cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57680/asep.0636276
title: Determination of nitrogen dioxide and ozone in ambient air using modified chemiluminescent online analysers

description: The production of the NO2 source, using a permeation tube, is strongly dependent on temperature. A gravimetric method was subsequently used to determine the production of NO2.
DOI: 10.17632/zdhy83cv4p.1
title: Python Script for Simulating, Analyzing, and Evaluating Statistical Mirroring-Based Ordinalysis and Other Estimators

description: This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the method methodology presented in the article titled "Statistical Mirroring-based Ordinalysis: A Sensitive, Robust, and Efficient Methodology for Analysis of Ordinal Assessments Data" authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing. 
 The validation process of ordinal data analysis methods (estimators) has the following specifications: 
• Simulation process: Monte Carlo simulation.
• Data generation distributions: categorical and normal distributions. 
• Data analysis: 
 - Classical estimator: average ordinal score.
 - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation. 
• Evaluation metrics: 
 - Overall average.
 - Overall median.
 - Efficiency (statistical absolute meanic deviation).
 - Sensitivity (entropy).
 - Normality and Mann-Whitney-U test.
DOI: 10.17632/zdhy83cv4p.2
title: Python Script for Simulating, Analyzing, and Evaluating Statistical Mirroring-Based Ordinalysis and Other Estimators

description: This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the method methodology presented in the article titled "Statistical Mirroring-Based Ordinalysis: A Sensitive, Robust, Efficient, and Ordinality-Preserving Method for Analyzing Ordinal Assessment Data" authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing. 
 The validation process of ordinal data analysis methods (estimators) has the following specifications: 
• Simulation process: Monte Carlo simulation.
• Data generation distributions: categorical, normal and multivariate model distributions. 
• Data analysis: 
 - Classical estimators: sum, average and median ordinal score.
 - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation. 
• Evaluation metrics: 
 - Overall estimates average.
 - Overall estimates median.
 - Efficiency (by statistical absolute meanic deviation method).
 - Sensitivity (by entropy method).
 - Normality, Mann-Whitney-U test and others.
DOI: 10.17632/zdhy83cv4p
title: Python Script for Simulating, Analyzing, and Evaluating Statistical Mirroring-Based Ordinalysis and Other Estimators

description: This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the methodology presented in the article titled "Statistical Mirroring-Based Ordinalysis: A Sensitive, Robust, Efficient, and Ordinality-Preserving Descriptive Method for Analyzing Ordinal Assessment Data," authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing. 

 The validation process of ordinal data analysis methods (estimators) has the following specifications: 
• Simulation process: Monte Carlo simulation.
• Data generation distributions: categorical, normal, and multivariate model distributions. 
• Data analysis: 
 - Classical estimators: sum, average, and median ordinal score.
 - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation. 
• Evaluation metrics: 
 - Overall estimates average.
 - Overall estimates median.
 - Efficiency (by statistical absolute meanic deviation method).
 - Sensitivity (by entropy method).
 - Normality, Mann-Whitney-U test, and others.
DOI: 10.17632/zdhy83cv4p.3
title: Python Script for Simulating, Analyzing, and Evaluating Statistical Mirroring-Based Ordinalysis and Other Estimators

description: This presentation involves simulation and data generation processes, data analysis, and evaluation of classical and proposed methods of ordinal data analysis. All the parameters and metrics used are based on the methodology presented in the article titled "Statistical Mirroring-Based Ordinalysis: A Sensitive, Robust, Efficient, and Ordinality-Preserving Descriptive Method for Analyzing Ordinal Assessment Data," authored by Kabir Bindawa Abdullahi in 2024. For further details, you can follow the paper's publication submitted to MethodsX Elsevier Publishing. 

 The validation process of ordinal data analysis methods (estimators) has the following specifications: 
• Simulation process: Monte Carlo simulation.
• Data generation distributions: categorical, normal, and multivariate model distributions. 
• Data analysis: 
 - Classical estimators: sum, average, and median ordinal score.
 - Proposed estimators: Kabirian coefficient of proximity, probability of proximity, probability of deviation. 
• Evaluation metrics: 
 - Overall estimates average.
 - Overall estimates median.
 - Efficiency (by statistical absolute meanic deviation method).
 - Sensitivity (by entropy method).
 - Normality, Mann-Whitney-U test, and others.
DOI: 10.17632/pnndxj662c.1
title: Computer Application for Statistical Mirroring-Based Ordinalysis

description: The Statistical Mirroring-Based Ordinalysis [3] Application (SM-Based Ordinalysis Application) is an advanced tool designed for analyzing ordinal data (numerically encoded ordinal scores) using the statistical mirroring-based ordinalysis methodology. A user-friendly interface facilitates in-depth analysis of ordinal data, including estimates of how closely or distant an individual’s composite set of ordinal assessment scores is to the highest positive ordinal scale point. Utilizing statistical mirroring [2], and Kabirian-based isomorphic optinalysis [1], the application allows users to select preprocessing parameters and compute estimates based on advanced models. Users can enter ordinal numerical data directly or upload CSV or Excel files, choose analysis parameters, and generate detailed results. The SM-Based Ordinalysis Application's intuitive design and powerful features make it an essential resource for researchers, students, and professionals seeking to explore and understand ordinal data assessment relationships effectively. Its broad applicability spans areas such as survey data analysis, clinical assessments, psychometrics, market research, and public health studies where ordinal data are common. 

References:
[1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. doi: 10.1016/j.mex.2023.102400
[2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 
[3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, and efficient methodology for analysis of ordinal assessments data, (2024). [You can follow the published version of the paper for the other reference details).
DOI: 10.17632/pnndxj662c.2
title: Computer Application for Statistical Mirroring-Based Ordinalysis

description: The Statistical Mirroring-Based Ordinalysis [3] Application (SM-Based Ordinalysis Application) is a computer tool designed for descriptive analysis of ordinal data (numerically encoded and homogeneous ordinal scores) using statistical mirroring-based ordinalysis methodology. 
A user-friendly interface facilitates in-depth analysis of ordinal assessment data, including estimates of how closely or distant an individual’s composite set of ordinal assessment scores is to the highest positive ordinal scale point. Utilizing statistical mirroring [2], and Kabirian-based isomorphic optinalysis [1], the application allows users to select preprocessing parameters and compute estimates based on the task needs. 
Users can enter ordinal score manually and directly or upload CSV or Excel files, choose analysis parameters, and generate detailed results. The SM-Based Ordinalysis Application's intuitive design and powerful features make it an essential resource for researchers, students, and professionals seeking to explore and describe ordinal assessment data effectively. Its broad applicability spans areas such as survey data analysis, clinical assessments, psychometrics, market research, and public health studies where ordinal data are common. 

References:
[1] K.B. Abdullahi, Kabirian-based optinalysis: A conceptually grounded framework for symmetry/asymmetry, similarity/dissimilarity, and identity/unidentity estimations in mathematical structures and biological sequences, MethodsX 11 (2023) 102400. doi: 10.1016/j.mex.2023.102400
[2] K.B. Abdullahi, Statistical mirroring: A robust method for statistical dispersion estimation, MethodsX 12 (2024) 102682. https://doi.org/10.1016/j.mex.2024.102682 
[3] K.B. Abdullahi, Statistical mirroring-based ordinalysis: A sensitive, robust, efficient, and ordinality-preserving descriptive method for analyzing ordinal assessment data, (2024). [You can follow the published version of the paper for the other reference details).
DOI: 10.15468/dl.f9vqm9
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Naufraga Constance & Cannon"
 ]
}

 The dataset includes 174 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021874-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6nw4jr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dioscorea alata",
 "Country is Saint Kitts and Nevis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027496-250811113504898/datasets/export for details.
DOI: 10.15468/dl.q6kdrr
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Odontotermes obesus"
 ]
}

 The dataset includes 64 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027497-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17611/dp/23119468
title: Global ShakeMovie synthetic for March 28, 2025 MW 6.1 CENTRAL MID-ATLANTIC RIDGE
DOI: 10.17611/dp/23119472
title: Global ShakeMovie synthetic for March 28, 2025 MW 6.7 MYANMAR
DOI: 10.17611/dp/23123938
title: Global ShakeMovie synthetic for March 28, 2025 MW 6.6 CENTRAL MID-ATLANTIC RIDGE
DOI: 10.17611/dp/23128254
title: Global ShakeMovie synthetic for March 30, 2025 MW 7.0 TONGA ISLANDS
DOI: 10.17611/dp/23128258
title: Global ShakeMovie synthetic for March 30, 2025 MW 6.1 TONGA ISLANDS
DOI: 10.17611/dp/23128262
title: Global ShakeMovie synthetic for March 30, 2025 MW 5.9 TONGA ISLANDS
DOI: 10.15468/dl.yd8wyg
title: Occurrence Download

description: A dataset containing 174 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is South America",
 "Country is Bolivia (Plurinational State of)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Psychotria viridis"
 ]
}

 The dataset includes 174 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027498-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17611/dp/23131449
title: Global ShakeMovie synthetic for March 28, 2025 MW 5.7 VANUATU ISLANDS
DOI: 10.17611/dp/23135558
title: Global ShakeMovie synthetic for March 31, 2025 MW 5.6 SUMBA REGION, INDONESIA
DOI: 10.17611/dp/23135562
title: Global ShakeMovie synthetic for April 01, 2025 MW 5.8 BANDA SEA
DOI: 10.17611/dp/23138446
title: Global ShakeMovie synthetic for April 02, 2025 MW 6.4 PACIFIC-ANTARCTIC RIDGE
DOI: 10.17611/dp/23138450
title: Global ShakeMovie synthetic for April 02, 2025 MW 6.1 KYUSHU, JAPAN
DOI: 10.17611/dp/23138454
title: Global ShakeMovie synthetic for April 02, 2025 MW 5.6 NEAR ISLANDS, ALEUTIAN ISLANDS
DOI: 10.17611/dp/23138458
title: Global ShakeMovie synthetic for April 02, 2025 MW 5.9 NORTHERN MOLUCCA SEA
DOI: 10.15468/dl.gwv972
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (CHN.6.4_1, CHN.6.15_1, CHN.6.21_1, CHN.6.3_1, CHN.6.6_1, CHN.6.2_1, CHN.6.20_1, CHN.6.7_1, CHN.6.19_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Egretta eulophotes",
 "Year 2020-2025"
 ]
}

 The dataset includes 11 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025618-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mm4ufm
title: Occurrence Download

description: A dataset containing 4540407 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ficedula albicollis, Jynx torquilla, Strix aluco, Muscicapa striata, Dendrocoptes medius, Dryocopus martius, Parus montanus, Ficedula albicollis, Picus viridis, Motacilla cinerea, Lophophanes cristatus, Picus canus, Dryobates minor, Upupa epops, Athene noctua)",
 "Year is one of (2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024)",
 "DecimalLatitude is greater than or equal to 44.95782311925129",
 "DecimalLatitude is less than or equal to 58.30331634439415",
 "DecimalLongitude is greater than or equal to -10.001896207643075",
 "DecimalLongitude is less than or equal to 30.31691447027875",
 "Year is not null",
 "HasCoordinate is TRUE",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 4540407 records from 4314 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0025129-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.skhn9k
title: Occurrence Download

description: A dataset containing 155 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "Continent is South America",
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Carex pichinchensis Kunth"
 ]
}

 The dataset includes 155 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018251-250525065834625/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.cc2mdn2s
title: CCDC 2425225: Experimental Crystal Structure Determination

description: Related Article: Dong-En Wu|2025|CSD Communication|||
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.cc2mdjl5
title: CCDC 2425117: Experimental Crystal Structure Determination

description: Related Article: TATYANA LEVENETS|2025|CSD Communication|||
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.cc2mdn90
title: CCDC 2425232: Experimental Crystal Structure Determination

description: Related Article: Shao-Han Sun|2025|CSD Communication|||
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.cc2mdp5x
title: CCDC 2425259: Experimental Crystal Structure Determination

description: Related Article: Ulan Kemelbekov|2025|CSD Communication|||
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.cc2mdq2v
title: CCDC 2425287: Experimental Crystal Structure Determination

description: Related Article: lian sun|2025|CSD Communication|||
description: Related Article: Lian Sun,  Xiao-Long Li,  Qiu-Shan Huang,  Wan-Sheng Ji,  Xiaohuan Li,  Jin-Bu Xu,  and Feng Gao|2025|J.Nat.Prod.|88|706|doi:10.1021/acs.jnatprod.4c01344
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.cc2mdtsn
title: CCDC 2425402: Experimental Crystal Structure Determination

description: Related Article: Jingyun Ren|2025|CSD Communication|||
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.cc2mdkn8
title: CCDC 2425150: Experimental Crystal Structure Determination

description: Related Article: Dongjing Hong|2025|CSD Communication|||
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: 10.21942/uva.28684382
title: Data

description: Data for the manuscript "A causal role of the NMDA receptor in recurrent processing during perceptual integration" published in eLife.
DOI: 10.21942/uva.28684382.v5
title: Data

description: Data for the manuscript "A causal role of the NMDA receptor in recurrent processing during perceptual integration" published in eLife.
DOI: 10.21942/uva.28684382.v4
title: Data

description: Data for the manuscript "A causal role of the NMDA receptor in recurrent processing during perceptual integration" published in eLife.
DOI: 10.15468/dl.mtucyf
title: Occurrence Download

description: A dataset containing 202 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pan paniscus Schwarz, 1929"
 ]
}

 The dataset includes 202 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018254-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3n9qn4
title: Occurrence Download

description: A dataset containing 36448 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Streptomyces Waksman & Henrici, 1943"
 ]
}

 The dataset includes 36448 records from 425 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018252-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c3qtev
title: Occurrence Download

description: A dataset containing 396 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-104.13124 17.74298,-99.67919 17.74298,-99.67919 20.61922,-104.13124 20.61922,-104.13124 17.74298))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sylvilagus floridanus (J.A.Allen, 1890)"
 ]
}

 The dataset includes 396 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018255-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.utqkjc
title: Occurrence Download

description: A dataset containing 420638 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is UK Butterfly Monitoring Scheme (UKBMS)",
 "Year 2020-2025"
 ]
}

 The dataset includes 420638 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018250-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/tnn5ra
title: Florida Keys Reef Visual Census 2009

description: In 1998, the Florida Fish and Wildlife Conservation Commission's (FWC) Fisheries Independent Monitoring (FIM) program began a long-term monitoring effort of key reef fish populations in the Florida Keys National Marine Sanctuary. This effort was aimed at evaluating the relative abundance, size structure, and habitat utilization of specific reef fish species that are targeted by commercial and recreational fisheries.
DOI: 10.15468/dl.75hynx
title: Occurrence Download

description: A dataset containing 5069 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Acer grandidentatum Nutt. ex Torr. & A.Gray)",
 "StateProvince is one of (Arizona, Colorado, Idaho, Mexico Northeast, Mexico Northwest, Montana, Nevada, New Mexico, Oklahoma, Texas, Utah, Wyoming)"
 ]
}

 The dataset includes 5069 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018257-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.