cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0154240.10fu
title: SH0154240.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0154241.10fu
title: SH0154241.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0154242.10fu
title: SH0154242.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0154243.10fu
title: SH0154243.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh0154244.10fu
title: SH0154244.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.57702/ibzt5zde
title: Wired Explaining Dialogue Corpus

description: A manually annotated corpus for studying how humans explain in dialogical settings
DOI: 10.57702/dsjwj80u
title: HW3

description: The dataset consists of 2,000 data points for digits 0 to 9.
DOI: 10.57702/efyhxe9w
title: Assembly Scenario Dataset

description: The dataset used in the paper is an assembly scenario consisting of 9 steps, with 30 participants, and includes task instructions, error rates, and user acceptance.
DOI: 10.57702/tk766suf
title: Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks

description: Uncertainty quantification of spatiotemporal travel demand with probabilistic graph neural networks.
DOI: 10.57702/rmdryini
title: XmediaNet

description: The XmediaNet dataset is a large-scale image-text dataset for cross-modal retrieval.
DOI: 10.57702/ejhan9lf
title: A Practical Two-Stage Training Strategy for Multi-Stream End-to-End Speech Recognition

description: The multi-stream paradigm of audio processing, in which several sources are simultaneously considered, has been an active research area for information fusion. The proposed technique has the following highlights: In Stage-1, a single-stream model is trained using all data for better model generalization. In Stage-2, a set of high-level UFE features are used to train a multi-stream model without requiring highly-parameterized parallel encoders.
DOI: 10.57702/rkrty9p1
title: TRANCOS

description: The dataset contains images of vehicles with varying densities and sizes.
DOI: 10.57702/i17oh9wb
title: NL4Opt Generation Dataset

description: The NL4Opt Generation Dataset consists of 1101 examples, divided into the train, dev, and test splits composed of 713, 99, and 289 examples, respectively. Each example consists of a linear programming word problem, labeled semantic entities, and the order mapping of variables.
DOI: 10.57702/4v9yv18c
title: Sub-ImageNet dataset

description: Sub-ImageNet dataset consists of 60,000 training and 10,000 testing samples.
DOI: 10.57702/cr0tdu3s
title: Stereo Vision Datasets

description: The dataset used in the paper is a set of stereo vision datasets.
DOI: 10.57702/f49wd501
title: Asset Allocation Example

description: The dataset used in the paper is a collection of historical returns data for ten asset classes, including stocks, bonds, and commodities, over a period of 14.5 years.
DOI: 10.57702/ouxfe33u
title: Large language models struggle to learn long-tail knowledge

description: Large language models struggle to learn long-tail knowledge
DOI: 10.57702/mxwdcs8c
title: Argoverse

description: The Argoverse dataset is a large-scale dataset for autonomous driving, containing 3D point clouds, semantic segmentation masks, and instance segmentation masks.
DOI: 10.57702/q5kxvpyx
title: Two-stage Linear Adaptive Robust Optimization

description: The dataset used in this paper is a collection of two-stage linear adaptive robust optimization (ARO) problems with binary here-and-now variables and polyhedral uncertainty sets.
DOI: 10.57702/rtebifwq
title: PyMAF-X

description: A dataset for 3D holistic body mesh reconstruction from in-the-wild videos, with synchronized speech.
DOI: 10.57702/5xmibgfh
title: Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting

description: The dataset is used to optimize the time-average sum of uplink transmission throughput rates for NOMA-based LPWA networks powered by ambient energy.
DOI: 10.57702/hienjqq8
title: Arch

description: A dataset for 3D human reconstruction from a single image. The approach uses a combination of 2D and 3D techniques to reconstruct the 3D human shape and texture.
DOI: 10.57702/3k2ar6qy
title: Point-VOS Kinetics

description: The Point-VOS Kinetics dataset consists of 23.9K videos with 120K objects annotated with points.
DOI: 10.57702/5gwvgm45
title: Expert Demonstrations

description: The expert demonstrations are generated according to the given optimal policy for the recovery. The length of each expert demonstration is 5-grid size trajectory length. Four algorithms for the evaluation includes MaxEnt, DeepMaxEnt, SIRL, and DSIRL.
DOI: 10.15468/dl.ubqg2k
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.3019 23.584271, 89.225278 23.578694, 89.150561 23.562102, 89.079606 23.534908, 89.014174 23.497787, 88.95588499999999 23.451661, 88.906176 23.397674, 88.866269 23.337164, 88.837136 23.27163, 88.81948300000001 23.202689, 88.81372500000001 23.132044, 88.819988 23.061436, 88.838097 22.992601, 88.867591 22.927231, 88.907731 22.866928, 88.957519 22.813171, 89.01572899999999 22.767274, 89.080928 22.73036, 89.151521 22.70333, 89.225782 22.686844, 89.3019 22.681303, 89.378018 22.686844, 89.452279 22.70333, 89.52287200000001 22.73036, 89.588071 22.767274, 89.646281 22.813171, 89.69606899999999 22.866928, 89.736209 22.927231, 89.765703 22.992601, 89.783812 23.061436, 89.790075 23.132044, 89.784317 23.202689, 89.76666400000001 23.27163, 89.737531 23.337164, 89.697624 23.397674, 89.647915 23.451661, 89.589626 23.497787, 89.52419399999999 23.534908, 89.453239 23.562102, 89.378522 23.578694, 89.3019 23.584271))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079909-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/4ujnipys
title: BDD-100K dataset

description: The dataset used in the paper is the BDD-100K dataset, a large-scale dataset for object detection tasks.
DOI: 10.57702/43vm84ek
title: Four-layer Phantom

description: Four-layer Phantom
DOI: 10.57702/5ll3x52a
title: Dynamic Proxy Domain Generalizes the Crowd

description: Crowd localization targets on predicting each instance precise location within an image. Current advanced methods propose the pixel-wise binary classification to tackle the congested prediction, in which the pixel-level thresholds binarize the prediction confidence of being the pedestrian head.
DOI: 10.5281/zenodo.15138636
title: TABLE 3 in Phylogeny of iguanodontian dinosaurs and the evolution of quadrupedality

description: TABLE 3. Distributions of modeled variables in Bayesian analysis.
StatisticESSMeanStandard DeviationPosterior365-559213.90Likelihood343-491911.89Birth rate (ʎ)Late Cretaceous9790.3710.106Early Cretaceous39500.3160.155Late Jurassic13180.3440.117Middle Jurassic38230.3140.155Early Jurassic38320.3150.158Late Triassic330.2880.087Death rate (µ)Late Cretaceous9950.3590.109Early Cretaceous40010.3050.154Late Jurassic30850.2540.121Middle Jurassic38090.3060.156Early Jurassic40010.3070.154Late Triassic320.2780.090Sampling rate (ψ)Late Cretaceous38361.22E-31.45E-3Early Cretaceous33232.52E-32.08E-3Late Jurassic40018.45E-49.15E-4Middle Jurassic2916.61E-33.99E-3Early Jurassic997.59E-32.90E-3Late Triassic1381.03E-23.72E-3Sampling proportion (ρ)-0.1-
description: Published as part of Poole, Karen E., 2016, Phylogeny of iguanodontian dinosaurs and the evolution of quadrupedality, pp. 15 in Palaeontologia Electronica (a 30) 25 (3) on page 15, DOI: 10.26879/702, http://zenodo.org/record/11063310
DOI: 10.5281/zenodo.15138637
title: TABLE 3 in Phylogeny of iguanodontian dinosaurs and the evolution of quadrupedality

description: TABLE 3. Distributions of modeled variables in Bayesian analysis.
StatisticESSMeanStandard DeviationPosterior365-559213.90Likelihood343-491911.89Birth rate (ʎ)Late Cretaceous9790.3710.106Early Cretaceous39500.3160.155Late Jurassic13180.3440.117Middle Jurassic38230.3140.155Early Jurassic38320.3150.158Late Triassic330.2880.087Death rate (µ)Late Cretaceous9950.3590.109Early Cretaceous40010.3050.154Late Jurassic30850.2540.121Middle Jurassic38090.3060.156Early Jurassic40010.3070.154Late Triassic320.2780.090Sampling rate (ψ)Late Cretaceous38361.22E-31.45E-3Early Cretaceous33232.52E-32.08E-3Late Jurassic40018.45E-49.15E-4Middle Jurassic2916.61E-33.99E-3Early Jurassic997.59E-32.90E-3Late Triassic1381.03E-23.72E-3Sampling proportion (ρ)-0.1-
description: Published as part of Poole, Karen E., 2016, Phylogeny of iguanodontian dinosaurs and the evolution of quadrupedality, pp. 15 in Palaeontologia Electronica (a 30) 25 (3) on page 15, DOI: 10.26879/702, http://zenodo.org/record/11063310
DOI: 10.15468/dl.k9e3et
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pinus gerardiana Wall. ex D.Don"
 ]
}

 The dataset includes 51 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005841-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15138640
title: Table 2 in Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India

description: Table 2. Pairwise uncorrected ND2 sequence divergence between members of the Cyrtodactylus khasiensis group.
Species12345678910111213141516171819202122232425OK247679 Cyrtodactylus exercitus sp. nov.OK247680 Cyrtodactylus exercitus sp. nov.0.00KM255194 Cyrtodactylus guwahatiensis4.254.21KM255183 Cyrtodactylus tripuraensis8.3310.312.9MH971164 Cyrtodactylus septentrionalis11.811.911.311.5MW596515 Cyrtodactylus agarwali12.412.5012.513.211.1MW367435 Cyrtodactylus bapme12.812.9013.612.311.67.24MW596514 Cyrtodactylus karsticola14.014.113.113.110.76.46.73MN911174 Cyrtodactylus urbanus15.815.815.211.513.415.615.114.4JX440526 Cyrtodactylus ayeyarwadyensis15.315.415.47.4914.614.814.212.713.7OK247677 Cyrtodactylus siahaensis sp. nov.17.918.021.118.419.222.720.823.320.019.9OK247678 Cyrtodactylus siahaensis sp. nov.18.318.421.318.619.522.920.823.520.320.20.39KM255188 Cyrtodactylus khasiensis18.918.716.912.515.717.216.915.314.215.619.519.8MT341522 Cyrtodactylus arunachalensis19.519.520.318.420.620.620.819.120.820.219.919.921.0MH764589 Cyrtodactylus aunglini20.120.121.618.820.820.820.719.321.320.118.418.421.424.2MW596516 Cyrtodactylus bengkhuaiai20.620.62218.420.423.521.823.821.419.88.338.3319.420.818.1MH764604 Cyrtodactylus chrysopylos20.720.722.919.621.721.421.921.121.520.720.920.922.123.121.419.1KM255170 Cyrtodactylus kazirangaensis20.920.919.714.518.619.919.617.117.316.722.322.318.421.522.422.823.6KM255200 Cyrtodactylus montanus21.321.221.918.219.622.920.522.021.820.010.911.221.722.619.612.021.523.2KM255192 Cyrtodactylus sp. Changlang 221.321.219.715.419.120.520.919.519.717.618.418.419.719.120.119.221.220.619.8MW596520 Cyrtodactylus aaronbaueri21.421.422.621.821.224.322.922.923.821.911.311.422.723.419.611.921.724.710.320.3MN059875 Cyrtodactylus mombergi21.621.621.817.120.620.419.919.620.519.319.419.420.320.420.220.322.320.919.67.5120.0JX440537 Cyrtodactylus gansi22.622.621.116.719.821.020.921.220.018.818.818.820.022.018.318.119.821.719.617.319.218.2KM255195 Cyrtodactylus jaintiaensis23.323.322.418.621.423.122.521.821.821.817.817.821.522.919.117.222.022.918.819.319.219.719.0KM255199 Cyrtodactylus nagalandensis25.525.523.619.023.624.823.821.822.921.717.517.522.022.319.819.122.723.019.618.921.520.618.019.0JX440559 Cyrtodactylus slowinskii28.528.426.621.725.525.625.523.625.125.324.024.025.524.026.624.127.025.226.224.027.824.925.125.227.2
description: Published as part of Purkayastha, Jayaditya, Lalremsanga, Hmar Tlawmte, Litho, Beirathie, Rathee, Yashpal Singh, Bohra, Sanath Chandra, Mathipi, Vabeiryureilai, Biakzuala, Lal & Muansanga, Lal, 2022, Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India, pp. 120 in European Journal of Taxonomy 794 on page 120, DOI: 10.5852/ejt.2022.794.1659, http://zenodo.org/record/6204929
DOI: 10.5281/zenodo.15138641
title: Table 2 in Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India

description: Table 2. Pairwise uncorrected ND2 sequence divergence between members of the Cyrtodactylus khasiensis group.
Species12345678910111213141516171819202122232425OK247679 Cyrtodactylus exercitus sp. nov.OK247680 Cyrtodactylus exercitus sp. nov.0.00KM255194 Cyrtodactylus guwahatiensis4.254.21KM255183 Cyrtodactylus tripuraensis8.3310.312.9MH971164 Cyrtodactylus septentrionalis11.811.911.311.5MW596515 Cyrtodactylus agarwali12.412.5012.513.211.1MW367435 Cyrtodactylus bapme12.812.9013.612.311.67.24MW596514 Cyrtodactylus karsticola14.014.113.113.110.76.46.73MN911174 Cyrtodactylus urbanus15.815.815.211.513.415.615.114.4JX440526 Cyrtodactylus ayeyarwadyensis15.315.415.47.4914.614.814.212.713.7OK247677 Cyrtodactylus siahaensis sp. nov.17.918.021.118.419.222.720.823.320.019.9OK247678 Cyrtodactylus siahaensis sp. nov.18.318.421.318.619.522.920.823.520.320.20.39KM255188 Cyrtodactylus khasiensis18.918.716.912.515.717.216.915.314.215.619.519.8MT341522 Cyrtodactylus arunachalensis19.519.520.318.420.620.620.819.120.820.219.919.921.0MH764589 Cyrtodactylus aunglini20.120.121.618.820.820.820.719.321.320.118.418.421.424.2MW596516 Cyrtodactylus bengkhuaiai20.620.62218.420.423.521.823.821.419.88.338.3319.420.818.1MH764604 Cyrtodactylus chrysopylos20.720.722.919.621.721.421.921.121.520.720.920.922.123.121.419.1KM255170 Cyrtodactylus kazirangaensis20.920.919.714.518.619.919.617.117.316.722.322.318.421.522.422.823.6KM255200 Cyrtodactylus montanus21.321.221.918.219.622.920.522.021.820.010.911.221.722.619.612.021.523.2KM255192 Cyrtodactylus sp. Changlang 221.321.219.715.419.120.520.919.519.717.618.418.419.719.120.119.221.220.619.8MW596520 Cyrtodactylus aaronbaueri21.421.422.621.821.224.322.922.923.821.911.311.422.723.419.611.921.724.710.320.3MN059875 Cyrtodactylus mombergi21.621.621.817.120.620.419.919.620.519.319.419.420.320.420.220.322.320.919.67.5120.0JX440537 Cyrtodactylus gansi22.622.621.116.719.821.020.921.220.018.818.818.820.022.018.318.119.821.719.617.319.218.2KM255195 Cyrtodactylus jaintiaensis23.323.322.418.621.423.122.521.821.821.817.817.821.522.919.117.222.022.918.819.319.219.719.0KM255199 Cyrtodactylus nagalandensis25.525.523.619.023.624.823.821.822.921.717.517.522.022.319.819.122.723.019.618.921.520.618.019.0JX440559 Cyrtodactylus slowinskii28.528.426.621.725.525.625.523.625.125.324.024.025.524.026.624.127.025.226.224.027.824.925.125.227.2
description: Published as part of Purkayastha, Jayaditya, Lalremsanga, Hmar Tlawmte, Litho, Beirathie, Rathee, Yashpal Singh, Bohra, Sanath Chandra, Mathipi, Vabeiryureilai, Biakzuala, Lal & Muansanga, Lal, 2022, Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India, pp. 120 in European Journal of Taxonomy 794 on page 120, DOI: 10.5852/ejt.2022.794.1659, http://zenodo.org/record/6204929
DOI: 10.5281/zenodo.15138642
title: Table 3 in Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India

description: Table 3. (continued on the next page). Morphometric and meristic data for Cyrtodactylus exercitus sp. nov. and C. siahaensis sp. nov. (all in millimeters).
SpeciesCyrtodactylus exercitus sp. nov.Cyrtodactylus siahaensis sp. nov.Voucher no.MZMU2542 (holotype)MZMU2543MZMU2544MZMU2545MZMU2445 (holotype)MZMU2443MZMU2444MZMU2446MZMU2448MZMU2449LocalityUmroi, MeghalayaUmroi, MeghalayaUmroi, MeghalayaUmroi, MeghalayaSiaha, MizoramSiaha, MizoramSiaha, MizoramSiaha, MizoramSiaha, MizoramSiaha, MizoramSexMFMMMFFMFFSVL6866.7848.1661.857.3863.967261.1442.6243.24TRL363127.527.522932.9233.9302222.3BW9.59.886.787.848.1811.4411.59.75.45.5TL81.12-5868.6465.469.7272.58-52.852TW5.424.53.84.644.024.345.4853.363.36HL19.419.6214.2418.1418.3619.3821.0219.221919.1HW12.2812.128.9810.8410.1810.5412.410.327.487.52HD7.266.525.066.286.246.37.56.244.824.78FL10.5810.28.29.89.69.8210.649.27.87.98CL12.2412.39.2811.4210.9411.1612.4118.68.58OD4.244.223.34.2843.84.23.8233.1NO4.524.633.944.445.24.52.72.72SO7.5265.186.425.086.827.86.924.824.86OE4.084.542.63.343.324.044.44.122.242.28EL2.2421.5621.581.71.941.51.421.4IN1.721.22.061.561.481.941.540.80.82IO332.12.642.322.562.922.541.721.72F14.8253.944.64.44.1254.423.723.76F26.26.24.926.55.4255.45.664.244.28F36.646.65.66.85.665.365.75.724.324.36F477.86.527.86.86.47.266.084.464.5F56.747.65.526.385.95.45.865.84.364.4T16.065.84.65.44.824.844.944.964.24.4T27.46.95.747.346.25.226.2464.364.38T38.128.16.686.96.327.66.955T48.48.487.18.787.447.88.167.65.725.8T588.536.38.276.747.86.65.165.24RL221.22.11.521.71.761.641.181.2RW2.242.21.82.241.68221.921.241.26PcP/ PcFP15-12117--7--MVSR37373535343635373437DTR22212224232422222224PVT32343234363838363939SL(L/R)10.1110.911.1110.1010. 910.1010.1110.1010.1010.11IL(L/R)9.1010.109.99.99.98.1011.1010.910.1010.9F1L4(2)64(1)64(1)64(1)84(1)63(1)65(3)54(1)64(1)64(1)7F2L5(1)86(2)64(0)84(2)85(1)75(1)76(1)85(2)75(1)74(1)8F3L5(1)106(0)105(0)95(3)85(1)85(2)85(2)95(2)95(1)84(0)10F4L6(1)106(1)95(0)105(2)95(1)85(1)86(2)85(1)86(2)85(0)8F5L4(3)85(0)83(1)86(3)73(1)74(1)84(0)85(1)85(1)74(1)9T1L5(0)74(1)84(1)74(3)73(1)73(1)73(2)73(2)65(2)64(2)7T2L5(1)96(0)95(1)96(1)94(1)85(1)95(1)85(1)86(1)85(1)9T3L6(1)106(1)106(1)105(4)95(0)116(3)85(1)106(0)106(1)85(1)9T4L6(3)107(1)107(1)107(1)107(0)117(3)86(2)97(1)117(1)117(1)10T5L5(1)96(1)104(1)86(2)115(2)96(2)87(3)95(1)106(2)95(1)10Dorsal spots9101199898910
description: Published as part of Purkayastha, Jayaditya, Lalremsanga, Hmar Tlawmte, Litho, Beirathie, Rathee, Yashpal Singh, Bohra, Sanath Chandra, Mathipi, Vabeiryureilai, Biakzuala, Lal & Muansanga, Lal, 2022, Two new Cyrtodactylus (Squamata, Gekkonidae) from Northeast India, pp. 123-124 in European Journal of Taxonomy 794 on page 123, DOI: 10.5852/ejt.2022.794.1659, http://zenodo.org/record/6204929