• Digital Humanities and Cultural Heritage
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• ZENODO close
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Résumé (Français): La nature et la localisation de l’âme ont longtemps intrigué philosophes et médecin dans leur quête de la vérité. Depuis la nuit des temps, l’humanité plaça en son cœur l’âme du monde, le brasier de ses passions, les arcanes de sa raison et toutes les facultés qui font de nous ce qu’on est. L’effort des penseurs à travers les siècles et les civilisations a permis de remettre en question la domination du cardiocentrisme ancestrale. L’exploration de l’âme étant intimement lié à la découverte du cerveau, elle devait nécessairement traverser la plus décisif des évolutions, celle de l’encéphalocentrisme. Ce mémoire retracera les fondements civilisationnels du cardiocentrisme, les courants philosophiques clés et les révolutions majeurs qui menèrent à notre compréhension actuelle du cerveau. Nous voguerons doctrines, dogmes et paradigmes des origines aux Temps modernes sous le prisme de la médecine, de la philosophie et de la religieux puisque ainsi furent bâtit nos mondes et ainsi ils disparaitront. Title: Of the Soul or the cardiocentric drift of Brains. Abstract: The nature and location of the soul have long puzzled philosophers and physicians in their quest for the Truth. Since the dawn of time, humanity has lodged in its heart the soul of the world, the inferno of its passions, the arcane of its reason and all the faculties that make us who we are. The effort of thinkers through the centuries and civilizations has made it possible to question the domination of the ancestral cardiocentrism. The exploration of the soul being intimately linked to the discovery of the brain, necessarily had to go through the most crucial evolution, that of encephalocentrism. In this work, we will trace the civilizational foundations of cardiocentrism, the key philosophical currents and the major revolutions that led to our present understanding of the brain. We will navigate doctrines, dogmas, and paradigms from the earliest beginnings to Modern Times through the prism of medicine, philosophy, and religion, since this is how our worlds were built and how they will disappear.

In this article, we present an approach designed to extend the metadata schema of the Zenodo data management platform to strengthen the FAIRness of the published dataset. We focus on a bottom-up approach starting from a series of datasets ranging from the 3D digitalisation of monuments and sites to the creation of reconstructive records (including the scientific documentation they are based on), to the implementation of digital storytelling and to the development of open source-based web-apps. We propose the simplest possible set of metadata to be included in the Zenodo platform with the possibility, for the community, to adopt and further develop/modify them. This article will describe in detail the formalisation and the digital formats adopted providing the related metadata templates developed within the projects.

This resource defines the Hierarchical Event Descriptor (HED) specification, including the core specification with detailed rules about the handling of the vocabulary, tool behavior, and errors. This specification lays out the rules that HED-compliant tools must follow to correctly handle HED annotations. It is meant for tool developers and for users who need to understand the details of the behavior. If you are new to HED, please visit the HED homepage or the HED resources site. The current, officially released specification can also be browsed in HTML format. The HED specification is maintained in the GitHub hed-specification repository which is part of the GitHub HED Standard organization. HED is the annotation standard for events and other tabular metadata in the Brain Imaging Data Structure (BIDS) standard. Release 3.2.0 added the Inset tag for labeling intermediate points as well as partnered and rooted library schemas and the curly braces notation for sidecars..

This talk outlines a vision for Common European Data Spaces, proposed by the European Commission, where FAIR principles (Wilkinson et al. 2016) and FAIR Digital Objects (FDOs) (De Smedt et al. 2020, Schwardmann 2020) can play a role in bringing together research infrastructures, data aggregators and other stakeholders working with curated objects in museums, herbaria, libraries and archives. The organisations and stakeholders involved represent a wide range of disciplines and data types including biodiversity, ecology, anthropology, archaeology, cultural history, digital storytelling, art conservation, and history of science among others (ICEDIG 2020, Ortolja-Baird and Nyhan 2021). The context and the history of the curated objects also span the natural sciences and cultural heritage domains (Nadim 2021, Weber 2021). Despite this heterogeneity, various common themes in the area of digital curation, open access, and data usage (Tasovac et al. 2020) appear where FDOs and Common European Data Spaces can be a useful venue for supporting the European Strategy for Data. In particular, FDOs, as an abstraction mechanism to structure and describe digital artefacts from a specific domain yet at the same time provide interoperability (De Smedt et al. 2020), can help realise the vision behind a common data space to “bring together relevant data infrastructures and governance frameworks in order to facilitate data pooling and sharing” (European Commission 2022:2). A May 2022 report on the challenges and opportunities of European Common Data Spaces highlights the following points: Open data holders have extensive experience in data publishing, metadata management, data quality, dataset discovery, data federation, as well as tried-and-tested standards (e.g. DCAT) and technologies. There seems to be very little knowledge/technology transfer from the open data community to the data spaces community, which is a missed opportunity. Data space implementations should not reinvent wheels that the open data community has already developed, tested, and used extensively. Whether the data is private, shared, or open, using data from multiple sources requires interoperability at several levels, from identifiers to vocabularies. The question of which data intermediaries will act as neutral agents to ensure interoperability is underexplored in the data space context. Public administrations, building on their experience of publishing open data, are best placed to take on such roles Open data holders have extensive experience in data publishing, metadata management, data quality, dataset discovery, data federation, as well as tried-and-tested standards (e.g. DCAT) and technologies. There seems to be very little knowledge/technology transfer from the open data community to the data spaces community, which is a missed opportunity. Data space implementations should not reinvent wheels that the open data community has already developed, tested, and used extensively. Whether the data is private, shared, or open, using data from multiple sources requires interoperability at several levels, from identifiers to vocabularies. The question of which data intermediaries will act as neutral agents to ensure interoperability is underexplored in the data space context. Public administrations, building on their experience of publishing open data, are best placed to take on such roles Building on previous conversations facilitated by DiSSCo, DARIAH, Europeana, and Archives Portal Europe Foundation, (Europeana Conference 2021, DARIAH Annual Event 2022), this talk will address the above points from the perspective of bringing together the domains of natural history museums, cultural heritage, and digital humanities. Within our collaboration, we have identified several common areas such as data discoverability, linking, and providing contextual information, which align with the goal of FDO implementation. DiSSCo and DARIAH as European infrastructures, on the one hand, and Europeana and Archives Portal as data aggregators, on the other hand, are involved in improving access to data and the researchers' capacity to work with heterogeneous data sources. One of the biggest shared challenges across the diverse workflows in the arts and humanities and natural history domains is that the data curation processes form a natural continuum between a range of different actors working either in cultural heritage institutions or in academia. In reality, these different layers of curation, enrichment and analysis are separated by legal, institutional, infrastructural and even funding silos (as in many countries, these institutions belong to different ministries, and fall under different legislative frameworks). How can this continuum, from a scholarly point of view, be supported within common data space and FDO framework? At the same time, implementing a common data space requires not just interoperability but stewardship and strategy for sharing resources (Keller 2021). The data infrastructure and FAIR related activities explored in our collaboration are of strategic importance to help Europe and the rest of the world deal with important societal issues. Therefore, bringing this collaboration within the context of FDO provides an ideal avenue to explore potential data, policy, and implementation matters, in order to address the two gaps outlined above for Common Data Spaces. Furthermore, the ideas expressed in Common European Data Space for Cultural Heritage (with Europeana as the core stakeholder) and Green Deal Data Spaces need further clarification concerning implementation planning and most importantly, how multiple commons would work together. With DARIAH coming from the humanities and DiSSCo from the natural sciences side, such collaborations and synergy should align with the Common Data Spaces vision. The philosophy and ideas behind data and digital commons are not new (Fuchs 2020, Kashwan et al. 2021). However, it is crucial to contextualise the implementation strategy and benefits within data intensive, multidisciplinary research and FAIR principles. Given that curated objects are informational resources for the researchers, but can also provide contexts, and make visible the relationships between artefacts, people, publications, organisations, provenance, and events, it is important to think of them as much more than just records in a database. Additionally, FDOs as the digital representations of the curated objects have the potential of fostering cross-disciplinary collaborations (such as between biology, history, art or anthropology) and of providing a wider lens for understanding materiality and the role of data (Ribes 2019). As interdisciplinarity and data-driven foci are gaining traction via applications of artificial intelligence and machine learning, it is vital to understand what FDO adoption and implementation can contribute to common data spaces. We believe FDOs can be a successful foundation for Common European Data Spaces because they can can connect multiple commons -- from Green Deal to Cultural Heritage -- in order to drive forward the vision for interdisciplinary collaboration.

Essential Biodiversity Variables (EBVs) make it possible to evaluate and monitor the state of biodiversity over time at different spatial scales. Its development is led by the Group on Earth Observations Biodiversity Observation Network (GEO BON) to harmonize, consolidate and standardize biodiversity data from varied biodiversity sources. This document presents a mechanism to obtain baseline data to feed the Species Traits Variable Phenology or other biodiversity indicators by extracting species characters and structure names from morphological descriptions of specimens and classifying such descriptions using machine learning (ML). A workflow that performs Named Entity Recognition (NER) and Classification of morphological descriptions using ML algorithms was evaluated with excellent results. It was implemented using Python, Pytorch, Scikit-Learn, Pomegranate, Python-crfsuite, and other libraries applied to 106,804 herbarium records from the National Biodiversity Institute of Costa Rica (INBio). The text classification results were almost excellent (F1 score between 96% and 99%) using three traditional ML methods: Multinomial Naive Bayes (NB), Linear Support Vector Classification (SVC), and Logistic Regression (LR). Furthermore, results extracting names of species morphological structures (e.g., leaves, trichomes, flowers, petals, sepals) and character names (e.g., length, width, pigmentation patterns, and smell) using NER algorithms were competitive (F1 score between 95% and 98%) using Hidden Markov Models (HMM), Conditional Random Fields (CRFs), and Bidirectional Long Short Term Memory Networks with CRF (BI-LSTM-CRF).

Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. argumentative texts. In this project, we adopt a complementary perspective: we aim to develop an argumentation machine that supports users during the argumentation process in a scientific context, enabling them to follow ongoing argumentation in a scientific community and to develop their own arguments. To achieve this ambitious goal, we will focus on a particular phase of the scientific argumentation process, namely the initial phase of claim or hypothesis development. According to argumentation theory, the starting point of an argument is a claim, and also data that serves as a basis for the claim. In scientific argumentation, a carefully developed and thought-through hypothesis (which we see as Toulmin's "claim'' in a scientific context) is often crucial for researchers to be able to conduct a successful study and, in the end, present a new, high-quality finding or argument. Thus, an initial hypothesis needs to be specific enough that a researcher can test it based on data, but, at the same time, it should also relate to previous general claims made in the community. We investigate how argumentation machines can (i) represent concrete and more abstract knowledge on hypotheses and their underlying concepts, (ii) model the process of hypothesis refinement, including data as a basis of refinement, and (iii) interactively support a user in developing her own hypothesis based on these resources. This project will combine methods from different disciplines: natural language processing, knowledge representation and semantic web, philosophy of science and -- as an example for a scientific domain -- invasion biology. Our starting point is an existing resource in invasion biology that organizes and relates core hypotheses in the field and associates them to meta-data for more than 1000 scientific publications, which was developed over the course of several years based on manual analysis. This network, however, is currently static (i.e. needs substantial manual curation to be extended to incorporate new claims) and, moreover, is not easily accessible for users who miss specific background and domain knowledge in invasion biology. Our goal is to develop (i) a semantic model for representing knowledge on concepts and hypotheses, such that also non-expert users can use the network; (ii) a tool that automatically computes links from publication abstracts (and data) to these hypotheses; and (iii) an interactive system that supports users in refining their initial, potentially underdeveloped hypothesis.

PURPOSE: Measuring longitudinal changes in amyloid load in the asymptomatic stage of Alzheimer's disease is of high relevance for clinical research and progress towards more efficacious, timely treatments. Apolipoprotein E ε4 (APOE4) has a well-established effect on the rate of amyloid accumulation. Here we investigated which region of interest and which reference region perform best at detecting the effect of APOE4 on longitudinal amyloid load in individuals participating in the Flemish Prevent Alzheimer's Disease Cohort KU Leuven (F-PACK). METHODS: Ninety cognitively intact F-PACK participants (baseline age: 68 (52-80) years, 46 males, 42 APOE4 carriers) received structural MRI and 18F-Flutemetamol PET scans at baseline and follow-up (6.2 (3.4-10.9) year interval). Standardised uptake value ratios (SUVRs) and Centiloids (CLs) were calculated in a composite cortical volume of interest (SUVRcomp/CL) and in the precuneus (SUVRprec), and amyloid rate of change derived: (follow-up amyloid load - baseline amyloid load) / time interval (years). Four reference regions were used to derive amyloid load: whole cerebellum, cerebellar grey matter, eroded subcortical white matter, and pons. RESULTS: When using whole cerebellum or cerebellar grey matter as reference region, APOE4 carriers had a significantly higher SUVRcomp amyloid rate of change than non-carriers (pcorr = 0.004, t = 3.40 (CI 0.005-0.018); pcorr = 0.036, t = 2.66 (CI 0.003-0.018), respectively). Significance was not observed for eroded subcortical white matter or pons (pcorr = 0.144, t = 2.13 (CI 0.0003-0.008); pcorr = 0.116, t = 2.22 (CI 0.005-0.010), respectively). When using CLs as the amyloid measurement, and whole cerebellum, APOE4 carriers had a higher amyloid rate of change than non-carriers (pcorr = 0.012, t = 3.05 (CI 0.499-2.359)). Significance was not observed for the other reference regions. No significance was observed with any of the reference regions and amyloid rate of change in the precuneus (SUVRprec). CONCLUSION: In this cognitively intact cohort, a composite neocortical volume of interest together with whole cerebellum or cerebellar grey matter as reference region are the methods of choice for detecting APOE4-dependent differences in amyloid rate of change. ispartof: NEUROIMAGE-CLINICAL vol:37 ispartof: location:Netherlands status: published