cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.3535/tfw-gp5-7a9
title: Desmana Güldenstädt, 1777

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/1wx-1dy-y29
title: Hibolithes

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/pzl-ras-ap6
title: Brachiopoda Duméril, 1805

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/a56-jph-vz5
title: Animalia

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/agd-8vb-63e
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/n8j-xv1-h0g
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/0a9-521-tba
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.3535/3ep-9fy-508
title: Mammalia Linnaeus, 1758

description: Digital Specimen for the physical specimen hosted at Naturalis Biodiversity Center.
DOI: 10.17632/nhvyvytn2h.1
title: Legitimate domains and DGA categorized morphologically and by families.

description: Dataset Composition
Examples: 4,090,661
Legitimate: 998,313
DGA: 3,092,348
DGA Families: 160
Morphological Types: 5

Dataset composed of a collection from the DGA DGArchive feed between 09/28/2025 and 10/28/2025, plus the Majestic Million domain set collected on 09/28/2025.
Examples found in both lists were disregarded in order to improve the quality and reliability of the labels presented by the dataset.
This dataset was conceived during research work for the detection and classification of DGA using deep learning and natural language processing techniques.
This research resulted in the publication of two articles and a master's thesis:

- Class Incremental Deep Learning: A Computational Scheme to Avoid Catastrophic Forgetting in Domain Generation Algorithm Multiclass Classification. https://doi.org/10.3390/app14167244
- Deep Convolutional Neural Network and Character Level Embedding for DGA Detection. http://dx.doi.org/10.5220/0012605700003690
- Detecção de domínios gerados por algoritmos com aprendizado profundo incremental e DNS passivo. https://hdl.handle.net/11449/313556

The DGA example set, we organized it by families. This data already came from the original set obtained from the DGArchive. In type, we identified five major morphological groups, which are described below:

- Random: DGA families that generate their domain names in a way that forms unintelligible character sequences. Although we know that this sequence is not exactly random, it gives the impression of being random, hence the choice of the label for the morphological type.
Examples: 15zkgsh1n100ax15m265x1cnkdk7.org, uhovosxkjkcg.ru

- Pseudo-words: This morphological format seeks to emulate the formation of real words, permuting vowels and consonants. This technique seeks to evade security layers that are based on entropy to detect DGA domains.
Examples: stajaq.com, nonafudazage.name

- Seed-domain: Some DGAs start with an existing, often legitimate, domain name and add characters at the beginning or end, allow characters, and change the initial domain's TLD.
Examples: tlzeitudeconscientiousavl.com, agirtvolveras.com

- Wordlist: Another very effective way that some threats have found to evade automated DGA detection analyses is by forming their domains using dictionaries of real words and creating domains with the permission of those words. These DGAs are particularly difficult to detect because of their morphology, which is very close to legitimate domains.
Examples: kneemanualgirl.com, leadunabledeal.art, christinashaquila.ru

- Subdomain: Another strategy recently used by attackers to try to evade automated DGA analysis is the use of a subdomain instead of the initial domain. Some even use subdomains of legitimate Dynamic DNS providers. We found DGA families that have examples of both morphologies (domain and subdomain), but there are already threats that rely exclusively on this morphology.
Examples: odtzcjajsrxh.dyndns.org, eboxmj56grwjs2afs6i.ddns.net
DOI: 10.15468/dl.syrttr
title: Occurrence Download

description: A dataset containing 2653 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ambystoma californiense Gray, 1853"
 ]
}

 The dataset includes 2653 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319743-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hxgj8r
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON((-425.95337 -68.1012,-412.86743 -68.1012,-412.86743 -60.82581,-425.95337 -60.82581,-425.95337 -68.1012))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000365-180920211646547/datasets/export for details.
DOI: 10.15468/dl.yjr4tf
title: Occurrence Download

description: A dataset containing 805072 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Unknown evidence"
 ]
 },
 {
 "or" : [
 "Country is Benin",
 "Country is Burkina Faso",
 "Country is Cameroon",
 "Country is Cabo Verde",
 "Country is Côte d’Ivoire",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Liberia",
 "Country is Mali",
 "Country is Mauritania",
 "Country is Niger",
 "Country is Nigeria",
 "Country is Senegal",
 "Country is Sierra Leone",
 "Country is Togo"
 ]
 },
 "TaxonKey is Plantae"
 ]
}

 The dataset includes 805072 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006190-171025204727650/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xk9ktw
title: Occurrence Download

description: A dataset containing 102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydalis crassifolia Royle",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 102 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319740-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnhrfs
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000624-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wjnnfp
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydalis crispa Prain",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 53 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319741-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yafg72
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydalis dajingensis C.Y.Wu & Z.Y.Su",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319744-200613084148143/datasets/export for details.
DOI: 10.15468/dl.byrnez
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydalis ecristata (Prain) D.G.Long",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 6 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319747-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nfetdg
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000422-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r6sx68
title: Occurrence Download

description: A dataset containing 60 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Corydalis dasyptera Maxim.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 60 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0319745-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9swo1k
title: Occurrence Download

description: A dataset containing 1630226566 species occurrences available in GBIF matching the query:
{ }

 The dataset includes 1630226566 records from 18525 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000373-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17333841
title: Damage Risk Index (DRI): Mathematical Definition, Computational Model, and Quantitative Application Using Environmental Sensor Data

description:  

This section introduces the Damage Risk Index (DRI), a synthetic indicator designed to quantify the probability of material decay in heritage structures based on microclimatic stressors. The DRI integrates normalized relative humidity, surface moisture, and short-term temperature fluctuations into a weighted composite score, reflecting their empirical contribution to deterioration processes. A logistic regression model is applied to translate the DRI into a probabilistic estimate of decay risk. A worked example using real sensor data illustrates the full computational workflow, including normalization, index calculation, and probability mapping. This framework supports predictive conservation strategies by enabling continuous, data-driven risk assessment across monitored heritage assets.
DOI: 10.5281/zenodo.17333842
title: Damage Risk Index (DRI): Mathematical Definition, Computational Model, and Quantitative Application Using Environmental Sensor Data

description:  

This section introduces the Damage Risk Index (DRI), a synthetic indicator designed to quantify the probability of material decay in heritage structures based on microclimatic stressors. The DRI integrates normalized relative humidity, surface moisture, and short-term temperature fluctuations into a weighted composite score, reflecting their empirical contribution to deterioration processes. A logistic regression model is applied to translate the DRI into a probabilistic estimate of decay risk. A worked example using real sensor data illustrates the full computational workflow, including normalization, index calculation, and probability mapping. This framework supports predictive conservation strategies by enabling continuous, data-driven risk assessment across monitored heritage assets.
DOI: 10.22033/esgf/cmip6.2872
title: BCC BCC-CSM2HR model output prepared for CMIP6 HighResMIP control-1950

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.HighResMIP.BCC.BCC-CSM2-HR.control-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The BCC-CSM 2 HR climate model, released in 2017, includes the following components:
 atmos: BCC_AGCM3_HR (T266; 800 x 400 longitude/latitude; 56 levels; top level 0.1 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. 
The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 50 km, land: 50 km, ocean: 50 km, seaIce: 50 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.562
title: NCC NorESM2-LM model output prepared for CMIP6 LUMIP

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.LUMIP.NCC.NorESM2-LM' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The NorESM2-LM (low atmosphere-medium ocean resolution, GHG concentration driven) climate model, released in 2017, includes the following components:
 aerosol: OsloAero, atmos: CAM-OSLO (2 degree resolution; 144 x 96; 32 levels; top level 3 mb), atmosChem: OsloChemSimp, land: CLM, landIce: CISM, ocean: MICOM (1 degree resolution; 360 x 384; 70 levels; top grid cell minimum 0-2.5 m [native model uses hybrid density and generic upper-layer coordinate interpolated to z-level for contributed data]), ocnBgchem: HAMOCC, seaIce: CICE. 
The model was run by the NorESM Climate modeling Consortium consisting of CICERO (Center for International Climate and Environmental Research, Oslo 0349), MET-Norway (Norwegian Meteorological Institute, Oslo 0313), NERSC (Nansen Environmental and Remote Sensing Center, Bergen 5006), NILU (Norwegian Institute for Air Research, Kjeller 2027), UiB (University of Bergen, Bergen 5007), UiO (University of Oslo, Oslo 0313) and UNI (Uni Research, Bergen 5008), Norway. Mailing address: NCC, c/o MET-Norway, Henrik Mohns plass 1, Oslo 0313, Norway (NCC) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, atmosChem: 250 km, land: 250 km, landIce: 250 km, ocean: 100 km, ocnBgchem: 100 km, seaIce: 100 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.6084/m9.figshare.29083565
title: Raw Data on Proline Prompting Serum Killing of KP

description: This study found that proline is an effective metabolite capable of entering the pyruvate/TCA cycle through metabolic processes, reversing serum resistance of Klebsiella pneumoniae both in vitro and in vivo. The pyruvate/TCA cycle upregulates the expression of complement-binding proteins and PMF, permitting the formation of MAC (membrane attack complex), which ultimately leads to cell death.
DOI: 10.22033/esgf/cmip6.2899
title: BCC BCC-CSM2MR model output prepared for CMIP6 LUMIP deforest-globe

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.LUMIP.BCC.BCC-CSM2-MR.deforest-globe' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The BCC-CSM 2 MR climate model, released in 2017, includes the following components:
 atmos: BCC_AGCM3_MR (T106; 320 x 160 longitude/latitude; 46 levels; top level 1.46 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. 
The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.
DOI: 10.22033/esgf/cmip6.2912
title: BCC BCC-CSM2MR model output prepared for CMIP6 C4MIP esm-ssp585

description: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets: These data include all datasets published for 'CMIP6.C4MIP.BCC.BCC-CSM2-MR.esm-ssp585' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.
 
 The BCC-CSM 2 MR climate model, released in 2017, includes the following components:
 atmos: BCC_AGCM3_MR (T106; 320 x 160 longitude/latitude; 46 levels; top level 1.46 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. 
The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 50 km, seaIce: 50 km. 

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.