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Folie 1 Statusreport Masterthesis > Miriam Ney > 13 .01.2011 Enabling a data management system to support the good laboratory practice Masterthesis Status Report Miriam Ney (13.01.2011)

Transcript of Enabling a data management system to support the good ...

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Statusreport Masterthesis > Miriam Ney > 13 .01.2011

Enabling a data management system to support the

good laboratory practice

Masterthesis Status Report – Miriam Ney (13.01.2011)

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Vortrag > Autor > Dokumentname > Datum

Overview

Description of Task

Approach to Complete Task

Phase 1: Requirements Analysis

Phase 2: Implementation

Phase 3: Testing and Evaluation

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Enabling a data management system to support the

good laboratory practice

Data management system: DataFinder

Open Source Project developed by DLR

Data management and workflow management

Heterogenous data store concept

Meta Data handling

Good laboratory practice (GLP)

Scientific conduct

Regulatories from DFG, OECD, Universities, …

Part of GLP: Laboratory Notebook

Description of Task:

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Requirements analysis (November, December)

What is the good laboratory practice?

How does a scientific workflow look like?

What do other implementation have?

What is part of a laboratory notebook?

Implementation

Feature 1: Origin of Data (January, February)

Feature 2: Evidentially Archiving (February)

Feature 3: Signing digitally (March)

Testing the Implementation (April)

NANO in GBK

Writing the thesis (concurrently)

Vortrag > Autor > Dokumentname > Datum

Plan:

Organizing the Master Thesis

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Requirements analysis

What is the good laboratory practice?

The principles of Good Laboratory Practice (GLP) have been developed

to promote the quality and validity of test data used for determining the

safety of chemicals and chemicals products.OECD Principles on Good Laboratory Practice

(as revised in 1997)

[The recommendations] are designed to provide a framework for the

deliberations and measures which each institution will have to conduct

for itself according to its constitution and its mission Deutsche Forschungsgemeinschaft:

Sicherung guter wissenschaftlicher Praxis (Safeguarding good scientific practice) 1998 (p.50).

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Requirements analysis

How does a scientific workflow look like?

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Requirements analysis

What do other implementations have?

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Requirements analysis:

What is part of a laboratory notebook?

Paper based

Immediate documentation

Protocol style

Short notes

Attesting authentication

Genuineness

Authenticity in general

Integrity

Chain of events

“Das Laborbuch ist ein Tagebuch des experimentierenden Naturwissenschaftlers”

(The laboratory notebook is the diary ofthe experimenting scientist)

(Schreiben und Publizieren in den NaturwissenschaftenVon Hans F. Ebel,Claus Bliefert,Walter Greulich; chapter 1.3 - page 16)

Electronically based

Durability

Collaboration

Versioning

Rights management

Variety of dataformats

Searchability

Device integration

Individual Sorting

Infrastructure

Environmental specialisation

Availability

Complexity

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Implementation

Feature 1: Origin of Data – Provenance IntegrationProvenance (lat. pro venire = to come from): origin of data , source

Motivation:

Requirements:

Integrity

Chain of events

Genuineness

TODO:

Provenance Model

Provenance System adjusting

„noblivious“

Integration into DataFinder

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Implementation

Feature 1: Origin of Data – Provenance Integration

Model

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Implementation

Feature 2: Evidentially Archiving

Motivation:

„Recommendation 7: Primary data as the basis for publications shall be

securely stored for ten years in a durable form in the institution of their

origin.“

Deutsche Forschungsgemeinschaft:

Sicherung guter wissenschaftlicher Praxis (Safeguarding good scientific

practice) 1998 (p.55).

TODO:

Requirements analysis:

BeLab project results

Developing concept:

DataStore integration in DataFinder of WS-Secure

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Implementation

Feature 3: Signing digitally

Motivation:

Requirements:

Authenticity in general

Attesting authentication

TODO:

Developing concept:

Authentification methods

Saving methods of signatures

Integration into DataFinder

Signing of data as MetaData

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Testing the Implementation

NANO: „Gasdynamisch initiierte Partikelerzeugung“

TODO:

Adjusting DataFinder for test data (datamodel)

Testing the system with the data

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Stand #

Fragen?

Kontakt:

Miriam Ney

DLR Simulations- und

Softwaretechnik, Berlin

Email: [email protected]