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Enregistrement W4200153775 · doi:10.3897/biss.5.79084

Challenges in Curating Interdisciplinary Data in the Biodiversity Research Community

2021· article· en· W4200153775 sur OpenAlex
Inna Kouper, Kimberly Cook

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Notice bibliographique

RevueBiodiversity Information Science and Standards · 2021
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueEnvironmental DNA in Biodiversity Studies
Établissements canadiensnon disponible
Organismes subventionnairesInstitute of Museum and Library Services
Mots-clésBiodiversityData scienceComputer scienceWorld Wide WebSociologyEcologyBiology

Résumé

récupéré en direct d'OpenAlex

Panelists: James Macklin, Agriculture and Agri-Food Canada; Anne Thessen, University of Colorado Anschutz Medical Campus; Robbie Burger, University of Kentucky; Ben Norton, North Carolina Museum of Natural Sciences Organizers: Kimberly Cook, University of Kentucky; Inna Kouper, Indiana University As research incentives become increasingly focused on collaborative work, addressing the challenges of curating interdisciplinary data becomes a priority. A panel convened at the TDWG 2021 virtual conference on October 19 discussed these issues and provided the space where people with a variety of experience curating interdisciplinary biodiversity data shared their knowledge and expertise. The panel started with a brief introduction to the challenges of interdisciplinary and highly collaborative research (IHCR), which the panel organizers have previously observed (Kouper et al. 2021). In addition to varying definitions that focus on crossing the disciplinary boundaries or synthesizing knowledge, IHCR is characterized by an increasing emphasis on computation, integration of heterogeneous data sources, and work with multiple stakeholders. As such, IHCR data does not fit with traditional lifecycle models as it requires more iterations, coordination, and shared language. Narrowing the scope to biodiversity data, the panelists acknowledged that biodiversity is a truly interdisciplinary domain where researchers and practitioners bring their diverse expertise to take care of data. The domain has a variety of contributors, including data producers, users, and curators. While they share common goals, these contributors are often fragmented in separate projects that prioritize academic disciplines or public engagement. Lack of knowledge and awareness about contributors and their projects and expertise as well as a certain vulnerability in branching out into new areas, are among the factors that make it difficult to tear down silos. As James Macklin put it, “... you're crossing a boundary into a place you don't maybe know a lot about, and for some people, that's hard to do. Right? It takes a lot of listening and thinking.” Due to their complex and interactive nature, IHCR projects almost always have a higher overhead in terms of communication, coordination, and management. Panelists agreed that for such projects there needs to be a collaboration handbook that assigns roles and responsibilities and establishes rules for various aspects of collaboration, including authorship and handling disagreements. Successful IHCR projects create such handbooks at the beginning and revisit them regularly. Another useful strategy mentioned was to hold debriefing sessions that evaluate what went well and what didn’t. Strong leadership that takes IHCR complexities into account and builds a network of capable facilitators and “bridge-builders” or “translators” is a big factor that makes projects succeed. Recognizing and encouraging the role of facilitators from the onset of the project helps to develop productive relationships across disciplines and areas of expertise. It also enables everyone to focus on their strengths and build trust. Data and metadata integration is one of the big challenges in biodiversity, although it is not unique to it. Biodiversity brings together many disciplines and each of them identifies its own problems and collects data to address them. Data silos stem from disciplinary silos, and it will take a different, more integrated, kind of cyberinfrastructure and modeling to bring these pieces together. Creating such infrastructures and standards around interdisciplinary data and metadata are serious needs, although they are not valued and rewarded enough compared to, say publishing academic papers. Lack of standardization and infrastructure also stands in the way of improving the quality of data in biodiversity. To evaluate the quality of data and to trust its creators, data users need to know who gathered and processed the data and how. When the data is re-used within a collaborative project, there is an opportunity to ask questions and find out why, for example, someone had certain naming conventions or processing and analytical approaches. Long-term data such as species’ life history traits, however, can be collected over long periods of time. Improving the quality of biodiversity data requires going beyond interpersonal communication and addressing the issues of metadata and standards more systematically. Panelists also discussed the issue of openness in connection to biodiversity data. Openness contributes to the improved quality of data and an increased return on public investment in science and research. Panelists’ positions diverged in the degree to which biodiversity data should be open and approaches to address competitiveness and sensitivity in research. On one hand, they acknowledged the need for some form of embargo on data sharing to allow data originators to benefit from their effort; on the other, they argued that lack of openness promotes silos and diminishes the quality of research and its reproducibility. Panelists briefly discussed the COVID pandemic data as an example of how lack of openness and silos can be detrimental to finding solutions: “COVID has given us the best example we have of how silos do damage to things that could have gone better. ... the data wasn't available, if it had been open or not even necessarily open but had anybody had any idea that it existed somewhere, that would have helped a lot. … We are learning those lessons, governments are changing the way they do business because of it. And so for us, I mean, our community, I think this has been one of the best things that could have happened to us in some ways, simply because it forced a change of mindset. And it has forced citizens to get engaged.” [James Macklin] The panelists, who brought a wide range of expertise to the discussion, including semantic and digitization technologies, agricultural data, evolutionary biology, and mineralogy among others, discussed projects they work on, which engaged the audience and stimulated a discussion among all participants about the role of end users in biodiversity data curation, non-traditional careers in biodiversity, and approaches to reviewing data similar to traditional research publications. Panelists and the audience also discussed the differences between “cleaning” and “annotating” data, making annotations part of the biodiversity record and data reviews. These productive discussions provide a foundation for further developments in the research and practice of curating biodiversity data and building strong interdisciplinary communities.

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)0,014
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies, Science ouverte
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 enseignants0,132
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0140,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,002
Communication savante0,0000,004
Science ouverte0,0010,009
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 Opus0,202
Tête enseignante GPT0,371
Écart entre enseignants0,169 · 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