cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48577/jpl.8fdbzp
title: Autonomous Robotics is Driving Perseverance Rover’s Progress on Mars

description: NASA's Perseverance Mars rover is making unprecedented use of Robotic Autonomy to achieve its mission goals. During its first two years of operation, the self-driving Autonomous Navigation system (AutoNav) has been used to evaluate 88.7% of its 17.7km distance travelled, more than any other Mars mission. AutoNav has set multiple Planetary Rover records, including greatest distance driven without human review (699.9m) and greatest single-day drive distance (347.7m). The Autonomous Exploration for Gathering Increased Science system (AEGIS) provides the science team with targeted mineralogy knowledge right after a drive into unknown terrain, without requiring ground-in-the-loop processing to select scientifically interesting targets. And a new OnBoard Planner (OBP) scheduling capability planned for operational use in August 2023 has the potential to reduce energy usage by up to 20%, and complete drive and arm-contact science campaigns in 25% fewer days on Mars. This paper presents the AutoNav, AEGIS, and OBP capabilities used on Perseverance, and compares them against similar systems deployed on other missions.
DOI: 10.48577/jpl.imq1wv
title: An f/0.27 High-Gain Lens-Antenna for Ultra-Small Platforms at THz Frequencies

description: The development of a low focal number and low mass lens antenna is presented that enables Terahertz (THz) spectroscopy applications on ultra-compact platforms. The antenna operates efficiently over a 20% fractional bandwidth, from 450 GHz to 550 GHz, with a gain of 50 dBi at 500 GHz. The antenna consists of a hyperbolic silicon lens that is placed in a record low focal number configuration (f/# = 0.27) with respect to an advanced waveguide feed. An incident field-matching analysis is applied to investigate the optimal feed radiation pattern that maximizes the lens aperture efficiency, which would result in a 20% increase in aperture efficiency (&gt;80%) with respect to a standard open ended waveguide (&lt;60% aperture efficiency). A multi-layer leaky-wave (LW) stratification is quasi-analytically optimized to approximate the optimal feeding pattern, resulting in a &gt; 70% lens aperture efficiency. An example LW stratification is synthesized using silicon micro-machining technology and is fully characterized in combination with the dielectric lens.
DOI: 10.6084/m9.figshare.23681688
title: One

description: raw datd
DOI: 10.6084/m9.figshare.23681688.v1
title: One

description: raw datd
DOI: 10.5281/zenodo.8118182
title: CICE tx1 JRA55do Forcing Data by year - 2023.07.03

description: These files contain the tx1 JRA55do forcing data by year for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1). These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.5281/zenodo.8118183
title: CICE tx1 JRA55do Forcing Data by year - 2023.07.03

description: These files contain the tx1 JRA55do forcing data by year for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1). These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.5281/zenodo.8118061
title: CICE gx1 JRA55do Forcing Data by year - 2023.07.03

description: These files contain the gx1 JRA55do forcing data by year for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1). These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.5281/zenodo.8117762
title: CICE gx3 JRA55do Forcing Data - 2023.07.03

description: This file contains the gx3 JRA55do forcing data for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1). These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.5281/zenodo.8117763
title: CICE gx3 JRA55do Forcing Data - 2023.07.03

description: This file contains the gx3 JRA55do forcing data for CICE. The latest information about CICE forcing data and files can be found at the GitHub Resource Index: https://github.com/CICE-Consortium/About-Us/wiki/Resource-Index under the "Input Data" link. This is a dataset remapped from the original JRA55do dataset (Tsujino et al. 2018; https://doi.org/10.1016/j.ocemod.2018.07.002; version 1.5.0.1). These data are provided by the CICE Consortium (https://github.com/CICE-Consortium).
DOI: 10.6084/m9.figshare.23681706
title: Interactive NSGA-III for an interactive search-based PLA design approach

description: Interactive NSGA-III for an interactive search-based PLA design approach
DOI: 10.6084/m9.figshare.23681706.v1
title: Interactive NSGA-III for an interactive search-based PLA design approach

description: Interactive NSGA-III for an interactive search-based PLA design approach
DOI: 10.21227/23sj-wv86
title: Original ultrasound images and Hough transformed images

description: The morphological characteristics of skeletal muscles, such as fascicle orientation, fascicle length, and muscle thickness, contain valuable mechanical information that aids in understanding muscle contractility and excitation due to commands from the central nervous system. Ultrasound (US) imaging, a non-invasive measurement technique, has been employed in clinical research to provide visualized images that capture morphological characteristics. However, accurately and efficiently detecting the fascicle in US images is challenging. In the current study, we employed computer vision techniques based on shallow/light neural networks (SNN) to detect the locations and orientations of fascicles in US images. To enhance the linear/tubular feature of the fascicle, we developed a weighted Hough transform algorithm to transform the original gray-scaled images to a weighted Hough space (WHS), which is expected to reduce the demand in layers for the neural network (NN) design. Compared to many baseline methods, including Vgg, ResNet, AlexNet, Unet, and UltraTrack, the proposed SNN was found to be more accurate in detection. Among the SNN methods, a single-layer convolutional neural network outperformed the others. Our study found that WHS usually improved the detection accuracy for SNN models, and the regression with an $\mathcal{L}_{2}$ regularization provided satisfactory detection even without WHS, which is suitable for real-time applications. Moreover, our proposed methods are robust to disturbances like data loss, time delay, and out-of-order, as they do not require the image frames to be closely interlinked or temporally related.  The path of the file is stated in the code, which can be found here: https://github.com/XBao06093030/SCNN_for_Fascicle_Detection/blob/main/SCNN_sample.ipynb
DOI: 10.5281/zenodo.8145356
title: CTIPe model simulations

description: Simulation output such as atomic oxygen to molecular nitrogen ratio (O/N2), peak electron density (NmF2), and total electron content (TEC) from the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model for 2019-2021. The data files are in support of the publication “Ionospheric response to solar EUV radiation variations using GOLD observations and the CTIPe model”
DOI: 10.5281/zenodo.8145355
title: CTIPe model simulations

description: Simulation output such as atomic oxygen to molecular nitrogen ratio (O/N2), peak electron density (NmF2), and total electron content (TEC) from the Coupled Thermosphere Ionosphere Plasmasphere electrodynamics (CTIPe) model for 2019-2021. The data files are in support of the publication “Ionospheric response to solar EUV radiation variations using GOLD observations and the CTIPe model”
DOI: 10.5281/zenodo.8145094
title: Graph Positional and Structural Encodings (model weights and precomputed encodings)

description: Model weights for pre-trained GPSE model and selected pre-computed GPSE encodings using the model pre-trained on MolPCBA
DOI: 10.5281/zenodo.8145095
title: Graph Positional and Structural Encodings (model weights and precomputed encodings)

description: Model weights for pre-trained GPSE model and selected pre-computed GPSE encodings using the model pre-trained on MolPCBA
DOI: 10.5281/zenodo.8145344
title: Graph Positional and Structural Encodings (model weights and precomputed encodings)

description: Model weights for pre-trained GPSE model and selected pre-computed GPSE encodings using the model pre-trained on MolPCBA
DOI: 10.6084/m9.figshare.19632769
title: Evidence-based-practice profile among physiotherapists: a cross-sectional survey in France

description: To establish an inventory of the Evidence-Based Practice (EBP) among physiotherapists (PT) in France assessing EBP knowledge, perception, and utilisation in practice. A cross-sectional survey was performed using a French translation of the EBP<sup>2</sup> questionnaire including the 5 EBP domains (Relevance, Terminology, Confidence, Practice and Sympathy). Participants completed the online survey from February 26th to August 31st, 2021. Scores were summarised by EBP domain for each participant. Pairwise Pearson’s correlations between domains scores and a hierarchical clustering on principal component analysis were conducted. In total, 542 participants were included in the analysis. Majority of participants were male with median age of 30 years (IQR: 26–36 years). Regarding EBP domain scores, PT perceived relevance of EBP but had difficulty to apply it in practice. Furthermore, positive correlations exist between the 5 domains. Participants were divided into three clusters. Those reporting poor scientific analysis skills and high barriers were older, less graduated, less trained in EBP and graduated earlier. French PT perceived research to be important in their current practice and had a favourable opinion of EBP. However, they reported a lack of confidence, difficulty to understand terminology and to use it in practice. These findings and their profiles may help to improve PT’s EBP education in France.
DOI: 10.5281/zenodo.10081251
title: Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology

description: This dataset includes the current version of the indicative distribution maps and profiles for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology (v2.1). Please refer to Keith et al. (2020) and Keith et al. (2022).


The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group.


Maps are indicative of global distribution patterns and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications.


We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into functional biomes (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.
DOI: 10.5281/zenodo.3546513
title: Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology

description: This dataset includes the current version of the indicative distribution maps and profiles for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology (v2.1). Please refer to Keith et al. (2020) and Keith et al. (2022).


The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group.


Maps are indicative of global distribution patterns and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications.


We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into functional biomes (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.
DOI: 10.5281/zenodo.4018314
title: Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology

description: This dataset includes the original version of the indicative distribution maps and profiles for <strong>Ecosystem Functional Groups</strong> - Level 3 of IUCN Global Ecosystem Typology (v2.0). Please refer to Keith <em>et al.</em> (2020). The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group. Maps are indicative of global distribution patterns are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications. We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into<strong> functional biomes</strong> (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.
description: This dataset is part of the publication: Keith, D.A., Ferrer-Paris, J.R., Nicholson, E. and Kingsford, R.T. (eds.) (2020). The IUCN Global Ecosystem Typology 2.0: Descriptive profiles for biomes and ecosystem functional groups. Gland, Switzerland: IUCN. DOI:10.2305/IUCN.CH.2020.13.en. The PLuS Alliance supported a workshop in London to initiate development. DAK, EN, RTK, JRFP, JAR & NJM were supported by ARC Linkage Grants LP170101143 and LP180100159 and the MAVA Foundation. The IUCN Commission on Ecosystem Management supported travel for DAK to present aspects of the research to peers and stakeholders at International Congresses on Conservation Biology in 2017 and 2019, and at meetings in Africa, the middle east and Europe.
DOI: 10.5281/zenodo.5089835
title: Indicative distribution maps for Ecosystem Functional Groups - Level 3 of IUCN Global Ecosystem Typology

description: This dataset includes the original version of the indicative distribution maps and profiles for <strong>Ecosystem Functional Groups</strong> - Level 3 of IUCN Global Ecosystem Typology (v2.0). Please refer to Keith <em>et al.</em> (2020). The descriptive profiles provide brief summaries of key ecological traits and processes for each functional group of ecosystems to enable any ecosystem type to be assigned to a group. Maps are indicative of global distribution patterns are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Most maps were prepared using a coarse-scale template (e.g. ecoregions), but some were compiled from higher resolution spatial data where available (see details in profiles). Higher resolution mapping is planned in future publications. We emphasise that spatial representation of Ecosystem Functional Groups does not follow higher-order groupings described in respective ecoregion classifications. Consequently, when Ecosystem Functional Groups are aggregated into<strong> functional biomes</strong> (Level 2 of the Global Ecosystem Typology), spatial patterns may differ from those of biogeographic biomes. Differences reflect the distinctions between functional and biogeographic interpretations of the term, “biome”.
description: This dataset is part of the publication: Keith DA, Ferrer-Paris JR, Nicholson E, Kingsford RT (Eds.) (2020) 'The IUCN Global Ecosystem Typology v2.0: Descriptive profiles for Biomes and Ecosystem Functional Groups'. The International Union for the Conservation of Nature (IUCN), Gland. DOI:10.2305/IUCN.CH.2020.13.en. The PLuS Alliance supported a workshop in London to initiate development. DAK, EN, RTK, JRFP, JAR & NJM were supported by ARC Linkage Grants LP170101143 and LP180100159 and the MAVA Foundation. The IUCN Commission on Ecosystem Management supported travel for DAK to present aspects of the research to peers and stakeholders at International Congresses on Conservation Biology in 2017 and 2019, and at meetings in Africa, the middle east and Europe.
DOI: 10.25582/data.2022-03.2453331/2193985
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477617/2193986
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-05.2516500/2193987
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-04.2477713/2193988
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-05.2517310/2193989
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2453281/2193990
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477608/2193991
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-04.2477615/2193992
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-03.2453359/2193993
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453218/2193994
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453306/2193995
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453272/2193996
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2460594/2193997
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2450277/2193998
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2459956/2193999
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2450207/2194000
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-05.2516477/2194001
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2450282/2194002
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477672/2194003
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-03.2450167/2194004
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453265/2194005
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-05.2516485/2194006
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2453357/2194007
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-05.2518696/2194008
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-05.2518686/2194009
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2460035/2194010
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2461376/2194011
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2450217/2194012
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477638/2194013
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-04.2477666/2194014
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-03.2453261/2194015
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477684/2194016
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-05.2518633/2194017
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-05.2518697/2194018
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2450237/2194019
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2450300/2194020
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453253/2194021
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2464567/2194022
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453257/2194023
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453232/2194024
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2453271/2194025
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-03.2461429/2194026
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-05.2518670/2194027
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-03.2453287/2194028
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-05.2516644/2194029
title: Data for EMSL Project 60189 from May 2022
DOI: 10.25582/data.2022-04.2477674/2194030
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-03.2461223/2194031
title: Data for EMSL Project 60189 from March 2022
DOI: 10.25582/data.2022-04.2477569/2194032
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-04.2477710/2194033
title: Data for EMSL Project 60189 from April 2022
DOI: 10.25582/data.2022-03.2453275/2194034
title: Data for EMSL Project 60189 from March 2022