Title:	Read and Manipulate Camera Trap Data Packages
Version:	0.4.0
Date:	2025-06-06
Description:	Read and manipulate Camera Trap Data Packages ('Camtrap DP'). 'Camtrap DP' (https://camtrap-dp.tdwg.org) is a data exchange format for camera trap data. With 'camtrapdp' you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. 'camtraptor' or 'camtrapR'.
License:	MIT + file LICENSE
URL:	https://github.com/inbo/camtrapdp, https://inbo.github.io/camtrapdp/
BugReports:	https://github.com/inbo/camtrapdp/issues
Depends:	R (≥ 3.6.0)
Imports:	cli, dplyr, EML, frictionless (≥ 1.2.1), memoise, purrr, readr, rlang, stringr, uuid
Suggests:	jsonlite, lubridate, testthat (≥ 3.0.0), tibble, xml2
Config/testthat/edition:	3
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-06-06 14:31:05 UTC; peter_desmet
Author:	Peter Desmet [aut, cre] (affiliation: Research Institute for Nature and Forest (INBO)), Sanne Govaert [aut] (affiliation: Research Institute for Nature and Forest (INBO)), Pieter Huybrechts [aut] (affiliation: Research Institute for Nature and Forest (INBO)), Damiano Oldoni [aut] (affiliation: Research Institute for Nature and Forest (INBO)), Research Institute for Nature and Forest (INBO) [cph], Research Foundation - Flanders [fnd] (https://lifewatch.be)
Maintainer:	Peter Desmet <peter.desmet@inbo.be>
Repository:	CRAN
Date/Publication:	2025-06-11 11:40:05 UTC

camtrapdp: Read and Manipulate Camera Trap Data Packages

Description

Read and manipulate Camera Trap Data Packages ('Camtrap DP'). 'Camtrap DP' (https://camtrap-dp.tdwg.org) is a data exchange format for camera trap data. With 'camtrapdp' you can read, filter and transform data (including to Darwin Core) before further analysis in e.g. 'camtraptor' or 'camtrapR'.

Author(s)

Maintainer: Peter Desmet peter.desmet@inbo.be (ORCID) (Research Institute for Nature and Forest (INBO))

Authors:

• Sanne Govaert sanne.govaert@inbo.be (ORCID) (Research Institute for Nature and Forest (INBO))

• Pieter Huybrechts pieter.huybrechts@inbo.be (ORCID) (Research Institute for Nature and Forest (INBO))

• Damiano Oldoni damiano.oldoni@inbo.be (ORCID) (Research Institute for Nature and Forest (INBO))

Other contributors:

• Research Institute for Nature and Forest (INBO) (00j54wy13) [copyright holder]

• Research Foundation - Flanders (https://lifewatch.be) [funder]

See Also

Useful links:

• https://github.com/inbo/camtrapdp

• https://inbo.github.io/camtrapdp/

• Report bugs at https://github.com/inbo/camtrapdp/issues

Pipe operator

Description

See magrittr::pipe() for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling rhs(lhs).

Check a Camera Trap Data Package object

Description

Checks if an object is a Camera Trap Data Package object with the required properties.

Usage

check_camtrapdp(x)

Arguments

Value

x invisibly or an error.

Examples

x <- example_dataset()
check_camtrapdp(x) # Invisible return of x if valid

Get or set contributors

Description

contributors() gets contributors from the x$contributors property in a Camera Trap Data Package object and returns it as a tibble data frame.

⁠contributors()<-⁠ is the assignment equivalent.

Usage

contributors(x)

contributors(x) <- value

Arguments

Value

A tibble::tibble() data frame with the contributors, containing the following columns (columns absent in x$contributors will be created):

• title

• firstName: if absent, this will be set to the first word in title, except if it is a single word or the role is rightsHolder or publisher.

• lastName: if absent, this will be set to the remaining words in title, with the same exceptions as firstName.

• email

• path

• role

• organization

See Also

Other accessor functions: deployments(), events(), individuals(), locations(), media(), observations(), taxa()

Examples

x <- example_dataset()
# Get contributors
contributors(x)

# Set contributors
contributors(x) <- head(contributors(x), 1)

Get or set deployments

Description

deployments() gets the deployments from a Camera Trap Data Package object.

⁠deployments()<-⁠ is the assignment equivalent.

• It should only be used within other functions, where the expected data structure can be guaranteed.

• Metadata (x$spatial and x$temporal) are updated to match the assigned deployments.

Usage

deployments(x)

deployments(x) <- value

Arguments

Value

A tibble::tibble() data frame with deployments.

See Also

Other accessor functions: contributors(), events(), individuals(), locations(), media(), observations(), taxa()

Examples

x <- example_dataset()
# Get deployments
deployments(x)

# Set deployments (not recommended outside a function)
deployments(x) <- head(deployments(x), 1)

Get events

Description

Gets the (unique) events from the observations of a Camera Trap Data Package object. Only observations with observationLevel == "event" are considered.

Usage

events(x)

Arguments

Value

A tibble::tibble() data frame with the events, containing the following columns:

• deploymentID

• eventID

• eventStart

• eventEnd

See Also

Other accessor functions: contributors(), deployments(), individuals(), locations(), media(), observations(), taxa()

Examples

x <- example_dataset()
events(x)

Read the Camtrap DP example dataset

Description

Reads the Camtrap DP example dataset. This dataset is maintained and versioned with the Camtrap DP standard.

Usage

example_dataset()

Value

Camera Trap Data Package object.

Examples

example_dataset()

Filter deployments

Description

Subsets deployments in a Camera Trap Data Package object, retaining all rows that satisfy the conditions.

• Media are filtered on associated deploymentID.

• Observations are filtered on associated deploymentID.

• Metadata (x$spatial, x$temporal and x$taxonomic) are updated to match the filtered deployments.

Usage

filter_deployments(x, ...)

Arguments

Value

x filtered.

See Also

Other filter functions: filter_media(), filter_observations()

Examples

x <- example_dataset()

# Filtering returns x, so pipe with deployments() to see the result
x %>%
  filter_deployments(deploymentID == "62c200a9") %>%
  deployments()

# Filtering on deployments also affects associated media and observations
x_filtered <- filter_deployments(x, deploymentID == "62c200a9")
media(x_filtered)
observations(x_filtered)

# Filtering on multiple conditions (combined with &)
x %>%
  filter_deployments(latitude > 51.0, longitude > 5.0) %>%
  deployments()

# Filtering on dates is easiest with lubridate
library(lubridate, warn.conflicts = FALSE)
x %>%
  filter_deployments(
    deploymentStart >= lubridate::as_date("2020-06-19"),
    deploymentEnd <= lubridate::as_date("2020-08-30")
  ) %>%
  deployments()

Filter media

Description

Subsets media in a Camera Trap Data Package object, retaining all rows that satisfy the conditions.

• Deployments are not filtered.

• Observations are filtered on associated mediaID (for media-based observations) and eventID (for event-based observations).

• Metadata (x$taxonomic) are updated to match the filtered observations.

Usage

filter_media(x, ...)

Arguments

Value

x filtered.

See Also

Other filter functions: filter_deployments(), filter_observations()

Examples

x <- example_dataset()

# Filtering returns x, so pipe with media() to see the result
x %>%
  filter_media(captureMethod == "timeLapse") %>%
  media()

# Filtering on media also affects associated observations, but not deployments
x_filtered <- filter_media(x, favorite == TRUE)
observations(x_filtered)

# Filtering on multiple conditions (combined with &)
x %>%
  filter_media(captureMethod == "activityDetection", filePublic == FALSE) %>%
  media()

# Filtering on datetimes is easiest with lubridate
library(lubridate, warn.conflicts = FALSE)
x %>%
  filter_media(
    timestamp >= lubridate::as_datetime("2020-08-02 05:01:00"),
    timestamp <= lubridate::as_datetime("2020-08-02 05:02:00")
  ) %>%
  media()

Filter observations

Description

Subsets observations in a Camera Trap Data Package object, retaining all rows that satisfy the conditions.

• Deployments are not filtered.

• Media are filtered on associated mediaID (for media-based observations) and eventID (for event-based observations). Filter on observationLevel == "media" to only retain directly linked media.

• Metadata (x$taxonomic) are updated to match the filtered observations.

Usage

filter_observations(x, ...)

Arguments

Value

x filtered.

See Also

Other filter functions: filter_deployments(), filter_media()

Examples

x <- example_dataset()

# Filtering returns x, so pipe with observations() to see the result
x %>%
  filter_observations(observationType == "animal") %>%
  observations()

# Filtering on observations also affects associated media, but not deployments
x %>%
  filter_observations(
    scientificName == "Vulpes vulpes",
    observationLevel == "event"
  ) %>%
  media()
x %>%
  filter_observations(
    scientificName == "Vulpes vulpes",
    observationLevel == "media"
  ) %>%
  media()

# Filtering on multiple conditions (combined with &)
x %>%
  filter_observations(
    deploymentID == "577b543a",
    scientificName %in% c("Martes foina", "Mustela putorius")
  ) %>%
  observations()

# Filtering on datetimes is easiest with lubridate
library(lubridate, warn.conflicts = FALSE)
x %>%
  filter_observations(
    eventStart >= lubridate::as_datetime("2020-06-19 22:00:00"),
    eventEnd <= lubridate::as_datetime("2020-06-19 22:10:00")
  ) %>%
  observations()

Get individuals

Description

Gets the (unique) individuals from the observations of a Camera Trap Data Package object.

Usage

individuals(x)

Arguments

Value

A tibble::tibble() data frame with the individuals that have an individualID, containing the following columns:

• individualID

• scientificName

• lifeStage

• sex

See Also

Other accessor functions: contributors(), deployments(), events(), locations(), media(), observations(), taxa()

Examples

x <- example_dataset()
individuals(x)

Get locations

Description

Gets the (unique) locations from the deployments of a Camera Trap Data Package object.

Usage

locations(x)

Arguments

Value

A tibble::tibble() data frame with the locations, containing the following columns:

• locationID

• locationName

• latitude

• longitude

• coordinateUncertainty

See Also

Other accessor functions: contributors(), deployments(), events(), individuals(), media(), observations(), taxa()

Examples

x <- example_dataset()
locations(x)

Get or set media

Description

media() gets the media from a Camera Trap Data Package object.

⁠media()<-⁠ is the assignment equivalent.

• It should only be used within other functions, where the expected data structure can be guaranteed.

Usage

media(x)

media(x) <- value

Arguments

Value

A tibble::tibble() data frame with media.

See Also

Other accessor functions: contributors(), deployments(), events(), individuals(), locations(), observations(), taxa()

Examples

x <- example_dataset()
# Get media
media(x)

# Set media (not recommended outside a function)
media(x) <- head(media(x), 1)

Merge two Camera Trap Data Packages

Description

Merges two Camera Trap Data Package objects into one. Repeat to merge multiple datasets.

Usage

merge_camtrapdp(x, y)

Arguments

Value

A single Camera Trap Data Package object that is the combination of x and y.

Transformation details

Both x and y must have a unique dataset name x$name and y$name. This name is used to prefix identifiers in the data that occur in both datasets. For example:

• x contains deploymentIDs c("a", "b").

• y contains deploymentIDs c("b", "c").

• Then merged xy will contain deploymentIDs c("a", "x_b", "y_b", "c").

Data are merged as follows:

• Deployments are combined, with deploymentID kept unique.

• Media are combined, with mediaID, deploymentID and eventID kept unique.

• Observations are combined, with observationID, deploymentID, mediaID and eventID kept unique.

• Additional resources are retained, with the resource name kept unique.

Metadata properties are merged as follows:

• name: Removed.

• id: Removed.

• created: Set to current timestamp.

• title: Removed.

• contributors: Combined, with duplicates removed.

• description: Combined as two paragraphs.

• version: Set to 1.0.

• keywords: Combined, with duplicates removed.

• image: Removed.

• homepage: Removed.

• sources: Combined, with duplicates removed.

• licenses: Combined, with duplicates removed.

• bibliographicCitation: Removed.

• project$id: Removed.

• project$title: Combined.

• project$acronym: Removed.

• project$description: Combined as two paragraphs.

• project$path: Removed.

• project$samplingDesign: Sampling design of x.

• project$captureMethod: Combined, with duplicates removed.

• project$individuals: TRUE if one of the datasets has TRUE.

• project$observationLevel: Combined, with duplicates removed.

• coordinatePrecision: Set to the least precise coordinatePrecision.

• spatial: Reset based on the new deployments.

• temporal: Reset based on the new deployments.

• taxonomic: Combined, with duplicates removed.

• relatedIdentifiers: Combined, with duplicates removed.

• references: Combined, with duplicates removed.

• Custom properties of x are also retained.

See Also

Other transformation functions: round_coordinates(), shift_time(), update_taxon(), write_dwc(), write_eml()

Examples

x <- example_dataset() %>%
  filter_deployments(deploymentID %in% c("00a2c20d", "29b7d356"))
y <- example_dataset() %>%
  filter_deployments(deploymentID %in% c("577b543a", "62c200a9"))
x$name <- "x"
y$name <- "y"
merge_camtrapdp(x, y)

Get or set observations

Description

observations() gets the observations from a Camera Trap Data Package object.

⁠observations()<-⁠ is the assignment equivalent.

• It should only be used within other functions, where the expected data structure can be guaranteed.

• Metadata (x$taxonomic) are updated to match the assigned observations.

Usage

observations(x)

observations(x) <- value

Arguments

Value

A tibble::tibble() data frame with observations.

See Also

Other accessor functions: contributors(), deployments(), events(), individuals(), locations(), media(), taxa()

Examples

x <- example_dataset()
# Get the observations
observations(x)

# Set observations (not recommended outside a function)
observations(x) <- head(observations(x), 1)

Print a Camera Trap Data Package

Description

Prints a human-readable summary of a Camera Trap Data Package, as an extension of frictionless::print.datapackage().

Usage

## S3 method for class 'camtrapdp'
print(x, ...)

Arguments

Value

print() with a summary of the Camera Trap Data Package object.

Examples

x <- example_dataset()

# Print a summary
print(x)

# Print a summary after filtering
filter_deployments(x, deploymentID == "62c200a9")

Read a Camera Trap Data Package

Description

Reads a Camera Trap Data Package (Camtrap DP) dataset into memory.

Usage

read_camtrapdp(file)

Arguments

Value

A Camera Trap Data Package object.

Older versions

The read_camtrapdp() function supports older versions of Camtrap DP and will automatically upgrade such datasets to the latest version of the standard. It currently supports versions 1.0 and 1.0.1 (latest).

Events

Observations can contain classifications at two levels:

• Media-based observations (observationLevel = "media") are based on a single media file and are directly linked to it via mediaID.

• Event-based observations (observationLevel = "event") are based on an event, defined as a combination of eventID, eventStart and eventEnd. This event can consist of one or more media files, but is not directly linked to these.

The read_camtrapdp() function will automatically assign eventIDs to media, using media.deploymentID = observations.deploymentID and ⁠observations.eventStart <= media.timestamp <= observations.eventEnd⁠. Note that this can result in media being linked to multiple events (and thus being duplicated), for example when events and sub-events were defined.

Taxonomic information

Camtrap DP metadata has a taxonomic property that can contain extra information for each scientificName found in observations. Such information can include higher taxonomy (family, order, etc.) and vernacular names in multiple languages.

The read_camtrapdp() function will automatically include this taxonomic information in observations, as extra columns starting with taxon.. It will then update the taxonomic scope in the metadata to the unique taxa() found in the data.

Spatial/temporal coverage

Camtrap DP metadata has a spatial and temporal property that contains the spatial and temporal coverage of the package respectively.

The read_camtrapdp() function will automatically update (or create) the spatial and temporal scopes in the metadata based on the data. It also does this for the taxonomic scope (see higher).

Additional resources

A Camtrap DP can contain Data Resources not described by the standard. Those are listed with the tables supported by the standard (i.e. deployments, media, observations) in the resources property.

The read_camtrapdp() function will ignore these additional resources and only read the tables described by the standard. Additional resources can be read with frictionless::read_resource() if they are tabular.

Examples

file <- "https://raw.githubusercontent.com/tdwg/camtrap-dp/1.0/example/datapackage.json"
x <- read_camtrapdp(file)
x

Round coordinates to generalize camera trap locations

Description

Rounds deployment coordinates to a certain number of digits to fuzzy/generalize camera trap locations. This function can be used before publishing data in order to protect sensitive species and/or prevent theft of active cameras.

Usage

round_coordinates(x, digits)

Arguments

Value

x with chosen coordinatePrecision in metadata and rounded coordinates and calculated coordinateUncertainty in deployments.

Details

Rounding coordinates is a recommended method to generalize sensitive biodiversity information (see Section 4.2 in Chapman 2020). Use this function to do so for your data. Determine the category of sensitivity (see Section 2.2 in Chapman 2020) and choose the associated number of digits :

The function will:

1. Set the coordinatePrecision in the metadata (original values will be overwritten):

   digits	coordinatePrecision
   1	0.1
   2	0.01
   3	0.001

2. Round all coordinates in the deployments to the selected number of digits.

3. Update the coordinateUncertainy (in meters) in the deployments. This uncertainty is based on the number of digits and the latitude, following Table 3 in Chapman & Wieczorek 2020:

   digits	0° latitude	30° latitude	60° latitude	85° latitude
   1	15691 m	14697 m	12461 m	11211 m
   2	1570 m	1470 m	1246 m	1121 m
   3	157 m	147 m	125 m	112 m

   If a coordinatePrecision is already present, the function will subtract the coordinateUncertainty associated with it before setting a new uncertainty (e.g. 0.001 to 0.01 = ⁠original value - 157 + 1570 m⁠). If ⁠original value⁠ is NA, the function will assume the coordinates were obtained by GPS and set ⁠original value = 30⁠.

See Also

Other transformation functions: merge_camtrapdp(), shift_time(), update_taxon(), write_dwc(), write_eml()

Examples

x <- example_dataset()

# Original precision
x$coordinatePrecision

# Original coordinates and uncertainty
deployments(x)[c("latitude", "longitude", "coordinateUncertainty")]

# Round coordinates to 1 digit
x_rounded <- round_coordinates(x, 1)

# Updated coordinatePrecision
x_rounded$coordinatePrecision

# Updated coordinates and uncertainty (original 187 - 147 + 14697 = 14737)
deployments(x_rounded)[c("latitude", "longitude", "coordinateUncertainty")]

Shift date-times

Description

Shifts date-times for selected deployments (and associated media and observations) by a specified duration. This function can be used to correct date-time issues such as incorrectly set time zones.

• Deployments: deploymentStart and deploymentEnd are updated and timestampIssues is set to FALSE.

• Media: timestamp is updated.

• Observations: eventStart and eventEnd are updated.

• Metadata (x$temporal) are updated to match the new temporal scope.

Usage

shift_time(x, deployment_id, duration)

Arguments

Value

x with shifted date-times.

See Also

Other transformation functions: merge_camtrapdp(), round_coordinates(), update_taxon(), write_dwc(), write_eml()

Examples

# Set desired duration between current and new date-times (e.g. 4 hours earlier)
library(lubridate, warn.conflicts = FALSE)
duration(-4, units = "hours")

# Or calculate one based on two date-times
current <- ymd_hms("2024-04-01T04:00:00", tz = "UTC")
new <- ymd_hms("2024-04-01T00:00:00", tz = "UTC")
duration <- as.duration(interval(current, new))

# Shift date-times for 2 deployments
x <- example_dataset()
x_shifted <- shift_time(x, c("00a2c20d", "29b7d356"), duration)

# Inspect results
deployments(x)[, c("deploymentID", "deploymentStart", "deploymentEnd")]
deployments(x_shifted)[, c("deploymentID", "deploymentStart", "deploymentEnd")]

Get taxa

Description

Gets the (unique) scientific names and associated taxonomic information from the observations of a Camera Trap Data Package object. Duplicate taxa (i.e. with the same scientificName) are removed, retaining the taxon with (first) a taxonID and (second) the most taxonomic information.

Usage

taxa(x)

Arguments

Value

A tibble::tibble() data frame with the taxonomic information, containing at least a scientificName column.

See Also

Other accessor functions: contributors(), deployments(), events(), individuals(), locations(), media(), observations()

Examples

x <- example_dataset()
taxa(x)

Update a taxon

Description

Updates taxonomic information in data and metadata for a provided taxon. This allows to:

1. Update a taxon: provide the same name in to and from$scientificName.

2. Replace a taxon: provide a new name in from$scientificName.

3. Lump a taxon: provide a name in from$scientificName that is already present in the dataset. In all cases, existing information will be overwritten with the provided information.

Usage

update_taxon(x, from, to)

Arguments

Value

x with updated taxon information.

See Also

Other transformation functions: merge_camtrapdp(), round_coordinates(), shift_time(), write_dwc(), write_eml()

Examples

x <- example_dataset()

# Update taxonomic information for "Anas platyrhynchos"
updated_x <- update_taxon(
  x,
  from = "Anas platyrhynchos",
  to = list (
    scientificName = "Anas platyrhynchos",
    taxonID = "https://www.checklistbank.org/dataset/COL2023/taxon/DGP6",
    taxonRank = "species",
    vernacularNames.fra = "canard colvert"
  )
)

# Lump "Ardea cinerea" into already present "Ardea", using the provided info
updated_x <- update_taxon(
  x,
  from = "Ardea cinerea",
  to = list(scientificName = "Ardea", vernacularname.fra = "grands hérons")
)

Get Camtrap DP version

Description

Extracts the version number used by a Camera Trap Data Package object. This version number indicates what version of the Camtrap DP standard was used.

Usage

version(x)

Arguments

Details

The version number is derived as follows:

1. The version attribute, if defined.

2. A version number contained in x$profile, which is expected to contain the URL to the used Camtrap DP standard.

3. x$profile in its entirety (can be NULL).

Value

Camtrap DP version number (e.g. 1.0).

Examples

x <- example_dataset()
version(x)

Write a Camera Trap Data Package to disk

Description

Writes a Camera Trap Data Package and its related Data Resources to disk as a datapackage.json and CSV files.

Usage

write_camtrapdp(x, directory, ...)

Arguments

Value

datapackage.json and CSV files written to disk.

Examples

x <- example_dataset()

# Filter (and therefore change) the dataset
x <- filter_deployments(x, deploymentID == "00a2c20d")

# Write the Camera Trap Data Package to disk
write_camtrapdp(x, directory = "my_directory")

# Check files
list.files("my_directory")

# Clean up (don't do this if you want to keep your files)
unlink("my_directory", recursive = TRUE)

Transform a Camera Trap Data Package to a Darwin Core Archive

Description

Transforms a Camera Trap Data Package object to a Darwin Core Archive.

Usage

write_dwc(x, directory)

Arguments

Value

CSV and meta.xml files written to disk. And invisibly, a list of data frames with the transformed data.

Transformation details

This function follows recommendations in Reyserhove et al. (2023) doi:10.35035/doc-0qzp-2x37 and transform data to:

• An Occurrence core.

• An Audubon/Audiovisual Media Description extension.

• A meta.xml file.

Key features of the Darwin Core transformation:

• The Occurrence core contains one row per observation (dwc:occurrenceID = observationID).

• Only observations with observationType = "animal" and are included, thus excluding observations that are (of) humans, vehicles, blanks, unknowns and unclassified.

• Either observations with observationLevel = "event" or "media" are used, never both to avoid duplicates. The level be defined with x$gbifIngestion$observationLevel, with "event" as default.

• Observations classified by humans with 100% certainty get a dwc:identificationVerificationStatus = "verified using recorded media".

• Deployment information is included in the Occurrence core, such as location, habitat, dwc:samplingProtocol, deployment duration in dwc:samplingEffort and dwc:parentEventID = deploymentID as grouping identifier.

• Event information is included in the Occurrence core, as event duration in dwc:eventDate and dwc:eventID = eventID as grouping identifier.

• Media files are included in the Audubon/Audiovisual Media Description extension, with a foreign key to the observation. A media file that is used for more than one observation is repeated.

• Metadata are used to set the following record-level terms:

  • dwc:datasetID: x$id.

  • dwc:datasetName: x$title.

  • dwc:collectionCode: first source in x$sources.

  • dcterms:license: license name (e.g. CC0-1.0) in x$licenses with scope data. The license name with scope media is used as dcterms:rights in the Audubon Media Description extension.

  • dcterms:rightsHolder: first contributor in x$contributors with role rightsHolder.

  • dwc:dataGeneralizations: set if x$coordinatePrecision is defined.

See Also

Other transformation functions: merge_camtrapdp(), round_coordinates(), shift_time(), update_taxon(), write_eml()

Examples

x <- example_dataset()
write_dwc(x, directory = "my_directory")

# Clean up (don't do this if you want to keep your files)
unlink("my_directory", recursive = TRUE)

Transform a Camera Trap Data Package to EML

Description

Transforms the metadata of a Camera Trap Data Package object to an Ecological Metadata Language (EML) file.

Usage

write_eml(x, directory, derived_paragraph = TRUE)

Arguments

Value

eml.xml file written to disk. And invisibly, an EML::eml object.

Transformation details

Metadata are derived from what is provided in x. The following properties are set:

• title: Title as provided in x$title.

• type: Set to Occurrence in keywords.

• subtype: Set to Observation in keywords.

• update frequency: Set to unknown.

• description: Description as provided in x$description. If derived_paragraph = TRUE a generated paragraph is added, e.g.:

  Data have been standardized to Darwin Core using the camtrapdp R package and only include observations (and associated media) of animals. Excluded are records that document blank or unclassified media, vehicles and observations of humans.

• license: License with scope data as provided in x$licenses.

• creators: Contributors as provided in x$contributors, excluding those with roles rightsHolder and publisher.

• contact: Contributors with role contact. If none exist, first creator.

• metadata provider: Same as contact.

• keywords: Keywords as provided in x$keywords.

• geographic coverage: Bounding box as provided in x$spatial.

• taxonomic coverage: Taxa as provided in x$taxonomic.

• temporal coverage: Date range as provided in x$temporal.

• project data: Title, acronym as identifier, description, and sampling design as provided in x$project.

• alternative identifier: Identifier as provided in x$id. If this is a DOI, no new DOI will be created when publishing to GBIF.

• external link: URL of the project as provided in x$project$path.

The following properties are not set:

• publishing organization

• associated parties

• sampling methods

• citations

• collection data: not applicable.

See Also

Other transformation functions: merge_camtrapdp(), round_coordinates(), shift_time(), update_taxon(), write_dwc()

Examples

x <- example_dataset()
(write_eml(x, directory = "my_directory"))

# Clean up (don't do this if you want to keep your files)
unlink("my_directory", recursive = TRUE)