Skip to content

Latest commit

 

History

History
127 lines (95 loc) · 9.23 KB

data-access.en.adoc

File metadata and controls

127 lines (95 loc) · 9.23 KB
Raw

Data access

Note
 In this module, you will learn how GBIF’s data is used and accessed. You will also learn about the handling of data quality and GBIF’s data quality flags.

How is GBIF-mediated data used?

The GBIF literature tracking system has identified over 5,000 uses of GBIF mediated data, most of which are in peer-reviewed articles. The majority of these uses are in the field of ecology, but others relate to climate change, conservation, human health and agriculture. A systematic review of the use of GBIF-mediated data by Heberling et al. (2020) showed:

  • Both data availability and data use have increased over time.

  • Data integration facilitates global research and access.

  • Uses of GBIF-mediated data span disciplinary boundaries.

  • The scientific areas using GBIF-mediated data are conceptually diverse and change in prevalence over time.

  • Globally integrated datasets enable researchers to ask both basic and applied questions at taxonomic, temporal and spatial scales that would be otherwise impossible.

  • The synergistic roles of observation- and specimen-based biodiversity data highlight the value and need for deeper integration with phylogenetic, environmental, phenotypic, ecological and genetic sources of data.

fig3
Structural topic model results from 4,035 studies that used GBIF-mediated data published between 2003 and 2019.

GBIF-mediated data is also used for monitoring the state of biodiversity and progress towards achieving the targets of the Convention on Biological Diversity. The increase in availability of GBIF occurrence data is one of the indicators for tracking progress towards the achievement of Aichi Biodiversity target 19 and GBIF is a key data source in the creation of a number of other indicators, including the Species Status Information Index, Species Habitat Index and the Biodiversity Habitat Index.

While the utility of GBIF-mediated data is clear, the wide variety of sources of data accessible through GBIF, spanning museum collections, citizen science, metagenomics, among others, means that not all GBIF-mediated data will be fit for every use. Key components of using GBIF-mediated data are understanding how to access the specific data that you need from what is available in GBIF and understanding some of the common data quality issues that affect the data so as to facilitate processing of the data before analysis.

Accessing GBIF-mediated data

There are two main points of access to GBIF-mediated data: GBIF.org and the Application Programming Interface (API) services. Using the website requires no programming experience and allows for quick and easy search, filter and download functions for GBIF-mediated data, as well as a range of additional tools and metrics that are not available through API services. API services allow continued access to GBIF-mediated data through other systems and can be the basis for the development of tools that allow for the interrogation of the data. Examples include a number of R packages, such as rgbif and CoordinateCleaner, as well as more specialized tools that allow for more specific use cases, such as GeoCat for Red List assessments.

What is available to me?

Through the search functions on the website, users can access data that can either be directly downloaded through GBIF or accessed from the original sources following links that GBIF provides.

Note
 Remember that as a data user you should read and agree with the terms of the GBIF Data User Agreement that include correctly citing the use of GBIF-mediated data.
downloadoptions

The data available to you are:

  • Primary biodiversity data - occurrence, checklist and sampling event data that is provided to users through the one of the 3 download formats:

    • Simple: Tab delimited CSV. Only contains the data after GBIF interpretation. No multimedia included.

    • Darwin Core Archive: The Darwin Core Archive (DwC-A) contains both the original data as the publisher provided it and the GBIF interpretation. Links (but not files) to multimedia included.

    • Species list: Tab delimited CSV with the distinct list of names in the search result and as a map visualization of the data.

  • A range of metrics are provided for countries and regions, data publishers, datasets and data searches that provide taxonomic breakdowns, trends in data collection and highlight data quality issues. For countries, these metrics can be also be downloaded in the form of a PDF activity report.

  • Searchable database of publications that have used GBIF-mediated data.

Searches can be performed on the occurrences, species, datasets, publishers and resources, and each search function carries a set of filters that allow for more refined searching and additional data associated with the data, for example, images, can be found in tabs associated with the search.

Handling data quality

Determining the precision and accuracy of the data for use is a key step in determining the usefulness of the data for any intended purpose. While GBIF can support the identification of some quality issues that arise from within the data publishing workflow, handling some quality issues requires additional expert knowledge. The two most common issues for which this may be required are:

  • Data gaps - sampling across taxonomic groups and geographic regions is not equal and users may need to take into account sampling bias in their analyses before the data can be used effectively.

  • Taxonomic misidentification - some taxonomic groups may require additional information to ensure that taxa have been correctly identified such as images, videos and audio recordings that accompany data or collector information.

GBIF Flags for Data Quality Issues

During the indexation process, GBIF assigns issues and flags to data for common data quality issues. These most frequently occur from data entry errors or missing data fields whose interpretation can be automated centrally by GBIF. These interpretations are classified as

  • Excluded - where the original data couldn’t be interpreted, so is excluded in the interpreted fields.

  • Altered - where the original data is modified in the interpretation process to be indexed in GBIF.org.

  • Inferred - where an empty field is inferred using other record information.

Tip
 Be aware that if you are filtering for data quality issues, you should reverse the filter to exclude those data that have been flagged with that issue. You can also see the verbatim data i.e. the non-interpreted data in a Darwin Core Archive if you would like to validate the interpretation process.

How can I improve data quality?

Data publishers have the responsibility for improving the quality of the data, and as a user, you play a key role in identifying where there are errors. If you should find an error in the data, you should contact the publisher directly using the contact details that GBIF provides on the publisher page. GBIF also provides the ability for users to log data quality issues using the "Feedback and questions" button on the menu bar of GBIF.org.

Review

Note
 Quiz yourself on the concepts learned in this section.

  1. How can you access GBIF data?

    - [x] GBIF.org search interface
- [x] GBIF API
- [x] rGBIF

  2. Which file formats are available for downloads of data?

    - [x] simple
- [ ] XML
- [x] Darwin Core Archive
- [x] species list

  3. What kind of flags does GBIF apply to data to alert you to the quality?

    - [x] altered
- [ ] amended
- [ ] translated
- [x] excluded
- [ ] interpreted
- [x] inferred