TY - JOUR A1 - Röblitz, Thomas A1 - Schintke, Florian A1 - Reinefeld, Alexander A1 - Bärring, Olof A1 - Lopez, Maite Barroso A1 - Cancio, German A1 - Chapeland, Sylvain A1 - Chouikh, Karim A1 - Cons, Lionel A1 - Poznanski, Piotr A1 - Defert, Philippe A1 - Iven, Jan A1 - Kleinwort, Thorsten A1 - Panzer-Steindel, Bernd A1 - Polok, Jaroslaw A1 - Rafflin, Catherine A1 - Silverman, Alan A1 - Smith, Tim A1 - van Eldik, Jan A1 - Front, David A1 - Biasotto, Massimo A1 - Aiftimiei, Cristina A1 - Ferro, Enrico A1 - Maron, Gaetano A1 - Chierici, Andrea A1 - dell’Agnello, Luca A1 - Serra, Marco A1 - Michelotto, Michele A1 - Hess, Lord A1 - Lindenstruth, Volker A1 - Pister, Frank A1 - Steinbeck, Timm A1 - Groep, David A1 - Steenbakkers, Martijn A1 - Koeroo, Oscar A1 - de Cerff, Wim A1 - Venekamp, Gerben A1 - Anderson, Paul A1 - Colles, Tim A1 - Holt, Alexander A1 - Scobie, Alastair A1 - George, Michael A1 - Washbrook, Andrew A1 - Leiva, Rafael T1 - Autonomic Management of Large Clusters and Their Integration into the Grid JF - J. Grid Comput. Y1 - 2004 U6 - https://doi.org/10.1007/s10723-004-7647-3 VL - 2 IS - 3 SP - 247 EP - 260 ER - TY - JOUR A1 - Dai, Chengxin A1 - Füllgrabe, Anja A1 - Pfeuffer, Julianus A1 - Solovyeva, Elizaveta M. A1 - Deng, Jingwen A1 - Moreno, Pablo A1 - Kamatchinathan, Selvakumar A1 - Kundu, Deepti Jaiswal A1 - George, Nancy A1 - Fexovy, Silvie A1 - Grüning, Björn A1 - Föll, Melanie Christine A1 - Griss, Johannes A1 - Vaudel, Marc A1 - Audain, Enrique A1 - Locard-Paulet, Marie A1 - Turewicz, Michael A1 - Eisenacher, Martin A1 - Uszkoreit, Julian A1 - Van Den Bossche, Tim A1 - Schwämmle, Veit A1 - Webel, Henry A1 - Schulze, Stefan A1 - Bouyssié, David A1 - Jayaram, Savita A1 - Duggineni, Vinay Kumar A1 - Samaras, Patroklos A1 - Wilhelm, Mathias A1 - Choi, Meena A1 - Wang, Mingxun A1 - Kohlbacher, Oliver A1 - Brazma, Alvis A1 - Papatheodorou, Irene A1 - Bandeira, Nuno A1 - Deutsch, Eric W. A1 - Vizcaíno, Juan Antonio A1 - Bai, Mingze A1 - Sachsenberg, Timo A1 - Levitsky, Lev I. A1 - Perez-Riverol, Yasset T1 - A proteomics sample metadata representation for multiomics integration and big data analysis JF - Nature Communications N2 - The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets. Y1 - 2021 U6 - https://doi.org/https://doi.org/10.1038/s41467-021-26111-3 VL - 12 IS - 5854 ER -