MétaCan
Menu
Back to cohort
Record W2951967024 · doi:10.3389/fneur.2019.00425

10Kin1day: A Bottom-Up Neuroimaging Initiative

2019· article· en· W2951967024 on OpenAlexafffund
Martijn P. van den Heuvel, Lianne H. Scholtens, Hannelore K. van der Burgh, Federica Agosta, Clara Alloza, Celso Arango, Bonnie Auyeung, Simon Baron‐Cohen, Silvia Basaia, Manon J.N.L. Benders, Frauke Beyer, Linda Booij, Kees P. J. Braun, Geraldo F. Busatto, Wiepke Cahn, Dara M. Cannon, Tiffany Chaim-Avancini, Sandra Sau Man Chan, Eric Chen, Benedicto Crespo‐Facorro, Eveline A. Crone, Udo Dannlowski, Sonja M. C. de Zwarte, Bruno Dietsche, Gary Donohoe, Stefan S. du Plessis, Sarah Durston, Covadonga M. Díaz‐Caneja, Ana M. Díaz‐Zuluaga, Robin Emsley, Massimo Filippi, Thomas Frodl, Martin Gorges, Beata Graff, Dominik Grotegerd, Dariusz Gąsecki, Julie M. Hall, Laurena Holleran, Rosemary Holt, Helene Hopman, Andreas Jansen, Joost Janssen, Krzysztof Jodzio, Lutz Jäncke, Vasiliy G. Kaleda, Jan Kassubek, Shahrzad Kharabian Masouleh, Tilo Kircher, Martijn Koevoets, Vladimir Kostić, Axel Krug, Stephen M. Lawrie, И. С. Лебедева, Edwin Lee, Tristram A. Lett, Simon J.G. Lewis, Franziskus Liem, Michael Lombardo, Carlos López‐Jaramillo, Daniel S. Margulies, Sebastian Markett, Paulo Marques, Ignacio Martínez‐Zalacaín, Colm McDonald, Andrew M. McIntosh, Genevieve McPhilemy, Susanne Meinert, José M. Menchón, Christian Montag, Pedro Silva Moreira, Pedro Morgado, David Mothersill, Susan Mérillat, Hans-Peter Müller, Leila Nabulsi, Pablo Najt, Krzysztof Narkiewicz, Patrycja Naumczyk, Bob Oranje, Víctor Ortiz‐García de la Foz, Jiska S. Peper, Julián Pineda, Paul E. Rasser, Ronny Redlich, Jonathan Repple, Martin Reuter, Pedro G. P. Rosa, Amber Ruigrok, Agnieszka Sabisz, Ulrich Schall, Soraya Seedat, Maurício H. Serpa, Stavros Skouras, Carles Soriano‐Mas, Nuno Sousa, Edyta Szurowska, A. S. Tomyshev, Diana Tordesillas‐Gutiérrez, Sofie L. Valk, Leonard H. van den Berg, Theo G.M. van Erp, Neeltje E.M. van Haren, Judith van Leeuwen, Arno Villringer, Christiaan H. Vinkers, Christian Vollmar, Lea Waller, Henrik Walter, Heather C. Whalley, M Witkowska, A. Veronica Witte, Marcus V. Zanetti, Rui Zhang, Siemon C. de Lange

Bibliographic record

VenueFrontiers in Neurology · 2019
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsConcordia University
FundersCanadian Institutes of Health ResearchNational Institute of Biomedical Imaging and BioengineeringUniversiteit UtrechtNational Institute of Mental HealthNational Center for Research ResourcesHealth and Medical Research FundInstituto de Salud Carlos IIIMedical Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekMcDonnell Center for Systems NeuroscienceNational Institutes of HealthWestern Sydney UniversityInternational Max Planck Research School for Advanced Methods in Process and Systems EngineeringWellcome TrustMeath FoundationDeutsche ForschungsgemeinschaftScience Foundation IrelandAustralian Schizophrenia Research BankBundesamt für GesundheitEuropean CommissionVelux StiftungNational Institute for Health and Care ResearchNational Health and Medical Research CouncilNIH Blueprint for Neuroscience ResearchRussian Foundation for Basic Research
KeywordsNeuroimagingHuman Connectome ProjectConnectomeNeuroscienceEvent (particle physics)Top-down and bottom-up designData sciencePsychologyComputer scienceFunctional connectivityCognitive science

Abstract

fetched live from OpenAlex

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.024
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.002
Science and technology studies0.0050.002
Scholarly communication0.0110.009
Open science0.0060.029
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0440.031

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.

Opus teacher head0.025
GPT teacher head0.248
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in NeurologySame topicFunctional Brain Connectivity StudiesFrench-language works237,207