cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/1cbx-1q81
title: Measurement and Simulation of Human Sitting and Standing Movement Biomechanics

description: Previous studies of robotic lower-limb exoskeletons and prostheses with regenerative actuators have focused exclusively on level-ground walking applications. Here we analyzed the lower-limb joint mechanical power during stand-to-sit movements using inverse dynamic simulations to estimate the biomechanical energy available for regeneration. Nine subjects performed 20 sitting and standing movements while lower-limb kinematics and ground reaction forces were measured. Subject-specific body segment parameters were estimated using parameter identification. Joint mechanical power was calculated from net joint torques and rotational velocities and numerically integrated to determine joint biomechanical energy. The hip produced the largest peak negative mechanical power (1.8 ± 0.5 W/kg), followed by the knee (0.8 ± 0.3 W/kg) and ankle (0.2 ± 0.1 W/kg). Negative mechanical work from the hip, knee, and ankle per stand-to-sit movement were 0.35 ± 0.06 J/kg, 0.15 ± 0.08 J/kg, and 0.02 ± 0.01 J/kg, respectively. Assuming known regenerative actuator efficiencies (i.e., maximum 63%), robotic lower-limb exoskeletons and prostheses could theoretically regenerate ~26 Joules of electrical energy while sitting down, compared to ~19 Joules per walking stride. Given that these regeneration performance calculations are based on healthy young adults, future research should involve seniors and/or rehabilitation patients to better estimate the biomechanical energy available for regeneration in individuals with mobility impairments.Reference:Laschowski B, Razavian RS, and McPhee J. (2020). Simulation of Stand-to-Sit Biomechanics for Design of Lower-Limb Exoskeletons and Prostheses with Energy Regeneration. bioRxiv. DOI: 10.1101/801258.
DOI: 10.21227/mps3-aa84
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.21227/kbyj-tj48
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.21227/74ep-7n17
title: Wearable RGB Camera Images of Human Locomotion Environments

description: Drawing inspiration from autonomous vehicles, using future environment information could improve the control of wearable biomechatronic devices for assisting human locomotion. To the authors knowledge, this research represents the first documented investigation using machine vision and deep convolutional neural networks for environment recognition to support the predictive control of robotic lower-limb prostheses and exoskeletons. One participant was instrumented with a battery-powered, chest-mounted RGB camera system. Approximately 10 hours of video footage were experimentally collected while ambulating throughout unknown outdoor and indoor environments. The sampled images were preprocessed and individually labelled. A deep convolutional neural network was developed and trained to automatically recognize three walking environments: level-ground, incline staircases, and decline staircases. The environment recognition system achieved 94.85% overall image classification accuracy. Extending these preliminary findings, future research should incorporate other environment classes (e.g., incline ramps) and integrate the environment recognition system with electromechanical sensors and/or surface electromyography for automated locomotion mode recognition. The challenges associated with implementing deep learning on wearable biomechatronic devices are discussed.Reference: Laschowski B, McNally W, McPhee J, and Wong A. (2019). Preliminary Design of an Environment Recognition System for Controlling Robotic Lower-Limb Prostheses and Exoskeletons. IEEE International Conference on Rehabilitation Robotics (ICORR), pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.18112/openneuro.ds003363.v1.0.1
title: GSP1000_Connectome
DOI: 10.18112/openneuro.ds003368.v1.0.0
title: GSP1000_Connectome
DOI: 10.25387/g3.13143596
title: Supplemental Material for Liu et al., 2020

description: <b>Figure S1. Principal component analysis.</b><b><br></b><b>Figure S2. Comparison of gene expression changes. </b><b><br></b><b><br></b><b>Figure S3. WormCat analysis of genes with significant changes in regulation upon exposure to <i>E. faecalis </i>from previous studies.</b><b><br></b><b><br></b><b>Figure S4. Heat map of genes encoding hedgehog-like proteins that changed significantly in at least one condition.</b><b><br></b><b><br></b><b>Figure S5. Brood size.</b><b><br></b><b>Table S1. All RNA seq data.</b><b>Table S2. WormCat enrichment analysis of genes with significant changes in regulation (|log2FC| &gt;= 1, Q<i> &lt; </i>0.05). </b><b><br></b><b>Table S3. KEGG pathway enrichment analysis.</b><b><br></b>
DOI: 10.25387/g3.13143596.v1
title: Supplemental Material for Liu et al., 2020

description: <b>Figure S1. Principal component analysis.</b><b><br></b><b>Figure S2. Comparison of gene expression changes. </b><b><br></b><b><br></b><b>Figure S3. WormCat analysis of genes with significant changes in regulation upon exposure to <i>E. faecalis </i>from previous studies.</b><b><br></b><b><br></b><b>Figure S4. Heat map of genes encoding hedgehog-like proteins that changed significantly in at least one condition.</b><b><br></b><b><br></b><b>Figure S5. Brood size.</b><b><br></b><b>Table S1. All RNA seq data.</b><b>Table S2. WormCat enrichment analysis of genes with significant changes in regulation (|log2FC| &gt;= 1, Q<i> &lt; </i>0.05). </b><b><br></b><b>Table S3. KEGG pathway enrichment analysis.</b><b><br></b>
DOI: 10.25810/xgdx-5358
title: Dataset associated with Floodplain large wood and organic matter jam formation after a large flood: investigating the influence of floodplain forest stand characteristics and river corridor morphology 

description: This dataset is associated with the article in review titled, "Floodplain large wood and organic matter jam formation after a large flood: investigating the influence of floodplain forest stand characteristics and river corridor morphology." The controls on LW and CPOM deposition on floodplains have rarely been assessed, and there are relatively few studies that explore the bi-directional interactions between wood, standing trees, and geomorphic processes. We use field data from West Creek, Colorado, to assess the influence of river corridor morphology and forest stand density on the depositional patterns of floodplain LW and CPOM accumulations (i.e., jams) resulting from a large flood. Average LW loads per area (mean ± SE = 678.6 ± 192.3 m3 ha-1; median = 382.1 m3 ha-1) were much higher than most previously published values from other environments, pointing to the importance of large disturbances delivering LW to floodplains. CPOM loads per area averaged 10.7 ± 4.8 m3 ha-1 (median = 5.0 m3 ha-1), and LW jam count per area (70.4 ± 0.2 jams ha-1) was higher than CPOM jam count per area (19.0 ± 3.7 jams ha-1). We find that LW jams decreased in size with distance from and height above the channel, but that CPOM jams did not. Steeper reaches contained lower amounts of LW load per area, and as forest stand density increased, the number of CPOM jams per area increased. A majority of jams were pinned by standing trees. LW and CPOM provide habitat and nutrients to river corridor ecosystems and influence geomorphic processes. Floodplain LW and CPOM provide an opportunity to promote ecological processes in river corridors while reducing potential hazards caused by in-channel LW.
DOI: 10.6084/m9.figshare.13217444
title: Supplemental Data_Lynn

description: Supplemental Data and Methods
DOI: 10.6084/m9.figshare.13217444.v1
title: Supplemental Data_Lynn

description: Supplemental Data and Methods
DOI: 10.21227/3xyw-5s03
title: AURORA, A multi sensor dataset for robotic ocean exploration

description: The current maturity of autonomous underwater vehicles (AUVs) has made their deployment practical and cost-effective, such that many scientific, industrial and military applications now include AUV operations. However, the logistical difficulties and high costs of operating at-sea are still critical limiting factors in further technology development, the benchmarking of new techniques and the reproducibility of research results. To overcome this problem, we present a freely available dataset suitable to test control, navigation, sensor processing algorithms and others tasks. This dataset combines AUV navigation data, side-scan sonar, multibeam echosounder data and seafloor camera image data, and associated sensor acquisition meta-data to provide a detailed characterisation of surveys carried out by the National Oceanography Centre (NOC) in the Greater Haig Fras Marine Conservazion Zone (MCZ) of the U.K in 2015.
DOI: 10.5067/modis/am1ephne_nrt.006
title: AM1EPHNE Extrapolated Orbital Data
DOI: 10.6084/m9.figshare.12568793
title: Korarchaeota gene table

description: Table of select genes identified in Korarchaeota MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12568127.v1
title: FeGenie assembly annotation

description: FeGenie annotations of Brothers volcano assemblies. Only annotations passing the bitscore cutoff were retained.<br>
DOI: 10.6084/m9.figshare.12559103.v1
title: MAG translations individual, S009-S142

description: Open reading frames predicted for Brothers volcano MAGs using Prokka. Each file contains an individual MAG translation. MAGs from the following samples are included: S009, S010, S011, S012, S013, S014, S015, S016, S017, S139, S140, S141, S142.<br>
DOI: 10.6084/m9.figshare.12558833.v1
title: S21 Bacteria KAAS annotations

description: Annotations of S21 bacterial MAGs predicted using KAAS.<br>
DOI: 10.6084/m9.figshare.12558830.v1
title: S21 Archaea arCOG and KAAS annotations

description: Annotations of the S21_7 Woesearcheota and a reference genome predicted using arCOGs and KAAS. Annotations flagged as inconsistent are marked in red and are not included in the final summary chart. Reasoning for each flagged gene is explained in the Notes column.<br>
DOI: 10.6084/m9.figshare.12551528.v1
title: Aquificae KAAS annotations

description: Annotations of Aquificae MAGs and publicly available reference genomes predicted using KAAS. An additional annotation file shows KAAS annotations of two <i>Desulfurobacterium aclAB </i>genes that were re-classified in KAAS using <i>Desulfurobacterium</i> as the only reference input parameter.<br>
DOI: 10.5517/ccdc.csd.cc249t7j
title: CCDC 1975637: Experimental Crystal Structure Determination

description: Related Article: Qi-Jun Yao, Pei-Pei Xie, Yong-Jie Wu, Ya-Lan Feng, Ming-Ya Teng, Xin Hong, Bing-Feng Shi|2020|J.Am.Chem.Soc.|142|18266|doi:10.1021/jacs.0c09400
DOI: 10.5517/ccdc.csd.cc249tdp
title: CCDC 1975642: Experimental Crystal Structure Determination

description: Related Article: Qi-Jun Yao, Pei-Pei Xie, Yong-Jie Wu, Ya-Lan Feng, Ming-Ya Teng, Xin Hong, Bing-Feng Shi|2020|J.Am.Chem.Soc.|142|18266|doi:10.1021/jacs.0c09400
DOI: 10.5517/ccdc.csd.cc22k561
title: CCDC 1923122: Experimental Crystal Structure Determination

description: Related Article: Sina K. Goetzfried, Caroline M. Gallati, Monika Cziferszky, Radu A. Talmazan, Klaus Wurst, Klaus R. Liedl, Maren Podewitz, Ronald Gust|2020|Inorg.Chem.|59|15312|doi:10.1021/acs.inorgchem.0c02298
DOI: 10.5517/ccdc.csd.cc24hsl1
title: CCDC 1981383: Experimental Crystal Structure Determination

description: Related Article: Mengna Tao, Wenbo Li, Junliang Zhang|2020|Chem.Commun.|56|13125|doi:10.1039/D0CC04996D
DOI: 10.5517/ccdc.csd.cc24hsn3
title: CCDC 1981385: Experimental Crystal Structure Determination

description: Related Article: Mengna Tao, Wenbo Li, Junliang Zhang|2020|Chem.Commun.|56|13125|doi:10.1039/D0CC04996D
DOI: 10.5517/ccdc.csd.cc24sxnj
title: CCDC 1990158: Experimental Crystal Structure Determination

description: Related Article: Vladislav V. Krisyuk, Samara Urkasym kyzy, Tatyana V. Rybalova, Ilya V. Korolkov, Sergey V. Sysoev, Tatyana P. Koretskaya, Boris M. Kuchumov, Asiya E. Turgambaeva|2020|Polyhedron|191|114806|doi:10.1016/j.poly.2020.114806
DOI: 10.5281/zenodo.4243023
title: University of Denver Collections as Data - HTR Train and Validation Set JCRS_2020_5_27

description: HTR_Train_Set_JCRS_2020_5_27.zip <br> Description
DOI: 10.5281/zenodo.4242885
title: University of Denver Collections as Data - HTR Train and Validation Set JCRS_2020_5_27

description: HTR_Train_Set_JCRS_2020_5_27.zip <br> Description
DOI: 10.5281/zenodo.4266857
title: Dataset for Identity Development and Integration through Interest paper

description: Transcripts.
DOI: 10.5281/zenodo.4266856
title: Dataset for Identity Development and Integration through Interest paper

description: Transcripts.
DOI: 10.25345/c5v20z
title: MassIVE MSV000086433 - Host interactome of select SARS-CoV-2 ORFs in human bronchial epithelial cells (16HBEo-).
title: Host interactome of select SARS-CoV-2 ORFs in human bronchial epithelial cells (16HBEo-).
DOI: 10.6084/m9.figshare.13215500.v1
title: Genome sequence assembly of Passion Fruit (Passiflora edulis Sims)

description: The genome sequence assembly of Passion Fruit (Passiflora edulis Sims). For the passion fruit genome sequencing was used a semi-inbred plant of self-compatible accession “CPAC MJ-M-21” (Roxo Jaboticaba) previously evaluated for maximum homozygosity via genotyping with microsatellites. This dataset contains the FASTA formatted file and many of basic contig and scaffold-level statistics calculated on the basis of the script assemblathon_stats.pl (github.com/ucdavis-bioinformatics/assemblathon2-analysis).<br>
DOI: 10.21227/k228-8t82
title: another_submission

description: Just another submission
DOI: 10.6084/m9.figshare.13217558
title: Genome-wide triallelic single nucleotide polymorphisms as genetic markers to separate three chemotypes of cannabis strains

description: The whole-genomes of 23 cannabis strains marketed in Canada were sequenced, aligned to a reference genome assembly ASM23057v4 of a drug type strain Purple Kush (PK) in the NCBI BioProject database under accession number PRJNA73819. Here we deposited the SNP files for these 23 strains, which were used to study the genetic structure and identify genetic markers to differentiate three chemotypes strains.
DOI: 10.6084/m9.figshare.13217558.v2
title: Genome-wide triallelic single nucleotide polymorphisms as genetic markers to separate three chemotypes of cannabis strains

description: The whole-genomes of 23 cannabis strains marketed in Canada were sequenced, aligned to a reference genome assembly ASM23057v4 of a drug type strain Purple Kush (PK) in the NCBI BioProject database under accession number PRJNA73819. Here we deposited the SNP files for these 23 strains, which were used to study the genetic structure and identify genetic markers to differentiate three chemotypes strains.
DOI: 10.6084/m9.figshare.13217558.v1
title: Genome-wide triallelic single nucleotide polymorphisms as genetic markers to separate three chemotypes of cannabis strains

description: whole-genome of 23 cannabis strains marketed in Canada were sequenced, aligned to a reference genome assembly ASM23057v4 of a drug type strain Purple Kush (PK) in the NCBI BioProject database under accession number PRJNA73819. Here we deposited the SNP files for these 23 strains, which were used to study the genetic structure and identify genetic markers to differentiate three chemotypes strains.
DOI: 10.21227/y0ne-yg96
title: another_submission2

description: just another one
DOI: 10.6084/m9.figshare.12415325
title: Short-term impact of arbuscular mycorrhizal fungi and different phosphorus levels on selected soil properties and growth components of two dry bean cultivars in phosphorus-deficient soils

description: Low yields of dry beans are often associated with lack of inoculation with biofertilisers and deficiencies in soil phosphorus (P). A field experiment was conducted to investigate the effect of arbuscular mycorrhizal fungi (AMF) and P fertiliser on dry bean growth and soil properties in Limpopo province, South Africa. The experiment was laid out in a split-split plot design. The main treatments were: (a) dry bean cultivars (red-speckled and small-white haricot), (b) sub-plot treatments: AMF inoculation (mycoroots) levels (with and without inoculation) and (c) sub-sub plot treatments: different levels of P (0, 20, 40 and 60 kg P ha<sup>−1</sup>). The soil and plant growth parameters were analysed using Analysis of Variance (<i>p </i>= 0.05). The interaction of cultivar and P levels indicated that P application improved growth and yield of both cultivars and also suggested that the optimum P level for red-speckled beans was 40 kg ha<sup>–1</sup> while that of small-white haricot beans was 30 kg ha<sup>−1</sup>. Red-speckled beans showed greater growth and yield than the small-white haricot beans. There was no clear impact on soil properties in the short term. These results indicate that raising the soil P levels may improve dry bean growth and yield, and red-speckled beans could be a better option for farmers than small-white haricot beans.
DOI: 10.6084/m9.figshare.12854432
title: Relationships between commonly-used South African and international soil phosphorus extraction tests on pristine and cultivated soils

description: The availability of soil phosphorus (P) norms for various plant available P tests is limited due to the cost of establishing these norms. Consequently a need exists for a means of converting between the values of commonly used South-African and international soil P extraction tests for advisory purposes or comparing the results of soil P studies from different regions. Therefore, the aim of this study was to compare and correlate eight widely used soil P extraction tests on a wide variety of South African soils (48 pristine and cultivated samples). It was found that the relative effectiveness of total P extraction was a follows: 1:2 H<sub>2</sub>O &lt; Olsen &lt; Colwell &lt; Bray I &lt; Ambic I &lt; Bray II &lt; Mehlich II ≈ citric acid. The strongest linear correlations were observed between Mehlich III, Bray I and Bray II (<i>r</i> = 0.92–0.99, <i>p</i> &lt; 0.001), Ambic I and citric acid (<i>r</i> = 0.96, <i>p</i> &lt; 0.001), and Olsen and Bray I, Bray II and Mehlich III (<i>r</i> = 0.85–0.89, <i>p</i> &lt; 0.001). Thus, it is possible to directly convert between the soil P values of these tests with more confidence using linear regression equations derived in this study.
DOI: 10.6084/m9.figshare.12415325.v2
title: Short-term impact of arbuscular mycorrhizal fungi and different phosphorus levels on selected soil properties and growth components of two dry bean cultivars in phosphorus-deficient soils

description: Low yields of dry beans are often associated with lack of inoculation with biofertilisers and deficiencies in soil phosphorus (P). A field experiment was conducted to investigate the effect of arbuscular mycorrhizal fungi (AMF) and P fertiliser on dry bean growth and soil properties in Limpopo province, South Africa. The experiment was laid out in a split-split plot design. The main treatments were: (a) dry bean cultivars (red-speckled and small-white haricot), (b) sub-plot treatments: AMF inoculation (mycoroots) levels (with and without inoculation) and (c) sub-sub plot treatments: different levels of P (0, 20, 40 and 60 kg P ha<sup>−1</sup>). The soil and plant growth parameters were analysed using Analysis of Variance (<i>p </i>= 0.05). The interaction of cultivar and P levels indicated that P application improved growth and yield of both cultivars and also suggested that the optimum P level for red-speckled beans was 40 kg ha<sup>–1</sup> while that of small-white haricot beans was 30 kg ha<sup>−1</sup>. Red-speckled beans showed greater growth and yield than the small-white haricot beans. There was no clear impact on soil properties in the short term. These results indicate that raising the soil P levels may improve dry bean growth and yield, and red-speckled beans could be a better option for farmers than small-white haricot beans.