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540
datasets available to search
ShareScore release 0.9.0
Dataset results
540 results for “data normalization”
Feasibility and Interpretation of the Genome-wide Infinium Methylation Data Derived from Ligated Formalin Fixed Paraffin Embedded Paired Tumor and Normal Tissue.
GEO Series GSE33181. Homo sapiens. 48 samples. Type: Methylation profiling by array.
DNA methylation data from human parthenogenetic haploid ESCs (hPGES), normal human ESCs(H9) and human forskin fibroblast
GEO Series GSE80226. Homo sapiens. 6 samples. Type: Methylation profiling by genome tiling array.
Expression data for tumor and normal tissue samples from deceased sepsis and non-sepsis colorectal cancer patients
GEO Series GSE198103. Homo sapiens. 55 samples. Type: Expression profiling by array.
DNA methylation data from 11 normal and 9 chronic periodontitis patients from site USC
GEO Series GSE59939. Homo sapiens. 20 samples. Type: Methylation profiling by genome tiling array.
Affymetrix SNP array data for beak deformity and normal Beijing-You Chickens
GEO Series GSE101703. Gallus gallus. 96 samples. Type: Genome variation profiling by SNP array; SNP genotyping by SNP array.
DNA methylation data from pilocytic astrocytoma tumor samples and normal cerebellum controls
GEO Series GSE44684. Homo sapiens. 67 samples. Type: Methylation profiling by array.
Normal keratinocyte cell line and skin cancer cell line RNA-seq data
GEO Series GSE236291. Homo sapiens. 5 samples. Type: Expression profiling by high throughput sequencing.
Gene expression data for tumor and paired adjacent normal tissues from colorectal cancer patients
GEO Series GSE156355. Homo sapiens. 12 samples. Type: Expression profiling by array.
ChIP-seq data processing and relative and quantitative signal normalization for Saccharomyces cerevisiae
GEO Series GSE288548. Saccharomyces cerevisiae. 24 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
mRNA-seq data for mouse ovarian tumors arising from oviduct with combinations of (Tamoxifen-inducible) inactivation of Brca1, Trp53, Rb1, and Nf1, or Apc and Pten, and normal ovaries and oviducts.
GEO Series GSE135590. Mus musculus. 35 samples. Type: Expression profiling by high throughput sequencing.
Normalization of whole-genome SNP data from non-Hodgkin lymphoma patients for copy number variation.
GEO Series GSE47357. Homo sapiens. 53 samples. Type: SNP genotyping by SNP array; Genome variation profiling by SNP array.
MicroRNA expression data for human primary and metastatic prostate cancer samples and control normal adjacent benign prostate
GEO Series GSE21036. Homo sapiens. 142 samples. Type: Non-coding RNA profiling by array.
Normalization of Illumina Infinium whole-genome SNP data improves copy number estimates and allelic intensity ratios
GEO Series GSE11977. Homo sapiens. 6 samples. Type: Genome variation profiling by SNP array.
Mast cell expression data from murine aryl hydrocarbon receptor (AhR) null, AhR_d and normal control mice
GEO Series GSE44715. Mus musculus. 9 samples. Type: Expression profiling by array.
circRNA/miRNA/mRNA sequence data of BPA exposed GC-2 cells and normally cultured cells.
GEO Series GSE120921. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing.
RNA sequencing data of whole blood cells of normal glucose tolerant (NGT) and gestational diabetes (GDM) pregnant women
GEO Series GSE92772. Homo sapiens. 32 samples. Type: Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Data from: Monoallelic chromatin conformation flanking long-range silenced domains in cancer-derived and normal cells
Epigenetic inactivation of chromatin plays an important role in determining cell phenotype in both normal and cancer cells, but our knowledge is still incomplete with respect to any potential monoallelic nature of the phenomenon. We have genotyped DNA isolated from chromatin of two colorectal cancer-derived lines and a culture of normal human intestinal epithelial cells (HIEC), which was immunoprecipitated with antibodies to acetylated vs. methylated histone H3K9, and presented the data as B allele frequency differences over multiple single-nucleotide polymorphism (SNP) moving window averages. [B allele is an arbitrary term defined as one of the two alleles at any given SNP, named A and B]. Three different validation tests confirmed that peaks exhibiting differences represented monoallelic domains. These complementary tests confirmed the following: 1) genes in the regions of high B allele frequency difference were expressed monoallelically; 2) in normal cells all five imprinting control regions which carried heterozygous SNPs were characterized by B allele difference peaks; and 3) the haplotypes in the B allele difference peaks were faithfully maintained in the chromatin immunoprecipitated with the respective antibodies. In both samples most of the monoallelic domains were found at the boundaries between regions of open and closed chromatin. With respect to the cancer line, this supports the established concept of conformation spreading, but the results from the normal cells were unexpected. Since these cells were polyclonal, the monoallelic structures were probably not determined by random choice as occurs in X-inactivation, so we propose that epigenetic inactivation in some domains may be heritable and polymorphic in normal human cells.
Data from: Prognosis of chronic kidney disease with normal-range proteinuria: The CKD-ROUTE study
Background: Although lower estimated glomerular filtration rate (eGFR) and higher proteinuria are high risks for mortality and kidney outcomes, the prognosis of chronic kidney disease (CKD) in patients with normal-range proteinuria remains unclear. Methods: In this prospective cohort study, 1138 newly visiting stage G2-G5 CKD patients were stratified into normal-range and abnormal-range proteinuria groups. Study endpoints were CKD progression (>50% eGFR loss or initiation of dialysis), cardiovascular events, and all-cause death. Results: In total, 927 patients who were followed for >6 months were included in the analysis. The mean age was 67 years, and 70.2% were male. During a median follow-up of 35 months, CKD progression, cardiovascular events, and mortality were observed in 223, 110, and 55 patients, respectively. Patients with normal-range proteinuria had a significantly lower risk for CKD progression (hazard ratio, 0.20; 95% confidence interval, 0.10-0.38) than those with abnormal-proteinuria by multivariate Cox proportional hazard analysis. We also analyzed patients with normal-range proteinuria (n=351). Nephrosclerosis was the most frequent cause of CKD among all patients with normal-range proteinuria (59.7%). During a median follow-up of 36 months, CKD progression, cardiovascular events, and mortality were observed in 10, 28, and 18 patients, respectively. The Kaplan-Meyer analysis demonstrated that the risks of CKD progression and cardiovascular events were not significantly different among CKD stages, whereas the risk of death was significantly higher in patients with advanced-stage CKD. Multivariate Cox proportional hazard analysis showed that the risk of three endpoints did not significantly differ among CKD stages. Conclusion: Newly visiting CKD patients with normal-range proteinuria, who tend to be overlooked during health checkups did not exhibit a decrease in kidney function even in advanced CKD stages under specialized nephrology care.
FEM data sets for heat generation due to plastic deformation and the and associated contact temperature during a normal impact between an elastic-perfectly-plastic particle and a rigid surface.
<p>This dataset contains essential data from the Finite Element Method (FEM) model predicting heat generation due to plastic deformation during the normal impact of a deformable spherical particle and a rigid flat substrate. Part of this data was processed and featured in a publication of a journal article (https://doi.org/10.1016/j.ijimpeng.2023.104831). The following is the description of the data files and the associated figures in the original paper. </p><p>'Energy_Vy200_1200 .xlsx' and 'Temp_Vy200_1200 .xlsx' - data for the evolution of heat and nodal temperature, respectively for varying impact velocities. Data was used for Figs.5 -7 in the published paper.</p><p>'Energy_YM_5_1000.xlsx' and 'Temp_YM_5_1000.xlsx' - data for the evolution of heat and nodal temperature, respectively for varying Young moduli. Data was used for Figs. 8 and 9 in the published paper.</p><p>'Energy_YS_50_800.xlsx' , 'Temp_YS_50_800.xlsx' - data for the evolution of heat and nodal temperature, respectively for varying yield strengths. Data was used for Figs. 10 and 11 in the published paper.</p><p>'Energy_Den_500_8000.xlsx' , 'TempC_Den_500_8000.xlsx'- data for the evolution of heat and nodal temperature, respectively for varying densities. Data was used for Figs. 12 and 13 in the published paper.</p><p>'Temp_TC.xlsx' , 'Temp_HC.xlsx' - data for the evolution of nodal temperatures for varying thermal conductivity and specific heat capacities, respectively. Data was used for Figs. 14 -17 in the published paper.</p><p> </p><p> </p><p> </p><p> </p><p> </p>
Data from: Use of normalized difference vegetation index to assess N status and predict grain yield in rice
Fine tuning N recommendations requires an understanding of crop N status and yield potential early enough in the growing season when changes to N management can influence yields. Recent studies have demonstrated the ability of Normalized Difference Vegetation Index (NDVI) to assess crop N status and predict yield in wheat (Tricticum aestivum L.) and maize (Zea mays L.); however, there has been relatively little such research on rice (Oryza sativa L.). The objectives of this study were to determine how well NDVI measured at the panicle initiation (PI) rice growth stage assesses crop N status and predicts final grain yield. Nitrogen response trials were established over a 4-yr period (10 site-years) at various locations throughout the Sacramento Valley rice growing region of California. Additionally, the relationship between NDVI and crop N status was characterized across 28 on-farm plots representing a range of environmental conditions and management practices. The NDVI at PI was best correlated with total N uptake (NUP, r2 = 0.66), followed by N concentration (NCONC, r2 = 0.54), and aboveground biomass (AGB, r2 = 0.51). The utility of NDVI was greatest at lower values of crop N status, whereas at higher values, NDVI saturated. The NDVI at PI was positively correlated with final grain yield (r2 = 0.58) indicating utility for developing in-season yield predictions. While NDVI is a potentially useful tool to improve N fertilizer management and develop in-season yield predictions in rice, alternative indices that do not saturate would likely provide a basis for a better tool.
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.