The Allotrope Data Format (ADF) is a family of specifications designed to standardize the acquisition, exchange, storage and access of analytical data captured in laboratory workflows. This document is an overview of ADF. It provides an entry point to its specifications and documentation:



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Table of Contents

1. Introduction

The Allotrope Data Format (ADF) defines an interface for storing scientific observations from analytical chemistry. It is intended for long-term stability of archived analytical data and fast real-time access to it. This document provides an overview of the main components and links to corresponding specifications.

The document is structured as follows: Within the remaining part of the introduction, document conventions are described. In particular, naming conventions and a list of namespaces and corresponding prefixes are given that are used throughout different ADF specifications. Then, the general requirements for the ADF API stack and the different components are described. Detailed descriptions are available at the corresponding specifications.

1.1 Document Conventions

1.1.1 Namespaces

The IRI of an entity has two parts: the namespace and the local identifier. Within one RDF document the namespace might be associated by a shorter prefix. For instance the namespace IRI http://www.w3.org/2002/07/owl# is commonly associated with the prefix owl: and one can write owl:Class instead of the full IRI http://www.w3.org/2002/07/owl#Class.

Within the biomedical domain the local identifier is often an alphanumeric ID which is not human readable. The Allotrope Foundation Ontologies [AFO] follow this approach, e.g., a process is represented as af-p:AFP_0001617. To enhance readability within this document, the preferred label from the ontology or taxonomy is used for the corresponding entity. I.e., instead of af-p:AFP_0001617 the corresponding entity is named as af-p:process. If the namespace is clear by the context the prefix MAY be omitted and the entity is named simply process. If the label contains spaces, the entity MAY be surrounded by guillemets to avoid ambiguities, e.g. «af-p:experimental method».

Within the ADF specifications, the following namespace prefix bindings are used:

Prefix Namespace

Allotrope SHOULD use the common prefixes registered at prefix.cc.

1.1.2 Indication of Requirement Levels

Within this document the definitions of MUST, SHOULD and MAY are used as defined in [rfc2119].

1.1.3 Number Formatting

Within this document, decimal numbers will use a dot "." as the decimal mark.

2. Requirements

This section describes the key requirements to the Allotrope Data Format (ADF).

The key requirements for ADF are:

3. ADF High-Level File Structure

This section describes the high-level structure of the Allotrope Data Format (ADF). The following figure illustrates the high-level structure of ADF:

Fig. 1 The high-level structure of ADF file
An ADF file has six components where all data is stored. The data description component stores meta data in form of RDF [rdf11-concepts] statements. The named graphs store where user defined graphs can be stored in form of RDF [rdf11-concepts] statements The data cube component stores n-dimensional source or processed data that are the output of experimental processes. The data package component is a general purpose file storage (e.g. for reports or images). The audit store contains the audit trail. Finally, there is a component for checksums.

4. ADF Components Overview

The following figure illustrates the high-level structure of the Allotrope Data Format (ADF) API stack.

Fig. 2 The high-level structure of ADF API stack
According to the high-level file structure there are three APIs for data package, data cube and data description (with a more technical RDF store API). The analytical data API provides specific methods for specific analytical techniques. Further, there is an API for the HDF5 file format and one for the audit trail. The taxonomies and ontologies provide the vocabulary and data structures for RDF representations.

4.1 ADF API Stack

4.1.1 ADF Data Package API

The Data Package API specification [ADF-DP] defines how to store multiple (proprietary, binary, text) data files in a single package. The purpose of packaging is to ensure consistency and integrity of data files and metadata during storage and transfer. Files stored in the data package can represent source measurements or results of an experiment or process described in the data description. The vocabulary used within the ADF Data Package operations is described in the ADF Data Package Ontology [ADF-DPO].

4.1.2 ADF Data Cube API

The Data Cube API specification [ADF-DC] defines how to store one- or multi-dimensional data. This can be source or processed data, which may be sparse. Data cubes represent measurements or results of an experiment or process described in the data description. The vocabulary used within the ADF Data Cube operations is described in the ADF Data Cube Ontology [ADF-DCO].

4.1.3 ADF Data Description API

The Data Description API specification [ADF-DD] defines how to store experiment or process descriptions and contextual metadata as well as metadata of Data Package and Data Cubes.

4.1.4 ADF Quadruple Store API

The Quadruple Store API specification [ADF-QS] provides an efficient persistence layer for the RDF Data Model based on HDF5 [HDF5]. It allows to store, query and retrieve RDF statements of the RDF Data Model.

4.2 Allotrope Foundation Ontology Stack

The following figure shows the different ontologies that are used to describe data stored in ADF:

Fig. 3 The ontology stack.

4.2.1 ADF Data Package Ontology

The ADF Data Package Ontology [ADF-DPO] provides the vocabulary for the operations of the ADF Data Package API [ADF-DP] on files and folders.

4.2.2 ADF Data Cube Ontology

The ADF Data Cube Ontology [ADF-DCO] provides the vocabulary for the descriptions of multi-dimensional data and the operations on them. It is used by the ADF Data Cube API [ADF-DC].

4.2.3 ADF Data Cube to HDF5 Mapping Ontology

The ADF Data Cube to HDF5 Mapping Ontology [ADF-DCO-HDF] provides the vocabulary for the descriptions of the mapping between the functional representation of data cubes to their physical representation in HDF5 data sets.

4.2.4 ADF Audit Ontology

The ADF Audit Ontology [ADF-AUDIT] provides the vocabulary for the description of audit trail entries and electronic signatures.

4.3 Platform Independent File Format

ADF uses HDF5 [HDF5] as the underlying file format. ​The following figure illustrates the basic layout of an ADF file in HDFView with the six components for data description, named graphs, data cube, data package, checksums and audit trail:

Fig. 4 The ADF file layout.

5. Change History

Version Release Date Remarks
0.3.0 2015-04-30 Initial Working Draft version
0.4.0 2015-06-18
  • included references to all related ADF specifications
  • included disclaimer information
  • harmonized layout across ADF specifications
  • harmonized structure across ADF specifications
  • added namespace overview
1.0.0 RC 2015-09-17
  • added new specifications and documentation
  • updated links
  • updated dates
1.0.0 2015-09-29
  • added section for blueprints
  • added section for ADF Data Cube to HDF5 mapping ontology
  • updated versions, dates and document status
  • updated references
  • updated figure in section Platform Independent File Format
1.1.0 RC 2016-03-11
  • updated versions, dates and document status
  • added section on number formatting to document conventions
  • updated Fig. 2 and Fig. 3
1.1.0 RF 2016-03-31
  • updated versions, dates and document status
1.1.5 2016-05-13
  • updated versions and dates
1.2.0 Preview 2016-09-23
  • updated versions and dates
1.2.0 RC 2016-12-07
  • updated versions and dates
  • updated Fig. 2
1.3.0 Preview 2017-03-31
  • updated versions and dates
  • updated abstract and references
  • uodated ADF components overview
  • updated section on platform independent file format
  • added section for ADF Audit Ontology
  • updated Fig. 1 and Fig. 4
1.3.0 RF 2017-06-30
  • updated versions and dates
  • adaptations to new business model
  • minor edits

A. References

A.1 Normative references

Allotrope. Allotrope Data Format Audit Trail and Electronic Signature Specification. URL: http://purl.allotrope.ord/TR/adf-audit/
Allotrope. ADF Check Sum Computation. URL: http://purl.allotrope.ord/TR/adf-checksum/
Allotrope. ADF Data Cube API. URL: http://purl.allotrope.org/TR/adf-dc/
Allotrope. ADF Data Cube Ontology. URL: http://purl.allotrope.org/TR/adf-dco/
Allotrope. ADF Data Cube to HDF5 Mapping Ontology. URL: http://purl.allotrope.org/TR/adf-dco-hdf/
Allotrope. ADF Data Description API. URL: http://purl.allotrope.org/TR/adf-dd/
Allotrope. ADF Data Package API. URL: http://purl.allotrope.org/TR/adf-dp/
Allotrope. ADF Data Package Ontology. URL: http://purl.allotrope.org/TR/adf-dpo/
Allotrope. ADF RDF Store API. URL: http://purl.allotrope.org/TR/adf-qs/
Allotrope. Allotrope Foundation Ontologies. URL: http://purl.allotrope.org/TR/afo/
The HDF Group. HDF5 File Format Specification 2.0. URL: http://www.hdfgroup.org/HDF5/doc/H5.format.html
Richard Cyganiak; David Wood; Markus Lanthaler. W3C. RDF 1.1 Concepts and Abstract Syntax. 25 February 2014. W3C Recommendation. URL: https://www.w3.org/TR/rdf11-concepts/
S. Bradner. IETF. Key words for use in RFCs to Indicate Requirement Levels. March 1997. Best Current Practice. URL: https://tools.ietf.org/html/rfc2119

A.2 Informative references

Allotrope. ADF Audit Trail Developer's Guide. URL: http://purl.allotrope.org/TR/adf-audit-dg/
Allotrope. ADF Data Cube API Developer's Guide. URL: http://purl.allotrope.org/TR/adf-dc-dg/
Allotrope. ADF Data Description API Developer's Guide. URL: http://purl.allotrope.org/TR/adf-dd-dg/
Allotrope. ADF Data Package API Developer's Guide. URL: http://purl.allotrope.org/TR/adf-dp-dg/
Allotrope. ADF Java Documentation. URL: http://purl.allotrope.org/TR/adf-java-doc/
Allotrope. Allotrope Framework OWL Documentation. URL: http://purl.allotrope.org/TR/af-owl-doc/