This USGS data release represents tabular data and water-level modeling files for the 16 Pahute Mesa multiple-well aquifer tests conducted from 2009–2014. This dataset represents water-level models used to estimate observation-well drawdown during the 16 multiple-well aquifer tests. Water-level models are organized by aquifer test in zipped files. Water-level models are created using an Excel Add-in called SeriesSEE (Halford and others, 2012). The SeriesSEE Excel Add-in also is inlcuded so that water-level models can be reactivated. Once the SeriesSEE Add-In is loaded into Excel, water-level model files can be activated by opening the file, scrolling to the SeriesSEE toolbar menu, and selecting the "WLM" utility. See Halford and others (2012) for more information about SeriesSEE. Reference Cited: Halford, K.J., Garcia, C.A., Fenlon, J.M., and Mirus, B.B., 2012, Advanced methods for modeling water-levels and estimating drawdowns with SeriesSEE, an Excel add-in: U.S. Geological Survey Techniques and Methods Report, 4-F4. Reston, Virginia: USGS.

This dataset contains a structured HR Analytics report for the year 2024–2025, prepared entirely in Microsoft Excel. It has been designed to help students, analysts, HR professionals, and data enthusiasts practice real-world HR analytics using clean and well-organized data.

The dataset covers key HR areas such as employee demographics, salary structure, performance scores, promotions, attendance, and attrition indicators. All data is synthetic and manually curated for educational and analytical purposes.

The main purpose of this dataset is to provide users with a practical Excel-based resource to:

Explore and analyze employee trends

Build HR dashboards in Excel

Practice pivot tables, formulas, and HR KPIs

Learn workforce analytics without Power BI or coding

Work on beginner-friendly and professional HR case studies

This dataset does not contain personally identifiable information and is safe for public sharing. It serves purely as a digital learning resource inspired by real HR scenarios.

This archive contains raw data of visual and acoustic mapping of perforations in Utah FORGE well 16A(78)-32 acquired during the August 2024 circulation program. The dataset includes downhole images captured by EV, a downhole visual analytics company, providing visual records of each perforation. Images are organized in two folders: one set with perforation visualization overlays and one without. An included Excel spreadsheet provides the organized raw data.

This dataset is comprised of temporal variations in groundwater elevation data for the 24 monitoring wells located throughout the East River watershed. Seasonal to annual variations in groundwater elevations are a critical property of mountainous watersheds needed to understand both hydrological and below ground biogeochemical processes. Such data serve as a critical constraint for numerical models describing coupled groundwater-surface water behavior within the watershed. Additionally, the offset between the maximum and minimum groundwater elevations defines the extent of the bedrock weathering zone, with annual excursions in the groundwater hydrographic (i.e., the rising and falling hydrographic limbs) imposing primary controls on bedrock saturation state and redox conditions that govern biogeochemical reactions impacting nitrogen, carbon, and metals cycling. Manufacturer-specific software is used to download pressure data from each transducer, with broadly available spreadsheet software (e.g. Microsoft Excel) used to convert temporal variations in water pressure to elevations in units of meters above mean sea level. As additional monitoring wells are installed within the East River watershed and new groundwater monitoring wells are installed in the Taylor River watershed, temporal groundwater elevation data will be included as a part of this master dataset. Details regarding the metadata associated with each monitoring well location, including well depths, screened intervals, well location coordinates, and bedrock type, are included, as is a standard operating procedure for generating groundwater elevation data from water pressure values recorded by the pressure transducers. This dataset includes: (1) a zip file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.zip), containing (a) PNG of groundwater hydrographs, (b) a CSV file with groundwater elevation data, and (c) CSV file containing metadata organized by location; (2) an Excel file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.xlsx) with the groundwater elevation data, groundwater hydrographs, and metadata organized by location; (3) a Word file (Groundwater_elevation_data_protocols.docx) and a PDF file version (Groundwater_elevation_data_protocols.pdf) containing field protocols and methods; (4) a location metadata (locations.csv) file; (5) a file level metadata (flmd.csv); and (6) data dictionary (dd.csv) file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

The GAPs Data Repository provides a comprehensive overview of available qualitative and quantitative data on national return regimes, now accessible through an advanced web interface at https://data.returnmigration.eu/.

This updated guideline outlines the complete process, starting from the initial data collection for the return migration data repository to the development of a comprehensive web-based platform. Through iterative development, participatory approaches, and rigorous quality checks, we have ensured a systematic representation of return migration data at both national and comparative levels.

The Repository organizes data into five main categories, covering diverse aspects and offering a holistic view of return regimes: country profiles, legislation, infrastructure, international cooperation, and descriptive statistics. These categories, further divided into subcategories, are based on insights from a literature review, existing datasets, and empirical data collection from 14 countries. The selection of categories prioritizes relevance for understanding return and readmission policies and practices, data accessibility, reliability, clarity, and comparability. Raw data is meticulously collected by the national experts.

The transition to a web-based interface builds upon the Repository’s original structure, which was initially developed using REDCap (Research Electronic Data Capture). It is a secure web application for building and managing online surveys and databases.The REDCAP ensures systematic data entries and store them on Uppsala University’s servers while significantly improving accessibility and usability as well as data security. It also enables users to export any or all data from the Project when granted full data export privileges. Data can be exported in various ways and formats, including Microsoft Excel, SAS, Stata, R, or SPSS for analysis. At this stage, the Data Repository design team also converted tailored records of available data into public reports accessible to anyone with a unique URL, without the need to log in to REDCap or obtain permission to access the GAPs Project Data Repository. Public reports can be used to share information with stakeholders or external partners without granting them access to the Project or requiring them to set up a personal account. Currently, all public report links inserted in this report are also available on the Repository’s webpage, allowing users to export original data.

This report also includes a detailed codebook to help users understand the structure, variables, and methodologies used in data collection and organization. This addition ensures transparency and provides a comprehensive framework for researchers and practitioners to effectively interpret the data.

The GAPs Data Repository is committed to providing accessible, well-organized, and reliable data by moving to a centralized web platform and incorporating advanced visuals. This Repository aims to contribute inputs for research, policy analysis, and evidence-based decision-making in the return and readmission field.

Explore the GAPs Data Repository at https://data.returnmigration.eu/.

🧾 All India Book And Stationery Shop Database – Verified And Updated Contact Directory in ExcelThe All India Book & Stationery Shop Database is a verified, well-organized, and regularly updated Excel directory featuring bookstores, stationery retailers, school supply shops, and paper product se...

Data describing incidents of spill and loss of well control in the U.S. Gulf of Mexico from 1996 to 2010 have been accessed from the BSEE public records and organized in an Excel spreadsheet. Incidents reported over the past 15 years were reviewed and organized in a spreadsheet. A total of 90 non-pipeline incidents were identified as including enough description to be useful. Most of these incidents were blowouts and/or spills greater than 50 barrels. To the extent possible, the descriptions include the operation in progress at the time of the release of hydrocarbons, the cause of the release, the flow path taken from the formation to the point in the well or production system where the fluids were released, the release point, the types and volumes of fluids released, the barriers that were ultimately used to reestablish control, and other factors considered useful in describing these inidents and determining what kinds of responses should be expected to be most effective. Only 15 of the incidents occurred in water depths greater than 1000 ft. The spreadsheet is organized to allow sorting be any of the factors described, to focused on particular factors, or to be used with a built-in coding to establish correlations between particular factors.

This repository was created to store, organize, and share data collected for the Eastern Kentucky Project, focusing on hydrological research in the region. It serves as a centralized platform to manage data efficiently and facilitate collaboration among researchers and stakeholders involved in the project.

The repository primarily contains data from level loggers, which are crucial for monitoring and recording water levels, temperature, and other hydrological parameters over time. The collected data has been carefully extracted, processed, and stored in Excel files to ensure compatibility with various analysis tools. This structured format enables easy access and seamless integration into research workflows.

In addition to providing secure storage, the repository is designed to support efficient data sharing, transparency, and interdisciplinary collaboration. By offering a well-organized dataset, it enables researchers to analyze and build upon existing data, promoting high-quality research outputs. The repository ultimately aims to advance understanding and inform decision-making in water resource management for Eastern Kentucky.

The data file entitled “Emergy analysis of maize production in Ghana” is based on an empirical study to assess the resource as well as energy use efficiency of maize production systems using the Emergy-Data Envelopment Analysis approach, which was developed within the context of the BiomassWeb Project. The study area was Bolgatanga and Bongo Districts, Ghana, sub-Saharan Africa. The approach was developed by coupling Emergy Analysis and Data Envelopment Analysis methods into a framework, and integrating the concept of eco-efficiency into the framework to assess the resource as well as energy use efficiency and sustainability of agroecosystems as a whole. In this data file, the Emergy Analysis method is applied to achieve enviromental and economic accounting of maize production systems in Ghana. The Agricultural Production Systems sIMulator (APSIM) was used to model five maize-based production scenarios as follows: 1. Extensive rainfed maize system if the external input is 0 kg/ha/yr urea, with/ without manure (Extensive0). 2. Extensive rainfed maize system if the external input is 12 kg/ha/yr NPK, with/ without manure (Extensive12). 3. Rainfed maize-legume (cowpea - Vigna unguiculata, soybean - Glycine max, or groundnut - Arachis hypogaea) intercropping system if the external input is 20 kg/ha/yr urea, with/ without manure (Intercrop20). 4. Intensive maize system if the external input is 50 kg/ha/yr urea, including supplemental irrigation (Intensive50). 5. Intensive maize system if the external input is 100 kg/ha/yr urea, including supplemental irrigation (Intensive100). The five scenarios were compared on the basis of the evaluation that was achieved using the Emergy Analysis to account for resource as well as energy use efficiency and sustainability. The data were processed using mathemathical functions in Microsoft Excel. The data file is organized in seven sheet tabs, and they are linked. Comments have been added to make the content self-explanatory. Where secondary data have been used, the sources have been cited. This data file was authored by Mwambo, Francis Molua.

This repository was created to store, organize, and share data collected for two closely related NSF EPSCoR projects, both committed to better understanding flash flooding in headwater streams and mitigating flood impacts in Appalachia. The repository primarily contains data from water pressure leveloggers and various real-time telemetered water quality and flow sensors. The collected data has been carefully extracted, processed, and stored in Excel files (.xlsx and .csv) to ensure compatibility with various analysis tools.

The EPSCoR projects include the FLASH (Flooding in Appalachian Streams and Headwaters) Initiative: Mitigating impacts of climate change and flash flooding in Appalachia, and CLIMBS (Climate Resilience through Multidisciplinary Big Data Learning, Prediction and Building Response Systems) Project 4: Mitigate Flood Impacts. Both projects aim to enhance understanding of flash flooding and co-create effective mitigation strategies across Appalachia and the United States. FLASH further aims to empower vulnerable communities with knowledge, tools, resources, and technologies to build flash flood resilience via heavy focus on community relationships, involvement, and local student participation. CLIMBS Project 4 focuses more on improvement of flash flooding modeling and prediction to help provide actionable information to mitigate the loss of life and property.

In addition to providing secure storage, this repository is designed to support efficient data sharing, transparency, and interdisciplinary collaboration. By offering a well-organized dataset, it enables researchers to analyze and build upon existing data, promoting high-quality research outputs. The repository ultimately aims to advance understanding and inform decision-making in water resource management for Eastern Kentucky and Appalachia.

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 the results of a Readiness for Interprofessional Learnings Scale (RIPLS) pre-test and post-test that was administered in association with a health professions course in inter-professional education (IPE). Between 2016 and 2018, 251 surveys were collected from medical and nursing students at the University of Miami who participated in a one-day course on inter-professional education. All 251 participant surveys were entered into the data file. 245 surveys were complete and used in the study analysis. The survey is comprised of demographic questions, followed by 19 pre-test and 19 post-test questions assessing student perceptions of IPE. The excel spreadsheet is organized with each participant’s demographic data, as well as their RIPLS responses, which fall on a Likert scale (1-5). Each participant row includes all collected data for that participant. No individual respondent can be identified using this data.

This dataset contains a well-structured collection of data suitable for building and analyzing Bayesian Networks. The data is organized in an Excel file format, making it easy to manipulate, visualize, and use with various statistical software and programming languages. Node Information: Each column represents a variable (node) in the Bayesian Network. Edge Information: The relationships (edges) between the variables are implicitly defined by the data. Observations: Each row in the dataset corresponds to an observation or a data point, providing the values for each variable. Potential Applications:

Predictive Modeling: Use the dataset to build predictive models that can estimate the probability of certain outcomes based on observed data. Decision Support: Develop decision support systems that can suggest optimal actions based on probabilistic reasoning. Educational Purposes: Ideal for students and educators to understand and demonstrate the principles of Bayesian Networks. Columns:

Healthcare: Predict the likelihood of a patient developing a certain condition based on their medical history and symptoms. Finance: Model the probability of credit default based on financial indicators and borrower characteristics. Marketing: Determine the likelihood of a customer purchasing a product based on their browsing and purchasing history. Acknowledgements: This dataset was compiled and organized to support the research and application of Bayesian Networks. We encourage users to explore, analyze, and share their findings.

🧾 Andhra Pradesh 12th Standard (BPC) 2024–25 Batch Database – Verified Student Leads in ExcelThe Andhra Pradesh 12th Standard (BPC) 2024–25 Batch Database is a verified, well-organized, and regularly updated Excel directory of students enrolled in the Biology, Physics, Chemistry (BPC) stream for th...

All experimental measurement data are provided in the MS Excel file titled "SupplementaryMaterial.xlsx." The file includes an introduction with a brief explanation of how to use the data, as well as the full measurements, organized for ease of use. The specific spreadsheets titled "Recapitulation" and "Recapitulation - Transient" contain a summary of all measurements. Additionally, the file provides details regarding the Mesh Independence Study.