The African Open Science Platform: The Future Of Science And The Science Of The Future
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.025 | 0.047 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".