The core data set are citations, typically in a basically Scopus dataset format, for 92 reports published between 1987 and 2025. Included are various views of the data set and tables summarizing some of the characteristics of the data set. Also included are a few sheets explaining calculations that do one of two things: convert Return On Investment (ROI) data from the form initially presented in a report to the units used in our analyses; or how Return on Investment figures were calculated from reports that included enough data to enable ROI calculation but within which an ROI figure was not calculated.
Snapp-Childs, W., Hancock, D., Smith, P., Towns, J., Stewart, C. (2025) "Overview of best practices for quantitative analysis of economic and academic benefits of research-enabling facilities." Indiana University. https://hdl.handle.net/2022/34680. https://hdl.handle.net/2022/34680
Title:
PRISMA Data and Analyses Regarding University-based Research-enabling Facilities and ROI
In addition to participant demographics, the number and type of herbs and spices used, supplements taken, specific diseases had, and number of prescription medications taken where analyzed in this dataset. Only this select information on the forms was loaded into the dataset. SPSS was used to run the dataset statistics.
The data was collected through an online Qualtrics survey. R and R Studio were used to subset the variables of interest that can be used in statistical analysis. No specific software or scripts, however, are required to access the final CSV file.
Jupyter Notebook pages that include automation protocols were created for each step in the the multistep continuous-flow synthesis of a D-glucuronic acid building block through a series of optimised and modular transformations, including O-p-methoxyphenyl (PMP) glycosylation, Zemplén deacylation, 4,6-O-benzylidene protection/deprotection, 2-O-benzoylation, and C6 oxidation/methylation. The Jupyter Notebooks serve as a versatile platform for both writing Python code and comprehensively documenting automated chemical procedures, including reaction setups, protocols, and execution logs. To ensure experimental reproducibility, the necessary chemical information is stored externally in JSON files. The Notebooks process this data to dynamically generate stoichiometry tables and apparatus descriptions, while simultaneously controlling the laboratory’s liquid handling systems.
This collection documents open-source Python-based code used to build synthesizers and execute synthetic protocols to produce various chemical compounds.
This subcollection contains Russian Military Topographic Maps in the 1:50,000 scale. The IU collection of these (mostly) former Soviet Red Army topographic maps came to us from the duplicate map room of the Library of Congress Map Collection. While by no means complete, this collection is a fine addition to our existing international map holdings. These maps have a great story to tell: some carry the stamp from both the University of Berlin and the University of Bonn Geography Departments, some are stamped "Captured Map", some carry the ID of the CIA Map Library or the Bureau of Geographic Names, and still others are hand-annotated. They are in a variety of conditions (paper, laminated, photographically reproduced in color or black and white, plasticized, or muslin-backed). The collection ranges from around 1880-1945 with a geographic extent mostly centered around Eastern Europe and Western Russia.
