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This file contains a preprocessed subset of the MIMIC-IV dataset (Medical Information Mart for Intensive Care, Version IV), specifically focusing on laboratory event data related to glucose levels. It has been curated and processed for research on data normalization and integration within Clinical Decision Support Systems (CDSS) to improve Human-Computer Interaction (HCI) elements.
The dataset includes the following key features:
This data has been used to analyze the impact of normalization and integration techniques on improving data accuracy and usability in CDSS environments. The file is provided as part of ongoing research on enhancing clinical decision-making and user interaction in healthcare systems.
The data originates from the publicly available MIMIC-IV database, developed and maintained by the Massachusetts Institute of Technology (MIT). Proper ethical guidelines for accessing and preprocessing the dataset have been followed.
MIMIC-IV_LabEvents_Subset_Normalization.xlsx
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Working Excel spreadsheet compilation of recently published GMarc normalized datasets mapped onto granular segments of canonical Luke and related statistical findings. There are approximately 25000 total word tokens mapped so far.
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Additional file 2 Data set (excel file). The excel data file data_set_of_extracted_data_Buchka_et_al.xlsx contains the data from our bibliographical survey.
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), 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.
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Additional file 1: 994 Prodigal ortholog sets with inconsistent start sites. The Excel file provides information about the 994 ortholog sets with inconsistent start sites, including the genes within each set and the gene start site revisions required to achieve consistency within each set. (XLS 1 MB)
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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.
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Finding a good data source is the first step toward creating a database. Cardiovascular illnesses (CVDs) are the major cause of death worldwide. CVDs include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other heart and blood vessel problems. According to the World Health Organization, 17.9 million people die each year. Heart attacks and strokes account for more than four out of every five CVD deaths, with one-third of these deaths occurring before the age of 70 A comprehensive database for factors that contribute to a heart attack has been constructed , The main purpose here is to collect characteristics of Heart Attack or factors that contribute to it. As a result, a form is created to accomplish this. Microsoft Excel was used to create this form. Figure 1 depicts the form which It has nine fields, where eight fields for input fields and one field for output field. Age, gender, heart rate, systolic BP, diastolic BP, blood sugar, CK-MB, and Test-Troponin are representing the input fields, while the output field pertains to the presence of heart attack, which is divided into two categories (negative and positive).negative refers to the absence of a heart attack, while positive refers to the presence of a heart attack.Table 1 show the detailed information and max and min of values attributes for 1319 cases in the whole database.To confirm the validity of this data, we looked at the patient files in the hospital archive and compared them with the data stored in the laboratories system. On the other hand, we interviewed the patients and specialized doctors. Table 2 is a sample for 1320 cases, which shows 44 cases and the factors that lead to a heart attack in the whole database,After collecting this data, we checked the data if it has null values (invalid values) or if there was an error during data collection. The value is null if it is unknown. Null values necessitate special treatment. This value is used to indicate that the target isn’t a valid data element. When trying to retrieve data that isn't present, you can come across the keyword null in Processing. If you try to do arithmetic operations on a numeric column with one or more null values, the outcome will be null. An example of a null values processing is shown in Figure 2.The data used in this investigation were scaled between 0 and 1 to guarantee that all inputs and outputs received equal attention and to eliminate their dimensionality. Prior to the use of AI models, data normalization has two major advantages. The first is to avoid overshadowing qualities in smaller numeric ranges by employing attributes in larger numeric ranges. The second goal is to avoid any numerical problems throughout the process.After completion of the normalization process, we split the data set into two parts - training and test sets. In the test, we have utilized1060 for train 259 for testing Using the input and output variables, modeling was implemented.
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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:
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This is the raw data of the performances of three open-loop synchronization techniques using a proposed benchmark. The RMSE folder contains the data of Table.2 of the article. In the main script, the data has been normalized. The folder contains the original data for each case of the benchmark. The radar chart file contains the original data of the radar chart (Fig .14). The data has been manipulated to better present it in a radar chart format. First, the values are inverted. Then, we normalize the data according to the highest value. The method that has the highest value (better performances) will take 10, the other methods will be below that value. Formulas are included in the MS Excel file.
Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2006 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.
Data fields requiring description are detailed below.
APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded.
LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.
LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).
Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.
Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2006 to present. Frequency: Data is updated daily.
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Table of gene enrichmend results for ESC vs neuron. This excel file contains a worksheet for the GO MF, GO BP, and KEGG pathway analysis results for the gene enrichment analysis between the ESC and neuron discussed in the results section. (XLSX 46 kb)
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We provide the data used for this research in both Excel (one file with one matrix per sheet, 'Allmatrices.xlsx'), and CSV (one file per matrix).
Patent applications (Patent_applications.csv) Patent applications from residents and no residents per million inhabitants. Data obtained from the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.
High-tech exports (High-tech_exports.csv) The proportion of exports of high-level technology manufactures from total exports by technology intensity, obtained from the Trade Structure by Partner, Product or Service-Category database (Lall, 2000; UNCTAD, 2019)
Expenditure on education (Expenditure_on_education.csv) Per capita government expenditure on education, total (2010 US$). The data was obtained from the government expenditure on education (total % of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.
Scientific publications (Scientific_publications.csv) Scientific and technical journal articles per million inhabitants. The data were obtained from the scientific and technical journal articles and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.
Expenditure on R&D (Expenditure_on_R&D.csv) Expenditure on research and development. Data obtained from the research and development expenditure (% of GDP), GDP (constant 2010 US$), and population indicators of the World Development Indicators database (World Bank 2020). Normalization by the number of inhabitants was made by the authors.
Two centuries of GDP (GDP_two_centuries.csv) GDP per capita that accounts for inflation. Data obtained from the Maddison Project Database, version 2018 (Inklaar et al. 2018), and available from the Open Numbers community (open-numbers.github.io).
Inklaar, R., de Jong, H., Bolt, J., & van Zanden, J. (2018). Rebasing “Maddison”: new income comparisons and the shape of long-run economic development (GD-174; GGDC Research Memorandum). https://www.rug.nl/research/portal/files/53088705/gd174.pdf
Lall, S. (2000). The Technological Structure and Performance of Developing Country Manufactured Exports, 1985‐98. Oxford Development Studies, 28(3), 337–369. https://doi.org/10.1080/713688318
Unctad. 2019. “Trade Structure by Partner, Product or Service-Category.” 2019. https://unctadstat.unctad.org/EN/.
World Bank. (2020). World Development Indicators. https://databank.worldbank.org/source/world-development-indicators
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Additional file 1. Hyperparameter tuning. Full results of the hyperparameter-tuning process, performed over the training set in 6-fold cross-validation. The file is an \emph{Open Document Format} table (.ods), which can be viewed and analysed with spreadsheet applications like MS Excel or LibreOffice Calc.
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This spreadsheet implements the FA normalization technique for analyzing a set of male Drosophila cuticular hydrocarbons. It is intended for GC-FID output. Sample data is included. New data can be copied into the file to apply the normalization. (0.07 MB DOC)
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.
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Full dataset for the siderite + R. palustris experiment. Includes:
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In this study, blood proteome characterization in face transplantation using longitudinal serum samples from six face transplant patients was carried out with SOMAscan platform. Overall, 24 serum samples from 13 no-rejection, 5 nonsevere rejection and 6 severe rejection episodes were analyzed.Files attached:HMS-16-007.20160218.adat - raw SomaScan dataset presented in adat format.HMS-16-007_SQS_20160218.pdf - technical validation report on the dataset.HMS-16-007.HybNorm.20160218.adat - SomaScan dataset after hybridization control normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.20160218.adat - SomaScan dataset after hybridization control normalization and median signal normalization presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in adat format.HMS-16-007.HybNorm.MedNorm.Cal.20160218.xls - SomaScan dataset after hybridization control normalization, median signal normalization, and calibration presented in Microsoft Excel Spreadsheet format.Patients_metadata.txt – metadata file containing patients’ demographic and clinical information presented in tab-delimited text format. Metadata is linked to records in the SomaScan dataset via ‘SampleType’ column.SciData_R_script.R – this script is given as an example of a downstream statistical analysis of the HMS-16-007.HybNorm.MedNorm.Cal.20160218.adat dataset.SciData_R_script_SessionInfo - Session information for SciData_R_script.R script.
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Expression (normalized read count) for breast cancer specific 79 fusion-protein and 419 3′-truncated protein transcripts. Expression is the normalized RNA-Seq read counts as estimated using RSEM and followed by upper quartile normalization. File contains expression data for breast cancer specific fusion-protein and 3′-truncated protein transcripts only. The first sheet in the excel file contains the data columns and a key describing the data is on the second excel sheet. (XLSX 33 kb)
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LipidMaps, NIST interlab, and this studies lipid values for NIST SRM 1950. An excel table with the lipids identified in the LIPID MAPS, NIST Interlaboratory Study for Lipidomics, and this study for NIST SRM 1950, and the resulting lipid levels. (XLSX 23 kb)
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This file contains a preprocessed subset of the MIMIC-IV dataset (Medical Information Mart for Intensive Care, Version IV), specifically focusing on laboratory event data related to glucose levels. It has been curated and processed for research on data normalization and integration within Clinical Decision Support Systems (CDSS) to improve Human-Computer Interaction (HCI) elements.
The dataset includes the following key features:
This data has been used to analyze the impact of normalization and integration techniques on improving data accuracy and usability in CDSS environments. The file is provided as part of ongoing research on enhancing clinical decision-making and user interaction in healthcare systems.
The data originates from the publicly available MIMIC-IV database, developed and maintained by the Massachusetts Institute of Technology (MIT). Proper ethical guidelines for accessing and preprocessing the dataset have been followed.
MIMIC-IV_LabEvents_Subset_Normalization.xlsx