Forked from bitpay/bitcore-explorers Blockchain APIs for smartcoin

DFTBpy is an open source project, intended for education and simulation, which implements and explores the DFTB/SCC-DFTB method and its features. It is developed in Python with C extensions.

Block Explorer for SCC Layer 2 & Smart Contracts

Standard Cross-Cultural Sample (SCCS) Crosstabulation Tool - Web-based tool for cross-tabulating variables from Ethnographic Atlas

Introduction Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere. Approximatly every 3 years, the EPA releases its database on emissions of PM2.5. This database is known as the National Emissions Inventory (NEI). You can read more information about the NEI at the EPA National Emissions Inventory web site. For each year and for each type of PM source, the NEI records how many tons of PM2.5 were emitted from that source over the course of the entire year. The data that you will use for this assignment are for 1999, 2002, 2005, and 2008. Data The data for this assignment are available from the course web site as a single zip file: Data for Peer Assessment [29Mb] The zip file contains two files: PM2.5 Emissions Data (summarySCC_PM25.rds): This file contains a data frame with all of the PM2.5 emissions data for 1999, 2002, 2005, and 2008. For each year, the table contains number of tons of PM2.5 emitted from a specific type of source for the entire year. Here are the first few rows. ## fips SCC Pollutant Emissions type year ## 4 09001 10100401 PM25-PRI 15.714 POINT 1999 ## 8 09001 10100404 PM25-PRI 234.178 POINT 1999 ## 12 09001 10100501 PM25-PRI 0.128 POINT 1999 ## 16 09001 10200401 PM25-PRI 2.036 POINT 1999 ## 20 09001 10200504 PM25-PRI 0.388 POINT 1999 ## 24 09001 10200602 PM25-PRI 1.490 POINT 1999 fips: A five-digit number (represented as a string) indicating the U.S. county SCC: The name of the source as indicated by a digit string (see source code classification table) Pollutant: A string indicating the pollutant Emissions: Amount of PM2.5 emitted, in tons type: The type of source (point, non-point, on-road, or non-road) year: The year of emissions recorded Source Classification Code Table (Source_Classification_Code.rds): This table provides a mapping from the SCC digit strings in the Emissions table to the actual name of the PM2.5 source. The sources are categorized in a few different ways from more general to more specific and you may choose to explore whatever categories you think are most useful. For example, source “10100101” is known as “Ext Comb /Electric Gen /Anthracite Coal /Pulverized Coal”. You can read each of the two files using the readRDS() function in R. For example, reading in each file can be done with the following code: ## This first line will likely take a few seconds. Be patient! NEI <- readRDS("summarySCC_PM25.rds") SCC <- readRDS("Source_Classification_Code.rds") as long as each of those files is in your current working directory (check by calling dir() and see if those files are in the listing). Assignment The overall goal of this assignment is to explore the National Emissions Inventory database and see what it say about fine particulate matter pollution in the United states over the 10-year period 1999–2008. You may use any R package you want to support your analysis. Questions You must address the following questions and tasks in your exploratory analysis. For each question/task you will need to make a single plot. Unless specified, you can use any plotting system in R to make your plot. Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008. Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (fips == "24510") from 1999 to 2008? Use the base plotting system to make a plot answering this question. Of the four types of sources indicated by the type (point, nonpoint, onroad, nonroad) variable, which of these four sources have seen decreases in emissions from 1999–2008 for Baltimore City? Which have seen increases in emissions from 1999–2008? Use the ggplot2 plotting system to make a plot answer this question. Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008? How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City? Compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles County, California (fips == "06037"). Which city has seen greater changes over time in motor vehicle emissions? Making and Submitting Plots For each plot you should Construct the plot and save it to a PNG file. Create a separate R code file (plot1.R, plot2.R, etc.) that constructs the corresponding plot, i.e. code in plot1.R constructs the plot1.png plot. Your code file should include code for reading the data so that the plot can be fully reproduced. You must also include the code that creates the PNG file. Only include the code for a single plot (i.e. plot1.R should only include code for producing plot1.png) Upload the PNG file on the Assignment submission page Copy and paste the R code from the corresponding R file into the text box at the appropriate point in the peer assessment.

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Source code of https://country-level-scc.github.io/explorer

Mood Light tutorial and code for Exploring Computer Science Workshops at SCC.

Arduino Twitter Bot Starter code for Exploring Computer Science Workshops at SCC.

Project for an Exploring Computer Science Workshop for High School students visiting SCC.

Exploring new approaches to likelihood profiling and how they apply to designs other than Cox models (e.g. SCCS)

Program that takes a graph and splits it into SCCs. Numbering is based off order of exploration. First explored and components is 0 next is 1 and so on.

Used Matplotlib 📊 to visualize tumor growth trends in a clinical trial for Pymaceuticals, Inc. Analyzed SCC tumor responses to various drug regimens, cleaned data with Pandas, and explored correlations with regression modeling.

The primary goal of analysis was to evaluate the effectiveness of anti-cancer medications for a pharmaceutical company. This study was conducted to explore potential treatments for squamous cell carcinoma (SCC), a common type of skin cancer.

SCC-ALAR: An experimental study on skin lesion classification using hybrid CNN-Transformer architectures. This project explores the impact of architectural complexity versus inductive bias on the imbalanced HAM10000 dataset, featuring custom weighting schemes and pre-extracted feature embeddings.

🔌 Excited to share our EEE-301 project on Performance Analysis of a 3-Phase Synchronous Generator! ⚡ We explored OCC & SCC tests, voltage regulation, efficiency & load effects—bridging theory with practice. Grateful to teammates & S M Nur Pranto for guidance. 🙌 #EEE #PowerSystems #Engineering