It started as a lightweight alternative to Excel, tucked quietly inside the broader Google ecosystem. But fast-forward to 2025, and Google Sheets isn’t just a spreadsheet tool; it’s a platform reshaping how individuals and businesses collaborate with data. Whether you’re a startup founder tracking KPIs, a school administrator running reports,...

The G Suite Creative Tools market has become a cornerstone of productivity and creativity for businesses and individuals alike. As organizations increasingly shift to digital orientation, tools like Google Docs, Sheets, Slides, and other integrated applications within G Suite have emerged as vital resources for coll

Education System In India (District Level)

Analyzing Educational Performance at the District Level

By Inder Sethi [source]

About this dataset

This comprehensive District Information System for Education (DISE) dataset collects district-level educational statistics in India and provides the most up-to-date data on the nation's schools. The project tracks and compiles data on primary and upper primary school students, teachers, institutions, infrastructures and more from all districts in India. It has drastically reduced the time lag between data collection to analysis - from seven to eight years down to only a few months at both district and state levels. DISE is fully supported by the Ministry of Human Resource Development (MHRD) as well as UNICEF so precise regional insights are available regarding Indian education standards. With this institutionalized flow of raw data being collected, verified at Block Education Offices/Coordinators then computerized at a District level before eventually being aggregated into State level analysis – it’s easier than ever before to understand where educational improvements need to be made. From tracking key performance indicators amongst students across all ages right through to measuring access teacher resources - this DISE dataset serves as an invaluable resource towards unlocking potential within the Indian learning system!

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How to use the dataset

Guide: How to Use the Indian District Level School Data 2015-16

• Familiarize yourself with the features of this data set. The dataset consists of five columns which provides an overview at district level educational statistics in India for the year 2015-16. Each row contains individual district-level data with corresponding educational information and statistics like Total Number of Schools, Number of Girls' Schools, Enrolment and more for each district in India during that year.

• Understand what kind of analysis can be done using this dataset once imported into a statistical software program or spreadsheet program such as Microsoft Excel or Google Sheets. You can use this dataset to analyze many different aspects related to education in India at a district level; including total number of schools, number and percent girls enrolled, teacher qualifications and more across districts throughout all states in India during the year 2015-16 period covered by this data set.

• Pull up a visual representation of your data within a statistical program like SPSS or perhaps one online such as Tableau Public, depending on your preference and needs for analysis purposes - either way it is necessary to have these setup beforehand before attempting to import any given subset into them; click upload file option within them (or any other appropriate action), select all files in your local machine directory where you saved our downloaded csv file “report card” from kaggle above – then just wait until it’s completely uploaded after selecting open/import/apply/etc…and if no errors about encoding appear then begin your desired data mining experience (visualization & analytical techniques).

• Once inside your preferred visualization environment, try out different methods for analyzing individual rows which correspond directly onto specific districts located inside this geographic territory that are meant by our target sheet observations mentioned prior – refer back often if lost & take time understanding what any given county contributes when computer processing their respective responses accordingly without overlooking any particular variables taken into account unlike secondary “missing values” under consideration also..

• Then define relationships between similar items according figures gathered - notice patterns found among these locations while focusing attention isolation instead – graphic qualities captured midst these demographics we choose visualize key representing intent anyways… therefor aim transform knowledge through effective strategy meant enable more meaningful representation ideas presented starting place develops further details follow courtesy

Research Ideas

• Analyzing literacy rate and measure the educational advancement of different districts in India.
• Tracking the progress of various Governmental programs like Sarva Shiksha Abhiyan that focus on improving access to education for children across districts.
• Predicting trends in the quality of school resources, educational infrastructure and student performance to guide district-level decision making processes for improved education outcomes

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This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: https://doi.org/10.17868/65389/.

Macgregor, G. (2018). Repository optimisation techniques to improve discoverability and web impact: an evaluation. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: https://doi.org/10.17868/65389/

The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.

Elections in Belarus attract much attention around the world. The election result is declared as a victory of Mr. Lukashenko with 80% votes. It is interesting to give the simplest statistical analysis of this victory. According to Belarus law, protocols of precinct election commissions (PECs) must be posted up just after the election procedure, so that everybody could take a photograph of the protocols. Currently, 1527 of the 5767 protocols of PECs are available in the open access at https://docs.google.com/spreadsheets/d/17aK3JxBTGtzULB0-YZGOF0hJwhuViHO3/edit#gid=84585767. We focus an attention on two arrays of numbers taken from these photographs. Namely, the number Ni of voters at some polling station and the number Mi of voters for Mr. Lukashenko at the same polling station. These numbers give a possibility to calculate the average percentage of those who voted for Mr. Lukashenko, which turns out to be about 60%. That is, a random sample approximately of ¼ of total number of protocol gives a value that differs at about 20% from the declared total value 80%. Using Monte-Carlo simulation we have calculated a probability of this event and obtain less than one part in million. Next we have considered Ni and Mi as the random variables and calculate probability distribution functions for Mi/Ni and Mi/

Methods Notebook is for Wolfram Mathematica, version 12. It use smoothed Gaussian kernel to calculate probability distribution functions. Files *.txt contain Ni and Mi arrays for city of Minsk, ans five regions of Belarus. A notebook DistributionsGenBel.nb calculates probability distribution functions for x=Mi/Ni and y=Mi/

Beginning undergraduate students in biology need basic laboratory, data analysis, and science process skills to pursue more complex questions in course-based undergraduate research experiences (CUREs). To this end, we designed an introductory lesson that helps students learn to use common laboratory equipment such as analytical balances and micropipettes, analyze and present data in Google and Microsoft spreadsheet software, and perform simple descriptive and inferential statistics for hypothesis testing. In this lesson, students first learn to use micropipettes by pipetting specific volumes of water correctly and incorrectly. After determining the masses of the water samples pipetted, students enter the data into a shared Google spreadsheet and then use statistics to test a null hypothesis; ultimately, they determine if there is a statistically significant difference between the mass of water pipetted correctly versus incorrectly. Together, these activities introduce students to important data analysis and science process skills while also orienting them to basic laboratory equipment.

Background Pelvic health conditions significantly impact quality of life and are prevalent in the general population. Urinary and fecal incontinence, pelvic organ prolapse, and pelvic pain are examples of pelvic health conditions. A scoping review was conducted to understand what is currently known about pelvic health conditions experienced by Indigenous populations worldwide. To date, no such review has been reported. Methods A scoping review methodology was used. In February 2024, a search was conducted, capturing both primary and grey literature. An iterative process of abstract and full text screening was conducted by two reviewers before proceeding to data extraction. Inclusion criteria focused on English publications and reports of pelvic health conditions experienced by Indigenous peoples. Data was collected in Google Sheets, and then underwent descriptive statistical analysis. Publications that provided qualitative data were further analyzed using thematic analysis. Results A total of 242 publications were included in the analysis. Several patterns emerged: most publications originated from English-speaking regions, fewer than half of publications specifically recruited Indigenous peoples, women participated in more studies than men, and bladder conditions were most frequently reported. Perceptions of pelvic health conditions and experiences with help seeking and the health care system were described. Notable gaps were a lack of publications and representation of Indigenous peoples from China, Russia, and Nordic countries, minimal representation of gender diverse populations, few publications reporting on auto-immune and bowel conditions, and limited mention of trauma-informed and culturally safe approaches. Conclusions This study highlights gaps in the current literature around gender representation, bowel and auto-immune conditions, regional representation, and the use of safety frameworks, which may inform future research initiatives. It also summarizes the existing literature, which may inform clinical and health system-level decision making.

Background Pelvic health conditions significantly impact quality of life and are prevalent in the general population. Urinary and fecal incontinence, pelvic organ prolapse, and pelvic pain are examples of pelvic health conditions. A scoping review was conducted to understand what is currently known about pelvic health conditions experienced by Indigenous populations worldwide. To date, no such review has been reported. Methods A scoping review methodology was used. In February 2024, a search was conducted, capturing both primary and grey literature. An iterative process of abstract and full text screening was conducted by two reviewers before proceeding to data extraction. Inclusion criteria focused on English publications and reports of pelvic health conditions experienced by Indigenous peoples. Data was collected in Google Sheets, and then underwent descriptive statistical analysis. Publications that provided qualitative data were further analyzed using thematic analysis. Results A total of 242 publications were included in the analysis. Several patterns emerged: most publications originated from English-speaking regions, fewer than half of publications specifically recruited Indigenous peoples, women participated in more studies than men, and bladder conditions were most frequently reported. Perceptions of pelvic health conditions and experiences with help seeking and the health care system were described. Notable gaps were a lack of publications and representation of Indigenous peoples from China, Russia, and Nordic countries, minimal representation of gender diverse populations, few publications reporting on auto-immune and bowel conditions, and limited mention of trauma-informed and culturally safe approaches. Conclusions This study highlights gaps in the current literature around gender representation, bowel and auto-immune conditions, regional representation, and the use of safety frameworks, which may inform future research initiatives. It also summarizes the existing literature, which may inform clinical and health system-level decision making.

Various population statistics, including structured demographics data.