cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3967537
title: Lee2020 GSE132465 Primary Colorectal Cancer Dataset for Besca

description: The gene expression matrix was downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE132465), originally published by Lee HO, Hong Y, Etlioglu HE, et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer. <em>Nat Genet</em>. 2020;52(6):594-603. doi:10.1038/s41588-020-0636-z. We reprocessed the dataset using the Besca package (https://github.com/bedapub/besca).
description: {"references": ["Lee HO, Hong Y, Etlioglu HE, et al. Lineage-dependent gene expression programs influence the immune landscape of colorectal cancer.\u00a0Nat Genet. 2020;52(6):594-603. doi:10.1038/s41588-020-0636-z"]}
DOI: 10.6084/m9.figshare.11870523.v1
title: Additional file 1 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 1: Table S1. p53 ChIP data generated in this study and the public datasets used (in Additional file 4: Figure S1). For each cell line and condition, we also individually called peaks and report the number of peaks identified in each condition.
DOI: 10.6084/m9.figshare.11870529.v1
title: Additional file 2 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 2: Table S2. Table of 8742 p53 ChIP peaks reported in this study. Table includes chromosome number, peak start and end coordinates, whether the peak was variably bound across 12 cell lines in this study (494 peaks marked by ‘1’ in the ‘variable peak’ column), the nearest associated gene to each peak as well as the normalized peak intensities for each cell line in this study and in published datasets (Additional file 4: Figure S1, Additional file 1: Table S1).
DOI: 10.6084/m9.figshare.11870538.v1
title: Additional file 3 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 3: Table S3. List of p53 target genes used for analysis in Additional file 4: Figure S4. This list was based on [12] and removing genes with low read coverage in our dataset.
DOI: 10.6084/m9.figshare.11870766
title: Additional file 1 of Socio-economic impacts of brucellosis on livestock production and reproduction performance in Koibatek and Marigat regions, Baringo County, Kenya

description: Additional file 1. This is the raw data generated from the quantitative questionnaire administered to the respondents.
DOI: 10.5281/zenodo.3674275
title: Data on Memorable Tourism Experiences

description: This is a data collected from Indian and International tourist who have travelled for leisure. The focus is on memorable tourism experiences. It has also captured the visitors intention to revisit or extend their stay. It was collected from December 2018 - February 2020. I want research scholars, students, and academicians to make best use of this data and share any kind of analysis or papers published with me on chervendias@gmail.com.
description: {"references": ["Cheryl Dias & Venan Dias (2019) Memorable Tourism Experiences Data, DOI:10.5281/zenodo.3674276", "Cheryl Dias & Venan Dias (2019) Memorable tourism experience design an effective destination marketing tool, Anatolia, 30:4, 626-628, DOI: 10.1080/13032917.2019.1668727"]}
DOI: 10.6084/m9.figshare.11870523
title: Additional file 1 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 1: Table S1. p53 ChIP data generated in this study and the public datasets used (in Additional file 4: Figure S1). For each cell line and condition, we also individually called peaks and report the number of peaks identified in each condition.
DOI: 10.6084/m9.figshare.11870529
title: Additional file 2 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 2: Table S2. Table of 8742 p53 ChIP peaks reported in this study. Table includes chromosome number, peak start and end coordinates, whether the peak was variably bound across 12 cell lines in this study (494 peaks marked by ‘1’ in the ‘variable peak’ column), the nearest associated gene to each peak as well as the normalized peak intensities for each cell line in this study and in published datasets (Additional file 4: Figure S1, Additional file 1: Table S1).
DOI: 10.6084/m9.figshare.11870538
title: Additional file 3 of Identification of universal and cell-type specific p53 DNA binding

description: Additional file 3: Table S3. List of p53 target genes used for analysis in Additional file 4: Figure S4. This list was based on [12] and removing genes with low read coverage in our dataset.
DOI: 10.5281/zenodo.3968485
title: eeg-workshops/mkpy_data_examples/data

description: EEG data files for eeg-workshops/mkpy_data_examples. For more information see the docs and github source. Version bump for docs release v0.0.3.
DOI: 10.17632/3zz6txz35t.6
title: FOSD Male Speech Dataset

description: This is FOSD-based (extracted from approximately 30-hour of FPT Open Speech Data, released publicly in 2018 by FPT Corporation, under FPT Public License) Male Speech Dataset which is useful for creating text-to-speech model. It comprises of 9474 audio files totalling more than 10.5 recording hours. All files are in *.wav format (16 kHz sampling rate, 32-bit float, mono). This dataset is useful for various TTS-related applications. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
DOI: 10.17632/4gzzc9k49n.4
title: FOSD Female Speech Dataset

description: This is FOSD-based (extracted from approximately 30-hour of FPT Open Speech Data, released publicly in 2018 by FPT Corporation, under FPT Public License) Female Speech Dataset which is useful for creating text-to-speech model. It comprises of 7637 audio files totalling more than 9.5 recording hours. All files are in *.wav format (16 kHz sampling rate, 32-bit float, mono). This dataset is useful for various TTS-related applications. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
DOI: 10.17632/4gzzc9k49n.5
title: FOSD Female Speech Dataset

description: This is FOSD-based (extracted from approximately 30-hour of FPT Open Speech Data, released publicly in 2018 by FPT Corporation, under FPT Public License) Female Speech Dataset which is useful for creating text-to-speech model. It comprises of 7637 audio files totalling more than 9.5 recording hours. All files are in *.wav format (16 kHz sampling rate, 32-bit float, mono). This dataset is useful for various TTS-related applications. Copyright 2018 FPT Corporation Permission is hereby granted, free of charge, non-exclusive, worldwide, irrevocable, to any person obtaining a copy of this data or software and associated documentation files (the “Data or Software”), to deal in the Data or Software without restriction, including without limitation the rights to use, copy, modify, remix, transform, merge, build upon, publish, distribute and redistribute, sublicense, and/or sell copies of the Data or Software, for any purpose, even commercially, and to permit persons to whom the Data or Software is furnished to do so, subject to the following conditions: The above copyright notice, and this permission notice, and indication of any modification to the Data or Software, shall be included in all copies or substantial portions of the Data or Software. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
DOI: 10.17632/3zz6txz35t.7
title: FOSD Male Speech Dataset

description: This is FOSD-based (extracted from approximately 30-hour of FPT Open Speech Data, released publicly in 2018 by FPT Corporation, under FPT Public License) Male Speech Dataset which is useful for creating text-to-speech model. It comprises of 9474 audio files totalling more than 10.5 recording hours. All files are in *.wav format (16 kHz sampling rate, 32-bit float, mono). This dataset is useful for various TTS-related applications. Copyright 2018 FPT Corporation Permission is hereby granted, free of charge, non-exclusive, worldwide, irrevocable, to any person obtaining a copy of this data or software and associated documentation files (the “Data or Software”), to deal in the Data or Software without restriction, including without limitation the rights to use, copy, modify, remix, transform, merge, build upon, publish, distribute and redistribute, sublicense, and/or sell copies of the Data or Software, for any purpose, even commercially, and to permit persons to whom the Data or Software is furnished to do so, subject to the following conditions: The above copyright notice, and this permission notice, and indication of any modification to the Data or Software, shall be included in all copies or substantial portions of the Data or Software. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
DOI: 10.17632/dsmrndnmyy.2
title: The First FOSD-Tacotron-2-based Text-to-Speech Model Dataset for Vietnamese

description: This is the 1st FPT Open Speech Data (FOSD) and Tacotron-2 -based Text-to-Speech Model Dataset for Vietnamese. It comprises of: - A configuration file in *.json format; - Training and validation text input files (in *.csv format); - A trained model (checkpoint file, after 225,000 steps); - Sample generated audios from the trained model. This dataset is useful for research related to TTS and its applications, text processing and especially TTS output optimization given a set of predefined input texts. Copyright 2018 FPT Corporation Permission is hereby granted, free of charge, non-exclusive, worldwide, irrevocable, to any person obtaining a copy of this data or software and associated documentation files (the “Data or Software”), to deal in the Data or Software without restriction, including without limitation the rights to use, copy, modify, remix, transform, merge, build upon, publish, distribute and redistribute, sublicense, and/or sell copies of the Data or Software, for any purpose, even commercially, and to permit persons to whom the Data or Software is furnished to do so, subject to the following conditions: The above copyright notice, and this permission notice, and indication of any modification to the Data or Software, shall be included in all copies or substantial portions of the Data or Software. THE DATA OR SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATA OR SOFTWARE OR THE USE OR OTHER DEALINGS IN THE DATA OR SOFTWARE. Patent and trademark rights are not licensed under this FPT Public License.
DOI: 10.25345/c5p67f
title: MassIVE MSV000084977 - Fusarium dPKS7 dataset submission to MassIVE
title: LC-MS data from the Fusarium PKS7 k/o project. Containing WT, dPKS7 (k/o), OEPKS7, dand PKS7;10;11;12 (k/o) strains. B means broth while CE means cell extracts.
DOI: 10.14457/tu.the.2018.1442
title: Teacher’s code-switching: a case of native Chinese teachers teaching in Thailand

description: This study focuses on the native Chinese teachers’ code-switching practices, which refers to the frequency and reasons for teachers’ shifting from Chinese to Thai and English when teaching Chinese in a Thai classroom setting. Besides, this research aims at exploring the Chinese teachers’ and school authorities’ attitudes towards teachers’ code-switching. The researcher employed a 5-point Likert scale questionnaire and semi-structured interviews to answer the research questions. Altogether, 194 native Chinese teachers responded to the questionnaire while five Chinese teachers and four school authorities participated in the semi-structured interviews. The findings revealed that most of the Chinese teachers code switched frequently to achieve various teaching goals including curriculum access, classroom management, and teacher-student relationship building. Among these three functions, code-switching for the teacher-student social relationship was the most frequently used. In addition, the interview findings reveal that despite some drawbacks of code-switching, both Chinese teachers and school authorities hold supportive attitudes towards code-switching in general. Teacher code-switching in Chinese classrooms in Thailand is regarded as an effective teaching strategy.
DOI: 10.14457/tu.the.2018.1443
title: A corpus-based analysis of English synonymous verbs: predict and forecast

description: This study aimed to explore the collocations of two English synonymous verbs: ‘predict’ and ‘forecast’. Four hundred concordance lines of ‘predict’ and ‘forecast’ were obtained from the Corpus of Contemporary American English (COCA) for collocation analysis. The results of the information about ‘predict’ and ‘forecast’ were compared and investigated in order to see the similarities and differences. The results showed that the two synonymous verbs shared some core meanings and can be used interchangeably in some particular contexts. However, the collocations of the verb ‘predict’ had a wider range of themes which implied that they can be used in more variety of situations.
DOI: 10.14457/tu.the.2018.1444
title: A corpus-based study of English synonyms: ill and sick

description: This research is a corpus-based study which aims to broaden the knowing of the two synonyms: Ill and Sick. The research instruments used in this study were: 1) Oxford Advanced Learner’s Dictionary Online, and 2) Corpus of Contemporary American English (COCA). The researcher collected the data from the first 300 concordance lines from COCA and compared the data with Oxford Dictionary Online to find the differences in meaning and grammatical pattern. The study shows that the two synonyms are both similar and different in grammatical pattern, meaning, and collocation. Although the words “ill” and “sick” share the same meaning of feeling unwell or about the illness, both of them have the other different meanings: “ill” means “ poor in quality, badly, difficulty, misfortune” while “ sick” means “ disappointed, annoyed, and unpleasant” and “sick” can be used as a verb without “up” to give a meaning of “ill”. The differences of the definitions are related to their part of speech which affected their grammatical pattern also. In terms of the collocations, both “ill” and “sick” can be considered as a weak collocations since they can be used with many words, but there are some collocations which are unique collocation such as ill at ease, good or ill, and sick of.
DOI: 10.14457/tu.the.2018.1445
title: A corpus-based study of English synonyms: goal, purpose and aim

description: The corpus-based study aims to examine the similarities and differences of three synonyms which are goal, purpose and aim, by focusing on three aspects: lexical meaning, grammatical patterns and collocations. The English dictionaries, Longman’s Dictionary of Contemporary English and Oxford Advanced Learner’s Dictionary, were used in this study to compare the lexical meaning of the synonyms while the Corpus of Contemporary American English (COCA) was used to collect the data from two hundred concordance lines to analyze the grammatical patterns and collocations of the synonyms. The results show that these three synonyms share the same core meanings, have some similar grammatical patterns and also occur with some collocations in common. However, there are some different points in specific grammatical patterns shown from the data in concordance lines, and the collocations (especially verb) are strong collocations for each. Furthermore, these synonyms have the same core meaning yet they cannot be interchangeable in any context, especially in academic contexts, that prefer use of purpose to goal and aim.
DOI: 10.14457/tu.the.2018.1446
title: A corpus-based study of synonyms: fight and battle

description: The study aimed to investigate the substitution of two verb synonyms, namely fight and battle, focusing on the similarities and differences in terms of collocation information and grammatical patterns. The data were collected from two sources which were the Longman Dictionary of Contemporary English Online (2019) and the 560-million-word Corpus of Contemporary American English (COCA). The results reflected that although both synonyms shared several noun collocations in the category of ‘Warfare and Military,’ ‘Health and Medicine,’ as well as ‘Social Issues,’ some specific characteristics of each, appearing in these groups, and differentiating usage of fight and battle were discovered. Fight had more preference in the ‘Law’ and ‘Economy’ categories whereas battle more frequently co-occurred with those in the groups of ‘Environment, ‘Sports,’ and ‘Entertainment.’ Regarding grammatical patterns, the way the two synonyms were followed by their direct objects without any propositional phrases was found to be the most common pattern used in the authentic contexts. When the prepositional phrases occurred, the preposition for was the most frequently used one. For differences, fight had a tendency to produce many more patterns while battle carried the unique characteristic to be followed by gerund phrases. Therefore, it is crucial for language learners especially those who study English as a foreign language to always take these kinds of similarities and differences of any synonyms into consideration in order to be capable of using the synonyms appropriately.
DOI: 10.14457/tu.the.2018.1447
title: Parents’ attitudes towards mobile learning among young children in Thailand

description: This study examined parents’ attitudes towards their preschool children’s use of mobile learning. Participants were 123 parents of preschool-aged children (age 3 – 6 years old) who resided in Bangkok, Thailand. The focus of the study is on investigating parents’ attitudes towards benefits and risks associated with preschoolers’ use of mobile learning at home. It also explored the ways parents protected their child from potential harm caused by mobile learning. The result showed that most parents displayed positive attitudes towards m-learning and believed in its advantages which were improving children’s memory, supporting children’s cognitive skill development, and creating a positive learning environment at home. Among those parents who had positive views, they still concerned over health problems relating to technology usage such as eye problem, radiation exposure, and technology addiction. They also agreed that m-learning may provide negative feedback if they allowed children use technologies without supervision.
DOI: 10.14457/tu.the.2018.1448
title: Factors affecting the English speaking ability of Thai university students

description: This study investigated problems that hinder Thai EFL students to speak English and the contributing factors that support students’ speaking skills. The participants were 60 non-English major students who enrolled in the English for Communication class in 2018 at a private university. The data were collected using a 22-item questionnaire related to the problems and supporting factors of English speaking. The questionnaires were distributed to the students and collected back within one day. The results of the analyses revealed that most students found it difficult to speak English because they were afraid of making mistakes and they felt anxious while speaking. However, the most significant factors that support English speaking skills found were the teachers’ support and frequent exposure to an English environment. The findings can be a valuable resource with pedagogical implications for teachers to develop proper English teaching methods and help students overcome their difficulties in oral performance in order to communicate effectively in their real lives.
DOI: 10.14457/tu.the.2018.1449
title: Thai EFL students' attitudes towards English teachers' accents amidst the emergence of world Englishes

description: The increasing popularity of the use of English as a lingua franca has encouraged the development of English language teaching (ELT) and learning, especially in the outer circle and expanding circle countries where English is used as a second and foreign language. In Thailand, where English is used as a foreign language, Thai EFL students have struggled to improve their English proficiency as reports from multiple institutions indicate that Thais’ English proficiency is lower than the average. One measure to improve the situation, supported by the government sector, is the employment of foreign English teachers in formal and non-formal school systems. The increasing number of native and non-native English teachers in Thailand has raised a debate about what accent should be a model for language learning and teaching. Research on attitudes towards English accents have been conducted to explore the learners’ attitudes and the effects such attitudes have on English language learning. The current study was conducted in order to investigate Thai EFL students’ ability to identify five English accents (American, Australian, British, Filipino, and Thai), their attitudes towards native and non-native English accents, and their perceptions of English teachers’ accent acceptability.Qualitative and quantitative data was collected for interpretation. The study employed a listening task, a questionnaire, and semi-structured interviews. 60 EFL participants enrolling at a language school in Bangkok took part in the study. The results revealed that the participants’ ability to accurately identify English accents was low, with the accuracy rate of 43.33%. Thai English accent,; however, was correctly identified by most participants (85%). In terms of attitudes towards accents, native English accents were reported to be preferred by the majority of the participants. Native English accents were rated higher in terms of status (intelligence, professionalism, confidence and etc.) when compared to non-native English accents. In terms of solidarity, Thai English accent was rated with higher scores for most traits (friendliness, sincerity, kindness etc.).