cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2khqtm
title: CCDC 2368611: Experimental Crystal Structure Determination

description: Related Article: zhang jianwei|2024|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.2ctf9b
title: Occurrence Download

description: A dataset containing 64 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Heterolamium debile (Hemsl.) C.Y.Wu)"
 ]
}

 The dataset includes 64 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002011-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ca82nm
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Tetrathyrium subcordatum Benth.)"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002010-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10141556
title: NSL KDD V2
title: NSL-KDD V2
title: NSL-KDD Version 2
title: NSL KDD Version 2

description: The NSL-KDD V2 is an extended version of NSL-KDD original dataset. The dataset is normalised and 1 additional class is synthesised by mixing multiple non-benign classes. 

To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here.

 

Citation info:

Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645. 

 

This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.
DOI: 10.5281/zenodo.10141555
title: NSL KDD V2
title: NSL-KDD V2
title: NSL-KDD Version 2
title: NSL KDD Version 2

description: The NSL-KDD V2 is an extended version of NSL-KDD original dataset. The dataset is normalised and 1 additional class is synthesised by mixing multiple non-benign classes. 

To cite the dataset, please reference the original paper with DOI: 10.1109/SmartNets61466.2024.10577645. The paper is published in IEEE SmartNets and can be accessed here.

 

Citation info:

Madhubalan, Akshayraj & Gautam, Amit & Tiwary, Priya. (2024). Blender-GAN: Multi-Target Conditional Generative Adversarial Network for Novel Class Synthetic Data Generation. 1-7. 10.1109/SmartNets61466.2024.10577645. 

 

This dataset was made by Abluva Inc, a Palo Alto based, research-driven Data Protection firm. Our data protection platform empowers customers to secure data through advanced security mechanisms such as Fine Grained Access control and sophisticated depersonalization algorithms (e.g. Pseudonymization, Anonymization and Randomization). Abluva's Data Protection solutions facilitate data democratization within and outside the organizations, mitigating the concerns related to theft and compliance. The innovative intrusion detection algorithm by Abluva employs patented technologies for an intricately balanced approach that excludes normal access deviations, ensuring intrusion detection without disrupting the business operations. Abluva’s Solution enables organizations to extract further value from their data by enabling secure Knowledge Graphs and deploying Secure Data as a Service among other novel uses of data. Committed to providing a safe and secure environment, Abluva empowers organizations to unlock the full potential of their data.
DOI: 10.5281/zenodo.13860141
title: Empowering Autonomous Shuttles with Next-Generation Infrastructure

description: This dataset accompanies the paper: Empowering Autonomous Shuttles with Next-Generation Infrastructure presented at the European Conference on Computer Vision (ECCV) 2024 in Milan. 

We provide a dataset in the form of CSV-file recordings. The dataset was recorded during the three-day congress IT-TRANS 2024 in Karlsruhe. It contains 207 individual recordings, each recording representing one trip between the two intelligent bus stops, with a total driving duration of 7 hours and 28 minutes. We publish V2X messages, information regarding the autonomous driving of the shuttle, and recorded information from the bus stops and the intelligent traffic light.

More information about the data and the setup can be read in the paper.This work was partially funded by KAMO: Karlsruhe Mobility High Performance Center (www.kamo.one).
DOI: 10.5281/zenodo.13860142
title: Empowering Autonomous Shuttles with Next-Generation Infrastructure

description: This dataset accompanies the paper: Empowering Autonomous Shuttles with Next-Generation Infrastructure presented at the European Conference on Computer Vision (ECCV) 2024 in Milan. 

We provide a dataset in the form of CSV-file recordings. The dataset was recorded during the three-day congress IT-TRANS 2024 in Karlsruhe. It contains 207 individual recordings, each recording representing one trip between the two intelligent bus stops, with a total driving duration of 7 hours and 28 minutes. We publish V2X messages, information regarding the autonomous driving of the shuttle, and recorded information from the bus stops and the intelligent traffic light.

More information about the data and the setup can be read in the paper.This work was partially funded by KAMO: Karlsruhe Mobility High Performance Center (www.kamo.one).
DOI: 10.5281/zenodo.14221812
title: Data and code to reproduce analyses in Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites"

description: This datasets archives the GitHub version found at https://github.com/ThieleLab/CodeBase to reproduce simulations for the article Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites", Cell Systems, in press.
DOI: 10.5281/zenodo.14221811
title: Data and code to reproduce analyses in Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites"

description: This datasets archives the GitHub version found at https://github.com/ThieleLab/CodeBase to reproduce simulations for the article Heinken et al, "A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites", Cell Systems, in press.
DOI: 10.15468/dl.32mh84
title: Occurrence Download

description: A dataset containing 215 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is UKR",
 "OccurrenceStatus is Present",
 "TaxonKey is Chrysopidae"
 ]
}

 The dataset includes 215 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002012-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n4sx6e
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Heterolamium flaviflorum (Z.Y.Zhu) L.Wei)"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002015-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7mmkt5
title: Occurrence Download

description: A dataset containing 147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is one of (Sinowilsonia henryi Hemsl.)"
 ]
}

 The dataset includes 147 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002014-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x5pdkb
title: Occurrence Download

description: A dataset containing 386210 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Taraxacum officinale Weber ex F.H.Wigg."
 ]
}

 The dataset includes 386210 records from 2254 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001900-241125135418005/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.iwjegt
title: Occurrence Download

description: A dataset containing 1362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "TaxonKey is Trillium recurvatum L.C.Beck",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1362 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001430-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/8534255
title: Baybayin Character Dataset
DOI: 10.15468/dl.sdpfb6
title: Occurrence Download

description: A dataset containing 6829 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-180 -60,180 -60,180 -10,-180 -10,-180 -60))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Poa annua L."
 ]
}

 The dataset includes 6829 records from 102 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0109044-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uvwexp
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Biscutella pichiana Raffaelli"
 ]
}

 The dataset includes 83 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060613-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yctzsi
title: Occurrence Download

description: A dataset containing 223112 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-92.39502 -1.93323,-88.52783 -1.93323,-88.52783 1.62576,-92.39502 1.62576,-92.39502 -1.93323))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 223112 records from 224 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001415-190813142620410/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3gbmbj
title: Occurrence Download

description: A dataset containing 35950010 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Norway",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 35950010 records from 1303 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0126836-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6j46cz
title: Occurrence Download

description: A dataset containing 154232 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "OccurrenceStatus is Present",
 "RecordedBy is obsr433118"
 ]
}

 The dataset includes 154232 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071030-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7gdxcu
title: Occurrence Download

description: A dataset containing 422 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Solidago salicina"
 ]
}

 The dataset includes 422 records from 50 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071039-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8fgs8a
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "MediaType is Image",
 "TaxonKey is Campanula simulans"
 ]
}

 The dataset includes 10 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071040-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mz5v4
title: Occurrence Download

description: A dataset containing 95 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CollectionKey is bc3d39fd-a7d4-4763-b951-2bdeddd8a5d2",
 {
 "and" : [
 "TaxonKey is one of (Plantae, Glyceria canadensis)",
 "VerbatimScientificName is one of (glyceria canadensis (michx.) trin.)"
 ]
 }
 ]
}

 The dataset includes 95 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071044-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14372617
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.ue58u9
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Schistidium maritimum",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2020"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0071045-251120083545085/datasets/export for details.
DOI: 10.5281/zenodo.18131854
title: Source Apportionment of Weathered Oil Spills
title: data-Resolving Mixed-Oil Complexity via Isotopic Signatures and Non-Negative Matrix Factorization (NMF) Algorithms

description: In oil pollution research, identifying spill sources is crucial but challenging due to mixed/weathered samples. This study simulated 300 days of controlled abiotic weathering on oil mixtures with two crude oils and analyzed the resulting isotopic signatures using GC/C/IRMS. Carbon and hydrogen isotope profile of mixed oil is mainly influenced by the weight proportion of different end-numbers. Six new oil end numbers are founded by different change modes of these two isotope values (carbon (δ13C) and hydrogen (δD) isotope compositions) of n-alkanes in mixed oil samples. The contributions of different end numbers can be quantified by δD values of Pr and Ph in the oil samples. Initial mixing ratios of the original crude oils were determined as 42.4%–26.9% and 66.3%–5.0%. By integrating non-negative matrix factorization (NMF) with gradient descent and coordinate descent algorithms, refined mixing ratios of 35.8%–28.6% and 64.0%–8.3% were derived, which strongly correlated with prior results and validated the algorithm accuracy. Further analysis revealed source resolution limits: for one original crude oil, carbon isotopes indicated a maximum of four-end-member mixing (38.0%–19.0%), consistent with hydrogen isotopes (40.4%–17.2%), suggesting a four-source cap. In contrast, the other crude oil tolerated up to five end-members under both carbon (38.7%–8.0%) and hydrogen (34.3%–6.6%) isotopic analyses, demonstrating a five-source resolution capacity. This study integrates isotopic analysis with computational algorithms, enhancing the accuracy of oil spill source attribution and clarifying the theoretical limits of source resolution in complex mixtures.
DOI: 10.5281/zenodo.18131853
title: Source Apportionment of Weathered Oil Spills
title: data-Resolving Mixed-Oil Complexity via Isotopic Signatures and Non-Negative Matrix Factorization (NMF) Algorithms

description: In oil pollution research, identifying spill sources is crucial but challenging due to mixed/weathered samples. This study simulated 300 days of controlled abiotic weathering on oil mixtures with two crude oils and analyzed the resulting isotopic signatures using GC/C/IRMS. Carbon and hydrogen isotope profile of mixed oil is mainly influenced by the weight proportion of different end-numbers. Six new oil end numbers are founded by different change modes of these two isotope values (carbon (δ13C) and hydrogen (δD) isotope compositions) of n-alkanes in mixed oil samples. The contributions of different end numbers can be quantified by δD values of Pr and Ph in the oil samples. Initial mixing ratios of the original crude oils were determined as 42.4%–26.9% and 66.3%–5.0%. By integrating non-negative matrix factorization (NMF) with gradient descent and coordinate descent algorithms, refined mixing ratios of 35.8%–28.6% and 64.0%–8.3% were derived, which strongly correlated with prior results and validated the algorithm accuracy. Further analysis revealed source resolution limits: for one original crude oil, carbon isotopes indicated a maximum of four-end-member mixing (38.0%–19.0%), consistent with hydrogen isotopes (40.4%–17.2%), suggesting a four-source cap. In contrast, the other crude oil tolerated up to five end-members under both carbon (38.7%–8.0%) and hydrogen (34.3%–6.6%) isotopic analyses, demonstrating a five-source resolution capacity. This study integrates isotopic analysis with computational algorithms, enhancing the accuracy of oil spill source attribution and clarifying the theoretical limits of source resolution in complex mixtures.
DOI: 10.5281/zenodo.18131975
title: Intelligent Product Data Management in E-commerce: A Scientometric Analysis Database

description: Data Availability Statement The full dataset comprising the 868 publications analyzed in this study
DOI: 10.5281/zenodo.18131976
title: Intelligent Product Data Management in E-commerce: A Scientometric Analysis Database

description: Data Availability Statement The full dataset comprising the 868 publications analyzed in this study
DOI: 10.3886/e242260v1
title: Abia State University, Uturu 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Abia State University, Uturu 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242261v1
title: Adekunle Ajasin University, Akungba 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Adekunle Ajasin University, Akungba 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242263v1
title: Ambrose Alli University, Ekpoma 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Ambrose Alli University, Ekpoma 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242254v1
title: Federal University of Education Kontagora 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 Education Kontagora 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242262v1
title: Akwa Ibom State University of Technology, Uyo 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: Akwa Ibom State University of Technology, Uyo 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e242255v1
title: University of Maritime Studies, Oron 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542

description: University of Maritime Studies, Oron 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542
DOI: 10.3886/e239749v1-194472
title: HIFLD OPEN NERC Regions

description: This feature class/shapefile represents North American Electric Reliability Corporation (NERC) Regions and Subregions. NERC is an international regulatory authority that works to improve the reliability of the bulk power system in North America. NERC works with many different regional entities to improve the coordination and supply of electricity. This data set of vector layer of polygons represents the boundaries of the regional and subregional entities associated with NERC. The following updates have been made since the previous release: 6 features removed, geometry and attribution updated using best available sources.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.
DOI: 10.3886/e242256v1
title: Federal University of Applied Sciences, Kachia 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 Applied Sciences, Kachia 2026/2027 Pre-Degree Form, Part-time Form Is Out Call 07076040542 To Apply and further admission assistance. Call Prof. Ben +2347076040542