The text file "Dewpoint temperature.txt" contains hourly dewpoint temperature data in degrees Fahrenheit and associated data-source flags from January 1, 1948, to September 30, 2021. The primary data for water year 2021 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2022) and they were processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN20.WDM (Bera, 2021) and renamed as ARGN21.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as "backup." The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2022) provided the hourly dewpoint temperature data collected by the National Weather Service from the station at O'Hare International Airport and was used as "backup." Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2022, Meteorological data, accessed on January 17, 2022, at https://www.atmos.anl.gov/ANLMET/numeric/. Bera, M., 2021, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2020: U.S. Geological Survey data release, https://doi.org/10.5066/P9GP8COF. Midwestern Regional Climate Center, 2022, Meteorological data, accessed on March 2, 2022, at https://mrcc.purdue.edu/CLIMATE/. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

The text file "Dewpoint temperature.txt" contains hourly dewpoint temperature data in degrees Fahrenheit and associated data-source flags from January 1, 1948, to September 30, 2020. The primary data for water year 2020 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2020) and they were processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN19.WDM (Bera, 2020) and renamed as ARGN20.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Midwestern Regional Climate Center (Midwestern Regional Climate Center, 2020) provided the hourly dewpoint temperature data collected by the National Weather Service from the station at O'Hare International Airport and was used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2020, Meteorological data, accessed on November 17, 2020, at http://gonzalo.er.anl.gov/ANLMET/. Bera, M., 2020, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2019: U.S. Geological Survey data release, https://doi.org/10.5066/P9X0P4HZ. Midwestern Regional Climate Center, 2020, Meteorological data, accessed on November 3, 2020, at https://mrcc.illinois.edu/CLIMATE/. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

Station data from 255 U.S. surface stations are archived in this dataset. Station observation data include temperature, dew point, wind, visibility, weather, and clouds. Prior to October 1972, observation data are available at 6-hourly intervals, and from October 1972 on, they are available at 3-hourly intervals. The station reports also include 6-hourly precipitation amounts and daily maximum temperature.

The text file "Air temperature.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2015. The primary source of the data is the Argonne National Laboratory, Illinois. The first four columns give year, month, day and hour of the observation. Column 5 is the data in degrees Fahrenheit. Column 6 is the three-digit data-source flag. They indicate if the air temperature data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. These flags consist of a three-digit sequence in the form "xyz". The user of the data should consult Over and others (2010) for the detailed documentation of this hourly data-source flag series. Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

The atmospheric boundary layer (ABL) is the layer of air closest to the ground which is directly influenced on a daily basis by the heating and cooling of the earth's surface. The exact depth of the ABL varies according synoptic weather conditions and the time of day. During the daytime it is usually between 1 and 3 km; during the night it is much shallower. The ABL is important because it links the fluxes of heat and water vapor observed at the surface to the general circulation of the atmosphere. To model climate correctly, it is necessary for the ABL to be well understood and represented in the model. Because the air in the ABL is turbulent, small scale variations (about 1 km or less) in evaporation and heat flux at the surface are smoothed, with the temperature, humidity and depth of the ABL being uniform over the entire area. Larger scale variations (on the scale of 10 km or more) may lead to differences in ABL properties between the different surface types. Such differences may cause local atmospheric circulations to develop which may be important for the local climate of an area. During ABRACOS, three ABL measurement campaigns were carried out. These campaigns were called the Rondonia Boundary Layer Experiment (RBLE) 1, 2 and 3 and were held at Ji-Parana where the scale of the forested and deforested areas is large enough for each surface type to develop its own ABL. Refer to the related data set, Pre-LBA Anglo-Brazilian Amazonian Climate Observation Study (ABRACOS) Data, for additional information.The processed, quality controlled and integrated data in the documented Pre-LBA Data sets were originally published as a set of three CD_ROMs (Marengo and Victoria, 1998) but are now archived individually. The campaigns were held during the dry season when the difference in evaporation between the two surfaces types, forest and pasture, is at its greatest. Measurements were made with both free-flying radiosondes which measure temperature, humidity, and wind up to about 12 km and with a tethered balloon which makes more detailed measurements in the lowest 1 km of the atmosphere. Measurements were made at both the forest and clearing sites. Profiles of potential temperature measured during RBLE2 show that the daytime ABL was deeper over the clearing than the forest. The data have been used to test several models of ABL development. It appears that the ABL over pastures or over clearings grows more rapidly than predicted by the models, possibly because of the increased turbulence generated by the strips of forest typical of this area. The data have also been used to initialize one-dimensional climate models used in experiments to investigate the sensitivity of climate to land surface parameters, and to initialize a mesoscale model which can predict local effects on climate caused by the pattern of deforestation in this area.

The text file "Air temperature.txt" contains hourly air temperature data in degrees Fahrenheit and associated data-source flags from January 1, 1948, to September 30, 2018. The primary data for water year 2018 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2018) and processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN17.WDM (Bera and Over, 2018) and renamed as ARGN18.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2018) station at St. Charles, Illinois, was used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2018, Meteorological data, accessed on October 10, 2018, at http://www.atmos.anl.gov/ANLMET/. Bera, M., and Over, T.M., 2018, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2017: U.S. Geological Survey data release, ​https://doi.org/10.5066/F7H1318R. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2018, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on October 30, 2018, at http://dx.doi.org/10.13012/J8MW2F2Q.

The text file "Air temperature.txt" contains hourly air temperature data in degrees Fahrenheit and associated data-source flags from January 1, 1948, to September 30, 2019. The primary data for water year 2019 (a water year is the 12-month period, October 1 through September 30, designated by the calendar year in which it ends) were downloaded from the Argonne National Laboratory (ANL) (Argonne National Laboratory, 2019) and they were processed following the guidelines documented in Over and others (2010). The processed data were appended to ARGN18.WDM (Bera, 2019) and renamed as ARGN19.WDM. Missing and apparently erroneous data values were replaced with adjusted values from nearby weather stations used as “backup”. The Illinois Climate Network (Water and Atmospheric Resources Monitoring Program, 2019) station at St. Charles, Illinois, was used as "backup". Each data source flag is of the form "xyz", which allows the user to determine its source and the methods used to process the data (Over and others, 2010). References Cited: Argonne National Laboratory, 2019, Meteorological data, accessed on November 6, 2019, at http://www.atmos.anl.gov/ANLMET/. Bera, M., 2019, Meteorological Database, Argonne National Laboratory, Illinois, January 1, 1948 - September 30, 2018: U.S. Geological Survey data release, ​https://doi.org/10.5066/P9H8P0F7. Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open-File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/. Water and Atmospheric Resources Monitoring Program, 2019, Illinois Climate Network: Champaign, Ill., Illinois State Water Survey, accessed on November 6, 2019, at http://dx.doi.org/10.13012/J8MW2F2Q.

This data set contains the Coordinated Energy and Water Cycle Observation Project (CEOP) Enhanced Observing Period 3 (EOP-3) Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) Santarem Soil Temperature and Soil Moisture Data Set. This data set contains half-hourly data from a single station for the complete CEOP EOP-3 time period (01 October 2002 to 30 September 2003). This data set contains both ASCII data and netCDF data. The ASCII data file covers the entire time period for all stations. The netCDF data file covers the entire time period with one netCDF file for each station.

This submission comprises two downloadable .zip archives. Each archive contains spreadsheets with 3-D data and a .txt document describing the data. The Mesh Files .zip download contains data regarding the lithologic contacts of the granitoid and overlying sedimentary basin fill. The Initial Conditions .zip download contains data regarding temperature, pressure, and stress. These data were generated during Utah FORGE Phase 2 and exported from Seequent Leapfrog Geothermal. They are intended for use in 3-D earth modeling.

The formation of large scale structures in three-dimensional (3D) turbulent flows. How small-scale dynamics organize in turbulent flows to grow large scale coherent circulation? is at the heart of fundamental studies in fluid dynamics. It appears to be equally important for our understanding of atmospheric dynamics, oceanography, meteorology and more generally geophysical fluid dynamics. Here, we deliver a data collection that (1) gathers measurements of 3D turbulent flows that emulate planetary atmospheres of the gas giants. Turbulent flows are explored using three different approaches, laboratory experiments, numerical simulations and direct planetary observations. All data set are computed in order to easily extract flow properties, i.e. high resolution maps of the different velocity components and flow vorticity (useful for further diagnostic). The data collected are fully discribed in Cabanes et al GRL (2020) "Revealing the intensity of turbulent energy transfer in planetary atmospheres" and can be used to compute (2) theoretical diagnostics with the numerical codes that allow to reveal the physical meaning of flow measurements. Numerical codes are available on https://github.com/scabanes

We deliver (1) data collection and (2) numerical codes in the following files attached:

(1) Data collection:

A PDF file named JUMP-zonal-jets-data-collection-GRL.pdf that describes the following data files and nomenclature.

A zip File of the velocity fields in the lab, interpolated on Polar and Cartesian grids

JUMP-JetsInTheLab.zip

A netcdf file of velocity fields of our Saturn reference simulation

uvData-SRS-istep-312000-nstep-50-niz-12.nc

Two netcdf files of velocity fields from Cassini observations of Jupiter

uvData-JupObs-istep-0-nstep-4-niz-1.nc

StatisticalData-JupObs.nc

A zip file of potential vorticity profiles for Saturn and Jupiter observations

IPV-QGPV-Jupiter-Saturn.zip

(2) Numerical codes:

Codes for statistical analysis in spherical geometry on Github. --> https://github.com/scabanes/POST

Codes for statistical analysis in cylindrical geometry on Github. --> https://github.com/scabanes/JUMP

Codes for statistical analysis in cartesian geometry on Github. --> https://github.com/scabanes/JUMP

The purpose of this data collection is to reveal statistical properties of planetary flows. By computing the same analysis on different data sets the researcher allows direct confrontation of planetary observations with idealized laboratory and numerical models. Idealized models are specially designed to sweep on a large array of parameters in order to understand what parameters control planetary global circulation. The data collected and generated by the researcher deliver (1) velocity measurements of 3D turbulent flows using the different approaches (observations-laboratory-numerics) and (2) guidelines to compute the appropriate statistical analysis through the PTST. Here, the ground-breaking novelty is that the researcher deliver the possibility to compute statistical diagnostics adapted to the different geometries: the spherical geometry of planetary flows, i.e. 2D latitude-longitude maps, the cylindrical geometry of laboratory experiments, i.e. 2D flows in a rotating cylindrical tank, and the Cartesian geometry of idealized numerical simulations. Indeed, the math behind each statistical diagnostics must account for the different geometrical configurations in order to properly confront the different approaches. The PTST is also designed to be easily re-used by different communities such as experimentalists, numericists and atmosphericists that deal with 3D or 2D turbulent flows.

Acknowledgments

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement N° 797012.

Three .csv files. Two years of data collected on critical thermal maxima and minima (CTmax and CTmin) from 14 colonies in Oklahoma from 2017-2018. Additional data files represent thermal traits from 10 colonies in a short term (10 days) acclimation experiment and projections using environmental data for potential activity differences.Abstract from paper:How do individuals tolerate both the hot and the cold climate of our planet? One possibility is that organisms have plastic traits like thermal tolerance that allow them to function in highly variable environments. In this study, we tested whether phenotypic plasticity of temperature tolerance (i.e. acclimatization in the field and acclimation in the lab) occurs in the red harvester ant, Pogonomyrmex barbatus, at two temporal scales. We first measured the upper and lower critical thermal limits (CTmax and CTmin) of ants monthly for two years while concurrently measuring environmental conditions. Both CTmax and CTmin co-varied with temperature in a predictable way; values increased in a positive, linear manner. We then experimentally tested whether CTmax and CTmin could shift within a shorter time period by exposing subcolonies of ants to cool (10°C), moderate (20°C), and hot (30°C) temperatures for 10 days. CTmax increased only slightly at the hottest temperature treatment (+1.2°C), however CTmin increased considerably under both moderate (+2.6°C) and hot treatments (+3.8°C). Combined, our results suggest that thermal tolerance of ants may be more plastic than originally hypothesized, potentially aiding an already thermophilic clade.Methods from paper:Study site and environmental temperatureWe sampled ant workers monthly during their annual active period in 2017 and 2018 (i.e. March-November) from 14 colonies in a 30-ha grazed prairie in the central Great Plains of Oklahoma (34.5478º N, -98.2311º W, 330 m elevation). Over two years, ground temperature was recorded every 10 minutes using HOBO U23 Pro v2 External Temperature Data loggers at three equidistant locations within the sampling area. Temperature values were then averaged per month.Thermal tolerance across monthsDuring each sampling event (n = 18), we collected ~20 workers directly outside the nest of each colony and used 5 workers to measure critical thermal maximum (CTmax) and 5 workers to measure critical thermal minimum (CTmin). We did so using a heating/cooling assay to determine the temperature at which individuals lost muscle control. Thermal assays were conducted by placing individual ants into 1.5ml microcentrifuge tubes and plugging the tops with cotton to remove a potential thermal refuge in the cap. For CTmax, tubes were placed randomly into a Thermal-Lok 2-position dry heat bath that was prewarmed to 36ºC. Every 10 minutes, individuals were checked to see if they had reached their critical thermal limit by rotating the tube to check for a righting response. The temperature was then increased by 2ºC, with the process repeated until all individuals had reached their critical thermal maximum. CTmin was assayed in a similar manner, but we used a EchoThermTM IC20 chilling/heating dry bath that was precooled to 20ºC, following the methods above except with temperature lowered 2°C every 10 minutes. Additional ants from each colony were kept at ambient conditions as a control during each thermal assay, all of which survived. During each trial, we also confirmed the interior temperature of one unused vial using a thermocouple attached to an Extech MN35 Digital Mini MultiMeter. CTmax and CTmin values were averaged per colony for each month.Thermal tolerance within a monthIn April of 2019, we collected ~200 workers from each of 10 separate colonies to assess if critical thermal limits could change within a single cohort of ants over a short period of time. We split each group of 200 workers into three sub-colonies containing 50 workers and placed these newly created sub-colonies into three environmental chambers set at 10ºC, 20ºC, and 30ºC with a 12:12 L:D cycle and 85% RH. The selected temperatures span the approximate range of average monthly temperatures at our study site during which ants were active. Each sub-colony was provided with water and 20% sucrose solution ad libitum in cotton plugged vials and a small petri dish with Plaster of Paris that was moistened every other day. Critical thermal limits (CTmax and CTmin) were assayed using five individuals from each colony immediately prior to the start of the experiment and for five individuals from each sub-colony after 10 days in the environmental chambers. CTmax and CTmin values were averaged per colony for each temperature treatment.

The text file "Dewpoint temperature.txt" contains hourly data and associated data-source flag from January 1, 1948, to September 30, 2016. The primary source of the data is the Argonne National Laboratory, Illinois. The first four columns give year, month, day and hour of the observation. Column 5 is the data in degrees Fahrenheit. Column 6 is the data-source flag consist of a three-digit sequence in the form "xyz". They indicate if the dewpoint temperature data are original or missing, the method that was used to fill the missing periods, and any other transformations of the data. The user of the data should consult Over and others (2010) for the detailed documentation of this hourly data-source flag series. Reference Cited: Over, T.M., Price, T.H., and Ishii, A.L., 2010, Development and analysis of a meteorological database, Argonne National Laboratory, Illinois: U.S. Geological Survey Open File Report 2010-1220, 67 p., http://pubs.usgs.gov/of/2010/1220/.

The TIROS-3 Low-Resolution Omnidirectional Radiometer Level 1 Temperature Data product contains the black and white sensor temperature values in degrees Celsius. The experiment consisted of two sets of bolometers in the form of hollow aluminum hemispheres, mounted on opposite sides of the spacecraft, and whose optical axes were parallel to the spin axis. The bolometers were thermally isolated from but in close proximity to reflecting mirrors so that the hemispheres behaved like isolated spheres in space. The experiment was designed to measure the amount of solar energy absorbed, reflected, and emitted by the earth and its atmosphere in order to calculate the Earth's radiation budget. The data were originally written on IBM 7094 machines, and these have been recovered from magnetic tapes, referred to as the Omnidirectional Radiometer Temperature (ORT) tapes. The data are archived in their original text format.The TIROS-3 satellite was successfully launched on July 12, 1961. The Low-Resolution Omnidirectional Radiometer experiment returned data for about three months. Two follow-on instruments were flown on TIROS-4 and -7, while a similar instrument flew on Explorer-7.The Principal Investigator for these data was Verner E. Suomi from the University of Wisconsin. This product was previously available from the NSSDC with the identifier ESAD-00187 (old id 61-017A-01A).

Understanding dispersal potential, or the probability a species will move a given distance, under different environmental conditions is essential to predicting species’ ability to move across the landscape and track shifting ecological niches. Two important drivers of dispersal ability are climatic differences and variation in local habitat type. Despite the likelihood these global drivers act simultaneously on plant populations, and thus dispersal potential is likely to change as a result, their combined effects on dispersal are rarely examined. To understand the effect of climate and varying habitat types on dispersal potential, we studied Geum triflorum - a perennial grassland species that spans a wide range of environments, including both prairie and alvar habitats. We explored how the climate of the growing season and habitat type (prairie vs alvar) interact to alter dispersal potential. We found a consistent interactive effect of climate and habitat type on dispersal potential. Ac..., 1) geum_data.csv - trait data for geum. Trait data measured in the lab. 2) terminalvelocity_data.csv - terminal velocity data collected from dropping seeds through a tunnel with light receptors in the lab 3) height_data.csv - data on heights of G. triflorum individuals collected from digital herbarium specimens provided by: University of Minnesota, University of Manitoba, the Canadian Museum of Nature (CAN), and the Agriculture and Agri-Food Canada National Collection of Vascular Plants (DAO). 4) wind_data.csv - wind data from a weather station near Moorehead, MN 5) PCA_results.xlsx - the PCA results for our climate variables to demonstrate how we chose the variables we did in our analysis. 6) climate_data_tomerge_trait.csv: The .csv file to merge with geum_data.csv for analyses from that dataset (code merges these together). Created from ClimateNA 7) climate_data_tomerge_tv.csv: The .csv file to merge with terminalvelocity_data.csv for analyses from that dataset (code merges these to..., , # Climate and habitat type interact to influence contemporary dispersal potential in Prairie Smoke (Geum triflorum)

https://doi.org/10.5061/dryad.3bk3j9kt0

In this experiment we sampled seeds from natural populations of Geum triflorum plants across the northern Midwestern US and Canada to determine their dispersal ability in their two habitat types (prairie and alvar habitats) across the range of climate that exists across G. triflorum's range. We collected dispersal traits on the seeds (e.g. mass, length, width and area), as well as terminal velocity on these seeds. We also collected data on the height at seed release of these plants, as well as the climate data in these populations from the years the seeds were collected from climateNA (a climate database). We then ran the WALD dispersal model on these seeds to estimate their dispersal potential. We found that as climate warmed in a particular year (as the number of growing degree...

This data set contains the Coordinated Energy and Water Cycle Observation Project (CEOP) Enhanced Observing Period 3 and 4 (EOP-3 and EOP-4) Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) Brasilia Soil Temperature and Soil Moisture Data Set. This data set contains data from the Brasila Site for the CEOP EOP-3 and EOP-4 time period (26 June 2003 to 31 December 2004). This data set contains both ASCII data and netCDF data. The ASCII data file covers the entire time period for all stations. The netCDF data file covers the entire time period with one netCDF file for each station.

This dataset provides Daymet Version 3 model output data as gridded estimates of daily weather parameters for North America and Hawaii: including Canada, Mexico, the United States of America, and Puerto Rico. The island areas of Hawaii and Puerto Rico are available as files separate from the continental land mass. Daymet output variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980 to December 31 of the most recent full calendar year. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are continuous surfaces provided as individual files, by variable and year, at a 1-km x 1-km spatial resolution and a daily temporal resolution. Data are in a Lambert Conformal Conic projection for North America and are distributed in a netCDF file format compliant with Climate and Forecast (CF) metadata conventions (version 1.6).

1. Body size is a fundamental ecological trait and is correlated with population dynamics, community structure and function, and ecosystem fluxes. Laboratory data from broad taxonomic groups suggest that a widespread response to a warming world may be an overall decrease in organism body size. However, given the myriad of biotic and abiotic factors that can also influence organism body size in the wild, it is unclear whether results from these laboratory assays hold in nature. 2. Here we use data sets spanning 30 to 100 years to examine whether the body size of wild-caught beetles has changed over time, whether body size changes are correlated with increased temperatures, and we frame these results using predictions derived from a quantitative review of laboratory responses of 22 beetle species to temperature. 3. We found that 95% of lab-reared beetles decreased in size with increased rearing temperature, with larger-bodied species shrinking disproportionately more than smaller-bodied beetles. In addition, the museum data sets revealed that larger-bodied beetle species have decreased in size over time, that mean beetle body size explains much of the interspecific variation in beetle responses to temperature, and that long-term beetle size changes are explained by increases in autumn temperature and decreases in spring temperature in this region. 4. Our data demonstrate that the relationship between body size and temperature of wild-caught beetles matches relatively well with results from laboratory studies, and that variation in this relationship is largely explained by interspecific variation in mean beetle body size. 5. This long-term beetle dataset is one of the most comprehensive arthropod body size datasets compiled to date, it improves predictions regarding the shrinking of organisms with global climate change, and together with the meta-analysis data, call for new hypotheses to explain why larger-bodied organisms may be more sensitive to temperature.

Monitoring streambed elevation changes is important for many engineering and ecological applications. This contribution contains the data and the numerical code written in R used in the publication of DeWeese et al, (2017), who tested a new methodology based on stream water temperature as a signal to monitor local streambed elevation changes at the daily time scale. This contribution contains: (1) laboratory experiment time series of water temperature in the surface and within the sediment, (2) times series of sediment surface elevation changes in the laboratory, (3) field experiment time series of sediment elevation and (4) field experiment time series of surface and pore waters temperatures and (5) R code of the model to analyze the temperature data to extract streambed elevation changes and interstitial flows.

Reference:Timothy DeWeese, Daniele Tonina, Charles Luce, Monitoring streambed scour/deposition under non-ideal temperature signal and flood conditions, Water Resources Research, doi: 10.1002/2017WR020632

Details about the Data * Outdoor experiment was conducted at the open plaza in SDE4

• Indoor experiment was conducted at the education resource centre in SDE4

Context

Thermal comfort affects the well-being of different genders differently. However, due to practical and time limitations, the number of studies that we are able to conduct is limited. Since our studies involves the study of both indoor and outdoor conditions, longitudinal data are therefore a valuable resource to understand how different genders perceive temperature. For our analysis, we chose to use bar graphs to showcase our data instead of other graphs, particularly bubble graphs as the size of the bubbles will not be impactful in showing the differencd between both genders.

Content For both Indoor and Outdoor datasets, it is done based on longitudinal subjective feedback of the different genders' preference to determine the differing levels of thermal comfort. The experiment was conducted with 6 participants (3 female, 3 male) over the course of 8 hours in a day. This produced 360 surveys for thermal preference.

For the whole duration of the study, each survey was conducted at 15 minute intervals. To make sure that the experiment is fair and constant, we ensured that the location was kept constant for each dataset, leading to environmental variables (e.g. temperature, relative humidity) being kept constant as well. Participants completed comfort surveys from the screen of their smartwatches using an open-source application named Cozie. Location data were used to time and spatially align environmental measurements to thermal preference responses provided by the participants. Background information of participants, such as physical characteristics was collected using an on-boarding survey administered at the beginning of the experiment.

The Temperature Severity Indicator data identifies areas subject to extreme heat and cold events in the contiguous United States in an effort to inform temperature-related housing and planning research. The indicators, conveyed as a grid of 1-degree latitude by 1-degree longitude cells, are created from observational data (Berkeley Earth Lab gridded daily maximum and minimum temperature ) and consider the frequency, intensity, and duration of extreme heat and extreme cold weather events that occurred in the US between 1913 and 2012.DEFINING EXTREME TEMPERATURE EVENTS

For the purposes of this data, a daytime extreme heat event is defined as daily maximum temperature (tmax) that meets or exceeds the 90th percentile daily tmax for June, July, and August (JJA) during the reference period 1961-1990 and lasting for at least 3 consecutive days. A lower bound is set to 90 degrees Fahrenheit (F) to define the minimum temperature qualifying as a daytime heat event. Likewise, a night time extreme heat event is defined as daily minimum temperature (tmin) that meets or exceeds the 90th percentile daily tmin for JJA during the reference period 1961-1990 and lasting for at least 3 consecutive nights. A lower bound is set to 75 F to define the minimum temperature qualifying as a night time heat event. A daytime extreme cold event is defined as daily maximum temperature (tmax) that is at least 10 F less than the median daily climatological January tmax over the reference period 1961-1990 and lasting for at least 3 consecutive days. An upper bound is set at 32 F to define the maximum temperature qualifying as a daytime cold event, and a lower bound is set to -10 F, where any 3 or more consecutives days colder than this limit is considered a cold event. A night time extreme cold event is defined as daily minimum temperature (tmin) that is at least 10 F less than the median daily climatological January tmin over the reference period 1961-1990 and lasting for at least 3 consecutive days. An upper bound is set at 32 F to define the maximum temperature qualifying as a night time cold event, and a lower bound is set to -10 F, where any 3 or more consecutives nights colder than this limit is considered a cold event.CREATING EXTREME TEMPERATURE SEVERITY INDEXES

The average annual event frequency (events/yr), average event intensity compared to a seasonally representative temperature (F), and the average event duration (days) are computed using the Berkeley Earth temperature observations as well as the above definitions for extreme heat and cold events. Results of those calculations are classified according to a quartile distribution of all values relative to attribute, and each cell receives a score according to its quartile class: 0 points for a cell value less than the 25th percentile, 1 point if between the 25th and 50th percentile, 2 points if between the 50th and 75th percentile, 3 points if greater than the 75th percentile. The index value represents the aggregation of quartile points awarded for each attribute of a particular cell.SUGGESTED USE OF DATA Fields ending with the suffix, “_INDX” provide spatially relevant severity indices for min/max cold snaps and heat waves. As described previously, the value for each index represents the summation of attributes scores determined by a quartile distribution of all values for each facet of analysis. Index scores for these fields range from 0 to 9 providing for a relatively smooth surface map illustrating spatial variability. In contrast, fields ending with the suffix, “_IND” are binary attributes that indicate areas where the index values for both night-time (tmin) and day-time (tmax) is >= 5 relative to each event type. Given the boolean nature of data in these fields they are best used to quickly identify areas of extreme temperature to answer policy related questions, and not necessarily for illustration or spatial analysis.For questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Temperature Severity IndexDate of Coverage: 1913 - 2013