cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7743858
title: Correlative study of liquid in human bone by 3D neutron microscopy and lab-based X-ray µCT

description: X-ray (xray_data.tiff) and neutron microscopy (neutron_data.tiff) of the same piece of human cortical bone. Neutron data were collected at the neutron microscope at SINQ, PSI, Switzerland while the x-ray data were collected in house at an VERSA 620 X-ray microscope. The voxel size is in both case 2.7 µm
description: We acknowledge support from the ESS lighthouse on hard materials in 3D, SOLID, funded by the Danish Agency for Science and Higher Education, grant number 8144-00002B. We thank the Danish Agency for Science, Technology, and Innovation for funding the instrument center DanScatt. Use of the Novo Nordisk Foundation research infrastructure AXIA (grant NNF19OC0055801) and support from Dr. Nina K. Wittig is gratefully acknowledged. Technical support in sample preparation by Dr. Carsten Pedersen is gratefully acknowledged. This work is based on experiments performed at the Swiss spallation neutron source SINQ, Paul Scherrer Institute, Villigen, Switzerland.
DOI: 10.6084/m9.figshare.22298215
title: Population genetics of pBI143 in mothers and their infants

description: This datapack contains the primary output files for four mother-infant datasets from Finalnd, Italy, Sweeden, and the United Stats generated by anvi'o. <br> The generation of these project files is very straightforward with the program anvi-run-workflow, to which we essentially provided the plasmid sequence and a list of metagenomes of interest. The program anvi-run-workflow simply (1) recruited reads using the plasmid sequence from each metagenome it was given, (2) profiled each read recruitment result using the program anvi-profile, and finally (3) merged all single profile databases using the program anvi-merge. <br> See the analysis details at https://merenlab.org/data/pBI143
DOI: 10.6084/m9.figshare.22298215.v2
title: Population genetics of pBI143 in mothers and their infants

description: This datapack contains the primary output files for four mother-infant datasets from Finalnd, Italy, Sweeden, and the United Stats generated by anvi'o. <br> The generation of these project files is very straightforward with the program anvi-run-workflow, to which we essentially provided the plasmid sequence and a list of metagenomes of interest. The program anvi-run-workflow simply (1) recruited reads using the plasmid sequence from each metagenome it was given, (2) profiled each read recruitment result using the program anvi-profile, and finally (3) merged all single profile databases using the program anvi-merge. <br> See the analysis details at https://merenlab.org/data/pBI143
DOI: 10.6084/m9.figshare.22298215.v1
title: Population genetics of pBI143 in mothers and their infants

description: This datapack contains the primary output files for four mother-infant datasets from Finalnd, Italy, Sweeden, and the United Stats generated by anvi'o. <br> The generation of these project files is very straightforward with the program anvi-run-workflow, to which we essentially provided the plasmid sequence and a list of metagenomes of interest. The program anvi-run-workflow simply (1) recruited reads using the plasmid sequence from each metagenome it was given, (2) profiled each read recruitment result using the program anvi-profile, and finally (3) merged all single profile databases using the program anvi-merge. <br> See the analysis details at https://merenlab.org/data/pBI143
DOI: 10.6084/m9.figshare.22298230
title: Table 6. Results from multiple regression analysis

description: <em><strong>Table 6. Results from multiple regression analysis</strong></em>
DOI: 10.34740/kaggle/dsv/4893723
title: Ranjana Lipi
DOI: 10.6084/m9.figshare.22298245
title: Data set

description: Data set
DOI: 10.6084/m9.figshare.22298245.v1
title: Data set

description: Data set
DOI: 10.5281/zenodo.6834997
title: TrypTag: plate 327048 (replicate dated 20170310)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate 327048, replicate dated 20170310.
description: Fix malformed/partial upload zip file
DOI: 10.5281/zenodo.6834998
title: TrypTag: plate 327048 (replicate dated 20170310)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <i>Trypanosoma brucei</i> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate 327048, replicate dated 20170310.
DOI: 10.5281/zenodo.7748321
title: TrypTag: plate 327048 (replicate dated 20170310)

description: TrypTag genome-wide protein localisation project data. Widefield epifluorescence microscope images of protein subcellular localisation in the unicellular eukaryotic pathogen <em>Trypanosoma brucei</em> by endogenous tagging with mNeonGreen (mNG). Raw microscopy data and per-cell line localisation annotation for plate 327048, replicate dated 20170310.
description: Fix malformed/partial upload zip file
DOI: 10.6084/m9.figshare.22122974
title: ERIraos Estonian validation

description: This dataset was used for the validation study of the Estonian version of the psychosis early recognition instrument ERIraos.
DOI: 10.6084/m9.figshare.22122974.v2
title: ERIraos Estonian validation

description: This dataset was used for the validation study of the Estonian version of the psychosis early recognition instrument ERIraos.
DOI: 10.6084/m9.figshare.22122974.v1
title: ERIraos Estonian validation

description: This dataset was used for the validation study of the Estonian version of the psychosis early recognition instrument ERIraos.
DOI: 10.34740/kaggle/dsv/5188789
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/dsv/5188798
title: UNET Lung Segmentation Weights for Chest X Rays
DOI: 10.5281/zenodo.7747534
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2010

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 LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2010. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7747533
title: MUSES Leaf Area Index (LAI) Monthly Global 500m SIN Grid in 2010

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 LAI product at 500m spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2010. Please <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2009</strong></em><strong>, </strong>and <em><strong>click here</strong> to download the MUSES LAI product <strong>in 2011</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2010 Spatial Resolution: 500m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.6084/m9.figshare.22298497.v1
title: outputs.rar

description: · Identified and quantified air pollutant values, including Sentinel-5P TROPOMI-derived air pollutants (NO2 and SO2,) and MODIS-derived AOD data before and during the COVID-19 outbreak in six provinces in Iran. · Relatively high temporal NO2 and SO2 variation across the COVID-19 outbreak. · Acceptable consistency of ground-based measurement at air pollutants monitoring sites by the remote-sensing measurements in these locations in the study periods. · PM2.5 concentration increased oinearly 2021 from the value in baseline period,.
DOI: 10.6084/m9.figshare.22298497
title: outputs.rar

description: · Identified and quantified air pollutant values, including Sentinel-5P TROPOMI-derived air pollutants (NO2 and SO2,) and MODIS-derived AOD data before and during the COVID-19 outbreak in six provinces in Iran. · Relatively high temporal NO2 and SO2 variation across the COVID-19 outbreak. · Acceptable consistency of ground-based measurement at air pollutants monitoring sites by the remote-sensing measurements in these locations in the study periods. · PM2.5 concentration increased oinearly 2021 from the value in baseline period,.
DOI: 10.6084/m9.figshare.22298497.v3
title: outputs.rar

description: · Identified and quantified air pollutant values, including Sentinel-5P TROPOMI-derived air pollutants (NO2 and SO2,) and MODIS-derived AOD data before and during the COVID-19 outbreak in six provinces in Iran. · Relatively high temporal NO2 and SO2 variation across the COVID-19 outbreak. · Acceptable consistency of ground-based measurement at air pollutants monitoring sites by the remote-sensing measurements in these locations in the study periods. · PM2.5 concentration increased oinearly 2021 from the value in baseline period,.
DOI: 10.6084/m9.figshare.22298497.v2
title: outputs.rar

description: · Identified and quantified air pollutant values, including Sentinel-5P TROPOMI-derived air pollutants (NO2 and SO2,) and MODIS-derived AOD data before and during the COVID-19 outbreak in six provinces in Iran. · Relatively high temporal NO2 and SO2 variation across the COVID-19 outbreak. · Acceptable consistency of ground-based measurement at air pollutants monitoring sites by the remote-sensing measurements in these locations in the study periods. · PM2.5 concentration increased oinearly 2021 from the value in baseline period,.
DOI: 10.6084/m9.figshare.22045055
title: Met RNA-seq

description: Met RNA-seq
DOI: 10.6084/m9.figshare.22045055.v1
title: Met RNA-seq

description: Met RNA-seq
DOI: 10.34740/kaggle/dsv/5188879
title: 2021 World Population (updated daily)
DOI: 10.4121/21930768
title: Data underlying: Stacking domain morphology in epitaxial graphene on silicon carbide

description: This dataset contains stitched AC-LEEM overviews visualizing stacking domain boundaries in three different, high-quality graphene on SiC samples, grown in three different manners. The samples exhibit domain boundaries with different morphology, as explored in the corresponding paper. More details on the files can be found in the README.
DOI: 10.15156/bio/sh1959129.08fu
title: SH1959129.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1495569.08fu
title: SH1495569.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1495570.08fu
title: SH1495570.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1959130.08fu
title: SH1959130.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1959131.08fu
title: SH1959131.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1495571.08fu
title: SH1495571.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1495572.08fu
title: SH1495572.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1495573.08fu
title: SH1495573.08FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.