Datasets

This dataset matches to a thesis of the same title: Can fair use be adequately taught to Librarians? Assessing Librarians' confidence and comprehension in explaining fair use following an expert workshop.

Objectives: This study follows-up on previous work that began examining data deposited in an institutional repository. The work here extends the earlier study by answering the following lines of research questions: (1) what is the file composition of datasets ingested into the University of Illinois at Urbana-Champaign campus repository? Are datasets more likely to be single file or multiple file items? (2) what is the usage data associated with these datasets? Which items are most popular? Methods: The dataset records collected in this study were identified by filtering item types categorized as "data" or "dataset" using the advanced search function in IDEALS. Returned search results were collected in an Excel spreadsheet to include data such as the Handle identifier, date ingested, file formats, composition code, and the download count from the item's statistics report. The Handle identifier represents the dataset record's persistent identifier. Composition represents codes that categorize items as single or multiple file deposits. Date available represents the date the dataset record was published in the campus repository. Download statistics were collected via a website link for each dataset record and indicates the number of times the dataset record has been downloaded. Once the data was collected, it was used to evaluate datasets deposited into IDEALS. Results: A total of 522 datasets were identified for analysis covering the period between January 2007 and August 2016. This study revealed two influxes occurring during the period of 2008-2009 and in 2014. During the first time frame a large number of PDFs were deposited by the Illinois Department of Agriculture. Whereas, Microsoft Excel files were deposited in 2014 by the Rare Books and Manuscript Library. Single file datasets clearly dominate the deposits in the campus repository. The total download count for all datasets was 139,663 and the average downloads per month per file across all datasets averaged 3.2. Conclusion: Academic librarians, repository managers, and research data services staff can use the results presented here to anticipate the nature of research data that may be deposited within institutional repositories. With increased awareness, content recruitment, and improvements, IRs can provide a viable cyberinfrastructure for researchers to deposit data, but much can be learned from the data already deposited. Awareness of trends can help librarians facilitate discussions with researchers about research data deposits as well as better tailor their services to address short-term and long-term research needs.

Monthly water withdrawal records (total pumpage and per-capita consumption) for the City of Austin, Texas (2000-2014). Data were provided by Austin Water Utility.

This file contains the supplemental appendix for the article "Farmer Preferences for Agricultural Soil Carbon Sequestration Schemes" published in Applied Economic Policy and Perspectives (accepted 2017).

List of Chinese Students Receiving a Ph.D. in Chemistry between 1905 and 1964. Based on two books compiling doctoral dissertations by Chinese students in the United States. Includes disciplines; university; advisor; year degree awarded, birth and/or death date, dissertation title. Accompanies Chapter 5 : History of the Modern Chemistry Doctoral Program in Mainland China by Vera V. Mainz published in "Igniting the Chemical Ring of Fire : Historical Evolution of the Chemical Communities in the Countries of the Pacific Rim", Seth Rasmussen, Editor. Published by World Scientific. Expected publication 2017.

This dataset enumerates the number of geocoded tweets captured in geographic rectangular bounding boxes around the metropolitan statistical areas (MSAs) defined for 49 American cities, during a four-week period in 2012 (between April and June), through the Twitter Streaming API. More information on MSA definitions: https://www.census.gov/population/metro/

Copyright Review Management System renewals by year, data from Table 2 of the article "How Large is the ‘Public Domain’? A comparative Analysis of Ringer’s 1961 Copyright Renewal Study and HathiTrust CRMS Data."

These data are the result of a multi-step process aimed at enriching BIBFRAME RDF with linked data. The process takes in an initial MARC XML file, transforms it to BIBFRAME RDF/XML, and then four separate python files corresponding to the BIBFRAME 1.0 model (Work, Instance, Annotation, and Authority) are run over the BIBFRAME RDF/XML output. The input and outputs of each step are included in this data set. Input file types include the CSV; MARC XML; and Master RDF/XML Files. The CSV contain bibliographic identifiers to e-books. From CSVs a set of MARC XML are generated. The MARC XML are utilized to produce the Master RDF file set. The major outputs of the enrichment code produce BIBFRAME linked data as Annotation RDF, Instance RDF, Work RDF, and Authority RDF.

This dataset was extracted from a set of metadata files harvested from the DataCite metadata store (https://search.datacite.org/ui) during December 2015. Metadata records for items with a resourceType of dataset were collected. 1,647,949 total records were collected. This dataset contains three files: 1) readme.txt: A readme file. 2) version-results.csv: A CSV file containing three columns: DOI, DOI prefix, and version text contents 3) version-counts.csv: A CSV file containing counts for unique version text content values.

This dataset was extracted from a set of metadata files harvested from the DataCite metadata store (http://search.datacite.org/ui) during December 2015. Metadata records for items with a resourceType of dataset were collected. 1,647,949 total records were collected. This dataset contains four files: 1) readme.txt: a readme file. 2) language-results.csv: A CSV file containing three columns: DOI, DOI prefix, and language text contents 3) language-counts.csv: A CSV file containing counts for unique language text content values. 4) language-grouped-counts.txt: A text file containing the results of manually grouping these language codes.