cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/nc4cg9bdcd.1
title: Data for: Can we locate shrimp aquaculture ponds from space? – A case study for Thailand

description: This shapefile (generated with ArcGis Desktop Version 10.8) contains the identified water areas in Thailand that have a high likelihood to be a shrimp pond and the identified land cover change. -9999 = NoData Pond 1= Shrimp water area 2= Non-Shrimp water area Farm_Unit = Identification number of the corresponding "farm unit" Water_Cat =Water category obtained from the global water surface explorer (Pekel, J.-F.; Cottam, A.; Gorelick, N.; Belward, A. S., High-resolution mapping of global surface water and its long-term changes. Nature 2016, 540, 418.) 1=Permanent 2=New permanent 4=Seasonal 5=New seasonal 7=Seasonal to permanent 8=Permanent to seasonal 9=Ephemeral permanent 10=Ephemeral seasonal Coastal = defines the location 1 = inland region 2 = costal region Area_km2 = Area of the related polygon in km2; measured by using the WGS 1984 World Mercator projection. Forest_km2 = “primary forested land” area in km2, that has potentially been converted by shrimp pond construction NFores_km2 = “primary non-forested” area in km2, that has potentially been converted by shrimp pond construction NPrima_km2 = “non-primary land” area in km2, that has potentially been converted by shrimp pond construction likelihood = Attributed shrimp pond water area likelihood class Class 1 has the comparably lowest likelihood to be a shrimp pond and class 3 has the comparably highest likelihood to be a shrimp pond.
DOI: 10.5281/zenodo.4064894
title: Drug-interaction annotations over full text articles reporting on clinical studies reports or case reports

description: Drug interaction annotations over full text reports. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)
description: National Library of Medicine R01LM011838
DOI: 10.5281/zenodo.4064893
title: Drug-interaction annotations over full text articles reporting on clinical studies reports or case reports

description: Drug interaction annotations over full text reports. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)
description: National Library of Medicine R01LM011838
DOI: 10.5281/zenodo.1404313
title: Drug-interaction annotations over a large set of drug product labels

description: Drug interaction annotations over a large set of drug product labels. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)
description: National Library of Medicine R01LM011838
DOI: 10.5281/zenodo.1404312
title: Drug-interaction annotations over a large set of drug product labels

description: Drug interaction annotations over a large set of drug product labels. These annotations were done as part of the National Library of Medicine funded research project "Addressing gaps in clinically useful evidence on drug-drug interactions" (R01LM011838)
description: National Library of Medicine R01LM011838
DOI: 10.21227/byrn-j931
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: 10.21227/581y-r149
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, pp. 868-873. DOI: 10.1109/ICORR.2019.8779540.
DOI: 10.21227/fhrn-mh73
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 includes over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone 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 provides researchers 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 humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.21227/nsjq-k919
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 includes over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone 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 humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.21227/0ydf-k565
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 includes over 5.6 million RGB images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone 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 provides 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 humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Frontiers in Robotics and Artificial Intelligence. Under Review.
DOI: 10.5285/2cc63301f1854239aa61c70e58c61207
title: ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from TANSAT, generated with the OCFP algorithm, for selected validation sites, version 1.0

description: This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (CO2), derived from the TANSAT satellite, using the University of Leicester Full-Physics Retrieval Algorithm (UoL-FP, also known as OCFP). This dataset is also referred to as CO2_TAN_OCFP. The data covers the period from March 2017 to May 2018 and is provided for TCCON (Total Carbon Column Observing Network) validation sites only. A full global dataset is in production. For further information on the dataset, please see the linked documentation. This data has been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme, with support from the UK's National Centre for Earth Observation (NCEO).
DOI: 10.5285/5d8221e070e64823891bed7a87840447
title: FRANC: Doppler radar radial wind observations and associated observation-minus-model residuals from the Met Office UKV 3D Var assimilation scheme

description: The dataset is provided to support the publication 'Diagnosing Observation Error Correlations for Doppler Radar Radial Winds in the Met Office UKV Model Using Observation-Minus-Background and Observation-Minus-Analysis Statistics' by Waller et al (2016). The dataset was created as part of the NERC Flooding from Intense Rainfall (FRANC) project in order to study the observation uncertainties associated with Doppler radar radial wind observations assimilated in to the Met Office UK variable resolution model. The dataset is processed output of the Met Office UKV 3D var assimilation scheme for June, July and August 2013 for four different experimental scenarios. Full details and equations are given in Waller et al (2016) but the four different experimental cases are summarised as follows: - Case 1: Control experiment using standard UKV settings in place in January 2014 - Case 2: As Case 1, but with a different background error covariance matrix used in the data assimilation - Case 3: As Case 1, but with raw Doppler radial wind observations rather than superobservations - Case 4: As Case 3, but with an improved observation operator. For each case the dataset consists of the radial wind observations assimilated at each assimilation cycle valid between 01/06/2013 and 31/08/2016 along with the associated observation-minus-background and observation-minus-analysis residuals. Each observation also has metadata that describes the location of the observation (both in latitude/longitude co-ordinates, and co-ordinates relative to the radar station) , the assimilation cycle at which it was assimilated and the observation error variance that the observation was assigned in the data assimilation scheme. These data are published under the Open Government License (http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/) © Crown Copyright, 2020, Met Office”.
DOI: 10.5517/ccdc.csd.cc254lmk
title: CCDC 2000418: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lpm
title: CCDC 2000420: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lqn
title: CCDC 2000421: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lrp
title: CCDC 2000422: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lsq
title: CCDC 2000423: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254ltr
title: CCDC 2000424: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lvs
title: CCDC 2000425: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254lwt
title: CCDC 2000426: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc254ygr
title: CCDC 2000754: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254yhs
title: CCDC 2000755: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254yjt
title: CCDC 2000756: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254ykv
title: CCDC 2000757: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254ylw
title: CCDC 2000758: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254ymx
title: CCDC 2000759: Experimental Crystal Structure Determination

description: Related Article: Xunjin Jiang, Zhixiong Zeng, Yuhui Hua, Beibei Xu, Yang Shen, Jing Xiong, Huijuan Qiu, Yifan Wu, Tianhui Hu, Yandong Zhang|2020|J.Am.Chem.Soc.|142|15585|doi:10.1021/jacs.0c07680
DOI: 10.5517/ccdc.csd.cc254zcp
title: CCDC 2000782: Experimental Crystal Structure Determination

description: Related Article: Raúl Díaz-Torres, Wasinee Phonsri, Keith S. Murray, Lujia Liu, Manan Ahmed, Suzanne M. Neville, Phimphaka Harding, David J. Harding|2020|Inorg.Chem.|59|13784|doi:10.1021/acs.inorgchem.0c02201
DOI: 10.5517/ccdc.csd.cc255hrm
title: CCDC 2001290: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Song, Bei-Bei Liu, Wen-Bo Liu, Zheng-Lei Tan|2020|RSC Advances|10|32069|doi:10.1039/D0RA04344C
DOI: 10.5517/ccdc.csd.cc255hsn
title: CCDC 2001291: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Song, Bei-Bei Liu, Wen-Bo Liu, Zheng-Lei Tan|2020|RSC Advances|10|32069|doi:10.1039/D0RA04344C
DOI: 10.5517/ccdc.csd.cc255htp
title: CCDC 2001292: Experimental Crystal Structure Determination

description: Related Article: Li-Cheng Song, Bei-Bei Liu, Wen-Bo Liu, Zheng-Lei Tan|2020|RSC Advances|10|32069|doi:10.1039/D0RA04344C
DOI: 10.5517/ccdc.csd.cc256n02
title: CCDC 2002383: Experimental Crystal Structure Determination

description: Related Article: Dong Zhang, Zhishan Su, Qianwen He, Zhikun Wu, Yuqiao Zhou, Chenjing Pan, Xiaohua Liu, Xiaoming Feng|2020|J.Am.Chem.Soc.|142|15975|doi:10.1021/jacs.0c07066
DOI: 10.5517/ccdc.csd.cc256sv1
title: CCDC 2002533: Experimental Crystal Structure Determination

description: Related Article: Li-feng Yao, Xue-long Huang, Hong-ying Xia, Hai-feng He, Liang Shen|2021|Dyes Pigm.|184|108747|doi:10.1016/j.dyepig.2020.108747
DOI: 10.5517/ccdc.csd.cc256sw2
title: CCDC 2002534: Experimental Crystal Structure Determination

description: Related Article: Li-feng Yao, Xue-long Huang, Hong-ying Xia, Hai-feng He, Liang Shen|2021|Dyes Pigm.|184|108747|doi:10.1016/j.dyepig.2020.108747
DOI: 10.5517/ccdc.csd.cc2571l2
title: CCDC 2002773: Experimental Crystal Structure Determination

description: Related Article: Jesús A. Lara-Cerón, Víctor M. Jiménez Pérez, Leonardo Xochicale-Santana, María E. Ochoa, Arturo Chávez-Reyes, Blanca M. Muñoz-Flores|2020|RSC Advances|10|31748|doi:10.1039/D0RA05948J
DOI: 10.5517/ccdc.csd.cc2574wg
title: CCDC 2002875: Experimental Crystal Structure Determination

description: Related Article: Alexander G. Shtukenberg, Xiaolong Zhu, Yongfan Yang, Bart Kahr|2020|Cryst.Growth Des.|20|6186|doi:10.1021/acs.cgd.0c00908
DOI: 10.5517/ccdc.csd.cc2574vf
title: CCDC 2002874: Experimental Crystal Structure Determination

description: Related Article: Alexander G. Shtukenberg, Xiaolong Zhu, Yongfan Yang, Bart Kahr|2020|Cryst.Growth Des.|20|6186|doi:10.1021/acs.cgd.0c00908
DOI: 10.5517/ccdc.csd.cc2574xh
title: CCDC 2002876: Experimental Crystal Structure Determination

description: Related Article: Alexander G. Shtukenberg, Xiaolong Zhu, Yongfan Yang, Bart Kahr|2020|Cryst.Growth Des.|20|6186|doi:10.1021/acs.cgd.0c00908
DOI: 10.5517/ccdc.csd.cc244myw
title: CCDC 1970668: Experimental Crystal Structure Determination

description: Related Article: Jiří Pinkas, Jiří Kubišta, Karel Mach, Róbert Gyepes, Michal Horáček|2020|J.Organomet.Chem.|927|121536|doi:10.1016/j.jorganchem.2020.121536
DOI: 10.5517/ccdc.csd.cc25c1l6
title: CCDC 2006617: Experimental Crystal Structure Determination

description: Related Article: Jun Du, Yanan Zhang, Zeming Huang, Shuangliu Zhou, Huayi Fang, Peng Cui|2020|Dalton Trans.|49|12311|doi:10.1039/D0DT02708A
DOI: 10.5517/ccdc.csd.cc25c1m7
title: CCDC 2006618: Experimental Crystal Structure Determination

description: Related Article: Jun Du, Yanan Zhang, Zeming Huang, Shuangliu Zhou, Huayi Fang, Peng Cui|2020|Dalton Trans.|49|12311|doi:10.1039/D0DT02708A
DOI: 10.5517/ccdc.csd.cc25c1n8
title: CCDC 2006619: Experimental Crystal Structure Determination

description: Related Article: Jun Du, Yanan Zhang, Zeming Huang, Shuangliu Zhou, Huayi Fang, Peng Cui|2020|Dalton Trans.|49|12311|doi:10.1039/D0DT02708A
DOI: 10.25721/r55j-2b94
title: Money, gold and foreign reserves in the interwar gold standard and the Bretton Woods era
DOI: 10.6084/m9.figshare.11497128.v3
title: Premature termination codons signal nonsense-mediated mRNA editing and gene repair in E. coli

description: S1: the list of SNPs in the genomes; S2: the list of SNPs in the plasmid; S3: the read count and FPKM for all genes; S4: the list of upregulated genes; S5: the list of enriched KEGG pathways; S6: Genes annotated in the duplicate regions; S7: the list of structure variations in the genomes; S8: Peptides identified in the proteome