FAQ

GBIF 服务器在互联网上公开访问，供所有人使用。 我们的数据处理基础设施还需要与已注册的数据存储库建立出站连接，以便将数据集导入 GBIF 索引。

在某些情况下，网络限制可能会阻止访问；要么阻止用户访问 GBIF 服务，要么阻止 GBIF 连接到您的系统。 这些限制有时是由机构网络管理员施加的，例如通过屏蔽 GBIF IP 地址范围。 如果这种情况发生在您的机构内，通常可以通过联系您的网络管理员来解决。

如果该限制措施在国家或更广泛的网络层面实施，那么很遗憾，GBIF 几乎无法进行干预或提供变通方案。

GBIF 服务器的 IP 地址范围为 130.225.43.0/24。 Web 服务可以通过 [Cloudflare] 进行代理。（https://www.cloudflare.com/ips/）。

To have events related to a project show up on the project's page, please suggest the event and make sure to include your project ID under GBIF-funded projects.

You can access a full history of all previous downloads in your GBIF.org account. After logging in, click you username in the upper-right corner and then click the "Downloads" tab. Downloads are shown in reverse chronological order, i.e., with the most recent downloads at the top.

All GBIF servers have IP addresses in the range 130.225.43.0/24, and data publishers and hosts should adjust their firewall filters make sure that access is allowed from this range.

These would be user-defined, integrated data products and communication tools derived from these products that could include

• Data summary tables indicating population trends, species richness and community composition
• Map products such as species distributions or geographical data gap analyses
• Indicators informing Sustainable Development Goals or CBD targets

This may seem like a simple question, but the answer isn't. For a detailed explanation of why GBIF doesn't show a species count on the home page, please refer to this article.

This simple format provides a view of the data with the most commonly used columns, separated by tabs. The table includes only the data after it has gone through interpretation and quality control. Tools such as Microsoft Excel can be used to read this format.

See also: How do I open tab-delimited CSV files downloaded from GBIF.org in Excel?

You can access Feedback and questions by clicking the speech bubble () in the upper right-hand corner. Here, you can report bugs or issues on specific content–as well as submit ideas for new developments on the site.

All feedback submitted is public and available through Github.

This means that the taxon has been deleted from the GBIF Backbone Taxonomy, but the identifier is preserved for historical purposes. Taxa are deleted for a number of reasons, e.g. duplicate entries, being removed from source checklist, etc.

A publishing institution is a GBIF data publisher that has published at least one dataset. As newly endorsed publishers may not yet have published any data, the total number of data publishers is slightly higher.

Downloads bigger than four gigabytes (4 GB) need to be compressed using an extension of the original zip format called ZIP64. Not all operating systems support this extension natively. MS Windows XP and Mac OS X systems are among those. Please make sure that the software you are using to decompress the file is compatible with the ZIP64 extension.

Please use the feedback button at the top right of each page or contact us by email.

In future GBIF work programmes, it may be possible to extend this work further to include other interesting trends around data mobilization in GBIF. Please use the feedback button to provide any additional ideas or comments on the current charts, or consider contributing to the project.

The original unprocessed data resides in Hadoop. Hive is used for the SQL processing on the Hadoop data using custom UDFs wrapping the GBIF core processing libraries (Java). Hive is used to digest the data into CSV tables. All other processing is in R.

The colour palettes come from colorbrewer2.org, and an attempt was made to select colours that would be colour-blind safe. It is difficult to find suitable colour palettes that work on all charts (e.g. global and country specific) and input would be greatly appreciated to help improve these.

This is a first iteration of work. Future versions could be more interactive, although one has to consider if a PDF view or simple images for (e.g.) annual reports are required. As an open project, anyone with interest in improving the data visualization is welcome to get involved. Please contact us.

Please use the feedback button on the top of the page to log any suggestions.

The charts may reveal patterns that represent biases in data collection (seasonality, public holidays) or potential issues in data management (disproportionate numbers of records shown for the first or last days in the year or each month or week). Such issues may arise at various stages in data processing and require further investigation.

This is due to the removal of data sets from GBIF. This might occur if a publisher wishes to remove their data, but is often due to the removal of datasets that were inadvertently published twice (duplicate datasets).

All data is processed to the latest GBIF backbone taxonomy, to ensure that species counts are comparable over time.

The project is documented on the GitHub project site. Approximately four historical views per year of the GBIF index are restored (totalling approximately 8 Billion records in May 2014), and the raw data is processed to the latest quality control and taxonomic backbone. Various scripts are then used to digest the records into smaller views which are then processed in R to produce the charts.

This project is being developed openly on the GitHub project site. While some data preparation stages require access to the GBIF index and Hadoop infrastructure, other stages run using R and can be developed remotely. Please contact us if you would like to contribute to the work.

The charts show data trending from the end of 2007 until recent weeks and will be recalculated periodically; approximately quarterly.

Yes, however we encourage that they be reviewed before doing so.

The data used for the data trend charts and reports is available to download at https://analytics-files.gbif.org/

The GBIF Secretariat is producing information on data mobilization trends observed on the GBIF network. Showing trends on the data mobilized by the GBIF network can help with planning data mobilization efforts, showing the results of previous investments in digitization or data mobilization, or in highlighting issues to be targeted to improve the fitness-for-use of the data.

GBIF currently supports four classes of datasets. GBIF currently only indexes species occurrence records though, which can be provided as either core records or as extension records. In the case of sampling-event datasets, species occurrences in extension records will be augmented with information coming from its core event record wherever possible.

You’ll find a slightly more formal description of the logic behind automatic citation generation in this GBIF GitHub repository.

In cases where no authors are named, or where only metadata authors are named without any originating authors, the citation text will start with the name of the publishing institution, followed by the publication year and the other elements.