Is Your Collection Ambiguous?
Pourquoi ce travail est dans la base
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Notice bibliographique
Résumé
The natural history specimens of the world have been documented on paper labels, often physically attached to the specimen itself. As we transcribe these data to make them digital and more useful for analysis, we make interpretations. Sometimes these interpretations are trivial, because the label is unambiguous, but often the meaning is not so clear, even if it is easily read. One key element that suffers from considerable ambiguity is people’s names. Though a person is indivisible, their name can change, is rarely unique and can be written in many ways. Yet knowing the people associated with data is incredibly useful. Data on people can be used to validate other data, simplify data capture, link together data across domains, reduce duplication-of-effort and facilitate data-gap-analysis. In addition, people data enable the discovery of individuals unique to our collections, the collective charting of the history of scientific researchers and the provision of credit to the people who deserve it (Groom et al. 2020). We foresee a future where the people associated with collections are not ambiguous, are shared globally, and data of all kinds are linked through the people who generate them. The TDWG People in Biodiversity Data Task Group is therefore working on a guide to the disambiguation of people in natural history collections. The ultimate goal is to connect the various strings of characters on specimen labels and other documentation to persistent identifiers (PIDs) that unambiguously link a name “string” to the identity of a person. In working towards this goal, 150 volunteers in the Bionomia project have linked 21 million specimens to persistent identifiers for their collectors and determiners. An additional 2 million specimens with links to identifiers for people have already emerged directly from collections that make use of the recently ratified Darwin Core terms recordedByID and identifiedByID. Furthermore, the CETAF Botany Pilot conducted among a group of European herbaria and museums has connected over 1.4 million specimens to disambiguated collectors (Güntsch et al. 2021). Still, given the estimated 2 billion (Ariño 2010) natural history specimens globally, there is much more disambiguation to be done. The process of disambiguation starts with a trigger, which is often the transcription of a specimen’s label data. Unambiguous identification of the collector may facilitate this transcription, as it offers knowledge of their biographical details and collecting habits, allowing us to infer missing information such as collecting date or locality. Another trigger might be the flagging of inconsistent data during data entry or resulting from data quality processes, revealing for instance that multiple collectors have been conflated. A disambiguation trigger is followed by the gathering of data, then the evaluation of the results and finally by the documentation of the new information. Disambiguation is not always straightforward and there are many pitfalls. It requires access to biographical data, and identifiers to be minted. In the case of living people, they have to cooperate with being disambiguated and we have to follow legal and ethical guidelines. In the case of dead people, particularly those long dead, disambiguation may require considerable research. We will present the progress made by the People in Biodiversity Data Task Group and their recommendations for disambiguation in collections. We want to encourage other institutions to engage with a global effort of linking people to persistent identifiers to collaboratively improve all collection data.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,002 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle