cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/v2dw-dk32
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Recently added: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 241,501 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.
DOI: 10.6084/m9.figshare.13088624
title: curated_perturbed_descriptor

description: Processed data used to generate figures and tables in the manuscript "Curated gene expression dataset of differentiating 3T3-L1 adipocytes under pharmacological and genetic perturbations"
DOI: 10.21227/7qpn-g438
title: India-specific Tweets During COVID-19 Pandemic Lockdowns (Phase 1,2,3,4)

description: To be updated soon.
DOI: 10.21227/4gss-c056
title: Tweets Originating from India During COVID-19 Lockdowns 1,2,3,4

description: To be updated soon.
DOI: 10.6084/m9.figshare.13088864
title: Supplementary Material for: Development of a Fluorescence-Based Caries Scoring System for an Intraoral Scanner: An in vitro Study

description: <b><i>Objectives:</i></b> To develop an automated fluorescence-based caries scoring system for an intraoral scanner and to<i></i>test the performance of the system compared to state-of-the-art methods. <b><i>Methods:</i></b> Seventy-three permanent posterior teeth were scanned with a three-dimensional (3D) intraoral scanner prototype which emitted light at 415 nm. An overlay representing the fluorescence signal from the tissue was mapped onto 3D models of the teeth. Multiple examination sites (<i>n</i> = 139) on the occlusal surfaces were chosen, and their red and green fluorescence signal components were extracted. These components were used to calculate 4 mathematical functions upon which a caries scoring system for the scanner prototype could be based. Visual-tactile (International Caries Detection and Assessment System, ICDAS), radiographic (ICDAS), and histological assessments were conducted on the same examination sites. <b><i>Results:</i></b> Most index tests showed significant correlation with histology. The strongest correlation was observed for the visual-tactile examination (<i>r</i><sub>s</sub> = 0.80) followed by the scanner supported by the caries classification function that quantifies the overall fluorescence compared to sound surfaces (<i>r</i><sub>s</sub> = 0.78). Additionally, this function resulted in the highest intra-examiner reliability (κ = 0.964), and the highest sum of sensitivity (SE) and specificity (SP) (sum SE-SP: 1.60–1.84) at the 2 histological levels where the comparison with visual-tactile assessment was possible (κ = 0.886, sum SE-SP = 1.57–1.81) and at the 3 out of 4 histological levels where the comparison with radiographic assessment was possible (κ = 0.911, sum SE-SP = 1.37–1.78); the only exception was for the lesions in the outer third of dentin, where the radiographic assessment showed the highest sum SE-SP (1.78). <b><i>Conclusion:</i></b> A fluorescence-based caries scoring system was developed for the intraoral scanner showing promising performance compared to state-of-the-art caries detection methods. The intraoral scanner accompanied by an automated caries scoring system may improve objective caries detection and increase the efficiency and effectiveness of oral examinations. Furthermore, this device has the potential to support reliable monitoring of early caries lesions.
DOI: 10.6084/m9.figshare.13088864.v1
title: Supplementary Material for: Development of a Fluorescence-Based Caries Scoring System for an Intraoral Scanner: An in vitro Study

description: <b><i>Objectives:</i></b> To develop an automated fluorescence-based caries scoring system for an intraoral scanner and to<i></i>test the performance of the system compared to state-of-the-art methods. <b><i>Methods:</i></b> Seventy-three permanent posterior teeth were scanned with a three-dimensional (3D) intraoral scanner prototype which emitted light at 415 nm. An overlay representing the fluorescence signal from the tissue was mapped onto 3D models of the teeth. Multiple examination sites (<i>n</i> = 139) on the occlusal surfaces were chosen, and their red and green fluorescence signal components were extracted. These components were used to calculate 4 mathematical functions upon which a caries scoring system for the scanner prototype could be based. Visual-tactile (International Caries Detection and Assessment System, ICDAS), radiographic (ICDAS), and histological assessments were conducted on the same examination sites. <b><i>Results:</i></b> Most index tests showed significant correlation with histology. The strongest correlation was observed for the visual-tactile examination (<i>r</i><sub>s</sub> = 0.80) followed by the scanner supported by the caries classification function that quantifies the overall fluorescence compared to sound surfaces (<i>r</i><sub>s</sub> = 0.78). Additionally, this function resulted in the highest intra-examiner reliability (κ = 0.964), and the highest sum of sensitivity (SE) and specificity (SP) (sum SE-SP: 1.60–1.84) at the 2 histological levels where the comparison with visual-tactile assessment was possible (κ = 0.886, sum SE-SP = 1.57–1.81) and at the 3 out of 4 histological levels where the comparison with radiographic assessment was possible (κ = 0.911, sum SE-SP = 1.37–1.78); the only exception was for the lesions in the outer third of dentin, where the radiographic assessment showed the highest sum SE-SP (1.78). <b><i>Conclusion:</i></b> A fluorescence-based caries scoring system was developed for the intraoral scanner showing promising performance compared to state-of-the-art caries detection methods. The intraoral scanner accompanied by an automated caries scoring system may improve objective caries detection and increase the efficiency and effectiveness of oral examinations. Furthermore, this device has the potential to support reliable monitoring of early caries lesions.
DOI: 10.21227/2kyb-vr46
title: Tweets Originating from India During COVID-19 Lockdowns 1,2,3,4

description: To be updated soon.
DOI: 10.21227/3zh5-8y69
title: Tweets Originating from India During COVID-19 Lockdowns 1,2,3,4

description: To be updated soon.
DOI: 10.25919/5f869383d035e
title: Parkes observations for project P455 semester 2020APRS_04

description: This project is to continue timing and profile studies of the first double-pulsar system, a unique laboratory for gravitational physics. Recent results, including the first measurements of higher-order light-propagation effects and of the relativistic deformation of the orbit, highlight the importance of a long term observational campaign for this remarkable system. The main aims of this proposal are to provide the strongest tests to date for general relativity and to measure for the first time the moment-of-inertia of a neutron star. Additionally, we will determine the system geometry and map the pulsar beams via geodetic precession. Whenever possible, we will exploit the high sensitivity of the new UWL receiver.
DOI: 10.17909/t9-sykz-ya77
title: HST archival data used in Saifollahi et al. 2020
DOI: 10.6084/m9.figshare.12895469.v3
title: Supplementary data for manuscript "DNA methylation haplotype block markers efficiently discriminate follicular thyroid carcinoma from follicular adenoma"

description: <b>Supplementary Data 1 </b>Details of clinical and molecular features in thyroid neoplasms.<b></b> <b>Supplementary Data 2 </b>DMCs markers that specific for FTC or FA samples.<b></b> <b>Supplementary Data 3</b> Characteristics of candidate methylation markers in identification FTC from FA samples <b>Supplementary Data 4 </b>Differentially Methylated in FTC and GO Analysis Results<b></b> <b>Supplementary Data 5 </b>Classification of 26 patients according to their DNA methylation score and pathology <br>
DOI: 10.21227/04q3-db83
title: Tweets Originating from India During COVID-19 Lockdowns 1,2,3,4

description: To be updated soon.
DOI: 10.14457/cmu.res.2001.71
title: ปัจจัยที่เกี่ยวข้องกับการตัดสินใจของนักศึกษาที่เข้าศึกษาต่อในหลักสูตรการศึกษาต่อเนื่อง ระดับปริญญาตรี คณะศึกษาศาสตร์ มหาวิทยาลัยเชียงใหม่
DOI: 10.14457/cmu.the.2000.208
title: การสร้างบทเรียนคอมพิวเตอร์ช่วยสอนวิชาเขียนแบบสำหรับนักศึกษาระดับปริญญาตรี สาขาวิชาอุตสาหกรรม
DOI: 10.14457/cmu.the.2001.354
title: พฤติกรรมของนกแอ่นตะโพกขาวหางแฉก (Apus pacificus) บริเวณถ้ำน้ำลอด จังหวัดแม่ฮ่องสอน
DOI: 10.14457/cmu.the.2000.209
title: การรับรู้เกี่ยวกับการฟื้นฟูสมรรถภาพหัวใจของบุคลากรทีมสุขภาพโรงพยาบาลศูนย์ภาคเหนือ
DOI: 10.14457/cmu.the.2001.355
title: การชักนำยอดจากส่วนต่างๆ ของต้นกล้าน้อยหน่าโดยวิธี Thin Cell Layers
DOI: 10.14457/cmu.the.2000.210
title: การรู้จำเค้าโครงหน้ามนุษย์ด้วยนิวรอลเน็ตเวอร์คแบบหน่วยความจำร่วม
DOI: 10.14457/cmu.the.2000.211
title: คุณภาพและความสามารถในการเก็บรักษาของเมล็ดพันธุ์ถั่วเหลืองภายหลังการปรับปรุงสภาพ
DOI: 10.14457/cmu.the.2000.212
title: ผลของความชื้นในดินต่อการเจริญเติบโตและคุณภาพเส้นใยของสายพันธุ์ปอสา
DOI: 10.14457/cmu.the.2001.356
title: อิทธิพลของวัฒนธรรมท้องถิ่นต่อพฤติกรรมทางการเมือง : กรณีศึกษาพิธีแซนยะของชุมชนชาวกูย ตำบลแจนแวน กิ่งอำเภอศรีณรงค์ จังหวัดสุรินทร์
DOI: 10.14457/cmu.the.2001.357
title: การเปลี่ยนแปลงการผลิตทางการเกษตรของชุมชนชนบท
DOI: 10.14457/cmu.the.2001.358
title: การเกิดยอดและแคลลัสของหงส์เหินดอกขาวในสภาพปลอดเชื้อ
DOI: 10.14457/cmu.the.2001.359
title: การจัดแรงต้านทางอากาศพลศาสตร์ของเมล็ดข้าวเปลือก
DOI: 10.14457/cmu.the.2001.360
title: การวิเคราะห์ต้นทุนการผลิตไฟฟ้าจากก๊าซชีวภาพ
DOI: 10.14457/cmu.the.2001.361
title: ความต้องการในภาวะสูญเสียและเศร้าโศกของสมาชิกในครอบครัวผู้ป่วยภาวะใกล้ตาย
DOI: 10.14457/cmu.the.2000.213
title: ผลการดำเนินงานส่งเสริมสุขภาพในโรงงานอุตสาหกรรมปิโตรเคมี เขตนิคมอุตสาหกรรมมาบตาพุด จังหวัดระยอง
DOI: 10.6084/m9.figshare.13089020
title: Burraco_etal_Oikos2021

description: Dataset from the manuscript "Limits to compensatory responses to altered phenology in amphibian larvae" , Oikos 2021.
DOI: 10.6084/m9.figshare.13089020.v1
title: Burraco_etal_Oikos2021

description: Dataset from the manuscript "Limits to compensatory responses to altered phenology in amphibian larvae" , Oikos 2021.
DOI: 10.14457/cmu.the.2000.214
title: ความสัมพันธ์ระหว่างความสามารถในด้านการคิดจินตนาการกับความสามารถใช้ภาษาในการเขียนของนักเรียนชั้นมัธยมศึกษาปีที่ 1
DOI: 10.14457/cmu.the.2001.362
title: ผลกระทบของการฝังไนโตรเจนไอออนที่มีต่อดัชนีหักเหและการดูดกลืนแสงสีม่วงของอะลูมิเนียมออกไซด์
DOI: 10.14457/cmu.the.2001.363
title: การสร้างแบบวัดความฉลาดทางอารมณ์สำหรับนักเรียนชั้นมัธยมศึกษาปีที่ 1
DOI: 10.14457/cmu.the.2000.215
title: การศึกษาสภาวะการเดินระบบในการกำจัดความขุ่นโดยซูเปอร์ฟิลเตอร์
DOI: 10.14457/cmu.the.2000.216
title: การละเมิดพระวินัยของพระสงฆ์ในเขตปกครองสงฆ์ระดับอำเภอในภาคเหนือตอนบน
DOI: 10.14457/cmu.the.2001.364
title: การผสมว่านนางคุ้มข้ามสกุลกับไม้ดอกสี่สกุล
DOI: 10.14457/cmu.the.2001.365
title: กระบวนการกลายเป็นคนจน กรณีศึกษาชาวบ้านปางอีกา จังหวัดเชียงใหม่ (พ.ศ.2500-พ.ศ.2543)
DOI: 10.14457/cmu.the.2001.366
title: ฐานข้อมูลทางคอมพิวเตอร์ของยาที่ใช้สำหรับปัจจัยเสี่ยงต่อการเกิดโรคหลอดเลือดหัวใจ ซึ่งส่งผลต่อสมรรถภาพทางการบินของนักบิน
DOI: 10.14457/cmu.the.2001.367
title: การศึกษาวิเคราะห์เพลงตลกคำเมือง
DOI: 10.14457/cmu.the.2001.368
title: กระบวนการในการพัฒนาและจัดสรรกิจกรรมและโครงการขององค์การบริหารส่วนตำบล
DOI: 10.14457/cmu.the.2001.369
title: การสร้างชุดฝึกสถานการณ์จำลองเครื่องยนต์แก๊สโซลีน ระบบฉีดเชื้อเพลิงด้วยอิเล็กทรอนิกส์
DOI: 10.14457/cmu.the.2001.370
title: ปัจจัยที่เกี่ยวขัองกับการจัดทำแผนการสอนให้มีประสิทธิภาพของครูมัธยมศึกษา จังหวัดพะเยา
DOI: 10.21227/xemh-9198
title: Tweets Originating from India During COVID-19 Lockdowns 1,2,3,4

description: This Indian-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets dataset, which currently contains more than 600 million IDs of COVID-19 specific English tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset.Lockdown period tweets:Lockdown1.zip: March 25, 2020 - April 02, 2020; (all files) corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; (all files) corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; (all files) corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; (all files) corona_tweets_61.csv to corona_tweets_66.csvExtras:extras_june1_june7.zip: (all files) corona_tweets_75.csv to corona_tweets_80.csvextras_june24_july1.zip: (only even-numbered files) corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: (only even-numbered files) corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: (only even-numbered files) corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: (only even-numbered files) corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: (only even-numbered files) corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: (only even-numbered files) corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.21227/akye-yt90
title: Model5b

description: Resubmission
DOI: 10.21227/pprz-7m32
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This Indian-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets dataset, which currently contains more than 600 million IDs of COVID-19 specific English tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.21227/y57k-cs44
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This Indian-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 600 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset,Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Coronavirus (COVID-19) Tweets Sentiment Trend (Global) -------------------------------------What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: Below, (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.21227/dtwp-ww25
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This Indian-specific COVID-19 tweets dataset has been developed using the large-scale Coronavirus (COVID-19) Tweets Dataset, which currently contains more than 600 million COVID-19 specific English language tweets. This dataset contains tweets originating from India during the first week of each four phases of nationwide lockdowns initiated by the Government of India. For more information on filtering keywords, please visit the primary dataset page.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Coronavirus (COVID-19) Tweets Sentiment Trend (Global)-------------------------------------What's inside the dataset? The files in the dataset contain IDs of the tweets present in the Coronavirus (COVID-19) Tweets Dataset. Note: Below, (all files) means that all the files mentioned and in-between have been considered to develop the ID file, while (only even-numbered files) suggests that only the even-numbered files have been considered.Lockdown period tweets: (all files)Lockdown1.zip: March 25, 2020 - April 02, 2020; corona_tweets_08.csv to corona_tweets_14.csvLockdown2.zip: April 14, 2020 - April 21, 2020; corona_tweets_27.csv to corona_tweets_33.csvLockdown3.zip: May 01, 2020 - May 07, 2020; corona_tweets_44.csv to corona_tweets_49.csvLockdown4.zip: May 18, 2020 - May 23, 2020; corona_tweets_61.csv to corona_tweets_66.csvExtras: (all files)extras_june1_june7.zip: corona_tweets_75.csv to corona_tweets_80.csvExtras: (only even-numbered files)extras_june24_july1.zip: corona_tweets_96.csv to corona_tweets_104.csvextras_july2_july15.zip: corona_tweets_106.csv to corona_tweets_118.csvextras_july16_august4.zip: corona_tweets_120.csv to corona_tweets_138.csvextras_august5_august18.zip: corona_tweets_140.csv to corona_tweets_152.csvextras_august19_september1.zip: corona_tweets_154.csv to corona_tweets_166.csvextras_september2_september15.zip: corona_tweets_168.csv to corona_tweets_180.csv
DOI: 10.21227/94zp-c239
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 241,501 tweets— Coverage : Global— Language : English (EN)— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Usage policy : As per Twitter's Developer Policy— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time of each CSV file present in this dataset.