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Enregistrement W2950653319 · doi:10.3897/biss.3.35074

Progress in Authority Management of People Names for Collections

2019· article· en· W2950653319 sur OpenAlex
Quentin Groom, Chloé Besombes, Josh Brown, Simon Chagnoux, Teodor Georgiev, Nicole Kearney, Arnald Marcer, Nicky Nicolson, Roderic Page, Sarah T. Phillips, Heimo Rainer, Greg Riccardi, Dominik Röpert, David Peter Shorthouse, Павел Стоев, Elspeth Haston

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Notice bibliographique

RevueBiodiversity Information Science and Standards · 2019
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental DNA in Biodiversity Studies
Établissements canadiensAgriculture and Agri-Food Canada
Organismes subventionnairesnon disponible
Mots-clésIdentifierComputer scienceWorld Wide WebPublicationData scienceHerbariumInformation retrievalEcologyPolitical science

Résumé

récupéré en direct d'OpenAlex

The concept of building a network of relationships between entities, a knowledge graph, is one of the most effective methods to understand the relations between data. By organizing data, we facilitate the discovery of complex patterns not otherwise evident in the raw data. Each datum at the nodes of a knowledge graph needs a persistent identifier (PID) to reference it unambiguously. In the biodiversity knowledge graph, people are key elements (Page 2016). They collect and identify specimens, they publish, observe, work with each other and they name organisms. Yet biodiversity informatics has been slow to adopt PIDs for people and people are currently represented in collection management systems as text strings in various formats. These text strings often do not separate individuals within a collecting team and little biographical information is collected to disambiguate collectors. In March 2019 we organised an international workshop to find solutions to the problem of PIDs for people in collections with the aim of identifying people unambiguously across the world's natural history collections in all of their various roles. Stakeholders were represented from 11 countries, representing libraries, collections, publishers, developers and name registers. We want to identify people for many reasons. Cross-validation of information about a specimen with biographical information on the specimen can be used to clean data. Mapping specimens from individual collectors across multiple herbaria can geolocate specimens accurately. By linking literature to specimens through their authors and collectors we can create collaboration networks leading to a much better understanding of the scientific contribution of collectors and their institutions. For taxonomists, it will be easier to identify nomenclatural type and syntype material, essential for reliable typification. Overall, it will mean that geographically dispersed specimens can be treated much more like a single distributed infrastructure of specimens as is envisaged in the European Distributed Systems of Scientific Collections Infrastructure (DiSSCo). There are several person identifier systems in use. For example, the Virtual International Authority File (VIAF) is a widely used system for published authors. The International Standard Name Identifier (ISNI), has broader scope and incorporates VIAF. The ORCID identifier system provides self-registration of living researchers. Also, Wikidata has identifiers of people, which have the advantage of being easy to add to and correct. There are also national systems, such as the French and German authority files, and considerable sharing of identifiers, particularly on Wikidata. This creates an integrated network of identifiers that could act as a brokerage system. Attendees agreed that no one identifier system should be recommended, however, some are more appropriate for particular circumstances. Some difficulties have still to be resolved to use those identifier schemes for biodiversity : 1) duplicate entries in the same identifier system; 2) handling collector teams and preserving the order of collectors; 3) how we integrate identifiers with standards such as Darwin Core, ABCD and in the Global Biodiversity Information Facility; and 4) many living and dead collectors are only known from their specimens and so they may not pass notability standards required by many authority systems. The participants of the workshop are now working on a number of fronts to make progress on the adoption of PIDs for people in collections. This includes extending pilots that have already been trialed, working with identifier systems to make them more suitable for specimen collectors and talking to service providers to encourage them to use ORCID iDs to identify their users. It was concluded that resolving the problem of person identifiers for collections is largely not a lack of a solution, but a need to implement solutions that already exist.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,310

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,009
Tête enseignante GPT0,234
Écart entre enseignants0,226 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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