cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1280083.10fu
title: SH1280083.10FU

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/sh1280086.10fu
title: SH1280086.10FU

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: 10.6084/m9.figshare.31049596
title: Optimal plug-in Gaussian processes for modeling derivatives

description: Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating the posterior distribution with GP priors for derivatives of any order. This practically appealing plug-in GP method has been previously perceived as suboptimal and degraded, but this is not necessarily the case. We provide posterior contraction rates for plug-in GPs and establish that they achieve optimal rates simultaneously for all derivative orders. We show that the posterior measure of the regression function and its derivatives, with the same choice of hyperparameter that does not depend on the order of derivatives, converges at the minimax optimal rate up to a logarithmic factor for functions in certain classes. We analyze a data-driven hyperparameter tuning method based on empirical Bayes, and show that it satisfies the optimal rate condition while maintaining computational efficiency. This article to the best of our knowledge provides the first positive result for plug-in GPs in the context of inferring derivative functionals, and leads to a practically simple nonparametric Bayesian method with optimal and adaptive hyperparameter tuning for simultaneously estimating the regression function and its derivatives. Simulations show competitive finite sample performance of the plug-in GP method. A climate change application for analyzing the global sea-level rise is discussed.
DOI: 10.6084/m9.figshare.31049596.v1
title: Optimal plug-in Gaussian processes for modeling derivatives

description: Derivatives are a key nonparametric functional in wide-ranging applications where the rate of change of an unknown function is of interest. In the Bayesian paradigm, Gaussian processes (GPs) are routinely used as a flexible prior for unknown functions, and are arguably one of the most popular tools in many areas. However, little is known about the optimal modeling strategy and theoretical properties when using GPs for derivatives. In this article, we study a plug-in strategy by differentiating the posterior distribution with GP priors for derivatives of any order. This practically appealing plug-in GP method has been previously perceived as suboptimal and degraded, but this is not necessarily the case. We provide posterior contraction rates for plug-in GPs and establish that they achieve optimal rates simultaneously for all derivative orders. We show that the posterior measure of the regression function and its derivatives, with the same choice of hyperparameter that does not depend on the order of derivatives, converges at the minimax optimal rate up to a logarithmic factor for functions in certain classes. We analyze a data-driven hyperparameter tuning method based on empirical Bayes, and show that it satisfies the optimal rate condition while maintaining computational efficiency. This article to the best of our knowledge provides the first positive result for plug-in GPs in the context of inferring derivative functionals, and leads to a practically simple nonparametric Bayesian method with optimal and adaptive hyperparameter tuning for simultaneously estimating the regression function and its derivatives. Simulations show competitive finite sample performance of the plug-in GP method. A climate change application for analyzing the global sea-level rise is discussed.
DOI: 10.15156/bio/sh1280090.10fu
title: SH1280090.10FU

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/sh1280091.10fu
title: SH1280091.10FU

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/sh1280092.10fu
title: SH1280092.10FU

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.p2wtan
title: Occurrence Download

description: A dataset containing 190600 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Fusigobius duospilus, Fusigobius maximus, Fusigobius neophytus, Fusigobius pallidus, Corcyrogobius liechtensteini, Corcyrogobius lubbocki, Evermannia longipinnis, Evermannia zosterura, Evermannia erici, Evermannia panamensis, Gymneleotris seminuda, Siphonogobius nue, Eleotrica cableae, Porogobius schlegelii, Coryphopterus glaucofraenum, Coryphopterus urospilus, Coryphopterus tortugae, Coryphopterus kuna, Coryphopterus hyalinus, Coryphopterus punctipectophorus, Coryphopterus venezuelae, Coryphopterus alloides, Coryphopterus eidolon, Coryphopterus lipernes, Coryphopterus thrix, Coryphopterus personatus, Fusigobius humeralis, Fusigobius gracilis, Coryphopterus dicrus, Mangarinus waterousi, Grallenia arenicola, Grallenia lipi, Ebomegobius goodi, Nematogobius brachynemus, Nematogobius maindroni, Hetereleotris poecila, Hetereleotris tentaculata, Hetereleotris margaretae, Hetereleotris kenyae, Hetereleotris georgegilli, Hetereleotris apora, Hetereleotris readerae, Hetereleotris nebulofasciata, Hetereleotris zonata, Hetereleotris vulgaris, Hetereleotris vinsoni, Hetereleotris bipunctata, Hetereleotris caminata, Hetereleotris zanzibarensis, Hetereleotris diademata, Pseudapocryptes borneensis, Caspiosoma caspium, Brachyamblyopus brachysoma, Kelloggella cardinalis, Kelloggella tricuspidata, Kelloggella oligolepis, Kelloggella quindecimfasciata, Kelloggella disalvoi, Wheelerigobius wirtzi, Wheelerigobius maltzani, Pandaka trimaculata, Pandaka silvana, Pandaka rouxi, Pandaka pygmaea, Pandaka pusilla, Pandaka lidwilli, Oxyurichthys mindanensis, Oxyurichthys takagi, Oxyurichthys lemayi, Oxyurichthys microlepis, Oxyurichthys amabalis, Oxyurichthys ophthalmonema, Oxyurichthys tentacularis, Oxyurichthys auchenolepis, Oxyurichthys papuensis, Oxyurichthys lonchotus, Oxyurichthys saru, Oxyurichthys paulae, Oxyurichthys cornutus, Oxyurichthys heisei, Oxyurichthys viridis, Oxyurichthys guibei, Oxyurichthys notonema, Oxyurichthys formosanus, Oxyurichthys visayanus, Oxyurichthys petersenii, Oxyurichthys uronema, Oxyurichthys stigmalophius, Sufflogobius bibarbatus, Caragobius urolepis, Caragobius rubristriatus, Caragobius burmanicus, Parawaous megacephalus, Schismatogobius vanuatuensis, Schismatogobius deraniyagalai, Schismatogobius insignus, Schismatogobius ampluvinculus, Schismatogobius vitiensis, Schismatogobius pallidus, Schismatogobius roxasi)",
 "HasCoordinate is true",
 "LastInterpreted is greater than or equal to 2023-01-01",
 "LastInterpreted is less than or equal to 2026-01-01"
 ]
}

 The dataset includes 190600 records from 253 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004066-260108223611665/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.31049599
title: The issue of sparse networks in sports competitions: can Elo ratings efficiently compare football teams that never play a match?

description: This study assesses the accuracy of Elo-based rating models in sparse, fragmented networks of pairwise comparisons. Three different modifications to the standard Elo rating are introduced and combined to eight model variants which were tested on both real-world and artificially generated data from the domain of football. The first modification implements a noise-free input by replacing the match outcome with the matches betting odds. The second modification adapts the k-parameter to adhere for competition differences. The last introduces a league-consistent Elo adaptation. Forecasting accuracy, ranking quality, and comparison accuracy were assessed to compare model performance based on both a rich and a limited version of the dataset. Results show that Elo ratings are suitable for comparisons of teams that never meet in matches. Additionally, noise-free Elo continuously shows the best accuracy, while league-consistent Elo enhances accuracy when data availability is limited. The artificial dataset showed strong similarities to the real data, supporting its suitability for controlled experiments. Overall, the findings highlight the importance of the interplay of model choice and data availability, while demonstrating the general ability of Elo ratings to evaluate and compare teams, even in sparse network structures.
DOI: 10.6084/m9.figshare.31049599.v1
title: The issue of sparse networks in sports competitions: can Elo ratings efficiently compare football teams that never play a match?

description: This study assesses the accuracy of Elo-based rating models in sparse, fragmented networks of pairwise comparisons. Three different modifications to the standard Elo rating are introduced and combined to eight model variants which were tested on both real-world and artificially generated data from the domain of football. The first modification implements a noise-free input by replacing the match outcome with the matches betting odds. The second modification adapts the k-parameter to adhere for competition differences. The last introduces a league-consistent Elo adaptation. Forecasting accuracy, ranking quality, and comparison accuracy were assessed to compare model performance based on both a rich and a limited version of the dataset. Results show that Elo ratings are suitable for comparisons of teams that never meet in matches. Additionally, noise-free Elo continuously shows the best accuracy, while league-consistent Elo enhances accuracy when data availability is limited. The artificial dataset showed strong similarities to the real data, supporting its suitability for controlled experiments. Overall, the findings highlight the importance of the interplay of model choice and data availability, while demonstrating the general ability of Elo ratings to evaluate and compare teams, even in sparse network structures.
DOI: 10.15156/bio/sh1280094.10fu
title: SH1280094.10FU

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.9tggfz
title: Occurrence Download

description: A dataset containing 37156 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Trimma unisquamis, Gillichthys seta, Gillichthys mirabilis, Leucopsarion petersii, Sueviota larsonae, Sueviota lachneri, Sueviota atrinasa, Sueviota aprica, Eucyclogobius newberryi, Acanthogobius hasta, Synechogobius ommaturus, Lesueurigobius suerii, Lesueurigobius friesii, Lesueurigobius heterofasciatus, Lesueurigobius koumansi, Lesueurigobius sanzi, Gnatholepis argus, Gnatholepis gymnocara, Gnatholepis pascuensis, Gnatholepis anjerensis, Gnatholepis ophthalmotaenia, Gnatholepis knighti, Gnatholepis yoshinoi, Gnatholepis thompsoni, Vomerogobius flavus, Discordipinna griessingeri, Vanderhorstia attenuata, Vanderhorstia auronotata, Vanderhorstia kizakura, Vanderhorstia rapa, Vanderhorstia steelei, Vanderhorstia bella, Vanderhorstia macropteryx, Vanderhorstia auropunctata, Vanderhorstia longimanus, Vanderhorstia ambanoro, Vanderhorstia ornatissima, Vanderhorstia belloides, Vanderhorstia delagoae, Vanderhorstia opercularis, Vanderhorstia mertensi, Vanderhorstia nannai, Vanderhorstia nobilis, Vanderhorstia flavilineata, Vanderhorstia dorsomacula, Vanderhorstia hiramatsui, Vanderhorstia papilio, Cabillus macrophthalmus, Cabillus lacertops, Cabillus tongarevae, Cabillus caudimacula, Cabillus atripelvicus, Lebetus guilleti, Lebetus scorpioides, Amblyeleotris latifasciata, Amblyeleotris melanocephala, Amblyeleotris periophthalma, Amblyeleotris triguttata, Amblyeleotris delicatulus, Amblyeleotris diagonalis, Amblyeleotris callopareia, Amblyeleotris bleekeri, Amblyeleotris steinitzi, Amblyeleotris macronema, Amblyeleotris fontanesii, Amblyeleotris novaecaledoniae, Amblyeleotris japonica, Amblyeleotris yanoi, Amblyeleotris neglecta, Amblyeleotris gymnocephala, Amblyeleotris guttata, Amblyeleotris taipinensis, Amblyeleotris ellipse, Amblyeleotris ogasawarensis, Amblyeleotris stenotaeniata, Amblyeleotris neumanni, Amblyeleotris masuii, Amblyeleotris randalli, Amblyeleotris marquesas, Amblyeleotris harrisorum, Amblyeleotris fasciata, Amblyeleotris aurora, Amblyeleotris downingi, Amblyeleotris sungami, Amblyeleotris biguttata, Amblyeleotris morishitai, Amblyeleotris bellicauda, Amblyeleotris rhyax, Amblyeleotris wheeleri, Amblyeleotris arcupinna, Amblyeleotris rubrimarginata, Benthophilus baeri, Benthophilus macrocephalus, Benthophilus granulosus, Benthophilus leptocephalus, Benthophilus ragimovi, Benthophilus stellatus, Benthophilus kessleri, Benthophilus leptorhynchus, Benthophilus magistri)",
 "HasCoordinate is true",
 "LastInterpreted is greater than or equal to 2023-01-01",
 "LastInterpreted is less than or equal to 2026-01-01"
 ]
}

 The dataset includes 37156 records from 263 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004077-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1280095.10fu
title: SH1280095.10FU

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/sh1280097.10fu
title: SH1280097.10FU

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: 10.3886/e243376v1
title: College of Nursing Sciences, Malumfashi, Katsina State 2026/2027 Admission Form is Out Call ☎️(08039478772) DR MERCY ☎️(+23408039478772), Midwifery Form, Post basic nursing form ,Post basic midwifery Form, Transfer form

description: College of Nursing Sciences, Malumfashi, Katsina State 2026/2027 Admission Form is Out Call ☎️(08039478772) DR MERCY ☎️(+23408039478772), Midwifery Form, Post basic nursing form ,Post basic midwifery Form, Transfer form are all out Call on ☎️(08039478772) on how to purchase the form and register online, For Admission Process on how to be admitted and mode of payment of School Fee And Acceptances Fee Contact Office of the registrar on ☎️(+23408039478772) more Inquiry On AND ADMISSION ASSISTANCE CALL DR. MRS MERCY ON ☎️(08039478772)
DOI: 10.15156/bio/sh1280104.10fu
title: SH1280104.10FU

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.