Using someone else’s data: problems, pragmatics and provisions

In the current climate of requirements for ethical research, qualitative research data is often archived at the end of each unique research project. Yet qualitative data is capable of being revisited from multiple perspectives, and used to answer different research questions to those envisaged by the original data collector. Using other people’s data saves time, avoids unnecessarily burdening your research participants, and adds confidence in interpreting your own data. This paper is a case of how data from one research project was acquired and then analysed to ground the analysis of a separate project using Distributed Cognition (Dcog) theory and its associated methodology, cognitive ethnography. Theoretical considerations were the benefits and difficulties of using multiple sources and types of data in creating a theoretical account of the observed situation. Methodological issues included how to use (and not misuse) other people’s data and coherently integrate data collected over time and for different purposes. Current ethics guidelines come from a paradigm of control suited to experimental, quantitative research approaches. A new paradigm that recognises researchers’ inherent lack of control over qualitative research contexts needs to be developed. This research demonstrates the benefits of designing an ethics application to provide for data reuse and giving participants choice over the level of protection they require

Interannual variability of the stratospheric wave driving during northern winter

The strength of the stratospheric wave driving during northern winter is often quantified by the January–February mean poleward eddy heat flux at 100 hPa, averaged over 40°–80° N (or a similar area and period). Despite the dynamical and chemical relevance of the wave driving, the causes for its variability are still not well understood. In this study, ERA-40 reanalysis data for the period 1979–2002 are used to examine several factors that significantly affect the interannual variability of the wave driving. The total poleward heat flux at 100 hPa is poorly correlated with that in the troposphere, suggesting a decoupling between 100 hPa and the troposphere. However, the individual zonal wave-1 and wave-2 contributions to the wave driving at 100 hPa do exhibit a significant coupling with the troposphere, predominantly their stationary components. The stationary wave-1 contribution to the total wave driving significantly depends on the latitude of the stationary wave-1 source in the troposphere. The results suggest that this dependence is associated with the varying ability of stationary wave-1 activity to enter the tropospheric waveguide at mid-latitudes. The wave driving anomalies are separated into three parts: one part due to anomalies in the zonal correlation coefficient between the eddy temperature and eddy meridional wind, another part due to anomalies in the zonal eddy temperature amplitude, and a third part due to anomalies in the zonal eddy meridional wind amplitude. It is found that year-to-year variability in the zonal correlation coefficient between the eddy temperature and the eddy meridional wind is the most dominant factor in explaining the year-to-year variability of the poleward eddy heat flux

BridgeDb: standardized access to gene, protein and metabolite identifier mapping services

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

Many interesting problems in bioinformatics require integration of data from various sources. For example when combining microarray data with a pathway database, or merging co-citation networks with protein-protein interaction networks. Invariably this leads to an identifier mapping problem, where different datasets are annotated with identifiers that are related, but originate from different databases.

Solutions for the identifier mapping problem exist, such as Biomart, Synergizer, Cronos, PICR, HMS and many more. This creates an opportunity for bioinformatics tool developers. Tools can be made to flexibly support multiple mapping services or mapping services could be combined to get broader coverage. This approach requires an interface layer between tools and mapping services. BridgeDb provides such an interface layer, in the form of both a Java and REST API.

Because of the standardized interface layer, BridgeDb is not tied to a specific source of mapping information. You can switch easily between flat files, relational databases and several different web services. Mapping services can be combined to support multi-omics experiments or to integrate custom microarray annotations. BridgeDb isn't just yet another mapping service: it tries to build further on existing work, and integrate multiple partial solutions. The framework is intended for customization and adaptation to any identifier mapping service. 

BridgeDb makes it easy to add an important capability to existing tools. BridgeDb has already been integrated into several popular bioinformatics applications, such as Cytoscape, WikiPathways, PathVisio, Vanted and Taverna. To encourage tool developers to start using BridgeDb, we've created code examples, online documentation, and a mailinglist to ask questions. 

We believe that, to meet the challenges that are encountered in bioinformatics today, the software development process should follow a few essential principles: user friendliness, code reuse, modularity and open source. BridgeDb adheres to these principles, and can serve as a useful model for others to follow. BridgeDb can function to increase user-friendliness of graphical applications. It re-uses work from other projects such as BioMart and MIRIAM. BridgeDb consists of several small modules, integrated through a common interface (API). Components of BridgeDb can be left out or replaced, for maximum flexibility. BridgeDb was open source from the very beginning of the project. The philosophy of open source is closely aligned to academic values, of building on top of the work of giants. 

The BridgeDb library is available at "http://www.bridgedb.org":http://www.bridgedb.org.
A paper about BridgeDb was published in BMC _Bioinformatics_, 2010 Jan 4;11(1):5.

BridgeDb blog: "http://www.helixsoft.nl/blog/?tag=bridgedb":http://www.helixsoft.nl/blog/?tag=bridged