The documentation is very outdated and needs to be fixed:
See: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0186170
Ecology has become a data intensive science where researchers integrate data across spatial, temporal and organismic scales in order to answer questions of global interest. The annotation of data and the efficient discovery are crucial to support researchers finding relevant data suitable to answer their questions. This GitHub repository serves as a platform for the development of standards and tools that help with the annotation and the discovery of ecological data. This includes an ecological vocabulary, an XML schema and an OWL ontology as well as a tool for the annotation, the discovery and the sharing of data.
A vocabulary that describes ecological search objects has been created in close collaboration with ecological domain experts as well as experts from closely related disciplines. The vocabulary in a top down manner where the top most concepts have been defined first before the vocabulary was extended for more detail.
The top level concepts are namely time, space, sphere, biome, method, process and chemicals. Next to expert knowledge, scientific literature, textbooks, existing metadata standards (Access To Biological Collection Data, Darwin Core, Ecological Metadata Language), ontologies (e.g. CheBi, ENVO), and established standards (e.g. International Chronostratigraphic Chart) served as source of information for the vocabulary. The vocabulary adheres to the following design principles.
It does not provide too much detailed concepts to cover a specific type of information. The idea behind that is that the vocabulary should be easy to use by somebody with a bit of background in ecology or with a little bit of training (e.g. data curator).
Despite the fact that the vocabulary on one hand aims to be parsimonious, on the other hand it aims for a comprehensive description of ecological search objects. Thus the vocabulary provides more detail to cover a specific topic if there is a good reason to do so.
Concepts that can be found in one part of the vocabulary do not appear somewhere else again. Before a new concept is going to be added first it is checked from an annotation perspective if a combination of concepts from different parts of the vocabulary could express the same meaning.
The XML schema provides a simple annotation format capturing metadata based on concepts specified in the vocabulary. The exposure of this metadata in faceted search approach provides a filter which significantly speeds up the discovery of specific research data.
The schema also adheres to some desing principles
Do not do:
<space>
<location>
<range>
<item>
<type>country</type>
<name>Andorra</name>
</item>
</range>
</location>
</space>But rather do:
<space>
<locations>
<location>
<locationType>country</locationType>
<locationName>Andorra</locationName>
...
</locationItem>
</locationRange>
</space>Full-text fields are useful in general and are used in all existing metadata standards like Ecological Metadata Language (EML) or the Access to Biological Collection Data (ABCD). The information in full text fields however is hard to extract and make use for a facet data discovery.
An annotation tool on its way which will allow to create annotations based on the schema. The tool will also provide search and data sharing features. You can browse the mock-ups that show the annotation part of the tool here here.
| EML | ABCD | DwC | |
|---|---|---|---|
| Time | x | x | x |
| Space | x | x | x |
| Sphere | - | - | - |
| Biome | - | ~ | ~ |
| Organism | x | x | x |
| Chemical | - | - | - |
| Process | - | - | - |
| Method | x | x | x |
The contents in this repository is developed and maintained in the context of the German Federation for Biological data (GFBio).
A good data discovery mechanism is crucial to answer questions in ecology more efficiently. Facet search strategies are famous and spread across the internet used successfully in may different applications. Facets represent a powerful tool to restrict a search space with search objects by certain numerical and categorical criteria of the object for a faster discovery. In ecology this means a more efficient discovery of research data.
If you want to get involved please contact:
claas-thido.pfaff@uni-leipzig.de
Contributing content to the repository is simple given you have a GitHub account. You can read into the issues to get you started with existing tasks. However you can also create a new issue to discuss your ideas or just to give us feedback if something is missing or needs improvement. The issues are then referenced in the commit messages to track the changes and the progress so always stay up to date with the development around certain discussions.
Committing to the repository works as follows.
NOTE:
Be as precise as possible in your commit messages. Please always use present tense. Give the commit a meaningful title (max 50 chars long). After the title leave an empty line and then describe your changes. Be as verbose as you want to in your descriptions. Bullet points can be used as well to represent your changes (use asterisk as bullet points '*'). Provide references to existing issues the commit is addressing. This is done using the hastag and the ID of the commit (#id_of_the_issue).
Copyright (c) [2015] [The GFBio project]