"If you like interesting snippets on all sorts of subjects relevant to academia, information, the world, highly recommended is @grip54 's collection:"
La curation de contenus, la mémoire partagée d'une veille scientifique et sociétale
Gilbert C FAURE's insight:
... designed to collect posts and informations I found and want to keep available but not relevant to the other topics I am curating on Scoop.it (on behalf of ASSIM):
because we have a long standing collaboration through a french speaking medical training program between Faculté de Médecine de Nancy and WuDA, Wuhan university medical school and Zhongnan Hospital
Congratulations Ceinwen Jones, AALIA (CP) and the SCIS Data team for another fantastic issue of SCIS Connections! It is a privilege to have contributed my article, How to build a collection for purpose, with purpose. Read this, and the other terrific articles online at https://lnkd.in/g8NbxjBe .
Young people today are the loneliest generation on record.
That's why third places matter more than ever: the cafés, plazas, libraries and markets where community actually happens. The concept comes from Ray Oldenburg's The Great Good Place (1989); the empirical link to neighborhood quality of life was established by Jeffres et al. (2009) in Applied Research in Quality of Life.
I've experienced (and suffered the lack of) this myself living in different cities, so I built this for my current one, Palma de Mallorca.
For every census section, it scores third place density and diversity within a 5 / 10 / 15 minute walk.
Built on Overture Maps Foundation + OpenStreetMap, INE & IBESTAT, OSRM walking isochrones.
If loneliness is a public health issue, we should measure the infrastructure that prevents it. Third places make community. And that community, that sense of belonging and connection with people, can truly make a difference in our lives.
How would you use something like this for decision-making?
Here's a trend in KM roles I've been watching with interest: the rise of the Knowledge Engineer.
Not a pure engineering role, despite the name. What's appearing in job postings right now is more multi-dimensional — and I think it may be a signpost about where KM is heading.
What strikes me most about these roles is their connector quality. They sit at the intersection of people, content, processes, and AI systems, using KM expertise as the bridge between human knowledge needs and machine knowledge structures. A recent posting from a global engineering firm captures this: the role spans AI-enabled solution design, KM process analysis and automation, taxonomy and ontology governance, knowledge community development, and translating complex AI concepts for non-technical audiences. That's not a narrow technical job. That's a connector role.
These roles are also emerging in an interesting context. As organizations have moved from experimenting with AI to building AI-powered knowledge experiences, a clearer picture has formed: the outputs are only as good as the knowledge infrastructure underneath them. Good metadata, controlled vocabulary, well-structured taxonomies — these turn out to matter enormously. KM disciplines that were sometimes hard to make the case for are now on the critical path.
But these roles aren't just about fixing what AI can get wrong. Knowledge Engineer roles also work in the other direction: identifying where AI and automation can make KM processes themselves more effective. Metadata tagging, content classification, knowledge gap analysis — work that was once painstakingly manual, now augmentable. The Knowledge Engineer understands both sides of that relationship.
For KM professionals, the path into these roles isn't about becoming an engineer. It's about recognizing that the semantic and behavioral infrastructure AI requires is knowledge management infrastructure by another name — and that your ability to think across people, content, and process is exactly what makes these hub roles work.
The "we don't need KM anymore" wave has crested. What's coming in behind it is more interesting. But you might not find these new roles called a "Knowledge Manager" anymore!
A new Science Magazine study published today examines how a large language model (LLM) performs on the reasoning tasks of a physician. Using information including real emergency department data -- which is a genuine step forward -- it reports that the LLM can be good at taking the information available about a patient and suggesting which diagnoses should be considered. A related Perspective explores what these findings do (and don’t) mean in clinical practice.
Given the level of interest in AI and medicine, earlier this week, the Science Press Package team hosted an embargoed media briefing on this paper. It featured four authors on the study: Arjun Manrai, Adam Rodman, Peter Brodeur, MD, MA, and Thomas Buckley. Reporters brought a wide range of thoughtful questions, and the discussion benefited from the authors’ clinical experience (in two cases, as practicing physicians), which helped ground the results in real-world care.
Kudos to these authors for careful, clear and compelling communication on this topic.
One of the clearest takeaways: these findings don’t point to AI replacing doctors. Instead, they offer a pathway for studying how clinicians and AI systems might work together—where there’s promise, and where careful evaluation is still needed. It’s encouraging to see that nuance reflected in early coverage.
We have a recording of the briefing available. (Please message me directly for the recording.)
If you’re following the evolving role of AI in clinical reasoning, both the study and the accompanying Perspective are well worth a look.
Links in comments.
Beth Israel Deaconess Medical Center Harvard Medical School
Most researchers are using AI tools for literature reviews the wrong way.
They ask AI to “find papers” and hope for the best.
That is not a literature review strategy. That is search outsourcing.
Used properly, AI can save time, improve structure, and help you think more clearly.
But it should support your judgment, not replace it.
Here are practical tips I give research students:
1. Start with your question, not the tool A vague research question creates vague results. Define your topic, population, variables, or context first.
2. Use AI for search expansion Ask AI for synonyms, related terms, alternate spellings, and discipline-specific keywords. This improves database searching.
3. Use AI to screen faster Paste abstracts and ask for relevance against your inclusion criteria. This helps with first-pass screening.
4. Use AI to compare studies Ask it to summarise differences in methods, sample sizes, findings, and limitations across papers.
5. Use AI to identify patterns Good reviews are not summaries. Ask: What themes repeat? Where do studies disagree? What populations are ignored? What methods dominate?
6. Verify every citation Never trust references blindly. Cross-check authors, journal, DOI, and publication year.
7. Use AI for structure, not authorship AI can help organise themes and draft outlines, but your interpretation must lead the review.
8. Keep a decision trail Document search terms, databases, inclusion criteria, and why papers were included or excluded.
Use AI as an assistant, not as a scholar.
PS: What AI tool has actually helped your literature review most? Share in the comments
REPOST to help others.
Follow Dr Priya Singh, Founder Research Made Clear for more insights
For research tutorials and AI tool guides, subscribe to my YT channel: https://lnkd.in/e8zWuWV2 | 24 comments on LinkedIn
Une étude du Imperial College of London et de Internet Archive révèle que plus de la moitié des sites web est généré en partie ou totalement par l’IA (52,9%), en forte augmentation. #GenAI https://lnkd.in/eac76_aG | 12 comments on LinkedIn
✍️🎓Dear PhD Scholar:Starting your first research article can feel overwhelming… but with the right steps, it becomes simple and structured. Here’s your easy roadmap 👇
🔹 1. Choose the Right Topic
Pick a clear, focused, and interesting research problem.
Your topic should answer a question or solve a gap.
🔹 2. Conduct a Literature Review
Read existing studies to understand what’s already done—and where your research fits in.
🔹 3. Define Your Research Objective
What exactly are you trying to find?
Be specific and concise.
🔹 4. Design Your Methodology
Decide how you will collect and analyze data (qualitative, quantitative, or mixed methods).
🔹 5. Collect & Analyze Data
Follow your method carefully and ensure your data is accurate and reliable.
🔹 6. Write the Structure
A standard research article includes:
Abstract
Introduction
Literature Review
Methodology
Results
Discussion
Conclusion
🔹 7. Interpret Your Findings
Explain what your results mean and how they contribute to existing knowledge.
🔹 8. Cite Properly
Always give credit using the required citation style (APA, MLA, etc.).
🔹 9. Edit & Proofread
Refine your writing for clarity, grammar, and flow.
🔹 10. Choose the Right Journal & Submit
Select a suitable journal and follow its guidelines carefully.
You don’t need to be perfect to start… you just need to start to become perfect.
😊Happy Researching & Best of Luck, Future Scholars! 👍
More about paper mills and authorship for sale in today's Science issue. Need one more paper on your CV? Price depends... from $57 to $5600. Of course, this paper has a good chance of being entirely fabricated (fake) but who cares?
If you think this is a minor problem: - 18,710 advertisements related to authorship for sale were traced to Russia, Ukraine, Uzbekistan, India... (those were mostly written in English and do not count paper mills in other countries such as China) - A study published in January in The BMJ found that nearly 10% of 2.6 million cancer-research papers published from 2019 to ’24 seem to be paper mills products. That's... 250,000 papers. And this crap is used to train AI.
Publisher should aggressively fight this problem. But they won't unless it hurts their business... and I am not only talking of predatory publishers. Too many problems with many journals handled by "respectable" publishers with comfortable margins.
Link to the Science paper in comments. | 13 comments on LinkedIn
Qu’est-ce qu’un algorithme est capable de vous montrer, à vous ou à vos proches ? Ouest-France a construit un bras robotique pour scroller sur TikTok en continu afin de le savoir. Pendant 100 heures, la machine a visionné des milliers de vidéos. Certaines donnent des conseils pour s’affamer, d’autres enseignent comment faire un nœud coulant. D’autres encore renvoient vers des contenus pédocriminels sur Telegram. Enquête sur une mécanique qui amplifie tout, y compris le pire.
#tiktok #algorithme #enquete
00:00 Introduction — Marie, 15 ans 2:05 Un robot 4:24 L'algorithme de TikTok 5:25 Make-Up et cuisine 8:12 Dans la bulle mascu 10:09 les Tartariens 11:37 La faille dans le système 16:30 Des vidéos contrevantes aux règles de TikTok 16:28 Les conséquences humaines des algorithmes 20:34 Le compte sans nom 21:51 La réponse de TikTok 22:10 : Ce qu'un algorithme est capable de vous montrer --------------------------------------------------------------------------- Retrouvez toute l’actualité sur :
🌳 🤩 Magnifique ! L'ONF (Office National des Forêts) a publié une carte interactive avec une sélection de 30 forêts exceptionnelles à découvrir en France.
Les forestières et les forestiers de l'ONF les racontent en images, en vidéo et avec passion.
Voici cinq choses à savoir sur les forêts françaises.
1. Le patrimoine forestier français a connu une extension forte et continue depuis 150 ans et atteint désormais une surface de 17M d'hectares soit 31 % du territoire français (source : IGN https://buff.ly/42BakSd); C'est la 4ème forêt européenne après la Finlande, la Suède et l'Espagne - le niveau de boisement de la France est revenu à celui du XVe siècle. Voir cette infographie de Jules Grandin et Clara DeAlberto https://buff.ly/3J46xWC
Mais les massifs sont jeunes, leur vitalité et leur résilience pose problème puisque 50% des arbres ont moins de 60 ans et 20% seulement ont plus d'un siècle.
2. La #Corse est la région la plus boisée de métropole - 63% et la forêt d'Orléans est la plus grande forêt domaniale (appartenant à l'Etat) en France métropolitaine.
3. Les chênes sont les arbres les plus répandus en métropole. Avec 190 essences d'arbres, notre forêt métropolitaine compte près de 75% de toutes les essences présentes en Europe. C'est fou !
4. Les forêts d’Outre-mer représentent près de la moitié de la superficie forestière, soit 8 millions d’hectares, et abritent la biodiversité la plus riche.
5. En métropole, 75% des forêts sont privées. Les 25% restants sont des forêts domaniales ou des forêts appartenant à des collectivités ou établissements publics.
Mais globalement, nos forêts ne sont pas en bon état. Elle pourrait cesser de stocker du carbone.
Les arbres interviennent à plusieurs titres dans l'équation climatique. Ils sont utiles pour l'atténuation (en jouant un rôle de stock de carbone) et pour l'adaptation (en jouant un rôle de régulateur climatique, dans le cycle de l'eau, des sols et de la biodiversité).
Mais les forêts françaises sont, comme les autres forêts du monde, soumises à une pression croissante. Elles absorbaient 30M de tonnes absorbées en 2020, soit environ 7,5 % des émissions nationales. Mais c’est deux fois moins qu’il y a dix ans.
En terme de biodiversité en 2023, 17 % des oiseaux de forêt, 7 % des mammifères, 8 % des reptiles et amphibiens, 12 % des papillons sont menacés d’extinction (Source : IGN)
Les effets des sécheresses se font sentir sur les écosystèmes. La tendance est à la monoculture et les essences ne sont plus aussi résilientes qu'autrefois.
Alors pensons nos forêts dans un souci de variété et de diversité.
Les arbres tissent des liens entre les règnes du vivant. Soyons clairs, nous ne pouvons pas nous en passer.
#foret #arbre #onf #france
🥚 Petite annonce ! Mon futur livre a besoin de votre soutien. Contactez moi si vous souhaitez m'aider à préparer la campagne => https://buff.ly/P06fQIn| 10 commentaires sur LinkedIn
"Can you remain active in wet-lab research as a biomedical educator?" This was the question I tackled during the Department of Pharmacology | NUS Medicine's academic staff retreat.
Surrounded by the goodwill and positive energy of longtime colleagues and friends, I was able to share my personal journey honestly, both achievements and failures, fulfilment and dejection.
My take homes for the session: 1. Prioritize: You may have loved bench-side experiments as a PhD student / post-doc, and may even have excelled in it, but as a biomedical educator, wet-lab research is no longer your primary KPI. Spend the bulk of your efforts getting good on reflective teaching, curriculum development / revamp, education outreach, pedagogical research & service... THEN, if you still have the bandwidth, go for it (see 2).
2. Understand Your Motivation: It is 6pm, you are tired from a whole day of teaching, meetings and exam marking. Do you still want to analyze that cell-culture data, write that paper, take that Zoom meeting with your students? Why? What's in it for you now that the work won't (directly) help with your career advancement?
3. Develop Expertise: I started my PhD working on dementia neurochemistry, then at some point fell into blood-based biomarkers (BBMs). Through opportunities and generous support (see 6), I began to develop a real interest in this fast-moving field, and published fairly regularly on it. Gradually, I got invited to lecture on BBMs, and to participate in programmatic grant proposals in which BBM is a component. So, take the time (multiple years in my case) to build expertise and be a go-to person for an area in which you have strong interest (see 4).
4. "Give up" and Collaborate: A colleague once told me their paper from a C(ell) N(ature) S(cience)-level journal took multiple years to publish (it was >1.5 years just to do the requested experiments for a revision). Given the high bar set by top multidisciplinary journals nowadays, it is difficult enough to lead all aspects of the research (including procuring resources) for such papers as a tenure-track PI, and virtually impossible (IMHO) to do so as an educator. So I focus on being a good collaborator / theme lead and contributing to worthwhile, impactful research programs needing my area of expertise.
5. Make Your Research Edu-Relevant: At every opportunity, translate your research into your teaching. I have given lectures and seminars on BBMs to diverse audiences, from secondary school students through to practicing doctors, a deeply rewarding experience!
6. Be Thankful: "It takes a village..." to nurture a biomedical researcher. I certainly have my share of mentors who generously guided, taught and enabled me, collaborators who enthusiastically shared their expertise and data with me, and students / team members who steadfastly supported me through peaks and valleys. To all these wonderful people I say a loud "thank you" for enriching my professional life so abundantly!
En 2007, Apple lance l'iPhone. Et en 2009, la fécondité mondiale décroche.
Pendant longtemps, les démographes ont cru à une oscillation passagère. Précisément parce qu’avant 2009, la fécondité baissait, remontait, baissait. Alors on s’est dit qu’il n’y avait rien d'alarmant, que les couples faisaient des enfants plus tard, pas qu’il en faisaient moins.
Sauf que, 15 ans plus tard, il a bien fallu se rendre à l'évidence : États-Unis, Mexique, Italie, Argentine, Iran, Tunisie, Corée, France, la fécondité n'est jamais remontée, et pour la première fois dans l'histoire moderne, l’effondrement est synchrone et planétaire.
Ce qui est planétaire aussi, c'est ce petit rectangle lumineux glissé dans la poche de 4 milliards d'êtres humains. Car oui, je crois profondément que le smartphone et les réseaux sociaux ont détruit la rencontre. Ils nous donnent l'illusion d'une proximité relationnelle qui est en réalité superficielle. Une bulle de notifications, de stories et de likes qui simule la présence des autres tout en nous laissant assis seul sur notre canapé. On pense « voir » ses amis parce qu'on défile dans leur quotidien sur Instagram. On pense « parler » parce qu'on s'envoie des memes. Mais on ne s'embrasse plus, on ne se dispute plus, on ne se touche plus 😕
A tel point qu’aux États-Unis, la part des adolescents qui voient leurs amis en chair et en os moins d'une fois par mois est passée de 3 % entre 1990 et 2010 à 10 % en 2019, pendant que 46 % d’entre eux se déclarent « constamment en ligne » d’après le Financial Times.
Or, c'est précisément cette génération-là qui devrait, aujourd'hui, tomber amoureuse. Sauf qu'on ne tombe amoureux de personne quand on ne rencontre personne…
Au fond, je crois que le smartphone n'a pas tué le désir d'enfant. Il a tué l'écosystème dans lequel ce désir naît. La fête improvisée, la conversation qui dérive jusqu'à 3h du matin, l'ennui partagé qui pousse deux individus à inventer quelque chose ensemble. Le smartphone a supprimé le temps mort. Et, avec lui, l'élan instinctif qui pousse à se tourner vers la personne assise à côté 👋
Avant d'être une crise de berceaux, la dénatalité est donc avant tout une crise de canapés. Parce qu’il n'y a tout simplement plus deux personnes dessus. | 69 comments on LinkedIn
One concept worth understanding in evidence-based work is the Woozle Effect.
It occurs when a claim gains the appearance of strong evidential support through repetition and citation, even though the original research did not clearly establish it.
- A preliminary or limited finding is published. - Later summaries describe it with slightly more certainty. - Secondary sources cite those summaries rather than the primary paper. - Caveats, exclusions and uncertainty gradually disappear. - Repetition creates familiarity. - Familiarity is mistaken for reliability. - The proposition becomes treated as established knowledge.
Eventually, the claim can feel settled simply because it has circulated widely enough.
The Woozle Effect is usually driven less by dishonesty than by repetition, assumption and uncritical citation. It often reflects ordinary human tendencies: confirmation bias, inherited beliefs, deference to consensus and failure to revisit primary sources.
The result is that non-facts can become common sense. In any field that relies on research, policy or expert opinion, that carries obvious risk.
The Woozle Effect is not merely about misinformation. It is about how intelligent people, acting in good faith, inherit certainty they did not build.
That is why disciplined analysis often begins with a deceptively simple question:
What did the original evidence actually show?
Repetition can amplify a claim but it cannot validate one. | 23 comments on LinkedIn
[Contenu proposé par SGPB]⚖️ Loi de finances 2026 : Le budget de la France en perspective. Voté en ce début d'année, il définit le cadre des dépenses publiques. Décryptez les 1 672 milliards d'euros répartis entre Sécurité sociale, État et collectivités.⤵️
Nearly 20 years ago, when I began my academic career as an assistant professor, I made a big decision: to make my teaching materials freely available to anyone who wanted them
In a world that cares about monetizing knowledge, I chose to give away my content. Why? Knowledge is a strange thing - unlike many things, it grows when you give it away!
My new essay is all about the benefits of creating a teaching website
Thanks to Trudi Jacobson for writing this very insightful blog post about how #AI defines #metaliteracy. This is the start of a series at Metaliteracy.org. https://lnkd.in/emnXpVWk
Ceci n’est pas ENCORE une projection de Spilhaus ❌ Enfin si… mais surtout, c’est une vraie carte de la France.
👨🏫 Nicolas Imbert signe ici une œuvre magistrale, qu’il nous faut afficher dans toutes les écoles de France, de Navarre et d’Antarctique. Edouard GEFFRAY Catherine Chabaud
Deuxième domaine maritime mondial, la France est définitivement un archipel 🏝️ Contrairement à ce que laissait penser la carte d’hier. Et beaucoup trop de cartes dans notre pays.
Il est donc essentiel de maritimiser les esprit de nos concitoyens, de soutenir le GASPE - Compagnies Maritimes de Proximité, et d’affirmer une stratégie océanocratique.
Ne pas le faire serait contre nature pour la France.
🗺️ #30daysmapchallenge, jour 28/30 : participez en commentaire avec vos idées 👇
__ ❤️ Retrouvez moi sur Instagram pour (re)découvrir le monde maritime : https://lnkd.in/dHDE78kB
Comment enseigner l’IA en santé ? Une belle journée autour du symposium organisé par CAP Santé numérique - université de Bordeaux et CAP IA - université de Bordeaux pour évoquer l’enseignement de l’IA en Santé. Nous avons un véritable défi à relever pour la formation des étudiants et des enseignants/formateurs! Une fresque générée en direct par une artiste Sophie Bougrat, facilitatrice graphique, résume le symposium et le tout sans aucune IA générative!
Merci à tous les intervenants Xavier Blanc Philippe Nauche Frederic ALEXANDRE Bonnemains Carole Gouvernance stratégique de la Data IA Caroline Receveur Cedric Gil-Jardine Fleur Mougin Vianney JOUHET Laurent Beaumont Ingrid MONTEIL Christelle SOARES Laurent Simon Olivier Cousin Andy Smith Jean Benoit Corcuff et l’équipe organisatrice en particulier Ines Hizebry Camille Bachellerie Hugo Corvaisier
"The adoption of artificial intelligence (AI)-powered tools is accelerating rapidly across all layers of healthcare systems. Predictive models, decision support tools and generative tools have entered clinical environments, and large language models are increasingly being used by the general public to seek medical information and advice. Yet evidence that AI tools create value for patients, providers or health systems remains scarce."
"Without a clear connection between claims and evidence, medical AI risks being adopted faster than its real value can be understood."
There are numerous examples of transformational technologies & products introducing substantial harms before broad based benefits. (or before we could mitigate harms). We should avoid adopting faster than we can adapt.
🚨 561 pages c'est beaucoup Peut-être un peu trop pour un jeudi matin 😁
Alors avec mon collègue Claude, nous vous avons préparé une page de synthèse interactive de ma thèse : son fil conducteur, ses trois contributions (pragmatisme, protocole F-T-T, modèle H-O-T), ses points saillants, le tout en quelques minutes de lecture.
Pour ceux qui veulent aller à l'essentiel avant (éventuellement) de plonger dans le document complet, c'est par ici : https://lnkd.in/ggYwvrWH
(cc Nicolas MOINET Christian Marcon Audrey Knauf Stephane Goria Philippe clerc et Olivier Le Deuff)
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