Designing precise queries across disciplines

A retrieval is only as good as its query. This article shows how to compose correct, field-tagged ‘Scopus’ queries with scopus_query(), which spares you pasting fragments together by hand, where a missing bracket or a mistyped tag quietly returns the wrong records. Everything here is string construction, so it all runs offline, and each query is shown as the literal string it produces.

Field tags decide where to look

A field tag restricts a query to part of a record. scopus_field_tags() lists the common ones.

scopus_field_tags()

tag	searches
TITLE	Words in the document title
TITLE-ABS-KEY	Title, abstract and keywords
TITLE-ABS-KEY-AUTH	Title, abstract, keywords and author names
ABS	Abstract text
KEY	Indexed and author keywords
AUTH	Author names
AUTHKEY	Author-supplied keywords
AFFIL	Affiliation, any part
AFFILORG	Affiliation organisation name
SRCTITLE	Source (publication) title
DOI	Digital Object Identifier
ALL	All available fields

The most generally useful tag is TITLE-ABS-KEY, which searches the title, abstract and keywords together, broad enough to catch a topic without the noise of a full-text match.

One term, many disciplines

The same builder serves any field. Each call below returns the exact query string that would be sent to ‘Scopus’.

# molecular biology
scopus_query("CRISPR", .field = "TITLE-ABS-KEY")

[1] "TITLE-ABS-KEY(CRISPR)"

scopus_query("gravitational waves", .field = "TITLE-ABS-KEY") # physics

[1] "TITLE-ABS-KEY(gravitational waves)"

# environmental science
scopus_query("microplastics", .field = "TITLE-ABS-KEY")

[1] "TITLE-ABS-KEY(microplastics)"

scopus_query("blockchain", .field = "TITLE-ABS-KEY")          # computer science

[1] "TITLE-ABS-KEY(blockchain)"

scopus_query("digital humanities", .field = "AUTHKEY")        # humanities

[1] "AUTHKEY(digital humanities)"

The last example uses AUTHKEY, the author-supplied keywords, which isolates work that self-identifies with a field and so cuts incidental mentions.

Combining terms with boolean operators

Passing several terms joins them. The default operator is AND, and OR or AND NOT are available through .op.

# Two concepts that must co-occur (materials science).
scopus_query("perovskite", "solar cell", .field = "TITLE-ABS-KEY")

[1] "TITLE-ABS-KEY(perovskite) AND TITLE-ABS-KEY(solar cell)"

# Spelling variants, either of which will do (economics).
scopus_query("behavioral economics", "behavioural economics", .op = "OR")

[1] "behavioral economics OR behavioural economics"

# A family of related tools (molecular biology).
scopus_query("CRISPR", "Cas9", "Cas12", .op = "OR")

[1] "CRISPR OR Cas9 OR Cas12"

# Excluding a dominant homonym, so the snake stays out of the results.
scopus_query("python", "snake", .op = "AND NOT", .field = "TITLE-ABS-KEY")

[1] "TITLE-ABS-KEY(python) AND NOT TITLE-ABS-KEY(snake)"

From a query to a plan

A composed query drops straight into the rest of the workflow. Here it anchors a year-partitioned plan, which keeps each cell under the API’s 5000-record ceiling.

q <- scopus_query("gut microbiome", "immunology", .field = "TITLE-ABS-KEY")
q

[1] "TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)"

plan <- scopus_plan(q, years = 2015:2022, partition = "year")
plan

cell	query	date	year	view	page_size
1	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2015	2015	STANDARD	200
2	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2016	2016	STANDARD	200
3	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2017	2017	STANDARD	200
4	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2018	2018	STANDARD	200
5	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2019	2019	STANDARD	200
6	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2020	2020	STANDARD	200
7	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2021	2021	STANDARD	200
8	TITLE-ABS-KEY(gut microbiome) AND TITLE-ABS-KEY(immunology)	2022	2022	STANDARD	200

The plan is ready to size and run, which contacts the API.

scopus_count(q, years = 2015:2022)
records <- scopus_fetch_plan(plan)

Without a key, vignette("scopusflow") carries the same two steps through to a record set, standing the bundled corpus of real articles in for the harvest, since ‘Scopus’ records may not be redistributed.

Searching by affiliation

Field tags reach beyond topics. AFFILORG searches the affiliation, which turns a query into an institution-level view of output.

scopus_query("Max Planck", .field = "AFFILORG")

[1] "AFFILORG(Max Planck)"

When a term is empty

The builder validates its input, so a stray empty term is caught at construction, well before it can produce a malformed query.

tryCatch(
  scopus_query("graphene", ""),
  scopus_error_bad_input = function(e) conditionMessage(e)
)

[1] "`...` must be one or more non-empty character terms."