diff --git a/DESCRIPTION b/DESCRIPTION
index 1b14fb83e..b49b83390 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9126
-Date: 2025-01-26
+Version: 2.1.1.9127
+Date: 2025-01-27
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by
diff --git a/NAMESPACE b/NAMESPACE
index d9ca17aa2..78f103355 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -221,7 +221,6 @@ export(g.test)
 export(geom_sir)
 export(get_AMR_locale)
 export(get_episode)
-export(get_long_numeric_format)
 export(get_mo_source)
 export(ggplot_pca)
 export(ggplot_sir)
diff --git a/NEWS.md b/NEWS.md
index 5483caddd..9f354c607 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# AMR 2.1.1.9126
+# AMR 2.1.1.9127
 
 *(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://msberends.github.io/AMR/#latest-development-version).)*
 
@@ -16,7 +16,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
   * The `antibiotics` data set contains all veterinary antibiotics, such as pradofloxacin and enrofloxacin. All WHOCC codes for veterinary use have been added as well.
   * `ab_atc()` now supports ATC codes of veterinary antibiotics (that all start with "Q")
   * `ab_url()` now supports retrieving the WHOCC url of their ATCvet pages
-* Support for WISCA antibiograms**  
+* **Support for WISCA antibiograms**  
   * The `antibiogram()` function now supports creating true Weighted-Incidence Syndromic Combination Antibiograms (WISCA), a powerful Bayesian method for estimating regimen coverage probabilities using pathogen incidence and antimicrobial susceptibility data. WISCA offers improved precision for syndrome-specific treatment, even in datasets with sparse data. A dedicated `wisca()` function is also available for easy usage.
 * **Major update to fungal taxonomy and tools for mycologists**
   * MycoBank has now been integrated as the primary taxonomic source for fungi. The `microorganisms` data set has been enriched with new columns (`mycobank`, `mycobank_parent`, and `mycobank_renamed_to`) that provide detailed information for fungal species.
diff --git a/PythonPackage/AMR/AMR.egg-info/PKG-INFO b/PythonPackage/AMR/AMR.egg-info/PKG-INFO
index b8389d8fe..bbf2afcfc 100644
--- a/PythonPackage/AMR/AMR.egg-info/PKG-INFO
+++ b/PythonPackage/AMR/AMR.egg-info/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9126
+Version: 2.1.1.9127
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends
@@ -123,48 +123,6 @@ print(df)
 
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 
-
-## Taxonomic Data Sets Now in Python!
-
-As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
-
-```python
-AMR.microorganisms
-```
-
-| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
-|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
-| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
-| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
-| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
-| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
-
-```python
-AMR.antibiotics
-```
-
-| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
-|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
-| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
-| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
-| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
-| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
-| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
-| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
-| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
-
-
 ## Calculating AMR
 
 ```python
@@ -215,6 +173,47 @@ print(result2b)
 
 In this example, we generate an antibiogram by selecting various antibiotics.
 
+## Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
+
+```python
+AMR.microorganisms
+```
+
+| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
+|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
+| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
+| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
+| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
+| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
+
+```python
+AMR.antibiotics
+```
+
+| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
+|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
+| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
+| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
+| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
+| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
+| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
+| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
+| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
+
+
 # Conclusion
 
 With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
diff --git a/PythonPackage/AMR/AMR/datasets.py b/PythonPackage/AMR/AMR/datasets.py
index 6d1dccd68..efc5d87d8 100644
--- a/PythonPackage/AMR/AMR/datasets.py
+++ b/PythonPackage/AMR/AMR/datasets.py
@@ -19,6 +19,9 @@ r_lib_path = os.path.join(venv_path, "R_libs")
 os.makedirs(r_lib_path, exist_ok=True)
 # Set the R library path in .libPaths
 base = importr('base')
+# Turn off warnings
+base.options(warn = -1)
+
 base._libPaths(r_lib_path)
 r_amr_lib_path = base._libPaths()[0]
 
@@ -46,6 +49,9 @@ if r_amr_version != python_amr_version:
     except Exception as e:
         print(f"{BLUE}AMR:{RESET} Could not update: {e}{RESET}", flush=True)
 
+# Restore warnings to default
+base.options(warn = 0)
+
 print(f"{BLUE}AMR:{RESET} Setting up R environment and AMR datasets...", flush=True)
 
 # Activate the automatic conversion between R and pandas DataFrames
diff --git a/PythonPackage/AMR/README.md b/PythonPackage/AMR/README.md
index c28f09998..de26a764e 100755
--- a/PythonPackage/AMR/README.md
+++ b/PythonPackage/AMR/README.md
@@ -95,48 +95,6 @@ print(df)
 
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 
-
-## Taxonomic Data Sets Now in Python!
-
-As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
-
-```python
-AMR.microorganisms
-```
-
-| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
-|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
-| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
-| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
-| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
-| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
-
-```python
-AMR.antibiotics
-```
-
-| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
-|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
-| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
-| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
-| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
-| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
-| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
-| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
-| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
-
-
 ## Calculating AMR
 
 ```python
@@ -187,6 +145,47 @@ print(result2b)
 
 In this example, we generate an antibiogram by selecting various antibiotics.
 
+## Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
+
+```python
+AMR.microorganisms
+```
+
+| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
+|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
+| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
+| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
+| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
+| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
+
+```python
+AMR.antibiotics
+```
+
+| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
+|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
+| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
+| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
+| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
+| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
+| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
+| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
+| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
+
+
 # Conclusion
 
 With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
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diff --git a/PythonPackage/AMR/setup.py b/PythonPackage/AMR/setup.py
index e1ceb8d41..49c451f84 100644
--- a/PythonPackage/AMR/setup.py
+++ b/PythonPackage/AMR/setup.py
@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 setup(
     name='AMR',
-    version='2.1.1.9126',
+    version='2.1.1.9127',
     packages=find_packages(),
     install_requires=[
         'rpy2',
diff --git a/R/antibiogram.R b/R/antibiogram.R
index 6115a5db4..503273433 100755
--- a/R/antibiogram.R
+++ b/R/antibiogram.R
@@ -60,7 +60,7 @@
 #' 
 #' For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top *n* species encountered in the data. You can filter on this top *n* using [top_n_microorganisms()]. For example, use `top_n_microorganisms(your_data, n = 10)` as a pre-processing step to only include the top 10 species in the data.
 #' 
-#' Using [get_long_numeric_format()], the antibiogram is converted to a long format containing numeric values. This is ideal for e.g. advanced plotting.
+#' The numeric values of an antibiogram are stored in a long format as the [attribute] `long_numeric`. You can retrieve them using `attributes(x)$long_numeric`, where `x` is the outcome of [antibiogram()] or [wisca()]. This is ideal for e.g. advanced plotting.
 #' 
 #' ### Formatting Type
 #' 
@@ -345,7 +345,8 @@
 #'
 #' ab1 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-#'   mo_transform = "gramstain"
+#'   mo_transform = "gramstain",
+#'   wisca = TRUE
 #' )
 #' ab2 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
@@ -648,14 +649,14 @@ antibiogram.default <- function(x,
   }
   
   if (wisca == TRUE) {
-    out_numeric <- out %pm>%
+    long_numeric <- out %pm>%
       pm_summarise(percentage = percentage,
                    lower = lower,
                    upper = upper,
                    numerator = numerator,
                    total = total)
   } else {
-    out_numeric <- out %pm>%
+    long_numeric <- out %pm>%
       pm_summarise(percentage = numerator / total,
                    numerator = numerator,
                    total = total)  
@@ -727,7 +728,7 @@ antibiogram.default <- function(x,
     out
   }
   out$ab <- ab_naming_function(out$ab, t = ab_transform, l = language, s = sep)
-  out_numeric$ab <- ab_naming_function(out_numeric$ab, t = ab_transform, l = language, s = sep)
+  long_numeric$ab <- ab_naming_function(long_numeric$ab, t = ab_transform, l = language, s = sep)
   
   # transform long to wide
   long_to_wide <- function(object) {
@@ -801,14 +802,14 @@ antibiogram.default <- function(x,
   
   out <- as_original_data_class(new_df, class(x), extra_class = "antibiogram")
   rownames(out) <- NULL
-  rownames(out_numeric) <- NULL
+  rownames(long_numeric) <- NULL
   
   structure(out,
             has_syndromic_group = has_syndromic_group,
             combine_SI = combine_SI,
             wisca = wisca,
             conf_interval = conf_interval,
-            out_numeric = as_original_data_class(out_numeric, class(out))
+            long_numeric = as_original_data_class(long_numeric, class(out))
   )
 }
 
@@ -868,7 +869,7 @@ antibiogram.grouped_df <- function(x,
                            conf_interval = conf_interval,
                            interval_side = interval_side,
                            info = i == 1 && info == TRUE)
-    new_out_numeric <- attributes(new_out)$out_numeric
+    new_long_numeric <- attributes(new_out)$long_numeric
     
     if (i == 1) progress$tick()
     
@@ -878,7 +879,7 @@ antibiogram.grouped_df <- function(x,
     
     # remove first column 'Pathogen' (in whatever language)
     new_out <- new_out[, -1, drop = FALSE]
-    new_out_numeric <- new_out_numeric[, -1, drop = FALSE]
+    new_long_numeric <- new_long_numeric[, -1, drop = FALSE]
     
     # add group names to data set
     for (col in rev(seq_len(NCOL(groups) - 1))) {
@@ -886,17 +887,17 @@ antibiogram.grouped_df <- function(x,
       col_value <- groups[i, col, drop = TRUE]
       new_out[, col_name] <- col_value
       new_out <- new_out[, c(col_name, setdiff(names(new_out), col_name))] # set place to 1st col
-      new_out_numeric[, col_name] <- col_value
-      new_out_numeric <- new_out_numeric[, c(col_name, setdiff(names(new_out_numeric), col_name))] # set place to 1st col
+      new_long_numeric[, col_name] <- col_value
+      new_long_numeric <- new_long_numeric[, c(col_name, setdiff(names(new_long_numeric), col_name))] # set place to 1st col
     }
     
     if (i == 1) {
       # the first go
       out <- new_out
-      out_numeric <- new_out_numeric
+      long_numeric <- new_long_numeric
     } else {
       out <- rbind_AMR(out, new_out)
-      out_numeric <- rbind_AMR(out_numeric, new_out_numeric)
+      long_numeric <- rbind_AMR(long_numeric, new_long_numeric)
     }
   }
 
@@ -907,7 +908,7 @@ antibiogram.grouped_df <- function(x,
                    combine_SI = isTRUE(combine_SI),
                    wisca = isTRUE(wisca),
                    conf_interval = conf_interval,
-                   out_numeric = as_original_data_class(out_numeric, class(x)))
+                   long_numeric = as_original_data_class(long_numeric, class(x)))
 }
 
 #' @export
@@ -947,14 +948,6 @@ wisca <- function(x,
               info = info)
 }
 
-#' @export
-#' @param antibiogram the outcome of [antibiogram()] or [wisca()]
-#' @rdname antibiogram
-get_long_numeric_format <- function(antibiogram) {
-  stop_ifnot(inherits(antibiogram, "antibiogram"), "This function only works for the output of `antibiogram()` and `wisca()`.", call = FALSE)
-  attributes(antibiogram)$out_numeric
-}
-
 calculate_priors <- function(data, combine_SI = TRUE) {
   # Ensure data has required columns
   stopifnot(all(c("mo", "total_rows", "total", "S") %in% colnames(data)))
@@ -1009,9 +1002,10 @@ tbl_format_footer.antibiogram <- function(x, ...) {
 #' @export
 #' @rdname antibiogram
 plot.antibiogram <- function(x, ...) {
-  df <- attributes(x)$out_numeric
+  df <- attributes(x)$long_numeric
   if (!"mo" %in% colnames(df)) {
-    stop_("Plotting antibiograms using plot() is only possible if they were not created using dplyr groups. Consider using `get_long_numeric_format()` to retrieve raw antibiogram values.")
+    stop_("Plotting antibiograms using `plot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
+          call = FALSE)
   }
   if ("syndromic_group" %in% colnames(df)) {
     # barplot in base R does not support facets - paste columns together
@@ -1063,9 +1057,10 @@ barplot.antibiogram <- function(height, ...) {
 #' @rdname antibiogram
 # will be exported using s3_register() in R/zzz.R
 autoplot.antibiogram <- function(object, ...) {
-  df <- attributes(object)$out_numeric
+  df <- attributes(object)$long_numeric
   if (!"mo" %in% colnames(df)) {
-    stop_("Plotting antibiograms using plot() is only possible if they were not created using dplyr groups. Consider using `get_long_numeric_format()` to retrieve raw antibiogram values.")
+    stop_("Plotting antibiograms using `autoplot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
+          call = FALSE)
   }
   out <- ggplot2::ggplot(df,
                          mapping = ggplot2::aes(
@@ -1107,7 +1102,7 @@ knit_print.antibiogram <- function(x, italicise = TRUE, na = getOption("knitr.ka
   meet_criteria(italicise, allow_class = "logical", has_length = 1)
   meet_criteria(na, allow_class = "character", has_length = 1, allow_NA = TRUE)
   
-  if (isTRUE(italicise) && "mo" %in% colnames(attributes(x)$out_numeric)) {
+  if (isTRUE(italicise) && "mo" %in% colnames(attributes(x)$long_numeric)) {
     # make all microorganism names italic, according to nomenclature
     names_col <- ifelse(isTRUE(attributes(x)$has_syndromic_group), 2, 1)
     x[[names_col]] <- italicise_taxonomy(x[[names_col]], type = "markdown")
diff --git a/data-raw/_generate_python_wrapper.sh b/data-raw/_generate_python_wrapper.sh
index 841d57b6e..b9b629f8d 100644
--- a/data-raw/_generate_python_wrapper.sh
+++ b/data-raw/_generate_python_wrapper.sh
@@ -63,6 +63,9 @@ r_lib_path = os.path.join(venv_path, "R_libs")
 os.makedirs(r_lib_path, exist_ok=True)
 # Set the R library path in .libPaths
 base = importr('base')
+# Turn off warnings
+base.options(warn = -1)
+
 base._libPaths(r_lib_path)
 r_amr_lib_path = base._libPaths()[0]
 
@@ -90,6 +93,9 @@ if r_amr_version != python_amr_version:
     except Exception as e:
         print(f"{BLUE}AMR:{RESET} Could not update: {e}{RESET}", flush=True)
 
+# Restore warnings to default
+base.options(warn = 0)
+
 print(f"{BLUE}AMR:{RESET} Setting up R environment and AMR datasets...", flush=True)
 
 # Activate the automatic conversion between R and pandas DataFrames
diff --git a/data-raw/gpt_training_text_v2.1.1.9126.txt b/data-raw/gpt_training_text_v2.1.1.9127.txt
similarity index 99%
rename from data-raw/gpt_training_text_v2.1.1.9126.txt
rename to data-raw/gpt_training_text_v2.1.1.9127.txt
index f16964c22..e030aade0 100644
--- a/data-raw/gpt_training_text_v2.1.1.9126.txt
+++ b/data-raw/gpt_training_text_v2.1.1.9127.txt
@@ -1,5 +1,5 @@
 This files contains all context you must know about the AMR package for R.
-First and foremost, you are trained on version 2.1.1.9126. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9127. Remember this whenever someone asks which AMR package version you’re at.
 --------------------------------
 
 THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'NAMESPACE':
@@ -228,7 +228,6 @@ export(g.test)
 export(geom_sir)
 export(get_AMR_locale)
 export(get_episode)
-export(get_long_numeric_format)
 export(get_mo_source)
 export(ggplot_pca)
 export(ggplot_sir)
@@ -1609,7 +1608,6 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antibiogram.Rd':
 \name{antibiogram}
 \alias{antibiogram}
 \alias{wisca}
-\alias{get_long_numeric_format}
 \alias{plot.antibiogram}
 \alias{autoplot.antibiogram}
 \alias{knit_print.antibiogram}
@@ -1641,8 +1639,6 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
   combine_SI = TRUE, sep = " + ", simulations = 1000,
   info = interactive())
 
-get_long_numeric_format(antibiogram)
-
 \method{plot}{antibiogram}(x, ...)
 
 \method{autoplot}{antibiogram}(object, ...)
@@ -1689,8 +1685,6 @@ get_long_numeric_format(antibiogram)
 
 \item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
 
-\item{antibiogram}{the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}}
-
 \item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
 
 \item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
@@ -1711,7 +1705,7 @@ This function returns a table with values between 0 and 100 for \emph{susceptibi
 
 For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top \emph{n} species encountered in the data. You can filter on this top \emph{n} using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}. For example, use \code{top_n_microorganisms(your_data, n = 10)} as a pre-processing step to only include the top 10 species in the data.
 
-Using \code{\link[=get_long_numeric_format]{get_long_numeric_format()}}, the antibiogram is converted to a long format containing numeric values. This is ideal for e.g. advanced plotting.
+The numeric values of an antibiogram are stored in a long format as the \link{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
 \subsection{Formatting Type}{
 
 The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} is the susceptibility percentage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
@@ -1985,7 +1979,8 @@ if (requireNamespace("knitr")) {
 
 ab1 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-  mo_transform = "gramstain"
+  mo_transform = "gramstain",
+  wisca = TRUE
 )
 ab2 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
@@ -8596,48 +8591,6 @@ print(df)
 
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 
-
-## Taxonomic Data Sets Now in Python!
-
-As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
-
-```python
-AMR.microorganisms
-```
-
-| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
-|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
-| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
-| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
-| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
-| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
-
-```python
-AMR.antibiotics
-```
-
-| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
-|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
-| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
-| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
-| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
-| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
-| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
-| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
-| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
-
-
 ## Calculating AMR
 
 ```python
@@ -8688,6 +8641,47 @@ print(result2b)
 
 In this example, we generate an antibiogram by selecting various antibiotics.
 
+## Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
+
+```python
+AMR.microorganisms
+```
+
+| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
+|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
+| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
+| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
+| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
+| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
+
+```python
+AMR.antibiotics
+```
+
+| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
+|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
+| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
+| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
+| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
+| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
+| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
+| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
+| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
+
+
 # Conclusion
 
 With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.
diff --git a/inst/tinytest/test-_deprecated.R b/inst/tinytest/test-_deprecated.R
index a9e1139c9..95bfcd045 100644
--- a/inst/tinytest/test-_deprecated.R
+++ b/inst/tinytest/test-_deprecated.R
@@ -27,4 +27,5 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
 
-
+expect_warning(example_isolates[, ab_class("mycobact")])
+expect_warning(example_isolates[, ab_selector(name %like% "trim")])
diff --git a/inst/tinytest/test-ab_selectors.R b/inst/tinytest/test-ab_selectors.R
index e41f138d3..ac1a45837 100644
--- a/inst/tinytest/test-ab_selectors.R
+++ b/inst/tinytest/test-ab_selectors.R
@@ -28,7 +28,8 @@
 # ==================================================================== #
 
 # antibiotic class selectors
-expect_equal(ncol(example_isolates[, ab_class("antimyco"), drop = FALSE]), 1, tolerance = 0.5)
+expect_equal(ncol(example_isolates[, amr_class("antimyco"), drop = FALSE]), 1, tolerance = 0.5)
+expect_equal(ncol(example_isolates[, amr_selector(name %like% "trim"), drop = FALSE]), 2, tolerance = 0.5)
 expect_equal(ncol(example_isolates[, aminoglycosides(), drop = FALSE]), 4, tolerance = 0.5)
 expect_equal(ncol(example_isolates[, aminopenicillins(), drop = FALSE]), 2, tolerance = 0.5)
 expect_equal(ncol(example_isolates[, betalactams(), drop = FALSE]), 16, tolerance = 0.5)
diff --git a/inst/tinytest/test-antibiogram.R b/inst/tinytest/test-antibiogram.R
index 6614a902e..8725436c1 100644
--- a/inst/tinytest/test-antibiogram.R
+++ b/inst/tinytest/test-antibiogram.R
@@ -128,9 +128,7 @@ expect_silent(plot(ab5))
 expect_silent(plot(ab6))
 expect_silent(plot(ab7))
 expect_silent(plot(ab8))
-if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
-  expect_silent(plot(ab9))
-}
+expect_error(plot(ab9))
 
 if (AMR:::pkg_is_available("ggplot2")) {
   expect_inherits(ggplot2::autoplot(ab1), "gg")
@@ -141,7 +139,5 @@ if (AMR:::pkg_is_available("ggplot2")) {
   expect_inherits(ggplot2::autoplot(ab6), "gg")
   expect_inherits(ggplot2::autoplot(ab7), "gg")
   expect_inherits(ggplot2::autoplot(ab8), "gg")
-  if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
-    expect_inherits(ggplot2::autoplot(ab9), "gg")
-  }
+  expect_error(ggplot2::autoplot(ab9))
 }
diff --git a/man/antibiogram.Rd b/man/antibiogram.Rd
index bc63ccfd2..c3023111a 100644
--- a/man/antibiogram.Rd
+++ b/man/antibiogram.Rd
@@ -3,7 +3,6 @@
 \name{antibiogram}
 \alias{antibiogram}
 \alias{wisca}
-\alias{get_long_numeric_format}
 \alias{plot.antibiogram}
 \alias{autoplot.antibiogram}
 \alias{knit_print.antibiogram}
@@ -35,8 +34,6 @@ wisca(x, antibiotics = where(is.sir), mo_transform = "shortname",
   combine_SI = TRUE, sep = " + ", simulations = 1000,
   info = interactive())
 
-get_long_numeric_format(antibiogram)
-
 \method{plot}{antibiogram}(x, ...)
 
 \method{autoplot}{antibiogram}(object, ...)
@@ -83,8 +80,6 @@ get_long_numeric_format(antibiogram)
 
 \item{info}{a \link{logical} to indicate info should be printed - the default is \code{TRUE} only in interactive mode}
 
-\item{antibiogram}{the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}}
-
 \item{...}{when used in \link[knitr:kable]{R Markdown or Quarto}: arguments passed on to \code{\link[knitr:kable]{knitr::kable()}} (otherwise, has no use)}
 
 \item{object}{an \code{\link[=antibiogram]{antibiogram()}} object}
@@ -105,7 +100,7 @@ This function returns a table with values between 0 and 100 for \emph{susceptibi
 
 For estimating antimicrobial coverage, especially when creating a WISCA, the outcome might become more reliable by only including the top \emph{n} species encountered in the data. You can filter on this top \emph{n} using \code{\link[=top_n_microorganisms]{top_n_microorganisms()}}. For example, use \code{top_n_microorganisms(your_data, n = 10)} as a pre-processing step to only include the top 10 species in the data.
 
-Using \code{\link[=get_long_numeric_format]{get_long_numeric_format()}}, the antibiogram is converted to a long format containing numeric values. This is ideal for e.g. advanced plotting.
+The numeric values of an antibiogram are stored in a long format as the \link{attribute} \code{long_numeric}. You can retrieve them using \code{attributes(x)$long_numeric}, where \code{x} is the outcome of \code{\link[=antibiogram]{antibiogram()}} or \code{\link[=wisca]{wisca()}}. This is ideal for e.g. advanced plotting.
 \subsection{Formatting Type}{
 
 The formatting of the 'cells' of the table can be set with the argument \code{formatting_type}. In these examples, \code{5} is the susceptibility percentage (for WISCA: \code{4-6} indicates the confidence level), \code{15} the numerator, and \code{300} the denominator:
@@ -379,7 +374,8 @@ if (requireNamespace("knitr")) {
 
 ab1 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-  mo_transform = "gramstain"
+  mo_transform = "gramstain",
+  wisca = TRUE
 )
 ab2 <- antibiogram(example_isolates,
   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
diff --git a/vignettes/AMR_for_Python.Rmd b/vignettes/AMR_for_Python.Rmd
index c28f09998..de26a764e 100755
--- a/vignettes/AMR_for_Python.Rmd
+++ b/vignettes/AMR_for_Python.Rmd
@@ -95,48 +95,6 @@ print(df)
 
 * **ab_name**: Similarly, this function standardises antimicrobial names. The different representations of ciprofloxacin (e.g., "Cipro", "CIP", "J01MA02", and "Ciproxin") are all converted to the standard name, "Ciprofloxacin".
 
-
-## Taxonomic Data Sets Now in Python!
-
-As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
-
-```python
-AMR.microorganisms
-```
-
-| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
-|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
-| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
-| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
-| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
-| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
-| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
-| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
-
-```python
-AMR.antibiotics
-```
-
-| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
-|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
-| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
-| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
-| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
-| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
-| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
-| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
-| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
-| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
-| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
-
-
 ## Calculating AMR
 
 ```python
@@ -187,6 +145,47 @@ print(result2b)
 
 In this example, we generate an antibiogram by selecting various antibiotics.
 
+## Taxonomic Data Sets Now in Python!
+
+As a Python user, you might like that the most important data sets of the `AMR` R package, `microorganisms`, `antibiotics`, `clinical_breakpoints`, and `example_isolates`, are now available as regular Python data frames:
+
+```python
+AMR.microorganisms
+```
+
+| mo           | fullname                           | status   | kingdom  | gbif      | gbif_parent | gbif_renamed_to | prevalence |
+|--------------|------------------------------------|----------|----------|-----------|-------------|-----------------|------------|
+| B_GRAMN      | (unknown Gram-negatives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_GRAMP      | (unknown Gram-positives)           | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-NEG  | (unknown anaerobic Gram-negatives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER-POS  | (unknown anaerobic Gram-positives) | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| B_ANAER      | (unknown anaerobic bacteria)       | unknown  | Bacteria | None      | None        | None            | 2.0        |
+| ...          | ...                                | ...      | ...      | ...       | ...         | ...             | ...        |
+| B_ZYMMN_POMC | Zymomonas pomaceae                 | accepted | Bacteria | 10744418  | 3221412     | None            | 2.0        |
+| B_ZYMPH      | Zymophilus                         | synonym  | Bacteria | None      | 9475166     | None            | 2.0        |
+| B_ZYMPH_PCVR | Zymophilus paucivorans             | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| B_ZYMPH_RFFN | Zymophilus raffinosivorans         | synonym  | Bacteria | None      | None        | None            | 2.0        |
+| F_ZYZYG      | Zyzygomyces                        | unknown  | Fungi    | None      | 7581        | None            | 2.0        |
+
+```python
+AMR.antibiotics
+```
+
+| ab  | cid         | name                 | group                      | oral_ddd | oral_units | iv_ddd | iv_units |
+|-----|-------------|----------------------|----------------------------|----------|------------|--------|----------|
+| AMA | 4649.0      | 4-aminosalicylic acid| Antimycobacterials         | 12.00    | g          | NaN    | None     |
+| ACM | 6450012.0   | Acetylmidecamycin    | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ASP | 49787020.0  | Acetylspiramycin     | Macrolides/lincosamides    | NaN      | None       | NaN    | None     |
+| ALS | 8954.0      | Aldesulfone sodium   | Other antibacterials       | 0.33     | g          | NaN    | None     |
+| AMK | 37768.0     | Amikacin             | Aminoglycosides            | NaN      | None       | 1.0    | g        |
+| ... | ...         | ...                  | ...                        | ...      | ...        | ...    | ...      |
+| VIR | 11979535.0  | Virginiamycine       | Other antibacterials       | NaN      | None       | NaN    | None     |
+| VOR | 71616.0     | Voriconazole         | Antifungals/antimycotics   | 0.40     | g          | 0.4    | g        |
+| XBR | 72144.0     | Xibornol             | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZID | 77846445.0  | Zidebactam           | Other antibacterials       | NaN      | None       | NaN    | None     |
+| ZFD | NaN         | Zoliflodacin         | None                       | NaN      | None       | NaN    | None     |
+
+
 # Conclusion
 
 With the `AMR` Python package, Python users can now effortlessly call R functions from the `AMR` R package. This eliminates the need for complex `rpy2` configurations and provides a clean, easy-to-use interface for antimicrobial resistance analysis. The examples provided above demonstrate how this can be applied to typical workflows, such as standardising microorganism and antimicrobial names or calculating resistance.