cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7781228
title: Supporting Information for: Deoxygenation Before the End-Permian Mass Extinction: Evidence from Secular Variation of Magnetic Mineral Assemblages, Meishan Section, China

description: Supporting Information for: <strong>Deoxygenation Before the End-Permian Mass Extinction: Evidence from Secular </strong><strong>Variation of Magnetic Mineral Assemblages, Meishan Section, China</strong> The supporting information includes: (a) supplemental figures: Figure S1, Rock magnetic experiments of selected sample and Figure S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; (2) measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the trace element results of the bulk carbonate samples
DOI: 10.5281/zenodo.8098330
title: Supporting Information for: Ocean Deoxygenation Before the End-Permian Mass Extinction: Evidence from Secular Variation of Magnetic Mineral Assemblages, Meishan Section, China

description: Supporting Information for: <strong>Ocean</strong><strong> Deoxygenation </strong><strong>Before</strong><strong> the End-Permian Mass Extinction: Evidence from Secular Variation of Magnetic Mineral Assemblages, Meishan Section, China</strong> The supporting information includes: (a) supplemental figures: Figure S1, Rock magnetic experiments of selected sample; Figure S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; Figure S3, Conceptual model for the end-Permian mass extinction (EPME) linked to environmental and climatic perturbations; (2) measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.10146616
title: Supporting Information for: Ocean deoxygenation before the end-Permian mass extinction shown by secular variation of magnetic mineral assemblages

description: Supporting Information for:
Ocean deoxygenation before the end-Permian mass extinction shown by secular variation of magnetic mineral assemblages
The supporting information includes: (a) supplemental figures: Fig. S1, Rock magnetic experiments of selected sample; Fig. S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; (2) Measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.10456991
title: Supporting Information for: Ocean deoxygenation before the end-Permian mass extinction shown by secular variation of magnetic mineral assemblages

description: Supporting Information for:

Ocean deoxygenation before the end-Permian mass extinction shown by secular variation of magnetic mineral assemblages

The supporting information includes: (a) supplemental figures: Fig. S1, Rock magnetic experiments of selected sample; Fig. S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; Fig. S3, PAAS normalized rare earth element (REE) patterns; Fig. S4, Y/Ho ratio vs. Th, Ti, Cu, and Sc concentrations, respectively. (2) Measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.10848261
title: Supporting Information for: Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

description: Supporting Information for:

Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

The supporting information includes: (a) supplemental figures: Fig. S1, Rock magnetic experiments of selected sample; Fig. S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; Fig. S3, PAAS normalized rare earth element (REE) patterns; Fig. S4, Y/Ho ratio  and ΩCe vs. Th and Ti concentrations, respectively. (2) Measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.10851614
title: Supporting Information for: Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

description: Supporting Information for:

Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

The supporting information includes: (a) supplemental figures: Fig. S1, Rock magnetic experiments of selected sample; Fig. S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; Fig. S3, PAAS normalized rare earth element (REE) patterns; Fig. S4, Y/Ho ratio  and ΩCe vs. Th and Ti concentrations, respectively. (2) Measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.7508663
title: Supporting Information for: Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

description: Supporting Information for:

Shifts in magnetic mineral assemblages support ocean deoxygenation before the end-Permian mass extinction

The supporting information includes: (a) supplemental figures: Fig. S1, Rock magnetic experiments of selected sample; Fig. S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; Fig. S3, PAAS normalized rare earth element (REE) patterns; Fig. S4, Y/Ho ratio  and ΩCe vs. Th and Ti concentrations, respectively. (2) Measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis; Table S2, Magnetic experiments results; and Table S3, the Rare Earth element results of the bulk carbonate samples
DOI: 10.5281/zenodo.7508690
title: Supporting Information for: Deoxygenation Prior to the End-Permian Mass Extinction: Evidence from Secular Variation of Magnetic Mineral Assemblage, Meishan Section, China

description: Supporting Information for: <strong>Deoxygenation Prior to the End-Permian Mass Extinction: Evidence from Secular </strong><strong>Variation of Magnetic Mineral Assemblage, Meishan Section, China</strong> The supporting information includes: (a) supplemental figures: Figure S1, Rock magnetic experiments of selected sample and Figure S2, Energy dispersive X-ray spectroscopy (EDXS) of Fe-oxide; (2) measurement data: Table S1, Rock magnetic parameters of coercivity spectrum analysis and Table S2, Magnetic experiments results
DOI: 10.6084/m9.figshare.22601287
title: Additional file 1 of Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients

description: Additional file 1: Appendix 1. Summary of the data extraction and results of methodological quality assessment.
DOI: 10.6084/m9.figshare.22601287.v1
title: Additional file 1 of Combining robot-assisted therapy with virtual reality or using it alone? A systematic review on health-related quality of life in neurological patients

description: Additional file 1: Appendix 1. Summary of the data extraction and results of methodological quality assessment.
DOI: 10.5281/zenodo.10849443
title: Oracle bone fragment image dataset
title: OBFI-dataset
title: 甲骨碎片图像数据集

description: Oracle bone inscriptions record valuable information about ancient Chinese history in Shang Dynasty. However, many complete oracle bones have broken into pieces over the years, resulting in disjointing sentences. This poses a serious challenge in rejoining oracle bone fragments and interpreting the inscriptions. With oracle bone fragments scattered around the world, researchers have turned their attention to artificial intelligence algorithms to piece together oracle bone fragment images. This paper presents a benchmark datasets for rejoining oracle bone fragment. The benchmark consists of three parts. The first part consists of 5374 high-resolution oracle bone fragment images. The second part consists of 110 oracle bone fragment image pairs that can be rejoined together, including the rejoinable location. The third part consists of the images of oracle bone pairs with matching shapes from multiple sources, divided into two categories: 23,072 rejoinable images and 116003 unrejoinable images. The dataset can be used to evaluate the performance oracle bone rejoining algorithms, the dataset also contains undiscovered rejoinable fragments, which offer promising opportunities for further analysis. 
description: The OBFI-dataset for experiment and code for training and testing oracle bone fragment image rejoining models are also publicly available without restriction, DeepRejoin software, machine learning and deep learning methods are given in the code.rar file,  DeepRejoin software's instruction is linked by the second URL in related work.
DOI: 10.5281/zenodo.10849444
title: Oracle bone fragment image dataset
title: OBFI-dataset
title: 甲骨碎片图像数据集

description: Oracle bone inscriptions record valuable information about ancient Chinese history in Shang Dynasty. However, many complete oracle bones have broken into pieces over the years, resulting in disjointing sentences. This poses a serious challenge in rejoining oracle bone fragments and interpreting the inscriptions. With oracle bone fragments scattered around the world, researchers have turned their attention to artificial intelligence algorithms to piece together oracle bone fragment images. This paper presents a benchmark datasets for rejoining oracle bone fragment. The benchmark consists of three parts. The first part consists of 5374 high-resolution oracle bone fragment images. The second part consists of 110 oracle bone fragment image pairs that can be rejoined together, including the rejoinable location. The third part consists of the images of oracle bone pairs with matching shapes from multiple sources, divided into two categories: 23,072 rejoinable images and 116003 unrejoinable images. The dataset can be used to evaluate the performance oracle bone rejoining algorithms, the dataset also contains undiscovered rejoinable fragments, which offer promising opportunities for further analysis. 
description: The OBFI-dataset for experiment and code for training and testing oracle bone fragment image rejoining models are also publicly available without restriction, DeepRejoin software, machine learning and deep learning methods are given in the code.rar file,  DeepRejoin software's instruction is linked by the second URL in related work.
DOI: 10.6084/m9.figshare.25458916
title: Data Chemotherapy

description: Skin, hair, and nail (SHN) manifestations are a relatively common adverse effect of chemotherapy that affects the quality of life of cancer patients. However, little is known about their incidence among cancer patients who received anthracycline-based chemotherapy and their associated risk factors.
DOI: 10.5517/ccdc.csd.cc2h4bww
title: CCDC 2298088: Experimental Crystal Structure Determination

description: Related Article: Xing Zhao, Zhaopeng Zheng, Chao Chen, Huan Wang, Hanfei Liu, Jinyu Li, Chao Sun, Huayong Lou, Weidong Pan|2024|Fitoterapia|174|105878|doi:10.1016/j.fitote.2024.105878
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2h4bxx
title: CCDC 2298089: Experimental Crystal Structure Determination

description: Related Article: Xing Zhao, Zhaopeng Zheng, Chao Chen, Huan Wang, Hanfei Liu, Jinyu Li, Chao Sun, Huayong Lou, Weidong Pan|2024|Fitoterapia|174|105878|doi:10.1016/j.fitote.2024.105878
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.25458946
title: DATA AND ANALYSIS.xlsx

description: This dataset contains questionnaire with response 407 respondents. The data is analyzed for realiability,validatity,covaraiance ,Average varaiance extracted, fit indices ,significnce of path and test of normality.
DOI: 10.6084/m9.figshare.25458946.v1
title: DATA AND ANALYSIS.xlsx

description: This dataset contains questionnaire with response 407 respondents. The data is analyzed for realiability,validatity,covaraiance ,Average varaiance extracted, fit indices ,significnce of path and test of normality.
DOI: 10.6084/m9.figshare.25458862
title: The data needed for the experiment

description: Road network selection is a key focus in the field of cartographic generalization. With the rapid updating of road data, there are higher requirements for the quality and intelligence of road selection. Based on the knowledge graph composition method, this article combines graph neural networks to achieve automatic selection of road networks.
DOI: 10.5281/zenodo.10851821
title: The data for The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector

description: The data is used to describe the figure in the paper "The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector"
DOI: 10.5281/zenodo.10851822
title: The data for The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector

description: The data is used to describe the figure in the paper "The Simulation of the Equinox Asymmetry of Ionospheric Electrodynamics at East-Asian Sector"
DOI: 10.5281/zenodo.10583247
title: NeoRdRp 2.0

description: This dataset is part of the NeoRdRp and supplements the information provided in our GitHub repository. For the most comprehensive and up-to-date information, as well as additional resources related to this dataset, please visit our GitHub page:

https://github.com/shoichisakaguchi/NeoRdRp

The GitHub repository contains detailed documentation, usage instructions, and the latest updates on our ongoing work. We encourage users to refer to the GitHub repository for the most current data, analysis, and findings related to this project.
DOI: 10.5281/zenodo.10807561
title: NeoRdRp 2.0

description: This dataset is part of the NeoRdRp and supplements the information provided in our GitHub repository. For the most comprehensive and up-to-date information, as well as additional resources related to this dataset, please visit our GitHub page:

https://github.com/shoichisakaguchi/NeoRdRp

The GitHub repository contains detailed documentation, usage instructions, and the latest updates on our ongoing work. We encourage users to refer to the GitHub repository for the most current data, analysis, and findings related to this project.
DOI: 10.5281/zenodo.10067141
title: Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse  modeling of geomorphic processes"

description: Code and datasets for:

Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. 

For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).
DOI: 10.5281/zenodo.10067142
title: Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse  modeling of geomorphic processes"

description: Code and datasets for
Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, in review, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. 
For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).
DOI: 10.5281/zenodo.10851801
title: Code and datasets for "Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse  modeling of geomorphic processes"

description: Code and datasets for:

Delaney I., M. A. Werder, D. Felix, I. Albayrak, R. M. Boes, D. Farinotti, 2024, Controls on sediment transport from a glacierized catchment in the Swiss Alps established through inverse modeling of geomorphic processes. Water Resources Research. 

For more information, contact Ian Delaney (ianarburua.delaney@unil.ch).
DOI: 10.18419/darus-2826/1
title: 5.zip

description: :unav
DOI: 10.18419/darus-2826/2
title: group9.tab

description: :unav
DOI: 10.18419/darus-2826/3
title: 1_blur.zip

description: :unav
DOI: 10.18419/darus-2826/4
title: 6.zip

description: :unav
DOI: 10.18419/darus-2826/5
title: 7.zip

description: :unav
DOI: 10.18419/darus-2826/6
title: 3.zip

description: :unav
DOI: 10.18419/darus-2826/7
title: 4.zip

description: :unav
DOI: 10.5281/zenodo.3381751
title: Patent Citations to Science

description: This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018. The main file, <strong>pcs.tsv</strong>, contains the resolved citations. Fields are tab-separated. Each match has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1. There is also a PubMed-specific match in <strong>pcs-pubmed.tsv</strong>. The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. All of these files are compressed using ZIP compression under CentOS5. Original files, documented at https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema, can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see _<strong>reliance_on_science.pdf</strong> for full details, the latest version of which is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3331686). Source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif. Although MAG contains authors and affiliations for each paper, it does not contain the location for affiliations. We have created a dataset of locations for affiliations appearing at least 100x using Bing Maps and Google Maps; however, it is unclear to us whether the API licensing terms allow us to repost their data. In any case, you can download our source code for doing so here: https://github.com/ksjiaxian/api-requester-locations. MAG extracts field keywords for each paper (paperfieldid.zip and fieldidname.zip) --more than 200,000 fields in all! When looking to study industries or technical areas you might find this a bit overwhelming. We mapped the MAG subjects to six OECD fields and 39 subfields, defined here: http://www.oecd.org/science/inno/38235147.pdf. Clarivate provides a crosswalk between the OECD classifications and Web of Science fields, so we include WoS fields as well. This file is <strong>magfield_oecd_wos_crosswalk.zip</strong>.
DOI: 10.5281/zenodo.3381755
title: Patent Citations to Science

description: This dataset contains citations from USPTO patents granted 1947-2018 to articles captured by the Microsoft Academic Graph (ID) from 1800-2018. The main file, <strong>pcs.tsv</strong>, contains the resolved citations. Fields are tab-separated. Each match has the patent number, MAG ID, the original citation from the patent, an indicator for whether the citation was supplied by the applicant, examiner, or unknown, and a confidence score (1-10) indicating how likely this match is correct. Note that this distribution does not contain matches with confidence 2 or 1. There is also a PubMed-specific match in <strong>pcs-pubmed.tsv</strong>. The remaining files are a redistribution of the 1 January 2019 release of the Microsoft Academic Graph. All of these files are compressed using ZIP compression under CentOS5. Original files, documented at https://docs.microsoft.com/en-us/academic-services/graph/reference-data-schema, can be downloaded from https://aka.ms/msracad; this redistribution carves up the original files into smaller, variable-specific files that can be loaded individually (see <strong>_README.pdf</strong> for full details.. Source code for generating the patent citations to science in pcs.tsv is available at https://github.com/mattmarx/reliance_on_science. Source code for generating jif.zip and jcif.zip (Journal Impact Factor and Journal Commercial Impact Factor) is at https://github.com/mattmarx/jcif. Although MAG contains authors and affiliations for each paper, it does not contain the location for affiliations. We have created a dataset of locations for affiliations appearing at least 100x using Bing Maps and Google Maps; however, it is unclear to us whether the API licensing terms allow us to repost their data. In any case, you can download our source code for doing so here: https://github.com/ksjiaxian/api-requester-locations. MAG extracts field keywords for each paper (paperfieldid.zip and fieldidname.zip) --more than 200,000 fields in all! When looking to study industries or technical areas you might find this a bit overwhelming. We mapped the MAG subjects to six OECD fields and 39 subfields, defined here: http://www.oecd.org/science/inno/38235147.pdf. Clarivate provides a crosswalk between the OECD classifications and Web of Science fields, so we include WoS fields as well. This file is <strong>magfield_oecd_wos_crosswalk.zip</strong>.
DOI: 10.17632/7r5vmkx6w3.1
title: Data for: Stability of Revolutionary Governments in the Face of Mass Protest

description: This data (from Cross-National Time-Series Data Archive (CNTS)) is used for production of illustrative figures.
DOI: 10.48443/naax-q953
title: Wet deposition chemical analysis (DP1.00013.001)
title: RELEASE-2023

description: This data product contains the quality-controlled, native sampling resolution data from NEON's wet deposition protocol. Major ion concentrations, pH, and conductivity are measured in precipitation samples.

Latency:
The expected time from data and/or sample collection in the field to data publication is as follows, for each of the data tables (in days) in the downloaded data package. See the Data Product User Guide for more information.

wdp_chemLab:  90

wdp_collection:  30

wdp_collectionChem:  30

wdp_sensor:  30
description: Wet deposition chemistry is sampled in conjunction with stable isotope concentration in precipitation (DP1.00038.001). Samples are collected in a climate controlled wet deposition collector located at the tower top of terrestrial sites, and at the meteorologic tower of select aquatic sites. The automated assembly detects precipitation with an optical sensor and opens to collect wet deposition during all rain events. Every two weeks samples are retrieved, filtered, and sent for analysis. Instrumentation: N- Con Systems Company Wet Deposition Collector, Manufacture Model No: NEON 00-127-7