So, I've been through my publications list and broken it down in various respects - and I challenge my colleagues to do the same to see how they fare also.
I've looked at gender, LGBTQ+ representation and disability, but in most of the following lists and statistics people are not mentioned by name as I've not received their explicit permission to publish these personal details in this context. For the same reason, I am not making the full version of my data set publicly available, although I would be willing to share it for academic studies with sufficient personal data safeguards. There are some areas where the numbers will not be entirely accurate: my LGBTQ+ and disability figures include only those people where I know personally that they identify in this way and so I could be under-counting in these categories. Also, there are some coauthors whose sex/gender is unknown as I've never met them and whom I can't easily find online (mainly some East Asian coauthors on very large multi-authored papers). Similar comments also apply to BAME authors, where I am unaware of how some mixed heritage colleagues self-identify. In all of the theses cases, where the answer isn't clear I have either not included the name or erred on the most conservative view of assuming that they are white, straight, etc. in order to give the most critical view of the data. Finally, this data set draws on a subset of my total publications list: it includes all papers in peer-reviewed international journals, those in edited books and those in conference proceedings volumes. It excludes books that I have edited/authored, abstracts, popular articles, commentaries, web publications, encyclopedia articles, etc. It also includes three papers with >20 authors. Initially, I considered excluding these in case they introduced major biases, but in the in end I included them, again to stack the odds for the least diverse scenario (and also many of these papers include coauthors that were already included in my dataset for other reasons).
The dataset includes 199 published papers (1994–2020), of which 183 were coauthored with at least one other author (average number of coauthors 3.21). Of these I am first or senior author of 116 publications, for which I should assume the majority of responsibility for the composition of the group forming the collaboration. I had a total number of 341 coauthors (only six of whom lack gender information).
In terms of male/female balance (including at least one trans man who identifies as male):
Number of male coauthors = 255
Number of female coauthors = 81
So, a 76% to 24% male to female bias (similar to the overall results from the Pal Ass survey)
Taking a view of all authors has the following results for other categories of individuals:
BAME coauthors = 47 (14% of total)
LGBTQ+ coauthors = 10 (3% of total)
Disabled coauthors = 2 (0.5% of total)
Of the 199 papers, 88 had female coauthors (44% of total) of which 39 had female senior or first authors (20% of total).
Many of my papers have been published with a relatively small number of individuals:
>10 papers coauthored - six people
5-8 papers coauthored - 14 people
2-4 papers coauthored - 69 people
1 paper coauthored - 252 people (skewed by several mega-authored papers)
Delving into this reveals some good news in terms of balance as two of my top five coauthors are female:
1. Richard Butler (29; M; UK)
2. Paul Upchurch (27; M; UK)
3. Susannah Maidment (22; F; UK)
4. Roger Benson (16; M; UK)
5. Emily Rayfield (13; F; UK)
However, only two other female coauthors currently fall into the 5+ category (Angela Milner [6, UK] and Kimi Chapelle [5, South Africa]). Also, it should be noted that all of my top 5 coauthors were either former students, postdocs or close contemporaries (and were all based at Cambridge, although most of the papers have been produced while we've held posts elsewhere). The majority of other 5+ authors are not from the UK and include representatives from South Africa, China, Canada, Switzerland and USA.
In terms of other diversity, I also tabulated the countries where I have coauthors (though note this is generally based on country where they are based, not country of origin, and for simplicity some international moves have been ignored in favour of the country where most of this work was carried out.
South Africa: 16
Finally, although not publication related, it's also possible to look at these issues with respect to students and postdocs. In my case, I've had seven postdocs (4 M, 3F) and 24 PhD students that I've advised or co-advised (16 M, 9 F, 1 trans).
So, what next? I'm glad to see that my stats do reflect at least some diversity (in terms of top coauthors, lab membership, geographic diversity), but can see many other areas where it would be open to criticism. I think, at least, this has given me a clearer idea of some of the issues that are being highlighted by broader studies and the stream of comments I see, though I would be most interested in seeing comparative data from other colleagues at similar career stages. Also, I can see definite trends in my own data towards greater inclusion, largely as my own networks have grown (for example, the majority of my South African collaborators are female) and as projects have become more multidisciplinary (rather than narrowly focused). Having data in hand makes me more aware of the issue from a very personal perspective and is certainly making me think more about how future networks might be opened up.