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- Researchers propose novel methods for ascribing authorship contributions, which involve assigning authorship to each result in a manuscript.<\/li>\n<\/ul>\n
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<\/p>\nThe last few years have seen concerted efforts to bring more consistency and quantification to the way that authorship and author contributions are assigned. In addition to existing tools such as <\/span>Contributory Roles Taxonomy (CRediT)<\/a>, various bodies have suggested new methods to facilitate <\/span>transparency<\/a> and ensure authorship and author contributions are easily and appropriately assigned. These include the International Society for Medical Publications Professionals (<\/span>ISMPP<\/a>) <\/span>authorship algorithm tool<\/a> and initiatives such as the <\/span>quantitative authorship decision support tool<\/a> and <\/span>Author Contribution Index<\/a>. Now, Oded Rechavi and Pavel Tomancak provide an alternative method in a recent <\/span>commentary<\/a> published in <\/span>Nature Reviews<\/a><\/em>.<\/span><\/p>\n
Rechavi and Tomancak\u2019s approach involves assigning credit to each result in a manuscript. They \u201cargue that it should be known who thought of each idea, who ran each experiment, and who analysed the data.\u201d But how exactly would this be achieved? The authors propose two ways. Rechavi suggests substituting the word \u201cwe\u201d for the names of specific, responsible authors. For instance, \u201cwe sequenced RNA\u201d would become \u201cRechavi sequenced RNA\u201d. Alternatively, Tomancak proposes assigning a number to each author in the author list and citing these for each contribution. For example, \u201cwe sequenced RNA1<\/sup>\u201d would credit the first author in the author list.<\/p>\n
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\u201cIt should be known who thought of each idea, who ran each experiment, and who analysed the data.<\/em>\u201d<\/p>\n<\/blockquote>\n
The authors list multiple advantages of ascribing authorship to each result, irrespective of how it is achieved. These include:<\/p>\n
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- vague author contribution statements become redundant<\/li>\n
- unexpected contributions are recognised (eg, theorists performing experimental work)<\/li>\n
- the semi-quantitative data provided could help to justify or assign author order.<\/li>\n<\/ul>\n
Nevertheless, they acknowledge several concerns raised by their peers, including:<\/p>\n
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- extra work will be needed to recall \u2018who did what\u2019 <\/em>for each sentence<\/li>\n
- reading the names of authors throughout a manuscript may be cumbersome<\/li>\n
- disputes may arise when discussing who contributed to a specific study.<\/li>\n<\/ul>\n
Rechavi and Tomancak counter this by calling on researchers to experiment with this alternative method in their own papers and suggest that bioRxiv<\/a><\/em>, the preprint server, is an ideal place to try it out. They end with a clear call to action: \u2018bottom-up\u2019 adoption by the scientific community is needed to implement meaningful, lasting changes to the way in which author contributions are assigned.<\/p>\n
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- extra work will be needed to recall \u2018who did what\u2019 <\/em>for each sentence<\/li>\n