cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.dsagxp
title: Occurrence Download

description: A dataset containing 193 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Edentomyia Galati, Andrade-Filho, Silva & Falcão, 2003"
 ]
}

 The dataset includes 193 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042041-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.26368855
title: <b>Printing 3D interconnects by filament drawing</b>

description: A micro-printing method was proposed based on filament drawing to fabricate 3D interconnects. The process involves forming a meniscus under the tension of the ink, and the delicate structure is drawn out from the meniscus when printing. The printing material is an ink composed of silver nanoparticles (AgNPs) and polyvinylpyrrolidone (PVP). The viscosity is controlled bsy evaporating the ink’s solvent to facilitate meniscus formation. This unique printing method utilizes a single needle, controlled by precise air pressure and speed, to construct 3D structures with varying wire widths. The 3D architectures exhibit superior structural retention and enhanced conductivity following thermal treatment. The drawing printing method has been successfully implemented on flexible circuits, including light-emitting diode (LED) arrays, thermal imaging displays, and multivibrator circuits. These applications reveal significant potential for flexible, integrated circuits and personalized manufacturing applications. The proposed technique promises the manufacturing of 3D interconnects essential for future integrated, flexible electronics.
DOI: 10.6084/m9.figshare.26368855.v2
title: <b>Printing 3D interconnects by filament drawing</b>

description: A micro-printing method was proposed based on filament drawing to fabricate 3D interconnects. The process involves forming a meniscus under the tension of the ink, and the delicate structure is drawn out from the meniscus when printing. The printing material is an ink composed of silver nanoparticles (AgNPs) and polyvinylpyrrolidone (PVP). The viscosity is controlled bsy evaporating the ink’s solvent to facilitate meniscus formation. This unique printing method utilizes a single needle, controlled by precise air pressure and speed, to construct 3D structures with varying wire widths. The 3D architectures exhibit superior structural retention and enhanced conductivity following thermal treatment. The drawing printing method has been successfully implemented on flexible circuits, including light-emitting diode (LED) arrays, thermal imaging displays, and multivibrator circuits. These applications reveal significant potential for flexible, integrated circuits and personalized manufacturing applications. The proposed technique promises the manufacturing of 3D interconnects essential for future integrated, flexible electronics.
DOI: 10.6084/m9.figshare.26368855.v3
title: <b>Printing 3D interconnects by filament drawing</b>

description: A micro-printing method was proposed based on filament drawing to fabricate 3D interconnects. The process involves forming a meniscus under the tension of the ink, and the delicate structure is drawn out from the meniscus when printing. The printing material is an ink composed of silver nanoparticles (AgNPs) and polyvinylpyrrolidone (PVP). The viscosity is controlled bsy evaporating the ink’s solvent to facilitate meniscus formation. This unique printing method utilizes a single needle, controlled by precise air pressure and speed, to construct 3D structures with varying wire widths. The 3D architectures exhibit superior structural retention and enhanced conductivity following thermal treatment. The drawing printing method has been successfully implemented on flexible circuits, including light-emitting diode (LED) arrays, thermal imaging displays, and multivibrator circuits. These applications reveal significant potential for flexible, integrated circuits and personalized manufacturing applications. The proposed technique promises the manufacturing of 3D interconnects essential for future integrated, flexible electronics.
DOI: 10.34740/kaggle/ds/4060677
title: Predict lifespan of a comet goldfish
DOI: 10.15468/dl.pessm2
title: Occurrence Download

description: A dataset containing 844 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Viburnum rugosum Pers.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 844 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042042-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8ugttx
title: Occurrence Download

description: A dataset containing 20488671 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Diptera"
 ]
}

 The dataset includes 20488671 records from 5348 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042037-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.a4ayuf
title: Occurrence Download

description: A dataset containing 1062279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Falco peregrinus Tunstall, 1771"
 ]
}

 The dataset includes 1062279 records from 127 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001082-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fqq24a
title: Occurrence Download

description: A dataset containing 22716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "OccurrenceStatus is Present",
 "TaxonKey is Vultur gryphus Linnaeus, 1758"
 ]
}

 The dataset includes 22716 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001096-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ky483b
title: Occurrence Download

description: A dataset containing 52771 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lycalopex culpaeus (Molina, 1782)"
 ]
}

 The dataset includes 52771 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001091-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kabqg9
title: Occurrence Download

description: A dataset containing 8939 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera onca (Linnaeus, 1758)"
 ]
}

 The dataset includes 8939 records from 280 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001092-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15485/2222681
title: Solute chemistry for streams draining geomorphic features and varying land cover gradients in the East River watershed, Colorado

description: This dataset contains solute chemistry data and GPS coordinates for surface water samples collected in the East River watershed in the Elk and West Elk Ranges of Colorado from August 3rd through August 11th, 2022. Samples were collected from streams and groundwater seeps as part of a comprehensive analysis within the watershed of how different geomorphic features (such as landslides and rock glaciers) along with variations in land cover (such as the presence of vegetation) affect stream water quality in this Rocky Mountain headwater environment. This dataset includes concentrations of the following in a single csv file: fluoride, chloride, sulfate, nitrate, calcium, potassium, magnesium, sodium, silicon, and total organic carbon (TOC). All concentrations are reported in mg/L. The sulfate data in this version have been updated to account for an earlier ion chromatograph instrument method error whereby sulfate and bromide peaks overlapped in calibration solution samples, biasing calculated sulfate concentrations low by 30-50%. Sulfate data reported herein are from a re-run of the samples performed in December 2024 using a corrected instrument method. All samples were stored under refrigerated conditions and filtered on initial collection.
DOI: 10.15468/dl.snvhmp
title: Occurrence Download

description: A dataset containing 2688 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "TaxonKey is Vultur gryphus Linnaeus, 1758"
 ]
}

 The dataset includes 2688 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001100-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zarzg5
title: Occurrence Download

description: A dataset containing 22716 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Chile",
 "OccurrenceStatus is Present",
 "TaxonKey is Vultur gryphus Linnaeus, 1758"
 ]
}

 The dataset includes 22716 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001099-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t7aurd
title: Occurrence Download

description: A dataset containing 23223 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "OccurrenceStatus is Present",
 "TaxonKey is Didelphis marsupialis Linnaeus, 1758"
 ]
}

 The dataset includes 23223 records from 343 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001098-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/ppkp-5h63
title: Data from The Resolved Structure of a Low Metallicity Photodissociation Region

description: JWST: NIRSpec and MIRI-MRS, HST: Photometry. 
Abstract:
Photodissociation Regions (PDRs) are key to understanding the feedback processes that shape interstellar matter in galaxies. One important type of PDR is the interface between \hii regions and molecular clouds, where far-ultraviolet (FUV) radiation from massive stars heats gas and dissociates molecules. Photochemical models predict that the C/CO transition occurs deeper in the PDR compared to the \htohtwo transition in low-metallicity environments, increasing the extent of CO-dark \htwo gas. This prediction has been difficult to test outside the Milky Way due to the lack of high spatial resolution observations tracing \htwo and CO. This study examines a low-metallicity PDR in the N13 region of the Small Magellanic Cloud (SMC) where we spatially resolve the ionization front, the \htwo dissociation front, and the C/CO transition. Using $^{12}$CO J=2$-$1, 3$-$2 and [CI] (1-0) observations from the Atacama Large Millimeter/sub-mm Array (ALMA) and near-infrared spectroscopy of \htwo vibrational lines from the James Webb Space Telescope (JWST), we resolve the layers of the PDR in N13. Our analysis shows that the separation between the H/\htwo and C/CO boundaries is approximately \seppc (equivalent to \separc at the distance of the SMC), defining the spatial extent of the CO-dark \htwo region. Compared to our plane-parallel PDR models, we find that a constant pressure model matches the observed structure better than a constant density one. Overall, we find that the PDR model does well at predicting the extent of the CO-dark \htwo layer in N13. This study represents the first resolved benchmark for low metallicity PDRs.
DOI: 10.5281/zenodo.14555860
title: Effects of climate change on rhizosphere microbiota
DOI: 10.5281/zenodo.14555861
title: Effects of climate change on rhizosphere microbiota
DOI: 10.25582/data.2024-01.3017027/2505875
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2025-01.3363327/2506090
title: Data for EMSL Project 60923 from January 2025
DOI: 10.15473/2506146
title: Drifting Acoustic Measurements around C-Power's SeaRay WEC

description: The repository contains underwater noise measurements and associated metadata collected around C-Power's SeaRay wave energy converter on July 15, 2024 and July 16, 2024 while it was deployed at the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Measurements were obtained using Drifting Acoustic Instrumentation SYstems (DAISYs). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is a compressed archive (.zip format), containing multiple MATLAB binary data files (.mat format). The structure of each file is included in the repository as a Word document (Data Description MHK-DR.docx). Each file contains time series information for a single DAISY deployment (file naming convention: WETS_DAISY_[Drift #].mat) consisting of processed hydrophone data and associated metadata. During these measurements, C-Power's SeaRay was located at approximately 21.48112 N, 157.74451 W.
DOI: 10.15473/2506147
title: HERO WEC Belt Test Data

description: The following submission includes raw and processed data from the 2024 Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC) belt tests conducted using NREL's Large Amplitude Motion Platform (LAMP). A description of the motion profiles run during testing can be found in the run log document. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in the form of TDMS files. Data was then processed using Python and MATLAB and converted to MATLAB workspace, parquet, and csv file formats. During Data processing, a low pass filter was applied to each array and the arrays were then resampled to common 10Hz timestamps. A MATLAB data viewer script is provided to quickly visualize these data sets. The following arrays are contained in each test data file: - Time: Unix seconds timestamp - Test_Time: Time in seconds since beginning of test - POS_OS_1001: Encoder position in degrees (the encoder is located on the secondary shaft of the spring return and is driven by the winch after a 4.5:1 gear reduction) - LC_ST_1001: Anchor load cell data in lbf - PRESS_OS_2002: Air spring pressure in psi This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.
DOI: 10.15473/2506148
title: TEAMER: Twin Ocean Power Wave Energy Converter Comprehensive Overview

description: These files collectively provide a comprehensive overview of the testing process, data analysis, and validation for the Twin Ocean Power device tested at the O.H. Hinsdale Wave Research Laboratory, supported by TEAMER funding. This resource includes an overview of power results for a series of 7 trials. The files included in this comprehensive overview include a comprehensive log sheet for each trial, a summary of all trials, and processing scripts for the raw data. It includes all raw data in .tsv and MATLAB compatible formats, an average power chart, angular velocity charts for each trial, trial metrics, and power output files. This resource includes images of the Twin Ocean Power Wave Energy Converter device components and movement during testing and video recordings of each trial.
DOI: 10.15473/2506149
title: HERO WEC V1 Upgrade - 2023 Laboratory Testing (Raw Data)

description: This submission contains the original, unprocessed data from the 2023 Large Amplitude Motion Platform (LAMP) testing of NREL's Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This data serves as a companion to MHKDR #520. Data was collected using NREL's Modular Ocean Data AcQuisition (MODAQ) system in TDMS format. Specifications of TDMS files can be found on the NI website. The TDMS files have been separated into zip files corresponding to either Drivetrain, Hydraulic, or Electric configuration runs representing the respective test cases that were run. The drivetrain runs were used to characterize the drivetrain only (no pump or generator). The Hydraulic runs represent the configuration when the seawater pump is installed, and the Electric runs represents the configuration when the generator is installed. The following sub-categories of data are included for each type: - DW - Deep water (monochromatic sine wave) profile (not run in drivetrain configuration) - Heave - Heave only (monochromatic sine wave) profile - Heave_NoRO (hydraulic configuration only) - Heave_ACC (hydraulic configuration only) - IR - Surge and heave irregular wave profile (not run in drivetrain configuration) - RW - Heave only profile created from real world encoder data (not run in drivetrain configuration) Reference documents: - "HERO WEC Lamp Test Run Log.xlsx": contains specifications for each test run - "Lamp Data Description.docx": provides detailed information about data types and processing methods For those interested in the processed data the authors have created a separate submission, MHKDR #520, linked below. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.
DOI: 10.25582/data.2024-01.3017239/2506150
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017240/2506151
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017244/2506152
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017249/2506153
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017250/2506154
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017252/2506155
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017253/2506156
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017254/2506157
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017259/2506158
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017260/2506159
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017269/2506160
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017273/2506161
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017279/2506162
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017281/2506163
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017284/2506164
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017285/2506165
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017286/2506166
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017291/2506167
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017293/2506168
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017294/2506169
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017296/2506170
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017297/2506171
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017298/2506172
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017300/2506173
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017301/2506174
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017304/2506175
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017305/2506176
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017308/2506177
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017309/2506178
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017311/2506179
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017313/2506180
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017314/2506181
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017315/2506182
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017323/2506183
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017324/2506184
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017325/2506185
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017328/2506186
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017330/2506187
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017331/2506188
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017332/2506189
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017333/2506190
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017334/2506191
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017335/2506192
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017336/2506193
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017337/2506194
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017338/2506195
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017339/2506196
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017340/2506197
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017341/2506198
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017342/2506199
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017343/2506200
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017345/2506201
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017346/2506202
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017347/2506203
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017348/2506204
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017359/2506205
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017363/2506206
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017365/2506207
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017371/2506208
title: Data for EMSL Project 51287 from January 2024
DOI: 10.25582/data.2024-01.3017372/2506209
title: Data for EMSL Project 51287 from January 2024