Dataspice
:hot_pepper: Create lightweight schema.org descriptions of your datasets
Install / Use
/learn @ropensci/DataspiceREADME
dataspice
The goal of dataspice is to make it easier for researchers to create
basic, lightweight, and concise metadata files for their datasets by
editing the kind of files they’re probably most familiar with: CSVs. To
spice up their data with a dash of metadata. These metadata files can
then be used to:
- Make useful information available during analysis.
- Create a helpful dataset README webpage for your data similar to how pkgdown creates websites for R packages.
- Produce more complex metadata formats for richer description of your datasets and to aid dataset discovery.
Metadata fields are based on Schema.org/Dataset and other metadata standards and represent a lowest common denominator which means converting between formats should be relatively straightforward.
Example
An basic example repository for demonstrating what using dataspice
might look like can be found at
https://github.com/amoeba/dataspice-example.
From there, you can also check out a preview of the HTML dataspice
generates at
https://amoeba.github.io/dataspice-example
and how Google sees it at
https://search.google.com/test/rich-results?url=https%3A%2F%2Famoeba.github.io%2Fdataspice-example%2F.
A much more detailed example has been created by Anna Krystalli at https://annakrystalli.me/dataspice-tutorial/ (GitHub repo).
Installation
You can install the latest version from CRAN:
install.packages("dataspice")
Workflow
create_spice()
# Then fill in template CSV files, more on this below
write_spice()
build_site() # Optional
Create spice
create_spice() creates template metadata spreadsheets in a folder (by
default created in the data folder in the current working directory).
The template files are:
- biblio.csv - for title, abstract, spatial and temporal coverage, etc.
- creators.csv - for data authors
- attributes.csv - explains each of the variables in the dataset
- access.csv - for files, file types, and download URLs (if appropriate)
Fill in templates
The user needs to fill in the details of the four template files. These csv files can be directly modified, or they can be edited using either the associated helper function and/or Shiny app.
Helper functions
-
prep_attributes()populates thefileNameandvariableNamecolumns of theattributes.csvfile using the header row of the data files. -
prep_access()populates thefileName,nameandencodingFormatcolumns of theaccess.csvfile from the files in the folder containing the data.
To see an example of how prep_attributes() works, load the data files
that ship with the package:
data_files <- list.files(system.file("example-dataset/", package = "dataspice"),
pattern = ".csv",
full.names = TRUE
)
This function assumes that the metadata templates are in a folder called
metadata within a data folder.
attributes_path <- file.path("data", "metadata", "attributes.csv")
Using purrr::map(), this function can be applied over multiple files
to populate the header names
data_files %>%
purrr::map(~ prep_attributes(.x, attributes_path),
attributes_path = attributes_path
)
The output of prep_attributes() has the first two columns filled out:
Shiny helper apps
Each of the metadata templates can be edited interactively using a Shiny app:
edit_attributes()opens a Shiny app that can be used to editattributes.csv. The Shiny app displays the currentattributestable and lets the user fill in an informative description and units (e.g. meters, hectares, etc.) for each variable.edit_access()opens an editable version ofaccess.csvedit_creators()opens an editable version ofcreators.csvedit_biblio()opens an editable version ofbiblio.csv
Remember to click on Save when finished editing.
Completed metadata files
The first few rows of the completed metadata tables in this example will look like this:
access.csv has one row for each file
| fileName | name | contentUrl | encodingFormat | |:----------------|:----------------|:-----------|:---------------| | StockInfo.csv | StockInfo.csv | NA | CSV | | BroodTables.csv | BroodTables.csv | NA | CSV | | SourceInfo.csv | SourceInfo.csv | NA | CSV |
attributes.csv has one row for each variable in each file
| fileName | variableName | description | unitText | |:----------------|:-------------|:-------------------------------------------------|:---------| | BroodTables.csv | Stock.ID | Unique stock identifier | NA | | BroodTables.csv | Species | species of stock | NA | | BroodTables.csv | Stock | Stock name, generally river where stock is found | NA | | BroodTables.csv | Ocean.Region | Ocean region | NA | | BroodTables.csv | Region | Region of stock | NA | | BroodTables.csv | Sub.Region | Sub.Region of stock | NA |
biblio.csv is one row containing descriptors including spatial and
temporal coverage
| title | description | datePublished | citation | keywords | license | funder | geographicDescription | northBoundCoord | eastBoundCoord | southBoundCoord | westBoundCoord | wktString | startDate | endDate | |:----------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------|:---------|:---------------------------|:--------|:-------|:----------------------|----------------:|---------------:|----------------:|---------------:|:----------|:--------------------|:--------------------| | Compiled annual statewide Alaskan salmon escapement counts, 1921-2017 | The number of mature salmon migrating from the marine environment to freshwater streams is defined as escapement. Escapement data are the enumeration of these migrating fish as they pass upstream, … | 2018-02-12 08:00:00 | NA | salmon, alaska, escapement | NA | NA | NA | 78 | -131 | 47 | -171 | NA | 1921-01-01 08:00:00 | 2017-01-01 08:00:00 |
creators.csv has one row for each of the dataset authors
| id | name | affiliation | email | |:----|:---------------|:------------------------------------------------------|:---------------------------| | NA | Jeanette Clark | National Center for Ecological Analysis and Synthesis | jclark@nceas.ucsb.edu | | NA | Rich,Brenner | Alaska Department of Fish and Game | richard.brenner.alaska.gov |
Save JSON-LD file
write_spice() generates a json-ld file (“linked data”) to aid in
dataset
discovery,
creation of more extensive metadata
(e.g. EML), and creating a website.
Here’s a view of the dataspice.json file of the example d
