cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7922502
title: ARISE-SAI_1.5: UKESM1 Extreme Temperature and Precipitation Indices

description: The UKESM1 ARISE experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere. The injections occur at four different latitudes: 15 degrees N & S and 30 degrees N & S, at an altitude of approximately 20 km. The simulations are based on the medium-emissions CMIP6 scenario ssp245 and cover the years 2035 to 2070. They form a 5-member ensemble with initial conditions taken from the corresponding five members of the ssp245 simulations upon which they were based. Data for the UKESM1 ARISE-SAI-1.5 simulations is available at https://data.ceda.ac.uk/badc/deposited2022/arise and the data for the UKESM1 SSP2-4.5 simulations is available on the Earth System Grid Federation database. The files included here contain processed annual daily extremes of surface temperature (TREFHT) and total precipitation (PRECT) from the ARISE-SAI-1.5 simulations and companion SSP245 simulations. Indices are those recommended by the WCRP Expert Team on Climate Change Detection Indices, Zhang et al. 2011). Methods to calculate the indices are also described in Tye et al. (2022). <strong>Precipitation Indices</strong> PRCPTOT, SDII, RX1D, RX5D, R10mm, R20mm, CDD, CWD, P95TOT, P99TOT <strong>Temperature Indices</strong> TNN, TNX, FD, TR, TN90, TN10, TXX, TXN, ID, SU, TX90, TX10
description: More information can be found here: https://www.cesm.ucar.edu/projects/community-projects/ARISE-SAI/ Computing resources (doi:10.5065/D6RX99HX) were provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation.
description: {"references": ["Haywood, J.; Jones, A.; Dalvi, M. (2022): UKESM1 ARISE-SAI climate simulations. NERC EDS Centre for Environmental Data Analysis, 10 May 2023. https://catalogue.ceda.ac.uk/uuid/26b89d8d76bd40bfbaf9fedfa383e9cf", "Tye, M. R., Dagon, K., Molina, M. J., Richter, J. H., Visioni, D., Kravitz, B., & Tilmes, S. (2022). Indices of Extremes: Geographic patterns of change in extremes and associated vegetation impacts under climate intervention. Earth System Dynamics, 13, 1233\u20131257. https://doi.org/10.5194/esd-13-1233-2022", "Zhang, Xuebin et al. (2011) Indices for monitoring changes in extremes based on daily temperature and precipitation data 10.1002/wcc.147"]}
DOI: 10.5281/zenodo.7839928
title: Multiplexed histology of COVID-19 post-mortem lung samples - Single-cell Mean Fluorescence Intensities

description: Data table containing single-cell mean fluorescence intensities (MFI) of all markers analyzed by multiplexed histology in all COVID-19 post-mortem lung samples and non-COVID-related pneumonia controls (14 lung samples, stratified based on disease duration into control, acute, chronic and prolonged). It contains information at the single-cell level about approx 50 proteins in around 40.000 lung cells. Data shown has been arcsin(h) transformed with a co-factor of 0.2. Additionally, cells expressing less than 0.15 MFI of all markers have been labeled as non-defined and excluded from the data set. Seurat package 4.0.0 was used in R to perform mean centering and scaling, followed by PCA, and reduced the dimensions of the data to the top 11 principal components. UMAP was initialized in this PCA space to visualize the data on reduced UMAP dimensions. The cells were clustered on PCA space using the SNN algorithm implemented as <em>FindNeighbors</em> and <em>FindClusters </em>with <em>n.epochs = 500</em> and default parameters (<em>res = 0.8</em>). We obtained 26 clusters that we merged to get relevant populations for our analysis based on canonical lineage markers. We ended up with 8 cell clusters that were manually annotated based on cell-type-specific markers found to be differentially expressed.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
description: {"references": ["Mothes, R. and Pascual-Reguant, A. et al. Local CCL18 and CCL21 expand lung fibrovascular niches and recruit lymphocytes, leading to tertiary lymphoid structure formation in prolonged COVID-19. medRxiv 2022.03.24.22272768 (2022) doi:10.1101/2022.03.24.22272768."]}
DOI: 10.5281/zenodo.7473755
title: Multiplexed histology of COVID-19 post-mortem lung samples - Single-cell Mean Fluorescence Intensities

description: Data table containing single-cell mean fluorescence intensities (MFI) of all markers analyzed by multiplexed histology in all COVID-19 post-mortem lung samples and non-COVID-related pneumonia controls (14 lung samples, stratified based on disease duration into control, acute, chronic and prolonged). It contains information at the single-cell level about approx 50 proteins in around 40.000 lung cells. Data shown has been arcsin(h) transformed with a co-factor of 0.2. Additionally, cells expressing less than 0.15 MFI of all markers have been labeled as non-defined and excluded from the data set. Seurat package 4.0.0 was used in R to perform mean centering and scaling, followed by PCA, and reduced the dimensions of the data to the top 11 principal components. UMAP was initialized in this PCA space to visualize the data on reduced UMAP dimensions. The cells were clustered on PCA space using the SNN algorithm implemented as <em>FindNeighbors</em> and <em>FindClusters </em>with <em>n.epochs = 500</em> and default parameters (<em>res = 0.8</em>). We obtained 26 clusters that we merged to get relevant populations for our analysis based on canonical lineage markers. We ended up with 8 cell clusters that were manually annotated based on cell-type-specific markers found to be differentially expressed.
description: Funding: Deutsche Forschungsgemeinschaft HA5354/10-1, SPP1937 (HA5354/8-2) and TRR130 P17 and C01 (to Anja Hauser)
description: {"references": ["Mothes, R. and Pascual-Reguant, A. et al. Local CCL18 and CCL21 expand lung fibrovascular niches and recruit lymphocytes, leading to tertiary lymphoid structure formation in prolonged COVID-19. medRxiv 2022.03.24.22272768 (2022) doi:10.1101/2022.03.24.22272768."]}
DOI: 10.5281/zenodo.7779963
title: Open Surface Drifter Data - Pirita river

description: This dataset contains the surface drifter tracks collected in Pirita River (Estonia) to test the open drifter presented in the publication https://doi.org/10.3390/s22249918
DOI: 10.5281/zenodo.7779962
title: Open Surface Drifter Data - Pirita river

description: This dataset contains the surface drifter tracks collected in Pirita River (Estonia) to test the open drifter presented in the publication https://doi.org/10.3390/s22249918
DOI: 10.11588/data/tjnqzg
title: 3D Point Cloud from Nakadake Sanroku Kiln Site Center, Japan: Sample Data for the Application of Adaptive Filtering with the AFwizard

description: This data set represents 3D point clouds acquired with LiDAR technology and related files from a subregion of 150*436 sqm in the ancient Nakadake Sanroku Kiln Site Center in South Japan. It is a densely vegetated mountainous region with varied topography and vegetation. The data set contains the original point cloud (reduced from a density of 5477 points per square meter to 100 points per square meter), a segmentation of the area based on characteristics in vegetation and topography, and filter pipelines for segments with different characteristics, and other data necessary. The data serve to test the AFwizard software which can create a DTM from the point cloud with varying filter and filter parameter selections based on varying segment characteristics (https://github.com/ssciwr/afwizard). The AFwizard adds flexibility to ground point filtering of 3D point clouds, which is a crucial step in a variety of applications of LiDAR technology. Digital Terrain Models (DTM) derived from filtered 3D point clouds serve various purposes and therefore, rather than creating one representation of the terrain that is supposed to be "true", a variety of models can be derived from the same point cloud according to the intended usage of the DTM. The sample data were acquired during an archaeological research project in a mountainous and densely forested region in South Japan -- the Nakadake-Sanroku Kiln Site Center: LiDAR data were acquired in a subregion of 0.5 sqkm, a relatively small area characterized by frequent and sudden changes in topography and vegetation. The point cloud is very dense due to the technology chosen (UAV multicopter GLYPHON DYNAMICS GD-X8-SP; LiDAR scanner RIEGL VUX-1 UAV). Usage of the data is restricted to the citation of the article mentioned below. Version 2.01: 2023-05-11; Article citation updated; 2022-07-21; Documentation (HowTo - Minimal Workflow) updated, data files tagged.
DOI: 10.6084/m9.figshare.22800596
title: <strong>Research Data [Bibliometric Analysis of Mekong River Issues from 1997 to 2022]</strong>

description: The 517 document results from 1997 to 2022 from the Scopus database were exported in BibTex, RIS, and Excel format.
DOI: 10.6084/m9.figshare.22800596.v1
title: <strong>Research Data [Bibliometric Analysis of Mekong River Issues from 1997 to 2022]</strong>

description: The 517 document results from 1997 to 2022 from the Scopus database were exported in BibTex, RIS, and Excel format.
DOI: 10.5281/zenodo.7915682
title: Hatchery produced blue mussel (Mytilus edulis) larvae length measurements

description: Hatchery produced blue mussel (<em>Mytilus edulis</em>) larvae length measurements.
DOI: 10.5281/zenodo.7915683
title: Hatchery produced blue mussel (Mytilus edulis) larvae length measurements

description: Hatchery produced blue mussel (<em>Mytilus edulis</em>) larvae length measurements.
DOI: 10.6084/m9.figshare.22689751.v4
title: The annual 10-m maize mapping in China during 2017-2021

description: <strong>The annual 10-m maize mapping in China during 2017-2021</strong> <br> <strong>The format of data is: [adcode]_[year].tif</strong> <br> <strong>the [adcode] is a unique code for province of China. (</strong> More detail: DataV.GeoAtlas地理小工具系列 (aliyun.com) <strong>)</strong>
DOI: 10.25345/c53n20q5k
title: MassIVE MSV000091914 - GNPS membrane vesicles P1B7_P3D2
title: Metabolites from membrane vesicles of terrestrial bacteria.
DOI: 10.26180/22133105.v1
title: The DREAM database

description: <em><strong>NOTE:</strong></em><em> Files are currently pending release.</em> Overview The Dream EEG and Mentation (DREAM) database collects and stores metadata about DREAM datasets, and is accessible to the public. DREAM datasets provide polysomnography and associated subjective mentation reports. Some datasets may also contain personally identifiable information about participants, but such information are not stored by the DREAM database. Datasets are contributed to DREAM from many different labs in many different studies and, where possible, made openly accessible in the hope of pushing the fields of sleep, dream, brain-computer interface, and consciousness research forward. If you have data that others in the community might find useful, please consider contributing it to DREAM. Contents The DREAM database consists of a following data tables: Datasets Data records People The records in <em>Datasets</em> list all officially accepted DREAM datasets and their summary metadata. <em>Data records</em> lists metadata of each individual datum from these datasets. <em>People</em> provides information on the data contributors, referred to by <em>Key ID</em> in <em>Datasets</em>.
DOI: 10.6084/m9.figshare.22791170
title: <strong>Effect of U1 AMO (antisense morpholino oligonucleotides) on translational landscape</strong>

description: U1 snRNP could protect eukaryotic pre-mRNAs from premature mRNA 3’ end processing at thousands of intronic polyadenylation sites (PASs), a phenomenon known as U1 snRNP telescripting, or U1 snRNP inhibition of intronic premature cleavage and polyadenylation (PCPA). An outstanding question in the field is if these U1-regulated truncated forms of pre-mRNAs could be exported to the cytoplasm and have the potential to be translated. It was previously shown that some of the intronic PCPAed products induced by Spliceostatin A, a splicing modulator, might be translated in cells using Ribo-seq analysis. Here we performed similar analysis to address the question that if thousands of U1-regulated PCPAed products have the potential to be translated in the cytoplasm.
DOI: 10.6084/m9.figshare.22791170.v4
title: <strong>Effect of U1 AMO (antisense morpholino oligonucleotides) on translational landscape</strong>

description: U1 snRNP could protect eukaryotic pre-mRNAs from premature mRNA 3’ end processing at thousands of intronic polyadenylation sites (PASs), a phenomenon known as U1 snRNP telescripting, or U1 snRNP inhibition of intronic premature cleavage and polyadenylation (PCPA). An outstanding question in the field is if these U1-regulated truncated forms of pre-mRNAs could be exported to the cytoplasm and have the potential to be translated. It was previously shown that some of the intronic PCPAed products induced by Spliceostatin A, a splicing modulator, might be translated in cells using Ribo-seq analysis. Here we performed similar analysis to address the question that if thousands of U1-regulated PCPAed products have the potential to be translated in the cytoplasm.
DOI: 10.6084/m9.figshare.22791170.v3
title: <strong>Effect of U1 AMO (antisense morpholino oligonucleotides) on translational landscape</strong>

description: U1 snRNP could protect eukaryotic pre-mRNAs from premature mRNA 3’ end processing at thousands of intronic polyadenylation sites (PASs), a phenomenon known as U1 snRNP telescripting, or U1 snRNP inhibition of intronic premature cleavage and polyadenylation (PCPA). An outstanding question in the field is if these U1-regulated truncated forms of pre-mRNAs could be exported to the cytoplasm and have the potential to be translated. It was previously shown that some of the intronic PCPAed products induced by Spliceostatin A, a splicing modulator, might be translated in cells using Ribo-seq analysis. Here we performed similar analysis to address the question that if thousands of U1-regulated PCPAed products have the potential to be translated in the cytoplasm.
DOI: 10.6084/m9.figshare.22689751.v5
title: The annual 10-m maize mapping in China during 2017-2021

description: <strong>The annual 10-m maize mapping in China during 2017-2021</strong> <br> <strong>The format of data is: [adcode]_[year].tif</strong> <br> <strong>the [adcode] is a unique code for province of China. (</strong> More detail: DataV.GeoAtlas地理小工具系列 (aliyun.com) <strong>)</strong>
DOI: 10.6084/m9.figshare.22800563
title: GSE193337serut.RData

description: Single-cell RNA seqencing analysis of GSE193337 by seurat.
DOI: 10.6084/m9.figshare.22689751.v6
title: The annual 10-m maize mapping in China during 2017-2021

description: <strong>The annual 10-m maize mapping in China during 2017-2021</strong> <br> <strong>The format of data is: [adcode]_[year].tif</strong> <br> <strong>the [adcode] is a unique code for province of China. (</strong> More detail: DataV.GeoAtlas地理小工具系列 (aliyun.com) <strong>)</strong>
DOI: 10.25375/uct.22716874
title: Data required for an updated assessment of the toothfish resource in the Prince Edward Islands EEZ

description: This paper sets out the data required for the assessment analyses of the toothfish resource in the Prince Edward Islands EEZ.
DOI: 10.25375/uct.22716874.v1
title: Data required for an updated assessment of the toothfish resource in the Prince Edward Islands EEZ

description: This paper sets out the data required for the assessment analyses of the toothfish resource in the Prince Edward Islands EEZ.
DOI: 10.5281/zenodo.7922793
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2003

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2003. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2002</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2004</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2003 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7922792
title: MUSES Fractional Vegetation Coverage (FVC) 8-Day Global 1km SIN Grid in 2003

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 1 km spatial resolution and 8-day temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 1 km resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2003. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2002</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2004</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2003 Spatial Resolution: 1 km Temporal Resolution: 8 days Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22689751.v7
title: The annual 10-m maize mapping in China during 2017-2021

description: <strong>The annual 10-m maize mapping in China during 2017-2021</strong> <br> <strong>The format of data is: [adcode]_[year].tif</strong> <br> <strong>the [adcode] is a unique code for province of China. (</strong> More detail: DataV.GeoAtlas地理小工具系列 (aliyun.com) <strong>)</strong>
DOI: 10.5281/zenodo.3737102
title: Crowdsourced air traffic data from The OpenSky Network 2020

description: <strong>Motivation</strong> The data in this dataset is derived and cleaned from the full OpenSky dataset to illustrate the development of air traffic during the COVID-19 pandemic. It spans all flights seen by the network's more than 2500 members between 1 January 2020 and 1 April 2020. More data will be periodically included in the dataset until the end of the COVID-19 pandemic. <strong>Disclaimer</strong> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous. Origin and destination airports are computed online based on the ADS-B trajectories on approach/takeoff: no crosschecking with external sources of data has been conducted.<br> Fields <strong>origin</strong> or <strong>destination</strong> are empty when no airport could be found. Aircraft information come from the OpenSky aircraft database. Fields <strong>typecode</strong> and <strong>registration</strong> are empty when the aircraft is not present in the database. <strong>Description of the dataset</strong> One file per month is provided as a csv file with the following features: <strong>callsign</strong>: the identifier of the flight displayed on ATC screens (usually the first three letters are reserved for an airline: AFR for Air France, DLH for Lufthansa, etc.) <strong>number</strong>: the commercial number of the flight, when available (the matching with the callsign comes from public open API) <strong>icao24</strong>: the transponder unique identification number; <strong>registration</strong>: the aircraft tail number (when available); <strong>typecode</strong>: the aircraft model type (when available); <strong>origin</strong>: a four letter code for the origin airport of the flight (when available); <strong>destination</strong>: a four letter code for the destination airport of the flight (when available); <strong>firstseen</strong>: the UTC timestamp of the first message received by the OpenSky Network; <strong>lastseen</strong>: the UTC timestamp of the last message received by the OpenSky Network; <strong>day</strong>: the UTC day of the last message received by the OpenSky Network. <strong>Examples</strong> Possible visualisations and a more detailed description of the data are available at the following page:<br> &lt;https://traffic-viz.github.io/scenarios/covid19.html&gt; <strong>Credit</strong> If you use this dataset, please cite the original OpenSky paper: Matthias Schäfer, Martin Strohmeier, Vincent Lenders, Ivan Martinovic and Matthias Wilhelm.<br> "Bringing Up OpenSky: A Large-scale ADS-B Sensor Network for Research".<br> In<em> Proceedings of the 13th IEEE/ACM International Symposium on Information Processing in Sensor Networks (IPSN)</em>, pages 83-94, April 2014. and the traffic library used to derive the data: Xavier Olive.<br> "traffic, a toolbox for processing and analysing air traffic data."<br> <em>Journal of Open Source Software</em> 4(39), July 2019.<br>