cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2nsdb6
title: CCDC 2466308: Experimental Crystal Structure Determination

description: Related Article: Nobuto Kaneko, Misao Himeno, Yuhi Kobayashi, Hiroki Kubota, Ryo Tanifuji, Haruki Mizoguchi, Makoto Muroi, Takehiro Suzuki, Masaya Sugiyama, Meng Ling Moi, Naoshi Dohmae, Hiroyuki Osada, Taketomo Kido, Atsushi Miyajima, Hiroki Oguri|2025|RSC Chem. Biol.|||doi:10.1039/D5CB00268K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2nsdhc
title: CCDC 2466313: Experimental Crystal Structure Determination

description: Related Article: Nobuto Kaneko, Misao Himeno, Yuhi Kobayashi, Hiroki Kubota, Ryo Tanifuji, Haruki Mizoguchi, Makoto Muroi, Takehiro Suzuki, Masaya Sugiyama, Meng Ling Moi, Naoshi Dohmae, Hiroyuki Osada, Taketomo Kido, Atsushi Miyajima, Hiroki Oguri|2025|RSC Chem. Biol.|||doi:10.1039/D5CB00268K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2c4z7r
title: CCDC 2179524: Experimental Crystal Structure Determination

description: Related Article: Nobuto Kaneko, Misao Himeno, Yuhi Kobayashi, Hiroki Kubota, Ryo Tanifuji, Haruki Mizoguchi, Makoto Muroi, Takehiro Suzuki, Masaya Sugiyama, Meng Ling Moi, Naoshi Dohmae, Hiroyuki Osada, Taketomo Kido, Atsushi Miyajima, Hiroki Oguri|2025|RSC Chem. Biol.|||doi:10.1039/D5CB00268K
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.4211/hs.7062dca5d72f42e193c0cee9bc1cc2bc
title: Kīholo Bay Submarine Groundwater Discharge Dataset_v2

description: Submarine groundwater discharge (SGD) dataset from Kīholo Bay, Hawai’i Island from 2014-2016. Radon data (Bq/m^3) were collected using the SGD Sniffer (Dulai et al., 2016), an autonomous gamma spectrometer. Salinity was derived from specific conductivity collected by an onboard CTD Diver. SGD (cm/day) was calculated using a radon mass balance following methods described in Dulaiova et al., 2010. 

Please contact: Trista McKenzie <tristam@hawaii.edu>, or  Henrietta Dulai <hdulaiov@hawaii.edu> for information related to this

 References: 1. Dulai, H. et al. Autonomous long-term gamma-spectrometric monitoring of submarine groundwater discharge trends in Hawaii. J. Radioanal. Nucl. Chem. 307, 1865–1870 (2016).    2. Dulaiova, H., Camilli, R., Henderson, P. B. & Charette, M. A. Coupled radon, methane and nitrate sensors for large-scale assessment of groundwater discharge and non-point source pollution to coastal waters. J. Environ. Radioact. 101, 553–563 (2010).
DOI: 10.4211/hs.ae2dd1c92e1b4cd7b77d6115db1d8011
title: UNM WR 573 Fall 2021 - Las Huertas data

description: The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body.  In this investigation, we seek to answer the following Research Question: 

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river? 

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U. 

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.
DOI: 10.4211/hs.a0dc51142fe249f89877c4005e0b6947
title: Spatio-temporal variations of water sources and mixing spots in a riparian zone: supplementary data

description: This resource is linked to the following manuscript:
Nogueira et al.: "Spatio-temporal variations of water sources and mixing spots in a riparian zone"

The observational data, as well as model files and scripts stored at this repository were used for the development and further analyses presented and discussed in the manuscript, supporting the study carried out at the Selke stream floodplain, central Germany.

>The data presented here include the field observations used for model creation (e.g., topography data), and its further evaluation (e.g., stream discharge, hydraulic heads, and hydrochemical data). Additional HydroGeoSphere model files (e.g., grok file + HMC extension embedded, soil property files) are also provided.
>Details on each file and collected data are provided on the "Read_me.txt" file below.
DOI: 10.4211/hs.6a7c90d586b34b3da0f1f675dc95888a
title: Supplement materials for publication (after Revision): Linking different drought types in a small catchment from a statistical perspective – case study in the Wernersbach catchment, Germany.

description: - Monthly data (P,Q,SM)
- Calculated indexes (with SPI and Empirical Quantile for 1,3,6,9,12 month aggregation period foe each variable)
- Code for each method
- BROOK90 data (scheme, list  of used parameters, files with calibrated parameters, modelled SM)
DOI: 10.4211/hs.f0f53cbd821842cb817b8c60c94c4e6f
title: Coastal Permafrost and Groundwater Discharge Model

description: This resource expands on a previously published FlexPDE code (doi: 10.4211/hs.98d4192a4f8841149e1502f82b777f69), which solves the coupled partial differential equations describing variable-density fluid flow and solute transport, and heat transfer with salinity-dependent freeze-thaw, by incorporating additional text necessary for analyzing groundwater discharge in the coastal zone. Also included are scripts for -4 degree C and -2 degree C initial surface temperature scenarios.
DOI: 10.4211/hs.6bc577cabe0341398f78d976e23f51c7
title: UNM WR 573: Field Methods Fall 2021

description: The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body.  In this investigation, we seek to answer the following research question: 

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river? 

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U. 

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.
DOI: 10.4211/hs.812a28a8a2814eac9760e34737c54fa2
title: UNM WR 573 Fall 2021 - Rio Grande data

description: The hydrology, chemistry, and biology of a stream are strongly interconnected, and must all be considered when assessing the overall state of a water body.  In this investigation, we seek to answer the following research question: 

What are the differences in water quality and quantity between a rural headwater stream and an urban main-stem river? 

For our investigation, we measured, analyzed, and compared water quality and quantity characteristics in a rural headwater stream (Las Huertas Creek, abbreviated as LH) and an urban main-stem river (The Rio Grande, abbreviated as RG) located near and in Albuquerque, New Mexico. At each of our two locations, we measured water quality and quantity at a downstream site (abbreviated as D), a midstream site (abbreviated as M), and an upstream site (abbreviated as U) for a total of six sites in our study. We defined these areas as the location abbreviation followed by the site abbreviation; for example, the Las Huertas Downstream site was defined as LH_D while the Rio Grande Upstream site was defined as RG_U. 

To answer our research question, we measured hydrologic, chemical, and biological parameters at each of our six sites. For hydrology, we measured discharge and soil hydraulic conductivity; for chemistry, we measured temperature, specific conductivity, conductivity, total dissolved solids, salinity, dissolved oxygen, pH, turbidity, alkalinity, anions, and cations; for biology, we measured chlorophyll a, benthic macroinvertebrates, organic matter, and riparian vegetation. Below is a description of our study locations and our parameter methods followed by parameter results and a discussion.
DOI: 10.4211/hs.f970981d5dde4e60a01beff47197670a
title: AW_betasdam Final data and code

description: Hydrological, biological, geomorphological, and geospatial datasets collected from the Arid West (AW) used to develop the Beta Streamflow Duration Assessment Method (SDAM) for the AW. SDAMs are rapid, reach-scale indices or models that use physical and/or biological indicators to predict flow duration class. Three flow duration classes are used in the AW Beta SDAM: perennial, intermittent, and ephemeral. Perennial reaches have continuous surface flow and do not experience drying outside of extreme drought. Intermittent reaches have continuous surface flow for part of the year that is sustained by snowmelt and/or groundwater. Ephemeral reaches have surface flow only during and immediately following precipitation or snowmelt. These datasets are also located at the United States Environmental Protection Agency data repository at: https://doi.org/10.23719/1523371
DOI: 10.4211/hs.b143432cfe72462fac90376631c9a2b3
title: Dataset for "Boreal headwater catchment as hot spot of carbon processing from headwater to fjord" Clayer et al.

description: This dataset includes dissolved O2 and greenhouse gas (GHG: CO2, CH4 and N2O) concentrations as well as GHG diffusive emission fluxes from lakes and streams in the Langtjern catchment in 2018 and 2019 as well as sensor data and TOC concentrations from the inlet, buoy and outlet stations in Lake Langtjern (discharge, water temperature, pH, conductivity, pCO2, O2 , fDOM) over 2015-2019. Detailed information about the methodology can be found in Clayer et al. (in press). The content of this resource serves as the data for "Boreal headwater catchment as hot spot of carbon processing from headwater to fjord" by Clayer et al. 2022, JGR-Biogeosciences
DOI: 10.4211/hs.39d5402c01af4fc8b0eaaf3d381223cb
title: Stochastic Decadal Projections of Colorado River Streamflow and Reservoir Pool Elevations Conditioned on Temperature Projections

description: Decadal (~10-years) scale flow projections in the Colorado River Basin (CRB) are increasingly important for water resources management and planning of its reservoir system. Physical models – Ensemble Streamflow Prediction (ESP) – do not have skill beyond interannual time scales. However, Global Climate Models have good skill in projecting decadal temperatures. This, combined with the sensitivity of CRB flows to temperature from recent studies, motivate the research question - can skill in decadal temperature projections be translated to operationally skillful flow projections and consequently, water resources management? To explore this, we used temperature projections from the Community Earth System Model – Decadal Prediction Large Ensemble (CESM-DPLE) along with past basin runoff efficiency as covariates in a Random Forest (RF) method to project ensembles of multi-year mean flow at the key aggregate gauge of Lees Ferry, Arizona. RF streamflow projections outperformed both ESP and climatology in a 1982-2017 hindcast, as measured by ranked probability skill score. The projections were disaggregated to monthly and sub-basin scales to drive the Colorado River Mid-term Modeling System to generate ensembles of water management variables. The projections of pool elevations in Lakes Powell and Mead – the two largest U.S. reservoirs that are critical for water resources management in the basin – were found to reduce the hindcast median root mean square error by up to -20 and -30% at lead times of 48- and 60-months, respectively, relative to projections generated from ESP. This suggests opportunities for enhancing water resources management in the CRB and potentially elsewhere.
DOI: 10.4211/hs.14e9c14880ad4a079a6112149160d489
title: Thermal infrared imagery, Wailupe, HI_v2

description: Title of dataset	Thermal infrared imagery, Wailupe, HIAbstract	Coastal groundwater dependent ecosystems take advantage of low salinity, nutrient rich submarine groundwater discharge (SGD). Across the Pacific islands marine macroalgae have been challenged by and adapted to the stress of lowered salinity with a trade-off of nutrient subsidies delivered by SGD. Human alterations of groundwater resources and climate change-driven shifts brought modifications to the magnitude and composition of SGD. This paper discusses how native macroalgae have adapted to SGD nutrient and salinity gradients, but that invasive algae are outcompeting the native ones near SGD with nutrient pollution, due to their higher salinity tolerance. It is important to re-evaluate land and water use practices by modifying groundwater sustainable yields and improving wastewater infrastructure to keep SGD reductions minimal and nitrogen inputs in optimal ranges. This task may be particularly challenging amidst global sea level rise and reductions in groundwater recharge, which threaten coastal groundwater systems and ecosystems dependent on them.Keywords	submarine groundwater discharge, thermal infrared imagery, temperatureDataset lead author 	Eunhee LeePosition of data author	Senior ResearcherAddress of data author	Korea Institute of Geoscience and Mineral Resources (KIGAM)Email address of data author	eunheelee@kigam.re.krPrimary contact person for dataset	Henrietta DulaiPosition of primary contact person	Professor, principal investigatorAddress of primary contact person	1680 East-West Rd POST 707 Honolulu, HI 96822Email address of primary contact person	hdulaiov@hawaii.eduOrganization associated with the data	University of Hawaiʻi at MānoaUsage Rights	publicly available and free to useGeographic region	Wailupe, O’ahu, Hawai’I, USAGeographic coverage 	21.2759N, 21.2751N, 157.7624W, 157.7606WTemporal coverage - Begin date	April 1, 2015Temporal coverage - End date	April 1, 2015General study design	Imagery of known groundwater discharge spots was performed at low tide, during a single flight.Methods description	The thermal camera (FLIR T450sc) with a field of view (FOV, 258 3198) was mounted on a S1000 Octocopter drone (DJI Inc) using a three-axis direct drive gimbal (DYS Eagle Eye) mounting system. Laboratory, field, or other analytical methods	Infrared images were collected, georeferenced based on AUV flight information, a false color SST map was produced and draped over a visible light image from Google Earth.	Quality control	The following parameters were checked for quality control: UAV battery, transmitter, and GPS, thermal sensor, flight information dataAdditional information
DOI: 10.4211/hs.567d7bdc7b84465ca333b6e0c011853a
title: Supplement materials for publication: Modelling evaporation with local, regional and global BROOK90 frameworks: importance of parameterization and forcing.

description: - Raw eddy-covariance and meteorological measurement daily data with location files
- Raw results of model runs for each framework, including model calibration and FAO simulations
- R-scripts to reproduce figures and tables for the manuscript

Remark: the archive should be extracted with exactly the given name so that all scripts work correctly.
DOI: 10.4211/hs.26e8238f0be14fa1a49641cd8a455e29
title: QUADICA - water quality, discharge and catchment attributes for large-sample studies in Germany

description: This data set provides data of water quality, discharge, driving forces (meteorological and nitrogen surplus), and catchment attributes for a set of 1386 German catchments covering a wide range of natural and anthropogenic conditions. A corresponding paper "Water quality, discharge and catchment attributes for large-sample studies in Germany - QUADICA" by Ebeling et al. describing the data set in detail will be made available in the Journal Earth System Science Data (https://www.earth-system-science-data.net/). 

This repository includes:

1.) Water quality data as annual medians of observed concentration data of N, P and C species (c_annual.csv)
2.) Water quantity data as annual medians of observed discharge (q_annual.csv)
3.) Monthly medians over the whole time series of water quality variables and discharge (c_months.csv)
4.) Monthly and annual median concentrations, flow-normalized concentrations, and mean fluxes estimated using Weighted Regressions on Time, Discharge, and Season (WRTDS) for stations with enough data availability (for details see the corresponding paper Ebeling et al.; wrtds_monthly.csv, wrtds_annual.csv).
5.) Meteorological data as monthly median average temperatures and sums of precipitation and potential evapotranspiration (tavg_monthly.csv, pre_monthly.csv, pet_monthly.csv)
6.) N surplus time series on annual basis (n_surplus.csv)
7.) Summary statistics for the stations including number of samples, covered time periods, degree of censoring (concentrations below the detection limit), availability of discharge data, and availability and performance of WRTDS models (metadata.csv).
8.) Description of data tables (Metadata_QUADICA.pdf).

Data on catchment attributes and geodata also part of the QUADICA data set are available at "CCDB - catchment characteristics data base Germany" (https://doi.org/10.4211/hs.82f8094dd61e449a826afdef820a2c19). The metadata of the water quality and quantity data base is available at "WQQDB - water quality and quantity data base Germany" (https://doi.org/10.4211/hs.a42addcbd59a466a9aa56472dfef8721). 

Conditions: The data set is freely and easily accessible. Please refer to the corresponding paper Ebeling et al. when using or referring to this data set.
DOI: 10.4211/hs.82f8094dd61e449a826afdef820a2c19
title: CCDB - catchment characteristics data base Germany

description: This data set provides geoinformation data, natural and anthropogenic characteristics of 1386 catchments across Germany as part of the QUADICA data set.
The attributes include information on topography, land cover, lithology, soils, climate, hydrology, population density and nutrient sources and heterogeneity.
The calculated catchment attributes base on various publicly available and published resources referenced in the metadata of this repository.
The data set from this repository combines the two existing CCDB repositories for the German catchments (Ebeling, 2021: https://doi.org/10.4211/hs.0fc1b5b1be4a475aacfd9545e72e6839; Ebeling & Dupas, 2021: https://doi.org/10.4211/hs.c7d4df3ba74647f0aa83ae92be2e294b).
A corresponding paper "Water quality, discharge and catchment attributes for large-sample studies in Germany - QUADICA" by Ebeling et al. describing the QUADICA data set in detail will be made available in the Journal Earth System Science Data (https://www.earth-system-science-data.net/). 

This repository includes:

1.) Data table with catchment attributes
2.) Metadata with the description of each catchment attribute and references to original publications and data resources. 
3.) Shapefile with delineated catchment polygons
4.) Shapefile with stations.

Water quality and quantity data, as well as meteorological and N surplus time series are available in the "QUADICA - water quality, discharge and catchment attributes for large-sample studies in Germany" repository (https://doi.org/10.4211/hs.26e8238f0be14fa1a49641cd8a455e29). All repositories use the same unique identifier OBJECTID for each water quality station.

Note: the stations do not always fall within the delineated catchments as the catchment outlets were adapted according the stream network and the topographic flow accumulation grid.

Conditions: Please, reference both the original data publisher and this repository/corresponding paper Ebeling et al. for credits, when using the provided data.
DOI: 10.4211/hs.43601618877945c5a46b715aa98db729
title: Annual mean water quality metrics for catchments draining to German drinking water reservoirs

description: This data describes water quality parameters and catchment characteristics for 88 catchments draining into German drinking water reservoirs.
This data was used in more detail in Musolff et al. (2017).
The data comprises:
  Catchment - number of the catchment
  Year - year for which the data was averaged
  SUVA254 -  Specific ultraviolet absorbance at a wavelenght of 254 nm of the filtered water sample [L m-1 mg-1]
  NO3 - nitrate concentration of the filtered water sample [µmol L-1]
  Fe - dissolved iron concentration [µmol L-1]
  DOC - dissolved organic carbon concentration [mmol L-1]
  PO4 - soluble reactive phosphorus concentrations of the filteres water sample [µmol L-1]
  Forest - share of the catchment covered by forest following CLC (2016), static metric  [%]
  TWI90 - topographic wetness index following Beven and Kirkby (1979) using a 10 m digital elevation model, static metric [-]
The hydrochemical data was averaged using Box-Cox-transformation (Box and Cox, 1964) for the samples of each year, arithmetic mean and backtransformation. On average 11 samples per years have been averaged.
The data is stored as a CSV file.

References
Beven, K. J., & Kirkby, M. J. (1979). A physically based, variable contributing area model of basin hydrology. Hydrological Sciences Journal, 24(1), 43-69.
Box, G. E. P., & Cox, D. R. (1964). An Analysis of Transformations. Journal of the Royal Statistical Society Series B-Statistical Methodology, 26(2), 211-252.
CLC. (2016). CORINE Land Cover 2012 v18.5. . https://land.copernicus.eu/pan-european/corine-land-cover.