Cleaning GBIF Data

This article walks through a practical case study using the barRoso package to standardize and harmonize plant specimen records downloaded from the Global Biodiversity Information Facility (GBIF) portal.

Goal

Perform a full cleaning workflow for GBIF records using barRoso, including:

Standardizing collector names and record numbers
Harmonizing taxonomic and geographic fields
Flagging and filtering duplicate specimens
Generating a cleaned dataset ready for downstream use

Step 1: Download GBIF Records

Use the rgbif package to download occurrence records. Here’s an example for the Fabaceae family:

# install.packages("rgbif")
library(rgbif)

occ <- occ_search(scientificName = "Fabaceae", limit = 1000, hasCoordinate = TRUE)
df <- occ$data

Step 2: Load barRoso and Standardize Records

# install.packages("devtools")
devtools::install_github("DBOSlab/barRoso")

library(barRoso)

cleaned <- barroso_std(df,
                       flag_duplicates = TRUE,
                       rm_duplicates = FALSE)

Step 3: Review Standardized Output

table(cleaned$duplicate)
head(cleaned[, c("recordedBy", "recordNumber", "duplicate")])

Step 4: Save Results

write.csv(cleaned, "gbif_cleaned.csv", row.names = FALSE)

Summary

In this case study, we:

Programmatically downloaded GBIF data for a plant family
Used barRoso to standardize and harmonize specimen metadata
Flagged duplicates and prepared a clean dataset for biodiversity research

You can apply this pipeline to other sources such as SEINet, REFLORA Virtual Herbarium, JABOT, or speciesLink.

---
title: "Cleaning GBIF Data"
format: html
---

This article walks through a practical case study using the `barRoso` package to standardize and harmonize plant specimen records downloaded from the [Global Biodiversity Information Facility (GBIF)](https://www.gbif.org/) portal.

## Goal

Perform a full cleaning workflow for GBIF records using `barRoso`, including:

- Standardizing collector names and record numbers
- Harmonizing taxonomic and geographic fields
- Flagging and filtering duplicate specimens
- Generating a cleaned dataset ready for downstream use

## Step 1: Download GBIF Records

Use the [`rgbif`](https://docs.ropensci.org/rgbif/) package to download occurrence records. Here's an example for the Fabaceae family:

```r
# install.packages("rgbif")
library(rgbif)

occ <- occ_search(scientificName = "Fabaceae", limit = 1000, hasCoordinate = TRUE)
df <- occ$data
```

## Step 2: Load barRoso and Standardize Records

```r
# install.packages("devtools")
devtools::install_github("DBOSlab/barRoso")

library(barRoso)

cleaned <- barroso_std(df,
                       flag_duplicates = TRUE,
                       rm_duplicates = FALSE)
```

## Step 3: Review Standardized Output

```r
table(cleaned$duplicate)
head(cleaned[, c("recordedBy", "recordNumber", "duplicate")])
```

## Step 4: Save Results

```r
write.csv(cleaned, "gbif_cleaned.csv", row.names = FALSE)
```

## Summary

In this case study, we:

- Programmatically downloaded GBIF data for a plant family
- Used `barRoso` to standardize and harmonize specimen metadata
- Flagged duplicates and prepared a clean dataset for biodiversity research

You can apply this pipeline to other sources such as [SEINet](https://swbiodiversity.org/seinet), [REFLORA Virtual Herbarium](https://floradobrasil.jbrj.gov.br/reflora/herbarioVirtual/), [JABOT](https://jabot.jbrj.gov.br/v3/consulta.php), or [speciesLink](https://specieslink.net/).