cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5122537
title: Vehicle Trips Dataset without Missing Values

description: This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response. The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).
description: {"references": ["fivethirtyeight, 2015. uber-tlc-foil-response. URL https://github.com/fivethirtyeight/uber-tlc-foil-response"]}
DOI: 10.5281/zenodo.5122536
title: Vehicle Trips Dataset without Missing Values

description: This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response. The original dataset contains missing values and they have been replaced by carrying forward the corresponding last seen observations (LOCF method).
description: {"references": ["fivethirtyeight, 2015. uber-tlc-foil-response. URL https://github.com/fivethirtyeight/uber-tlc-foil-response"]}
DOI: 10.5281/zenodo.5122535
title: Vehicle Trips Dataset with Missing Values

description: This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response.
description: {"references": ["fivethirtyeight, 2015. uber-tlc-foil-response. URL https://github.com/fivethirtyeight/uber-tlc-foil-response"]}
DOI: 10.5281/zenodo.5122534
title: Vehicle Trips Dataset with Missing Values

description: This dataset contains 329 daily time series representing the number of trips and vehicles belonging to a set of for-hire vehicle (FHV) companies. The dataset was originally extracted from https://github.com/fivethirtyeight/uber-tlc-foil-response.
description: {"references": ["fivethirtyeight, 2015. uber-tlc-foil-response. URL https://github.com/fivethirtyeight/uber-tlc-foil-response"]}
DOI: 10.6084/m9.figshare.15001053.v1
title: Supplementary data for "An Omics-based Framework for Assessing the Health Risk of Antimicrobial Resistance Genes"

description: Supplementary data for "An Omics-based Framework for Assessing the Health Risk of Antimicrobial Resistance Genes"
DOI: 10.6084/m9.figshare.15001053.v2
title: Supplementary data for "An Omics-based Framework for Assessing the Health Risk of Antimicrobial Resistance Genes"

description: Supplementary data for "An Omics-based Framework for Assessing the Health Risk of Antimicrobial Resistance Genes"
DOI: 10.5517/ccdc.csd.cc24ngjt
title: CCDC 1985876: Experimental Crystal Structure Determination

description: Related Article: Tzu-Hao Chiu, Jian-Hong Liao, Franck Gam, Isaac Chantrenne, Samia Kahlal, Jean-Yves Saillard, C. W. Liu|2021|Nanoscale|13|12143|doi:10.1039/D1NR02540F
DOI: 10.5517/ccdc.csd.cc12944n
title: CCDC 991880: Experimental Crystal Structure Determination

description: Related Article: Qiaozhen Pi, Dongqin Bi, Dongfang Qiu, Hongwei Wang, Xinfeng Cheng, Yuquan Feng, Qian Zhao, Mingdong Zhou|2021|J.Mater.Chem.C|9|8994|doi:10.1039/D1TC01425K
DOI: 10.5517/ccdc.csd.cc26lwl9
title: CCDC 2043941: Experimental Crystal Structure Determination

description: Related Article: Hector Miranda-Salinas, Yi-Tzu Hung, Yi-Sheng Chen, Dian Luo, Hao-Che Kao, Chih-Hao Chang, Ken-Tsung Wong, Andrew Monkman|2021|J.Mater.Chem.C|9|8819|doi:10.1039/D1TC02316K
DOI: 10.5517/ccdc.csd.cc26lwmb
title: CCDC 2043942: Experimental Crystal Structure Determination

description: Related Article: Hector Miranda-Salinas, Yi-Tzu Hung, Yi-Sheng Chen, Dian Luo, Hao-Che Kao, Chih-Hao Chang, Ken-Tsung Wong, Andrew Monkman|2021|J.Mater.Chem.C|9|8819|doi:10.1039/D1TC02316K
DOI: 10.5517/ccdc.csd.cc26lwnc
title: CCDC 2043943: Experimental Crystal Structure Determination

description: Related Article: Hector Miranda-Salinas, Yi-Tzu Hung, Yi-Sheng Chen, Dian Luo, Hao-Che Kao, Chih-Hao Chang, Ken-Tsung Wong, Andrew Monkman|2021|J.Mater.Chem.C|9|8819|doi:10.1039/D1TC02316K
DOI: 10.5517/ccdc.csd.cc1m9v81
title: CCDC 1499013: Experimental Crystal Structure Determination

description: Related Article: Hector Miranda-Salinas, Yi-Tzu Hung, Yi-Sheng Chen, Dian Luo, Hao-Che Kao, Chih-Hao Chang, Ken-Tsung Wong, Andrew Monkman|2021|J.Mater.Chem.C|9|8819|doi:10.1039/D1TC02316K
DOI: 10.5517/ccdc.csd.cc27grl2
title: CCDC 2069764: Experimental Crystal Structure Determination

description: Related Article: Tzu-Hao Chiu, Jian-Hong Liao, Franck Gam, Isaac Chantrenne, Samia Kahlal, Jean-Yves Saillard, C. W. Liu|2021|Nanoscale|13|12143|doi:10.1039/D1NR02540F
DOI: 10.5517/ccdc.csd.cc27grp5
title: CCDC 2069767: Experimental Crystal Structure Determination

description: Related Article: Tzu-Hao Chiu, Jian-Hong Liao, Franck Gam, Isaac Chantrenne, Samia Kahlal, Jean-Yves Saillard, C. W. Liu|2021|Nanoscale|13|12143|doi:10.1039/D1NR02540F
DOI: 10.5517/ccdc.csd.cc27k8wz
title: CCDC 2072191: Experimental Crystal Structure Determination

description: Related Article: Qui Pham Xuan, Jana Glatz, Amina Benchohra, Juan-Ramón Jiménez, Rémi Plamont, Lise-Marie Chamoreau, Alexandrine Flambard, Yanling Li, Laurent Lisnard, Damien Dambournet, Olaf J. Borkiewicz, Marie-Laure Boillot, Laure Catala, Antoine Tissot, Rodrigue Lescouëzec|2021|J.Mater.Chem.C|9|8882|doi:10.1039/D1TC01825F
DOI: 10.5517/ccdc.csd.cc27wh1p
title: CCDC 2081992: Experimental Crystal Structure Determination

description: Related Article: Omar J. Garcia, Laure Vendier, Michel Etienne, Steven Gwaltney, Abigail Ressler, Miguel-Ángel Muñoz-Hernández|2021|Inorg.Chem.|60|10757|doi:10.1021/acs.inorgchem.1c01434
DOI: 10.5517/ccdc.csd.cc27wh2q
title: CCDC 2081993: Experimental Crystal Structure Determination

description: Related Article: Omar J. Garcia, Laure Vendier, Michel Etienne, Steven Gwaltney, Abigail Ressler, Miguel-Ángel Muñoz-Hernández|2021|Inorg.Chem.|60|10757|doi:10.1021/acs.inorgchem.1c01434
DOI: 10.5517/ccdc.csd.cc280n21
title: CCDC 2085992: Experimental Crystal Structure Determination

description: Related Article: Jesús Bello-García, Damián Padín, Jesús A. Varela, Carlos Saá|2021|Org.Lett.|23|5539|doi:10.1021/acs.orglett.1c01881
DOI: 10.5517/ccdc.csd.cc280n32
title: CCDC 2085993: Experimental Crystal Structure Determination

description: Related Article: Jesús Bello-García, Damián Padín, Jesús A. Varela, Carlos Saá|2021|Org.Lett.|23|5539|doi:10.1021/acs.orglett.1c01881
DOI: 10.5517/ccdc.csd.cc280n43
title: CCDC 2085994: Experimental Crystal Structure Determination

description: Related Article: Jesús Bello-García, Damián Padín, Jesús A. Varela, Carlos Saá|2021|Org.Lett.|23|5539|doi:10.1021/acs.orglett.1c01881
DOI: 10.25505/fiz.icsd.cc280p11
title: CSD 2086022: Experimental Crystal Structure Determination

description: Related Article: Pascale Armand, Dominique Granier, Corine Reibel, Léa Daenens, Monique Tillard|2021|J.Alloys Compd.|884|161074|doi:10.1016/j.jallcom.2021.161074
DOI: 10.5517/ccdc.csd.cc280pqq
title: CCDC 2086043: Experimental Crystal Structure Determination

description: Related Article: Sourav Biswas, Zahra A. Tabasi, Jian-Bin Lin, Yuming Zhao, Graham J. Bodwell|2021|Org.Lett.|23|5461|doi:10.1021/acs.orglett.1c01791
DOI: 10.5517/ccdc.csd.cc280prr
title: CCDC 2086044: Experimental Crystal Structure Determination

description: Related Article: Sourav Biswas, Zahra A. Tabasi, Jian-Bin Lin, Yuming Zhao, Graham J. Bodwell|2021|Org.Lett.|23|5461|doi:10.1021/acs.orglett.1c01791
DOI: 10.5517/ccdc.csd.cc280pss
title: CCDC 2086045: Experimental Crystal Structure Determination

description: Related Article: Sourav Biswas, Zahra A. Tabasi, Jian-Bin Lin, Yuming Zhao, Graham J. Bodwell|2021|Org.Lett.|23|5461|doi:10.1021/acs.orglett.1c01791
DOI: 10.5517/ccdc.csd.cc281h50
title: CCDC 2086801: Experimental Crystal Structure Determination

description: Related Article: Min Seo Park, Kyeongwon Moon, Harin Oh, Ji Yoon Lee, Prithwish Ghosh, Ju Young Kang, Jung Su Park, Neeraj Kumar Mishra, In Su Kim|2021|Org.Lett.|23|5518|doi:10.1021/acs.orglett.1c01866
DOI: 10.5517/ccdc.csd.cc281h61
title: CCDC 2086802: Experimental Crystal Structure Determination

description: Related Article: Min Seo Park, Kyeongwon Moon, Harin Oh, Ji Yoon Lee, Prithwish Ghosh, Ju Young Kang, Jung Su Park, Neeraj Kumar Mishra, In Su Kim|2021|Org.Lett.|23|5518|doi:10.1021/acs.orglett.1c01866
DOI: 10.25919/206k-ba53
title: Parkes observations for project P456 semester 2021APRS_06

description: The Parkes Pulsar Timing Array (PPTA) project has three primary goals: (a) detection of gravitational waves from astronomical sources, (b) establishment of a pulsar timescale, and (c) improvement of our understanding of Solar-system dynamics. The PPTA is the oldest of three international pulsar timing groups. We have the smallest telescope and the smallest group, but the best measurements and the best results. Our bound on the strength of the incoherent gravitational wave background is the only bound which significantly constrains theoretical models. We have maintained our leading position because: we have the southern sky; we have a small but well-focussed group; we have the best-calibrated receivers; and we have been able to observe with an almost regular cadence of 15 to 20 days over a wide bandwidth. With this proposal we aim to maintain our pre-eminent position in the field. Unlike most observing proposals, this is a continuing proposal for which the observations will continue to improve bounds on ultra-low-frequency gravitational waves until they are finally detected. Continued Parkes observations will remain valuable at least until the first stage of the SKA is able to improve on our sensitivity and observing cadence. Even after the gravitational wave background is detected we will want to continue observations in support of the nascent field of gravitational wave astronomy!
DOI: 10.34808/md4y-e980
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (Białystok University of Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of  the Białystok University of Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.34808/92k2-wd31
title: Depth profile of the composition of 8 nm Al2O3 thin film

description: 


8 nm layer of aluminum oxide (Al2O3) was deposited by ALD method on a s.  Atomic layer deposition provides precise thickness control down to a single atomic layer. The precursors used were trimethylaluminum (Sigma-Aldrich) and purified water. The deposition of the atomic layer was carried out at 200 °C. To investigate the profile of concenration of elements film was etched by Ar ion gun 5 times. Each etching takes 30 sec. XPS measurements were carried out at room temperature under ultrahigh vacuum conditions and pressures below 1.1 x 10-6 Pa using Omicron NanoScience equipment. Data analysis was performed with the CASA XPS software package using Shirley background subtraction and Gauss-Lorentz curve fitting algorithm by the least-squares method - GL (30). The resulting spectra were calibrated to obtain a binding energy of 285.00 eV for C 1s.
DOI: 10.34808/e3yh-0934
title: Investigation of the uniformity of TeO2:Eu layer

description: TeO2 doped by Eu thin films manufactured by magnetron sputtering method were measured by XPS method.  Te-Eu mosaic target with diameter of 50.8 mm was sputtered for about 45 min in argon-oxygen atmosphere what resulted in 300 nm film thickness deposition. The pressure in the deposition chamber was below 0.2 Pa and substrate was heated  at 200 oC during deposition. The distance between sputtered targets and the Corning 1737 glass substrate was about 10 cm. Uniformity of the film was measured by XPS method with Ar ion gun etching. For analysis etching for 2, 4, 6 and 8 minutes was performed. Results showed good uniformity of film.
DOI: 10.5281/zenodo.5121880
title: 7 T MRI rawdata for (part 2): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.
DOI: 10.5281/zenodo.5121879
title: 7 T MRI rawdata for (part 2): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.
DOI: 10.5281/zenodo.5120627
title: 7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.
DOI: 10.5281/zenodo.5120626
title: 7T MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI 7 T raw data for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset only contains additional raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively.
DOI: 10.6084/m9.figshare.15036012.v1
title: MXtrodes: MXene-infused bioelectronic interfaces

description: Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a novel class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti<sub>3</sub>C<sub>2</sub>­ MXene and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in a number of applications ranging from mapping large scale neuromuscular networks in humans to delivering cortical microstimulation in small animal models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.<br>This repository contains the files necessary to reproduce the analysis for this manuscript:(https://doi.org/10.1101/2021.03.01.433237)<br><br>
DOI: 10.34808/f9gz-cg03
title: Bibliometric data for a research study on scientific productivity of Polish technical universities (AGH University of Science & Technology 2016-2020) retrieved by InCites benchmarking tool.

description: This dataset was created for the purpose of research on scientific productivity at Polish technical universities.The raw data was retrieved in July 2021 by the InCites benchmarking tool in xlsx format and will be used to create the research profiles of the universities and underlying data of journals articles. The most common definition of research productivity (interchangeable with academic productivity or scientific productivity)  states as research productivity are the number of publications per researchers. However, a more extended definition of scientific productivity, connecting quantity and quality approach involves producing quality research represented by publishing academic papers in reputable international journals, citing these papers, gaining research funding (e.g. national and international grants), and collaborating in scientific teams.
The dataset prepared for the scientific profile of  the AGH University of Science & Technology consists records from 2016-2020: Number of Web of Science Documents, Times Cited, % Docs Cited, Rank, Category Normalized Citation Impact, International Collaborations, All Open Access Documents, % International Collaborations, Documents in JIF Journals, Documents in Q1 Journals, Documents in Q2 Journals, Documents in Q3 Journals, Documents in Q4 Journals.
DOI: 10.5281/zenodo.5120590
title: MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).
description: {"references": ["Emmerich et al. (2021). A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry. 10.1016/j.ejmp.2021.07.015"]}
DOI: 10.5281/zenodo.5120589
title: MRI raw data for: A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. The dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 1.5 T and 3 T (additional 7 T data are provided in separate datasets).
description: {"references": ["Emmerich et al. (2021). A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry. 10.1016/j.ejmp.2021.07.015"]}
DOI: 10.5281/zenodo.5121472
title: 7 T MRI rawdata for (part 1): A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry

description: MRI raw data from three different magnetic field strength (1.5 T, 3 T, 7T; 7T data are in separate datasets) for the publication 'A novel phantom with dia- and paramagnetic substructure for quantitative susceptibility mapping and relaxometry', in which a phantom was presented that allows for an experimental evaluation of QSM reconstruction algorithms. The phantom contains susceptibility producing particles with dia- and paramagnetic properties embedded in an MRI visible medium (gelatin and agarose gel) and is suitable to assess the performance of algorithms that attempt to separate isotropic dia- and paramagnetic susceptibility at the sub-voxel level. This dataset additionally contains raw data for a phantom that only contains diamagnetic and paramagnetic particles, respectively, for magnetic field strengths of 7 T.