cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.14457/mu.the.2000.761
title: Birth spacing practice among married women after first and second birth in Chittagong and Khulna division in Bangladesh : a comparative analysis
DOI: 10.14457/mu.the.2000.762
title: Women's social participation and contraceptive use in Bangladesh
DOI: 10.14457/mu.the.2000.763
title: The effect of selected socio-demographic characteristics on desire for additional children among couples in Bangladesh
DOI: 10.14457/mu.the.2000.764
title: Development of the reproductive system and localization of egg-laying hormone (ELH) in the gonad of a tropical abalone, Haliotis asinina Linnaeus
DOI: 10.14457/mu.the.2000.765
title: Factors affecting breastfeeding practice among Myanmar mothers in northern district of Yangon
DOI: 10.14457/mu.the.2000.766
title: The effectiveness of health education program on the complication of pregnancy among pregnant women attending antenatal care clinic at Ratchaburi hospital
DOI: 10.14457/mu.the.2000.767
title: A study of number from a translated version of Many Lives by Meredith Borthwick
DOI: 10.14457/mu.the.2000.768
title: Structure and development of nerve ganglia, peripheral nervous system and special sensory organs in Haliotis asinina Linnaeus
DOI: 10.14457/mu.the.2000.769
title: Monitoring shrimp farm expansion and assessing its compliance to land use plan along Prasae and Pangrat river, Rayong province
DOI: 10.14457/mu.the.2000.770
title: Heme and nonheme iron content in raw and cooked animal products
DOI: 10.5281/zenodo.3889168
title: YouTube-ASMR-300K

description: The YouTube-ASMR dataset contains URLS for over 900 hours of ASMR video clips with stereo/binaural audio produced by various YouTube artists. The following paper contains a detailed description of the dataset and how it was compiled: K. Yang, B. Russell and J. Salamon, "Telling Left from Right: Learning Spatial Correspondence of Sight and Sound", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference, June 2020.
DOI: 10.5281/zenodo.3889167
title: YouTube-ASMR-300K

description: The YouTube-ASMR dataset contains URLS for over 900 hours of ASMR video clips with stereo/binaural audio produced by various YouTube artists. The following paper contains a detailed description of the dataset and how it was compiled: K. Yang, B. Russell and J. Salamon, "Telling Left from Right: Learning Spatial Correspondence of Sight and Sound", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Virtual Conference, June 2020.
DOI: 10.5439/1632815
title: ARM: AAFWCM

description: wcm-air
DOI: 10.5439/1632816
title: ARM: AAFCAMMOVIE

description: cam-air
DOI: 10.17632/tnshkjd6x5.1
title: Data for: WEST: Water Economy Simulation Tool to Predict Impacts of Economic and Environmental Shocks

description: Model code including data required for model
DOI: 10.5519/qd.m1l4pv89
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (111 records)
DOI: 10.21227/h62t-2c28
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 170+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data, which currently does not exist or is private.
DOI: 10.21227/f87k-fa45
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 170+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.21227/h33x-0a42
title: Defective photonic bandgap crystals using Finite Difference Time Domain (FDTD)

description: This is a dataset of Finite Difference Time Domain (FDTD) simulation results of 13 defective crystals and one non-defective crystal. There are 4 fields in the dataset, namely: Real, Img, Int, and Attribute. The header real shows a real part of the simulated result, img shows the imaginary part, int gives the intensity all in superimposed form. Attribute denotes the label of a crystal simulated. The label 0 is for the simulated crystal, which is non-defective. Other 13 labels, from crystal 1 to crystal 13 are assigned to the 13 different crystals whose simulations are studied.
DOI: 10.21227/5ww9-2h17
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything aboutnbsp;“corona”, coronavirus, covid and possible variants of sarscov2, nCov,nbsp;covid-19, ncov2019.nbsp;Complying with Twitter's content redistribution policy,nbsp;only thenbsp;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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 170+ million tweets) shared in thenbsp;Coronavirus (COVID-19) Tweetsnbsp;Dataset.If you need the geolocation-based data starting March 20, 2020, then use thenbsp;Coronavirus (COVID-19) Tweetsnbsp;Datasetnbsp;and hydrate the IDs while adding the following condition:data = json.loads(data)if data[coordinates]:nbsp; nbsp; nbsp; nbsp;longitude, latitude = data[coordinates][coordinates]The data is available in two formats: CSV and JSON.nbsp;I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared?nbsp;Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet ornbsp;make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.25919/5ee30b9fc16be
title: Parkes observations for project P595 semester 2020APRS_06

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the FERMI Gamma ray mission, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.21227/xp90-9e38
title: 300-Dimensional Word Embeddings for Nepali Language

description: This pre-trained Word2Vec model has 300-dimensional vectors for more than 0.5 million Nepali words and phrases. A separate Nepali language text corpus was created using the news contents freely available in the public domain. The text corpus contained more than 90 million running words.Word2Vec model details: Embeddings Dimension: 300, Architecture: Continuous - BOW, Training algorithm: Negative sampling = 15, Context (window) size: 10, Token minimum count: 2, Encoded in UTF-8.
DOI: 10.21227/f2w0-v624
title: Twitter Sentiment Analysis Data

description: This dataset page is currently being updated. The tweets collected by the model deployed at https://live.rlamsal.com.np/ are shared here. However, because of COVID-19, all computing resources I have are being used for a dedicated collection of the tweets related to the pandemic. You can go through the following datasets to access those tweets:Coronavirus (COVID-19) Tweets Dataset:https://ieee-dataport.org/open-access/coronavirus-covid-19-tweets-datasetCoronavirus (COVID-19) Geo-tagged Tweets Dataset:https://ieee-dataport.org/open-access/coronavirus-covid-19-geo-tagged-tweets-dataset
DOI: 10.21227/jaa1-rs62
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains the IDs of geo-tagged tweets. The tweets were captured by an on-going project deployed at https://live.rlamsal.com.np. The geolocation data was extracted from the tweets which mentioned anything about “corona”, "coronavirus", "covid" and possible variants of "sarscov2", "nCov", "covid-19", "ncov2019". 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. Please note that this dataset should be solely used for non-commercial research purposes (ignore every other LICENSE category given on this page).Note: I started sharing the IDs of the tweets that had exact 'point' location information, only since April 28, 2020, with some genuine requests coming in from academic researchers who did not want to hydrate the whole lists of IDs (above 170+ million tweets) shared in the Coronavirus (COVID-19) Tweets Dataset.If you need the geolocation-based data starting March 20, 2020, then use the Coronavirus (COVID-19) Tweets Dataset and hydrate the IDs while adding the following condition:data = json.loads(data)if data["coordinates"]: longitude, latitude = data["coordinates"]["coordinates"]The data is available in two formats: CSV and JSON. I'll be sharing new files every day, and the files will be named period-wise. For example, april28-june5.zip will contain tweet ID and sentiment score of the tweets (in CSV and JSON formats) that were created between April 28, 2020, and June 05, 2020.Why are only tweet IDs being shared? Twitter's content redistribution policy restricts the sharing of tweet information other than tweet IDs and/or user IDs. Twitter wants researchers always to pull fresh data. It is because a user might delete a tweet or make their profile protected. If the same tweet has already been pulled and shared on a public domain, it might make the user/community vulnerable to many inferences coming out of the shared data which currently does not exist or is private.
DOI: 10.14457/cu.res.2016.71
title: การสร้างคลังข้อมูลภาษาไทยแบบสั่งสมและการสกัดหาคำเขียนผิดอักขรวิธีและคำปรากฏใหม่
DOI: 10.14457/cu.res.2014.110
title: โครงการ การจำลองการปลูกฟิล์มบางแบบโมเลกุลาร์บีมเอพิแทกซีด้วยทฤษฎีกราฟ
DOI: 10.14457/cu.res.2012.141
title: โครงการฤทธิ์สารสกัดเนื้อส้มโอต่อการยับยั้งการย่อยคาร์โบไฮเดรตและไขมัน และการต้านไกลเคชั่น
DOI: 10.14457/cu.res.2016.72
title: โครงการ การศึกษารูปแบบ วิธีการสอน และวิเคราะห์เปรียบเทียบผลสัมฤทธิ์ของ การสอนอ่าน เขียนภาษาไทยในระดับประถมศึกษา ในสมัยรัชกาลที่ ๙
DOI: 10.14457/cu.res.2017.38
title: โครงการ พัฒนาการรูปแบบวิธีสอนอ่าน เขียนภาษาไทย ในระดับประถมศึกษาตั้งแต่สมัยรัชกาลที่ 5 จนถึงปัจจุบัน
DOI: 10.14457/cu.res.2010.134
title: ศูนย์วิจัยระบบการวางแผนจัดการทรัพยากรน้ำเพื่อความมั่นคงระดับจังหวัด
DOI: 10.14457/cu.res.2009.140
title: โครงการการพัฒนาเครื่องมือสนับสนุนการบริหารเชิงกลยุทธ์สำหรับชีวมวลจากอุตสาหกรรมยางพาราและปาล์มน้ำมัน
DOI: 10.14457/cu.res.2010.135
title: การศึกษากระบวนการการเข้าเป็นภาคีอนุสัญญาสหประชาชาติว่าด้วยกฎหมายทะเล ค.ศ. 1982
DOI: 10.14457/cu.res.2017.39
title: โครงการ ไทยกับการกู้ชาติเวียดนามในช่วงกลางคริสต์ศตวรรษที่ 20
DOI: 10.14457/cu.res.2008.141
title: โครงการการผลิตอิฐดินเชิงประกอบจากแกลบและน้ำยางธรรมชาติ
DOI: 10.14457/cu.res.2013.108
title: การประเมินสภาวะความรุนแรงสภาพภูมิอากาศของประเทศไทย: การวิเคราะห์ความเสี่ยง และความล่อแหลมของพื้นที่วิกฤติ ระยะที่ 2: การวิเคราะห์ความล่อแหลมและการประเมินความเสี่ยงจากภาวะความรุนแรง ของฝนในพื้นที่วิกฤติ
DOI: 10.14457/cu.res.2017.40
title: การศึกษาโครงสร้างเศรษฐกิจการเงินของประเทศสิงคโปร์
DOI: 10.14457/cu.res.2017.41
title: การพัฒนาหัวเชื้อแบคทีเรียผสมเพื่อส่งเสริมการเจริญและการฟื้นตัวของพืชเศรษฐกิจในสภาวะแห้งแล้ง
DOI: 10.14457/cu.res.2017.42
title: รายงานการวิเคราะห์ สถานการณ์ น้ำของประเทศไทย: ทรัพยากรน้ำกับการพัฒนาเศรษฐกิจ
DOI: 10.14457/cu.res.2002.32
title: การสร้างระบบประสานงานข้อมูลการนำเข้าสารเคมีอันตราย
DOI: 10.14457/cu.res.2012.142
title: คุณลักษณะและผลของสารสกัดผลหม่อนต่อการเจริญเติบโต และการเปลี่ยนแปลงไปเป็นกระดูกของเซลล์ต้นกำเนิดไขกระดูกหนูนอกร่างกาย
DOI: 10.14457/cu.res.2017.43
title: โครงการ อัตราภาษีเงินได้นิติบุคคลที่มีผลจริงสำหรับการลงทุนทางตรงของธุรกิจไทยในประเทศอาเซียนที่สำคัญ
DOI: 10.14457/cu.res.2018.30
title: โครงการ สิทธิป้องกันตนเองร่วมในการวางยุทธศาสตร์ความมั่นคงของญี่ปุ่น กรณีศึกษาทศวรรษ 1990 และทศวรรษ 2010
DOI: 10.14457/cu.res.2018.31
title: โครงการ การบริหารจัดการ Denken เขตเพื่อการอนุรักษ์กลุ่มอาคาร สถาปัตยกรรม และสิ่งปลูกสร้างดั้งเดิมทรงคุณค่าในประเทศญี่ปุ่น
DOI: 10.14457/cu.res.2012.143
title: การประเมินประสิทธิผลของการแก้ไขปัญหาการกัดเซาะ ชายฝั่งด้วยโครงสร้างและวิเคราะห์การเปลี่ยนแปลงพื้นท้องทะเลและแนว ชายฝั่งในพื้นที่นำร่อง บ้านขุนสมุทรจีน บริเวณอ่าวไทยตอนบน
DOI: 10.14457/cu.res.2010.136
title: การเตรียมความพร้อมเพื่อการจัดการก๊าซเรือนกระจกสำหรับอุตสาหกรรมเหล็กและเหล็กกล้าในประเทศไทย
DOI: 10.14457/cu.res.2018.32
title: โครงการวิจัยและประสานงานเพื่อสังคมไทยไร้คอร์รัปชัน
DOI: 10.14457/cu.res.2001.22
title: โครงการศึกษาเบื้องต้น เรื่อง ระบบการบริหารจัดการท้องถิ่น
DOI: 10.14457/cu.res.2015.87
title: การศึกษาเครื่องปฏิกรณ์แบบหลายหน้าที่และการรวมกระบวนการสำหรับอุตสาหกรรมเคมี ปิโตรเคมี และไบโอรีไฟเนอรี
DOI: 10.14457/cu.res.2011.106
title: Demographic Change, Perceived Vulnerabilities in Old Age and Economic Welfare in Rural Thailand
DOI: 10.14457/cu.res.2015.88
title: โครงการ ปมอิดิปุสและแผลฝังใจในวรรณกรรม และภาพยนตร์ไทยสมัยใหม่
DOI: 10.14457/cu.res.2017.44
title: โครงการวิธีสำหรับตัวแบบสถิติที่มีข้อจำกัดการจัดลำดับ
DOI: 10.14457/cu.res.2009.141
title: ไทยในการรับรู้และความเข้าใจของประเทศเพื่อนบ้าน
DOI: 10.14457/cu.res.2013.109
title: การเตรียมความพร้อมรับมือโรคอุบัติใหม่และอุบัติซ้ำเชิงบูรณาการ
DOI: 10.14457/cu.res.2013.110
title: โครงการ การแยกสกัดและการศึกษาสมบัติทางเคมีกายภาพของเฮมิเซลลูโลสในรำที่ได้จากการสีข้าว พันธุ์ขาวดอกมะลิ 105 และข้าวสีนิล
DOI: 10.18170/dvn/fbwpvh
title: C677T polymorphism in the MTHFR gene is associated with risperidone-induced weight gain in schizophrenia

description: To explore the association of methylenetetrahydrofolate reductase (MTHFR) C677T polymorphism with risperidone-induced weight gain. We analyzed the association between MTHFR C677T polymorphism and risperidone-induced weight gain in 356 schizophrenia patients. We found a significant association between MTHFR C677T polymorphism and body mass index (BMI) change after 8-week treatment with risperidone. After 8- week treatment with risperidone, increased BMI values (kg/m2) of MTHFR C677 carriers was significantly higher than that of TT genotype carriers
DOI: 10.17632/8bxy2hss6b.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 3-1)
DOI: 10.17632/rw2p53j2dj.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part1)
DOI: 10.17632/k8ynpz4dn5.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-seq part 7)
DOI: 10.17632/89pj8hgt4w.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 3)
DOI: 10.17632/thgf5wc65n.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 2)
DOI: 10.17632/tpy74b58rm.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 6)
DOI: 10.17632/447vzpstp4.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 5)
DOI: 10.17632/fgm74n5nrx.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 4)
DOI: 10.17632/wn9vffxx9g.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 8)
DOI: 10.17632/khdf275tcv.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 9)
DOI: 10.17632/g8wwg7sczf.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 10)
DOI: 10.17632/6f9c39bz8x.2
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 11)
DOI: 10.17632/jsghd3xxv4.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 995772T)
DOI: 10.17632/5rw8sxf9g7.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 9916464T)
DOI: 10.17632/dmfm24py4h.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 997270T section 2)
DOI: 10.17632/njvkxf4m7x.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample 997270T section 1)
DOI: 10.17632/76sk4t9hkf.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 1)
DOI: 10.17632/p7phgdts95.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 2)
DOI: 10.17632/m6hjdjsrgx.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 3-2)
DOI: 10.17632/fb59x46pdn.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 3-4)
DOI: 10.17632/v8dsnt99gc.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 3-3)
DOI: 10.17632/5kwrwvmz4h.4
title: Fusion Pten-NOLC1 Promotes C-Met and EGFR Signals in Human Cancers (Chip-Seq part 12)
DOI: 10.17632/tgnfkxpn7n.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample FB174T section 3-5)
DOI: 10.17632/7h6pw45gnx.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 1)
DOI: 10.17632/tshz3zw7w7.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 2)
DOI: 10.17632/jw2gkwwwng.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 2 & section 3-1)
DOI: 10.17632/zjv62vx29h.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 3-2)
DOI: 10.17632/by5tpsf7yj.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 3-3)
DOI: 10.17632/xs5p5npjzz.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB207T section 3-4)
DOI: 10.17632/r3srrxnpkm.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB568T section 1)
DOI: 10.17632/6576ynbtvc.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB568T section 2)
DOI: 10.17632/jr7fk92bb2.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample IB483T section 1)
DOI: 10.17632/xt4hn459fp.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample HB483T section 2)
DOI: 10.17632/n4d7mymn5c.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 1)
DOI: 10.17632/f5nwfyhxk9.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 2 & section 3-1)
DOI: 10.17632/nktv5xy357.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 3-2)
DOI: 10.17632/s6ypnpxk72.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 3-3)
DOI: 10.17632/z82b3599tn.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 3-4)
DOI: 10.17632/pwrwdyy375.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 3-5)
DOI: 10.17632/9945b5fhk6.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample KB170T section 1)
DOI: 10.17632/5fn3xw2bhv.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample JB197T section 3-6)
DOI: 10.17632/fbm7h4fyhn.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample KB170T section 2-2)
DOI: 10.17632/5k3f8mw22y.2
title: Pten-NOLC1 Fusion Promotes C-MET and EGFR Signaling in Human Cancers (RNAseq Sample KB170T section 2-1)