cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26208/x1d1-gs56
title: Investigating the relationship between polarimetric radar signatures of hydrometeor size sorting and tornadic potential in simulated supercells

description: The national upgrade of the operational weather radar network to polarimetric capabilities has lead to numerous studies focusing on polarimetric radar signatures commonly observed in supercells. One such signature is the horizontal separation of regions of enhanced differential reflectivity (ZDR) and specific differential phase (KDP) values due to hydrometeor size sorting. Recent observational studies have shown that the orientation of this separation tends to be more perpendicular to storm motion in supercells that produce tornadoes. Although this finding has potential operational utility, the physical relationship between this observed radar signature and tornadic potential is not known. This study uses an ensemble of tornadic and nontornadic supercell simulations to investigate this connection. The tendency for tornadic supercells to have a more perpendicular separation orientation was reproduced, although to a lesser degree. This difference in orientation angles was caused by stronger rearward storm-relative flow in the nontornadic supercells, leading to a rearward shift of precipitation and, therefore, the enhanced KDP region within the supercell. Further, this resulted in an unfavorable rearward shift of the main negative buoyancy region, which led to an order of magnitude less baroclinic generation of circulation in the nontornadic simulations compared to tornadic simulations.
DOI: 10.5281/zenodo.6706730
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26

description: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: repackage-gbif-backbone.sh:<br> hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:<br> hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502<br> hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:<br> hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4<br> hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:<br> hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2<br> hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)
DOI: 10.6084/m9.figshare.20134994.v1
title: Database

description: Database
DOI: 10.25919/capn-mv43
title: Parkes observations for project P885 semester 2022APRS_07

description: We request a continued reduced monitoring cadence for the magnetars 1E 1547.0-5408 and Swift J1818.0-1607 at once every 10 days, to track their rotation, flux density, and polarisation. Swift J1818.0-1607 remains very dynamic, and the unique UWL capabilities promise new opportunities to probe still mysterious radio emission mechanisms in magnetars. Our timing results indicate that 1E 1547.0-5408 is currently a more stable rotator, supporting the diminished cadence. We will observe XTE J1810-197 and PSR J1622-4950 less frequently, to complement monitoring elsewhere. The overall request is 18 hours.
DOI: 10.5281/zenodo.6652128
title: Hourly average and marginal electricity mixes for Spain for assessing systems with variable load or power

description: This upload contains the Supplementary Information file and the underlying data as Excel-file for the referenced Journal article ("Hourly marginal electricity mixes and their relevance for assessing the environmental performance of installations with variable load or power") More specifically, it provides time series of the Spanish electricity generation mix for the year 2021 for energy system analysis (as used as basis for the underlying publication), including imports and environmental impacts of the hourly generation mix for all EF3.0 impact categories (<em>Marginal_Electricity_Results_Analysis_2021_V17.xls)</em> a spreadshhet calculator for determining the average and marginal hourly benefits of a generator with variable load (a PV installation, but the generation profile can be substituted by any other) <em>Load-Gen_Balancing__V15.xlsm</em> life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request to the main author.
DOI: 10.5281/zenodo.6652127
title: Hourly average and marginal electricity mixes for Spain for assessing systems with variable load or power

description: This upload contains the Supplementary Information file and the underlying data as Excel-file for the referenced Journal article ("Hourly marginal electricity mixes and their relevance for assessing the environmental performance of installations with variable load or power") More specifically, it provides time series of the Spanish electricity generation mix for the year 2021 for energy system analysis (as used as basis for the underlying publication), including imports and environmental impacts of the hourly generation mix for all EF3.0 impact categories (<em>Marginal_Electricity_Results_Analysis_2021_V17.xls)</em> a spreadshhet calculator for determining the average and marginal hourly benefits of a generator with variable load (a PV installation, but the generation profile can be substituted by any other) <em>Load-Gen_Balancing__V15.xlsm</em> life cycle inventory data for import into openLCA and re-use in combination with the ecoinvent databse (Version 3.7.1). Further details are available on request to the main author.
DOI: 10.6084/m9.figshare.20135003
title: Simultaneous imaging of ER-lysosome interactions

description: MAI-SIM data of lysosome-ER interactions imaged in simultaneous acquisition.
DOI: 10.5281/zenodo.6606333
title: VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

description: This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on github. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip. To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).
description: {"references": ["A. Konrad, J. McDonald and R. Villing, \"VGQ-CNN: Moving beyond fixed cameras and top-grasps for grasp quality prediction,\" to appear in International Joint Conference on Neural Networks (IJCNN), 2022.", "J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, \"Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,\" in Robotics: Science and Systems (RSS), 2017."]}
DOI: 10.5281/zenodo.6606332
title: VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

description: This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on github. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip. To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).
description: {"references": ["A. Konrad, J. McDonald and R. Villing, \"VGQ-CNN: Moving beyond fixed cameras and top-grasps for grasp quality prediction,\" to appear in International Joint Conference on Neural Networks (IJCNN), 2022.", "J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, \"Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,\" in Robotics: Science and Systems (RSS), 2017."]}
DOI: 10.5066/p95dygmu
title: Aerial images, digital elevation models, channel width maps, and river metrics along the Colorado River in Canyonlands National Park, Utah (1940 - 2018)

description: These data consist of rectified aerial photographs, measurements of active channel width, measurements of river and floodplain bathymetry and topography, and ancillary data. These data are specific to the corridor of the Colorado River in Canyonlands National Park between Potash, Utah and the confluence of the Green and Colorado Rivers near Spanish Bottom, Utah. The time period for these data are 1940 to 2018. The shapefile data are measurements of features of the active river channel and floodplains of the Colorado River. The raster data are aerial images and digital elevation models (DEMs) for segments of the Colorado River in Canyonlands National Park, Utah. The aerial images depict the river channel and adjacent floodplains for [...]
DOI: 10.6084/m9.figshare.20135036
title: Wood nutrient (i.e., P, K, Mg and Ca) concentrations of tropical trees from eastern Amazonia

description: This dataset contains wood nutrient concentrations, as wellas wood specific gravity and xylem parenchyma fractions, for 22 tree species of eastern Amazonia (French Guiana)
DOI: 10.5066/p9p6kwr5
title: Surface Geophysical Data Used for Mapping the Top of the Dockum Group on Cannon Air Force Base in Curry County, New Mexico, 2020

description: The hydrogeology of the Cannon Air Force Base (AFB) in Curry County, is being investigated to better understand the mechanics of groundwater flow in the area, including the identification of preferential groundwater flow paths. Mapping the top of the Dockum Group was done to help identify potential paleochannels that may have eroded the surface of the Dockum Group and subsequently filled in with coarse-grained sediments of the overlying Ogallala Formation. The Dockum Group is composed of sandstone, siltstone, mudstone, and shale that were originally deposited in fluvial and lacustrine environments (McGowen and others, 1979). The upper part of the Dockum Group is a thick layer of red clay referred to locally as the "red beds" and is a confining unit between the Ogallala aquifer and the Dockum aquifer. The lower part of the Ogallala Formation consists of heterogeneous sequences of gravel and coarse-grained sand that grades upward into sand, silt, and clay (Peckham and Ashworth, 1993). Paleochannels in the top part of the Dockum Group are believed to be preferential flow paths for groundwater in the area. The U.S. Geological Survey (USGS) in cooperation with the U.S. Air Force Civil Engineer Center (AFCEC) used surface geophysical resistivity methods to analyze the subsurface hydrogeology and map potential paleochannels within the Dockum Group, which can further aid in the understanding of the groundwater flow beneath Cannon AFB. This data release contains the results of 60 time-domain electromagnetic (TDEM) soundings and two electrical resistivity tomography (ERT) profiles collected on the AFB that will be used map the top of the Dockum Group.
DOI: 10.5281/zenodo.6707049
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26

description: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: repackage-gbif-backbone.sh:<br> hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:<br> hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502<br> hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:<br> hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4<br> hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:<br> hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2<br> hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)
DOI: 10.5281/zenodo.6706729
title: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF) - 2021-11-26

description: A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)<br> --- Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth. To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world. At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1]. This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes. Pre-process steps currently include: 1. reducing amount of columns<br> 2. reverse sort by id<br> 3. reverse sort by name <br> Contents<br> --- README:<br> this file repackage-gbif-backbone.sh:<br> script used to repackage GBIF Simple Backbone. backbone-current-simple.txt.gz:<br> original GBIF backbone archive gbif-backbone-by-name.tsv.gz:<br> two columns, gzipped, tab-separated text file with columns name, and id<br> reverse sorted by name gbif-backbone-by-name.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz gbif-backbone-by-id.tsv.gz:<br> 20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file<br> reverse sorted by id gbif-backbone-by-id.tsv.sha256:<br> sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz References<br> --- [1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2021-08-18.<br> [2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2021-08-18. <br> Hash URIs<br> ---<br> This publication includes the following content uris: repackage-gbif-backbone.sh:<br> hash://sha256/073ac5490252c4ccbbd4f516d391faebe62c9fde9e4d75ae870441a86c382527 backbone-current-simple.txt.gz:<br> hash://sha256/15cbfc038e666356af27248935f79e408ed51fd8c0b49a668fed8dbf72591502<br> hash://sha256/1f78788a4a046dcbcf1e36c7658a1e333ca60e7586a372238d58b938d91fde51 (uncompressed) gbif-backbone-by-name.tsv.gz:<br> hash://sha256/6e11ae9961a9498b60d4bdeb489d6c1f5da9c2732310edaecdc79bd287b79ef4<br> hash://sha256/934ce05dbd067abb209168bd1d9389f122d051e1b7374b5d757a12e86f8da9a5 (uncompressed) gbif-backbone-by-id.tsv.gz:<br> hash://sha256/c434c7d3622421b17dadcd119391b32a66edee59f484d4cab924d92fd17713e2<br> hash://sha256/e2cf9116a21966315b0482d391052223e21c8e916ae0c097dfd37bed017b815b (uncompressed)
DOI: 10.25547/jtes-sg10
title: My publication

description: &lt;p&gt;Test&lt;/p&gt;
DOI: 10.21227/n7b4-8p83
title: Objective analysis of available water contents

description: The dataset contains maps with the objective analysis (OA) fields of available water contents.  OA is based on ground-base and remote sensing (ASCAT) observations in the upper 10 and 20 cm soil layers for the period from September 10 to November 15, 2022.
DOI: 10.5281/zenodo.6506282
title: Cajas Lake Chemistry Data - Survey 2014-2015

description: Lake water chemistry data and lake characteristics used in the publication: Mosquera, P.V.; Hampel, H.; Vázquez, R.F.; Catalan, J. Water chemistry variation in tropical high-mountain lakes on old volcanic bedrocks. Limnology & Oceanography 2022, in press.
DOI: 10.5281/zenodo.6506281
title: Cajas Lake Chemistry Data - Survey 2014-2015

description: Lake water chemistry data and lake characteristics used in the publication: Mosquera, P.V.; Hampel, H.; Vázquez, R.F.; Catalan, J. Water chemistry variation in tropical high-mountain lakes on old volcanic bedrocks. Limnology & Oceanography 2022, in press.
DOI: 10.18164/d92ed3cf-4ba0-4473-beec-357ec45b0e78
title: Whitehorse, Yukon (CYXY) Enhanced Site for Canadian Arctic Weather Science (CAWS) Project
title: Whitehorse, Yukon (CYXY) – Site amélioré pour le Projet de sciences météorologiques dans l’Arctique canadien (CAWS)

description: The Whitehorse enhanced meteorological site is located at Erik Nielsen Whitehorse International Airport (CYXY, 60°43N, 135°04W). This scientific observation site aims to provide a better understanding of the region’s weather conditions to help improve local weather forecasts across the Canadian Arctic and sub-Arctic. For instance, new radar technology is being tested at this site to assess its suitability for detecting and analyzing this region’s weather systems. A companion site in Iqaluit NU (CYFB, 63°45N, 68°33W), in the eastern Canadian Arctic, has also been established. It is important to note that Iqaluit is located above the tree line, while Whitehorse is below, resulting in a significantly more humid atmosphere at Whitehorse suggesting different weather monitoring requirements. The Whitehorse site includes an X-band radar, a Doppler lidar, ceilometers, radiation flux and precipitation sensors, and others. Data collection for a subset of instruments at Whitehorse began in January 2018.
description: Le site météorologique amélioré de Whitehorse est situé à l’aéroport international Erik Nielsen de Whitehorse (CYXY, 60°43N, 135°04O). Le site d’observation scientifique vise à fournir une meilleure compréhension des conditions météorologiques qui prévalent dans la région afin de contribuer à l’amélioration des prévisions météorologiques locales dans la région arctique et subarctique du Canada. Par exemple, une nouvelle technologie radar est mise à l’essai à ce site afin d’en évaluer la capacité à détecter et à analyser les systèmes météorologiques de cette région. Un site compagnon à Iqaluit, au Nunavut (CYFB, 63°45N, 68°33O), dans l’Est de l’Arctique canadien, a également été établi. Il est important de souligner qu’Iqaluit est située au-delà de la limite des arbres, alors que Whitehorse se trouve sous cette limite, ce qui entraîne un climat plus humide à Whitehorse et impose des exigences différentes relativement à la surveillance des conditions météorologiques. Le site de Whitehorse comprend un radar fonctionnant dans la bande X, un lidar à effet Doppler, des télémètres de nuages, des capteurs de précipitations et de flux énergétiques, ainsi que d’autres instruments. La collecte de données destinées à un sous-ensemble d’instruments à Whitehorse a commencé en janvier 2018.