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:

• Raw Lab Data: Original values of glucose levels as recorded in the clinical setting.
• Normalized Data: Glucose levels transformed into a standardized range for comparison and analysis.
• Demographic Information: Includes patient age and gender to support subgroup analyses.

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.

Key Applications:

• Research on the effects of data normalization on clinical outcomes.
• Study of demographic variations in laboratory values to support personalized healthcare.
• Exploration of data integration and its role in reducing cognitive load in CDSS.

Data Source:

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.

File Content:

• Filename: MIMIC-IV_LabEvents_Subset_Normalization.xlsx
• File Format: Microsoft Excel
• Number of Rows: 100 samples for demonstration purposes.
• Fields Included: Patient ID, Age, Gender, Raw Glucose Value, Normalized Glucose Value, and additional derived statistics.

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.

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.

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.

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)

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.

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)

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.

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

This dataset contains excel files corresponding to the data presented in the figures of the manuscript titled "Rabphilin-3A negatively regulates neuropeptide release, through its SNAP25 interaction". The title of the excel files indicates the specific figure it relates to. Each excel file is organized into multiple sheets, that represent the specific panel of the figure (e.g. Figure 1A, Figure 1B). When data in the figures are normalized, the raw data are also provided within the same excel file.

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.

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.

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.

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.

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 data set is comprised of five files related to the modification and scoring of Index of Waterbird Community Integrity (IWCI) scores for all waterbirds of the Chesapeake Bay. One Excel file (A) contains a list of 100+ Chesapeake waterbird species and their species attribute and IWCI scores. Another Excel file (B) contains case study data from recent surveys of breeding and migratory waterbirds in Chesapeake Bay and shoreline delineations across a disturbance gradient that were used to demonstrate the utility of the modified index. Finally, three supplemental files include an Access database (C), R code (D) and a protocol (E) for running the complex steps to calculate index scores.

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.

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)

A post-processed Excel-file (xlsx-file) that contains stride-normalized data. This data includes saggital plane ankle angle, knee angle, hip angle and pelvis angles, and EMG of the Gastrocnemius (GAS), Rectus femoris (RF), vastus lateralis (VL), biceps femoris (BF), semitendinosis (ST) and erector spinae (ERS). This data is similar to the MAT-files.