cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4usrt7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.87161173107167 -89.99999846875383, -26.871961794461058 -89.99999846874357, -26.872379828266606 -89.99999846873537, -26.8728498765068 -89.99999846872947, -26.873353839664066 -89.99999846872616, -26.873872295349724 -89.99999846872554, -26.874385445069002 -89.99999846872763, -26.87487344830725 -89.99999846873234, -26.875317656487685 -89.99999846873952, -26.87570088023113 -89.99999846874888, -26.87600855800963 -89.99999846876004, -26.87622866608043 -89.9999984687726, -26.876352893965304 -89.99999846878607, -26.876376458074787 -89.99999846879993, -26.876298374706415 -89.99999846881364, -26.8761217078778 -89.99999846882667, -26.875853194314647 -89.99999846883854, -26.875503135225408 -89.9999984688488, -26.8750850872272 -89.99999846885702, -26.87461500847555 -89.99999846886291, -26.87411100314638 -89.99999846886624, -26.873592500035564 -89.99999846886685, -26.87307930485699 -89.99999846886477, -26.87259126506445 -89.99999846886004, -26.872147034769707 -89.99999846885287, -26.87176380677099 -89.99999846884351, -26.871456143149054 -89.99999846883235, -26.87123606561393 -89.99999846881977, -26.871111879917535 -89.99999846880631, -26.871088363198815 -89.99999846879246, -26.87116649201733 -89.99999846877874, -26.871343195391866 -89.9999984687657, -26.87161173107167 -89.99999846875383))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)",
 "Modified is greater than or equal to 2026-02-06"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017035-260129131611470/datasets/export for details.
DOI: 10.15468/dl.sfm4d6
title: Occurrence Download

description: A dataset containing 2562 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material citation, Material sample, Human Observation)",
 "Continent is one of (South America, North America)",
 "Country is one of (Bolivia (Plurinational State of), Panama, Colombia, Brazil, French Guiana, Uruguay, Argentina, Trinidad and Tobago, Suriname)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Rivulidae",
 "Year 2000-2026"
 ]
}

 The dataset includes 2562 records from 121 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017036-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18509379
title: "THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ACADEMIC WRITING SKILLS OF EFL STUDENTS"

description: The integration of Artificial Intelligence into education has significantly influenced the way students develop academic writing skills, particularly in English as a Foreign Language (EFL) contexts. This study explores the impact of AI-based tools on the academic writing performance of EFL learners. The research aims to examine how applications such as ChatGPT, Grammarly, and other digital assistants affect students’ grammar accuracy, vocabulary use, organization of ideas, and overall writing quality. A mixed-method approach was employed, including questionnaires and comparative analysis of student writing samples produced with and without AI support. The findings indicate that AI tools contribute positively to improving language accuracy and writing efficiency. However, the study also reveals potential drawbacks, such as reduced critical thinking, creativity, and originality due to overdependence on AI. The paper concludes that while Artificial Intelligence can serve as a helpful educational resource, its use in academic writing should be balanced and guided to ensure effective learning outcomes.
DOI: 10.5281/zenodo.18509380
title: "THE IMPACT OF ARTIFICIAL INTELLIGENCE ON ACADEMIC WRITING SKILLS OF EFL STUDENTS"

description: The integration of Artificial Intelligence into education has significantly influenced the way students develop academic writing skills, particularly in English as a Foreign Language (EFL) contexts. This study explores the impact of AI-based tools on the academic writing performance of EFL learners. The research aims to examine how applications such as ChatGPT, Grammarly, and other digital assistants affect students’ grammar accuracy, vocabulary use, organization of ideas, and overall writing quality. A mixed-method approach was employed, including questionnaires and comparative analysis of student writing samples produced with and without AI support. The findings indicate that AI tools contribute positively to improving language accuracy and writing efficiency. However, the study also reveals potential drawbacks, such as reduced critical thinking, creativity, and originality due to overdependence on AI. The paper concludes that while Artificial Intelligence can serve as a helpful educational resource, its use in academic writing should be balanced and guided to ensure effective learning outcomes.
DOI: 10.5281/zenodo.18508628
title: SuperDARN data in netCDF format (2015-Aug)

description: 2015-Aug SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6426166
title: SuperDARN data in netCDF format (2015-Aug)

description: 2015-Aug SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.q88j8m
title: Occurrence Download

description: A dataset containing 1004 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nigritella rhellicani, Nigritella angustifolia)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 1004 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017037-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.svsn5j
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.871670491669143 -89.99999842500515, -26.872010791772908 -89.9999984249949, -26.872417296110736 -89.99999842498667, -26.872874260400895 -89.99999842498082, -26.873364259542665 -89.99999842497748, -26.87386840928192 -89.99999842497685, -26.874367286642983 -89.99999842497894, -26.87484183114841 -89.99999842498366, -26.87527368020736 -89.99999842499084, -26.875646350893305 -89.99999842500019, -26.875945454397257 -89.99999842501136, -26.87615953026954 -89.99999842502393, -26.876280325162654 -89.99999842503739, -26.876303187771445 -89.99999842505126, -26.876227276574685 -89.99999842506497, -26.876055455200376 -89.99999842507802, -26.87579438869868 -89.99999842508988, -26.87545409262114 -89.99999842510013, -26.87504757487466 -89.99999842510834, -26.87459058174417 -89.99999842511423, -26.874100542728353 -89.99999842511755, -26.873596348171105 -89.99999842511818, -26.873097427842183 -89.9999984251161, -26.872622848756336 -89.99999842511137, -26.872190978829583 -89.9999984251042, -26.871818304087455 -89.99999842509484, -26.87151921400076 -89.99999842508367, -26.871305166954727 -89.99999842507111, -26.871184411921686 -89.99999842505764, -26.87116159416955 -89.99999842504377, -26.871237548332683 -89.99999842503006, -26.871409404267162 -89.99999842501701, -26.871670491669143 -89.99999842500515))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)",
 "Modified is greater than or equal to 2026-02-06"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017038-260129131611470/datasets/export for details.
DOI: 10.15468/dl.9xks6n
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.871670491669143 -89.99999842500515, -26.872010791772908 -89.9999984249949, -26.872417296110736 -89.99999842498667, -26.872874260400895 -89.99999842498082, -26.873364259542665 -89.99999842497748, -26.87386840928192 -89.99999842497685, -26.874367286642983 -89.99999842497894, -26.87484183114841 -89.99999842498366, -26.87527368020736 -89.99999842499084, -26.875646350893305 -89.99999842500019, -26.875945454397257 -89.99999842501136, -26.87615953026954 -89.99999842502393, -26.876280325162654 -89.99999842503739, -26.876303187771445 -89.99999842505126, -26.876227276574685 -89.99999842506497, -26.876055455200376 -89.99999842507802, -26.87579438869868 -89.99999842508988, -26.87545409262114 -89.99999842510013, -26.87504757487466 -89.99999842510834, -26.87459058174417 -89.99999842511423, -26.874100542728353 -89.99999842511755, -26.873596348171105 -89.99999842511818, -26.873097427842183 -89.9999984251161, -26.872622848756336 -89.99999842511137, -26.872190978829583 -89.9999984251042, -26.871818304087455 -89.99999842509484, -26.87151921400076 -89.99999842508367, -26.871305166954727 -89.99999842507111, -26.871184411921686 -89.99999842505764, -26.87116159416955 -89.99999842504377, -26.871237548332683 -89.99999842503006, -26.871409404267162 -89.99999842501701, -26.871670491669143 -89.99999842500515))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)",
 "Modified is greater than or equal to 2026-02-06"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017039-260129131611470/datasets/export for details.
DOI: 10.15468/dl.h4asv5
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-26.871670491669143 -89.99999842500515, -26.872010791772908 -89.9999984249949, -26.872417296110736 -89.99999842498667, -26.872874260400895 -89.99999842498082, -26.873364259542665 -89.99999842497748, -26.87386840928192 -89.99999842497685, -26.874367286642983 -89.99999842497894, -26.87484183114841 -89.99999842498366, -26.87527368020736 -89.99999842499084, -26.875646350893305 -89.99999842500019, -26.875945454397257 -89.99999842501136, -26.87615953026954 -89.99999842502393, -26.876280325162654 -89.99999842503739, -26.876303187771445 -89.99999842505126, -26.876227276574685 -89.99999842506497, -26.876055455200376 -89.99999842507802, -26.87579438869868 -89.99999842508988, -26.87545409262114 -89.99999842510013, -26.87504757487466 -89.99999842510834, -26.87459058174417 -89.99999842511423, -26.874100542728353 -89.99999842511755, -26.873596348171105 -89.99999842511818, -26.873097427842183 -89.9999984251161, -26.872622848756336 -89.99999842511137, -26.872190978829583 -89.9999984251042, -26.871818304087455 -89.99999842509484, -26.87151921400076 -89.99999842508367, -26.871305166954727 -89.99999842507111, -26.871184411921686 -89.99999842505764, -26.87116159416955 -89.99999842504377, -26.871237548332683 -89.99999842503006, -26.871409404267162 -89.99999842501701, -26.871670491669143 -89.99999842500515))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)",
 "Modified is greater than or equal to 2026-02-06"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017040-260129131611470/datasets/export for details.
DOI: 10.5281/zenodo.18495814
title: Multimodal dataset for cohort-wide spatial autocorrelation-based MIR imaging analysis with correlative MSI lipidomics

description: This record contains the multimodal raw datasets of which analysis results are published in "Cohort-scale Spatial Autcorrelation for Tumor Prediction in Mid-infrared Pathology and Spatial Biomarker Discovery using MALDI Imaging Lipidomics" in Advanced Science by Rittel et al.

The study was conducted on twelve surgical tissue specimens of colorectal cancer liver metastasis (CRLM) obtained from patients with informed consent, prepared as fresh frozen (FF) material. The dataset includes images of hematoxylin and eosin (H&E) staining conducted on adjacent sections and MIR imaging as well as MS imaging (positive and negative ionization mode) data obtained subsequently on the same section for all twelve tumor samples. In short data was generated via



Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) on a timsTOF flex mass spectrometer (Bruker Daltonics), imzML format

Fourier-transform infrared (FT-IR) imaging (Spotlight 400, Perkin Elmer), fsm format

Optical images of hematoxylin and eosin (H&E) stainings recorded with an Aperio CS2 Scanner (objective: 20×, Leica Biosystems), svs format


(more detailed information can be obtained from the corresponding publication)

Additionally, an example dataset to showcase basic functionality of the published code (https://github.com/CeMOS-Mannheim/InSpIRe) is provided here.
DOI: 10.5281/zenodo.18495815
title: Multimodal dataset for cohort-wide spatial autocorrelation-based MIR imaging analysis with correlative MSI lipidomics

description: This record contains the multimodal raw datasets of which analysis results are published in "Cohort-scale Spatial Autcorrelation for Tumor Prediction in Mid-infrared Pathology and Spatial Biomarker Discovery using MALDI Imaging Lipidomics" in Advanced Science by Rittel et al.

The study was conducted on twelve surgical tissue specimens of colorectal cancer liver metastasis (CRLM) obtained from patients with informed consent, prepared as fresh frozen (FF) material. The dataset includes images of hematoxylin and eosin (H&E) staining conducted on adjacent sections and MIR imaging as well as MS imaging (positive and negative ionization mode) data obtained subsequently on the same section for all twelve tumor samples. In short data was generated via



Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) on a timsTOF flex mass spectrometer (Bruker Daltonics), imzML format

Fourier-transform infrared (FT-IR) imaging (Spotlight 400, Perkin Elmer), fsm format

Optical images of hematoxylin and eosin (H&E) stainings recorded with an Aperio CS2 Scanner (objective: 20×, Leica Biosystems), svs format


(more detailed information can be obtained from the corresponding publication)

Additionally, an example dataset to showcase basic functionality of the published code (https://github.com/CeMOS-Mannheim/InSpIRe) is provided here.
DOI: 10.5517/ccdc.csd.cc2q1h6h
title: CCDC 2503876: Experimental Crystal Structure Determination

description: Related Article: Congcong Tian, Anxin Sun, Jinling Chen, Rongshan Zhuang, Chen Chen, Jiawei Zheng, Shuo Liu, Jiajun Du, Qianwen Chen, Lei Cai, Shulin Han, Feng Tian, Chun-Chao Chen|2026|Nat.Photo.|||doi:10.1038/s41566-025-01827-6
description: Related Article: Congcong Tian, Anxin Sun, Jinling Chen, Rongshan Zhuang, Chen Chen, Jiawei Zheng, Shuo Liu, Jiajun Du, Qianwen Chen, Lei Cai, Shulin Han, Feng Tian, Chun-Chao Chen*|2025|CSD Communication|||
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.cc2q1h7j
title: CCDC 2503877: Experimental Crystal Structure Determination

description: Related Article: Congcong Tian, Anxin Sun, Jinling Chen, Rongshan Zhuang, Chen Chen, Jiawei Zheng, Shuo Liu, Jiajun Du, Qianwen Chen, Lei Cai, Shulin Han, Feng Tian, Chun-Chao Chen|2026|Nat.Photo.|||doi:10.1038/s41566-025-01827-6
description: Related Article: Congcong Tian, Anxin Sun, Jinling Chen, Rongshan Zhuang, Chen Chen, Jiawei Zheng, Shuo Liu, Jiajun Du, Qianwen Chen, Lei Cai, Shulin Han, Feng Tian, Chun-Chao Chen*|2025|CSD Communication|||
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: 10.15468/dl.55jen4
title: Occurrence Download

description: A dataset containing 1691 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Cnidoscolus aconitifolius"
 ]
}

 The dataset includes 1691 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017041-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18509341
title: Teoría de la Inversión por Aniquilación de Polaridad (MIAP): Un modelo de expansión universal mediante el colapso y cruce de vectores gravitatorios

description: Teoría de la Inversión por Aniquilación de Polaridad (MIAP): Un modelo de expansión universal mediante el colapso y cruce de vectores gravitatorios.

Este trabajo presenta la Teoría de la Inversión por Aniquilación de Polaridad (MIAP), un modelo cosmológico disruptivo que propone una explicación mecánica para la energía oscura y la expansión acelerada del universo. A diferencia de los modelos convencionales, la MIAP postula que la gravedad posee un punto de saturación crítica. Mediante una descripción basada en dinámica de vectores, el autor demuestra cómo, en centros de densidad extrema (agujeros negros hipermasivos), los vectores de atracción convergen hasta un punto de interposición, cruzándose y transformándose en vectores de repulsión.

Este fenómeno da origen a "Agujeros Blancos" situados en los centros de los grandes vacíos cósmicos, los cuales actúan como motores de expansión al aniquilar materia remanente y convertirla en potencial repulsivo. El modelo ofrece, además, una solución coherente a la Paradoja de Olbers, vinculando la oscuridad del cielo nocturno con la finitud del combustible bariónico y el ciclo de reciclaje energético del cosmos.
DOI: 10.5281/zenodo.18509241
title: Full Moon Perigee Erathquake

description: Earthquakes on Full Moon and Perigee
DOI: 10.5281/zenodo.18509240
title: Full Moon Perigee Erathquake

description: Earthquakes on Full Moon and Perigee
DOI: 10.15468/dl.5txuqd
title: Occurrence Download

description: A dataset containing 580085 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Specimen, Material citation)",
 "Country is Colombia",
 "DatasetKey is one of (Herbario Universidad de Antioquia (HUA), Tropicos MO Specimen Data, Herbario Amazónico Colombiano Dairon Cárdenas López, Herbario Pontificia Universidad Javeriana, Colección Herbario Federico Medem Bogotá - FMB, Herbario Nacional Colombiano (COL), Herbario de la Universidad Industrial de Santander)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (eac88d99-9f6c-4031-8fc4-8088f0e0dfe7, 9d77fdeb-100f-4b29-98ad-4effdd824457, cccff716-2694-4209-9f9e-2f7a484465a0, 2a7e3080-28a9-11dd-97cd-b8a03c50a862, 0e2f2e28-7790-4c82-b8fb-6ef7b4c764e2, 90fd6680-349f-11d8-aa2d-b8a03c50a862, 6c147991-c3bf-453d-a778-3bea9a534804)",
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 580085 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017019-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n4zzt3
title: Occurrence Download

description: A dataset containing 36468 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Triodanis"
 ]
}

 The dataset includes 36468 records from 444 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017042-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yc4zdy
title: Occurrence Download

description: A dataset containing 808 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Noccaea caerulescens, Thlaspi alpestre, Thlaspi alpestre, Thlaspi alpestre, Thlaspi alpestre, Thlaspi alpestre, Thlaspi caerulescens, Thlaspi caerulescens)",
 "Year is greater than or equal to 1900",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 808 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017043-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.