ToDo

ODAM: Development roadmap

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Data extraction

1. Extension of the Rodam package

   • Data exploitation only based on the datapackage.json file - an equivalent of the datapackage-r package for ODAM datapackage.
   • It will facilitate the reuse of the data provided with this structure by installing a single R package.
   • Development of a python package equivalent to Rodam

2. Build a lightweight interface (jQuery/Bootstrap) for

   • Data extraction - equivalent to Web API functions
   • Generation of the datapackage.json file with additional metadata
     • e.g. description of the dataset, choice of license, keywords, sources, ...

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Data annotation

Development carried out in interaction with the FooSIN project

1. Build a lightweight interface (jQuery/Bootstrap) for

   • Attribute annotations with terms selected from ontologies.
   • The choice of ontologies will be based on predefined sets according to the domain under consideration. For example, in the domain of plants, a set of ontologies will be predefined according to AgroPortal.
   • The chosen solution will be greatly inspired by the RightField project, the Swate tool or the annotation module of the MeRyB database

2. Automatic proposals for annotation

   • based on the annotation module of AgroPortal through its API.

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Linked Data

Development carried out in interaction with the FooSIN project

1. Build an ontology-based model to describe the structure of tabular and relational data, primarily focused on concepts of data entity¹, data attributes², and categories of variables (identifier³, factor⁴, quantitative⁵, qualitative⁶), as shown below :

   • See Make your data great again : How to ensure that open data works for research - Towards Linked Data
   
   
   

2. Make a proposal to convert a ODAM datapackage to JSON-LD and then to RDF.

   • Mainly based on the ontology defined at point 1.
   • the Tbox will be based on the AgroPortal ontologies as a Proof of Concept.

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1. A data entity is an object in a data model. Data is typically designed by breaking things down into their smallest parts that are useful for representing data relationships. For example, a plant can generate a list of samples. Each sample can be associated with several types of analytical variables. All three objects: plant, sample and type of analytical variables are considered data entities. ↩

2. Data attributes are characteristics of a data object. From data science view, they are the features of a data entity. They exist most often as a column in a data table. ↩

3. Identifiers precisely reference within the experiment each of the elements belonging to the same observation entity forming a coherent observation unit. For example, each plant, each sample has its own identifier, ↩

4. A factor of an experiment is a controlled independent variable; a variable whose levels are set by the experimenter. Treatments (control vs. stress), genotype (WT vs. mutant), the course of time (development stages) or even tissues, are typical factors of experiments. ↩

5. Quantitative data are values that describe a measurable quantity, in the form of numbers that can be calculated. ↩

6. Qualitative data describe qualities or characteristics. They answer questions such as "what type" or "what category". These values ↩