• Digital Humanities and Cultural Heritage
• 2018-2022close
• Open Access close
• Research software close
• Other research products close
• EU close
• DK close
• English close
• Digital Humanities and Cultural Her... close

This blog post is a synthesis of the papers given by the members of the DHARMA project during the international symposium "Regards sur l'autre, regards de l'autre" organised by GREI in November 2022. dharma.hypotheses.org

The Social Sciences and Humanities communities needed a central point to gather and exchange information about their tools, services, and datasets. Although plenty of project websites, service registries, and data repositories existed, they were mostly fragmented. The lack of contextualisation connecting these assets and offering domain-relevant means to discuss and enrich them was evident. Also, there was a need to digitalise this research domain which, in most cases, was not yet digital native. This siloed environment was the challenge SSHOC set out to bridge in January 2019. The solution to solve this problems was found in the SSH Open Marketplace, delivered in 2022. It is a discovery platform supporting researchers in the multi-faceted domain of humanities and social sciences at every step of the research data lifecycle. The SSH Open Marketplace is available on the EOSC Portal and was developed by SSHOC The full list of EOSC in practice stories is available here

The presented Open Educational Resources help mitigate common problems that arise when scientific data is used for educational purposes: Often, finding, getting and analyzing data is a time-consuming task. To address this problem, we demonstrate how teachers and their students can use the methods to directly download data. Good analysis scripts begin with code that fetches the data from a repository. This contributes to making research reproducible and supports proper citation of research data. Two examples, one executable in Python, one in a local R installation, will show how to download datafiles from a Dataverse repository, and take first steps in analysing data.

This is the initial release of the research compendium for the article by Tsirintoulaki, K., Matzig, D.N., Riede, F. (2022) "A 2D geometric morphometric assessment of chrono-cultural trends in osseous barbed points of the European Final Palaeolithic and Early Mesolithic" submitted to Open Archaeology. It contains all data and code, and will generate the results as found in the publication. The files hosted at https://github.com/yesdavid/Osseous_Barbed_Points_2D_GMM are the development versions and may have changed since the publication.

Abstract: Phytolith research contributes to our understanding of plant-related studies such as plant use in archaeological contexts and past landscapes in palaeoecology. This multi-disciplinarity combined with the specificities of phytoliths themselves (multiplicity, redundancy, naming issues) produces a wide variety of methodologies. Combined with a lack of data sharing and transparency in published studies, it means data are hard to find and understand, and therefore difficult to reuse. This situation is challenging for phytolith researchers to collaborate from the same and different disciplines for improving methodologies and conducting meta-analyses. Implementing The FAIR Data principles (Findable, Accessible, Interoperable and Reusable) would improve transparency and accessibility for greater research data sustainability and reuse. This paper sets out the method used to conduct a FAIR assessment of existing phytolith data. We sampled and assessed 100 articles of phytolith research (2016-2020) in terms of the FAIR principles. The end goal of this project is to use the findings from this dataset to propose FAIR guidance for more sustainable publishing of data and research in phytolith studies. Link to the data and documents are here: https://doi.org/10.5281/zenodo.7215572

This is an interactive live modelling system which can automatically convert simple tab separated triples or JSON-LD (experimental) into graphical models using the mermaid library. General updates and improvements have been carried out, with the addition of pan and zoom functions for the displayed models, simple tooltips, alternative display labels for links, an alternative format to fix property labels to relationships lines and an update default instructions model. Instructions: Several worked examples, including some simple instructions relating to how this system works are included in the interactive web site: dynamic modelling, with all of the related code presented in GitHub.

Heritage Sciences, i.e. the application of scientific experimental methods to the analysis of cultural heritage artefacts, produces a large quantity of numeric data that are only loosely related to the cultural object to which the analyses were applied. The lack of standard data models for the different technologies employed makes interoperability between datasets almost impossible. On the other hand, the same cultural objects and activities on them (studies, interventions, etc.) are documented in textual documents usually with very basic metadata. This situation requires the intervention of a human to link the documentation of scientific analyses to the documentation of the cultural object, e.g. chemical analyses and physical to a study by an art historian; this in the end prevents data re-use and data-driven research. Learn more

This paper describes the organization and the results of the second edition of EvaLatin, the campaign for the evaluation of Natural Language Processing tools for Latin. The three shared tasks proposed in EvaLatin 2022, i. e. Lemmatization, Part-of-Speech Tagging and Features Identification, are aimed to foster research in the field of language technologies for Classical languages. The shared dataset consists of texts mainly taken from the LASLA corpus. More specifically, the training set includes only prose texts of the Classical period, whereas the test set is organized in three sub-tasks: a Classical sub-task on a prose text of an author not included in the training data, a Cross-genre sub-task on poetic and scientific texts, and a Cross-time sub-task on a text of the 15th century. The results obtained by the participants for each task and sub-task are presented and discussed.

The information uploaded here contains the code for the relational database Prosobab. A dump of the collected data has been put in DANS (https://dans.knaw.nl/) Prosobab is an online database of recorded inhabitants of Babylonia between c. 620 and 330 BCE. Its main focus is on individuals who lived in southern Mesopotamia under Persian rule (539-330 BCE), but it also includes the preceding period of the Neo-Babylonian Empire in an attempt to contextualize their lives and those of their family members. In addition to person data, Prosobab collects information on the texts and archives in which the individuals are recorded. The database is developed in the framework of ERC project “Persia and Babylonia” at Leiden University and is intended for everybody with an interest in Babylonian history or the Persian Empire. {"references": ["C. Waerzeggers, M. Gross, et al., Prosobab: Prosopography of Babylonia (c. 620-330 BCE) (Leiden University, 2019)"]} Programming language: PHP, JavaScript, jQuery Database management system: MySQL

Reproducible research (Reproducible research is when data can be reanalysed taking the same steps and producing the same or similar result) is being implemented at different speeds in different disciplines, and Archaeology, as a discipline that sits at the intersection of the sciences and humanities, is at the start of this journey. Enabling reproducibility of your work by others is an important step in ensuring research quality. There are currently many barriers to moving towards reproducible research such as upskilling researchers in the practices, software and infrastructure needed to do reproducible research and also the need to address how we can, as a discipline, deal with issues like sensitive data. In this article, we seek to introduce reproducible research in an understandable manner so that archaeological researchers can learn where and how to start with this approach. We describe what reproducible archaeological research can look like and suggest three different computational skill levels of constructing reproducible research workflows (a research workflow is the different parts of a research lifecycle such as data collection, data analysis, data archiving, etc, and making all stages reproducible by using a history tracking system (version control) and transparent documation). Finally, in an extensive appendix, we address common questions about reproducible research to remove the stigma about these issues and suggest ways to overcome them.