cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh1135737.10fu
title: SH1135737.10FU

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/sh1135740.10fu
title: SH1135740.10FU

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/sh1135744.10fu
title: SH1135744.10FU

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/sh1135745.10fu
title: SH1135745.10FU

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/sh1135750.10fu
title: SH1135750.10FU

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/sh1135751.10fu
title: SH1135751.10FU

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/sh1135753.10fu
title: SH1135753.10FU

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/sh1135756.10fu
title: SH1135756.10FU

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/sh1135757.10fu
title: SH1135757.10FU

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.a5xpay
title: Occurrence Download

description: A dataset containing 2280369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CAN.5_1",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 2280369 records from 635 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002655-260108223611665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/q5nx0d2a
title: Extracting Blockchain Concepts from Text

description: The dataset is used to extract information from whitepapers and academic articles focused on the blockchain area to organize this information and aid users to navigate the space.
DOI: 10.57702/099wgxvm
title: PADT, EWT, GSD, HDT, and SynTagRus

description: PADT, EWT, GSD, HDT, and SynTagRus are UD treebanks.
DOI: 10.57702/n9q461ao
title: Hybrid BLE-UWB Localization System

description: The dataset used in the paper is a hybrid BLE-UWB localization system, where the users are primarily localized based on the RSS measured for BLE signals. The UWB part of the system is used incidentally.
DOI: 10.57702/dpm5rldd
title: Statlog (Australian Credit Approval)

description: credit card applications with a good mix of attributes: continuous, small and large numbers of nominal values plus some missing values.
DOI: 10.57702/k71g7ais
title: Statlog (German Credit Data)

description: credit data provided by Prof. Hofmann and the numerical dataset provided by Strathclyde University.
DOI: 10.57702/e4284sio
title: WIMCOR: A Large Harvested Corpus of Location Metonymy

description: WIMCOR is a large and rich dataset of location metonymy, extracted using Wikipedia. It is suitable for metonymy detection and entity linking tasks.
DOI: 10.57702/7tf5w1pa
title: Schema-Guided Dataset (SGD)

description: Schema-Guided Dataset (SGD) is the official dataset for the schema-guided state tracking challenge at DSTC8. The schema-guided dataset consists of 20 domains with a total of 45 services among those domains. The dataset consists of both single and multi-domain dialogues.
DOI: 10.57702/3onkc01v
title: Domain-to-Domain Translation Model for Recommender System

description: Propose a multi-domain recommender system that can extract both homogeneous and divergent features among domains.
DOI: 10.57702/hf33i4z6
title: CuratorNet: Visually-aware Recommendation of Art Images

description: A neural network architecture for visually-aware recommendation of art images.
DOI: 10.6084/m9.figshare.c.3611879_d16.v1
title: Additional file 16 of The Local Edge Machine: inference of dynamic models of gene regulation

description: Table: Compilation of literature that supports the designation of circadian core nodes. The first column gives the names of the genes that were considered to be circadian core genes in our analysis. The second column provides any alternative gene names, and the third column lists literature references that support the designation of the corresponding gene as being part of the circadian core. (XLSX 54 kb)
DOI: 10.57702/t2rk8n7s
title: Art Dataset

description: A dataset of user transactions over physical paintings.
DOI: 10.6084/m9.figshare.c.3611879_d14.v1
title: Additional file 2 of The Local Edge Machine: inference of dynamic models of gene regulation

description: Table: Biological prior information about gene function used for LEM inference on yeast cell-cycle networks 1â  5. LEM can incorporate biological priors to remove any impossible edges (e.g., a TF known to be an activator cannot repress a given target), which improves inference on real data (see Additional files 5 and 13). Here we provide literature evidence for TFs that function as an activator of target gene transcription, repressor, or both. Genes marked N/A do not function as TFs. Note 1. CLN3 is included as a regulator in networks 1, 2, and 5 because it is known to inhibit Whi5 at cell-cycle START [2]. The other cyclins, CLN2 and CLB2, are not included as regulators because their effects on TFs are redundant with other edges included in the networks (instead, CLN2 and CLB2 are used to represent canonical targets of TFs in the networks). Note 2. PLM2 is lacking specific literature evidence for its role as a TF activator. However, PLM2 is a paralog of TOS4 from the S. cerevisiae whole-genome duplication, and we propose that the activator function is shared between the paralogs. (XLSX 43 kb)
DOI: 10.6084/m9.figshare.c.3611879_d11.v1
title: Additional file 7 of The Local Edge Machine: inference of dynamic models of gene regulation

description: Table: Saccharomyces cerevisiae gene expression dynamics used in this study. The wild-type gene expression data of 28 genes over approximately two cell cycles were obtained from previous work [2]. Expression values from each profile were smoothed from 13 experimental time points to 40 time points by fitting a cubic spline (the function splinefun in R package stats). A Rescon Ltd. tool was used to rank the splined profiles by 50 % of the peak expression value. Genes are ordered from earliest (top) to latest (bottom) half-maximal expression and presented in the heat map in Fig. 1. (XLSX 51 kb)
DOI: 10.57702/rkco133v
title: Simulators for Small-Scale Autonomous Cars

description: A survey of simulators for small-scale autonomous cars
DOI: 10.6084/m9.figshare.c.3611879_d9.v1
title: Additional file 3 of The Local Edge Machine: inference of dynamic models of gene regulation

description: Table: Evidence for regulatory interactions in yeast cell-cycle networks 1â  5. Each row corresponds to a regulatory interaction (edge), where an upstream regulator acts on a target gene. p values from four high-throughput chromatin immunoprecipitation (ChIP) studies are shown to provide evidence (when available) for a regulator transcription factor (TF) binding to a target promoter [11, 50, 51]. ChIP p values were combined using Fisherâ  s method [52]. Combined p values less than 0.001 were considered high-confidence evidence for a given edge (shown in bold red). Where available, edges are supported by additional literature references (see Additional file 8). In the absence of ChIP data, literature evidence was used to determine edges. Evidence for many edges provided here is also documented in the YEASTRACT database [42]. The direction of each interaction (activation, repression, or N/A unknown) is derived from the YEASTRACT database, literature evidence, and/or biological priors about gene function (see Additional file 1). (XLSX 75 kb)
DOI: 10.57702/4bmztqs8
title: TruthfulQA

description: The TruthfulQA dataset is a dataset that contains 817 questions designed to evaluate language models' preference to mimic some human falsehoods.
DOI: 10.57702/r7sciz7v
title: RECCON Dataset

description: The RECCON dataset contains partial utterance-level causal indicators.
DOI: 10.57702/xdhzxafp
title: Sentiment in Twitter events

description: Sentiment in Twitter events.
DOI: 10.57702/x7zc7pax
title: Cost behaviors of binary classiﬁcation measures in class-imbalanced problems

description: This work investigates into cost behaviors of binary classiﬁcation measures in a background of class-imbalanced problems.
DOI: 10.15468/dl.7cznwr
title: Occurrence Download

description: A dataset containing 99374 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Mustela putorius Linnaeus, 1758"
 ]
}

 The dataset includes 99374 records from 936 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037160-241126133413365/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/44cci8xj
title: FASTINJECT: Injecting Unpaired Text Data into CTC-Based ASR Training

description: This paper proposes a flat-start joint training method, named FastInject, to inject unpaired text data into CTC-based ASR training.
DOI: 10.57702/h9qwaxti
title: GigaSpeech

description: GigaSpeech: An evolving, multi-domain ASR corpus with 10,000 hours of transcribed audio.
DOI: 10.6084/m9.figshare.c.3611564_d12.v1
title: Additional file 13: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Optimization dataset sample identification, protocol information, and RNA sequencing stats. As Additional file 1 for samples used in protocol optimization analyses. (XLS 37 kb)
DOI: 10.6084/m9.figshare.c.3611564_d11.v1
title: Additional file 11: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Measurement reliability at low sequencing depth. As Fig.Â 4kâ  m, but using low-depth in silico samples in place of individual 10Â pg. replicates. (A) aRNA. (B) SmartSeq Plus. (C) NuGen. (XLS 329 kb)
DOI: 10.6084/m9.figshare.c.3611564_d10.v1
title: Additional file 9: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Precision outliers. Genes with residuals within the upper or lower 1%ile with respect to regression of standard deviation on the mean (see Methods ). â  Gene setâ   indicates whether gene is classified as computationally unambiguous (1) or not (2). Only genes whose mean is within the range of fitted model were included. Column values indicate whether indicate whether the gene standard deviation is unexpectedly low (L) or high (H), given mean. (XLS 114 kb)
DOI: 10.6084/m9.figshare.c.3611564_d9.v1
title: Additional file 6: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Gene detection logistic regression fit and validation. Model was fit using randomly selected 90% of 10 pg. data, excluding 17 large influence genes. Fit was evaluated on the remaining 10% of the data. Fit was also evaluated on sequence data that was in silico truncated to 50 base pair single end (“Truncated”), ERCC read counts (“ERCC”), and 100 pg. dilution replicates (“100 pg.”). AUC (area under receiver operating characteristic curve) reported as mean values ± 2 Sd. calculated over 10,000 bootstrap samples. AUC (molecules) predicts detection based on number of input molecules alone. See Methods for further details. (XLS 29 kb)
DOI: 10.6084/m9.figshare.c.3611564_d8.v1
title: Additional file 10: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Accuracy outliers. Genes were identified as accuracy outliers if its median fold deviation, taken across dilution replicates, was contained in the upper or lower 1%ile of all considered genes (see Methods ). Columns labeled by single-cell protocol contain an “H” if a gene was identified as an overestimated outlier, and an “L” if a gene was identified as an underestimated outlier. “Gene set” indicates whether gene is classified as computationally unambiguous (1) or not (2). (XLS 218 kb)
DOI: 10.15468/dl.cvkj77
title: Occurrence Download

description: A dataset containing 191 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Skeletonemataceae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 191 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0037167-241126133413365/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.c.3611564_d7.v1
title: Additional file 5: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Gene detection logistic regression model. See model details in Methods . Abbreviations: M expected number of input molecules; L gene length (kilobases); G gene GC content; S strength of gene local secondary structure (kilocalories per mole); hasA presence of A-hexamer internal to gene body; D Depth (per 10,000,000 reads); S.E. standard error; Wald Z Wald test statistic; Pr(&gt;|Z|) Wald test p-value. (XLS 40 kb)
DOI: 10.57702/fm7q1qho
title: EuSQuAD

description: EuSQuAD: Automatically Translated and Aligned SQuAD2.0 for Basque
DOI: 10.6084/m9.figshare.c.3611564_d5.v1
title: Additional file 4: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Number of detected genes for low-depth in silico samples. As Fig.Â 2a, except that each dilution replicate has been subsampled to a depth of 500,000 unique genic reads. (XLS 74 kb)
DOI: 10.6084/m9.figshare.c.3611564_d4.v1
title: Additional file 1: of Assessing characteristics of RNA amplification methods for single cell RNA sequencing

description: Control dataset sample identification, protocol information, and RNA sequencing stats. Experimental group, protocol information and RNA sequencing statistics for each sample used in primary analyses. Alignment statistics were based on STAR alignment to hg19 and were with respect to reads retained after trimming for primer or poly-A sequences [21]. (XLS 62 kb)
DOI: 10.57702/nqa4j45c
title: Language Ancestry Analysis using 3-Spider

description: The dataset is used for language ancestry analysis using the 3-spider method.