cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.17632/d5h73x6xst.2
title: Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20. Vieira-Vieira et al. Figure 5A. WT RBM20

description: Cellular mRNA-binding proteins (mRBPs) are major regulators of gene expression at the post-transcriptional level. While many posttranslational modification sites in mRBPs have been identified, little is known about how these modifications regulate mRBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular mRBPs pulled down with polyadenylated mRNAs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of mRBPs. Almost 200 phosphorylation events increased or decreased pull-down efficiency compared to the unmodified mRBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytoplasmic ribonucleoprotein granules and alternative splicing. Together, we show that qRIC in conjunction with phosphoproteomics is a scalable method to identify functional posttranslational modification sites in mRBPs.
DOI: 10.17632/bdx89nn3tp.2
title: Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20. Vieira-Vieira et al. Figure 5A. D10 RBM20

description: Cellular mRNA-binding proteins (mRBPs) are major regulators of gene expression at the post-transcriptional level. While many posttranslational modification sites in mRBPs have been identified, little is known about how these modifications regulate mRBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular mRBPs pulled down with polyadenylated mRNAs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of mRBPs. Almost 200 phosphorylation events increased or decreased pull-down efficiency compared to the unmodified mRBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytoplasmic ribonucleoprotein granules and alternative splicing. Together, we show that qRIC in conjunction with phosphoproteomics is a scalable method to identify functional posttranslational modification sites in mRBPs.
DOI: 10.17632/sp3xdygfxj.2
title: Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20. Vieira-Vieira et al. Figure 5A. A10 RBM20

description: Cellular mRNA-binding proteins (mRBPs) are major regulators of gene expression at the post-transcriptional level. While many posttranslational modification sites in mRBPs have been identified, little is known about how these modifications regulate mRBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular mRBPs pulled down with polyadenylated mRNAs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of mRBPs. Almost 200 phosphorylation events increased or decreased pull-down efficiency compared to the unmodified mRBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytoplasmic ribonucleoprotein granules and alternative splicing. Together, we show that qRIC in conjunction with phosphoproteomics is a scalable method to identify functional posttranslational modification sites in mRBPs.
DOI: 10.17632/643rgkftv2.2
title: A study of Notch-mediated lactate metabolism regulated myeloid cell development through Hes1/MCT2/c-Jun axis. Zhao JL et al.

description: All raw western blots and analysis data have been deposited at Mendeley data.
DOI: 10.5281/zenodo.6462470
title: Dataset del análisis de los juegos de Fight COVID Jam 2019

description: Dataset realizado por medio del modelo "Modelo de análisis de serious games en el entorno de las game jam" (https://zenodo.org/record/6462394) en la muestra de juegos presentados en la Fight COVID Jam 2019 (https://fightcovid.gamebcn.co/).
DOI: 10.5281/zenodo.6462469
title: Dataset del análisis de los juegos de Fight COVID Jam 2019

description: Dataset realizado por medio del modelo "Modelo de análisis de serious games en el entorno de las game jam" (https://zenodo.org/record/6462394) en la muestra de juegos presentados en la Fight COVID Jam 2019 (https://fightcovid.gamebcn.co/).
DOI: 10.6084/m9.figshare.19601956
title: camd2022.tar.gz

description: This dataset is composed of autonomously discovered crystal structures from CAMD. 96640 distinct crystal structures with accompanying formation energy. 26,435 distinct formulas, 1,457 distinct element combinations (chemical systems). 26826 structures are less than 0.2 eV/atom above the hull, 894 are less than 0.001 eV/atom above the hull.
DOI: 10.7916/d8-8cam-r455
title:  Mechanical Response of Mouse Cervices Lacking Decorin and Biglycan during Pregnancy: Data
DOI: 10.17632/n938bchmv2.2
title: Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20. Vieira-Vieira et al. Figure S5C

description: Cellular mRNA-binding proteins (mRBPs) are major regulators of gene expression at the post-transcriptional level. While many posttranslational modification sites in mRBPs have been identified, little is known about how these modifications regulate mRBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular mRBPs pulled down with polyadenylated mRNAs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of mRBPs. Almost 200 phosphorylation events increased or decreased pull-down efficiency compared to the unmodified mRBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytoplasmic ribonucleoprotein granules and alternative splicing. Together, we show that qRIC in conjunction with phosphoproteomics is a scalable method to identify functional posttranslational modification sites in mRBPs.
DOI: 10.17632/tcggv58kjk.2
title: Proteome-wide quantitative RNA interactome capture (qRIC) identifies phosphorylation sites with regulatory potential in RBM20. Vieira-Vieira et al. Figure 5A. S635A RBM20

description: Cellular mRNA-binding proteins (mRBPs) are major regulators of gene expression at the post-transcriptional level. While many posttranslational modification sites in mRBPs have been identified, little is known about how these modifications regulate mRBP function. Here, we developed quantitative RNA-interactome capture (qRIC) to quantify the fraction of cellular mRBPs pulled down with polyadenylated mRNAs. Combining qRIC with phosphoproteomics allowed us to systematically compare pull-down efficiencies of phosphorylated and non-phosphorylated forms of mRBPs. Almost 200 phosphorylation events increased or decreased pull-down efficiency compared to the unmodified mRBPs and thus have regulatory potential. Our data captures known regulatory phosphorylation sites in ELAVL1, SF3B1 and UPF1 and identifies new potentially regulatory sites. Follow-up experiments on the cardiac splicing regulator RBM20 revealed that multiple phosphorylation sites in the C-terminal disordered region affect nucleo-cytoplasmic localization, association with cytoplasmic ribonucleoprotein granules and alternative splicing. Together, we show that qRIC in conjunction with phosphoproteomics is a scalable method to identify functional posttranslational modification sites in mRBPs.
DOI: 10.6084/m9.figshare.19602091
title: model-14-04-22:10

description: This description is a placeholder
DOI: 10.6084/m9.figshare.19602091.v1
title: model-14-04-22:10

description: This description is a placeholder
DOI: 10.6084/m9.figshare.19593409
title: Historical, generalized built-up areas in U.S. core-based statistical areas 1900 - 2015

description: An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing. <br> Additionally, there is a CSV file (HISDAC-US_patch_statistics.csv) containing the counts of built-up property records (BUPR), and -locations (BUPL), as well as total building indoor area (BUI) and built-up area (BUA) per CBSA, year, and patch, extraced from the HISDAC-US data (Uhl & Leyk 2018, Uhl et al. 2021). This CSV can be joined to the shapefile (column uid2) by concatenating the columns msaid_year_Id. <br> Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. <em>Scientific data</em>, <em>5</em>(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. <em>Earth system science data</em>, <em>13</em>(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021
DOI: 10.6084/m9.figshare.19593409.v1
title: Historical, generalized built-up areas in U.S. core-based statistical areas 1900 - 2015

description: An ESRI Shapfile containing spatially generalized built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical settlement layers from the Historical settlement data compilation for the U.S. (HISDAC-US, Leyk & Uhl 2018). See Burghardt et al. (2022) for details on the data processing. Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers. This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details. All data created by Johannes H. Uhl, University of Colorado Boulder, USA. Code available at https://github.com/johannesuhl/USRoadNetworkEvolution. References: Burghardt, K., Uhl, J., Lerman, K., & Leyk, S. (2022). Road Network Evolution in the Urban and Rural United States Since 1900. Computers, Environment and Urban Systems. Leyk, S., & Uhl, J. H. (2018). HISDAC-US, historical settlement data compilation for the conterminous United States over 200 years. <em>Scientific data</em>, <em>5</em>(1), 1-14. DOI: https://doi.org/10.1038/sdata.2018.175 Uhl, J. H., Leyk, S., McShane, C. M., Braswell, A. E., Connor, D. S., & Balk, D. (2021). Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States. <em>Earth system science data</em>, <em>13</em>(1), 119-153. DOI: https://doi.org/10.5194/essd-13-119-2021
DOI: 10.25345/c50g3h27z
title: MassIVE Reanalysis RMSV000000651.1 - CPTAC MSV000084243/ S023 Time-Dependent Proteome Study: Human colorectal tumor samples (0, 10, 30, 60 min), annotation file only
title: Annotation file for CPTAC MSV000084243/ S023 Time-Dependent Proteome Study: Human colorectal tumor samples (0, 10, 30, 60 min)
DOI: 10.25345/c54746w19
title: MassIVE Reanalysis RMSV000000652.1 - CPTAC MSV000084244/ S024 Site-specific mapping and quantification of protein S-sulphenylation in cells, annotation file only
title: Annotation file for CPTAC MSV000084244/ S024 Site-specific mapping and quantification of protein S-sulphenylation in cells
DOI: 10.5281/zenodo.6462483
title: IO500 10 node challenge

description: 10 node challenge data
DOI: 10.5281/zenodo.6462482
title: IO500 10 node challenge

description: 10 node challenge data
DOI: 10.5281/zenodo.6462490
title: ISC19 IO500 Data Sets

description: Full, 10 node, and standard IO500 lists
DOI: 10.5281/zenodo.6462489
title: ISC19 IO500 Data Sets

description: Full, 10 node, and standard IO500 lists
DOI: 10.5281/zenodo.6462493
title: IO500 SC19 Lists

description: The historical, Full, 10 node challenge, and student cluster competition IO500 lists
DOI: 10.5281/zenodo.6462492
title: IO500 SC19 Lists

description: The historical, Full, 10 node challenge, and student cluster competition IO500 lists
DOI: 10.5281/zenodo.6462501
title: IO500 SC20 Lists

description: IO500 lists for SC20
DOI: 10.5281/zenodo.6462500
title: IO500 SC20 Lists

description: IO500 lists for SC20
DOI: 10.25345/c5vq2sf2k
title: MassIVE Reanalysis RMSV000000653.1 - CPTAC MSV000084247/ S028 Integrated bottom-up and top-down proteomics of patient-derived breast tumor xenografts, annotation file only
title: Annotation file for CPTAC MSV000084247/ S028 Integrated bottom-up and top-down proteomics of patient-derived breast tumor xenografts
DOI: 10.6084/m9.figshare.19602088
title: kASA Human database

description: Built with kASA v1.4.7 on 13-April-2022 as described here: https://hackmd.io/@SilvioWeging/GL-kASA-human-db-setup
DOI: 10.6084/m9.figshare.19602088.v1
title: kASA Human database

description: Built with kASA v1.4.7 on 13-April-2022 as described here: https://hackmd.io/@SilvioWeging/GL-kASA-human-db-setup
DOI: 10.5281/zenodo.6462505
title: IO500 ISC21 Lists

description: IO500 lists from ISC21
DOI: 10.5281/zenodo.6462504
title: IO500 ISC21 Lists

description: IO500 lists from ISC21
DOI: 10.6084/m9.figshare.19602145
title: Dynamic recognition and mirage using neuro-metamaterials: Datasets

description: The datasets are associated with the work "Dynamic recognition and mirage using neuro-metamaterials".
DOI: 10.5517/ccdc.csd.cc296mms
title: CCDC 2121535: Experimental Crystal Structure Determination

description: Related Article: Sevval Efe, Carsten Schauerte, Klaus Merz|2022|Cryst.Growth Des.|22|2272|doi:10.1021/acs.cgd.1c01356
DOI: 10.5517/ccdc.csd.cc296mnt
title: CCDC 2121536: Experimental Crystal Structure Determination

description: Related Article: Sevval Efe, Carsten Schauerte, Klaus Merz|2022|Cryst.Growth Des.|22|2272|doi:10.1021/acs.cgd.1c01356
DOI: 10.5517/ccdc.csd.cc296nv1
title: CCDC 2121573: Experimental Crystal Structure Determination

description: Related Article: Tian-Yi Xu, Fei Tong, Hui Xu, Meng-Qi Wang, He Tian, Da-Hui Qu|2022|J.Am.Chem.Soc.|144|6278|doi:10.1021/jacs.1c12485
DOI: 10.5517/ccdc.csd.cc296nw2
title: CCDC 2121574: Experimental Crystal Structure Determination

description: Related Article: Tian-Yi Xu, Fei Tong, Hui Xu, Meng-Qi Wang, He Tian, Da-Hui Qu|2022|J.Am.Chem.Soc.|144|6278|doi:10.1021/jacs.1c12485
DOI: 10.5517/ccdc.csd.cc29cxf1
title: CCDC 2126613: Experimental Crystal Structure Determination

description: Related Article: Marcos Escolano, María Jesús Cabrera-Afonso, Maria Ribagorda, Shorouk O. Badir, Gary A. Molander|2022|J.Org.Chem.|87|4981|doi:10.1021/acs.joc.1c03041
DOI: 10.5281/zenodo.6015605
title: Results for the national decarbonization study (2022)

description: <em>A set of data files for figures in the paper on national decarbonization (2022)</em>
DOI: 10.5281/zenodo.6015604
title: Results for the national decarbonization study (2022)

description: <em>A set of data files for figures in the paper on national decarbonization (2022)</em>
DOI: 10.6084/m9.figshare.19602124
title: Raw data for Overexpression of angiogenic factors and matrix metalloproteinases in the saliva of oral squamous cell carcinoma patients: potential non-invasive diagnostic and therapeutic biomarkers

description: Raw data for Overexpression of angiogenic factors and matrix metalloproteinases in the saliva of oral squamous cell carcinoma patients: potential non-invasive diagnostic and therapeutic biomarkers
DOI: 10.6084/m9.figshare.19602124.v1
title: Raw data for Overexpression of angiogenic factors and matrix metalloproteinases in the saliva of oral squamous cell carcinoma patients: potential non-invasive diagnostic and therapeutic biomarkers

description: Raw data for Overexpression of angiogenic factors and matrix metalloproteinases in the saliva of oral squamous cell carcinoma patients: potential non-invasive diagnostic and therapeutic biomarkers
DOI: 10.5281/zenodo.6391179
title: Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"

description: For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act" Description: Data/code used in energy-economic impacts and health impacts analysis. Directory contents: <strong>Energy Economic Impacts</strong> <strong>Code </strong>used for producing figures and data tables 'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>) 'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper <strong>Table</strong>: data tables for figures in the paper and supplementary materials <strong>Figure</strong>: figures in the paper and supplementary materials <strong>Data</strong>: USREP-ReEDS results and data from other sources 'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper. 'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper. 'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021. 'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018. 'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G. <strong>Health Impacts</strong> <strong>analysis</strong>: <strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis <strong>data</strong>: outputs <strong>inmap</strong>: spatial inputs/outputs for inmap <strong>working</strong>: intermediate procssed output files <strong>final</strong>: final health impacts results <strong>data</strong>: raw data used in analysis <strong>working</strong>: processed intermediate raw data for faster loading in R
DOI: 10.5281/zenodo.6391178
title: Data and code for "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act"

description: For the files and data associated with the Yuan et al. 2022 "Meeting U.S. Greenhouse Gas Emissions Goals with the International Air Pollution Provision of the Clean Air Act" Description: Data/code used in energy-economic impacts and health impacts analysis. Directory contents: <strong>Energy Economic Impacts</strong> <strong>Code </strong>used for producing figures and data tables 'paperFigs_March2022.Rmd' contains the R code used for data analysis and visualization in the paper. (<em>The code runs with R v4.0.0, RStudio v1.4.1106, and the following packages: scales_1.1.1, ggpubr_0.4.0, cowplot_1.1.0, readxl_1.3.1, here_0.1, forcats_0.5.0, stringr_1.4.0, dplyr_1.0.4, purrr_0.3.4, readr_1.3.1, tidyr_1.1.0, tibble_3.0.6, ggplot2_3.3.4, and tidyverse_1.3.0.</em>) 'ERL_Figure4.py' contains the Python code used for generating Figure 4 in the paper <strong>Table</strong>: data tables for figures in the paper and supplementary materials <strong>Figure</strong>: figures in the paper and supplementary materials <strong>Data</strong>: USREP-ReEDS results and data from other sources 'rrpt_subset.csv' contains the portions of the ReEDS output from February 26, 2021 that are necessary to create the figures in the paper. 'urpt_subset.csv' contains the portions of the USREP output from February 26, 2021 that are necessary to create the figures in the paper. 'urpt_welfare_subset.csv' contains more detailed USREP welfare output from February 26, 2021. 'cooper_pop_proj.csv' contains U.S. population projections from the University of Virginia Weldon Cooper Center for Public Service published in 2018. 'carbon_price_comparison.csv' contains data from other recent carbon pricing studies, as described in supplementary materials G. <strong>Health Impacts</strong> <strong>analysis</strong>: <strong>lib</strong>: annotated code library, which loads raw data from the root data folder and conducts health impacts analysis <strong>data</strong>: outputs <strong>inmap</strong>: spatial inputs/outputs for inmap <strong>working</strong>: intermediate procssed output files <strong>final</strong>: final health impacts results <strong>data</strong>: raw data used in analysis <strong>working</strong>: processed intermediate raw data for faster loading in R
DOI: 10.6084/m9.figshare.16816654.v3
title: TCGA cancer genomic data and pathway information

description: TCGA cancer genomic data (ESCA and LUAD) and pathway information (GO-BP, GO-CC, GO-MF, KEEG, Biocarter)
DOI: 10.6084/m9.figshare.16816654.v4
title: TCGA cancer genomic data and pathway information

description: TCGA cancer genomic data (ESCA , PAAD and LUAD) and pathway information (GO-BP, GO-CC, GO-MF, KEEG, Biocarter)
DOI: 10.6084/m9.figshare.17427443.v1
title: source data-2nd Ver.xlsx

description: Source data of " Nuclear Progestin Receptor Mediated Linkage of Blood Coagulation and Ovulation" <br>
DOI: 10.6084/m9.figshare.19602379
title: raw_data.tiff

description: Reconstructed raw volume acquired during methane hydrate formation in sandy sample
DOI: 10.6084/m9.figshare.19602379.v1
title: raw_data.tiff

description: Reconstructed raw volume acquired during methane hydrate formation in sandy sample
DOI: 10.6084/m9.figshare.19602400
title: segmented_data

description: Results of the raw volume segmentation using our hybrid machine learning approach. <br> presented phases: 0 - background 1 - sand grains 2 - methane gas 3 - metahne hydrate 4 - mixture of brine and hydrate 5 - NaBr brine <br>