cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/un3c1t
title: Birds fallen at Danish Lighthouses 1883 through 1939

description: This dataset presents occurrence records and notes of birds extracted from the multiple volumes of “Fuglene ved de danske Fyr 1883-1939” (i.e. “Birds at the Danish Lighthouses 1883-1939”).The dataset was created firstly by digitizing the book volumes "Fuglene ved de danske Fyr" into PDF files, OCR-ing the PDF files and finally extracting relevant data and compiling them in a database file. In the period 1883-1939, there were 45 lighthouses and lightships functioning in Denmark. This dataset contains specific occurrence records and notes on bird species that struck 2 of these, namely Lodbjerg Fyr and Hanstholm Fyr. The dataset covers records from during the nights of bird migration period from the years 1886 through 1939 (there were no occurrence records or notes on Lodbjerg Fyr and Hanstholm Fyr between 1883- 1885 and 1887-1888). A total of 1212 records were generated with 742 records from Lodbjerg Fyr and 470 records from Hanstholm Fyr. A detailed description of the sampling and digitization process may be found at: https://samlinger.snm.ku.dk/digitale-ressourcer/research-files/fyrfaldne-fugle/. 

 The two specific lighthouses Lodbjerg Fyr and Hanstholm Fyr were selected for extraction from the book volumes through a suggestion from the Thy National Park, Denmark (http://nationalparkthy.dk/). They wanted to use these specific occurrence data for an exhibition project. This databasing effort has been co-funded by Thy National Park and DanBIF.

 Future projects could include to extract data on more lighthouses from these digitized book volumes, all available at https://samlinger.snm.ku.dk/digitale-ressourcer/research-files/fyrfaldne-fugle/. 

 The number of specimens of the fallen birds that were sent by light keepers to the Zoological Museum of Copenhagen was also recorded in this dataset. These birds were carefully preserved and catalogued by collection workers at the museum and the specimens can still be found there today. Observations on weather conditions during nights when birds were observed by light keepers were also included.

 Data from this dataset were extracted from “Report on Birds in Danmark, 1886” (published by Ornis) and Fuglene ved de danske Fyr volumes (published in Videnskabelige Meddelelser fra den Naturhistoriske Forening i København). Most of the book volumes were compiled by Herluf Winge (1857-1923), a Danish zoologist who studied mammalian dentition. After the early death of his brother, Oluf Winge (1855-1889), who was a leading Danish ornithologist at his time, Herluf helped to compile "Fuglene ved de danske Fyr": Birds at the Danish Lighthouses 1891-1910 and until 1912 he supplemented this with his own bird observations.
DOI: 10.5281/zenodo.17478881
title: Simplified Chinese word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Simplified Chinese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:





Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)




Window size: varying context windows (e.g., 2, 5, 10, …)




Each file corresponds to a unique configuration (dimension × window size). 



Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:





Manuscript: https://doi.org/10.5281/zenodo.17243812 




Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17478882
title: Simplified Chinese word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Simplified Chinese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:





Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)




Window size: varying context windows (e.g., 2, 5, 10, …)




Each file corresponds to a unique configuration (dimension × window size). 



Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:





Manuscript: https://doi.org/10.5281/zenodo.17243812 




Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.15468/dl.gmf4mj
title: Occurrence Download

description: A dataset containing 3105 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lonomia"
 ]
}

 The dataset includes 3105 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033473-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17603186
title: English word2vec embeddings trained on OpenSubtitles Part 5

description: This dataset contains the subs2vec embeddings for English, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:





Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)




Window size: varying context windows (e.g., 2, 5, 10, …)




Each file corresponds to a unique configuration (dimension × window size). 



Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1).

If you use this dataset, please cite:





Manuscript: https://doi.org/10.5281/zenodo.17243812 




Data: This Zenodo dataset (using the DOI provided here)



sha256sum checksums:



en_300_3_sg_wxd.csv.bz2 2e6f80ec20aa7bab032d0def845aecaed881ae7dfabb2141112af972d1e51139 

en_300_4_cbow_wxd.csv.bz2 d40266f317c0e615d75ab0bd3a7ac4dd21adde50369a58ec4f17f91e34ebd029

en_300_4_sg_wxd.csv.bz2 6603c6f3ae494a90605c1e8f48312fd43474f84ee86a271f418a4ec5b45f9433 

en_300_5_cbow_wxd.csv.bz2 2c98360e7af1f2d729bec0cd926266fae34e3e1a62028dc8d7a130d4914bdfe8

en_300_5_sg_wxd.csv.bz2 0b84df4ab4a2d6f4a91ac1cd0c6aae67495116eda5a6e2385f474f3c59151dc2
DOI: 10.5281/zenodo.17603185
title: English word2vec embeddings trained on OpenSubtitles Part 5

description: This dataset contains the subs2vec embeddings for English, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:





Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)




Window size: varying context windows (e.g., 2, 5, 10, …)




Each file corresponds to a unique configuration (dimension × window size). 



Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1).

If you use this dataset, please cite:





Manuscript: https://doi.org/10.5281/zenodo.17243812 




Data: This Zenodo dataset (using the DOI provided here)



sha256sum checksums:



en_300_3_sg_wxd.csv.bz2 2e6f80ec20aa7bab032d0def845aecaed881ae7dfabb2141112af972d1e51139 

en_300_4_cbow_wxd.csv.bz2 d40266f317c0e615d75ab0bd3a7ac4dd21adde50369a58ec4f17f91e34ebd029

en_300_4_sg_wxd.csv.bz2 6603c6f3ae494a90605c1e8f48312fd43474f84ee86a271f418a4ec5b45f9433 

en_300_5_cbow_wxd.csv.bz2 2c98360e7af1f2d729bec0cd926266fae34e3e1a62028dc8d7a130d4914bdfe8

en_300_5_sg_wxd.csv.bz2 0b84df4ab4a2d6f4a91ac1cd0c6aae67495116eda5a6e2385f474f3c59151dc2
DOI: 10.21227/t25k-9f90
title: "oasis dataset"

description: "Early, reliable detection of Alzheimer\u2019s disease (AD) from routine structural MRI is challenging because models often overfit single sites, leak patient identity across splits, and degrade under class imbalance. We present ResidualSE\u2013Transformer, a compact hybrid that combines a Residual Squeeze-and-Excitation CNN with a  Transformer encoder to capture local anatomy and global context. We enforce patient-level partitions and first attempted four-class staging on OASIS (control, very mild, mild, moderate), to a binary stage (control vs. dementia) because the moderate class had only two subjects total, and adjacent-stage confusion dominated; the binary formulation matches clinical screening and yields stable estimates. Training uses class-weighted loss and classical augmentation. Generalization is assessed by pretraining on OASIS with fine-tuning on MIRIAD\/ADNI and by leave-one-dataset-out (LODO) testing across OASIS, MIRIAD, and ADNI. On OASIS (binary), the model achieves an accuracy of 0.791 and a weighted F1 of 0.784. LODO AUCs are 0.867 (MIRIAD), 0.535 (OASIS), and 0.503 (ADNI), with calibration consistent with these gaps. Grad-CAM highlights AD-relevant regions (medial temporal\/hippocampal band, ventricular borders, posterior cingulate) most clearly on MIRIAD. The study provides a transparent, leakage-free, calibration-aware baseline and a practical recipe for pooled multi-site training with per-site threshold calibration and patient-level aggregation for moving MRI-based AD screening toward robust cross-site deployment."
DOI: 10.5519/qd.n7f5w7tt
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.15468/kxsxk9
title: INBO collaborator Occurrences of invertebrates recorded in waarnemingen.be in Flanders, Belgium

description: This dataset comprises invertebrates occurrence records sourced from the Waarnemingen database, with a focus on data tagged to the Research Institute for Nature and Forest (INBO). The majority of observations stem from INBO-led fieldwork, systematically recorded using waarnemingen.be and the ObsMapp mobile application. In addition, the dataset includes a selection of ad hoc observations contributed by INBO users within the platform.

All records were extracted from the central Waarnemingen database, managed by Natuurpunt and part of the observation.org network. Each entry includes species identification, observation date, location details, observer information, and method of recording. This curated dataset provides valuable insights into invertebrate biodiversity across Flanders and supports research, monitoring, and conservation initiatives.

INBO tagged occurrences of non-native plants and animals are already published in https://www.gbif.org/dataset/9a0b66df-7535-4f28-9f4e-5bc11b8b096c and https://www.gbif.org/dataset/7f5e4129-0717-428e-876a-464fbd5d9a47 and are kept out of this dataset.

To allow anyone to use this dataset, we have released the data to the public domain under a Creative Commons Zero waiver. We would appreciate however, if you read and follow these norms for data use and provide a link to the original dataset whenever possible.
DOI: 10.34740/kaggle/dsv/13733630
title: Mahanagar Yatayat Transport Dataset
DOI: 10.15468/dl.gkdgk3
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Genipa caruto",
 "Year 1970-2025"
 ]
}

 The dataset includes 27 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033474-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17582530
title: Portuguese word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Portuguese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17582531
title: Portuguese word2vec embeddings trained on OpenSubtitles Part 1

description: This dataset contains the subs2vec embeddings for Portuguese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17549328
title: Raw Data for the publication Mechanical and electrical properties of a nano-gap or how to play the nano-accordion

description: Raw data for the publication Mechanical and electrical properties of a nano-gap or how to play the nano-accordion
DOI: 10.5281/zenodo.17549327
title: Raw Data for the publication Mechanical and electrical properties of a nano-gap or how to play the nano-accordion

description: Raw data for the publication Mechanical and electrical properties of a nano-gap or how to play the nano-accordion
DOI: 10.5281/zenodo.17605883
title: Portuguese word2vec embeddings trained on OpenSubtitles Part 2

description: This dataset contains the subs2vec embeddings for Portuguese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17605882
title: Portuguese word2vec embeddings trained on OpenSubtitles Part 2

description: This dataset contains the subs2vec embeddings for Portuguese, as presented in https://zenodo.org/records/17243814. The embeddings were trained on large-scale subtitle corpora and represent semantic vector spaces derived from naturalistic language use in films and television from the OpenSubtitles 2018 datasets: https://opus.nlpl.eu/OpenSubtitles/corpus/version/OpenSubtitles. 

For this language, we provide all embedding variants explored in the study. Specifically, the dataset includes vectors generated under different combinations of:



Dimensionality: multiple vector sizes (e.g., 100, 200, 300, …)

Window size: varying context windows (e.g., 2, 5, 10, …)

Each file corresponds to a unique configuration (dimension × window size). 


Each file contains the vocabulary for that language (column 1) and then the embedding values (columns 2 through dimension size + 1). 

If you use this dataset, please cite:



Manuscript: https://doi.org/10.5281/zenodo.17243812 

Data: This Zenodo dataset (using the DOI provided here)
DOI: 10.5281/zenodo.17610754
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114p Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114p Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.17610755
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS251114p Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS251114p Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.15468/dl.36sdxw
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 2a7e3080-28a9-11dd-97cd-b8a03c50a862",
 "TaxonKey is Panthera onca"
 ]
}

 The dataset includes 53 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033475-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25934/pr00004448.0
title: Available IPD datapackage for study 'Duloxetine Versus Placebo in the Treatment of Patients With Generalized Anxiety Disorder in China'

description: This is a 15 week study comparing how well duloxetine and placebo treatments improve generalized anxiety disorder
DOI: 10.48580/dgnz3-37s
title: The Reptile Database
DOI: 10.48580/dgtp3-37v
title: FishBase
DOI: 10.48580/dgsv8-38s
title: Vespidae based on Zoological-Botanical Database
DOI: 10.48580/dgtp3-388
title: Orthoptera Species File
DOI: 10.48580/dgtp3-38g
title: World Scarabaeidae Database
DOI: 10.48580/dgtp3-392
title: World Porifera Database
DOI: 10.48580/dgtp3-398
title: Phasmida Species File
DOI: 10.48580/dgtp3-39k
title: World Ophiuroidea Database
DOI: 10.48580/dgjc7-39v
title: Taxapad Ichneumonoidea
DOI: 10.48580/dgtp3-39r
title: Plecoptera Species File
DOI: 10.48580/dgtp3-3bb
title: World List of Bryozoa
DOI: 10.48580/dgt98-3bg
title: World Nemertea Database
DOI: 10.48580/dgtp3-3bm
title: World Polychaeta Database
DOI: 10.48580/dgtp3-3br
title: World Marine, Freshwater and Terrestrial Isopod Crustaceans database
DOI: 10.48580/dgsv8-3bs
title: World Asteroidea Database
DOI: 10.48580/dgtp3-3bx
title: World List of Marine Oligochaeta
DOI: 10.48580/dgtp3-3c7
title: World List of Holothuroidea
DOI: 10.48580/dgtp3-3c8
title: World List of marine Brachyura
DOI: 10.48580/dgtp3-3cq
title: World Gastrotricha Database
DOI: 10.48580/dgtp3-3cv
title: World List of Monogenea
DOI: 10.48580/dgtp3-3cw
title: World List of Cestoda
DOI: 10.48580/dgtp3-3cx
title: World List of Trematoda
DOI: 10.48580/dgtp3-3cy
title: World List of Myxozoa
DOI: 10.48580/dgtp3-3cz
title: MolluscaBase
DOI: 10.48580/dgtp3-3d2
title: World List of Octocorallia
DOI: 10.48580/dgtp3-3dc
title: Checklist of Ferns and Lycophytes of the World
DOI: 10.48580/dgtp3-3dd
title: Synonymic Checklists of the Vascular Plants of the World
DOI: 10.48580/dgtp3-3dx
title: World Foraminifera Database
DOI: 10.48580/dgtp3-3dy
title: Dermaptera Species File
DOI: 10.15468/dl.mcjcjf
title: Occurrence Download

description: A dataset containing 66780 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "TaxonKey is Halyomorpha halys"
 ]
}

 The dataset includes 66780 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033472-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dgtp3-3f8
title: World Entiminae Database
DOI: 10.48580/dgtp3-3fk
title: World Ostracoda Database
DOI: 10.48580/dgtp3-3fl
title: World List of Actiniaria
DOI: 10.48580/dgsv8-3fq
title: Phylum Ctenophora: list of all valid species names. Electronic internet document
DOI: 10.48580/dgsv8-3fx
title: Ascidiacea World Database
DOI: 10.48580/dgtp3-3ft
title: Pycnobase: World Pycnogonida Database
DOI: 10.48580/dgtp3-3g4
title: World of Copepods Database
DOI: 10.48580/dgtp3-3g6
title: World List of turbellarian worms: Acoelomorpha, Catenulida, Rhabditophora
DOI: 10.48580/dgtp3-3g9
title: World List of Scleractinia
DOI: 10.48580/dgsv8-3gd
title: Catalogue of the Pterophoroidea of the World
DOI: 10.48580/dgtp3-3gc
title: Isoptera Species File
DOI: 10.48580/dgtp3-3gf
title: MilliBase
DOI: 10.48580/dgtp3-3gh
title: World Amphipoda Database
DOI: 10.48580/dgtp3-4f5
title: Pinopsida from WFO
DOI: 10.48580/dgtp3-4f8
title: The Interim Register of Marine and Nonmarine Genera
DOI: 10.48580/dg9ld-4kv
title: Hymenoptera Information System, Pompilidae of the World
DOI: 10.48580/dgtp3-4ky
title: The Integrated Taxonomic Information System
DOI: 10.48580/dgtp3-4pt
title: World Catalogue of Opiliones
DOI: 10.48580/dgtp3-4rb
title: World Brachiopoda Database
DOI: 10.48580/dgtp3-4rf
title:  Nemys, World Database of Nematodes
DOI: 10.48580/dgt98-49cf
title: An Online Catalog of the Ants of the World
DOI: 10.48580/dgt98-4th
title: Catalogue of World Gelechiidae
DOI: 10.48580/dgtp3-4rx
title: 3i World Auchenorrhyncha Database
DOI: 10.48580/dgqdn-758p
title: Universal Chalcidoidea Database curated in TaxonWorks
DOI: 10.48580/dgt98-49xk
title: Global Lepidoptera Index
DOI: 10.48580/dgtp3-75rv
title: The World Odonata List
DOI: 10.15468/dl.ejamhm
title: Occurrence Download

description: A dataset containing 3949 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.11_1, USA.34_1, USA.41_1, USA.10_1)",
 "TaxonKey is Ophisaurus ventralis"
 ]
}

 The dataset includes 3949 records from 37 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033476-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48580/dgt98-dwyf
title: A Revised World Catalogue of Ghost Moths (Lepidoptera: Hepialidae)