cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.2279cac6748944d39035dacb6c2c8a85
title: Analysis codes to conduct Quantile-Kendall trend analysis across flow duration curve of streamflow in urbanizing watersheds across U.S.

description: These are a set of codes to accompany the Water Resources Research submission by Bhaskar et al.

The following codes are run in this order: 
1. GAGESII_subset_urbanizing.R 

        This codes identifies urbanizing watersheds and associated peak 20-year periods of urbanization from GAGES-II based on the criteria of: 
        1. Drainage area < 200 km2
        2. 20 years of completely gap-free daily flow
        3. Unaffected by regulation or diversion
        4. Housing density increased by > 40% during period of analysis 
        5. Housing density > 200 housing units/km2 at end of analysis period
        6. Imperviousness > 20% in 2012


2. selection_of_reference_gages.R 

        This code picks a reference gage from GAGES-II reference gages for each urbanizing gage based on the minimization of a function with 4 criteria: distance, similar drainage area, similar precipitation, and similar geology. 

3. compare_urbanizing_gage_trends.R 

        This code calculates quantile-Kendall trend slopes for just the urbanizing gages. 
        
4. gages_reference_vs_urbanizing.R

        This code calculates quantile-Kendall trend slopes for the urbanizing and reference gages, subtracts them, creates plots, and has regression analysis. This code uses gages_urbanizing_2019-11-27_Plotting.groups.csv to group urbanizing gages into regional groups for plotting purposes.
DOI: 10.5281/zenodo.17915226
title: Dataset for: Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals

description: This record contains raw and processed data supporting:"Functional imaging of nine distinct neuronal populations under a miniscope in freely behaving animals"

Imaging modalities:- Miniscope calcium imaging (nVoke2)- In vivo multiplexed confocal spectral imaging (LSM 980)

Folder descriptions:

4FluorophoreData/: Reduced-complexity experiments using four retrograde fluorophores, organized by animal ID.



Inscopix/: CNMF-E spatial maps and anatomical reference images, organized by imaging day

ZEISS/: Confocal spectral images and imported ROIs


9FluorophoreData/: Full Neuroplex experiments using nine injected fluorophores (plus GCaMP), organized by animal ID.



Behavior/: Timestamped behavioral probabilities for each session

Inscopix/: CNMF-E spatial maps and anatomical reference images, organized by imaging day

ZEISS/: Confocal spectral images and imported ROIs

ProcessedData/: ROI definitions, raw and normalized spectral fingerprints, and fluorophore assignments


CalibrationData/: Optical calibration and validation datasets.



ArgoSlide: Fluorescent calibration slide data used for image-to-image registration and GRIN lens aberration characterization

Hek293Cells: In vitro spectral imaging data from HEK293T cells expressing individual fluorophores, used to generate pure fluorophore spectral fingerprints


 

Corresponding analysis code available at:

GitHub - Neurocipher/PythonPipeline: Python pipeline for Neurocipher
DOI: 10.4211/hs.e8768f4cb4d5478a96d2b1cbd00d9e85
title: FEMA - Harvey Flood Depths Grid

description: This resource describes a dataset of gridded depth at horizontal resolution of 3 meters, published as an Esri ArcGIS geodatabase on November 15, 2017 by FEMA. This dataset is no longer accessible from FEMA, but is now uploaded to this HydroShare resource in the contents list. This product utilized Triangulated Irregular Network (TIN) interpolation, four quality assurance measures (identifying dips, spikes, duplication, and inaccurate/unrealistic measurements). High Water Marks were obtained from the Harris County Flood Control District (HCFCD), US Geological Survey (USGS), and other inspection data. Elevation data comprised a mosaic of 3 meter resampled elevations from 1M & 3M LiDAR, and IFSAR data. One section of the IfSAR data was found to be erroneous, and replaced with a blended 10 meter section. 
[This method description was from correspondence January 22, 2018, from Mark English, GeoSpatial Risk Analyst, FEMA Region VIII, Mitigation Division.]

See FEMA's Natural Disasters data site [1] for additional HWM-based depth grids and inundation polygons:
- Harris County Areas of Interest (AOIs) and Inundation Boundaries 
- Harris County Depth Grids
- Aransas, Nueces, and San Patricio Coastal Depth Grids and Boundaries 

Some of the data available below in the contents list is from the FIMA NHRAP program, which is no longer available from FEMA, as of the summer of 2019. 

FEMA notes on these Modeled Preliminary Observations:
o Based on observed Water Levels at stream gauges interpolated along rivers, downsampled to 5m resolution DEM
o Depth grids updated with new observed peak crest as they become available
o Will include High Water Mark information as it becomes available
o Extents validated with remote sensing
o Use for determining damage levels on specific structures

References:
[1] FEMA Harvey data [https://disasters.geoplatform.gov/publicdata/NationalDisasters/2017/HurricaneHarvey/Data/] [link is no longer accessible]
DOI: 10.4211/hs.a52d209d46eb42578be0a7472c48e2d5
title: FEMA - Harvey Damage Assessments and Claims

description: This resource contains shapefiles for FEMA Damage Assessments, Auto Claims, Property Claims, and geodatabases for Hazus windfield data, publicly available here [see NOTE 1].  
Damage assessments are organized in daily map layers. These appear to be cumulative, but some days' records do not include all the previous days' records. 
   - Aug 27, 2017:  Coastal damage assessments (26,027 records)
   - Aug 28: Damage assessments (78,218)
   - Aug 29: Damage assessments (115,412)
   - Aug 30: Damage assessments (137,754)
   - Aug 31: Damage assessments (161,366)
   - Sep 02: Damage assessments (156,099)
A document is provided that explains the damage assessment methodology. 

Auto and Property Claims are each in a single shapefile, containing all records from Aug 25-Sep 08: 
   - Auto claims, Aug 25-Sep 08 (203, 312 records)
   - Property claims, Aug 25-Sep 08 (226,167)
These identify location, date and type of loss. These are all claims submitted during this period, which may include damages not caused by Hurricane Harvey. 

Other damage assessments and inundation depth grids are available at the FEMA Natural Hazard Risk Assessment Program (NHRAP) [2]. These include:
- Windfield contours [3]
- PDC Hazus Wind Adv26 (Hurrevac) [4]
- PDC Hazus Windfield geodatabases for Harvey [5]

References
NOTE 1: As of the summer of 2019, FEMA damage data was reorganized and moved to new URLs, which affected references [1,2,3,4,5]. Most of these data sources have been moved to https://disasters.geoplatform.gov/publicdata/NationalDisasters/2017/HurricaneHarvey/Data/. Some of the original datasets are no longer available from FEMA. The original and current datasets are available for download below in the contents list. 

[1] FEMA Damage Assessments [https://disasters.geoplatform.gov/publicdata/NationalDisasters/2017/HurricaneHarvey/Data/DamageAssessments/]
[2] FEMA Natural Hazard Risk Assessment Program (NHRAP) [link no longer available]
[3] Harvey_WindSpeedContours.zip, see contents list below. 
[4] PDC_HAZUS_Damage_Loss_Assessment_ADV26_26AUG17_2100UTC.PDF, see contents list below. 
[5] PDC_HarveyResults.gdb.zip and PDC_HarveyWindfield.gdb.zip from [https://disasters.geoplatform.gov/publicdata/NationalDisasters/2017/HurricaneHarvey/Data/Hazus/PDC/Wind/], also in contents list below.
DOI: 10.4211/hs.9ff2b9ad3eb74b06a5af8491c399ee57
title: ECMWF GloFAS - Harvey+Irma Flood Area Grids

description: These datasets were obtained from ECMWF/GloFAS on November 13, 2017, to include the flood forecast (area grid) for Hurricanes Harvey and Irma in the USA from August 15 - September 15, 2017. These are contained in netCDF files, one per day. 

Note that while folders and files may have the words "areagrid_for_Harvey" in the name, all the data here are for the southeast USA, encompassing both Harvey and Irma impact areas. 

Dataset variables:
      - dis = forecasted discharge (for all forecast step 1+30 as initial value and 30 daily average values, with ensemble members as 1+50 where the first is the so-called control member and the 50 perturbed members)
     - ldd = local drainage direction within routing model
     - ups = upstream area of each point within routing model
     - rl2,rl5,rl20 = forecast exceedance thresholds for 2-, 5- and 20-year return period flows, based on gumbel distribution from ERA-interim land reanalysis driven through the lisflood routing.

Models used (see [1] for further details):
    - Hydrology: River discharge is simulated by the Lisflood hydrological model (van der Knijff et al., 2010) for the flow routing in the river network and the groundwater mass balance. The model is set up on global coverage with horizontal grid resolution of 0.1° (about 10 km in mid-latitude regions) and daily time step for input/output data.
    - Meteorology: To set up a forecasting and warning system that runs on a daily basis with global coverage, initial conditions and input forcing data must be provided seamlessly to every point within the domain. To this end, two products are used. The first consists of operational ensemble forecasts of near-surface meteorological parameters. The second is a long-term dataset consistent with daily forecasts, used to derive a reference climatology.

Suggestions for usage: 
     - Selected software: ArcGIS or QGIS 
     - Select dis for example, then any of the bands (51*31 in total), then set the range manually to 0-1000 or something like that.

Agency:
GloFAS [1]
From its public website: "The Global Flood Awareness System (GloFAS), jointly developed by the European Commission and the European Centre for Medium-Range Weather Forecasts (ECMWF), is independent of administrative and political boundaries. It couples state-of-the art weather forecasts with a hydrological model and with its continental scale set-up it provides downstream countries with information on upstream river conditions as well as continental and global overviews. GloFAS produces daily flood forecasts in a pre-operational manner since June 2011."

References
[1] GloFAS home page [http://www.globalfloods.eu/]
DOI: 10.5286/isis.e.rb2520478-1
title: Phonon density of states of barocaloric [choline]2ZnCl4 ionic plastic crystals via variable temperature inelastic neutron scattering studies

description: Ionic plastic crystals (IPCs) are solids which undergo order-disorder structural phase transitions leading to significant orientational disorder in the crystal structure in the high-temperature phase. As a result, these materials have a rich landscape of temperature driven solid-solid phase transitions. Recent discoveries of colossal barocaloric effects in molecular plastic crystals (MPCs) suggest that PCs in general could have a huge impact on the future of solid-state cooling technology. We have recently synthesised an IPC based on the choline cation [(CH3)3(NCH2CH2OH)]ZnCl4 (CHOZn) which exhibits a pressure-induced entropy change of ~130 J/kg K across its order-disorder phase transition. Crystallography studies in tandem with ab initio molecular dynamics (AIMD) simulations show that hydrogen bonding plays a crucial role in the order-disorder phase transition. The phonon density of states calculated from the AIMD simulations, for both the ordered and disordered phases, show that the major contribution to the total entropy change is the change in vibrational entropy and is dominated by the weak intermolecular interactions (soft phonons). To compare against the phonon density of states calculated from AIMD simulations, we propose to experimentally determine the phonon density of states for CHOZn using inelastic neutron scattering experiments on the MARI spectrometer and observe its evolution with temperature across the temperature-driven phase transition.
DOI: 10.4211/hs.35d4502200764c2985c24ae5c8836ab9
title: NOAA NWC - Harvey National Water Model Streamflow Forecasts

description: The National Water Model (NWM) is a water forecasting model operated by the National Water Center (NWC) of the NOAA National Weather Service. The NWM continually forecasts flows on 2.7 million stream reaches covering 3.2 million miles of streams and rivers in the continental United States [1]. It operates as part of the national weather forecasting system, with inputs from NOAA numerical weather prediction models, and from weather and water conditions observed through the US Geological Survey's National Water Information System. Reference materials for the computational framework behind NWM is published by NCAR [9] [10]. 

The NWC generates NWM streamflow forecasts for the continental US (CONUS) with multiple forecast horizons and time steps. Due to the output file sizes, these are normally not available for download more than a couple days at a time [2]. However, for a time a 40-day rolling window of these forecasts was maintained by HydroShare at RENCI [3], and a complete retrospective (August 2016 to the present) of the NWM Analysis & Assimilation outputs is maintained as well (contact help@cuahsi.org for access).    

An archive of all NWM forecasts for the period Aug 18 to Sept 10, 2017 has been compiled at RENCI [4] [5], available as netCDF (.nc) files totaling 8TB. These can be browsed, subsetted, visualized, and downloaded (see [6] [7] [8]). In addition to these output files, we have uploaded to this HydroShare resource the input parameter files needed to re-run the NWM for the Harvey period, or for any time period covered by NWM v1.1 and 1.2 (August 2016 to this publication date in August 2018).  These parameter files are also made available at [1]. 

See README for further details and usage guidance. Please see NOAA contacts listed on [1] for questions about the NWM data contents, structure and formats. Contact help@cuahsi.org if any questions about HydroShare-based tools and data access.  

References
[1] Overview of the NWM framework and output files [http://water.noaa.gov/about/nwm] 
[2] Free access to all National Water Model output for the most recent two days [http://water.noaa.gov/about/nwm - scroll down to links under "Downloading Output"]
[3] NWM outputs for rolling 40-day window, maintained by HydroShare [link is no longer available]
[4] Archived Harvey NWM outputs via RENCI THREDDS server [http://thredds.hydroshare.org/thredds/catalog/nwm/harvey/catalog.html] [link is no longer accessible]
[5] RENCI is an Institute at the University of North Carolina at Chapel Hill 
[6] Live map for National Water Model forecasts [http://water.noaa.gov/map]
[7] NWM Forecast Viewer app [no longer available]
[8] CUAHSI JupyterHub example scripts for subsetting NWM output files [https://hydroshare.org/resource/3db192783bcb4599bab36d43fc3413db/]
[9] WRF-Hydro Overview [https://ral.ucar.edu/projects/wrf_hydro/overview]
[10] WRF-Hydro User Guide 2015 [https://ral.ucar.edu/sites/default/files/public/images/project/WRF_Hydro_User_Guide_v3.0.pdf]
DOI: 10.5286/isis.e.rb2520478
title: Phonon density of states of barocaloric [choline]2ZnCl4 ionic plastic crystals via variable temperature inelastic neutron scattering studies

description: Ionic plastic crystals (IPCs) are solids which undergo order-disorder structural phase transitions leading to significant orientational disorder in the crystal structure in the high-temperature phase. As a result, these materials have a rich landscape of temperature driven solid-solid phase transitions. Recent discoveries of colossal barocaloric effects in molecular plastic crystals (MPCs) suggest that PCs in general could have a huge impact on the future of solid-state cooling technology. We have recently synthesised an IPC based on the choline cation [(CH3)3(NCH2CH2OH)]ZnCl4 (CHOZn) which exhibits a pressure-induced entropy change of ~130 J/kg K across its order-disorder phase transition. Crystallography studies in tandem with ab initio molecular dynamics (AIMD) simulations show that hydrogen bonding plays a crucial role in the order-disorder phase transition. The phonon density of states calculated from the AIMD simulations, for both the ordered and disordered phases, show that the major contribution to the total entropy change is the change in vibrational entropy and is dominated by the weak intermolecular interactions (soft phonons). To compare against the phonon density of states calculated from AIMD simulations, we propose to experimentally determine the phonon density of states for CHOZn using inelastic neutron scattering experiments on the MARI spectrometer and observe its evolution with temperature across the temperature-driven phase transition.
DOI: 10.4211/hs.af1e61b719ec4d0caef391bd076fbfc9
title: Reno ET

description: Climate data was collected using a meteorological station in Reno, Nevada for the period 3/1/2020 - 4/30-2020. Variables measured were net radiation, wind speed, precipitation, temperature, and relative humidity. These data were used to calculate grass reference evapotranspiration (ETo) using the FAO 56 Penman Monteith hourly method. Two datasets are available. The file “met_station_data.csv” contains station data. The file “reno_et.csv” contains station data sampled to 1 hour and ETo calculated using Penman Monteith. The file metadata provides information on methods used to calculate hourly grass reference ET.
DOI: 10.4211/hs.4de42db6485f47b290bd9e17b017bb51
title: Supporting data for "A low-cost, open source monitoring system for collecting high-resolution water use data on magnetically-driven residential water meters"

description: The files provided here are the supporting data and code files for the analyses presented in "A low-cost, open source monitoring system for collecting high-resolution water use data on magnetically-driven residential water meters," an article in Sensors (https://doi.org/10.3390/s20133655). The data included in this resource were collected in laboratory testing and field deployment of the Cyberinfrastructure for Intelligent Water Supply (CIWS) datalogger, an open source, low cost device capable of collecting high temporal resolution data on magnetically driven water meters. The code included allows replication of the analyses presented in the article, and the raw data included allow for extension of the analyses conducted. In the article we present a low-cost (≈ $150) monitoring system for collecting high resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically-driven, positive displacement, residential water meters and can collect data at variable time resolution intervals. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah for a period of over 1 month. Battery life for the device was estimated to be over 5 weeks with continuous data collection at a 4 second time interval. Data collected using this system, under ideal installation conditions, was within 2% of the volume recorded by the register of the meter on which they were installed. Results from field deployments are presented to demonstrate the accuracy, functionality, and applicability of the system. Results indicate the device is capable of collecting data at a resolution sufficient for identifying individual water use events and analyzing water use at coarser temporal resolutions. This system is of special interest for water end-use studies, future projections of residential water use, water infrastructure design, and for advancing our understanding of water use timing and behavior. The system’s hardware design and software are open source, are available for potential reuse, and can be customized for specific research needs.
DOI: 10.4211/hs.e0a7f2a216e9456a8567b850db1cf1f9
title: IkeWai marine Geophysics -  raw CSEM data, Survey towlines time windows, and power spectrograms images

description: The CSEM data acquired during 8 days as part of the IkeWai marine Geophysics survey conducted in Sep-Oct 2019 offshore of the Kona coastline, west of Hawaii.

The three zipped files attached below contains the following CSEM data/information:
1) CSEM raw data recordings (binary files).
2) Survey towlines time windows of the CSEM data recording.
3) Power spectrograms images of the recorded raw CSEM data.
DOI: 10.4211/hs.c3ea08faa88a46a4a3ce596a09686198
title: Water quality data - Selke catchment

description: This resource provides water quality data for Winter et al. (2020) from the Selke catchment (456 km², Germany), a sub-catchment of the Bode catchment, which is part of the network of TERrestrial ENvironmental Observatories (TERENO). Within the Selke catchment, we consider three nested sub-catchments that are delineated by the following gauging stations: i) Silberhütte (SH) , ii) Meisdorf (MEIS) and iii) Hausneindorf (HAUS). More specifically, this resource provides the following datasets:

1) Annual and seasonal averages of flow-normalized (FN) and non-FN nitrate concentrations (C in mg/L), loads (L in kg/d) and CQ-slopes (ß)between 1983 - 2016 for all three nested sub-catchments, calculated via Weighted Regression on Time, Discharge and Season (WRTDS, Hirsch et al. 2010). 
2) Annual nitrogen (N) input data for all three sub-catchments (in kg/ ha * a) between 1950 - 2015. This N-input data is the sum of N-input from agricultural areas, N-input from non-agricultural areas (atm. deposition and biological fixation) and to a minor part from wastewater treatment plants (WWTPs).
3) A table of event characteristics from high-frequency measurements between 2013 - 2016 in Silberhütte and between 2010 - 2016 in Meinsdorf and Hausneindorf (15 min resolution, Qmax  in mm and Cmax in mg/L, R2 is the coefficient of determination).
DOI: 10.4211/hs.d76491f6a4584241a7cafd8683911f65
title: SISS model data and script

description: This resource is linked to the following manuscript: 
Lutz et al.: How Important is Denitrification in Riparian Zones? Combining Endmember Mixing and Isotope Modeling to Quantify Nitrogen Removal Processes, in review for Water Resources Research

This resource provides concentration and isotope data in a groundwater well field along a 2 km stream section in central Germany. We developed a mathematical model combining endmember mixing and isotope modeling to account for mixing of river water and groundwater, and quantify nitrate transformation at the study site (i.e., the SISS model). This enabled us to explicitly determine the extent of denitrification (as permanent nitrate removal process) and nitrate removal by additional processes associated with negligible isotope fractionation (e.g., plant uptake, microbial assimilation and dissimilatory nitrate reduction to ammonium) in the riparian system.

Content:
*the SISS model code 
*chloride and nitrate concentration data from the field site
*nitrate and stable water isotope data from the field site
DOI: 10.4211/hs.fe607ea8296342d48e17b9732ba8a295
title: Dataset for the manuscript "Role of pore-scale disorder in fluid displacement: Experiments and theoretical model" by Wu et al.

description: This is the data for the manuscript "Role of pore-scale disorder in fluid displacement: Experiments and theoretical model" by Wu, Hu, Lan and Chen , 2020.
DOI: 10.4211/hs.79522f0d75ce41269604288d8f9468e3
title: Dataset for the manuscript "Role of pore-scale disorder in fluid displacement: Experiments and theoretical model" by Wu et al.

description: This is the data for the manuscript "Role of pore-scale disorder in fluid displacement: Experiments and theoretical model" by Wu, Hu, Lan and Chen , 2020.