cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30359347
title: Replication Package: "The SBOM Gap: Adoption and Compliance in Open Source Software"

description: Replication Package Structure:The replication package contains all data and scripts necessary to reproduce the analyses and results presented in the paper:M. F. Rabbi, A. K. Turzo, A. I. Champa, and M. Zibran, “The SBOM Gap: Adoption and Compliance in Open Source Software,” in <i>2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)</i>, Limassol, Cyprus, 2026, pp. 1–12.<br>replication_package/├── data/│ ├── sbom_repo_paths.csv # Repository paths and metadata for analyzed projects│ ├── sbom_project_features.csv # Extracted features for SBOM projects│ ├── non_sbom_project_features.csv # Extracted features for non-SBOM projects│ └── SBOM_files/ # Raw SBOM files collected from selected repositories│└── code/├── RQ1_regression/ # Scripts for regression analysis (RQ1)│ ├── regression.R # Main regression analysis script│ └── common.R # Shared functions for data filtering and formatting│└── RQ2_compliance/ # Scripts for compliance and coverage checks (RQ2)├── check_component_name.py├── check_component_version.py├── check_supplier.py├── check_unique_identifiers.py├── check_sbom_author.py├── check_timestamp.py├── check_dependency.py├── check_hash.py├── check_lifecycle_phase.py├── check_license.py├── check_vex.py├── check_transitive_dependency.py├── check_circular_dep.py└── check_all_7_min_req_files.py<br><br>Folder Descriptions:data/: Contains datasets and raw SBOM files used in the analysis.- sbom_repo_paths.csv: Maps each SBOM file to its corresponding GitHub repository.- sbom_project_features.csv: Contains 21 extracted features for each SBOM-using project.- non_sbom_project_features.csv: Contains the same 21 features for matched non-SBOM projects.- SBOM_files/: Includes all valid SBOM files collected from open-source projects, in SPDX or CycloneDX formats.<br>code/: Contains source code for reproducing both research questions.- RQ1_regression/:- regression.R: Runs multivariate logistic regression across 100 bootstrapped samples.- common.R: Defines helper functions for feature selection, multicollinearity removal, and LaTeX formatting of regression outputs.- RQ2_compliance/:- 14 Python scripts that check SBOM compliance against NTIA's minimum elements and best practices.<br>CitationIf you use this dataset or replication package, please cite:@inproceedings{rabbi2026sbomgap, author = {Md Fazle Rabbi and Asif Kamal Turzo and Arifa Islam Champa and Minhaz Zibran}, title = {The SBOM Gap: Adoption and Compliance in Open Source Software}, booktitle = {2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}, year = {2026}, pages = {1--12}, address = {Limassol, Cyprus}, publisher = {IEEE}}<br>
DOI: 10.6084/m9.figshare.30359347.v1
title: Replication Package: "The SBOM Gap: Adoption and Compliance in Open Source Software"

description: Replication Package Structure:The replication package contains all data and scripts necessary to reproduce the analyses and results presented in this study.<br>replication_package/│├── data/│ ├── sbom_repo_paths.csv # Repository paths and metadata for analyzed projects│ ├── sbom_project_features.csv # Extracted features for SBOM projects│ ├── non_sbom_project_features.csv # Extracted features for non-SBOM projects│ └── SBOM_files/ # Raw SBOM files collected from selected repositories│└── code/ ├── RQ1_regression/ # Scripts for regression analysis (RQ1) │ ├── regression.R # Main regression analysis script │ └── common.R # Shared functions for data filtering and formatting │ └── RQ2_compliance/ # Scripts for compliance and coverage checks (RQ2) ├── check_component_name.py ├── check_component_version.py ├── check_supplier.py ├── check_unique_identifiers.py ├── check_sbom_author.py ├── check_timestamp.py ├── check_dependency.py ├── check_hash.py ├── check_lifecycle_phase.py ├── check_license.py ├── check_vex.py ├── check_transitive_dependency.py ├── check_circular_dep.py └── check_all_7_min_req_files.py<br><br><br>Folder Descriptions:<br>data/: Contains datasets and raw SBOM files used in the analysis. - sbom_repo_paths.csv: Maps each SBOM file to its corresponding GitHub repository. - sbom_project_features.csv: Contains 23 extracted features for each SBOM-using project. - non_sbom_project_features.csv: Contains the same 23 features for matched non-SBOM projects. - SBOM_files/: Includes all valid SBOM files collected from open-source projects, in SPDX or CycloneDX formats.<br>code/: Contains source code for reproducing both research questions. - RQ1_regression/: - regression.R: Runs multivariate logistic regression across 100 bootstrapped samples. - common.R: Defines helper functions for feature selection, multicollinearity removal, and LaTeX formatting of regression outputs. - RQ2_compliance/: - 14 Python scripts that check SBOM compliance against NTIA's minimum elements and best practices.
DOI: 10.6084/m9.figshare.30359347.v2
title: Replication Package: "The SBOM Gap: Adoption and Compliance in Open Source Software"

description: Replication Package Structure:The replication package contains all data and scripts necessary to reproduce the analyses and results presented in the paper:M. F. Rabbi, A. K. Turzo, A. I. Champa, and M. Zibran, “The SBOM Gap: Adoption and Compliance in Open Source Software,” in <i>2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)</i>, Limassol, Cyprus, 2026, pp. 1–12.<br>replication_package/├── data/│ ├── sbom_repo_paths.csv # Repository paths and metadata for analyzed projects│ ├── sbom_project_features.csv # Extracted features for SBOM projects│ ├── non_sbom_project_features.csv # Extracted features for non-SBOM projects│ └── SBOM_files/ # Raw SBOM files collected from selected repositories│└── code/├── RQ1_regression/ # Scripts for regression analysis (RQ1)│ ├── regression.R # Main regression analysis script│ └── common.R # Shared functions for data filtering and formatting│└── RQ2_compliance/ # Scripts for compliance and coverage checks (RQ2)├── check_component_name.py├── check_component_version.py├── check_supplier.py├── check_unique_identifiers.py├── check_sbom_author.py├── check_timestamp.py├── check_dependency.py├── check_hash.py├── check_lifecycle_phase.py├── check_license.py├── check_vex.py├── check_transitive_dependency.py├── check_circular_dep.py└── check_all_7_min_req_files.py<br><br>Folder Descriptions:data/: Contains datasets and raw SBOM files used in the analysis.- sbom_repo_paths.csv: Maps each SBOM file to its corresponding GitHub repository.- sbom_project_features.csv: Contains 21 extracted features for each SBOM-using project.- non_sbom_project_features.csv: Contains the same 21 features for matched non-SBOM projects.- SBOM_files/: Includes all valid SBOM files collected from open-source projects, in SPDX or CycloneDX formats.<br>code/: Contains source code for reproducing both research questions.- RQ1_regression/:- regression.R: Runs multivariate logistic regression across 100 bootstrapped samples.- common.R: Defines helper functions for feature selection, multicollinearity removal, and LaTeX formatting of regression outputs.- RQ2_compliance/:- 14 Python scripts that check SBOM compliance against NTIA's minimum elements and best practices.<br>CitationIf you use this dataset or replication package, please cite:@inproceedings{rabbi2026sbomgap, author = {Md Fazle Rabbi and Asif Kamal Turzo and Arifa Islam Champa and Minhaz Zibran}, title = {The SBOM Gap: Adoption and Compliance in Open Source Software}, booktitle = {2026 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)}, year = {2026}, pages = {1--12}, address = {Limassol, Cyprus}, publisher = {IEEE}}<br>
DOI: 10.48577/jpl.yyykm6
title: NISAR Mission Commissioning and Early Science Results

description: No abstract available.
DOI: 10.21227/hcva-yq74
title: "attribute_change_detection"

description: "This supplementary submission provides the dataset splits used in the experiments for the manuscript \u201cDetecting Attribute Changes in Entity Records Using Cross-Attention Mechanisms.\u201d The package includes three Parquet files corresponding to the train, validation, and test sets. Each example contains a paired representation of a current record and an earlier record, along with a binary label indicating whether an attribute change is present. The data is synthetically generated from a public multilingual product-metadata source to emulate realistic change and non-change scenarios, and the evaluation splits emphasize difficult partial-substitution cases to support stress testing."
DOI: 10.5281/zenodo.18099107
title: Improving carbon modelling in aspen-dominated forests: Lessons from CBM-CFS3 evaluation against long-term field data

description: The CBM-CFS3 simulates forest C dynamics, and ongoing model evaluation and calibration are essential to maintain its accuracy. This study assesses the model's performance across different pools using independent ground plot data from 64 aspen-dominated stands, monitored since 2000 as part of the Climate Impacts on the Productivity and Health of Aspen project.
DOI: 10.5281/zenodo.18099108
title: Improving carbon modelling in aspen-dominated forests: Lessons from CBM-CFS3 evaluation against long-term field data

description: The CBM-CFS3 simulates forest C dynamics, and ongoing model evaluation and calibration are essential to maintain its accuracy. This study assesses the model's performance across different pools using independent ground plot data from 64 aspen-dominated stands, monitored since 2000 as part of the Climate Impacts on the Productivity and Health of Aspen project.
DOI: 10.6084/m9.figshare.29196023
title: MINFLUX RyR2 DNA-PAINT

description: Raw data files of RyR2 MINFLUX DNA-PAINT imaging.<br>The data and analysis scripts provided in this repository accompany the publication MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. The reference isClowsley, A. H., Meletiou, A., Janicek, R., Bokhobza, A. F. E., Lučinskaitė, E., Bleuer, G., Jansen, I., Jones, P. P., Louch, W. E. & Soeller, C. MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. <i>Nat Commun</i> https://doi.org/10.1038/s41467-025-67801-6 (2025) doi:10.1038/s41467-025-67801-6.Software requirements to open and analyse the data are given in the file SoftwareUseAndInstallation.md. The quickest way to install the python software to open and analyse these files, however, has since become an install using the python pip command, as detailed here. The file SoftwareUseAndInstallation.md is still the authorative source for the purposes of the publication, but most users might find the pip install considerably easier.Authored by Christian Soeller and co-authors.
DOI: 10.6084/m9.figshare.29196023.v1
title: MINFLUX RyR2 DNA-PAINT

description: Raw data files of RyR2 MINFLUX DNA-PAINT imaging.<br>The data and analysis scripts provided in this repository accompany a preprint Analysis of RyR2 distribution in HEK293 cells and mouse cardiac myocytes using 3D MINFLUX microscopy. Once published in a peer reviewed journal a link will be providedto the published article.Authored by Christian Soeller and co-authors.
DOI: 10.6084/m9.figshare.29196023.v2
title: MINFLUX RyR2 DNA-PAINT

description: Raw data files of RyR2 MINFLUX DNA-PAINT imaging.<br>The data and analysis scripts provided in this repository accompany the publication MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. The reference isClowsley, A. H., Meletiou, A., Janicek, R., Bokhobza, A. F. E., Lučinskaitė, E., Bleuer, G., Jansen, I., Jones, P. P., Louch, W. E. & Soeller, C. MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. <i>Nat Commun</i> https://doi.org/10.1038/s41467-025-67801-6 (2025) doi:10.1038/s41467-025-67801-6.Software requirements to open and analyse the data are given in the file SoftwareUseAndInstallation.md. The quickest way to install the python software to open and analyse these files, however, has since become an install using the python pip command, as detailed here. The file SoftwareUseAndInstallation.md is still the authorative source for the purposes of the publication, but most users might find the pip install considerably easier.Authored by Christian Soeller and co-authors.
DOI: 10.6084/m9.figshare.30974398
title: Supporting qualitative materials for: Service providers’ perspectives on youth SRHS provision in Calabar South, Nigeria.

description: This Figshare record contains supporting qualitative materials for the study “Service providers’ perspectives on youth sexual and reproductive health services in Calabar South, Nigeria: A qualitative interview study.”The files include:1. anonymised interview excerpts organised by analytic themes;2, an analytic framework documenting the development of codes, subthemes, and final themes using reflexive thematic analysis; and3. the semi-structured interview guide used for data collection.These materials are provided to support transparency and methodological rigour. Full interview transcripts are not publicly available due to ethical and confidentiality restrictions.
DOI: 10.6084/m9.figshare.30974398.v1
title: Supporting qualitative materials for: Service providers’ perspectives on youth SRHS provision in Calabar South, Nigeria.

description: This Figshare record contains supporting qualitative materials for the study “Service providers’ perspectives on youth sexual and reproductive health services in Calabar South, Nigeria: A qualitative interview study.”The files include:1. anonymised interview excerpts organised by analytic themes;2, an analytic framework documenting the development of codes, subthemes, and final themes using reflexive thematic analysis; and3. the semi-structured interview guide used for data collection.These materials are provided to support transparency and methodological rigour. Full interview transcripts are not publicly available due to ethical and confidentiality restrictions.
DOI: 10.6084/m9.figshare.30974746
title: Data Set-1

description: SCHOOL INFRASTRUCTURE AND STUDENT SUCCESS
DOI: 10.6084/m9.figshare.30974746.v1
title: Data Set-1

description: SCHOOL INFRASTRUCTURE AND STUDENT SUCCESS
DOI: 10.5281/zenodo.17990589
title: RAG4C Evaluation Datasets, prompts, scripts, solver encoding, results
DOI: 10.5281/zenodo.18099726
title: Data from Skills2Capabilities WP3 Interviews in Italy

description: This dataset includes the summaries of semi-structured expert interviews of Skills2Capabilities WP3 on the responsiveness and proactiveness of VET systems in Italy.
DOI: 10.5281/zenodo.18099500
title: The Record Path: An Ontological Analysis of Blockchain Technology and the Digital Law of Karma

description: This article conceptualizes the "Record Path"—defined as the systematic logging of data and actions within a traceable, immutable, and predictable framework—by situating it within a theoretical structure from the perspective of blockchain technology. The inquiry elucidates the transition from traditional "trust-based" social contracts toward mathematical proof-oriented, "truth-based" digital systems. By leveraging concrete empirical evidence, such as the reduction of food traceability duration from 6 days and 18 hours to a mere 2.2 seconds within the Walmart-IBM Food Trust pilot implementations, the study demonstrates how blockchain technology absolutizes operational efficiency and transparency. Furthermore, the transformation of individual accountability is analyzed through the lens of the Massachusetts Institute of Technology’s (MIT) digital diploma initiatives and Self-Sovereign Identity (SSI) models in healthcare data. Synthesizing contemporary literature spanning the period of 2018–2025, the paper discusses critical technical constraints, including the "Oracle Problem" and energy expenditures. Ultimately, it seeks to contribute a socio-technical responsibility model titled the "Digital Law of Karma" to the academic discourse—a model constructed upon a universal ethical denominator bridging Eastern karma doctrine, the Western social contract, and Islamic accountability principles. The conclusion addresses the theoretical implications of the Record Path, such as self-censorship and self-regulatory pressures on human consciousness, while offering legal and psychological projections for future scholarly inquiry.
description: academia.edu, calameo.com, scribd.com, archive.org, fliphtml5.com
DOI: 10.5281/zenodo.18099501
title: The Record Path: An Ontological Analysis of Blockchain Technology and the Digital Law of Karma

description: This article conceptualizes the "Record Path"—defined as the systematic logging of data and actions within a traceable, immutable, and predictable framework—by situating it within a theoretical structure from the perspective of blockchain technology. The inquiry elucidates the transition from traditional "trust-based" social contracts toward mathematical proof-oriented, "truth-based" digital systems. By leveraging concrete empirical evidence, such as the reduction of food traceability duration from 6 days and 18 hours to a mere 2.2 seconds within the Walmart-IBM Food Trust pilot implementations, the study demonstrates how blockchain technology absolutizes operational efficiency and transparency. Furthermore, the transformation of individual accountability is analyzed through the lens of the Massachusetts Institute of Technology’s (MIT) digital diploma initiatives and Self-Sovereign Identity (SSI) models in healthcare data. Synthesizing contemporary literature spanning the period of 2018–2025, the paper discusses critical technical constraints, including the "Oracle Problem" and energy expenditures. Ultimately, it seeks to contribute a socio-technical responsibility model titled the "Digital Law of Karma" to the academic discourse—a model constructed upon a universal ethical denominator bridging Eastern karma doctrine, the Western social contract, and Islamic accountability principles. The conclusion addresses the theoretical implications of the Record Path, such as self-censorship and self-regulatory pressures on human consciousness, while offering legal and psychological projections for future scholarly inquiry.
description: academia.edu, calameo.com, scribd.com, archive.org, fliphtml5.com
DOI: 10.6084/m9.figshare.23733579
title: MINFLUX RyR2 imaging data - analysis and simulation scripts

description: Data and analysis scripts for RyR MINFLUX imagingThe data and analysis scripts provided to accompany the publication MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. The reference isClowsley, A. H., Meletiou, A., Janicek, R., Bokhobza, A. F. E., Lučinskaitė, E., Bleuer, G., Jansen, I., Jones, P. P., Louch, W. E. & Soeller, C. MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. <i>Nat. Comm.</i> https://doi.org/10.1038/s41467-025-67801-6 (2025) doi:10.1038/s41467-025-67801-6.Software requirements to open and analyse the data are given in the file SoftwareUseAndInstallation.md. The quickest way to install the python software to open and analyse these files, however, has since become an install using the python pip command, as detailed here. The file SoftwareUseAndInstallation.md is still the authorative source for the purposes of the publication, but most users might find the pip install considerably easier.AuthorsData authored by Clowsley, Meletiou, Soeller and coauthors.
DOI: 10.6084/m9.figshare.23733579.v2
title: MINFLUX RyR2 imaging data - analysis and simulation scripts

description: Data and analysis scripts for RyR MINFLUX imagingThe data and analysis scripts provided to accompany the preprint MINFLUX imaging of RyR2 in HEK293 cells and mouse cardiac myocytes. Once published in a peer reviewed journal a link will be providedto the published article.AuthorsData authored by Clowsley, Meletiou, Soeller and coauthors.
DOI: 10.6084/m9.figshare.23733579.v3
title: MINFLUX RyR2 imaging data - analysis and simulation scripts

description: Data and analysis scripts for RyR MINFLUX imagingThe data and analysis scripts provided to accompany the publication MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. The reference isClowsley, A. H., Meletiou, A., Janicek, R., Bokhobza, A. F. E., Lučinskaitė, E., Bleuer, G., Jansen, I., Jones, P. P., Louch, W. E. & Soeller, C. MINFLUX microscopy resolves subunits of the cardiac ryanodine receptor and its 3D orientation in cells. <i>Nat. Comm.</i> https://doi.org/10.1038/s41467-025-67801-6 (2025) doi:10.1038/s41467-025-67801-6.Software requirements to open and analyse the data are given in the file SoftwareUseAndInstallation.md. The quickest way to install the python software to open and analyse these files, however, has since become an install using the python pip command, as detailed here. The file SoftwareUseAndInstallation.md is still the authorative source for the purposes of the publication, but most users might find the pip install considerably easier.AuthorsData authored by Clowsley, Meletiou, Soeller and coauthors.
DOI: 10.15468/dl.xegktn
title: Occurrence Download

description: A dataset containing 418442 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "OccurrenceStatus is Present",
 "TaxonKey is Apus apus",
 "Year 1995-2025"
 ]
}

 The dataset includes 418442 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069405-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14347438
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.cny2kt
title: Occurrence Download

description: A dataset containing 643 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hordeum tetraploidum, Solanum gonocladum, Solanum grandidentatum, Solanum pygmaeum, Tropaeolum azureum)",
 "BasisOfRecord is Specimen",
 "HasCoordinate is true"
 ]
}

 The dataset includes 643 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069407-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18099794
title: RAG4C Evaluation Datasets, prompts, scripts, solver encoding, results

description: RAG4C: Observable-Centric Consistency Checking of Requirements and Models — Research Artifacts

This archive contains:





Datasets used in our experiments




Prompts for observable extraction, synonym construction, and constraints formalisation


Results (intermediate and final metrics)



Scripts to reproduce tables and figures



Implementation





The RAG4C implementation is available upon request.



These materials support reproducibility and enable further comparison and extension of observable-centric consistency checking approaches.
DOI: 10.5281/zenodo.18099795
title: RAG4C Evaluation Datasets, prompts, scripts, solver encoding, results

description: RAG4C: Observable-Centric Consistency Checking of Requirements and Models — Research Artifacts

This archive contains:





Datasets used in our experiments




Prompts for observable extraction, synonym construction, and constraints formalisation


Results (intermediate and final metrics)



Scripts to reproduce tables and figures



Implementation





The RAG4C implementation is available upon request.



These materials support reproducibility and enable further comparison and extension of observable-centric consistency checking approaches.
DOI: 10.5067/caliop/calipso/cal_lid_l2_vfm-standard-v5-00
title: CALIPSO Lidar Level 2 Vertical Feature Mask (VFM), V5-00
DOI: 10.5281/zenodo.18099628
title: URBANIZATION EFFECT ON SOCIO-SPATIAL LANDSCAPE OF LODWAR MUNICIPALITY, TURKANA, KENYA.