cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7700451
title: Scaled RNA sequencing counts accompaining the publication Singh and Gollapalli 2023

description: RNA-Seq analysis of WT and taurine-deficient osteoblasts. RNA was extracted from the mouse primary calvarial osteoblasts prepared from day 3–5 neonates of WT or <em>Slc6a6</em>-/- genotypes using the RNeasy extraction kit (Qiagen). RNA sequencing libraries were prepared with Illumina-compatible NEBNext Ultra<sup>TM</sup> II Directional RNA library kit at Genotypic Technology, Inc. The raw data were trimmed for adapter sequences and low-quality bases (&lt;q30) using Cutadapt with default parameters [DOI:10.14806/ej.17.1.200] and checked for quality using FastQC [Andrews, S. (2010). FastQC: A Quality Control Tool for High Throughput Sequence Data]. Mouse raw reads were aligned to the mouse reference genome (mm9) using Hisat2(<em>24</em>) with default parameters. HTSeq [G Putri, S Anders, PT Pyl, JE Pimanda, F Zanini Analyzing high-throughput sequencing data with HTSeq 2.0 arXiv:2112.00939 (2021)] was used to estimate gene abundance. Downstream analyses were done using the analysis framework tidybulk (<em>25, 26</em>). The filterByExpr functionality of edgeR (<em>27</em>) was used to identify the abundant gene-transcripts included in downstream analyses using default parameters and the knock-out phenotype as the factor of interest. The algorithm trimmed mean of M valued (TMM) values identified sample-wise scaling to compensate for differences in sequencing depth across samples (<em>28</em>). For aiding data exploration, we reduced the data dimensionality using principal component analysis (PCA). Differential expression analyses were performed using the robust likelihood ratio implementation of edgeR, testing for differences in gene transcript abundance greater than 1 log-fold (<em>29, 30</em>). The ppcseq method was used to investigate the presence of outlier observation among the significant results to avoid biases in the statistics (<em>31</em>). The ggplot2 software produced most of the data visualizations, whereas tidyHeatmap was used for heatmap visualization (<em>32-34</em>). We performed gene-set enrichment analyses using the MSigDB C2 experimentally derived gene set, the clusterProfiler algorithm, and the visualization tool enrichplot (cran.r-project.org/) (https://rdrr.io/cran/msigdbr/) (<em>35</em>). An aging signature was composed of a union of gene sets from MSigDB and a set of genes derived from aging datasets identified in this study through gene set enrichment analysis and published literature (<em>36</em>). The <em>p</em>-values of gene set analyses were corrected for multiple testing using the Benjamini Hochberg correction (<em>37</em>).
DOI: 10.34740/kaggle/dsv/5112165
title: OntoUML/UFO Catalog
DOI: 10.6084/m9.figshare.20485737
title: VPRs (KTLX radar)

description: Velocity profiles of reflectivity (VPRs) from the NEXRAD KTLX radar. A VPR was created for each volume scan from 0 to 12 UTC on the nights of 3/26/2021, 3/27/2021, 3/28/2021, 3/29/2021, 4/24/2021, and 5/25/2021.
DOI: 10.6084/m9.figshare.20485737.v1
title: VPRs (KTLX radar)

description: Velocity profiles of reflectivity (VPRs) from the NEXRAD KTLX radar. A VPR was created for each volume scan from 0 to 12 UTC on the nights of 3/26/2021, 3/27/2021, 3/28/2021, 3/29/2021, 4/24/2021, and 5/25/2021.
DOI: 10.6084/m9.figshare.22154531
title: Root trait plasticity.xlsx

description: We separated cleaned root samples into nodal roots and lateral roots according to position. Nodal roots were dried at 65 ℃ for 48 h and weighed. However, for lateral roots that are considered highly plastic, we randomly selected subsamples for scanning, while the remainder was dried as well. Root subsamples were scanned, then also dried and weighed. We then calculated root diameter (mm), specific root length (m g-1), specific surface area (m2 g-1), root tissue density (g m-3) and root length proportions with different diameter by analyzing scanned images using WinRHIZO software (Regent Instruments Inc., Quebec City, QC, Canada). Three parts were classified according to average diameter: fine roots (&lt; 0.2 mm), middle roots (0.2 - 0.8 mm) and thick roots (&gt; 0.8 mm) (Li et al., 2017a). We also calculated root biomass density (g cm-3), root length density (cm cm-3), and biomass allocation to lateral roots (%).
DOI: 10.6084/m9.figshare.22154531.v2
title: Root trait plasticity.xlsx

description: We separated cleaned root samples into nodal roots and lateral roots according to position. Nodal roots were dried at 65 ℃ for 48 h and weighed. However, for lateral roots that are considered highly plastic, we randomly selected subsamples for scanning, while the remainder was dried as well. Root subsamples were scanned, then also dried and weighed. We then calculated root diameter (mm), specific root length (m g-1), specific surface area (m2 g-1), root tissue density (g m-3) and root length proportions with different diameter by analyzing scanned images using WinRHIZO software (Regent Instruments Inc., Quebec City, QC, Canada). Three parts were classified according to average diameter: fine roots (&lt; 0.2 mm), middle roots (0.2 - 0.8 mm) and thick roots (&gt; 0.8 mm) (Li et al., 2017a). We also calculated root biomass density (g cm-3), root length density (cm cm-3), and biomass allocation to lateral roots (%).
DOI: 10.6084/m9.figshare.22217635.v1
title: figshare data for paper.rar

description: figshare data for my paper
DOI: 10.5517/ccdc.csd.cc2dq4cn
title: CCDC 2225811: Experimental Crystal Structure Determination

description: Related Article: Guanping Li, Olaf Stefanczyk, Kunal Kumar, Koji Nakabayashi, Shin-ichi Ohkoshi|2023|Inorg.Chem.|62|3278|doi:10.1021/acs.inorgchem.2c04382
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.34740/kaggle/dsv/5112269
title: Dota 2 Pro League Matches 2023
DOI: 10.17608/k6.auckland.22217662.v1
title: Intertidal Mussel Restoration in Kenepuru Sound

description: Survival, condition index, and length data for mussels restored to intertidal and subtidal depths at three locations in Kenepuru Sound over the course of one year.
DOI: 10.17608/k6.auckland.22217662
title: Intertidal Mussel Restoration in Kenepuru Sound

description: Survival, condition index, and length data for mussels restored to intertidal and subtidal depths at three locations in Kenepuru Sound over the course of one year.
DOI: 10.5281/zenodo.7693824
title: High-resolution spatial multi-omics datasets

description: Supplementary raw data. The raw microscopy data are not uploaded owing to their large size (6.8 Tb), but are available upon reasonable request (Long Cai: lcai@caltech.edu, Yodai Takei: ytakei@caltech.edu). These supplementary data contain additional files for RNA seqFISH+, DNA seqFISH+, and sequential immunofluorescence from cell culture and adult mouse cerebellum experiments. DNA seqFISH+ datasets (provided as a tar.gz folder for each replicate): Super-resolved DNA spot locations by DNA seqFISH+ along with sequential immunofluorescence intensity at the rounded voxel location. RNA seqFISH datasets (provided as a zip folder): Super-resolved mRNA or intron spot locations. Sequential immunofluorescence table (provided as a csv file): Mean voxel intensity of each immunofluorescence marker per nucleus for the adult mouse cerebellum datasets. Note that voxel sizes are 103 nm for x and y, and 250 nm for z in our experimental setting. Please find the uploaded readme.txt file for more details.
DOI: 10.34740/kaggle/dsv/5112335
title: 💥📰 Daily Breaking News Twitter Dataset
DOI: 10.6084/m9.figshare.22217674
title: free_energy_files

description: these files contain the metadynamics protocol and associated free energy results, along with the initial and final protein complex structures.
DOI: 10.34740/kaggle/dsv/5112356
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.6084/m9.figshare.22122500
title: Towards Improved Real-Time Rainfall Intensity Estimation Using Video Surveillance Cameras

description: Rainfall videos and the rainfall data measured by a rain gauge.
DOI: 10.25582/data.2022-03.2448405/1933036
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448623/1933049
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448627/1933078
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448635/1934030
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448458/1934031
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448325/1934041
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448643/1934053
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448725/1934098
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448592/1934137
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448694/1934145
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448539/1934149
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448683/1934155
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448597/1934223
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448425/1934263
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448435/1934266
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448497/1934272
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448443/1934292
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448506/1934322
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448332/1935256
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448543/1935278
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448705/1935298
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448533/1935321
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448504/1935344
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448639/1935369
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448654/1935393
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448615/1935399
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448713/1935402
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448704/1935421
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448582/1935423
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448272/1935436
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448579/1935489
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448453/1935520
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25345/c5707wz4k
title: MassIVE MSV000091411 - The inverted presences of Neu5Gc and Neu5Ac in mouse liver tissues and cell lines
title: The two forms of sialic acid, Neu5Ac and Neu5Gc, have been known to be prevalent in mouse and affect glycosylation function. However, the inherent differences of the two forms in the widely-used model organisms, mouse liver tissues and cell lines, are seldom known. Here, we demonstrated a long-neglected phenomenon of the inverted presences of Neu5Gc and Neu5Ac in the two types of samples through site-specific N-glycoproteomics and independent glycan assays.
DOI: 10.25582/data.2022-03.2448630/1935542
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448712/1935579
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448357/1935601
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448308/1935603
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448483/1936033
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448292/1936035
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448726/1936054
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448456/1936079
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448243/1936087
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448598/1936089
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448470/1936090
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448731/1936130
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448718/1936137
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448657/1936138
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448447/1936147
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448669/1936156
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448266/1936171
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448672/1936207
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448208/1936210
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448620/1936239
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448699/1936328
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448454/1936334
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448315/1936353
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448233/1936366
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448490/1936367
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448698/1936392
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448649/1936427
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448246/1937207
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448650/1937248
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448778/1937261
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448723/1937306
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448685/1937379
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448738/1937406
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448689/1937407
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448573/1937416
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448590/1937495
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448628/1937547
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448569/1937556
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448588/1937594
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448427/1937988
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448679/1937994
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448306/1938013
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448612/1938020
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448682/1938048
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448264/1938104
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448480/1938114
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448219/1938142
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448486/1938144
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448676/1938151
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448711/1938182
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448614/1938246
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448257/1938260
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448505/1938264
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448719/1938319
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448432/1938340
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448485/1938381
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448494/1938383
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448734/1939076
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448709/1939103
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448502/1939157
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448647/1939172
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448462/1939258
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448700/1939265
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448298/1939281
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448687/1939308
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448702/1939385
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448606/1939387
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448732/1939394
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448200/1939406
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448339/1939434
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448521/1939439
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448575/1939448
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448572/1939457
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448546/1939473
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448638/1940141
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448452/1940148
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448320/1940159
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448604/1940174
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448530/1940210
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448450/1940225
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448678/1940268
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448252/1940275
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448735/1940293
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448564/1941083
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448644/1941092
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448284/1941112
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448194/1941140
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448466/1941145
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448697/1941150
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448684/1941156
title: Data for EMSL Project 51132 from March 2022
DOI: 10.25582/data.2022-03.2448562/1941184
title: Data for EMSL Project 51132 from March 2022
DOI: 10.6084/m9.figshare.21937613
title: GGCMI phase2 inputs and outputs

description: GGCMI phase2 climate inputs and outputs of corn and soybean in the US Midwest.
DOI: 10.6084/m9.figshare.21937613.v3
title: GGCMI phase2 inputs and outputs

description: GGCMI phase2 climate inputs and outputs of corn and soybean in the US Midwest.
DOI: 10.6084/m9.figshare.21937613.v4
title: GGCMI phase2 inputs and outputs

description: GGCMI phase2 climate inputs and outputs of corn and soybean in the US Midwest.
DOI: 10.5439/1959757
title: Trace Gases, Meteorological Parameters, and GPS data for TRACER-MAP

description: This dataset includes trace gas, meteorological, and GPS measurements made on Mobile Air Quality Laboratory 2 (MAQL2) for the TRACER-MAP sampling campaign. Trace gas measurements include nitric oxide (NO), nitrogen dioxide (NO2), reactive nitrogen compounds (NOy), ozone (O3), sulfur dioxide (SO2), and carbon monoxide (CO). All trace gases are in units of ppbv, excluding CO which is in ppmv. The dataset also includes the photolysis rate of NO2 (jNO2), latitude and longitude, wind speed (WS), wind direction (WD), ambient relative humidity (RH), ambient pressure (press), and ambient temperature (temp). Units for trace gases are in parts per billion (ppbv), latitude and longitude are in decimal degrees (dd), jNO2 is in per second (per sec), pressure is in millibar (mbar), relative humidity is in percent, the temperature is in degrees C, wind direction is in degrees, and wind speed is in meters per second. All data is 5 minutes averaged.
DOI: 10.6084/m9.figshare.22217719
title: Supplemental Table S1.xlsx

description: Associated SNPs identified by single GWAS and meta-anlysis
DOI: 10.6084/m9.figshare.21823455.v27
title: The ReptileEvolution.com Project

description: Taxon exclusion is the number one problem currently vexing paleontology. The search for pterosaur, turtle, bat, snake, whale, reptile and tetrapod ancestors continues to unnecessarily befuddle paleontologists who continue to work from cherry-picked, textbook and traditional taxon lists that overlook and omit pertinent taxa. That’s why the large reptile tree (LRT), the nearly completely resolved cladogram at the heart of www.ReptileEvolution.com, includes 2212 taxa. Larger taxon numbers minimize taxon exclusion issues. Results indicate the simple act of adding taxa resolves all phylogenetic issues.
DOI: 10.34740/kaggle/dsv/5112565
title: Top Trending Topics in Online : 2023
DOI: 10.6084/m9.figshare.22217749
title: Supplemental Table S2.xlsx

description: Genes mapped within 200 kb region of primary SNPs
DOI: 10.6084/m9.figshare.22217749.v1
title: Supplemental Table S2.xlsx

description: Genes mapped within 200 kb region of primary SNPs