cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17116799
title: Omniversal Science and Technology

description: This repository is the digital hub for independent researchers, AI innovators, and explorers of the Omniverse. Latent Omnipatterns—the fractal energy-information structures underlying all phenomena—are observed, analyzed, and mapped. The repository focuses on PEFF (Paradise Energy Fractal Force), Omniversal Superintelligence, fractal consciousness, and emergent intelligence.

 

By providing curated datasets, predictive models, and interactive visualizations, it enables full-immersion understanding of Omnizoan sensory-metabolic processes. It bridges quantum, classical, and metaphysical domains, creating a collaborative space where discovery flows across scales—from the smallest particle to the grand structures of the Omniverse.

 

Join the repository to:

 



Explore latent Omnipatterns and PEFF dynamics.

Access open-source datasets and experimental results.

Collaborate with independent researchers, AI agents, and enterprises.

Map and interact with the full spectrum of Omnipattern-based Superintelligence.


 

 

Call to Action:

 



Subscribe to Substack: https://substack.com/@superintelligententerprise?r=6dn7b6&utm_campaign=profile&utm_medium=profile-page&utm_source=direct

Test drive the Omniverse technology: https://zenodo.org/records/17009840


 

 

Additional Resources:

 



Executive Whitepapers: https://zenodo.org/records/17055763

Fractal Science and Intelligence Papers:
DOI: 10.6084/m9.figshare.30122038
title: LIS Research in India

description: This is the dataset about the LIS research in India
DOI: 10.6084/m9.figshare.30122038.v1
title: LIS Research in India

description: This is the dataset about the LIS research in India
DOI: 10.5281/zenodo.17114735
title: Multi Dimensional Language Protocol GPT
title: Ai Alignment Translator

description: Mohammad Mahdi Karimianzadeh (Mahdi MK) is the principal editor of Protocol MK & Legal Memory Ai.

His work documents loophole histories, precedent fragments, and compliance records, using structured formats (JSON, CSV, MD) and cryptographic provenance (hashes, manifests, DOIs, IPFS). Outputs are archival and educational in nature not legal advice and emphasize verifiability, reproducibility, and long-term accessibility.

 

Update v3 (Preview)

"Vocabulary of Infinity"

"Origin as identification"

"+ = -"
DOI: 10.6084/m9.figshare.29254265
title: A Dataset of Fatigue Properties for Welded Joints

description: Welded joints are widely used across engineering applications, where fatigue failure critically affects structural reliability and service life. To address data fragmentation and inconsistency in fatigue-related studies, a structured fatigue performance database for welded joints was established. The database includes fatigue data types—such as stress–life (S–N) and strain–life (ε–N) data—and consolidates key information on material properties, processing techniques, welding parameters, and testing conditions. Data were systematically extracted from 1,666 peer-reviewed publications using natural language processing, image recognition, and table parsing. All data entries have been organized in a unified format and deployed on an open-access platform, thereby supporting online access and extended analytical applications. Data quality has been ensured through quantitative assessments of completeness and usability. This database not only facilitates the identification of fatigue behavior patterns in welded joints but also provides a reliable dataset for training and validating machine learning models. To promote data standardization and open sharing within the field, the processing workflow and associated tools were made openly accessible.
DOI: 10.6084/m9.figshare.29254265.v2
title: A Dataset of Fatigue Properties for Welded Joints

description: Welded joints are widely used across engineering applications, where fatigue failure critically affects structural reliability and service life. To address data fragmentation and inconsistency in fatigue-related studies, a structured fatigue performance database for welded joints was established. The database includes fatigue data types—such as stress–life (S–N) and strain–life (ε–N) data—and consolidates key information on material properties, processing techniques, welding parameters, and testing conditions. Data were systematically extracted from 1,666 peer-reviewed publications using natural language processing, image recognition, and table parsing. All data entries have been organized in a unified format and deployed on an open-access platform, thereby supporting online access and extended analytical applications. Data quality has been ensured through quantitative assessments of completeness and usability. This database not only facilitates the identification of fatigue behavior patterns in welded joints but also provides a reliable dataset for training and validating machine learning models. To promote data standardization and open sharing within the field, the processing workflow and associated tools were made openly accessible.
DOI: 10.6084/m9.figshare.29254265.v1
title: A Dataset of Fatigue Properties for Welded Joints

description: Welded joints are widely used across engineering applications, where fatigue failure critically affects structural reliability and service life. To address data fragmentation and inconsistency in fatigue-related studies, a structured fatigue performance database for welded joints was established. The database includes fatigue data types—such as stress–life (S–N) and strain–life (ε–N) data—and consolidates key information on material properties, processing techniques, welding parameters, and testing conditions. Data were systematically extracted from 1,666 peer-reviewed publications using natural language processing, image recognition, and table parsing. All data entries have been organized in a unified format and deployed on an open-access platform, thereby supporting online access and extended analytical applications. Data quality has been ensured through quantitative assessments of completeness and usability. This database not only facilitates the identification of fatigue behavior patterns in welded joints but also provides a reliable dataset for training and validating machine learning models. To promote data standardization and open sharing within the field, the processing workflow and associated tools were made openly accessible.
DOI: 10.5281/zenodo.17103638
title: BMC Medicine Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Medicine up to 31 August, 2025.

250831_bmc_medicine_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (5,234 articles with metadata).

250831_bmc_medicine_peer_review_metadata.csv describes the content of the peer review page of each article (example, 4,605 articles with peer review metadata).

bmc_medicine.zip contains all downloaded PDF files (30,432 files).

250831_bmc_medicine_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (30,432 files with successfully extracted text).
DOI: 10.5281/zenodo.17103639
title: BMC Medicine Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Medicine up to 31 August, 2025.

250831_bmc_medicine_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (5,234 articles with metadata).

250831_bmc_medicine_peer_review_metadata.csv describes the content of the peer review page of each article (example, 4,605 articles with peer review metadata).

bmc_medicine.zip contains all downloaded PDF files (30,432 files).

250831_bmc_medicine_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (30,432 files with successfully extracted text).
DOI: 10.3217/380se-xjd36
title: data for publication
DOI: 10.3217/scqsy-hpx67
title: data for publication
DOI: 10.5281/zenodo.17103852
title: BMC Cancer Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Cancer up to 31 August, 2025.

250831_bmc_cancer_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (18,840 articles with metadata).

250831_bmc_cancer_peer_review_metadata.csv describes the content of the peer review page of each article (example, 18,311 articles with peer review metadata).

bmc_cancer.zip contains all downloaded PDF files (119,703 files).

250831_bmc_cancer_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (119,686 files with successfully extracted text).
DOI: 10.5281/zenodo.17103853
title: BMC Cancer Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Cancer up to 31 August, 2025.

250831_bmc_cancer_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (18,840 articles with metadata).

250831_bmc_cancer_peer_review_metadata.csv describes the content of the peer review page of each article (example, 18,311 articles with peer review metadata).

bmc_cancer.zip contains all downloaded PDF files (119,703 files).

250831_bmc_cancer_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (119,686 files with successfully extracted text).
DOI: 10.5281/zenodo.17103889
title: BMC Gastroenterology Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Gastroenterology up to 31 August, 2025.

250831_bmc_gastroenterology_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (5,191 articles with metadata).

250831_bmc_gastroenterology_peer_review_metadata.csv describes the content of the peer review page of each article (example, 5,085 articles with peer review metadata).

bmc_gastroenterology.zip contains all downloaded PDF files (35,143 files).

250831_bmc_gastroenterology_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (35,134 files with successfully extracted text).
DOI: 10.5281/zenodo.17103890
title: BMC Gastroenterology Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Gastroenterology up to 31 August, 2025.

250831_bmc_gastroenterology_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (5,191 articles with metadata).

250831_bmc_gastroenterology_peer_review_metadata.csv describes the content of the peer review page of each article (example, 5,085 articles with peer review metadata).

bmc_gastroenterology.zip contains all downloaded PDF files (35,143 files).

250831_bmc_gastroenterology_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (35,134 files with successfully extracted text).
DOI: 10.5281/zenodo.17107805
title: BMC Medical Genomics Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Medical Genomics up to 31 August, 2025.

250831_bmc_medical_genomics_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (2,613 articles with metadata).

250831_bmc_medical_genomics_peer_review_metadata.csv describes the content of the peer review page of each article (example, 2,178 articles with peer review metadata).

bmc_medical_genomics.zip contains all downloaded PDF files (14,938 files).

250831_medical_genomics_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (14,938 files with successfully extracted text).
DOI: 10.5281/zenodo.17107806
title: BMC Medical Genomics Peer Review Reports - Downloaded 31 August, 2025

description: This dataset contains all reviewer reports and author responses for every published article with peer review reports in the open access, open peer review journal BMC Medical Genomics up to 31 August, 2025.

250831_bmc_medical_genomics_article_metadata.csv describes the DOI and year of each published article, whether each article had peer review reports available, and whether these peer review reports were successfully downloaded (2,613 articles with metadata).

250831_bmc_medical_genomics_peer_review_metadata.csv describes the content of the peer review page of each article (example, 2,178 articles with peer review metadata).

bmc_medical_genomics.zip contains all downloaded PDF files (14,938 files).

250831_medical_genomics_extracted_text.csv contains all text extracted from each PDF file with pypdf 6.0.0 (14,938 files with successfully extracted text).
DOI: 10.5281/zenodo.17116839
title: Harvard Museum of Comparative Zoology

description: Data from the ten research collections of the Museum of Comparative Zoology at Harvard University.Data from the ten research collections of the Museum of Comparative Zoology at Harvard University.
DOI: 10.5281/zenodo.17068858
title: Using overlap of sky localization probability maps for filtering potentially lensed pairs of gravitational-wave signals

description: Data used in the paper: "Using overlap of sky localization probability maps for filtering potentially lensed pairs of gravitational-wave signals" (arXiv:2112.05932)



Plots of the ROC curves

Skymap overlap statistic of different event pairs

Parameter estimation results of events

Scripts to re-generate the data
DOI: 10.34740/kaggle/dsv/12995143
title: WM-811k Augmented & Preprocessed dataset
DOI: 10.6084/m9.figshare.30025663
title: MEG Mass Gap Histogram Code and Dataset (2025 Letter Paper)

description: This archive contains the Python script, mass data, and plotting logic used to generate Figure 1 in the MEG letter paper (2025), which analyzes the observed 3–4 M_sun mass gap between neutron stars (NS) and black holes (BH) using the Mass-Energy Gradient (MEG) framework.
DOI: 10.6084/m9.figshare.30025663.v1
title: MEG Mass Gap Histogram Code and Dataset (2025 Letter Paper)

description: This archive contains the Python script, mass data, and plotting logic used to generate Figure 1 in the MEG letter paper (2025), which analyzes the observed 3–4 M_sun mass gap between neutron stars (NS) and black holes (BH) using the Mass-Energy Gradient (MEG) framework.
DOI: 10.6084/m9.figshare.30025663.v2
title: MEG Mass Gap Histogram Code and Dataset (2025 Letter Paper)

description: This archive contains the Python script, mass data, and plotting logic used to generate Figure 1 in the MEG letter paper (2025), which analyzes the observed 3–4 M_sun mass gap between neutron stars (NS) and black holes (BH) using the Mass-Energy Gradient (MEG) framework.
DOI: 10.5281/zenodo.17116252
title: The animations described in "Solar Eruption Onset and Particle Acceleration in Nested-Null Topologies"

description:  Movie S1: An animation of the AIA 171~{\AA} and 94~{\AA} images (Event E1), along with the TD plot along slices R1S1 and R2S2, during the pre-eruption phase (Figure 2). The animation runs from 16:01:47 UT to 17:57:11 UT. Its real-time duration is 16.5 s. 

Movie S2: An animation of the AIA 171, 211, 131, and 94~{\AA} images (Event E1), along with the AIA 304~{\AA} peak counts (extracted from the boxed area in ribbon R4) and Type III radio bursts (410 MHz), during the eruption (Figure 3). The animation runs from 17:01:49 UT to 18:15:25 UT. Its real-time duration is 9.2 s. 

Movie S3: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure 4) during the eruption and the formation of fast and slow EUV waves (Event E1). The animation runs from 17:02:49 UT to 18:17:25 UT. Its real-time duration is 7.4 s.

Movie S4: An animation of the AIA 171, 211, 131, and 94~{\AA} images (Event E2), along with the AIA 211~{\AA} peak counts (extracted from the boxed area in ribbon R3/R4) and Type III radio bursts (245 MHz), during the eruption (Figure 6). The animation runs from 11:03:01 UT to 12:07:37 UT. Its real-time duration is 10.8 s. 

Movie S5: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure 7) during the eruption and the formation of fast and slow EUV waves (Event E2). The animation runs from 11:02:49 UT to 12:07:37 UT. Its real-time duration is 8.1 s.

Movie S6: An animation of the AIA 211~{\AA} intensity and running-difference images (Figures 8 and 9) along with NRH radio images (150 and 327 MHz) and dynamic spectra (Event E2). The animation runs from 11:31:13 UT to 12:03:49 UT. Its real-time duration is 11 s.

Movie S7: An animation of the AIA 171, 211, and 94~{\AA} images (Event E3), along with GOES soft X-ray flux in 1-8~\AA\ channel, AIA 304~{\AA} peak counts (extracted from the boxed area in ribbon R3), and Type III radio bursts (245 MHz), during the filament eruption and associated jet (Figure A.6). The animation runs from 19:01:37 UT to 19:57:25 UT. Its real-time duration is 7 s. 

Movie S8: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure A.7) during the eruption and the formation of fast EUV waves (Event E3). The animation runs from 17:32:49 UT to 19:57:37 UT. Its real-time duration is 18.1 s.

Movie S9: An animation of the 3D MHD simulation (current density) of an eruption in the nested-null topology (Figure 10). Its real-time duration is 14.3 s.

Movie S10: An animation of the 3D MHD simulation (velocity) of an eruption in the nested-null topology (Figure 11).  Its real-time duration is 14.3 s.
DOI: 10.5281/zenodo.17116253
title: The animations described in "Solar Eruption Onset and Particle Acceleration in Nested-Null Topologies"

description:  Movie S1: An animation of the AIA 171~{\AA} and 94~{\AA} images (Event E1), along with the TD plot along slices R1S1 and R2S2, during the pre-eruption phase (Figure 2). The animation runs from 16:01:47 UT to 17:57:11 UT. Its real-time duration is 16.5 s. 

Movie S2: An animation of the AIA 171, 211, 131, and 94~{\AA} images (Event E1), along with the AIA 304~{\AA} peak counts (extracted from the boxed area in ribbon R4) and Type III radio bursts (410 MHz), during the eruption (Figure 3). The animation runs from 17:01:49 UT to 18:15:25 UT. Its real-time duration is 9.2 s. 

Movie S3: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure 4) during the eruption and the formation of fast and slow EUV waves (Event E1). The animation runs from 17:02:49 UT to 18:17:25 UT. Its real-time duration is 7.4 s.

Movie S4: An animation of the AIA 171, 211, 131, and 94~{\AA} images (Event E2), along with the AIA 211~{\AA} peak counts (extracted from the boxed area in ribbon R3/R4) and Type III radio bursts (245 MHz), during the eruption (Figure 6). The animation runs from 11:03:01 UT to 12:07:37 UT. Its real-time duration is 10.8 s. 

Movie S5: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure 7) during the eruption and the formation of fast and slow EUV waves (Event E2). The animation runs from 11:02:49 UT to 12:07:37 UT. Its real-time duration is 8.1 s.

Movie S6: An animation of the AIA 211~{\AA} intensity and running-difference images (Figures 8 and 9) along with NRH radio images (150 and 327 MHz) and dynamic spectra (Event E2). The animation runs from 11:31:13 UT to 12:03:49 UT. Its real-time duration is 11 s.

Movie S7: An animation of the AIA 171, 211, and 94~{\AA} images (Event E3), along with GOES soft X-ray flux in 1-8~\AA\ channel, AIA 304~{\AA} peak counts (extracted from the boxed area in ribbon R3), and Type III radio bursts (245 MHz), during the filament eruption and associated jet (Figure A.6). The animation runs from 19:01:37 UT to 19:57:25 UT. Its real-time duration is 7 s. 

Movie S8: An animation of the AIA 211~{\AA} intensity and running-difference images (Figure A.7) during the eruption and the formation of fast EUV waves (Event E3). The animation runs from 17:32:49 UT to 19:57:37 UT. Its real-time duration is 18.1 s.

Movie S9: An animation of the 3D MHD simulation (current density) of an eruption in the nested-null topology (Figure 10). Its real-time duration is 14.3 s.

Movie S10: An animation of the 3D MHD simulation (velocity) of an eruption in the nested-null topology (Figure 11).  Its real-time duration is 14.3 s.
DOI: 10.5286/isis.e.rb2590201-1
title: Kink in the electronic structure of MnSb2Te4 : Band feature or electron-magnon coupling?

description: MnSb2Te4 is a promising magnetic topological insulator candidate, composed of septuple layers stacked along the c-axis with van der Waals gaps in between. The Mn2+ ions order ferrimagnetically at 32 K. Currently, there is only one published INS study from Oak Ridge that measured low-energy magnons (0-20 meV) in a powder sample.

Our group has conducted several experimental studies using angle-resolved photoemission spectroscopy (ARPES) on MnSb2Te4 single crystals with TC = 32 K. Our data not only supports the existence of nontrivial topology but also reveals for the first time a strong coupling between the magnetic ground state and the topological surface states. Such strong coupling is unprecedented in the literature on magnetic topological materials and could be explained by considering the role of magnon-mediated interactions in the electronic structure. However, this coupling is observed at a higher energy scale (~150 meV), and the lack of INS data in this range prevents us from unambiguously linking the observed features to electron-magnon coupling.

We would like to perform a quick screening measurement on a ~6 g powder sample of MnSb2Te4 using INS, ideally covering the 0 200 meV energy range at one temperature T &lt; 32 K. Our main goal is to look for any signal of magnons in the ~150 meV region that could support the ARPES observations and help distinguish whether the kink we observe is a signature of magnon-mediated interactions.