cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/5y1t-0b53
title: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.5525/gla.researchdata.1002
title: Title of the publication: Artificial double-helix for geometrical control of magnetic chirality
DOI: 10.21227/062f-sc82
title: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.14457/cu.the.2016.24
title: การใช้วัชพืชในงานออกแบบภูมิทัศน์
DOI: 10.14457/cu.the.2015.17
title: ผลของว่านหางจระเข้ต่อการลดการอักเสบ และการตายของเนื้อเยื่อตับในหนูแรท ที่มีภาวะตับอักเสบจากไขมันลงตับที่ไม่ได้เกิดจากแอลกอฮอล์
DOI: 10.14457/cu.the.2015.18
title: การพัฒนารูปแบบสื่อการเรียนรู้ดิจิทัลโดยผสานหลักการดึงความสนใจและเทคนิคช่วยจำเพื่อส่งเสริมการคงอยู่ของความรู้สำหรับนักศึกษาสายงานแพทย์ พยาบาลและสาธารณสุข
DOI: 10.14457/cu.the.1997.270
title: การศึกษาความคิดเห็นเรื่องปัจจัยด้านโครงสร้างสังคมและทัศนคติของคนไทยที่สัมพันธ์กับพัฒนาการของอินเทอร์เนต
DOI: 10.14457/cu.the.2015.19
title: การศึกษาความสามารถในการขับขี่รถยนต์และพุทธิปัญญาของผู้สูงอายุโดยใช้เครื่องจำลองการขับขี่
DOI: 10.14457/cu.the.1997.271
title: การศึกษาสื่อสารมวลชนด้วยทฤษฎีวิพากษ์ (Critical Theory) : แนวคิดและตัวอย่างงานวิจัย
DOI: 10.14457/cu.the.1997.272
title: การสื่อสารทางการเมืองของชั้นชนกลาง
DOI: 10.14457/cu.the.2015.20
title: ความสัมพันธ์ระหว่างการเกิดเมทิลเลชั่นของไลน์วันกับการใช้บุหรี่และเมทแอมเฟตามีน
DOI: 10.14457/cu.the.2016.25
title: ผลกระทบของการได้รับยาพาราเซตามอลอย่างเรื้อรังต่อการแสดงออกของสารไซโตไคน์ที่เกี่ยวข้องกับการอักเสบในเซลล์แอสโทรไซต์
DOI: 10.14457/cu.the.2016.26
title: ผลกระทบของการได้รับยาพาราเซตามอลเป็นเวลานานต่อการเปลี่ยนแปลงของเซลล์ประสาทบริเวณซีรีบรัลคอร์เท็กซ์ของหนูแรท
DOI: 10.14457/cu.res.2015.99
title: การวิเคราะห์วัจนลีลาภาษาวิชาการไทย
DOI: 10.14457/cu.res.2018.52
title: Conductive and electroactive polymers
DOI: 10.14457/cu.res.2017.52
title: แนวทางการจัดทำแผนที่โครงข่ายน้ำและการกระจายน้ำ ของกลุ่มลุ่มน้ำภาคกลาง
DOI: 10.21227/bctf-9f50
title: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.21227/x4rw-am14
title: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.21227/5yrx-2359
title: Top Indian Fruits with quality

description: INDIA is the second-largest fruit and vegetable exporter in the world after China. It ranked first in the production of Bananas, Papayas, and Mangoes. Public datasets of fruits are available but they are limited to general fruit classes and failed to classify the fruits according to the fruit quality. The main objectives to create this dataset were: 1) Target the top six Indian fruits which are exported or highly consumed. 2) Create a dataset for fruit classification with the quality of fruit. 3) Dataset consists of 12000 high-quality images of 12 different classes of fruits.We use mobile phones rear camera to take the images of fruits. The dataset consists of total 12000 images with 12 classes namely, Bad Apple, Good Apple, Bad Banana, Good Banana, Bad Guava, Good Guava, Bad Lime, Good Lime, Bad Orange, Good Orange, Bad Pomegranate, and Good Pomegranate. In the dataset, each class consists of 1000 images of size 256x256. The images had taken with different angles, with different backgrounds, and in different lighting conditions. As we considered the most consumed fruits this dataset is very useful for researchers.
DOI: 10.5281/zenodo.3930398
title: The Microsoft Academic Graph in RDF: A Linked Data Source with 8 Billion Triples of Scholarly Data

description: We provide an updated version of the <strong>Microsoft Academic Knowledge Graph (MAKG)</strong>. The MAKG is a <strong>large RDF knowledge graph </strong>with <strong>over eight billion triples</strong> containing information about scientific <strong>publications</strong> and related entities, such as <strong>authors</strong>, <strong>institutions</strong>, <strong>journals</strong>, and <strong>fields of study</strong>. The provided data is based on the MAG data as of <strong>2019-12-26</strong>. Besides the MAKG core data, also the owl:sameAs-links to Wikidata and OpenCitations were updated. More information can be found at <strong>http://ma-graph.org/</strong> and in the ISWC'19 paper <strong>The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data</strong> (author copy available here). If you use the data set, please cite it as follows (see also in DBLP): Michael Färber: "The Microsoft Academic Knowledge Graph: A Linked Data Source with 8 Billion Triples of Scholarly Data". Proceedings of the 18th International Semantic Web Conference (ISWC'19). Auckland, New Zealand, 2019, pp. 113-129. <pre><code>@inproceedings{DBLP:conf/semweb/Farber19, author = "{Michael F{\"{a}}rber}", title = "{The Microsoft Academic Knowledge Graph: {A} Linked Data Source with 8 Billion Triples of Scholarly Data}", booktitle = "{Proceedings of the 18th International Semantic Web Conference}", series = "{ISWC'19}", location = "{Auckland, New Zealand}", pages = {113--129}, year = {2019}, url = {https://doi.org/10.1007/978-3-030-30796-7\_8}, doi = {10.1007/978-3-030-30796-7\_8} } </code></pre>
DOI: 10.5281/zenodo.3706495
title: New 16S rRNA primers to uncover Bdellovibrio and like organisms diversity and abundance

description: Alignment files and OTU tables
DOI: 10.5281/zenodo.3706494
title: New 16S rRNA primers to uncover Bdellovibrio and like organisms diversity and abundance

description: Alignment files and OTU tables
DOI: 10.5281/zenodo.3935319
title: Biocuration - mapping resources and needs - Underlying Data

description: A variety of teams and individuals are involved in biocuration worldwide. However, they may not self-identify as biocurators <em>per se</em>, as they may be unaware of biocuration as a career path or because biocuration is only part of their role. The lack of a clear, up-to-date profile of biocuration creates challenges for organisations like ELIXIR, the ISB and GOBLET to systematically support biocurators and for biocurators themselves to develop their own careers. Therefore, the ELIXIR Training Platform launched an Implementation Study in order to i) identify communities of biocurators, ii) map the type of curation work being done, iii) assess biocuration training, and iv) draw a picture of biocuration career development. To achieve the goals of the study we carried out a global survey about the nature of biocuration work, the tools and resources that are used, training that has been received and additional training needs. To examine these topics in more detail we ran workshop-based discussions at ISB Biocuration Conference<strong> </strong>2019 and the ELIXIR All Hands Meeting 2019. We also had guided conversations with selected people from the EMBL-European Bioinformatics Institute. The following files represent the underlying data for this study: Pilot survey questions.docx (questionnaire sent to staff of Wellcome Genome Campus) Questions to guide conversations with biocurators.docx (conversation guide outlines the type of questions to be asked) Global survey questions.docx (globally disseminated questionnaire revised on the basis of the pilot survey) Global_survey_deidentified.xlsx (de-identified responses to the global survey) This file includes de-identified responses to the survey questions. Responses that may lead to the identification of respondents have been redacted. Free text responses to questions 6, 14 and 15 have been categorised into tasks, topics and skills, respectively. Bar graphs of global survey.xlsx (quantitative responses to multiple choice questions in the global survey. For some questions, respondents could choose more than one option) Tools and resources.xlsx (Tools and resources used for biocuration work and listed by the respondents of the global survey) Biocuration training course list.xlsx (formal training courses listed by respondents of the global survey)
description: This work was done in the context of an ELIXIR Implementation Study - Mapping the landscape of Biocuration in ELIXIR: Practice, capability and training requirements - linked to the ELIXIR Training Platform and was funded by the ELIXIR Hub.
DOI: 10.7910/dvn/grpjhb/fmubvk
title: metadata.tab

description: Metadata of the dataset
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