cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57760/sciencedb.o00008.00039
title: Phenological data of four woody plants monitored at 10 sites in China from 1963 to 2020
title: 1963-2020年中国4种代表性木本植物10 个站点监测物候数据

description: The long-term ground observation phenological data of 4 woody plants at 10 stations of the Chinese Phenological Observation Network (CPON) are made into a key phenological data set through unified data quality control. Time range: 1963-2020;Site name: Beijing, Xi'an, Shenyang, Minqin, Harbin, Hohhot, Nanjing, Changsha, Hefei, Kunming;Species name: Fraxinus chinensis, Robinia pseudoacacia, Ulmus pumila, Syringa oblata;Phenophases (unit: day of year): first leaf date (FLD) and leaf coloring date (LCD).
description: The long-term ground observation phenological data of 4 woody plants at 10 stations of the Chinese Phenological Observation Network (CPON) are made into a key phenological data set through unified data quality control. Time range: 1963-2020;Site name: Beijing, Xi'an, Shenyang, Minqin, Harbin, Hohhot, Nanjing, Changsha, Hefei, Kunming;Species name: Fraxinus chinensis, Robinia pseudoacacia, Ulmus pumila, Syringa oblata;Phenophases (unit: day of year): first leaf date (FLD) and leaf coloring date (LCD).
DOI: 10.57760/sciencedb.o00026.00001
title: The effect of tranexamic acid and epsilon-aminocaproic acid on bleeding during hip and knee arthroplasty
title: 氨甲环酸和氨基己酸对髋关节和膝关节置换出血的影响

description: Bleeding analysis of hip and knee arthroplasty in the Third Affiliated Hospital of Chongqing Medical University from February 2019 to March 2021
description: Bleeding analysis of hip and knee arthroplasty in the Third Affiliated Hospital of Chongqing Medical University from February 2019 to March 2021
DOI: 10.57760/sciencedb.o00035.00004
title: Five years (2015-2019) of global eddy product from SEIA
title: Five years (2015-2019) of global eddy product from SEIA

description: Five years of global eddy product from 2015 to 2019 is generated from a scale-selective eddy identification algorithm (SEIA) based on closed sea level anomalies (SLAs) dataset from the French Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) project. SEIA features two improvements in the detection and tracking processes over earlier algorithms. First, based on the data resolution and eddy spatial scale, the scale-selective scheme replaces the previously used threshold for defining the eddy boundary and restricts the numbers of upper and lower grid points. The eddy boundary is as large as possible under such conditions, yet the eddy region is not overestimated. Furthermore, a novel and effective overlap scheme is used to track eddies by calculating the intersecting ratio of time-step-successive eddies. SEIA generates 278,630 anticyclonic eddies and 274,351 cyclonic eddies over five years for both the north and south hemispheres. SEIA runs on the MATLAB and outputs series of eddies as mat files with information of eddy existing time, boundary, radius and so on.
description: Five years of global eddy product from 2015 to 2019 is generated from a scale-selective eddy identification algorithm (SEIA) based on closed sea level anomalies (SLAs) dataset from the French Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO) project. SEIA features two improvements in the detection and tracking processes over earlier algorithms. First, based on the data resolution and eddy spatial scale, the scale-selective scheme replaces the previously used threshold for defining the eddy boundary and restricts the numbers of upper and lower grid points. The eddy boundary is as large as possible under such conditions, yet the eddy region is not overestimated. Furthermore, a novel and effective overlap scheme is used to track eddies by calculating the intersecting ratio of time-step-successive eddies. SEIA generates 278,630 anticyclonic eddies and 274,351 cyclonic eddies over five years for both the north and south hemispheres. SEIA runs on the MATLAB and outputs series of eddies as mat files with information of eddy existing time, boundary, radius and so on.
DOI: 10.57760/sciencedb.o00064.00001
title: The role of eye-specific attention in ocular dominance plasticity
title: The role of eye-specific attention in ocular dominance plasticity

description: It is well known how selective attention biases information processing in real time, but few work investigates its aftereffects. To examine perceptual aftereffect after prolonged attention to a monocular pathway, movie images played normally were presented to normal adult’s one eye (attended eye), while movie images of the same episode but played backwards were presented to the opposite eye (unattended eye). One hour of watching this dichoptic movie caused a shift of perceptual ocular dominance towards the unattended eye. Interestingly, the aftereffect positively correlated with the advantage of neural activity for the attended-eye over unattended-eye signals at the frontal electrodes measured with steady-state visual evoked potentials (SSVEP). Moreover, the aftereffect disappeared when interocular competition was minimized during adaptation. These results suggest that top-down eye-specific attention can induce ocular dominance plasticity through binocular rivalry mechanisms. The ocular-opponency-neuron model for binocular rivalry is then discussed for possible mechanisms underlying the present findings.
description: It is well known how selective attention biases information processing in real time, but few work investigates its aftereffects. To examine perceptual aftereffect after prolonged attention to a monocular pathway, movie images played normally were presented to normal adult’s one eye (attended eye), while movie images of the same episode but played backwards were presented to the opposite eye (unattended eye). One hour of watching this dichoptic movie caused a shift of perceptual ocular dominance towards the unattended eye. Interestingly, the aftereffect positively correlated with the advantage of neural activity for the attended-eye over unattended-eye signals at the frontal electrodes measured with steady-state visual evoked potentials (SSVEP). Moreover, the aftereffect disappeared when interocular competition was minimized during adaptation. These results suggest that top-down eye-specific attention can induce ocular dominance plasticity through binocular rivalry mechanisms. The ocular-opponency-neuron model for binocular rivalry is then discussed for possible mechanisms underlying the present findings.
DOI: 10.57760/sciencedb.o00114.00030
title: Application of neural network word slicing method in Mongolian-Chinese ma-chine translation
title: 神经网络词切分在蒙汉机器翻译中的应用

description: Mongolian word slicing is a major task in Mongolian information processing. The accuracy and reasonableness of the word slicing can alleviate the data sparsity problem and directly affect the subsequent work of Mongolian information processing. The paper firstly presents a pre-processing method based on neural network for Mongolian word slicing to address the stickiness of Mongolian and the rich variation of word morphology. The experimental results show that the accuracy of the neural network word slicing method reaches 99.47%. Secondly, the Transformer Mongolian-Chinese neural machine translation model based on Mongolian word segmentation is constructed. Finally, various Mongolian word -sorting methods on the Transformer translation model were compared. The experimental results show that the word slicing method of BiLSTM-CRF-CNN neural network with filtering of semantically missing concatenated letters and deactivated words achieves a BLEU value of 73.30% in machine translated translations, which can improve the quality of machine tr anslation to a certain extent.
description: Mongolian word slicing is a major task in Mongolian information processing. The accuracy and reasonableness of the word slicing can alleviate the data sparsity problem and directly affect the subsequent work of Mongolian information processing. The paper firstly presents a pre-processing method based on neural network for Mongolian word slicing to address the stickiness of Mongolian and the rich variation of word morphology. The experimental results show that the accuracy of the neural network word slicing method reaches 99.47%. Secondly, the Transformer Mongolian-Chinese neural machine translation model based on Mongolian word segmentation is constructed. Finally, various Mongolian word -sorting methods on the Transformer translation model were compared. The experimental results show that the word slicing method of BiLSTM-CRF-CNN neural network with filtering of semantically missing concatenated letters and deactivated words achieves a BLEU value of 73.30% in machine translated translations, which can improve the quality of machine tr anslation to a certain extent.
DOI: 10.57760/sciencedb.o00015.00006
title: Chuquiraga_jussieui_chloroplast_genome
title: Chuquiraga_jussieui_chloroplast_genome

description: This is the first chloroplast genome of Chuquiraga jussieui. It has also been released in NCBI GenBank database with the accession number of MZ292544.
description: This is the first chloroplast genome of Chuquiraga jussieui. It has also been released in NCBI GenBank database with the accession number of MZ292544.
DOI: 10.57760/sciencedb.o00114.00049
title: A Speech Synthesis Method based on End-to-End Model for Tibetan multi-speaker
title: 基于端到端模型的藏语多说话人语音合成方法

description: "Tibetan TTS generally focuses on a single speaker or a single dialect, and there is a lack of research on Tibetan speech synthesis technology with multi-speaker. This paper proposes a speech synthesis method, which can synthesize the Tibetan speech for different speakers. First, the Wiley transliteration program is used to convert Tibetan text into Latin letters. This step will effectively enhance the effect of model learning and reduce the required corpus data set and the workload of text data processing. The following step is to establish a sequence-to-sequence feature prediction network model, extract the Tibetan Latin letters’ feature vector through text preprocessing, map the vector to the mel spectrogram, and combine different WaveNet vocoders trained by different speakers to combine mel spectrogram synthesizes time-domain waveforms. The subjective and objective experimental results show that the speech synthesis effect obtained by
description: "Tibetan TTS generally focuses on a single speaker or a single dialect, and there is a lack of research on Tibetan speech synthesis technology with multi-speaker. This paper proposes a speech synthesis method, which can synthesize the Tibetan speech for different speakers. First, the Wiley transliteration program is used to convert Tibetan text into Latin letters. This step will effectively enhance the effect of model learning and reduce the required corpus data set and the workload of text data processing. The following step is to establish a sequence-to-sequence feature prediction network model, extract the Tibetan Latin letters’ feature vector through text preprocessing, map the vector to the mel spectrogram, and combine different WaveNet vocoders trained by different speakers to combine mel spectrogram synthesizes time-domain waveforms. The subjective and objective experimental results show that the speech synthesis effect obtained by
DOI: 10.57760/sciencedb.o00049.00001
title: Raw Data for the article Design, Synthesis, and Biological Evaluation of 1-(Indolizin-3-yl)ethan-1-ones as CBP Bromodomain Inhibitors for the Treatment of Prostate Cancer
title: Raw Data for the article Design, Synthesis, and Biological Evaluation of 1-(Indolizin-3-yl)ethan-1-ones as CBP Bromodomain Inhibitors for the Treatment of Prostate Cancer

description: This zip package includes all the raw data involving in the article "Design, Synthesis, and Biological Evaluation of 1-(Indolizin-3-yl)ethan-1-ones as CBP Bromodomain Inhibitors for the Treatment of Prostate Cancer"submitted to the journal of the Journal of Medicinal Chemistry.
description: This zip package includes all the raw data involving in the article "Design, Synthesis, and Biological Evaluation of 1-(Indolizin-3-yl)ethan-1-ones as CBP Bromodomain Inhibitors for the Treatment of Prostate Cancer"submitted to the journal of the Journal of Medicinal Chemistry.
DOI: 10.57760/sciencedb.o00049.00005
title: Raw Data for the article "Hepatic CPT1A Facilitates Liver–Adipose Cross Talk via Induction of FGF21 in Mice"
title: Raw Data for the article "Hepatic CPT1A Facilitates Liver–Adipose Cross Talk via Induction of FGF21 in Mice"

description: This zip package includes all the raw data involving in the article "Hepatic CPT1A Facilitates Liver–Adipose Cross Talk via Induction of FGF21 in Mice" submitted to the journal diabetes.
description: This zip package includes all the raw data involving in the article "Hepatic CPT1A Facilitates Liver–Adipose Cross Talk via Induction of FGF21 in Mice" submitted to the journal diabetes.
DOI: 10.57760/sciencedb.o00061.00006
title: The 16srRNA data of EcN C6 on gut microbes in the hyperuricemia rat model.
title: The 16srRNA data of EcN C6 on gut microbes in the hyperuricemia rat model.

description: Feces collected from hyperuricemia rats were frozen in -80 ℃ until use. DNA was extracted using the E.Z.N.A. Stool DNA Kit (OMEGA, USA) following the instructions. 16S rDNA (V4 region) was amplified by a two-step PCR enrichment using barcodes for multiplexing. The pooled DNA was cleaned utilizing Ampure XP beads (Beckman, USA). DNA libraries were constructed using NEBNext Ultra II FS DNA Library Prep kit (NEB, USA) and were sequenced by Illumina HiSeq X Ten platform.
description: Feces collected from hyperuricemia rats were frozen in -80 ℃ until use. DNA was extracted using the E.Z.N.A. Stool DNA Kit (OMEGA, USA) following the instructions. 16S rDNA (V4 region) was amplified by a two-step PCR enrichment using barcodes for multiplexing. The pooled DNA was cleaned utilizing Ampure XP beads (Beckman, USA). DNA libraries were constructed using NEBNext Ultra II FS DNA Library Prep kit (NEB, USA) and were sequenced by Illumina HiSeq X Ten platform.
DOI: 10.57760/sciencedb.o00063.00001
title: 抚仙湖不同位点、不同水层浮游细菌高通量测序数据
title: 抚仙湖不同位点、不同水层浮游细菌高通量测序数据

description: 以北部湖区S1为起点，在抚仙湖纵截面上每隔5 km设置1个采样点、共设置5个采样点（S1-S5，图1）。2013年8月25日在此5个采样点采集水样：在S1采样点分9层采样，分别在水面以下0.5 m、10 m、20 m、30 m、40 m、50 m、60 m、100 m、130 m各设置一采样层；其余4个采样点只在水面以下0.5 m处采样。使用过滤在孔径为0.2 µm的聚碳酸酯膜以收集水样细菌。利用前端引物515F（5’-GTGCCAGCMGCCGCGG-3’）和末端引物806R （5’-GGACTACHVGGGTWTCTAAT-3’）对所有样品基因组DNA进行聚合酶链式反应（PCR）扩增，并使用Illumina Miseq平台进行高通量测序。
description: 以北部湖区S1为起点，在抚仙湖纵截面上每隔5 km设置1个采样点、共设置5个采样点（S1-S5，图1）。2013年8月25日在此5个采样点采集水样：在S1采样点分9层采样，分别在水面以下0.5 m、10 m、20 m、30 m、40 m、50 m、60 m、100 m、130 m各设置一采样层；其余4个采样点只在水面以下0.5 m处采样。使用过滤在孔径为0.2 µm的聚碳酸酯膜以收集水样细菌。利用前端引物515F（5’-GTGCCAGCMGCCGCGG-3’）和末端引物806R （5’-GGACTACHVGGGTWTCTAAT-3’）对所有样品基因组DNA进行聚合酶链式反应（PCR）扩增，并使用Illumina Miseq平台进行高通量测序。
DOI: 10.57760/sciencedb.o00069.00004
title: FRB 190520B FAST Data set
title: FRB 190520B FAST Data set

description: This Data set include the data that referred to the FRB 190520B discovery Paper.
description: This Data set include the data that referred to the FRB 190520B discovery Paper.
DOI: 10.57760/sciencedb.o00114.00046
title: Data Augmentation for Low resource Neural Machine Translation Based on Se mantic Related Word Replacement and Grammatical Error Correction
title: 基于语义相关词替换和语法纠错的低资源语言神经机器翻译数据增强方法

description: This paper proposes a low-resource language neural machine translation data enhancement method based on semantically related word replacement and grammatical error correction. Firstly, the low-resource language is data-enhanced through the method of semantically related word replacement; secondly, the data-enhanced bilingual parallel corpus is grammatically corrected to make it conform to linguistic syntax and common sense reasoning. The results show that the method proposed in this paper not only guarantees the quantity of training corpus, but also improves the quality of training corpus, realizes effective data enhancement for low-resource languages, and further improves the effect of neural machine translation for low-resource languages.
description: This paper proposes a low-resource language neural machine translation data enhancement method based on semantically related word replacement and grammatical error correction. Firstly, the low-resource language is data-enhanced through the method of semantically related word replacement; secondly, the data-enhanced bilingual parallel corpus is grammatically corrected to make it conform to linguistic syntax and common sense reasoning. The results show that the method proposed in this paper not only guarantees the quantity of training corpus, but also improves the quality of training corpus, realizes effective data enhancement for low-resource languages, and further improves the effect of neural machine translation for low-resource languages.
DOI: 10.57760/sciencedb.o00114.00048
title: Construction of Kazakh Knowledge Graph in Tourism
title: 面向旅游领域的哈萨克语知识图谱构建

description: The construction of knowledge graph is to organize and store knowledge from the objective world to form aknowledge system. However, building a high-quality, large-scale knowledge graph is an important challenge for the low-resource Kazakh language. For the needs of the tourism field, this paper proposes a method for constructing Kazakh knowledge graphs based on named entity recognition and relation extraction. First, we use the deep learning method to obtain the semantic information of the Kazakh entity features, use Bi-LSTM to learn from different directions to obtain effective context information. Through multi-feature fusion, the word and word location features and entity tags are merged to obtain the association between the word and the target entity. Second, we use R-BERT to capture the semantic information of sentences and target entities to better adapt to relation classification tasks. Finally, we use the graph database neo4j to store the triple knowledge of the model layer, the ontology and the data layer to construct the Kazakh tourism knowledge graph。
description: The construction of knowledge graph is to organize and store knowledge from the objective world to form aknowledge system. However, building a high-quality, large-scale knowledge graph is an important challenge for the low-resource Kazakh language. For the needs of the tourism field, this paper proposes a method for constructing Kazakh knowledge graphs based on named entity recognition and relation extraction. First, we use the deep learning method to obtain the semantic information of the Kazakh entity features, use Bi-LSTM to learn from different directions to obtain effective context information. Through multi-feature fusion, the word and word location features and entity tags are merged to obtain the association between the word and the target entity. Second, we use R-BERT to capture the semantic information of sentences and target entities to better adapt to relation classification tasks. Finally, we use the graph database neo4j to store the triple knowledge of the model layer, the ontology and the data layer to construct the Kazakh tourism knowledge graph。
DOI: 10.57760/sciencedb.o00114.00051
title: A Review of the Research Status of Computational Pragmatics
title: 计算语用学的研究现状述评

description: Computational pragmatics is a branch of computational linguistics, which is based on pragmatics. This paper combs the research results of computational pragmatics from three aspects: discipline history, language theory research and the application of computational pragmatics, and understands the development trends of the discipline at home and abroad, in order to enlighten the in-depth research in this field.
description: Computational pragmatics is a branch of computational linguistics, which is based on pragmatics. This paper combs the research results of computational pragmatics from three aspects: discipline history, language theory research and the application of computational pragmatics, and understands the development trends of the discipline at home and abroad, in order to enlighten the in-depth research in this field.
DOI: 10.57760/sciencedb.o00114.00060
title: A Method of Tokenization Based on Neural Network Natural Language Processing Model
title: 基于神经网络的自然语言处理方法中子词切分方法综述

description: This report will review the Tokenization method in natural language processing based on neural networks. First, I will explain the out-of-vocabulary (OOV) problem caused by the closed vocabulary in the natural language processing based on neural network, and introduce two common methods for solving this problem, BPE and WordPiece, and a derivative method, SentencePiece technology. Although BPE is simple and efficient, there might be a insufficient learning problem of some sub-words. In that way, I will introduce BPE Dropout technology to solve this problem. Character-based BBPE and WordPiece still have out-of-vocabulary(OOV) problem when facing mult-languages large character sets (especially CJK languages). I will introduce an effective method to solve this problem: a tokenization technology based on UTF-8 characters (mainly BBPE), and its derivative technology BBPE-based SentencePiece. Finally, I will introduce VOLT, a general vocabulary size optimization technique proposed by ACL2021 best paper.
description: This report will review the Tokenization method in natural language processing based on neural networks. First, I will explain the out-of-vocabulary (OOV) problem caused by the closed vocabulary in the natural language processing based on neural network, and introduce two common methods for solving this problem, BPE and WordPiece, and a derivative method, SentencePiece technology. Although BPE is simple and efficient, there might be a insufficient learning problem of some sub-words. In that way, I will introduce BPE Dropout technology to solve this problem. Character-based BBPE and WordPiece still have out-of-vocabulary(OOV) problem when facing mult-languages large character sets (especially CJK languages). I will introduce an effective method to solve this problem: a tokenization technology based on UTF-8 characters (mainly BBPE), and its derivative technology BBPE-based SentencePiece. Finally, I will introduce VOLT, a general vocabulary size optimization technique proposed by ACL2021 best paper.