cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.16894954
title: May 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>May, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16894957
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16894957.v1
title: April 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>April, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16894963
title: Supplementary Material for: Drug Adherence and Persistence of Patients with Moderate to Severe Psoriasis Treated with Biologic Medications in a US Commercially Insured Population

description: <b><i>Background:</i></b> Adalimumab (ADA), certolizumab pegol (CER), etanercept (ETA), guselkumab (GUS), ixekizumab (IXE), secukinumab (SEC), and ustekinumab (UST) are biologic medications approved in the USA for the treatment of moderate to severe psoriasis. We examined drug adherence and persistence of patients with moderate to severe psoriasis who initiated these seven biologic medications. <b><i>Methods:</i></b> Adult patients with ≥1 pharmacy/medical claim for any of the seven psoriasis medications and ≥1 diagnosis of psoriasis in the previous 6 months between July 1, 2014 and June 30, 2019 were selected from the IBM MarketScan® Commercial Claims and Encounters Database. The index date was defined as the date of the first prescription fill. Patients were required to have continuous health plan enrollment during the 6 months prior to their index date and ≥9 months after. Patients were grouped into seven study cohorts based upon their index biologic medication. Adherence was measured using the proportion of days covered (PDC) and defined by a PDC ≥80%. Adherence and persistence with index biologic medications were examined during fixed follow-up periods of 3, 6, and 9 months, with a subpopulation analysis carried out among patients with 12 months of follow-up. <b><i>Results:</i></b> Among psoriasis patients with ≥9 months of continuous enrollment included in the study population, the number of those who initiated each biologic medication was 10,324 for ADA, 431 for CER, 3,092 for ETA, 821 for GUS, 1,766 for IXE, 4,132 for SEC, and 5,441 for UST. The mean age at the time of initiating biologic treatment was 46.9 years. During the 9-month follow-up period, the proportions of adherent patients (i.e., PDC ≥80%) were numerically higher among those treated with UST (59.9%) and GUS (56.9%), followed by those treated with SEC (46.1%), IXE (45.5%), ADA (44.7%), ETA (33.9%), and CER (22.0%). The proportions of patients who were persistent with their index biologic medication during the 9-month follow-up period were numerically higher among those treated with UST (70.1%) and GUS (67.8%), followed by those treated with IXE (47.3%), SEC (46.9%), ADA (28.7%), CER (14.8%), and ETA (10.7%). <b><i>Conclusions:</i></b> In this large healthcare claims database analysis of psoriasis patients treated with seven different biologic medications, adherence was numerically higher among those treated with UST or GUS. UST and GUS were also associated with numerically greater persistence.
DOI: 10.6084/m9.figshare.16894960
title: January 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in January, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16894966
title: December 2020 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>December, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16894966.v1
title: December 2020 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>December, 2020</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.5281/zenodo.5599925
title: Wikidata Dump wow

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.5599924
title: Wikidata Dump wow

description: RDF dump of wikidata produced with wdumps. <br> View on wdumper <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0
DOI: 10.5281/zenodo.5600882
title: Wikidata Dump Simple Data Cut

description: RDF dump of wikidata produced with wdumps. Just filtering all items which are not an instance of or subclass of and must contain an english entry<br> View on wdumper <b>entity count</b>: 16067, <b>statement count</b>: 208513, <b>triple count</b>: 2139493
DOI: 10.5281/zenodo.5600881
title: Wikidata Dump Simple Data Cut

description: RDF dump of wikidata produced with wdumps. Just filtering all items which are not an instance of or subclass of and must contain an english entry<br> View on wdumper <b>entity count</b>: 16067, <b>statement count</b>: 208513, <b>triple count</b>: 2139493
DOI: 10.5281/zenodo.5606431
title: Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"

description: Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments." Data includes representative real and simulated bead trajectories used in the manuscript. Code includes all simulations, analysis, and plot details for the Figures in the manuscript. See included README.txt for more details.
description: All bead trajectory data is in nms.
DOI: 10.5281/zenodo.4948542
title: Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments"

description: Data and code for "Tweezepy: A Python package for calibrating forces in single-molecule video-tracking instruments." Data includes representative real and simulated bead trajectories used in the manuscript. Code includes all simulations, analysis, and plot details for the Figures in the manuscript. See included README.txt for more details.
description: All bead trajectory data is in nms.
DOI: 10.15468/4ersgb
title: Taxonomic and identification review of adventive Fiorinia Targioni Tozzetti (Hemiptera, Coccomorpha, Diaspididae) of the United States

description: This dataset contains the digitized treatments in Plazi based on the original journal article Ahmed, Muhammad Z., Moore, Matthew R., Rohrig, Eric A., McKenzie, Cindy L., Liu, Di, Feng, Jinian, Normark, Benjamin B., Miller, Douglass R. (2021): Taxonomic and identification review of adventive Fiorinia Targioni Tozzetti (Hemiptera, Coccomorpha, Diaspididae) of the United States. ZooKeys 1065: 141-203, DOI: http://dx.doi.org/10.3897/zookeys.1065.69171, URL: http://dx.doi.org/10.3897/zookeys.1065.69171
DOI: 10.15151/esrf-es-538779994
title: Atomic layer deposition: unraveling process-structure relationships

description: Atomic layer deposition (ALD) is a growing technology, enabling breakthroughs in semiconductor manufacturing, photovoltaics and energy storage. However, despite its technological success, lots of fundamental questions remain, and deposited materials do not always perform as envisioned. A lack of understanding of how the ALD process parameters impact the atomic scale structure of the as-deposited amorphous thin-films hampers us to improve their performance based on fundamental insights. Here, we aim to access the local- and medium-range order of atoms in ALD thin-films via pair distribution function analysis of grazing incidence X-ray total scattering measurements (GI-PDF). GI-PDF at ID15A will enable us to relate structural properties to ALD process parameters, such as the choice of reactants, deposition temperature and conditions used for plasma-activation of the reactants, providing the ALD field with a piece of fundamental insight it urgently needs.
DOI: 10.6084/m9.figshare.16895014
title: February 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in February, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895014.v1
title: February 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in February, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895017.v1
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in September, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895017
title: September 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in September, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895020
title: February 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895020.v1
title: February 2021 Covid-19 + Vaccines Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in <b>February, 2021</b> from <b>Twitter's COVID-19 Streaming Endpoint </b>via a custom script developed by the <b>Social Media Lab (https://socialmedialab.ca/)</b>.<br>Visit <b>our interactive dashboard at https://stream.covid19misinfo.org</b>/ for a preview and some general stats about this COVID-19 Twitter streaming dataset.<br>For more info about <b>Twitter's COVID-19 Streaming Endpoint</b>, visit <b>https://developer.twitter.com/en/docs/labs/covid19-stream/overview</b><br><b>Note</b>: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895023
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895023.v1
title: April 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in April, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895026
title: March 2021 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in March, 2021 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).
DOI: 10.6084/m9.figshare.16895011
title: Supplementary Material for: Therapeutic Impact and Management of Persistent Head and Neck Atopic Dermatitis in Dupilumab-Treated Patients

description: <b><i>Background:</i></b> Localization of atopic dermatitis (AD) in exposed areas such as the hands, head, and neck has been considered as a negative factor impacting on dupilumab response, although a comparison of exposed versus unexposed areas is not currently available. <b><i>Objectives:</i></b> The aim of this study is to evaluate the clinical response to dupilumab depending on the presence or persistency of AD skin manifestations in specific body areas. <b><i>Methods:</i></b> The study retrospectively collected clinical and demographic data of adult patients affected by moderate to severe AD. Based on the anatomical sites involved, 5 subcohorts of patients were identified. <b><i>Results:</i></b> A total of 41 patients were included in the study. Disease amelioration was detected during the study period, although baseline head/neck and hand localization was associated with a significantly lower likelihood of achieving an Eczema Area Severity Index (EASI) ≤1. In addition, patients with head/neck persistency showed a significantly lower response when compared to patients without persistency of head/neck AD in terms of both mean EASI and Dermatology Life Quality Index (DLQI) reduction. <b><i>Conclusion:</i></b> AD localization in exposed areas at the baseline and AD persistency at the head/neck may have a negative impact on certain treatment response parameters to dupilumab therapy.
DOI: 10.6084/m9.figshare.16893796.v1
title: Data Set1

description: This folder contains raw dataset of CW Z-scan of L-Tryptophan aqueous solution at different powers with red laser.
DOI: 10.6084/m9.figshare.16895029.v1
title: December 2020 Covid-19 Twitter Streaming Dataset

description: The file contains Tweet IDs* for COVID-19 related tweets collected in December, 2020 from Twitter's COVID-19 Streaming Endpoint via a custom script developed by the Social Media Lab (https://socialmedialab.ca/). Visit our interactive dashboard at https://stream.covid19misinfo.org/ for a preview and some general stats about this COVID-19 Twitter streaming dataset. For more info about Twitter's COVID-19 Streaming Endpoint, visit https://developer.twitter.com/en/docs/labs/covid19-stream/overview * Note: In accordance with Twitter API Terms, the dataset only includes Tweet IDs (as opposed to the actual tweets and associated metadata). To recollect tweets contained in this dataset, you can use programs such as Hydrator (https://github.com/DocNow/hydrator/) or the Python library Twarc (https://github.com/DocNow/twarc/).