cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ha56-wh71
title: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.14457/tu.the.2019.556
title: การเปรียบเทียบกลยุทธ์การแข่งขันของทรูมันนี่วอลเล็ท และแรบบิทไลน์เพย์

description: งานวิจัยนี้มีวัตถุประสงค์เพื่อศึกษาเปรียบเทียบกลยุทธ์การแข่งขันของทรูมันนี่วอลเล็ทและแรบบิทไลน์เพย์ และเพื่อศึกษาความแตกต่างของผู้ใช้บริการทั้ง 2 ราย ด้วยวิธีการศึกษาตามกรอบแนวคิด S-C-P (Structure Conduct Performance) ประกอบกับการศึกษาความแตกต่างของผู้ใช้บริการ จากการเก็บแบบสอบถามผู้ที่ใช้บริการทรูมันนี่วอลเล็ทและแรบบิทไลน์เพย์ ในเขตพื้นที่ สีลมและสาธร จังหวัดกรุงเทพมหานคร จำนวน 310 คนจากการศึกษาพบว่า โครงสร้างตลาดของเงินอิเล็กทรอนิกส์ที่มิใช่สถาบันการเงิน (Non-Bank) มีลักษณะเป็นตลาดผู้ขายน้อยราย (Oligopoly) ที่มีทรูมันนี่วอลเล็ทและแรบบิทไลน์เพย์เป็นผู้เล่นรายใหญ่ในตลาด อุปสรรคในการเข้าสู่ตลาดมีมากทั้ง กฎหมาย และเทคโนโลยี ลักษณะการให้บริการแต่ละรายไม่แตกต่างกัน แต่แตกต่างกันที่จำนวนของผู้ร่วมให้บริการ โดยผู้เล่นที่มีผู้ร่วมให้บริการที่มากจะเป็นผู้เล่นรายใหญ่ในตลาด กลยุทธ์ที่ใช้ในการตั้งราคาเป็นแบบขายควบ (Bundling) ซึ่งเป็นการขายควบบริการการชำระพร้อมกับมอบส่วนลดค่าสินค้าให้กับผู้ใช้บริการ ผลการศึกษาจากแบบสอบถาม พบว่า ข้อมูลทั่วไปของผู้ใช้บริการส่วนใหญ่ไม่แตกต่างกัน แต่พฤติกรรมของผู้ใช้บริการมีความแตกต่างกันอย่างมีนัยสำคัญทางสถิติ ได้แก่ เครือข่ายมือถือที่ใช้บริการ วิธีในการเลือกผู้ให้บริการ ช่องทางการรับรู้สิทธิประโยชน์ และความถี่ในการใช้บริการ ในส่วนพฤติกรรมอื่น ๆ แต่ละกลุ่มผู้ใช้บริการมีพฤติกรรมที่ไม่แตกต่างกัน รวมไปถึงความพึงพอใจในแต่ละด้านของกลุ่มผู้ใช้บริการก็มีความพึงพอใจที่ไม่แตกต่างกัน
DOI: 10.21227/m0py-zd30
title: Coronavirus (COVID-19) Tweets Sentiment Trend (Global)

description: This dataset gives a cursory glimpse at the overall sentiment trend of the public discourse regarding the COVID-19 pandemic on Twitter. The live scatter plot of this dataset is available as The Overall Trend block at https://live.rlamsal.com.np. The trend graph reveals multiple peaks and drops that need further analysis. The n-grams during those peaks and drops can prove beneficial for better understanding the discourse.The dataset will be updated weekly and will continue until the development of the Coronavirus (COVID-19) Tweets Dataset is ongoing.-------------------------------------Related: Coronavirus (COVID-19) Tweets Dataset, Coronavirus (COVID-19) Geo-tagged Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------A quick overview of the datasetThe sentiment scores are defined in the range [-1,0), 0, and (0,+1] for negative sentiment, neutral sentiment, and positive sentiment. Since the number of negative sentiment tweets is always less than the combined number of neutral and positive sentiment tweets, the majority of the time, the average sentiment falls pretty close to +0.05. So if we consider +0.05 as the neutral point for average sentiment score, then any score greater than +0.1 (peaks) and smaller than 0 (drops) can be regarded as a point of interest for further scrutinizing. Following are the dates when the Twitter stream (based on the tweets present in the Coronavirus (COVID-19) Tweets Dataset) experienced those peaks and drops:Positive peaks: In 2020 (April 30, May 3, May 23, May 24, May 25, May 26, June 2, June 22, June 28, July 3, July 12, July 26, August 15, August 16, August 18, August 21, August 24, August 31, September 1, September 2, September 4, September 5, September 9, September 21, September 23, October 2)Negative Peaks: In 2020 (May 28, May 30, May 31, June 1, June 2, June 7, June 8, June 12, June 13, June 14, June 15, June 21, June 24, june 25, July 6, July 7, July 10, August 26, September 1, September 3, September 13, September 17, September 25, September 26, September 28, October 5)What's inside the dataset files?Tweets collected every 10 minutes are sampled together, and an average sentiment score is computed. This dataset contains TXT files, each with two columns: (i) date/time (in UTC) and (ii) average sentiment. The first column is date/time and is by default in Unix timestamp (in ms). You can use this formula =cell/1000/60/60/24 + DATE(1970,1,1) in Spreadsheets, or this pd.to_datetime(dataframe_name[column],unit='ms') if you're comfortable with Python, to convert the Unix timestamp to human-readable format. There are 44 instances where the average sentiment score is NULL because of networking issues with the cloud service.
DOI: 10.5281/zenodo.4064682
title: Dataset for DISC reconstruction based on novel approach to protein-protein docking

description: Coordinates of protein-protein complexes involved in the CD95 based DISC formation, Homology models along with MD simulation trajectories. Homology modeling: YASARA Model quality assessment: MOE software and QMEAN sever Protein-protein docking: HADDOCK, ClusPro, ZDOCK, GRAMMX, HDOCK RMSD-based Clustering: Meastro Schrodinger MD simulation: Desmond Schrodinger Alanine scanning and MMGBSA: Maestro Schrodinger
description: Funding from the Vinnova Seal-of-Excellence programme 2019-02205 (CaTheDRA) is gratefully acknowledged (SJM). Sven and Lilly Lawski Foundation are gratefully acknowledged for a postdoctoral fellowship to MT, and MSCA-RISE 777995 "DISCOVER" for funding of secondments. The Faculty of Science at the University of Gothenburg, the Swedish Science Research Council (VR; grant number 2019-3684), and MSCA-RISE 734749 "INSPIRED" are gratefully acknowledged for financial support (LAE) and the Swedish National Infrastructure for Computing for allocations of computing time at supercomputing center C3SE, in part funded by the Swedish Research Council through grant agreement no. 2018-05973.
DOI: 10.14457/tu.the.2019.555
title: ศึกษาความสัมพันธ์ระหว่างปัจจัยทางเศรษฐกิจกับดัชนีผลตอบแทน จากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์

description: งานศึกษามีวัตถุประสงค์เพื่อศึกษาความสัมพันธ์ระหว่างปัจจัยทางเศรษฐกิจกับดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ โดยแบ่งปัจจัยเป็น 2 กลุ่ม คือ ปัจจัยทางเศรษฐกิจมหภาค ได้แก่ อัตราเงินเฟ้อ, ดัชนีผลผลิตอุตสาหกรรม, ส่วนต่างของผลตอบแทนพันธบัตรรัฐบาลอายุ 10 ปี และ 1 ปี และ ปัจจัยที่เกี่ยวข้องกับอสังหาริมทรัพย์ ได้แก่ ดัชนีราคาที่ดินและปริมาณสินเชื่ออสังหาริมทรัพย์ของธนาคารพาณิชย์ เพื่อสร้างแบบจำลองในการพยากรณ์ดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ โดยวิธีการประมาณค่าแบบจำลอง Vector Autoregressive (VAR) และวิเคราะห์ความสัมพันธ์เชิงเหตุและผลของตัวแปรด้วยวิธี Granger causality test วิเคราะห์การตอบสนองของตัวแปรด้วยวิธี Impulse Response Function และวิเคราะห์แยกส่วนประกอบของค่าคลาดเคลื่อนของการพยากรณ์ด้วยวิธี Variance Decomposition จากการศึกษาแบบจำลอง VAR พบว่า อัตราการเปลี่ยนแปลงของดัชนีราคาที่ดิน, อัตราการเปลี่ยนแปลงของดัชนีผลผลิตอุตสาหกรรม และ อัตราการเปลี่ยนแปลงของส่วนต่างอัตราผลตอบแทนพันธบัตรรัฐบาลระยะเวลา 10 ปี และ 1 ปี ส่งผลกระทบต่อ อัตราการเปลี่ยนแปลงของดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ในปัจจุบัน อัตราเงินเฟ้อ และ อัตราการเปลี่ยนแปลงของปริมาณสินเชื่ออสังหาริมทรัพย์ของธนาคารพาณิชย์ ไม่ส่งผลกระทบต่ออัตราการเปลี่ยนแปลงของดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ในปัจจุบัน การทดสอบความสัมพันธ์เชิงเป็นเหตุเป็นผลของตัวแปรแต่ละคู่ ด้วยวิธี Granger causality test พบว่าตัวแปรมีความสัมพันธ์กันทั้งทางตรงและทางอ้อมต่ออัตราการเปลี่ยนแปลงของดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ (RPFREIT) และ พบว่า เมื่อเกิดการเปลี่ยนแปลงอย่างฉับพลัน (Shock) กับตัวแปรต่างๆ จะส่งผลกระทบต่อ ดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ (RPFREIT) อย่างรวดเร็วในเดือนที่ 1 ทันที จากนั้นจะค่อยๆ ปรับขนาดของผลกระทบลดลงแต่ยังคงมีความผันผวนอย่างต่อเนื่อง และใช้ระยะเวลานานกว่า 15 ถึง 20 เดือน ในการปรับตัวเข้าสู่ภาวะดุลภาพอีกครั้งหนึ่ง ในส่วนการวิเคราะห์ Variance Decomposition พบว่าสัดส่วนค่าความแปรปรวนของค่าพยากรณ์เกิดจากอัตราการเปลี่ยนแปลงของดัชนีผลตอบแทนจากกองทุนรวมอสังหาริมทรัพย์และกองทรัสต์ (RPFREIT) เองมากถึง 90% และจากตัวแปรอื่นๆ มีสัดส่วนค่อนข้างน้อย
DOI: 10.6084/m9.figshare.13089098
title: S7_Original data and analysis

description: Original collected data and anaysis performed: HCA, ERGM, Frequency stats, Kruskal-Wallis results<br>
DOI: 10.6084/m9.figshare.13089098.v1
title: S7_Original data and analysis

description: Original collected data and anaysis performed: HCA, ERGM, Frequency stats, Kruskal-Wallis results<br>
DOI: 10.25405/data.ncl.13084148
title: Automatic recognition of feeding and foraging behaviour in pigs using deep learning

description: The file includes the following items:<br>1) Datasets used to train, validate and test the method.2) Code used to train and validate the method.<br>More details about the datasets and code and provided in the below link:<br>https://www.sciencedirect.com/science/article/pii/S1537511020301707
DOI: 10.25405/data.ncl.12472859
title: Machine learning-based detection of FLOSS version release events from Stack Overflow message data

description: Reprodicibility package for "Machine learning-based detection of FLOSS version release events from Stack Overflow message data" paper.
DOI: 10.25405/data.ncl.12472859.v1
title: Machine learning-based detection of FLOSS version release events from Stack Overflow message data

description: Reprodicibility package for "Machine learning-based detection of FLOSS version release events from Stack Overflow message data" paper.
DOI: 10.17632/h325b4jzgv.5
title: Data for: Mineralogical and isotopic characterization of graphite deposits in the western part of the North Qaidam Orogen and East Kunlun Orogen, northeast Tibetan Plateau, China

description: Data for X-ray diffraction, Carbon-oxygen isotopes of graphite and calcite
DOI: 10.6084/m9.figshare.12098523
title: upper-elementary school students (5th,6th,7th grade), MHV, MOAV, GAVM, gender, grade

description: This study analyzes a newly developed measure for assessing the knowledge of generalacademic vocabulary in upper-elementary school students in Croatia. This researcher-designedinstrument assesses students’ command of synonymy and the limits of collocational use as aproxy for academic vocabulary breadth and depth. Elementary school students in the 5th , 6th ,and 7th grade participated in the study (N = 152, 69,7% girls). In addition to the newly developedmeasure for assessing general academic vocabulary, students completed the Mill HillVocabulary Scales, a standardized measure of general vocabulary knowledge. The results showthat the newly developed instrument for measuring general academic vocabulary knowledge isreliable and valid—it has adequate internal consistency, shows to be sensitive to the expectedage and gender differences in vocabulary, and correlates highly with the results obtained bythe Mill Hill Vocabulary Scales. This new instrument could fill the gap in academic vocabulary-related research since the currently available vocabulary assessment measures in Croatian donot focus on this specific register. More specifically, our new measure could be used to evaluatevarious types of academic vocabulary interventions in schools.<br><br><br><br><br><br>
DOI: 10.6084/m9.figshare.12098523.v1
title: upper-elementary school students (5th,6th,7th grade), MHV, MOAV, GAVM, gender, grade

description: This study analyzes a newly developed measure for assessing the knowledge of generalacademic vocabulary in upper-elementary school students in Croatia. This researcher-designedinstrument assesses students’ command of synonymy and the limits of collocational use as aproxy for academic vocabulary breadth and depth. Elementary school students in the 5th , 6th ,and 7th grade participated in the study (N = 152, 69,7% girls). In addition to the newly developedmeasure for assessing general academic vocabulary, students completed the Mill HillVocabulary Scales, a standardized measure of general vocabulary knowledge. The results showthat the newly developed instrument for measuring general academic vocabulary knowledge isreliable and valid—it has adequate internal consistency, shows to be sensitive to the expectedage and gender differences in vocabulary, and correlates highly with the results obtained bythe Mill Hill Vocabulary Scales. This new instrument could fill the gap in academic vocabulary-related research since the currently available vocabulary assessment measures in Croatian donot focus on this specific register. More specifically, our new measure could be used to evaluatevarious types of academic vocabulary interventions in schools.<br><br><br><br><br><br>
DOI: 10.6084/m9.figshare.13089113
title: Supplementary Material for: Immunohistochemical Analysis of Mismatch Repair Gene Proteins in Early Gastric Cancer Based on Microsatellite Status

description: <b><i>Background:</i></b> Microsatellite instability (MSI) is a major pathway involved in gastric carcinogenesis and is observed in 10–20% of early gastric cancers (EGCs). Early detection of EGCs with an MSI-high phenotype would be useful for elucidating the mechanisms of gastric carcinogenesis and improving outcomes in patients with GC. <b><i>Objective:</i></b> We explored the usefulness of immunohistochemical expression of mismatch repair (MMR) proteins, including MLH1, PMS2, MSH2, and MSH6 in EGC. <b><i>Methods:</i></b> We examined the expression of 4 MMR proteins using immunohistochemistry in 119 patients with EGC based on MS status, as determined by polymerase chain reaction-microsatellite analysis. In addition, methylation of the <i>MLH1</i> gene was quantified by pyrosequencing. <b><i>Results:</i></b> EGCs were classified into 46 MSI-high phenotypes and 73 microsatellite stable (MSS) phenotypes. Although loss of MLH1 expression was associated with loss of PMS2 expression in the MSI-high phenotype, discordant cases of loss of expression between MLH1 and PMS2 were found (MLH1 [−]/PMS2 [+], 3 cases). Loss of MLH1/PMS2 expression was observed in 2 of 73 MSS phenotypes. Loss of MSH2/MSH6 expression was found in 4 of 46 MSI-high phenotypes, whereas loss of MSH2/MSH6 expression was not detected in the MSS phenotype. In addition, loss of MLH1 expression was correlated with methylation of <i>MLH1</i>. However, there were discordant cases in which loss of MLH1 expression was not accompanied by methylation of <i>MLH1</i>. <b><i>Conclusion:</i></b> Although immunostaining of MMR proteins could help predict MSI in EGCs, immunostaining did not have the same value as genetic testing for determination of MSI.
DOI: 10.6084/m9.figshare.13089113.v1
title: Supplementary Material for: Immunohistochemical Analysis of Mismatch Repair Gene Proteins in Early Gastric Cancer Based on Microsatellite Status

description: <b><i>Background:</i></b> Microsatellite instability (MSI) is a major pathway involved in gastric carcinogenesis and is observed in 10–20% of early gastric cancers (EGCs). Early detection of EGCs with an MSI-high phenotype would be useful for elucidating the mechanisms of gastric carcinogenesis and improving outcomes in patients with GC. <b><i>Objective:</i></b> We explored the usefulness of immunohistochemical expression of mismatch repair (MMR) proteins, including MLH1, PMS2, MSH2, and MSH6 in EGC. <b><i>Methods:</i></b> We examined the expression of 4 MMR proteins using immunohistochemistry in 119 patients with EGC based on MS status, as determined by polymerase chain reaction-microsatellite analysis. In addition, methylation of the <i>MLH1</i> gene was quantified by pyrosequencing. <b><i>Results:</i></b> EGCs were classified into 46 MSI-high phenotypes and 73 microsatellite stable (MSS) phenotypes. Although loss of MLH1 expression was associated with loss of PMS2 expression in the MSI-high phenotype, discordant cases of loss of expression between MLH1 and PMS2 were found (MLH1 [−]/PMS2 [+], 3 cases). Loss of MLH1/PMS2 expression was observed in 2 of 73 MSS phenotypes. Loss of MSH2/MSH6 expression was found in 4 of 46 MSI-high phenotypes, whereas loss of MSH2/MSH6 expression was not detected in the MSS phenotype. In addition, loss of MLH1 expression was correlated with methylation of <i>MLH1</i>. However, there were discordant cases in which loss of MLH1 expression was not accompanied by methylation of <i>MLH1</i>. <b><i>Conclusion:</i></b> Although immunostaining of MMR proteins could help predict MSI in EGCs, immunostaining did not have the same value as genetic testing for determination of MSI.
DOI: 10.6084/m9.figshare.13089107
title: Supplementary Material for: A Targeted Multiomics Approach to Identify Biomarkers Associated with Rapid eGFR Decline in Type 1 Diabetes

description: <b><i>Background:</i></b> Individuals with type 1 diabetes (T1D) demonstrate varied trajectories of estimated glomerular filtration rate (eGFR) decline. The molecular pathways underlying rapid eGFR decline in T1D are poorly understood, and individual-level risk of rapid eGFR decline is difficult to predict. <b><i>Methods:</i></b> We designed a case-control study with multiple exposure measurements nested within 4 well-characterized T1D cohorts (FinnDiane, Steno, EDC, and CACTI) to identify biomarkers associated with rapid eGFR decline. Here, we report the rationale for and design of these studies as well as results of models testing associations of clinical characteristics with rapid eGFR decline in the study population, upon which “omics” studies will be built. Cases (<i>n</i> = 535) and controls (<i>n</i> = 895) were defined as having an annual eGFR decline of ≥3 and &lt;1 mL/min/1.73 m<sup>2</sup>, respectively. Associations of demographic and clinical variables with rapid eGFR decline were tested using logistic regression, and prediction was evaluated using area under the curve (AUC) statistics. Targeted metabolomics, lipidomics, and proteomics are being performed using high-resolution mass-spectrometry techniques. <b><i>Results:</i></b> At baseline, the mean age was 43 years, diabetes duration was 27 years, eGFR was 94 mL/min/1.73 m<sup>2</sup>, and 62% of participants were normoalbuminuric. Over 7.6-year median follow-up, the mean annual change in eGFR in cases and controls was −5.7 and 0.6 mL/min/1.73 m<sup>2</sup>, respectively. Younger age, longer diabetes duration, and higher baseline HbA1c, urine albumin-creatinine ratio, and eGFR were significantly associated with rapid eGFR decline. The cross-validated AUC for the predictive model incorporating these variables plus sex and mean arterial blood pressure was 0.74 (95% CI: 0.68–0.79; <i>p</i> &lt; 0.001). <b><i>Conclusion:</i></b> Known risk factors provide moderate discrimination of rapid eGFR decline. Identification of blood and urine biomarkers associated with rapid eGFR decline in T1D using targeted omics strategies may provide insight into disease mechanisms and improve upon clinical predictive models using traditional risk factors.
DOI: 10.6084/m9.figshare.13089143
title: Han et al., WEOM and BEOM in Yangtze River-East China Searaw data.xlsx

description: Bulk geochemical data of surface sediments as well as water soluble organic matter (WEOM) and base soluble organic matter (BEOM) from surface sediments in lower Yangtze River, Estuary and East China Sea<br>
DOI: 10.34812/qfd4-3d81
title: Rolls of Honour

description: This dataset represents the Scottish First World War Rolls of Honour, which contain listings of casualties and those who died while on active service during the War.
Date range: 1914-1919
OCR quality: original OCR
File types/numbers: 9,952 ALTO XML files; 9,952 image files; 64 METS files; 64 plain text files.
DOI: 10.21227/m2dk-zy75
title: Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4

description: This India-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