Project Title: Add title here

Project Team: Add contact information for research project team members

Summary: Provide a descriptive summary of the nature of your research project and its aims/focal research questions.

Relevant publications/outputs: When available, add links to the related publications/outputs from this data.

Data availability statement: If your data is not linked on figshare directly, provide links to where it is being hosted here (i.e., Open Science Framework, Github, etc.). If your data is not going to be made publicly available, please provide details here as to the conditions under which interested individuals could gain access to the data and how to go about doing so.

Data collection details: 1. When was your data collected? 2. How were your participants sampled/recruited?

Sample information: How many and who are your participants? Demographic summaries are helpful additions to this section.

Research Project Materials: What materials are necessary to fully reproduce your the contents of your dataset? Include a list of all relevant materials (e.g., surveys, interview questions) with a brief description of what is included in each file that should be uploaded alongside your datasets.

List of relevant datafile(s): If your project produces data that cannot be contained in a single file, list the names of each of the files here with a brief description of what parts of your research project each file is related to.

Data codebook: What is in each column of your dataset? Provide variable names as they are encoded in your data files, verbatim question associated with each response, response options, details of any post-collection coding that has been done on the raw-response (and whether that's encoded in a separate column).

Examples available at: https://www.thearda.com/data-archive?fid=PEWMU17 https://www.thearda.com/data-archive?fid=RELLAND14

The zip file contains data and code used in the following paper: Varble, A. C., Ma, P.-L., Christensen, M. W., Mülmenstädt, J., Tang, S., and Fast, J.: Evaluation of Liquid Cloud Albedo Susceptibility in E3SM Using Coupled Eastern North Atlantic Surface and Satellite Retrievals, Atmospheric Chemistry and Physics, 23, 13523-13553, https://doi.org/10.5194/acp-23-13523-2023, 2023. All E3SMv1 and observation data used in the study can be found in the data folder. These files are limited to data in the column (or columns) over the ARM ENA site in the Azores and include variables from E3SMv1 output or ARM datasets (https://adc.arm.gov/discovery/#/results/site_code::ena) in addition to retrievals performed on those variables. The python notebooks used to create the files from the raw model output and ARM datasets can be found in the python_notebooks folder. The E3SM run script can be used to run E3SMv1 (https://github.com/E3SM-Project/E3SM) to reproduce model output used in the study. We are archiving E3SMv1 model output as long as possible, but it is a very large dataset. Please send inquiries for downloading these data to Adam Varble (adam.varble@pnnl.gov). If you have any further questions, please contact Adam Varble at adam.varble@pnnl.gov.

General Info

This dataset contains monthly output from two 20-year (1979-1998) variable-resolution (VR) CESM2 simulations (HMA_VR7a and HMA_VR7b). The coupled atmosphere-land simulations were run with a newly generated VR grid that has regional grid refinements up to 7 km over High Mountain Asia. The HMA_VR7b simulation was performed with an updated glacier-cover dataset (https://doi.org/10.5281/zenodo.7864689) and includes snow and glacier model modifications. Further, monthly output from a globally uniform 1-degree CESM simulation (NE30), used for evaluation of the HMA VR simulations, is also included. The monthly output have been used for analysis and discussion in the paper “Exploring the ability of the variable-resolution CESM to simulate cryospheric-hydrological variables in High Mountain Asia” that is currently under review in the Cryosphere Discussions, https://tc.copernicus.org/preprints/tc-2022-256/.

Contact

René Wijngaard (r.r.wijngaard.uu@gmail.com / r.r.wijngaard@uu.nl)

Raw Data

Raw monthly and daily unstructured HMA VR model output are available on request.

Dataset Contents

NE30.tar

HMA_VR7a.tar

HMA_VR7b.tar 

These files contain atmosphere (CAM) and land (CLM) model output that are regridded to a 1-degree finite volume (0.9 x 1.25 degrees latitude/longitude) grid. The following variables are included: CLDLIQ, OMEGA, Q, STEND_CLUBB, SWCF, T, Z3, EFLX_LH_TOT, FGR, FIRE, FLDS, FSA, FSDS, FSH, FSM, FSNO, FSM, FSR, H2OSNO, PCT_LANDUNIT, QICE_MELT, QSNOFRZ, RAIN, SNOW, and TSA.

SMB_HMA_VR7a.tar

SMB_HMA_VR7b.tar

These files contain unstructured SMB-related CLM model output (i.e., on the HMA VR grid). The following variables are included: PCT_LANDUNIT, QRUNOFF_ICE, QSNOFRZ_ICE, QSNOMELT_ICE, QSOIL_ICE, RAIN_ICE, and SNOW_ICE.

Vegetation variables in the case study dataset.

The data are based on the 2011 Census Microdata Teaching File, with the first 18 variables exactly the same as those found in the original file, which can be downloaded from: http://www.scotlandscensus.gov.uk/microdataThe final 10 variables found in the file, highlighted in yellow, are synthetic data. Those variables corresponding to a 2001 state are based on the transitional probabilities taken from the ONS longitudinal study, accurate to 10 year age groups.Details of the synthetic variables can be found in the Synthetic Variables sheet in this file. Details of the original variables can be found in the meta data accompanying the original microdata teaching file.

This dataset accompanies the study Preferences in the Area of Nontypical Roles-in-Sex, conducted online via the FetSide platform in May–June 2022. The research explores the self-reported sexual role preferences among individuals, including autogynephilic ideation, with a focus on adult heterosexual males.

The dataset includes:

A fully coded matrix of survey responses (survey_answers.csv),

A complete codebook with all variables and response categories (codebook.xlsx),

A technical variable dictionary (variable_dictionary.csv),

And a detailed README.txt file describing the structure and purpose of each component.

The study was conducted anonymously. No personal data were collected, and all responses are fully de-identified.

This dataset contains comprehensive annual data from 30 elite female race walkers collected during 2021–2024. The dataset includes anthropometric variables (e.g., body mass, height, fat mass), physiological indicators (e.g., VO₂max, heart rate, lactate threshold, oxygen pulse), and biomechanical measures (e.g., step length, walking speed), as well as neuromuscular performance parameters (e.g., 1RM, power output, RFD).

The dataset is structured across four Excel sheets representing consecutive years. Each sheet includes anonymized rows for each athlete and columns for the assessed variables. These data were used in the study: "Optimizing Race Walking Performance through Advanced Modeling and AI-based Training Analysis."

This resource supports time-series analysis, seasonality modeling, and development of machine learning algorithms in elite sport research.

Details of the 10 additional datasets (the top five datasets are on species-habitat interactions; the second five datasets are wider biological datasets).

The data are based on the 2011 Census Microdata Teaching File, with the first 18 variables in the OriginalTeachingFileData worksheet exactly the same as those found in the original file. This can be downloaded from: http://www.nisra.gov.uk/Census/2011_results_specialist_products.html. It is also available on the Northern Ireland Neighbourhood Information Service (NINIS) website.The final 8 variables found in the SYLLS_Synthetic_NILS_Spine worksheet, are synthetic data. Those variables corresponding to a 2001 state are based on the transitional probabilities taken from the NILS, accurate to 10 year age groups.

Species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations. However, species response to climate change is poorly understood and species range shifts may be related to climate change exposure. This project was designed to find when a new climate normal emerges beyond different thresholds of natural climate variability with the goal to help natural resource managers, other practitioners, and scientists concerned with emerging climate signals. Estimates are provided for the time (year) when a biologically-relevant temperature signal emerges (time of emergence - ToE) above natural variability considering an early industrial period climate and the strength of the signal (degrees C) at the ToE. The year-to-year “natural” variability is estimated as the noise in which the signal must persistently surpass for the emergence of a cl ...

Annual or summer (JJA) mean variables from two CESM2-CISM2 simulations: 'F09' uses the f09 grid for the atmosphere and land components, 'ARCTIC' uses the variable-resolution arctic grid. Three periods - piControl, 1pctCO2 and 4xext are included.

CAM variables: CLDTOT, PHIS, PS, T, TGCLDLWP, TREFHT, Z3

CLM variables: EFLX_LH_TOT, FGR, FIRA, FLDS, FSDS, FSH, FSM, FSR, PCT_LANDUNIT, QFLX_EVAP_TOT, QICE_MELT, QRUNOFF, QSNOMELT, RAIN, SNOW

CISM variables: iarea, ice_sheet_mask, ivol, thk, total_bmb_flux, total_calving_flux, total_smb_flux

POP variables: MOC

CICE variables: aice, hi

Data at native resolutions smaller or larger than 1km have been aggregated to 1km.†QSCAT annual mean and standard deviation are based on monthly data from the year 2001 with complete data coverage.‡LAImax, LAImin, and LAIrange are derived from monthly mean values based on the first 5 year of MODIS data (2000–2004 [26]).§Percent Tree Cover is based on MODIS data from 2001 [25].¶WorldClim data are based on monthly climatologies from 1950–2000 [23].*Cost distances are computed either as Leas-Cost-Paths [48] or resistance distances [49].**See [21].

Association between variables and time to recurrent admission after AIS hospitalization (using a Weibull accelerated failure time model) in univariate analysis (Model 1) and after controlling for patient age, sex, NIHSS category, LOS, discharge disposition, hospital bed size, location/teaching status, and ownership (Model 2).

This dataset was created with the aim of understanding how a farm’s local climate contributes to its sale price. It includes both the transaction prices of farmland plots, as well as factors that may have an influence on those prices. Variables were selected from the best currently available data, and are guided by a literature review of previous studies. However – and importantly – this dataset may not include all of the factors that explain the price of a piece of farmland. A unique aspect of this dataset is the effort made to collect variables with a similarly granular spatial scale.

General Info

This data archive contains the updated glacier-cover and glacier regions for the Community Land Model version 5 (CLM5)/Community Terrestrial Systems Model (CTSM). The updated glacier-cover and glacier regions are used for a study on the evaluation of variable-resolution (VR) CESM2 in High Mountain Asia (https://tc.copernicus.org/preprints/tc-2022-256/). The data archive also contains the model scripts and input files that have been used to create the glacier-cover dataset. The global glacier outlines used for the glacier-cover dataset were retrieved from the Randolph Glacier Inventory version 6 (RGI-Consortium, 2017). The vector data for the Greenland and Antarctic ice sheets were retrieved from the masks of Bedmachine version 4 (Morlighem et al., 2017, 2021) and version 2 (Morlighem et al., 2020; Morlighem, 2020), respectively.

Contact

René Wijngaard (r.r.wijngaard.uu@gmail.com / r.r.wijngaard@uu.nl)

Dataset Contents

mksrf_glacier_3x3min_simyr2000.c210708.nc

The updated glacier-cover dataset, encompassing three 3-minute datasets: 1) fractional land ice coverage, including both glaciers and ice sheets (PCT_GLACIER), 2) distributions of areal glacier coverage by elevation (PCT_GLC_GIC), and 3) distributions of areal ice-sheet coverage by elevation (PCT_GLC_ICESHEET).

mksrf_GlacierRegion_10x10min_nomask_c200813.nc

The updated glacier regions, encompassing five different glacier regions (0 - Other regions, 1 - Inside standard CISM grid but outside Greenland itself, 2 - Greenland, 3 - Antarctica, and 4 - High Mountain Asia (new)), used to set the ice melt and runoff behaviour in CLM5/CTSM (more detailed information can be found in the CLM5 Documentation, https://escomp.github.io/ctsm-docs/)

model_scripts.tar

Model scripts used for creating the glacier-cover dataset. A README file is included that lists instructions on how to make the glacier-cover dataset.

glacier_final.tar

Input files used to create the glacier-cover dataset. The following files are included: a global 30-arcsec merged BedMachine/GMTED2010 elevation dataset (gmted_bedmachine_stitched.nc) and land-sea mask (gmted2010_modis-rawdata-lonshift.nc), Antarctica land mask (BedMachineAntarcticaRotate2RotateBack_2020-07-15_v02_lonshift.map_TO_30arcsec.nc), Greenland land mask (BedMachineGreenland-2021-04-20.map_TO_30arcsec.nc), and 30-arcsec datasets encompassing glacier-cover (30arcsec_00_rgi60_World.nc) and ice-sheet cover (30arcsec_00_BM_World.nc).

This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.

Precipitation is a key input variable in distributed surface water-groundwater models, and its spatial variability is expected to impact watershed hydrologic response via changes in subsurface flow dynamics. Gridded precipitation datasets based on gauge observations, however, are plagued by uncertainty, especially in mountainous terrain where gauge networks are sparse. To examine the mechanisms via which uncertainty in precipitation data propagates through a watershed, we perform a series of numerical experiments using an integrated surface water-groundwater hydrologic model, ParFlow.CLM. The Kaweah River watershed in California, USA is used as our virtual catchment laboratory to characterize watershed response to variable precipitation forcing from headwaters to groundwaters. By applying the three cornered hat method, we quantify the spatially distributed uncertainty in four publically available precipitation forcing datasets and their simulated hydrology. Simulations demonstrate that uncertainty in the simulated groundwater storage is primarily a result of topographic redistribution of uncertainty in precipitation forcing. Soil water redistribution is the primary pathway that redistributes uncertainty downslope. We also find that topography exerts a larger impact than variable subsurface parameters on propagating uncertainty in simulated fluxes. Finally, we find that improvement in model performance metrics is higher for a single simulation forced with the mean precipitation from the available datasets than the averaged simulated results of separate simulations forced with each dataset. Results from this study highlight the importance of topography-moderated flow through the critical zone in shaping the groundwater response to climate variability.

Data archive for Tier 1 of the ForceSMIP project, which is described in "Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)" by Wills et al., submitted to Journal of Climate. Please cite that paper for any usage of this data.

Types of data included here are:

• Evaluation-Tier1: Raw data for 10* evaluation members, including reanalysis/observations (member "1I")
• ensmeans-Tier1: The "true forced response", from the corresponding large ensemble mean, for the 9* evaluation members from models (all except "1I")
• ForceSMIP-estimates-Tier1: ForcesSMIP method estimates of the forced response in each evaluation member

Each of these types of data is provided at monthly temporal resolution over 1950-2022, for each of 8* variables: tos (sea-surface temperature), tas (surface air temperature), pr (precipitation), psl (sea-level pressure), monmaxtasmax (monthly maximum daily maximum temperature), monmintasmin (monthly minimum daily minimum temperature), monmaxpr (monthly maximum daily precipitation), and zmta (zonal-mean atmospheric temperature).

*At the time of initial submission, only 6 of 10 evaluation members are included (the 5 unseen models and observations), and data is only provided for 3 out of 8 variables. The full dataset requires a Zenodo quota increase, which will be requested as the publication is finalized. Please also note that in this version, the "member" labels are swapped between the methods "RegGMST" and RegGMST-LENSem".

Dataset characteristics for the whole market data and water and sewer revenue bonds only, for both response variables: market spread and spread at issue.