DOI: 10.5061/dryad.tmpg4f518
title: Data for: Representation in sea turtle science: slow progress towards gender equity and globalization revealed from thirty years of symposium abstracts

description: Sea turtles are a circumglobal taxon that receive considerable research
 attention, yet there is little information about the demographics of sea
 turtle researchers. To assess long-term trends in demographic, geographic,
 and institutional representation within the sea turtle community, we
 quantified information from 7041 abstracts presented at the International
 Sea Turtle Symposium (ISTS) between 1988–2018. The dataset from this study
 is presented here.
description: <strong><em>Data
 Source</em></strong> The first ISTS
 occurred in 1980 and has been repeated annually until 2020, when
 subsequent events were put on hold due to the COVID-19 pandemic. The first
 seven meetings were based around informal discussions and meetings. Since
 1988, however, the event has followed a more structured format based
 around several days of short presentations (8-20 mins each). The “Book of
 Abstracts” from these presentations are published after each symposium and
 made available at https://www.internationalseaturtlesociety.org/publications/proceedings/ or https://repository.library.noaa.gov/. We gleaned data from every abstract presented in the annual Book of Abstracts between the 8th ISTS in 1988 and the 30th in 2018 (for full references see Supplementary Materials 1). However, we did not compile any data from the 32nd, 35th, 37th, and 39th ISTS as the official Book of Abstracts for these events were not publicly available at the time of writing this manuscript. We only report on data that was shared within these Books of Abstracts and did not access any additional personal information. Furthermore, no specific individuals or institutions will be named in this manuscript. From each manuscript, we extracted the following information: (1) whether the abstract was for an oral or a poster presentation, (2) the number of authors per abstract, (3) the inferred gender of the first author and last author using their first name, (4) the category (defined below) of the first author’s affiliated institution, and (5) the location of both the author’s affiliated institution as well as the location where the study took place. We will expand on each of these topics in the sub-headers below. <em><strong>Oral or poster</strong></em> In the database, we recorded the location of the event, the total number of presentations per year, and whether each abstract was given as either an oral or a poster presentation. Presentations are occasionally made in other formats, such as video presentations, but for consistency these were not included. We also did not include any key-note presentations. <em><strong>Number of authors per abstract</strong></em> We recorded the total number of authors, including the first author, on each abstract. When authors listed a collective of individuals under a single heading (e.g. J.J. Jamieson and volunteers), we counted the collective as a single individual. <em><strong>Inferred gender of first and last author</strong></em> We inferred the gender of the first and last author by consulting the Oxford Dictionary of First Names (Hanks et al. 2006), which indicated whether these names were predominantly used for male or females. For example, Alexander would be listed as male, Alexandra would be listed female, and Alex would not have a categorized gender. If the author had initialized their first name, we used their next listed name as long as it was not their final name (i.e. J. Jonah Jameson would be shortened to Jonah Jameson but J. J. Jameson would not be categorized) or if the author’s full name appeared elsewhere in our dataset. For those names that either did not appear in the Oxford Dictionary of First Names or were categorized as being unisex names, we used the platform Gender-API (https://gender-api.com/) to determine the most likely gender. Names that could not be categorized by Gender-API or were given a 50 % probability of being either gender were left as non-categorized. We acknowledge that gender is not binary, and that the gender inferred via author’s name may not match the author’s self-identified gender. Consequently, this study’s representation of gender lacks complexity and will not represent the holistic array of genders attending each ISTS. Nevertheless, we believe this simplification can still provide insightful details about gender representation at the ISTS. <em><strong>Affiliated institution of the first author</strong></em> We recorded and categorized the affiliated institution for all first authors. When the author listed more than one affiliation, we only used the first one that was listed. We assigned each affiliated institution to one of the following five categories. (1) <em>Academic </em>– this included all traditional education institutions, both public and private, such as high schools, colleges, and universities (e.g., Oxford University, Duke University). (2) <em>Governmental </em>– this included any local, federal, or national governmental entities. This also included governmental run initiatives such as National Park services and government funded research centers (e.g., National Oceanographic and Atmospheric Administration run institutes in the USA or Consejo Superior de Investigaciones Científicias in Spain). (3) <em>Non</em>-<em>profit </em>– this included all non-profits, charities, and non-commercial organizations as well as research centers/institutes that are not directly affiliated with universities or colleges (e.g., the Sea Turtle Conservancy). The decision was made to include non-academic research centers in this category because they are frequently part of a broader non-profit (e.g., the Cape Eleuthera Institute in The Bahamas is part of the Cape Eleuthera Foundation). (4) <em>Industry </em>– including all for-profit institutions such as consulting agencies, aquariums, and private museums (e.g., the New England Aquarium). Museums associated with academic institutions were listed under the associated academic institution (e.g., the Peabody Museum of Natural History was considered part of Yale University and thus included in the academic category). (5) <em>Unknown </em>/ <em>Not Listed </em>– This was used when no affiliation was listed, or it was not possible to assign the affiliation to one of the previously listed categories with certainty. <em><strong>Institutional and study site</strong></em> We determined the geographic location where the study took place (hereafter referred to as the study site) as well as the location of the author’s institutional affiliation (hereafter referred to as the institutional site). As each abstract could only have a single affiliation (see previous section), this meant that it also could only have a single institutional site. In contrast, it was possible that the research was conducted in multiple locations and a single abstract could have several study sites. We defined the study site as the area where the sampling took place and not where the analyses were conducted. For example, if samples were collected from turtles in Costa Rica but then exported to the U.S.A. for analysis, the study site would remain listed as Costa Rica and not the U.S.A. Similarly, if samples were collected / bio-logging devices were placed on turtles in one country but the turtles migrated into the waters of another country, only the country where the samples were collected / devices were deployed was listed. We only recorded the location of the study if it could be deduced with certainty. When possible, we defined the study and institutional site to the level of both country and continent. When determining countries, we used political borders. Thus, all territories, islands, or dependencies were listed as part of their broader country. For example, the U.S. Virgin Islands were part of the U.S.A and French Guyana was part of France. In contrast, when determining continents, we used geographic borders. This meant that territories, islands, or dependencies may be listed as being in a separate continent to the principal country (for guidance see Supplementary Material 2). For example, the U.S. Virgin Islands were part of Central America and the Caribbean and French Guyana was part of South America. The definition of continent is not fixed and varies worldwide and so we delineated the continents, following the United Nations global geoscheme (UNSD 2022), as follows: Africa, Antarctica, Asia, Central America and the Caribbean, Europe, North America, Oceania, and South America. Each country was only considered to be part of a single continent following designations in  Supplementary Materials 3. As a single abstract could have study sites in several countries, when calculating the representation of each continent we considered that an abstract with a single study site was counted as 1. However, if the abstract had study sites in multiple continents, then representation was divided between those continents. E.g., a study in both Asia and Europe would be counted at 0.5 for each continent. When studies stated that they occurred in a particular region, we still assigned them a continent even if they did not state specific countries. However, when studies stated that they were global, we did not assign continents or countries if neither nor any specific region was stated specifically in the abstract.
description: # Data for: Representation in sea turtle science: slow progress towards
 gender equity and globalization revealed from thirty years of symposium
 abstracts Give a brief
 summary of dataset contents, contextualized in experimental procedures and
 results. ## Description of the data and file structure This is a freeform
 section for you to describe how the data are structured and how a
 potential consumer might use them. Be as descriptive as necessary. Keep in
 mind that users of your data might be new to the field and unfamiliar with
 common terminology, metrics, etc. Describe relationships between data
 files, missing data codes, other abbreviations used. Be as descriptive as
 possible. ## Sharing/Access information This is a section for linking to
 other ways to access the data, and for linking to sources the data is
 derived from, if any. Links to other publicly accessible locations of the
 data: * Data was derived from the following sources: *
 ## Code/Software This is an optional, freeform section for describing any
 code in your submission and the software used to run it. Describe any
 scripts, code, or notebooks (e.g., R, Python, Mathematica, MatLab) as well
 as the software versions (including loaded packages) that you used to run
 those files. If your repository contains more than one file whose
 relationship to other scripts is not obvious, provide information about
 the workflow that you used to run those scripts and notebooks. ## Human
 subjects data The database includes the names of authors who published
 abstracts in International Sea Turtle Symposiums. As this information is
 consolidated from information that is already in the public domain, I
 believe the dataset is suitable for publication.