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Tutorial to be held in connection with TPDL 2013, 22 September 2013, Valletta, Malta

From Preserving Data to Preserving Research: Curation of Process and Context


In the domain of eScience, investigations are increasingly collaborative. Most scientific and engineering domains benefit from building on the outputs of other research: by sharing information to reason over and data to incorporate in the modeling task at hand. This raises the need for preserving and sharing entire eScience workflows and processes for later reuse. We need to define which information is to be collected, create means to preserve it and approaches to enable and validate the re-execution of a preserved process. This includes and goes beyond preserving the data used in the experiments, as the process underlying its creation and use is essential.

The TIMBUS project and Wf4Ever project team up for this half-day tutorial to provide an introduction to the problem domain and discuss solutions for the curation of eScience processes.

1. TUTORIAL LEVEL: Introductory level

2. DURATION: Half-day


The tutorial will cover the following topics:

Introduction to Process and Context Preservation: The introduction will motivate the need for process and context preservation, illustrate how this task is difficult in an evolving domain, and introduce a use case for the rest of the tutorial to illustrate approaches and tools.

Data Citation: Data forms the basis of the results of many research publications, and thus needs to be referenced with the same accuracy as bibliographic data. Only if data can be identified with high precision can it be reused, validated, verified and reproduced. Citing a specific data set is however not trivial - it exists in a vast plurality of specifications and instances, can potentially be huge in size, and its location might change. We will provide an overview over existing approaches to overcoming these challenges. Further, we will present the issue of creating data citations of data held in databases, especially of dynamic data sets where data is added or updated on a regular basis.

Re-usability and traceability of workflows and processes: The processes creating and interpreting data are complex objects. Curating and preserving them requires special effort, as they are dynamic, and highly dependent on software, configuration, hardware, and other aspects. We will discuss these issues in detail, and provide an introduction to two complementary approaches.

The first approach is based on the concept of Research Objects, which adopts a workflow-centric approach and thereby aims at facilitating the reuse and reproducibility. It allows packaging the data and the methods as one Research Object to share and cite it, and thus enable publishers to grant access to the actual data and methods that contribute to the findings reported in scholarly articles.

A second approach focuses on describing and preserving a process and the context it is embedded in. The artifacts that may need to be captured range from data, software and accompanying documentation, to legal and human resource aspects. Some of this information can be automatically extracted from an existing process, and tools for this will be presented. Ways to archive the process and to perform preservation actions on the process environment, such as recreating a controlled execution environment or migration of software components, are presented. Finally, the challenge of evaluating the re-execution of a preserved process is discussed, addressing means of establishing its authenticity.


The tutorial is targeted at researchers, publishers and curators in eScience disciplines who want to learn about methods of ensuring the long-term availability of experiments forming the basis of scientific research.


The tutorial participants will become understand

Motivations and challenges of process preservation

Motivations, stakeholders and challenges of making data citable

How Data is Cited Today: OECD [1] report on data citability, Google search of data sets, requirements, guidelines, metadata, locators and identifiers, approaches to naming schemes and properties.

Available technologies for identifiers: Archival Resource Key (ARK), Digital Object Identifiers (DOI), Extensible Resource Identifier (XRI), HANDLE, Life Science ID (LSID), Object Identifiers (OID), Persistent Uniform Resource Locators (PURL), URI/URN/URL, Universally Unique Identifier (UUID)

Approaches and Initiatives for citing data: CODATA, Data Cite, OpenAire, challenges and opportunities: granularity, scalability, complexity and evolving data sets current research questions

Ontologies needed to capture research objects: Core Ontology of the RO family of vocabularies, workflow centric ROs, provenance traces, life cycle of research objects.

Wf4Ever Toolkit / technological infrastructure for the preservation and efficient retrieval and reuse of scientific workflows: software architecture, functionalities, software interfaces to functionalities, reference implementation as services and clients:

~ Collect, manage and preserve aggregations of scientific workflows and related objects and annotations

~ Workflow sharing through a social website

~ Execution of workflows

~ Testing completeness, execution, repeatability and other desired quality features

~ Testing the ability of a Research Object to achieve its original purpose after changes to its resources.

~ Recommendations of relevant users, Research Objects and their aggregated resources

~ Converting workflows into Research Objects

~ Search for workflows by input parameters or frequency of use

~ Collaborative environment

~ Access and use of research objects and aggregated resources.

~ Synchronization with remote repositories

~ Visualization of correlation between similar objects

TIMBUS context model and tools to semi-automatically capture the relevant context of a business process for preservation

~ The scope of context regarding business process preservation - technology, application and business context, aligned with enterprise architecture

~ The context meta-model, with domain independent and domain specific aspects

~ Demonstration of a context model instance of example processes (in the eScience domain)

~ Tools to automatically capture some parts of the context (software dependencies, data formats, licenses, ...)

~ Outlook on reasoning and preservation planning, based on the context model