Descriptive Metadata Research Articles

OpenOrgs

Building a connected open scholarly communication system requires unambiguously identifying research-related entities. It is not a simple task: for example, the same organization can have a range of names (eg. legal name, a shortened version, an abbreviation, in national language or in English), as well as varied metadata in other sources. Additionally, persistent IDs might not be helpful when several data sources (eg. ROR, ISNI, EC PIC) utilize various PID schemas to identify organizations. Due to this ambiguity, there are efficiency issues with information sharing, discoverability of research outputs, keeping track of activities, and ultimately building an integrated open scholarly communication system and OS services (Artini et al., 2022).A new tool called OpenOrgs was developed to address this old issue: the disambiguation of organizations engaged in the research process (Pavone, 2021) as well as the parentchild relationships between departments and organizations. OpenOrgs tackles the ambiguity in the data that OpenAIRE collects from several research organization registries (eg. ROR), as well as other sources including institutional repositories, scientific journals, and CRIS systems, and aggregates them to populate the OpenAIRE Graph (OpenAIRE Graph, n.d.). To make up for the lack of information and increase the organization's discoverability and recognition, OpenOrgs combines automated processes with human curation. Numerous data sources are used to gather and merge information on organizations. Their metadata are automatically compared and combined, then these suggested identities are manually checked by data curators assigned at a national or multi-national level.These two steps work as follows:1. The deduplication algorithm (De Bonis, 2022) suggests a similarity between organizations that emerge in various sources by comparing their metadata (eg. the organization name, URL, country).2. The automatic procedure is then verified by a manual curation process. By indicating whether two or more entities pertain to the same organization or not, data curators can clear up any ambiguity surrounding duplicates detected using the automated approach. Furthermore, they can themselves suggest new duplicates unidentified by the algorithm and make up for the information shortage by editing metadata description of the organization, compensating the lack of information from sources and enhancing the organization records' completeness and discoverability, for example by adding a persistent identifier, an alternative name (OpenAIRE, 2023), or establishing parent- child relationships (eg. university and departments). As of now, there are more than 70 registered data curators from over 40 countries, with more than 100,000 curated organizations.OpenOrgs offers a number of advantages for researchers, Research Performing Organizations (RPOs), Research Funding Organizations (RFOs), and all other stakeholders of Open Science services. It improves the findability of digital objects for academics and provides RPOs with a consistent showcase of the overall scientific production. It offers RFOs consistent data on the impact of resources. Finally, OpenOrgs offers functional and up-to-date services to all parties involved in Open Science.In the OpenAIRE ecosystem, OpenOrgs plays an important role. For example, OpenAIRE Explore displays the curated metadata from OpenOrgs, giving researchers quick and easy access to details about the organizations involved in the research process (OpenAIRE EXPLORE, n.d.). These data are also used by the OpenAIRE Monitor service, which tracks and monitors research activities and Open Science trends of organizations (OpenAIRE MONITOR, n.d.). This integration improves these organizations’ discoverability and recognition even more, fostering a more open and cooperative research environment. Therefore, rather than just a tool, OpenOrgs is a game-changer for the research community, and we believe it will contribute positively to build and maintain an integrated open scholarly communication system in the years to come.

Visual methods for desire and wonder in the digital heritage

The digitalisation of cultural heritage creates expectations for improved research methods and more diverse and inclusive memory institutions. However, it is difficult to take advantage of the opportunities the quantification might give owing to deficient and inadequate metadata. The diversity of standards and wilfulness in different historical archive practices creates problems when aggregating data from various sources. The ambition to create more diverse and inclusive memory institutions, compensating for the historical lack of justice, also creates the risk of excluding important contexts from the digital collections. To develop research methods that take this archival wilfulness into account, in this study I have used speculative design to explore images from Europeana, the digital archive that aggregates data from memory institutions all over Europe. Instead of seeing this archive as something lacking in terms of shared standards and inclusive vocabularies, I suggest we see Europeana as a queer collection of wilful archival practices, by showing the desires and imaginations represented in the archival context. By contrasting the visual content of an image with the metadata that describes the image, the norms and desires in the archival practice come into focus, as the metadata points out what at the time was considered interesting about an image, and the reason the photograph was taken. Images described as ‘Swedes’, for example, rarely show pictures of Swedes in Sweden. Swedes are described as Swedes when they are outside Sweden. It is the exotic and foreign that are categorised. Most importantly, the person who controls the camera is not in the picture, but the choice of perspective and the metadata description of the image tell us something about the photographer’s and archivist’s will and desires. By visually reversing the perspective and making visible both norms and deviations, I show how one can approach this digital heritage with a methodology of feminist wonder.

Describing the HSS Commons: The View from Metadata

In the first year since the launch of the HSS Commons, members have added over 100 publications and the features of the repository’s design are beginning to manifest. Building from Kim Martin’s talk on the possibilities for serendipity in browsing the Commons (Martin, 2022), I explore the descriptive metadata for this first cohort of publications, with particular focus on author-supplied keywords. Information studies predicts that author-supplied metadata enacts diverse sense of the intent and operation of fields, a tendency toward very high-level or general terminology, and the relevance of interaction design choices. This paper presents the metadata practices already visible in the HSS Commons and suggests interaction and maintenance design that can facilitate serendipitous discovery without compromising on the usability and creativity of the platform.

A Crowdsourcing Recommendation Model for Image Annotations in Cultural Heritage Platforms

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.

Description: Its meaning, epistemology, and use with emphasis on information science

AbstractThis study examines the concept of “description” and its theoretical foundations. The literature about it is surprisingly limited, and its usage is vague, sometimes even conflicting. Description should be considered in relation to other processes, such as representation, data capturing, and categorizing, which raises the question about what it means to describe something. Description is often used for any type of predication but may better be limited to predications based on observations. Research aims to establish criteria for making optimal descriptions; however, the problems involved in describing something have seldom been addressed. Specific ideals are often followed without examine their fruitfulness. This study provides evidence that description cannot be a neutral, objective activity; rather, it is a theory‐laden and interest‐based activity. In information science, description occurs in processes such as document description, descriptive metadata assignment, and information resource description. In this field, description has equally been used in conflicting ways that mostly do not evince a recognition of the value‐ and theory‐laden nature of descriptions. It is argued that descriptive activities in information science should always be based on consciously explicit considerations of the goals that descriptions are meant to serve.

Unearthing the Past for a Sustainable Future: Extracting and transforming data in the Biodiversity Heritage Library for climate action

As the urgency to address the climate crisis intensifies, the availability of accurate and comprehensive biodiversity data has become crucial for informing climate change studies, tracking key environmental indicators, and building global biodiversity monitoring platforms. The Biodiversity Heritage Library (BHL) plays a vital role in the core biodiversity infrastructure, housing over 60 million pages of digitized literature about life on Earth. Recognizing the value of over 500 years of data in BHL, a global network of BHL staff is working to establish a scalable data pipeline to provide actionable occurrence data from BHL’s vast and diverse collections. However, transforming textual content into FAIR (findable, accessible, interoperable, reusable) data poses challenges due to missing descriptive metadata and error-ridden unstructured outputs from commercial text engines. (Fig. 1) Despite the wealth of knowledge in BHL now available to global audiences, the underutilization of biodiversity and climate data contained in BHL's textual corpus hinders scientific research, hampers informed decision-making for conservation efforts, and limits our understanding of biodiversity patterns crucial for addressing the climate crisis. By leveraging recent advancements in text recognition engines, along with cutting-edge AI (Artificial Intelligence) models like OpenAI’s CLIP (Contrastive Language-Image Pre-Training) and nascent features in transcription platforms, BHL staff are beginning to process vast amounts of textual and image data and transform centuries worth of data from BHL collections into computationally usable formats. Recent technological breakthroughs now offer a transformative opportunity to empower the global biodiversity community with prescient insights from our shared past and facilitate the integration of historical knowledge into climate action initiatives. To bridge gaps in the historical record and unlock the potential of the Biodiversity Heritage Library (BHL), a multi-pronged effort utilizing innovative cross-disciplinary approaches is being piloted. These technical approaches were selected for their efficiency and ability to generate rapid results that could be applied across the diverse range of materials in BHL. (Fig. 2) Piloting a data pipeline that is scalable to 60 million pages requires considerable investigation, experimentation, and resources but will have an appreciable impact on global conservation efforts by informing and establishing historic baselines deeper into time. This presentation will focus on the identification, extraction, and transformation of OCR into structured data outputs in BHL. Approaches include: Upgrading legacy OCR text using Tesseract OCR engine to improve data quality by 20% and openly publish 40 GBs of textual data as FAIR data; Evaluating handwritten text recognition (HTR) engines (Microsoft Azure Computer Vision, Google Cloud Vision API (Application Programming Interface), and Amazon Textract) to improve scientific name-finding in BHL’s handwritten archival materials using algorithms developed by Global Names Architecture; Extracting data from collecting events using HTR coordinate outputs with Python library Pandas DataFrame to create structured data; Classifying BHL page-level images with OpenAI's CLIP, a neural network model to accurately identify the handwritten sub-corpus of primary source materials in BHL; Running an A/B test to evaluate the efficiency and accuracy of human-keyed transcription data extraction to provide high-quality, human-vetted datasets that can be deposited with data aggregators. Upgrading legacy OCR text using Tesseract OCR engine to improve data quality by 20% and openly publish 40 GBs of textual data as FAIR data; Evaluating handwritten text recognition (HTR) engines (Microsoft Azure Computer Vision, Google Cloud Vision API (Application Programming Interface), and Amazon Textract) to improve scientific name-finding in BHL’s handwritten archival materials using algorithms developed by Global Names Architecture; Extracting data from collecting events using HTR coordinate outputs with Python library Pandas DataFrame to create structured data; Classifying BHL page-level images with OpenAI's CLIP, a neural network model to accurately identify the handwritten sub-corpus of primary source materials in BHL; Running an A/B test to evaluate the efficiency and accuracy of human-keyed transcription data extraction to provide high-quality, human-vetted datasets that can be deposited with data aggregators. The ongoing development of a scalable data pipeline of BHL’s relevant biodiversity and climate-related datasets requires sustained support and partnership with the biodiversity community. Initial results demonstrate that liberating data from archival and handwritten field notes is arduous but feasible. Extending these methodologies to the broader scientific literature presents new research opportunities. Extracting and normalizing data from unstructured textual sources can significantly advance biodiversity research and inform environmental policy. The Biodiversity Heritage Library staff are committed to building multiple scalable data pipelines with the ultimate goal of erecting a global biodiversity knowledge graph, rich in interconnected data and semantic meaning, enabling informed decisions for the preservation and sustainable management of Earth's biodiversity.

Mapping.bio: Piloting FAIR semantic mappings for biodiversity digital twins

Biodiversity research has a strong focus on the links between environment and functional traits, e.g., to assess how anthropogenic drivers of change impact ecological systems (Díaz et al. 2013). Interoperable exchange and integration of such data is enabled through the use of ontologies that provide ”meaning” to data and enable downstream processing involving learning and inference over graph-structured models of these data (Kulmanov et al. 2020). However, the development of thematically similar semantic artifacts, e.g., the Environmental Ontology (ENVO, Buttigieg et al. 2016) and the Semantic Web for Earth and Environment Technology Ontology (SWEET, DiGiuseppe et al. 2014), in biodiversity-related disciplines (e.g., environmental genomics and earth observation) can introduce substantial conceptual overlaps, and highlights the need for bridging technologies to facilitate reuse of biodiversity data across those knowledge fields (Karam et al. 2020). A recent design study, funded by the European Open Science Cloud (EOSC), proposes a framework to create, document and publish mappings and crosswalks linking different semantic artifacts within a particular scientific community and across scientific domains under the label of "Flexible Semantic Mapping Framework" (SEMAF, Broeder et al. 2021). SEMAF puts a strong emphasis on so-called pragmatic mappings, i.e., mappings that are driven by specific interoperability goals such as translations between specific observation measurements (e.g., sensor configurations) and metadata descriptions. Within the Horizon Europe Project “Biodiversity Digital Twin for Advanced Modelling, Simulation and Prediction Capabilities" (BioDT), a mapping tool leveraging SEMAF is currently under development: Mapping.bio provides a lightweight web service to read semantic artifacts, visualize them, add mappings as graphical connections and store the mappings as FAIR (Findable, Accessible, Interoperable Reusable) Digital Objects (FDOs, De Smedt et al. 2020) in a repository. To foster reusability, sustainably and long-term availability of digital objects, mapping.bio features mappings compliant with the Simple Standard for Sharing Ontological Mappings (SSSOM, Matentzoglu et al. 2022), a machine-interpretable and extensible vocabulary enabling the self-contained exploration and processing of annotated mappings by machines (machine actionability, Jacobsen et al. 2020).

Collection insight and interconnectivity through artificial intelligence image analysis: A collaboration with the National Archives of Estonia

Descriptive metadata information is paramount to creating a holistic historical record for researchers, scholars and the general public, as well as other institutions to use both internally and externally. Metadata contextualise image-based collections, providing insight into the individuals, objects and stories contained within them, allowing for the sharing and cross-referencing of information and collections. However, gathering descriptive metadata for image collections is time-consuming and costly to generate, often relying the knowledge of a single person. And yet, if researchers do not extract, catalogue and link this information to the images, valuable context can be lost, obscured by the sheer volume of images that one would have to review manually in order to find specific content. This paper demonstrates how the application of artificial intelligence (AI) image analysis offers an alternative to the traditional labour-intensive manual approach and provides important identifications within collections that allow for further research, collaboration and accessibility. The paper describes how, in partnership with the National Archives of Estonia, we digitised over 100,000 film frames and glass-plate negatives, collected from 22 different institutions around the country, and applied AI image analysis to the digital assets in order to take the first step in successfully identifying individuals within the collections, unlocking their stories, interconnecting these institutions’ collections and the broader cultural heritage community.

Reproducibility, verifiability, and computational historical research

Digital humanities methods have been at the heart of a recent series of high-profile historical research projects. But these approaches raise new questions about reproducibility and verifiability in a field of research where grounding one’s conclusions in a body of historical evidence is crucial. While there have been extensive debates about the nature and methods of historical research since the nineteenth century, the underlying assumption has generally been that documenting one’s sources in a series of footnotes is essential to enable other researchers to test the validity of the research. Even if this approach never amounted to “reproducibility” in the sense of scientific experimentation, it might still be seen as broadly analogous, since the evidence can be reassembled to see the basis for the explanations that were offered and to test their validity. This essay examines how new digital methods like topic modelling, network analysis, knowledge graphs, species models, and various kinds of visualizations are affecting the process of reproducing and verifying historical research. Using examples drawn from recent research projects, it identifies a need for thorough documentation and publication of the different layers of digital research: digital and digitized collections, descriptive metadata, the software used for analysis and visualizations, and the various settings and configurations.

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Key-Value Pairs and NoSQL Databases: A Novel Concept to Manage Biologging Data in Data Repositories

Proactive Institutional Repository Collection Development Techniques: Archiving Gold Open Access Articles and Metadata Retrieved with Web Scraping

Many institutions face low deposit rates with their institutional repositories despite investing substantial resources in implementing and supporting these systems. Deposit rates are higher in IRs that offer mediated deposits; however, this can be a time and labor intensive process. This article describes a method for copying open access articles and corresponding descriptive metadata from open repositories for archiving in an institutional repository using Beautiful Soup and Selenium as web scraping tools. This method quickly added hundreds of articles to an IR without relying on faculty participation or consulting publisher policies, increasing repository downloads and usage.

EUSEDcollab: a network of data from European catchments to monitor net soil erosion by water

As a network of researchers we release an open-access database (EUSEDcollab) of water discharge and suspended sediment yield time series records collected in small to medium sized catchments in Europe. EUSEDcollab is compiled to overcome the scarcity of open-access data at relevant spatial scales for studies on runoff, soil loss by water erosion and sediment delivery. Multi-source measurement data from numerous researchers and institutions were harmonised into a common time series and metadata structure. Data reuse is facilitated through accompanying metadata descriptors providing background technical information for each monitoring station setup. Across ten European countries, EUSEDcollab covers over 1600 catchment years of data from 245 catchments at event (11 catchments), daily (22 catchments) and monthly (212 catchments) temporal resolution, and is unique in its focus on small to medium catchment drainage areas (median = 43 km2, min = 0.04 km2, max = 817 km2) with applicability for soil erosion research. We release this database with the aim of uniting people, knowledge and data through the European Union Soil Observatory (EUSO).

Python scripts for web scraping metadata from descriptions of the international scenario of research data repositories

Objective: Research data repositories are an evolution of document repositories that aim to access and preserve all materials used before, during, and after scientific research. In this context, this study aims to conduct an exploratory and descriptive investigation of the international scenario of data repositories by monitoring the descriptive metadata of the international register of this type of repositories in the Registry of Research Data Repositories (re3data.org). Methods: The process requires applying knowledge inherent to the techniques and technologies used for descriptive data analysis, information retrieval, manipulation, analysis, and data visualization. Consequently, three scripts in Python 3.11 are provided for collecting metadata from re3data and scripts and converting the metadata to enable visualization in software such as VOSviewer, a dataset with metadata descriptions of repositories and conversions for visualization of networks. The datasets produced in this study can be found in the ZENODO Data Repository (https://doi.org/10.5281/zenodo.7903109). In a collection on (05/05/2023), 3108 links to the repository descriptions were retrieved. Data and scripts were created for this methodological experiment and shared at (DOI: doi.org/10.5281/zenodo.7903109). The dataset contains a root directory with three subdirectories: (scripts) with (.py) Python codes, another directory called (data) with textual files containing tab-separated values (.TSV), and the file (Information Systems Research, RIS). The third directory (env) contains the Python libraries required to run the scripts. Potential for reuse: The research method applied to manipulate this dataset is based on automated re3data metadata extraction and network visualization; after the data collection and analysis process, it is possible to trigger a study based on the descriptions extracted from the Registry of Research Data Repositories (re3data), researchers can visualize the international scenario of research data repositories, verified by re3data, which allows ethical monitoring of the number of research data repositories that are registered in re3data, what are their areas, institutions, countries, the language of research data, the typology of repositories and deposited data, their themes, areas of knowledge, types of access, licenses and software used. In addition, other issues can be raised while interpreting the data. The community of Librarianship and Information Science professionals need to share data and the extraction technique these research data. Finally, it can be concluded whether information about research data repositories allows us to state that they are heterogeneous data sources that enable access and preservation of a wide range of research data types

A Metadata-Assisted Cascading Ensemble Classification Framework for Automatic Annotation of Open IoT Data

Public Internet of Things (IoT) platforms, such as Thingspeak, significantly increased the availability of open IoT data and enabled faster and cheaper development of novel IoT applications by reducing or even eliminating the need for deploying their own IoT sensors and platforms. However, open IoT data is often heterogeneous, sparse, fuzzy, and lacks accurate description (which we refer to as IoT metadata). These limitations make open IoT data challenging to integrate and use, and prevent the efficient development of IoT applications. In fact, while several sensor data description models have been proposed and standardized, open IoT data currently lack or include only partial metadata description. Therefore, novel techniques for automatically annotating open IoT data are needed to fully unleash the power of open IoT. This paper proposes a novel Metadata-Assisted Cascading Ensemble classification framework (MACE) for the automatic annotation of IoT data. MACE is capable of sequentially combining standalone classifiers, enabling it to cope with heterogeneous IoT data and different domains of information (e.g. numerical and textual), which have not been considered previously. MACE incorporates a novel ensemble approach for automatically selecting, sorting, filtering, and assembling classifiers in a way that improves annotation performance. The paper presents extensive experimental evaluations of MACE using public IoT datasets. Results demonstrate that the MACE framework significantly outperforms existing solutions for open IoT data by as much as 10% in classification accuracy.

Repository for Digital Editions of Legal Historical Sources: ‘IURA. Sources of Law from the Past’The research for this publication has been supported by a grant from the Priority Research Area Heritage under the Strategic Programme Excellence Initiative at Jagiellonian University, project “Edition of Polish Legal Writings 16th–18th cent. Preliminary (H.1.3.2021.67)”.)

Summary The article is a short presentation of the knowledge base ‘IURA. Sources of Law from the Past’ (https://iura.uj.edu.pl/dlibra). The main goal of the project is to build a comprehensive knowledge base with interlinked and easily searchable content from various sources of historical law. The system of content links will help the user to get a broader picture of various sections or dimensions of the legal world, i.e. texts of the law and normative regulations, references to the law or laws, legal literature, or references to the implementation of the law. A set of advanced search tools will enable the user to find and select, with some degree of precision, the scope of relevant content throughout the digitalized source material. Moreover, it will be possible to conduct searches using standardized metadata descriptions. The article contains a short description of selected sources collections.

ScrutinAI: A visual analytics tool supporting semantic assessments of object detection models

We present ScrutinAI, a Visual Analytics tool to leverage semantic understanding for deep neural network (DNN) prediction analysis, focusing on models for object detection and semantic segmentation. Typical fields of application for such models, e.g., autonomous driving or healthcare, have a high demand for uncovering and mitigating data- and model-inherent shortcomings. Our approach helps analysts and/or auditors use their semantic understanding to identify and investigate potential weaknesses in DNN models. ScrutinAI includes interactive visualizations of the model’s inputs and outputs, interactive plots with linked brushing, data filtering with textual queries on descriptive meta data and an interactive similarity based image retrieval feature. The different views and data filtering options enable global and local inspection of DNN predictions. Overall, the tool fosters hypothesis driven knowledge generation which aids in understanding the model’s inner reasoning. Insights gained during the analysis process mitigate the “black-box character” of the DNN and thus support model improvement and the generation of a safety argumentation for AI applications. We present two case studies on the investigation of DNN models for pedestrian detection from the automotive domain.

PANGAEA - Data Publisher for Earth & Environmental Science

The information system PANGAEA provides targeted support for research data management as well as long-term data archiving and publication. PANGAEA is operated as an open access library for archiving, publishing, and distributing georeferenced data from earth and environmental sciences. It focuses on observational and experimental data. Citability, comprehensive metadata descriptions, interoperability of data and metadata, a high degree of structural and semantic harmonization of the data inventory as well as the commitment of the hosting institutions ensures the long-term usability of archived data. PANGAEA is a pioneer of FAIR and open data infrastructures to enable data intensive science and an integral component of national and international science and technology activities. This paper provides an overview of the recent organisational, structural, and technological advancements in developing and operating the information system.

Reflections on digitising the tear sheets in the Walt Reed Illustration Archive

The D.B. Dowd Modern Graphic History Library is a repository of primary source materials that supports faculty scholarship and teaching in the field of visual culture. One of its cornerstone collections is the Walt Reed Illustration Archive, which consists of materials amassed at Illustration House, a business founded around collecting and selling key pieces of narrative art, in addition to related materials, of some of the most notable illustrators of the 20th century. The materials in this collection were used to create a publicly accessible digital collection of over 150,000 assets. Administrative metadata have been added to the collection, but work has been ongoing to enhance the descriptive metadata, which is crucial for research and discovery. Presently, only 30,000 records contain fully descriptive data. This paper discusses the challenges and successes of the project, as well as the innovative systems created to enhance image records and increase accessibility.

Metadata implementation and data discoverability: A survey on university libraries' Dataverse portals

The purpose of this practical case study is to examine the development of Dataverse, a global research data management consortium. This paper is the second in a project focusing on data discoverability and current metadata implementation on the Dataverse portals established by 27 university libraries worldwide. Five research questions were proposed to identify the most popular metadata standards and elements, search interface options, and result display formats by those portals. The data were collected from 27 university libraries worldwide between December 1, 2020 and January 31, 2021. According to the results of the descriptive analyses, the most popular metadata elements for the dataset overview were Subject and Description, while Dataset persistent ID, Publication Date, Title, Author, Contact, Deposit Date, Depositor, Description, and Subject were the most popular elements for the metadata record of each dataset. Publication Year, Author Names, and Subject were found to be the most common search facets used by the portals. English was the most common language used for the search interfaces and metadata descriptions. Based on their findings from this evidence-based study, the authors recommend future research on the development of institutional data portal infrastructure, on stakeholder outreach and training, and on user studies on dataset retrieval.

Toward a Data Fusion Index for the Assessment and Enhancement of 3D Multimodal Reconstruction of Built Cultural Heritage

In the field of digital cultural heritage (DCH), 2D/3D digitization strategies are becoming more and more complex. The emerging trend of multimodal imaging (i.e., data acquisition campaigns aiming to put in cooperation multi-sensor, multi-scale, multi-band and/or multi-epochs concurrently) implies several challenges in term of data provenance, data fusion and data analysis. Making the assumption that the current usability of multi-source 3D models could be more meaningful than millions of aggregated points, this work explores a “reduce to understand” approach to increase the interpretative value of multimodal point clouds. Starting from several years of accumulated digitizations on a single use-case, we define a method based on density estimation to compute a Multimodal Enhancement Fusion Index (MEFI) revealing the intricate modality layers behind the 3D coordinates. Seamlessly stored into point cloud attributes, MEFI is able to be expressed as a heat-map if the underlying data are rather isolated and sparse or redundant and dense. Beyond the colour-coded quantitative features, a semantic layer is added to provide qualitative information from the data sources. Based on a versatile descriptive metadata schema (MEMoS), the 3D model resulting from the data fusion could therefore be semantically enriched by incorporating all the information concerning its digitization history. A customized 3D viewer is presented to explore this enhanced multimodal representation as a starting point for further 3D-based investigations.