- freely available
Data 2019, 4(2), 83; https://doi.org/10.3390/data4020083
- Scientists use specific or customized tools, software and data formats with good reason. A research data management system (RDMS) must be built around their workflows and practices and be open for change. It furthermore should assist in integrating data, even if stored in propriertary formats. Enforcing one of the individual systems cannot work in a heterogeneous scientific environment.
- If the RDMS requires too much extra work for learning and understanding, it is likely that the individual scientist will be unable to use it efficiently or just be unwilling to use it at all. This holds in particular for the construction of queries which should retrieve data according to powerful criteria while being simple and intuitive at the same time. For this reason it is unlikely that a scientist without computer science background will be able to use Structured Query Language (SQL) or SPARQL efficiently [8,9,10].
- The system should strongly encourage to develop and use standards for workflows and data models without being overly restrictive. Users of the database can only profit from the system when data is organized sufficiently structured to enable everyone to search and retrieve data easily and to understand the structure of the data intuitively. At the same time the database has to be prepared for constantly evolving data models and standards .
- File systems and other types of storage usually organize data into some kind of hierarchy which can be folders or projects. In scientific environments this can raise issues, especially when data belongs to multiple projects or is part of cooperations.
- Simple and intuitive access to complex and changing data models
- Reuse and integration of existing workflows and technologies
- Findable (F1–F4)
- Accessible (A1–A2)
- Interoperable (I1–I3)
- Reusable (R1–R1.3)
- Entities with subtyping
- User-defined n-ary relationships and properties
- Integration of files and directories as entities
- Native support for primitive data types which include several numeric data types with their physical units and uncertainties, standard compliant date and time values, booleans, strings, and undefined values
- Compound data types for lists, sets, tuples, and dictionaries
SELECT flavour, rating, ingredients FROM Experiment WHICH HAS A room_temperature > 26C AND WHICH IS REFERENCED BY ExperimentSeries WHICH HAS A name LIKE *ice cream testing*
- offers a flexible data model
- can be seamlessly integrated into existing workflows
- allows search for values of specific fields (not just full text search), with automatic unit conversion and search for (back)references of linked objects.
2.4. Data Model
2.5. Query Language
(?date >= xsd:date("2017-01-01") && ?date < xsd:date("2018-01-01"))The implementation of a temperature filter covering unit conversion would even rely on external unit conversion extensions.
Find Experiment with date in 2017 and room temperature=293.15K
- A query starting with Count returns a non-negative integer.
- A query starting with Find returns a list of entities.
- A query starting with Select returns a table containing the values of selected Properties.
COUNT Experiment with date in 2017will return the number of experiments from 2017. In this query, Experiment is typically the name of a Record Type with a possibly large number of subtypes and instances. All Entities which have the name Experiment or have a parent with this name are filtered for those which have a Property with the name date and a date value in the year 2017. CQL filters can also express the equivalence of complex SQL joins in an easily understandable syntax:
FIND Person which is referenced as an Author by an Article which has aTitle like *terminating ventricular fibrillation*
SELECT first name, family name from person with date of birth > 2000will appear as an HTML table in the WebUI (downloadable as a tsv table), with three columns—id, first name, and family name. This feature is intended to provide one of the interfaces between CaosDB and existing scientific workflows.
2.6. User Management and Access Control
Conflicts of Interest
|RDMS||Research Data Management System|
|FAIR||Findable, Accessible, Interoperable and Reusable|
|REST||Representational State Transfer|
|ACID||Atomicity, Consistency, Isolation, Durability|
|API||Application Programming Interface|
|XML||Extensible Markup Language|
|HTTP||Hypertext Transfer Protocol|
|XSLT||Extensible Stylesheet Language Transformations|
|HTML||Hypertext Markup Language|
|SQL||Structured Query Language|
|CQL||CaosDB Query Language|
|GNU||GNU’s not Unix|
|PAM||Pluggable Authentication Modules|
|EBNF||Extended Backus-Naur Form|
|RDF||Resource Description Framework|
|OWL||Web Ontology Language|
|UML||Unified Modeling Language|
|SPARQL||SPARQL Protocol and RDF Query Language|
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Atomicity, Consistency, Isolation, Durability.
Representational State Transfer.
Extended Backus-Naur Form.
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