Using Provenance to support Good Laboratory Practice in Grid Environments

📝 Abstract

Conducting experiments and documenting results is daily business of scientists. Good and traceable documentation enables other scientists to confirm procedures and results for increased credibility. Documentation and scientific conduct are regulated and termed as “good laboratory practice.” Laboratory notebooks are used to record each step in conducting an experiment and processing data. Originally, these notebooks were paper based. Due to computerised research systems, acquired data became more elaborate, thus increasing the need for electronic notebooks with data storage, computational features and reliable electronic documentation. As a new approach to this, a scientific data management system (DataFinder) is enhanced with features for traceable documentation. Provenance recording is used to meet requirements of traceability, and this information can later be queried for further analysis. DataFinder has further important features for scientific documentation: It employs a heterogeneous and distributed data storage concept. This enables access to different types of data storage systems (e. g. Grid data infrastructure, file servers). In this chapter we describe a number of building blocks that are available or close to finished development. These components are intended for assembling an electronic laboratory notebook for use in Grid environments, while retaining maximal flexibility on usage scenarios as well as maximal compatibility overlap towards each other. Through the usage of such a system, provenance can successfully be used to trace the scientific workflow of preparation, execution, evaluation, interpretation and archiving of research data. The reliability of research results increases and the research process remains transparent to remote research partners.

💡 Analysis

Conducting experiments and documenting results is daily business of scientists. Good and traceable documentation enables other scientists to confirm procedures and results for increased credibility. Documentation and scientific conduct are regulated and termed as “good laboratory practice.” Laboratory notebooks are used to record each step in conducting an experiment and processing data. Originally, these notebooks were paper based. Due to computerised research systems, acquired data became more elaborate, thus increasing the need for electronic notebooks with data storage, computational features and reliable electronic documentation. As a new approach to this, a scientific data management system (DataFinder) is enhanced with features for traceable documentation. Provenance recording is used to meet requirements of traceability, and this information can later be queried for further analysis. DataFinder has further important features for scientific documentation: It employs a heterogeneous and distributed data storage concept. This enables access to different types of data storage systems (e. g. Grid data infrastructure, file servers). In this chapter we describe a number of building blocks that are available or close to finished development. These components are intended for assembling an electronic laboratory notebook for use in Grid environments, while retaining maximal flexibility on usage scenarios as well as maximal compatibility overlap towards each other. Through the usage of such a system, provenance can successfully be used to trace the scientific workflow of preparation, execution, evaluation, interpretation and archiving of research data. The reliability of research results increases and the research process remains transparent to remote research partners.

📄 Content

Using Provenance to support Good Laboratory Practice in Grid Environments Miriam Ney1, Guy K. Kloss2, and Andreas Schreiber1 Abstract Conducting experiments and documenting results is daily business of sci- entists. Good and traceable documentation enables other scientists to confirm pro- cedures and results for increased credibility. Documentation and scientific conduct are regulated and termed as “good laboratory practice.” Laboratory notebooks are used to record each step in conducting an experiment and processing data. Orig- inally, these notebooks were paper based. Due to computerised research systems, acquired data became more elaborate, thus increasing the need for electronic note- books with data storage, computational features and reliable electronic documenta- tion. As a new approach to this, a scientific data management system (DataFinder) is enhanced with features for traceable documentation. Provenance recording is used to meet requirements of traceability, and this information can later be queried for further analysis. DataFinder has further important features for scientific documenta- tion: It employs a heterogeneous and distributed data storage concept. This enables access to different types of data storage systems (e. g. Grid data infrastructure, file servers). In this chapter we describe a number of building blocks that are available or close to finished development. These components are intended for assembling an electronic laboratory notebook for use in Grid environments, while retaining maxi- mal flexibility on usage scenarios as well as maximal compatibility overlap towards each other. Through the usage of such a system, provenance can successfully be used to trace the scientific workflow of preparation, execution, evaluation, interpre- tation and archiving of research data. The reliability of research results increases and the research process remains transparent to remote research partners. Miriam Ney · Andreas Schreiber Simulation and Software Technology, German Aerospace Centre, Berlin, Cologne, Germany e-mail: NeyMiriam@googlemail.com,Andreas.Schreiber@dlr.de Guy K. Kloss School of Computing + Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand e-mail: Guy.Kloss@aut.ac.nz 1 arXiv:1112.3062v1 [cs.DC] 13 Dec 2011 2 Miriam Ney, Guy K. Kloss, and Andreas Schreiber 1 Introduction With the “Principles of Good Laboratory Practice and Compliance Monitoring” the OECD provides research institutes with guidelines and a framework to ensure good and reliable research. It defines “Good Laboratory Practice” as “a quality system concerned with the organisational process and the conditions under which non- clinical health and environmental safety studies are planned, performed, monitored, recorded, archived and reported” (p. 14 in [8]). This definition can be extended to other fields of research. To prove the quality of research is of relevance for cred- ibility and reliability in the research community. Next to organisational processes and environmental guidelines, part of the good laboratory practice is to maintain a laboratory notebook when conducting experiments. The scientist documents each step, either taken in the experiment or afterwards when processing data. Due to computerised research systems, acquired data in- creases in volume and becomes more elaborate. This increases the need to migrate from originally paper-based to electronic notebooks with data storage, computa- tional features and reliable electronic documentation. For these purposes suitable data management systems for scientific data are available. 1.1 A Sample Use Case As an example use case a group of biologists are conducting research. This task includes the collection of specimen samples in the field. Such samples may need to be archived physically. The information on these samples must be present within the laboratory system to refer to it from further related entries. Information regarding these samples possibly includes the archival location, information on name, type, date of sampling, etc. The samples form the basis for further studies in the biological (wet) laboratories. Researchers in these environments are commonly not computer scientists, but biol- ogists who just “want to get their research done.” An electronic laboratory notebook application therefore must be similarly easy to operate in day-to-day practice like a paper-based notebook. All notes regarding experimentation on the samples and further derivative stages (processing, treatments, etc.) must be recorded, and linked to a number of other artifacts (other specimen, laboratory equipment, substances, etc.). As a result of this experimentation further artifacts are derived, which need to be managed. These could be either further physical samples, or information (data, measurements, digital images, instrument readings, etc.). Along with these artifacts the team manages documents outlining the project plan, documents on experimental procedures,

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