Benchmarking of Genomics and Health Biotechnology in Seven Developing Countries, 1991-2002: Brazil, China, Cuba, Egypt, India, Republic of Korea and South Africa. Prepared for the University of Toronto Joint Center for Bioethics
Notice bibliographique
Résumé
This scientometric study provides an extensive quantitative analysis of the performance of leading countries, as well as seven selected developing countries, in the domains of genomics and health biotechnology using the Science Citation Index (SCI) Expanded scientific papers database.Papers were retrieved from SCI Expanded using two sets of keywords, one for genomics and one for health biotechnology.All genomics and health biotechnology papers were normalized at the country and city levels.Therefore, significant effort was put into normalizing data for developing countries, in order to categorize institutional sectors of activity with a minimum level (1% to 4%) of unknowns, and to precisely identify the most active institutions and researchers.Using a ten year time frame (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001), the report outlines the evolution of genomics and health biotechnology at the international and national levels in leading developed countries, as well as in seven developing countries.The report presents the scientific output performances of the developing countries using five scientometric indicators and a combined multicriteria ranking.Increasingly, developing countries are rapidly gaining a presence in the world's scientific community for life sciences.China and the Republic of Korea in particular will, in the near future, enter the league of leading countries and will overtake so-called developed countries in terms of absolute scientific output in genomics and health biotechnology.As the international community stresses the need to apply genomics and health biotechnology R&D to improve global health and sustainability in developing countries, the next decade might provide these countries with the necessary knowledge and know-how to solve the most prevalent issues of poverty, disease, high population density, and environmental problems.However, it would be of great interest to monitor and study the integration and application of the blossoming domains of biotechnological and genome science to local health and well-being needs, in order to provide insight to the developing nations that have limited scientific and technological resources. Key findings Genomics at the international levelBetween 1991 and 2002, papers in genomics increased by almost 60% at the world level; specifically, from approximately 35,000 to over 55,000 scientific papers annually. Genomics and Health Biotechnology in Seven Developing Countries vi Most active cities and institutions BrazilIn terms of the number of publications, Sao Paulo was the most active city in Brazil with 1,637 papers in genomics and 428 in health biotechnology.The leading institutions include the Universidade de São Paulo in 1 st place for both genomics and health biotechnology, the Universidade Federal do Rio de Janeiro in 2 nd place in genomics and 3 rd place in health biotechnology, and the Fundação Oswaldo Cruz governmental institute in 3 rd place in genomics and 2 nd place in health biotechnology. ChinaIn China, Beijing is the most active city in both domains, having published 2,472 papers in genomics and 623 in health biotechnology.In China, the main institutions contributing to publications in genomics and health biotechnology come from the governmental and the university sectors.The Chinese Academy of Sciences dominates in terms of scientific output.This governmental institution ranks 1 st in genomics with 1,703 papers and also in health biotechnology with 318.Fudan University leads the university sector with 590 papers in genomics and 170 papers in health biotechnology.Overall, seven universities published more papers than the country average for most active institutions in genomics, and eight universities did so in health biotechnology. CubaIn Cuba, most of the scientific activity in genomics and health biotechnology is concentrated in Havana, which holds 90% of the papers in genomics and 95% of the papers in health biotechnology.Most scientific activity in Cuba is concentrated in the government.The Centro de Ingenieria Genética y Biotechnologià on its own accounts for half of Cuba's production in both domains, with 174 papers in genomics and 119 papers in health biotechnology. EgyptWith 253 papers in genomics and 91 papers in health biotechnology, Cairo is the most active city in Egypt.In Egypt, the main institutions contributing to publications in genomics and health biotechnology come from the university and governmental sectors.The Cairo University occupied the 1 st rank in genomics with 123 papers and in health biotechnology with 33 publications.The National Research Center is the leading institution within the government and ranked 4 th in genomics and 3 rd in health biotechnology. IndiaIn India, New Delhi, with 1,294 papers in genomics and 429 papers in health biotechnology, is the most active city.With 524 papers in genomics and 117 papers in health biotechnology, the most active institution, the Indian Institute of Science, is from the university sector.However, when grouping institutes from the same governmental entity, the Council of Scientific & Industrial Research (CSIR) ranks 1 st , followed by the Indian Institute of Science and the Indian Council of Agricultural Research (ICAR). Republic of KoreaIn the Republic of Korea, Seoul is the clear leader in both genomics and health biotechnology, holding about 50% of the country's papers in both domains.Seoul National University is clearly the country's leader with 1, 587 papers in genomics and 471 papers in health biotechnology.Among developing countries, South Korean companies are the most active in peerreviewed publishing. South AfricaJohannesburg and Cape Town are clearly the two most active South African cities in both genomics and health biotechnology in terms of the absolute number of papers.Leading institutions are principally from the academic sector.The four most prolific universities in both domains are the
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 | 0,002 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,008 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».