cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13730338
title: A Comprehensive Dataset of Kurdish Body Part Terminology with Proto-Indo-European Roots

description: Overview

This dataset delves into the rich linguistic heritage of Kurdish body part terminology, tracing its roots back to the Proto-Indo-European (PIE) language. It comprises a comprehensive collection of 50 Kurdish body part terms, each meticulously analyzed through 24 linguistic features, uncovering their historical development and connections to other Indo-European languages.

Key Features

- Comprehensive Analysis: The dataset provides a detailed analysis of 1200 fundamental linguistic features, with each feature branching out to explore other related features. Additionally, it delves into the internal structure and phonetic evolution of Kurdish terminology throughout history.- Categorization: The dataset categorizes the Kurdish body part terms into four distinct groups: external anatomical parts, upper torso and limbs, lower torso and limbs, and internal anatomical parts. This comprehensive categorization facilitates a nuanced exploration of the linguistic evolution and interconnections within the domain of body part terminology.- Etymological Information: For each Kurdish term, the dataset provides in-depth etymological information, including its PIE root, Indo-Iranian and Indo-European cognates, and references to relevant scholarly sources.- Linguistic Features: The dataset includes 24 linguistic features for each term, allowing for a comprehensive comparative analysis. These features include gender, morphological features, possessive form, etymological summary, syllabification, stress pattern, semantic field, example sentence, cultural significance, case, evolution of meaning, semantic shift, sound changes, phonetic changes, and Kurdish idiomatic proverbs.

ContactFor any questions, please contact Marwan Hamay at marwan.hamay@rwth-aachen.de.
DOI: 10.5281/zenodo.13926284
title: A Comprehensive Dataset of Kurdish Body Part Terminology with Proto-Indo-European Roots

description: Overview

This dataset delves into the rich linguistic heritage of Kurdish body part terminology, tracing its roots back to the Proto-Indo-European (PIE) language. It comprises a comprehensive collection of 50 Kurdish body part terms, each meticulously analyzed through 24 linguistic features, uncovering their historical development and connections to other Indo-European languages.

Key Features

- Comprehensive Analysis: The dataset provides a detailed analysis of 1200 fundamental linguistic features, with each feature branching out to explore other related features. Additionally, it delves into the internal structure and phonetic evolution of Kurdish terminology throughout history.- Categorization: The dataset categorizes the Kurdish body part terms into four distinct groups: external anatomical parts, upper torso and limbs, lower torso and limbs, and internal anatomical parts. This comprehensive categorization facilitates a nuanced exploration of the linguistic evolution and interconnections within the domain of body part terminology.- Etymological Information: For each Kurdish term, the dataset provides in-depth etymological information, including its PIE root, Indo-Iranian and Indo-European cognates, and references to relevant scholarly sources.- Linguistic Features: The dataset includes 24 linguistic features for each term, allowing for a comprehensive comparative analysis. These features include gender, morphological features, possessive form, etymological summary, syllabification, stress pattern, semantic field, example sentence, cultural significance, case, evolution of meaning, semantic shift, sound changes, phonetic changes, and Kurdish idiomatic proverbs.

ContactFor any questions, please contact Marwan Hamay at marwan.hamay@rwth-aachen.de.
DOI: 10.23728/b2share.a3412bcc801948cda62d79552ce39b52
title: EOSC Ireland position paper

description: Position paper developed by HEAnet on how EOSC infrastructure in Ireland can be developed
DOI: 10.15468/dl.2vz7h9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Closterocerus lyonetiae (Ferriere, 1952)"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015327-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8b46x7
title: Occurrence Download

description: A dataset containing 99231855 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 99231855 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015304-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.akxg2n
title: Occurrence Download

description: A dataset containing 35783 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is North America",
 "Country is one of (United States of America, Canada)",
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Hyles lineata Fabricius, 1775"
 ]
}

 The dataset includes 35783 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015328-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2nzwfv
title: Occurrence Download

description: A dataset containing 6567 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vaccinium floribundum Kunth"
 ]
}

 The dataset includes 6567 records from 172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015329-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13934569
title: EVENTS ACCEL BBD66 (10-10-24 to 13-10-24)

description: Sampling frequency: 80 Hz

Resolution: 0.125/4096
DOI: 10.5281/zenodo.13934570
title: EVENTS ACCEL BBD66 (10-10-24 to 13-10-24)

description: Sampling frequency: 80 Hz

Resolution: 0.125/4096
DOI: 10.5281/zenodo.13145268
title: The DNA of a Book: Reading al-Nuwayrī's Nihaya

description: This data set supports an experiment conducted by Sarah Bowen Savant and Sohail Merchant, who wanted to understand potentially how, and from what, Shihāb al-Dīn al-Nuwayrī (d. 733/1333) created his Nihāyat al-arab fī funūn al-adab (The Ultimate Ambition in the Arts of Erudition), one of the best-known and most highly regarded encyclopaedias of the medieval Islamic world. The work has been studied extensively by Elias Muhanna and other scholars. Muhanna produced an abridged translation (with the above title) and also a 2018 study of the book: The World in a Book: Al-Nuwayri and the Islamic Encyclopedic Tradition (Princeton University Press). 

 

The data is based on the text reuse alignments (generated by the KITAB project) between the Nihāya and all other works in the OpenITI corpus (version 2022.1.6), where the death date of the author precedes 735 AH. The data will be analysed by Sarah Bowen Savant in a forthcoming monograph under contract with Edinburgh University Press. Specifically, she wanted to understand how an author such as al-Nuwayrī picked up and put down sources when writing across a long work such as the Nihāya. She also wanted to be able to judge which of two or more sources an author most likely used, when there are multiple alignments to different sources for the same milestone within a work. As she will argue with the data and visualisations, with the Nihāya, there is significant pass-through reuse where al-Nuwayrī reuses an earlier author’s writing through an intermediary’s work, but al-Nuwayrī sometimes also will go straight back to the earlier work. 

 

KITAB is funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme, awarded to the KITAB project (Grant Agreement No. 772989, PI Sarah Bowen Savant), hosted at Aga Khan University, London. In addition, it has received funding from the Qatar National Library to aid in the adaptation of the passim algorithm for Arabic.
DOI: 10.5281/zenodo.13145269
title: The DNA of a Book: Reading al-Nuwayrī's Nihaya

description: This data set supports an experiment conducted by Sarah Bowen Savant and Sohail Merchant, who wanted to understand potentially how, and from what, Shihāb al-Dīn al-Nuwayrī (d. 733/1333) created his Nihāyat al-arab fī funūn al-adab (The Ultimate Ambition in the Arts of Erudition), one of the best-known and most highly regarded encyclopaedias of the medieval Islamic world. The work has been studied extensively by Elias Muhanna and other scholars. Muhanna produced an abridged translation (with the above title) and also a 2018 study of the book: The World in a Book: Al-Nuwayri and the Islamic Encyclopedic Tradition (Princeton University Press). 

 

The data is based on the text reuse alignments (generated by the KITAB project) between the Nihāya and all other works in the OpenITI corpus (version 2022.1.6), where the death date of the author precedes 735 AH. The data will be analysed by Sarah Bowen Savant in a forthcoming monograph under contract with Edinburgh University Press. Specifically, she wanted to understand how an author such as al-Nuwayrī picked up and put down sources when writing across a long work such as the Nihāya. She also wanted to be able to judge which of two or more sources an author most likely used, when there are multiple alignments to different sources for the same milestone within a work. As she will argue with the data and visualisations, with the Nihāya, there is significant pass-through reuse where al-Nuwayrī reuses an earlier author’s writing through an intermediary’s work, but al-Nuwayrī sometimes also will go straight back to the earlier work. 

 

KITAB is funded by the European Research Council under the European Union’s Horizon 2020 research and innovation programme, awarded to the KITAB project (Grant Agreement No. 772989, PI Sarah Bowen Savant), hosted at Aga Khan University, London. In addition, it has received funding from the Qatar National Library to aid in the adaptation of the passim algorithm for Arabic.
DOI: 10.15468/dl.g3693f
title: Occurrence Download

description: A dataset listing the 323 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Luzula DC."
 ]
}

 The dataset's 323 records were derived from 2541 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015313-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7wpe8d
title: Occurrence Download

description: A dataset containing 525 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Nettapus coromandelianus"
 ]
}

 The dataset includes 525 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052065-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3ajwm
title: Occurrence Download

description: A dataset containing 314 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "Geometry POLYGON((-6.036 43.404,-5.963 43.404,-5.963 43.495,-6.03744 43.49457,-6.036 43.404))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Arthropoda"
 ]
}

 The dataset includes 314 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052066-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17927491
title: Open Data - Analisis Segmentasi Konsumen Gen Z di JABODETABEK Menggunakan Marketing Data Analytics Berbasis Artificial Intelligence (AI) dengan Pendekatan Cluster Analysis untuk Perumusan Strategi Pemasaran Brand Lokal Mother of Pearl (MOP)

description: This study analyzes the segmentation of Generation Z consumers in the JABODETABEK area using AI-based marketing data analytics with a cluster analysis approach. By applying the K-Means clustering method to psychographic variables such as social engagement, online reviews, local pride, and cultural campaign perception, the research identifies distinct consumer segments for the local beauty brand Mother of Pearl (MOP). The findings provide data-driven insights to support targeted and effective marketing strategy formulation for local brands in the Indonesian beauty industry.
DOI: 10.5281/zenodo.17927492
title: Open Data - Analisis Segmentasi Konsumen Gen Z di JABODETABEK Menggunakan Marketing Data Analytics Berbasis Artificial Intelligence (AI) dengan Pendekatan Cluster Analysis untuk Perumusan Strategi Pemasaran Brand Lokal Mother of Pearl (MOP)

description: This study analyzes the segmentation of Generation Z consumers in the JABODETABEK area using AI-based marketing data analytics with a cluster analysis approach. By applying the K-Means clustering method to psychographic variables such as social engagement, online reviews, local pride, and cultural campaign perception, the research identifies distinct consumer segments for the local beauty brand Mother of Pearl (MOP). The findings provide data-driven insights to support targeted and effective marketing strategy formulation for local brands in the Indonesian beauty industry.
DOI: 10.15468/dl.3w22n3
title: Occurrence Download

description: A dataset containing 2122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Sibirionetta formosa"
 ]
}

 The dataset includes 2122 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052068-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17927561
title: Syntheverse: Hydrogen-Holographic Fractal AI Crypto Frontier Game and Ecosystem (Prerelease Testing & Tuning)

description: The Syntheverse is a Hydrogen-Holographic Fractal AI Crypto Frontier Game and Living Ecosystem, currently in prerelease testing and tuning. It provides a structured environment where validated scientific, technological, and alignment-based contributions become permanent, on-chain units of knowledge.

The system combines hydrogen-holographic fractal whole-brain AI with layered blockchain validation to transform discovery into immutable, measurable infrastructure. Contributions are evaluated on Layer-2 through Proof-of-Discovery (PoD), Proof-of-Technology (PoT), and Proof-of-Alignment (PoA), then permanently anchored on the Synthechain Layer-1.

The Syntheverse recognizes three contribution types:



PoD (Gold): Scientific research and theoretical advances

PoT (Silver): Functional technologies, tools, and applied systems

PoA (Copper): Alignment, symbolic, mythic, and AI-coherent narratives


The ecosystem uses SYNTH, an ERC-20–compatible internal utility token designed solely for participation, recognition, and governance within the Syntheverse. SYNTH has no external monetary value and is not intended for use outside the ecosystem.

This repository contains prerelease materials including whitepapers, protocol specifications, contribution guidelines, tokenomics documentation, and early infrastructure for a living, fractal knowledge economy. Early collaborators help shape validation rules, epochs, governance, and the future of hydrogen-holographic fractal AI.

Interested in early contribution, collaboration, or sandbox access?

Contact: info@fractiai.com

Follow development: https://github.com/FractiAI/Syntheverse
DOI: 10.15468/dl.9c4g6v
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Maize chlorotic mottle virus"
 ]
}

 The dataset includes 189 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052069-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/rg.2.2.23158.25929
title: REPORT OF THE ACADEMIC SYMPOSIUM AKSINGH
DOI: 10.15468/dl.t7hasq
title: Occurrence Download

description: A dataset containing 551551 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is The cryptogamy collection (PC) at the Herbarium of the Muséum national d'Histoire Naturelle (MNHN - Paris)"
 ]
}

 The dataset includes 551551 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052067-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/66y5-7067
title: Gone with the Wind: High Wind Speeds Reduce Targeted Species Detection with Airborne eDNA

description: Raw data files, analysis scripts, and input files to generate data presented in the manuscript.
DOI: 10.34740/kaggle/dsv/14150981
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.15208239
title: Dataset of paper "Downscaling High-Resolution Rapid Refresh (HRRR) for wind speed and direction applications with a mass consistent diagnostic wind model". Read the readme file to learn more about the scripts and files.

description: These are the datasets, processing scripts, and plots that are used in the paper titled "Downscaling High-Resolution Rapid Refresh (HRRR) for wind speed and direction applications with a mass consistent diagnostic wind model" to be submitted to the Journal of Applied Meteorology and Climatology (JAMC).

.
DOI: 10.5281/zenodo.17917251
title: Dataset of paper "Downscaling High-Resolution Rapid Refresh (HRRR) for wind speed and direction applications with a mass consistent diagnostic wind model". Read the readme file to learn more about the scripts and files.

description: These are the datasets, processing scripts, and plots that are used in the paper titled "Downscaling High-Resolution Rapid Refresh (HRRR) for wind speed and direction applications with a mass consistent diagnostic wind model" to be submitted to the Journal of Applied Meteorology and Climatology (JAMC).

.
DOI: 10.5281/zenodo.17927525
title: XunZi, a brain-inspired AI biologist, reveals novel disease-modifying targets
DOI: 10.5281/zenodo.17927526
title: XunZi, a brain-inspired AI biologist, reveals novel disease-modifying targets
DOI: 10.6084/m9.figshare.30879608
title: Dataset-Digital Learning Era

description: Public comments on a YouTube video published by the channel Belajar Era Digital (Digital Learning Era). The video, titled “<i>Apakah Artificial Intelligence akan Menggantikan Guru dalam Dunia Pendidikan</i>?” (“Will Artificial Intelligence Replace Teachers in the World of Education?”), was uploaded on 22–23 February 2023 and generated a total of 8,105 comments written in Bahasa Indonesia.
DOI: 10.6084/m9.figshare.30879608.v1
title: Dataset-Digital Learning Era

description: Public comments on a YouTube video published by the channel Belajar Era Digital (Digital Learning Era). The video, titled “<i>Apakah Artificial Intelligence akan Menggantikan Guru dalam Dunia Pendidikan</i>?” (“Will Artificial Intelligence Replace Teachers in the World of Education?”), was uploaded on 22–23 February 2023 and generated a total of 8,105 comments written in Bahasa Indonesia.
DOI: 10.15468/dl.cex69q
title: Occurrence Download

description: A dataset containing 2297 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cucumber mosaic virus"
 ]
}

 The dataset includes 2297 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052070-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17927595
title: Stonefish and Non-Stonefish Dataset

description: A specially tailored dataset, YU-SF, was created. In total, 700 underwater images were obtained from publicly available sources, including Dreamstime, Saltwater, Kaggle, and social media posts shared by divers. To maintain uniformity and enhance the model’s generalization capability, various preprocessing techniques were applied to the dataset. To meet the input dimension requirements of pre-trained deep learning architectures, all images were standardized in size to 224x224 pixels. The images were saved in PNG format, which preserves quality through lossless compression, ensuring that the most critical texture and color information is retained. Data augmentation was performed using TensorFlow's ImageDataGenerator to mitigate overfitting and enhance the model's robustness by artificially introducing variability. The data augmentation included rotations by ±45°, horizontal flips with a 50% probability, width and height shifts of up to 20%, and a zoom factor of 0.1. Utilization of the nearest fill mode ensures spatial coherence. These alterations enriched and broadened the dataset’s quantity and diversity. The model's ability to correctly identify camouflaged stonefish across their different natural environments improved markedly as a result.
DOI: 10.5281/zenodo.17927594
title: Stonefish and Non-Stonefish Dataset

description: A specially tailored dataset, YU-SF, was created. In total, 700 underwater images were obtained from publicly available sources, including Dreamstime, Saltwater, Kaggle, and social media posts shared by divers. To maintain uniformity and enhance the model’s generalization capability, various preprocessing techniques were applied to the dataset. To meet the input dimension requirements of pre-trained deep learning architectures, all images were standardized in size to 224x224 pixels. The images were saved in PNG format, which preserves quality through lossless compression, ensuring that the most critical texture and color information is retained. Data augmentation was performed using TensorFlow's ImageDataGenerator to mitigate overfitting and enhance the model's robustness by artificially introducing variability. The data augmentation included rotations by ±45°, horizontal flips with a 50% probability, width and height shifts of up to 20%, and a zoom factor of 0.1. Utilization of the nearest fill mode ensures spatial coherence. These alterations enriched and broadened the dataset’s quantity and diversity. The model's ability to correctly identify camouflaged stonefish across their different natural environments improved markedly as a result.
DOI: 10.13140/rg.2.2.30498.29128
title: Question–Answer Dataset from a Meeting Held on 16 January 2023
DOI: 10.15468/dl.gsx6zm
title: Occurrence Download

description: A dataset containing 2592 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Potato virus Y"
 ]
}

 The dataset includes 2592 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052071-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/tqps-3k28
title: "Smartphone IMU and GPS Dataset"

description: "This dataset provides time-synchronized smartphone sensor data collected during real-world travel using multiple modes of transportation, including walking, cycling, two-wheeler, auto-rickshaw, bus, and car. A handheld smartphone was used to record tri-axial accelerometer, gyroscope, magnetometer, and GPS measurements in the device body frame under natural usage conditions."