cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/5rq7-hy22
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. 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.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 278,128 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/9c5y-rq54
title: Non Linear Battery

description: This dataset is in support of my planned research paper to be submitted to IEEE Transactions. This dataset include results of my new proposed model for the battery and the results with the existing models. Technical scientific answers will be in the Transaction paper. (paper submission will take some time as I also involved in other personal works)There had been studies regarding Unscented Kalman Filter (UKF), Hinfinity, Solar power harvested from Sun is collected in the lithium- ion battey bank.The thermal model , non linear
DOI: 10.21258/1579880
title: Inventario forestale nazionale svizzero - Tabella n. 898015
DOI: 10.21227/9hfr-t965
title: Non Linear Battery

description: This dataset is in support of my planned research paper to be submitted to IEEE Transactions. This dataset include results of my new proposed model for the battery and the results with the existing models. Technical scientific answers will be in the Transaction paper. (paper submission will take some time as I also involved in other personal works)There had been studies regarding Unscented Kalman Filter (UKF), Hinfinity, Solar power harvested from Sun is collected in the lithium- ion battey bank.The thermal model , non linear
DOI: 10.21258/1578401
title: Inventario forestale nazionale svizzero - Tabella n. 899371
DOI: 10.21258/1549233
title: Inventaire forestier national suisse - Tableau no 899416
DOI: 10.21258/1610464
title: Inventario forestale nazionale svizzero - Tabella n. 898742
DOI: 10.21258/1564107
title: Inventaire forestier national suisse - Tableau no 899450
DOI: 10.21258/1594262
title: Inventario forestale nazionale svizzero - Tabella n. 897367
DOI: 10.21258/1529655
title: Inventaire forestier national suisse - Tableau no 899202
DOI: 10.21258/1579998
title: Inventario forestale nazionale svizzero - Tabella n. 899451
DOI: 10.21258/1583935
title: Inventario forestale nazionale svizzero - Tabella n. 899290
DOI: 10.21258/1613282
title: Inventario forestale nazionale svizzero - Tabella n. 898154
DOI: 10.21258/1605335
title: Inventario forestale nazionale svizzero - Tabella n. 892995
DOI: 10.21258/1594013
title: Inventario forestale nazionale svizzero - Tabella n. 898789
DOI: 10.21258/1593266
title: Inventario forestale nazionale svizzero - Tabella n. 902190
DOI: 10.21258/1621946
title: Inventario forestale nazionale svizzero - Tabella n. 898868
DOI: 10.21258/1559779
title: Inventaire forestier national suisse - Tableau no 898952
DOI: 10.21258/1545112
title: Inventaire forestier national suisse - Tableau no 898940
DOI: 10.6084/m9.figshare.13369733
title: Data for "Effect of grain shape on quasi-static fluid-fluid displacement in porous media"

description: Simulation results using the Extended Pore Network Model (EPONM) described in the manuscript "Effect of grain shape on quasi-static fluid-fluid displacement in porous media"<br>"outputData.zip" contains the data from EPONM, which can be opened by MATLAB. (5 simulations for each contact angle and particle shape)"sampleDataStructureDescription" describes the data structure of a sample result.<br>For more information, see the manuscript.
DOI: 10.21258/1589056
title: Inventario forestale nazionale svizzero - Tabella n. 902047
DOI: 10.21258/1545299
title: Inventaire forestier national suisse - Tableau no 898858
DOI: 10.21258/1613534
title: Inventario forestale nazionale svizzero - Tabella n. 899242
DOI: 10.21258/1558778
title: Inventaire forestier national suisse - Tableau no 892931
DOI: 10.21258/1588599
title: Inventario forestale nazionale svizzero - Tabella n. 898955
DOI: 10.21258/1616220
title: Inventario forestale nazionale svizzero - Tabella n. 898325
DOI: 10.21258/1559294
title: Inventaire forestier national suisse - Tableau no 897460
DOI: 10.21258/1534167
title: Inventaire forestier national suisse - Tableau no 897517
DOI: 10.21227/x8me-5r37
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. 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.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related datasets: (a) Coronavirus (COVID-19) Tweets Sentiment Trend (Global)(b) Tweets Originating from India During COVID-19 Lockdowns-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 278,758 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.21227/n06j-7e22
title: Aviation droplet detection based on DNN

description: Deep learning has been widely used in various sensor detection fields to realize intelligent detection tasks. However, there are always restrictions on how these functions can be integrated into embedded devices to complete mobile and convenient sensing detection tasks. Therefore, the MobileNet_SSD, A lightweight model after simplification and optimization, is used in this paper to design a portable visual sensor system for the detection of spray droplet deposition in drone applications. The sensing system includes a droplet deposition image loop acquisition device and a supporting host computer interactive platform. It introduces the Sarsa algorithm to establish an adaptive model of light intensity to enhance the robustness and combines Deep Neural Networks and image processing technology to achieve rapid loop detection of droplet deposition parameters on the mobile terminal. Experimental results indicate that the proposed sensor can adapt to light changes in complex environments, and accurately measure the deposition parameters of different droplet density images. Furthermore, it is of great significance to detect the quality of drone sprays, master the rules of droplet deposition, and understand the effects of pesticide spraying.
DOI: 10.21258/1599920
title: Inventario forestale nazionale svizzero - Tabella n. 902176
DOI: 10.21258/1596677
title: Inventario forestale nazionale svizzero - Tabella n. 891937
DOI: 10.21258/1578495
title: Inventario forestale nazionale svizzero - Tabella n. 891873
DOI: 10.21258/1576590
title: Inventario forestale nazionale svizzero - Tabella n. 896928
DOI: 10.5281/zenodo.4035168
title: DTM - VALENCIA

description: DTM FROM IGN
DOI: 10.5281/zenodo.4035167
title: DTM - VALENCIA

description: DTM FROM IGN
DOI: 10.6084/m9.figshare.12417533.v4
title: Railway network of Galicia and Austrian Silesia (1847-1914)

description: We describe and share reconstructed data of the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire that covered more than 80 000 km<sup>2</sup> that are currently divided among Czechia, Poland and Ukraine. The network dataset includes the times of railway appearance and of the most dynamic development until 1914, which marked the outbreak of the First World War. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed down between 1914 and 2019 and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imagery and historical maps. Altogether, the network dataset covers nearly 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. the year of construction, length, starting point, finish point, and type (normal, narrow-gauge, etc.), and are available for download in the shp format.
DOI: 10.6084/m9.figshare.12417533
title: Railway network of Galicia and Austrian Silesia (1847-1914)

description: We describe and share reconstructed data of the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire that covered more than 80 000 km<sup>2</sup> that are currently divided among Czechia, Poland and Ukraine. The network dataset includes the times of railway appearance and of the most dynamic development until 1914, which marked the outbreak of the First World War. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed down between 1914 and 2019 and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imagery and historical maps. Altogether, the network dataset covers nearly 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. the year of construction, length, starting point, finish point, and type (normal, narrow-gauge, etc.), and are available for download in the shp format.
DOI: 10.6084/m9.figshare.12108642
title: UAV-based remote sensing of immediate changes in geomorphology following a glacial lake outburst flood at the Zackenberg river, northeast Greenland

description: Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m<sup>2</sup>. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.
DOI: 10.6084/m9.figshare.12108642.v1
title: UAV-based remote sensing of immediate changes in geomorphology following a glacial lake outburst flood at the Zackenberg river, northeast Greenland

description: Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m<sup>2</sup>. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.
DOI: 10.6084/m9.figshare.12417533.v3
title: Railway network of Galicia and Austrian Silesia (1847-1914)

description: We describe and share reconstructed data of the historical railway network of Galicia and Austrian Silesia – two regions of the Habsburg Empire that covered more than 80 000 km<sup>2</sup> that are currently divided among Czechia, Poland and Ukraine. The network dataset includes the times of railway appearance and of the most dynamic development until 1914, which marked the outbreak of the First World War. Most of the lines were reconstructed based on OpenStreetMap data, and the lines, which were closed down between 1914 and 2019 and are no longer available in spatial datasets, were reconstructed based on high-resolution satellite imagery and historical maps. Altogether, the network dataset covers nearly 5000 km on 127 lines. The data are accompanied by a set of attributes, i.e. the year of construction, length, starting point, finish point, and type (normal, narrow-gauge, etc.), and are available for download in the shp format.
DOI: 10.6084/m9.figshare.12084315.v1
title: Interactive display of surnames distributions in historic and contemporary Great Britain

description: We introduce a method to calculate and store approximately 1.2 million surname distributions calculated for surnames found in Great Britain for six years of historic population data and 20 years of contemporary population registers compiled from various consumer sources. We subsequently show how this database can be incorporated into an interactive web-environment specifically designed for the public dissemination of detailed surname statistics. Additionally, we argue that the database can be used in the quantitative analysis of surnames in Great Britain and potentially offer valuable insights into processes of contagious and hierarchical diffusion of populations as well as the regional distinctiveness of demographic change and stasis.
DOI: 10.6084/m9.figshare.12854315
title: Epidemics and pandemics in maps – the case of COVID-19

description: Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019–2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.
DOI: 10.6084/m9.figshare.12084315
title: Interactive display of surnames distributions in historic and contemporary Great Britain

description: We introduce a method to calculate and store approximately 1.2 million surname distributions calculated for surnames found in Great Britain for six years of historic population data and 20 years of contemporary population registers compiled from various consumer sources. We subsequently show how this database can be incorporated into an interactive web-environment specifically designed for the public dissemination of detailed surname statistics. Additionally, we argue that the database can be used in the quantitative analysis of surnames in Great Britain and potentially offer valuable insights into processes of contagious and hierarchical diffusion of populations as well as the regional distinctiveness of demographic change and stasis.
DOI: 10.6084/m9.figshare.12854315.v2
title: Epidemics and pandemics in maps – the case of COVID-19

description: Epidemics and pandemics are geographical in nature and constitute spatial, temporal, and thematic phenomena across large ranges of scales: local infections with a global spread; short-term decisions by governments and institutions with long-term effects; and diverse effects of the disease on many aspects of our lives. Pandemics pose particular challenges to their visual representation by cartographic means. This article briefly summarizes some of these challenges and outlines ways to approach these. We discuss how to use the information usually available for telling the story of an epidemic, illustrated by the example of the 2019–2020 COVID-19 pandemic. The maps attached to this article demonstrate the discussed cartographic means.
DOI: 10.21258/1531142
title: Inventaire forestier national suisse - Tableau no 898941
DOI: 10.21258/1531093
title: Inventaire forestier national suisse - Tableau no 898055
DOI: 10.21258/1558819
title: Inventaire forestier national suisse - Tableau no 902120
DOI: 10.21258/1556065
title: Inventaire forestier national suisse - Tableau no 897993
DOI: 10.21258/1607746
title: Inventario forestale nazionale svizzero - Tabella n. 898845
DOI: 10.21258/1547773
title: Inventaire forestier national suisse - Tableau no 891894
DOI: 10.21258/1537860
title: Inventaire forestier national suisse - Tableau no 897411
DOI: 10.21258/1545304
title: Inventaire forestier national suisse - Tableau no 891680
DOI: 10.21258/1559625
title: Inventaire forestier national suisse - Tableau no 899353
DOI: 10.21258/1617886
title: Inventario forestale nazionale svizzero - Tabella n. 891913
DOI: 10.21258/1600268
title: Inventario forestale nazionale svizzero - Tabella n. 897100
DOI: 10.21258/1607649
title: Inventario forestale nazionale svizzero - Tabella n. 898975
DOI: 10.21258/1566702
title: Inventaire forestier national suisse - Tableau no 897601
DOI: 10.21258/1615200
title: Inventario forestale nazionale svizzero - Tabella n. 898814
DOI: 10.21258/1570939
title: Inventaire forestier national suisse - Tableau no 899433
DOI: 10.21258/1569243
title: Inventaire forestier national suisse - Tableau no 899637
DOI: 10.21258/1574237
title: Inventaire forestier national suisse - Tableau no 897656
DOI: 10.21258/1570910
title: Inventaire forestier national suisse - Tableau no 899534
DOI: 10.21258/1595376
title: Inventario forestale nazionale svizzero - Tabella n. 897147
DOI: 10.21258/1531433
title: Inventaire forestier national suisse - Tableau no 892867
DOI: 10.21258/1624644
title: Inventario forestale nazionale svizzero - Tabella n. 891859
DOI: 10.21258/1586199
title: Inventario forestale nazionale svizzero - Tabella n. 897956
DOI: 10.21258/1626248
title: Inventario forestale nazionale svizzero - Tabella n. 899065
DOI: 10.21258/1581189
title: Inventario forestale nazionale svizzero - Tabella n. 892065
DOI: 10.21258/1559929
title: Inventaire forestier national suisse - Tableau no 891824
DOI: 10.21258/1592947
title: Inventario forestale nazionale svizzero - Tabella n. 897566
DOI: 10.34740/kaggle/dsv/1735992
title: COVID-19 Indonesia Dataset
DOI: 10.25345/c56j5s
title: MassIVE MSV000086583 - the iTRAQ data for plasma-derived exosmoe
title: All analyses were performed by a Q-Exactive mass spectrometer (Thermo, USA) equipped with a Nanospray Flex source (Thermo, USA). Samples were loaded by a capillary C18 trap column and then separated by a C18 column on an EASY-nLCTM 1200 system (Thermo, USA). The flow rate was 300 nL/min and linear gradient was 90 min.
DOI: 10.34740/kaggle/dsv/1736093
title: COVID19 cases by Continent
DOI: 10.6084/m9.figshare.13369793
title: Wilms' tumor lncRNA and mRNA sequence in the blood figshare

description: Total RNA was extracted from blood samples from 2 WT and 2 hydronephrosis patients using Trizol reag ChIP was next performed using the human lncRNA array v4 lab-on-a-chip kit (CapitalBio Technology, Beijing, China) according to the manufacturer's instructionsent.
DOI: 10.6084/m9.figshare.13369793.v1
title: Wilms' tumor lncRNA and mRNA sequence in the blood figshare

description: Total RNA was extracted from blood samples from 2 WT and 2 hydronephrosis patients using Trizol reag ChIP was next performed using the human lncRNA array v4 lab-on-a-chip kit (CapitalBio Technology, Beijing, China) according to the manufacturer's instructionsent.
DOI: 10.5281/zenodo.4318615
title: MFS-M-00001 Air temperature at +2 m, raised bog-ridge, Thermochron (DS1921G-F5)

description: Air temperature at 2m measured in a raised bog ecosystem (ridge) by Thermochron logger, 2009-present (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).
DOI: 10.5281/zenodo.4318614
title: MFS-M-00001 Air temperature at +2 m, raised bog-ridge, Thermochron (DS1921G-F5)

description: Air temperature at 2m measured in a raised bog ecosystem (ridge) by Thermochron logger, 2009-present (with several breaks) as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).
DOI: 10.5281/zenodo.4318626
title: MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)

description: Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).
DOI: 10.5281/zenodo.4318625
title: MFS-M-00002 Air temperature at 2m measured in a raised bog-ridge, DS18B20 (APIK)

description: Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2018-2019, 30 min frequency, N60.89494 E68.66999, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).
DOI: 10.5281/zenodo.4318634
title: Retraction gap in Neurosurgery Journals

description: This is the final data set for our study on the retraction gap in Neurosurgery.
DOI: 10.5281/zenodo.4241904
title: Retraction gap in Neurosurgery Journals

description: This is the final data set for our study on the retraction gap in Neurosurgery.
DOI: 10.5281/zenodo.4318659
title: MFS-M-00003 Air temperature at 2m, raised bog-ryam, DS18B20 (APIK)

description: Air temperature at 2m measured in a raised bog ecosystem by DS18B20 (temperature logger), 2012-2018, 30 and 60 min frequency, several gaps in data, N60.89471 E68.68176, as part of meteorological monitoring in Mukhrino Field Station (https://mukhrinostation.com/).