D-PLACE contains cultural, linguistic, environmental and geographic information
for over 1400 human cultural groups. This data is aggregated from a number of
D-PLACE includes cultural data from four major cross-cultural datasets: the
Ethnographic Atlas (Murdock 1962-1971; Barry 1980; Gray 1999; Korotayev et al. 2004; Bondarenko et al. 2005),
the Binford Hunter-Gatherer dataset (Binford 2001; Binford and Johnson 2006) (as described in
Kirby et al. (2016)),
the Standard Cross Cultural Sample (White and Murdock 1969) and the
Western North American Indians dataset (Jorgensen 1980, 1999a and 1999b).
All four 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
Most of these sources are original ethnographies published as academic journal articles or books. While Murdock,
and their successors carefully documented their sources, the references were for the most part excluded from
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
the primary sources and comments included in results tables and data downloads. We encourage users to draw on
information when considering intracultural variation and uncertainty in cultural codes, and to return to the
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. In addition ot the four datasets described
above, 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) and the CHIRILA database of Australian languages (Bowern 2016).
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).
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. 2015). 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 Glottolog ID
and an ISO 639-3 code, and dialects by a Glottolog ID (Hammarström et al. 2015; SIL International 2015). For
languages for which an ISO 639-3 code has not been assigned, we use a D-PLACE serial number as a place-holder
(x01, x02...; all within the ISO-639-3 private use range). 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).
We sampled environmental variables at the localities reported for each society in each dataset (EA, Binford,
WNAI, SCCS), with some adjustments to geographic coordinates as outlined in Kirby et al. (2016). Both the
original and revised latitude and longitude for all societies are included in CSV downloads of search results
from this site.
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).
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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