cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17916255
title: МІСЬКЕ ПРАВО: СУЧАСНИЙ ПРАВОВИЙ ЛАНДШАФТ

description: The article argues that the rapid strengthening of cities as centers of social life and governance requires revising modern-era concepts of the relationship between national and local (urban) law and recognizing urban law as an autonomous legal locus shaped by spatial criteria. Against the background of global urbanization and the historical role of urban law in the formation of national legal orders, it shows how the once homogeneous legal space of the nation-state differentiates into national and local (urban) legal orders, each interacting in its own way with international legal processes.Using an urbanist approach, the author characterizes the city as a legal center with a much higher concentration of legal relations and a specific legal culture, which justifies orienting legal analysis to the city as an independent legal unit. At the same time, many cities are burdened with legislation that poorly corresponds to urban realities and lack institutional capacity to adapt norms, which generates widespread informality, parallel regulatory systems, and “informal urban law” as a de facto norm.The article links these dynamics to a broader crisis of state legitimacy, especially in “criminal states” (aggressor, oppressive, corrupt, or negligent), where national law becomes a decorative façade for criminal governance and loses the ability to mediate growing tensions between urban communities and discredited state institutions. In such conditions, the traditional functional division in which cities specialize in finance and states in warfare becomes exhausted, and the balance between cities and nation-states is shifting, which the author illustrates through the impact of Russia’s aggression against Ukraine.On this basis, the article conceptualizes “urban law” as the body of norms that shape cities, their land use, institutions, and finances, and that structure the relationship between city and state within a differentiated legal landscape. The concluding thesis is that future legal development will increasingly depend on negotiated compromises between powerful cities and effective nation-states, and on constructing urban legal frameworks capable of reconciling intense local legal cultures with broader national and international legal orders.
DOI: 10.4211/hs.eb6eeeccdbe546fc941f3c219cb05a34
title: TSEB modeling and the comparison between the model results and the eddy-covariance monitored data within the footprint area

description: The widely used two-source energy balance (TSEB) model coupled with AggieAir (https:// uwrl.usu.edu/aggieair/, a type of small Unmanned Aerial System) data can provide high-resolution modeled energy components, evapotranspiration partitioning, etc., at a subfield scale.  When the research area is equipped with eddy-covariance (EC) towers, researchers often want to compare between the modeling results and the EC monitoring data within the footprint area once they run the TSEB model. However, we found the process to be time-consuming due to the large number of AggieAir flight images in the archive, as well as the uncertainty of some parameters (e.g., the G ratio). In order to increase the efficiency of modeling and verifying modeling results, this project adds some python scripts after the published TSEB model runs that consider the connection among the modeling results, the AggieAir platform, and the EC tower. This project is a part of our pending paper. Other researchers can also consider using this project if the available data are similar to ours.
DOI: 10.4211/hs.fc621d75985c4695b6758ade312241c6
title: SnowClim: Pre-industrial Snow Data

description: This resource contains snow metrics for the pre-industrial period and represents a subset of the SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). The SnowClim Dataset was developed following the methods presented in Lute et al., (in prep). The pre-industrial snow data was created by first downscaling 4 km climate forcings from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) over a thirteen year period (1 Oct 2000 to 30 Sep 2013) and then perturbing the downscaled data using multi-model mean deltas from CMIP5 to create climate forcing data that reflects conditions during 1850-1879. This climate data was then used to force the SnowClim snow model. Snow model outputs were summarized into snow metrics at ~210 m spatial resolution for the western US. 

Additional details about forcing data preparation, model physics, model calibration, and application to the western US domain can be found in:
Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.
DOI: 10.4211/hs.0c852b12f668438fb9f0188a1cc6e8a9
title: SnowClim: Pre-industrial Climate Data

description: This resource is part of the larger SnowClim Dataset (https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/). This resource contains pre-industrial climate metrics. Climate metrics were created by first downscaling outputs of the Weather Research and Forecasting Model (WRF; Rasmussen and Liu, 2017) for the present-day period (1 Oct 2000 to 30 Sep 2013) using a combination of local lapse rates and terrain corrections for solar radiation as described in Lute et al., (in prep). Downscaled data was then perturbed by the multi-model mean delta from CMIP5 to create climate date reflecting pre-industrial conditions (1850-1879). Climate metrics are available on a ~210 m grid for the western United States in both netCDF and GeoTiff formats.

Additional information is available in:
Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.
DOI: 10.4211/hs.89478d0a27c646e3b6a92c2483c0174d
title: Dataset for 'Towards the closure of mass and energy budgets at the watershed scale: the Wahoo Creek Case' article

description: This dataset is part of the article entitled 'Towards the closure of mass and energy budgets at the watershed scale: the Wahoo Creek Case' from Hoeltgebaum and Dias, submitted to the Water Resources Research journal in November 2021.

This dataset contains processed data from 6 rain gauges, 10 snowboard measurement points, 2 discharge gages, measurements of latent heat flux and soil moisture (at 4 depths) for 3 AmeriFlux stations, and remote sensing data over 2 basins, such as NDVI, land surface radiation, and soil moisture. Estimates of storage and evapotranspiration over the basins are also included. More detail on the processing of data and methods of calculations can be seen in the article 'Towards the closure of mass and energy budgets at the watershed scale: the Wahoo Creek Case'.
DOI: 10.4211/hs.18db8e1da5354e318b57e9d873fd5e35
title: Sinking Creek, Giles County, Virginia - Tree, flow, geomorphology

description: This dataset contains data collected along Sinking Creek in Giles County, Virginia. 

This dataset includes data on tree characteristics, flow stage and discharge, bed sediment grain size, and channel geomorphology extracted from lidar.

This data supports the following publication:

Christensen, N.D., J.A. Czuba, S. Triantafillou, C.A. Copenheaver, J.A. Peterson, and W.C. Hession, 
Establishment and persistence of trees growing in the channel of an intermittent stream in a temperate, karst environment.

The related tree core data are available in the International Tree-Ring Data Bank (ITRDB) as Christensen - Sinking Creek - New Zion Road - PLOC - ITRDB VA044, located here:
https://www.ncei.noaa.gov/access/paleo-search/study/34572
DOI: 10.4211/hs.4478fc64e802496d86badefffe809ab4
title: After the Storm: Fate and leaching of particulate nitrogen (PN) in the fluvial network and the influence of watershed sources and moisture conditions

description: Large storms can erode, transport, and deposit substantial amounts of particulate ni-trogen (PN) in the fluvial network. The fate of this input and its consequence for water quality is poorly understood. This study investigated the transformation and leaching of PN using a 56-day incubation experiment with five PN sources: forest floor humus, upland mineral A hori-zon, stream bank, storm deposits, and stream bed. Experiments were subjected to two moisture regimes: continuously moist and dry-wet cycles. Sediment and porewater samples were collected through the incubation and analyzed for N and C species, and quantification of nitrifying and denitrifying genes (amoA, nirS, nirK). C and N rich watershed sources experienced decomposi-tion, mineralization, and nitrification and released large amounts of dissolved N, but the amount of N released varied by PN source and moisture regime. Drying and rewetting stimulated nitri-fication and suppressed denitrification in most PN sources. Storm deposits released large amounts of porewater N regardless of the moisture conditions, indicating that they can readily act as N sources under a variety of conditions. The inputs, processing, and leaching of large storm-driven PN inputs become increasingly important as the frequency and intensity of large storms is predicted to increase with global climate change.
DOI: 10.4211/hs.6d0c4a14289742d0951ba5ab9eca7dc0
title: Features extraction from the LAI2200C Plant Canopy Analyzer

description: Leaf area index (LAI) plays an important role in land-surface models to describe the energy, carbon, and water fluxes between the soil and canopy vegetation. Indirect ground LAI measurements, such as using the LAI2200C Plant Canopy Analyzer (PCA), can not only increase the measurement efficiency but also protect the vegetation compared with the direct and destructive ground LAI measurement. Additionally, indirect measurements provide opportunities for remote-sensing-based LAI monitoring. This project focuses on the extraction of several features observed using the LAI2200C PCA because the extracted features can help to explore the relationship between the ground measurements and remote sensing data. Although FV2200 software can provide convenient data calculation, data visualization, etc., it cannot generate features such as time, coordinates, and LAI from the data log for deeper exploration, especially when facing a large amount of collected data that needs to process. In order to increase efficiency, this project developed a simple python script for feature extraction, and demo data are provided.
DOI: 10.4211/hs.508fc30c230149d8b97977bea9c0f49b
title: Terrestrial Water Storage Forecasts

description: The terrestrial water storage (TWS) forecasts are produced using the Catchment Land Surface Model (CLSM) forced with NASA’s Goddard Earth Observing System (GEOS), and is a part of NASA’s Hydrological Forecast and Analysis System (NHyFAS). The initial conditions in this system do not use data assimilation. The TWS forecast data is over continental Africa and the Middle East at 25km or 0.25deg spatial resolution and monthly temporal resolution. It covers the period between 1982 to 2019, the forecasts are initialized on the first of every month from 1982 through 2018 and the forecast leads extend out to 6 months. The TWS forecasts are included as monthly files with the following coordinates: 37years(1982-2018) x 6leads x 4ensembles x 320latitudes x 320longitudes.
DOI: 10.4211/hs.826fbc5bb04e4674b2df002d979b5390
title: Data Files for "Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange"

description: Supplementary information for surveyed reaches, model mesh quality outputs, RSF inputs, p-values for Kruskal-Wallis tests, and percent differences for exchange flux and percent upwelling particles. Supplementary information is associated with, "Testing Hidden Assumptions of Representativeness in Reach-Scale Studies of Hyporheic Exchange" by Becker, Ward, Herzog, and Wondzell, 2022.
DOI: 10.4211/hs.acc4f39ad6924a78811750043d59e5d0
title: SnowClim Model and Dataset

description: The SnowClim Model and Dataset address the need for climate and snow data products that are based on physical principles, that are simulated at high spatial resolution, and that cover large geographic domains. 

The SnowClim Model is a physics-based snow model that incorporates key energy balance processes necessary for capturing snowpack spatiotemporal variability, including under future climate scenarios, while optimizing computational efficiency throughout several empirical simplifications. The model code can be downloaded or run in the cloud using MATLAB Online through HydroShare.

The SnowClim Dataset consists of climate forcing data for and snow outputs from the SnowClim Model. Climate forcing data was downscaled from 4 km climate data from the Weather Research and Forecasting (WRF) model (Rasmussen and Liu, 2017) to ~210 m across the contiguous western United States. Climate forcings were downscaled from WRF directly for a present day (2000-2013) period and a thirteen year pseudo global warming scenario reflecting conditions between 2071-2100 under RCP 8.5. Climate forcings were prepared for a third time period by perturbing present-day downscaled climate data by the multi-model mean from CMIP5 to reflect conditions under pre-industrial conditions (1850-1879).

Additional details regarding the SnowClim model physics, model calibration, climate data downscaling, model application to the western US, and model performance are available in:
Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.
DOI: 10.4211/hs.3b13760174174c31988120baeb84e2e8
title: LPMLEn - A code for estimating heat transport parameters in 1D

description: LPMLEn - A code for estimating heat transport parameters in 1D

The LPMLEn combines the local polynomial method (LP method) with a maximum likelihood estimator (MLE) to estimate 1D vertical streambed fluxes and thermal diffusivities using time-series from n temperature sensors. It operates in the frequency domain and can use multiple frequencies and sensors simultaneously for the parameter estimation. The LPMLEn is provided here with two models, (i) the semi-infinite domain model where only an upper temperature boundary condition is used to estimate the parameters and (ii) a bounded (finite) domain model where an additional lower local temperature boundary condition is assigned to estimate the parameters for a distinct section of the streambed.

Contents
The MATLAB scirpts that are used to create the figures in the paper are:
- Estimation_with_synthetic_dataset1_and_2_SI_vs_BD.m for Table 1, Fig. S1 and S2.
- Estimation_with_synthetic_dataset3_change_in_D.m for Fig. 1. 
- Estimation_with_synthetic_dataset4_change_in_D_from_low_to_high.m for Fig. S3. 
- Estimation_with_experimental_dataset.m for Fig. 2b, 2c, 3, 4, 5, S4 and S5.
- ML1_90.txt contains the measurement data of the experimental dataset.
The analysis performed on the dataset in Estimation_with_experimental_dataset.m is resource demanding. For this reason the computational results are saved in Estimation_experimental_dataset_workspace.mat, which can be loaded into MATLAB to bypass the computations.

To start using the LPMLEn, please check the simplified example Example_simplified_LPMLEn.m that uses the function MLEn_hydrology_time.m that only requires the time-series, measurement depths, and model choice as input.
The more advance user may want to use the LP-method (LocalPolyAnal.m) and MLEn (MLEn.m) sepperatly for more control and advanced settings. For this, the Estimation_with_experimental_dataset.m can be used as an example.
DOI: 10.4211/hs.dc3a40e067bf416d82d87c664d2edcc7
title: SnowClim Model

description: The SnowClim Model is a physics-based snow model that incorporates key energy balance processes necessary for capturing snowpack spatiotemporal variability, including under future climate scenarios, while optimizing computational efficiency through several empirical simplifications. The SnowClim Model is specifically designed for energy balance snow modeling at high spatial resolutions over large domains.

The model is written in MATLAB (R2020b). The model code can be downloaded below or run in the cloud through HydroShare by clicking the 'Open With' button above and selecting MATLAB Online. The code run_snowclim_model.m describes how to set parameters, import climate forcing data, run the model, and plot a few model outputs. This code uses default parameter values and the example climate forcing data found in /example_forcings but can be modified for new applications.

Additional details regarding the SnowClim model physics and application to the western US are available in:
Lute, A. C., Abatzoglou, J., and Link, T.: SnowClim v1.0: high-resolution snow model and data for the western United States, Geosci. Model Dev., 15, 5045–5071, https://doi.org/10.5194/gmd-15-5045-2022, 2022.

Climate and snow data from application of the SnowClim model to the western US under pre-industrial, present-day, and future time periods can be found here:
https://www.hydroshare.org/resource/acc4f39ad6924a78811750043d59e5d0/
DOI: 10.4211/hs.61bb11e383034bc194f85a57a1d251eb
title: Supporting Information dataset for article: "Buried paleo-channel detection with a groundwater model, tracer-based observations, and spatially varying, preferred anisotropy pilot point calibration"

description: This repository contains the Supporting Information Dataset DS1 for Schilling O.S., Partington, D.J., Doherty, J., Kipfer, R., Hunkeler, D., & Brunner, P. (2022): Buried Paleo-Channel Detection With a Groundwater Model, Tracer-Based Observations, and Spatially Varying, Preferred Anisotropy Pilot Point Calibration. Geophys. Res. Lett., 49(14): e2022GL098944. doi: 10.1029/2022GL098944
DOI: 10.4211/hs.6f96b71a982743c2905e00de35bdb817
title: Simulating the effects of fish canals on the Logone Floodplain

description: Assessing the impact of climate change on floodplain productivity poses unique challenges for hydrodynamic models. For example, the dynamics of floodplain fisheries are governed both by inundation dynamics across thousands of km2, and water storage timing within small depressions (which serve as fish habitat) connected to the river network by meter-scale manmade canals, controlled by flow across fishing weirs. Here, we propose to represent these features as a system of effective, interconnected sub-grid elements within a coarse-scale model. We test this strategy over the Logone floodplain in Cameroon, and its floodplain fishery. We first validate this strategy for a local study area (30 km2); we find that hydraulic models at resolutions from 30 m to 500 m are able to reproduce hydraulic dynamics as documented by in situ water level observations. When applied to the entire floodplain (16,000 km2), we find that the proposed modeling strategy allows accurate prediction of observed pattern of recession in the depressions. Artificially removing floodplain canals in the model causes residence time of water in depressions to be overpredicted by approximately 30 days. This study supports the strategy of modeling fine-scale interconnected features as a system of sub-grid elements in a coarse resolution model for applications such as assessing the sensitivity of floodplain fisheries to future climate change.

Shastry, Apoorva Michael Durand; Jeffrey Neal; Alfonso Fernández; Sui Chian Phang; Brandon Mohr; Hahn Chul Jung; Saïdou Kari; Mark Moritz; Bryan Mark; Sarah Laborde; Asmita Murumkar; Ian Hamilton. 2020. Small-scale anthropogenic changes impact floodplain hydraulics: simulating the effects of fish canals on the Logone Floodplain. Journal of Hydrology, 588: 125035. 125035.10.1016/j.jhydrol.2020.125035.
DOI: 10.5517/ccdc.csd.cc2c4z6q
title: CCDC 2179523: 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.cc2nsd95
title: CCDC 2466307: 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.cc2c4z1k
title: CCDC 2179518: 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.cc2c57b4
title: CCDC 2179775: 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.87dc9a2b40b348cf8b40caa11cc5e614
title: Kiholo SGD Time Series Data

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.