The African Open Science Platform: The Future Of Science And The Science Of The Future
Notice bibliographique
Résumé
This document presents a draft strategy and makes the scientific case for the African Open Science Platform (AOSP). It is based on an expert group meeting held in Pretoria on 27-28 March 2018. Its purpose is to act as a framework for detailed, work on the creation of the Platform and as a basis for discussion at a stakeholder meeting to be held on 3-4 September 2018, which will lead to a definitive strategy for implementation from 2019. Expert group members at the March meeting were drawn from the following organisations: African Academy of Sciences (AAS), Academy of Science of South Africa (ASSAf), Committee on Data for Science and Technology (CODATA), International Council for Science (ICSU), National Research and Education Networks (NRENS), Research Data Alliance (RDA), South African Department of Science & Technology (DST) and National Research Foundation (NRF), Square Kilometre Array (SKA), UNESCO. The African Open Science Platform. The Platform’s mission is to put African scientists at the cutting edge of contemporary, data-intensive science as a fundamental resource for a modern society. Its building blocks are: a federated hardware, communications and software infrastructure, including policies and enabling practices, to support Open Science in the digital era; a network of excellence in Open Science that supports scientists and other societal actors in accumulating and using modern data resources to maximise scientific, social and economic benefit. These objectives will be realised through seven related strands of activity: Strand 0: Register and portal for African and related international data collections & services. Strand 1: A federated network of computational facilities and services. Strand 2: Software tools and advice on policies & practices of research data management. Strand 3: A Data Science Institute at the cutting edge of data analytics and AI. Strand 4: Priority application programmes: e.g. cities, disease, biosphere, agriculture. Strand 5: A Network for Education and Skills in data and information. Strand 6: A Network for Open Science Access and Dialogue. The document also outlines the proposed governance, membership and management structure of the Platform, the approach to initial funding and the milestones in building up to the launch. The case for Open Science is based on the profound implications for society and for science, of the digital revolution and of the storm of data that it has unleashed and of the pervasive and novel means of communication that it has enabled. No state should fail to recognise this potential or to adapt their national intellectual infrastructure in exploiting benefits and minimising risks. Open Science is a vital enabler in maintaining the rigour and reliability of science; in creatively integrating diverse data resources to address complex modern challenges; in open innovation and in engaging with other societal actors as knowledge partners in tackling shared problems. It is fundamental to realisation of the Sustainable Development Goals. National science systems worldwide are struggling to adapt to this new paradigm. The alternatives are to do so or risk stagnating in a scientific backwater, isolated from creative streams of social, cultural and economic opportunity. Africa should adapt and capitalise on the opportunities, but in its own way, and as a leader not a follower, with broader, more societally-engaged priorities. It should seize the challenge with boldness and resolution.
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.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,048 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,010 | 0,013 |
| Communication savante | 0,025 | 0,047 |
| Science ouverte | 0,003 | 0,029 |
| Intégrité de la recherche | 0,019 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,007 |
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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».