cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17095483
title: Table 14 in Description of five new species of Acestrocephalus Eigenmann and redescription of A. sardina and A. boehlkei (Characiformes: Characidae)

description: Table 14. Morphometrics of Acestrocephalus pallidus. Standard length is expressed in mm; measurements through head length are percentages of standard length; last four entries are percentages of head length. Specimens are from: MZUSP 35624 (holotype), 89586, 76236, 73471, 73487, 76627, and 63311 (paratypes).
CharactersHolotypeNRangeMeanSDStandard length60.56040.0-66.0Body depth28.15024.1-28.426.11.0Snout to dorsal-fin origin51.76048.7-53.350.70.8Snout to pectoral-fin origin28.16026.2-29.728.00.6Snout to pelvic-fin origin42.16039.1-43.141.00.8Snout to anal-fin origin55.56053.2-57.455.30.9Caudal peduncle depth8.6607.5-8.78.10.2Caudal peduncle length9.6609.1-11.09.80.5Pectoral-fin length17.76016.0-18.317.20.5Pelvic-fin length15.36014.1-16.215.10.4Dorsal-fin base length11.96010.3-12.511.60.4Dorsal-fin height24.86023.2-26.524.80.8Anal-fin base length38.06035.8-40.037.71.1Anal-fin lobe length18.16016.2-18.817.70.7Eye to dorsal-fin origin35.56033.0-36.034.80.6Dorsal-fin origin to caudal-fin base53.06050.0-53.652.10.8Head length30.56028.8-31.230.00.5Horizontal eye diameter32.46029.0-34.132.21.0Snout length29.76026.7-30.028.10.8Least interorbital width19.06017.6-22.019.61.0Upper jaw length64.86060.3-65.663.41.1
description: Published as part of Menezes, Naércio A., 2006, Description of five new species of Acestrocephalus Eigenmann and redescription of A. sardina and A. boehlkei (Characiformes: Characidae), pp. 399 in Neotropical Ichthyology 4 (4) on page 399, DOI: 10.1590/S1679-62252006000400002, http://zenodo.org/record/5418787
DOI: 10.15468/bqcu3u
title: The establishment of the Onthophagus anthracinus (Coleoptera: Scarabaeidae) species complex and the description of a new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Arriaga-Jiménez, Alfonsina, Escobar-Hernández, Fernando, Rös, Matthias, Kohlmann, Bert (2020): The establishment of the Onthophagus anthracinus (Coleoptera: Scarabaeidae) species complex and the description of a new species. The Canadian Entomologist 152 (1): 1-17, DOI: 10.4039/tce.2019.62, URL: https://doi.org/10.4039/tce.2019.62AbstractThe Onthophagus anthracinus Harold, 1873 (Coleoptera: Scarabaeidae: Scarabaeinae: Onthophagini) species complex of the Onthophagus landolti Harold, 1880 species group is established and analysed. A new species, O. etlaensis, is described from this group. Onthophagus mextexus Howden and Cartwright, 1970 is considered a valid species and O. knulli Howden and Cartwright, 1963 is placed in synonymy with O. durangoensis Balthasar, 1939 new synonymy. A key for the O. anthracinus species complex is presented along with photographs and illustrations of the main taxonomic differences. An update of the O. landolti species group is presented.
DOI: 10.15468/dl.zekckp
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Paeonia jishanensis"
 ]
}

 The dataset includes 21 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066126-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17942968
title: Anisotropic Spheres (ASPH)

description: Overview

The Anisotropic Spheres (ASPH) dataset is a synthetic benchmark dataset designed for evaluating neural rendering methods and inverse rendering algorithms that handle anisotropic reflectance under complex illumination. This dataset was created to support the research presented in ShinyNeRF.

Dataset Description

ASPH contains controlled renderings of four distinct sphere instances, each rendered under multiple high-dynamic-range (HDR) environment maps. The dataset varies environment maps to study their effects on appearance under different lighting conditions.

Key Features



Simple, controlled geometry: Sphere primitives eliminate geometric complexity, allowing researchers to focus exclusively on material and illumination interactions

Systematic parameter variations: Structured sweeps across anisotropic and roughness material properties enable controlled experiments on each object

Multiple illumination conditions: Each sphere is rendered under diverse HDR environment maps to capture behavior across different lighting scenarios

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking


Dataset Contents



RGB renderings: Rendered images (270×270 pixels) for each sphere-environment combination

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, anisotropy (ani), roughness (ro), and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations
DOI: 10.5281/zenodo.17942969
title: Anisotropic Spheres (ASPH)

description: Overview

The Anisotropic Spheres (ASPH) dataset is a synthetic benchmark dataset designed for evaluating neural rendering methods and inverse rendering algorithms that handle anisotropic reflectance under complex illumination. This dataset was created to support the research presented in ShinyNeRF.

Dataset Description

ASPH contains controlled renderings of four distinct sphere instances, each rendered under multiple high-dynamic-range (HDR) environment maps. The dataset varies environment maps to study their effects on appearance under different lighting conditions.

Key Features



Simple, controlled geometry: Sphere primitives eliminate geometric complexity, allowing researchers to focus exclusively on material and illumination interactions

Systematic parameter variations: Structured sweeps across anisotropic and roughness material properties enable controlled experiments on each object

Multiple illumination conditions: Each sphere is rendered under diverse HDR environment maps to capture behavior across different lighting scenarios

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking


Dataset Contents



RGB renderings: Rendered images (270×270 pixels) for each sphere-environment combination

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, anisotropy (ani), roughness (ro), and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations
DOI: 10.57760/sciencedb.msdc.00071
title: The Railway Fastener Defect Dataset
title: The Railway Fastener Defect Dataset

description: The Railway Fastener Defect Dataset (RFDD) is a metrology-based, high-quality benchmark comprising 1,350 full-scene images (2048 × 2021 pixels) captured by operational high-speed railway inspection vehicles. Unlike conventional datasets that rely on cropped single-object images, RFDD preserves the original multi-fastener layout and strictly defines its six categories—Normal, Missing, Inverted, Displaced, Deformed, and Fractured—based on measurable engineering specifications and maintenance tolerances rather than subjective visual patterns. To ensure engineering fidelity, the dataset is constructed under a rigorous framework of six visual consistency criteria (Geometric, Optical, Boundary Gradient, Noise, Texture, and Occlusion), providing high-precision, instance-level semantic annotations that support detection, localization, and segmentation tasks. By maintaining metrology-grade quality and realistic large-field-of-view imaging conditions, RFDD facilitates the objective, quantitative assessment of both automated detection algorithms and hardware perception systems in field-level applications.
description: The Railway Fastener Defect Dataset (RFDD) is a metrology-based, high-quality benchmark comprising 1,350 full-scene images (2048 × 2021 pixels) captured by operational high-speed railway inspection vehicles. Unlike conventional datasets that rely on cropped single-object images, RFDD preserves the original multi-fastener layout and strictly defines its six categories—Normal, Missing, Inverted, Displaced, Deformed, and Fractured—based on measurable engineering specifications and maintenance tolerances rather than subjective visual patterns. To ensure engineering fidelity, the dataset is constructed under a rigorous framework of six visual consistency criteria (Geometric, Optical, Boundary Gradient, Noise, Texture, and Occlusion), providing high-precision, instance-level semantic annotations that support detection, localization, and segmentation tasks. By maintaining metrology-grade quality and realistic large-field-of-view imaging conditions, RFDD facilitates the objective, quantitative assessment of both automated detection algorithms and hardware perception systems in field-level applications.
DOI: 10.15468/ej5t3h
title: Species identity crisis among New World members of the genus Heliothrips (Thysanoptera: Thripidae: Panchaetothripinae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Silva, Larinne De Maria Rocha, O’Donnell, Cheryle A., Lima, Élison Fabrício Bezerra (2024): Species identity crisis among New World members of the genus Heliothrips (Thysanoptera: Thripidae: Panchaetothripinae). Zootaxa 5489 (1): 153-172, DOI: 10.11646/zootaxa.5489.1.11, URL: http://dx.doi.org/10.11646/zootaxa.5489.1.11AbstractUntil mid-1990s, Heliothrips was considered a genus without major identification problems, with only H. haemorrhoidalis spread worldwide. More recently, concerns have arisen regarding misidentifications of this pest species around the world, particularly with the description of H. longisensibilis from China and the subsequent records from Argentina and Brazil. Here we consider that species as a new synonym of H. angustior stat. rev., and records of this species under the name H. haemorrhoidalis from the Neotropics (and also India and Thailand) are rectified, whereas in the Nearctic region only H. haemorrhoidalis is present. Both species are widespread, and morphological variations suggest other cryptic species may occur. Heliothrips flamengo sp. n. is described from the Brazilian and Peruvian Amazon Rainforest, and an updated key to the five species of Heliothrips is provided.
DOI: 10.5281/zenodo.17954887
title: Cultural Heritage Anisotropic Objects (CHAO)

description: Overview

The Cultural Heritage Anisotropic Objects (CHAO) dataset is a synthetic benchmark dataset introduced in ShinyNeRF for evaluating neural rendering methods and inverse rendering algorithms on culturally significant objects with pronounced anisotropic reflectance. This dataset was created to support the research presented in ShinyNeRF, focusing on the challenges posed by complex geometry and environment lighting on heritage artifacts.

Dataset Description

CHAO contains renderings of two distinct cultural heritage objects: a Moroccan barad teapot and a German medieval helmet. Both objects exhibit strong anisotropic specularities characteristic of metallic craftsmanship. The 3D models were sourced from the Sketchfab catalog and processed to generate comprehensive ground truth data for material and geometry analysis under realistic lighting conditions.

Key Features



Cultural heritage authenticity: Genuine artifact models provide realistic test cases for heritage digitization applications

Environment map illumination: Complex HDR lighting captures realistic reflectance behavior under natural illumination conditions

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking on complex geometries


Dataset Contents



RGB renderings: Rendered images (512×512 pixels) for each object under environment map lighting

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations


Objects



Barad Teapot (Moroccan)

Closed Helmet with Etched Decor (German medieval)


3D models source:All objects obtained from Sketchfab with modified material parameters (anisotropy and roughness). Model references: barad teapot, closed helmet
DOI: 10.5281/zenodo.17954888
title: Cultural Heritage Anisotropic Objects (CHAO)

description: Overview

The Cultural Heritage Anisotropic Objects (CHAO) dataset is a synthetic benchmark dataset introduced in ShinyNeRF for evaluating neural rendering methods and inverse rendering algorithms on culturally significant objects with pronounced anisotropic reflectance. This dataset was created to support the research presented in ShinyNeRF, focusing on the challenges posed by complex geometry and environment lighting on heritage artifacts.

Dataset Description

CHAO contains renderings of two distinct cultural heritage objects: a Moroccan barad teapot and a German medieval helmet. Both objects exhibit strong anisotropic specularities characteristic of metallic craftsmanship. The 3D models were sourced from the Sketchfab catalog and processed to generate comprehensive ground truth data for material and geometry analysis under realistic lighting conditions.

Key Features



Cultural heritage authenticity: Genuine artifact models provide realistic test cases for heritage digitization applications

Environment map illumination: Complex HDR lighting captures realistic reflectance behavior under natural illumination conditions

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking on complex geometries


Dataset Contents



RGB renderings: Rendered images (512×512 pixels) for each object under environment map lighting

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations


Objects



Barad Teapot (Moroccan)

Closed Helmet with Etched Decor (German medieval)


3D models source:All objects obtained from Sketchfab with modified material parameters (anisotropy and roughness). Model references: barad teapot, closed helmet
DOI: 10.5281/zenodo.17954754
title: Anisotropic Synthetic Dataset (ASD)

description: Overview

The Anisotropic Synthetic Dataset (ASD) is a synthetic benchmark dataset originally introduced in Spec-Gaussian for evaluating neural rendering methods and inverse rendering algorithms on objects with pronounced specular reflections. This extended version was created to support the research presented in ShinyNeRF, providing comprehensive ground truth data for material and geometry recovery evaluation.

Dataset Description

ASD contains renderings of four distinct objects selected from the original dataset, each exhibiting distinctive specular characteristics where anisotropic material parameters remain roughly constant across the object's surface. While the original dataset provided high-quality RGB views, it did not include ground truth material properties and surface geometry. To enable quantitative benchmarking of material recovery, we re-rendered the scenes in Blender to generate complete ground truth data.

Key Features



Varying geometric complexity: Diverse object shapes eliminate material ambiguity, allowing researchers to focus on algorithm robustness across different surface structures

Homogeneous material parameters: Constant anisotropic properties per object enable controlled evaluation of material estimation

Controlled illumination conditions: Direct point lighting provides predictable interactions without environment map complexity

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking material and geometry recovery


Dataset Contents



RGB renderings: Rendered images (270×270 pixels) for each object under point light sources

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations
DOI: 10.5281/zenodo.17954755
title: Anisotropic Synthetic Dataset (ASD)

description: Overview

The Anisotropic Synthetic Dataset (ASD) is a synthetic benchmark dataset originally introduced in Spec-Gaussian for evaluating neural rendering methods and inverse rendering algorithms on objects with pronounced specular reflections. This extended version was created to support the research presented in ShinyNeRF, providing comprehensive ground truth data for material and geometry recovery evaluation.

Dataset Description

ASD contains renderings of four distinct objects selected from the original dataset, each exhibiting distinctive specular characteristics where anisotropic material parameters remain roughly constant across the object's surface. While the original dataset provided high-quality RGB views, it did not include ground truth material properties and surface geometry. To enable quantitative benchmarking of material recovery, we re-rendered the scenes in Blender to generate complete ground truth data.

Key Features



Varying geometric complexity: Diverse object shapes eliminate material ambiguity, allowing researchers to focus on algorithm robustness across different surface structures

Homogeneous material parameters: Constant anisotropic properties per object enable controlled evaluation of material estimation

Controlled illumination conditions: Direct point lighting provides predictable interactions without environment map complexity

High-quality ground truth: Physically-based rendering provides accurate reference data for benchmarking material and geometry recovery


Dataset Contents



RGB renderings: Rendered images (270×270 pixels) for each object under point light sources

Material property maps: Ground truth maps including RGB, surface normals, tangent vectors, and depth (alpha)

Blender scene files: Complete scene setups for reproducibility and custom rendering configurations
DOI: 10.15468/dl.7x2nmz
title: Occurrence Download

description: A dataset containing 9041 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Abrothrix olivaceus)"
 ]
}

 The dataset includes 9041 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066128-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/b2w46q
title: Feather mites (Acariformes: Astigmata) from the yellow-rumped cacique, Cacicus cela (Linnaeus, 1758) (Passeriformes: Icteridae) in Brazil, with description of four new species

description: This dataset contains the digitized treatments in Plazi based on the original journal article Hernandes, Fabio Akashi (2023): Feather mites (Acariformes: Astigmata) from the yellow-rumped cacique, Cacicus cela (Linnaeus, 1758) (Passeriformes: Icteridae) in Brazil, with description of four new species. Journal of Natural History 57 (2): 257-284, DOI: 10.1080/00222933.2023.2174459, URL: http://dx.doi.org/10.1080/00222933.2023.2174459ARTICLE HISTORYFour new species of feather mites (Acariformes: Astigmata:Analgoidea) are described from the yellow-rumped cacique, Cacicus cela (Linnaeus, 1758) (Passeriformes: Icteridae), in Brazil: Proctophyllodes truncatilobus sp. nov., Amerodectes gracilisimilis sp. nov., (Proctophyllodidae), Trouessartia cacica sp. nov. (Trouessartiidae), and Mesalgoides brasiliensis sp. nov. (Psoroptoididae).Received 18 August 2022 Accepted 24 January 2023
DOI: 10.5281/zenodo.18062011
title: The dataset used in the paper for the Backward cloud transformation algorithm based on Kullback Leibler divergence

description: The dataset used in the paper for the Backward cloud transformation algorithm based on Kullback Leibler divergence
DOI: 10.5281/zenodo.18062010
title: The dataset used in the paper for the Backward cloud transformation algorithm based on Kullback Leibler divergence

description: The dataset used in the paper for the Backward cloud transformation algorithm based on Kullback Leibler divergence
DOI: 10.15468/h8hpx4
title: Four new earthworm species from the highlands of Cameroon with description of a new genus Okudrilus gen. n. (Oligochaeta: Eudrilidae & Acanthodrilidae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Csuzdi, Cs., Sherlock, E., Kouete, M. Talla, Doherty-Bone, T. M. (2015): Four new earthworm species from the highlands of Cameroon with description of a new genus Okudrilus gen. n. (Oligochaeta: Eudrilidae & Acanthodrilidae). African Invertebrates 56 (1): 25-38, DOI: 10.5733/afin.056.0103, URL: http://dx.doi.org/10.5733/afin.056.0103ABSTRACTExamining a small collection of earthworms from the highlands of Cameroon resulted in clarifying the taxonomic status of Eminoscolex franzi Zicsi & Csuzdi, 1986 stat. n. and discovering a new eudrilid genus Okudrilus gen. n. containing three new species: O. monticolus, O. sulcatus and O. nyosensis spp. n. The new genus is similar to Metascolex Michaelsen, 1903; however, it differs from it in the position of the gizzard, the structure of the ovo-spermathecal system and furthermore in the presence of penial setae. It also resembles Vomia Segun, 1976 but differs from it in the metandric condition of the male genitalia and the long backward running vesicles. Apart from the new eudrilid taxa the material also contained a new acanthodrilid species, Dichogaster (Diplothecodrilus) asymmetrica sp. n., characterised by the asymmetric position of its spermathecal duct-ampulla junction.All members of this new genus were collected in North West Region, with O. monticolus endemic to the summit grasslands of Mount Oku, and is possibly threatened by climate change, and sOil degradatiOn thrOugh OVergrazing and fire.KEY WORDS: Afrotropical Region, Cameroon, Mount Oku, earthworms, Eudrilinae, new genus, new species.
DOI: 10.15468/dl.8bc9xp
title: Occurrence Download

description: A dataset containing 3838759 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (BRA.11_1, BRA.12_1)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3838759 records from 1136 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0066102-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hmps4a
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OrderKey is one of (Baetidae, Limnephilidae, Leptoceridae, Glossosomatidae, Hydroptilidae, Phryganeidae, Ameletidae, Nemouridae, Apataniidae, Astacidae, Atyidae, Beraeidae, Perlodidae, Brachycentridae, Taeniopterygidae, Caenidae, Capniidae, Parastacidae, Hydropsychidae, Philopotamidae, Chloroperlidae, Leptophlebiidae, Lepidostomatidae, Polycentropodidae, Perlidae, Heptageniidae, Ecnomidae, Varunidae, Cambaridae, Goeridae, Leuctridae, Psychomyiidae, Molannidae, Sericostomatidae, Odontoceridae, Oligoneuriidae, Potamanthidae, Ephemerellidae, Siphlonuridae, Rhyacophilidae)",
 "Country is one of (Germany, France, Italy, Austria, Netherlands, Denmark, Belgium, Poland, Czechia, Slovakia, Hungary, Slovenia, Romania, Bosnia and Herzegovina, Bulgaria, Moldova, Republic of, Croatia, Montenegro, Serbia, Ukraine, Switzerland, Belarus, Russian Federation, Greece, Spain, Türkiye, Lithuania, Latvia, Estonia, Finland, Norway, Sweden, United Kingdom of Great Britain and Northern Ireland, Ireland)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028628-241107131044228/datasets/export for details.