cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2n90ww
title: CCDC 2451538: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Maxim N. Sokolov, Pavel A. Abramov|2025|Crystals|15|607|doi:10.3390/cryst15070607
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.cc2n9101
title: CCDC 2451542: Experimental Crystal Structure Determination

description: Related Article: Taisiya S. Sukhikh, Maxim N. Sokolov, Pavel A. Abramov|2025|Crystals|15|607|doi:10.3390/cryst15070607
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: 10.17632/w5mfysjymp
title: heart rate variability and inflammatory

description: The autonomic nervous system regulates chronic inflammation, with heart rate variability (HRV) reflecting this regulation. We investigated the relationship between HRV and inflammatory markers (IL-6, hs-CRP) in 26 healthy young adults (19.9 ± 0.8 years). Bayesian correlation analysis was employed to estimate the relationship between natural log-transformed (Ln) HRV and inflammatory marker values. Ln-IL-6 showed substantial positive associations with 24-hour and night-time Ln-VLF (Bayes factor = 9.33 and 4.39, respectively), with 95% credible intervals entirely above zero. No compelling associations were found for hs-CRP or other HRV parameters. The VLF component is a unique indicator of serum IL-6 in young adults.
DOI: 10.17632/w5mfysjymp.1
title: heart rate variability and inflammatory

description: The autonomic nervous system regulates chronic inflammation, with heart rate variability (HRV) reflecting this regulation. We investigated the relationship between HRV and inflammatory markers (IL-6, hs-CRP) in 26 healthy young adults (19.9 ± 0.8 years). Bayesian correlation analysis was employed to estimate the relationship between natural log-transformed (Ln) HRV and inflammatory marker values. Ln-IL-6 showed substantial positive associations with 24-hour and night-time Ln-VLF (Bayes factor = 9.33 and 4.39, respectively), with 95% credible intervals entirely above zero. No compelling associations were found for hs-CRP or other HRV parameters. The VLF component is a unique indicator of serum IL-6 in young adults.
DOI: 10.17632/xg42r9t638
title: Data for: Assessing the relationship between blubber thickness and adipocyte size in beluga whales

description: The goal of this study was to assess the relationship between blubber thickness and adipocyte size to determine their use as proxies for beluga whale body condition. 
This work focuses on blubber sampled from Hudson Bay beluga whales provided by Inuit from Sanikiluaq, Nunavut, during subsistence harvests 2021-2023.

Blubber samples were also collected between 2015 and 2021 and analyzed to assess seasonal energy storage in Hudson Bay beluga whales. See Belanger et al., 2025 for study and dataset:

Belanger, A.M., Roth, J.D., Ferguson, S.H., Friesen, O.C., Watt, C.A. Seasonal Flexibility in Energy Stores of Hudson Bay Beluga Whales: Insights from Blubber Lipid Analysis. Can J Zool 2025, 103, doi:10.1139/cjz-2024-0114.

Dataset DOI: 10.5061/dryad.ht76hdrqk

Files and variables
File: Stewart et al. 2026.csv
Variables:

- whale_ID: Beluga whale individual
- layer: All cut in ~ 1cm3
 - Outer - 1 cm subsample starting from the skin, along the depth of the blubber
 - Inner - 1 cm subsample starting from the end of the blubber, along the depth of the blubber
- vac_count: Number of cells measured within the square analysis area
- total_vac_area (𝝁m^2): Total area of all measured cells
- mean_vac_aread (𝝁m^2): Mean area of all the measured cells
- year: Year the sample was collected 
- sex: Genetically determined sex from tissues
 -F = Female
 -M = Male
- bt (cm): Blubber thickness
DOI: 10.17632/xg42r9t638.1
title: Data for: Assessing the relationship between blubber thickness and adipocyte size in beluga whales

description: The goal of this study was to assess the relationship between blubber thickness and adipocyte size to determine their use as proxies for beluga whale body condition. 
This work focuses on blubber sampled from Hudson Bay beluga whales provided by Inuit from Sanikiluaq, Nunavut, during subsistence harvests 2021-2023.

Blubber samples were also collected between 2015 and 2021 and analyzed to assess seasonal energy storage in Hudson Bay beluga whales. See Belanger et al., 2025 for study and dataset:

Belanger, A.M., Roth, J.D., Ferguson, S.H., Friesen, O.C., Watt, C.A. Seasonal Flexibility in Energy Stores of Hudson Bay Beluga Whales: Insights from Blubber Lipid Analysis. Can J Zool 2025, 103, doi:10.1139/cjz-2024-0114.

Dataset DOI: 10.5061/dryad.ht76hdrqk

Files and variables
File: Stewart et al. 2026.csv
Variables:

- whale_ID: Beluga whale individual
- layer: All cut in ~ 1cm3
 - Outer - 1 cm subsample starting from the skin, along the depth of the blubber
 - Inner - 1 cm subsample starting from the end of the blubber, along the depth of the blubber
- vac_count: Number of cells measured within the square analysis area
- total_vac_area (𝝁m^2): Total area of all measured cells
- mean_vac_aread (𝝁m^2): Mean area of all the measured cells
- year: Year the sample was collected 
- sex: Genetically determined sex from tissues
 -F = Female
 -M = Male
- bt (cm): Blubber thickness
DOI: 10.17632/3dng3j76y9
title: Traffic Congestion Prediction Dataset

description: The dataset is designed for the study and development of machine learning and intelligent traffic prediction models for congestion classification. This dataset contains structured traffic observations collected using a virtual IoT-based traffic monitoring approach from selected road segments in the Vijay Nagar area, Indore. The dataset is organized into four congestion classes based on traffic delay values:
(1) FREE: Low or no traffic delay
(2) LIGHT: Minor traffic delay
(3) MODERATE: Noticeable traffic delay
(4) HEAVY: Significant traffic congestion
---------------------------------------------
Format: CSV (Comma Separated Values)
Type: Spatio-temporal traffic dataset
Features: Road ID, Hour, Day, Delay, Congestion
Status: Primary dataset (collected and structured for machine learning)
Applications:
* Multi-class classification of traffic congestion levels
* Training machine learning models for intelligent traffic prediction
* Development of IoT-based smart traffic monitoring systems
* Traffic pattern analysis and congestion forecasting
* Smart city and intelligent transportation research
DOI: 10.17632/3dng3j76y9.1
title: Traffic Congestion Prediction Dataset

description: The dataset is designed for the study and development of machine learning and intelligent traffic prediction models for congestion classification. This dataset contains structured traffic observations collected using a virtual IoT-based traffic monitoring approach from selected road segments in the Vijay Nagar area, Indore. The dataset is organized into four congestion classes based on traffic delay values:
(1) FREE: Low or no traffic delay
(2) LIGHT: Minor traffic delay
(3) MODERATE: Noticeable traffic delay
(4) HEAVY: Significant traffic congestion
---------------------------------------------
Format: CSV (Comma Separated Values)
Type: Spatio-temporal traffic dataset
Features: Road ID, Hour, Day, Delay, Congestion
Status: Primary dataset (collected and structured for machine learning)
Applications:
* Multi-class classification of traffic congestion levels
* Training machine learning models for intelligent traffic prediction
* Development of IoT-based smart traffic monitoring systems
* Traffic pattern analysis and congestion forecasting
* Smart city and intelligent transportation research
DOI: 10.17632/pdjwy9dwtx
title: Movie Success Rate

description: In this analysis, Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn are used to clean, process, and visualize the dataset. The success of a movie is typically defined using criteria such as profitability (revenue greater than budget), return on investment (ROI), audience ratings, or popularity scores.

The workflow begins with data preprocessing, where missing values are handled, duplicates are removed, and categorical variables are encoded. After cleaning, exploratory data analysis (EDA) is performed to understand relationships between features like budget, genre, runtime, and ratings with the movie’s success.
DOI: 10.17632/pdjwy9dwtx.1
title: Movie Success Rate

description: In this analysis, Python libraries such as Pandas, NumPy, Matplotlib, and Seaborn are used to clean, process, and visualize the dataset. The success of a movie is typically defined using criteria such as profitability (revenue greater than budget), return on investment (ROI), audience ratings, or popularity scores.

The workflow begins with data preprocessing, where missing values are handled, duplicates are removed, and categorical variables are encoded. After cleaning, exploratory data analysis (EDA) is performed to understand relationships between features like budget, genre, runtime, and ratings with the movie’s success.
DOI: 10.17632/7xwj6vksjc
title: Physical properties data of forearc basalts and boninites collected during the IODP Expedition 352

description: This dataset reports physical properties of forearc basalts and boninites collected by the IODP Expedition 352, cited in Akamatsu et al. (2026).
The csv file contains porosity, density, P-wave velocity, and magnetic susceptibility under atmospheric pressure and temperature conditions, along with whole-rock geochemical compositions.
DOI: 10.17632/7xwj6vksjc.1
title: Physical properties data of forearc basalts and boninites collected during the IODP Expedition 352

description: This dataset reports physical properties of forearc basalts and boninites collected by the IODP Expedition 352, cited in Akamatsu et al. (2026).
The csv file contains porosity, density, P-wave velocity, and magnetic susceptibility under atmospheric pressure and temperature conditions, along with whole-rock geochemical compositions.
DOI: 10.17632/6pbrvkdswf
title: Base-Gumanova N. G.

description: Metabolic Hormones, Nitric Oxide Signaling components, and Inflammation Markers Crosstalk in Coronary Artery Disease
DOI: 10.17632/6pbrvkdswf.1
title: Base-Gumanova N. G.

description: Metabolic Hormones, Nitric Oxide Signaling components, and Inflammation Markers Crosstalk in Coronary Artery Disease
DOI: 10.5878/h994-nk34
title: The National SOM Survey 2020
title: Den nationella SOM-undersökningen 2020
title: Riks-SOM 2020

description: In order to identify how the evolution of society affects Swedes’ attitudes and behaviour, the SOM Institute started its National SOM study in 1986. National SOM addresses three areas - society, opinion and mass media - and consists of several parallel surveys. All surveys include a large number of questions related to politics, society, media and social background, but their areas of focus differ.

In 2020, six different questionnaires were distributed.

The main purpose is to establish time series that enable researchers to analyse how various changes in society affect people's attitudes and behaviour.

The SOM Institute also has an online tool for data analysis, where you can work with data from the national SOM surveys directly in your web browser. You can find it at https://som-institutet.se/dataanalys
description: För att kunna förstå hur samhällsutvecklingen påverkar svenska folkets attityder och beteenden, har SOM-institutet sedan 1986 genomfört Riks-SOM. Första mätningen skedde genom samverkan mellan ett par forskningsprojekt vid Göteborgs universitet, men intresset för resultaten blev stort och SOM-institutet etablerades med ansvar att göra en ny undersökning varje höst.

Den nationella SOM-undersökningen fokuserar på tre teman - samhälle, opinion och massmedier. Ett stort antal frågor om politik, samhälle och medier samt social bakgrund finns med i alla undersökningar, men frågeformulären har ibland något olika inriktningar.

Undersökningen genomförs i samverkan med forskare från olika discipliner. I undersökningen deltar också externa parter, via särskilda forskningsprojekt. För 2020 gäller det exempelvis Kulturdepartementet, Riksgälden och Försäkringskassan.

2020 års undersökning utgjordes av sex parallella riksrepresentativa delundersökningar där datainsamlingen genomfördes parallellt enligt en gemensam fältplan. De sex formulären innehåller många gemensamma frågor, men undersökningen innehåller också ett stort antal frågor endast ställda i en eller två av de olika enkäterna. Datainsamlingen genomfördes parallellt och under identiska förhållanden.

En mixed mode undersökningsdesign används, vilket innebär att flera datainsamlingsmetoder används i en och samma frågeundersökning. I SOM-undersökningen samlas data primärt in via postenkäter, men respondenten ges även möjlighet att fylla i en webbenkät.

Huvudsyftet är att etablera tidsserier som gör det möjligt att analysera hur olika samhällsförändringar påverkar människors attityder och beteenden.

SOM-institutet har även ett onlineverktyg för dataanalys, där du kan arbeta med data från de nationella SOM-undersökningarna direkt i din webbläsare. Du hittar det på https://som-institutet.se/dataanalys
DOI: 10.5878/mdv9-tj53
title: The National SOM Survey 2020
title: Den nationella SOM-undersökningen 2020
title: Riks-SOM 2020

description: In order to identify how the evolution of society affects Swedes’ attitudes and behaviour, the SOM Institute started its National SOM study in 1986. National SOM addresses three areas - society, opinion and mass media - and consists of several parallel surveys. All surveys include a large number of questions related to politics, society, media and social background, but their areas of focus differ.

In 2020, six different questionnaires were distributed.

The main purpose is to establish time series that enable researchers to analyse how various changes in society affect people's attitudes and behaviour.

The SOM Institute also has an online tool for data analysis, where you can work with data from the national SOM surveys directly in your web browser. You can find it at https://som-institutet.se/dataanalys
description: För att kunna förstå hur samhällsutvecklingen påverkar svenska folkets attityder och beteenden, har SOM-institutet sedan 1986 genomfört Riks-SOM. Första mätningen skedde genom samverkan mellan ett par forskningsprojekt vid Göteborgs universitet, men intresset för resultaten blev stort och SOM-institutet etablerades med ansvar att göra en ny undersökning varje höst.

Den nationella SOM-undersökningen fokuserar på tre teman - samhälle, opinion och massmedier. Ett stort antal frågor om politik, samhälle och medier samt social bakgrund finns med i alla undersökningar, men frågeformulären har ibland något olika inriktningar.

Undersökningen genomförs i samverkan med forskare från olika discipliner. I undersökningen deltar också externa parter, via särskilda forskningsprojekt. För 2020 gäller det exempelvis Kulturdepartementet, Riksgälden och Försäkringskassan.

2020 års undersökning utgjordes av sex parallella riksrepresentativa delundersökningar där datainsamlingen genomfördes parallellt enligt en gemensam fältplan. De sex formulären innehåller många gemensamma frågor, men undersökningen innehåller också ett stort antal frågor endast ställda i en eller två av de olika enkäterna. Datainsamlingen genomfördes parallellt och under identiska förhållanden.

En mixed mode undersökningsdesign används, vilket innebär att flera datainsamlingsmetoder används i en och samma frågeundersökning. I SOM-undersökningen samlas data primärt in via postenkäter, men respondenten ges även möjlighet att fylla i en webbenkät.

Huvudsyftet är att etablera tidsserier som gör det möjligt att analysera hur olika samhällsförändringar påverkar människors attityder och beteenden.

SOM-institutet har även ett onlineverktyg för dataanalys, där du kan arbeta med data från de nationella SOM-undersökningarna direkt i din webbläsare. Du hittar det på https://som-institutet.se/dataanalys
DOI: 10.17632/z9x6n8676b
title: Replication Folder for "Impact of pension insurance and health insurance on pension savings"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/z9x6n8676b.3
title: Replication Folder for "Impact of pension insurance and health insurance on pension savings"

description: This replication folder contains the necessary codes to replicate the quantitative results of the paper.
DOI: 10.17632/dczbv2tdn7
title: BV-Class

description: This dataset contains 3001 Bangla sentences, each annotated with a grammatical verb class: Transitive (1634 sentences) or Intransitive (1,367 sentences). The sentences were manually collected and labeled by native Bangla speakers with linguistic expertise to ensure accuracy. Each entry consists of a raw Bangla sentence and its verb classification.

-------Value of the Data-------

-First publicly available dataset for Bangla verb transitivity classification.

-Supports research in syntactic parsing, semantic role labeling, machine translation, and verb-based NLP tasks.

-Balanced distribution ensures unbiased benchmarking of machine learning models.
DOI: 10.17632/dczbv2tdn7.3
title: BV-Class

description: This dataset contains 3001 Bangla sentences, each annotated with a grammatical verb class: Transitive (1634 sentences) or Intransitive (1,367 sentences). The sentences were manually collected and labeled by native Bangla speakers with linguistic expertise to ensure accuracy. Each entry consists of a raw Bangla sentence and its verb classification.

-------Value of the Data-------

-First publicly available dataset for Bangla verb transitivity classification.

-Supports research in syntactic parsing, semantic role labeling, machine translation, and verb-based NLP tasks.

-Balanced distribution ensures unbiased benchmarking of machine learning models.
DOI: 10.5281/zenodo.18679261
title: Thermographic data for manuscript Mus.4172-E-25 (SLUB)

description: This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.
DOI: 10.17632/c6n49ssrnh
title: Transport Poverty, Inequality and Absolute Poverty: Testing a Transport Kuznets Curve in a Dynamic Panel Framework- Bergantino, Intini, Nademi

description: Title:
Transport Poverty, Inequality and Absolute Poverty: Testing a Transport Kuznets Curve in a
Dynamic Panel Framework

Journal:
Economic Modelling

Authors:
Angela Stefania Bergantino, Mario Intini, Younes Nademi

Description
This replication package accompanies the above-mentioned manuscript submitted to Economic
Modelling. It provides all necessary materials to reproduce the empirical results reported in the
paper, including data construction, index development, econometric estimation, and robustness
analyses.
All original proprietary datasets have been replaced with synthetic data preserving the original
structure, variable names, and sample dimensions, in accordance with the journal’s data and
reproducibility requirements.
The package includes raw synthetic data files, Python scripts for index construction, R scripts for
unit root testing, and EViews workfiles for econometric estimation.
By following the instructions provided in this document, independent researchers can fully
reproduce the results presented in the main text and appendices
DOI: 10.17632/c6n49ssrnh.1
title: Transport Poverty, Inequality and Absolute Poverty: Testing a Transport Kuznets Curve in a Dynamic Panel Framework- Bergantino, Intini, Nademi

description: Title:
Transport Poverty, Inequality and Absolute Poverty: Testing a Transport Kuznets Curve in a
Dynamic Panel Framework

Journal:
Economic Modelling

Authors:
Angela Stefania Bergantino, Mario Intini, Younes Nademi

Description
This replication package accompanies the above-mentioned manuscript submitted to Economic
Modelling. It provides all necessary materials to reproduce the empirical results reported in the
paper, including data construction, index development, econometric estimation, and robustness
analyses.
All original proprietary datasets have been replaced with synthetic data preserving the original
structure, variable names, and sample dimensions, in accordance with the journal’s data and
reproducibility requirements.
The package includes raw synthetic data files, Python scripts for index construction, R scripts for
unit root testing, and EViews workfiles for econometric estimation.
By following the instructions provided in this document, independent researchers can fully
reproduce the results presented in the main text and appendices
DOI: 10.17632/fcnx4fdpcc
title: RNA-Puzzles Round V: Models from LCBio group

description: This dataset comprises the three-dimensional RNA structure models submitted by the LCBio group for the RNA-Puzzles Round V blind prediction challenge. The collection includes computational predictions for seven distinct RNA targets: PZ30, PZ31, PZ32, PZ33, PZ34, PZ35, and PZ36.

The models were generated using the LCBio group's pipeline, which combines consensus secondary structure prediction with coarse-grained 3D modeling and refinement. The LCBio approach focuses on:
 - Secondary Structure Prediction: A consensus secondary structure was derived using multiple methods (ViennaRNA, RNAStructure, ProbKnot, CentroidFold, ContraFold, IPknot) and homologous sequence information from Rfam and RNAcentral.
 - 3D Modeling: The SimRNA program was used for 3D structure modeling via replica-exchange Monte Carlo simulations, performed both with and without secondary structure restraints.
 - Refinement: The lowest-energy coarse-grained models were selected and refined using the QRNAs program to reconstruct all-atom details and mitigate modeling errors.
 - Homology Modeling: For specific targets like PZ30, homology modeling was employed based on identified structural homologs.

Each model reflects the group's methodology in predicting tertiary structures from primary sequences, contributing to the community-wide effort to improve RNA structure prediction accuracy. These submissions were part of the RNA-Puzzles Round V assessment, a blind challenge where participants predict structures before the experimental results are released. This dataset provides a valuable resource for researchers interested in benchmarking RNA folding software, analyzing prediction performance, or studying the structural characteristics of the specific targets involved in this round of the challenge.