cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19369266
title: Development of peptide vaccine candidate using highly antigenic PE-PGRS family proteins to stimulate the host immune response against <i>Mycobacterium tuberculosis</i> H<sub>37</sub>Rv: an immuno-informatics approach

description: Tuberculosis (TB) is a fast spreading; transmissible disease caused by the <i>Mycobacterium tuberculosis</i> (<i>M. tuberculosis</i>). <i>M. tuberculosis</i> has a high death rate in its endemic regions due to a lack of appropriate treatment and preventative measures. We have used a vaccinomics strategy to create an effective multi-epitope vaccine against <i>M. tuberculosis</i>. The antigenic proteins with the highest antigenicity were utilised to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes. CTL and HTL epitopes were covered in 99.97% of the population. Seven epitopes each of CTL, HTL, and LBL were ultimately selected and utilised to develop a multi-epitope vaccine. A vaccine design was developed by combining these epitopes with suitable linkers and LprG adjuvant. The vaccine chimera was revealed to be highly immunogenic, non-allergenic, and non-toxic. To ensure a better expression within the <i>Escherichia coli</i> K12 (<i>E. coli</i> K12) host system, codon adaptation and in silico cloning were accomplished. Following that, various validation studies were conducted, including molecular docking, molecular dynamics simulation, and immunological simulation, all of which indicated that the designed vaccine would be stable in the biological environment and effective against <i>M. tuberculosis</i> infection. The immune simulation revealed higher levels of T-cell and B-cell activity, which corresponded to the actual immune response. Exposure simulations were repeated several times, resulting in increased clonal selection and faster antigen clearance. These results suggest that, if proposed vaccine chimera would test both in-vitro and in-vivo, it could be a viable treatment and preventive strategy for TB. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.19369266.v1
title: Development of peptide vaccine candidate using highly antigenic PE-PGRS family proteins to stimulate the host immune response against <i>Mycobacterium tuberculosis</i> H<sub>37</sub>Rv: an immuno-informatics approach

description: Tuberculosis (TB) is a fast spreading; transmissible disease caused by the <i>Mycobacterium tuberculosis</i> (<i>M. tuberculosis</i>). <i>M. tuberculosis</i> has a high death rate in its endemic regions due to a lack of appropriate treatment and preventative measures. We have used a vaccinomics strategy to create an effective multi-epitope vaccine against <i>M. tuberculosis</i>. The antigenic proteins with the highest antigenicity were utilised to predict cytotoxic T-lymphocyte (CTL), helper T-lymphocyte (HTL), and linear B-lymphocyte (LBL) epitopes. CTL and HTL epitopes were covered in 99.97% of the population. Seven epitopes each of CTL, HTL, and LBL were ultimately selected and utilised to develop a multi-epitope vaccine. A vaccine design was developed by combining these epitopes with suitable linkers and LprG adjuvant. The vaccine chimera was revealed to be highly immunogenic, non-allergenic, and non-toxic. To ensure a better expression within the <i>Escherichia coli</i> K12 (<i>E. coli</i> K12) host system, codon adaptation and in silico cloning were accomplished. Following that, various validation studies were conducted, including molecular docking, molecular dynamics simulation, and immunological simulation, all of which indicated that the designed vaccine would be stable in the biological environment and effective against <i>M. tuberculosis</i> infection. The immune simulation revealed higher levels of T-cell and B-cell activity, which corresponded to the actual immune response. Exposure simulations were repeated several times, resulting in increased clonal selection and faster antigen clearance. These results suggest that, if proposed vaccine chimera would test both in-vitro and in-vivo, it could be a viable treatment and preventive strategy for TB. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.19328871
title: An <i>in silico</i> discovery of potential 3CL protease inhibitors of SARS-CoV-2 based upon inactivation of the cysteine 145-Histidine 41 catalytic dyad

description: Coronavirus disease 19 (COVID19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Currently, several countries are at risk of the pandemic caused by this virus. In the absence of any vaccine or virus-specific antiviral treatments, the need is to fast track search for potential drug candidates to combat the virus. Though there are known drugs that are being repurposed to fight against the SARS-CoV-2, there is a requirement for the virus-specific drugs at the earliest. One of the main drug targets of SARS-CoV-2 is an essential non-structural protein, 3CL protease, critical for the life cycle of the virus. We have used molecular docking studies to screen a chemically diverse set of small molecules to identify potential drug candidates to target this protein. Of the 22,630 molecules from varied small molecule libraries, based on the binding affinities and physicochemical properties, we finalized 30 molecules to be potential drug candidates. Eight of these molecules bind in a manner allowing for the scope of a nearly orthogonal backside nucleophilic attack on their suitably placed electrophilic carbonyl groups by the thiol group of cysteine residue 145, while remaining inside a 4 Ǻ distance range. It is interesting since carbonyl groups are known to be attacked in a similar fashion by external nucleophiles and can be relevant when considering these molecules as potential mechanism-based irreversible inhibitors of the 3CL<sup>Pro</sup>. Further, ADMET analysis and Molecular dynamics simulations and available bioactive assays led to the identification of three molecules with high potential to be explored as drug candidates/lead molecules to target 3CL<sup>Pro</sup> of SARS-CoV-2. Communicated by Ramaswamy H. Sarma
DOI: 10.5281/zenodo.7882306
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2014

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 1 km 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 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 1 km 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.7882305
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2014

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 1 km 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 2014. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2013</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2015</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2014 Spatial Resolution: 1 km 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.22725863
title: BEH 3813 Appendix

description: <strong>Table A1.</strong> Functional traits, mobbing frequency and abundance of bird species.
DOI: 10.6084/m9.figshare.22725863.v1
title: BEH 3813 Appendix

description: <strong>Table A1.</strong> Functional traits, mobbing frequency and abundance of bird species.
DOI: 10.5519/qd.6vefy1r8
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1079955 records)
DOI: 10.5281/zenodo.7619887
title: Merging and imputation of flow cytometry data: a critical assessment

description: This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>. Scripts for reproducing our analyses are available at: https://github.com/AUMC-HEMA/imputation-manuscript
DOI: 10.5281/zenodo.7619888
title: Merging and imputation of flow cytometry data: a critical assessment

description: This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>. Scripts for reproducing our analyses are available at: https://github.com/AUMC-HEMA/imputation-manuscript
DOI: 10.5281/zenodo.7883291
title: Merging and imputation of flow cytometry data: a critical assessment

description: This dataset contains the data necessary to reproduce the analyses by Mocking <em>et al</em>. Scripts for reproducing our analyses are available at: https://github.com/AUMC-HEMA/imputation-manuscript
DOI: 10.5519/qd.evz5un87
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (187 records)
DOI: 10.5281/zenodo.7882317
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2015

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 500 m spatial resolution and monthly 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 500 m 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 2015. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 500 m Temporal Resolution: 1 month 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.7882316
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2015

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 500 m spatial resolution and monthly 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 500 m 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 2015. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2014</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2016</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2015 Spatial Resolution: 500 m Temporal Resolution: 1 month 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: 10.5519/qd.3imo1r3q
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (19 records)
DOI: 10.6084/m9.figshare.22725905
title: Recursos tecnológicos para utlizar en educación infantil

description: Esta guía recoge diferentes recursos tecnológicos que pueden ser utilizados por los docentes en la etapa de educación infantil.
DOI: 10.6084/m9.figshare.22725905.v1
title: Recursos tecnológicos para utlizar en educación infantil

description: Esta guía recoge diferentes recursos tecnológicos que pueden ser utilizados por los docentes en la etapa de educación infantil.
DOI: 10.5281/zenodo.7883119
title: Intraassay experiment: Asinh transformed, rescaled CD3 exported, fcs-format.

description: See https://git.uni-regensburg.de/datasrc/cytometry/ukr/2022-10-17_Intraassay_variation. The pipeline has been run and the results of the following commit hash presented here: 81ae31eead30ba9904672b4057970e25d6717a54 These files are the .fcs-format files.
DOI: 10.5281/zenodo.7883118
title: Intraassay experiment: Asinh transformed, rescaled CD3 exported, fcs-format.

description: See https://git.uni-regensburg.de/datasrc/cytometry/ukr/2022-10-17_Intraassay_variation. The pipeline has been run and the results of the following commit hash presented here: 81ae31eead30ba9904672b4057970e25d6717a54 These files are the .fcs-format files.
DOI: 10.6084/m9.figshare.22725908
title: <strong>Meta-learning in head fixed mice navigating in virtual reality</strong>

description: Data and Codes for: <strong>Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis </strong> <br> Please start from the Readme file.
DOI: 10.6084/m9.figshare.22725908.v1
title: <strong>Meta-learning in head fixed mice navigating in virtual reality</strong>

description: Data and Codes for: <strong>Meta-learning in head fixed mice navigating in virtual reality: A Behavioral Analysis </strong> <br> Please start from the Readme file.
DOI: 10.34740/kaggle/dsv/5573733
title: Egypt Gold Prices | Daily Updated
DOI: 10.34740/kaggle/dsv/5573760
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5519/qd.2igdxr0t
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (18 records)
DOI: 10.6084/m9.figshare.22725614
title: Sequence data

description: RNA -SEQ data of TLR7-overexpressed HaCaT
DOI: 10.6084/m9.figshare.22725614.v2
title: Sequence data

description: RNA -SEQ data of TLR7-overexpressed HaCaT
DOI: 10.6084/m9.figshare.22725614.v1
title: Untitled Item

description: RNA -SEQ data of TLR7-overexpressed HaCaT
DOI: 10.6084/m9.figshare.22723177
title: CANDID-III Dataset

description: 322,473 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. 33,486 studies were manually labeled. 187,491 were labeled by deep learning models. Accuracy of the AI labeled portion of the dataset with respect to each label will be outlined in the published paper. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays. To access the data, an ethics training process is required and is divided into 2 steps: 1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to sijingfeng@gmail.com 2. Signing the Data Use Agreement. It can be accessed at Data Use Agreement- Unanonymised data.pdf. Once you signed the Data Use Agreement, please also send the signed copy to sijingfeng@gmail.com After successfully completion of both of above steps, a private link to download the dataset will be sent.
DOI: 10.6084/m9.figshare.22723177.v1
title: CANDID-III Dataset

description: 322,473 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. 33,486 studies were manually labeled. 187,491 were labeled by deep learning models. Accuracy of the AI labeled portion of the dataset with respect to each label will be outlined in the published paper. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays. To access the data, an ethics training process is required and is divided into 2 steps: 1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to sijingfeng@gmail.com 2. Signing the Data Use Agreement. It can be accessed at Data Use Agreement- Unanonymised data.pdf. Once you signed the Data Use Agreement, please also send the signed copy to sijingfeng@gmail.com After successfully completion of both of above steps, a private link to download the dataset will be sent.
DOI: 10.34740/kaggle/dsv/5573903
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.22725953
title: Untitled Item

description: field crop production, tillage practices. <br>
DOI: 10.6084/m9.figshare.22725953.v1
title: Untitled Item

description: field crop production, tillage practices. <br>