cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/pcra-c14-23744
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.2gpbr5
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Telipogon nunezii Dressler"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003385-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vvyqwd
title: Occurrence Download

description: A dataset containing 192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Peru",
 "TaxonKey is Theobroma obovatum Klotzsch ex Bernoulli"
 ]
}

 The dataset includes 192 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045282-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fhdvq5
title: Occurrence Download

description: A dataset containing 36 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Athyrium cuspidatum (Mett. ex Bedd.) M.Kato"
 ]
}

 The dataset includes 36 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0047245-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qz82qn
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Oldenlandia valerianelloides Pit."
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051526-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-23728
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.rn5hmj
title: Occurrence Download

description: A dataset containing 158 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brassica pekinensis (Lour.) Rupr."
 ]
}

 The dataset includes 158 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045247-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5jdey9
title: Occurrence Download

description: A dataset containing 52500 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Year 1990-2020",
 "CoordinateUncertaintyInMeters 0.0-159.0",
 "Geometry POLYGON((-6.93948 56.01784,-1.62759 56.01784,-1.62759 58.14370,-6.93948 58.14370,-6.93948 56.01784))",
 "HasCoordinate is true",
 "TaxonKey is Bryopsida",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 52500 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019157-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-23712
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.7jrynk
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is one of (Oeceoclades maculata (Lindl.) Lindl., Oenothera sandiana Hassk., Oldenlandia lancifolia (Schumach.) DC., Origanum majorana L., Oryza rufipogon Griff., Oxalis micrantha Bojer ex Progel, Oxalis micrantha Bojer, Oxalis micrantha Bertero, Oxalis micrantha Bertero ex Savi, Oxalis micrantha Bertero ex Colla)"
 ]
}

 The dataset includes 44 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017905-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.k6bndx
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brassica boissieri Munby"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045268-220831081235567/datasets/export for details.
DOI: 10.25549/pcra-c14-24014
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.aqmqsy
title: Occurrence Download

description: A dataset containing 1769 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "Country is Kenya",
 "Country is Tanzania, United Republic of",
 "Country is Uganda",
 "Country is Rwanda",
 "Country is Ethiopia",
 "Country is Congo, Democratic Republic of the",
 "Country is Burundi",
 "Country is Malawi"
 ]
 },
 "TaxonKey is Buteo oreophilus Hartert & Neumann, 1914"
 ]
}

 The dataset includes 1769 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024661-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.53hfra
title: Occurrence Download

description: A dataset listing the 5 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Machine Observation, Human Observation, Material sample, Material citation, Specimen, Occurrence evidence)",
 "Country is Greece",
 "OccurrenceStatus is Present",
 "TaxonKey is Iduna pallida (Hemprich & Ehrenberg, 1833)",
 "Year 1990-2022"
 ]
}

 The dataset's 5 records were derived from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0302912-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxaess
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solanum triunfense S.Knapp"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003386-240425142415019/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-23979
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.pyawxh
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brassica fruticulosa var. numidica Coss."
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045297-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25549/pcra-c14-24006
title: Letters of Mundell, Thomas, Secretary of Home Reference Missionary Council, PMU, June 1925-July 1927

description: [description english/roman)] Pentecostal Missionary Union [PMU]/Assemblies of God [AoG] Home Reference Missionary Council outgoing correspondence from Home Reference Missionary Council secretary Thomas Mundell, including index for correspondence 109 pages and correspondence 2026 pages. Outgoing letters sent by Mundell on behalf of Assemblies of God [AoG] Home Reference Missionary Council
DOI: 10.15468/dl.4rnudk
title: Occurrence Download

description: A dataset containing 9644 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Zalophus californianus (Lesson, 1828)"
 ]
}

 The dataset includes 9644 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012626-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/j5zzhy652g.1
title: Systemic risk spillovers incorporating investor sentiment：Evidence from an improved TENET analysis

description: The folder "code" contains the code for data processing and empirical results.
It includes two folders, data is used to store data, and model is used to store running python and R code.

1.Data Description：
1.1.The folder "TENET network data at each time point" stores the adjacency matrix and other data of each time node in the TENET network. It is called in "Network topology analysis.R".
1.2.Ping An Bank Investor Sentiment (Bayesian Machine Learning).csv is Ping An Bank's investor sentiment data based on machine learning methods
1.3.Ping An Bank Investor Sentiment (Financial Dictionary).csv is Ping An Bank's investor sentiment data based on Financial Dictionary methods
1.4.Ping An Bank Investor Sentiment (Pre-trained Deep Learning (ERNIE)).csv is Ping An Bank's investor sentiment data based on ERNIE model.
1.5aligned_sentiment_indices.csv stores variables related to market sentiment, among which ISI, CICSI and Confidence index are derived from the CSMAR database, and BI is the investor sentiment index calculated by ERNIE based on Baidu AI platform.


2.Figure：
2.1Figure 1 can be obtained through the ''Sentiment Comparison of Three Approaches for Individual Financial Institutions.py''.
2.2Figure 2 can be obtained via ''Comparison of Market sentiment.py''.
2.3Figures 3 can be obtained through ''Change in average λ for systematic risk (compare to inclusion of sentiment variables).py''.
2.4Figure 4 requires you to choose to run ''Comparison of elemental standardisation treatments for TENET.py''.
2.5Figure 5 requires you to choose to run ''Comparison of average λ and spillover intensity.py''.
2.6Figure 6-11 are obtained by running ''Network topology analysis.R''.
2.7Figure 12 is obtained by running ''Evolution of Cross-Sector Tail Risk Spillovers and Spill-Ins.py''.
2.8Figure 13 is obtained by running ''Tail risk spillovers between any financial sector of a financial institution and any other financial sector.py''.
2.9Figure 14 is obtained by running ''Tail Risk Spillovers within the Financial Sector.py''.
DOI: 10.17632/j5zzhy652g.2
title: Systemic risk spillovers incorporating investor sentiment：Evidence from an improved TENET analysis

description: The folder "code" contains the code for data processing and empirical results.
It includes two folders, data is used to store data, and model is used to store running python and R code.

1.Data Description：
1.1.The folder "TENET network data at each time point" stores the adjacency matrix and other data of each time node in the TENET network. It is called in "Network topology analysis.R".
1.2.Ping An Bank Investor Sentiment (Bayesian Machine Learning).csv is Ping An Bank's investor sentiment data based on machine learning methods
1.3.Ping An Bank Investor Sentiment (Financial Dictionary).csv is Ping An Bank's investor sentiment data based on Financial Dictionary methods
1.4.Ping An Bank Investor Sentiment (Pre-trained Deep Learning (ERNIE)).csv is Ping An Bank's investor sentiment data based on ERNIE model.
1.5aligned_sentiment_indices.csv stores variables related to market sentiment, among which ISI, CICSI and Confidence index are derived from the CSMAR database, and BI is the investor sentiment index calculated by ERNIE based on Baidu AI platform.


2.Figure：
2.1Figure 1 can be obtained through the ''Sentiment Comparison of Three Approaches for Individual Financial Institutions.py''.
2.2Figure 2 can be obtained via ''Comparison of Market sentiment.py''.
2.3Figures 3 can be obtained through ''Change in average λ for systematic risk (compare to inclusion of sentiment variables).py''.
2.4Figure 4 requires you to choose to run ''Comparison of elemental standardisation treatments for TENET.py''.
2.5Figure 5 requires you to choose to run ''Comparison of average λ and spillover intensity.py''.
2.6Figure 6-11 are obtained by running ''Network topology analysis.R''.
2.7Figure 12 is obtained by running ''Evolution of Cross-Sector Tail Risk Spillovers and Spill-Ins.py''.
2.8Figure 13 is obtained by running ''Tail risk spillovers between any financial sector of a financial institution and any other financial sector.py''.
2.9Figure 14 is obtained by running ''Tail Risk Spillovers within the Financial Sector.py''.
DOI: 10.17632/j5zzhy652g
title: Systemic risk spillovers incorporating investor sentiment：Evidence from an improved TENET analysis

description: The folder "code and data" contains the code for data processing and empirical results.
It includes two folders, data is used to store data, and model is used to store running python and R code.

1.Data Description：
1.1.The folder "TENET network data at each time point" stores the adjacency matrix and other data of each time node in the TENET network. It is called in "Network topology analysis.R".
1.2.Ping An Bank Investor Sentiment (Bayesian Machine Learning).csv is Ping An Bank's investor sentiment data based on machine learning methods
1.3.Ping An Bank Investor Sentiment (Financial Dictionary).csv is Ping An Bank's investor sentiment data based on Financial Dictionary methods
1.4.Ping An Bank Investor Sentiment (Pre-trained Deep Learning (ERNIE)).csv is Ping An Bank's investor sentiment data based on ERNIE model.
1.5aligned_sentiment_indices.csv stores variables related to market sentiment, among which ISI, CICSI and Confidence index are derived from the CSMAR database, and BI is the investor sentiment index calculated by ERNIE based on Baidu AI platform.
1.6 The IIC.csv file contains data on tail risk spillovers within the financial sector.
1.7 The DS.csv file contains data on tail risk spillovers between any financial sector of a financial institution and any other financial sector.
1.8 The BIC.csv file contains data on how much risk each sector spillsover to others.
1.9 The BIC_receive.csv contains data on how much risk each sector receives from others.
1.10 The three files HHI.csv, NAS.csv, and AS.csv store network topology indicator data.
1.11 The code number.xlsx store the stock codes and abbreviations of all financial institutions.
1.12 The Stock Market Value.csv is the market value data of financial institutions, which is used to identify Systemically Important Financial Institutions (Härdle et al. (2016)).


2.Figure：
2.1Figure 1 can be obtained through the ''Sentiment Comparison of Three Approaches for Individual Financial Institutions.py''.
2.2Figure 2 can be obtained via ''Comparison of Market sentiment.py''.
2.3Figures 3 can be obtained through ''Change in average λ for systematic risk (compare to inclusion of sentiment variables).py''.
2.4Figure 4 requires you to choose to run ''Comparison of elemental standardisation treatments for TENET.py''.
2.5Figure 5 requires you to choose to run ''Comparison of average λ and spillover intensity.py''.
2.6Figure 6-11 are obtained by running ''Network topology analysis.R''.The same procedure is also run for Tables 5 and 6 concerning the rankings of risk emitters and receivers.
2.7Figure 12 is obtained by running ''Evolution of Cross-Sector Tail Risk Spillovers and Spill-Ins.py''.
2.8Figure 13 is obtained by running ''Tail risk spillovers between any financial sector of a financial institution and any other financial sector.py''.
2.9Figure 14 is obtained by running ''Tail Risk Spillovers within the Financial Sector.py''.
DOI: 10.17632/j5zzhy652g.3
title: Systemic risk spillovers incorporating investor sentiment：Evidence from an improved TENET analysis

description: The folder "code and data" contains the code for data processing and empirical results.
It includes two folders, data is used to store data, and model is used to store running python and R code.

1.Data Description：
1.1.The folder "TENET network data at each time point" stores the adjacency matrix and other data of each time node in the TENET network. It is called in "Network topology analysis.R".
1.2.Ping An Bank Investor Sentiment (Bayesian Machine Learning).csv is Ping An Bank's investor sentiment data based on machine learning methods
1.3.Ping An Bank Investor Sentiment (Financial Dictionary).csv is Ping An Bank's investor sentiment data based on Financial Dictionary methods
1.4.Ping An Bank Investor Sentiment (Pre-trained Deep Learning (ERNIE)).csv is Ping An Bank's investor sentiment data based on ERNIE model.
1.5aligned_sentiment_indices.csv stores variables related to market sentiment, among which ISI, CICSI and Confidence index are derived from the CSMAR database, and BI is the investor sentiment index calculated by ERNIE based on Baidu AI platform.
1.6 The IIC.csv file contains data on tail risk spillovers within the financial sector.
1.7 The DS.csv file contains data on tail risk spillovers between any financial sector of a financial institution and any other financial sector.
1.8 The BIC.csv file contains data on how much risk each sector spillsover to others.
1.9 The BIC_receive.csv contains data on how much risk each sector receives from others.
1.10 The three files HHI.csv, NAS.csv, and AS.csv store network topology indicator data.
1.11 The code number.xlsx store the stock codes and abbreviations of all financial institutions.
1.12 The Stock Market Value.csv is the market value data of financial institutions, which is used to identify Systemically Important Financial Institutions (Härdle et al. (2016)).


2.Figure：
2.1Figure 1 can be obtained through the ''Sentiment Comparison of Three Approaches for Individual Financial Institutions.py''.
2.2Figure 2 can be obtained via ''Comparison of Market sentiment.py''.
2.3Figures 3 can be obtained through ''Change in average λ for systematic risk (compare to inclusion of sentiment variables).py''.
2.4Figure 4 requires you to choose to run ''Comparison of elemental standardisation treatments for TENET.py''.
2.5Figure 5 requires you to choose to run ''Comparison of average λ and spillover intensity.py''.
2.6Figure 6-11 are obtained by running ''Network topology analysis.R''.The same procedure is also run for Tables 5 and 6 concerning the rankings of risk emitters and receivers.
2.7Figure 12 is obtained by running ''Evolution of Cross-Sector Tail Risk Spillovers and Spill-Ins.py''.
2.8Figure 13 is obtained by running ''Tail risk spillovers between any financial sector of a financial institution and any other financial sector.py''.
2.9Figure 14 is obtained by running ''Tail Risk Spillovers within the Financial Sector.py''.
DOI: 10.17632/46x2m539xc.1
title: BOLT (Bangla Offensive and Lethal Texts)

description: The widespread dissemination of aggressive content on social media platforms poses significant threats to social cohesion and communal harmony. Social media has emerged as a prevalent venue for discussing diverse topics, which frequently result in debates. These debates often fuel animosity, incite violence, and spread threatening messages that disrupt societal peace and security. To address this challenge, we developed a novel Bengali dataset, BOLT, accompanied by English translations, to identify and classify aggression in texts. 

The dataset was obtained from several online platforms, including Facebook, YouTube, blogs, online news portals, and group discussions. We executed multiple stages for data preprocessing, including the elimination of duplicates, special characters, emoji to improve the coherence of the dataset. Each instance in the dataset was annotated by two of the lists of four annotators with diverse academic backgrounds, with any discrepancies resolved by expert review.

The ALERT dataset is a collection of 4,027 Bangla texts categorized as 
1. Hate speech (995), 
2. Vandalism (909), 
3. Atrocity (1117), 
4. No aggression (1,006). 
The dataset is structured with the following fields:
• Final Annotation
• Text
• English Translation 
Our developed dataset contains a mix of formal and informal Bangla texts, reflecting how people communicate in real life. Instead of simply labeling content as aggressive or not, it offers more detailed categories, helping with more precise content moderation. The dataset is publicly accessible for research purposes to promote innovation and collaboration within the Bengali NLP community.