cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28678420
title: Hypothesis Testing for a Functional Parameter via Self-Normalization

description: Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN) method is tuning parameter free when applied to the inference of a finite-dimensional parameter but its applicability to a functional parameter is unknown. In this article, we propose a sample splitting based approach to generalize the SN method to hypothesis testing of a functional parameter. Our SS-SN (sample splitting plus self-normalization) idea is broadly applicable to many testing problems for functional parameters, including testing for simple/composite hypothesis on marginal cumulative distribution function, testing for time-reversibility and testing for a change point on the spectral distribution of a multivariate time series. Specifically, we derive the pivotal limiting distributions of our SS-SN test statistics under the null for both simple and composite null hypothesis, and derive the limiting power function under the local alternatives. Numerical simulations show that our new tests tend to yield accurate size with competitive power performance as compared to many existing ones. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28678420.v2
title: Hypothesis Testing for a Functional Parameter via Self-Normalization

description: Testing simple or composite hypothesis on a functional parameter has attracted considerable attention in time series analysis. To accommodate for the unknown temporal dependence, classical nonparametric approaches such as block bootstrapping and subsampling all involve a bandwidth parameter, the choice of which can substantially affect the finite sample performance. The self normalization (SN) method is tuning parameter free when applied to the inference of a finite-dimensional parameter but its applicability to a functional parameter is unknown. In this article, we propose a sample splitting based approach to generalize the SN method to hypothesis testing of a functional parameter. Our SS-SN (sample splitting plus self-normalization) idea is broadly applicable to many testing problems for functional parameters, including testing for simple/composite hypothesis on marginal cumulative distribution function, testing for time-reversibility and testing for a change point on the spectral distribution of a multivariate time series. Specifically, we derive the pivotal limiting distributions of our SS-SN test statistics under the null for both simple and composite null hypothesis, and derive the limiting power function under the local alternatives. Numerical simulations show that our new tests tend to yield accurate size with competitive power performance as compared to many existing ones. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.548nyx
title: Occurrence Download

description: A dataset containing 194737 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acleris stadiana (Barnes & Busck, 1920), Euchlaena tigrinaria (Guenée), Euchlaena johnsonaria (Fitch, 1870), Hethemia pistasciaria (Guenée, 1857), Hydrelia albifera Walker, 1866, Phaeoura quernaria (Smith, 1797), Eueretagrotis attentus Grote, 1874, Ortholepis pasadamia Dyar, 1917, Meroptera pravella Grote, 1878, Apotomis funerea (Meyrick, 1920), Choristoneura zapulata (Robinson, 1869), Olethreutes diallacta (Meyrick, 1932), Pococera aplastella Hulst, 1888, Swammerdamia lutarea Haworth, 1828, Euchlaena irraria Barnes & McDunnough, 1916, Epinotia nisella (Clerck, 1759), Anacampsis innocuella (Zeller, 1873), Bucculatrix canadensisella Chambers, 1875, Enargia infumata Grote, 1874, Psilocorsis cryptolechiella Chambers, 1872, Tolype velleda (Stoll, 1791), Charadra deridens Guenée, 1852, Acrobasis tricolorella Grote, 1878, Furcula cinerea Walker, 1865, Antaeotricha schlaegeri Zeller, 1854, Cepphis armataria (Herrich-Schäffer, 1857), Sphinx chersis Hübner, Anisota virginiensis Drury, 1773, Ceratomia undulosa Walker, 1856, Paonias excaecata Smith, 1797, Acronicta radcliffei Harvey, 1875, Habrosyne scripta (Gosse, 1840), Acleris nigrolinea (Robinson, 1869), Aethalura intertexta (Walker, 1860), Sicya macularia (Harris, 1850), Elasmucha lateralis (Say, 1831), Apotomis capreana (Hübner, 1817), Bedellia somnulentella (Zeller, 1847), Hypena scabra (Fabricius, 1798), Agrilus anxius Gory, 1841, Polygonia vaualbum (Denis & Schiffermüller), 1775, Cycnia oregonensis Stretch, Phigalia titea (Cramer, 1780), Achatia distincta Hübner, 1813, Olethreutes appendiceum (Zeller, 1875), Limenitis archippus Cramer, 1775, Heterocampa umbrata Walker, 1855, Acronicta superans Guenée, 1852, Psilocorsis reflexella Clemens, 1860, Smerinthus jamaicensis Drury, 1773)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 194737 records from 391 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014818-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5517/ccdc.csd.cc2m6l5m
title: CCDC 2419400: Experimental Crystal Structure Determination

description: Related Article: Debashis Patra, Michael G.B. Drew, Tapas Ghosh|2025|Inorg.Chem.Commun.|179|114651|doi:10.1016/j.inoche.2025.114651
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.cc2m6xvm
title: CCDC 2419731: Experimental Crystal Structure Determination

description: Related Article: Zhen-Xiang Liu, Xue-Yu Zhao, Jin-Da Li, Liu-Dong Song, Lin-Fen Ding, Xing-De Wu|2025|Fitoterapia|183|106588|doi:10.1016/j.fitote.2025.106588
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.cc2m9010
title: CCDC 2421721: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9021
title: CCDC 2421722: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9032
title: CCDC 2421723: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9054
title: CCDC 2421725: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9065
title: CCDC 2421726: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9076
title: CCDC 2421727: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9087
title: CCDC 2421728: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2m9043
title: CCDC 2421724: Experimental Crystal Structure Determination

description: Related Article: Victoria E. Gontcharenko, Marat M. Islamov, Trofim A. Polikovskiy, Tatiana B. Shatalova, Ilya V. Taydakov, Yury A. Belousov|2025|Inorg.Chem.Commun.|178|114640|doi:10.1016/j.inoche.2025.114640
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.cc2mh81g
title: CCDC 2427735: Experimental Crystal Structure Determination

description: Related Article: Ziyang Zhang, Wenjing Bao, Guangxing Gu, Zhenjiang Liu, Yanchuan Zhao|2025|Org.Lett.|27|4842|doi:10.1021/acs.orglett.5c00862
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: 10.15468/dl.s35gjs
title: Occurrence Download

description: A dataset containing 72 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Potamotrygon brachyura (Günther, 1880)"
 ]
}

 The dataset includes 72 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014820-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15492532
title: IODP Expedition 399 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.5281/zenodo.15492533
title: IODP Expedition 399 Alkalinity and pH

description: Alkalinity was determined by Gran titration with an autotitrator (Metrohm 794 basic Titrino) using 0.1 M HCl at 20 degrees C. Report includes alkalinity, correction factor (if applicable), and pH.
DOI: 10.15468/dl.bzwukp
title: Occurrence Download

description: A dataset containing 280 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Peromyscus aztecus (Saussure, 1860)",
 "BasisOfRecord is one of (Human Observation, Observation, Machine Observation, Occurrence evidence)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 280 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014822-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17909/zrjp-rw13
title: Data for Kepler-51d Transmission Spectrum

description: Single transit of Kepler-51d used in Libby-Roberts et al. (2025)
DOI: 10.15468/dl.zag8cs
title: Occurrence Download

description: A dataset containing 2725 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-40.50958 -9.55368,-41.1504 -11.25704,-40.93132 -11.39944,-40.31241 -9.71799,-40.50958 -9.55368))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2725 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014819-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8174335
title: Wikipedia Multilingual Vandalism Detection Dataset

description: This dataset accompanies a research paper that introduces a novel system designed to support the Wikipedia community in combating vandalism on the platform. The dataset has been prepared to enhance the accuracy and efficiency of Wikipedia patrolling in multiple languages.

The release of this comprehensive dataset aims to encourage further research and development in vandalism detection techniques, fostering a safer and more inclusive environment for the Wikipedia community. Researchers and practitioners can utilize this dataset to train and validate their models for vandalism detection and contribute to improving online platforms' content moderation strategies.

Dataset Details:



Number of Languages: 47

Observation period: 6 months training, one week hold-out testing

Use Case: The dataset is primarily intended for training and evaluating vandalism detection systems.

Features: Each record characterizes the corresponding revision of the Wikipedia page, including revision metadata, user details, text inserted, removed, or changed, and corresponding MLMs-based features. 

Data Filtering and Feature Engineering: Advanced filtering and feature engineering techniques were applied to ensure the dataset's quality and relevance for effectively training the vandalism detection system.

Files: Training and hold-out testing datasets of anonymous and all users. 


 

Related paper citation:

@inproceedings{10.1145/3580305.3599823,
author = {Trokhymovych, Mykola and Aslam, Muniza and Chou, Ai-Jou and Baeza-Yates, Ricardo and Saez-Trumper, Diego},
title = {Fair Multilingual Vandalism Detection System for Wikipedia},
year = {2023},
isbn = {9798400701030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3580305.3599823},
doi = {10.1145/3580305.3599823},
abstract = {This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. To achieve this, we collected a massive dataset of 47 languages, and applied advanced filtering and feature engineering techniques, including multilingual masked language modeling to build the training dataset from human-generated data. The performance of the system was evaluated through comparison with the one used in production in Wikipedia, known as ORES. Our research results in a significant increase in the number of languages covered, making Wikipedia patrolling more efficient to a wider range of communities. Furthermore, our model outperforms ORES, ensuring that the results provided are not only more accurate but also less biased against certain groups of contributors.},
booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {4981–4990},
numpages = {10},
location = {Long Beach, CA, USA},
series = {KDD '23}
}

 
description: This work has been funded by MCIN/AEI /10.13039/501100011033 under the Maria de Maeztu Units of Excellence Programme (CEX2021-001195-M)
DOI: 10.5281/zenodo.8174336
title: Wikipedia Multilingual Vandalism Detection Dataset

description: This dataset accompanies a research paper that introduces a novel system designed to support the Wikipedia community in combating vandalism on the platform. The dataset has been prepared to enhance the accuracy and efficiency of Wikipedia patrolling in multiple languages.

The release of this comprehensive dataset aims to encourage further research and development in vandalism detection techniques, fostering a safer and more inclusive environment for the Wikipedia community. Researchers and practitioners can utilize this dataset to train and validate their models for vandalism detection and contribute to improving online platforms' content moderation strategies.

Dataset Details:



Number of Languages: 47

Observation period: 6 months training, one week hold-out testing

Use Case: The dataset is primarily intended for training and evaluating vandalism detection systems.

Features: Each record characterizes the corresponding revision of the Wikipedia page, including revision metadata, user details, text inserted, removed, or changed, and corresponding MLMs-based features. 

Data Filtering and Feature Engineering: Advanced filtering and feature engineering techniques were applied to ensure the dataset's quality and relevance for effectively training the vandalism detection system.

Files: Training and hold-out testing datasets of anonymous and all users. 


 

Related paper citation:

@inproceedings{10.1145/3580305.3599823,
author = {Trokhymovych, Mykola and Aslam, Muniza and Chou, Ai-Jou and Baeza-Yates, Ricardo and Saez-Trumper, Diego},
title = {Fair Multilingual Vandalism Detection System for Wikipedia},
year = {2023},
isbn = {9798400701030},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3580305.3599823},
doi = {10.1145/3580305.3599823},
abstract = {This paper presents a novel design of the system aimed at supporting the Wikipedia community in addressing vandalism on the platform. To achieve this, we collected a massive dataset of 47 languages, and applied advanced filtering and feature engineering techniques, including multilingual masked language modeling to build the training dataset from human-generated data. The performance of the system was evaluated through comparison with the one used in production in Wikipedia, known as ORES. Our research results in a significant increase in the number of languages covered, making Wikipedia patrolling more efficient to a wider range of communities. Furthermore, our model outperforms ORES, ensuring that the results provided are not only more accurate but also less biased against certain groups of contributors.},
booktitle = {Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {4981–4990},
numpages = {10},
location = {Long Beach, CA, USA},
series = {KDD '23}
}

 
description: This work has been funded by MCIN/AEI /10.13039/501100011033 under the Maria de Maeztu Units of Excellence Programme (CEX2021-001195-M)
DOI: 10.15468/dl.45462x
title: Occurrence Download

description: A dataset containing 5403 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is one of (Spain, Czechia, Switzerland, Austria)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Salvia officinalis L.",
 "Year 1943-2025"
 ]
}

 The dataset includes 5403 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0014826-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.