cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3603610
title: Semantic text mining in early drug discovery for type 2 diabetes

description: BACKGROUND: Surveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g.\ recently discovered modes of action) and identifies potential novel drug target, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs.<br> <br> METHODS: We extracted over 7 million <em>n</em>-grams from PubMed and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant `semantic concepts'. To score papers, these concepts were weighted depending on co-mentioning with core T2D proteins. A protein's current T2D relevance was determined by combining the scores of the papers mentioning it in the preceeding five years. The significance of a jump in a protein's rank was assessed by comparing it to previously observed jumps.<br> <br> RESULTS: We show that T2D relevant papers, also those not mentioning T2D explicitly, got assigned high scores by mentioning semantic concepts often used in connection with T2D, as shown by the enrichment of well known T2D proteins among the top scoring proteins. Our `high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.
DOI: 10.5281/zenodo.3603609
title: Semantic text mining in early drug discovery for type 2 diabetes

description: BACKGROUND: Surveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g.\ recently discovered modes of action) and identifies potential novel drug target, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs.<br> <br> METHODS: We extracted over 7 million <em>n</em>-grams from PubMed and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant `semantic concepts'. To score papers, these concepts were weighted depending on co-mentioning with core T2D proteins. A protein's current T2D relevance was determined by combining the scores of the papers mentioning it in the preceeding five years. The significance of a jump in a protein's rank was assessed by comparing it to previously observed jumps.<br> <br> RESULTS: We show that T2D relevant papers, also those not mentioning T2D explicitly, got assigned high scores by mentioning semantic concepts often used in connection with T2D, as shown by the enrichment of well known T2D proteins among the top scoring proteins. Our `high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline.
DOI: 10.5281/zenodo.3895551
title: Cinque Terre Flooding | 25 Oct 2011 | WRF, Meso-NH, MOLOCH Ensemble Reforecasts

description: Cinque Terre Flooding | 25 October 2011 Ensemble-based reforecasts using the WRF, Meso-NH and MOLOCH models Variables are described in the control (.ctl) files Starting dates: 2011/10/23 00 UTC, 2011/10/23 12 UTC, 2011/10/24 00 UTC, 2011/10/24 12 UTC Ending date: 2011/10/26 00 UTC
DOI: 10.5281/zenodo.3895550
title: Cinque Terre Flooding | 25 Oct 2011 | WRF, Meso-NH, MOLOCH Ensemble Reforecasts

description: Cinque Terre Flooding | 25 October 2011 Ensemble-based reforecasts using the WRF, Meso-NH and MOLOCH models Variables are described in the control (.ctl) files Starting dates: 2011/10/23 00 UTC, 2011/10/23 12 UTC, 2011/10/24 00 UTC, 2011/10/24 12 UTC Ending date: 2011/10/26 00 UTC
DOI: 10.25439/rmt.12356363
title: Courier Trajectories 'eCourier dataset'

description: Group of movement trajectories of couriers operating in London, UK.<br>https://rd.eres.rmit.edu.au/experiment/view/2/<br><br>This dataset is a collection of courier trajectories, captured principally around London over a continuous eight week period. This is a useful example of real movement trajectories, which could potentially be used for benchmarking or for the development of spatio-temporal analytics. In addition to the principle data file,which contains 9,917,703 discrete data points, sixteen different spatial and temporal summaries have been included in the related experiment to aid analysis.
DOI: 10.25439/rmt.12356363.v1
title: Courier Trajectories 'eCourier dataset'

description: Group of movement trajectories of couriers operating in London, UK.<br>This dataset is a collection of courier trajectories, captured principally around London over a continuous eight week period. This is a useful example of real movement trajectories, which could potentially be used for benchmarking or for the development of spatio-temporal analytics. In addition to the principle data file,which contains 9,917,703 discrete data points, sixteen different spatial and temporal summaries have been included in the related experiment to aid analysis.
DOI: 10.5281/zenodo.3888942
title: Supplemental Material to Article "Impact of manufacture-induced blade shape distortion on turbine loads and energy yield"

description: This set supplements the figure data to the article "Impact of manufacture-induced blade shape distortion on turbine loads and energy yield", DOI: .
DOI: 10.5281/zenodo.3888941
title: Supplemental Material to Article "Impact of manufacture-induced blade shape distortion on turbine loads and energy yield"

description: This set supplements the figure data to the article "Impact of manufacture-induced blade shape distortion on turbine loads and energy yield", DOI: .
DOI: 10.5281/zenodo.3895376
title: List of genomes used in Identification of a low pathogenicity clade of SARS-CoV-2

description: Supplemental table 1: list of genomes used in the analysis.
DOI: 10.5281/zenodo.3895375
title: List of genomes used in Identification of a low pathogenicity clade of SARS-CoV-2

description: Supplemental table 1: list of genomes used in the analysis.
DOI: 10.5281/zenodo.3895265
title: Covid tweets (Feb-May)

description: Tweets about COVID-19 from users with 1000+ followers. Data from WHO.
DOI: 10.5281/zenodo.3895264
title: Covid tweets (Feb-May)

description: Tweets about COVID-19 from users with 1000+ followers. Data from WHO.
DOI: 10.14457/tu.res.2009.267
title: ประมวลศัพท์ เรื่อง "พระราชพิธีพระศพ สมเด็จพระเจ้าพี่นางเธอเจ้าฟ้ากัลยาณิวัฒนา กรมหลวงนราธิวาสราชนครินทร์" พร้อมบทวิเคราะห์
DOI: 10.14457/tu.res.2010.35
title: ปัจจัยที่มีผลต่อการยอมรับนำพาณิชย์อิเล็กทรอนิกส์ไปใช้ในธุรกิจขนาดเล็ก :
DOI: 10.14457/tu.res.2010.36
title: การสร้างแบบจำลองพฤติกรรมผู้บริโภคแบบธุรกิจกับธุรกิจผ่านทางช่องทางพาณิชย์อิเล็กทรอนิกส์เพื่อสนับสนุนกิจกรรมทางการตลาดโดยการทำเหมืองข้อมูล
DOI: 10.14457/tu.res.2010.37
title: ปัจจัยที่มีผลต่อพฤติกรรมการบริโภคหนังสือแปลประเภทนวนิยายของวัยรุ่นในเขตกรุงเทพมหานคร
DOI: 10.14457/tu.res.2010.38
title: ผลกระทบเขตการค้าเสรีอาเซียนต่ออุตสาหกรรมเม็ดพลาสติกพีวีซี
DOI: 10.14457/tu.res.2010.39
title: กลยุทธ์การตลาดของธุรกิจรถเช่า :
DOI: 10.14457/tu.res.2010.40
title: ปัจจัยที่มีผลต่อการยอมรับโอเพ่นซอร์สซอฟต์แวร์ในสถาบันการศึกษาระดับอุดมศึกษา :
DOI: 10.14457/tu.res.2010.41
title: การขอบริจาคเงินเพื่อการกุศลบนเครือข่ายทางสังคมออนไลน์ :
DOI: 10.14457/tu.res.2010.42
title: ปัจจัยที่มีผลต่อการยอมรับในการใช้งาน e-service เพื่อชำระค่าบริการของผู้ใช้บริการโทรศัพท์เคลื่อนที่ AIS ในกรุงเทพมหานคร
DOI: 10.14457/tu.res.2010.43
title: การศึกษาปัจจัยที่มีอิทธิพลต่อความพึงพอใจในการดำเนินงานบริหารอาคารของบริษัทรับบริหารอาคาร :
DOI: 10.14457/tu.res.2010.44
title: ศึกษาคุณภาพบริการของสถาบันเทคโนโลยีปัญญาภิวัฒน์ที่มีผลต่อความพึงพอใจของนักศึกษา
DOI: 10.14457/tu.res.2010.45
title: ปัจจัยที่ส่งผลต่อการตัดสินใจใช้บริการธนาคารทางอินเทอร์เน็ตอย่างต่อเนื่อง
DOI: 10.5281/zenodo.3250651
title: Wikidata Vandalism Corpus 2015 (WDVC-15)

description: The Wikidata vandalism corpus 2015 (WDVC-15) is a corpus for the evaluation of automatic vandalism detectors for Wikidata. For research purposes the corpus can be used free of charge.
description: {"references": ["Stefan Heindorf, Martin Potthast, Benno Stein, and Gregor Engels. Towards Vandalism Detection in Knowledge Bases: Corpus Construction and Analysis. In Ricardo Baeza-Yates, Mounia Lalmas, Alistair Moffat, and Berthier Ribeiro-Neto, editors, 38th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2015), pages 831-834, August 2015. ACM. ISBN 978-1-4503-3621-5"]}
DOI: 10.5281/zenodo.3250650
title: Wikidata Vandalism Corpus 2015 (WDVC-15)

description: The Wikidata vandalism corpus 2015 (WDVC-15) is a corpus for the evaluation of automatic vandalism detectors for Wikidata. For research purposes the corpus can be used free of charge.
description: {"references": ["Stefan Heindorf, Martin Potthast, Benno Stein, and Gregor Engels. Towards Vandalism Detection in Knowledge Bases: Corpus Construction and Analysis. In Ricardo Baeza-Yates, Mounia Lalmas, Alistair Moffat, and Berthier Ribeiro-Neto, editors, 38th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2015), pages 831-834, August 2015. ACM. ISBN 978-1-4503-3621-5"]}
DOI: 10.5281/zenodo.3866327
title: Danish Research Projects within Smart Grids 2010-2019

description: List and assessment of nationally funded projects in Denmark within the Smart Grids field, covering years 2010-2019. The data regarding the projects is retrieved from www.energiforskning.dk.
DOI: 10.5281/zenodo.3866326
title: Danish Research Projects within Smart Grids 2010-2019

description: List and assessment of nationally funded projects in Denmark within the Smart Grids field, covering years 2010-2019. The data regarding the projects is retrieved from www.energiforskning.dk.
DOI: 10.5519/qd.pi8654xs
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2051 records)
DOI: 10.21227/cn5z-5380
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages. For more details, please refer to this paper: https://arxiv.org/abs/2005.11177
DOI: 10.5281/zenodo.3895561
title: Genoa Flooding | 4 Nov 2011 | WRF, Meso-NH, MOLOCH Ensemble Reforecasts

description: Genoa Flooding | 4 November 2011 Ensemble-based reforecasts using the WRF, Meso-NH and MOLOCH models Variables are described in the control (.ctl) files Starting dates: 2011/11/02 00 UTC, 2011/11/02 12 UTC, 2011/11/03 00 UTC, 2011/11/03 12 UTC Ending date: 2011/11/05 00 UTC
DOI: 10.5281/zenodo.3895560
title: Genoa Flooding | 4 Nov 2011 | WRF, Meso-NH, MOLOCH Ensemble Reforecasts

description: Genoa Flooding | 4 November 2011 Ensemble-based reforecasts using the WRF, Meso-NH and MOLOCH models Variables are described in the control (.ctl) files Starting dates: 2011/11/02 00 UTC, 2011/11/02 12 UTC, 2011/11/03 00 UTC, 2011/11/03 12 UTC Ending date: 2011/11/05 00 UTC
DOI: 10.21227/f572-zx96
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.The dataset was collected using more than 800 multilingual keywords and hashtags. The complete list of keywords can be downloaded from here: https://crisisnlp.qcri.org/covid19 For more details, please refer to this paper: https://arxiv.org/abs/2005.11177
DOI: 10.21227/mmgq-6n43
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.The dataset was collected using more than 800 multilingual keywords and hashtags. The complete list of keywords can be downloaded from here: https://crisisnlp.qcri.org/covid19 For more details, please refer to this paper: https://arxiv.org/abs/2005.11177
DOI: 10.5281/zenodo.3894718
title: Single-cell data on lac operon induction by lactose in E. coli

description: Raw data from experiments presented in https://doi.org/10.1101/2020.01.04.894766. Escherichia coli cells are exposed alternatively to glucose and lactose as carbon source, while their growth and lac operon expression are monitored using phase contrast and epifluorescence microscopy. Data from 36 independent experiments are provided: details about conditions are given in Julou_2020_lacInduction_assays.xlsx, and the list of data files in Julou_2020_lacInduction_GL_Preproc_fileList.txt
DOI: 10.21227/7hgd-fs84
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: Abstract:We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.The dataset was collected using more than 800 multilingual keywords and hashtags. The complete list of keywords can be downloaded from here: https://crisisnlp.qcri.org/covid19 For more details, please refer to this paper: https://arxiv.org/abs/2005.11177
DOI: 10.5281/zenodo.3895734
title: DCASE2020_v4

description: DCASE2020 pretrained task6 v4
DOI: 10.5281/zenodo.3895542
title: DCASE2020_v4

description: DCASE2020 pretrained task6 v4
DOI: 10.5258/soton/d1384
title: Dataset for: Hypoxia drives the assembly of the multi-enzyme purinosome complex

description: Dataset supporting the paper: Doigneaux, C., Pedley, A., Mistry, I. N., Papayova, M., Benkovic, S., & Tavassoli, A. (Accepted/In press). Hypoxia drives the assembly of the multi-enzyme purinosome complex. The Journal of Biological Chemistry. DOI:10.1074/jbc.RA119.012175
DOI: 10.5258/soton/d1420
title: Dataset for 'Feeling the Difference: Discrimination of Vibration Frequency and Acceleration Magnitude in the Tactile Channels'

description: Excel file containing difference limens and weber fractions for 4 psycho-physical experiments investigating vibration frequency and acceleration magnitude discrimination performance on the glabrous skin of the hand. Supports the PhD thesis: Feeling the Difference: Discrimination of Vibration Frequency and Acceleration Magnitude in the Tactile Channels (2020)
DOI: 10.5281/zenodo.3878599
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.
DOI: 10.5281/zenodo.3878598
title: GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

description: We present GeoCoV19, a large-scale Twitter dataset related to the ongoing COVID-19 pandemic. The dataset has been collected over a period of 90 days from February 1 to May 1, 2020 and consists of more than 524 million multilingual tweets. As the geolocation information is essential for many tasks such as disease tracking and surveillance, we employed a gazetteer-based approach to extract toponyms from user location and tweet content to derive their geolocation information using the Nominatim (Open Street Maps) data at different geolocation granularity levels. In terms of geographical coverage, the dataset spans over 218 countries and 47K cities in the world. The tweets in the dataset are from more than 43 million Twitter users, including around 209K verified accounts. These users posted tweets in 62 different languages.
DOI: 10.5281/zenodo.3895681
title: Cutting through the Jungle: Disambiguating Model-based Traceability Terminology — Terminology Mapping Table

description: This dataset contains the mapping of model-based traceability terminology developed in "Cutting through the Jungle: Disambiguating Model-based Traceability Terminology" to the secondary and primary sources that have been used as part of the validation of the terminology.
DOI: 10.5281/zenodo.3672745
title: Cutting through the Jungle: Disambiguating Model-based Traceability Terminology — Terminology Mapping Table

description: This dataset contains the mapping of model-based traceability terminology developed in "Cutting through the Jungle: Disambiguating Model-based Traceability Terminology" to the secondary and primary sources that have been used as part of the validation of the terminology.
DOI: 10.25405/data.ncl.9892238.v1
title: Low temperature methane conversion via exsolved Co particles

description: Lowering the temperature at which CH<sub>4</sub> is converted to useful products has been long-sought in energy conversion applications. Selective conversion to syngas is additionally desirable. Generally, most of the current CH<sub>4</sub> activation processes operate at temperatures between 600 and 900 °C when non-noble metal systems are used. These temperatures can be even higher for redox processes where a gas phase-solid reaction must occur. Here we employ the endogenous-exsolution concept to create a perovskite oxide with surface and embedded metal nanoparticles able to activate methane at temperatures as low as 450 °C in a cyclic redox process. We achieve this by using a non-noble, Co-Ni-based system with tailored nano- and micro-structure. The materials designed and prepared in this study demonstrate long-term stability and resistance to deactivation mechanisms while still being selective when applied for chemical looping partial oxidation of methane. <br>
DOI: 10.25919/5ee88fe3e0210
title: Parkes observations for project PX600 semester 2020APRS_09

description: The Galactic Center (GC) is a key target for the radio component of Breakthrough Listen (BL) program. Offering the largest amount of stars in any given direction in the sky, the GC is widely cited as a location believed to harbour advanced civilizations, and it is also the most energetic region in the Milky Way. We aim to conduct BL-GC Survey with Parkes radio telescope for around 350 hours. We will cover the frequency range from 700 MHz to 4 GHz, utilizing the newly installed ultra-wideband receiver. Our team has already leveraged both standard and bespoke tools to construct a flexible software stack to search data for signals of interest. We will look for signals from extraterrestrial intelligence (ETI) by searching for both simple narrow-band signals and complex modulated signals. Along with that, as an ancillary science, we will also search for accelerated pulsars — likely orbiting a supermassive black hole at the centre of the Milky Way Galaxy. Data products produced during this program will also be publically available to engage larger pulsar community