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677 results for “Inversion”
Seismic profiles, migration velocities and tomographic inversion results of the deep reflection profiles in the central South China
<h1><strong>Overview</strong></h1> <p>The following set of data and scripts are meant to accompany the paper:</p> <p>Jiang, W. B., Wang, Q., Zhang, Y.Q., Dong, S.W., Ruan, Y.Q., Cui, J.J., Kuang, Z.Y., Paleoproterozoic collision to Mesozoic crustal reworking in central South China: evidence from borehole data and seismic crustal structure, Submitted to JGR: Solid Earth</p> <p>The data and scripts are intended to reproduce seismic profiles, migration velocities, and tomographic inversion results shown in the paper.</p> <p>The repository contains five directories:</p> <p><strong>./01_Seismic_Profiles/</strong> -> Four seismic profiles shown in the manuscript. (1) psdm_line01.sgy (Figure 7a); (2) psdm_line02.sgy (Figure 7b); (3) SCB_PSDM.segy (Figure 9); (4) SCB_PSTM.segy (Figure S5a).</p> <p><strong>./02_Tomographic_Data_Velocity/</strong> -> picked_traveltimes.tt, picked traveltime for the normal shots (Figure 6a). The file contains location of shots and receivers, picked traveltimes; Tomo_inv.mdl, P-wave velocity model derived from first-arrival traveltime tomography (Figure 8a); Tomo_Fx1_Fz1_ray.mdl, Ray density distribution calculated using the Vp model (Figure 8b); traveltime_data_FILE_FORMAT.pdf, this pdf file describes the format of *.tt file; mdl_data_FILE_FORMAT.pdf, this pdf file describes the format of *.mdl file.</p> <p><strong>./03_Migration_Velocity_Models/ </strong>-> Migration velocity models to produce prestack time migration profile and prestack depth migration profile. VEL_RMS.mdl, RMS velocity field used in prestack time migration (Figure S4a); VEL_INTERVAL.mdl, Interval velocity field used in prestack depth migration (Figure S4b).</p> <p><strong>./04_Bouguer_Gravity_Anomaly/ </strong>-> gravity_data.grv, the Bouguer gravity anomaly data used in 2-D gravity modelling; gravity_data_FILE_FORMAT.pdf, this pdf file describes the format of *.grv file.</p> <p><strong>./05_Scripts/</strong> -> Matlab scripts to reproduce seismic profiles, migration velocities, and tomography inversion results shown in the paper. </p>
Crustal structure of the Volgo-Uralian subcraton revealed by inverse and forward gravity modeling [dataset]
<p>This collection contains data that were used to build a 3D crustal model of the Volgo-Uralian subcraton through inverse and forward gravity modeling.</p> <p>The dataset is subdivided into two folders: (1) Gravity field inversion; (2) Forward gravity modeling. </p>
Probabilistic linear inversion of satellite gravity gradient data applied to the northeast Atlantic
<p>% MATLAB scripts to calculate and plot figures as in manuscript by<br> %<br> % Minakov, A., & Gaina, C. (2021).<br> % Probabilistic linear inversion of satellite gravity gradient data applied<br> % to the northeast Atlantic. Journal of Geophysical Research: Solid Earth,<br> % 126, e2021JB021854. https://doi.org/10.1029/2021JB021854<br> % <br> % Last modified by alexamin@uio.no, 26/11/2021<br> %<br> % version v1.1<br> % </p> <p>% Contents of arhcive<br> % /data contains requiried and generated datasets <br> % /fig folder for output figures <br> % /plot scripts to produce figures <br> % /tools additional matlab tools and routines</p> <p>% Dataset in ..data/GOCE_NEATLANTIC is structure containing the full model<br> % <br> % Cm: [6670×6670 double] posterior model covariance matrix<br> % m: [29×23×10 double] mean denstity perturbation model<br> % Cd: [667×667 double] data covariance matrix<br> % d: [29×23 double] data vector (Trr)<br> % r: [1×10 double] distance<br> % lat: [29×1 double] latitute<br> % lon: [23×1 double] longitude<br> %<br> % Run /plot/fig_results.m to produce all figures <br> %<br> % Some scripts require GMT (Wessel et al. 2019) and SHBUNDLE (Sneeuw et al. 2018) software to be installed</p> <p>% and corresponding folders must be added to the matlab search path.</p> <p>% Also ScientificColorMaps7 by F. Crameri (2021) maybe required and have been included in the archive.</p>
Processed data and models in support of manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion"
<p>Data and model files in original format used in the manuscript "Deciphering the state of the lower crust and upper mantle with multi-physics inversion". These files are accompanied by a set of python scripts to reproduce several of the figures in the Manuscript. Please refer to the Manuscript and the included files for further information on data origin and how to use the scripts. A link will be added upon acceptance.</p>
MIROC4-ACTM CH4 inversion fluxes (2000-2016)
<p>This version of inversion is prepared by Dmitry Belikov, Chiba University. Results updated from Chandra et al. (Chandra et al., 2021)</p> <p>Methods detailed in</p> <p>Emissions from the Oil and Gas Sectors, Coal Mining and Ruminant Farming Drive Methane Growth over the Past Three Decades</p> <p><a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Naveen+CHANDRA">Naveen CHANDRA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Prabir+K.+PATRA">Prabir K. PATRA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Jagat+S.+H.+BISHT">Jagat S. H. BISHT</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Akihiko+ITO">Akihiko ITO</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Taku+UMEZAWA">Taku UMEZAWA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Nobuko+SAIGUSA">Nobuko SAIGUSA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Shinji+MORIMOTO">Shinji MORIMOTO</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Shuji+AOKI">Shuji AOKI</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Greet+JANSSENS-MAENHOUT">Greet JANSSENS-MAENHOUT</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Ryo+FUJITA">Ryo FUJITA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Masayuki+TAKIGAWA">Masayuki TAKIGAWA</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Shingo+WATANABE">Shingo WATANABE</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Naoko+SAITOH">Naoko SAITOH</a>, <a href="https://www.jstage.jst.go.jp/search/global/_search/-char/en?item=8&word=Josep+G.+CANADELL">Josep G. CANADELL</a></p> <p>DOI <a href="https://doi.org/10.2151/jmsj.2021-015">https://doi.org/10.2151/jmsj.2021-015</a></p>
Traveltime Inversion for the Earthquake Focal Depth based on the Inner Core Phases PKIKP and pPKIKP Time Differences
<p>This dataset contained the codes and seismogram records associated with 2022GLO98624. (1) The raw seismograms of Chile, 2014-08-23 22:32:22 UTC, and Bolivia, 2017-02-21 14:09:04 UTC, which recorded at a seismic array deployed at Guangxi, China. (2) the GMT plotting shell and the python codes for earthquake depth determination.</p> <p>The input file format for determing the depth of an earthquake is in the follow:</p> <p>taking Bolivia_EHB.dat as an example:</p> <p>line1 --> evla (event latitude) evlo (event longitude) evdp (event depth)</p> <p>line2 --> number of station (ns)</p> <p>line3, 1 of ns --> stla, stlo, dt</p> <p>......</p> <p>_________________________________________</p> <p>evla: event latitude</p> <p>evlo: event longitude</p> <p>evdp: event depth</p> <p>stla: station latitude</p> <p>stlo: staion longitude</p> <p>dt: pPKIKP minus PKIKP</p>
An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis
<p>This Zenodo archive contains the dataset analysed in the paper "An allozyme polymorphism is associated with a large chromosomal inversion in the marine snail Littorina fabalis" published in Evolutionary Application in 2022:</p> <ul> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_LG3_maf1_SNP_Hexcess_depth10.vcf">FAB_LG3_maf1_SNP_Hexcess_depth10.vcf </a>: vcf for LG3 unpruned for LD containing 295 individuals genotyped at 58,246 filtered SNPs</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_LG3_maf1_SNP_Hexcess_depth10_thin.vcf">FAB_LG3_maf1_SNP_Hexcess_depth10_thin.vcf </a>: vcf for LG3 pruned for LD containing 295 individuals genotyped at 9,905 filtered SNPs</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/FAB_AK_maf1_SNP_Hexcess_depth10.vcf">FAB_AK_maf1_SNP_Hexcess_depth10.vcf</a> : vcf for contig265 containing the arginine kinase gene: 295 individuals genotyped at 70 filtered SNPs</li> </ul> <p>The archive also include some of the R script used to performed the analyses of the manuscrit:</p> <ul> <li> </li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Population_genetic_Ark_analyses.R">Population_genetic_Ark_analyses.R </a>: Script to perform PCA +phenotypic cline + FST + Hobs + FIS</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Suspension_bridge_fit.R">Suspension_bridge_fit.R </a>: Script to perform the suspension bridge fit used to found evidence of gene flux inside the inversion.</li> <li><a href="https://zenodo.org/api/files/cd560cff-56d4-4f72-95be-e70939f2b85f/Cline_function.R">Cline_function.R </a>: function used to fit the allelic frequency variation (cline) along the transect</li> </ul> <p>The raw sequences are available in NCBI.</p> <p>Abstract of the study: Understanding the genetic targets of natural selection is one of the most challenging goalsof population genetics. Some of the earliest candidate genes were identified from associations between allozyme allele frequencies and environmental variation. One such example is the clinal polymorphism in the arginine kinase (<em>Ak</em>) gene in the marine snail <em>Littorina fabalis</em>. While other enzyme loci do not show differences in allozyme frequencies among populations, the <em>Ak</em> alleles are near differential fixation across repeated wave exposure gradients in Europe. Here, we use this case to illustrate how a new sequencing toolbox can be employed to characterize the genomic architecture associated with historical candidate genes. We found that the <em>Ak</em> alleles differ by 9 non-synonymous substitutions, which perfectly explain the different migration patterns of the allozymes during electrophoresis. Moreover, by exploring the genomic context of the <em>Ak</em> gene, we found that the three main <em>Ak</em> alleles are located on different arrangements of a putative chromosomal inversion that reaches near fixation at the opposing ends of two transects covering a wave exposure gradient. This shows <em>Ak</em> is part of a large (3/4 of the chromosome) genomic block of differentiation, in which <em>Ak</em> is unlikely to be the only target of divergent selection. Nevertheless, the non-synonymous substitutions among <em>Ak</em> alleles and the complete association of one allele with one inversion arrangement suggest that the <em>Ak</em> gene is a strong candidate to contribute to the adaptive significance of the inversion.</p> <p> </p> <p> </p>
Data from: Inversion Invasions: when the genetic basis of local adaptation is concentrated within inversions in the face of gene flow
<p><span></span></p> <p>Across many species where inversions have been implicated in local adaptation, genomes often evolve to contain multiple, large inversions that arise early in divergence. Why this occurs has yet to be resolved. To address this gap, we built forward-time simulations in which inversions have flexible characteristics and can invade a metapopulation undergoing spatially divergent selection for a highly polygenic trait. In our simulations, inversions typically arose early in divergence, captured standing genetic variation upon mutation, and then accumulated many small-effect loci over time. Under special conditions, inversions could also arise late in adaptation and capture locally adapted alleles. Polygenic inversions behaved similarly to a single supergene of large effect and were detectable by genome scans. Our results show that characteristics of adaptive inversions found in empirical studies (e.g., multiple large, old inversions that are FST outliers, sometimes overlapping with other inversions) are consistent with a highly polygenic architecture, and inversions do not need to contain any large-effect genes to play an important role in local adaptation. By combining a population and quantitative genetic framework, our results give a deeper understanding of the specific conditions needed for inversions to be involved in adaptation when the genetic architecture is polygenic.</p>
Recombination experiments with inversion heterozygotes
<p>Recombination suppression in chromosomal inversion heterozygotes is a well-known but poorly understood phenomenon. Surprisingly, recombination suppression extends far outside of inverted regions where there are no intrinsic barriers to normal chromosome pairing, synapsis, double-strand break formation, or recovery of crossover products. The interference hypothesis of recombination suppression proposes heterozygous inversion breakpoints possess chiasma-like properties such that recombination suppression extends from these breakpoints in a process analogous to crossover interference. This hypothesis is qualitatively consistent with chromosome-wide patterns of recombination suppression extending to both inverted and uninverted regions of the chromosome. The present study generated quantitative predictions for this hypothesis using a probabilistic model of crossover interference with gamma-distributed inter-event distances. These predictions were then tested with experimental genetic data (>40,000 meioses) on crossing-over in intervals that are external and adjacent to four common inversions of Drosophila melanogaster. The crossover interference model accurately predicted the partially suppressed recombination rates in euchromatic intervals outside inverted regions. Furthermore, assuming interference does not extend across centromeres dramatically improved model fit and partially accounted for excess recombination observed in pericentromeric intervals. Finally, inversions with breakpoints closest to the centromere had the greatest excess of recombination in pericentromeric intervals, an observation that is consistent with negative crossover interference previously documented near Drosophila melanogaster centromeres. In conclusion, the experimental data support the interference hypothesis of recombination suppression, validate a mathematical framework for integrating distance-dependent effects of structural heterozygosity on crossover distribution, and highlight the need for improved modeling of crossover interference in pericentromeric regions.</p>
Data of FigS2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS2, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as nine files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3_M1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format.</p>
Data of FigS4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of FigS4, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_FigS4.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_6_1-3.csv).</p>
Data of Fig7, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig7, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig7.PNG). The Corresponding raw data and subsequent data analysis obtained for TCGA analysis contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M.txt), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M1.pdf)</p> <p> </p>
Data of Fig8, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig8, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig8.PNG). The Corresponding raw data and subsequent data analysis obtained for immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M .txt) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M1 .pdf)</p> <p> </p>
Data of Fig4, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig4, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig4.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_3_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_3_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_4_M_1.pdf).</p>
Data of Fig2, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig2, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig2.PNG). The corresponding raw data and subsequent data analysis obtained from proteomics analysis are provided as three files in CSV format (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1-3.csv). All further experiment related information provided as one meta-data-file (31003A-179400_10.3390-cancers14133074_SS_SA_DHRS7_5_1_M .txt) in txt format. Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_1_M_1 .pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_1_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_2_M_1.pdf).</p>
Data of Fig5, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig5, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig5.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_4_3.csv). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M .txt) and three files in CSV format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5-1-3.csv), all further experiment related information provided as three meta-data-files in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_5_M_1-3.pdf).</p>
Data of Fig6, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig6, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig6.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_M.txt) and three files in CSV-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_5_1-3.csv).</p> <p> </p>
Data of Fig3, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig3, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig3.PNG). The Corresponding raw data and subsequent data analysis obtained from western blot analysis contains the original figures of the raw blots and antibody dilutions as PDF-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M_1.pdf), data analysis (densitometry) and all further experiment related information provided as one meta-data-file in txt format (31003A-179400_10.3390-cancers14133074_SSDHRS7_2_2_M.txt). Corresponding raw data and subsequent data analysis obtained from RT-PCR analysis provided as one files in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M.txt), all further experiment related information provided as one meta-data-file in pdf format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_3_M_1.pdf).</p>
Data of Fig1, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig1, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (doi: 10.3390/cancers14133074) contains the original figures as PNG-format (10.3390-cancers14133074_Fig1.PNG). Raw data and related Meta data are provides as one file in TXT format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M.txt) and one file in PDF-Format (31003A-179400_10.3390-cancers14133074_SSDHRS7_1_1_M_1.pdf).</p>
Data of Fig9, "The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples"
<p>Data of Fig9, “The Potential Tumor-Suppressor DHRS7 Inversely Correlates with EGFR Expression in Prostate Cancer Cells and Tumor Samples”</p> <p>The Dataset (original publication: doi: 10.3390/cancers14133074) contains the original figure as PNG-format (10.3390-cancers14133074_Fig9.PNG). The Corresponding raw data and subsequent data analysis obtained for immunohistochemistry contains one file in txt-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1_M.pdf) and one file in csv-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_12_1.csv), and all further related information provided as one meta-data-file in pdf-format (31003A-179400_10.3390-cancers14133074_SSDHRS7_23_1_M .txt)</p> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.