Abstract

Abstract
An institution must understand its users to provide quality services, and archives are no exception. Over the years, archives have adapted to the technological world, and their users have also changed. To understand archive users' characteristics and motivations, we conducted a study in the context of the Portuguese Archives. For this purpose, we analysed a survey and complemented this analysis with information gathered in interviews with archivists. Based on the most frequent reasons for visiting the archives, we defined six main archival profiles (genealogical research, historical research, legal purposes, academic work, institutional purposes and publication purposes), later characterised using the results of the previous analysis. For each profile, we created a persona for a more visual and realistic representation of users.

Abstract

Abstract
Linked data is used in various fields as a new way of structuring and connecting data. Cultural heritage institutions have been using linked data to improve archival descriptions and facilitate the discovery of information. Most archival records have digital representations of physical artifacts in the form of scanned images that are non-machine-readable. Optical Character Recognition (OCR) recognizes text in images and translates it into machine-encoded text. This article evaluates the impact of image processing methods and parameter tuning in OCR applied to typewritten cultural heritage documents. The approach uses a multi-objective problem formulation to minimize Levenshtein edit distance and maximize the number of words correctly identified with a non-dominated sorting genetic algorithm (NSGA-II) to tune the methods' parameters. Evaluation results show that parameterization by digital representation typology benefits the performance of image pre-processing algorithms in OCR. Furthermore, our findings suggest that employing image pre-processing algorithms in OCR might be more suitable for typologies where the text recognition task without pre-processing does not produce good results. In particular, Adaptive Thresholding, Bilateral Filter, and Opening are the best-performing algorithms for the theater plays' covers, letters, and overall dataset, respectively, and should be applied before OCR to improve its performance.

Abstract
Archives are facing numerous challenges. On the one hand, archival assets are evolving to encompass digitized documents and increasing quantities of born-digital information in diverse formats. On the other hand, the audience is changing along with how it wishes to access archival material. Moreover, the interoperability requirements of cultural heritage repositories are growing. In this context, the Portuguese Archives started an ambitious program aiming to evolve its data model, migrate existing records, and build a new archival management system appropriate to both archival tasks and public access. The overall goal is to have a fine-grained and flexible description, more machine-actionable than the current one. This work describes ArchOnto, a linked open data model for archives, and rules for its automatic population from existing records. ArchOnto adopts a semantic web approach and encompasses the CIDOC Conceptual Reference Model and additional ontologies, envisioning interoperability with datasets curated by multiple communities of practice. Existing ISAD(G)-conforming descriptions are being migrated to the new model using the direct mappings provided here. We used a sample of 25 records associated with different description levels to validate the completeness and conformity of ArchOnto to existing data. This work is in progress and is original in several respects: (1) it is one of the first approaches to use CIDOC CRM in the context of archives, identifying problems and questions that emerged during the process and pinpointing possible solutions; (2) it addresses the balance in the model between the migration of existing records and the construction of new ones by archive professionals; and (3) it adopts an open world view on linking archival data to global information sources.

Abstract
Indispensable in many contexts, images are fundamental in the tasks of representation and transmission of information. In the scientific context, images can be tools for researchers seeking to see their data properly managed. Research data management guides in this direction as it determines necessary phases in the life cycle of projects. The description phase is fundamental as it is an essential means for data context, safeguarding, and reuse. The description often occurs through metadata models composed of descriptors capable of attributing context. However, there is one common aspect: the values associated with these descriptors are always textual or numeric. Through studies and work developed over the last few years, we propose a new approach to description, where images can have a preponderant role in the description of data, assuming the role of metadata. We present several pieces of evidence, point out their challenges and determine the opportunities this new perspective can have in the research. Images have specific characteristics that can be leveraged in improving data description. Historical evidence establish that images have always been used and produced in research, yet their representational ability has never been harnessed to describe data and give more context to the scientific process. ©, Joana Rodrigues and Carla Teixeira Lopes. Published with license by Taylor & Francis Group, LLC.