--- title: package overview subtitle: "suwo: access nature media repositories" pagetitle: suwo package overview author: - Jorge Elizondo, Marcelo Araya-Salas & Alejandro Rico-Guevara date: "2026-02-10" output: rmarkdown::html_document: self_contained: yes toc: true toc_depth: 3 toc_float: collapsed: false smooth_scroll: true vignette: > %\VignetteIndexEntry{1. Package overview} %\usepackage[utf8]{inputenc} %\VignetteEncoding{UTF-8} %\VignetteEngine{knitr::rmarkdown} editor_options: chunk_output_type: console params: EVAL: !r identical(Sys.getenv("NOT_CRAN"), "true") --- ::: {.alert .alert-info} The [suwo](https://docs.ropensci.org/suwo/) package aims to simplify the retrieval of nature media (mostly photos, audio files and videos) across multiple online biodiversity databases. This vignette provides an overview of the package’s core querying functions, the searching and downloading of media files, and the compilation of metadata from various sources. For detailed information on each function, please refer to the [function reference](https://docs.ropensci.org/suwo/reference/index.html) or use the help files within R (e.g., `?query_gbif`). ::: ::: {.alert .alert-warning} **Intended use and responsible practices** This package is designed exclusively for non-commercial, scientific purposes, including research, education, and conservation. **Commercial use of data or media retrieved through this package is the user’s responsibility and is allowed only when the applicable license of the source database explicitly permits such use, or when explicit, separate permission has been obtained directly from the original source platforms or rights holders**. Users must comply with the specific terms of service and data-use policies of each source database, which may require attribution and may further restrict commercial application. The package developers assume no liability for misuse of the retrieved data or for violations of third-party terms of service. ::: # Installation Install the latest development version from GitHub: ``` r # install package remotes::install_github("ropensci/suwo") #load packages library(suwo) ``` # Basic workflow for obtaining nature media files Obtaining nature media using [suwo](https://docs.ropensci.org/suwo/) follows a basic sequence. The following diagram illustrates this workflow and the main functions involved:

Flowchart of the suwo workflow for obtaining nature media files. Step 1, 'Get metadata', includes multiple boxes representing queries to different repositories, such as query_wikiaves() and query_xenocanto(), plus additional possible query_() calls. Arrows from all these queries converge into Step 2, 'Combine metadata', using merge_metadata(). The process then moves to Step 3, 'Remove duplicates', using find_duplicates() and remove_duplicates(). Next is Step 4, 'Download media files', using download_media(). Finally, Step 5, 'Update metadata', using update_metadata(), loops back toward the earlier steps, indicating that metadata can be updated after downloading and re-enter the workflow.

Here is a description of each step: 1. Get metadata: * Queries regarding a species are submitted through one of the available query functions (`query_repo_name()`) that connect to five different online repositories (Xeno-Canto, Inaturalist, GBIF, Macaulay Library and WikiAves). The output of these queries is a data frame containing metadata associated with the media files (e.g., species name, date, location, etc, see below). * Users can update their datasets with new records using the [update_metadata()](https://docs.ropensci.org/suwo/reference/update_metadata.html) function. 2. Curate metadata: * If multiple repositories are queried, the resulting metadata data frames can be merged into a single data frame using the [merge_metadata()](https://docs.ropensci.org/suwo/reference/merge_metadata.html) function. * Check for duplicate records in their datasets using the [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html) function. Candidate duplicated entries are identified based on matching species name, country, date, user name, and geographic coordinates. User can double check the candidate duplicates and decide which records to keep, which can be done with [remove_duplicates()](https://docs.ropensci.org/suwo/reference/remove_duplicates.html). 3. Get media: * Download the media files associated with the metadata using the [download_media()](https://docs.ropensci.org/suwo/reference/download_media.html) function. # Obtaining metadata: the query functions The following table summarizes the available [suwo](https://docs.ropensci.org/suwo/) query functions and the types of metadata they retrieve: ``` r # Load suwo package library(suwo) ```

Table 1: Summary of query functions and the associated repositories.
Function	Repository	URL link	File types	Requires api key	Taxonomic level	Geographic coverage	Taxonomic coverage	Other features
query_gbif	GBIF	https://www.gbif.org/	image, sound, video, interactive resource	No	Species	Global	All life	Specify query by data base
query_inaturalist	iNaturalist	https://www.inaturalist.org/	image, sound	No	Species	Global	All life
query_macaulay	Macaulay Library	https://www.macaulaylibrary.org/	image, sound, video	No	Species	Global	Mostly birds but also other vertebrates and invertebrates	Interactive
query_wikiaves	WikiAves	https://www.wikiaves.com.br/	image, sound	No	Species	Brazil	Birds
query_xenocanto	Xeno-Canto	https://www.xeno-canto.org/	sound	Yes	Species, subspecies, genus, family, group	Global	Birds, frogs, non-marine mammals and grasshoppers	Specify query by taxonomy, geographic range and dates

These are some example queries: 1. Images of Sarapiqui Heliconia (_Heliconia sarapiquensis_) from iNaturalist (we print the first 4 rows of each output data frame): ``` r # Load suwo package library(suwo) h_sarapiquensis <- query_inaturalist(species = "Heliconia sarapiquensis", format = "image") ``` ``` ✔ Obtaining metadata (29 matching records found) 🎊 ``` ``` r head(h_sarapiquensis, 4) ``` ``` repository format key species date time user_name 1 iNaturalist image 330280680 Heliconia sarapiquensis 2025-12-08 13:47 Carlos g Velazco-Macias 2 iNaturalist image 330280680 Heliconia sarapiquensis 2025-12-08 13:47 Carlos g Velazco-Macias 3 iNaturalist image 330280680 Heliconia sarapiquensis 2025-12-08 13:47 Carlos g Velazco-Macias 4 iNaturalist image 263417773 Heliconia sarapiquensis 2025-02-28 14:23 Original Madness country locality latitude longitude 1 NA 10.159645,-83.9378766667 10.15964 -83.93788 2 NA 10.159645,-83.9378766667 10.15964 -83.93788 3 NA 10.159645,-83.9378766667 10.15964 -83.93788 4 NA 10.163116739,-83.9389050007 10.16312 -83.93891 file_url file_extension 1 https://inaturalist-open-data.s3.amazonaws.com/photos/598874322/original.jpg jpeg 2 https://inaturalist-open-data.s3.amazonaws.com/photos/598874346/original.jpg jpeg 3 https://inaturalist-open-data.s3.amazonaws.com/photos/598874381/original.jpg jpeg 4 https://inaturalist-open-data.s3.amazonaws.com/photos/473219810/original.jpeg jpeg ```
2. Harpy eagles (_Harpia harpyja_) audio recordings from WikiAves: ``` r h_harpyja <- query_wikiaves(species = "Harpia harpyja", format = "sound") ``` ``` ✔ Obtaining metadata (78 matching records found) 🌈 ``` ``` r head(h_harpyja, 4) ``` ``` repository format key species date time user_name country 1 WikiAves sound 25867 Harpia harpyja Gustavo Pedersoli Brazil 2 WikiAves sound 2701424 Harpia harpyja 2020-10-20 Bruno Lima Brazil 3 WikiAves sound 878999 Harpia harpyja 2013-03-20 Thiago Silveira Brazil 4 WikiAves sound 3027120 Harpia harpyja 2016-06-20 Ciro Albano Brazil locality latitude longitude 1 Alta Floresta/MT NA NA 2 Itanhaém/SP NA NA 3 Alta Floresta/MT NA NA 4 Camacan/BA NA NA file_url 1 https://s3.amazonaws.com/media.wikiaves.com.br/recordings/52/25867_a73f0e8da2179e82af223ff27f74a912.mp3 2 https://s3.amazonaws.com/media.wikiaves.com.br/recordings/1072/2701424_e0d533b952b64d6297c4aff21362474b.mp3 3 https://s3.amazonaws.com/media.wikiaves.com.br/recordings/878/878999_c1f8f4ba81fd597548752e92f1cdba50.mp3 4 https://s3.amazonaws.com/media.wikiaves.com.br/recordings/7203/3027120_5148ce0fed5fe99aba7c65b2f045686a.mp3 file_extension 1 mp3 2 mp3 3 mp3 4 mp3 ```
3. Common raccoon (_Procyon lotor_) videos from GBIF: ``` r p_lotor <- query_gbif(species = "Procyon lotor", format = "video") ``` ``` ✔ Obtaining metadata (13 matching records found) 😸 ``` ``` r head(p_lotor, 4) ``` ``` repository format key species date time user_name country locality latitude 1 GBIF video 3501153129 Procyon lotor 2015-07-21 Luxembourg NA 49.7733 2 GBIF video 3501153135 Procyon lotor 2015-07-04 Luxembourg NA 49.7733 3 GBIF video 3501153159 Procyon lotor 2015-07-04 Luxembourg NA 49.7733 4 GBIF video 3501153162 Procyon lotor 2015-07-04 Luxembourg NA 49.7733 longitude 1 5.94092 2 5.94092 3 5.94092 4 5.94092 file_url 1 https://archimg.mnhn.lu/Observations/Taxons/Biomonitoring/063_094_S2_K2_20150721_063004AM.mp4 2 https://archimg.mnhn.lu/Observations/Taxons/Biomonitoring/063_094_S2_K1_20150704_072418AM.mp4 3 https://archimg.mnhn.lu/Observations/Taxons/Biomonitoring/063_094_S2_K1_20150704_072402AM.mp4 4 https://archimg.mnhn.lu/Observations/Taxons/Biomonitoring/063_094_S2_K1_20150704_072346AM.mp4 file_extension 1 m4a 2 m4a 3 m4a 4 m4a ```
--- By default all query function return the 13 most basic metadata fields associated with the media files. Here is the definition of each field: - **repository**: Name of the repository - **format**: Type of media file (e.g., sound, photo, video) - **key**: Unique identifier of the media file in the repository - **species**: Species name associated with the media file (Note taxonomic authority may vary among repositories) - **date***: Date when the media file was recorded/photographed (in YYYY-MM-DD format or YYYY if only year is available) - **time***: Time when the media file was recorded/photographed (in HH:MM format) - **user_name***: Name of the user who uploaded the media file - **country***: Country where the media file was recorded/photographed - **locality***: Locality where the media file was recorded/photographed - **latitude***: Latitude of the location where the media file was recorded/photographed (in decimal degrees) - **longitude***: Longitude of the location where the media file was recorded/photographed (in decimal degrees) - **file_url**: URL link to the media file (used to download media files) - **file_extension**: Extension of the media file (e.g., .mp3, .jpg, .mp4) _* Can contain missing values (NAs)_ Users can also download all available metadata by setting the argument `all_data = TRUE`. These are the additional metadata fields, on top of the basic fields, that are retrieved by each query function:

Table 2: Additional metadata per query function.
Function	Additional data
query_gbif	datasetkey, publishingorgkey, installationkey, hostingorganizationkey, publishingcountry, protocol, lastcrawled, lastparsed, crawlid, basisofrecord, occurrencestatus, taxonkey, kingdom_code, phylum_code, class_code, order_code, family_key, genus_code, species_code, acceptedtaxonkey, scientificnameauthorship, acceptedscientificname, kingdom, phylum, order, family, genus, genericname, specific_epithet, taxonrank, taxonomicstatus, iucnredlistcategory, continent, year, month, day, startdayofyear, enddayofyear, lastinterpreted, license, organismquantity, organismquantitytype, issequenced, isincluster, datasetname, recordist, identifiedby, samplingprotocol, geodeticdatum, class, countrycode, gbifregion, publishedbygbifregion, recordnumber, identifier, habitat, institutionid, verbatimeventdate, dynamicproperties, verbatimcoordinatesystem, eventremarks, gbifid, collectioncode, occurrenceid, institutioncode, identificationqualifier, media_type, page, state_province, comments
query_inaturalist	quality_grade, taxon_geoprivacy, uuid, cached_votes_total, identifications_most_agree, species_guess, identifications_most_disagree, positional_accuracy, comments_count, site_id, created_time_zone, license_code, observed_time_zone, public_positional_accuracy, oauth_application_id, created_at, description, time_zone_offset, observed_on, observed_on_string, updated_at, captive, faves_count, num_identification_agreements, identification_disagreements_count, map_scale, uri, community_taxon_id, owners_identification_from_vision, identifications_count, obscured, num_identification_disagreements, geoprivacy, spam, mappable, identifications_some_agree, place_guess, id, license_code_1, attribution, hidden
query_macaulay	common_name, background_species, caption, year, month, day, country_state_county, state_province, county, age_sex, behavior, playback, captive, collected, specimen_id, home_archive_catalog_number, recorder, microphone, accessory, partner_institution, ebird_checklist_id, unconfirmed, air_temp__c_, water_temp__c_, media_notes, observation_details, parent_species, species_code, taxon_category, taxonomic_sort, recordist_2, average_community_rating, number_of_ratings, asset_tags, original_image_height, original_image_width
query_wikiaves	user_id, species_code, common_name, repository_id, verified, locality_id, number_of_comments, likes, visualizations, duration
query_xenocanto	genus, specific_epithet, subspecies, taxonomic_group, english_name, altitude, vocalization_type, sex, stage, method, url, uploaded_file, license, quality, length, upload_date, other_species, comments, animal_seen, playback_used, temp, regnr, auto, recorder, microphone, sampling_rate, sonogram_small, sonogram_med, sonogram_large, sonogram_full, oscillogram_small, oscillogram_med, oscillogram_large, sonogram

**Obtaining raw data** By default the package standardizes the information in the basic fields (detailed above) in order to facilitate the compilation of metadata from multiple repositories. However, in some cases this may result in loss of information. For instance, some repositories allow users to provide "morning" as a valid time value, which are converted into NAs by [suwo](https://docs.ropensci.org/suwo/). In such cases, users can retrieve the original data by setting the `raw_data = TRUE` in the query functions and/or global options (`options(raw_data = TRUE)`). Note that subsequent data manipulation functions (e.g., [merge_metadata()](https://docs.ropensci.org/suwo/reference/merge_metadata.html), [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html), etc) will not work as the basic fields are not standardized.

The code above examplifies the most common use of query functions, which applies also to the function [query_gbif()](https://docs.ropensci.org/suwo/reference/query_gbif.html). The following sections provide more details on the two query functions that require special considerations: [query_macaulay()](https://docs.ropensci.org/suwo/reference/query_macaulay.html) and [query_xenocanto()](https://docs.ropensci.org/suwo/reference/query_xenocanto.html). ## query_macaulay() ### Interactive retrieval of metadata [query_macaulay()](https://docs.ropensci.org/suwo/reference/query_macaulay.html) is the only interactive function. This means that when users run a query the function opens a browser window to the [Macaulay Library's search page](https://search.macaulaylibrary.org/catalog), where the users must download a .csv file with the metadata. Here is a example of a query for strip-throated hermit (_Phaethornis striigularis_) videos: ``` r p_striigularis <- query_macaulay(species = "Phaethornis striigularis", format = "video") ``` ``` A browser will open the macaulay library website. Save the .csv file ('export' button) to this directory: /home/m/Dropbox/R_package_testing/suwo/vignettes/ (R is monitoring for new CSV files. Press ESC to stop the function) File: ML__2026-02-10T16-08_stther2_video.csv ``` ``` ✔ 24 matching records found 🎉 ``` Users must click on the "Export" button to save the .csv file with the metadata: Screen shot of the Macaulay library search site showing the first result of a query for Stripe-throated hermit videos

Screen shot of the Macaulay library search site showing the first result of a query for Stripe-throated hermit videos

Note that for bird species the species name must be valid according to the Macaulay Library taxonomy (which follows the Clements checklist). For non-bird species users must use the argument `taxon_code`. The species taxon code can be found by running a search at the [Macaulay Library's search page](https://search.macaulaylibrary.org/catalog) and checking the URL of the species page. For instance, the taxon code for jaguar (_Panthera onca_) is "t-11032765": Screen shot of the Macaulay library search site showing the first result of a query for jaguar videos, highlighting the taxon code in the URL adress

Screen shot of the Macaulay library search site showing the first result of a query for jaguar videos, highlighting the taxon code in the URL adress

Once you have the taxon code, you can run the query as follows: ``` r jaguar <- query_macaulay(taxon_code = "t-11032765", format = "video") ``` ``` A browser will open the macaulay library website. Save the .csv file ('export' button) to this directory: /home/m/Dropbox/R_package_testing/suwo/vignettes/ (R is monitoring for new CSV files. Press ESC to stop the function) File: ML__2026-02-10T16-08_t-11032765_video.csv ``` ``` ✔ 11 matching records found 🎊 ``` Here are some tips for using this function properly: * Valid bird species names can be checked at `suwo:::ml_taxon_code$SCI_NAME` * The exported csv file must be saved in the directory specified by the argument `path` of the function (default is the current working directory) * The function will not proceed until the file is saved (press ESC to stop the function) * Do not overwritte files : if the file is saved overwriting a pre-existing file (i.e. same file name) the function will not detect it * Users must log in to the Macaulay Library/eBird account in order to access large batches of observations After saving the file, the function will read the file and return a data frame with the metadata. Here we print the first 4 rows of the output data frame: ``` r head(p_striigularis, 4) ``` ``` repository format key species date time 1 Macaulay Library video 647905148 Phaethornis striigularis 2021-08-28 06:21 2 Macaulay Library video 646200424 Phaethornis striigularis 2025-11-13 13:31 3 Macaulay Library video 630814231 Phaethornis striigularis 2025-01-17 09:23 4 Macaulay Library video 628258211 Phaethornis striigularis 2024-12-13 14:26 user_name country locality latitude 1 Edison🦉 Ocaña Ecuador Finca Blanca Margarita - Chicao Chocolate 0.1575163 2 Gregg Severson Honduras Valle Esmeralda 14.9254324 3 Carlos Roberto Chavarria Costa Rica Tirimbina Rainforest Center 10.4156200 4 Russell Campbell Costa Rica Reserva El Copal (Tausito) 9.7840400 longitude file_url file_extension 1 -79.22496 https://cdn.download.ams.birds.cornell.edu/api/v1/asset/647905148/ mp4 2 -88.04940 https://cdn.download.ams.birds.cornell.edu/api/v1/asset/646200424/ mp4 3 -84.12078 https://cdn.download.ams.birds.cornell.edu/api/v1/asset/630814231/ mp4 4 -83.75147 https://cdn.download.ams.birds.cornell.edu/api/v1/asset/628258211/ mp4 ``` ### Bypassing record limit Even if logged in, a maximum of 10000 records per query can be returned. This can be bypassed by using the argument `dates` to split the search into a sequence of shorter date ranges. The rationale is that by splitting the search into date ranges, users can download multiple .csv files, which are then combined by the function into a single metadata data frame. Of course users must download the csv for each data range. The following code looks for photos of costa's hummingbird (_Calypte costae_). As Macaulay Library hosts more than 30000 costa's hummingbird records, we need to split the query into multiple date ranges: ``` r # test a query with more than 10000 results paging by date cal_cos <- query_macaulay( species = "Calypte costae", format = "image", dates = c(1976, 2020, 2022, 2024, 2025, 2026) ) ``` ``` A browser will open the macaulay library website. Save the .csv file ('export' button) to this directory: /home/m/Dropbox/R_package_testing/suwo/vignettes/ (R is monitoring for new CSV files. Press ESC to stop the function) ``` ``` • Query 1 of 5 (1976-2019): ``` ``` File: ML__2026-02-10T16-08_coshum_photo.csv ``` ``` • Query 2 of 5 (2020-2021): ``` ``` File: ML__2026-02-10T16-09_coshum_photo.csv ``` ``` • Query 3 of 5 (2022-2023): ``` ``` File: ML__2026-02-10T16-09_coshum_photo2.csv ``` ``` • Query 4 of 5 (2024): ``` ``` File: ML__2026-02-10T16-10_coshum_photo3.csv ``` ``` • Query 5 of 5 (2025-2026): ``` ``` File: ML__2026-02-10T16-10_coshum_photo4.csv ``` ``` ✔ 37904 matching records found 🥳 ``` Users can check at the Macaulay Library website how many records are available for their species of interest (see image below) and then decide how to split the search by date ranges accordingly so each sub-query has less than 10000 records. Screen shot of the Macaulay library search site showing the first result of a query for Costa's hummingbird and highlighting how to check the number of records for that query

Screen shot of the Macaulay library search site showing the first result of a query for Costa's hummingbird and highlighting how to check the number of records for that query

[query_macaulay()](https://docs.ropensci.org/suwo/reference/query_macaulay.html) can also read metadata previously downloaded from [Macaulay Library website](https://www.macaulaylibrary.org/). To do this, users must provide 1) the name of the csv file(s) to the argument `files` and 2) the directory path were it was saved to the argument `path`. ## query_xenocanto() ### API key [Xeno-Canto](https://www.xeno-canto.org/) requires users to obtain a free API key to use [their API v3](https://xeno-canto.org/admin.php/explore/api). Users can get their API key by creating an account at [Xeno-Canto's registering page](https://xeno-canto.org/auth/register). Once users have their API key, they can set it as a variable in your R environment using `Sys.setenv(xc_api_key = "YOUR_API_KEY_HERE")` and [query_xenocanto()](https://docs.ropensci.org/suwo/reference/query_xenocanto.html) will use it. Here is an example of a query for Spix's disc-winged bat (_Thyroptera tricolor_) audio recordings: ``` r # set your Xeno-Canto key as environmental variable (run it on the console) # Sys.setenv(xc_api_key = "YOUR_API_KEY_HERE") # query Xeno-CAnto t_tricolor <- query_xenocanto(species = "Thyroptera tricolor") ``` ``` Error in `query_xenocanto()`: ! An API key is required for Xeno-Canto API v3. Get yours at https://xeno-canto.org/account. ``` ``` r head(t_tricolor, 4) ``` ``` repository format key species date time user_name country 1 Xeno-Canto sound 879621 Thyroptera tricolor 2023-07-15 12:30 José Tinajero Costa Rica 2 Xeno-Canto sound 820604 Thyroptera tricolor 2013-01-10 19:00 Sébastien J. Puechmaille Costa Rica locality latitude longitude file_url 1 Hacienda Baru, Dominical, Costa Rica 9.2635 -83.8768 https://xeno-canto.org/879621/download 2 Pavo, Provincia de Puntarenas 8.4815 -83.5945 https://xeno-canto.org/820604/download file_extension genus specific_epithet subspecies taxonomic_group english_name 1 wav Thyroptera tricolor bats Spix's Disk-winged Bat 2 wav Thyroptera tricolor bats Spix's Disk-winged Bat altitude vocalization_type sex stage method url 1 10 social call male adult in enclosure //xeno-canto.org/879621 2 30 echolocation female adult hand-release //xeno-canto.org/820604 uploaded_file 1 XC879621-ch1T2023-07-15_12-01-42_0000021.wav 2 XC820604-Thyroptera_tricolor-2013.01.10-CL00176_A_30.wav license quality length upload_date other_species 1 //creativecommons.org/licenses/by-nc-sa/4.0/ A 1:28 2024-03-06 2 //creativecommons.org/licenses/by-nc-sa/4.0/ A 0:00 2023-08-05 comments animal_seen 1 Inquiry call playback and response call of an individual yes 2 High-pass filter applied (Time domain filter:30 kHz) due to background noise. yes playback_used temp regnr auto recorder microphone sampling_rate 1 yes no Avisoft UltraSoundGate 116Hme CM16/CMPA 500000 2 no no Pettersson D1000X Advanced electret 384000 sonogram_small 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/ffts/XC879621-small.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/ffts/XC820604-small.png sonogram_med 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/ffts/XC879621-med.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/ffts/XC820604-med.png sonogram_large 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/ffts/XC879621-large.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/ffts/XC820604-large.png sonogram_full 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/ffts/XC879621-full.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/ffts/XC820604-full.png oscillogram_small 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/wave/XC879621-small.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/wave/XC820604-small.png oscillogram_med 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/wave/XC879621-med.png 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/wave/XC820604-med.png oscillogram_large sonogram 1 //xeno-canto.org/sounds/uploaded/MNBWPPOPGM/wave/XC879621-large.png NA 2 //xeno-canto.org/sounds/uploaded/WNXRUCNUAZ/wave/XC820604-large.png NA [ reached 'max' / getOption("max.print") -- omitted 2 rows ] ``` ## Special queries [query_xenocanto()](https://docs.ropensci.org/suwo/reference/query_xenocanto.html) allows users to perform special queries by specifying additional query tags. Users can also search by country, taxonomy (taxonomic group, family, genus, subspecies), geography (country, location, geographic coordinates) date, sound type (e.g. female song, calls) and recording properties (quality, length, sampling rate) ([see list of available tags here](https://xeno-canto.org/admin.php/explore/api#examples)). Here is an example of a query for audio recordings of pale-striped poison frog (_Ameerega hahneli_, 'sp:"Ameerega hahneli") from French Guiana (cnt:"French Guiana") and with the highest recording quality (q:"A"): ``` r # assuming you already set your API key as in previous code block a_hahneli <- query_xenocanto( species = 'sp:"Ameerega hahneli" cnt:"French Guiana" q:"A"') ``` ``` Error in `query_xenocanto()`: ! An API key is required for Xeno-Canto API v3. Get yours at https://xeno-canto.org/account. ``` ``` r head(a_hahneli, 4) ``` ``` repository format key species date time user_name country 1 Xeno-Canto sound 928987 Ameerega hahneli 2024-05-14 16:00 Augustin Bussac French Guiana 2 Xeno-Canto sound 928972 Ameerega hahneli 2024-04-24 17:00 Augustin Bussac French Guiana locality latitude longitude file_url file_extension 1 Sentier Gros-Arbre 3.6132 -53.2169 https://xeno-canto.org/928987/download mp3 2 Camp Bonaventure 4.3226 -52.3387 https://xeno-canto.org/928972/download mp3 genus specific_epithet subspecies taxonomic_group english_name altitude vocalization_type sex 1 Ameerega hahneli frogs 220 male 2 Ameerega hahneli frogs 60 stage method url uploaded_file 1 adult field recording //xeno-canto.org/928987 XC928987-Hahneli-top-Saul.mp3 2 field recording //xeno-canto.org/928972 XC928972-Ameerega-hahneli-belizon.mp3 license quality length upload_date other_species comments 1 //creativecommons.org/licenses/by-nc-sa/4.0/ A 0:47 2024-08-21 2 //creativecommons.org/licenses/by-nc-sa/4.0/ A 0:28 2024-08-21 animal_seen playback_used temp regnr auto recorder microphone sampling_rate 1 no no no 44100 2 no no no 44100 sonogram_small 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928987-small.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928972-small.png sonogram_med 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928987-med.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928972-med.png sonogram_large 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928987-large.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928972-large.png sonogram_full 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928987-full.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/ffts/XC928972-full.png oscillogram_small 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928987-small.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928972-small.png oscillogram_med 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928987-med.png 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928972-med.png oscillogram_large sonogram 1 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928987-large.png NA 2 //xeno-canto.org/sounds/uploaded/KDZSESJSYF/wave/XC928972-large.png NA [ reached 'max' / getOption("max.print") -- omitted 1 rows ] ``` # Update metadata The [update_metadata()](https://docs.ropensci.org/suwo/reference/update_metadata.html) function allows users to update a previous query to add new information from the corresponding repository of the original search. This function takes as input a data frame previously obtained from any query function (i.e. `query_reponame()`) and returns a data frame similar to the input with new data appended. To show case the function, we first query metadata of Eisentraut's Bow-winged Grasshopper sounds from iNaturalist. Let's assume that the initial query was done a while ago and we want to update it to include any new records that might have been added since then. The following code removes all observations recorded after 2024-12-31 to simulate an old query: ``` r # initial query c_eisentrauti <- query_inaturalist(species = "Chorthippus eisentrauti") ``` ``` ✔ Obtaining metadata (113 matching records found) 😀 ``` ``` r head(c_eisentrauti, 3) ``` ``` repository format key species date time user_name 1 iNaturalist image 335245347 Chorthippus eisentrauti 2019-11-16 12:48 Eliot Stein-Deffarges J. 2 iNaturalist image 335245344 Chorthippus eisentrauti 2019-11-16 12:36 Eliot Stein-Deffarges J. 3 iNaturalist image 334597801 Chorthippus eisentrauti 2026-01-11 12:01 Eliot Stein-Deffarges J. country locality latitude longitude 1 NA 43.967696755,7.6218244195 43.96770 7.621824 2 NA 43.967696755,7.6218244195 43.96770 7.621824 3 NA 44.0786166389,7.6128199722 44.07862 7.612820 file_url file_extension 1 https://inaturalist-open-data.s3.amazonaws.com/photos/608983424/original.jpg jpeg 2 https://inaturalist-open-data.s3.amazonaws.com/photos/608982971/original.jpg jpeg 3 https://inaturalist-open-data.s3.amazonaws.com/photos/607665238/original.jpg jpeg ``` ``` r # exclude new observations (simulate old data) old_c_eisentrauti <- c_eisentrauti[c_eisentrauti$date <= "2024-12-31" | is.na(c_eisentrauti$date), ] # update "old" data upd_c_eisentrauti <- update_metadata(metadata = old_c_eisentrauti) ``` ``` ✔ Obtaining metadata (113 matching records found) 🥳 ``` ``` ✔ 95 new entries found 🎉 ``` ``` r # compare number of records nrow(c_eisentrauti) == nrow(upd_c_eisentrauti) ``` ``` [1] TRUE ``` # Combine metadata from multiple repositories The [merge_metadata()](https://docs.ropensci.org/suwo/reference/merge_metadata.html) function allows users to combine metadata data frames obtained from multiple query functions into a single data frame. The function will match the basic columns of all data frames. Data from additional columns (for instance when using `all_data = TRUE` in the query) will only be combined if the column names from different repositories match. The function will return a data frame that includes a new column called `source` indicating the name of the original metadata data frame: ``` r truf_xc <- query_xenocanto(species = "Turdus rufiventris") ``` ``` Error in `query_xenocanto()`: ! An API key is required for Xeno-Canto API v3. Get yours at https://xeno-canto.org/account. ``` ``` r truf_gbf <- query_gbif(species = "Turdus rufiventris", format = "sound") ``` ``` ✔ Obtaining metadata (738 matching records found) 🎊 ``` ``` ! 2 observations do not have a download link and were removed from the results (inlcuded as an attribute called 'excluded_results'). ``` ``` r truf_ml <- query_macaulay(species = "Turdus rufiventris", format = "sound") ``` ``` A browser will open the macaulay library website. Save the .csv file ('export' button) to this directory: /home/m/Dropbox/R_package_testing/suwo/vignettes/ (R is monitoring for new CSV files. Press ESC to stop the function) File: ML__2026-02-10T16-13_rubthr1_audio.csv ``` ``` ✔ 1371 matching records found 😀 ``` ``` r # merge metadata merged_metadata <- merge_metadata(truf_xc, truf_gbf, truf_ml) head(merged_metadata, 4) ``` ``` repository format key species date time user_name 1 Xeno-Canto sound 1071699 Turdus rufiventris 2025-10-26 11:20 Franco Vushurovich 2 Xeno-Canto sound 1070609 Turdus rufiventris 2025-12-29 05:59 Jayrson Araujo De Oliveira country locality latitude longitude 1 Argentina Victoria, Entre Ríos -32.8606 -60.6486 2 Brazil Reserva do Setor Sítio de Recreio Caraíbas-Goiânia, Goiás -16.5631 -49.2850 file_url file_extension genus specific_epithet subspecies 1 https://xeno-canto.org/1071699/download wav Turdus rufiventris 2 https://xeno-canto.org/1070609/download mp3 Turdus rufiventris taxonomic_group english_name altitude vocalization_type sex stage method 1 birds Rufous-bellied Thrush 10 song uncertain adult in the hand 2 birds Rufous-bellied Thrush 750 alarm call uncertain nestling in the hand url 1 //xeno-canto.org/1071699 2 //xeno-canto.org/1070609 uploaded_file 1 XC1071699-ZorzalColorado26deOctubredel2025Isla.wav 2 XC1070609-29-12-2025-5e59-Sabia-laranjeira-filhote-(Turdus-rufiventris)-GYN-CARAIBAS.mp3 license quality length upload_date 1 //creativecommons.org/licenses/by-nc-sa/4.0/ A 2:12 2026-01-10 2 //creativecommons.org/licenses/by-nc-sa/4.0/ A 1:35 2026-01-04 other_species 1 Saltator coerulescens-Vireo chivi-Colaptes melanochloros-Phacellodomus ruber-Lepidocolaptes angustirostris-Zonotrichia capensis-Agelaioides badius 2 Patagioenas picazuro-Brotogeris chiriri comments 1 Zorzal vocalizando dentro del albardón a 100mt del Río Paraná. \r\n\r\nThrush singing at the gallery forest on the Paraná river.\r\n\r\n Coordinates: -32.9022245, -60.6654077 2 animal_seen playback_used temp regnr auto recorder microphone sampling_rate 1 yes no no Zoom H1 Sennheiser MKE 600 44100 2 yes no no Panassonic Embutido 16000 sonogram_small 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/ffts/XC1071699-small.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/ffts/XC1070609-small.png sonogram_med 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/ffts/XC1071699-med.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/ffts/XC1070609-med.png sonogram_large 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/ffts/XC1071699-large.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/ffts/XC1070609-large.png sonogram_full 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/ffts/XC1071699-full.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/ffts/XC1070609-full.png oscillogram_small 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/wave/XC1071699-small.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/wave/XC1070609-small.png oscillogram_med 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/wave/XC1071699-med.png 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/wave/XC1070609-med.png oscillogram_large sonogram source 1 //xeno-canto.org/sounds/uploaded/VLDFGFKOWN/wave/XC1071699-large.png NA truf_xc 2 //xeno-canto.org/sounds/uploaded/LXKLWEDKEM/wave/XC1070609-large.png NA truf_xc [ reached 'max' / getOption("max.print") -- omitted 2 rows ] ``` Note that in such a multi-repository query, all query functions use the same search species (i.e. species name) and media format (e.g., sound, image, video). To facilitate this, users can set the global options `species` and `format` so they do not need to specify them in each query function: ``` r # query at multiple repositories setting global options options(species = "Turdus rufiventris", format = "sound") truf_xc <- query_xenocanto() # assuming you already set your API key truf_gbf <- query_gbif() truf_ml <- query_macaulay() # merge metadata merged_metadata <- merge_metadata(truf_xc, truf_gbf, truf_ml) ``` # Find and remove duplicated records When compiling data from multiple repositories, duplicated media records are a common issue, particularly for sound recordings. These duplicates occur both through data sharing between repositories like Xeno-Canto and GBIF, and when users upload the same file to multiple platforms. To help users efficiently identify these duplicate records, [suwo](https://docs.ropensci.org/suwo/) provides the [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html) function. Duplicates are identified based on matching species name, country, date, user name, and locality. The function uses a fuzzy matching approach to account for minor variations in the data (e.g., typos, different location formats, etc).The output is a data frame with the candidate duplicate records, allowing users to review and decide which records to keep. In this example we look for possible duplicates in the merged metadata data frame from the previous section: ``` r # find duplicates dups_merged_metadata <- find_duplicates(merged_metadata) ``` ``` ℹ 668 potential duplicates found ``` ``` r # look first 6 columns head(dups_merged_metadata) ``` ``` repository format key species date time user_name 10 Xeno-Canto sound 1032061 Turdus rufiventris 2025-07-19 18:01 Jacob Wijpkema 3513 GBIF sound 5995345374 Turdus rufiventris 2025-07-19 18:01 Jacob Wijpkema country locality latitude 10 Bolivia Lagunillas, Cordillera, Santa Cruz Department -19.6348 3513 Bolivia (Plurinational State of) Lagunillas, Cordillera, Santa Cruz Department -19.6348 longitude file_url 10 -63.6711 https://xeno-canto.org/1032061/download 3513 -63.6711 https://xeno-canto.org/sounds/uploaded/WETAASKWUP/XC1032061-250719_8737_2.wav file_extension genus specific_epithet subspecies taxonomic_group english_name 10 wav Turdus rufiventris birds Rufous-bellied Thrush 3513 wav altitude vocalization_type sex stage method url 10 900 call, dawn song, dusksong male adult field recording //xeno-canto.org/1032061 3513 uploaded_file license quality length 10 XC1032061-250719_8737_2.wav //creativecommons.org/licenses/by-nc-sa/4.0/ A 1:14 3513 upload_date other_species comments animal_seen playback_used temp regnr auto recorder 10 2025-08-23 yes no no Olympus LS-P4 3513 microphone sampling_rate sonogram_small 10 Telinga 44100 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-small.png 3513 sonogram_med 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-med.png 3513 sonogram_large 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-large.png 3513 sonogram_full 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-full.png 3513 oscillogram_small 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-small.png 3513 oscillogram_med 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-med.png 3513 oscillogram_large sonogram source 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-large.png NA truf_xc 3513 NA truf_gbf duplicate_group 10 1 3513 1 [ reached 'max' / getOption("max.print") -- omitted 4 rows ] ``` Note that the [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html) function adds a new column called "duplicate_group" to the output data frame. This column assigns a unique identifier to each group of potential duplicates, allowing users to easily identify and review them. For instance, in the example above, records from duplicated group 92 belong to the same user, were recorded on the same date and time and in the same country: ``` r subset(dups_merged_metadata, duplicate_group == 92) ``` ``` repository format key species date time user_name country 145 Xeno-Canto sound 273100 Turdus rufiventris 2013-10-19 18:00 Peter Boesman Argentina 147 Xeno-Canto sound 273098 Turdus rufiventris 2013-10-19 18:00 Peter Boesman Argentina locality latitude longitude file_url file_extension 145 Calilegua NP, Jujuy -23.74195 -64.85777 https://xeno-canto.org/273100/download mp3 147 Calilegua NP, Jujuy -23.74195 -64.85777 https://xeno-canto.org/273098/download mp3 genus specific_epithet subspecies taxonomic_group english_name altitude 145 Turdus rufiventris birds Rufous-bellied Thrush 147 Turdus rufiventris birds Rufous-bellied Thrush vocalization_type sex stage method url 145 call field recording //xeno-canto.org/273100 147 call field recording //xeno-canto.org/273098 uploaded_file license 145 XC273100-Rufous-bellied Thrush QQQ call A.mp3 //creativecommons.org/licenses/by-nc-nd/4.0/ 147 XC273098-Rufous-bellied Thrush QQ call A 1.mp3 //creativecommons.org/licenses/by-nc-nd/4.0/ quality length upload_date other_species comments animal_seen playback_used temp regnr 145 A 0:36 2015-08-26 ID certainty 90%. unknown unknown 147 A 0:35 2015-08-26 unknown unknown auto recorder microphone sampling_rate 145 no 44100 147 no 44100 sonogram_small 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273100-small.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273098-small.png sonogram_med 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273100-med.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273098-med.png sonogram_large 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273100-large.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273098-large.png sonogram_full 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273100-full.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/ffts/XC273098-full.png oscillogram_small 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273100-small.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273098-small.png oscillogram_med 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273100-med.png 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273098-med.png oscillogram_large sonogram source 145 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273100-large.png NA truf_xc 147 //xeno-canto.org/sounds/uploaded/OOECIWCSWV/wave/XC273098-large.png NA truf_xc duplicate_group 145 92 147 92 [ reached 'max' / getOption("max.print") -- omitted 4 rows ] ``` In this case all the observations seem to refer to the same media file. Therefore only one copy is needed. Also note that the locality is not exactly the same for these records, but the fuzzy matching approach used by [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html) was able to identify them as potential duplicates. By default, the criteria is set to `country > 0.8 & locality > 0.5 & user_name > 0.8 & time == 1 & date == 1` which means that two entries will be considered duplicates if they have a country similarity greater than 0.8, locality similarity greater than 0.5, user_name similarity greater than 0.8, and exact matches for time and date (similarities range from 0 to 1). These values have been found to work well in most cases. Nonetheless, users can adjust the sensitivity based on their specific needs using the argument `criteria`. Once users have reviewed the candidate duplicates, they can apply the [remove_duplicates()](https://docs.ropensci.org/suwo/reference/remove_duplicates.html) function to eliminate unwanted duplicates from their metadata data frames. This function takes as input a metadata output data frame from [find_duplicates()](https://docs.ropensci.org/suwo/reference/find_duplicates.html): ``` r # remove duplicates dedup_metadata <- remove_duplicates(dups_merged_metadata) ``` ``` ℹ 304 duplicates removed ``` The output is a data frame similar to the input but without the specified duplicate records: ``` r # look at first 4 columns of deduplicated metadata head(dedup_metadata, 4) ``` ``` repository format key species date time user_name 10 Xeno-Canto sound 1032061 Turdus rufiventris 2025-07-19 18:01 Jacob Wijpkema 3513 GBIF sound 5995345374 Turdus rufiventris 2025-07-19 18:01 Jacob Wijpkema country locality latitude 10 Bolivia Lagunillas, Cordillera, Santa Cruz Department -19.6348 3513 Bolivia (Plurinational State of) Lagunillas, Cordillera, Santa Cruz Department -19.6348 longitude file_url 10 -63.6711 https://xeno-canto.org/1032061/download 3513 -63.6711 https://xeno-canto.org/sounds/uploaded/WETAASKWUP/XC1032061-250719_8737_2.wav file_extension genus specific_epithet subspecies taxonomic_group english_name 10 wav Turdus rufiventris birds Rufous-bellied Thrush 3513 wav altitude vocalization_type sex stage method url 10 900 call, dawn song, dusksong male adult field recording //xeno-canto.org/1032061 3513 uploaded_file license quality length 10 XC1032061-250719_8737_2.wav //creativecommons.org/licenses/by-nc-sa/4.0/ A 1:14 3513 upload_date other_species comments animal_seen playback_used temp regnr auto recorder 10 2025-08-23 yes no no Olympus LS-P4 3513 microphone sampling_rate sonogram_small 10 Telinga 44100 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-small.png 3513 sonogram_med 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-med.png 3513 sonogram_large 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-large.png 3513 sonogram_full 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/ffts/XC1032061-full.png 3513 oscillogram_small 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-small.png 3513 oscillogram_med 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-med.png 3513 oscillogram_large sonogram source 10 //xeno-canto.org/sounds/uploaded/WETAASKWUP/wave/XC1032061-large.png NA truf_xc 3513 NA truf_gbf duplicate_group 10 1 3513 1 [ reached 'max' / getOption("max.print") -- omitted 2 rows ] ``` When duplicates are found, one observation from each group of duplicates is retained in the output data frame. However, if multiple observations from the same repository are labeled as duplicates, by default (`same_repo = FALSE`) all of them are retained in the output data frame. This is useful as it can be expected that observations from the same repository are not true duplicates (e.g. different recordings uploaded to Xeno-Canto with the same date, time and location by the same user), but rather have not been documented with enough precision to be told apart. This behavior can be modified. If `same_repo = TRUE`, only one of the duplicated observations from the same repository will be retained in the output data frame (and all other excluded). The function will give priority to repositories in which media downloading is more straightforward (i.e. Xeno-Canto, GBIF), but this can be modified with the argument `repo_priority`. # Download media files The last step of the workflow is to download the media files associated with the metadata. This can be done using the [download_media()](https://docs.ropensci.org/suwo/reference/download_media.html) function, which takes as input a metadata data frame (obtained from any query function or any of the other metadata managing functions) and downloads the media files to a specified directory. For this example we will download images from a query on zambian slender Caesar (_Amanita zambiana_) (a mushroom) on GBIF: ``` r # query GBIF for Amanita zambiana images a_zam <- query_gbif(species = "Amanita zambiana", format = "image") ``` ``` ✔ Obtaining metadata (7 matching records found) 🌈 ``` ``` r # create folder for images out_folder <- file.path(tempdir(), "amanita_zambiana") dir.create(out_folder) # download media files to a temporary directory azam_files <- download_media(metadata = a_zam, path = out_folder) ``` ``` Downloading media files: ``` ``` ✔ All files were downloaded successfully 😸 ``` The output of the function is a data frame similar to the input metadata but with two additional columns indicating the file name of the downloaded files ('downloaded_file_name') and the result of the download attempt ('download_status', with values "success", 'failed', 'already there (not downloaded)' or 'overwritten'). Here we print the first 4 rows of the output data frame: ``` r head(azam_files, 4) ``` ``` repository format key species date time user_name country locality 1 GBIF image 4430877067 Amanita zambiana 2023-01-25 10:57 Allanweideman Mozambique 2 GBIF image 4430877067 Amanita zambiana 2023-01-25 10:57 Allanweideman Mozambique 3 GBIF image 4430877067 Amanita zambiana 2023-01-25 10:57 Allanweideman Mozambique 4 GBIF image 5104283819 Amanita zambiana 2023-03-31 13:41 Nick Helme Zambia latitude longitude file_url 1 -21.28456 34.61868 https://inaturalist-open-data.s3.amazonaws.com/photos/253482452/original.jpg 2 -21.28456 34.61868 https://inaturalist-open-data.s3.amazonaws.com/photos/253482473/original.jpg 3 -21.28456 34.61868 https://inaturalist-open-data.s3.amazonaws.com/photos/253484256/original.jpg 4 -12.44276 31.28535 https://inaturalist-open-data.s3.amazonaws.com/photos/268158445/original.jpeg file_extension downloaded_file_name download_status 1 jpeg Amanita_zambiana-GBIF4430877067-1.jpeg saved 2 jpeg Amanita_zambiana-GBIF4430877067-2.jpeg saved 3 jpeg Amanita_zambiana-GBIF4430877067-3.jpeg saved 4 jpeg Amanita_zambiana-GBIF5104283819.jpeg saved ``` ... and check that the files were saved in the path supplied: ``` r fs::dir_tree(path = out_folder) ``` ``` /tmp/RtmpHOiu3v/amanita_zambiana ├── Amanita_zambiana-GBIF3759537817-1.jpeg ├── Amanita_zambiana-GBIF3759537817-2.jpeg ├── Amanita_zambiana-GBIF4430877067-1.jpeg ├── Amanita_zambiana-GBIF4430877067-2.jpeg ├── Amanita_zambiana-GBIF4430877067-3.jpeg ├── Amanita_zambiana-GBIF5069132689-1.jpeg ├── Amanita_zambiana-GBIF5069132689-2.jpeg ├── Amanita_zambiana-GBIF5069132691.jpeg ├── Amanita_zambiana-GBIF5069132696-1.jpeg ├── Amanita_zambiana-GBIF5069132696-2.jpeg ├── Amanita_zambiana-GBIF5069132732.jpeg └── Amanita_zambiana-GBIF5104283819.jpeg ``` Note that the name of the downloaded files includes the species name, an abbreviation of the repository name and the unique record key. If more than one media file is associated with a record, a sequential number is added at the end of the file name. This is a multipanel plot of 6 of the downloaded images (just for illustration purpose): ``` png 2 ``` ``` r # create a 6 pannel plot of the downloaded images opar <- par(mfrow = c(2, 3), mar = c(1, 1, 2, 1)) for (i in 1:6) { img <- jpeg::readJPEG(file.path(out_folder, azam_files$downloaded_file_name[i])) plot( 1:2, type = 'n', axes = FALSE ) graphics::rasterImage(img, 1, 1, 2, 2) title(main = paste( azam_files$country[i], azam_files$date[i], sep = "\n" )) } ```

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``` r # reset par par(opar) ``` Example images obtain from a query of Amanita zambiana

Users can also save the downloaded files into sub-directories with the argument `folder_by`. This argument takes a character or factor column with the names of a metadata field (a column in the metadata data frame) to create sub-directories within the main download directory (suplied with the argument `path`). For instance, the following code searches/downloads images of longspined porcupinefish (_Diodon holocanthus_) from GBIF, and saves images into sub-directories by country (for simplicity only 6 of them): ``` r # query GBIF for longspined porcupinefish images d_holocanthus <- query_gbif(species = "Diodon holocanthus", format = "image") ``` ``` ✔ Obtaining metadata (3814 matching records found) 🥇 ``` ``` ! 1 observation does not have a download link and was removed from the results (inlcuded as an attribute called 'excluded_results'). ``` ``` r # keep only JPEG records (for simplicity for this vignette) d_holocanthus <- d_holocanthus[d_holocanthus$file_extension == "jpeg", ] # select 6 random JPEG records set.seed(666) d_holocanthus <- d_holocanthus[sample(seq_len(nrow(d_holocanthus)), 6),] # create folder for images out_folder <- file.path(tempdir(), "diodon_holocanthus") dir.create(out_folder) # download media files creating sub-directories by country dhol_files <- download_media(metadata = d_holocanthus, path = out_folder, folder_by = "country") ``` ``` Downloading media files: ``` ``` ✔ All files were downloaded successfully 🥳 ``` ``` r fs::dir_tree(path = out_folder) ``` ``` /tmp/RtmpHOiu3v/diodon_holocanthus ├── Chinese Taipei │ └── Diodon_holocanthus-GBIF5230440534.jpeg ├── Costa Rica │ └── Diodon_holocanthus-GBIF4936464164.jpeg ├── Japan │ └── Diodon_holocanthus-GBIF5827555622.jpeg ├── Mexico │ └── Diodon_holocanthus-GBIF4528339523.jpeg └── United States of America ├── Diodon_holocanthus-GBIF1880606498.jpeg └── Diodon_holocanthus-GBIF5154762137.jpeg ``` In such case the 'downloaded_file_name' column will include the sub-directory name: ``` r dhol_files$downloaded_file_name ``` ``` [1] "Mexico/Diodon_holocanthus-GBIF4528339523.jpeg" [2] "Japan/Diodon_holocanthus-GBIF5827555622.jpeg" [3] "United States of America/Diodon_holocanthus-GBIF1880606498.jpeg" [4] "Chinese Taipei/Diodon_holocanthus-GBIF5230440534.jpeg" [5] "United States of America/Diodon_holocanthus-GBIF5154762137.jpeg" [6] "Costa Rica/Diodon_holocanthus-GBIF4936464164.jpeg" ``` This is a multipanel plot of the downloaded images (just for fun): ``` png 2 ``` ``` r # create a 6 pannel plot of the downloaded images opar <- par(mfrow = c(2, 3), mar = c(1, 1, 2, 1)) for (i in 1:6) { img <- jpeg::readJPEG(file.path(out_folder, dhol_files$downloaded_file_name[i])) plot( 1:2, type = 'n', axes = FALSE ) graphics::rasterImage(img, 1, 1, 2, 2) title(main = paste( substr(dhol_files$country[i], start = 1, stop = 14), dhol_files$date[i], sep = "\n" )) } ```

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``` r # reset par par(opar) ``` Example images obtain from a query of porcupinefish

## Session information {.unnumbered .unlisted}

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``` R version 4.5.2 (2025-10-31) Platform: x86_64-pc-linux-gnu Running under: Ubuntu 22.04.5 LTS Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 LAPACK version 3.10.0 locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=es_CR.UTF-8 [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=es_CR.UTF-8 LC_MESSAGES=en_US.UTF-8 [7] LC_PAPER=es_CR.UTF-8 LC_NAME=C LC_ADDRESS=C [10] LC_TELEPHONE=C LC_MEASUREMENT=es_CR.UTF-8 LC_IDENTIFICATION=C time zone: America/Costa_Rica tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] suwo_0.1.0 knitr_1.51 loaded via a namespace (and not attached): [1] viridisLite_0.4.3 blob_1.3.0 farver_2.1.2 fastmap_1.2.0 [5] promises_1.3.3 digest_0.6.39 rpart_4.1.24 timechange_0.4.0 [9] mime_0.13 lifecycle_1.0.5 ellipsis_0.3.2 survival_3.8-3 [13] processx_3.8.6 RSQLite_2.4.6 magrittr_2.0.4 compiler_4.5.2 [17] rlang_1.1.7 tools_4.5.2 yaml_2.3.12 data.table_1.18.2.1 [21] htmlwidgets_1.6.4 bit_4.6.0 pkgbuild_1.4.8 curl_7.0.0 [25] xml2_1.5.2 RColorBrewer_1.1-3 pkgload_1.4.1 miniUI_0.1.2 [29] purrr_1.2.0 desc_1.4.3 nnet_7.3-20 grid_4.5.2 [33] urlchecker_1.0.1 profvis_0.4.0 xtable_1.8-4 e1071_1.7-17 [37] future_1.69.0 globals_0.19.0 scales_1.4.0 MASS_7.3-65 [41] cli_3.6.5 crayon_1.5.3 rmarkdown_2.30 generics_0.1.4 [45] remotes_2.5.0 rstudioapi_0.17.1 RecordLinkage_0.4-12.6 future.apply_1.20.1 [49] sessioninfo_1.2.3 DBI_1.2.3 cachem_1.1.0 proxy_0.4-29 [53] stringr_1.6.0 splines_4.5.2 parallel_4.5.2 vctrs_0.7.1 [57] devtools_2.4.5 Matrix_1.7-4 jsonlite_2.0.0 callr_3.7.6 [61] bit64_4.6.0-1 listenv_0.10.0 jpeg_0.1-11 systemfonts_1.3.1 [65] evd_2.3-7.1 glue_1.8.0 parallelly_1.46.1 codetools_0.2-20 [69] ps_1.9.1 lubridate_1.9.5 stringi_1.8.7 later_1.4.2 [73] tibble_3.3.0 pillar_1.11.1 rappdirs_0.3.4 htmltools_0.5.9 [77] ipred_0.9-15 lava_1.8.2 ff_4.5.2 R6_2.6.1 [81] httr2_1.2.2 textshaping_1.0.4 evaluate_1.0.5 shiny_1.10.0 [85] kableExtra_1.4.0 lattice_0.22-7 backports_1.5.0 memoise_2.0.1 [89] httpuv_1.6.16 class_7.3-23 Rcpp_1.1.1 svglite_2.2.2 [93] prodlim_2025.04.28 checkmate_2.3.4 xfun_0.56 fs_1.6.6 [97] usethis_3.1.0 pkgconfig_2.0.3 ```