cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.12576935
title: Breakdown of selected MAG annotations from GhostKoala and HydDB

description: Selected annotations from GhostKoala and HydDB discussed in publication. Results are broken down by gene, with one sheet (labeled in the format <b>gene_hits</b>) showing all annotation hits for that particular gene and a second sheet (labeled in the format <b>gene_MAG_counts</b>) showing the breakdown of MAGs with hit(s) for that specific gene. The following genes are included in the analysis: <i>dfx, SOD2, coxA, cydA, coxL, aclA, rbcL/cbbL, </i>[NiFe] Group 1b hydrogenase gene and [NiFe] Group 1d hydrogenase gene.<br>
DOI: 10.6084/m9.figshare.12551438.v1
title: MAG translations (by site)

description: Open reading frames (ORFs) predicted for Brothers volcano MAGs using Prokka. Each document contains MAG translations from one site, with MAG numbers indicated in the ORF headers.<br>
DOI: 10.6084/m9.figshare.12551984.v1
title: Aigarchaeota KAAS annotations

description: Annotations of Aigarchaeota MAGs and publicly available reference genomes, predicted with KAAS.<br>
DOI: 10.6084/m9.figshare.12551570.v1
title: Hydrothermarchaeota KAAS annotations

description: Annotations of Hydrothermarchaeota MAGs and publicly available reference genomes predicted with KAAS.<br>
DOI: 10.6084/m9.figshare.12551675.v1
title: Hydrothermarchaeota gene table

description: Table of select genes identified in Hydrothermarchaeota MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12568082.v1
title: Altiarchaeota Prokka annotations

description: Annotation of Altiarchaeota MAG S014_51 predicted using Prokka. This annotation was used for comparison with existing, publicly available Prokka annotations of Alti-2 Altiarchaeota.<br>
DOI: 10.6084/m9.figshare.12551420.v1
title: GhostKoala MAG annotation (by site)

description: Annotations of Brothers volcano MAGs, as predicted using GhostKoala. Each document contains MAG annotations from a single site, with MAG number indicated in the protein identifiers.<br>
DOI: 10.6084/m9.figshare.12559400.v1
title: MAG translations individual, S143-S147

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: S143, S144, S145, S146, S147.<br>
DOI: 10.6084/m9.figshare.12551966.v1
title: Aigarchaeota gene table

description: Table of select genes identified in Aigarchaeota MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12559103
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.12559496.v1
title: Hydrothermarchaeota reference translations

description: Open reading frames predicted for publicly available reference genomes in the Hydrothermarchaeota. ORFs were predicted in Prokka, since no translations were available for download on Genbank.<br>
DOI: 10.6084/m9.figshare.12568127
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.12551303
title: Assembly translations

description: Open reading frames for Brothers volcano metagenome assemblies predicted using Prodigal.<br>
DOI: 10.6084/m9.figshare.12559400
title: MAG translations individual, S143-S147

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: S143, S144, S145, S146, S147.<br>
DOI: 10.6084/m9.figshare.12558638
title: DPANN reference annotations

description: Annotations of DPANN superphylum reference genomes, produced using the arCOG and KO databases, Prokka, and dbCAN-seq. Sheets are arranged as follows:<br><b>DPANN_ref_annotations:</b> Annotations of DPANN superphylum reference genomes. E-values &gt;1.0e-5 are highlighted in pink.<br><b>DPANN_ref_annotations_flagged:</b> Annotations of interest that were flagged as inconsistent during curation and are not included in the final metabolism summary. The reasoning for each removal is explained in the Notes column.<br>
DOI: 10.6084/m9.figshare.12558833
title: S21 Bacteria KAAS annotations

description: Annotations of S21 bacterial MAGs predicted using KAAS.<br>
DOI: 10.6084/m9.figshare.12559496
title: Hydrothermarchaeota reference translations

description: Open reading frames predicted for publicly available reference genomes in the Hydrothermarchaeota. ORFs were predicted in Prokka, since no translations were available for download on Genbank.<br>
DOI: 10.6084/m9.figshare.12568082
title: Altiarchaeota Prokka annotations

description: Annotation of Altiarchaeota MAG S014_51 predicted using Prokka. This annotation was used for comparison with existing, publicly available Prokka annotations of Alti-2 Altiarchaeota.<br>
DOI: 10.6084/m9.figshare.12568787
title: Korarchaeota arCOG, KAAS annotations

description: Annotation of Brothers volcano MAGs and reference genomes from the Korarchaeota. Sheets are broken down as follows:<b>Kor_MAG_and_ref_annotations: </b>Annotations of Korarchaeota MAGs and reference genomes predicted with the arCOG database and KAAS. E-values &gt;1.0e-5 are shown in pink. <b>Kor_flagged annotations: </b>Annotations of interest that were flagged as inconsistent during curation and are not included in the final metabolism summary. The reasoning for each removal is explained in the Notes column.<b>CD_database_curation: </b>Genes with conflicting annotations that were further curated using Conserved Domain database batch searches.<br>
DOI: 10.6084/m9.figshare.12558779
title: S21 MAG and reference translations

description: Open reading frames (ORFs) predicted for S21 MAGs using Prokka. An additional translation file for the reference genome Woesearchaeota UBA119 (Genbank accession DAWM00000000.1) was also predicted using Prokka, since no translation file was available for download on Genbank.<br>
DOI: 10.6084/m9.figshare.12558440
title: DPANN MAG annotations

description: Annotations of Brothers volcano MAGs from the DPANN superphylum, produced using the arCOG and KO databases, Prokka, and dbCAN-seq. Hydrogenases were predicted using KO annotations and verified with HydDB. Sheets are broken down as follows: <br><br><b>DPANN_MAG_annotations:</b> Annotations of DPANN superphylum MAGs from Brothers volcano. E-values &gt;1.0e-5 are highlighed in pink.<br><b>DPANN_MAG_hydrogenases: </b>Hydrogenases from Brothers volcano MAGs identified using HMM searches and verified with the HydDB database.<br><b>DPANN_MAG_annotations_flagged: </b>Annotations of interest that were flagged as inconsistent during curation and are not included in the final metabolism summary. The reasoning for each removal is explained in the Notes column.<br><br>
DOI: 10.6084/m9.figshare.12551315
title: GhostKoala assembly annotation

description: Annotations of Brothers volcano assemblies predicted using GhostKoala.<br>
DOI: 10.6084/m9.figshare.12558830
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.12551675
title: Hydrothermarchaeota gene table

description: Table of select genes identified in Hydrothermarchaeota MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12551984
title: Aigarchaeota KAAS annotations

description: Annotations of Aigarchaeota MAGs and publicly available reference genomes, predicted with KAAS.<br>
DOI: 10.6084/m9.figshare.12551528
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: 10.6084/m9.figshare.12551966
title: Aigarchaeota gene table

description: Table of select genes identified in Aigarchaeota MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12558638.v1
title: DPANN reference annotations

description: Annotations of DPANN superphylum reference genomes, produced using the arCOG and KO databases, Prokka, and dbCAN-seq. Sheets are arranged as follows:<br><b>DPANN_ref_annotations:</b> Annotations of DPANN superphylum reference genomes. E-values &gt;1.0e-5 are highlighted in pink.<br><b>DPANN_ref_annotations_flagged:</b> Annotations of interest that were flagged as inconsistent during curation and are not included in the final metabolism summary. The reasoning for each removal is explained in the Notes column.<br>
DOI: 10.6084/m9.figshare.12551315.v1
title: GhostKoala assembly annotation

description: Annotations of Brothers volcano assemblies predicted using GhostKoala.<br>
DOI: 10.6084/m9.figshare.12551570
title: Hydrothermarchaeota KAAS annotations

description: Annotations of Hydrothermarchaeota MAGs and publicly available reference genomes predicted with KAAS.<br>
DOI: 10.6084/m9.figshare.12551672
title: Aquificae gene table

description: Table of select genes identified in Aquificae MAGs and reference genomes.<br>
DOI: 10.6084/m9.figshare.12558440.v1
title: DPANN MAG annotations

description: Annotations of Brothers volcano MAGs from the DPANN superphylum, produced using the arCOG and KO databases, Prokka, and dbCAN-seq. Hydrogenases were predicted using KO annotations and verified with HydDB. Sheets are broken down as follows: <br><br><b>DPANN_MAG_annotations:</b> Annotations of DPANN superphylum MAGs from Brothers volcano. E-values &gt;1.0e-5 are highlighed in pink.<br><b>DPANN_MAG_hydrogenases: </b>Hydrogenases from Brothers volcano MAGs identified using HMM searches and verified with the HydDB database.<br><b>DPANN_MAG_annotations_flagged: </b>Annotations of interest that were flagged as inconsistent during curation and are not included in the final metabolism summary. The reasoning for each removal is explained in the Notes column.<br><br>
DOI: 10.6084/m9.figshare.12551303.v1
title: Assembly translations

description: Open reading frames for Brothers volcano metagenome assemblies predicted using Prodigal.<br>
DOI: 10.6084/m9.figshare.12551438
title: MAG translations (by site)

description: Open reading frames (ORFs) predicted for Brothers volcano MAGs using Prokka. Each document contains MAG translations from one site, with MAG numbers indicated in the ORF headers.<br>
DOI: 10.6084/m9.figshare.12551420
title: GhostKoala MAG annotation (by site)

description: Annotations of Brothers volcano MAGs, as predicted using GhostKoala. Each document contains MAG annotations from a single site, with MAG number indicated in the protein identifiers.<br>
DOI: 10.21227/05mc-tp74
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2020.562061.
DOI: 10.25345/c5zv2k
title: MassIVE MSV000086432 - Regional Proteomics in rTg-DI Rats
title: This project utilized UPLC TripleTOF MS/MS and data independent acquisition (DIA), and spectral analysis in Spectronaut (Biognosys) to study brain regional proteomic changes in a cerebral amyloid angiopathy pre-clinical model, rTg-DI. Results revealed similar and regionally distinct differentially expressed proteins in rTg-DI rats.
DOI: 10.21227/6bgc-dw46
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. IEEE Transactions on Medical Robotics and Bionics. Under Review.
DOI: 10.5517/ccdc.csd.cc1pzwwf
title: CCDC 1578826: 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: 10.5517/ccdc.csd.cc1q60b8
title: CCDC 1584699: 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: 10.5517/ccdc.csd.cc1zzcx7
title: CCDC 1846450: 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: 10.5517/ccdc.csd.cc1zzmn7
title: CCDC 1846690: 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: 10.5517/ccdc.csd.cc20xzjg
title: CCDC 1874896: Experimental Crystal Structure Determination

description: Related Article: Swaraj Sengupta, Sahanwaj Khan, Binitendra Naath Mongal, William Lewis, Michel Fleck, Shyamal K. Chattopadhyay, Subhendu Naskar|2020|Polyhedron|191|114798|doi:10.1016/j.poly.2020.114798
DOI: 10.5517/ccdc.csd.cc20n4qk
title: CCDC 1866408: Experimental Crystal Structure Determination

description: Related Article: Saheli Karmakar, Debdas Ray|2020|J.Lumin.|223|117229|doi:10.1016/j.jlumin.2020.117229
DOI: 10.5517/ccdc.csd.cc20n4rl
title: CCDC 1866409: Experimental Crystal Structure Determination

description: Related Article: Saheli Karmakar, Debdas Ray|2020|J.Lumin.|223|117229|doi:10.1016/j.jlumin.2020.117229
DOI: 10.5517/ccdc.csd.cc242x7d
title: CCDC 1969003: Experimental Crystal Structure Determination

description: Related Article: Jebiti Haribabu, Othman I. Alajrawy, Kumaramangalam Jeyalakshmi, Chandrasekar Balachandran, Dhanabalan Anantha Krishnan, Nattamai Bhuvanesh, Shin Aoki, Karuppannan Natarajan, Ramasamy Karvembu|2021|Spectrochim.Acta,Part A|246|118963|doi:10.1016/j.saa.2020.118963
DOI: 10.5517/ccdc.csd.cc14bdgb
title: CCDC 1052681: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc242x8f
title: CCDC 1969004: Experimental Crystal Structure Determination

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title: CCDC 1974193: Experimental Crystal Structure Determination

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title: CCDC 1974199: Experimental Crystal Structure Determination

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DOI: 10.5517/ccdc.csd.cc248k33
title: CCDC 1974424: Experimental Crystal Structure Determination

description: Related Article: Zikai Wang, Rong Bi, Jiadi Liu, Kuaibing Wang, Feifei Mao, Hua Wu, Yuanqing Bu, Ninghui Song|2021|Chem.Eng.Journal|404|127117|doi:10.1016/j.cej.2020.127117
DOI: 10.21227/52ft-jw19
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. IEEE Transactions on Medical Robotics and Bionics. Under Review.
DOI: 10.21227/86s7-dn10
title: ExoNet Database: Wearable Camera Images of Human Locomotion Environments

description: Abstract: Advances in computer vision and artificial intelligence are allowing researchers to develop environment recognition systems for powered lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for classifying human walking environments. To address these limitations, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in scale and diversity, ExoNet contains over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable camera system throughout the summer, fall, and winter seasons. Approximately 923,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented communal platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Besides the control of powered lower-limb exoskeletons and prostheses, applications of ExoNet could extend to humanoids and autonomous legged robots.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and AI. DOI: 10.3389/frobt.2020.562061.