cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4270585
title: Selecting the Most Feasible Construction Phasing Plans for Urban Highway Rehabilitation Projects

description: Despite the abundance of research that has aimed to understand the effects of highway work zones, very little definitive<br> information is available concerning the determination of work zone length (WZL). Quantitative studies that holistically<br> model WZL are very rare. To fill this gap, this study identifies critical factors affecting WZL and develops decision<br> support models that determine the optimal WZL in a balanced tradeoff between motorists’ inconvenience due to traffic<br> disruption and their opportunity cost. A high-confidence dataset was created by conducting a series of scheduling and<br> traffic simulations and analyses. The results revealed that traffic loading and work zone duration are critical factors,<br> with traffic loading at approximately 41,000 vehicles-per-day being an important benchmarking point. Based on these<br> findings, a decision support model was developed to determine the most feasible WZL. As the first of its kind, this<br> study will help state transportation agencies devise sounder construction phasing plans by providing a point of<br> reference when establishing WZL in a viable way to minimize traffic disruption during construction.
description: Tran-SET Project: 19ITSLSU07
DOI: 10.6084/m9.figshare.13232537
title: Amazon Reviews Full

description: Amazon Review Full Score Dataset<br>Version 3, Updated 09/09/2015<br>ORIGIN<br>The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. For more information, please refer to the following paper: J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys, 2013.<br>The Amazon reviews full score dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).<br><br>DESCRIPTION<br>The Amazon reviews full score dataset is constructed by randomly taking 600,000 training samples and 130,000 testing samples for each review score from 1 to 5. In total there are 3,000,000 trainig samples and 650,000 testing samples.<br>The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 5), review title and review text. The review title and text are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".<br>
DOI: 10.5281/zenodo.4270616
title: Real-Time Work Zone Traffic Management via Unmanned Air Vehicles

description: Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in real- time traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research.
description: Tran-SET Project: ITSOSU01
DOI: 10.5281/zenodo.4270615
title: Real-Time Work Zone Traffic Management via Unmanned Air Vehicles

description: Highway work zones are prone to traffic accidents when congestion and queues develop. Vehicle queues expand at a rate of 1 mile every 2 minutes. Back-of-queue, rear-end crashes are the most common work zone crash, endangering the safety of motorists, passengers, and construction workers. The dynamic nature of queuing in the proximity of highway work zones necessitates traffic management solutions that can monitor and intervene in real time. Fortunately, recent progress in sensor technology, embedded systems, and wireless communication coupled to lower costs are now enabling the development of real-time, automated, “intelligent” traffic management systems that address this problem. The goal of this project was to perform preliminary research and proof of concept development work for the use of UAS in real- time traffic monitoring of highway construction zones in order to create real-time alerts for motorists, construction workers, and first responders. The main tasks of the proposed system was to collect traffic data via the UAV camera, analyze that a UAV based highway construction zone monitoring systems would be capable of detecting congestion and back-of-queue information, and alerting motorists of stopped traffic conditions, delay times, and alternate route options. Experiments were conducted using UAS to monitor traffic and collect traffic videos for processing. Prototype software was created to analyze this data. The software was successful in detecting vehicle speed from zero mph to highway speeds. Review of available mobile traffic apps were conducted for future integration with advanced iterations of the UAV and software system that has been created by this research. This project has proven that UAS monitoring of highway construction zones and real-time alerts to motorists, construction crews, and first responders is possible in the near term and future research is needed to further development and implement the innovative UAS traffic monitoring system developed by this research.
description: Tran-SET Project: ITSOSU01
DOI: 10.5517/ccdc.csd.cc24zykn
title: CCDC 1995952: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zylp
title: CCDC 1995953: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zynr
title: CCDC 1995955: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zyps
title: CCDC 1995956: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zyqt
title: CCDC 1995957: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zyrv
title: CCDC 1995958: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.5517/ccdc.csd.cc24zytx
title: CCDC 1995960: Experimental Crystal Structure Determination

description: Related Article: Victor Mamane, Paola Peluso, Emmanuel Aubert, Robin Weiss, Emmanuel Wenger, Sergio Cossu, Patrick Pale|2020|Organometallics|39|3936|doi:10.1021/acs.organomet.0c00633
DOI: 10.6084/m9.figshare.13232501
title: Amazon Review Polarity

description: <b>Amazon Review Polaridy Dataset</b><br>Version 3, Updated 09/09/2015<br>ORIGIN<br>The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review. For more information, please refer to the following paper: J. McAuley and J. Leskovec. Hidden factors and hidden topics: understanding rating dimensions with review text. RecSys, 2013.<br>The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).<br><br>DESCRIPTION<br>The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. In the dataset, class 1 is the negative and class 2 is the positive. Each class has 1,800,000 training samples and 200,000 testing samples.<br>The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 or 2), review title and review text. The review title and text are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".<br>
DOI: 10.6084/m9.figshare.13232582.v1
title: AACM.narrowPeak

description: narrow peak
DOI: 10.6084/m9.figshare.13232582
title: AACM.narrowPeak

description: narrow peak
DOI: 10.6084/m9.figshare.13232585
title: AAEM

description: narrow peak
DOI: 10.6084/m9.figshare.13232588
title: naive

description: narrow peak
DOI: 10.6084/m9.figshare.13232450
title: AG News

description: <b>AG's News Topic Classification Dataset</b><br>Version 3, Updated 09/09/2015<br><br><b>ORIGIN</b><br>AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .<br>The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).<br><br><b>DESCRIPTION</b><br>The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.<br>The file classes.txt contains a list of classes corresponding to each label.<br>The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".<br><b>CLASSES</b><b><br></b>WorldSportsBusinessSci/Tech<br><br>
DOI: 10.6084/m9.figshare.13232450.v1
title: AG News

description: <b>AG's News Topic Classification Dataset</b><br>Version 3, Updated 09/09/2015<br><br><b>ORIGIN</b><br>AG is a collection of more than 1 million news articles. News articles have been gathered from more than 2000 news sources by ComeToMyHead in more than 1 year of activity. ComeToMyHead is an academic news search engine which has been running since July, 2004. The dataset is provided by the academic comunity for research purposes in data mining (clustering, classification, etc), information retrieval (ranking, search, etc), xml, data compression, data streaming, and any other non-commercial activity. For more information, please refer to the link http://www.di.unipi.it/~gulli/AG_corpus_of_news_articles.html .<br>The AG's news topic classification dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the dataset above. It is used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).<br><br><b>DESCRIPTION</b><br>The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600.<br>The file classes.txt contains a list of classes corresponding to each label.<br>The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 3 columns in them, corresponding to class index (1 to 4), title and description. The title and description are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".<br><b>CLASSES</b><b><br></b>WorldSportsBusinessSci/Tech<br><br>
DOI: 10.21227/kn1c-zx22
title: Fine-Grained Balanced Cyberbullying Dataset

description: Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim’s age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications of such magnitude, and existing cyberbullying datasets suffer from quite severe class imbalances. To combat these challenges, we establish a framework for the automatic generation of balanced data by using a semi-supervised online Dynamic Query Expansion (DQE) process to extract more natural data points of a specific class from Twitter. We also propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings. With our DQE-augmented dataset, which we have made publicly available, we compare our GCN model using eight different tweet embedding methods and six other classification models over two sizes of datasets. Our results show that our proposed GCN model matches or exceeds the performance of the baseline models, as indicated by McNemar statistical tests.
DOI: 10.21227/mekx-d381
title: Fine-Grained Balanced Cyberbullying Dataset

description: Amidst the COVID-19 pandemic, cyberbullying has become an even more serious threat. Our work aims to investigate the viability of an automatic multiclass cyberbullying detection model that is able to classify whether a cyberbully is targeting a victim’s age, ethnicity, gender, religion, or other quality. Previous literature has not yet explored making fine-grained cyberbullying classifications of such magnitude, and existing cyberbullying datasets suffer from quite severe class imbalances. To combat these challenges, we establish a framework for the automatic generation of balanced data by using a semi-supervised online Dynamic Query Expansion (DQE) process to extract more natural data points of a specific class from Twitter. We also propose a Graph Convolutional Network (GCN) classifier, using a graph constructed from the thresholded cosine similarities between tweet embeddings. With our DQE-augmented dataset, which we have made publicly available, we compare our GCN model using eight different tweet embedding methods and six other classification models over two sizes of datasets. Our results show that our proposed GCN model matches or exceeds the performance of the baseline models, as indicated by McNemar statistical tests.
DOI: 10.6084/m9.figshare.13232390
title: Yelp Review Polarity

description: Yelp Review Polarity Dataset<br>Version 1, Updated 09/09/2015<br>ORIGIN<br>The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to http://www.yelp.com/dataset_challenge<br>The Yelp reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).
DOI: 10.21227/0wq5-6v48
title: Zh-Probase

description: This is a large Chinese taxonomic knowledge base, which is translated from Probase by the neural network.It has 11,292,493 IsA pairs with an accuracy of 86.6%.
DOI: 10.21227/738j-ew54
title: Zh-Probase

description: This is a large Chinese taxonomic knowledge base, which is translated from Probase by the neural network.It has 11,292,493 IsA pairs with an accuracy of 86.6%.
DOI: 10.6084/m9.figshare.13231931
title: yelp_2014.csv

description: Yelp Challenge 2014
DOI: 10.6084/m9.figshare.13228796
title: yelp_2013.csv

description: Yelp challenge 2013
DOI: 10.6084/m9.figshare.13228796.v1
title: yelp_2013.csv

description: Yelp challenge 2013
DOI: 10.5517/ccdc.csd.cc25mk9p
title: CCDC 2014792: Experimental Crystal Structure Determination

description: Related Article: Haruka Takeuchi, Shinsuke Inuki, Kohei Nakagawa, Takaaki Kawabe, Atsuhiko Ichimura, Shinya Oishi, Hiroaki Ohno|2020|Angew.Chem.,Int.Ed.|59|21210|doi:10.1002/anie.202009399
DOI: 10.5517/ccdc.csd.cc25mkds
title: CCDC 2014795: Experimental Crystal Structure Determination

description: Related Article: Haruka Takeuchi, Shinsuke Inuki, Kohei Nakagawa, Takaaki Kawabe, Atsuhiko Ichimura, Shinya Oishi, Hiroaki Ohno|2020|Angew.Chem.,Int.Ed.|59|21210|doi:10.1002/anie.202009399
DOI: 10.5517/ccdc.csd.cc25mkft
title: CCDC 2014796: Experimental Crystal Structure Determination

description: Related Article: Haruka Takeuchi, Shinsuke Inuki, Kohei Nakagawa, Takaaki Kawabe, Atsuhiko Ichimura, Shinya Oishi, Hiroaki Ohno|2020|Angew.Chem.,Int.Ed.|59|21210|doi:10.1002/anie.202009399
DOI: 10.21227/s6te-k265
title: HIS-RING-abdomen

description: This dataset contains 1216 data, which are scaned by HIS-RING PACT system.the data sampling rate of our system is 40 MSa/s,a 128-elements full-view ring-shaped transducer with 2.5MHz.
DOI: 10.21227/e5qg-cd73
title: HIS-RING-abdomen

description: This dataset contains 1216 data, which are scaned by HIS-RING PACT system.the data sampling rate of our system is 40 MSa/s,a 128-elements full-view ring-shaped transducer with 2.5MHz.
DOI: 10.21227/jwbg-cn78
title: HIS-RING-abdomen

description: This dataset contains 1216 data, which are scanned by HIS-RING PACT system.the data sampling rate of our system is 40 MSa/s,a 128-elements full-view ring-shaped transducer with 2.5MHz.
DOI: 10.21227/gjxe-ga27
title: HIS-RING-abdomen

description: This dataset contains 1216 data, which are scanned by HIS-RING PACT system.the data sampling rate of our system is 40 MSa/s,a 128-elements full-view ring-shaped transducer with 2.5MHz.
DOI: 10.21227/hvzc-wk94
title: HIS-RING-abdomen

description: This dataset contains 1216 data, which are scanned by HIS-RING PACT system.the data sampling rate of our system is 40 MSa/s,a 128-elements full-view ring-shaped transducer with 2.5MHz.continuous updating.....
DOI: 10.6084/m9.figshare.4823518
title: Instances and solutions of the article "Repayment policy for multiple loans", Plos-one PloS one 12 (4), e0175782, 2017

description: <pre>Instances and Gurobi code in C++ for the article: </pre><pre><pre>Repayment policy for multiple loans</pre><pre>Plos-one</pre><pre>Yasmin Agueda Rios-Solis</pre><pre>Mario Alberto Saucedo-Espinosa</pre><pre>Gabriel Arturo Caballero-Robledo</pre><pre>2017</pre></pre> <pre><br></pre>
DOI: 10.6084/m9.figshare.4823518.v2
title: Instances and solutions of the article "Repayment policy for multiple loans", Plos-one PloS one 12 (4), e0175782, 2017

description: <pre>Instances for the article: </pre><pre><pre>Repayment policy for multiple loans</pre><pre>Plos-one</pre><pre>Yasmin Agueda Rios-Solis</pre><pre>Mario Alberto Saucedo-Espinosa</pre><pre>Gabriel Arturo Caballero-Robledo</pre><pre>2017</pre></pre> <pre><br></pre>
DOI: 10.14457/tu.the.2019.706
title: ความสัมพันธ์ระหว่างการกำกับดูแลกิจการกับผลการจัดอันดับความน่าเชื่อถือของบริษัทจดทะเบียนในตลาดหลักทรัพย์แห่งประเทศไทยในกลุ่ม SET100

description: การค้นคว้าอิสระมีจุดประสงค์เพื่อศึกษาถึงความสัมพันธ์ระหว่างการกำกับดูแลกิจการกับผลการจัดอันดับความน่าเชื่อถือของบริษัทจดทะเบียนในตลาดหลักทรัพย์แห่งประเทศไทยในกลุ่ม SET100 ระหว่างปี 2559 – 2561 รวมทั้งสิ้น 95 กลุ่มตัวอย่าง โดยข้อมูลที่ใช้ศึกษามาจากแหล่งข้อมูลทุติยภูมิที่รวบรวมจากแบบแสดงรายการข้อมูลประจำปี (แบบ 56-1) รายงานข้อมูลประจำปี (แบบ 56-2) ฐานข้อมูล SET-SMART รายงานการกำกับดูแลกิจการบริษัทจดทะเบียน (Corporate Governance Report of Thai Listed Company: CGR) www.set.or.th และ www.trisrating.com/th โดยใช้การวิเคราะห์สถิติเชิงพรรณนาและสถิติเชิงอนุมานในการวิเคราะห์ข้อมูลจากการศึกษาพบว่าคะแนนการกำกับดูแลกิจการ สัดส่วนการถือหุ้นของผู้ถือหุ้นสถาบัน อัตราส่วนผลตอบแทนต่อสินทรัพย์มีความสัมพันธ์เชิงบวกต่อผลการจัดอันดับความน่าเชื่อถือ ในขณะที่การควบรวมตำแหน่งประธานบริหารและประธานกรรมการ และอัตราส่วนหนี้สินต่อส่วนของผู้ถือหุ้นมีความสัมพันธ์เชิงลบต่อผลการจัดอันดับความน่าเชื่อถือ สำหรับคะแนนการเปิดเผยข้อมูลไม่ส่งผลต่อผลการจัดอันดับความน่าเชื่อถือ