cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4739460
title: Data for "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks"

description: This deposit provides the data for replicating the figures and table in the paper "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks". The full access is available upon request before June 2022. The complementary code is stored in a separate deposit (10.5281/zenodo.4739416).
DOI: 10.5281/zenodo.5046772
title: Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"

description: This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment" Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.
DOI: 10.5281/zenodo.4745484
title: Dataset related to article "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment"

description: This record contains raw data related to article “Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment" Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the "most infected volume" composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively.
DOI: 10.25345/c57n9n
title: MassIVE MSV000087927 - The immunopeptidome of HCT116 cell line
title: The HCT116 cell line was obtained from ATCC (American Type Culture Collection, Manassas,VA). Cells were grown in T75 flasks to a density of 1e9 cells before purification of HLA-I peptides for MS experiments.
DOI: 10.34740/kaggle/dsv/2487070
title: LUFlow Network Intrusion Detection Data Set
DOI: 10.5281/zenodo.5151211
title: Dataset related to article "A 'multiomic' approach to separate COVID-19 inpatients from outpatients."

description: *These authors contributed equally to the work<sup> #</sup>Corresponding author This record contains raw data related to article “A ‘multiomic’ approach to separate COVID-19 inpatients from outpatients." Abstract: The SARS-CoV-2 pandemic has overwhelmed the treatment capacity of the healthcare systems during the highest viral diffusion rate. Patients reaching the emergency department had to be either hospitalized or discharged. Still, the decision was taken based on the individual assessment of the actual clinical condition, without specific biomarkers to predict future improvement or deterioration. Often discharged patients returned to the hospital for aggravation of their condition. Here we have developed a new combined approach of omics to identify factors that could distinguish COVID-19 inpatients from outpatients. We tested the metabolome in the saliva and identified nine metabolites that separated the inpatient from the outpatient population, but not completely. When combined with serum biomarkers, just two salivary metabolites (myo-inositol and 2-pyrollidine acetic acid) and one serum protein, Chitinase 3-like-1(CHI3L1) were sufficient to separate the two groups completely. These metabolites positively or negatively correlated with four modulated microbiota taxa. This is a proof-of-concept that a combined omic analysis can be used to stratify patients.
DOI: 10.5281/zenodo.5147547
title: Data for "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks"

description: This deposit provides the data for replicating the figures and table in the paper "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks".
DOI: 10.5281/zenodo.4739461
title: Data for "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks"

description: This deposit provides the data for replicating the figures and table in the paper "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks".
DOI: 10.5281/zenodo.5147927
title: Data for "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks"

description: This deposit provides the data for replicating the figures and table in the paper "How Contagious was the Panic of 1907? New Evidence from Trust Company Stocks". The full access is available upon request until Jan 2022. The complimentary code is stored in a separate deposit (10.5281/zenodo.4739416).
DOI: 10.5281/zenodo.5151298
title: Azotea Dataset

description: Latest monthly AZOTEA reduced CSV files
DOI: 10.15161/oar.it/73998
title: CovidStat project data

description: This record contains the daily updated data of the CovidStat project. CovidStat is a project carried out by the CovidStat Working Group at INFN, whose creation was promoted within the Italian National Institute of Nuclear Physics with the aim of making a statistical analysis of the data provided daily by the Civil Protection on the spread of the pandemic in Italy. CovidStat data can interactively be browsed at https://covid19.infn.it.
description: The software to process this Root TTree is in the related identifier. Be sure to use the right corresponding version, based on the data in which Root TTree was generated.
DOI: 10.5281/zenodo.5151337
title: R-data-independent

description: 人工データ 生成する人工データの種類1: 独立変数から説明できるいくつかの分布のYを生成する 10000行の独立変数1000個について線形モデルで説明できるYを下記分布で生成<br> 同じ変数Xについて、それぞれ分布の違うYを生成する 正規分布 ポアソン分布 混合正規分布 最初10個の変数について、(10,9,8,..,1)と大きい係数、次の90個の変数について0.1、それ以外はYに関係しないものとする github
DOI: 10.5281/zenodo.5151227
title: Model parameters for EBSD indexing model

description: These files are parameters of a machine learning model mentioned in the paper: Indexation and calibration of EBSD by convolutional neural networks To use these parameters, please refer to: https://github.com/Pandenotium/EBSD_indexing These files are in an old version. A new version of parameters will be published soon.
DOI: 10.5281/zenodo.5151226
title: Model parameters for EBSD indexing model

description: These files are parameters of a machine learning model mentioned in the paper: Indexation and calibration of EBSD by convolutional neural networks To use these parameters, please refer to: https://github.com/Pandenotium/EBSD_indexing These files are in an old version. A new version of parameters will be published soon.
DOI: 10.25646/8870
title: Tagesdaten-CSV aus dem DIVI-Intensivregister

description: Die Tagesdaten-CSV entspricht einem Auszug der Daten des DIVI-Intensivregisters. Die Datei enthält eine Aggregation der aktuellsten Meldungen pro Landkreis. Es werden die aktuell gemeldeten Anzahlen der COVID-19 Intensivfälle sowie die gemeldeten intensivmedizinischen Behandlungskapazitäten angezeigt. Die Tagesdaten-CSV liefert dabei ausschließlich einen Blick auf die Daten gemäß dem Stand des betrachteten Tages. Die Daten sind im situationsbedingten Kontext aufbereitet, damit sind verschiedene Tagesdaten-CSVs u.U. nicht direkt miteinander vergleichbar. Die aktuellsten Meldungen werden im gewählten Betrachtungszeitfenster über alle Meldebereiche und Standorte aufsummiert. Weitere Informationen sind zu finden unter https://www.intensivregister.de/#/faq
DOI: 10.6084/m9.figshare.15087771
title: Supplementary file

description: FASTA format sequences of three positive genes and 10 convergent genes.
DOI: 10.6084/m9.figshare.15087771.v1
title: Supplementary file

description: FASTA format sequences of three positive genes and 10 convergent genes.
DOI: 10.5281/zenodo.5151264
title: Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors

description: The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.
description: {"references": ["Marilena Daquino, Silvio Peroni, David Shotton, Giovanni Colavizza, Benham Ghavimi, Anne Lauscher, Philipp Mayr, Matteo Romanello, Philipp Zumstein (2020). The OpenCitations Data Model. In Proceedings of the 20th International Semantic Web Conference (ISWC 2020). https://doi.org/10.1007/978-3-030-62466-8_28."]}
DOI: 10.5281/zenodo.5151263
title: Bibliographic dataset based on Scientometrics, containing provenance information compliant with the OpenCitations Data Model and non disambigued authors

description: The dataset contains bibliographical information about scholarly works in the journal Scientometrics only if the DOI is known. The data was extracted via Crossref. It is a temporal dataset in which provenance information and change-tracking have been managed by adopting the OpenCitations Data Model. Moreover, the dataset contains information on all the cited academic works. Journals and bibliographic resources always appear unambiguously, without duplicates. On the contrary, the authors have not been disambigued. Finally, heuristics have been applied to recover the DOI of the cited works in case Crossref did not provide such information.
description: {"references": ["Marilena Daquino, Silvio Peroni, David Shotton, Giovanni Colavizza, Benham Ghavimi, Anne Lauscher, Philipp Mayr, Matteo Romanello, Philipp Zumstein (2020). The OpenCitations Data Model. In Proceedings of the 20th International Semantic Web Conference (ISWC 2020). https://doi.org/10.1007/978-3-030-62466-8_28."]}
DOI: 10.5281/zenodo.5151396
title: ROM model data for North Western Mediterranean Deep Water Formation

description: ROM model data for Will deep water formation collapse in the North Western Mediterranean Sea by the end of the 21<sup>st</sup> century?
description: {"references": ["Sein, D. V., Mikolajewicz, U., Gr\u00f6ger, M., Fast, I., Cabos, W., Pinto, J. G., Hagemann, S., Semmler, T., Izquierdo, A., & Jacob, D. (2015). Regionally coupled atmosphere-ocean- sea ice-marine biogeochemistry model ROM: 1. Description and validation, Journal of Advance in Modelling Earth Systems, 7(1), 268\u2013304. https://doi.org/10.1002/2014MS000357"]}
DOI: 10.5281/zenodo.5151404
title: R-data-independent

description: 人工データ 生成する人工データの種類1: 独立変数から説明できるいくつかの分布のYを生成する 10000行の独立変数1000個について線形モデルで説明できるYを下記分布で生成<br> 同じ変数Xについて、それぞれ分布の違うYを生成する 正規分布 ポアソン分布 混合正規分布 最初10個の変数について、(10,9,8,..,1)と大きい係数、次の90個の変数について0.1、それ以外はYに関係しないものとする github
DOI: 10.5281/zenodo.5151258
title: Very Early Postseismic Deformation following the 2015 Mw 8.3 Illapel Earthquake, Chile Revealed from High-rate GPS

description: Daily.zip: daily GPS solutions at 12 Chilean stations Subdaily.zip: 1-Hz GPS Solutions at 12 Chilean stations.
DOI: 10.5281/zenodo.5151257
title: Very Early Postseismic Deformation following the 2015 Mw 8.3 Illapel Earthquake, Chile Revealed from High-rate GPS

description: Daily.zip: daily GPS solutions at 12 Chilean stations Subdaily.zip: 1-Hz GPS Solutions at 12 Chilean stations.
DOI: 10.5281/zenodo.5148693
title: 1117 Russian cities with population estimates (as of January 1, 2020) and geographic coordinates

description: Files and columns 1. towns.csv: <code>city_name</code> <code>population</code> - Rosstat estimate, thousand people, as of 1.1.2020 <code>region_name</code> - subnational region (oblast, republic or krai) To be added (via dadata.ru and OSM): <code>lat,lon</code> - geographic coordinates (from OSM) <code>federal_district</code>, eg "Центральный" <code>region_iso_code</code> - ISO 3166 code, eg <code>RU-VLD</code> <code>fias_id</code> - <code>код ФИАС</code> <code>kladr_id</code> - <code>код КЛАДР</code> (depreciated) <code>place_id</code> - OpenStreetMap (OSM) identifier 2. regions.csv - list of Russian Federation regions <strong>Sources</strong> City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files. City list corresponds to this Wikipedia article. <strong>Comments</strong> <code>Белоозерский</code> not found in Rosstat publication, but should be considered a city as of 1.1.2020 <code>Дмитриев</code> and <code>Дмитриев-Льговский</code> are the same city. <code>Ханты-Мансийский</code> and <code>Ямало-Ненецкий</code> autonomous regions excluded to avoid duplication (parts of <code>Тюменская область</code>). Several notable towns are classified as administrative part of larger cities (eg <code>Сестрорецк</code> is a municpality at <code>Санкт-Петербург</code>) and not reported in this dataset.
description: See code at Github: https://github.com/epogrebnyak/ru-cities
DOI: 10.34740/kaggle/dsv/2487827
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/2487861
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.5517/ccdc.csd.cc1m9yc7
title: CCDC 1499109: Experimental Crystal Structure Determination

description: Related Article: Yeliz Kaya, Ayşe Erçağ, Özlem Uğuz, Atıf Koca, Yunus Zorlu, Mayram Hacıoğlu, Ayşe Seher Birteksöz Tan|2021|Polyhedron|207|115372|doi:10.1016/j.poly.2021.115372
DOI: 10.25505/fiz.icsd.cc255pqs
title: CSD 2001475: Experimental Crystal Structure Determination

description: Related Article: Yi Long, Yeming Xian, Songyang Yuan, Kun Liu, Mingyuan Sun, Yang Guo, Naveed Ur Rahman, Jiandong Fan, Wenzhe Li|2021|Nano Energy|89|106340|doi:10.1016/j.nanoen.2021.106340
DOI: 10.5517/ccdc.csd.cc25st60
title: CCDC 2019842: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25stb4
title: CCDC 2019846: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25stjb
title: CCDC 2019852: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25stkc
title: CCDC 2019853: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25stld
title: CCDC 2019854: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25strk
title: CCDC 2019859: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25stvn
title: CCDC 2019862: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc25styr
title: CCDC 2019865: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc269n39
title: CCDC 2035060: Experimental Crystal Structure Determination

description: Related Article: Takashi Ikawa, Yuta Yamamoto, Akito Heguri, Yutaka Fukumoto, Tomonari Murakami, Akira Takagi, Yuto Masuda, Kenzo Yahata, Hiroshi Aoyama, Yasuteru Shigeta, Hiroaki Tokiwa, Shuji Akai|2021|J.Am.Chem.Soc.|143|10853|doi:10.1021/jacs.1c05434
DOI: 10.5517/ccdc.csd.cc276sv3
title: CCDC 2062115: Experimental Crystal Structure Determination

description: Related Article: Fu-Qiang Song, Hao Cheng, Na-Na Zhao, Xue-Qin Song|2021|J.Lumin.|239|118338|doi:10.1016/j.jlumin.2021.118338
DOI: 10.5517/ccdc.csd.cc276sw4
title: CCDC 2062116: Experimental Crystal Structure Determination

description: Related Article: Fu-Qiang Song, Hao Cheng, Na-Na Zhao, Xue-Qin Song|2021|J.Lumin.|239|118338|doi:10.1016/j.jlumin.2021.118338
DOI: 10.15468/sq4xee
title: Transfer of the Honduran species Calathea carolineae to the genus Goeppertia (Marantaceae)

description: This dataset contains the digitized treatments in Plazi based on the original journal article Braga, João Marcelo Alvarenga (2014): Transfer of the Honduran species Calathea carolineae to the genus Goeppertia (Marantaceae). Phytotaxa 175 (4): 237-238, DOI: 10.11646/phytotaxa.175.4.6, URL: http://dx.doi.org/10.11646/phytotaxa.175.4.6
DOI: 10.5281/zenodo.5151442
title: R-data-independent

description: 人工データ 生成する人工データの種類1: 独立変数から説明できるいくつかの分布のYを生成する 10000行の独立変数1000個について線形モデルで説明できるYを下記分布で生成<br> 同じ変数Xについて、それぞれ分布の違うYを生成する 正規分布 ポアソン分布 混合正規分布 最初10個の変数について、(10,9,8,..,1)と大きい係数、次の90個の変数について0.1、それ以外はYに関係しないものとする github
DOI: 10.5281/zenodo.5151336
title: R-data-independent

description: 人工データ 生成する人工データの種類1: 独立変数から説明できるいくつかの分布のYを生成する 10000行の独立変数1000個について線形モデルで説明できるYを下記分布で生成<br> 同じ変数Xについて、それぞれ分布の違うYを生成する 正規分布 ポアソン分布 混合正規分布 最初10個の変数について、(10,9,8,..,1)と大きい係数、次の90個の変数について0.1、それ以外はYに関係しないものとする github
DOI: 10.25625/h5oixl/gndwvn
title: Bengaluru's DPS - Reichenbach et al., 2021.tab

description: :unav
DOI: 10.25625/h5oixl
title: Bengaluru's dairy production systems (Survey data)

description: Data from 337 interviews of dairy producers from the Rural-Urban Interface of Bengaluru, India, done in 2016
DOI: 10.21227/2g7j-e111
title: LoRa based Wireless Sensor Network for Environmental Monitoring - Dataset

description: As Science and technology evolve, the environment is getting affected daily. These cause major environmental issues like Global Warming, Ozone layer depletion, Natural resource depletion, etc. These are measured and regulated by local bodies. The data given by the local bodies are average values for a large area, those data might be inaccurate for a small sector or isolated zone. However, there are few techniques such as WSN (Wireless Sensor Networks), IoT (Internet of things) which measures and updates real-time data to a cloud server to overcome the trouble. But, in some cases, internet or even WiFi connectivity may be unavailable and sometimes it is necessary to stay away from IoT insecurities and vulnerabilities. To solve the above-mentioned issue by creating a local server with an alternate wireless technology such as LoRa, NB-IoT is one of the ways. This paper explains a method where LoRa is used as a transmission medium that ensures low interference and provides long-range communication along with a private/local server. Various nodes have different sensors embedded along with a microcontroller that samples and transfers the data over LoRa. These nodes are modular making the entire network scalable. The LoRa gateway further processes the data that is received and sends it to a web server for storing it in the database. The raw data thus stored is fetched and processed for easy visualization in a web based GUI.