Getting started with scholid

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scholid is a lightweight, dependency-free (base R only) toolkit for working with scholarly and academic identifiers. It provides small, well-tested helpers to detect, normalize, classify, and extract common identifier strings.

This vignette introduces the interface and typical workflows for mixed, messy identifier data.

Installation

install.packages("scholid")

Interface

scholid exposes a small set of user-facing functions that operate consistently across identifier types:

scholid_types() lists supported identifier types.
is_scholid(x, type) checks whether values match the identifier type.
normalize_scholid(x, type) returns canonical identifier strings.
extract_scholid(text, type) extracts identifiers from free text.
classify_scholid(x) guesses the identifier type per element.
detect_scholid_type(x) detects identifier types from canonical or wrapped input values (e.g., URLs or labels).

These generic helpers dispatch internally to type-specific implementations such as is_doi(), normalize_orcid(), and extract_isbn().

Supported identifier types

scholid::scholid_types()

## [1] "arxiv" "doi"   "isbn"  "issn"  "orcid" "pmcid" "pmid"

Detect: `is_scholid()`

is_scholid() checks whether each value matches a specific identifier type. It is vectorized and preserves missing values.

x <- c(
    "10.1000/182",
    "not a doi",
    NA
)
scholid::is_scholid(
    x    = x,
    type = "doi"
)

## [1]  TRUE FALSE    NA

Normalize: `normalize_scholid()`

Normalization removes common wrappers and enforces a canonical representation. This is particularly useful when identifiers are stored as URLs or prefixed labels.

x <- c(
  "https://doi.org/10.1000/182.",
  "doi:10.1000/182",
  " 10.1000/182 "
)
scholid::normalize_scholid(
    x    = x, 
    type = "doi"
)

## [1] "10.1000/182" "10.1000/182" "10.1000/182"

For ORCID iDs, normalization removes URL prefixes and enforces hyphenated grouping.

x <- c(
  "https://orcid.org/0000-0002-1825-0097",
  "0000000218250097"
)
scholid::normalize_scholid(
    x    = x,
    type = "orcid"
)

## [1] "0000-0002-1825-0097" "0000-0002-1825-0097"

Normalization is designed to be predictable: - NA input stays NA. - Invalid inputs typically become NA_character_.

Extract: `extract_scholid()`

Extraction is for harvesting identifiers from unstructured text. The result is a list with one element per input element. Each element is a character vector of matches (possibly empty).

txt <- c(
  "See https://doi.org/10.1000/182 and doi:10.5555/12345678.",
  "No identifier here.",
  NA
)
scholid::extract_scholid(
    text = txt,
    type = "doi"
)

## [[1]]
## [1] "10.1000/182"       "10.5555/12345678."
## 
## [[2]]
## character(0)
## 
## [[3]]
## character(0)

The list return type is intentional: a single text string can contain multiple identifiers.

Classify: `classify_scholid()`

classify_scholid() returns the best-guess identifier type per element for mixed identifier columns. Classification is based on the set of available is_<type>() checks and the precedence order defined by scholid_types().

x <- c(
  "10.1000/182",
  "0000-0002-1825-0097",
  "PMC12345",
  "2101.00001v2",
  "not an id",
  NA
)
scholid::classify_scholid(x = x)

## [1] "doi"   "orcid" "pmcid" "arxiv" NA      NA

Normalization + classification in messy data

Many identifiers appear wrapped (URLs, prefixes, trailing punctuation). Classification is strict and expects canonical strings. A common pattern is:

Extract identifiers from text.
Normalize extracted values.
Classify and/or validate.

txt <- "Read https://doi.org/10.1000/182 (and ORCID 0000-0002-1825-0097)."
dois <- scholid::extract_scholid(txt, "doi")[[1]]
orcids <- scholid::extract_scholid(txt, "orcid")[[1]]

dois_n <- scholid::normalize_scholid(dois, "doi")
orcids_n <- scholid::normalize_scholid(orcids, "orcid")

scholid::classify_scholid(c(dois_n, orcids_n))

## [1] "doi"   "orcid"

scholid::is_scholid(dois_n, "doi")

## [1] TRUE

scholid::is_scholid(orcids_n, "orcid")

## [1] TRUE

Detect: `detect_scholid_type()`

detect_scholid_type() performs best-effort type detection for mixed, messy identifier input. In contrast to classify_scholid(), detection also recognizes common wrapped forms such as URLs and prefixed labels (e.g., doi:, https://orcid.org/, arXiv:, PMID:).

Detection is useful when working with raw data where identifiers may not yet be normalized.

For example, wrapped identifiers are not classified strictly:

x <- c(
  "https://doi.org/10.1000/182",
  "ORCID: 0000-0002-1825-0097",
  "arXiv:2101.00001",
  "PMID: 12345",
  "not an id"
)
scholid::classify_scholid(x)

## [1] NA NA NA NA NA

However, they can be detected directly:

scholid::detect_scholid_type(x)

## [1] "doi"   "orcid" "arxiv" "pmid"  NA

Whitespace and minor formatting irregularities are handled conservatively:

scholid::detect_scholid_type(
  c(
    " 0000-0002-1825-0097 ",
    " 10.1000/182 ",
    "ISSN 0317-8471"
  )
)

## [1] "orcid" "doi"   "issn"

detect_scholid_type() does not modify values. Once the identifier type is known, use normalize_scholid() to convert to canonical form and is_scholid() for strict validation.

A typical workflow for messy data is:

Detect identifier types.
Normalize by detected type.
Validate canonical identifiers.

This separation keeps detection permissive and normalization predictable, while preserving strict validation where needed.

Design notes

scholid is intentionally small and conservative:

It uses base R only at runtime.
Functions are vectorized and return stable types.
Type-specific logic is kept in small is_*(), normalize_*(), and extract_*() helpers.
The package is designed to be a low-level building block for other packages and for workflows.

Session information

sessionInfo()

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=de_AT.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=de_AT.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=de_AT.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=de_AT.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Europe/Vienna
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## loaded via a namespace (and not attached):
##  [1] digest_0.6.39     R6_2.6.1          fastmap_1.2.0     xfun_0.56        
##  [5] cachem_1.1.0      knitr_1.51        htmltools_0.5.9   rmarkdown_2.30   
##  [9] lifecycle_1.0.5   cli_3.6.5         scholid_0.1.0     sass_0.4.10      
## [13] jquerylib_0.1.4   compiler_4.5.2    rstudioapi_0.18.0 tools_4.5.2      
## [17] evaluate_1.0.5    bslib_0.10.0      yaml_2.3.12       otel_0.2.0       
## [21] jsonlite_2.0.0    rlang_1.1.7

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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Getting started with scholid

Installation

Interface

Supported identifier types

Detect: is_scholid()

Normalize: normalize_scholid()

Extract: extract_scholid()

Classify: classify_scholid()

Normalization + classification in messy data

Detect: detect_scholid_type()

Design notes

Session information

Detect: `is_scholid()`

Normalize: `normalize_scholid()`

Extract: `extract_scholid()`

Classify: `classify_scholid()`

Detect: `detect_scholid_type()`