cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11588/data/c56quv/e1pcfs
title: configuration_xlm_roberta.py

description: :unav
DOI: 10.11588/data/c56quv/5eow3z
title: get_model_files.sh

description: :unav
DOI: 10.11588/data/c56quv/uqvcej
title: classify.sh

description: :unav
DOI: 10.11588/data/c56quv/htfkdl
title: tokenization_transfo_xl.py

description: :unav
DOI: 10.11588/data/c56quv/z9jrmn
title: evaluate.py

description: :unav
DOI: 10.11588/data/c56quv/l07vww
title: train_MASK_baseline.txt

description: :unav
DOI: 10.11588/data/c56quv/qluwg5
title: utils.cpython-36.pyc

description: :unav
DOI: 10.11588/data/c56quv/b6hs2u
title: models.cpython-38.pyc

description: :unav
DOI: 10.11588/data/c56quv/f4mbda
title: modeling_xlm_roberta.py

description: :unav
DOI: 10.11588/data/c56quv/sxbltn
title: modeling_xlm.py

description: :unav
DOI: 10.11588/data/c56quv/x0zlqj
title: utils.cpython-38.pyc

description: :unav
DOI: 10.11588/data/c56quv/tubfql
title: modeling_utils.py

description: :unav
DOI: 10.11588/data/c56quv/s1ue3l
title: modeling_tf_auto.py

description: :unav
DOI: 10.11588/data/c56quv/slxgi7
title: conceptnet_generate.py

description: :unav
DOI: 10.11588/data/c56quv/raa6s3
title: tokenization_bert_japanese.py

description: :unav
DOI: 10.11588/data/c56quv/mzcmzl
title: utils.cpython-35.pyc

description: :unav
DOI: 10.11588/data/c56quv/qz2yao
title: utils.cpython-36.pyc

description: :unav
DOI: 10.11588/data/c56quv/wcs9be
title: sampler.cpython-36.pyc

description: :unav
DOI: 10.11588/data/c56quv/sguxhk
title: knowledge_paths.zip

description: :unav
DOI: 10.11588/data/c56quv/wuvj1c
title: conceptnet_train.py

description: :unav
DOI: 10.11588/data/c56quv/4mzcvm
title: train.cpython-36.pyc

description: :unav
DOI: 10.11588/data/c56quv/wspxeh
title: atomic.cpython-35.pyc

description: :unav
DOI: 10.11588/data/c56quv/kp67jh
title: conceptnet.cpython-36.pyc

description: :unav
DOI: 10.11588/data/c56quv/u2jede
title: tokenization_xlm.py

description: :unav
DOI: 10.11588/data/c56quv/dt90or
title: run.sh

description: :unav
DOI: 10.11588/data/c56quv/legww2
title: configuration_camembert.py

description: :unav
DOI: 10.11588/data/c56quv/umwd49
title: Readme.md

description: :unav
DOI: 10.11588/data/c56quv/8ekgr6
title: modeling_tf_roberta.py

description: :unav
DOI: 10.11588/data/c56quv/gonmgt
title: train_debate_concept.txt

description: :unav
DOI: 10.11588/data/c56quv/gmr0zk
title: Readme.md

description: :unav
DOI: 10.11588/data/c56quv/lavpcj
title: evaluator_knowledge.py

description: :unav
DOI: 10.11588/data/c56quv/wqvicp
title: Readme.md

description: :unav
DOI: 10.11588/data/c56quv/7rjnm9
title: test_debate_wordnet.txt

description: :unav
DOI: 10.11588/data/c56quv/tfdwzy
title: FAIA-326-FAIA200515.pdf

description: :unav
DOI: 10.11588/data/c56quv/kwy6ti
title: Readme.md

description: :unav
DOI: 10.11588/data/c56quv/lucvsb
title: Readme.md

description: :unav
DOI: 10.11588/data/c56quv/vpnsax
title: EurNLP.pdf

description: :unav
DOI: 10.11588/data/c56quv/5wt5b7
title: COMMA_67_slides.pdf

description: :unav
DOI: 10.11588/data/c56quv/f9bmpi
title: train_essay_wordnet_concept.txt

description: :unav
DOI: 10.11588/data/c56quv/kvze3x
title: test_essay_wordnet+concept.txt

description: :unav
DOI: 10.11588/data/c56quv/4kaial
title: _COMMA_2020__CSK_improves_Argumentative_Relation_Classification.pdf

description: :unav
DOI: 10.11588/data/c56quv/97ixdz
title: experiment_knowledge.py

description: :unav
DOI: 10.11588/data/c56quv/5zmoww
title: dev_debate_wordnet+concept.txt

description: :unav
DOI: 10.11588/data/c56quv/su5ixf
title: test_debate_concept.txt

description: :unav
DOI: 10.11588/data/c56quv/doao9v
title: dev_essay_wordnet_concept.txt

description: :unav
DOI: 10.11588/data/c56quv/lkfw3o
title: Argumentation_knowledge.py

description: :unav
DOI: 10.11588/data/c56quv/fbqvmf
title: conf_know.txt

description: :unav
DOI: 10.5517/ccdc.csd.cc248fxs
title: CCDC 1974325: Experimental Crystal Structure Determination

description: Related Article: Xiao-Yong Zhang, Xue-Lin Ma, Jun-Fang Gao, Mei-Rong Wang, Yu-Heng Zhang, Sheng-Li Yong|2022|J.Indian Chem.Soc.|99|100394|doi:10.1016/j.jics.2022.100394
DOI: 10.5517/ccdc.csd.cc26ptk9
title: CCDC 2046761: Experimental Crystal Structure Determination

description: Related Article: Jianxiao Li, Dan He, Zidong Lin, Liying Cen, Wanqing Wu, Huanfeng Jiang|2022|Green Chemistry|24|1983|doi:10.1039/D1GC04556C
DOI: 10.5517/ccdc.csd.cc273k42
title: CCDC 2058993: Experimental Crystal Structure Determination

description: Related Article: Arkalekha Mandal|2022|CrystEngComm|24|2072|doi:10.1039/D2CE00006G
DOI: 10.5517/ccdc.csd.cc285kpq
title: CCDC 2090723: Experimental Crystal Structure Determination

description: Related Article: Shaoqun Zhu, Yingjie Pan, Yue Sun, Xiaoyan Hong, Xiaoyun Chen, Ying Han, Chaoguo Yan, Yaocheng Shi, Hong Hou|2022|J.Org.Chem.|87|4455|doi:10.1021/acs.joc.1c02949
DOI: 10.5517/ccdc.csd.cc288rw6
title: CCDC 2093798: Experimental Crystal Structure Determination

description: Related Article: Sravan K. Jonnalagadda, Bader I. Huwaimel, Shirisha Jonnalagadda, Jered C. Garrison, Paul C. Trippier|2022|J.Org.Chem.|87|4476|doi:10.1021/acs.joc.2c00018
DOI: 10.5517/ccdc.csd.cc288rx7
title: CCDC 2093799: Experimental Crystal Structure Determination

description: Related Article: Sravan K. Jonnalagadda, Bader I. Huwaimel, Shirisha Jonnalagadda, Jered C. Garrison, Paul C. Trippier|2022|J.Org.Chem.|87|4476|doi:10.1021/acs.joc.2c00018
DOI: 10.5517/ccdc.csd.cc288ry8
title: CCDC 2093800: Experimental Crystal Structure Determination

description: Related Article: Sravan K. Jonnalagadda, Bader I. Huwaimel, Shirisha Jonnalagadda, Jered C. Garrison, Paul C. Trippier|2022|J.Org.Chem.|87|4476|doi:10.1021/acs.joc.2c00018
DOI: 10.5517/ccdc.csd.cc288rz9
title: CCDC 2093801: Experimental Crystal Structure Determination

description: Related Article: Sravan K. Jonnalagadda, Bader I. Huwaimel, Shirisha Jonnalagadda, Jered C. Garrison, Paul C. Trippier|2022|J.Org.Chem.|87|4476|doi:10.1021/acs.joc.2c00018
DOI: 10.5517/ccdc.csd.cc28v62f
title: CCDC 2110544: Experimental Crystal Structure Determination

description: Related Article: Moeko Kawana, Ryohei Mizoue, Takashi Takeda, Norihisa Hoshino, Tomoyuki Akutagawa|2022|J.Mater.Chem.C|10|4208|doi:10.1039/D1TC05001J
DOI: 10.5517/ccdc.csd.cc2909vh
title: CCDC 2115466: Experimental Crystal Structure Determination

description: Related Article: Yan Long, Yun Wang, Yue-You Chen, Wen-Yong Han, Nan-Wei Wan, Wei-Cheng Yuan, Yong-Zheng Chen, Bao-Dong Cui|2022|J.Org.Chem.|87|4112|doi:10.1021/acs.joc.1c02909
DOI: 10.5517/ccdc.csd.cc296vzc
title: CCDC 2121763: Experimental Crystal Structure Determination

description: Related Article: Shaoqun Zhu, Yingjie Pan, Yue Sun, Xiaoyan Hong, Xiaoyun Chen, Ying Han, Chaoguo Yan, Yaocheng Shi, Hong Hou|2022|J.Org.Chem.|87|4455|doi:10.1021/acs.joc.1c02949
DOI: 10.5517/ccdc.csd.cc297l7d
title: CCDC 2122453: Experimental Crystal Structure Determination

description: Related Article: Manjari Jayendran, M.R. Prathapachandra Kurup|2022|Chem. Data Collect.|39|100853|doi:10.1016/j.cdc.2022.100853
DOI: 10.5517/ccdc.csd.cc29c7qn
title: CCDC 2125971: Experimental Crystal Structure Determination

description: Related Article: Zhen-Yao Lei, Kui Hu, Yuan-Xiang He, Shu Geng, Li-Na Chen, Shuai Zou, Li Pan, Zhi-Jun Ding, Feng Huang|2022|Org.Biomol.Chem.|20|2397|doi:10.1039/D2OB00317A
DOI: 10.5517/ccdc.csd.cc29f2xr
title: CCDC 2127744: Experimental Crystal Structure Determination

description: Related Article: Han-Wen Zheng, Dong-Dong Yang, Qiong-Fang Liang, Yan-Ping Liu, Jia-Hui Shan, Qian-Qian Liu, Hong-Wei Tan, Ling Chen, Xiang-Jun Zheng|2022|J.Mater.Chem.C|10|3901|doi:10.1039/D1TC05937H
DOI: 10.5517/ccdc.csd.cc29gbw0
title: CCDC 2128952: Experimental Crystal Structure Determination

description: Related Article: Oksana Yanshyna, Liat Avram, Linda J. W. Shimon, Rafal Klajn|2022|Chem.Commun.|58|3461|doi:10.1039/D1CC07081A
DOI: 10.5517/ccdc.csd.cc29clny
title: CCDC 2126310: Experimental Crystal Structure Determination

description: Related Article: Oksana Yanshyna, Liat Avram, Linda J. W. Shimon, Rafal Klajn|2022|Chem.Commun.|58|3461|doi:10.1039/D1CC07081A
DOI: 10.5517/ccdc.csd.cc1pgfzk
title: CCDC 1563019: Experimental Crystal Structure Determination

description: Related Article: Sevgi Kansız, Arda Tolan, Mohammad Azam, Necmi Dege, Mahboob Alam, Yusuf Sert, Saud I. Al-Resayes, Hasan İçbudak|2022|Polyhedron|218|115762|doi:10.1016/j.poly.2022.115762
DOI: 10.5517/ccdc.csd.cc29k748
title: CCDC 2131719: Experimental Crystal Structure Determination

description: Related Article: Zhen-Yao Lei, Kui Hu, Yuan-Xiang He, Shu Geng, Li-Na Chen, Shuai Zou, Li Pan, Zhi-Jun Ding, Feng Huang|2022|Org.Biomol.Chem.|20|2397|doi:10.1039/D2OB00317A
DOI: 10.5517/ccdc.csd.cc29k9w2
title: CCDC 2131804: Experimental Crystal Structure Determination

description: Related Article: Jia-Jing Wu, Yue Guo, Wen-Dong Yao, Wen-Long Liu, Sheng-Ping Guo|2022|Dalton Trans.|51|4878|doi:10.1039/D2DT00451H
DOI: 10.5517/ccdc.csd.cc29ld8l
title: CCDC 2132839: Experimental Crystal Structure Determination

description: Related Article: Han-Wen Zheng, Dong-Dong Yang, Qiong-Fang Liang, Yan-Ping Liu, Jia-Hui Shan, Qian-Qian Liu, Hong-Wei Tan, Ling Chen, Xiang-Jun Zheng|2022|J.Mater.Chem.C|10|3901|doi:10.1039/D1TC05937H
DOI: 10.5517/ccdc.csd.cc29zglc
title: CCDC 2144443: Experimental Crystal Structure Determination

description: Related Article: Arun Kumar, Samya Banerjee, Nishant Sharma, Mohd Nazish, Nico Graw, Regine Herbst-Irmer, Dietmar Stalke, Upakarasamy Lourderaj, Herbert W. Roesky|2022|Dalton Trans.|51|4898|doi:10.1039/D2DT00317A
DOI: 10.5517/ccdc.csd.cc29zgmd
title: CCDC 2144444: Experimental Crystal Structure Determination

description: Related Article: Arun Kumar, Samya Banerjee, Nishant Sharma, Mohd Nazish, Nico Graw, Regine Herbst-Irmer, Dietmar Stalke, Upakarasamy Lourderaj, Herbert W. Roesky|2022|Dalton Trans.|51|4898|doi:10.1039/D2DT00317A
DOI: 10.5517/ccdc.csd.cc29zgnf
title: CCDC 2144445: Experimental Crystal Structure Determination

description: Related Article: Arun Kumar, Samya Banerjee, Nishant Sharma, Mohd Nazish, Nico Graw, Regine Herbst-Irmer, Dietmar Stalke, Upakarasamy Lourderaj, Herbert W. Roesky|2022|Dalton Trans.|51|4898|doi:10.1039/D2DT00317A
DOI: 10.5517/ccdc.csd.cc29zgpg
title: CCDC 2144446: Experimental Crystal Structure Determination

description: Related Article: Arun Kumar, Samya Banerjee, Nishant Sharma, Mohd Nazish, Nico Graw, Regine Herbst-Irmer, Dietmar Stalke, Upakarasamy Lourderaj, Herbert W. Roesky|2022|Dalton Trans.|51|4898|doi:10.1039/D2DT00317A
DOI: 10.5517/ccdc.csd.cc29y7r8
title: CSD 2143270: Experimental Crystal Structure Determination

description: Related Article: Shan-Shan Han, Qian-Ting Xu, Wenlong Liu, Sheng-Ping Guo|2022|Dalton Trans.|51|4619|doi:10.1039/D2DT00268J
DOI: 10.5281/zenodo.6375540
title: A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset

description: A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (Martinez et al., 2022) and can be downloaded and cited from this landing page. <strong>The list is open for additions by the community via comments directly to this link. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the FAIR4RS Principles. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong> This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the FAIR4RS Principles; however, these still support their implementation. The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports. <strong>Data collection</strong> This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!
DOI: 10.5281/zenodo.6375539
title: A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset

description: A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (Martinez et al., 2022) and can be downloaded and cited from this landing page. <strong>The list is open for additions by the community via comments directly to this link. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the FAIR4RS Principles. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong> This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the FAIR4RS Principles; however, these still support their implementation. The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports. <strong>Data collection</strong> This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!
DOI: 10.6084/m9.figshare.19407539.v1
title: Untitled Item

description: Raw data for the article published in STOTEN by Qiao Yang et al. Any use of the data should contact at kyleyuechina@163.com.
DOI: 10.6084/m9.figshare.19407539
title: Raw data for Yang et al. STOTEN

description: Raw data for the article published in STOTEN by Qiao Yang et al. Any use of the data should contact at kyleyuechina@163.com.
DOI: 10.6084/m9.figshare.19407539.v3
title: Raw data for Yang et al. STOTEN

description: Raw data for the article published in STOTEN by Qiao Yang et al. Any use of the data should contact at kyleyuechina@163.com.
DOI: 10.6084/m9.figshare.19407539.v2
title: Raw data for Yang et al. STOTEN

description: Raw data for the article published in STOTEN by Qiao Yang et al. Any use of the data should contact at kyleyuechina@163.com.
DOI: 10.6084/m9.figshare.16947391.v1
title: JWP_SupTable

description: Sup table 1 List of samples used in this study "A DNA barcode database for the woody plants of Japan"<br>Sup table 2 A summary of the representation of genera and species, sequence success of each barcode region, and identification rate at the speceis, genus and family level for each plant family native to Japan included in the Japanese Woody Plant DNA Barcoding Database.
DOI: 10.6084/m9.figshare.19407554
title: 冀豆17基因组特异性及结瘤过程中DNA甲基化研究论文附表

description: 冀豆17基因组特异性及结瘤过程中DNA甲基化研究<br>博士毕业论文附表
DOI: 10.6084/m9.figshare.19407554.v1
title: 冀豆17基因组特异性及结瘤过程中DNA甲基化研究论文附表

description: 冀豆17基因组特异性及结瘤过程中DNA甲基化研究<br>博士毕业论文附表
DOI: 10.5281/zenodo.6358322
title: Data to: Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China

description: This dataset contains the data to generate the results in the manuscript "Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China". The downscaled results (BCSD, BCCI, BCCAQ, and CDF-t) are created by R.
DOI: 10.5281/zenodo.4471180
title: Data to: Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China

description: This dataset contains the data to generate the results in the manuscript "Downscaling and uncertainty analysis of future compound long-duration dry and hot events in China". The downscaled results (BCSD, BCCI, BCCAQ, and CDF-t) are created by R.
DOI: 10.5517/ccdc.csd.cc2900hv
title: CCDC 2115176: Experimental Crystal Structure Determination

description: Related Article: Yunhou Huang, Na Liu, Qiuyue Ning, Min Zhou, Ning Zang, Taoyuan Liang, Wanxing Wei|2022|J.Mol.Struct.|1260|132789|doi:10.1016/j.molstruc.2022.132789
DOI: 10.5517/ccdc.csd.cc2bbjlt
title: CCDC 2155076: Experimental Crystal Structure Determination

description: Related Article: Andrey Ilyukhin, Petr Buykin|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bbyf2
title: CCDC 2155474: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, Z. Yuan|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bbyg3
title: CCDC 2155475: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, Z. Yuan|2022|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2bdj5g
title: CCDC 2156985: Experimental Crystal Structure Determination

description: Related Article: W. Clegg, Z. Yuan, G.S. Nichol|2022|CSD Communication|||
DOI: 10.5281/zenodo.6381027
title: SUNRISE Campaign 2021

description: Observational data of SUNRISE Campaign 2021
DOI: 10.5281/zenodo.6381026
title: SUNRISE Campaign 2021

description: Observational data of SUNRISE Campaign 2021
DOI: 10.5281/zenodo.5854312
title: Tropospheric delays and precipitable water vapor retrieved from global radiosonde observations from 2014 to 2019

description: The data consists of a set of meteorological quantities including tropospheric delays (zenith wet delay, zenith hydrostatic delay, and zenith total delay), precipitable water vapor, and surface temperature and pressure. The data is retrieved from the observations of 414 globally distributed radiosonde stations from 2014 to 2019. In addition to the geographic information of radiosonde stations, the profiles of tropospheric delays and precipitable water vapor are contained in the data file. This data has a wide range of applications, e.g., validating the tropospheric delays and precipitable water vapor derived from other techniques, investigating the spatial-temporal variations of water vapor, and acting as training data of machine learning to build tropospheric delay models. In the manuscript "Machine Learning-based Model for Real-time GNSS Precipitable Water Vapor Sensing", this data is used to train a machine learning model to map the zenith total delays to precipitable water vapor. The data is split into training data and test data, where the data from 2014 to 2018 are employed for model training, and the data of 2019 are used for testing. If you use these data for scientific research, we would appreciate a cite of both the data and the paper. A suitable reference is: Zheng, Y., Lu, C., Wu, Z., Liao, J., Zhang, Y., & Wang, Q. (2022). Machine Learning‐Based Model for Real‐Time GNSS Precipitable Water Vapor Sensing. <em>Geophysical Research Letters</em>, <em>49</em>(3), e2021GL096408.
DOI: 10.25345/c55x25h1d
title: MassIVE MSV000089118 - Nitrogen competition and proteome allocation in a surface-ocean microbial community
title: Dataset from a shipboard incubation experiment of an ocean surface-water microbial community sampled at 25m depth at Station ALOHA in the North Pacific Subtropical Gyre. Incubations were amended with ammonium, glutamate, leucine, nitrate and urea, in two isotopic variants: 15N (to track incorporation by various community members) and 14N (for quantitation of abundance changes by diDO-IPTL).
DOI: 10.6084/m9.figshare.19158710
title: Data of A Real-time Safe Path Planning System For Cooperative Robot

description: Data of A Real-time Safe Path Planning System For Cooperative Robot
DOI: 10.6084/m9.figshare.19158710.v3
title: Data of A Real-time Safe Path Planning System For Cooperative Robot

description: Data of A Real-time Safe Path Planning System For Cooperative Robot
DOI: 10.6084/m9.figshare.19158710.v2
title: Data of A Real-time Safe Path Planning System For Cooperative Robot

description: Data of A Real-time Safe Path Planning System For Cooperative Robot
DOI: 10.5281/zenodo.6381052
title: Usability Evaluation of OFLAT Platform

description: Data and surveys used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.5281/zenodo.6381051
title: Usability Evaluation of OFLAT Platform

description: Data and surveys used in Usability Evaluation of OFLAT Platform Dissertation.
DOI: 10.5281/zenodo.6381073
title: PC338 major elements and Sr-Nd isotopes

description: Major elements and Sr-Nd isotopes data for the core PC338 from South China Sea.
DOI: 10.5281/zenodo.5720429
title: PC338 major elements and Sr-Nd isotopes

description: Major elements and Sr-Nd isotopes data for the core PC338 from South China Sea.
DOI: 10.14457/cu.the.2014.1585
title: Life cycle material flow analysis of PVC products in Thailand
DOI: 10.5281/zenodo.6381043
title: TXLA simulation and particle tracking

description: Output of the simulation and particle tracking in the TXLA model