Digitisation of herbarium specimens to the benefit of research: An African perspective focusing on South Africa and Western Indian Ocean Island states
Bibliographic record
Abstract
Societal Impact Statement Botanical exploration, discovery and conservation rely heavily on access to herbarium collections. Recently, digital access to label information, including georeferenced locality data, and images of herbarium specimens available online, have greatly increased usage of herbarium specimen data. However, access to online resources from African herbaria is lacking, despite being critical for promoting local and global research and conservation. Here, we explore digital access to herbarium collections from South Africa and Western Indian Ocean Islands and call on all African countries to unite and collectively find innovative ways of digitising and mobilising data from our botanical collections. Some excellent examples of such endeavours already exist. Summary Herbarium digitisation—transcribing and imaging specimens—is revolutionising biodiversity research by accelerating the description of species new to science, enhancing conservation assessments, and informing policy to address global challenges like climate change and biodiversity loss. Digitised herbarium data underpin critical research, such as alpha taxonomy, IUCN Red List assessments and mapping ecosystems. The historical legacy of botanical exploration has left many African specimens in herbaria located in the Northern Hemisphere, where large digitisation efforts are making data from these collections available online. A brief overview of some of these initiatives, as well as the history of digitisation of herbarium collections in South Africa and the Western Indian Ocean Islands, is provided, where projects such as the Southern African Botanical Diversity Network, African Plants Initiative, Western Indian Ocean Herbarium Network and Today's Flora for Tomorrow have advanced data mobilisation from African collections. Much work is still required towards digitising the collections held in herbaria across Africa; however, African herbaria face barriers that inhibit digitisation, such as limited funding, capacity and physical and digital infrastructure. This imbalance hinders the continent's ability to effectively meet goals like those of the Kunming‐Montreal Global Biodiversity Framework. We urge African nations to unite in collaborative networks and harness innovative technologies like artificial intelligence to accelerate digitisation and mobilise herbarium data. This will empower the continent to comprehensively assess its plant biodiversity and implement robust conservation strategies before critical opportunities are lost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".