This data release contains six different datasets that were used in the report SIR 2018-5108. These datasets contain discharge data, discrete dissolved-solids data, quality-control discrete dissolved data, and computed mean dissolved solids data that were collected at various locations between the Hoover Dam and the Imperial Dam. Study Sites: Site 1: Colorado River below Hoover Dam Site 2: Bill Williams River near Parker Site 3: Colorado River below Parker Dam Site 4: CRIR Main Canal Site 5: Palo Verde Canal Site 6: Colorado River at Palo Verde Dam Site 7: CRIR Lower Main Drain Site 8: CRIR Upper Levee Drain Site 9: PVID Outfall Drain Site 10: Colorado River above Imperial Dam Discrete Dissolved-solids Dataset and Replicate Samples for Discrete Dissolved-solids Dataset: The Bureau of Reclamation collected discrete water-quality samples for the parameter of dissolved-solids (sum of constituents). Dissolved-solids, measured in milligrams per liter, are the sum of the following constituents: bicarbonate, calcium, carbonate, chloride, fluoride, magnesium, nitrate, potassium, silicon dioxide, sodium, and sulfate. These samples were collected on a monthly to bimonthly basis at various time periods between 1990 and 2016 at Sites 1-5 and Sites 7-10. No data were collected for Site 6: Colorado River at Palo Verde Dam. The Bureau of Reclamation and the USGS collected discrete quality-control replicate samples for the parameter of dissolved-solids, sum of constituents measured in milligrams per liter. The USGS collected discrete quality-control replicate samples in 2002 and 2003 and the Bureau of Reclamation collected discrete quality-control replicate samples in 2016 and 2017. Listed below are the sites where these samples were collected at and which agency collected the samples. Site 3: Colorado River below Parker Dam: USGS and Reclamation Site 4: CRIR Main Canal: Reclamation Site 5: Palo Verde Canal: Reclamation Site 7: CRIR Lower Main Drain: Reclamation Site 8: CRIR Upper Levee Drain: Reclamation Site 9: PVID Outfall Drain: Reclamation Site 10: Colorado River above Imperial Dam: USGS and Reclamation Monthly Mean Datasets and Mean Monthly Datasets: Monthly mean discharge data (cfs), flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1990 to 2016 were computed using raw data from the USGS and the Bureau of Reclamation. This data were computed for all 10 sites. Flow weighted monthly mean dissolved-solids concentration and monthly mean dissolved-solids load were not computed for Site 2: Bill Williams River near Parker. The monthly mean datasets that were calculated for each month for the period between 1990 and 2016 were used to compute the mean monthly discharge and the mean monthly dissolved-solids load for each of the 12 months within a year. Each monthly mean was weighted by how many days were in the month and then averaged for each of the twelve months. This was computed for all 10 sites except mean monthly dissolved-solids load were not computed at Site 2: Bill Williams River near Parker. Site 8a: Colorado River between Parker and Palo Verde Valleys was computed by summing the data from sites 6, 7 and 8. Bill Williams Daily Mean Discharge, Instantaneous Dissolved-solids Concentration, and Daily Means Dissolved-solids Load Dataset: Daily mean discharge (cfs), instantaneous solids concentration (mg/L), and daily mean dissolved solids load were calculated using raw data collected by the USGS and the Bureau of Reclamation. This data were calculated for Site 2: Bill Williams River near Parker for the period of January 1990 to February 2016. Palo Verde Irrigation District Outfall Drain Mean Daily Discharge Dataset: The Bureau of Reclamation collected mean daily discharge data for the period of 01/01/2005 to 09/30/2016 at the Palo Verde Irrigation District (PVID) outfall drain using a stage-discharge relationship.

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data ...

Contains data and code for the manuscript 'Mean landscape-scale incidence of species in discrete habitats is patch size dependent'.

Raw data consist of 202 published datasets collated from primary and secondary (e.g., government technical reports) sources. These sources summarise metacommunity structure for different taxonomic groups (birds, invertebrates, non-avian vertebrates or plants) in different types of discrete metacommunities including 'true' islands (i.e., inland, continental or oceanic archipelagos), habitat islands (e.g., ponds, wetlands, sky islands) and fragments (e.g., forest/woodland or grass/shrubland habitat remnants).

The aim of the study was to test whether the size of a habitat patch influences the mean incidences of species within it, relative to the incidence of all species across the landscape. In other words, whether high-incidence (widespread) or low-incidence (narrow-range) species are found more often than expected in smaller or larger patches. To achieve this, a new standardized effect size metric was developed that quantifies the mean observed incidence of all species present in every patch (the geometric mean of the number of patches in which all species were observed) and compares this with an expectation based on re-sampling the incidences of all species in all patches. Meta-regression of the 202 datasets was used to test the relationship between this metric, the 'mean species landscape-scale incidences per patch' (MSLIP), and the size of habitat patches, and for differences in response among metacommunity types and taxonomic groups.

Monthly rollup of the discrete and daily-aligned groundwater levels were created from Robinson, Asquith, and Seanor (2020) data products with removal of the paired groundwater and surface-water sites listed by Robinson, Killian, and Asquith (2020). The monthly rollup is composed of (1) computed monthly "mean" values regardless of whether a well had one measurement in the month or up to about 30 days of daily-mean values, (2) standard deviation of the water levels within the month (sample size is generally just one day but for recorder sites could be up to about 30 days), (3) the last water level in the month, and (4) monthly counts of water levels. The algorithm is available within the sources of visGWDBmrva (Asquith and others, 2019). A comment is made that the string 1980-01-01_2019-12-31 is retained in the file naming to parallel that for Robinson, Asquith, and Seanor (2020) files although the day of the month has no meaning for a monthly rollup. There are 18,736 unique wells of statistics; 18,736 wells in the metadata; and 107,568 year-month entries in the monthly rollup product. References: Asquith, W.H., Seanor, R.C., McGuire, V.L. (contributor), and Kress, W.H. (contributor), 2019, Source code in R to quality assure, plot, summarize, interpolate, and extend groundwater-level information, visGWDB—Groundwater-level informatics with demonstration for the Mississippi River Valley alluvial aquifer: U.S. Geological Survey software release, Reston, Va., https://doi.org/10.5066/P9W004O6.

This dataset contains simulated data that is meant to represent sensitive finance data. # porftolio_id = unique identifer for portfolio (discrete) # date = unique date/time (discrete) # ticker = company stock ticker (discrete) # price = stock price (USD) (conintious, increasing mean, sd equals 5) # shares = number of shares held (count) # revenue = revenue in billion (USD) (continuous) # operating_income = operating income in billion (USD) (continuous) # profit = profit in billion (USD) (continuous) # total_assets = total assets in billion (USD) (continuous) # total_equity = total equity in billion (USD) (continuous) # industry = 'Basic Materials', 'Consumer Goods', 'Consumer Services', 'Financials', 'Health Care', 'Industrials', 'Oil and Gas', 'Technology', 'Telecom', 'Utilities' # country = Correlates of War Code (discrete) # intl = International or Domestic company (dichotomous) # ceo_salary = Salary of CEO in million (USD) (continuous) # no_employees = employees = 'lt 500', '500 - 1,000', '1,000 - 10,000', '10,000plus' # founded = year founded (discrete)

This data release is comprised of data tables of input variables for seawaveQ and surrogate models used to predict concentrations of select pesticides at six U.S. Geological Survey National Water Quality Network (NWQN) river sites (Fanno Creek at Durham, Oregon; White River at Hazleton, Indiana; Kansas River at DeSoto, Kansas; Little Arkansas River near Sedgwick, Kansas; Missouri River at Hermann, Missouri; Red River of the North at Grand Forks, North Dakota). Each data table includes discrete concentrations of one select pesticide (Atrazine, Azoxystrobin, Bentazon, Bromacil, Imidacloprid, Simazine, or Triclopyr) at one of the NWQN sites; daily mean streamflow; 30-day and 1-day flow anomalies; daily median values of pH and turbidity; daily mean values of dissolved oxygen, specific conductance, and water temperature; and 30-day and 1-day anomalies for pH, turbidity, dissolved oxygen, specific conductance, and water temperature. Two pesticides were modeled at each site with three types of regression models. Also included is a zip file with outputs from seawaveQ model summary. The processes for retrieving and preparing data for regression models followed those outlined in the SEAWAVE-Q R package documentation (Ryberg and Vecchia, 2013; Ryberg and York, 2020). The R package waterData (Ryberg and Vecchia, 2012) was used to import daily mean values for discharge and either daily mean or daily median values for continuous water-quality constituents directly into R depending on what data were available at each site. Pesticide concentration, streamflow, and surrogate data (continuously measured field parameters) were imported from and are available online from the USGS National Water Information System database (USGS, 2020). The waterData package was used to screen for missing daily mean discharge values (no missing values were found for the sites) and to calculate short-term (1 day) and mid-term (30 day) anomalies for flow and short-term anomalies (1 day) for each water-quality variable. A mid-term streamflow anomaly, for instance, is the deviation of concurrent daily streamflow from average conditions for the previous 30 days (Vecchia and others, 2008). Anomalies were calculated as additional potential model variables. Pesticide concentrations for select constituents from each site were pulled into R using the dataRetrieval package (De Cicco and others, 2018). Three of the six sites (Kansas River at DeSoto, Kansas; Missouri River at Hermann, Missouri; and White River at Hazleton, Indiana) pulled pesticide data for WY 2013–17 whereas the other three sites (Fanno Creek at Durham, Oregon; Little Arkansas River near Sedgwick, Kansas; and Red River of the North at Grand Forks, North Dakota) pulled pesticide data for WY 2013–18. Discrete pesticide data were matched with daily mean discharge and daily mean or median water-quality constituents and the associated calculated short-term (1-day) and mid-term (30-day) anomalies from the date of sampling. Pesticide concentrations were estimated using the SEAWAVE-Q (with surrogates) model using 19 combinations of surrogate variables (table 2 in the associated SIR, "Comparison of Surrogate Models to Estimate Pesticide Concentrations at Six U.S. Geological Survey National Water Quality Network Sites During Water Years 2013–18.") at each of 12 site-pesticide combinations (table 3 in the associated SIR). Three measures of model performance—the generalized coefficient of determination (R2), Akaike’s Information Criteria (AIC), and scale—were included in the output and used to select best-fit models (Table 4 of the associated SIR). The three to four best-fit SEAWAVE-Q (with surrogates) models with sample sizes at least five times the number of variables were selected for each site-pesticide combination based on generalized R2 values—the higher, the better. If generalized R2 values were the same, the model with the lower AIC value was used. The standard surrogate regression and base SEAWAVE-Q models were then applied using the same samples that were used for each of the best-fit SEAWAVE-Q (with surrogates) models so that direct comparisons could be made for each site-pesticide-surrogate instance. The input data used to estimate daily pesticide concentrations for each of the best fit models have been included in this data release. An example of one output file for each model type is included in a .zip file named "output_examples.zip". Each of the output files shows the three measures of model performance. (1) The output file for the standard regression model named "HAZ8_Atrazine_Standard_Regression_Output.txt" includes: Pseudo R-square (Allison) of 0.631, Model AIC of 174.0232, and a Scale of 0.961. (2) The output file for the base SEAWAVE-Q model named "HAZ8_Atrazine_Base_Seawave-Q_Output.txt" includes: Generalized r-squared of 0.82, AIC (Akaike's An Information Criterion) of 36.38, and a Scale of 0.288. (3) The output file for the SEAWAVE-Q w/Surrogates model named "HAZ8_Atrazine_Seawave-Q_w_Surrogates_Output.txt" includes: Generalized r-squared of 0.85, AIC (Akaike's An Information Criterion) of 33.76, and a Scale of 0.268. These values match those for Site ID = HAZ, Pesticide = Atrazine, and Surrogate variable group 8 for each model type in Table 4 of the associated SIR.

A time-varying parameter model with Markov-switching conditional heteroscedasticity is employed to investigate two sources of shifts in real interest rates: (1) shifts in the coefficients relating the ex ante real rate to the nominal rate, the inflation rate and a supply shock variable and (2) unconditional shifts in the variance of the stochastic process. The results underscore the importance of modelling continual change in the ex ante real rate in terms of other economic variables rather than relying on a statistical characterization that permits only a limited number of discrete jumps in the mean of the process.

Worker movement dataFile containing ant worker movement data within sample triplets along with distance between nests and nest volumes. For full description of labelling see readmemovement_data.txtMicrosatellite dataData for microsatellite variation across 12 loci. Four columns preceeding mirosatellite data describing sampling (see readme), next 12 columns are variation across microsatellite datamicrosatellite_data.txtResource movement dataA four column file with data on the absorbance of individual ants following and ELISA assay. Arranges as follows: colony - the name of the tested triplet, n=10; nest - B, C or U for base, connected or unconnected (see paper Fig. 1 for details); sample - bl (blank, no ant), ctrl (control, known negative ant), 1-100 individual ants being tested for absorbance; absorb - absorbance valueabsorbance_data.txtSample locationsLocations of nests used within this study along with triplet ID and their distance to forest cover historicallysample_locations.txt

This data release collates stream water temperature observations across the Chesapeake Bay watershed from the USGS National Water Information System (NWIS), Water Quality Portal (WQP) and the USGS Aquarius (AQ) Time-Series database. Data retrieved from NWIS consists of aggregate (minimum, maximum and mean) daily values and continuous data from USGS monitoring stations. Values from the WQP contain discrete data from multiple agencies. The dataset compiled from AQ includes miscellaneous stream temperature observations collected during discharge measurements. This stream temperature data release was completed to support the USGS goal to make scientific data publicly usable, easily discoverable, and widely available. A subset of these data will be used and published in the future to assess the status and trends of key indicators of stream health in the Chesapeake Bay watershed.

No description is available. Visit https://dataone.org/datasets/763057b62ce59448ffaa72406876ca7e for complete metadata about this dataset.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.3.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2023

• Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)

• Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)

• Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)

• Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)

• The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.

• For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).

• For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.

• For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.

• We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.

• Net changes to the input station data:

• Total of 126970983 observations

• 125384735 (98.75%) unchanged

• 28487 (0.02%) modified for this version

• 1557761 (1.23%) added in this version

• 188522 (0.15%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

All the data were entered into two separate Microsoft Excel spreadsheets (i.e., manual double-key data entry) and compared to assure data quality before analysis. Then, all relevant statistical calculations were completed using MedCalc® (version 20.011). Efficacy analysis was done on the modified intention to treat (ITT) anonymized dataset (n=73), whereas safety analysis was done on an intention to treat anonymized dataset (n=80). A summary statistic for all the parameters was performed and the results presented as mean with standard deviation (SD) for continuous variables. Categorical and discrete data were presented as counts with percentages. Change in values form baseline to 8 weeks were computed from and compared between the two groups using unpaired t-test. Since the baseline values were not similar for gender, BMI and chest circumference, the effect of these parameters on different parameters with respect to the change from baseline were analyzed using analysis of covariance (ANCOVA).

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2022 but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.2.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2022

• Added newly digitised data for monthly sunshine 1910-1918

  • Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242

  • Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files

  • Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs

  • Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.

  • Updated ordering of regions within regional values files. Alphabetical ordering.

  • Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression

• Net changes to the input station data used to generate this dataset:

• Total of 125601744 observations

• 122621050 (97.6%) unchanged

• 26700 (0.02%) modified for this version

• 2953994 (2.35%) added in this version

• 16315 (0.01%) deleted from this version

• Changes to monthly rainfall 1836-1960

• Total of 4823973 observations

• 3315657 (68.7%) unchanged

• 21029 (0.4%) modified for this version

• 1487287 (30.8%) added in this version

• 11155 (0.2%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2021, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.1.0.0 HadUK-Grid datasets are as follows:

• The addition of data for calendar year 2021

• The addition of 30 year averages for the new reference period 1991-2020

• An update to 30 year averages for 1961-1990 and 1981-2010. This is an order of operation change. In this version 30 year averages have been calculated from the underlying monthly/seasonal/annual grids (grid-then-average) in previous version they were grids of interpolated station average (average-then-grid). This order of operation change results in small differences to the values, but provides improved consistency with the monthly/seasonal/annual series grids. However this order of operation change means that 1961-1990 averages are not included for sfcWind or snowlying variables due to the start date for these variables being 1969 and 1971 respectively.

• A substantial new collection of monthly rainfall data have been added for the period before 1960. These data originate from the rainfall rescue project (Hawkins et al. 2022) and this source now accounts for 84% of pre-1960 monthly rainfall data, and the monthly rainfall series has been extended back to 1836.

Net changes to the input station data used to generate this dataset:

-Total of 122664065 observations

-118464870 (96.5%) unchanged

-4821 (0.004%) modified for this version

-4194374 (3.4%) added in this version

-5887 (0.005%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

Many practical problems in fluid dynamics demand an empirical approach, where statistics estimated from data inform understanding and modelling. In this context data-driven probabilistic modelling offers an elegant alternative to ad hoc estimation procedures. Probabilistic models are useful as emulators, but also offer an attractive means of estimating particular statistics of interest. In this paradigm one can rely on probabilistic scoring rules for model comparison and validation. Stochastic neural networks provide a particularly rich class of probabilistic models, which, when paired with modern optimisation algorithms and GPUs, can be remarkably efficient. We demonstrate this approach by learning the single particle transition density of ocean surface drifters from observations using a mixture density network. This provides a comprehensive description of drifter dynamics, from which we derive maps of various single-particle statistics. Our model also offers a means of simulating drifter trajectories as a discrete-time Markov process. A drifter release simulation using our model shows the emergence of concentrated clusters in the subtropical gyres, in agreement with previous studies on the formation of garbage patches. The dataset is intended to accompany the code repository archived at doi.org/10.5281/zenodo.7737161 . They are both related to the upcoming paper Brolly, M.T. (in submission), 'Inferring ocean transport statistics with probabilistic neural networks'.

HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.

The gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.

This data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2018, see linked documentation).

The changes for v1.3.0.ceda HadUK-Grid datasets are as follows:

• Added data for calendar year 2023

• Added newly digitised data for monthly sunshine 1910-1918

  • Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242

  • Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files

  • Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs

  • Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.

  • Updated ordering of regions within regional values files. Alphabetical ordering.

  • Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression

• Net changes to the input station data used to generate this dataset:

• Total of 125601744 observations

• 122621050 (97.6%) unchanged

• 26700 (0.02%) modified for this version

• 2953994 (2.35%) added in this version

• 16315 (0.01%) deleted from this version

• Changes to monthly rainfall 1836-1960

• Total of 4823973 observations

• 3315657 (68.7%) unchanged

• 21029 (0.4%) modified for this version

• 1487287 (30.8%) added in this version

• 11155 (0.2%) deleted from this version

The primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project "Analysis of historic drought and water scarcity in the UK"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence.

Continuous data are displayed as mean (SD; range) and discrete data are presented as count (%).*p

An upward-looking acoustic Doppler velocity meter (ADVM, SonTek SW, 3.0 MHz) located in a tidal channel of the Nisqually River Delta at site D1 (N 47d 05’ 37”/W 122d 43’ 17”) measured water level and current velocity at 15-minute intervals from October 14, 2016 to May 31, 2017 (175 days, excluding missing periods). This site is in a tidal channel at a levee breach where flow is tidally influenced. The water depth of the sensor ranged from 0.44 to 4.41 m. The elevation (NAVD88) of the ADVM sensor was survey by RTN-GPS. The offset to convert all water depth time-series data to water surface elevation (NAVD88) is -0.61 m. The water temperature ranged from -0.4 to 22.7 degrees C but may have been bias during periods of low tide due to solar heating of the instrument. Water level in the channel dropped below the elevation of the sensor during periods of low tide. The mean downstream velocity component (Vx) ranged from -0.93 to 0.77 m/s. The mean vertical velocity component (Vy) ranged from -0.14 to 0.14 m/s and mean signal to noise ratio ranged from 12.5 to 45.8 dB. The ADVM measured the water-velocity profile using a dynamic boundary adjustment mode allowing up to 10 velocity measurements in the water-column profile. Time-series gaps of 15-minutes or more are reported as “NA” and occurred when the instrument was offline, when error thresholds were exceeded, or when the water level in the channel dropped below the elevation of the sensor during periods of extreme low tide. Missing periods caused by power failure occurred from November 18-28, 2016; January 6-22, 2017; February 5-17, 2017; and April 1-11, 2017. This site is also referenced as "USGS 12081515 Area One near Olympia, WA". Discrete discharge data at this site are available at: https://waterdata.usgs.gov/wa/nwis/inventory/?site_no=12081515&agency_cd=USGS&