Excel spreadsheets by species (4 letter code is abbreviation for genus and species used in study, year 2010 or 2011 is year data collected, SH indicates data for Science Hub, date is date of file preparation). The data in a file are described in a read me file which is the first worksheet in each file. Each row in a species spreadsheet is for one plot (plant). The data themselves are in the data worksheet. One file includes a read me description of the column in the date set for chemical analysis. In this file one row is an herbicide treatment and sample for chemical analysis (if taken). This dataset is associated with the following publication: Olszyk , D., T. Pfleeger, T. Shiroyama, M. Blakely-Smith, E. Lee , and M. Plocher. Plant reproduction is altered by simulated herbicide drift toconstructed plant communities. ENVIRONMENTAL TOXICOLOGY AND CHEMISTRY. Society of Environmental Toxicology and Chemistry, Pensacola, FL, USA, 36(10): 2799-2813, (2017).

Sample data for exercises in Further Adventures in Data Cleaning.

Dataset

This dataset was created by Pinky Verma

Contents

Raw data outputs 1-18 Raw data output 1. Differentially expressed genes in AML CSCs compared with GTCs as well as in TCGA AML cancer samples compared with normal ones. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 2. Commonly and uniquely differentially expressed genes in AML CSC/GTC microarray and TCGA bulk RNA-seq datasets. This data was generated based on the results of AML microarray and TCGA data analysis. Raw data output 3. Common differentially expressed genes between training and test set samples the microarray dataset. This data was generated based on the results of AML microarray data analysis. Raw data output 4. Detailed information on the samples of the breast cancer microarray dataset (GSE52327) used in this study. Raw data output 5. Differentially expressed genes in breast CSCs compared with GTCs as well as in TCGA BRCA cancer samples compared with normal ones. Raw data output 6. Commonly and uniquely differentially expressed genes in breast cancer CSC/GTC microarray and TCGA BRCA bulk RNA-seq datasets. This data was generated based on the results of breast cancer microarray and TCGA BRCA data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 7. Differential and common co-expression and protein-protein interaction of genes between CSC and GTC samples. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. CSC, and GTC are abbreviations of cancer stem cell, and general tumor cell, respectively. Raw data output 8. Differentially expressed genes between AML dormant and active CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 9. Uniquely expressed genes in dormant or active AML CSCs. This data was generated based on the results of AML scRNA-seq data analysis. Raw data output 10. Intersections between the targeting transcription factors of AML key CSC genes and differentially expressed genes between AML CSCs vs GTCs and between dormant and active AML CSCs or the uniquely expressed genes in either class of CSCs. Raw data output 11. Targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 12. CSC-specific targeting desirableness score of AML key CSC genes and their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 13. The protein-protein interactions between AML key CSC genes with themselves and their targeting transcription factors. This data was generated based on the results of AML microarray and STRING database-based protein-protein interaction data analysis. Raw data output 14. The previously confirmed associations of genes having the highest targeting desirableness and CSC-specific targeting desirableness scores with AML or other cancers’ (stem) cells as well as hematopoietic stem cells. These data were generated based on a PubMed database-based literature mining. Raw data output 15. Drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 16. CSC-specific drug score of available drugs and bioactive small molecules targeting AML key CSC genes and/or their targeting transcription factors. These scores were generated based on an in-house scoring function described in the Methods section. Raw data output 17. Candidate drugs for experimental validation. These drugs were selected based on their respective (CSC-specific) drug scores. CSC is the abbreviation of cancer stem cell. Raw data output 18. Detailed information on the samples of the AML microarray dataset GSE30375 used in this study.

Download Employee Travel Excel SheetThis dataset contains information about the employee travel expenses for the year 2021. Details are provided on the employee (name, title, department), the travel (dates, location, purpose) and the cost (expenses, recoveries). Expenses are broken down in separate tabs by Quarter (Q1, Q2, Q3 and Q4). Updated quarterly when expenses are prepared. Expenses for other years are available in separate datasets.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The Adventure Works dataset is a comprehensive and widely used sample database provided by Microsoft for educational and testing purposes. It's designed to represent a fictional company, Adventure Works Cycles, which is a global manufacturer of bicycles and related products. The dataset is often used for learning and practicing various data management, analysis, and reporting skills.

Key Features of the Adventure Works Dataset:

1. Company Overview: - Industry: Bicycle manufacturing - Operations: Global presence with various departments such as sales, production, and human resources.

2. Data Structure: - Tables: The dataset includes a variety of tables, typically organized into categories such as: - Sales: Information about sales orders, products, and customer details. - Production: Data on manufacturing processes, inventory, and product specifications. - Human Resources: Employee details, departments, and job roles. - Purchasing: Vendor information and purchase orders.

3. Sample Tables: - Sales.SalesOrderHeader: Contains information about sales orders, including order dates, customer IDs, and total amounts. - Sales.SalesOrderDetail: Details of individual items within each sales order, such as product ID, quantity, and unit price. - Production.Product: Information about the products being manufactured, including product names, categories, and prices. - Production.ProductCategory: Data on product categories, such as bicycles and accessories. - Person.Person: Contains personal information about employees and contacts, including names and addresses. - Purchasing.Vendor: Information on vendors that supply the company with materials.

4. Usage: - Training and Education: It's widely used for teaching SQL, data analysis, and database management. - Testing and Demonstrations: Useful for testing software features and demonstrating data-related functionalities.

5. Tools: - The dataset is often used with Microsoft SQL Server, but it's also compatible with other relational database systems.

The Adventure Works dataset provides a rich and realistic environment for practicing a range of data-related tasks, from querying and reporting to data modeling and analysis.

This is the Excel template used to structure the databases that provide data at the legislature / party level for each of the six countries studied in Tasks 4.1 and 4.2 of the ActEU project.

An excel template with data elements and conventions corresponding to the openLCA unit process data model. Includes LCA Commons data and metadata guidelines and definitions Resources in this dataset:Resource Title: READ ME - data dictionary. File Name: lcaCommonsSubmissionGuidelines_FINAL_2014-09-22.pdfResource Title: US Federal LCA Commons Life Cycle Inventory Unit Process Template. File Name: FedLCA_LCI_template_blank EK 7-30-2015.xlsxResource Description: Instructions: This template should be used for life cycle inventory (LCI) unit process development and is associated with an openLCA plugin to import these data into an openLCA database. See www.openLCA.org to download the latest release of openLCA for free, and to access available plugins.

Blank database or template for the Labour Relations databases. Format: Microsoft Excel 2007 or higher

Adventure Works 2022 dataset

How this Dataset is created?

On the official website the dataset is available over SQL server (localhost) and CSVs to be used via Power BI Desktop running on Virtual Lab (Virtaul Machine). As per first two steps of Importing data are executed in the virtual lab and then resultant Power BI tables are copied in CSVs. Added records till year 2022 as required.

How this Dataset may help you?

this dataset will be helpful in case you want to work offline with Adventure Works data in Power BI desktop in order to carry lab instructions as per training material on official website. The dataset is useful in case you want to work on Power BI desktop Sales Analysis example from Microsoft website PL 300 learning.

How to use this Dataset?

Download the CSV file(s) and import in Power BI desktop as tables. The CSVs are named as tables created after first two steps of importing data as mentioned in the PL-300 Microsoft Power BI Data Analyst exam lab.

The Alaska Geochemical Database Version 2.0 (AGDB2) contains new geochemical data compilations in which each geologic material sample has one "best value" determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database (AGDB) before it, the AGDB2 was created and designed to compile and integrate geochemical data from Alaska in order to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, environmental impact assessments, and studies in medical geology. This relational database, created from the Alaska Geochemical Database (AGDB) that was released in 2011, serves as a data archive in support of present and future Alaskan geologic and geochemical projects, and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 85 laboratory and field analytical methods on 264,095 rock, sediment, soil, mineral and heavy-mineral concentrate samples. Most samples were collected by U.S. Geological Survey (USGS) personnel and analyzed in USGS laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various USGS programs and projects from 1962 through 2009. In addition, mineralogical data from 18,138 nonmagnetic heavy mineral concentrate samples are included in this database. The AGDB2 includes historical geochemical data originally archived in the USGS Rock Analysis Storage System (RASS) database, used from the mid-1960s through the late 1980s and the USGS PLUTO database used from the mid-1970s through the mid-1990s. All of these data are currently maintained in the National Geochemical Database (NGDB). Retrievals from the NGDB were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. This arduous process of reviewing, verifying and, where necessary, editing all USGS geochemical data resulted in a significantly improved Alaska geochemical dataset. USGS data that were not previously in the NGDB because the data predate the earliest USGS geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB2 and will be added to the NGDB. The AGDB2 data provided here are the most accurate and complete to date, and should be useful for a wide variety of geochemical studies. The AGDB2 data provided in the linked database may be updated or changed periodically.

Template (Excel Spreadsheet) designed for the EJPSOIL CarboSeq WP2 to collect (meta)data of long term agricultural field experiments for Tier2 (SOC) and where available Tier 3 (carbon input) from Europe.

Hypertension poses a significant public health challenge in sub-Saharan Africa due to various risk factors. Community-based intervention for prevention and control of hypertension is an effective strategy to minimize the negative health outcomes. However, comprehensive systematic review evidence to inform effective community-based interventions for prevention and control of hypertension in low resource settings is lacking. This study aimed to synthesize the effectiveness of community-based interventions on prevention and control of hypertension in sub-Saharan Africa. A comprehensive search for studies was carried out on PubMed, CINAHL, Web of Science Core Collection, Embase, Scopus, and Google scholar databases. The result of the review was reported according to PRISMA guidelines. Studies published in English language were included. Two independent reviewers conducted critical appraisal of included studies and extracted the data using predefined excel sheet. Experimental, quasi experimental, cohort and analytical cross-sectional studies conducted on adults who have received community-based interventions for prevention and controls of hypertension in sub-Saharan Africa were included. In this systematic review, a total of eight studies were included, comprising of two interventional studies, two quasi-experimental studies, three cohort studies, and one comparative cross-sectional study. The interventions included health education, health promotion, home-based screening and diagnosis, as well as referral and treatment of hypertensive patients. The sample sizes ranged from 236 to 13,412 in the intervention group and 346 to 6,398 in the control group. This systematic review shows the effect of community-based interventions on reduction of systolic and diastolic blood pressure. However, the existing evidence is inconsistence and not strong enough to synthesize the effect of community-based interventions for the prevention and control of hypertension in sub-Saharan Africa. Hence, further primary studies need on the effect of community-based interventions for the prevention and control of hypertension in sub-Saharan Africa.Systematic review registration number: PROSPERO CRD42022342823.

The 1986 Census was the first mid-decade census to undertake detailed enumeration. Data on demographic, social and economic characteristics, as well as on dwellings, were collected from Canadians. The information is recorded on two data bases, the 100% data base and the 20% sample data base. The 100% data bases includes general demographic, dwelling and household data (for example: age, sex, marital status, mother tongue and structural type of dwelling) collected from the entire population. The 20% sample data base includes the general demographic data, detailed socio-economic data (for example: ethnic origin, labour force activity, schooling, income and dwellings information) collected from one-fifth of the population. The range of the 1986 Census products and services differs somewhat from the 1981 Census. The major changes are: A 40% reduction in the number of publications The replacement of the 1981 Census Summary Tapes program by the Basic Summary Cross-Tabulations Improvements in the Custom Tabulations Service The implementation of a new Semi-Custom product line Focus series is the aggregate statistics (multi-variate cross-tabulations) at census subdivision, census tract, and enumeration area levels. These 7 tables do not correspond to the print Focus series print publications. At present, EA-level tables are available on CD-ROM only.

This CD contains part of the data from the Benthos Information system, the database of the Monitor Task-group of the NIOO. AccConID=24 AccConstrDescription=This license lets others remix, tweak, and build upon your work non-commercially, and although their new works must also acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. AccConstrEN=Attribution-NonCommercial (CC BY-NC) AccessConstraint=Attribution-NonCommercial (CC BY-NC) AccessConstraints=only presence data are available through EurOBIS & EMODnet under the given license. For access to additional data, the provider needs to be contacted Acronym=None added_date=2009-03-02 15:36:53.227000 BrackishFlag=1 CDate=2005-09-27 cdm_data_type=Other CheckedFlag=1 Citation=Hummel H. 2004: BIS dataset of the south-western part of Netherlands (1985-2004). Netherlands Institute of Ecology; Centre for Estuarine and Marine Ecology, Netherlands. Metadata available at http://mda.nioo.knaw.nl/imis.php?module=dataset&dasid=599 Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=599 DasOrigin=Monitoring: field survey DasType=Data DasTypeID=1 DateLastModified={'date': '2025-08-26 01:35:20.203183', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=4.285 EmbargoDate=None EngAbstract=This CD contains part of the data from the Benthos Information system, the database of the Monitor Task-group of the NIOO. EngDescr=The data included are the available data from 1985 until 2004. All included samples are from the South-Western part of the Netherlands (Delta and coastal area) taken with a Reineck Box-core, a sub-sampling with one or more cores from a box-core, or one or more cores directly taken from the sediment. All samples are taken approximately 30 cm deep into the sediment. All data is presented in two-types databases (Access and Paradox), each containing two tables. And as an Excel workbook. One table holds all data of the macrobenthos, the other table holds information about the sediment (mean grain-size, silt content). The two tables can be linked through the Sample_id. Sediment data are only available for part of the data. The sediment data are not included in the Excel workbook. Samples with two or more replicates and samples which where divided in several slices are aggregated to one sample.

All actual counted numbers and measured weights are included as well as the calculated density and weights (mg) per square meter. Positions are given in the Dutch system and as geographic coordinates (datum: European 1950 and World 1984). Depth is given in meters below NAP. Species-names are matched with the European Register of Marine Species as much as possible. Taxonomy and feeding-type are included. All samples whithout any benthic fauna, are added to the databases (not in the Excel-workbook) as entries with empty fields for spesies name and taxonomy. The number, density, AFDW and biomass are set to 0.

DISCLAIMER: All data on the CD are owned by the Monitor Taskforce of the NIOO-KNAW. Although the data is collected with extreme care, the monitor Taskforce is not responsible for any possible errors.

RESTRICTIONS: Use of the data is restricted to the Marbef Theme 1 Workshop at Crete, October 2005. For any use of the data in publications or presentations, co-authorship is required. It is NOT allowed to make a (partial) copy of the data.

INFO ABOUT THE MONITOR TASKFORCE: All the information can be found at our webpage http://www.monitortaskforce.com/

CONTACT: For any questions or other use of the data please contact W.C.H. Sistermans (datamanager) Email: w.sistermans@nioo.knaw.nl or H. Hummel (Taskforce leader) Email: h.hummel@nioo.knaw.nl FreshFlag=0 geospatial_lat_max=52.12 geospatial_lat_min=51.34 geospatial_lat_units=degrees_north geospatial_lon_max=4.285 geospatial_lon_min=3.084 geospatial_lon_units=degrees_east infoUrl=None InputNotes=SOFT S:\datac\Original datasets\MARBEF\Europe\Theme1\NIOO\Excel\NIOO-MT-data[599].xls

license changed from restricted to CC-by-nc => equals the eurobis data policy; extra note to indicate only presence data can be shared institution=NIOZ-MON License=https://creativecommons.org/licenses/by-nc/4.0 Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=1 modified_sync=2025-09-02 00:00:00 Northernmost_Northing=52.12 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=None OrigTitleLangCode=None OrigTitleLangID=None OrigTitleLangNL=None Progress=In Progress PublicFlag=1 ReleaseDate=Mar 2 2009 12:00AM ReleaseDate0=2009-03-02 RevisionDate=None SizeReference=136677 distribution records; 522 species; 15564 sampling events sourceUrl=(local files) Southernmost_Northing=51.34 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=BIS dataset of the south-western part of Netherlands (1985-2004) StatusID=1 subsetVariables=ScientificName,BasisOfRecord,YearCollected,MonthCollected,DayCollected,aphia_id TerrestrialFlag=0 time_coverage_end=2004-11-04T01:00:00Z time_coverage_start=1974-06-21T01:00:00Z UDate=2025-03-26 VersionDate=Sep 27 2005 12:00AM VersionDay=18 VersionMonth=12 VersionName=2.0 VersionYear=2007 VlizCoreFlag=1 Westernmost_Easting=3.084

BackgroundMicrosoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue.ResultsHere, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/).ConclusionsEscape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

Summary Trend TablesThe HCUP Summary Trend Tables include information on hospital utilization derived from the HCUP State Inpatient Databases (SID), State Emergency Department Databases (SEDD), National Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). State statistics are displayed by discharge month and national and regional statistics are displayed by discharge quarter. Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD.The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics:Overview of trends in inpatient and emergency department utilizationAll inpatient encounter typesInpatient encounter typeNormal newbornsDeliveriesNon-elective inpatient stays, admitted through the EDNon-elective inpatient stays, not admitted through the EDElective inpatient staysInpatient service lineMaternal and neonatal conditionsMental health and substance use disordersInjuriesSurgeriesOther medical conditionsED treat-and-release visitsDescription of the data source, methodology, and clinical criteria (Excel file, 43 KB)Change log (Excel file, 65 KB)For each type of inpatient stay, there is an Excel file for the number of discharges, the percent of discharges, the average length of stay, the in-hospital mortality rate per 100 discharges,1 and the population-based rate per 100,000 population.2 Each Excel file contains State-specific, region-specific, and national statistics. For most files, trends begin in January 2017. Also included in each Excel file is a description of the HCUP databases and methodology.

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.

The data were composed of datasets from four experiments, a meta-analysis, and a subgroup analysis. The total sample size was 481 participants. There are six Excel workbooks of the datasets, each of which consists of two worksheets for database and statement, respectively (refer to the ZIP file in Appendix A). The first four sheets are for the four experiments, respectively. In the sheet for each experiment, each row represents a participant. It is important to note that the sheet also contains data for excluded participants, which are marked by gray shadow. Each column represents one of the experimental variables, consisting of age, gender, cues, self-construal, allocation amount (i.e., indicator of prosociality), perceived anonymity, etc. The last two sheets are for the meta-analysis and the subgroup analysis, respectively. The meta-analysis and the subgroup analysis used the same participants that were recruited in the analyses of the four prior experiments. For the meta-analysis (see “5 Meta-analysis” in Appendix A for database), the mean, standard deviation and sample size of each experiment were extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the experiments and the columns indicate related summaries including the experiment number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, and mean and standard deviation for the control condition. For the subgroup analysis (see “6 Subgroup analysis” in Appendix A), the participants of each experiment were further segmented into an independence subgroup and an interdependence subgroup according to the measurement or the manipulation of self-construal. The mean, standard deviation, and sample size were then extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the subgroups and the columns indicate related summaries including subgroup number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, mean and standard deviation for the control condition, and the subgroup assignment (i.e., 1 = independent self-construal; 2 = interdependent self-construal).