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Enregistrement W2041746984 · doi:10.1371/journal.pcbi.1003244

Ten Simple Rules for Cultivating Open Science and Collaborative R&D

2013· editorial· en· W2041746984 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.

Notice bibliographique

RevuePLoS Computational Biology · 2013
Typeeditorial
Langueen
DomaineEngineering
ThématiqueBiomedical and Engineering Education
Établissements canadiensThe Scarborough HospitalStructural Genomics ConsortiumUniversity of Toronto
Organismes subventionnairesBill and Melinda Gates Foundation
Mots-clésCrowdsourcingComputer scienceWorld Wide WebOpen scienceOpen innovationScalabilityThe InternetData scienceOpen dataCitizen scienceSimple (philosophy)Knowledge managementPhysics

Résumé

récupéré en direct d'OpenAlex

How can we address the complexity and cost of applying science to societal challenges? Open science and collaborative R&D may help [1]–[3]. Open science has been described as “a research accelerator” [4]. Open science implies open access [5] but goes beyond it: “Imagine a connected online web of scientific knowledge that integrates and connects data, computer code, chains of scientific reasoning, descriptions of open problems, and beyond …. tightly integrated with a scientific social web that directs scientists' attention where it is most valuable, releasing enormous collaborative potential.” [1]. Open science and collaborative approaches are often described as open source, by analogy with open-source software such as the operating system Linux which powers Google and Amazon—collaboratively created software which is free to use and adapt, and popular for Internet infrastructure and scientific research [6], [7]. However, this use of “open source” is unclear. Some people use “open source” when a project's results are free to use, others when a project's process is highly collaborative [4]. It is clearer to classify open source and open science within a broader class of collaborative R&D, which can be defined as scalable collaboration (usually enabled by information technology) across organizational boundaries to solve R&D challenges [8]. Many approaches to open science and collaborative R&D have been tried [1], [9]. The Gene Wiki has created over 10,000 Wikipedia articles, and aims to provide one for every notable human gene [10]. The crowdsourcing platform InnoCentive has reportedly facilitated solutions to roughly half of the thousands of technical problems posed on the site, including many in life sciences such as the $1 million ALS Biomarker Prize [11]. Other examples include prizes (X-Prize [12]), scientific games (FoldIt [13]), and licensing schemes inspired by open-source software (BIOS [14]). Collaborative R&D approaches vary in openness [15]. In some approaches, the R&D process and outputs are open to all—for example, open-science projects like the Gene Wiki described above. In other approaches which demonstrate what might be called controlled collaboration, there are strong controls on who contributes and benefits—for example, computational platforms like Collaborative Drug Discovery or InnoCentive that support both commercial and nonprofit research [9], [11]. Collaborative approaches can unleash innovation from unforeseen sources, as with crowdsourcing health technologies [11]–[13], [16]. They may help in global challenges like drug development [17], as with India's OSDD (Open Source Drug Discovery) project that recruited over 7,000 volunteers [16] and an open-source drug synthesis project that improved an existing drug without increasing its cost [18]. If you want to apply open science and collaborative R&D, what principles are useful? We suggest Ten Simple Rules for Cultivating Open Science and Collaborative R&D. We also offer eight conversational interviews exploring life experiences that led to these rules (Box 1). Box 1. Conversations on Open Science and Collaborative R&D Many commentators have considered challenges in translating open science and collaborative methods to biomedical research [2]–[4], [9], [17], [20], [24], [26], [28], [29]. How can protecting intellectual property be balanced with freeing researchers to build on previous knowledge? If R&D results are collaboratively created and freely available, who will take responsibility for costly clinical trials and quality control? What will be the Linux of open-source R&D? To explore such challenges and convey life experiences in biomedical open science and collaborative R&D, we offer eight conversational interviews by the first author of this article as supplementary material. The conversations were done on behalf of the Results for Development Institute and are with: Alph Bingham, cofounder of InnoCentive (Text S1) Barry Bunin, CEO of Collaborative Drug Discovery (Text S2) Leslie Chan, open access pioneer and director of Bioline International (Text S3) Aled Edwards, director of the Structural Genomics Consortium (Text S4) Benjamin Good, coleader of the Gene Wiki initiative (Text S5) Bernard Munos, pharmaceutical innovation thought leader (Text S6) Zakir Thomas, director of India's Open Source Drug Discovery (OSDD) project (Text S7) Matt Todd, open science and drug development pioneer (Text S8)

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)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,261
Score d'incertitude au seuil0,650

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
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 Opus0,019
Tête enseignante GPT0,297
Écart entre enseignants0,278 · 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