cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/56a2a23c0964d11565f8b80e4e42469c
title: Minimum temperature at El Verde Field Station, Rio Grande, Puerto Rico since 1975

description: Daily emperature has been measured at the El Verde Field Station since 1975 (see methods). Monthly averages have been calculated. Lowest average values for minimum temperature were recorded from January to April with nearly 18 Centigrades during this period of time (see chart). During these months, the lowest average minimum temperature was recorded in 1976 ranging from 20 to 22 Centigrades (see chart). Highest minimum montly average records are shown from June to October with peak in the vecinity of 20 Centigrades. The year with lowest minimum tempearture was 1995 and 2000 with the low values near 14 and 15 Centigrades, respectively.
DOI: 10.6084/m9.figshare.18427106
title: The Equity Implications of the Augmented Deployment of Educational Technologies in ESL Education at the University Level

description: The data set includes SPSS files and interview transcriptions.
DOI: 10.6084/m9.figshare.18427106.v1
title: The Equity Implications of the Augmented Deployment of Educational Technologies in ESL Education at the University Level

description: The data set includes SPSS files and interview transcriptions.
DOI: 10.34740/kaggle/ds/1864386
title: PortugalLegislativeElections
DOI: 10.48511/g1dj-j211
title: Whatshan Lake Field Water Quality

description: Ancillary water quality data (temperature, pH and Secchi depth) for Whatshan Lake, collected by CKISS as part of their invasive mussel monitoring program.
DOI: 10.5517/ccdc.csd.cc226g2v
title: CCDC 1912826: Experimental Crystal Structure Determination

description: Related Article: Falu Hu, Zhengyi Di, Mingyan Wu, Maochun Hong, Jing Li|2019|Cryst.Growth Des.|19|6381|doi:10.1021/acs.cgd.9b00866
description: Related Article: Falu Hu|2019|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc24z92j
title: CCDC 1995317: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2021|CSD Communication|||
description: Related Article: Chia-Ching Liaw, Hung-Tse Huang, Hui-Kang Liu, Yu-Chi Lin, Li-Jie Zhang, Wen-Chi Wei, Chien-Chang Shen, Chia-Lun Wu, Chung-Yi Huang, Yao-Haur Kuo|2021|Phytochemistry|195|113026|doi:10.1016/j.phytochem.2021.113026
DOI: 10.5517/ccdc.csd.cc27vvhg
title: CCDC 2081386: Experimental Crystal Structure Determination

description: Related Article: Marco Meyer, Lorenzo Mardegan, Daniel Tordera, Alessandro Prescimone, Michele Sessolo, Henk J. Bolink, Edwin C. Constable, Catherine E. Housecroft|2021|Dalton Trans.|50|17920|doi:10.1039/D1DT03239A
DOI: 10.5517/ccdc.csd.cc283mst
title: CCDC 2088866: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc283mvw
title: CCDC 2088868: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc283mwx
title: CCDC 2088869: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc2882rd
title: CCDC 2093143: Experimental Crystal Structure Determination

description: Related Article: Yuhan Lin, Jian Zhang, Haardik Pandey, Xinglong Dong, Qihan Gong, Hao Wang, Liang Yu, Kang Zhou, Wei Yu, Xiaoxi Huang, Timo Thonhauser, Yu Han, Jing Li|2021|J.Mater.Chem.A|9|26202|doi:10.1039/D1TA08055E
DOI: 10.5517/ccdc.csd.cc28y4s5
title: CCDC 2113387: Experimental Crystal Structure Determination

description: Related Article: Pu-Sheng Wang|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28y5t7
title: CCDC 2113419: Experimental Crystal Structure Determination

description: Related Article: Jiuyi Sun|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28y6bs
title: CCDC 2113435: Experimental Crystal Structure Determination

description: Related Article: Xiang-Jin Zhang, Zhuo Wang, Han Zhang, Jing-Jing Gao, Kai-Rui Yang, Wei-Yu Fan, Rui-Xue Wu, Meng-Lin Feng, Wei Zhu, Yan-Ping Zhu|2021|J.Org.Chem.|87|835|doi:10.1021/acs.joc.1c02508
DOI: 10.5517/ccdc.csd.cc28yt35
title: CCDC 2114017: Experimental Crystal Structure Determination

description: Related Article: Masami Sakamoto, Takumi Nakamura, Yasushi Yoshida, Takashi Mino|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ytbd
title: CCDC 2114024: Experimental Crystal Structure Determination

description: Related Article: Masami Sakamoto, Takumi Nakamura, Yasushi Yoshida, Takashi Mino|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ytcf
title: CCDC 2114025: Experimental Crystal Structure Determination

description: Related Article: Masami Sakamoto, Takumi Nakamura, Yasushi Yoshida, Takashi Mino|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc28ytdg
title: CCDC 2114026: Experimental Crystal Structure Determination

description: Related Article: Masami Sakamoto, Takumi Nakamura, Yasushi Yoshida, Takashi Mino|2021|CSD Communication|||
DOI: 10.5517/ccdc.csd.cc291tv1
title: CCDC 2116923: Experimental Crystal Structure Determination

description: Related Article: Anders Reinholdt, Seongyeon Kwon, Mehrafshan G. Jafari, Michael R. Gau, Patrick J. Caroll, Chad Lawrence, Jun Gu, Mu-Hyun Baik, Daniel J. Mindiola|2021|J.Am.Chem.Soc.|144|527|doi:10.1021/jacs.1c11215
DOI: 10.5517/ccdc.csd.cc291tw2
title: CCDC 2116924: Experimental Crystal Structure Determination

description: Related Article: Anders Reinholdt, Seongyeon Kwon, Mehrafshan G. Jafari, Michael R. Gau, Patrick J. Caroll, Chad Lawrence, Jun Gu, Mu-Hyun Baik, Daniel J. Mindiola|2021|J.Am.Chem.Soc.|144|527|doi:10.1021/jacs.1c11215
DOI: 10.5517/ccdc.csd.cc291tx3
title: CCDC 2116925: Experimental Crystal Structure Determination

description: Related Article: Anders Reinholdt, Seongyeon Kwon, Mehrafshan G. Jafari, Michael R. Gau, Patrick J. Caroll, Chad Lawrence, Jun Gu, Mu-Hyun Baik, Daniel J. Mindiola|2021|J.Am.Chem.Soc.|144|527|doi:10.1021/jacs.1c11215
DOI: 10.25390/caryinstitute.11689263.v2
title: Cary Environmental Monitoring Program UVB Solar Radiation Data: 2000-2020

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, the Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: The following is a standard citation for referencing data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program:<br>Cary Institute of Ecosystem Studies, Environmental Monitoring Program. 2019 (or current year). Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, www.caryinstitute.org.<br>Those wishing to publish data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact Data Manager Vicky Kelly, kellyv@caryinstitute.org.
DOI: 10.25390/caryinstitute.11689263.v3
title: Cary Environmental Monitoring Program UVB Solar Radiation Data: 2000-2020

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, the Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: The following is a standard citation for referencing data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program:<br>Cary Institute of Ecosystem Studies, Environmental Monitoring Program. 2019 (or current year). Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, www.caryinstitute.org.<br>Those wishing to publish data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact Data Manager Vicky Kelly, kellyv@caryinstitute.org.
DOI: 10.25390/caryinstitute.11572944.v2
title: Cary Environmental Monitoring Program 3-Minute Meteorological and Solar Radiation Data (2011-2020)

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, the Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: The following is a standard citation for referencing data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program:<br>Cary Institute of Ecosystem Studies, Environmental Monitoring Program. 2020 (or current year). Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, www.caryinstitute.org.<br>Those wishing to publish data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact Data Manager Vicky Kelly, kellyv@caryinstitute.org.
DOI: 10.25390/caryinstitute.11553219.v2
title: Cary Environmental Monitoring Program Daily Meteorological and Solar Radiation Data: 1988-2020

description: The Cary Institute of Ecosystem Studies Environmental Monitoring Program is a long-term data collection program designed to understand how the environment changes over time. The program includes monitoring of climate including temperature and precipitation, as well as variables related to air pollution, such as acid deposition and ozone, and water pollution and other streamwater chemistry. Our solar radiation monitoring includes diffuse and global photosynthetically active radiation (PAR), diffuse and global shortwave radiation, net radiation and UV. Long-term monitoring of solar radiation provides us with an understanding of atmospheric energy dynamics, which can affect natural and human systems. The Cary Institute of Ecosystem Studies, Environmental Monitoring Program furnishes data under the following conditions: The data have received quality assurance scrutiny by our program, and, although we are confident of the accuracy of these data, Cary Institute will not be held liable for errors in these data. Data are subject to change resulting from updates in data screening or models used. Data citation: The following is a standard citation for referencing data from the Cary Institute of Ecosystem Studies, Environmental Monitoring Program:Cary Institute of Ecosystem Studies, Environmental Monitoring Program. 2018 (or current year). Cary Institute of Ecosystem Studies, Box AB, Millbrook, NY 12545, www.caryinstitute.org.Those wishing to publish data from Cary Institute of Ecosystem Studies, Environmental Monitoring Program are encouraged to contact data manager Vicky Kelly, kellyv@caryinstitute.org.
DOI: 10.5281/zenodo.5850003
title: ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

description: There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings. Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/ This is the <strong>main dataset and OOD test set. </strong>Please see project page for links to the dataset variants.
DOI: 10.5281/zenodo.5850002
title: ClevrTex: A Texture-Rich Benchmark for Unsupervised Multi-Object Segmentation

description: There has been a recent surge in methods that aim to decompose and segment scenes into multiple objects in an unsupervised manner, i.e., unsupervised multi-object segmentation. Performing such a task is a long-standing goal of computer vision, offering to unlock object-level reasoning without requiring dense annotations to train segmentation models. Despite significant progress, current models are developed and trained on visually simple scenes depicting mono-colored objects on plain backgrounds. The natural world, however, is visually complex with confounding aspects such as diverse textures and complicated lighting effects. In this study, we present a new benchmark called ClevrTex, designed as the next challenge to compare, evaluate and analyze algorithms. ClevrTex features synthetic scenes with diverse shapes, textures and photo-mapped materials, created using physically based rendering techniques. ClevrTex has 50k examples depicting 3-10 objects arranged on a background, created using a catalog of 60 materials, and a further test set featuring 10k images created using 25 different materials. We benchmark a large set of recent unsupervised multi-object segmentation models on ClevrTex and find all state-of-the-art approaches fail to learn good representations in the textured setting, despite impressive performance on simpler data. We also create variants of the ClevrTex dataset, controlling for different aspects of scene complexity, and probe current approaches for individual shortcomings. Project webpage: https://www.robots.ox.ac.uk/~vgg/data/clevrtex/ This is the <strong>main dataset and OOD test set. </strong>Please see project page for links to the dataset variants.
DOI: 10.6084/m9.figshare.18427295
title: Dataset reliability validity responsiveness instrumented strength assessment

description: This dataset contains reliability, validity and responsiveness data of the instrumented strength assessment from typically developing children and children with cerebral palsy and Duchenne muscular dystrophy. This dataset is used in the paper: Instrumented strength assessment in typically developing children and children with a neural or neuromuscular disorder: a reliability, validity and responsiveness study
DOI: 10.5281/zenodo.5851364
title: Data for Sterzinger et al. (2022, in prep) - "Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol: evidence from observations and idealized simulations"

description: This is horizontally averaged data for Sterzinger et al. 2022 (in prep): Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol: evidence from observations and idealized simulations. Also included are horizontally averaged W^2 (vertical momentum flux) needed for the plotting code and otherwise unavailable in the raw horizontal average data.
DOI: 10.5281/zenodo.5847181
title: Data for Sterzinger et al. (2022, in prep) - "Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol: evidence from observations and idealized simulations"

description: This is horizontally averaged data for Sterzinger et al. 2022 (in prep): Arctic mixed-phase clouds sometimes dissipate due to insufficient aerosol: evidence from observations and idealized simulations
DOI: 10.48511/st1e-tz21
title: Pend D'Oreille River Field Water Quality

description: Ancillary water quality data (temperature, pH and Secchi depth) for the Pend D'Oreille River, collected by CKISS as part of their invasive mussel monitoring program.
DOI: 10.6084/m9.figshare.18429224
title: Data repository for submitted manuscript "A model combining patchy saturation and moisture-induced elastic weakening with application to ultrasonic monitoring under varying water saturation" in Geophysical Research Letters

description: Data repository for submitted manuscript "A model combining patchy saturation and moisture-induced elastic weakening with application to ultrasonic monitoring under varying water saturation" in Geophysical Research Letters, including the datasheet for two figures and one table.
DOI: 10.6084/m9.figshare.18429224.v1
title: Data repository for submitted manuscript "A model combining patchy saturation and moisture-induced elastic weakening with application to ultrasonic monitoring under varying water saturation" in Geophysical Research Letters

description: Data repository for submitted manuscript "A model combining patchy saturation and moisture-induced elastic weakening with application to ultrasonic monitoring under varying water saturation" in Geophysical Research Letters, including the datasheet for two figures and one table.
DOI: 10.34740/kaggle/dsv/3044511
title: Omicron daily cases by country (COVID-19 variant)
DOI: 10.25573/data.14994282
title: Supplementary Files Unraveling the record of a tropical continental Cretaceous-Paleogene boundary in northern Colombia, South America

description: Contains:<br>Supplementary File 1. Stratigraphic description of the Diablito-1 Core.<br>Supplementary File 2. Abundance distribution of the palynomorphs found in Diablito-1 core.<br>Supplementary File 3. Abundance of 47 elements in the Diablito-1 core.<br>Supplementary File 4. Plot of the abundance of major and trace elements along the Diablito-1 core.<br>Supplementary File 5. Plot of the abundance of key biostratigraphic palynomorphs along the Diablito-1 core<br>Supplementary File 6. Magnetic Susceptibility of the Diablito-1 core.<br>Supplementary File 7. Deposition setting for Magnetic Susceptibility samples in the Diablito-1 core.<br>Supplementary File 8. Sørensen Cluster showing two distinct clusters, Maastrichtian (blue) and Paleocene (orange) including all samples analyzed in the study.@font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}p.Acknowledgement, li.Acknowledgement, div.Acknowledgement {mso-style-name:Acknowledgement; mso-style-unhide:no; margin-top:6.0pt; margin-right:0in; margin-bottom:0in; margin-left:.5in; text-indent:-.5in; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt;}div.WordSection1 {page:WordSection1;} @font-face {font-family:"Cambria Math"; panose-1:2 4 5 3 5 4 6 3 2 4; mso-font-charset:0; mso-generic-font-family:roman; mso-font-pitch:variable; mso-font-signature:-536870145 1107305727 0 0 415 0;}p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-unhide:no; mso-style-qformat:yes; mso-style-parent:""; margin:0in; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}p.Acknowledgement, li.Acknowledgement, div.Acknowledgement {mso-style-name:Acknowledgement; mso-style-unhide:no; margin-top:6.0pt; margin-right:0in; margin-bottom:0in; margin-left:.5in; text-indent:-.5in; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman",serif; mso-fareast-font-family:"Times New Roman";}.MsoChpDefault {mso-style-type:export-only; mso-default-props:yes; font-size:10.0pt; mso-ansi-font-size:10.0pt; mso-bidi-font-size:10.0pt;}div.WordSection1 {page:WordSection1;}