Dataset and codes of the paper "Cai, Y., Yang, Y., & Shi, W. (2021). A predictive model of the knowledge-sharing intentions of social Q&A community members: A regression tree approach. International Journal of Human–Computer Interaction, 1-15. https://doi.org/10.1080/10447318.2021.1938393 ". Files: 1. codes.html: Codes to replicate the regression tree (HTML version) 2. codes.Rmd: Codes to replicate the regression tree (R version) 3. dataset.sav: Dataset incorporated into the decision tree 4. indicators calculation syntax.sps: spss syntax to calculate mean of variables 5. raw dataset.sav: raw data

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.

The perennial reaches of the Richmond river network were explicitly simulated using the MOFLOW River Package. Long-term average river stages for the steady state model were interpolated from measurements obtained at 12 gauge sites located within the model domain. Transient river stages at the 12 gauges were derived from rating curves (Look_up_tables) based on historical records and the runoff component of the AWRA-L model outputs (AWRA_discharge). The dataset was used to calculate the long-term average river stages at the 12 gauge sites and also the look-up tables to fit transient river stages.

Dataset History

1. Rating curves and look up tables were compiled using the monitoring data from the NSW Office of Water (see Lineage).

2. Runoff data were generated by AWRA-L with calibration to available stream flow data in the study area.

3. Calculate the future (2012 - 2102) transient river stages at the selected 12 gauges by substituting runoff data into the rating curves or look up tables.

Dataset Citation

Bioregional Assessment Programme (2015) Data for river stage interpolation in the CLM groundwater model. Bioregional Assessment Derived Dataset. Viewed 09 October 2017, http://data.bioregionalassessments.gov.au/dataset/c486f991-fc71-44c5-974f-85913ce82faf.

Dataset Ancestors

• Derived From CLM - AWRA Calibration Gauges SubCatchments

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

• Derived From CLM - Streamflow unified NSW

• Derived From CLM - NSW Office of Water Gauge Data for Tweed, Richmond & Clarence rivers. Extract 20140901

• Derived From CLM16swg Surface water gauging station data within the Clarence Moreton Basin

• Derived From CLM - NSW River Gauge pdf documents.

• Derived From GEODATA 9 second DEM and D8: Digital Elevation Model Version 3 and Flow Direction Grid 2008

• Derived From Climate model 0.05x0.05 cells and cell centroids

• Derived From CLM AWRA model

• Derived From Mean Annual Climate Data of Australia 1981 to 2012

This dataset identifies all regions in which the full 95% confidence interval is greater than 0.5 mg/m3 that were combined for the months available in each hemisphere for the blue mussel. The chlorophyll 2 data includes the mean chlorophyll 2 level per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

The distribution of data records for the maximum horizontal stress orientation SHmax in the Earth’s crust is sparse and very unequally. In order to analyse the stress pattern and its wavelength or to predict the mean SHmax orientation on a regular grid, statistical interpolation as conducted e.g. by Coblentz and Richardson (1995), Müller et al. (2003), Heidbach and Höhne (2008), Heidbach et al. (2010) or Reiter et al. (2014) is necessary. Based on their work we wrote the Matlab® script Stress2Grid that provides several features to analyse the mean SHmax pattern. The script facilitates and speeds up this analysis and extends the functionality compared to aforementioned publications. The script is complemented by a number of example and input files as described in the WSM Technical Report (Ziegler and Heidbach, 2017, http://doi.org/10.2312/wsm.2017.002). The script provides two different concepts to calculate the mean SHmax orientation on a regular grid. The first is using a fixed search radius around the grid point and computes the mean SHmax orientation if sufficient data records are within the search radius. The larger the search radius the larger is the filtered wavelength of the stress pattern. The second approach is using variable search radii and determines the search radius for which the variance of the mean SHmax orientation is below a given threshold. This approach delivers mean SHmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with conflicting information that result in a large variance. Furthermore, the script can also estimate the deviation between plate motion direction and the mean SHmax orientation.

GeoTif images of chlorophyll, turbidity, and SST were created of the region. Then an EASI script was run on the geotifs to extract the data (drill the data) from specific points (or bitmaps) in each scene for the timeseries. After the output was created in text file format, they were opened up in Microsoft Excel and spreadsheets were created for each _location. Chlorophyll, turbidity, and SST are all contained in one spreadsheet bearing the name of the _location. The drilled data shows the files used for the data drill, the mean of the pixels used in the data drill, and the number of pixels used to calculate the mean for each image used.

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. This catalogue entry provides post-processed ERA5 hourly single-level data aggregated to daily time steps. In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min, daily sum*) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

*The daily sum is only available for the accumulated variables (see ERA5 documentation for more details). Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5 hourly single-level data catalogue entry and the documentation found therein.

The mean size is calculated using all identified shocked quartz grains, except for North American samples for which 100 grains were used to calculate the mean. Shocked quartz numbers estimated for fireball layer samples

Introduction : Equations can calculate pulse wave velocity (ePWV) from blood pressure values (BP) and age. The ePWV predicts cardiovascular events beyond carotid-femoral PWV. We aimed to evaluate the correlation between four different equations to calculate ePWV. Methods: The ePWV was estimated utilizing mean BP (MBP) from office BP (MBPOBP) or 24-hour ambulatory BP (MBP24-hBP). We separated the whole sample into two groups: individuals with risk factors and healthy individuals. The e-PWV was calculated as follows:
We calculated the concordance correlation coefficient (Pc) between e1-PWVOBP vs e2-PWVOBP, e1-PWV24-hBP vs e2-PWV24-hBP, and mean values of e1-PWVOBP, e2-PWVOBP, e1-PWV24-hBP, and e2-PWV24-hBP . The multilevel regression model determined how much the ePWVs are influenced by age and MBP values. Results: We analyzed data from 1541 individuals; 1374 ones with risk factors and 167 healthy ones. The values are presented for the entire sample, for risk-factor patients and for healthy individuals, respectively. The correlation between e1-PWVOBP with e2-PWVOBP and e1-PWV24-hBP with e2-PWV24-hBP was almost perfect. The Pc for e1-PWVOBP vs e2-PWVOBP was 0.996 (0.995-0.996), 0.996 (0.995-0.996), and 0.994 (0.992-0.995); furthermore, it was 0.994 (0.993-0.995), 0.994 (0.994-0.995), 0.987 (0.983-0.990) to the e1-PWV24-hBP vs e2-PWV24-hBP. There were no significant differences between mean values (m/s) for e1-PWVOBP vs e2-PWVOBP 8.98±1.9 vs 8.97±1.8; p=0.88, 9.14±1.8 vs 9.13±1.8; p=0.88, and 7.57±1.3 vs 7.65±1.3; p=0.5; mean values are also similar for e1-PWV24-hBP vs e2-PWV24-hBP, 8.36±1.7 vs 8.46±1.6; p=0.09, 8.50±1.7 vs 8.58±1.7; p=0.21 and 7.26±1.3 vs 7.39±1.2; p=0.34. The multiple linear regression showed that age, MBP, and age² predicted more than 99.5% of all four e-PWV. Conclusion: Our data presents a nearly perfect correlation between the values of two equations to calculate the estimated PWV, whether utilizing office or ambulatory blood pressure. Methods This study is a secondary analysis of data obtained from two cross-sectional studies conducted at a specialized center in Brazil to diagnose and treat non-communicable diseases. In both studies, the inclusion criteria were adults aged 18 years and above, referred to undergo ambulatory blood pressure monitoring (ABPM) due to suspected non-treated or uncontrolled hypertension following initial blood pressure measurements by a physician. The combined databases included 1541 people. For the first database, we recruited participants between 28 January and 13 December 2013, and for the second database, between 23 January 2016 and 28 June 2019. Prior to being fitted with an AMBP device and assisted by a trained nurse, all participants signed a written consent form to partake in the research. Later, the nurse collected demographic and clinical data, including any previous reports of clinical cardiovascular disease (CVD), acute myocardial infarction, acute coronary syndrome, coronary or other arterial revascularization, stroke, transient ischemic attack, aortic aneurysm, peripheral artery disease and severe chronic kidney disease (CKD). All subjects had their BP, weight, height, and waist circumference measured and their body mass index (BMI) calculated. Although the ePWV data from the Reference Values for Arterial Stiffness Collaboration originated from cohorts lacking established cardiovascular disease, cerebrovascular disease, or diabetes, we included diabetes, CVD, CKD, smokers, and obese individuals. This choice reflects a sample that more closely resembles what can be seen in everyday Brazilian physician appointments. The study population was divided into two groups: healthy individuals and those with risk factors. Healthy individuals did not present any risk factors and a non-elevated BP (<140 and 90 mmHg). Conversely, the group with risk factors consisted of individuals with elevated BP (≥140 and-or 90 mmHg) or at least one risk factor, such as previous hypertension, dyslipidemia, diabetes, smoking, body obesity (BMI ≥ 30 kg/m2), or an increased waist circumference at risk (waist circumference > 102 cm in males and > 88 cm in females). Blood pressure measurement and ambulatory blood pressure monitoring During the data collection for both studies, office BP (OBP) measurements were conducted following recommended guidelines to ensure accurate pressure values. In the first database, a nurse performed seven consecutive BP measurements utilizing a Microlife device BP3BTOA (Onbo Electronic Co, Shenzhen, China). In the second database, a nurse assistant operated a Microlife device model BP3AC1-1PC (Onbo Electronic Co, Shenzhen, China). This device operated on Microlife Average Mode which takes three measurements in succession and calculates the average BP value. The assistant took two sets of three BP measurements sequentially. All individuals registered twenty-four hours of ABPM using a Dyna-Mapa / Mobil-O-Graph-NG monitor (Cardios, São Paulo, Brazil), equipped with an appropriately-sized cuff on their non-dominant arm. The readings were taken every 20 minutes during the day and every 30 minutes during the night, here understood as the period between going to bed and waking up. We respected all recommended protocols strictly to ensure quality recordings. Calculation of estimated pulse wave velocity The ePWV was calculated using the equations derived from the Reference Values for Arterial Stiffness Collaboration, incorporating age and MBP as follows:
MBP was also calculated as diastolic BP+ 0.4*(systolic BP/diastolic BP). Thus, the values of e1-PWV and e2-PWV were obtained for the total sample, as well as separately for the groups comprising healthy individuals and those with risk factors. We used MBP from OBP (MBPOBP) to calculate e1-PWVOBP and e2-PWVOBP, and MBP of twenty hours BP average (MBP24-hBP) to e1-PWV24-hBP and e2-PWV24-hBP. The Human Research Ethics Committee of Sirio Libanes Hospital and Federal University of the Triângulo Mineiro, provided ethical approval for data collection under protocol numbers 08930813.0.0000.5461 (first database) and 61985316.9.0000.5154 (second database), respectively.

This dataset describes mean flow path distance, in meters, to flowlines from every NHDPlus version 2.1 (NHDv2) flowline in every catchment. A National flow distance raster utilizes NHDPlus Version 2.1 flow direction rasters to compute a national flow path distance raster. For the purposes of this dataset, NHDPlus Version 2 catchments were overlaid to compute a mean flow path distance to flowline for each flowline catchment.

This dataset identifies all regions in which the full 95% confidence interval is wholly between 1 - 10, 10 - 100, and > 100 cm/s for all 12 months. The HYCOM current speed data included the mean current speed per month, the standard deviation and the number of observations used to calculate the mean. Based on these values, the 95% upper and lower confidence levels about the mean for each month have been generated.

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

This layer represents a proxy for sediment input to the nearshore marine environment from recent construction sites. Data are derived from the NOAA Coastal Change Analysis Program (C-CAP) High Resolution Change dataset from 2005 to 2010, except for Oahu and Lanai where data are for 2005 to 2011 (http://coast.noaa.gov/ccapftp/). The Ocean Tipping Points (OTP) project extracted pixels that changed from any undeveloped class to an impervious surface during the time period and calculated the area of new impervious surface within National Hydrography Dataset (NHD) HU12 watershed polygons. A Focal Statistics tool was used to calculate the mean area of new development within a 1.5-km circular radius of each offshore pixel. This area was dispersed offshore using a Gaussian decay function with distance from shore. Finally, values were linearly rescaled from 0-1 as this layer is a unitless proxy.

This is the ERA5 dataset that can be used to calculate air density for wind energy purposes using the equations presented in https://doi.org/10.3390/en12112038. The NetCDF file contains mean fields of monthly means from 2010 to 2020 of the variable temperature, surface pressure, specific humidity and the lapse rate. The horizontal grid resolution is 0.25 degrees and covers the whole globe. These fields are used in the WAsP software version 12.6 and above to calculate the air density at specified heights above mean sea level. The WAsP software (www.wasp.dk) is the industry standard method to calculate the annual energy production of wind farms. The ERA5 data are generated using Copernicus Climate Change Service information [2020]

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast. The shoreline position and change rate are used to inform management decisions regarding the erosion of coastal resources. In 2001, a shoreline from 1994 was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013, two oceanfront shorelines for Massachusetts were added using 2008-9 color aerial orthoimagery and 2007 topographic lidar datasets obtained from the National Oceanic and Atmospheric Administration's Ocean Service, Coastal Services Center. This 2018 data release includes rates that incorporate two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data collected between 2010 and 2014. The first new shoreline for the State includes data from 2010 along the North Shore and South Coast from lidar data collected by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise. Shorelines along the South Shore and Outer Cape are from 2011 lidar data collected by the U.S. Geological Survey's (USGS) National Geospatial Program Office. Shorelines along Nantucket and Martha’s Vineyard are from a 2012 USACE Post Sandy Topographic lidar survey. The second new shoreline for the North Shore, Boston, South Shore, Cape Cod Bay, Outer Cape, South Cape, Nantucket, Martha’s Vineyard, and the South Coast (around Buzzards Bay to the Rhode Island Border) is from 2013-14 lidar data collected by the (USGS) Coastal and Marine Geology Program. This 2018 update of the rate of shoreline change in Massachusetts includes two types of rates. Some of rates include a proxy-datum bias correction, this is indicated in the filename with “PDB”. The rates that do not account for this correction have “NB” in their file names. The proxy-datum bias is applied because in some areas a proxy shoreline (like a High Water Line shoreline) has a bias when compared to a datum shoreline (like a Mean High Water shoreline). In areas where it exists, this bias should be accounted for when calculating rates using a mix of proxy and datum shorelines. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates.

ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past. ERA5-Land uses ERA5 atmospheric variables, such as air temperature and air humidity, as input to control the simulated land fields. This is called the atmospheric forcing. Without the constraint of the atmospheric forcing, the model-based estimates can rapidly deviate from reality. Therefore, while observations are not directly used in the production of ERA5-Land, they have an indirect influence through the atmospheric forcing used to run the simulation. In addition, the input air temperature, air humidity and pressure used to run ERA5-Land are corrected to account for the altitude difference between the grid of the forcing and the higher resolution grid of ERA5-Land. This correction is called 'lapse rate correction'. This catalogue entry provides post-processed ERA5-land hourly data aggregated to daily time steps. Note that the accumulated variables are omitted (e.g. total precipitation, runoff, etc - please refer to table 3 in the ERA5-Land online documentation for a full list of accumulated variables). In addition to the data selection options found on the hourly page, the following options can be selected for the daily statistic calculation:

The daily aggregation statistic (daily mean, daily max, daily min) The sub-daily frequency sampling of the original data (1 hour, 3 hours, 6 hours) The option to shift to any local time zone in UTC (no shift means the statistic is computed from UTC+00:00)

Users should be aware that the daily aggregation is calculated during the retrieval process and is not part of a permanently archived dataset. For more details on how the daily statistics are calculated, including demonstrative code and advice on how to return daily statistics for the accumulated variables, please see the documentation. For more details on the hourly data used to calculate the daily statistics, please refer to the ERA5-land hourly data catalogue entry and the documentation found therein.

Mean ice grain size and crystal-preferred orientation data was obtained from vertical thin sections (ca. 6.7 x 9 cm²) of the East Greenland Ice Core Project (EGRIP) ice core to analyze the microstructural evolution and internal deformation with depth. Vertical ice thin sections were prepared and measured with the Fabric Analyzer G50 (resolution: 20 μm/pixel) in the field at the EGRIP site in the summer seasons between 2017 and 2019. 55 cm long sample were cut into six thin sections and used to calculate mean grain size values for these 9 cm long sections with the program cAxes for the upper 1340 m of the ice core. The stated depth of the section represents the center of the section.At depths of 138.92 m, 276.88 m, 415.3 m, 514.48 m, 613.3 m, 757.21, 899.94 m, 1062.65 m, 1141.2 m, 1256.98 m, and 1339.75 m the orientation of each crystal in the representitive thin section was measured with the Fabric Analyzer G50 to analyze the internal deformation at those depths. Crystal orientations per sample are represented in eleven c-axes stereo plots from those depths. Stereoplots are not geographically truly orientated due to loss of orientation during ice core retrieval.

The Massachusetts Office of Coastal Zone Management launched the Shoreline Change Project in 1989 to identify erosion-prone areas of the coast and support local land-use decisions. Trends of shoreline position over long and short-term timescales provide information to landowners, managers, and potential buyers about possible future impacts to coastal resources and infrastructure. In 2001, a 1994 shoreline was added to calculate both long- and short-term shoreline change rates along ocean-facing sections of the Massachusetts coast. In 2013 two oceanfront shorelines for Massachusetts were added using 2008-2009 color aerial orthoimagery and 2007 topographic lidar datasets obtained from NOAA's Ocean Service, Coastal Services Center. In 2018, two new mean high water (MHW) shorelines for the Massachusetts coast extracted from lidar data between 2010-2014 were added to the dataset. This 2021 data release includes rates that incorporate one new shoreline from lidar data extracted in 2018 by the U.S. Army Corps of Engineers (USACE) Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX), added to the existing database of all historical shorelines (1844-2014), for the North Shore, South Shore, Cape Cod Bay, Outer Cape, Buzzard’s Bay, South Cape, Nantucket, and Martha’s Vineyard. 2018 lidar data did not cover the Boston or Elizabeth Islands regions. Included in this data release is a proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (the high water Line shoreline) and a datum shoreline (the mean high water shoreline. This issue is explained further in Ruggiero and List (2009) and in the process steps of the metadata associated with the rates. This release includes both long-term (~150+ years) and short term (~30 years) rates. Files associated with the long-term rates have “LT” in their names, files associated with short-term rates have "ST” in their names.

We need landscape-scale approaches to design and manage agro-ecosystems that can sustain both agricultural production and biodiversity conservation. In this study, yield figures provided by 299 farmers served to quantify the energy-equivalents of food production across different crops in 49 1-km2 landscapes. Our results show that the relationship between bird diversity and food energy production depends on the proportion of farmland within the landscape, with a negative correlation observed in agriculture dominated landscapes (≥ 64–74% farmland). In contrast, neither typical farmland birds nor butterflies showed any significant relationship with total food energy production. We conclude that in European temperate regions consisting of small-scale, mixed farming systems (arable and livestock production), productivity and biodiversity conservation may not be purely antagonistic, particularly when (semi-)natural habitats make up a large fraction of the landscape (≥ 20%)., The yield data was collected during farmer interviews and information on crop area per landscape was obtained from cantonal agricultural surveys and crop mapping. The data on bird and butterflies was collected on the field by the Swiss Biodiversity Monitoring. The yield dataset was completed using multiple imputation. After post processing the completed data set was used to calculate the (mean) yield per crop per landscape, which was consecutively converted into food energy (given as metabolizable energy for human consumption), summed up and scaled to landscape level. Generalized linear models (GLM) were used to describe the relationship between biodiversity, total produced food energy and farmland area per landscape. Species richness, abundance and Pielou’s evenness index of birds and butterflies were used as response variables. Food energy production per landscape in gigajoule (GJ) and the amount of farmland in hectare (ha) were included as explanatory variables., Excell and R, # Food production and biodiversity are not incompatible in temperate heterogeneous agricultural landscapes

Authors: Silvia Zingg (a), Jan Grenz (a), Jean-Yves Humbert (b)

Summary

In this study, yield figures provided by 299 farmers served to quantify the energy-equivalents of food production across different crops in 49 1-km2 landscapes. The results show that the relationship between bird diversity and food energy production depends on the proportion of farmland within the landscape, with a negative correlation observed in agriculture dominated landscapes (≥ 64–74% farmland). In contrast, neither typical farmland birds nor butterflies showed any significant relationship with total food energy production.

Description of the data and file structure

We have submitted our R Script (Zingg_2023_R_Script_Imputation_Analyse_Figures.R), the raw data (Zingg_2023_RawData.csv), the post-processed imputed dataset (Zingg_2023_Completed_data_post_processed.csv), the dataset containin...

This data set provides median cover management factors (C-Factor) for agriculture, pasture, and barren land cover classes for each USDA Crop Management Zone. The C-Factors were calculated based on a Normalized Difference Vegetation Index. MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI values were obtained at 250 m resolution for 16-day intervals between 2000-2014 to calculate a mean annual NDVI. The data in this file correspond To Table 2 in the associated journal article. This dataset is associated with the following publication: Woznicki, S., P. Cada, J. Wickham, M. Schmidt, J. Baynes, M. Mehaffey, and A. Neale. Sediment retention by natural landscapes in the conterminous United States. SCIENCE OF THE TOTAL ENVIRONMENT. Elsevier BV, AMSTERDAM, NETHERLANDS, 745: 140972, (2020).

The Surface Ocean CO₂ Atlas (SOCAT) version 2019 dataset (Bakker et al., 2016) is a quality-controlled dataset containing 25.7 million surface ocean gaseous CO₂ measurements collated from thousands of individual submissions. These gaseous CO₂ measurements are typically collected at many different depths (of the order of several metres below the surface) using many different systems, and the sampling depth varies dependent upon the sampling platform and/or setup. Different platforms (e.g. ships of opportunity, research vessels) and systems will collect water samples at different depths, and the sampling depth can even vary dependent upon sea state. Therefore, the collated SOCAT dataset contains high quality data, but these data are all valid for different and inconsistent depths. Therefore the SOCAT provided individual gaseous CO₂ measurements and gridded data are sub-optimal for calculating global or regional atmosphere-ocean gas exchange (and the resultant net CO₂ sinks) and sub-optimal for verifying gas fluxes from (or assimilation into) numerical models. Accurate calculations of CO₂ flux between the atmosphere and oceans require CO₂ concentrations at the top and bottom of the mass boundary layer, the ~100 μm deep layer that forms the interface between the ocean and the atmosphere (Woolf et al., 2016). Ignoring vertical temperature gradients across this very small layer can result in significant biases in the concentration differences and the resulting gas fluxes (e.g. ~5 to 29% underestimate in global net CO₂ sink values, Woolf et al., 2016). It is currently impossible to measure the CO₂ concentrations either side of this very thin layer, but it is possible to calculate the concentrations either side of this layer using the SOCAT data, satellite observations and knowledge of the carbonate system. Therefore to enable the SOCAT data to be optimal for an accurate atmosphere-ocean gas flux calculation, a reanalysis methodology was developed to enable the calculation of the fugacity of CO₂ (fCO₂) for the bottom of the mass boundary layer (termed sub-skin value). The theoretical basis and justification for this is described in detail within Woolf et al., (2016) and the re-analysis methodology is described in detail in (Goddijn-Murphy et al., 2015). The re-analysis calculation exploits paired in situ temperature and fCO₂ measurements in the SOCAT dataset, and uses an Earth observation dataset to provide a depth-consistent (sub-skin) temperature field to which all fugacity data are reanalysed. The outputs provide paired fCO₂ (and partial pressure of CO₂) and temperature data that correspond to a consistent sub-skin layer temperature. These can then be used to accurately calculate concentration differences and atmosphere-ocean CO₂ gas fluxes. This data submission contains a reanalysis of the fugacity of CO₂ (fCO₂) from the SOCAT version 2019 dataset to a consistent sub-skin temperature field. The reanalysis was performed using a tool that is distributed within the FluxEngine open source software toolkit (https://github.com/oceanflux-ghg/FluxEngine) (Shutler et al., 2016; Holding et al., in-review). All data processing and driver scripts are available from the FluxEngine ancillary tools repository https://github.com/oceanflux-ghg/FluxEngineAncillaryTools. The NOAA Optimum Interpolation Sea Surface Temperature (OISST) dataset (Reynolds et al., 2007) was used to provide the climate quality and depth consistent temperature data. The original OISST data were first resampled to provide monthly mean values on a 1º by 1º degree grid. These data were then used as the temperature input for the reanalysis. The resulting reanalysed data are provided as a tab-separated value file (individual data points) and as netCDF-5 file (gridded monthly means). These are the same file formats as provided by SOCAT and analogous to the SOCAT single data point and gridded data. Each row in the tab-separated value file corresponds to a row in the original SOCAT version 2019 dataset. The original SOCAT version 2019 data are included in full, with four additional columns containing the reanalysed data: * T_reynolds - The temperature (in degrees C) taken from the consistent OISST temperature field for the corresponding time and location. * fCO2_reanalysed - The fugacity of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. * pCO2_SST - The partial pressure of CO₂ (in μatm) corresponding to the in situ (measured) temperature. * pCO2_reanalysed - The partial pressure of CO₂ (in μatm) reanalysed to the consistent surface temperature indicated by T_reynolds. The netCDF gridded version of the reanalysed dataset contains monthly mean data, binned into a 1º by 1º grid and uses the same units, missing value indicators and time and space resolution as the original SOCAT gridded product to maximise compatibility. The gridding is performed using the SOCAT gridding methodology (Sabine et al. 2013). The implementation of the gridding has been verified by performing the gridding on the original (non-reanalysed) SOCAT data and all results were identical to 8 decimal places. The result of gridding the original SOCAT data are included within these netCDF data, along with additional variables containing the equivalent results for the reanalysed SOCAT data. Statistical sample mean, minimum, maximum, standard deviation and count data for each grid cell are included, with unweighted and cruise-weighted versions (following the convention used by SOCAT). Full meta data are included within the file. Notes: 1. Due to the temporal range of the OISST dataset the reanalysed values are only available from 1981 onwards. Pre-1981 rows contain "NaN" (not-a-number) in the reanalysis columns. 2. The download for this submission is provided as a single .zip file (1.1 GB, uncompressed: 10.7GB) containing two files: SOCATv2019_reanalysed_subskin.tsv (containing every data point, ungridded) and SOCATv2019_reanalysed_subskin.nc (the gridded monthly mean data). How to cite these data: Please cite this PANGAEA submission, the theory (Woolf et al., 2016), the reanalysis methodology (Goddijn-Murphy et al., 2015), the FluxEngine toolbox which was used to perform the reanalysis (Shutler et al., 2016, Holding et al. in review) and the original SOCAT dataset (Bakker et al., 2016) and/or gridded equivalent (Sabine et al., 2013). Acknowledgements: The Surface Ocean CO₂ Atlas (SOCAT) is an international effort, endorsed by the International Ocean Carbon Coordination Project (IOCCP), the Surface Ocean Lower Atmosphere Study (SOLAS) and the Integrated Marine Biosphere Research (IMBeR) program, to deliver a uniformly quality-controlled surface ocean CO₂ database. The many researchers and funding agencies responsible for the collection of data and quality control are thanked for their contributions to SOCAT. These data were provided by two Integrated Carbon Observing System (ICOS) European Union (EU) readiness projects, Ringo (grant no. 730944) and BONUS Integral (grant no. 03FO773A).