cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4537208
title: Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines

description: <strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Pauls Stradins Clinical University Hospital. There are 3185 hand washing episodes in total, each of which is annotated by up to seven different persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code. This dataset is part on three dataset series all following the same format: https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital https://zenodo.org/record/5808764 - data collected in Jurmala Hospital https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University <strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff. <strong>Statistics:</strong> Frame rate: 30 FPS Resolution: 320x240 and 640x480 Number of videos: 3185 Number of annotation files: 6690 Movement codes (both in CSV and JSON files): 1: Hand washing movement — Palm to palm 2: Hand washing movement — Palm over dorsum, fingers interlaced 3: Hand washing movement — Palm to palm, fingers interlaced 4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked 5: Hand washing movement — Rotational rubbing of the thumb 6: Hand washing movement — Fingertips to palm 7: Turning off the faucet with a paper towel 0: Other hand washing movement Additional annotations (in JSON files only): Armband or watch present Ring present Long nails present <strong>Acknowledgments: </strong>The dataset collection was funded by the Ministry of Education and Science, Republic of Latvia, project “Integration of reliable technologies for protection against Covid-19 in healthcare and high-risk areas”, project No. VPP-COVID-2020/1-0004. <strong>References: </strong>For more detailed information, see this article: <em>M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis and A. Elsts. Hand Washing Video Dataset Annotated According to the World Health Organization’s Handwashing Guidelines. Submitted to MDPI Data, 2021.</em> <strong>Contact information: </strong>atis.elsts@edi.lv
DOI: 10.5281/zenodo.5814726
title: GABLS4, snow model intercomparison.

description: Content of the archive: <strong>FORCING</strong>: The near-surface variable forcing dataset used to drive the offline simulations. Time step of 30 minutes, start date 1/12/2009, 15 days. <strong>SIMULATIONS</strong>: NetCDF output files from participating models. <strong>OBSERVATIONS</strong>: Surface temperature observation time series and observations of snow temperature in the snowpack at different depths used for the validation. <strong>FIGURES</strong>: The datasets and the python scripts used to prepare the figures.
DOI: 10.5281/zenodo.5814725
title: GABLS4, snow model intercomparison.

description: Content of the archive: <strong>FORCING</strong>: The near-surface variable forcing dataset used to drive the offline simulations. Time step of 30 minutes, start date 1/12/2009, 15 days. <strong>SIMULATIONS</strong>: NetCDF output files from participating models. <strong>OBSERVATIONS</strong>: Surface temperature observation time series and observations of snow temperature in the snowpack at different depths used for the validation. <strong>FIGURES</strong>: The datasets and the python scripts used to prepare the figures.
DOI: 10.5281/zenodo.5808764
title: Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset

description: <strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code. This dataset is part on three dataset series all following the same format: https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital https://zenodo.org/record/5808764 - data collected in Jurmala Hospital https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University <strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff. <strong>Statistics:</strong> Frame rate: 30 FPS Resolution: 320x240 and 640x480 Number of videos: 2427 Number of annotation files: 4818 Movement codes (both in CSV and JSON files): 1: Hand washing movement — Palm to palm 2: Hand washing movement — Palm over dorsum, fingers interlaced 3: Hand washing movement — Palm to palm, fingers interlaced 4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked 5: Hand washing movement — Rotational rubbing of the thumb 6: Hand washing movement — Fingertips to palm 7: Turning off the faucet with a paper towel 0: Other hand washing movement <strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309. <strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project: M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038 <strong>Contact information: </strong>atis.elsts@edi.lv
DOI: 10.5281/zenodo.5808763
title: Hand Washing Video Dataset Annotated According to the World Health Organization's Handwashing Guidelines - Jurmala Hospital Subset

description: <strong>Overview:</strong> This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code. This dataset is part on three dataset series all following the same format: https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital https://zenodo.org/record/5808764 - data collected in Jurmala Hospital https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University <strong>Applications: </strong>The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff. <strong>Statistics:</strong> Frame rate: 30 FPS Resolution: 320x240 and 640x480 Number of videos: 2427 Number of annotation files: 4818 Movement codes (both in CSV and JSON files): 1: Hand washing movement — Palm to palm 2: Hand washing movement — Palm over dorsum, fingers interlaced 3: Hand washing movement — Palm to palm, fingers interlaced 4: Hand washing movement — Backs of fingers to opposing palm, fingers interlocked 5: Hand washing movement — Rotational rubbing of the thumb 6: Hand washing movement — Fingertips to palm 7: Turning off the faucet with a paper towel 0: Other hand washing movement <strong>Acknowledgments: </strong>The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309. <strong>References: </strong>For more detailed information, see this article, describing a similar dataset collected in a different project: M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. <em>Hand-Washing Video Dataset Annotated According to the World Health Organization’s Hand-Washing Guidelines</em>. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038 <strong>Contact information: </strong>atis.elsts@edi.lv
DOI: 10.5281/zenodo.5807274
title: On the determinants of low-carbon innovations: Roles of technological diversification and international collaborations

description: This data describes the role of technological diversification and international collaborations, and how they affect the intensity of low-carbon innovations. The dataset is made up of a global panel dataset consisting of patents and macroeconomic data for 90 countries, covering a period of 32 years. It also contains indices for technological diversifications and collaborations.
DOI: 10.5281/zenodo.5807273
title: On the determinants of low-carbon innovations: Roles of technological diversification and international collaborations

description: This data describes the role of technological diversification and international collaborations, and how they affect the intensity of low-carbon innovations. The dataset is made up of a global panel dataset consisting of patents and macroeconomic data for 90 countries, covering a period of 32 years. It also contains indices for technological diversifications and collaborations.
DOI: 10.5281/zenodo.5813993
title: Jemma Representations Methods - C2VC

description: Jemma Representations Methods - C2VC &gt; file: Jemma_Representations_Methods_C2VC.csv<br> &gt; size: 6.1 GB<br> &gt; num_rows: 1,000,000<br> &gt; columns: method_id, method_c2vc
DOI: 10.5281/zenodo.5813992
title: Jemma Representations Methods - C2VC

description: Jemma Representations Methods - C2VC &gt; file: Jemma_Representations_Methods_C2VC.csv<br> &gt; size: 6.1 GB<br> &gt; num_rows: 1,000,000<br> &gt; columns: method_id, method_c2vc
DOI: 10.6084/m9.figshare.17719370
title: Supplemental Material S1

description: The table shows the number of animals used in each study group and the number of recording sites in each frequency region
DOI: 10.6084/m9.figshare.17719370.v1
title: Supplemental Material S1

description: The table shows the number of animals used in each study group and the number of recording sites in each frequency region
DOI: 10.6084/m9.figshare.17718695
title: Supplemental Material S2

description: The table shows the parameter estimates of the linear mixed effects model for MUA and LFP for acute and chronic trauma. <b></b>
DOI: 10.6084/m9.figshare.17718695.v2
title: Supplemental Material S2

description: The table shows the parameter estimates of the linear mixed effects model for MUA and LFP for acute and chronic trauma. <b></b>
DOI: 10.6084/m9.figshare.17718695.v1
title: Supplemental Material S2

description: The table shows the parameter estimates of the linear mixed effects model for MUA and LFP for acute and chronic trauma. <b></b>
DOI: 10.25721/dx64-1592
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.25721/fqej-es75
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.25721/zz3t-x943
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.25721/z9d6-b639
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.6084/m9.figshare.17205863
title: Prevalence of xerostomia and its associated risk factors among Saudi population: A cross-sectional survey

description: Xerostomia is a condition that has been shown to have an impact on the quality of life. Thus, exploring the associated risk factors and conditions needs special attention. There is still a lack of complete information regarding this condition among the Saudi population
DOI: 10.6084/m9.figshare.17205863.v1
title: Prevalence of xerostomia and its associated risk factors among Saudi population: A cross-sectional survey

description: Xerostomia is a condition that has been shown to have an impact on the quality of life. Thus, exploring the associated risk factors and conditions needs special attention. There is still a lack of complete information regarding this condition among the Saudi population
DOI: 10.5519/qd.iyfeq1lk
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (283 records)
DOI: 10.5281/zenodo.5814855
title: Decomposing Monolithic Processes using the mCRL2 toolset

description: A set of experiments consisting of mCRL2 specifications that can be decomposed into two (or more) monolithic processes using the lpscleave tool of the mCRL2 toolset. The resulting monolithic processes can be subsequently composed to obtain the state space of the original specification more efficiently than by exploring the original specification itself. The technique is described in detail in the "Decomposing Monolithic Processes in a Process Algebra with Multi-actions" paper.
DOI: 10.5281/zenodo.5091849
title: Decomposing Monolithic Processes using the mCRL2 toolset

description: A set of experiments consisting of mCRL2 specifications that can be decomposed into two (or more) monolithic processes using the lpscleave tool of the mCRL2 toolset. The resulting monolithic processes can be subsequently composed to obtain the state space of the original specification more efficiently than by exploring the original specification itself. The technique is described in detail in the "Decomposing Monolithic Processes in a Process Algebra with Multi-actions" paper.
DOI: 10.25721/yhra-0133
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.5517/ccdc.csd.cc28sddw
title: CCDC 2108818: Experimental Crystal Structure Determination

description: Related Article: Divambal Appavoo, Juanita L. van Wyk, Lara C. Spencer, Ilia A. Guzei, James Darkwa|2022|Results in Chemistry|4|100261|doi:10.1016/j.rechem.2021.100261
DOI: 10.25721/artx-9r55
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.25721/msne-2a61
title: Fabrication du galoubet : tournage du bois (présentation du gabarit)
DOI: 10.25721/ws3y-b755
title: Fabrication du galoubet : tournage du bois (perce longitudinale et traçage des trous)
DOI: 10.25721/kzhh-q751
title: Fabrication du galoubet : tournage du bois (présentation du gabarit)
DOI: 10.25721/6qe0-mh57
title: Fabrication du galoubet : tournage du bois (présentation du gabarit)
DOI: 10.25721/xye7-fa18
title: Fabrication du galoubet : tournage du bois (perçage à la gouge)
DOI: 10.6084/m9.figshare.17722278
title: HiPerMovelets: high-performance movelet extraction for trajectory classification

description: In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories.
DOI: 10.6084/m9.figshare.17722278.v1
title: HiPerMovelets: high-performance movelet extraction for trajectory classification

description: In the last decade, trajectory classification has received significant attention. The vast amount of data generated on social media, the use of sensor networks, IOT devices and other Internet-enabled sources allowed the semantic enrichment of mobility data, making the classification task more challenging. Existing trajectory classification methods have mainly considered space, time and numerical data, ignoring the semantic dimensions. Only recently proposed methods as Movelets and MASTERMovelets can handle all types of dimensions. MASTERMovelets is the only method that automatically discovers the best dimension combination and subtrajectory size for trajectory classification. However, although it outperformed the state-of-the-art in terms of accuracy, MASTERMovelets is computationally expensive and results in a high dimensionality problem, which makes it unfeasible for most real trajectory datasets that contain a big volume of data. To overcome this problem and enable the application of the movelets approach on large datasets, in this paper we propose a new high-performance method for extracting movelets and classifying trajectories, called HiPerMovelets (High-performance Movelets). Experimental results show that HiPerMovelets is 10 times faster than MASTERMovelets, reduces the high-dimensionality problem, is more scalable, and presents a high classification accuracy in all evaluated datasets with both raw and semantic trajectories.
DOI: 10.6084/m9.figshare.16843180
title: anger in predicting the index futures returns.xlsx

description: The data is for replicating the main findings of <b><i>Anger in predicting the index futures returns</i></b>.
DOI: 10.6084/m9.figshare.16843180.v1
title: anger in predicting the index futures returns.xlsx

description: The data is for replicating the main findings of <b><i>Anger in predicting the index futures returns</i></b>.
DOI: 10.48370/ofd/uesrkw
title: Phaeosphaeria caricis (J. Schröt.) Leuchtm., 66

description: Fungarium Micromycetes
DOI: 10.25345/c5xs2k
title: MassIVE MSV000088630 - CLINT1 is phosphorylated by BIKE and mediates dengue virus infection
title: Identification of CLINT1 as a phosphorylatable target by BIKE protein.
DOI: 10.5281/zenodo.5814977
title: Strategic low-cost energy investment opportunities and challenges towards achieving universal access (SDG7) in each African nation.

description: This dataset underpins the study "Strategic low-cost energy investment opportunities and challenges towards achieving universal access (SDG7) in each African nation". The study provides insights into energy supply and demand, power generation, investments and total system costs, SD7 indicators, job creation as well as carbon dioxide emissions for each African nation (48 in total). An energy systems model enhanced with geospatial data was developed to evaluate energy supply requirements to cover the energy needs of the African continent during the period 2015-2030 and achieve universal access by 2030. The model was developed using the open-source modeling system for long-term energy planning OSeMOSYS and the geospatial electrification outlook (GEP). The objective function is to minimise the total energy system costs. The results can be found https://doi.org/10.5281/zenodo.5810529
DOI: 10.48370/ofd/fbouvb
title: Phaeosphaeria herpotrichoides (De Not) L. Holm, 3123

description: Fungarium Micromycetes
DOI: 10.5517/ccdc.csd.cc27vk3s
title: CCDC 2081095: Experimental Crystal Structure Determination

description: Related Article: Zhenzhen Gao, Lei Xie, Lusha Ji, Xin Ma, Xiaojing Li, Honglei Liu, Hongchao Guo|2021|RSC Advances|11|40136|doi:10.1039/D1RA07881J
DOI: 10.5517/ccdc.csd.cc285x0d
title: CCDC 2091043: Experimental Crystal Structure Determination

description: Related Article: I. Sivagami, RO. MU. Jauhar, Paavai. Era, V. Viswanathan, G. Vinitha, S. Ranjani, T. Sivanesan|2022|J.Cryst.Growth|580|126471|doi:10.1016/j.jcrysgro.2021.126471
DOI: 10.5517/ccdc.csd.cc288qpz
title: CCDC 2093761: Experimental Crystal Structure Determination

description: Related Article: Zhiqiang Xu, Xiaomeng Yang, Ziru Liu, Ming-Xing Zhang|2022|J.Photochem.Photobiol.A:Chem.|426|113731|doi:10.1016/j.jphotochem.2021.113731
DOI: 10.5517/ccdc.csd.cc28fw2p
title: CCDC 2098702: Experimental Crystal Structure Determination

description: Related Article: Ping Hai, Kairui Rao, Na Jiang, Dan Liu, Ruirui Wang, Yuan Gao, Xiaocong Liu, Sihao Deng, Yu Zhou, Xuanqin Chen, Xiaonian Li, Rongtao Li|2022|Bioorg.Chem.|119|105567|doi:10.1016/j.bioorg.2021.105567
DOI: 10.5517/ccdc.csd.cc28fw3q
title: CCDC 2098703: Experimental Crystal Structure Determination

description: Related Article: Ping Hai, Kairui Rao, Na Jiang, Dan Liu, Ruirui Wang, Yuan Gao, Xiaocong Liu, Sihao Deng, Yu Zhou, Xuanqin Chen, Xiaonian Li, Rongtao Li|2022|Bioorg.Chem.|119|105567|doi:10.1016/j.bioorg.2021.105567
DOI: 10.5517/ccdc.csd.cc28g2zt
title: CCDC 2098916: Experimental Crystal Structure Determination

description: Related Article: Josiéli Demetrio Siqueira, Sidnei Flores de Pellegrin, Liniquer André Fontana, Bernardo Almeida Iglesias, Michele Rorato Sagrillo, Pâmella Schramm Oliveira, Aline Rossato, Liana da Silva Fernandes, Ademir Neves, Otávio Augusto Chaves, Davi Fernando Back|2022|Polyhedron|212|115608|doi:10.1016/j.poly.2021.115608
DOI: 10.5517/ccdc.csd.cc28g31x
title: CCDC 2098918: Experimental Crystal Structure Determination

description: Related Article: Josiéli Demetrio Siqueira, Sidnei Flores de Pellegrin, Liniquer André Fontana, Bernardo Almeida Iglesias, Michele Rorato Sagrillo, Pâmella Schramm Oliveira, Aline Rossato, Liana da Silva Fernandes, Ademir Neves, Otávio Augusto Chaves, Davi Fernando Back|2022|Polyhedron|212|115608|doi:10.1016/j.poly.2021.115608
DOI: 10.5517/ccdc.csd.cc28g340
title: CCDC 2098921: Experimental Crystal Structure Determination

description: Related Article: Josiéli Demetrio Siqueira, Sidnei Flores de Pellegrin, Liniquer André Fontana, Bernardo Almeida Iglesias, Michele Rorato Sagrillo, Pâmella Schramm Oliveira, Aline Rossato, Liana da Silva Fernandes, Ademir Neves, Otávio Augusto Chaves, Davi Fernando Back|2022|Polyhedron|212|115608|doi:10.1016/j.poly.2021.115608
DOI: 10.5517/ccdc.csd.cc28g373
title: CCDC 2098924: Experimental Crystal Structure Determination

description: Related Article: Josiéli Demetrio Siqueira, Sidnei Flores de Pellegrin, Liniquer André Fontana, Bernardo Almeida Iglesias, Michele Rorato Sagrillo, Pâmella Schramm Oliveira, Aline Rossato, Liana da Silva Fernandes, Ademir Neves, Otávio Augusto Chaves, Davi Fernando Back|2022|Polyhedron|212|115608|doi:10.1016/j.poly.2021.115608
DOI: 10.5517/ccdc.csd.cc28l94b
title: CCDC 2102951: Experimental Crystal Structure Determination

description: Related Article: Guo-Jin Zha, Wei Ji, Zheng-Hang Qi, Wen-Jie Qiu, Ai-Min Li, Dun-Ru Zhu, Su Jing|2022|J.Catalysis|405|313|doi:10.1016/j.jcat.2021.12.007
DOI: 10.48370/ofd/ru0lro
title: Phaeosphaeria minima Shoemaker C.E. Babc., 1263

description: Fungarium Micromycetes
DOI: 10.34740/kaggle/dsv/2998552
title: Classification of Ripeness Stage of Mango Fruit