PKP/OJS multi-source journal enrichment

This notebook adds the complete available OpenAlex and Crossref journal responses to ten real PKP OJS rows. It matches records by exact ISSN. The ten rows were chosen to include missing ISSNs, no matches, one match, and multiple matches. This sample checks whether the code works; it does not measure coverage across the full dataset.

Set OPENALEX_API_KEY in the environment or the repository-root .env file before rendering. This notebook runs the ten-row sample. Before a formal full-cohort run, replace the sections marked FULL-COHORT REPLACEMENT with reviewed batching, caching, checkpointing, and error-provenance code.

Packages and paths

This chunk loads the required R packages and sets the sample input and output paths. It first looks for OPENALEX_API_KEY in the current environment and then in the repository-root .env file.

The commented lines mark the path replacement required for a formal full-cohort run.

library(dplyr)
library(httr2)
library(purrr)
library(readr)
library(tibble)

project_env_path <- file.path("..", "..", "..", ".env")
if (!nzchar(Sys.getenv("OPENALEX_API_KEY")) && file.exists(project_env_path)) {
  readRenviron(project_env_path)
}
if (!nzchar(Sys.getenv("OPENALEX_API_KEY"))) {
  stop("Set OPENALEX_API_KEY before running this notebook.", call. = FALSE)
}

input_path <- file.path(
  "..",
  "data",
  "ojs-journal-enrichment-sample.csv"
)
artifact_dir <- file.path("..", "artifacts", "ojs_journal_enrichment")
run_dir <- file.path(artifact_dir, "sample")
dir.create(run_dir, recursive = TRUE, showWarnings = FALSE)

output_path <- file.path(run_dir, "pkp-ojs-multisource-enriched.csv")

# FULL-COHORT REPLACEMENT:
# data_root <- Sys.getenv("DATA_ROOT")
# input_path <- file.path(data_root, "raw", "pkp-beacon-v6", "beacon.csv")
# run_dir <- file.path(artifact_dir, "full")

ISSN preparation

ISSNs may contain spaces, punctuation, or more than one value. This chunk converts each valid ISSN to the same XXXX-XXXX format and checks its final digit. It keeps missing, invalid, and valid-but-unmatched ISSNs as separate cases.

normalize_issn <- function(value) {
  compact <- gsub("[^0-9X]", "", toupper(trimws(as.character(value))))
  if (!grepl("^[0-9]{7}[0-9X]$", compact)) return(NA_character_)

  digits <- as.integer(strsplit(substr(compact, 1, 7), "")[[1]])
  check_value <- (11 - sum(digits * 8:2) %% 11) %% 11
  expected <- if (check_value == 10) "X" else as.character(check_value)
  if (substr(compact, 8, 8) != expected) return(NA_character_)

  paste0(substr(compact, 1, 4), "-", substr(compact, 5, 8))
}

parse_issns <- function(value) {
  if (is.na(value) || !nzchar(value)) return(character())

  tokens <- unlist(strsplit(value, "[[:space:],;|]+"))
  normalized <- vapply(tokens[nzchar(tokens)], normalize_issn, character(1))
  unique(normalized[!is.na(normalized)])
}

source_issns <- function(source) {
  values <- c(unlist(source$issn, use.names = FALSE), source$issn_l)
  normalized <- vapply(values, normalize_issn, character(1))
  unique(normalized[!is.na(normalized)])
}

Source retrieval

The sample sends all valid ISSNs to OpenAlex in one request. It asks Crossref about each ISSN separately and waits between requests. A Crossref 404 means that no journal record was found.

For a formal full-cohort run, replace this chunk. The full version must divide requests into small groups, read every page of results, identify the project to Crossref, retry temporary failures, save progress, and record which ISSNs failed.

fetch_openalex_sources <- function(
  issns,
  api_key = Sys.getenv("OPENALEX_API_KEY")
) {
  stopifnot(length(issns) <= 100L, nzchar(api_key))

  request("https://api.openalex.org/sources") |>
    req_url_query(
      filter = paste0("issn:", paste(issns, collapse = "|")),
      per_page = 100,
      api_key = api_key
    ) |>
    req_timeout(seconds = 30) |>
    req_perform() |>
    resp_body_json(simplifyVector = FALSE) |>
    pluck("results")
}

fetch_crossref_journal <- function(issn) {
  response <- request(paste0("https://api.crossref.org/journals/", issn)) |>
    req_timeout(seconds = 30) |>
    req_error(
      is_error = \(response) {
        resp_status(response) != 404L && resp_is_error(response)
      }
    ) |>
    req_perform()
  if (resp_status(response) == 404L) return(NULL)
  resp_body_json(response, simplifyVector = FALSE)$message
}

# FULL-COHORT REPLACEMENT:
# - send OpenAlex ISSNs in groups of at most 100 and read every result page;
# - identify Crossref requests with CROSSREF_MAILTO and use its request rate;
# - retry temporary failures, save responses, and record failed ISSNs.

Load the PKP/OJS sample

The sample file was taken from PKP Beacon V6 file 13173372 on 2026-07-17. The MD5 value confirms that the file has not changed. This chunk reads the file, keeps its ten OJS rows, and checks that the same PKP row does not appear twice.

For a formal full-cohort run, replace the sample path, MD5 value, and expected row count with the approved full-dataset values.

stopifnot(
  file.exists(input_path),
  unname(tools::md5sum(input_path)) == "b5fb99c10f38c2a5c3e7e6e0d4aa2212"
)

raw_input <- read_csv(
  input_path,
  col_types = cols(.default = col_character()),
  na = character(),
  show_col_types = FALSE
)
ojs_rows <- raw_input |>
  filter(tolower(application) == "ojs") |>
  select(-any_of("admin_email"))
row_identity <- paste(
  ojs_rows$oai_url,
  ojs_rows$repository_name,
  ojs_rows$set_spec,
  sep = "\r"
)
stopifnot(nrow(ojs_rows) == 10L, !anyDuplicated(row_identity))

# FULL-COHORT REPLACEMENT:
# stopifnot(unname(tools::md5sum(input_path)) ==
#   "9f43fa051c7ed1cc45d8592593542011")
# stopifnot(nrow(raw_input) == 87170L, nrow(ojs_rows) == 86282L)

Multi-source enrichment

For each PKP row, the sample looks for OpenAlex and Crossref records with the same ISSN. If the same source record appears more than once, it is counted once. Every returned top-level API field becomes a dataframe column; nested objects and arrays are stored as JSON in their field columns, and the complete candidate lists are also retained as lossless JSON.

Sample matching helpers

These helpers label each row as having no usable ISSN, no match, one match, or multiple matches. They also identify Crossref records by their ISSNs and convert every candidate field into a dataframe-safe value without filtering the response schema.

classify_status <- function(input_issns, candidate_ids) {
  case_when(
    length(input_issns) == 0L ~ "not_attempted",
    length(candidate_ids) == 0L ~ "unmatched",
    length(candidate_ids) == 1L ~ "consistent",
    TRUE ~ "inconsistent"
  )
}

crossref_key <- function(journal) {
  issns <- vapply(journal$ISSN, normalize_issn, character(1))
  paste(sort(unique(issns[!is.na(issns)])), collapse = "|")
}

serialize_json <- function(value) {
  as.character(jsonlite::toJSON(
    value,
    auto_unbox = TRUE,
    null = "null",
    na = "null",
    digits = NA
  ))
}

dataframe_value <- function(value) {
  if (is.null(value) || !length(value)) return(NA)
  if (is.atomic(value) && length(value) == 1L) return(value)
  serialize_json(value)
}

candidate_prefix <- function(prefix, index) {
  if (index == 1L) prefix else paste0(prefix, "_candidate_", index)
}

candidate_field_names <- function(match, prefix) {
  unlist(imap(match$candidates, \(candidate, index) {
    paste(candidate_prefix(prefix, index), names(candidate), sep = "_")
  }), use.names = FALSE)
}

expand_candidate_fields <- function(match, prefix) {
  cells <- list()
  for (index in seq_along(match$candidates)) {
    candidate <- match$candidates[[index]]
    values <- map(candidate, dataframe_value)
    names(values) <- paste(
      candidate_prefix(prefix, index),
      names(candidate),
      sep = "_"
    )
    cells <- c(cells, values)
  }
  cells[[paste0(prefix, "_candidates__json")]] <- serialize_json(
    match$candidates
  )
  as_tibble(cells, .name_repair = "check_unique")
}

Sample enrichment orchestration

This chunk collects the unique sample ISSNs, retrieves source records, matches them to the PKP rows, adds every returned source field, and writes one CSV row for each PKP row. The first candidate uses the ordinary source prefix; additional candidates use numbered prefixes so inconsistent matches remain complete and auditable.

For a formal full-cohort run, replace this chunk with the full version described in the comments at its end.

input_issns <- map(ojs_rows$issn, parse_issns)
tokens <- sort(unique(unlist(input_issns)))

openalex_sources <- fetch_openalex_sources(tokens)
crossref_journals <- set_names(
  map(tokens, \(token) {
    journal <- fetch_crossref_journal(token)
    Sys.sleep(0.25)
    journal
  }),
  tokens
)

openalex_matches <- map(input_issns, \(issns) {
  candidates <- keep(openalex_sources, \(source) {
    any(issns %in% source_issns(source))
  })
  candidate_ids <- map_chr(candidates, "id")
  candidates <- candidates[!duplicated(candidate_ids)]
  candidate_ids <- unique(candidate_ids)

  list(
    status = classify_status(issns, candidate_ids),
    matched_issns = intersect(
      issns,
      unique(unlist(map(candidates, source_issns)))
    ),
    candidate_ids = candidate_ids,
    candidates = candidates
  )
})

crossref_matches <- map(input_issns, \(issns) {
  matched_issns <- keep(issns, \(issn) {
    !is.null(crossref_journals[[issn]])
  })
  candidates <- unname(crossref_journals[matched_issns])
  candidate_ids <- map_chr(candidates, crossref_key)
  candidates <- candidates[!duplicated(candidate_ids)]
  candidate_ids <- unique(candidate_ids)

  list(
    status = classify_status(issns, candidate_ids),
    matched_issns = matched_issns,
    candidate_ids = candidate_ids,
    candidates = candidates
  )
})

openalex_data <- map_dfr(
  openalex_matches,
  expand_candidate_fields,
  prefix = "openalex"
)
crossref_data <- map_dfr(
  crossref_matches,
  expand_candidate_fields,
  prefix = "crossref"
)

result <- ojs_rows |>
  mutate(
    identifier_status = case_when(
      is.na(issn) | !nzchar(issn) ~ "missing",
      lengths(input_issns) == 0L ~ "invalid",
      TRUE ~ "valid"
    ),
    openalex_input_issns = map_chr(input_issns, paste, collapse = "|"),
    openalex_matched_issns = map_chr(
      openalex_matches,
      \(match) paste(match$matched_issns, collapse = "|")
    ),
    openalex_match_status = map_chr(openalex_matches, "status"),
    openalex_candidate_ids = map_chr(
      openalex_matches,
      \(match) paste(match$candidate_ids, collapse = "|")
    ),
    crossref_input_issns = map_chr(input_issns, paste, collapse = "|"),
    crossref_matched_issns = map_chr(
      crossref_matches,
      \(match) paste(match$matched_issns, collapse = "|")
    ),
    crossref_match_status = map_chr(crossref_matches, "status"),
    crossref_candidate_issn_sets = map_chr(
      crossref_matches,
      \(match) paste(match$candidate_ids, collapse = ";")
    )
  ) |>
  bind_cols(openalex_data, crossref_data)

write_csv(result, output_path, na = "")

# FULL-COHORT REPLACEMENT:
# - send OpenAlex requests in groups and read every result page;
# - save API responses so an interrupted run can continue;
# - retry temporary failures and record failed ISSNs;
# - record file checksums and run details in the metadata file.

Checks

These checks confirm that every input row appears in the output, every returned top-level API field is retained, the lossless candidate JSON matches the fetched records, the CSV file was written, and the sample contains the expected match outcomes.

expected_openalex_columns <- unique(unlist(map(
  openalex_matches,
  candidate_field_names,
  prefix = "openalex"
)))
expected_crossref_columns <- unique(unlist(map(
  crossref_matches,
  candidate_field_names,
  prefix = "crossref"
)))

stopifnot(
  exists("fetch_openalex_sources", mode = "function"),
  exists("fetch_crossref_journal", mode = "function"),
  nrow(result) == nrow(ojs_rows),
  file.exists(output_path),
  all(c(
    "openalex_match_status",
    "openalex_id",
    "openalex_summary_stats",
    "openalex_topics",
    "openalex_candidate_2_id",
    "openalex_candidates__json",
    "crossref_match_status",
    "crossref_title",
    "crossref_counts",
    "crossref_coverage",
    "crossref_candidates__json"
  ) %in% names(result)),
  all(expected_openalex_columns %in% names(result)),
  all(expected_crossref_columns %in% names(result)),
  identical(
    result$openalex_candidates__json,
    map_chr(openalex_matches, \(match) serialize_json(match$candidates))
  ),
  identical(
    result$crossref_candidates__json,
    map_chr(crossref_matches, \(match) serialize_json(match$candidates))
  ),
  all(c(
    "consistent",
    "unmatched",
    "inconsistent",
    "not_attempted"
  ) %in% result$openalex_match_status),
  "not_attempted" %in% result$crossref_match_status
)

Complete merged dataframe

This table displays all OJS, OpenAlex, and Crossref columns in the merged sample dataframe.

result |>
  knitr::kable(format = "html")

oai_url	application	version	earliest_datestamp	repository_name	set_spec	context_name	stats_id	total_record_count	issn	country_marc	country_issn	country_doaj	country_tld	country_ip	country_consolidated	best_doaj_url	last_completed_update	first_beacon	last_beacon	last_oai_response	unresponsive_endpoint	unresponsive_context	record_count_2020	record_count_2021	record_count_2022	record_count_2023	record_count_2024	record_count_2025	region	identifier_status	openalex_input_issns	openalex_matched_issns	openalex_match_status	openalex_candidate_ids	crossref_input_issns	crossref_matched_issns	crossref_match_status	crossref_candidate_issn_sets	openalex_id	openalex_issn_l	openalex_issn	openalex_display_name	openalex_host_organization	openalex_host_organization_name	openalex_host_organization_lineage	openalex_works_count	openalex_oa_works_count	openalex_cited_by_count	openalex_summary_stats	openalex_is_oa	openalex_is_in_doaj	openalex_is_in_doaj_since_year	openalex_is_high_oa_rate	openalex_is_high_oa_rate_since_year	openalex_is_in_scielo	openalex_is_ojs	openalex_is_preprint_repository	openalex_is_core	openalex_oa_flip_year	openalex_first_publication_year	openalex_last_publication_year	openalex_ids	openalex_homepage_url	openalex_apc_prices	openalex_apc_usd	openalex_country_code	openalex_societies	openalex_alternate_titles	openalex_type	openalex_topics	openalex_topic_share	openalex_counts_by_year	openalex_works_api_url	openalex_updated_date	openalex_created_date	openalex_candidates__json	openalex_candidate_2_id	openalex_candidate_2_issn_l	openalex_candidate_2_issn	openalex_candidate_2_display_name	openalex_candidate_2_host_organization	openalex_candidate_2_host_organization_name	openalex_candidate_2_host_organization_lineage	openalex_candidate_2_works_count	openalex_candidate_2_oa_works_count	openalex_candidate_2_cited_by_count	openalex_candidate_2_summary_stats	openalex_candidate_2_is_oa	openalex_candidate_2_is_in_doaj	openalex_candidate_2_is_in_doaj_since_year	openalex_candidate_2_is_high_oa_rate	openalex_candidate_2_is_high_oa_rate_since_year	openalex_candidate_2_is_in_scielo	openalex_candidate_2_is_ojs	openalex_candidate_2_is_preprint_repository	openalex_candidate_2_is_core	openalex_candidate_2_oa_flip_year	openalex_candidate_2_first_publication_year	openalex_candidate_2_last_publication_year	openalex_candidate_2_ids	openalex_candidate_2_homepage_url	openalex_candidate_2_apc_prices	openalex_candidate_2_apc_usd	openalex_candidate_2_country_code	openalex_candidate_2_societies	openalex_candidate_2_alternate_titles	openalex_candidate_2_type	openalex_candidate_2_topics	openalex_candidate_2_topic_share	openalex_candidate_2_counts_by_year	openalex_candidate_2_works_api_url	openalex_candidate_2_updated_date	openalex_candidate_2_created_date	crossref_last-status-check-time	crossref_counts	crossref_breakdowns	crossref_publisher	crossref_coverage	crossref_title	crossref_subjects	crossref_coverage-type	crossref_flags	crossref_ISSN	crossref_issn-type	crossref_candidates__json
https://journalskuwait.org/kjs/index.php/index/oai	ojs	3.2.1.1	2025-09-26 11:26:41	Kuwait Journal of Science	KJS	Kuwait Journal of Science	57e6e2b01e496	667	2307-4108 2307-4116	ku ku	KW KW			CA	KW		2025-09-26 11:27:13	2020-05-01 00:07:42	2020-10-11 02:40:55	2025-09-26 11:26:42	0	0	60	106	111	77	0	0	Middle East & North Africa	valid	2307-4108|2307-4116	2307-4108|2307-4116	consistent	https://openalex.org/S2764757238	2307-4108|2307-4116	2307-4108|2307-4116	consistent	2307-4108|2307-4116	https://openalex.org/S2764757238	2307-4108	["2307-4108","2307-4116"]	Kuwait Journal of Science	https://openalex.org/P4310320990	Elsevier BV	["https://openalex.org/P4310320990"]	931	929	4344	{"2yr_mean_citedness":3.5678391959799,"h_index":25,"i10_index":123}	TRUE	TRUE	2013	FALSE	NA	FALSE	FALSE	FALSE	TRUE	2012	1981	2026	{"openalex":"https://openalex.org/S2764757238","issn_l":"2307-4108","issn":["2307-4108","2307-4116"],"mag":"2764757238","wikidata":null}	https://www.sciencedirect.com/journal/kuwait-journal-of-science	[{"price":1100,"currency":"USD"},{"price":1000,"currency":"EUR"},{"price":880,"currency":"GBP"},{"price":123600,"currency":"JPY"}]	1100	KW	NA	["Kuwait journal of science"]	journal	[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","count":32,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10288","display_name":"Fractional Differential Equations Solutions","count":24,"subfield":{"id":"https://openalex.org/subfields/2611","display_name":"Modeling and Simulation"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10079","display_name":"Nanoparticles: synthesis and applications","count":22,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10225","display_name":"Essential Oils and Antimicrobial Activity","count":19,"subfield":{"id":"https://openalex.org/subfields/1106","display_name":"Food Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12543","display_name":"Groundwater and Watershed Analysis","count":19,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11476","display_name":"Graph theory and applications","count":18,"subfield":{"id":"https://openalex.org/subfields/2608","display_name":"Geometry and Topology"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10399","display_name":"Hydrocarbon exploration and reservoir analysis","count":17,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10968","display_name":"Statistical Distribution Estimation and Applications","count":17,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10104","display_name":"Nanofluid Flow and Heat Transfer","count":16,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10572","display_name":"Geophysical and Geoelectrical Methods","count":16,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11871","display_name":"Advanced Statistical Methods and Models","count":16,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","count":15,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13177","display_name":"Geological and Geophysical Studies","count":15,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10016","display_name":"Adsorption and biosorption for pollutant removal","count":14,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10035","display_name":"Phytochemicals and 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Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13010","display_name":"Agriculture and Agroindustry Studies","value":1.34e-05,"subfield":{"id":"https://openalex.org/subfields/1100","display_name":"General Agricultural and Biological Sciences"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13678","display_name":"Agricultural Research and Practices","value":1.32e-05,"subfield":{"id":"https://openalex.org/subfields/1100","display_name":"General Agricultural and Biological Sciences"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T14132","display_name":"Medical Research and Islamic Perspectives","value":1.31e-05,"subfield":{"id":"https://openalex.org/subfields/1212","display_name":"Religious studies"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T13485","display_name":"Marine and Coastal Ecosystems","value":1.26e-05,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13373","display_name":"Data Mining and Machine Learning Applications","value":1.01e-05,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer 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Raya	{"affiliations-current":0,"similarity-checking-current":0.868421052631579,"descriptions-current":0,"ror-ids-current":0,"funders-backfile":0,"licenses-backfile":0.909090909090909,"funders-current":0,"affiliations-backfile":0,"resource-links-backfile":0.909090909090909,"orcids-backfile":0,"update-policies-current":0,"ror-ids-backfile":0,"orcids-current":0,"similarity-checking-backfile":0.909090909090909,"references-backfile":0,"descriptions-backfile":0,"award-numbers-backfile":0,"update-policies-backfile":0,"licenses-current":0.868421052631579,"award-numbers-current":0,"abstracts-backfile":0.909090909090909,"resource-links-current":0.868421052631579,"abstracts-current":0.868421052631579,"references-current":0}	JURNAL TEKNIK PERTAMBANGAN	NA	{"all":{"last-status-check-time":1784337543320,"affiliations":0,"abstracts":0.877551020408163,"orcids":0,"licenses":0.877551020408163,"references":0,"funders":0,"similarity-checking":0.877551020408163,"award-numbers":0,"ror-ids":0,"update-policies":0,"resource-links":0.877551020408163,"descriptions":0},"backfile":{"last-status-check-time":1784337543320,"affiliations":0,"abstracts":0.909090909090909,"orcids":0,"licenses":0.909090909090909,"references":0,"funders":0,"similarity-checking":0.909090909090909,"award-numbers":0,"ror-ids":0,"update-policies":0,"resource-links":0.909090909090909,"descriptions":0},"current":{"last-status-check-time":1784337543320,"affiliations":0,"abstracts":0.868421052631579,"orcids":0,"licenses":0.868421052631579,"references":0,"funders":0,"similarity-checking":0.868421052631579,"award-numbers":0,"ror-ids":0,"update-policies":0,"resource-links":0.868421052631579,"descriptions":0}}	{"deposits-abstracts-current":true,"deposits-orcids-current":false,"deposits":true,"deposits-affiliations-backfile":false,"deposits-update-policies-backfile":false,"deposits-similarity-checking-backfile":true,"deposits-award-numbers-current":false,"deposits-resource-links-current":true,"deposits-ror-ids-current":false,"deposits-articles":true,"deposits-affiliations-current":false,"deposits-funders-current":false,"deposits-references-backfile":false,"deposits-ror-ids-backfile":false,"deposits-abstracts-backfile":true,"deposits-licenses-backfile":true,"deposits-award-numbers-backfile":false,"deposits-descriptions-current":false,"deposits-references-current":false,"deposits-resource-links-backfile":true,"deposits-descriptions-backfile":false,"deposits-orcids-backfile":false,"deposits-funders-backfile":false,"deposits-update-policies-current":false,"deposits-similarity-checking-current":true,"deposits-licenses-current":true}	["2087-1058","2988-7712"]	[{"value":"2087-1058","type":"print"},{"value":"2988-7712","type":"electronic"}]	[{"last-status-check-time":1784337543320,"counts":{"current-dois":38,"backfile-dois":11,"total-dois":49},"breakdowns":{"dois-by-issued-year":[[2024,20],[2025,16],[2023,11],[2026,2]]},"publisher":"Universitas 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Complete merged column list

This table lists every column in the CSV so collaborators can review its structure.

tibble(column = names(result)) |>
  knitr::kable()

column
oai_url
application
version
earliest_datestamp
repository_name
set_spec
context_name
stats_id
total_record_count
issn
country_marc
country_issn
country_doaj
country_tld
country_ip
country_consolidated
best_doaj_url
last_completed_update
first_beacon
last_beacon
last_oai_response
unresponsive_endpoint
unresponsive_context
record_count_2020
record_count_2021
record_count_2022
record_count_2023
record_count_2024
record_count_2025
region
identifier_status
openalex_input_issns
openalex_matched_issns
openalex_match_status
openalex_candidate_ids
crossref_input_issns
crossref_matched_issns
crossref_match_status
crossref_candidate_issn_sets
openalex_id
openalex_issn_l
openalex_issn
openalex_display_name
openalex_host_organization
openalex_host_organization_name
openalex_host_organization_lineage
openalex_works_count
openalex_oa_works_count
openalex_cited_by_count
openalex_summary_stats
openalex_is_oa
openalex_is_in_doaj
openalex_is_in_doaj_since_year
openalex_is_high_oa_rate
openalex_is_high_oa_rate_since_year
openalex_is_in_scielo
openalex_is_ojs
openalex_is_preprint_repository
openalex_is_core
openalex_oa_flip_year
openalex_first_publication_year
openalex_last_publication_year
openalex_ids
openalex_homepage_url
openalex_apc_prices
openalex_apc_usd
openalex_country_code
openalex_societies
openalex_alternate_titles
openalex_type
openalex_topics
openalex_topic_share
openalex_counts_by_year
openalex_works_api_url
openalex_updated_date
openalex_created_date
openalex_candidates__json
openalex_candidate_2_id
openalex_candidate_2_issn_l
openalex_candidate_2_issn
openalex_candidate_2_display_name
openalex_candidate_2_host_organization
openalex_candidate_2_host_organization_name
openalex_candidate_2_host_organization_lineage
openalex_candidate_2_works_count
openalex_candidate_2_oa_works_count
openalex_candidate_2_cited_by_count
openalex_candidate_2_summary_stats
openalex_candidate_2_is_oa
openalex_candidate_2_is_in_doaj
openalex_candidate_2_is_in_doaj_since_year
openalex_candidate_2_is_high_oa_rate
openalex_candidate_2_is_high_oa_rate_since_year
openalex_candidate_2_is_in_scielo
openalex_candidate_2_is_ojs
openalex_candidate_2_is_preprint_repository
openalex_candidate_2_is_core
openalex_candidate_2_oa_flip_year
openalex_candidate_2_first_publication_year
openalex_candidate_2_last_publication_year
openalex_candidate_2_ids
openalex_candidate_2_homepage_url
openalex_candidate_2_apc_prices
openalex_candidate_2_apc_usd
openalex_candidate_2_country_code
openalex_candidate_2_societies
openalex_candidate_2_alternate_titles
openalex_candidate_2_type
openalex_candidate_2_topics
openalex_candidate_2_topic_share
openalex_candidate_2_counts_by_year
openalex_candidate_2_works_api_url
openalex_candidate_2_updated_date
openalex_candidate_2_created_date
crossref_last-status-check-time
crossref_counts
crossref_breakdowns
crossref_publisher
crossref_coverage
crossref_title
crossref_subjects
crossref_coverage-type
crossref_flags
crossref_ISSN
crossref_issn-type
crossref_candidates__json