cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.418270e3396b4696b5de65bd7be2d6b8
title: AIMS Great Plains approach3 EEAS

description: This study was conducted in the South Fork of King’s Creek at Konza Prairie Biological Station, one of the most extensively studied prairie stream ecosystems. Kings Creek, a 5th-order intermittent stream, drains a 1,059-ha tallgrass prairie in the Kansas Flint Hills. The USGS gage (06879560; established in 1979) on the mainstem provides long-term hydrological data. The region has a mid-continental climate, with an annual mean temperature ranging from 5.6 to 16.7 °C and annual precipitation between 30 and 114 cm. The landscape is dominated by unplowed tallgrass prairie, with a band of deciduous trees lining the downstream reach of King’s Creek. The catchment soil features alternating limestone and shale layers, contributing to the area's complex subsurface hydrology and biodiversity.
This synoptic survey was conducted to support the sampling objectives of the Aquatic Intermittency Effects on Microbiomes in Streams (AIMS) Project. In June 2021, a field team collected hydrology and biogeochemistry datasets across 50 locations within a sub-drainage of the South Fork of King’s Creek. Site selection aimed to balance multiple priorities: (i) targeting existing monitoring infrastructure with long-term data (n=14); (ii) including locations near known springs and tributary junctions (n=9); and (iii) incorporating a range of drainage areas and topographic wetness index (TWI) values (n=27), both of which are linked to flow permanence.
At each site, samples of water (if present), biofilm, leaf litter, and sediment were collected. Microbial enzyme activities associated with organic matter decomposition (β-glucosidase, phenol oxidase, and peroxidase) and the mineralization of organic nitrogen (N-acetylglucosaminidase) and phosphorus (phosphatase) were measured. Enzyme activities were quantified fluorometrically using 4-methylumbelliferyl (MUB)-linked substrates or colorimetrically using 4-nitrophenyl (pNP)-linked and 3,4-dihydroxyphenylalanine (L-DOPA)-linked substrates.
DOI: 10.48597/bxhy-6br2
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.a79f3ec8559341bb83cd1dd5705a49f1
title: Dan River, North Carolina - velocity, sediment, and survey data from 2019

description: Velocity, stage, sediment, and survey data collected in 2019 in the Dan River, North Carolina, in support of:

Sumaiya Sumaiya, Jonathan A. Czuba, William T. Russ, and Rachael Hoch (in review) Potential for Juvenile Freshwater Mussels to Settle onto Riverbeds from Field Investigation

This dataset contains data collected in 2019 from the Dan River upstream of its confluence with Big Creek at US Highway 89 in North Carolina.

GIS shapefile data
Survey data
Sediment data
Uplooking velocity data
Cross section velocity data
Pressure transducer data
DOI: 10.48597/d3e6-7aaf
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/dnq6-sbxt
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/msz6-8m69
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.58b0f961c81a41939415526c8ad78d33
title: EMMALAB v. 1.0: Software for improved endmember mixing analysis

description: Quantifying water sources to rivers and streams is critical for managing water resources globally. Endmember mixing analysis (EMMA) is a commonly applied method to water sources to streams that uses tracers for hydrograph separation. Most EMMA applications follow similar methods, but several choices must be made such as selecting tracers, endmembers, and stream locations for mixing.  With no standardized EMMA approach, these choices may be made subjectively with little regard for resulting errors. We created an open-source software program called EMMALAB, developed in Matlab App Designer, to simplify and standardize the workflow associated with EMMA. EMMALAB guides the user through a uniform process to visualize and select endmembers via principal components analysis, calculate the fractional contribution of each endmember, and calculate errors in the mixing analysis. The files in this HydroShare resource include: EMMALAB v1.0 installers for Mac and PC, a data template, and example dataset from the Provo River, and the transcript for a YouTube video that provides instructions for using the software. The Provo River dataset is the example data used in the training video.
DOI: 10.48597/vtjk-zk97
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.15468/dl.bhb9e9
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix skvortzovii"
 ]
}

 The dataset includes 42 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049991-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48597/7qqs-jxzb
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.25574/26565
title: Chandra X-ray Observatory ObsId 26565

description: Confirmation of the First Helium Star Stripped by a Black Hole
DOI: 10.4211/hs.6382ee056239480389bcb9410f65ad67
title: Time-averaged SPIV data for Unveiling surface-subsurface flow interactions of a salmon redd

description: Female salmonids bury their eggs in streambed gravel by digging a pit where they lay their eggs, which they then cover with gravel from a second pit, forming a dune-like structure called a redd, having coarse grains on the surface due to the winnowing away of fine material. The interaction between a redd and the stream flow induces surface water to enter the sediment, flow through the egg pockets, and reemerge downstream of the redd crest. These downwelling and upwelling flows form the hyporheic exchange, which is vital for the embryos’ development because it regulates the egg pocket temperature regime and delivers oxygen-rich surface water to the embryos. Here, we experimentally investigate the effects of (1) redd surface roughness and (2) egg pocket presence on redd-induced hyporheic flows under different surface hydraulics. We use non-intrusive optical measurement techniques, including stereo particle image velocimetry (SPIV) and planar laser-induced fluorescence (PLIF), in a large-scale flume by matching the refractive index of the solid grains (made of THV) and the fluid (water mixed with 15% Epsom salt). This allows us to see through the solid matrix of grains and map the flow fields above and within the salmon redd for four flow scenarios, e.g. slow and fast velocities with shallow and deep depths, two redd constructions, e.g. a smooth and rough surface, and with and without an egg pocket. Results indicate that the flow through the redd is approximately proportional to the square of the Froude number for the smooth redd, and more complex for the rough redd because roughness impacts flow characteristics. The egg pocket, having larger grains than the surrounding matrix, enhances mechanical dispersion that increases water mixing within the egg pocket and causes convergence of the downwelling fluxes towards the egg pocket. Article DOI: 10.1016/j.advwatres.2025.104947
DOI: 10.48597/b9ye-en2b
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/cf4s-gq4f
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/md42-bw8u
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.42ee6fcbf9f64bf882c579df948176e2
title: Data and code for hydrologic data-constrained inversion of time-lapse electrical resistivity measurements [Dataset]

description: This dataset contains the codes and data used in the manuscript "Temporal hydrologic constraints on time-lapse electrical resistivity inversion in hydrogeophysics".In the first folder, Hydrology-constrain includes the three inversion codes and processed field ERT data for the inversion. In the second folder, Hydrologic modeling includes the codes for hydrologic modeling and generating synthetic datasets. Abstract for the manuscript: Time-lapse electrical resistivity method has been frequently used in hydrology to monitor dynamic water flows and storage changes in the subsurface. To construct temporal resistivity images for hydrologic interpretations, measured apparent resistivity datasets at different times need to be inverted. Traditionally, this time-lapse resistivity inversion either assumes no links between adjacent resistivity models (individual inversion) or enforces the maximum smoothness condition in the time domain (temporal smoothness-constrained inversion). While the former method applies no temporal constraint to the resistivity changes, the latter minimizes the temporal resistivity changes. Both inversions could introduce biases to the reconstructed resistivity models, especially in hillslopes where the subsurface moisture (and thus ground resistivity) is neither independent nor unchanged during a typical monitoring period (e.g., a water year). In this study, we propose to construct realistic temporal resistivity constraint from subsurface water storage data. To test this new method, we combined integrated hydrologic modeling and resistivity forward modeling to design a synthetic case. Comparing the inversion results to ground truth shows that the hydrologic data-constrained inversion captures the dynamic water flows and storage changes in the subsurface. Application of this new method to a field dataset was also performed. Compared to existing inversion methods, the new method better reveals the abrupt resistivity changes associated with some intense hydrologic events such as rainfall/snowmelt infiltration and soil drying in the summer. This study thus provides a useful tool for processing time-lapse resistivity data collected at many dynamic hydrologic systems such as hillslopes, drylands, agriculture fields, and forest land.
DOI: 10.48597/qsgz-hfvv
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.a249f3100f924ad09600c9d3de2183b6
title: High frequency turbidity data and storm event characteristics of three urban streams in Cleveland, Ohio

description: This dataset contains turbidity data and storm event characters of three urban watersheds in Cuyahoga County, Ohio. Turbidity data were collected at a frequency of 10 minutes using in-situ Cyclop-7 turbidimeters designed by Turner Designs and integrated  with a Cyclops-7 logger by Precision Measurement Engineering, Inc. Data were collected for three years from September 2018 to 2021. Turbidity data is harmonized with instantaneous discharge data from USGS stream gages. Event characteristics contains runoff, precipitation and antecedent characteristics. The data support the findings of the study titled "Urbanization and Suspended Sediment Transport Dynamics: A Comparative Study of Watersheds with Varying Degree of Urbanization using Concentration-Discharge Hysteresis".
DOI: 10.48597/xfpr-cr7k
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/3fhy-78u6
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.404e72b042f0444ca4a5b28cc94697a3
title: Non-negative Matrix Factorization (NMF) Demo for the AGU 2023 EMMA Workshop

description: River and groundwater geochemistry reflect the integrated results of natural and anthropogenic biogeochemical processes that generate and transport solutes from rocks and soil to rivers and aquifers. These solutes can be derived from multiple sources, such as the weathering of silicate, carbonate, and evaporite rocks, meteoric precipitation, and human pollution. Delineating the contribution of each source to measured solute chemistry is critical for understanding biogeochemical processes and for helping policymakers improve management strategies to safeguard water resources under a changing climate.

End Member Mixing Analysis (EMMA) refers to a group of methods that are increasingly used to identify solute sources and quantify their contributions to water chemistry. EMMA techniques include statistical methods, inverse models, and emerging tools based on machine learning such as non-negative matrix factorization (NMF). As the availability of large hydrogeochemistry datasets and computational resources improves, there is an increased need for the hydrogeochemistry community to become fluent in a variety of EMMA approaches. In this half-day workshop, we will teach how to apply EMMA techniques with genuine river and groundwater hydrogeochemistry datasets in R and MATLAB to solve Earth and environmental sciences problems.

Workshop attendees will use the CUAHSI JupyterHub and MATLAB online cloud computing environment to complete workshop activities. This computing environment will be pre-configured with all necessary software in order to maximize engagement in end member mixing analysis workflows. While attendees are required to bring a personal laptop, all software libraries and hardware requirements will be pre-configured for them to use, no software installation will be necessary.
DOI: 10.48597/957t-wc5y
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/hy6m-z7te
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/tdp2-8ppa
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/vhku-j465
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.4b5d7c4a47da4e05bc6507b3071d1c82
title: The CUAHSI HydroShare Platform for Hydrology Data and Model Sharing

description: Poster for AGU Fall Meeting, December 11, 2023  
https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1336263

HydroShare (http://www.hydroshare.org) is a web-based repository and hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) that enables users to share, collaborate around, and publish data, models, scripts, and applications associated with water related research to meet Findable, Accessible, Interoperable, and Reusable (FAIR) open data mandates. The HydroShare repository also links with connected computational systems, enabling users to reproducibly run models and analyses and share documented workflows.  This presentation will overview the capabilities and best practices developed for collaboration and sharing of data and other research products along with the use of HydroShare and linked computing. It will focus on successes and challenges in engaging scholars, researchers, and practitioners as individuals and as communities, including lessons learned in sharing data across large scientific communities such as the Critical Zone Collaborative Network. It will also include collaboration functions being developed for the Institute for Geospatial Understanding through an Integrative Discovery Environment (I-GUIDE) and Cooperative Institute for Research to Operations in Hydrology (CIROH), where challenges associated with large scale input/output data preparation, staging, and sub setting along with execution of large-scale models and data are faced.
DOI: 10.48597/wyq6-qcp3
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/6hsm-y2ww
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.bfdb0c003e2248cc90bc75845d008887
title: Smartphone evapotranspiration field campaign data Rietholzbach

description: This dataset contains observations made by smartphone and an additional handheld sensor in order to estimate evapotranspiration. These include luminence, surface temperature, air temperature, air pressure, relative humidity, and wind speed. The dataset also contains hourly routine meteorological observations, as well as evapotranspiration observations by a large weighing lysimeter and eddy covariance, all collected at the Büel meteorological station in the Rietholzbach catchment, which is operated by the Land-Climate Dynamics group at the Institute for Atmospheric and Climate Science (IAC), ETH Zurich. The smartphone observations were used to train a simple machine learning algorithm on the observed fluxes.
DOI: 10.48597/6skv-2tc4
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.48597/f5hx-b43f
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.4211/hs.5aa50a5f46fa41d29c013799cc22bf25
title: The data for radium isotopes in coastal aquifers

description: The variation in radium activity within an intertidal aquifer poses significant uncertainties in estimating submarine groundwater discharge (SGD) and associated chemical fluxes. This study presents the first global findings on the seasonal variation of radium isotopes in a two-dimensional cross-section of an intertidal aquifer. This dataset includes the radium isotopes data, other groundwater data, sea level data, and numerical modeling data
DOI: 10.48597/kzcs-h2j2
title: Measurement of Nitrogen_dioxide at Jarczew

description: Measurement of Nitrogen_dioxide at Jarczew
DOI: 10.25582/data.2023-09.2887298/2464347
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887299/2464348
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887300/2464349
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887301/2464350
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887302/2464351
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887303/2464352
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887304/2464353
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887305/2464354
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887306/2464355
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887307/2464356
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887308/2464357
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887309/2464358
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887310/2464359
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887311/2464360
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887312/2464361
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887313/2464362
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887314/2464363
title: Data for EMSL Project 60568 from September 2023
DOI: 10.25582/data.2023-09.2887315/2464364
title: Data for EMSL Project 60568 from September 2023
DOI: 10.5281/zenodo.11465042
title: Data and code for: Recombinant venom proteins in insect seminal fluid reduces female lifespan

description: Publication: https://doi.org/10.1101/2024.01.14.575309

This repository contains all the data as well as the scripts used for the analysis and visualisation. 




File
Description


TMT.ipynb
The Jupyter notebook containing the Python code for statistical analysis and figure generation. Scripts are sectioned in the order that results are presented in the study, with sub-headings and figure numbers where appropriate.


actingal4.csv
Offspring phenotype data from the UAS:venom x Act5C-GAL4 assay (Results: Testing functional expression of recombinant venoms).


femalelifespan_1-1.csv
Female lifespan data from the 1:1 male:female mating assay (Results: TMT males reduce the lifespan of mated females). Time = days since initial male exposure until death. Status: 1 = death observed; 0 = censored. 


femalelifespan_3-1.csv
Female lifespan data from the 3:1 male:female mating assay (Results: TMT males reduce the lifespan of mated females). Time = days since initial male exposure until death. Status: 1 = death observed; 0 = censored. 


singlemating.csv
Data from the single-pair courtship assay. 1 = successful courtship observed; 0 = successful courtship not observed.


competitivemating.csv
Offspring genotype data from the competitive mating assay. Female = group of offspring from a given mother. white = positive result from gDNA PCR using the 'white' primers (supplementary table S2). TMT = positive result from gDNA PCR using the 'UAS' primers (supplementary table S2).


malelifespan.csv
TMT male lifespan data from the male longevity assay. Time = days since eclosion. Status: 1 = death observed; 0 = censored. 




 

TMT-GAMA.db and TMT-lethality.db contains data from the GAMA model (doi:10.5281/zenodo.11439089), which is accessed by the Jupyter notebook.




Table
Description


TMT_step_output
Each simulation will write key values to this table each cycle once transgenic males have begun to be released.


TMT_female_output
Each female will write key values to this table upon their death once transgeni males have begun to be released.


TMT_PR50
Each simulation will write to this table when/if the female population reaches 50% of its initial size.


TMT_PR95
Each simulation will write to this table when/if the female population reaches 95% of its initial size.