cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1018895.10fu
title: SH1018895.10FU

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/sh1018896.10fu
title: SH1018896.10FU

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/sh1018897.10fu
title: SH1018897.10FU

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/sh1018898.10fu
title: SH1018898.10FU

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/sh1018899.10fu
title: SH1018899.10FU

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/sh1018900.10fu
title: SH1018900.10FU

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/sh1018901.10fu
title: SH1018901.10FU

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/sh1018902.10fu
title: SH1018902.10FU

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/sh1018903.10fu
title: SH1018903.10FU

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: 10.15468/dl.n9zsbt
title: Occurrence Download

description: A dataset containing 19859889 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Hymenoptera"
 ]
}

 The dataset includes 19859889 records from 6017 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0116260-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.17067647.v4
title: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study

description: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study"
DOI: 10.6084/m9.figshare.17067647.v5
title: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study

description: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study"
DOI: 10.4225/35/5922194b0d451
title: WALK 2.0 Baseline activity and Quality of Life
DOI: 10.6084/m9.figshare.19334870.v1
title: A novel intragenic marker targeting the ectodomain of bacterial blight-resistance gene <i>Xa21</i> for marker-assisted selection in rice

description: The rice (<i>Oryza sativa</i> L.) <i>Xa21</i> gene is known to confer resistance against <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, the causal organism of bacterial blight (BB) through its involvement in pathogen recognition and immunity responses. The closely linked sequence-tagged site marker <i>pTA248</i> is frequently used for marker-assisted selection (MAS) of <i>Xa21</i>. However, lower precision and linkage drag of linked-markers hinders its reliability and effectiveness in MAS compared to flanking/intergenic markers. In the current study, a diagnostic intragenic marker was developed for MAS of <i>Xa21</i> in rice. The new marker <i>ABUOP0001</i> targets a 19-bp insertion/deletion on the ectodomain of <i>Xa21</i> and amplifies a 200-bp amplicon from rice line IRBB 62 (known to carry the “resistance allele”), and a 181-bp amplicon from IRBB 7 (known to carry the “susceptible allele”). The <i>ABUOP0001</i> marker assay identified the resistance allele in 15 rice lines/accessions. In a field study, 14 of these lines/accessions had highly resistant BB responses, and one had an intermediate response. Further, <i>Xa21</i> resistance allele was identified in 1,675 rice accessions through an <i>in-silico</i> genomic sequence analysis. The marker <i>ABUOP0001</i> performs better than the previously used linked-marker <i>pTA248</i> (anchored to a gene 225 kbp upstream of <i>Xa21</i>) as <i>ABUOP0001</i> is an intragenic marker targeting the functionally important ectodomain of <i>Xa21</i>. Further, <i>ABUOP0001</i> is compatible for high-throughput screening with high-resolution melting and can be multiplexed effectively with an intragenic marker of BB resistance gene <i>Xa4</i>. Therefore, <i>ABUOP0001</i> can be recommended as a high-throughput diagnostic intragenic marker for MAS of <i>Xa21</i> in rice.
DOI: 10.6084/m9.figshare.19334870
title: A novel intragenic marker targeting the ectodomain of bacterial blight-resistance gene <i>Xa21</i> for marker-assisted selection in rice

description: The rice (<i>Oryza sativa</i> L.) <i>Xa21</i> gene is known to confer resistance against <i>Xanthomonas oryzae</i> pv. <i>oryzae</i>, the causal organism of bacterial blight (BB) through its involvement in pathogen recognition and immunity responses. The closely linked sequence-tagged site marker <i>pTA248</i> is frequently used for marker-assisted selection (MAS) of <i>Xa21</i>. However, lower precision and linkage drag of linked-markers hinders its reliability and effectiveness in MAS compared to flanking/intergenic markers. In the current study, a diagnostic intragenic marker was developed for MAS of <i>Xa21</i> in rice. The new marker <i>ABUOP0001</i> targets a 19-bp insertion/deletion on the ectodomain of <i>Xa21</i> and amplifies a 200-bp amplicon from rice line IRBB 62 (known to carry the “resistance allele”), and a 181-bp amplicon from IRBB 7 (known to carry the “susceptible allele”). The <i>ABUOP0001</i> marker assay identified the resistance allele in 15 rice lines/accessions. In a field study, 14 of these lines/accessions had highly resistant BB responses, and one had an intermediate response. Further, <i>Xa21</i> resistance allele was identified in 1,675 rice accessions through an <i>in-silico</i> genomic sequence analysis. The marker <i>ABUOP0001</i> performs better than the previously used linked-marker <i>pTA248</i> (anchored to a gene 225 kbp upstream of <i>Xa21</i>) as <i>ABUOP0001</i> is an intragenic marker targeting the functionally important ectodomain of <i>Xa21</i>. Further, <i>ABUOP0001</i> is compatible for high-throughput screening with high-resolution melting and can be multiplexed effectively with an intragenic marker of BB resistance gene <i>Xa4</i>. Therefore, <i>ABUOP0001</i> can be recommended as a high-throughput diagnostic intragenic marker for MAS of <i>Xa21</i> in rice.
DOI: 10.6084/m9.figshare.17067647.v6
title: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study

description: Analysis of inferior nasal turbinate volume in subjects with nasal septum deviation: a cone beam tomography study"
DOI: 10.5281/zenodo.6342558
title: Test-Data for RNA-seq HSA Chr21 all protein coding transcripts

description: Created with ART ART_Illumina (2008-2016) Q Version 2.5.8 (June 6, 2016) <pre><code>art_illumina -ss MSv3 -i chr21_proteincoding.fa -p -l 75 -f 175 -m 200 -s 10 --minQ 30 </code></pre> high:= higher expression low:= lower expression repA/B:=indicates replicates with slightly different sequencing depths R1/2:= forward/reverse read
DOI: 10.5281/zenodo.6342557
title: Test-Data for RNA-seq HSA Chr21 all protein coding transcripts

description: Created with ART ART_Illumina (2008-2016) Q Version 2.5.8 (June 6, 2016) <pre><code>art_illumina -ss MSv3 -i chr21_proteincoding.fa -p -l 75 -f 175 -m 200 -s 10 --minQ 30 </code></pre> high:= higher expression low:= lower expression repA/B:=indicates replicates with slightly different sequencing depths R1/2:= forward/reverse read
DOI: 10.5281/zenodo.6342553
title: Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE

description: This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset. This dataset has several signatures: It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling. The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect). This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial). The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine. In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features. Column names: Trial ID: 'trial_id'<br> Experiment group label: 'treatment_group_key'<br> Outcome variable (classification label): 'conversion'<br> Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']<br> True underlying control conversion probability: 'control_conversion_prob'<br> True underlying treatment conversion probability: 'treatment1_conversion_prob'<br> True treatment effect: 'treatment1_true_effect'
description: {"references": ["https://arxiv.org/abs/2005.03447"]}
DOI: 10.5281/zenodo.6342552
title: Synthetic Data for Uplift Modeling and Heterogenous Treatment Effect with Known Counterfactuals and ITE

description: This dataset is designed and simulated for evaluating uplift modeling. The data generation process is based on a logistic regression model - no real data is included or used for generating this dataset. This dataset has several signatures: It generates features with various patterns associated with the outcome variable and the causal effect (or treatment effect). Thus it is suitable for evaluating feature importance and model interpretation for uplift modeling. The true counterfactual outcomes under control and treatment are known for each user, as well as the true ITE (Individual treatment effect). This dataset consists of 50 trials (replicates with different random seeds), each trial with 20,000 samples and 36 features. The outcome variable is binary, which makes this dataset for classification problems. The samples are equally split for the control and treatment groups (10,000 samples in each group in each trial). The generated data has three types of features: (1) uplift features influencing the treatment effect on the conversion probability; (2) classification features affecting the conversion probability but independent of the treatment effect; and (3) irrelevant features that are independent of both conversion probability and the treatment effect. To simulate the relationship between uplift features and the treatment effect and classification features and outcome probability, we implement six types of association patterns in the data generation process: linear, quadratic, cubic, ReLU (Rectified Linear Unit), trigonometric function sine, and cosine. In this data set, there are 36 features in total, including 10 classification features, 6 uplift features, and 20 irrelevant features. Column names: Trial ID: 'trial_id'<br> Experiment group label: 'treatment_group_key'<br> Outcome variable (classification label): 'conversion'<br> Feature names: ['x1_informative',<br> 'x2_informative',<br> 'x3_informative',<br> 'x4_informative',<br> 'x5_informative',<br> 'x6_informative',<br> 'x7_informative',<br> 'x8_informative',<br> 'x9_informative',<br> 'x10_informative',<br> 'x11_irrelevant',<br> 'x12_irrelevant',<br> 'x13_irrelevant',<br> 'x14_irrelevant',<br> 'x15_irrelevant',<br> 'x16_irrelevant',<br> 'x17_irrelevant',<br> 'x18_irrelevant',<br> 'x19_irrelevant',<br> 'x20_irrelevant',<br> 'x21_irrelevant',<br> 'x22_irrelevant',<br> 'x23_irrelevant',<br> 'x24_irrelevant',<br> 'x25_irrelevant',<br> 'x26_irrelevant',<br> 'x27_irrelevant',<br> 'x28_irrelevant',<br> 'x29_irrelevant',<br> 'x30_irrelevant',<br> 'x31_uplift_increase',<br> 'x32_uplift_increase',<br> 'x33_uplift_increase',<br> 'x34_uplift_increase',<br> 'x35_uplift_increase',<br> 'x36_uplift_increase']<br> True underlying control conversion probability: 'control_conversion_prob'<br> True underlying treatment conversion probability: 'treatment1_conversion_prob'<br> True treatment effect: 'treatment1_true_effect'
description: {"references": ["https://arxiv.org/abs/2005.03447"]}
DOI: 10.5517/ccdc.csd.cc293q38
title: CCDC 2118729: Experimental Crystal Structure Determination

description: Related Article: Yue Zhao, Congjun Yu, Wenjing Liang, Iuliana L. Atodiresei, Frederic W. Patureau|2022|Chem.Commun.|58|2846|doi:10.1039/D1CC07057F
DOI: 10.5517/ccdc.csd.cc298flm
title: CCDC 2123270: Experimental Crystal Structure Determination

description: Related Article: Dong-Dong Yang, Han-Wen Zheng, Qiong-Fang Liang, Xiang-Jun Zheng|2022|Dyes Pigm.|200|110188|doi:10.1016/j.dyepig.2022.110188
DOI: 10.5517/ccdc.csd.cc299y9w
title: CCDC 2124718: Experimental Crystal Structure Determination

description: Related Article: Zhaoyang Yin, Wei Huang, Zhiwei Zeng, Yuji Liu, Yongxing Tang|2022|Cryst.Growth Des.|22|1867|doi:10.1021/acs.cgd.1c01411
DOI: 10.5517/ccdc.csd.cc299ybx
title: CCDC 2124719: Experimental Crystal Structure Determination

description: Related Article: Zhaoyang Yin, Wei Huang, Zhiwei Zeng, Yuji Liu, Yongxing Tang|2022|Cryst.Growth Des.|22|1867|doi:10.1021/acs.cgd.1c01411
DOI: 10.5517/ccdc.csd.cc29b5tn
title: CCDC 2124951: Experimental Crystal Structure Determination

description: Related Article: Yue Yang, Jiaxiang Guo, Kaiqi Ye, Chuandong Dou|2022|Mater. Chem. Front.|6|600|doi:10.1039/D1QM01601F