This FAQ answers questions regarding the editorial principles and design decisions that went into the creation of PHOIBLE. We appreciate and welcome feedback regarding these FAQs via our issue tracker or by contacting the editors directly.

When relevant, we provide R code snippets to elucidate the questions raised in this FAQ. To run these code snippets requires the following R packages:


This document was rendered with R version 4.0.3 (2020-10-10) and package versions dplyr: 1.0.2, readr: 1.4.0, stringr: 1.4.0, knitr: 1.30, ggplot2: 3.3.2.

How do I get the data?

You can get the most recent “official” release from our download page, get the most current version (with bugfixes or new additions since last release) from GitHub, or use the following code snippet to download the most current version from GitHub directly within R:

url_ <- ""
col_types <- cols(InventoryID='i', Marginal='l', .default='c')
phoible <- read_csv(url(url_), col_types=col_types)

Inventories, language codes, doculects, and sources

How are PHOIBLE inventories created?

For the most part, every phonological inventory in PHOIBLE is based on one-and-only-one language description (usually a research article, book chapter, dissertation, or descriptive grammar). The technical term for this in comparative linguistics is “doculect” (from “documented lect”), in which lect means a specific form of a language or dialect, i.e. an instance of documentation of an instance of linguistic behavior at a particular time and place (Cysouw & Good, 2013). A brief explanation and some history of why linguists use the term “doculect,” which has gained broad acceptance in light of the issues of language identification and the use of “language codes,” is given in this blog post by Michael Cysouw.

Contributors to PHOIBLE start with a doculect, extract the contrastive phonemes and allophones, and (if necessary) adapt the authors’ choice of symbols to align with PHOIBLE’s symbol guidelines. If the authors have not provided ISO 639-3 and Glottolog codes, these are determined before adding the inventory to PHOIBLE. Each inventory is then given a unique numeric ID. Doculects are tracked in PHOIBLE using BibTeX keys.

Why do some phonological inventories combine more than one doculect?

An exception to the “one doculect per inventory” rule arises for inventories that were originally part of a curated phonological database such as UPSID (Maddieson, 1984; Maddieson & Precoda, 1990) or SPA (Crothers, Lorentz, Sherman, & Vihman, 1979). In those collections, inventories were often based on multiple descriptions of linguistic behavior, written by different linguists; those descriptions were believed to be describing the same language, and disagreements between the descriptions were adjudicated by the experts who compiled the collection.

We can quickly see how many of PHOIBLE’s inventories are based on multiple doculects by looking at the mapping table between PHOIBLE inventory IDs and BibTeX keys:

url_ <- ""
id_to_bibtex_mapping <- read_csv(url(url_), col_types=cols(InventoryID='i',

id_to_bibtex_mapping %>%
    group_by(InventoryID) %>%
    tally(name="Number of doculects consulted") %>%
    group_by(`Number of doculects consulted`) %>%
    count(name="Number of inventories") %>%
Number of doculects consulted Number of inventories
1 2435
2 442
3 93
4 29
5 10
6 7
7 2
9 1
11 1

Clearly, the majority of inventories in PHOIBLE represent a phonological description from a single doculect. But it seems strange that a single phonological inventory in PHOIBLE could be based on 11 different doculects. Let’s examine it:

id_to_bibtex_mapping %>%
    group_by(InventoryID) %>%
    tally(name="Number of doculects consulted") %>%
    filter(`Number of doculects consulted` == 11) %>%
    pull(InventoryID) ->

phoible %>%
    filter(InventoryID == this_inventory_id) %>%
    distinct(Source, LanguageName, Glottocode, ISO6393) %>%
Glottocode ISO6393 LanguageName Source
haus1257 hau HAUSA upsid

As we can see, this inventory represents a description of Hausa and was added to PHOIBLE from the UPSID database (Maddieson, 1984; Maddieson & Precoda, 1990). To understand why this UPSID entry consulted 11 different sources, consider first that Hausa is typologically interesting (e.g., it has both ejective and implosive phonation mechanisms) and has tens of millions of speakers, making it relatively well-studied (the Glottolog reference catalog has more than 1400 references related to Hausa).

Second, note that Maddieson’s work on UPSID involved “typologizing” phonological inventories from different doculects, so that they were comparable across all entries in his database (cf. Hyman, 2008). Maddieson’s work was groundbreaking at the time because he was the first typologist to generate a stratified language sample aimed at being genealogically balanced, i.e. for each language family he chose one representative language. This allowed Maddieson to make statements about the cross-linguistic distribution of contrastive speech sounds with some level of statistical confidence. In fact, much about what we know about the distribution of the sounds of the world’s languages is due to Maddieson’s original language sample and his meticulous curation of the data.

Where do the language codes in PHOIBLE come from?

Every phonological inventory in PHOIBLE has a unique numeric inventory ID. Since most PHOIBLE inventories (aside from some UPSID or SPA ones, as mentioned above) are based on a single document, it is fairly straightforward to link each PHOIBLE inventory to the Glottolog, which provides links between linguistic description documents and unique identifiers for dialects, languages, and groupings of dialects and languages at various levels (the preferred term for any of these levels of specificity / grouping is “languoid”; see Cysouw & Good, 2013). Thus in PHOIBLE each inventory typically corresponds to a single languoid, and in most cases that languoid is a “leaf node” in the Glottolog tree, i.e., it represents a particular dialect known to be used at a particular place and time (rather than a group of dialects, a language, or a language family). However, in the few cases where multiple document sources were consulted for an inventory, it may not be possible to link that inventory to a unique Glottolog leaf node. In such cases, the inventory in PHOIBLE is linked to the lowest possible Glottolog node that dominates the leaf nodes of each source document.

For example, inventory 298 describes the Dan language, and was ingested from UPSID and ultimately based on a single journal article. At the time UPSID was compiled, Dan and Kla-Dan were considered a single language (isocode daf) but in 2013 a proposal was accepted to assign them separate isocodes (dnj and lda, respectively). Since it is unknown whether the consultants for the doculect were speakers of what we would now call “Dan” or “Kla-Dan,” the inventory is labeled with the old isocode daf and linked to the corresponding non-leaf node in the glottolog (dann1241).

In addition to providing Glottolog languoid codes (“glottocodes”) for each inventory, PHOIBLE also includes ISO 639-3 language codes (“isocodes”) for each inventory. The link between glottocodes and isocodes is maintained and provided by Glottolog. This situation can result in two possible problems:

Missing isocodes

There are cases of languages reported in the Glottolog for which there exists no isocode. For example, the Vach-Vasjugan variety of Khanty is a Uralic language classified in the Glottolog as vach1239. However, SIL only assigns an isocode for one variety of Northern Khanty (isocode kca, glottocode khan1273), and because SIL is the Registration Authority of ISO 639-3, Vach-Vasjugan Khanty has not been assigned its own isocode distinct from Northern Khanty.

When no isocode exists for a particular phonological inventory in PHOIBLE, as in the Vach-Vasjugan example above, PHOIBLE follows the recommended practice of using the isocode mis (“missing”) to denote that the language is not included in the ISO 639-3 standard. In PHOIBLE these are these inventories with missing isocodes:

phoible %>%
    filter(ISO6393 == "mis") %>%
    distinct(InventoryID, LanguageName, ISO6393, Glottocode) %>%
InventoryID Glottocode ISO6393 LanguageName
2143 pisa1245 mis Pisamira
2352 lizu1234 mis Lizu
2388 east2773 mis Dolakha Newar
2420 zhon1235 mis Zhongu Tibetan
2434 vach1239 mis Eastern Khanty
2450 fore1274 mis Forest Nenets
2691 mink1237 mis Minkin
2714 guwa1244 mis Guwar
2729 NA mis Djindewal
2748 mith1236 mis Mithaka
2773 cola1237 mis Kolakngat
2778 yari1243 mis Yari-Yari
2782 west2443 mis East Djadjawurung
2783 djad1246 mis Jardwadjali
2792 kera1256 mis Keramin
2793 lowe1402 mis Ngayawang
2794 ngin1247 mis Ngintait
2818 gudj1237 mis Gudjal
2882 kawa1290 mis Ogh Awarrangg
2883 kawa1290 mis Ogh Unyjan
2907 wala1263 mis Walangama
2911 tyan1235 mis Thaynakwithi
2913 luth1234 mis Luthigh
2914 mbiy1238 mis Mbiywom
2916 ngko1236 mis Ngkoth
2920 yadh1237 mis Yadhaykenu
2946 bula1255 mis Bularnu
2956 yulp1239 mis Yulparija
2988 west2443 mis West Djadjawurung
2999 sout2770 mis Ngunawal

Many of these inventories come from Erich Round’s contribution of Australian phonemic inventories to PHOIBLE. Unfortunately, some of these languages are extinct and have no representation in the Ethnologue, and hence, no code assigned in ISO 639-3.

For users, it is important to note that multiple phonological inventories in PHOIBLE may have the same isocode (e.g. “mis”) or the same glottocode (e.g., in cases of two different descriptions of the same lect). In essence, this means that any programmatic code that groups by isocode or glottocode risks combining inventories from different doculects into a single apparent inventory. This could lead to incorrect results (e.g., if the goal is to count the number of phonemes). Therefore, most analyses of inventory properties should be done on the level of inventory IDs rather than isocodes or glottocodes. See also the section on filtering and sampling below.

Why do some languages have multiple entries in PHOIBLE?

It is not uncommon that phonological descriptions of a particular language’s speech sounds have different sets of contrastive phonemes when analyzed by different linguists (or sometimes even by the same linguist throughout their career). For example, Kabardian is represented in PHOIBLE by five distinct inventories.

phoible %>%
    filter(ISO6393 == "kbd") %>%
    group_by(InventoryID) %>%
    summarise(`Number of phonemes`=n(),
              Phonemes=str_c(Phoneme, collapse=" "),
              .groups="drop") %>%
InventoryID Number of phonemes Phonemes
4 56 b d̻ d̻z̻ f fʼ j kʷʰ kʷʼ k̟ʲʰ k̟ʲʼ m n̻ pʰ pʼ qχ qχʷ qχʷʼ qχʼ r s̻ t̻s̻ t̻s̻ʼ t̻ʰ t̻ʼ v w xʷ x̟ʲ z̻ ç̟ ħ ɡʷ ɡ̟ʲ ɣ̟ʲ ɦ ɬʲ ɬʲʼ ɮʲ ʁ ʁʷ ʃ ʃʼ ʒ ʔ ʔʷ ʕ ʝ̟ χ χʷ a̟ː e̞ː iː o̞ː uː ɜ ɨ
391 56 b d̪ d̪z̪ f fʼ j kʲʰ kʲʼ kʷʰ kʷʼ m n̪ pʰ pʼ qʷʼ qʼ qχ qχʷ r s̪ t̪s̪ t̪s̪ʼ t̪ʰ t̪ʼ v w xʲ xʷ z̪ ħ ɡʲ ɡʷ ɣʲ ɦ ɬ̪ʲ ɬ̪ʲʼ ɮ̪ʲ ʁ ʁʷ ʃ ʃʼ ʃ͇ ʒ ʒ͇ ʔ ʔʷ ʕ χ χʷ a̟ː e̞ː iː o̞ː uː ɜ ɨ
2310 55 b d dz d̠ʒ f fʼ j kʷ kʷʼ m n p pʼ q qʷ qʷʼ qʼ r s t ts tsʼ tʼ t̠ʃ t̠ʃʼ v w x xʷ z ħ ɡʷ ɣ ɬ ɬʼ ɮ ʁ ʁʷ ʃ ʆ ʆʼ ʒ ʓ ʔ ʔʷ χ χʷ ä e̞ː iː o̞ o̞ː uː ɑː ə
2401 63 b d dz dʑ f fʼ j kʷ kʷʼ kʼ l m n p pʼ q qʷ qʷʼ qʼ r s t ts tsʷʼ tsʼ tɕ tɕʼ tʷʼ tʼ t̠ʃ t̠ʃʼ v w x xʷ z zʷ ħ ɕ ɡʷ ɣ ɬ ɬʼ ʁ ʁʷ ʃ ʆ ʆʼ ʑ ʒ ʓ ʔ ʔʷ χ χʷ ä e̞ː iː o̞ o̞ː uː ɑː ə
2610 51 b d dz f fʼ h j kʲʼ kʷʰ kʷʼ l m n pʰ pʼ qʰ qʷʰ qʷʼ qʼ s ts tsʼ tʰ tʼ w xʷ z ç ħ ɡʲ ɡʷ ɬ ɬʼ ɾ ʁ ʁʷ ʃ ʃʼ ʒ ʔ ʔʷ ʝ χ χʷ äː eː iː oː uː ɐ ə

The differences among them can be due to a variety of reasons, but the main reason is that these phonological descriptions represent different doculects (i.e., different instances of linguistic behavior at different places, times, or with different speakers). This should probably not surprise most linguists, since it has long been known that phoneme analysis is a non-deterministic process (Chao, 1934; Hockett, 1963). See Moran (2012, ch. 2.3.3) for a general discussion, and Hyman (2008, p. 99) for a detailed discussion of Kabardian in particular.

In light of the above discussion, it should come as no surprise that PHOIBLE can contain multiple inventories for “the same” languoid, depending on what kind of languoid you’re interested in. All it takes is the existence of two or more descriptive documents associated with the same group of speakers, where “group of speakers” is ultimately determined by your desired level of granularity. To give a concrete example, one researcher may be interested in comparing lects at the “language” level, and so might wish to treat all inventories of “English” as duplicates for the purposes of her analysis (regardless of any differences in regional dialect or sociolect represented in the original doculects and encoded in the phonological inventory). That researcher might filter or sample PHOIBLE’s inventories to include only one inventory for each isocode (how she chooses to implement that filter is a separate question; see “How can I filter or sample inventories?” for examples). Other researchers may not care about “duplicates” in that sense, and may choose to include all inventories in their analysis (or, they may filter the dataset to include only inventories with a particular feature of interest such as breathy-voiced vowels).

Below is a summary of the number of isocodes that are represented by multiple inventories in PHOIBLE:

offset <- 25
phoible %>%
    group_by(ISO6393) %>%
    summarise(y=n_distinct(InventoryID), .groups="drop") %>%
    count(y) ->
counts %>%
    ggplot(aes(y=as.factor(y), x=n)) +
    geom_point() +
    geom_text(aes(x=n+offset, label=n), hjust=0) +
    geom_segment(aes(xend=0, yend=as.factor(y))) +
    xlim(NA, max(counts$n) + 2*offset) +
    theme_light() +
    labs(title="Prevalence of multiple inventories per ISO code",
         x="Number of ISO codes having N inventories",
         y="N inventories")

So most ISO 639-3 codes have only 1 inventory (the long, bottom line).

Here is the same representation, for glottocodes instead of isocodes:

offset <- 25
phoible %>%
    group_by(Glottocode) %>%
    summarise(y=n_distinct(InventoryID), .groups="drop") %>%
    count(y) ->
counts %>%
    ggplot(aes(y=as.factor(y), x=n)) +
    geom_point() +
    geom_text(aes(x=n+offset, label=n), hjust=0) +
    geom_segment(aes(xend=0, yend=as.factor(y))) +
    xlim(NA, max(counts$n) + 2*offset) +
    theme_light() +
    labs(title="Prevalence of multiple inventories per glottocode",
         x="Number of glottocodes having N inventories",
         y="N inventories")

Again, most glottocodes are represented by just one or two inventories in PHOIBLE. Let’s see the few glottocodes that have the most inventories:

phoible %>%
    group_by(Glottocode) %>%
    summarise(Names=str_c(unique(LanguageName), collapse=", "),
              `Number of inventories`=n_distinct(InventoryID),
              .groups="drop") %>%
    arrange(desc(`Number of inventories`)) %>%
    filter(`Number of inventories` > 5) %>%
Glottocode Names Number of inventories
osse1243 Ossetian, Iron Ossetic 11
biri1256 Barna, Biri, Garingbal, Miyan, Wiri, Yambina, Yangga, Yilba, Yuwi, Wangan 10
stan1293 English, English (American), American English, English (Australian), English (British), English (New Zealand) 9
dutc1256 Dutch 8
kham1282 Rgyalthang Tibetan, Brag-g.yab Tibetan, Nangchenpa Tibetan, Soghpo Tibetan, Kami Tibetan, Sangdam Tibetan, Dongwang Tibetan, Kham Tibetan 8
basq1248 Basque, BASQUE 7
east2328 Cheremis, MARI, Meadow Mari, Eastern Mari 7
gwan1268 Gwandara (Karshi), Gwandara (Cancara), Gwandara (Toni), Gwandara (Gitata), Gwandara (Koro), Gwandara (Nimbia) 6
khan1273 Ostyak, KHANTY, Eastern Khanty, Northern Khanty 6
lazz1240 Laz 6
lith1251 Lithuanian, LITHUANIAN 6

Why are multiple inventories sometimes linked to the same document?

Occasionally, a single document may provide information about multiple phonological inventories. For example, a dissertation that describes and compares three related dialects or languages spoken in a particular region. In that case, three phonological inventories in PHOIBLE corresponding to three doculects might all be linked to the same document, but each inventory is still linked to only one document (it just happens to be the same document for those three inventories). One example is Terrill (1998), a grammar of Biri, which contains a chapter describing its dialects, many of which appear in PHOIBLE.

What are the different “sources” in PHOIBLE?

PHOIBLE contains inventories from various contributors. These contributions are grouped into so-called “sources,” denoted by abbreviations. Here they are in the chronological order that they were added to PHOIBLE:

The acronyms above link to the GitHub page for each data source, which provides information about the source and how it was aggregated into PHOIBLE. Some sources are quite specialized; for example, UPSID contains a quota sample, i.e., one language per genealogical grouping (see Section “How can I filter or sample inventories?”); AA contains descriptions of only African languages; RA represents languges of India; SAPHON represents languages of South America; GM represents languages of Africa and Asia; EA represents languages of Eurasia; ER represents languages of Australia. Other sources like PH and UZ were added mainly to fill in the typological gaps left by the more specialized sources (e.g., to add language isolates, or to increase coverage of poorly-represented geographic areas or language families). Here is a table showing the number of inventories per source:

phoible %>%
    group_by(Source) %>%
    summarise(`Number of inventories`=n_distinct(InventoryID),
              .groups="drop") %>%
    arrange(desc(`Number of inventories`)) %>%
Source Number of inventories
gm 460
upsid 451
er 392
ea 390
ph 389
saphon 355
aa 203
spa 197
ra 100
uz 83

Note that the same languoid may be reported in different sources as encoded in different doculects. Here are the lects included in the most sources:

phoible %>%
    group_by(Glottocode) %>%
    distinct(Glottocode, Source) %>%
    summarise(`Found in how many sources?`=n(),
              `Which ones?`=str_c(Source, collapse=", "),
              .groups="drop") %>%
    filter(`Found in how many sources?` > 3) %>%
Glottocode Found in how many sources? Which ones?
akan1250 4 spa, upsid, aa, gm
basq1248 4 spa, upsid, uz, ea
beng1280 5 spa, upsid, ra, uz, ea
bulg1262 4 spa, upsid, uz, ea
east2328 4 spa, upsid, ph, ea
hakk1236 4 spa, upsid, uz, ea
haus1257 4 spa, upsid, aa, uz
hind1269 5 spa, upsid, ra, uz, ea
hung1274 4 spa, upsid, uz, ea
iris1253 4 spa, upsid, uz, ea
khan1273 4 spa, upsid, ph, ea
khar1287 5 spa, upsid, ph, ra, ea
kore1280 4 spa, upsid, uz, ea
mand1415 4 spa, upsid, ph, ea
mode1248 4 spa, upsid, uz, ea
mund1320 4 spa, upsid, ra, ea
nepa1254 4 upsid, ra, uz, ea
nucl1301 4 spa, upsid, uz, ea
nucl1302 4 spa, upsid, uz, ea
nucl1310 4 spa, upsid, uz, ea
nucl1417 4 spa, upsid, aa, uz
stan1288 4 spa, upsid, uz, ea
stan1290 4 spa, upsid, uz, ea
stan1295 4 spa, upsid, uz, ea
tach1250 4 spa, upsid, gm, uz
taga1270 4 spa, upsid, gm, ea
telu1262 4 spa, upsid, ra, ea
viet1252 4 spa, upsid, uz, ea
west2369 4 spa, upsid, uz, ea
yuec1235 4 spa, upsid, uz, ea

Filtering and sampling

Different research questions will require including / excluding certain inventories from PHOIBLE. These sections describe how to filter and sample the PHOIBLE data based on various criteria.

Random sampling: one inventory per isocode/glottocode

If multiple inventories per isocode/glottocode are problematic for your analysis or research question, one approach is to select one inventory from each isocode/glottocode via random sampling:

phoible %>%
    distinct(InventoryID, Glottocode) %>%
    group_by(Glottocode) %>%
    sample_n(1) %>%
    pull(InventoryID) ->

phoible %>%
    distinct(InventoryID, ISO6393) %>%
    group_by(ISO6393) %>%
    sample_n(1) %>%
    pull(InventoryID) ->

message("Picking one inventory per glottocode reduces PHOIBLE from ",
        n_distinct(phoible$InventoryID), " inventories\nto ",
        " inventories. Picking one per ISO 639-3 code yields ",
        length(inventory_ids_sampled_one_per_isocode), " inventories.")
## Picking one inventory per glottocode reduces PHOIBLE from 3020 inventories
## to 2177 inventories. Picking one per ISO 639-3 code yields 2099 inventories.

You can then apply your sample like this:

phoible %>%
    filter(InventoryID %in% inventory_ids_sampled_one_per_glottocode) ->

Filtering by data source

Another approach is to only use only inventories from a data source that already provides a one-inventory-per-language sample. For example, UPSID represents a “quota” sample (one language per family, for some definition of “family”). To do this, you can filter the PHOIBLE data by the Source column:

phoible %>%
    filter(Source == "upsid") ->

# show that there is exactly one inventory per ISO 639-3 code:
upsid %>%
    group_by(ISO6393) %>%
              .groups="drop") %>%
    pull(n_inventories_per_isocode) %>%
    all(. == 1)
## [1] TRUE

Filtering and sampling based on inventory properties

Another approach is to select inventories based on properties of the inventories themselves, such as whether they include information about allophones, contrastive tone, etc. For example, one might wish to include phonological inventories from sources other than UPSID, when available, since it does not include allophones in its inventories.

# get lists of all sources, and sources that include allophones
phoible %>% distinct(Source) %>% pull() -> all_sources

phoible %>%
    filter(! %>%
    distinct(Source) %>%
    pull() ->

# make a vector encoding allophone absence/presence as 0/1
all_sources %in% sources_with_allophones %>%
    as.integer() %>%
    setNames(all_sources) ->

# example 1: one language per isocode, only keep if includes allophones
phoible %>%
    distinct(InventoryID, ISO6393, Source) %>%
    filter(source_weights[Source] == 1) %>%
    group_by(ISO6393) %>%
    slice_sample(n=1) %>%
    pull(InventoryID) ->

# example 2: one language per isocode, *preferentially* pick ones w/ allophones
new_weights <- source_weights + 1e-9  # so that all weights are non-zero
phoible %>%
    distinct(InventoryID, ISO6393, Source) %>%
    group_by(ISO6393) %>%
    slice_sample(n=1, weight_by=new_weights[Source]) %>%
    pull(InventoryID) ->

message("Sampling one inventory per ISO code while *requiring* allophones yielded ",
        " inventories; merely *preferring* allophones yielded ",
        " inventories.")
## Sampling one inventory per ISO code while *requiring* allophones yielded 1155 inventories; merely *preferring* allophones yielded 2099 inventories.

You can then extract your sample using filter() as seen above:

phoible %>% filter(InventoryID %in% sample_of_inventory_ids_with_allophones) ->

If you’re inclined to be a “phoneme splitter,” you might prefer to pick the largest inventory for a given isocode:

phoible %>%
    group_by(InventoryID) %>%
    summarise(n_phonemes=n(), isocode=unique(ISO6393), .groups="drop") %>%
    group_by(isocode) %>%
    arrange(desc(n_phonemes), .by_group=TRUE) %>%
    slice_head(n=1) %>%
    pull(InventoryID) ->

… and again, extracting your sample using filter():

phoible %>% filter(InventoryID %in% inventory_ids_of_biggest_inventories) ->

Integrating geographic information

One way to look at the geographic coverage of PHOIBLE is to merge its data with information about languages and dialects as provided by the Glottolog. Here we use only the PHOIBLE index (the mapping from InventoryID to Glottocode, without any Phoneme information) and merge it with the Glottolog geographic and genealogical data:

url_ <- ""
phoible_index <- read_csv(url(url_), col_types=cols(InventoryID='i', .default='c'))

url_ <- ""
glottolog <- read_csv(url(url_), col_types=cols(latitude='d', longitude='d', .default='c'))

phoible_geo <- left_join(phoible_index, glottolog,

# show the merged data
phoible_geo %>% head() %>% kable()
InventoryID ISO6393 Glottocode LanguageName Source name isocodes level macroarea latitude longitude
1 kor kore1280 Korean spa Korean kor language Eurasia 37.5000 128.00000
2 ket kett1243 Ket spa Ket ket language Eurasia 63.7551 87.54660
3 lbe lakk1252 Lak spa Lak lbe language Eurasia 42.1328 47.08090
4 kbd kaba1278 Kabardian spa Kabardian kbd language Eurasia 43.5082 43.39180
5 kat nucl1302 Georgian spa Georgian kat language Eurasia 41.8504 43.78613
6 bsk buru1296 Burushaski spa Burushaski bsk language Eurasia 36.2161 74.82360

We can then easily see how many languages there are in PHOIBLE for each macroarea:

phoible_geo %>%
    distinct(ISO6393, macroarea) %>%
    group_by(macroarea) %>%
    tally(name="Number of unique isocodes") %>%
macroarea Number of unique isocodes
Africa 707
Australia 292
Eurasia 440
North America 144
Papunesia 182
South America 334
NA 5

Or, we can count the total number of inventories per macroarea:

phoible_geo %>%
    group_by(macroarea) %>%
    tally(name="Number of inventories") %>%
macroarea Number of inventories
Africa 885
Australia 436
Eurasia 804
North America 184
Papunesia 216
South America 479
NA 16

Note that there are some langoids/glottocodes for which geographic information is unavailable (their macroarea is NA). Also note that each languoid is given a single latitude-longitude coordinate pair (i.e., there is no information about the spatial extent of a languoid’s use).

Finally, let’s look at the global distribution of languages represented in PHOIBLE:

ggplot(data=phoible_geo, aes(x=longitude, y=latitude)) +
    borders("world", colour="gray50", fill="gray50") +
    geom_point(alpha=0.5, size=1, colour="orange")
## Warning: Removed 134 rows containing missing values (geom_point).

Of course this does not show all of the data points for languages that are not in PHOIBLE!

Phonological features in PHOIBLE

In addition to phoneme inventories, PHOIBLE includes distinctive feature data for every phoneme in every inventory. The feature system was created by the PHOIBLE developers to be descriptively adequate cross-linguistically. In other words, if two phonemes differ in their graphemic representation, then they should necessarily differ in their featural representation as well (regardless of whether those two phonemes coexist in any known doculect). The feature system is loosely based on the feature system in Hayes (2009) with some additions drawn from Moisik & Esling (2011).

Note that the feature system is potentially subject to change as new languages are added in subsequent editions of PHOIBLE, and/or as errors are found and corrected. More information about the PHOIBLE feature set can be found here.

How is PHOIBLE used in academic research and/or industry?

The data in PHOIBLE have been used in many published research papers pertaining to a variety of scientific fields and industrial applications. For a sampling, view Google Scholar’s list of papers citing PHOIBLE.


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