Term Coverage of Dietary Supplements in Product Labels
2016-07-07
Loading...
Persistent link to this item
Statistics
View StatisticsCollection period
2016-02-01
2016-03-10
2016-03-10
Date completed
2016-07-07
Date updated
Time period coverage
Geographic coverage
Source information
Dietary Supplement Label Database (DSLD)
Journal Title
Journal ISSN
Volume Title
Title
Term Coverage of Dietary Supplements in Product Labels
Published Date
2016-07-07
Authors
Group
Author Contact
Wang, Yefeng
wang4688@umn.edu
wang4688@umn.edu
Type
Dataset
Programming Software Code
Programming Software Code
Abstract
The data includes the Python source code for downloading, extracting and normalizing the dietary supplement ingredients information in Dietary Supplement Label Database (DSLD). Moreover, the output of the normalizing results is also included.
Description
Includes lstProducts.csv, Python scripts (version 3), and output text files. See Readme.txt for more detail.
Referenced by
Term Coverage of Dietary Supplements in Product Labels - AMIA 2016 Manuscript
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333301/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5333301/
Related to
Replaces
item.page.isreplacedby
License
Publisher
Collections
Funding information
item.page.sponsorshipfunderid
item.page.sponsorshipfundingagency
item.page.sponsorshipgrant
Previously Published Citation
Other identifiers
Suggested citation
Wang, Yefeng. (2016). Term Coverage of Dietary Supplements in Product Labels. Retrieved from the Data Repository for the University of Minnesota (DRUM), http://doi.org/10.13020/D6GW2G.
View/Download File
File View/Open
Description
Size
Readme.txt
Text file describing the scripts and other files included in this dataset
(2.64 KB)
InfoDownload.py
Python source code for downloading information from DSLD
(1.85 KB)
type_stat.py
Code for counting the ingredient number under each LanguaL(TM) category
(1.34 KB)
ingrTypeStat.py
Code for retrieving the ingredient category, and counting ingredients under each category
(1.87 KB)
type_print.py
Code that uses output from InfoDownload.py to create SupType.csv.
(1.95 KB)
new_suptypelist.py
Code that defines regular expression filters and performs normalization.
(5.61 KB)
lstProducts.csv
CSV needed to execute the Python scripts
(17.39 MB)
amino acid.txt
Normalized ingredients - Amino Acid
(21.26 KB)
amino acid_new.txt
Comparison between original and normalized ingredients - amino acid
(1.03 MB)
animal part or source.txt
Normalized ingredients - Animal Source or Parts
(6.2 KB)
animal part or source_new.txt
Comparison between original and normalized ingredients - animal part or source
(83.38 KB)
bacteria.txt
Normalized ingredients - Bacteria
(11.26 KB)
bacteria_new.txt
Comparison between original and normalized ingredients - bacteria
(503.06 KB)
blend.txt
Normalized ingredients - Blend
(3.43 KB)
blend_new.txt
Comparison between original and normalized ingredients - blend
(41.84 KB)
botanical.txt
Normalized ingredients - Botanical
(125.32 KB)
botanical_new.txt
Comparison between original and normalized ingredients - botanical
(4.74 MB)
carbohydrate.txt
Normalized ingredients - Carbohydrate
(1.84 KB)
carbohydrate_new.txt
Comparison between original and normalized ingredients - carbohydrate
(529.58 KB)
chemical_new.txt
Comparison between original and normalized ingredients - chemical
(1.01 MB)
chemical.txt
Normalized ingredients - Chemical
(28.31 KB)
element.txt
Normalized ingredients - Element
(1.57 KB)
element_new.txt
Comparison between original and normalized ingredients - element
(404.15 KB)
enzyme.txt
Normalized ingredients - Enzyme
(8.55 KB)
enzyme_new.txt
Comparison between original and normalized ingredients - enzyme
(357.43 KB)
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.