cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17367371
title: PandasCorpus

description: Overview



PandasCorpus is a dataset collected from the GitHub repositories that contains a large number of Pandas workflows and extracted code features. A workflow is the Pandas code that is written and shared in Jupyter notebooks. It contains 139k Jupyter notebooks from approximately 100k unique repositories.


Contents

The PandasCorpus contains the following dataset files (CSV or Parquet versions)

Dataset:



code_cells.csv (or .parquet.gzip): 3.8 million cells of code cells with fields: notebook_hash, cell_id, and source

markdown_cells.csv (or .parquet.gzip): 1.4 million cells of markdown cells with the same fields as code_cells.csv


Metrics and links:



code_cells_metrics.csv: the metrics gathered from every code cell

pandas_ops_count.csv: count of pandas operations over all cells

code_links.csv: links to repositories and files of downloaded notebooks

link_hash_map.csv: lookup table connecting code cells with code links


API Operations and others:



csv_count.csv: number of csv files in each repository

csv_links.csv (or .parquet.gzip): links to csv files for each repository

pandas_ops.csv: dataframe and general functions that output dataframe, series, or array-like types

mpl_ops.csv: all plotting functions of the matplotlib library

seaborn_ops.csv: all plotting functions of the seaborn library


Workflow

This is the pipeline to download and process all the data:



search_notebooks.py: search GitHub for potential Jupyter notebooks

download_notebooks.py: download notebooks from GitHub

ipynb_to_csv.py: convert all notebooks into 2 dataframes

db_to_csv.py: convert SQLite database to csv file

analyze_code.py: analyze code cells, extracting metrics


Additional programs:



check_csv.py: check every repository for csv files

parquet_converter.py: compress the dataframe from csv to parquet

recreate_notebooks.py: recreates all notebooks from the dataset


Tunable Parameters

To obtain more or different results, these things can be tuned:



pandas_ops.csv: currently it contains only 136 operations of Dataframe and general functions. It can be extended, for instance, with index objects, window objects, and so on.

Query parameters (in search_notebooks.py): these parameters are already good, but can be extended.
DOI: 10.5281/zenodo.17367372
title: PandasCorpus

description: Overview



PandasCorpus is a dataset collected from the GitHub repositories that contains a large number of Pandas workflows and extracted code features. A workflow is the Pandas code that is written and shared in Jupyter notebooks. It contains 139k Jupyter notebooks from approximately 100k unique repositories.


Contents

The PandasCorpus contains the following dataset files (CSV or Parquet versions)

Dataset:



code_cells.csv (or .parquet.gzip): 3.8 million cells of code cells with fields: notebook_hash, cell_id, and source

markdown_cells.csv (or .parquet.gzip): 1.4 million cells of markdown cells with the same fields as code_cells.csv


Metrics and links:



code_cells_metrics.csv: the metrics gathered from every code cell

pandas_ops_count.csv: count of pandas operations over all cells

code_links.csv: links to repositories and files of downloaded notebooks

link_hash_map.csv: lookup table connecting code cells with code links


API Operations and others:



csv_count.csv: number of csv files in each repository

csv_links.csv (or .parquet.gzip): links to csv files for each repository

pandas_ops.csv: dataframe and general functions that output dataframe, series, or array-like types

mpl_ops.csv: all plotting functions of the matplotlib library

seaborn_ops.csv: all plotting functions of the seaborn library


Workflow

This is the pipeline to download and process all the data:



search_notebooks.py: search GitHub for potential Jupyter notebooks

download_notebooks.py: download notebooks from GitHub

ipynb_to_csv.py: convert all notebooks into 2 dataframes

db_to_csv.py: convert SQLite database to csv file

analyze_code.py: analyze code cells, extracting metrics


Additional programs:



check_csv.py: check every repository for csv files

parquet_converter.py: compress the dataframe from csv to parquet

recreate_notebooks.py: recreates all notebooks from the dataset


Tunable Parameters

To obtain more or different results, these things can be tuned:



pandas_ops.csv: currently it contains only 136 operations of Dataframe and general functions. It can be extended, for instance, with index objects, window objects, and so on.

Query parameters (in search_notebooks.py): these parameters are already good, but can be extended.
DOI: 10.17632/cdhtdm2wd8
title: Skin cancer risk factors and protective behaviors among veterans in the United States: A cross-sectional analysis of the National Health Interview Survey supplementary file

description: Supplementary file for Skin cancer risk factors and protective behaviors among veterans in the United States: A cross-sectional analysis of the National Health Interview Survey
DOI: 10.17632/cdhtdm2wd8.1
title: Skin cancer risk factors and protective behaviors among veterans in the United States: A cross-sectional analysis of the National Health Interview Survey supplementary file

description: Supplementary file for Skin cancer risk factors and protective behaviors among veterans in the United States: A cross-sectional analysis of the National Health Interview Survey
DOI: 10.15468/dl.azhajj
title: Occurrence Download

description: A dataset listing the 13649 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Romania",
 "OccurrenceStatus is Present",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset's 13649 records were derived from 414 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0026952-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/wcsxhpd4h4
title: Ultimate tensile capacity of LNG cargo containment systems

description: These data correspond to the research about the ultimate tensile capacity of the LNG cargo containment system.
DOI: 10.17632/wcsxhpd4h4.1
title: Ultimate tensile capacity of LNG cargo containment systems

description: These data correspond to the research about the ultimate tensile capacity of the LNG cargo containment system.
DOI: 10.17632/bd32wp6vhy
title: A Multimodal Dataset of Climate Change Narratives in China and the UK

description: The multimodal dataset focuses on climate change communication across two major social video platforms: BiliBili (China) and YouTube (United Kingdom). These platforms were selected because their long-form video formats lend themselves particularly well to narrative analysis, offering advantages over other forms of social media. The dataset comprises video cover images, metadata, engagement statistics, and textual content (titles, descriptions, and transcripts) for 2,798 videos (2,092 from BiliBili and 706 from YouTube), timeframe ranges from May 2007 to April 2025. The dataset offers substantial potential for researchers in environmental communication, digital humanities and computational social science, enabling cross-cultural analysis of public discourse and storytelling practices related to the climate crisis.
DOI: 10.17632/bd32wp6vhy.2
title: A Multimodal Dataset of Climate Change Narratives in China and the UK

description: The multimodal dataset focuses on climate change communication across two major social video platforms: BiliBili (China) and YouTube (United Kingdom). These platforms were selected because their long-form video formats lend themselves particularly well to narrative analysis, offering advantages over other forms of social media. The dataset comprises video cover images, metadata, engagement statistics, and textual content (titles, descriptions, and transcripts) for 2,798 videos (2,092 from BiliBili and 706 from YouTube), timeframe ranges from May 2007 to April 2025. The dataset offers substantial potential for researchers in environmental communication, digital humanities and computational social science, enabling cross-cultural analysis of public discourse and storytelling practices related to the climate crisis.
DOI: 10.17632/v9r8rvf8pt
title: A Multimodal Dataset of Climate Change Narratives in China and the UK

description: The multimodal dataset focuses on climate change communication across two major social video platforms: BiliBili (China) and YouTube (United Kingdom). These platforms were selected because their long-form video formats lend themselves particularly well to narrative analysis, offering advantages over other forms of social media. The dataset comprises video cover images, metadata, engagement statistics, and textual content (titles, descriptions, and transcripts) for 2,798 videos (2,092 from BiliBili and 706 from YouTube), timeframe ranges from May 2007 to April 2025. The dataset offers substantial potential for researchers in environmental communication, digital humanities and computational social science, enabling cross-cultural analysis of public discourse and storytelling practices related to the climate crisis.
DOI: 10.17632/v9r8rvf8pt.1
title: A Multimodal Dataset of Climate Change Narratives in China and the UK

description: The multimodal dataset focuses on climate change communication across two major social video platforms: BiliBili (China) and YouTube (United Kingdom). These platforms were selected because their long-form video formats lend themselves particularly well to narrative analysis, offering advantages over other forms of social media. The dataset comprises video cover images, metadata, engagement statistics, and textual content (titles, descriptions, and transcripts) for 2,798 videos (2,092 from BiliBili and 706 from YouTube), timeframe ranges from May 2007 to April 2025. The dataset offers substantial potential for researchers in environmental communication, digital humanities and computational social science, enabling cross-cultural analysis of public discourse and storytelling practices related to the climate crisis.
DOI: 10.17632/8jw2ny3yy9
title: Improving Sustainability Performance through AI-Enabled Decision Support: Evidence from MSMEs

description: This study involved 168 MSME owners and managers from various business sectors. The majority operate in the culinary sector, followed by fashion and tourism, while smaller proportions are found in retail, services, and manufacturing. This distribution reflects the dominance of food-related and creative businesses within the MSME structure.
Most enterprises are relatively young, with the largest proportion operating between two and five years, followed by businesses under two years. Only a small share has operated for more than ten years, indicating a dynamic and developing MSME environment.
In terms of financial scale, most respondents report monthly revenues between ten and fifty million rupiah, representing typical small-enterprise operations. Only a limited number operate at higher revenue levels. Consistent with this profile, most businesses employ between one and ten workers, confirming the small-scale and owner-driven nature of MSMEs.
Overall, the sample represents typical MSME characteristics, including small operational scale, moderate revenue, limited workforce, and relatively young business age, providing an appropriate context for examining technology-enabled decision making and sustainability performance.
The analysis was conducted using Partial Least Squares Structural Equation Modeling. The evaluation followed two main stages consisting of the measurement model and the structural model, followed by mediation testing, as recommended in SEM-PLS procedures.
DOI: 10.17632/8jw2ny3yy9.1
title: Improving Sustainability Performance through AI-Enabled Decision Support: Evidence from MSMEs

description: This study involved 168 MSME owners and managers from various business sectors. The majority operate in the culinary sector, followed by fashion and tourism, while smaller proportions are found in retail, services, and manufacturing. This distribution reflects the dominance of food-related and creative businesses within the MSME structure.
Most enterprises are relatively young, with the largest proportion operating between two and five years, followed by businesses under two years. Only a small share has operated for more than ten years, indicating a dynamic and developing MSME environment.
In terms of financial scale, most respondents report monthly revenues between ten and fifty million rupiah, representing typical small-enterprise operations. Only a limited number operate at higher revenue levels. Consistent with this profile, most businesses employ between one and ten workers, confirming the small-scale and owner-driven nature of MSMEs.
Overall, the sample represents typical MSME characteristics, including small operational scale, moderate revenue, limited workforce, and relatively young business age, providing an appropriate context for examining technology-enabled decision making and sustainability performance.
The analysis was conducted using Partial Least Squares Structural Equation Modeling. The evaluation followed two main stages consisting of the measurement model and the structural model, followed by mediation testing, as recommended in SEM-PLS procedures.
DOI: 10.17632/jhmjjb8nch
title: Digital Listening Research in ESL/EFL: A Bibliometric Analysis (2021–2025)

description: This dataset contains bibliographic records of digital and web‑based listening research in ESL/EFL contexts indexed in Scopus from 2021 to 2025, reflecting all available publications up to 22 December 2025.
DOI: 10.17632/jhmjjb8nch.1
title: Digital Listening Research in ESL/EFL: A Bibliometric Analysis (2021–2025)

description: This dataset contains bibliographic records of digital and web‑based listening research in ESL/EFL contexts indexed in Scopus from 2021 to 2025, reflecting all available publications up to 22 December 2025.
DOI: 10.17632/9tzcg34jzy
title: Comparative Study of Machine Learning Models for Classifying Sentiment in Google Gemini App Reviews

description: This study uses user review data collected from the Google Play Store with the data collection limited up to September 11, 2025, and restricted to reviews written in the Indonesian language. The data was obtained through a web scraping method using Python executed on Google Colab, enabling systematic extraction of reviews along with supporting attributes such as rating scores and review dates. The objective of this research is to compare the performance of the Naive Bayes, Support Vector Machine (SVM), and Random Forest models in sentiment classification tasks on the Google Gemini application based on evaluation metrics. To ensure data quality, a validation process was conducted by removing duplicate entries and filtering irrelevant or incomplete reviews. The dataset then underwent preprocessing, including case folding, tokenization, stopword removal, and stemming to standardize the textual data and reduce noise prior to modeling. After all cleaning and preprocessing stages were completed, a total of 12,823 clean reviews were obtained and used as the final dataset in this study. The processed data enables meaningful interpretation of user sentiment patterns toward the Google Gemini application and can be utilized to evaluate classification model performance as well as to provide insights into user perceptions as a basis for drawing research conclusions.
DOI: 10.17632/9tzcg34jzy.1
title: Comparative Study of Machine Learning Models for Classifying Sentiment in Google Gemini App Reviews

description: This study uses user review data collected from the Google Play Store with the data collection limited up to September 11, 2025, and restricted to reviews written in the Indonesian language. The data was obtained through a web scraping method using Python executed on Google Colab, enabling systematic extraction of reviews along with supporting attributes such as rating scores and review dates. The objective of this research is to compare the performance of the Naive Bayes, Support Vector Machine (SVM), and Random Forest models in sentiment classification tasks on the Google Gemini application based on evaluation metrics. To ensure data quality, a validation process was conducted by removing duplicate entries and filtering irrelevant or incomplete reviews. The dataset then underwent preprocessing, including case folding, tokenization, stopword removal, and stemming to standardize the textual data and reduce noise prior to modeling. After all cleaning and preprocessing stages were completed, a total of 12,823 clean reviews were obtained and used as the final dataset in this study. The processed data enables meaningful interpretation of user sentiment patterns toward the Google Gemini application and can be utilized to evaluate classification model performance as well as to provide insights into user perceptions as a basis for drawing research conclusions.
DOI: 10.17632/94p3j2nz5c
title: Untargeted metabolomics profiling of erythrocytes and plasma in head and neck squamous cell carcinoma

description: The study was designed to investigate erythrocyte-centered metabolic reprogramming associated with tumor hypoxia adaptation
1.Sample Collection and Processing: This dataset contains untargeted metabolomics data generated from human erythrocytes and plasma obtained from patients with head and neck squamous cell carcinoma (HNSCC) and matched healthy controls.
2. Sample Types: Isolated red blood cells and plasma from peripheral blood
3. Analytical Platforms and Methods: Untargeted metabolomic profiling was performed using liquid chromatography coupled with high-resolution mass spectrometry. Samples were analyzed in both positive and negative electrospray ionization modes to achieve broad metabolite coverage.
4. Raw Data Files: Red blood cell metabolomics mzXML files ; plasma metabolomics mzXML files .
DOI: 10.17632/94p3j2nz5c.1
title: Untargeted metabolomics profiling of erythrocytes and plasma in head and neck squamous cell carcinoma

description: The study was designed to investigate erythrocyte-centered metabolic reprogramming associated with tumor hypoxia adaptation
1.Sample Collection and Processing: This dataset contains untargeted metabolomics data generated from human erythrocytes and plasma obtained from patients with head and neck squamous cell carcinoma (HNSCC) and matched healthy controls.
2. Sample Types: Isolated red blood cells and plasma from peripheral blood
3. Analytical Platforms and Methods: Untargeted metabolomic profiling was performed using liquid chromatography coupled with high-resolution mass spectrometry. Samples were analyzed in both positive and negative electrospray ionization modes to achieve broad metabolite coverage.
4. Raw Data Files: Red blood cell metabolomics mzXML files ; plasma metabolomics mzXML files .
DOI: 10.17632/rt4hd757wh
title: Evaluating Pinterest User Experience and Usability Using AttrakDiff and PLS-SEM

description: This study uses user survey data collected through an online questionnaire administered via Google Forms and targeted at active Pinterest users in Indonesia. The questionnaire instrument was developed based on AttrakDiff, consisting of 28 bipolar adjective pairs to measure the dimensions of Pragmatic Quality, Hedonic Quality–Stimulation, Hedonic Quality–Identity, and Attractiveness. Data collection was conducted online through social media, user communities, and instant messaging channels to reach respondents widely. The objective of this research is to evaluate user experience and analyze the relationships among User Experience dimensions and the perceived attractiveness of the Pinterest application. To ensure data quality, a validation process was carried out by verifying respondent eligibility and eliminating invalid responses. From a total of 551 responses collected, 524 valid respondent data were retained and used as the final dataset. The validated data were then analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach with the SmartPLS software to assess both the measurement model and the structural model. The resulting dataset enables empirical interpretation of users’ perceptions of the quality of the Pinterest user experience and serves as the basis for drawing the study’s conclusions.
DOI: 10.17632/rt4hd757wh.1
title: Evaluating Pinterest User Experience and Usability Using AttrakDiff and PLS-SEM

description: This study uses user survey data collected through an online questionnaire administered via Google Forms and targeted at active Pinterest users in Indonesia. The questionnaire instrument was developed based on AttrakDiff, consisting of 28 bipolar adjective pairs to measure the dimensions of Pragmatic Quality, Hedonic Quality–Stimulation, Hedonic Quality–Identity, and Attractiveness. Data collection was conducted online through social media, user communities, and instant messaging channels to reach respondents widely. The objective of this research is to evaluate user experience and analyze the relationships among User Experience dimensions and the perceived attractiveness of the Pinterest application. To ensure data quality, a validation process was carried out by verifying respondent eligibility and eliminating invalid responses. From a total of 551 responses collected, 524 valid respondent data were retained and used as the final dataset. The validated data were then analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach with the SmartPLS software to assess both the measurement model and the structural model. The resulting dataset enables empirical interpretation of users’ perceptions of the quality of the Pinterest user experience and serves as the basis for drawing the study’s conclusions.
DOI: 10.17630/19a5e783-c2ea-4f24-993c-d037cd602145
title: Copper adatoms mediated adsorption of benzotriazole on a gold substrate (dataset)

description: dataset contains raw HREELS, XPS, NEXAFS, STM and DFT data shown in associated paper
DOI: 10.5517/ccdc.csd.cc2m152y
title: CCDC 2414189: Experimental Crystal Structure Determination

description: Related Article: Kai Chen, Jia Cao, Yu-Jing Shang, Jia-Yu Gu, Wen-Ya Ding, Kui-Yuan Wang, Jiang-Lin Zhao|2025|Inorg.Chem.|64|6648|doi:10.1021/acs.inorgchem.5c00200
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.cc2mhb6p
title: CCDC 2427802: Experimental Crystal Structure Determination

description: Related Article: Youssoupha Diack, Sonia Mallet-Ladeira, Denis Lesage, João V. S. Guerra, Didier Bourissou, György Szalóki|2025|Chem.Commun.|61|8003|doi:10.1039/D5CC01540E
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.cc2mhb7q
title: CCDC 2427803: Experimental Crystal Structure Determination

description: Related Article: Youssoupha Diack, Sonia Mallet-Ladeira, Denis Lesage, João V. S. Guerra, Didier Bourissou, György Szalóki|2025|Chem.Commun.|61|8003|doi:10.1039/D5CC01540E
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.