Thank you for your interest in the NFIWADS databases! Here are some advices for data handling and accessing the database files. To unzip the files, it is strongly recommended to use the open source software 7zip: http://www.7-zip.org/ instead of onboard OS system tools. Some of the database .sqlite-files are zipped twice and split up, to reduce diskspace and to not exceed the maximum allowed file size of the data server. These have to be unpacked in two steps: 1. unzip the .zip-archive 2. unzip the first resulting .7z-archive (_.7z.001). 7zip then automatically recognizes the associated archives and assembles them to the original .sqlite database file) To access the .sqlite database files, you have different options: 1. Use the R software for statistical computing https://www.r-project.org/ and its extension package RSQLite for querying and displaying the contents of the database files. 2. Use the SQLite command line tool (https://www.sqlite.org) to query data using SQL commands. 3. Use the DB Browser for SQLite (http://sqlitebrowser.org) for a quick overview of the database structure. Not recommended for browsing the data! Here are some code examples of how to access .sqlite database files from within R: * R-code examples ********************************************* # Install RSQLite extension: install.packages("RSQLite") # Load the RSQLite package library(RSQLite) # Establish the database connection conn <- dbConnect(SQLite(), "NFIWADS_SPRUCE_RCP45_2010_2100.sqlite") # List tables and views of the database dbListTables(conn) # List the column names of a database table dbListFields(conn, "nfiwads_spruce_rcp45_20102100_vegper") # Retrieve a complete table: vegper <- dbReadTable(conn, "nfiwads_spruce_rcp45_20102100_vegper") # Retrieve a subset of a table using an SQL-select-statement monthly_20112020 <- dbGetQuery(conn, "select * from nfiwads_spruce_rcp45_20102100_monthly where yr >2010 and yr <=2020") # Disconnect database dbDisconnect(conn) * R-code examples ********************************************* From the authors personal experience, the tables and views with the suffix "_monthly" have to be handled with care due to their file size. From these tables only subsets should be queried, selecting the data of several years. Also, when accessing some of the predefined views, the user has to be patient, as some aggregating select statements might take a longer time to execute. Using the R software and its extension RSQLite on my Desktop PC (Intel Core i5-3570 CPU with 8 GB RAM) with Windows 7 64bit OS, the following execution times were examplarily measured for retrieving single tables and views from the database "NFIWADS_SPRUCE_RCP45_2010_2100.sqlite": table name exec. time (seconds) --------------------------------------------------------------------------- view_nfiwads_basic_spruce_rcp45_20102100_yearly 841.32 view_nfiwads_basic_spruce_rcp45_20102100_junaug_yearly 780.96 view_nfiwads_basic_spruce_rcp45_20102100_monthly (10 years) 601.42 nfiwads_spruce_rcp45_20102100_monthly (10 years) 347.25 nfiwads_spruce_rcp45_20102100_vegper 61.13 view_nfiwads_basic_spruce_rcp45_20102100_vegper_yearly 31.25 param_b90_spruce_rcp45_20102100 10.66 soils 2.22 view_site_parameters 0.71 simulation_time_spruce_rcp45_20102100 0.45 Paul Schmidt-Walter, February 16, 2018