highest quintile, and 750.94% higher compared to the lowest quintile.

Content

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

Income Levels:

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

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Log Lane Village median household income. You can refer the same here

This dataset contains global gridded maps of Heating Degree Days (HDD) and Cooling Degree Days (CDD) for three climate scenarios: a historical scenario corresponding to a global mean temperature rise of 1.0°C above pre-industrial levels (based on observations from 2006 to 2016), and two future climate projections for global mean temperature increases of 1.5°C and 2.0°C, respectively, regardless of when these thresholds are reached. HDD and CDD are widely used indicators to measure how much the mean temperature exceeds a reference temperature each day over a given period. They are widely used indicators to examine global temperature-related climate and quantify heating and cooling demand.

Five different maps of HDD and CDD are available for each scenario as NetCDF V4 files (*.nc). These maps relate to different annual statistical indices calculated using 70 climate simulations over a 10-year period: mean, median, 10th percentile, 90th percentile, and standard deviation. The novelty of this dataset lies in the combination of two factors: the representation of global mean temperature rise scenarios for 1.5°C and 2.0°C globally, regardless of when these occur; and the bias-corrected global climate dataset used to calculate HDD and CDD, which involves a large ensemble size at a high global spatio-temporal resolution.

Methods:

The global gridded statistical maps of HDD and CDD were calculated considering 18°C as the baseline temperature. First, the annual HDD and CDD were calculated for each simulated year of each scenario at all geographic locations (a total of 700 simulated years per scenario). Then, the statistical indices across this variability were obtained. Global gridded maps have a spatial resolution of 0.833° x 0.556° (longitude x latitude) over the land surface.

Climate data used:

These global gridded maps of CDD and HDD were calculated using bias-corrected global climate simulations for mean temperature generated using the HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Each scenario involved an ensemble of 70 individual members with 6-hourly mean temperatures at a horizontal resolution of 0.833 longitude and 0.556 latitude for a 10-year period (700 runs per scenario), aiming to ensure internal climate variability. These simulation experiments were run within the climateprediction.net (CPDN) climate simulation environment, using the Berkeley Open Infrastructure for Network Computing (BOINC) framework to distribute a large number of individual computational tasks. This system utilises the computational power of publicly volunteered computers that are globally distributed. The bias-corrected global climate dataset used to calculate these CDD and HDD maps is available at:

Lizana, J.; Miranda, N.D.; Sparrow, S.; Zachau-Walker, M.; Watson, P.; Wallom, D.C.H.; McCulloch, M. (2023): Large ensemble of global mean temperatures: 6-hourly HadAM4 model run data using the Climateprediction.net platform. NERC EDS Centre for Environmental Data Analysis, 28 June 2023. doi:10.5285/9c41e3aa67024bbdad796290a861e968

About this dataset

Context

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

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 13,786, while the mean income for the highest quintile (20% of households with the highest income) is 163,952. This indicates that the top earners earn 12 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 293,901, which is 179.26% higher compared to the highest quintile, and 2131.88% higher compared to the lowest quintile.

Content

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

Income Levels:

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

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mexico median household income. You can refer the same here

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are: Mean temperature (.1 Fahrenheit) Mean dew point (.1 Fahrenheit) Mean sea level pressure (.1 mb) Mean station pressure (.1 mb) Mean visibility (.1 miles) Mean wind speed (.1 knots) Maximum sustained wind speed (.1 knots) Maximum wind gust (.1 knots) Maximum temperature (.1 Fahrenheit) Minimum temperature (.1 Fahrenheit) Precipitation amount (.01 inches) Snow depth (.1 inches) Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

The data presented here were used to produce the following paper:

Archibald, Twine, Mthabini, Stevens (2021) Browsing is a strong filter for savanna tree seedlings in their first growing season. J. Ecology.

The project under which these data were collected is: Mechanisms Controlling Species Limits in a Changing World. NRF/SASSCAL Grant number 118588

For information on the data or analysis please contact Sally Archibald: sally.archibald@wits.ac.za

Description of file(s):

File 1: cleanedData_forAnalysis.csv (required to run the R code: "finalAnalysis_PostClipResponses_Feb2021_requires_cleanData_forAnalysis_.R"

The data represent monthly survival and growth data for ~740 seedlings from 10 species under various levels of clipping.

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes)

File 2: Herbivory_SurvivalEndofSeason_march2017.csv (required to run the R code: "FinalAnalysisResultsSurvival_requires_Herbivory_SurvivalEndofSeason_march2017.R"

The data consist of one .csv file with the following column names:

treatment Clipping treatment (1 - 5 months clip plus control unclipped) plot_rep One of three randomised plots per treatment matrix_no Where in the plot the individual was placed species_code First three letters of the genus name, and first three letters of the species name uniquely identifies the species species Full species name sample_period Classification of sampling period into time since clip. status Alive or Dead standing.height Vertical height above ground (in mm) height.mm Length of the longest branch (in mm) total.branch.length Total length of all the branches (in mm) stemdiam.mm Basal stem diameter (in mm) maxSpineLength.mm Length of the longest spine postclipStemNo Number of resprouting stems (only recorded AFTER clipping) date.clipped date.clipped date.measured date.measured date.germinated date.germinated Age.of.plant Date measured - Date germinated newtreat Treatment as a numeric variable, with 8 being the control plot (for plotting purposes) genus Genus MAR Mean Annual Rainfall for that Species distribution (mm) rainclass High/medium/low

File 3: allModelParameters_byAge.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

Age.of.plant Age in days species_code Species pred_SD_mm Predicted stem diameter in mm pred_SD_up top 75th quantile of stem diameter in mm pred_SD_low bottom 25th quantile of stem diameter in mm treatdate date when clipped pred_surv Predicted survival probability pred_surv_low Predicted 25th quantile survival probability pred_surv_high Predicted 75th quantile survival probability species_code species code Bite.probability Daily probability of being eaten max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc genus genus rainclass low/med/high

File 4: EatProbParameters_June2020.csv (required to run the R code: "FinalModelSeedlingSurvival_June2021_.R"

Consists of a .csv file with the following column headings

shtspec species name species_code species code genus genus rainclass low/medium/high seed mass mass of seed (g per 1000seeds)
Surv_intercept coefficient of the model predicting survival from age of clip for this species Surv_slope coefficient of the model predicting survival from age of clip for this species GR_intercept coefficient of the model predicting stem diameter from seedling age for this species GR_slope coefficient of the model predicting stem diameter from seedling age for this species species_code species code max_bite_diam_duiker_mm Maximum bite diameter of a duiker for this species duiker_sd standard deviation of bite diameter for a duiker for this species max_bite_diameter_kudu_mm Maximum bite diameer of a kudu for this species kudu_sd standard deviation of bite diameter for a kudu for this species mean_bite_diam_duiker_mm mean etc duiker_mean_sd standard devaition etc mean_bite_diameter_kudu_mm mean etc kudu_mean_sd standard deviation etc AgeAtEscape_duiker[t] age of plant when its stem diameter is larger than a mean duiker bite AgeAtEscape_duiker_min[t] age of plant when its stem diameter is larger than a min duiker bite AgeAtEscape_duiker_max[t] age of plant when its stem diameter is larger than a max duiker bite AgeAtEscape_kudu[t] age of plant when its stem diameter is larger than a mean kudu bite AgeAtEscape_kudu_min[t] age of plant when its stem diameter is larger than a min kudu bite AgeAtEscape_kudu_max[t] age of plant when its stem diameter is larger than a max kudu bite

Estimated annual average wave height (metres) created by a Pelamis Wave Model for Accessible Wave Energy Resource Atlas. Wave height values are measured as lower and upper values in metres as calculated by the Pelamis wave model. Annual average wave height covers an area known as the Irish Exclusive Economic Zone (EEZ). Data model produced in 2005. The Pelamis Wave Model was an oceanographic model using the Pelamis wave energy converter device. The Accessible Wave Energy Resource Atlas was produced to provide data and information on the accessible wave energy resource potential around Ireland. Wave model developed by ESB International (ESBI) as part of the Accessible Wave Energy Atlas Ireland published by the Marine Institute and Sustainable Energy Authority Ireland. Model completed for time period run.

This dataset represents predictions made to individual, local NHDPlusV2 stream segments. Attributes were calculated for every local NHDPlusV2 stream segment. (See Supplementary Info for Glossary of Terms). These predictions were made to provide estimates of reference-condition stream temperatures in support of the 2008-2009 and 2013-2014 (forthcoming) National Rivers and Streams Assessments. These predictions were based on a set of published models (Hill et al. 2013; http://www.journals.uchicago.edu/doi/abs/10.1899/12-009.1). From Hill et al. (2013): "We modeled 3 ecologically important elements of the thermal regime: mean summer, mean winter, and mean annual stream temperature. These models used a set of least-disturbed USGS stations and sites to model stream temperatures from a set of landscape metrics. To build reference-condition models, we used daily mean ST data obtained from several thousand US Geological Survey temperature sites distributed across the conterminous USA and iteratively modeled ST with Random Forests to identify sites in reference condition. These data are summarized to produce local stream segment-level metrics as a continuous data type.

The Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) data set is a merged record of stratospheric ozone and water vapor measurements taken by a number of limb sounding and solar occultation satellites over the previous ~30 years. The SWOOSH record spans 1984 to present, and is comprised of data from the SAGE-II/III, UARS HALOE, UARS MLS, and Aura MLS instruments. The measurements are homogenized by applying corrections that are calculated from data taken during time periods of instrument overlap. The primary SWOOSH data product consists of monthly-mean zonal-mean values on a pressure grid. In addition to the primary (zonal-mean) grid, SWOOSH data are also available on 3D (longitude/latitude/pressure), equivalent latitude, and isentropic grids. The gridded data include the mean, standard deviation, number of observations, and mean uncertainty from each instrument. Also included is a merged (multi-instrument) product based on a weighted mean of the available measurements. Because the merged product contains missing data, a merged and filled product is also provided for (e.g., modeling) studies requiring a continuous dataset.

This is a Residential PV generation and consumption data set from an Estonian house. At the time of submission, one year (2023) of data was available. The data was logged at a 10-second resolution. The untouched dataset can be found in the raw data folder, which is separated month-wise. A few missing points in the dataset were filled with a simple KNN algorithm. However, improved data imputation methods based on machine learning are also possible. To carry out the imputing, run the scripts in the script folder one by one in the numerical serial order (SC1..py, SC2..py, etc.).

Data Descriptor (Scientific Data): https://doi.org/10.1038/s41597-025-04747-w">https://doi.org/10.1038/s41597-025-04747-w

General Information:

Duration: January 2023 – December 2023

Resolution: 10 seconds

Dataset Type: Aggregated consumption and PV generation data

Logging Device: Camile Bauer PQ1000 (×2)

Load/Appliance Information:

• 5 kW Rooftop PV array connected to AC Bus via 4.2kW 3-ϕ Inverter
• Air conditioner: 0.44 kW (Cooling), 0.62 kW (Heating)
• Air to Water (ATW) Heat Pump: 2.5kW (Cooling), 2.6 kW (Heating)
• ATW Cylinder unit: 0.21 kW (Controller), 9 kW (Booster Heater)
• Microwave oven: 0.9 kW
• Coffee Maker: 1 kW
• Cooktop Hot Plate: 4.6 kW
• TV: 0.103 kW
• Vacuum Cleaner: 1.5 kW
• Ventilation: 0.1 kW
• Washing Machine: 2.2 kW
• Electric Sauna: 10 kW
• Lighting: 0.25 kW
• EV charger: 2.4 kW 1-ϕ

Measurement Points:

1. PV converter-side current transformer, potential transformer (Measurement of PV generation).
2. Utility meter-side current transformer, potential transformer (Measurement of power exchange with the grid).

Measured Parameters:

• Per-phase mean power recorded within the sampling period
• Per-phase Minimum power recorded within the sampling period
• Per-phase maximum power recorded within the sampling period
• Quadrant-wise mean power recorded within the sampling period (1st + 3rd), (2nd + 4th)
• Quadrant-wise minimum power recorded within the sampling period (1st + 3rd), (2nd + 4th)
• Quadrant-wise maximum power recorded within the sampling period (1st + 3rd), (2nd + 4th)
• mean power Factor recorded within the sampling period
• Minimum power Factor recorded within the sampling period
• Maximum power Factor recorded within the sampling period
• System Voltage
• Minimum system Voltage
• Maximum system Voltage
• Mean Voltage between phase and neutral
• Minimum voltage between phase and neutral
• Maximum voltage between phase and neutral
• Zero displacement voltage 4-wire systems (mean, min, max)

Script Description:

SC1_PV_auto_sort.py : This fixes timestamp continuity by resampling at the original sampling rate for PV generation data.

SC2_L2_auto_sort.py : This fixes timestamp continuity by resampling at the original sampling rate for meter-side measurement data.

SC3_PV_KNN_impute.py : Filling missing data points by simple KNN for PV generation data.

SC4_L2_KNN_impute.py : Filling missing data points by simple KNN for meter-side measurement data.

SC5_Final_data_gen.py : Merge PV and meter-side measurement data, and calculate load consumption.

The dataset provides all the outcomes (CSV files) from the scripts. All processed variables (PV generation, load, power import, and export) are expressed in kW units.

Update: 'SC1_PV_auto_sort.py' & 'SC2_L2_auto_sort.py' are adequate for cleaning up data and making the missing point visible. 'SC3_PV_KNN_impute.py' & 'SC4_L2_KNN_impute.py' work fine for short-range missing data points; however, these two scripts won't help much for missing data points for a longer period. They are provided as examples of one method of processing data. Future updates will include proper ML-based forecasting to predict missing data points.

Funding Agency and Grant Number:

1. European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 955614.
2. Estonian Research Council under Grant PRG1086.
3. Estonian Centre of Excellence in Energy Efficiency, ENER, funded by the Estonian Ministry of Education and Research under Grant TK230.

Monthly global grids of data, derived fluxes, and anomalies were prepared from the COADS data.

This data set contains 1971-2000 mean annual precipitation estimates for west-central Nevada. This is a raster data set developed using the precipitation-zone method, which uses elevation-based regression equations to estimate mean annual precipitation for defined precipitation zones (Lopes and Medina, 2007.) This data set is based on the 30-meter National Elevation Dataset. Reference Cited Lopes, T.J., and Medina, R.L., 2007, Precipitation Zones of West-Central Nevada: Journal of Nevada Water Resources Association, v. 4, no 2, p. 21.