cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc25qg0d
title: CCDC 2017573: Experimental Crystal Structure Determination

description: Related Article: Phillip S. Grant, Daniel P. Furkert, Margaret A. Brimble|2020|Org.Lett.|22|8735|doi:10.1021/acs.orglett.0c03364
DOI: 10.5517/ccdc.csd.cc25qg1f
title: CCDC 2017574: Experimental Crystal Structure Determination

description: Related Article: Phillip S. Grant, Daniel P. Furkert, Margaret A. Brimble|2020|Org.Lett.|22|8735|doi:10.1021/acs.orglett.0c03364
DOI: 10.5517/ccdc.csd.cc25qg2g
title: CCDC 2017575: Experimental Crystal Structure Determination

description: Related Article: Phillip S. Grant, Daniel P. Furkert, Margaret A. Brimble|2020|Org.Lett.|22|8735|doi:10.1021/acs.orglett.0c03364
DOI: 10.5517/ccdc.csd.cc25qm3n
title: CCDC 2017731: Experimental Crystal Structure Determination

description: Related Article: Ji Yoon Lee, Harrison D. Root, Rashid Ali, Won An, Vincent M. Lynch, Steffen Bähring, In Su Kim, Jonathan L. Sessler, Jung Su Park|2020|J.Am.Chem.Soc.|142|19579|doi:10.1021/jacs.0c08106
DOI: 10.5517/ccdc.csd.cc25qm4p
title: CCDC 2017732: Experimental Crystal Structure Determination

description: Related Article: Ji Yoon Lee, Harrison D. Root, Rashid Ali, Won An, Vincent M. Lynch, Steffen Bähring, In Su Kim, Jonathan L. Sessler, Jung Su Park|2020|J.Am.Chem.Soc.|142|19579|doi:10.1021/jacs.0c08106
DOI: 10.5517/ccdc.csd.cc25qm5q
title: CCDC 2017733: Experimental Crystal Structure Determination

description: Related Article: Ji Yoon Lee, Harrison D. Root, Rashid Ali, Won An, Vincent M. Lynch, Steffen Bähring, In Su Kim, Jonathan L. Sessler, Jung Su Park|2020|J.Am.Chem.Soc.|142|19579|doi:10.1021/jacs.0c08106
DOI: 10.5517/ccdc.csd.cc25vplb
title: CCDC 2021652: Experimental Crystal Structure Determination

description: Related Article: Mingdong Jiao, Jihui Gao, Xianjie Fang|2020|Org.Lett.|22|8566|doi:10.1021/acs.orglett.0c03171
DOI: 10.5517/ccdc.csd.cc25y2cl
title: CCDC 2023939: Experimental Crystal Structure Determination

description: Related Article: Mingdong Jiao, Jihui Gao, Xianjie Fang|2020|Org.Lett.|22|8566|doi:10.1021/acs.orglett.0c03171
DOI: 10.5517/ccdc.csd.cc25z29k
title: CCDC 2024898: Experimental Crystal Structure Determination

description: Related Article: Liang-Jing Zou, Qiang Pan, Cai-Yi Li, Ze-Ting Zhang, Xiao-Wei Zhang, Xiang-Guo Hu|2020|Org.Lett.|22|8302|doi:10.1021/acs.orglett.0c02949
DOI: 10.5517/ccdc.csd.cc265zt6
title: CCDC 2031548: Experimental Crystal Structure Determination

description: Related Article: Mingdong Jiao, Jihui Gao, Xianjie Fang|2020|Org.Lett.|22|8566|doi:10.1021/acs.orglett.0c03171
DOI: 10.34740/kaggle/dsv/1667212
title: COVID-19 PPE Dataset for Object Detection
DOI: 10.34740/kaggle/dsv/1667213
title: COVID-19 PPE Dataset for Object Detection
DOI: 10.34740/kaggle/dsv/1667216
title: COVID-19 PPE Dataset for Object Detection
DOI: 10.5281/zenodo.4128741
title: AI-related patents (WIPO, category G06N) and market capitalisation by companies registering at least 2 new ones in 2019, sorted into four global regions (China, USA, EEA, rest of the world)

description: Note, this is the first version of our upload, when we were still learning about Zenodo. Please click on the second version link (version links are in a box on the lower right of this page), and you will see a) a preview of our own visualisation and b) a slight cosmetic improvement of that visualisation. But the data sets on both versions are identical. Datasets and presentations concerning the strength of the EU and "the rest of the world" relative to China and the USA, for the purpose of illustrating and counteracting / better informing narratives concerning a "new AI cold war". The materials authored by us may be freely used under the terms of the MIT License, which appears in its entirety in both the dataset and the presentation. The other materials are only curated by us, taken from Twitter as examples of misinformation pertaining to this concern. Authors: The original analysis was conducted primarily by Malikova in collaboration with Bryson. An associated publication is anticipated where Bryson is the lead author. Contributors: independently followed Malikova's procedures to check her work. Inconsistencies were triple checked and resolved.
description: A linked project contains embargoed versions of screen shots evidencing some numbers appearing in the dataset which are not publicly available.
DOI: 10.34740/kaggle/ds/856525
title: akkp69000
DOI: 10.25646/7667
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.12746198
title: Negative play contagion in calves

description: Data on play duration, play bouts, synchrony of play and diurnal pattern of play of dairy calves<br>
DOI: 10.6084/m9.figshare.13266698.v2
title: Optimization of Hydrolase Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.21227/dpeq-zj85
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, 7, 562061. DOI: 10.3389/frobt.2020.562061.
DOI: 10.21227/m8rh-1k15
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, 7, 562061. DOI: 10.3389/frobt.2020.562061.
DOI: 10.6084/m9.figshare.13266698.v3
title: Optimization of Hydrolase Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.6084/m9.figshare.13266698.v4
title: Optimization of Hydrolase Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.6084/m9.figshare.13266698
title: Optimization of Hydrolases Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.6084/m9.figshare.13266698.v6
title: Optimization of Hydrolases Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.6084/m9.figshare.13266698.v5
title: Optimization of Hydrolase Production (Dataset).pdf

description: The saccharification of biomass with hydrolases from fungi is considered as an efficient and sustainable approach to obtain fermentable sugars. The expression of these hydrolytic enzymes by fungi is mainly by a substrate-induced mechanism, which depends on prevailing environmental conditions. <i>Trametes polyzona, </i>a white rot fungus, is regarded as one of the most efficient producers of hydrolases for the hydrolysis of biomass. This study sought to examine and optimize cultivation conditions for the efficient production of hydrolases by <i>Trametes polyzona </i>(<i>T. polyzona</i>)<i>. </i>Hydrolase production by <i>T. polyzona</i> was examined using parametric optimization. Crude enzyme extracts obtained under varying cultivation conditions were profiled for endoglucanase, betaglucosidase, exoglucanase, xylanase and amylase activities to determine the optimal conditions for hydrolase production. Maximum hydrolase production was achieved by solid-state cultivation of <i>T. polyzona</i> on cassava peels at a moisture content of 60%, pH 6, supplemented with 0.3% urea and 0.03% Mg<sup>2+</sup>, and incubated at 30 <sup>o</sup>C for 14 days. A solid to liquid ratio of 1:2 was found to be ideal for the solid-liquid extraction of enzymes. This study therefore proves that optimization of cultivation conditions is an efficient approach to enhance the production of hydrolases required to breakdown lignocellulosic biomass by <i>T. polyzona.</i>
DOI: 10.5519/qd.ux1ds10a
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.34740/kaggle/dsv/1667620
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.13270199
title: Changes in China’s anthropogenic SO2 emissions during the COVID-19 pandemic

description: Changes in China’s monthly SO<sub>2</sub> emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270199.v1
title: Changes in China’s anthropogenic SO2 emissions during the COVID-19 pandemic

description: Changes in China’s monthly SO<sub>2</sub> emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270202.v1
title: Changes in China’s anthropogenic NOx emissions during the COVID-19 pandemic

description: Changes in China’s monthly NO<sub>x</sub> emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270202
title: Changes in China’s anthropogenic NOx emissions during the COVID-19 pandemic

description: Changes in China’s monthly NO<sub>x</sub> emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270205.v1
title: Changes in China’s anthropogenic CO emissions during the COVID-19 pandemic

description: Changes in China’s monthly CO emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270205
title: Changes in China’s anthropogenic CO emissions during the COVID-19 pandemic

description: Changes in China’s monthly CO emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270208
title: Changes in China’s anthropogenic NMVOCs emissions during the COVID-19 pandemic

description: Changes in China’s monthly NMVOCs emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270208.v1
title: Changes in China’s anthropogenic NMVOCs emissions during the COVID-19 pandemic

description: Changes in China’s monthly NMVOCs emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270211
title: Changes in China’s anthropogenic PM2.5 emissions during the COVID-19 pandemic

description: Changes in China’s monthly PM<sub>2.5</sub> emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270214.v1
title: Changes in China’s anthropogenic BC emissions during the COVID-19 pandemic

description: Changes in China’s monthly BC emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270214
title: Changes in China’s anthropogenic BC emissions during the COVID-19 pandemic

description: Changes in China’s monthly BC emissions by source sector and by province
DOI: 10.6084/m9.figshare.13270217
title: China's provincial mask

description: China's provincial mask at the resolution of 0.1° X 0.1°
DOI: 10.6084/m9.figshare.13270241
title: Predicted protein sequences from Nepenthes species

description: Transdecoder fasta files of predicted protein sequences from PacBio isoform sequencing of Nepenthes ampullaria, Nepenthes rafflesiana, and Nepenthes x hookeriana. A reference protein database was generated from combined analysis of all three iso-seq datasets.
DOI: 10.6084/m9.figshare.13270241.v1
title: Predicted protein sequences from Nepenthes species

description: Transdecoder fasta files of predicted protein sequences from PacBio isoform sequencing of Nepenthes ampullaria, Nepenthes rafflesiana, and Nepenthes x hookeriana. A reference protein database was generated from combined analysis of all three iso-seq datasets.
DOI: 10.21227/gxcx-bk38
title: Supplementary Material To: A Safety Benchmark for Capacitive Proximity Sensing in Robotics – Safe Transient Contact Compliant to ISO TS 15066: Power and Force Limiting

description: Supplementary Data for Publication "A Safety Benchmark for Capacitive Proximity Sensing in Robotics - Safe Transient Contact in Compliance to ISO TS 15066: Power and Force LimitingThis paper is currently under review to be published as RA-L with ICRA 2021 option. All supplementary material will be published upon acceptance to RA-L and/or ICRA 2021. For further information feel free to contact us at:Serkan ErgunInstitute for Smart Systems Technologies - Sensors and Actuators GroupKlagenfurt UniveristyUniversitätsstrasse 65-67,A- 9020 KlagenfurtE- Mail:serkan.ergun@aau.at
DOI: 10.21227/dd4m-q821
title: solar 2

description: c
DOI: 10.21227/a3b2-ss33
title: Non Linear 6.66 kVA Solar Optimizer

description: This dataset is in support of my original research paper " 6.66kVA Solar Optimizer " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.This is not connected to the grid. The other related dataset " " (https://dx.doi.org/10.21227/v7s8-pk56 ), where this will be connected to the inverter which in turn will be connected to grid.Though few similar kind papers are there, this paper is original and different from them as evident from the abstract and the transaction paper , and is based on the actual submitted project for funding. ( project didn't got any funding ; though from self finances, submission was acceptable !)
DOI: 10.21227/kyss-6f37
title: 6.66 kVA Solar Inverter

description: This dataset is in support of my original research paper " 6.66 kVA Solar Inverter " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63).Paper is under review.This experiment is different from the exisiting approaches in the following ways
DOI: 10.21227/7n77-x878
title: 25 kVA PFC

description: This dataset is in support of my original research paper " PFC " submitted to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63). Paper is under review.This PFC is a three phase and the input source of 25kVA can be any. Here in this case, this 25 kVA is generated from the 25kVA wind turbine.To connect it to any DISCOM around the world, it must have PFC approx 1, which is the purpose of this.
DOI: 10.21227/43hg-k159
title: solar 2

description: This dataset is in support of my original research paper " 6.66kVA Solar Optimizer " submitted to "IEEE Transactions on Energy Conversion". (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=60). Paper is under review.