cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ecf7ry
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Gymnophiona)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "Geometry POLYGON((-30 60,0 60,0 90,-30 90,-30 60))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054341-250920141307145/datasets/export for details.
DOI: 10.15468/dl.zcypgd
title: Occurrence Download

description: A dataset containing 97565 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "Geometry POLYGON((4.15529 50.89437,4.65752 50.89437,4.65752 51.18177,4.15529 51.18177,4.15529 50.89437))"
 ]
}

 The dataset includes 97565 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054265-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wt6dbu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , )",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054342-250920141307145/datasets/export for details.
DOI: 10.5281/zenodo.17280303
title: The number of all protest and violent demonstration events reported in Lithuania during the period of January 1, 2021-December 31, 2022. Based on the data obtained from the ACLED database.

description: This work was supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].
DOI: 10.5281/zenodo.17280302
title: The number of all protest and violent demonstration events reported in Lithuania during the period of January 1, 2021-December 31, 2022. Based on the data obtained from the ACLED database.

description: This work was supported by the National Science Centre, Poland [grant number 2021/43/B/HS5/00290].
DOI: 10.25592/uhhfdm.18010
title: Dataset Tensor-Programmable Quantum Circuits for Solving Differential Equations

description: We used a self-written Julia library to find unitary approximations of

matrix product operators (MPO). We implemented the variational quantum algorithms in python,

using pennylane, pyTorch and Jax.

 

This dataset contains: 

- Unitaries that represent the MPO operators

- Weights for all time steps parametrizing the solution of the linear Euler equation, the Burgers’

  equation and the Advection-Diffusion equation.

- Weights encoding the initial condition for the the Burgers’ equation and the Advection-Diffusion 

   equation.

- Data used for Fig.4, Fig.7 , Fig.8
DOI: 10.15468/dl.z627fq
title: Occurrence Download

description: A dataset containing 564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dipelta floribunda)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 564 records from 26 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054343-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ax8bwb
title: Occurrence Download

description: A dataset containing 14 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ophiostoma ulmi",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 14 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0054344-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26023/vm9c-1c16-h003
title: DC3: NASA DC-8 1 Minute Data Merge. Version 6.0

description: This dataset contains NASA DC-8 1 Minute Data Merge data collected during the Deep Convective Clouds and Chemistry Experiment (DC3) from 18 May 2012 through 22 June 2012. This dataset contains updated data provided by NASA. In most cases, variable names have been kept identical to those submitted in the raw data files. However, in some cases, names have been changed (e.g., to eliminate duplication). Units have been standardized throughout the merge. In addition, a "grand merge" has been provided. This includes data from all the individual merged flights throughout the mission. This grand merge will follow the following naming convention: "dc3-mrg60-dc8_merge_YYYYMMdd_R5_thruYYYYMMdd.ict" (with the comment "_thruYYYYMMdd" indicating the last flight date included). This dataset is in ICARTT format. Please see the header portion of the data files for details on instruments, parameters, quality assurance, quality control, contact information, and dataset comments. For more information on updates to this dataset, please see the readme file.
DOI: 10.15468/dl.bg4tsq
title: Occurrence Download

description: A dataset containing 19632 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "GadmGid is CHN.15.7.4_1",
 "OccurrenceStatus is Present",
 "Year 2021-2026"
 ]
}

 The dataset includes 19632 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070933-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e242240
title: Admiralty University Ibusa 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Admiralty University Ibusa 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.15468/dl.g6e4me
title: Occurrence Download

description: A dataset containing 572888 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Asia",
 "Country is China",
 "DatasetKey is one of (iNaturalist Research-grade Observations, Xeno-canto - Bird sounds from around the world)",
 "OccurrenceStatus is Present",
 "Year 2020-2026"
 ]
}

 The dataset includes 572888 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070929-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9qe73d
title: Occurrence Download

description: A dataset containing 10370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Hungary",
 "OccurrenceStatus is Present",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 10370 records from 85 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070934-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18116692
title: Dilema Penggunaan Artificial Intelligence(AI) Dalam Dunia Pendidikan: Antara Inovasi Pembelajaran Dan Ancaman Etika Profesi Guru

description: Artikel ini mengkaji dilema penggunaan Artificial Intelligence (AI) dalam dunia pendidikan yang menghadirkan peluang sebagai inovasi pembelajaran sekaligus tantangan terhadap etika profesi guru. Melalui pendekatan studi literatur, artikel ini menelaah peran AI dalam meningkatkan efektivitas pembelajaran serta mengidentifikasi berbagai permasalahan etis yang muncul, seperti kejujuran akademik, ketergantungan teknologi, dan perubahan peran guru. Kajian ini menekankan pentingnya pemanfaatan AI secara bijak dan bertanggung jawab agar tetap selaras dengan nilai-nilai profesionalisme dan etika kependidikan.
DOI: 10.5281/zenodo.18116693
title: Dilema Penggunaan Artificial Intelligence(AI) Dalam Dunia Pendidikan: Antara Inovasi Pembelajaran Dan Ancaman Etika Profesi Guru

description: Artikel ini mengkaji dilema penggunaan Artificial Intelligence (AI) dalam dunia pendidikan yang menghadirkan peluang sebagai inovasi pembelajaran sekaligus tantangan terhadap etika profesi guru. Melalui pendekatan studi literatur, artikel ini menelaah peran AI dalam meningkatkan efektivitas pembelajaran serta mengidentifikasi berbagai permasalahan etis yang muncul, seperti kejujuran akademik, ketergantungan teknologi, dan perubahan peran guru. Kajian ini menekankan pentingnya pemanfaatan AI secara bijak dan bertanggung jawab agar tetap selaras dengan nilai-nilai profesionalisme dan etika kependidikan.
DOI: 10.23668/psycharchives.15944
title: Dataset for: Athlete Burnout and Health: Testing Longitudinal Mediation via Biomarkers

description: Abstract (prior to peer review): Athletes often face pressure in competitive sports, which can cause physical and mental health problems including athlete burnout. Burnout is becoming increasingly prevalent in athletes and not only affects mental health but heightens the risk for further physical and mental health consequences. While studies have supported this link, few of these examinations have assessed the possible explanatory mechanisms. Therefore, this study aimed to test whether biomarkers of key physiological systems may mediate the relationship between athlete burnout and mental and physical health outcomes over time. We recruited 64 competitive athletes who completed measures of athlete burnout, physical symptoms, depressive symptoms, and insomnia as well as provided saliva samples for the analysis of biomarkers (testosterone, dehydroepiandrosterone-sulphate [DHEA-S], secretory Immunoglobulin A [sIgA]) at three timepoints over six months. At the between level, burnout was associated with all questionnaire measures, while testosterone was associated with physical symptoms. At the within level, burnout predicted depressive symptoms and sIgA predicted insomnia. Exploratory analyses with a Bayesian approach further showed burnout to predict reductions in testosterone, DHEA-S and sIgA. In contrast to our hypotheses, we found no indirect effects linking burnout with potential health consequences via changes in biomarkers. Thus, burnout appears to affect physical and mental health through predominantly direct links.
DOI: 10.5517/ccdc.csd.cc2mr1wb
title: CCDC 2435232: Experimental Crystal Structure Determination

description: Related Article: Nahid Hassan, Rishukumar Panday, Prashanth Gouli Chandru, Kekkar Subray Ananthram, Therese Mariya Jose, Umashis Bhoi, Adam Sieradzki, Jan K. Zaręba, Ramamoorthy Boomishankar, Kartick Tarafder, Nirmalya Ballav|2025|Angew.Chem.,Int.Ed.|64|n/a|doi:10.1002/anie.202512104
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.cc2mr1xc
title: CCDC 2435233: Experimental Crystal Structure Determination

description: Related Article: Nahid Hassan, Rishukumar Panday, Prashanth Gouli Chandru, Kekkar Subray Ananthram, Therese Mariya Jose, Umashis Bhoi, Adam Sieradzki, Jan K. Zaręba, Ramamoorthy Boomishankar, Kartick Tarafder, Nirmalya Ballav|2025|Angew.Chem.,Int.Ed.|64|n/a|doi:10.1002/anie.202512104
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.cc2myh14
title: CCDC 2441406: Experimental Crystal Structure Determination

description: Related Article: Yu‐Jie Long, Wei‐Chen Guo, Jun‐Feng Xiang, Ying Han, Zhi‐Qiang Hu, Chuan‐Feng Chen|2025|Angew.Chem.,Int.Ed.|64|n/a|doi:10.1002/anie.202519524
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.cc2myh25
title: CCDC 2441407: Experimental Crystal Structure Determination

description: Related Article: Yu‐Jie Long, Wei‐Chen Guo, Jun‐Feng Xiang, Ying Han, Zhi‐Qiang Hu, Chuan‐Feng Chen|2025|Angew.Chem.,Int.Ed.|64|n/a|doi:10.1002/anie.202519524
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: 10.5281/zenodo.18090324
title: Datasets for "Refining simulated mineral dust composition through modified size distributions: dual validation with mineral-specific and elemental observations"

description: The dataset consists of three files:





In-situ-NorthAfrica-compilation.csv: This file contains a compilation of in situ datasets used to produce the initial scatterplots comparing modeled and measured mineral mass fractions. All values are given as mass fraction percentages (%). The first column lists the authors associated with each published measurement.




Elemental_CM-DUSTRISK-Praia.csv: This file includes elemental mass concentration measurements used in the DUSTRISK2022 campaign comparison plots against modeled, mineral-derived elemental mass percentages. Measured total mass is reported in μg/m³, while elemental concentrations are given in ng/m³. The COSMO-MUSCAT outputs are identified by the prefix “CM” and are reported in μg/m³. “SD” denotes standard deviation. All variables ending in “_frac” represent mass fraction percentages (%).


measurements_and-CM_compilation-JATAC2022.csv: This file contains measured elemental and mineral mass fractions used for the results presented in the subsection “Cape Verde – JATAC 2022”. Particle size classes correspond to the MUSCAT size bins (see Table 1 of the associated manuscript) and are provided in separate columns following the format “X##”, where X denotes the mineral or element and ## corresponds to MUSCAT bins (i.e., 01, 03, 09, or 26). Modeled values are provided in columns starting with “CM”. The prefixes “CI025” and “CI975” denote the lower and upper confidence intervals, respectively.
DOI: 10.5281/zenodo.18090325
title: Datasets for "Refining simulated mineral dust composition through modified size distributions: dual validation with mineral-specific and elemental observations"

description: The dataset consists of three files:





In-situ-NorthAfrica-compilation.csv: This file contains a compilation of in situ datasets used to produce the initial scatterplots comparing modeled and measured mineral mass fractions. All values are given as mass fraction percentages (%). The first column lists the authors associated with each published measurement.




Elemental_CM-DUSTRISK-Praia.csv: This file includes elemental mass concentration measurements used in the DUSTRISK2022 campaign comparison plots against modeled, mineral-derived elemental mass percentages. Measured total mass is reported in μg/m³, while elemental concentrations are given in ng/m³. The COSMO-MUSCAT outputs are identified by the prefix “CM” and are reported in μg/m³. “SD” denotes standard deviation. All variables ending in “_frac” represent mass fraction percentages (%).


measurements_and-CM_compilation-JATAC2022.csv: This file contains measured elemental and mineral mass fractions used for the results presented in the subsection “Cape Verde – JATAC 2022”. Particle size classes correspond to the MUSCAT size bins (see Table 1 of the associated manuscript) and are provided in separate columns following the format “X##”, where X denotes the mineral or element and ## corresponds to MUSCAT bins (i.e., 01, 03, 09, or 26). Modeled values are provided in columns starting with “CM”. The prefixes “CI025” and “CI975” denote the lower and upper confidence intervals, respectively.
DOI: 10.5281/zenodo.18130530
title: distichodus_engycephalus_spades_01.fasta.gz
DOI: 10.5281/zenodo.18130531
title: distichodus_engycephalus_spades_01.fasta.gz
DOI: 10.17632/6vchc9d4sm
title: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision

description: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision Paper Data
DOI: 10.17632/6vchc9d4sm.4
title: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision

description: Beyond Appraisal: Emotion Regulation and the Opposing Effects of Relative Abusive Supervision Paper Data
DOI: 10.17632/5yv96kyc78
title: Fabric WeaveNet Dataset

description: The Fabric WeaveNet dataset contains balanced RGB images of five traditional Indian handloom fabrics—Banarasi, Bandhani, Kanjeevaram, Patola, and Tussar—curated for fine-grained pattern classification. Images are standardized to 224×224 resolution, capturing diverse motifs and textures. The dataset supports research in computer vision, textile analysis, and cultural heritage digitization.
DOI: 10.17632/5yv96kyc78.1
title: Fabric WeaveNet Dataset

description: The Fabric WeaveNet dataset contains balanced RGB images of five traditional Indian handloom fabrics—Banarasi, Bandhani, Kanjeevaram, Patola, and Tussar—curated for fine-grained pattern classification. Images are standardized to 224×224 resolution, capturing diverse motifs and textures. The dataset supports research in computer vision, textile analysis, and cultural heritage digitization.
DOI: 10.3886/e242241
title: African Aviation and Aerospace University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: African Aviation and Aerospace University 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.25574/32076
title: Chandra X-ray Observatory ObsId 32076

description: X-RAY SURVEYING RADIO-LOUD ACTIVE GALAXIES AND THEIR LARGE-SCALE ENVIRONMENTS
DOI: 10.3886/e242242
title: National University of Science and Technology, Abuja 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: National University of Science and Technology, Abuja 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.15468/dl.nk7krr
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Geometry POLYGON((23.17625 34.2209,27.45212 34.2209,27.45212 36.19899,23.17625 36.19899,23.17625 34.2209))",
 "HasGeospatialIssue is false",
 "TaxonKey is Campanula creutzburgii"
 ]
}

 The dataset includes 5 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0070936-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/n957vf24sp
title: A Bilingual Synthetic Dataset for Elderly Emotional Wellbeing and Agentic AI Support in Low-Resource Contexts

description: The dataset consists of 2,000 synthetic records, divided equally into two subsets: 1,000 English records and 1,000 Roman Urdu records. Each record represents a simulated elderly participant aged between 50 and 90 years. Both subsets share an identical schema comprising 13 attributes, enabling direct cross-lingual comparison and analysis.

No missing values are present in the dataset. Summary statistics confirm identical distributions across both language versions, indicating controlled generation and minimizing unintended linguistic bias.
Dataset Attributes

Each record includes the following attributes:

Scenario_ID: Unique identifier representing the contextual scenario

Age: Participant age (50–90)

Gender: Male or Female

Emotional_Tone: Annotated dominant emotional state

Cognitive_Difficulty: Self-reported difficulty level (1–5)

Coping_Strategy: Emotional or cognitive regulation behavior

Learning_Outcome: Outcome of AI interaction

Support_Needed: Type of AI assistance required

Technology_Interaction: Mode of technology use

Reflection_Tag: High-level reflective keyword

Depression_Level: Emotional difficulty indicator (0–10)

Perceived_Usefulness: AI usefulness rating (0–10)

AI_Utilization_Purpose: Primary intent of AI usage

Participant_Response: Free-text response in English or Roman Urdu
DOI: 10.17632/n957vf24sp.1
title: A Bilingual Synthetic Dataset for Elderly Emotional Wellbeing and Agentic AI Support in Low-Resource Contexts

description: The dataset consists of 2,000 synthetic records, divided equally into two subsets: 1,000 English records and 1,000 Roman Urdu records. Each record represents a simulated elderly participant aged between 50 and 90 years. Both subsets share an identical schema comprising 13 attributes, enabling direct cross-lingual comparison and analysis.

No missing values are present in the dataset. Summary statistics confirm identical distributions across both language versions, indicating controlled generation and minimizing unintended linguistic bias.
Dataset Attributes

Each record includes the following attributes:

Scenario_ID: Unique identifier representing the contextual scenario

Age: Participant age (50–90)

Gender: Male or Female

Emotional_Tone: Annotated dominant emotional state

Cognitive_Difficulty: Self-reported difficulty level (1–5)

Coping_Strategy: Emotional or cognitive regulation behavior

Learning_Outcome: Outcome of AI interaction

Support_Needed: Type of AI assistance required

Technology_Interaction: Mode of technology use

Reflection_Tag: High-level reflective keyword

Depression_Level: Emotional difficulty indicator (0–10)

Perceived_Usefulness: AI usefulness rating (0–10)

AI_Utilization_Purpose: Primary intent of AI usage

Participant_Response: Free-text response in English or Roman Urdu
DOI: 10.3886/e242243
title: Federal University of Agriculture Bassam-Biri 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Federal University of Agriculture Bassam-Biri 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542