A client for the Environmental Data Initiative repository REST API. The EDI data repository is for publication and reuse of ecological data with emphasis on metadata accuracy and completeness. It was developed in collaboration with the US LTER Network and is built upon the PASTA software stack. EDIutils includes functions to search and access existing data, evaluate and upload new data, and assist with related data management tasks.
Get the latest version:
install.packages("EDIutils")Get the development version:
remotes::install_github("ropensci/EDIutils", ref = "development")library(EDIutils)The unit of publication is the data package. It contains one or more data entities (i.e. files) described with EML metadata, a metadata quality report, and a manifest of package contents. Data packages are immutable for reproducible research, yet versionable to allow updates and improved data quality through time. Each version is assigned a DOI and a unique package ID of the form “scope.identifier.revision”. The “scope” is the organizational unit, “identifier” the series, and “revision” the version (e.g. “edi.100.2” is version “2” of data package “edi.100”).
Authentication is required by data evaluation and upload functions, and
to access user audit logs and services. Contact EDI for an account
[email protected]. Authenticate with the login() function.
The repository search service is a standard deployment of Apache Solr
and indexes select metadata fields of data package metadata. For a list
of searchable fields see search_data_packages(). For a browser based
search experience, use the EDI data
portal.
# List data packages containing the term "water temperature"
res <- search_data_packages(query = 'q="water temperature"&fl=*')
colnames(res)
#> [1] "abstract" "begindate" "doi"
#> [4] "enddate" "funding" "geographicdescription"
#> [7] "id" "methods" "packageid"
#> [10] "pubdate" "responsibleParties" "scope"
#> [13] "site" "taxonomic" "title"
#> [16] "authors" "spatialCoverage" "sources"
#> [19] "keywords" "organizations" "singledates"
#> [22] "timescales"
nrow(res)
#> [1] 798Data entities are downloaded in raw bytes and parsed by a reader function.
# List data entities of data package edi.1047.1
res <- read_data_entity_names(packageId = "edi.1047.1")
res
#> entityId entityName
#> 1 3abac5f99ecc1585879178a355176f6d Environmentals.csv
#> 2 f6bfa89b48ced8292840e53567cbf0c8 ByCatch.csv
#> 3 c75642ddccb4301327b4b1a86bdee906 Chinook.csv
#> 4 2c9ee86cc3f3ffc729c5f18bfe0a2a1d Steelhead.csv
#> 5 785690848dd20f4910637250cdc96819 TrapEfficiencyRelease.csv
#> 6 58b9500439a5671ea7fe13212e889ba5 TrapEfficiencySummary.csv
#> 7 86e61c1a501b7dcf0040d10e009bfd87 TrapOperations.csv
# Read raw bytes of Steelhead.csv (i.e. the 4th data entity)
raw <- read_data_entity(packageId = "edi.1047.1", entityId = res$entityId[4])
head(raw)
#> [1] ef bb bf 44 61 74
# Parse with a .csv reader
data <- readr::read_csv(file = raw)
data
#> # A tibble: 2,926 x 14
#> Date trapVisitID subSiteName catchRawID releaseID commonName
#> <chr> <dbl> <chr> <dbl> <dbl> <chr>
#> 1 1/12/~ 326 North Chan~ 32123 0 Steelhead ~
#> 2 1/14/~ 336 North Chan~ 33980 0 Steelhead ~
#> 3 1/15/~ 337 North Chan~ 32683 0 Steelhead ~
#> 4 1/16/~ 339 North Chan~ 32971 0 Steelhead ~
#> 5 1/17/~ 341 North Chan~ 33104 0 Steelhead ~
#> 6 1/18/~ 342 North Chan~ 33304 0 Steelhead ~
#> 7 1/19/~ 343 North Chan~ 33432 0 Steelhead ~
#> 8 1/21/~ 349 North Chan~ 34083 0 Steelhead ~
#> 9 1/21/~ 349 North Chan~ 34084 0 Steelhead ~
#> 10 1/23/~ 351 North Chan~ 34384 0 Steelhead ~
#> # ... with 2,916 more rows, and 8 more variables:
#> # lifeStage <chr>, forkLength <dbl>, weight <dbl>, n <dbl>,
#> # mort <chr>, fishOrigin <chr>, markType <chr>,
#> # CatchRaw.comments <chr>The EDI data repository has a “staging” environment to test the upload and rendering of new data packages before publishing to “production”. Authentication is required by functions involving data evaluation and upload. Request an account from [email protected].
# Authenticate
login()
#> User name: "my_name"
#> User password: "my_secret"Data package reservations prevent conflicting use of the same identifier.
# Reserve a data package identifier
identifier <- create_reservation(scope = "edi", env = "staging")
identifier
#> [1] 595Evaluation checks for metadata accuracy and completeness.
# Evaluate data package
transaction <- evaluate_data_package(
eml = paste0(tempdir(), "/edi.595.1.xml"),
env = "staging")
transaction
#> [1] "evaluate_163966785813042760"
# Check status
status <- check_status_evaluate(transaction, env = "staging")
status
#> [1] TRUE
# Read the evaluation report
report <- read_evaluate_report(transaction, as = "char", env = "staging")
message(report)
#> ===================================================
#> EVALUATION REPORT
#> ===================================================
#>
#> PackageId: edi.595.1
#> Report Date/Time: 2021-12-16T08:17:40
#> Total Quality Checks: 29
#> Valid: 21
#> Info: 8
#> Warn: 0
#> Error: 0
#>
#> ---------------------------------------------------
#> DATASET REPORT
#> ---------------------------------------------------
#>
#> IDENTIFIER: packageIdPattern
#> NAME: packageId pattern matches "scope.identifier.revision"
#> DESCRIPTION: Check against LTER requirements for scope.identifier.revision
#> EXPECTED: 'scope.n.m', where 'n' and 'm' are integers and 'scope' is one ...
#> FOUND: edi.595.1
#> STATUS: valid
#> EXPLANATION:
#> SUGGESTION:
#> REFERENCE:
#>
#> IDENTIFIER: emlVersion
#> NAME: EML version 2.1.0 or beyond
#> DESCRIPTION: Check the EML document declaration for version 2.1.0 or higher
#> EXPECTED: eml://ecoinformatics.org/eml-2.1.0 or higher
#> FOUND: https://eml.ecoinformatics.org/eml-2.2.0
#> STATUS: valid
#> EXPLANATION: Validity of this quality report is dependent on this check ...
#> SUGGESTION:
#> REFERENCE:
#> ...Upload after errors and warnings are fixed.
# Create a new data package
transaction <- create_data_package(
eml = paste0(tempdir(), "/edi.595.1.xml"),
env = "staging")
transaction
#> [1] "create_163966765080210573__edi.595.1"
# Check status
status <- check_status_create(
transaction = transaction,
env = "staging")
status
#> [1] TRUEOnce everything looks good in the “staging” environment, then repeat the above reservation and upload steps in the “production” environment where the data package will be assigned a DOI and made discoverable with other published data.
Use GitHub Issues for bug reporting, feature requests, and general questions/discussions. When filing bug reports, please include a minimal reproducible example.
Community contributions are welcome! Please reference our contributing guidelines for details.
Please note that this package is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.