Cultural data ⇫ ¶

D-PLACE aggregates cultural data from several major cross-cultural datasets. All datasets use codes to characterize the cultural practices of a ‘society’, or group of people with a shared language and cultural identity at a given location and point in time.

All cultural descriptions are tagged with the date to which they refer, a geographic location (using a reported latitude and longitude) and language. This allows users to simultaneously consider how cultural practices relate to linguistic ancestry, practices of neighbouring groups, and the environment.

The authors of the cultural datasets relied on a huge number of primary data sources to code cultural practices of societies in their samples. Most of these sources are original ethnographies published as academic journal articles or books. While Murdock, Binford and their successors carefully documented their sources, the references were for the most part excluded from early attempts to digitize the EA and Binford datasets. Also lost from early digital datasets were the authors’ comments regarding particular coding decisions, despite the insights and caveats these comments provide.

In D-PLACE, each cultural data point is tagged with both its primary sources and coding comments, with references for the primary sources and comments included in results tables and data downloads. We encourage users to draw on this information when considering intracultural variation and uncertainty in cultural codes, and to return to the primary sources for a better understanding of particular coding decisions.

In order to facilitate access to supplementary cultural data for D-PLACE societies, we provide information on whether each society appears in other major cross-cultural databases. We have included links to the Human Relations Area Files (HRAF) (Murdock 1983), a repository of annotated primary literature that can be searched by topic e.g., marriage system (see this page on eHRAF and D-PLACE collaboration).

These links to other cross-cultural datasets appear at the top of individual “Society” results pages. Kirby et al. (2016) includes a discussion regarding the combination of data from different cross-cultural datasets, including the importance of considering agreement among focal year and focal location. More thorough discussions are included in Ember et al. (1992) and Ember (2007).

Linguistic data ⇫ ¶

Language family

The language spoken by a society is an important indicator for historical relatedness, cultural identity and contact. D-PLACE specifies the broad language family affiliation for all societies, using the classification systems of Glottolog (Hammarström et al. 2023). Users can treat language family as a variable of interest itself, or can use it as a coarse-level control for relatedness among societies (e.g., Botero et al. 2014).

Historical relationships among languages: Glottolog trees

At a closer resolution, all societies in D-PLACE have been linked to a language and, in cases where the language was shared with another D-PLACE society, to a Glottolog dialect. Languages are identified by both a Glottocode and an ISO 639-3 code, and dialects by a Glottocode (Hammarström et al. 2023; SIL International 2015). Languages and dialects are used by D-PLACE to link each society to Glottolog’s language classification trees. These trees are topological only, representing genealogical hypotheses of how languages are nested, based on comparative historical linguistic work. The classifications are purely taxonomies and branch lengths do not represent time or amount of change.

Distance among languages: phylogenies

At the finest scale, many of the societies in each cross-cultural dataset belong to a language family for which a well-resolved and computationally-derived phylogenetic tree is available (for example: Gray et al 2009, Kitchen et al. 2009, Dunn et al. 2011, Lee and Hasegawa 2011, Bowern and Atkinson 2012, Bouckaert et al 2012, Chacon and List 2015, Grollemund et al. 2015, Sicoli & Holton 2015, Lee 2015). In focusing analyses on these societies, researchers gain the ability to conduct sophisticated hypothesis testing about evolutionary change using phylogenetic comparative methods, as well as robust control for historical relatedness. For example, the relative time since language divergence can be used as a measure of relative distance among societies. Of course, while language provides a highly effective proxy for shared history, language family affiliation may not always reflect deep cultural or linguistic ancestry. Numerous instances of language shift, contact, and borrowing occur when societies interact. For example, many Central African Pygmy groups have adopted the languages of their Bantu trading partners (Bahuchet 2012). In such cases, linguistic relationships still capture meaningful aspects of cultural interaction, but users will need to make their own context-specific judgments.

For details on how societies were matched to languages, please see Kirby et al. (2016).

Environmental data ⇫ ¶

We sampled environmental variables at the localities reported for each society in each dataset, with some adjustments to geographic coordinates as outlined in Kirby et al. (2016).

Climate

For each society, we computed the mean, variance, and predictability of the entire annual cycles of precipitation and temperature based on monthly global maps (0.5 by 0.5 degree cells) obtained from the Baseline Historical (1900-1949), CCSM ecoClimate model (Lima-Ribeiro,M. et al. 2015). Predictability was measured via Colwell’s (1974) Constancy, Contingency and Predictability indexes. These indexes capture the extent to which yearly cycles vary among years in terms of onset, intensity and duration, ranging from 0 (completely unpredictable) to 1 (fully predictable). We include constancy (the extent to which a variable can be predicted because it tends to stay fairly constant) and contingency (the extent to which predictions are possible because environmental cycles are highly repeatable) in order to allow interested users to explore the potentially different impacts of these two types of predictability. Because the cultural data for the vast majority of societies in D-PLACE was collected between 1901 and 1950, we sampled climatic variables at each locality for this particular time period.

Productivity and biodiversity

Ecoregion and biome locations of each society were obtained from Olson et al. (2001). Monthly net primary production data were obtained from the MODIS dataset (Running et al. 1999, Data range: 2000-2016). From these data we computed the annual mean, variance predictability, constancy and contingency of net primary productivity at each sampled locality. Estimates of the number of species at each site were obtained for birds, mammals, and amphibians from Jenkins et al. (2013) and for vascular plants from Kreft and Jetz (2007).

Physical environment

We also include estimates of distance from a coast, elevation, and slope for all societies, with topographical data provided by the Global Multi-resolution Terrain Elevation Data of the U.S. Geological Survey.

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