Add grant ID for EU-funded zenodo communities

.zenodo.json

@@ -1,6 +1,6 @@
 {
   "title": "dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty",
-  "version": "0.11.0",
+  "version": "0.11.1",
   "license": "MIT",
   "upload_type": "software",
   "description": "<p>This R package provides functions to explore data cubes using simple measures and cross-validation techniques. It can also be used for uncertainty calculation using the bootstrap resampling method, and functionality is provided for efficient interpretation and visualisation of uncertainty related to indicators based on occurrence cubes.<\/p>",
@@ -43,5 +43,11 @@
     {
       "identifier": "inbo"
     }
+  ],
+  "publisher": "Research Institute for Nature and Forest (INBO)",
+  "grants": [
+    {
+      "id": "101059592"
+    }
   ]
 }

CITATION.cff

@@ -34,4 +34,4 @@ identifiers:
   value: 10.5281/zenodo.14850237
 - type: url
   value: https://b-cubed-eu.github.io/dubicube/
-version: 0.11.0
+version: 0.11.1

DESCRIPTION

@@ -1,14 +1,15 @@
 Package: dubicube
 Title: Calculation and Interpretation of Data Cube Indicator Uncertainty
-Version: 0.11.0
+Version: 0.11.1
 Authors@R: c(
     person("Ward", "Langeraert", , "ward.langeraert@inbo.be", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-5900-8109", affiliation = "Research Institute for Nature and Forest (INBO)")),
     person("Toon", "Van Daele", , "toon.vandaele@inbo.be", role = "aut",
            comment = c(ORCID = "0000-0002-1362-853X", affiliation = "Research Institute for Nature and Forest (INBO)")),
-    person("Research Institute for Nature and Forest (INBO)", , , "info@inbo.be", role = "cph",
-           comment = c(ROR = "https://ror.org/00j54wy13")),
-    person("European Union (ID 101059592)", , , "b-cubedsupport@meisebotanicgarden.be", role = "fnd")
+    person("Research Institute for Nature and Forest (INBO)", , , "info@inbo.be", role = c("cph", "pbl"),
+           comment = c(ROR = "00j54wy13")),
+    person("European Union (ID 101059592)", , , "b-cubedsupport@meisebotanicgarden.be", role = "fnd",
+           comment = c(grant_id = "101059592"))
   )
 Description: This R package provides functions to explore data cubes using
     simple measures and cross-validation techniques. It can also be used

NEWS.md

@@ -1,3 +1,10 @@
+# dubicube 0.11.1
+
+* Add grant ID for Zenodo integration #94
+* Fix ROR url bug
+* Add publisher to metadata
+* Make pkgdown lighter #93
+
 # dubicube 0.11.0
 
 * Improve figures of whole-cube versus group-specific tutorial #88

codemeta.json

@@ -8,7 +8,7 @@
   "codeRepository": "https://github.com/b-cubed-eu/dubicube",
   "issueTracker": "https://github.com/b-cubed-eu/dubicube/issues",
   "license": "https://spdx.org/licenses/MIT",
-  "version": "0.11.0",
+  "version": "0.11.1",
   "programmingLanguage": {
     "@type": "ComputerLanguage",
     "name": "R",
@@ -216,7 +216,7 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "387.377KB",
+  "fileSize": "1846.596KB",
   "citation": [
     {
       "@type": "SoftwareSourceCode",
@@ -233,7 +233,7 @@
           "familyName": "Van Daele"
         }
       ],
-      "name": "dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.0",
+      "name": "dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.1",
       "identifier": "10.5281/zenodo.14850237",
       "url": "https://b-cubed-eu.github.io/dubicube/",
       "@id": "https://doi.org/10.5281/zenodo.14850237",

inst/CITATION

@@ -2,12 +2,12 @@ citHeader("To cite `dubicube` in publications please use:")
 # begin checklist entry
 bibentry(
   bibtype = "Manual",
-  title = "dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.0",
+  title = "dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.1",
   author = c(person(given = "Ward", family = "Langeraert"), person(given = "Toon", family = "Van Daele")),
   year = 2026,
   url = "https://b-cubed-eu.github.io/dubicube/",
   abstract = "This R package provides functions to explore data cubes using simple measures and cross-validation techniques. It can also be used for uncertainty calculation using the bootstrap resampling method, and functionality is provided for efficient interpretation and visualisation of uncertainty related to indicators based on occurrence cubes.",
-  textVersion = "Langeraert, Ward; Van Daele, Toon (2026) dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.0. https://b-cubed-eu.github.io/dubicube/",
+  textVersion = "Langeraert, Ward; Van Daele, Toon (2026) dubicube: Calculation and Interpretation of Data Cube Indicator Uncertainty. Version 0.11.1. https://b-cubed-eu.github.io/dubicube/",
   keywords = "uncertainty quantification; uncertainty visualisation; biodiversity indicators; data cubes",
   doi = "10.5281/zenodo.14850237",
 )

organisation.yml

@@ -9,7 +9,7 @@ info@inbo.be:
   email: info@inbo.be
   website: https://www.vlaanderen.be/inbo/en-gb
   logo: https://inbo.github.io/checklist/reference/figures/logo-en.png
-  ror: https://ror.org/00j54wy13
+  ror: 00j54wy13
   orcid: yes
   zenodo: inbo
   rightsholder: optional

vignettes/articles/bootstrap-interval-calculation.Rmd

@@ -136,15 +136,14 @@ b3data_package <- read_package(
 
 # Load bird cube data
 bird_cube_belgium <- read_resource(b3data_package, "bird_cube_belgium_mgrs10")
-head(bird_cube_belgium)
 ```
 
 We process the cube with **b3gbi**.
 First, we select 2000 random rows to make the dataset smaller.
 This is to reduce the computation time for this tutorial.
 We select the data from 2011 - 2020.
 
-```{r}
+```r
 set.seed(123)
 
 # Make dataset smaller
@@ -161,6 +160,47 @@ processed_cube <- process_cube(
 processed_cube
 ```
 
+```{r, echo=FALSE, message=FALSE, results='hide'}
+if (
+  system.file("bootstrapping", "processed_cube.rds", package = "dubicube") == ""
+) {
+  # Read data package
+  b3data_package <- read_package(
+    "https://zenodo.org/records/15211029/files/datapackage.json"
+  )
+
+  # Load bird cube data
+  bird_cube_belgium <- read_resource(b3data_package, "bird_cube_belgium_mgrs10")
+
+  set.seed(123)
+
+  # Make dataset smaller
+  rows <- sample(nrow(bird_cube_belgium), 2000)
+  bird_cube_belgium <- bird_cube_belgium[rows, ]
+
+  # Process cube
+  processed_cube <- process_cube(
+    bird_cube_belgium,
+    first_year = 2011,
+    last_year = 2020,
+    cols_occurrences = "n"
+  )
+
+  saveRDS(
+    processed_cube,
+    file.path("..", "..", "inst", "bootstrapping", "processed_cube.rds")
+  )
+} else {
+  processed_cube <- readRDS(
+    system.file("bootstrapping", "processed_cube.rds", package = "dubicube")
+  )
+}
+```
+
+```{r, echo=FALSE}
+processed_cube
+```
+
 ### Analysis of the data
 
 Let's say we are interested in the mean number of observations per grid cell per year.
@@ -171,8 +211,8 @@ We create a function to calculate this.
 ```
 
 ```{r}
-# Function to calculate statistic of interest
-# Mean number of observations per grid cell per year
+# Function to calculate the statistic of interest
+# Mean observations per grid cell per year
 mean_obs <- function(data) {
   data %>%
     dplyr::mutate(x = mean(obs), .by = "cellCode") %>%
@@ -197,7 +237,7 @@ On their own, these values don’t reveal how much uncertainty surrounds them. T
 
 We use the `bootstrap_cube()` function to perform bootstrapping (see also the [bootstrap tutorial](https://b-cubed-eu.github.io/dubicube/articles/bootstrap-method-cubes.html)).
 
-```{r}
+```r
 bootstrap_results <- bootstrap_cube(
   data_cube = processed_cube,
   fun = mean_obs,
@@ -207,6 +247,35 @@ bootstrap_results <- bootstrap_cube(
 )
 ```
 
+```{r, echo=FALSE, message=FALSE, results='hide'}
+if (
+  system.file(
+    "bootstrapping", "bootstrap_results.rds", package = "dubicube"
+  ) == ""
+) {
+  bootstrap_results <- bootstrap_cube(
+    data_cube = processed_cube,
+    fun = mean_obs,
+    grouping_var = "year",
+    samples = 1000,
+    seed = 123
+  )
+
+  saveRDS(
+    bootstrap_results,
+    file.path("..", "..", "inst", "bootstrapping", "bootstrap_results.rds")
+  )
+} else {
+  bootstrap_results <- readRDS(
+    system.file("bootstrapping", "bootstrap_results.rds", package = "dubicube")
+  )
+}
+```
+
+```{r, echo=FALSE}
+print("Performing whole-cube bootstrap with `boot::boot()`.")
+```
+
 ### Interval calculation
 
 Now we can use the `calculate_bootstrap_ci()` function to calculate confidence limits. It relies on the following arguments:

vignettes/articles/bootstrap-method-cubes.Rmd

@@ -101,23 +101,22 @@ library(dubicube)     # Analysis of data quality & indicator uncertainty
 We load the bird cube data from the **b3data** data package using **frictionless** (see also [here](https://github.com/b-cubed-eu/b3data-scripts)).
 It is an occurrence cube for birds in Belgium between 2000 en 2024 using the MGRS grid at 10 km scale.
 
-```{r}
+```r
 # Read data package
 b3data_package <- read_package(
   "https://zenodo.org/records/15211029/files/datapackage.json"
 )
 
 # Load bird cube data
 bird_cube_belgium <- read_resource(b3data_package, "bird_cube_belgium_mgrs10")
-head(bird_cube_belgium)
 ```
 
 We process the cube with **b3gbi**.
 First, we select 2000 random rows to make the dataset smaller.
 This is to reduce the computation time for this tutorial.
 We select the data from 2011 - 2020.
 
-```{r}
+```r
 set.seed(123)
 
 # Make dataset smaller
@@ -134,24 +133,71 @@ processed_cube <- process_cube(
 processed_cube
 ```
 
+```{r, echo=FALSE, message=FALSE, results='hide'}
+if (
+  system.file("bootstrapping", "processed_cube.rds", package = "dubicube") == ""
+) {
+  # Read data package
+  b3data_package <- read_package(
+    "https://zenodo.org/records/15211029/files/datapackage.json"
+  )
+
+  # Load bird cube data
+  bird_cube_belgium <- read_resource(b3data_package, "bird_cube_belgium_mgrs10")
+
+  set.seed(123)
+
+  # Make dataset smaller
+  rows <- sample(nrow(bird_cube_belgium), 2000)
+  bird_cube_belgium <- bird_cube_belgium[rows, ]
+
+  # Process cube
+  processed_cube <- process_cube(
+    bird_cube_belgium,
+    first_year = 2011,
+    last_year = 2020,
+    cols_occurrences = "n"
+  )
+
+  saveRDS(
+    processed_cube,
+    file.path("..", "..", "inst", "inst", "bootstrapping", "processed_cube.rds")
+  )
+} else {
+  processed_cube <- readRDS(
+    system.file("bootstrapping", "processed_cube.rds", package = "dubicube")
+  )
+}
+```
+
+```{r, echo=FALSE}
+processed_cube
+```
+
 ### Analysis of the data
 
 Let's say we are interested in the mean number of observations per grid cell per year.
 We create a function to calculate this.
 
+```{r, echo=FALSE}
+# nolint start: object_usage_linter.
+```
+
 ```{r}
 # Function to calculate the statistic of interest
 # Mean observations per grid cell per year
 mean_obs <- function(data) {
-  obs <- x <- NULL
-
   data %>%
     dplyr::mutate(x = mean(obs), .by = "cellCode") %>%
     dplyr::summarise(diversity_val = mean(x), .by = "year") %>%
     as.data.frame()
 }
 ```
 
+```{r, echo=FALSE}
+# nolint end: object_usage_linter.
+```
+
 We get the following results:
 
 ```{r}
@@ -182,7 +228,7 @@ We use the `bootstrap_cube()` function to do this. It relies on the following ar
 - **`progress`**:  
   Logical flag to show a progress bar. Set to `TRUE` to enable progress reporting; default is `FALSE`.
 
-```{r}
+```r
 bootstrap_results <- bootstrap_cube(
   data_cube = processed_cube,
   fun = mean_obs,
@@ -192,6 +238,35 @@ bootstrap_results <- bootstrap_cube(
 )
 ```
 
+```{r, echo=FALSE, message=FALSE, results='hide'}
+if (
+  system.file(
+    "bootstrapping", "bootstrap_results.rds", package = "dubicube"
+  ) == ""
+) {
+  bootstrap_results <- bootstrap_cube(
+    data_cube = processed_cube,
+    fun = mean_obs,
+    grouping_var = "year",
+    samples = 1000,
+    seed = 123
+  )
+
+  saveRDS(
+    bootstrap_results,
+    file.path("..", "..", "inst", "bootstrapping", "bootstrap_results.rds")
+  )
+} else {
+  bootstrap_results <- readRDS(
+    system.file("bootstrapping", "bootstrap_results.rds", package = "dubicube")
+  )
+}
+```
+
+```{r, echo=FALSE}
+print("Performing whole-cube bootstrap with `boot::boot()`.")
+```
+
 This returned a list of `"boot"` objects. We can convert this to a dataframe 
 
 ```{r}