Working Excel spreadsheet compilation of recently published GMarc normalized datasets mapped onto granular segments of canonical Luke and related statistical findings. There are now over 56400 word tokens mapped.

This dataset contains Normalized Difference Vegetation Index (NDVI) images of the 1999 growing season of the Toolik Lake Field station to document differences in on study site in control and treatment plots. For more information, please see the readme file. NOTE: This dataset contains the data in EXCEL format.

A Data. Full data set, organized by transfected plate number. Shown is the average of duplicate wells in each transfection, normalized as F/R/Ba (see S10B File). Samples used in the figures are highlighted in bold, red font. B Example. Illustration of the data processing. Raw firefly counts (F counts) are normalized to the renilla control (R counts) for each well to give “F/R”. The two Ba710 control samples in each plate are averaged to give “Ba”. Each F/R value is then normalized to the Ba value to give “F/R/Ba”. The duplicate F/R/Ba values are averaged to give the activity of each sample for that transfection. This number is used in further analysis as an “n” of 1. (XLSX)

This dataset contains Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI) and Phytomass data collected at the Ivotuk field site during the growing season of 1999. The worksheets within this Excel file contain Mean NDVI and LAI data, raw NDVI and LAI data, seasonal mean phytomass, peak phytomass data and raw phytomass data separated by sampling period.

Background The Infinium EPIC array measures the methylation status of > 850,000 CpG sites. The EPIC BeadChip uses a two-array design: Infinium Type I and Type II probes. These probe types exhibit different technical characteristics which may confound analyses. Numerous normalization and pre-processing methods have been developed to reduce probe type bias as well as other issues such as background and dye bias.
Methods This study evaluates the performance of various normalization methods using 16 replicated samples and three metrics: absolute beta-value difference, overlap of non-replicated CpGs between replicate pairs, and effect on beta-value distributions. Additionally, we carried out Pearson’s correlation and intraclass correlation coefficient (ICC) analyses using both raw and SeSAMe 2 normalized data.Â
Results The method we define as SeSAMe 2, which consists of the application of the regular SeSAMe pipeline with an additional round of QC, pOOBAH masking, was found to be the b...,

Study Participants and SamplesÂ

The whole blood samples were obtained from the Health, Well-being and Aging (Saúde, Ben-estar e Envelhecimento, SABE) study cohort. SABE is a cohort of census-withdrawn elderly from the city of São Paulo, Brazil, followed up every five years since the year 2000, with DNA first collected in 2010. Samples from 24 elderly adults were collected at two time points for a total of 48 samples. The first time point is the 2010 collection wave, performed from 2010 to 2012, and the second time point was set in 2020 in a COVID-19 monitoring project (9±0.71 years apart). The 24 individuals were 67.41±5.52 years of age (mean ± standard deviation) at time point one; and 76.41±6.17 at time point two and comprised 13 men and 11 women.

All individuals enrolled in the SABE cohort provided written consent, and the ethic protocols were approved by local and national institutional review boards COEP/FSP/USP OF.COEP/23/10, CONEP 2044/2014, CEP HIAE 1263-10, University o..., We provide data on an Excel file, with absolute differences in beta values between replicate samples for each probe provided in different tabs for raw data and different normalization methods.

This dataset contains Normalized Difference Vegetation Index (NDVI) images of the 1999 growing season of the Toolik Lake Field station to document differences in on study site in control and treatment plots. For more information, please see the readme file. NOTE: This dataset contains the data in EXCEL format.

Full dataset for the siderite + R. palustris experiment. Includes:

• Solution and pellet ferrozine assay data (Excel file)
• Raman data of samples and standards (Excel file)
• XRD data of samples and standards (Excel file)
• Normalized XAS absorption data for all standards used in Fe K-edge XANES (Excel file)
• Representative least-squares fits of 1) siderite-like (reduced) and 2) oxidized sample endmembers (Excel file)

Foraminiferal samples were collected from Chincoteague Bay, Newport Bay, and Tom’s Cove as well as the marshes on the back-barrier side of Assateague Island and the Delmarva (Delaware-Maryland-Virginia) mainland by U.S. Geological Survey (USGS) researchers from the St. Petersburg Coastal and Marine Science Center in March, April (14CTB01), and October (14CTB02) 2014. Samples were also collected by the Woods Hole Coastal and Marine Science Center (WHCMSC) in July 2014 and shipped to the St. Petersburg office for processing. The dataset includes raw foraminiferal and normalized counts for the estuarine grab samples (G), terrestrial surface samples (S), and inner shelf grab samples (G). For further information regarding data collection and sample site coordinates, processing methods, or related datasets, please refer to USGS Data Series 1060 (https://doi.org/10.3133/ds1060), USGS Open-File Report 2015–1219 (https://doi.org/10.3133/ofr20151219), and USGS Open-File Report 2015-1169 (https://doi.org/10.3133/ofr20151169). Downloadable data are available as Excel spreadsheets, comma-separated values text files, and formal Federal Geographic Data Committee metadata.

This dataset contains changes in Normalized Difference Vegetation Index (NDVI) data from ITEX 1999. This dataset is in Excel Format. For more information, please see the readme file.

A post-processed Excel file (xlsx-files) that contains stride-normalized data. This data includes saggital plane ankle angle, knee angle, hip angle, and non-dimentional ankle power, knee power, and hip power, as well as EMG of the Gastrocnemius (GAS), Rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), semitendinosis (ST) and erector spinae (ERS). At last, this xlsx file contains ground reaction force in the anterior-posterior (ap), medio-lateral (ml) and vertical (vert) direction. This data is similar to the MAT-files.

The dataset contains results from Nanostring Digital Spatial Profiling (DSP, trade name is now GeoMx) experiments using colonic punch biopsy FFPE thin sections from IBD and IBS patients. The multiplex probe panel includes barcode-linked antibodies against 26 immune-oncology relevant proteins and 4 reference/normalization proteins.

The IF labeling strategy included Pan-cytokeratin, Tryptase, and DAPI staining for epithelia, mast cells, and sub-mucosal tissues, respectively. 21 FFPE sections were used, representing 19 individuals. 14 pediatric samples included 8 IBD, 5 IBS, and 1 recurring abdominal pain diagnoses. 7 adult samples were studied - 2 normal tissue biopsies from a single healthy control, 3 X-linked Severe Combined Immuno Deficiency (XSCID) samples from 2 individuals, 1 graft-versus-host disease, and 1 eosinophilic gastroenteritis sample. 8 representative ROIs per slide were selected, with a 9th ROI selected representing a lymphoid aggregate where present. Each of the ROIs contained the three masks (PanCK/epithelia, Tryptase/Mast cell, Dapi/submucosa), and therefore generated 24 individual 30-plex protein expression profiles per slide, with a 25th lymphoid ROI per sample (when present).

The data include: 1) Matrix of metadata with sample identifiers and clinical diagnoses (Excel file). 2) A PowerPoint for each sample showing an image of the full slide, images of each selected ROI and QC expression data. 3) An Excel file for each sample containing raw and normalized protein counts. Three normalization methods are reported: a) Normalization by nuclei count, b) Normalization by tissue area, c) Normalization by housekeeping proteins (Histone H3, Ribosomal protein S6).

Analysis derived from these data have been published in two conference proceedings (see references below)

ERS annually calculates "normalized prices," which smooth out the effects of shortrun seasonal or cyclical variation, for key agricultural inputs and outputs. They are used to evaluate the benefits of projects affecting agriculture.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: Web page with links to Excel files For complete information, please visit https://data.gov.

Title:

Amazon Financial Dataset: R&D, Marketing, Campaigns, and Profit

Description:

This dataset provides fictional yet insightful financial data of Amazon's business activities across all 50 states of the USA. It is specifically designed to help students, researchers, and practitioners perform various data analysis tasks such as log normalization, Gaussian distribution visualization, and financial performance comparisons.

Each row represents a state and contains the following columns:
- R&D Amount (in $): The investment made in research and development.
- Marketing Amount (in $): The expenditure on marketing activities.
- Campaign Amount (in $): The costs associated with promotional campaigns.
- State: The state in which the data is recorded.
- Profit (in $): The net profit generated from the state.

Additional features include log-normalized and Z-score transformations for advanced analysis.

Use Cases:

This dataset is ideal for practicing:
1. Log Transformation: Normalize skewed data for better modeling and analysis.
2. Statistical Analysis: Explore relationships between financial investments and profit.
3. Visualization: Create compelling graphs such as Gaussian distributions and standard normal distributions.
4. Machine Learning Projects: Build regression models to predict profits based on R&D and marketing spend.

File Information:

• File Format: Excel (.xlsx)
• Number of Records: 50 (one for each state of the USA)
• Columns: 5 primary financial columns and additional preprocessed columns for normalization and Z-scores.

Important Note:

This dataset is synthetically generated and is not based on actual Amazon financial records. It is created solely for educational and practice purposes.

Tags:

• Financial Analysis
• Data Visualization
• Machine Learning
• Statistical Analysis
• Educational Dataset

Data are transition probabilities of moving across full-time employment, voluntary part-time employment, involuntary part-time employment, unemployment, and nonparticipation. Data are calculated at the monthly frequency and cover U.S. workers over the period from 1976 until 2019. The content of each *_baseline MS Excel data file is as follows: time series of seasonally adjusted stocks (normalized by the corresponding population size), and time-series of seasonally adjusted transition probabilities, corrected for margin error and time aggregation bias. The content of each *_reclassified MS Excel data file is identical, but transition probabilities are in addition adjusted for potentially spurious transitions.

Additional table:The compositional variation of the Formula Diet fed to the experimental mice (Table 1), Differentially expressed genes by effect of RS, DJ, and DJ526 (Table 2), The most highly significant up-regulated and down-regulated pathways in the livers of mice on RS, DJ and DJ526 towards those on Ctrl groups (Table 3)Excel file: Globally normalized data (Fold change raw data), Z transformed data (Z-ratio raw data), and GSEA results

This is the Normalized Difference Vegetation Index (NDVI) Raw Data taken from the Excel file of dataset "ATLAS: 1999, Ivotuk NDVI, LAI, and Phytomass Data (EXCEL) (Epstein)" and translated into Tabular ASCII.

This dataset contains changes in Normalized Difference Vegetation Index (NDVI) data from International Tundra Experiment (ITEX) 1999. This dataset is in Excel Format. For more information, please see the readme file.

This dataset supports a bibliometric and sociometric analysis of Spanish universities' scientific production related to the United Nations Sustainable Development Goals (SDGs) for the period 2019 to 2023. This research is framed in the R+D+i Project "Spanish Universities in the Media" (MECU-ESP): Methodological Strategies and Web Tool for the Study and Classification of News Dissimination" PID2023-153339NA-I00 funded by the Ministry of Science, Innovation and Universities. Co-funded by the European Union. State Research Agency. University of Malaga, Spain.

The data were extracted from the InCites database and compiled into three Excel files. The dataset enables a comprehensive evaluation of the volume, impact, and interrelations of scientific output aligned with the SDG framework within the Spanish higher education system.

Contents:The dataset includes the following five files:

Total Scientific Production (2019–2023) Spanish Universities:This file includes bibliometric data on the total scientific output of Spanish universities during the period, with metadata on institutions, publication counts, and research areas.

Average Normalized Impact per SDG (2019-2023) All Spanish Universities:Contains data on the average normalized citation impact of publications, aggregated by university and SDG category, enabling comparisons of scientific influence across institutions and goals.

SDG–University Relations Matrix:This file presents the distribution and intensity of research output by Spanish universities across the 17 SDGs, facilitating sociometric analysis of institutional focus areas.

Format: Microsoft Excel (.xlsx)

Source: InCites (Clarivate Analytics)

Coverage: Spain | Time Period: 2019–2023

This dataset can serve as a resource for researchers, policymakers, and academic administrators interested in evaluating the contribution of Spanish universities to the global sustainability agenda through their scientific output.

This dataset from the VIRTA Publication Information Service consists of the metadata of 241,575 publications of Finnish universities (publication years 2016–2021) merged from yearly datasets downloaded from https://wiki.eduuni.fi/display/cscvirtajtp/Vuositasoiset+Excel-tiedostot.

The dataset contains following information:

• Organisation: name of the university
• Publication year: the year of publication
• Subfield: one of 66 fields of science based on Statistics Finland field of science classification (in Finnish), see the classification in English: https://www2.stat.fi/en/luokitukset/tieteenala/
• Peer-reviewed: 1=peer-reviewed publications, 0=not peer-reviewed publications
• Science communication: 1=publications aimed at professional and general audiences, 0=peer-reviewed and not peer-reviewed publications aimed at academic audience.
• Bibliodiversity: 1=peer-reviewed book publications (chapters, monographs and edited volumes) and conference articles, 0=peer-reviewed journal articles.
• Multilingualism: share of peer-reviewed publications in languages other than English (Finnish, Swedish and other languages).
• Domestic publishing: 1=peer-reviewed publications in journals and books published in Finland, 0=peer-reviewed publications in journals and books published outside Finland.
• Domestic collaboration: 1=peer-reviewed publications with co-authors from more than one Finnish university, 0=peer-reviewed publications without co-authors from more than one Finnish university.
• International collaboration: 1=share of peer-reviewed publications with co-authors affiliated with foreign institutions, 0=share of peer-reviewed publications without co-authors affiliated with foreign institutions.
• Research performance: 1=peer-reviewed outputs in JUFO levels 2 (“leading”) and 3 (“top”) publication channels, 0=peer-reviewed outputs in JUFO levels 1 (“basic”) and 0 (“other”) publication channels.
• Open access: 1=peer-reviewed open access publications, including gold, hybrid and green OA, 0=peer-reviewed closed publications.

These are the data tables used to produce results in the publication:

"Multi-omic approach to identify phenotypic modifiers underlying cerebral demyelination in X-linked adrenoleukodystrophy."
Phillip A. Richmond & Frans van der Kloet et al.

Submitting to Frontiers in Cellular and Developmental Biology, 2020, Peroxisomal Special Issue.

These tables include normalized measurements from four omics technologies, with no identifying information included. For details on processing, see the manuscript or contact:

prichmond (at) cmmt (dot) ubc (dot) ca.

Description of Files

• Sample mapping
  • 20180314_sib_pairs.xlsx
    • Excel sheet describing family numbering, etc. used as a mapping table within the sheets below.
• Methylation:
  • DMRs_5_Families_ALL_0.10DB_Dec2019.csv
    • Significant methylated regions with delta beta at least 10 percent when a single family is left out
  • ALD_Deconvoluted_Betas_Dec2019.csv
    • All fitted betas for every subject (single CpG)
  • ALD_Limma_Final_Dec2019_CHR.csv
    • All fitted effects using limma modeling per CpG
• RNA:
  • Count_data.txt
    • The raw count table summed at the gene level using featureCounts.
  • Pvalues_all_23_01_2019.csv
    • All pvalues and log fold changes for the genes included in the modeling process (also with family left out)
  • Tmm_norm_counts_5_2_2020.csv
    • Tmm normalized RNA count data
• Proteomic
  • Report_Precursor_Peptides.xls
    • The proteomic data as an excel spreadsheet
• Pvalues_prot_13_3_2019.xlsx
  • The pvalues and log fold changes (also with family left out)
• Lipids:
  • Lipid_data.csv
    • The lipid data (metabolites with missings are removed)
  • Pvalues_lipids.csv
    • Pvalues for the lipid data (also with family left out)

NOTE: For use of these data files for processing and reproducing results of the manuscript, please see https://github.com/Phillip-a-richmond/ALD_Modifier_Project.