Exploring data provenance in handwritten text recognition infrastructure:Sharing and reusing ground truth data, referencing models, and acknowledging contributions. Starting the conversation on how we could get it done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, and ways to reference and acknowledge contributions to the creation and enrichment of data within these Machine Learning systems. We discuss how one can publish Ground Truth data in a repository and, subsequently, inform others. Furthermore, we suggest appropriate citation methods for HTR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of Machine Learning in archival and library contexts, and how the community should begin toacknowledge and record both contributions and data provenance

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance

Shielding the cationic charge of nanoparticle-formulated dermal DNA vaccines is essential for antigen expression and immunogenicity

Nanoparticle-formulated DNA vaccines hold promise for the design of in vivo vaccination platforms that target defined cell types in human skin. A variety of DNA formulations, mainly based on cationic liposomes or polymers, has been investigated to improve transfection efficiency in in vitro assays.\ud \ud Here we demonstrate that formulation of DNA into both liposomal and polymeric cationic nanoparticles completely blocks vaccination-induced antigen expression in mice and ex vivo human skin. Furthermore, this detrimental effect of cationic nanoparticle formulation is associated with an essentially complete block in vaccine immunogenicity. The blocking of DNA vaccine activity may be explained by immobilization of the nanoparticles in the extracellular matrix, caused by electrostatic interactions of the cationic nanoparticles with negatively charged extracellular matrix components. Shielding the surface charge of the nanoparticles by PEGylation improves in vivo antigen expression more than 55 fold. Furthermore, this shielding of cationic surface charge results in antigen-specific T cell responses that are similar as those induced by naked DNA for the two lipo- and polyplex DNA carrier systems. These observations suggest that charge shielding forms a generally applicable strategy for the development of dermally applied vaccine formulations. Furthermore, the nanoparticle formulations developed here form an attractive platform for the design of targeted nanoparticle formulations that can be utilized for in vivo transfection of defined cell types\u

Exploring Data Provenance in Handwritten Text Recognition Infrastructure: Sharing and Reusing Ground Truth Data, Referencing Models, and Acknowledging Contributions. Starting the Conversation on How We Could Get It Done

This paper discusses best practices for sharing and reusing Ground Truth in Handwritten Text Recognition infrastructures, as well as ways to reference and acknowledge contributions to the creation and enrichment of data within these systems. We discuss how one can place Ground Truth data in a repository and, subsequently, inform others through HTR-United. Furthermore, we want to suggest appropriate citation methods for ATR data, models, and contributions made by volunteers. Moreover, when using digitised sources (digital facsimiles), it becomes increasingly important to distinguish between the physical object and the digital collection. These topics all relate to the proper acknowledgement of labour put into digitising, transcribing, and sharing Ground Truth HTR data. This also points to broader issues surrounding the use of machine learning in archival and library contexts, and how the community should begin to acknowledge and record both contributions and data provenance