cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3929/ethz-b-000688161
title: Dataset for "Computational Design Methods for geometry-driven upcycling of found objects in construction"
DOI: 10.3929/ethz-c-000795388
title: Machine-Learned Hamiltonians for Quantum Transport Simulation of Valence Change Memories

description: The construction of the Hamiltonian matrix H is an essential, yet computationally expensive step in ab-initio device simulations based on density-functional theory (DFT). In homogeneous structures, the fact that a unit cell repeats itself along at least one direction can be leveraged to minimize the number of atoms considered and the calculation time. However, such an approach does not lend itself to amorphous or defective materials for which no periodicity exists. In these cases, (much) larger domains containing thousands of atoms might be needed to accurately describe the physics at play, pushing DFT tools to their limit. Here we address this issue by learning and directly predicting the Hamiltonian matrix of large structures through equivariant graph neural networks and so-called augmented partitioning training. We demonstrate the strength of our approach by modeling valence change memory (VCM) cells, achieving a Mean Absolute Error (MAE) of 3.39 to 3.58 meV, as compared to DFT, when predicting the Hamiltonian matrix entries of systems made of ∼5,000 atoms. We then replace the DFT-computed Hamiltonian of these VCMs with the predicted one to compute their energy-resolved transmission function with a quantum transport tool. A qualitatively good agreement between both sets of curves is obtained. Our work provides a path forward to overcome the memory and computational limits of DFT, thus enabling the study of large-scale devices beyond current ab-initio capabilities.
DOI: 10.15468/dl.6jah4p
title: Occurrence Download

description: A dataset containing 4681 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Patella ulyssiponensis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4681 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009921-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rnhnnd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Global Register of Introduced and Invasive Species - Iceland"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009922-260129131611470/datasets/export for details.
DOI: 10.15468/dl.ftd7mc
title: Occurrence Download

description: A dataset containing 323126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Continent is Europe",
 "Country is Netherlands",
 "OccurrenceStatus is Present",
 "TaxonKey is Capreolus capreolus",
 "Year 2011-2026"
 ]
}

 The dataset includes 323126 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009847-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18112/openneuro.ds007231.v1.0.3
title: Interactions between sensory-biased and supramodal working memory networks in the human cerebral cortex
DOI: 10.15468/dl.htrm46
title: Occurrence Download

description: A dataset containing 28 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Halothamnus glaucus",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 28 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009923-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18472083
title: OʻZBEK MUSIQA TARIXI FANINI OʻQITISH ORQALI TALABA YOSHLARNI MA'NAVIY-AXLOQIY TARBIYALASH MASALALARI

description: Mazkur maqolada oliy ta'lim muassasalarida “Oʻzbek musiqa tarixi” fanini oʻqitish jarayonining talaba yoshlarning ma'naviy-axloqiy kamolotiga ta'siri ilmiy-nazariy jihatdan tahlil qilinadi. Oʻzbek milliy musiqasi asrlar davomida xalqning ruhiyati, dunyoqarashi, axloqiy me'yorlari va estetik ideallarini aks ettirib kelgan bebaho madaniy meros sifatida qaraladi. Tadqiqotda musiqa tarixi darslari orqali talabalar ongida milliy oʻzlikni anglash, vatanparvarlik, insonparvarlik, estetik did va axloqiy qadriyatlarni shakllantirish imkoniyatlari yoritiladi. Shuningdek, mumtoz musiqa, maqom san'ati, xalq kuy-qoʻshiqlari va bastakorlik maktablarining tarbiyaviy salohiyati ochib beriladi. Maqolada ta'lim jarayonida musiqa tarixini zamonaviy pedagogik yondashuvlar asosida oʻqitish talaba yoshlarning ma'naviy immunitetini mustahkamlashda muhim omil ekanligi asoslab beriladi.
DOI: 10.5281/zenodo.18472084
title: OʻZBEK MUSIQA TARIXI FANINI OʻQITISH ORQALI TALABA YOSHLARNI MA'NAVIY-AXLOQIY TARBIYALASH MASALALARI

description: Mazkur maqolada oliy ta'lim muassasalarida “Oʻzbek musiqa tarixi” fanini oʻqitish jarayonining talaba yoshlarning ma'naviy-axloqiy kamolotiga ta'siri ilmiy-nazariy jihatdan tahlil qilinadi. Oʻzbek milliy musiqasi asrlar davomida xalqning ruhiyati, dunyoqarashi, axloqiy me'yorlari va estetik ideallarini aks ettirib kelgan bebaho madaniy meros sifatida qaraladi. Tadqiqotda musiqa tarixi darslari orqali talabalar ongida milliy oʻzlikni anglash, vatanparvarlik, insonparvarlik, estetik did va axloqiy qadriyatlarni shakllantirish imkoniyatlari yoritiladi. Shuningdek, mumtoz musiqa, maqom san'ati, xalq kuy-qoʻshiqlari va bastakorlik maktablarining tarbiyaviy salohiyati ochib beriladi. Maqolada ta'lim jarayonida musiqa tarixini zamonaviy pedagogik yondashuvlar asosida oʻqitish talaba yoshlarning ma'naviy immunitetini mustahkamlashda muhim omil ekanligi asoslab beriladi.
DOI: 10.15468/dl.7sgjpf
title: Occurrence Download

description: A dataset containing 24028 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Artemisia vulgaris, Artemisia vulgaris, Artemisia vulgaris, Artemisia vulgaris, Artemisia vulgaris)",
 "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 24028 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009924-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.prmwa5
title: Occurrence Download

description: A dataset containing 229019 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Xiphias gladius",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 229019 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009827-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.84nb6s
title: Occurrence Download

description: A dataset containing 2735 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Patella depressa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2735 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009925-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57745/firz0c
title: Dataset of the distribution of tick-borne microorganisms in human-biting ticks in France collected through a Citizen-science program (CiTIQUE)

description: The dataset concerns information on the ticks that were sent by citizens in France between 2017 to 2019, and the results of the detection of pathogens in the ticks. CiTIQUE programm (2017- ) is a national Citizen science programm (www.citique.fr) initiated in July 2017 by the Laboratory of Excellence ARBRE as a collaboration between INRAE (French National Research Institute for Agriculture, Food and Environment), the University of Lorraine, the “Tous Chercheurs” Laboratory, Anses (French Agency for Food, Environmental and Occupational Health & Safety), and CPIE (Permanent Center for Environment Initiatives). This program aims at understanding the ecology of ticks and tick-borne potential human pathogens that they transmit to improve disease prevention, including Lyme borreliosis. When reporting their tick bites, citizens can send the biting ticks that they found, which feed a tick bank.
DOI: 10.5281/zenodo.14955066
title: Development of Prediction model through linear multiple regression

description:  Embroidery through computer aided semi-automatic machines is one of the most widely used option for the surface ornamentation of apparel fabrics at present. Since the embroidery process include addition of embroidery threads depending upon the design motif, it is quite obvious that basic physical and functional properties of fabric are subject to change. It is therefore important to develop an algorithm or empirical equation for proper prediction of the properties of the embroidered fabric, relevant to its required end use in apparel industry. In this context, an efort has been made to determine a prediction equation through linear multiple re gressions for the prediction of longitudinal stifness of embroidered fabric in terms of fexural rigidity in warp direction of the base fabric, considering the input parameters as warp-way fexural rigidity of the base fabric, breaking load and linear density of the embroidery thread, stit ch density, average stitch length and average stitch angle of the embroidery design. The fnal Prediction model is statisticall verifed taking new embroidery sam-ples of diferent varieties. It is found that the model can predict with a very satisfactory level of accuracy. Also, the infuences of the embroidery parameters in this context have been analyzed through the corresponding re -gression coefcients and the three dimensional (3D) surface curves..Stitch density has been on emerged as the most infuential parameter, followed by the stitch length and the stitch angle.
DOI: 10.5281/zenodo.14955067
title: Development of Prediction model through linear multiple regression

description:  Embroidery through computer aided semi-automatic machines is one of the most widely used option for the surface ornamentation of apparel fabrics at present. Since the embroidery process include addition of embroidery threads depending upon the design motif, it is quite obvious that basic physical and functional properties of fabric are subject to change. It is therefore important to develop an algorithm or empirical equation for proper prediction of the properties of the embroidered fabric, relevant to its required end use in apparel industry. In this context, an efort has been made to determine a prediction equation through linear multiple re gressions for the prediction of longitudinal stifness of embroidered fabric in terms of fexural rigidity in warp direction of the base fabric, considering the input parameters as warp-way fexural rigidity of the base fabric, breaking load and linear density of the embroidery thread, stit ch density, average stitch length and average stitch angle of the embroidery design. The fnal Prediction model is statisticall verifed taking new embroidery sam-ples of diferent varieties. It is found that the model can predict with a very satisfactory level of accuracy. Also, the infuences of the embroidery parameters in this context have been analyzed through the corresponding re -gression coefcients and the three dimensional (3D) surface curves..Stitch density has been on emerged as the most infuential parameter, followed by the stitch length and the stitch angle.
DOI: 10.15468/dl.k3pb4y
title: Occurrence Download

description: A dataset containing 467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Haloxylon ammodendron",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 467 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009926-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18471482
title: MeAJOR: Merged email Assets from Joint Open-source Repositories

description: The MeAJOR dataset was created with multi-source legitimate emails and malicious emails from different attack campaigns to enable a more reliable phishing detection. MeAJOR provides a total of 108685 data samples, in preprocessed format with engineered features suitable for the training, validation, and testing of ML and DL models.

The dataset was created at GECAD, ISEP, Polytechnic of Porto. It contains data collected from publicly available spam email and phishing email datasets:



TREC-05

TREC-06

TREC-07

Nazario Phishing Corpus

Nigerian Fraud


The dataset contains the email messages anonymized, source dataset and label (0 = benign; 1 = phishing). It also contains several extracted features suitable for email phishing attack detection. The email messages are anonymized using Entity Removal, with tokens replacing them, enclosed between square brackets. The tokens are as follow: [PGP], [EMOJI], [SYMBOL], [NAME], [USERNAME], [INITIALS], [EMAIL_ADDRESS], [PHONE_NUMBER], [ADDRESS], [ORGANIZATION], [URL], [IP_ADDRESS], [FILE_PATH], [FILE_NAME], [FILE], [DATE], [TIME], [FINANCIAL_INFO], [PRODUCT], [REFERENCE_NUMBER].

The emails are available in both an Apache Parquet file and a comma-separated values (CSV) file. We recommend using the Apache Parquet files for more efficient querying and compression.
description: This research was funded by the Itea Project VESTA (#21011) and Project VESTA (NORTE2030-FEDER-00382400) co-funded by Portugal 2030. Furthermore, this work also received funding from the project UID/00760/2025.
DOI: 10.5281/zenodo.18471483
title: MeAJOR: Merged email Assets from Joint Open-source Repositories

description: The MeAJOR dataset was created with multi-source legitimate emails and malicious emails from different attack campaigns to enable a more reliable phishing detection. MeAJOR provides a total of 108685 data samples, in preprocessed format with engineered features suitable for the training, validation, and testing of ML and DL models.

The dataset was created at GECAD, ISEP, Polytechnic of Porto. It contains data collected from publicly available spam email and phishing email datasets:



TREC-05

TREC-06

TREC-07

Nazario Phishing Corpus

Nigerian Fraud


The dataset contains the email messages anonymized, source dataset and label (0 = benign; 1 = phishing). It also contains several extracted features suitable for email phishing attack detection. The email messages are anonymized using Entity Removal, with tokens replacing them, enclosed between square brackets. The tokens are as follow: [PGP], [EMOJI], [SYMBOL], [NAME], [USERNAME], [INITIALS], [EMAIL_ADDRESS], [PHONE_NUMBER], [ADDRESS], [ORGANIZATION], [URL], [IP_ADDRESS], [FILE_PATH], [FILE_NAME], [FILE], [DATE], [TIME], [FINANCIAL_INFO], [PRODUCT], [REFERENCE_NUMBER].

The emails are available in both an Apache Parquet file and a comma-separated values (CSV) file. We recommend using the Apache Parquet files for more efficient querying and compression.
description: This research was funded by the Itea Project VESTA (#21011) and Project VESTA (NORTE2030-FEDER-00382400) co-funded by Portugal 2030. Furthermore, this work also received funding from the project UID/00760/2025.
DOI: 10.15468/dl.he3h2r
title: Occurrence Download

description: A dataset containing 644 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Astragalus frigidus, Phaca frigida)",
 "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 644 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009928-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18472049
title: Conjuntode datos sobre formación doctoral en documentación
DOI: 10.5281/zenodo.18472050
title: Conjuntode datos sobre formación doctoral en documentación
DOI: 10.5281/zenodo.18472115
title: VEKTORLARNING PLANIMETRIK MASALALAR YECHISHDAGI TADBIQI

description: Ushbu maqolada vektorlarning planimetrik masalalarni yechishda tutgan o‘rni va ularning amaliy tadbiri keng yoritilgan. Vektorlarning nazariy asoslari va ularning geometrik shakllarning xossalarini aniqlashda qo‘llanilishi tahlil qilinib, planimetrik masalalarni samarali va aniq yechish uchun vektor yondashuvining afzalliklari ko‘rsatildi. Tadqiqot jarayonida vektorlar yordamida nuqtalar orasidagi masofa, burchaklar o‘lchami, shakllarning parallelligi va perpendikulyarligi kabi geometriyaga oid muhim parametrlarni aniqlash usullari amaliy misollar bilan namoyish etildi. Olingan natijalar vektor metodikasining an’anaviy yechim usullariga nisbatan hisoblashlarni soddalashtirish, yechim aniqligini oshirish va masalalar yechimini tezlashtirish imkonini berishini tasdiqladi. Shu bilan birga, maqolada vektor yondashuvining ta’lim jarayonida qo‘llanilishi geometriya fanining o‘quvchilarga yanada tushunarli va qiziqarli tarzda o‘rgatilishini ta’minlashda muhim ahamiyatga ega ekani ta’kidlandi. Mazkur tadqiqot vektorlar asosida planimetrik masalalarni yechishning nafaqat nazariy, balki amaliy jihatlarini ham rivojlantirishga xizmat qiladi.
DOI: 10.5281/zenodo.18472114
title: VEKTORLARNING PLANIMETRIK MASALALAR YECHISHDAGI TADBIQI

description: Ushbu maqolada vektorlarning planimetrik masalalarni yechishda tutgan o‘rni va ularning amaliy tadbiri keng yoritilgan. Vektorlarning nazariy asoslari va ularning geometrik shakllarning xossalarini aniqlashda qo‘llanilishi tahlil qilinib, planimetrik masalalarni samarali va aniq yechish uchun vektor yondashuvining afzalliklari ko‘rsatildi. Tadqiqot jarayonida vektorlar yordamida nuqtalar orasidagi masofa, burchaklar o‘lchami, shakllarning parallelligi va perpendikulyarligi kabi geometriyaga oid muhim parametrlarni aniqlash usullari amaliy misollar bilan namoyish etildi. Olingan natijalar vektor metodikasining an’anaviy yechim usullariga nisbatan hisoblashlarni soddalashtirish, yechim aniqligini oshirish va masalalar yechimini tezlashtirish imkonini berishini tasdiqladi. Shu bilan birga, maqolada vektor yondashuvining ta’lim jarayonida qo‘llanilishi geometriya fanining o‘quvchilarga yanada tushunarli va qiziqarli tarzda o‘rgatilishini ta’minlashda muhim ahamiyatga ega ekani ta’kidlandi. Mazkur tadqiqot vektorlar asosida planimetrik masalalarni yechishning nafaqat nazariy, balki amaliy jihatlarini ham rivojlantirishga xizmat qiladi.
DOI: 10.15468/dl.g52kct
title: Occurrence Download

description: A dataset containing 29755 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Katsuwonus pelamis",
 "HasCoordinate is true",
 "DecimalLatitude is greater than -90",
 "DecimalLongitude is greater than -180",
 "OccurrenceStatus is Present",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2014"
 ]
}

 The dataset includes 29755 records from 97 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009929-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gpnw8k
title: Occurrence Download

description: A dataset containing 2210 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Patella caerulea",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 2210 records from 64 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009931-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.utxgfc
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009932-260129131611470/datasets/export for details.
DOI: 10.15468/dl.g7hfe9
title: Occurrence Download

description: A dataset containing 103368 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Sialia sialis"
 ]
 },
 "HasCoordinate is TRUE",
 "Year is greater than or equal to 2010",
 "Year is less than or equal to 2024",
 "DecimalLatitude is greater than or equal to 42.73",
 "DecimalLatitude is less than or equal to 45.02",
 "DecimalLongitude is greater than or equal to -73.44",
 "DecimalLongitude is less than or equal to -71.47",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 103368 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0009868-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.