cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14973855
title: MassIVE-KB v1 30 million PSMs

description: The MassIVE-KB data are derived from PSMs used to compile the MassIVE-KB v1 spectral library and consists of approximately 30 million PSMs. The PSMs were obtained by collecting up to the top 100 PSMs for each of the 2,154,269 precursors (as defined by a peptidoform and charge) included in the MassIVE-KB v1 spectral library, resulting in 30,504,897 PSMs for 1,601,479 unique peptidoforms.

The dataset was originally compiled through the following steps:



On the MassIVE website, go to MassIVE Knowledge Base > Human HCD Spectral Library > All Candidate library spectra > Download.

This will give you a zipped TSV file with the metadata and peptide identifications for all 30 million PSMs.

Using the filename (column "filename") you can then retrieve the corresponding peak files from the MassIVE FTP server (done using a wget script) and extract the desired spectra using their scan number (column "scan").
DOI: 10.5281/zenodo.14973856
title: MassIVE-KB v1 30 million PSMs

description: The MassIVE-KB data are derived from PSMs used to compile the MassIVE-KB v1 spectral library and consists of approximately 30 million PSMs. The PSMs were obtained by collecting up to the top 100 PSMs for each of the 2,154,269 precursors (as defined by a peptidoform and charge) included in the MassIVE-KB v1 spectral library, resulting in 30,504,897 PSMs for 1,601,479 unique peptidoforms.

The dataset was originally compiled through the following steps:



On the MassIVE website, go to MassIVE Knowledge Base > Human HCD Spectral Library > All Candidate library spectra > Download.

This will give you a zipped TSV file with the metadata and peptide identifications for all 30 million PSMs.

Using the filename (column "filename") you can then retrieve the corresponding peak files from the MassIVE FTP server (done using a wget script) and extract the desired spectra using their scan number (column "scan").
DOI: 10.25386/genetics.28396100
title: Supplemental Material for Cridland, Polston, and Begun, 2025

description: Supplemental material to accompany Genetics-2025-307870
DOI: 10.25386/genetics.28396100.v1
title: Supplemental Material for Cridland, Polston, and Begun, 2025

description: Supplemental material to accompany Genetics-2025-307870
DOI: 10.5281/zenodo.14950617
title: Systematic Literature Review Dataset on Smart Contracts in the Agri-Food Sector

description: This dataset contains a systematic literature review on the use of smart contracts in the agri-food sector. The data was collected following the methodology of Webster and Watson (2002), ensuring a systematic selection and categorization of relevant academic papers. The dataset includes key metadata such as the title, author(s), publication year, and the platform through which the papers can be accessed.

File format: Excel (.xlsx)
DOI: 10.5281/zenodo.14950618
title: Systematic Literature Review Dataset on Smart Contracts in the Agri-Food Sector

description: This dataset contains a systematic literature review on the use of smart contracts in the agri-food sector. The data was collected following the methodology of Webster and Watson (2002), ensuring a systematic selection and categorization of relevant academic papers. The dataset includes key metadata such as the title, author(s), publication year, and the platform through which the papers can be accessed.

File format: Excel (.xlsx)
DOI: 10.5281/zenodo.15000405
title: VERA 2.0 Validation Dataset: 3D Measurement of Entheseal Changes in Human Hand Bones

description: This dataset was used to test the reliability of the new "Validated Entheses-based Reconstruction of Activity 2.0" (VERA 2.0) approach, as part of a study introducing this approach and protocol, which has been accepted for publication in PLOS One. An associated step-by-step protocol has also been published on protocols.io (dx.doi.org/10.17504/protocols.io.5jyl82z8dl2w/v3). The dataset consists of 3D area measurements (in square millimeters) of bone surface changes on enthesis-bearing structures of human hand bones from the 19th century skeletal collection Basel-Spitalfriedhof, curated at the Natural History Museum of Basel, Switzerland. The sample includes biological males and females and covers the attachment regions of the following muscles: opponens pollicis (first metacarpal), extensor pollicis brevis (first proximal phalanx), and opponens digiti minimi (fifth metacarpal). For additional information on this collection’s hand bones and data, please refer to the existing previous publications of this hand bone material (e.g., Karakostis & Hotz, 2022: https://onlinelibrary.wiley.com/doi/10.1002/ajpa.24636; Karakostis et al., 2017: https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.23253).
DOI: 10.5281/zenodo.15000406
title: VERA 2.0 Validation Dataset: 3D Measurement of Entheseal Changes in Human Hand Bones

description: This dataset was used to test the reliability of the new "Validated Entheses-based Reconstruction of Activity 2.0" (VERA 2.0) approach, as part of a study introducing this approach and protocol, which has been accepted for publication in PLOS One. An associated step-by-step protocol has also been published on protocols.io (dx.doi.org/10.17504/protocols.io.5jyl82z8dl2w/v3). The dataset consists of 3D area measurements (in square millimeters) of bone surface changes on enthesis-bearing structures of human hand bones from the 19th century skeletal collection Basel-Spitalfriedhof, curated at the Natural History Museum of Basel, Switzerland. The sample includes biological males and females and covers the attachment regions of the following muscles: opponens pollicis (first metacarpal), extensor pollicis brevis (first proximal phalanx), and opponens digiti minimi (fifth metacarpal). For additional information on this collection’s hand bones and data, please refer to the existing previous publications of this hand bone material (e.g., Karakostis & Hotz, 2022: https://onlinelibrary.wiley.com/doi/10.1002/ajpa.24636; Karakostis et al., 2017: https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.23253).
DOI: 10.15468/dl.q948ma
title: Occurrence Download

description: A dataset containing 244 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Machine Observation)",
 "Continent is Africa",
 "Country is Ethiopia",
 "OccurrenceStatus is Present",
 "TaxonKey is Terminalia brownii Fresen."
 ]
}

 The dataset includes 244 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000681-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p8gcuz
title: Occurrence Download

description: A dataset containing 40 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Uleastrum palmicola (Müll.Hal.) R.H.Zander"
 ]
}

 The dataset includes 40 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000682-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15000587
title: IoT-SQL Dataset: A Benchmark for Text-to-SQL and IoT Threat Classification

description: IoT-SQL Dataset: A Benchmark for Text-to-SQL and IoT Threat Classification

Overview

This dataset accompanies the paper:

"Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats"

Published in TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing, colocated with NAACL 2025.

This dataset is designed to facilitate research in:



Text-to-SQL: Generating SQL queries from natural language.

IoT Network Traffic Analysis: Detecting malicious activity in IoT environments.

Multimodal Learning: Combining structured database queries with network security classification.


Dataset Contents

The dataset consists of three main components:

1. IoT Database (iot_database.sql.gz)



SQL schema and data from IoT-23 logs and Smart Building Sensor datasets to be put into a database.


2. Text-to-SQL Data (text-to-SQL-data.zip)



Includes queries with joins, aggregations, temporal conditions, and nested clauses.

Data split into training (6,591), validation (2,197), and test (2,197) sets.


3. Network Traffic Data (network_traffic_data.zip)



Each record labeled as benign or malicious.

Features include timestamps, IPs, ports, protocols, byte counts, and connection history.

Malicious traffic includes DDoS, C&C, and botnet-related activity.


Usage Instructions

Setting Up the Database



Extract the database file:

gunzip iot_database.sql.gz



Import into MySQL:

mysql -u <username> -p <database_name> < iot_database.sql



Verify the schema:

SHOW TABLES;




Citation

If you use this dataset, please cite:

@inproceedings{pavlich2025beyond,
  author = {Ryan Pavlich and Nima Ebadi and Richard Tarbell and Billy Linares and Adrian Tan and Rachael Humphreys and Jayanta Kumar Das and Rambod Ghandiparsi and Hannah Haley and Jerris George and Rocky Slavin and Kim-Kwang Raymond Choo and Glenn Dietrich and Anthony Rios},
  title = {Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats},
  booktitle = {TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing},
  year = {2025},
  organization = {NAACL}
}


Contact

For questions or collaborations, contact Anthony Rios at Anthony.Rios@utsa.edu.
DOI: 10.5281/zenodo.15000588
title: IoT-SQL Dataset: A Benchmark for Text-to-SQL and IoT Threat Classification

description: IoT-SQL Dataset: A Benchmark for Text-to-SQL and IoT Threat Classification

Overview

This dataset accompanies the paper:

"Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats"

Published in TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing, colocated with NAACL 2025.

This dataset is designed to facilitate research in:



Text-to-SQL: Generating SQL queries from natural language.

IoT Network Traffic Analysis: Detecting malicious activity in IoT environments.

Multimodal Learning: Combining structured database queries with network security classification.


Dataset Contents

The dataset consists of three main components:

1. IoT Database (iot_database.sql.gz)



SQL schema and data from IoT-23 logs and Smart Building Sensor datasets to be put into a database.


2. Text-to-SQL Data (text-to-SQL-data.zip)



Includes queries with joins, aggregations, temporal conditions, and nested clauses.

Data split into training (6,591), validation (2,197), and test (2,197) sets.


3. Network Traffic Data (network_traffic_data.zip)



Each record labeled as benign or malicious.

Features include timestamps, IPs, ports, protocols, byte counts, and connection history.

Malicious traffic includes DDoS, C&C, and botnet-related activity.


Usage Instructions

Setting Up the Database



Extract the database file:

gunzip iot_database.sql.gz



Import into MySQL:

mysql -u <username> -p <database_name> < iot_database.sql



Verify the schema:

SHOW TABLES;




Citation

If you use this dataset, please cite:

@inproceedings{pavlich2025beyond,
  author = {Ryan Pavlich and Nima Ebadi and Richard Tarbell and Billy Linares and Adrian Tan and Rachael Humphreys and Jayanta Kumar Das and Rambod Ghandiparsi and Hannah Haley and Jerris George and Rocky Slavin and Kim-Kwang Raymond Choo and Glenn Dietrich and Anthony Rios},
  title = {Beyond Text-to-SQL for IoT Defense: A Comprehensive Framework for Querying and Classifying IoT Threats},
  booktitle = {TrustNLP: Fifth Workshop on Trustworthy Natural Language Processing},
  year = {2025},
  organization = {NAACL}
}


Contact

For questions or collaborations, contact Anthony Rios at Anthony.Rios@utsa.edu.
DOI: 10.15468/dl.gxu79n
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Napomyza evanescens (Hendel, 1920)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000683-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14473/csda/5rzsv4
title: Výběrové šetření romské populace 2024

description: Datový soubor pochází z výběrového šetření romské populace realizovaného v rámci projektu "Vytvoření systému sběru kvantitativních dat pro vyhodnocování situace Romů v české společnosti". Tento projekt byl podpořen z grantů Norska v rámci programu Fondů EHP a Norska 2014–2021. První výběrové šetření proběhlo v roce 2022 a zahrnovalo 1549 respondentů. Druhé šetření s obdobným zaměřením bylo realizováno na přelomu let 2023/2024 a zahrnovalo 1268 respondentů. Data z prvního šetření (2022) jsou také dostupná v Dataverse ČSDA.
Vzorek respondentů v obou šetřeních tvoří osoby, které se buď samy identifikují jako Romové/Romky, nebo u nichž na základě znalosti romského jazyka či znalosti romského jazyka alespoň jednoho z rodičů usuzujeme na romský původ.
Výzkum byl tematicky zaměřen zejména na socioekonomické postavení romské populace.
K výzkumu jsou dostupné tři matice dat. Data respondentů jsou vytvořena z pohledu respondenta. Data domácností a dětí jsou z pohledů členů domácností a dětí. Jedná se tedy o jedna a ta samá data, která jsou pouze v jiné podobě a není třeba je propojovat (podrobný popis je dostupný v dokumentu Popis dat - manuál).
description: SPSS, null
description: Vytvoření systému sběru kvantitativních dat pro vyhodnocování situace Romů v české společnosti.
DOI: 10.14473/csda/5rzsv4/1ve195
title: 2024_deti-1.tab

description: Soubor "Děti" ve formátu pro software SPSS
DOI: 10.14473/csda/5rzsv4/6bzbka
title: 2024_respondenti.tab

description: Soubor "Respondenti" ve formátu pro software STATA
DOI: 10.14473/csda/5rzsv4/b56sfe
title: 2024_setreni_dotaznik.pdf

description: Dotazník
DOI: 10.14473/csda/5rzsv4/be1geb
title: Data_popis_manual.docx

description: Popis dat - manuál. Tento dokument popisuje jednotlivé datové soubory a jejich vzájemný vztah.
DOI: 10.14473/csda/5rzsv4/cbitya
title: 2024_respondenti-1.tab

description: Soubor "Respondenti" ve formátu pro software SPSS
DOI: 10.14473/csda/5rzsv4/fcy2gt
title: 2024_technicka_zprava.pdf

description: Podrobná technická zpráva z výzkumu 2024
DOI: 10.14473/csda/5rzsv4/nvx3ie
title: 2024_deti.tab

description: Soubor "Děti" ve formátu pro software STATA
DOI: 10.14473/csda/5rzsv4/ppicif
title: 2024_domacnosti-1.tab

description: Soubor "Domácnosti" ve formátu pro software SPSS
DOI: 10.14473/csda/5rzsv4/rtbnk9
title: 2024_domacnosti.tab

description: Soubor "Domácnosti" ve formátu pro software STATA
DOI: 10.14473/csda/5rzsv4/yon48l
title: Prevodnik_promennych.tab

description: Převodník proměnných mezi výzkumy 2022 a 2024.
DOI: 10.5281/zenodo.15000673
title: Validation dataset for "Earthformer for rainfall retrieval" applied in Ghana

description: This contains the validation dataset used to train the Earthformer model. 

This data belongs to the repository of earthformer-for-rainfall-retrieval

And is part of my master thesis work, which can be found in

Implementation of a space-time transformer retrieval algorithm for high rainfall rates from the Meteosat satellite applied in Africa
DOI: 10.5281/zenodo.15000674
title: Validation dataset for "Earthformer for rainfall retrieval" applied in Ghana

description: This contains the validation dataset used to train the Earthformer model. 

This data belongs to the repository of earthformer-for-rainfall-retrieval

And is part of my master thesis work, which can be found in

Implementation of a space-time transformer retrieval algorithm for high rainfall rates from the Meteosat satellite applied in Africa
DOI: 10.15468/dl.pg8q6r
title: Occurrence Download

description: A dataset containing 3357846 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Orobanchaceae"
 ]
}

 The dataset includes 3357846 records from 4195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000662-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1070892.10fu
title: SH1070892.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1070895.10fu
title: SH1070895.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1070899.10fu
title: SH1070899.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1070904.10fu
title: SH1070904.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1070905.10fu
title: SH1070905.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1070906.10fu
title: SH1070906.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.