diff --git a/codemeta.json b/codemeta.json
index 1f3a8eac1eeb6004b04aa78fe1ae7049a0b858a2..ca7183a8d33951e4e00b51990be33d1cdd87bd71 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -1 +1 @@
-{"@context":["https:\/\/w3id.org\/codemeta\/3.0","https:\/\/schema.org"],"@type":"SoftwareSourceCode","description":[{"@value":"La plupart des m\u00e9thodes de r\u00e9duction de dimension, h\u00e9rit\u00e9es de l\u0027analyse multivari\u00e9e de donn\u00e9es et couramment impl\u00e9ment\u00e9es comme \u00e9l\u00e9ments d\u0027apprentissage statistique pour le traitement de tr\u00e8s grands jeux de donn\u00e9es (la dimensionalit\u00e9 des espaces et le nombre de mesures), se reposent sur une cha\u00eene de pr\u00e9-traitements, un c\u0153ur avec la SVD pour l\u0027approximation de rang faible d\u0027une matrice donn\u00e9e, et un post-traitement pour l\u0027interpr\u00e9tation des r\u00e9sultats. La partie couteuse en calcul est la SVD, qui a une complexit\u00e9 cubique. pydiodon est une liste de fonctions et de pilotes qui impl\u00e9mentent (i) pr\u00e9-traitements, SVD et post-traitements avec une grande diversit\u00e9 de m\u00e9thodes, (ii) m\u00e9thodes de projection al\u00e9atoire pour l\u0027ex\u00e9cution de la SVD qui permet de contourner la limitation de temps dans le calcul de la SVD, et (iii) une impl\u00e9mentation en numpy sous forme d\u0027une librairie. Ce projet est connect\u00e9 au projet Cppdiodon, maintenu par le SED (Florent Pruvost).","@language":"fr"},{"@value":"Most of dimension reduction methods inherited from Multivariate Data Analysis, and currently implemented as element in statistical learning for handling very large datasets (the dimension of spaces is the number of features) rely on a chain of pretreatments, a core with a SVD for low rank approximation of a given matrix, and a post-treatment for interpreting results. The costly part in computations is the SVD, which is in cubic complexity. Diodon is a list of functions and drivers which implement (i) pre-treatments, SVD and post-treatments on a large diversity of methods, (ii)  random projection methods for running the SVD which permits to bypass the time limit in computing the SVD, and (iii) an implementation in C++ of the SVD with random projection at prescribed rank or precision, connected to MDS.","@language":"en"}],"identifier":"pydiodon","domain":"Informatique [cs]","licence":"https:\/\/spdx.org\/licenses\/GPL-3.0-or-later.html","name":"pydiodon","relatedLink":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","url":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","version":"0.0.5","codeRepository":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","keywords":"Dimensionality reduction","programmingLanguage":["Python","C++"],"author":[{"type":"Person","id":"_:author_0","givenName":"Alain","familyName":"Franc","email":"alain.franc@inria.fr","affiliation":{"@type":"Organization","name":"Inrae"}},{"type":"Role","author":"_:author_0","roleName":"coding"},{"type":"Person","id":"_:author_1","givenName":"Florent","familyName":"Pruvost","email":"florent.pruvost@inria.fr","affiliation":{"@type":"Organization","name":"Inria"}},{"type":"Role","author":"_:author_1","roleName":"coding"},{"type":"Person","id":"_:author_2","givenName":"Romain","familyName":"Peressoni","email":"romain.peressoni@inria.fr","affiliation":{"@type":"Organization","name":"Universit\u00e9 de Bordeaux"}},{"type":"Role","author":"_:author_2","roleName":"coding","startDate":"2019-10-15","endDate":"2022-10-14"},{"type":"Person","id":"_:author_3","givenName":"Jean-Marc","familyName":"Frigerio","email":"jean-marc.frigerio@inria.fr","affiliation":{"@type":"Organization","name":"Inrae"}},{"type":"Role","author":"_:author_3","roleName":"coding","startDate":"2019-04-04","endDate":"2019-08-30"}],"datePublished":"2025-03-25"}
\ No newline at end of file
+{"@context":["https:\/\/w3id.org\/codemeta\/3.0","https:\/\/schema.org"],"@type":"SoftwareSourceCode","description":[{"@value":"La plupart des m\u00e9thodes de r\u00e9duction de dimension, h\u00e9rit\u00e9es de l\u0027analyse multivari\u00e9e de donn\u00e9es et couramment impl\u00e9ment\u00e9es comme \u00e9l\u00e9ments d\u0027apprentissage statistique pour le traitement de tr\u00e8s grands jeux de donn\u00e9es (la dimension des espaces et la quantit\u00e9 de donn\u00e9es), se reposent sur une cha\u00eene de pr\u00e9-traitements, un c\u0153ur avec la SVD pour l\u0027approximation de rang faible d\u0027une matrice donn\u00e9e, et un post-traitement pour l\u0027interpr\u00e9tation des r\u00e9sultats. La partie couteuse en calcul est la SVD, qui a une complexit\u00e9 cubique. pydiodon est une liste de fonctions qui impl\u00e9mentent (i) la cha\u00eene pr\u00e9-traitements, SVD et post-traitements pour des m\u00e9thodes diversifi\u00e9es, (ii) m\u00e9thodes de projection al\u00e9atoire pour l\u0027ex\u00e9cution de la SVD qui permet de repousser la limite due au temps de calcul de la SVD, et (iii) une impl\u00e9mentation en numpy sous forme d\u0027une librairie. Ce projet est parall\u00e8le au projet Cppdiodon, maintenu par le SED.","@language":"fr"},{"@value":"Most of dimension reduction methods inherited from Multivariate Data Analysis, and currently implemented as element in statistical learning for handling very large datasets (the dimension of spaces is the number of features) rely on a chain of pretreatments, a core with a SVD for low rank approximation of a given matrix, and a post-treatment for interpreting results. The costly part in computations is the SVD, which is in cubic complexity. Diodon is a list of functions and drivers which implement (i) pre-treatments, SVD and post-treatments on a large diversity of methods, (ii)  random projection methods for running the SVD which permits to bypass the time limit in computing the SVD, and (iii) an implementation in C++ of the SVD with random projection at prescribed rank or precision, connected to MDS.","@language":"en"}],"identifier":"pydiodon","domain":"Informatique [cs]","licence":"https:\/\/spdx.org\/licenses\/GPL-3.0-or-later.html","name":"pydiodon","relatedLink":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","url":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","version":"0.1.0","codeRepository":"https:\/\/gitlab.inria.fr\/diodon\/pydiodon","keywords":"Dimensionality reduction","programmingLanguage":"Python","author":[{"type":"Person","id":"_:author_0","givenName":"Alain","familyName":"Franc","email":"alain.franc@inria.fr","affiliation":{"@type":"Organization","name":"Inrae"}},{"type":"Role","author":"_:author_0","roleName":"coding"},{"type":"Person","id":"_:author_1","givenName":"Florent","familyName":"Pruvost","email":"florent.pruvost@inria.fr","affiliation":{"@type":"Organization","name":"Inria"}},{"type":"Role","author":"_:author_1","roleName":"coding"},{"type":"Person","id":"_:author_2","givenName":"Romain","familyName":"Peressoni","email":"romain.peressoni@inria.fr","affiliation":{"@type":"Organization","name":"Universit\u00e9 de Bordeaux"}},{"type":"Role","author":"_:author_2","roleName":"coding","startDate":"2019-10-15","endDate":"2022-10-14"},{"type":"Person","id":"_:author_3","givenName":"Jean-Marc","familyName":"Frigerio","email":"jean-marc.frigerio@inria.fr","affiliation":{"@type":"Organization","name":"Inrae"}},{"type":"Role","author":"_:author_3","roleName":"coding","startDate":"2019-04-04","endDate":"2019-08-30"}],"datePublished":"2025-04-04"}
\ No newline at end of file