cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc275rmt
title: CCDC 2061116: Experimental Crystal Structure Determination

description: Related Article: Christopher Timmermann, Paula Thiem, Dominik Wanitschke, Mareike Hüttenschmidt, Johanna Romischke, Alexander Villinger, Wolfram W. Seidel|2022|Chemical Science|13|123|doi:10.1039/D1SC06149F
DOI: 10.5517/ccdc.csd.cc275rnv
title: CCDC 2061117: Experimental Crystal Structure Determination

description: Related Article: Christopher Timmermann, Paula Thiem, Dominik Wanitschke, Mareike Hüttenschmidt, Johanna Romischke, Alexander Villinger, Wolfram W. Seidel|2022|Chemical Science|13|123|doi:10.1039/D1SC06149F
DOI: 10.5517/ccdc.csd.cc27x7m1
title: CCDC 2082723: Experimental Crystal Structure Determination

description: Related Article: Qian Shang, Haifang Tang, Yongping Liu, MingMing Yin, Lebin Su, Shimin Xie, Lixin Liu, Wen Yang, Yi Chen, Jianyu Dong, Yongbo Zhou, Shuang-Feng Yin|2021|Chemical Science|13|263|doi:10.1039/D1SC03865F
DOI: 10.5517/ccdc.csd.cc282b5w
title: CCDC 2087607: Experimental Crystal Structure Determination

description: Related Article: Martin Börner, Jennifer Klose, Matias Gutierrez Suburu, Cristian Strassert, Fangshun Yang, Kirill Monakhov, Bernd Abel, Berthold Kersting|2021|Chem.-Eur.J.|27|14899|doi:10.1002/chem.202102079
DOI: 10.5517/ccdc.csd.cc28h5pn
title: CCDC 2099961: Experimental Crystal Structure Determination

description: Related Article: Chunbo Duan, Ying Xin, Zicheng Wang, Jing Zhang, Chunmiao Han, Hui Xu|2022|Chemical Science|13|159|doi:10.1039/D1SC05753G
DOI: 10.5517/ccdc.csd.cc28jb5b
title: CCDC 2101061: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jb6c
title: CCDC 2101062: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jb7d
title: CCDC 2101063: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jb8f
title: CCDC 2101064: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jb9g
title: CCDC 2101065: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbbh
title: CCDC 2101066: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbcj
title: CCDC 2101067: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbdk
title: CCDC 2101068: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbfl
title: CCDC 2101069: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbgm
title: CCDC 2101070: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbhn
title: CCDC 2101071: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbjp
title: CCDC 2101072: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbkq
title: CCDC 2101073: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jblr
title: CCDC 2101074: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbms
title: CCDC 2101075: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbnt
title: CCDC 2101076: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbpv
title: CCDC 2101077: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jbqw
title: CCDC 2101078: Experimental Crystal Structure Determination

description: Related Article: Andryj M. Borys, Ewan R. Clark, Paul J. Saines, Antonio Alberola, Jeremy M. Rawson|2022|Chemical Science|13|149|doi:10.1039/D1SC04248C
DOI: 10.5517/ccdc.csd.cc28jx6y
title: CCDC 2101620: Experimental Crystal Structure Determination

description: Related Article: Wen-Run Zhu, Qiong Su, Xiao-Yi Deng, Jia-Sheng Liu, Tao Zhong, Shan-Shui Meng, Ji-Tao Yi, Jiang Weng, Gui Lu|2021|Chemical Science|13|170|doi:10.1039/D1SC05174A
DOI: 10.5517/ccdc.csd.cc28jxsj
title: CCDC 2101638: Experimental Crystal Structure Determination

description: Related Article: Wen-Run Zhu, Qiong Su, Xiao-Yi Deng, Jia-Sheng Liu, Tao Zhong, Shan-Shui Meng, Ji-Tao Yi, Jiang Weng, Gui Lu|2021|Chemical Science|13|170|doi:10.1039/D1SC05174A
DOI: 10.5517/ccdc.csd.cc28jxtk
title: CCDC 2101639: Experimental Crystal Structure Determination

description: Related Article: Wen-Run Zhu, Qiong Su, Xiao-Yi Deng, Jia-Sheng Liu, Tao Zhong, Shan-Shui Meng, Ji-Tao Yi, Jiang Weng, Gui Lu|2021|Chemical Science|13|170|doi:10.1039/D1SC05174A
DOI: 10.5517/ccdc.csd.cc28nr0q
title: CCDC 2105303: Experimental Crystal Structure Determination

description: Related Article: Jiahua Chen, Jun Wei Lim, Derek Yiren Ong, Shunsuke Chiba|2022|Chemical Science|13|99|doi:10.1039/D1SC05876B
DOI: 10.5517/ccdc.csd.cc28q8jt
title: CCDC 2106776: Experimental Crystal Structure Determination

description: Related Article: Adrian W. Markwell-Heys, Michael Roemelt, Ashley D. Slattery, Oliver M. Linder-Patton, Witold M. Bloch|2022|Chemical Science|13|68|doi:10.1039/D1SC05663H
DOI: 10.5517/ccdc.csd.cc28q8kv
title: CCDC 2106777: Experimental Crystal Structure Determination

description: Related Article: Adrian W. Markwell-Heys, Michael Roemelt, Ashley D. Slattery, Oliver M. Linder-Patton, Witold M. Bloch|2022|Chemical Science|13|68|doi:10.1039/D1SC05663H
DOI: 10.5517/ccdc.csd.cc28q8lw
title: CCDC 2106778: Experimental Crystal Structure Determination

description: Related Article: Adrian W. Markwell-Heys, Michael Roemelt, Ashley D. Slattery, Oliver M. Linder-Patton, Witold M. Bloch|2022|Chemical Science|13|68|doi:10.1039/D1SC05663H
DOI: 10.5517/ccdc.csd.cc28vcr8
title: CCDC 2110720: Experimental Crystal Structure Determination

description: Related Article: Alexander Kremsmair, Henrik R. Wilke, Matthias M. Simon, Quirin Schmidt, Konstantin Karaghiosoff, Paul Knochel|2021|Chemical Science|13|44|doi:10.1039/D1SC05315A
DOI: 10.6084/m9.figshare.17424266
title: DRY_WELL_MODEL_INPUTS

description: This Excel file contains the input data for the model used to simulate water infiltration from a dry well, used for groundwater recharge of rainwater harvesting.
DOI: 10.6084/m9.figshare.17424266.v1
title: DRY_WELL_MODEL_INPUTS

description: This Excel file contains the input data for the model used to simulate water infiltration from a dry well, used for groundwater recharge of rainwater harvesting.
DOI: 10.48511/bqxp-2f37
title:  Crawford Creek (NJCRA01) 

description: Water quality monitoring for Crawford Creek. From 2015 to 2017, data in this dataset was collected by the East Shore Freshwater Habitat Society as part of the Columbia Basin Water Quality Monitoring Project which was coordinated and administered by Mainstreams.
DOI: 10.5281/zenodo.5797049
title: Data from: Empirical estimation of skin resistance to water loss in amphibians: agar evaluation as a non-resistance model to evaporation

description: Total resistance (R<sub>T</sub>) to evaporative water loss (EWL) in amphibians is given by the sum of the boundary layer (<em>r</em><sub>b</sub>) and the skin (<em>r</em><sub>s</sub>) resistances. Thus, an amphibian’s <em>r</em><sub>s</sub> can be determined if the <em>r</em><sub>b</sub> component is defined (<em>r</em><sub>s</sub> = R<sub>T</sub> - <em>r</em><sub>b</sub>). The use of agar models has become the standard technique to derive empirical estimates of <em>r</em><sub>b</sub> under the assumption that agar model surface imposes no barrier to evaporation (<em>r<sub>s</sub></em> = 0). Herein, we evaluated the validity of this assumption by determining EWL rates and <em>r</em><sub>b</sub> values from exposed surfaces of free water, agar gels prepared at various concentrations, and a physiological solution mimicking the osmotic properties of a generalized amphibian. Our results indicate that the agar material, at concentrations of 5%, provides a competent proxy to the estimation of amphibian´s boundary layer resistance, thus mimicking the evaporative properties of amphibians in the absence of a tegumental barrier.
description: DATA AND FILE OVERVIEW 1. File List: "EWL_RT_petri_models.csv" EWL and RT from petri dish models Rawdata of evaporative water loss rates (EWL) and the total resistance to evaporation (RT) obtained from exposed surfaces (petri dishes) of free water, agar gels prepared at various concentrations, and a physiological solution (NaCl) mimicking the osmotic properties of a generalized amphibian body fluids. "rb_simulations.r" Relative error simulations R-code to run simulations over a range of hypothetical skin resistance (rs) values to evaluate the impact of potential errors in the determination of the boundary layer resistance due to differences in agar content. "README.txt" General data information FUNDING SOURCES THAT SUPPORTED THE COLLECTION OF DATA São Paulo Research Foundation – FAPESP (Brazil) The National Council for Scientific and Technological Development – CNPq (Brazil) The Coordination for the Improvement of Higher Education Personnel – CAPES (Brazil)
DOI: 10.48511/44n3-5e79
title:  Curtis Creek Temperature (Upper - Lower) 

description: This resource contains temperature data from Curtis Creek (includes data from two sites: Upper, Lower). These sites have been monitored for 4 years (2015-06-18 to 2019-10-01). The Salmo Watershed Streamkeepers Society has been monitoring water temperature in the Salmo River Watershed to better understand the health of the river system as a whole and to monitor impacts of climate change on stream health and possible impacts to fish populations.
DOI: 10.5281/zenodo.5799056
title: Tsunami Simulation: TsunAWI Testcase "Coquimbo 2015", Setups and Example Results

description: This dataset contains three setups with example results for TsunAWI simulations for the Chilean coast with a focus on<br> the Coquimbo region. They are used in the LEXIS project, WP6 "Earthquake and Tsunami Pilot". <strong>coquimbo_150m.tgz </strong>- coarse setup with down to 150m resolution in Coquimbo and other areas of interest along the Chilean coast <strong>coquimbo_20m.tgz </strong>- fine<strong> </strong>setup with down to 20m resolution in Coquimbo, also high resolution along other areas of interest along the Chilean coast <strong>coquimbo_20m_new.tgz </strong>- setup tuned for the LEXIS workflow aiming at a real time estimate of the inundation on the City of Coquimbo, thus a focus with 20m resolution only in Coquimbo region. Each dataset includes a README explaining the included files. The following description is taken as pars pro toto from coquimbo_20m_new.tgz. <strong>Computational Mesh</strong> mesh/Coquimbo_20m/nod2D.out <br> Location of mesh vertices in geographical coordinates, vertices on the open boundary are flagged with "1"<br> Resolution in Coquimbo area: 20m mesh/Coquimbo_20m/nodhn.out<br> Water depth at the respective vertices. Negative values denote elevation on land. <br> In the ocean: ‘The GEBCO_08 Grid, version 20090202 (http://www.gebco.net)’ <br> On land: SRTM, Shuttle radar topography mission, 1 arcsec resolution, https://www2.jpl.nasa.gov/srtm/ Bilinear interpolation to the mesh vertices. mesh/Coquimbo_20m/elem2D.out<br> Mesh geometry: List of the vertices forming the triangular elements. <strong>Input Parameters</strong> namelist.tsunami<br> Parameters read by TsunAWI at run time, e.g., path to the mesh and the output the time step dt and the time frame of the simulation T_end physical parameters like Manning friction, viscosity toggle the output Additional command line parameters<br> The results below are for an idealized source (Cosine bell at the location of the 2015 Coquimbo event, started with <br> ompTsuna.x -xy -71.674 -31.573 -mw 8.3 -id coquimbo_20m <strong>Result</strong> coquimbo_20m_new.nc <br> Example output in netcdf format, raw data on the triangular mesh, see<br> ncdump -h coquimbo_20m.nc coquimbo_20m_new_inundation.nc<br> The same raw data but restricted to the inundated area, for faster postprocessing. The TsunAWI version used is <br> https://gitlab.awi.de/tsunawi/tsunawi/-/tags/LEXIS<br> with one minor bugfix: Add <br> use mpi_parallel<br> to line 881 of tsunawi/src/parameters.F90 Compiled with tsunawi/src/Makefile.in_karolina<br> and run on karolina.it4i.cz in MPI-parallel mode with 128 tasks. This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under grant agreement No 825532.
DOI: 10.5281/zenodo.5799055
title: Tsunami Simulation: TsunAWI Testcase "Coquimbo 2015", Setups and Example Results

description: This dataset contains three setups with example results for TsunAWI simulations for the Chilean coast with a focus on<br> the Coquimbo region. They are used in the LEXIS project, WP6 "Earthquake and Tsunami Pilot". <strong>coquimbo_150m.tgz </strong>- coarse setup with down to 150m resolution in Coquimbo and other areas of interest along the Chilean coast <strong>coquimbo_20m.tgz </strong>- fine<strong> </strong>setup with down to 20m resolution in Coquimbo, also high resolution along other areas of interest along the Chilean coast <strong>coquimbo_20m_new.tgz </strong>- setup tuned for the LEXIS workflow aiming at a real time estimate of the inundation on the City of Coquimbo, thus a focus with 20m resolution only in Coquimbo region. Each dataset includes a README explaining the included files. The following description is taken as pars pro toto from coquimbo_20m_new.tgz. <strong>Computational Mesh</strong> mesh/Coquimbo_20m/nod2D.out <br> Location of mesh vertices in geographical coordinates, vertices on the open boundary are flagged with "1"<br> Resolution in Coquimbo area: 20m mesh/Coquimbo_20m/nodhn.out<br> Water depth at the respective vertices. Negative values denote elevation on land. <br> In the ocean: ‘The GEBCO_08 Grid, version 20090202 (http://www.gebco.net)’ <br> On land: SRTM, Shuttle radar topography mission, 1 arcsec resolution, https://www2.jpl.nasa.gov/srtm/ Bilinear interpolation to the mesh vertices. mesh/Coquimbo_20m/elem2D.out<br> Mesh geometry: List of the vertices forming the triangular elements. <strong>Input Parameters</strong> namelist.tsunami<br> Parameters read by TsunAWI at run time, e.g., path to the mesh and the output the time step dt and the time frame of the simulation T_end physical parameters like Manning friction, viscosity toggle the output Additional command line parameters<br> The results below are for an idealized source (Cosine bell at the location of the 2015 Coquimbo event, started with <br> ompTsuna.x -xy -71.674 -31.573 -mw 8.3 -id coquimbo_20m <strong>Result</strong> coquimbo_20m_new.nc <br> Example output in netcdf format, raw data on the triangular mesh, see<br> ncdump -h coquimbo_20m.nc coquimbo_20m_new_inundation.nc<br> The same raw data but restricted to the inundated area, for faster postprocessing. The TsunAWI version used is <br> https://gitlab.awi.de/tsunawi/tsunawi/-/tags/LEXIS<br> with one minor bugfix: Add <br> use mpi_parallel<br> to line 881 of tsunawi/src/parameters.F90 Compiled with tsunawi/src/Makefile.in_karolina<br> and run on karolina.it4i.cz in MPI-parallel mode with 128 tasks. This work was supported by the LEXIS project funded by the EU’s Horizon 2020 research and innovation programme (2014-2020) under grant agreement No 825532.
DOI: 10.6073/pasta/7b55db6fc3761919e8b5f051aa3127d2
title: PGL01 Litterfall collection in riparian gallery forest at Konza Prairie

description: Litterfall is collected monthly (more frequently during peak litterfall in October and November) at permanent sampling sites in the mixed deciduous gallery forest located along the lower reaches of Kings Creek at the Konza Prairie Biological Station. Thirty litterfall traps, 50 x 50 cm (.25 m2) are located along the north fork of Kings Creek, and two are located on the south fork of Kings Creek. The north fork boxes are numbered 31 to 60 and the south fork boxes are numbered 1 and 2. Originally, the south fork also had boxes 3 to 30 but these samplers were terminated in 1993 due to repeated damage by bison. (Boxes 1 and 2 are located just outside the bison area.) Samples are sorted in the lab, and mass of wood, seeds, and foliage are recorded separately.
DOI: 10.5281/zenodo.5800092
title: Exechia fusca decontaminated FSCR

description: .
DOI: 10.5281/zenodo.5800091
title: Exechia fusca decontaminated FSCR

description: .
DOI: 10.6084/m9.figshare.17425454
title: Kay et al 2021 data.zip

description: All data from publication "Therapeutic Effects of Hypoxic and Pro-Inflammatory Priming of Mesenchymal Stem Cell-Derived Extracellular Vesicles in Inflammatory Arthritis" in International Journal of Molecular Sciences 2021/2022
DOI: 10.48511/wp6d-bg88
title:  Erie Creek Temperature (Upper) 

description: This resource contains temperature data from Upper Erie Creek. These sites have been monitored for 6 years (2014-08-02 to 2020-08-17). The Salmo Watershed Streamkeepers Society has been monitoring water temperature in the Salmo River Watershed to better understand the health of the river system as a whole and to monitor impacts of climate change on stream health and possible impacts to fish populations.
DOI: 10.5281/zenodo.5800056
title: DATASET: Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose - FTIR data

description: Raw FTIR data for the article "Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose"
description: S.M.-G. thanks SINCHEM Joint Doctorate program selected under the Erasmus Mundus Action 1 Program (FPA 2013-0037) for her PhD grant. P.L.-D., V.L., S.C. and W.S.J.L thank Carnot Chimie Balard Cirimat for funding of SUBLATEX project No. 17S08R99-PS-LACROIX-D.
DOI: 10.5281/zenodo.5800055
title: DATASET: Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose - FTIR data

description: Raw FTIR data for the article "Biobased composites by photoinduced polymerization of car-danol methacrylate with microfibrillated cellulose"
description: S.M.-G. thanks SINCHEM Joint Doctorate program selected under the Erasmus Mundus Action 1 Program (FPA 2013-0037) for her PhD grant. P.L.-D., V.L., S.C. and W.S.J.L thank Carnot Chimie Balard Cirimat for funding of SUBLATEX project No. 17S08R99-PS-LACROIX-D.
DOI: 10.6084/m9.figshare.14880474.v1
title: Additional file 4 of Genome-Wide Association Mapping and Gene Expression Analysis Reveal the Negative Role of OsMYB21 in Regulating Bacterial Blight Resistance in Rice

description: Additional file 4: Table S4. Expression detection of 11 potential genes before and after bacterial inoculation.
DOI: 10.6084/m9.figshare.21172103
title: Additional file 9 of Regional gain and global loss of 5-hydroxymethylcytosine coexist in genitourinary cancers and regulate different oncogenic pathways

description: Additional file9: Table S1. The primers of hMeDIP-qPCR and RT-qPCR.
DOI: 10.6084/m9.figshare.12262355
title: Additional file 5 of Integrated analysis of genome-wide miRNAs and targeted gene expression in esophageal squamous cell carcinoma (ESCC) and relation to prognosis

description: Additional file 5: Table S4. Correlation of miRNA expression with clinical characteristics.
DOI: 10.5281/zenodo.10033216
title: Data set for the publication

description: Data set for the publication
Identification and validation of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles
DOI: 10.6084/m9.figshare.20648133
title: Additional file 4 of Combining metabolome and clinical indicators with machine learning provides some promising diagnostic markers to precisely detect smear-positive/negative pulmonary tuberculosis

description: Additional file 4. Detailed information of the 79 DAMs between the SNPT patients and controls.
DOI: 10.5281/zenodo.10033215
title: Data set for the publication

description: Data set for the publication
Identification and validation of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles
DOI: 10.5281/zenodo.10654767
title: Data set for the publication

description: Data set for the publication
Identification and validation of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles
DOI: 10.6084/m9.figshare.14880632
title: Additional file 1 of A phenome-wide association study of genetically mimicked statins

description: Additional file 1: Table S1. Established genetic variants in the HMGCR, PCSK9 and NCP1L1 gene that mimic statins, PCSK9 inhibitors and ezetimibe respectively]. Table S2. Correlation matrix for genetic variants mimicking statins. Table S3. Correlation matrix for genetic variants mimicking PCSK9 inhibitors. Table S4. Correlation matrix for genetic variants mimicking ezetimibe. Table S5. 56 established independent SNPs genetically predicting LDL-c. Table S6. PheWAS table of rs12916. Table S7. Number of significant associations of rs12916 with phenotypes by category. Table S8. Sex-specific associations of genetically mimicked statins, PCSK9 inhibitors and ezetimibe (based on rs12916, rs11591147 and rs10260606, respectively) with body composition traits. Table S9. Sex-specific univariable MR of genetically predicted LDL-c on binary traits and body composition traits. Table S10. Replication table. Table S11. Sensitivity analysis for genetically mimicking statins. Table S12. Sensitivity analysis for genetically mimicking PCSK9 inhibitors. Table S13. Sensitivity analysis for genetically mimicking ezetimibe. Table S14. Univariable MR results of BMI on platelet count and BMR. Table S15. Multivariable MR results of genetically mimicked statins on BMR adjusting for BMI.