cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2021-05.2232927/2102895
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232204/2102896
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232251/2102897
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1916244/2102898
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232020/2102899
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232316/2102900
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1915323/2102901
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232127/2102902
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232053/2102903
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232180/2102904
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232227/2102905
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232589/2102906
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232594/2102907
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232585/2102908
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232421/2102909
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233725/2102910
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232346/2102911
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232414/2102912
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233691/2102913
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232659/2102914
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-03.2181793/2102915
title: Data for EMSL Project 51241 from March 2021
DOI: 10.25582/data.2021-05.2232039/2102916
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232146/2102917
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232706/2102918
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1915282/2102919
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232683/2102920
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232504/2102921
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-03.1963021/2102922
title: Data for EMSL Project 51241 from March 2020
DOI: 10.25582/data.2021-05.2232201/2102923
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232309/2102924
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1915333/2102925
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2020-01.1915280/2102926
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232167/2102927
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232171/2102928
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232633/2102929
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232544/2102930
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232685/2102931
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232288/2102932
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232277/2102933
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232355/2102934
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232411/2102935
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233693/2102936
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233920/2102937
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232625/2102938
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232436/2102939
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232590/2102940
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232567/2102941
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232055/2102942
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232176/2102943
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232670/2102944
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232384/2102945
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232628/2102946
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232468/2102947
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232622/2102948
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232437/2102949
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232074/2102950
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232728/2102951
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233727/2102952
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2231987/2102953
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232374/2102954
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232597/2102955
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1915328/2102956
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232128/2102957
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232375/2102958
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232529/2102959
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232002/2102960
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232199/2102961
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232408/2102962
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232475/2102963
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232393/2102964
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-03.2181795/2102965
title: Data for EMSL Project 51241 from March 2021
DOI: 10.25582/data.2021-05.2232130/2102966
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-03.2188175/2102967
title: Data for EMSL Project 51241 from March 2021
DOI: 10.25582/data.2021-05.2232663/2102968
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232226/2102969
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232229/2102970
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232669/2102971
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2233714/2102972
title: Data for EMSL Project 51241 from May 2021
DOI: 10.34740/kaggle/dsv/6736432
title: 📒Meta Kaggle ported to Parquet Format
DOI: 10.25582/data.2021-05.2232209/2102973
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232325/2102974
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/rbpm4pw24r.1
title: A Versatile Corpus for Intrinsic Plagiarism Detection, Text Reuse Analysis, and Author Clustering in Urdu

description: Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.
DOI: 10.17632/rbpm4pw24r.2
title: A Versatile Corpus for Intrinsic Plagiarism Detection, Text Reuse Analysis, and Author Clustering in Urdu

description: Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.
DOI: 10.17632/rbpm4pw24r.3
title: A Versatile Corpus for Intrinsic Plagiarism Detection, Text Reuse Analysis, and Author Clustering in Urdu

description: Plagiarism detection (PD) is a process of identifying instances where someone has presented another person's work or ideas as their own. Plagiarism detection is categorized into two types (i) Intrinsic plagiarism detection primarily concerns the assessment of authorship consistency within a single document, aiming to identify instances where portions of the text may have been copied or paraphrased from elsewhere within the same document. Author clustering, closely related to intrinsic plagiarism detection, involves grouping documents based on their stylistic and linguistic characteristics to identify common authors or sources within a given dataset. On the other hand, (ii) extrinsic plagiarism detection delves into the comparative analysis of a suspicious document against a set of external source documents, seeking instances of shared phrases, sentences, or paragraphs between them, which is often referred to as text reuse or verbatim copying. Detection of plagiarism from documents is a long-established task in the area of NLP with remarkable contributions in multiple applications. A lot of research has already been conducted in the English and other foreign languages but Urdu language needs a lot of attention especially in intrinsic plagiarism detection domain. The major reason is that Urdu is a low resource language and unfortunately there is no high-quality benchmark corpus available for intrinsic plagiarism detection in Urdu language. This study presents a high-quality benchmark Corpus comprising 10,872 documents. The corpus is structured into two granularity levels: sentence level and paragraph level. This dataset serves multifaceted purposes, facilitating intrinsic plagiarism detection, verbatim text reuse identification, and author clustering in the Urdu language. Also, it holds significance for natural language processing researchers and practitioners as it facilitates the development of specialized plagiarism detection models tailored to the Urdu language. These models can play a vital role in education and publishing by improving the accuracy of plagiarism detection, effectively addressing a gap and enhancing the overall ability to identify copied content in Urdu writing.
DOI: 10.25582/data.2021-05.2231988/2102975
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232688/2102976
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232132/2102977
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232062/2102978
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/3rr4926xp5.1
title: Structural data collected along the Kornos-Aghios Ioannis Normal Fault, Lemnos Island, North Aegean Sea, Greece

description: This dataset is related to the publication  "Along-strike variation of damage zone parameters in the silicified Kornos-Aghios Ioannis Normal Fault, Lemnos Island, North Aegean Sea, Greece" by L. R. Berio, F. Balsamo, M. Pizzati, F. Storti, M. Curzi and G. Viola published in Journal of Structural Geology. Data are subdivided by publication figures (3-13). Geographic coordinates of structural sites are reported using UTM Zone 35N (EPSG: 32635) coordinate system. The attitude of different deformation structures (fractures and faults) and that of bedding planes are reported in the format strike/dip angle adopting the "right-hand rule" (i.e. dip direction in 90° clockwise from strike) and slickenlines on fault planes are expressed as rake or pitch (i.e. the angle of slickenlines on fault surfaces measured clockwise from the strike of the fault surface and spanning between 0 and 180°). For scanline data (Figs. 8, 9, 10, 12 and 13), the origin of the reference system corresponds to the master fault surface with negative and positive values (i.e. distance from fault core) for progressively increasing distances in the footwall and hanging wall, respectively. Further details about the data format are explained in the "Methods" of the published article.
DOI: 10.17632/3rr4926xp5.2
title: Structural data collected along the Kornos-Aghios Ioannis Normal Fault, Lemnos Island, North Aegean Sea, Greece

description: This dataset is related to the publication "Along-strike variation of damage zone parameters in the silicified Kornos-Aghios Ioannis Normal Fault, Lemnos Island, North Aegean Sea, Greece" by L. R. Berio, F. Balsamo, M. Pizzati, F. Storti, M. Curzi and G. Viola published in Journal of Structural Geology. Data are subdivided by publication figures (3-13). Geographic coordinates of structural sites are reported using UTM Zone 35N (EPSG: 32635) coordinate system. The attitude of different deformation structures (fractures and faults) and that of bedding planes are reported in the format strike/dip angle adopting the "right-hand rule" (i.e. dip direction in 90° clockwise from strike) and slickenlines on fault planes are expressed as rake or pitch (i.e. the angle of slickenlines on fault surfaces measured clockwise from the strike of the fault surface and spanning between 0 and 180°). For scanline data (Figs. 8, 9, 10, 12 and 13), the origin of the reference system corresponds to the master fault surface with negative and positive values (i.e. distance from fault core) for progressively increasing distances in the footwall and hanging wall, respectively. Further details about the data format are explained in the "Methods" of the published article.
DOI: 10.25582/data.2021-05.2232570/2102979
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/phcxdkrnc5.1
title: Estimation of historical daily PM2.5 concentrations for three Chinese megacities

description: Historical PM2.5 data and meteorological data for the three cities
DOI: 10.25582/data.2021-05.2232630/2102980
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/6fnggg5rzn.1
title: Integrated Analysis of UBE2C in Breast Invasive Carcinoma

description: The clinical and transcriptomic data of patients with BRCA were obtained from TCGA. The correlation of UBE2C with expression differences, prognosis and clinicopathological parameters was conducted according to the data of TCGA patients with BRCA and verified by meta-analysis. UBE2C-related functional analysis, immune infiltration, immune checkpoint-related gene expression levels and nomograms were investigated using the R system.
DOI: 10.17632/yvyd4kbxy3.1
title: Data for: Quantitative easing in the US and financial cycles in emerging markets

description: This is replication data for "Quantitative easing in the US and financial cycles in emerging markets" by Marcin Kolasa and Grzegorz Wesołowski, published in Journal of Economic Dynamics and Control 149: 104631 (2023).
DOI: 10.25582/data.2021-05.2232729/2102981
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232186/2102982
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/xdrwrh9zbk.1
title: Hemoglobin Photoplethysmography Dataset

description: Dataset consists of photoplethysmography (PPG) signal, Hb value, age, and gender. The PPG signal was measured by a MAX30102 module sensor that emitted two types of light (red and infrared light) and measured using a photodetector. The Hb value was measured by a Hb meter, Nesco Multicheck 2®. Total of 68 subjects (56% female and 44% male) within the age of 18-65 years were collected. The total dataset is 816 data from 68 subjects, which each subject provides 12 sets of red and infrared light signals. The data were collected from the 5th of May to the 10th of June, 2023 at Primary Health Center Jatiuwung, Tangerang City, Banten 15138, Indonesia. Researchers interested in anemia monitoring and those pursuing the development of non-invasive hemoglobin measurement devices can leverage this dataset.
DOI: 10.25582/data.2021-05.2232349/2102983
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232631/2102984
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2020-01.1916243/2102985
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232541/2102986
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232455/2102987
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232154/2102988
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232395/2102989
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232579/2102990
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232535/2102991
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232163/2102992
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232219/2102993
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232276/2102994
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232218/2102995
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232774/2102996
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-03.2181789/2102997
title: Data for EMSL Project 51241 from March 2021
DOI: 10.25582/data.2021-05.2232558/2102998
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232270/2102999
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232525/2103000
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232156/2103001
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232484/2103002
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232466/2103003
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232320/2103004
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232063/2103005
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232463/2103006
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232763/2103007
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232486/2103008
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232664/2103009
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232701/2103010
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232429/2103011
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232600/2103012
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/c8ghr44426.1
title: Supplementary Table 1.

description: Demographic and clinical characteristics of patients with GGA
DOI: 10.25582/data.2021-05.2232651/2103013
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232924/2103014
title: Data for EMSL Project 51241 from May 2021
DOI: 10.17632/674wng47kc.1
title: Supplementary Table 2.

description: Treatment response summary of patients with GGA
DOI: 10.25582/data.2020-01.1915329/2103015
title: Data for EMSL Project 51241 from January 2020
DOI: 10.25582/data.2021-05.2232064/2103016
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232223/2103017
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232746/2103018
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232345/2103019
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232675/2103020
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232510/2103021
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232141/2103022
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2231897/2103023
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232150/2103024
title: Data for EMSL Project 51241 from May 2021
DOI: 10.25582/data.2021-05.2232302/2103025
title: Data for EMSL Project 51241 from May 2021