cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5027278
title: Deep Learning Reach-level Estimates of Mean River Depth at the Conterminous United States Spatial Scale

description: Abstract: Estimates of riverine channel geometry play a vital role in the physical representation of stream networks in models used to predict flood and drought conditions, manage water resources, and increase our knowledge of fluvial conditions under a changing climate. A well established body of literature exists that explains the relationship between channel geometry parameters width, depth, and velocity to instantaneous river discharge using a log-log linear power-law regression. In this study, a state-of-the-art deep learning regression model is presented and compared against the power-law method to evaluate their ability to estimate cross-sectional mean river depth. Results reveal three key findings, the neural network: (1) decreases RMSE by 22% verse a CONUS scale power-law equation, (2) reduces prediction variance across Strahler stream orders, and (3) generally outperforms regional power-law equations with an average decrease in RMSE of 8.7% Lastly, a reach-level CONUS dataset of estimated mean river depth is delivered. The deep learning model was trained using the following features: AI - Mean aridity index of unit catchment - Trabucco and Zomer, 2019 area - Upstream drainage area (km2) - P. Lin et al., 2020 CLY - Mean clay content (mass percentage, %) of unit catchment - Hengl et al., 2017 DOR - Stream segment degree of dam regulation (Scale 0. – 100.) - Grill et al., 2019 Elev - Stream segment mean elevation - P. Lin et al., 2020 K - Mean bedrock permeability of unit catchment surrounding stream segment - Huscroft et al., 2018 LAI - Mean leaf area index of unit catchment - Zhu et al., 2013 order - Strahler-Horton stream order - P. Lin et al., 2020 P - Mean bedrock porosity of unit catchment - Huscroft et al., 2018 QMEAN - Stream segment mean annual discharge (m3/s) - P. Lin et al., 2019 Sin - Stream segment sinuosity - P. Lin et al., 2020 Slp - Stream segment mean longitudinal slope - P. Lin et al., 2020 SLT - Mean silt content (mass percentage, %) of unit catchment - Hengl et al., 2017 SND - Mean sand content (mass percentage, %) of unit catchment - Hengl et al., 2017 stream_wdth_va - Measured stream cross-sectional width (m) - Canova et al., 2016 Urb - Mean urban fraction of unit catchment - Liu et al., 2018 The deep learning model was trained using the following label: mean_depth_va - Measured stream mean depth (m) - Canova et al., 2016 Predictions of mean depth were made by replacing stream_wdth_va from Canova et al., (2016) with bankfull width estimates (width_m) from P. Lin et al., (2020). Missing records from the P. Lin et al., (2020) dataset were excluded when making predictions, thus there are missing reaches in the dataset.
DOI: 10.6084/m9.figshare.14842689
title: anx2016

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.6073/pasta/097a6c1c5e35159d751a75f8fe536aba
title: Daily summarized seasonal measurements of discharge, water temperature, and specific conductivity from the Onyx River at Lower Wright, McMurdo Dry Valleys, Antarctica (1972-2020, ongoing)

description: As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This package contains daily summaries derived from 15-minute measurements of water quality and quantity parameters measured with automatic recording devices on streams in this region. Specifically, this metadata record describes the daily hydrological summaries for the McMurdo Dry Valley's Onyx River, located in the Wright Valley. Measurements commenced in 1972 and are ongoing. This dataset extends through the first half of the 2019-20 field season.
DOI: 10.5066/p9acxxi7
title: PeakFQ software input files and selected output files for selected long-term streamgages near Jefferson County, Missouri, through water year 2019

description: Contents include U.S. Geological Survey (USGS) flood-frequency analysis software PeakFQ input files (.psf specification file and peak-flow data file) and output files (.prt file of flood-frequency results) for selected streamgaging stations near Jefferson County, Missouri. Ten stations are included with flood-frequency results updated from Southard and Veilleux (2014) and peak-flow record through the 2019 water year. Files are presented by the USGS 8-digit station identification number and the 10 stations include: 07010350, 07013000, 07015720, 07016500, 07017200, 07018100, 07019000, 07021000, 07035800, 07036100.
DOI: 10.5066/p92mqye7
title: Geospatial data and model archive associated with the two-dimensional hydraulic analysis of Joachim Creek, De Soto, Missouri

description: Digital flood-inundation maps for a 6.7-mile reach of Joachim Creek within and near the City of De Soto, Missouri, were created by the U.S. Geological Survey (USGS) in cooperation with the City of De Soto, Missouri. The flood-inundation maps depict estimates of the spatial extent, depth, and velocity corresponding to select flood events. Flood elevations were computed for Joachim Creek by means of a two-dimensional, finite-volume numerical model for river hydraulics. The hydraulic model was calibrated by using global positioning system measurements of water-surface elevations of high-water marks from the April 18, 2013 flood and the maximum measured discharge at the USGS streamgage Joachim Creek at De Soto, Missouri (station number 07019500). The calibrated hydraulic model was then used to compute the hydraulic conditions associated with the 10-, 4-, 2-, 1-, and 0.2-annual exceedance probability (AEP) flows (10-year, 25-year, 50-year, 100-year and 500-year recurrence interval). The water-surface elevation, velocity, and water depth maps associated with the 1- and 0.2-percent AEP flood events are available in GeoTIFF format. The hydraulic model is available as a model archive.
DOI: 10.6073/pasta/7c52a2cd6f9b906ba7adbb2a9c4af620
title: Soil seedbank analysis under experimental drought and delayed monsoon treatments in blue grama and black grama grassland at Sevilleta

description: This study investigated the question, "Does climate change affect vegetation and seed bank composition in desert grasslands?" The work was done in the Sevilleta National Wildlife Refuge, New Mexico, USA, in in the Extreme Drought in Grassland Experiment (EDGE). Vegetation and seed bank species composition were recorded in black grama (Bouteloua eriopoda) and blue grama (B. gracilis) grasslands at Sevilleta. At each site, two rainfall manipulations and ambient controls were established in 2013 (n=10). Treatments included extreme drought (-66% rainfall reduction) and delayed monsoon (precipitation captured during July-August and reapplied during September-October). Aboveground species composition was assessed and composite soil samples were collected in 2017, five years after the experiment started. Seed bank composition was evaluated using the seedling emergence method. Rainfall treatments increased aboveground species richness at both sites, and seed bank richness only in the blue grama community. Vegetation cover was reduced by both rainfall manipulations, but seed bank density increased or remained the same compared with controls. In aboveground vegetation, cover of annual and perennial forbs increased, and dominant perennial grasses decreased. In the soil seed bank, species composition was similar among all treatments and was dominated by annual and perennial forbs. The seed bank was more resistant to drought than aboveground vegetation. Because seed banks enhance long-term community stability, their drought resistance plays an important role in maintaining ecosystem processes during and following drought in these grassland communities.
DOI: 10.6084/m9.figshare.14842692
title: sud2020-cud

description: GWAS summary statistics from the Psychiatric Genomics Consortium (PGC)
DOI: 10.24431/rw1k59f
title: Zooplankton Rapid Analysis (RZA) and abundance samples, Arctic Integrated Ecosystem Assessment Survey, from 2019 Chukchi and Beaufort Seas

description: These data are part of the Arctic IES component of the Arctic Integrated Ecosystem Research Program (IERP), sponsored by the North Pacific Research Board (NPRB), Collaborative Alaskan Arctic Studies Program (formerly the North Slope Borough/Shell Baseline Studies Program), Bureau of Ocean Energy Management (BOEM), and the Office of Naval Research Marine Mammals and Biology Program. Zooplankton abundance was estimated from the 2019 Ocean Starr survey. Zooplankton abundance is estimated from bongo net collected samples collected at sea and preserved in formalin. Zooplankton samples are sorted, identified to lowest taxonomic resolution possible, and enumerated. The zooplankton are identified by the staff at the Poland Plankton Sorting and Identification Center in Szczecin, Poland through a Joint Studies Project Agreement between NOAA Fisheries and the Morski Institut Rybacki (Sea Fisheries Institute) Poland. Zooplankton were enumerated from the 20 cm diameter, 153 um mesh net and the 60 cm diameter, 505 um mesh net. The data file contains station information, including latitude, longitude, time, volume filtered from the net, and maximum gear depth. Biological data includes zooplankton taxonomic information, stage, size, sex, and abundance. File is comma separated values. Rapid Zooplankton Assessment (RZA) was performed at all grid stations on the 2019 Ocean Starr survey. The RZA takes place at sea, where collected samples are examined under a microscope, zooplankton are enumerated, and identified into broad taxonomic categories. Zooplankton were enumerated from the 20 cm diameter, 153 um mesh net and the 60 cm diameter, 505 um mesh net. The data file contains station information, including latitude, longitude, and time. Biological variables include abundance (number per cubic meter) in eleven broad taxonomic categories. File is comma separated values.
DOI: 10.24431/rw1k59g
title: Zooplankton Rapid Analysis (RZA) and abundance samples, Arctic Integrated Ecosystem Assessment Survey, from 2017 Chukchi and Beaufort Seas

description: These data are part of the Arctic IES component of the Arctic Integrated Ecosystem Research Program (IERP), sponsored by the North Pacific Research Board (NPRB), Collaborative Alaskan Arctic Studies Program (formerly the North Slope Borough/Shell Baseline Studies Program), Bureau of Ocean Energy Management (BOEM), and the Office of Naval Research Marine Mammals and Biology Program. Zooplankton abundance was estimated from the 2017 Ocean Starr survey. Zooplankton abundance is estimated from bongo net collected samples collected at sea and preserved in formalin. Zooplankton samples are sorted, identified to lowest taxonomic resolution possible, and enumerated. The zooplankton are identified by the staff at the Poland Plankton Sorting and Identification Center in Szczecin, Poland through a Joint Studies Project Agreement between NOAA Fisheries and the Morski Institut Rybacki (Sea Fisheries Institute) Poland. Zooplankton were enumerated from the 20 cm diameter, 153 um mesh net and the 60 cm diameter, 505 um mesh net. The data file contains station information, including latitude, longitude, time, volume filtered from the net, and maximum gear depth. Biological data includes zooplankton taxonomic information, stage, size, sex, and abundance. File is comma separated values. Rapid Zooplankton Assessment (RZA) was performed at all grid stations on the 2017 Ocean Starr survey. The RZA takes place at sea, where collected samples are examined under a microscope, zooplankton are enumerated, and identified into broad taxonomic categories. Zooplankton were enumerated from the 20 cm diameter, 153 um mesh net and the 60 cm diameter, 505 um mesh net. The data file contains station information, including latitude, longitude, and time. Biological variables include abundance (number per cubic meter) in eleven broad taxonomic categories. File is comma separated values.
DOI: 10.5281/zenodo.4630687
title: ReqExp: BERT-based ML Model for Extracting Software Requirements

description: Datasets that were used during experiments in ReqExp project.
DOI: 10.5281/zenodo.4630686
title: ReqExp: BERT-based ML Model for Extracting Software Requirements

description: Datasets that were used during experiments in ReqExp project.
DOI: 10.5066/f7n58kbs
title: Hydrogeologic, geologic, and water-level data for the groundwater component of the upper Rio Grande Focus Area Study, Colorado, New Mexico, and Texas, United States and Chihuahua, Mexico 2017

description: This Data Release contains various types of hydrologic and geologic data from the Upper Rio Grande Focus Area Study from 1921-2017, including groundwater-level measurement data compiled and synthesized from various sources, water-level altitude and water-level change maps developed from the water-level measurement data every 5 years from 1980-2015, and the horizontal extent of 13 alluvial basins in the Upper Rio Grande Basin
DOI: 10.5281/zenodo.4784301
title: Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment

description: Results of the Global Sensitivity Analysis experiment and R code for visualizing chord diagrams with indexes and parallel coordinate plots. These were used for the paper Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment (Jose M. Rodriguez-Flores, et. al)
DOI: 10.5281/zenodo.4784300
title: Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment

description: Results of the Global Sensitivity Analysis experiment and R code for visualizing chord diagrams with indexes and parallel coordinate plots. These were used for the paper Global Sensitivity Analysis of a Coupled Hydro-Economic Model and Groundwater Restriction Assessment (Jose M. Rodriguez-Flores, et. al)
DOI: 10.6084/m9.figshare.14815857
title: Pawellek_CAD response_ J Chem Inf_Table S1

description: This set is composed of 65 independent variables and 1 dependent variable. The latter is Charged Aerosol Detector (CAD) response of fatty acids (C12-C18), measured under 25 different experimental conditions. The shown values of target variable are the cube-root transformed CAD responses. The independent variables are experimental factors (4 quantitative and 1 qualitative factors) and 61 molecular descriptors. Values of experimental factors were varied according to DoE (Face-Centered Central Composite Design). Molecular descriptors were calculated for optimized structures of given analytes using Dragon 6.0.7. software (Talete srl, Milan, Italy). Descriptors that were strongly correlated to the other descriptors (using a correlation coefficient │r│ &gt; 0.90), descriptors with constant values (RSD &lt; 5%), i.e. descriptors that were not available for all analytes were excluded from the initial pool of descriptors. This datset was developed for mixed Quantitative Structure - Property Relationship (QSPR) modeling. <br>
DOI: 10.6084/m9.figshare.14815857.v3
title: Pawellek_CAD response_ J Chem Inf_Table S1

description: This set is composed of 65 independent variables and 1 dependent variable. The latter is Charged Aerosol Detector (CAD) response of fatty acids (C12-C18), measured under 25 different experimental conditions. The shown values of target variable are the cube-root transformed CAD responses. The independent variables are experimental factors (4 quantitative and 1 qualitative factors) and 61 molecular descriptors. Values of experimental factors were varied according to DoE (Face-Centered Central Composite Design). Molecular descriptors were calculated for optimized structures of given analytes using Dragon 6.0.7. software (Talete srl, Milan, Italy). Descriptors that were strongly correlated to the other descriptors (using a correlation coefficient │r│ &gt; 0.90), descriptors with constant values (RSD &lt; 5%), i.e. descriptors that were not available for all analytes were excluded from the initial pool of descriptors. This datset was developed for mixed Quantitative Structure - Property Relationship (QSPR) modeling. <br>
DOI: 10.6084/m9.figshare.14815857.v2
title: Pawellek_CAD response_ J Chem Inf_Table S1

description: This set is composed of 65 independent variables and 1 dependent variable. The latter is Charged Aerosol Detector (CAD) response of fatty acids (C12-C18), measured under 25 different experimental conditions. The shown values of target variable are the cube-root transformed CAD responses. The independent variables are experimental factors (4 quantitative and 1 qualitative factors) and 61 molecular descriptors. Values of experimental factors were varied according to DoE (Face-Centered Central Composite Design). Molecular descriptors were calculated for optimized structures of given analytes using Dragon 6.0.7. software (Talete srl, Milan, Italy). Descriptors that were strongly correlated to the other descriptors (using a correlation coefficient │r│ &gt; 0.90), descriptors with constant values (RSD &lt; 5%), i.e. descriptors that were not available for all analytes were excluded from the initial pool of descriptors. This datset was developed for mixed Quantitative Structure - Property Relationship (QSPR) modeling. <br>
DOI: 10.25934/00003099.0
title: Available IPD datapackage for study 'A Phase 2b, Multicenter, Open-Label Study in Rheumatoid Arthritis Subjects Who Completed Preceding Study M13-390 With Adalimumab'

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00003099.1
title: IPD datapackage for study 'A Phase 2b, Multicenter, Open-Label Study in Rheumatoid Arthritis Subjects Who Completed Preceding Study M13-390 With Adalimumab' and research request: 'Predicting the Treatment Response of Biologics for Individual Patients with Rheumatoid Arthritis: An Individual Participant Data Meta-Analysis '

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00003095.0
title: Data Package for study.

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.25934/00003095.1
title: IPD datapackage for study 'Study to Assess the Pharmacokinetic, Pharmacodynamic, Safety and Immunogenicity of a New Adalimumab Formulation in Subjects With Active Rheumatoid Arthritis' and research request: 'Predicting the Treatment Response of Biologics for Individual Patients with Rheumatoid Arthritis: An Individual Participant Data Meta-Analysis '

description: Vivli is an independent, non-profit organization that has developed a global data-sharing and analytics platform to serve all elements of the international research community. Our mission is to promote, coordinate, and facilitate scientific sharing and reuse of clinical research data through the creation and implementation of a sustainable global data-sharing enterprise. The Vivli platform includes an independent data repository, in-depth search engine and a cloud-based, secure analytics platform.
DOI: 10.6084/m9.figshare.14842761
title: The Optimal Machine Learning Modeling of Brent Crude Oil Price

description: This is the data on Brent crude oil price<br>
DOI: 10.6084/m9.figshare.14842761.v1
title: The Optimal Machine Learning Modeling of Brent Crude Oil Price

description: This is the data on Brent crude oil price<br>
DOI: 10.24431/rw1k59h
title: Midwater Trawl Catch Dataset, Arctic Integrated Ecosystem Research Program, Chukchi Sea August - September 2017

description: These data are part of the Arctic IES component of the Arctic Integrated Ecosystem Research Program (IERP), sponsored by the North Pacific Research Board (NPRB), Collaborative Alaskan Arctic Studies Program (formerly the North Slope Borough/Shell Baseline Studies Program), Bureau of Ocean Energy Management (BOEM), and the Office of Naval Research Marine Mammals and Biology Program. To investigate the distribution, life history, and interactions of biological communities in the U.S. Continental Region of the NE Chukchi Sea a survey was conducted from August - September 2017. Pelagic fishes were targeted using a modified Marinovich midwater trawl. Catches were processed (see methods) and specimen were collected for additional processing.. Files included in this dataset are as follows: Midwater Catch: OS201701MidwaterCatchSummary.csv Midwater Specimen: OS201701MidwaterSpecimenCollection.csv Midwater Event Data: OS201701MidwaterEvents.csv Length measurement detailed methodology: GadidLengthConversions.pdf Species identification verification and final classification narrative: GadidGeneticReassignment.pdf
DOI: 10.24431/rw1k59i
title: Midwater Trawl Catch Dataset, Arctic Integrated Ecosystem Research Program, August - September 2019

description: These data are part of the Arctic IES component of the Arctic Integrated Ecosystem Research Program (IERP), sponsored by the North Pacific Research Board (NPRB), Collaborative Alaskan Arctic Studies Program (formerly the North Slope Borough/Shell Baseline Studies Program), Bureau of Ocean Energy Management (BOEM), and the Office of Naval Research Marine Mammals and Biology Program. To investigate the distribution, life history, and interactions of biological communities in the U.S. Continental Region of the NE Chukchi Sea a survey was conducted from August - September 2019. Pelagic fishes were targeted using a modified Marinovich midwater trawl. Catches were processed (see methods) and specimen were collected for additional processing.. Files included in this dataset are as follows: Midwater Catch: OS201901MidwaterCatchSummary.csv Midwater Specimen: OS201901MidwaterSpecimenCollection.csv Midwater Event Data: OS201901MidwaterEvents.csv
DOI: 10.24431/rw1k59j
title: Midwater Trawl Catch Dataset, Arctic Integrated Ecosystem Research Program, August - September 2019

description: These data are part of the Arctic IES component of the Arctic Integrated Ecosystem Research Program (IERP), sponsored by the North Pacific Research Board (NPRB), Collaborative Alaskan Arctic Studies Program (formerly the North Slope Borough/Shell Baseline Studies Program), Bureau of Ocean Energy Management (BOEM), and the Office of Naval Research Marine Mammals and Biology Program. To investigate the distribution, life history, and interactions of biological communities in the U.S. Continental Region of the NE Chukchi Sea a survey was conducted from August - September 2019. Pelagic fishes were targeted using a modified Marinovich midwater trawl. Catches were processed (see methods) and specimen were collected for additional processing.. Files included in this dataset are as follows: Midwater Catch: OS201901MidwaterCatchSummary.csv Midwater Specimen: OS201901MidwaterSpecimenCollection.csv Midwater Event Data: OS201901MidwaterEvents.csv