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Methodology capture: discriminating between the "best" and the rest of community practice.

Eales JM, Pinney JW, Stevens RD, Robertson DL - BMC Bioinformatics (2008)

Bottom Line: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas.We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design.Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk

ABSTRACT

Background: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field.

Results: Our approach is to implement data extraction techniques on the full-text of scientific articles to obtain the set of experimental protocols used by an entire scientific discipline, molecular phylogenetics. Our methodology for identifying methodologies could in principle be applied to any scientific discipline, whether or not computer-based. We find a number of issues related to the nature of best practice, as opposed to community practice. We find that there is much heterogeneity in the use of molecular phylogenetic methods and software, some of which is related to poor specification of protocols. We also find that phylogenetic practice exhibits field-specific tendencies that have increased through time, despite the generic nature of the available software. We used the practice of highly published and widely collaborative researchers ("expert" researchers) to analyse the influence of authority on community practice. We find expert authors exhibit patterns of practice common to their field and therefore act as useful field-specific practice indicators.

Conclusion: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas. Best practice information can help to bridge such subtle differences by increasing communication of protocols to a wider audience. We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design. Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.

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Co-authorship network highlighting most expert authors. Co-authorship network according to research field. Nodes represent individual authors, edges represent three or more co-authorships between the two connected authors. The 20 expert authors (see Methods) are represented by larger nodes with numbered labels. Author Names, 1: Koonin, E.V., 2: Pace, N.R., 3: Wang, Y., 4: Zhang, Y., 5: Doolittle, W.F., 6: Hasegawa, M., 7: Okada, N., 8: Nei, M., 9: Roger, A.J., 10: Meyer, A., 11: Falsen, E., 12: Collins, M.D., 13: Stackebrandt, E., 14: Schumann, P., 15: Yoon, J.H., 16: Orito, E., 17: Mizokami, M., 18: Webster, R.G., 19: Sharp, P.M., 20: Gessain, A.
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Figure 5: Co-authorship network highlighting most expert authors. Co-authorship network according to research field. Nodes represent individual authors, edges represent three or more co-authorships between the two connected authors. The 20 expert authors (see Methods) are represented by larger nodes with numbered labels. Author Names, 1: Koonin, E.V., 2: Pace, N.R., 3: Wang, Y., 4: Zhang, Y., 5: Doolittle, W.F., 6: Hasegawa, M., 7: Okada, N., 8: Nei, M., 9: Roger, A.J., 10: Meyer, A., 11: Falsen, E., 12: Collins, M.D., 13: Stackebrandt, E., 14: Schumann, P., 15: Yoon, J.H., 16: Orito, E., 17: Mizokami, M., 18: Webster, R.G., 19: Sharp, P.M., 20: Gessain, A.

Mentions: In order to identify authors who were most active in this network, we restricted the node set to include only those that had co-authored three or more articles with one or more other authors. This reduced the largest connected component of the resultant network to 1,112 nodes and 2,412 edges; we refer to this network as the reduced collaboration network (Figure 5). When we consider the reduced collaboration network of authors (Figure 5) who have published in our phylogenetics corpus, we see a field-specific pattern similar to that in Figure 2. There are many authors who tend not to collaborate regularly with others outside their field (visible as the clusters of nodes of a single colour) and then there are other authors who link the clusters through interdisciplinary collaborations.


Methodology capture: discriminating between the "best" and the rest of community practice.

Eales JM, Pinney JW, Stevens RD, Robertson DL - BMC Bioinformatics (2008)

Co-authorship network highlighting most expert authors. Co-authorship network according to research field. Nodes represent individual authors, edges represent three or more co-authorships between the two connected authors. The 20 expert authors (see Methods) are represented by larger nodes with numbered labels. Author Names, 1: Koonin, E.V., 2: Pace, N.R., 3: Wang, Y., 4: Zhang, Y., 5: Doolittle, W.F., 6: Hasegawa, M., 7: Okada, N., 8: Nei, M., 9: Roger, A.J., 10: Meyer, A., 11: Falsen, E., 12: Collins, M.D., 13: Stackebrandt, E., 14: Schumann, P., 15: Yoon, J.H., 16: Orito, E., 17: Mizokami, M., 18: Webster, R.G., 19: Sharp, P.M., 20: Gessain, A.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2553348&req=5

Figure 5: Co-authorship network highlighting most expert authors. Co-authorship network according to research field. Nodes represent individual authors, edges represent three or more co-authorships between the two connected authors. The 20 expert authors (see Methods) are represented by larger nodes with numbered labels. Author Names, 1: Koonin, E.V., 2: Pace, N.R., 3: Wang, Y., 4: Zhang, Y., 5: Doolittle, W.F., 6: Hasegawa, M., 7: Okada, N., 8: Nei, M., 9: Roger, A.J., 10: Meyer, A., 11: Falsen, E., 12: Collins, M.D., 13: Stackebrandt, E., 14: Schumann, P., 15: Yoon, J.H., 16: Orito, E., 17: Mizokami, M., 18: Webster, R.G., 19: Sharp, P.M., 20: Gessain, A.
Mentions: In order to identify authors who were most active in this network, we restricted the node set to include only those that had co-authored three or more articles with one or more other authors. This reduced the largest connected component of the resultant network to 1,112 nodes and 2,412 edges; we refer to this network as the reduced collaboration network (Figure 5). When we consider the reduced collaboration network of authors (Figure 5) who have published in our phylogenetics corpus, we see a field-specific pattern similar to that in Figure 2. There are many authors who tend not to collaborate regularly with others outside their field (visible as the clusters of nodes of a single colour) and then there are other authors who link the clusters through interdisciplinary collaborations.

Bottom Line: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas.We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design.Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester, Manchester, UK. james.eales@postgrad.manchester.ac.uk

ABSTRACT

Background: The methodologies we use both enable and help define our research. However, as experimental complexity has increased the choice of appropriate methodologies has become an increasingly difficult task. This makes it difficult to keep track of available bioinformatics software, let alone the most suitable protocols in a specific research area. To remedy this we present an approach for capturing methodology from literature in order to identify and, thus, define best practice within a field.

Results: Our approach is to implement data extraction techniques on the full-text of scientific articles to obtain the set of experimental protocols used by an entire scientific discipline, molecular phylogenetics. Our methodology for identifying methodologies could in principle be applied to any scientific discipline, whether or not computer-based. We find a number of issues related to the nature of best practice, as opposed to community practice. We find that there is much heterogeneity in the use of molecular phylogenetic methods and software, some of which is related to poor specification of protocols. We also find that phylogenetic practice exhibits field-specific tendencies that have increased through time, despite the generic nature of the available software. We used the practice of highly published and widely collaborative researchers ("expert" researchers) to analyse the influence of authority on community practice. We find expert authors exhibit patterns of practice common to their field and therefore act as useful field-specific practice indicators.

Conclusion: We have identified a structured community of phylogenetic researchers performing analyses that are customary in their own local community and significantly different from those in other areas. Best practice information can help to bridge such subtle differences by increasing communication of protocols to a wider audience. We propose that the practice of expert authors from the field of evolutionary biology is the closest to contemporary best practice in phylogenetic experimental design. Capturing best practice is, however, a complex task and should also acknowledge the differences between fields such as the specific context of the analysis.

Show MeSH
Related in: MedlinePlus