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Metadata management for distributed data sources is a long-standing but ever-growing problem. To counter this challenge in a research-data and library-oriented setting, this work constructs a data architecture, derived from the data-lake: the metadata-lake. A proof-of-concept implementation of this proposed metadata system is presented and evaluated as well.

Biologists study Diatoms, a fundamental algae, to assess the health of aquatic systems. Diatom specimens have traditionally been preserved on analog slides, where a single slide can contain thousands of these microscopic organisms. Digitization of these collections presents both metadata challenges and opportunities. This paper reports on metadata research aimed at providing access to a digital portion of the Academy of Natural Sciences' Diatom Herbarium, Drexel University. We report results of a 3-part study covering 1) a review of relevant metadata standards and a microscopy metadata framework shared by Hammer et al., 2) a baseline metadata alignment mapping current diatom metadata properties to standard metadata types, and 3) a metadata risk analysis associated with the course of standard data curation practices. This research is part of an effort involving the transfer of these digital slides to an new system, DataFed, to support global accessible. The final section of this paper includes a conclusion and discusses next steps., Comment: 13 pages, 2 figures, 1 table, to be published in MTSR 2024 conference proceedings

Cardiac image segmentation is essential for automated cardiac function assessment and monitoring of changes in cardiac structures over time. Inspired by coarse-to-fine approaches in image analysis, we propose a novel multitask compositional segmentation approach that can simultaneously localize the heart in a cardiac image and perform part-based segmentation of different regions of interest. We demonstrate that this compositional approach achieves better results than direct segmentation of the anatomies. Further, we propose a novel Cross-Modal Feature Integration (CMFI) module to leverage the metadata related to cardiac imaging collected during image acquisition. We perform experiments on two different modalities, MRI and ultrasound, using public datasets, Multi-disease, Multi-View, and Multi-Centre (M&Ms-2) and Multi-structure Ultrasound Segmentation (CAMUS) data, to showcase the efficiency of the proposed compositional segmentation method and Cross-Modal Feature Integration module incorporating metadata within the proposed compositional segmentation network. The source code is available: https://github.com/kabbas570/CompSeg-MetaData., Comment: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2025

In-context learning (ICL) is a powerful paradigm where large language models (LLMs) benefit from task demonstrations added to the prompt. Yet, selecting optimal demonstrations is not trivial, especially for complex or multi-modal tasks where input and output distributions differ. We hypothesize that forming task-specific representations of the input is key. In this paper, we propose a method to align representations of natural language questions and those of SQL queries in a shared embedding space. Our technique, dubbed MARLO - Metadata-Agnostic Representation Learning for Text-tO-SQL - uses query structure to model querying intent without over-indexing on underlying database metadata (i.e. tables, columns, or domain-specific entities of a database referenced in the question or query). This allows MARLO to select examples that are structurally and semantically relevant for the task rather than examples that are spuriously related to a certain domain or question phrasing. When used to retrieve examples based on question similarity, MARLO shows superior performance compared to generic embedding models (on average +2.9\%pt. in execution accuracy) on the Spider benchmark. It also outperforms the next best method that masks metadata information by +0.8\%pt. in execution accuracy on average, while imposing a significantly lower inference latency., Comment: Accepted to NeurIPS 2024 Table Representation Learning workshop

Distributed filesystems typically employ synchronous metadata updates, facing inherent challenges for access efficiency, load balancing, and directory contention, especially under dynamic and skewed workloads. This paper argues that synchronous updates are overly conservative for distributed filesystems. We propose AsyncFS with asynchronous metadata updates, allowing operations to return early and defer directory updates until respective read to enable latency hiding and conflict resolution. The key challenge is efficiently maintaining the synchronous semantics of metadata updates. To address this, AsyncFS is co-designed with a programmable switch, leveraging the constrained on-switch resources to holistically track directory states in the network with negligible cost. This allows AsyncFS to timely aggregate and efficiently apply delayed updates using batching and consolidation before directory reads. Evaluation shows that AsyncFS achieves up to 13.34$\times$ and 3.85$\times$ higher throughput, and 61.6% and 57.3% lower latency than two state-of-the-art distributed filesystems, InfiniFS and CFS-KV, respectively, on skewed workloads. For real-world workloads, AsyncFS improves end-to-end throughput by 21.1$\times$, 1.1$\times$ and 30.1% over Ceph, IndexFS and CFS-KV, respectively.

Developing the capacity to effectively search for requisite datasets is an urgent requirement to assist data users in identifying relevant datasets considering the very limited available metadata. For this challenge, the utilization of third-party data is emerging as a valuable source for improvement. Our research introduces a new architecture for data exploration which employs a form of Retrieval-Augmented Generation (RAG) to enhance metadata-based data discovery. The system integrates large language models (LLMs) with external vector databases to identify semantic relationships among diverse types of datasets. The proposed framework offers a new method for evaluating semantic similarity among heterogeneous data sources and for improving data exploration. Our study includes experimental results on four critical tasks: 1) recommending similar datasets, 2) suggesting combinable datasets, 3) estimating tags, and 4) predicting variables. Our results demonstrate that RAG can enhance the selection of relevant datasets, particularly from different categories, when compared to conventional metadata approaches. However, performance varied across tasks and models, which confirms the significance of selecting appropriate techniques based on specific use cases. The findings suggest that this approach holds promise for addressing challenges in data exploration and discovery, although further refinement is necessary for estimation tasks.

Time series forecasting is prevalent in extensive real-world applications, such as financial analysis and energy planning. Previous studies primarily focus on time series modality, endeavoring to capture the intricate variations and dependencies inherent in time series. Beyond numerical time series data, we notice that metadata (e.g.~dataset and variate descriptions) also carries valuable information essential for forecasting, which can be used to identify the application scenario and provide more interpretable knowledge than digit sequences. Inspired by this observation, we propose a Metadata-informed Time Series Transformer (MetaTST), which incorporates multiple levels of context-specific metadata into Transformer forecasting models to enable informative time series forecasting. To tackle the unstructured nature of metadata, MetaTST formalizes them into natural languages by pre-designed templates and leverages large language models (LLMs) to encode these texts into metadata tokens as a supplement to classic series tokens, resulting in an informative embedding. Further, a Transformer encoder is employed to communicate series and metadata tokens, which can extend series representations by metadata information for more accurate forecasting. This design also allows the model to adaptively learn context-specific patterns across various scenarios, which is particularly effective in handling large-scale, diverse-scenario forecasting tasks. Experimentally, MetaTST achieves state-of-the-art compared to advanced time series models and LLM-based methods on widely acknowledged short- and long-term forecasting benchmarks, covering both single-dataset individual and multi-dataset joint training settings.

Distribution system operators (DSOs) must cope with new challenges such as the reconstruction of distribution grids along climate neutrality pathways or the ability to manage and control consumption and generation in the grid. In order to meet the challenges, measurements within the distribution grid often form the basis for DSOs. Hence, it is an urgent problem that measurement devices are not installed in many low-voltage (LV) grids. In order to overcome this problem, we present an approach to estimate pseudo-measurements for non-measured LV feeders based on the metadata of the respective feeder using regression models. The feeder metadata comprise information about the number of grid connection points, the installed power of consumers and producers, and billing data in the downstream LV grid. Additionally, we use weather data, calendar data and timestamp information as model features. The existing measurements are used as model target. We extensively evaluate the estimated pseudo-measurements on a large real-world dataset with 2,323 LV feeders characterized by both consumption and feed-in. For this purpose, we introduce peak metrics inspired by the BigDEAL challenge for the peak magnitude, timing and shape for both consumption and feed-in. As regression models, we use XGBoost, a multilayer perceptron (MLP) and a linear regression (LR). We observe that XGBoost and MLP outperform the LR. Furthermore, the results show that the approach adapts to different weather, calendar and timestamp conditions and produces realistic load curves based on the feeder metadata. In the future, the approach can be adapted to other grid levels like substation transformers and can supplement research fields like load modeling, state estimation and LV load forecasting., Comment: 17 pages, 9 figures, 8 tables

Metadata and comments are critical elements of any software development process. In this paper, we explain how metadata and comments in source code can play an essential role in comprehending software. We introduce a Python-based open-source framework, Nirjas, which helps in extracting this metadata in a structured manner. Various syntaxes, types, and widely accepted conventions exist for adding comments in source files of different programming languages. Edge cases can create noise in extraction, for which we use Regex to accurately retrieve metadata. Non-Regex methods can give results but often miss accuracy and noise separation. Nirjas also separates different types of comments, source code, and provides details about those comments, such as line number, file name, language used, total SLOC, etc. Nirjas is a standalone Python framework/library and can be easily installed via source or pip (the Python package installer). Nirjas was initially created as part of a Google Summer of Code project and is currently developed and maintained under the FOSSology organization., Comment: 2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence)

Clarivate's Web of Science (WoS) and Elsevier's Scopus have been for decades the main sources of bibliometric information. Although highly curated, these closed, proprietary databases are largely biased towards English-language publications, underestimating the use of other languages in research dissemination. Launched in 2022, OpenAlex promised comprehensive, inclusive, and open-source research information. While already in use by scholars and research institutions, the quality of its metadata is currently being assessed. This paper contributes to this literature by assessing the completeness and accuracy of OpenAlex's metadata related to language, through a comparison with WoS, as well as an in-depth manual validation of a sample of 6,836 articles. Results show that OpenAlex exhibits a far more balanced linguistic coverage than WoS. However, language metadata is not always accurate, which leads OpenAlex to overestimate the place of English while underestimating that of other languages. If used critically, OpenAlex can provide comprehensive and representative analyses of languages used for scholarly publishing. However, more work is needed at infrastructural level to ensure the quality of metadata on language., Comment: Reference list updated and corrected, corresponding author's email contact added, minor typographical errors corrected

Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the optimal way to incorporate metadata into a political stance detection task. We demonstrate that previous methods combining metadata with language-based data for political stance detection have not fully utilized the metadata information; our simple baseline, using only party membership information, surpasses the current state-of-the-art. We then show that prepending metadata (e.g., party and policy) to political speeches performs best, outperforming all baselines, indicating that complex metadata inclusion systems may not learn the task optimally.

Scientists increasingly recognize the importance of providing rich, standards-adherent metadata to describe their experimental results. Despite the availability of sophisticated tools to assist in the process of data annotation, investigators generally seem to prefer to use spreadsheets when supplying metadata, despite the limitations of spreadsheets in ensuring metadata consistency and compliance with formal specifications. In this paper, we describe an end-to-end approach that supports spreadsheet-based entry of metadata, while ensuring rigorous adherence to community-based metadata standards and providing quality control. Our methods employ several key components, including customizable templates that capture metadata standards and that can inform the spreadsheets that investigators use to author metadata, controlled terminologies and ontologies for defining metadata values that can be accessed directly from a spreadsheet, and an interactive Web-based tool that allows users to rapidly identify and fix errors in their spreadsheet-based metadata. We demonstrate how this approach is being deployed in a biomedical consortium known as HuBMAP to define and collect metadata about a wide range of biological assays.

Camera trap imagery has become an invaluable asset in contemporary wildlife surveillance, enabling researchers to observe and investigate the behaviors of wild animals. While existing methods rely solely on image data for classification, this may not suffice in cases of suboptimal animal angles, lighting, or image quality. This study introduces a novel approach that enhances wild animal classification by combining specific metadata (temperature, location, time, etc) with image data. Using a dataset focused on the Norwegian climate, our models show an accuracy increase from 98.4% to 98.9% compared to existing methods. Notably, our approach also achieves high accuracy with metadata-only classification, highlighting its potential to reduce reliance on image quality. This work paves the way for integrated systems that advance wildlife classification technology.

Unlike traditional proprietary data sources like Scopus and Web of Science (WoS), OpenAlex emphasizes its comprehensive coverage, particularly highlighting its inclusion of the humanities, non-English languages, and research from the Global South. Strengthening diversity and inclusivity in science is crucial for ethical and practical reasons. This paper analyses OpenAlex's coverage and metadata availability of African-based publications. For this purpose, we compare OpenAlex with Scopus, WoS, and African Journals Online (AJOL). We first compare the coverage of African research publications in OpenAlex against that of WoS, Scopus, and AJOL. We then assess and compare the available metadata for OpenAlex, Scopus, and WoS publications. Our analysis shows that OpenAlex offers the most extensive publication coverage. In terms of metadata, OpenAlex offers a high coverage of publication and author information. It performs worse regarding affiliations, references, and funder information. Importantly, our results also show that metadata availability in OpenAlex is better for publications that are also indexed in Scopus or WoS.

Two key issues hindering the transition towards FAIR data science are the poor discoverability and inconsistent instructions for the use of data extractor tools, i.e., how we go from raw data files created by instruments, to accessible metadata and scientific insight. If the existing format conversion tools are hard to find, install, and use, their reimplementation will lead to a duplication of effort, and an increase in the associated maintenance burden is inevitable. The Datatractor framework presented in this work addresses these issues. First, by providing a curated registry of such extractor tools their discoverability will increase. Second, by describing them using a standardised but lightweight schema, their installation and use is machine-actionable. Finally, we provide a reference implementation for such data extraction. The Datatractor framework can be used to provide a public-facing data extraction service, or be incorporated into other research data management tools providing added value.

Current version identification (VI) datasets often lack sufficient size and musical diversity to train robust neural networks (NNs). Additionally, their non-representative clique size distributions prevent realistic system evaluations. To address these challenges, we explore the untapped potential of the rich editorial metadata in the Discogs music database and create a large dataset of musical versions containing about 1,900,000 versions across 348,000 cliques. Utilizing a high-precision search algorithm, we map this dataset to official music uploads on YouTube, resulting in a dataset of approximately 493,000 versions across 98,000 cliques. This dataset offers over nine times the number of cliques and over four times the number of versions than existing datasets. We demonstrate the utility of our dataset by training a baseline NN without extensive model complexities or data augmentations, which achieves competitive results on the SHS100K and Da-TACOS datasets. Our dataset, along with the tools used for its creation, the extracted audio features, and a trained model, are all publicly available online.

The ISO standard Property Graph model has become increasingly popular for representing complex, interconnected data. However, it lacks native support for querying metadata and reification, which limits its abilities to deal with the demands of modern applications. We introduce the vision of Meta-Property Graphs, a backwards compatible extension of the property graph model addressing these limitations. Our approach enables first-class treatment of labels and properties as queryable objects and supports reification of substructures in a graph. We propose MetaGPML, a backwards compatible extension of the Graph Pattern Matching Language forming the core of the ISO standard GQL, to query these enhanced graphs. We demonstrate how these foundations pave the way for advanced data analytics and governance tasks that are challenging or impossible with current property graph systems.

Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of music audio and text, utilizing it to retrieve music tracks that exactly match descriptive queries related to musical attributes (i.e. genre, instrument) and contextual elements (i.e. mood, theme). However, users also articulate a need to explore music that shares similarities with their favorite tracks or artists, such as \textit{I need a similar track to Superstition by Stevie Wonder}. To address these concerns, this paper proposes an improved Text-to-Music Retrieval model, denoted as TTMR++, which utilizes rich text descriptions generated with a finetuned large language model and metadata. To accomplish this, we obtained various types of seed text from several existing music tag and caption datasets and a knowledge graph dataset of artists and tracks. The experimental results show the effectiveness of TTMR++ in comparison to state-of-the-art music-text joint embedding models through a comprehensive evaluation involving various musical text queries., Comment: Accepted for publication at the IEEE ICASSP 2024

Computer simulations are an essential pillar of knowledge generation in science. Understanding, reproducing, and exploring the results of simulations relies on tracking and organizing metadata describing numerical experiments. However, the models used to understand real-world systems, and the computational machinery required to simulate them, are typically complex, and produce large amounts of heterogeneous metadata. Here, we present general practices for acquiring and handling metadata that are agnostic to software and hardware, and highly flexible for the user. These consist of two steps: 1) recording and storing raw metadata, and 2) selecting and structuring metadata. As a proof of concept, we develop the Archivist, a Python tool to help with the second step, and use it to apply our practices to distinct high-performance computing use cases from neuroscience and hydrology. Our practices and the Archivist can readily be applied to existing workflows without the need for substantial restructuring. They support sustainable numerical workflows, facilitating reproducibility and data reuse in generic simulation-based research., Comment: 19 pages, 5 figures

Biobanks are indispensable resources for medical research collecting biological material and associated data and making them available for research projects and medical studies. For that, the biobank data has to meet certain criteria which can be formulated as adherence to the FAIR (findable, accessible, interoperable and reusable) principles. We developed a tool, CollectionLocator, which aims at increasing the FAIR compliance of biobank data by supporting researchers in identifying which biobank and which collection are likely to contain cases (material and data) satisfying the requirements of a defined research project when the detailed sample data is not available due to privacy restrictions. The CollectionLocator is based on an ontology-based metadata model to address the enormous heterogeneities and ensure the privacy of the donors of the biological samples and the data. Furthermore, the CollectionLocator represents the data and metadata quality of the collections such that the quality requirements of the requester can be matched with the quality of the available data. The concept of CollectionLocator is evaluated with a proof-of-concept implementation.

In this work, we introduce the demonstration of symbolic music generation, focusing on providing short musical motifs that serve as the central theme of the narrative. For the generation, we adopt an autoregressive model which takes musical metadata as inputs and generates 4 bars of multitrack MIDI sequences. During training, we randomly drop tokens from the musical metadata to guarantee flexible control. It provides users with the freedom to select input types while maintaining generative performance, enabling greater flexibility in music composition. We validate the effectiveness of the strategy through experiments in terms of model capacity, musical fidelity, diversity, and controllability. Additionally, we scale up the model and compare it with other music generation model through a subjective test. Our results indicate its superiority in both control and music quality. We provide a URL link https://www.youtube.com/watch?v=-0drPrFJdMQ to our demonstration video.

The rapid expansion of the non-fungible token (NFT) market has catalyzed new opportunities for artists, collectors, and investors, yet it has also unveiled critical challenges related to the storage and distribution of associated metadata. This paper examines the current landscape of NFT metadata storage, revealing a significant reliance on centralized platforms, which poses risks to the integrity, security, and decentralization of these digital assets. Through a detailed analysis of top-selling NFTs on the OpenSea marketplace, it was found that a substantial portion of metadata is hosted on centralized servers, making them susceptible to censorship, data breaches, and administrative alterations. Conversely, decentralized storage solutions, particularly the InterPlanetary File System (IPFS), were identified as a more secure and resilient alternative, offering enhanced transparency, resistance to tampering, and greater control for creators and collectors. This study advocates for the widespread adoption of decentralized storage architectures, incorporating digital signatures to verify ownership, as a means to preserve the value and trustworthiness of NFTs in an increasingly digital world. The findings underscore the necessity for NFT platforms to prioritize decentralized methodologies to ensure the long-term sustainability and integrity of the NFT

Most machine learning models require many iterations of hyper-parameter tuning, feature engineering, and debugging to produce effective results. As machine learning models become more complicated, this pipeline becomes more difficult to manage effectively. In the physical sciences, there is an ever-increasing pool of metadata that is generated by the scientific research cycle. Tracking this metadata can reduce redundant work, improve reproducibility, and aid in the feature and training dataset engineering process. In this case study, we present a tool for machine learning metadata management in dynamic radiography. We evaluate the efficacy of this tool against the initial research workflow and discuss extensions to general machine learning pipelines in the physical sciences., Comment: 14 pages, 9 figures

In recent years, the building sector has experienced an increasing legislative pressure to reduce the energy consumption. This has created a global need for affordable building management systems (BMS) in areas such as lighting-, temperature-, air quality monitoring and control. BMS uses 2D and 3D building representations to visualize various aspects of building operations. Today the creation of these visual building representations relies on labor-intensive and costly computer-aided design (CAD) processes. Hence, to create affordable BMS there is an urgent need to develop methods for cost-effective automatic creation of visual building representations. This paper introduces an automatic, metadata-driven method for constructing building visualizations using metadata from existing smart building infrastructure. The method presented in this study utilizes a Velocity Verlet integration-based physics particle simulation that uses metadata to define the force dynamics within the simulation. This process generates an abstract point cloud representing the organization of BMS components into building zones. The developed system was tested in two buildings of respectively 2,560 m2 and 18,000 m2. The method successfully produced visual building representations based on the available metadata, demonstrating its feasibility and cost-effectiveness., Comment: 15 pages, 11 figures

Data discovery is crucial for data management and analysis and can benefit from better utilization of metadata. For example, users may want to search data using queries like ``find the tables created by Alex and endorsed by Mike that contain sales numbers.'' They may also want to see how the data they view relates to other data, its lineage, or the quality and compliance of its upstream datasets, all metadata. Yet, effectively surfacing metadata through interactive user interfaces (UIs) to augment data discovery poses challenges. Constantly revamping UIs with each update to metadata sources (or providers) consumes significant development resources and lacks scalability and extensibility. In response, we introduce Humboldt, a new framework enabling interactive data systems to effectively leverage metadata for data discovery and rapidly evolve their UIs to support metadata changes. Humboldt decouples metadata sources from the implementation of data discovery UIs that support search and dataset visualization using metadata fields. It automatically generates interactive data discovery interfaces from declarative specifications, avoiding costly metadata-specific (re)implementations., Comment: TaDA Workshop at VLDB 2024

The production of media content has undergone tremendous changes in recent years. Multiple daily content updates are just as common for some platforms as is processing the provided content specifically for their target audiences. Such features are made possible through metadata, which make information accessible by categorizing it. In conjunction with AI-supported tools, metadata are shaping the future of audio-visual content production, distribution and consumption. It allows editors to effectively search through archives like in the Tailored Media Project, broadcasters to provide content that is adapted to users' surroundings like in the ARD Audiothek unterwegs project, or give users the ability to experience audio-visual content from different perspectives like in the ORPHEUS project. Although these projects provide comprehensive insight into the potential of metadata, their integration in existing infrastructures meets several limitations. For example, content-related metadata may initially be generated at some point during the production process but will then be lost at later stages due to current standards and incomplete software implementations. In our contribution, we will discuss requirements and potential approaches and give an outlook on possible fields of application and use-cases., Comment: 12 pages, 4 figures, submitted to the Tonmeistertagung 32

Cloud key-value (KV) stores provide businesses with a cost-effective and adaptive alternative to traditional on-premise data management solutions. KV stores frequently consist of heterogeneous clusters, characterized by varying hardware specifications of the deployment nodes, with each node potentially running a distinct version of the KV store software. This heterogeneity is accompanied by the diverse metadata that they need to manage. In this study, we introduce MetaHive, a cache-optimized approach to managing metadata in heterogeneous KV store clusters. MetaHive disaggregates the original data from its associated metadata to promote independence between them, while maintaining their interconnection during usage. This makes the metadata opaque from the downstream processes and the other KV stores in the cluster. MetaHive also ensures that the KV and metadata entries are stored in the vicinity of each other in memory and storage. This allows MetaHive to optimally utilize the caching mechanism without extra storage read overhead for metadata retrieval. We deploy MetaHive to ensure data integrity in RocksDB and demonstrate its rapid data validation with minimal effect on performance., Comment: Cloud Databases

The convergence of the physical and digital realms has ushered in a new era of immersive experiences and seamless interactions. As the boundaries between the real world and virtual environments blur and result in a "mixed reality," there arises a need for robust and efficient methods to connect physical objects with their virtual counterparts. In this thesis, we present a novel approach to bridging this gap through the design, fabrication, and detection of embedded machine-readable markers. We categorize the proposed marking approaches into three distinct categories: natural markers, structural markers, and internal markers. Natural markers, such as those used in SensiCut, are inherent fingerprints of objects repurposed as machine-readable identifiers, while structural markers, such as StructCode and G-ID, leverage the structural artifacts in objects that emerge during the fabrication process itself. Internal markers, such as InfraredTag and BrightMarker, are embedded inside fabricated objects using specialized materials. Leveraging a combination of methods from computer vision, machine learning, computational imaging, and material science, the presented approaches offer robust and versatile solutions for object identification, tracking, and interaction. These markers, seamlessly integrated into real-world objects, effectively communicate an object's identity, origin, function, and interaction, functioning as gateways to "ubiquitous metadata" - a concept where metadata is embedded into physical objects, similar to metadata in digital files. Across the different chapters, we demonstrate the applications of the presented methods in diverse domains, including product design, manufacturing, retail, logistics, education, entertainment, security, and sustainability., Comment: MIT PhD Thesis

In response to several cultural heritage initiatives at the Jagiellonian University, we have developed a new digitization workflow in collaboration with the Jagiellonian Library (JL). The solution is based on easy-to-access technological solutions -- Microsoft 365 cloud with MS Excel files as metadata acquisition interfaces, Office Script for validation, and MS Sharepoint for storage -- that allows metadata acquisition by domain experts (philologists, historians, philosophers, librarians, archivists, curators, etc.) regardless of their experience with information systems. The ultimate goal is to create a knowledge graph that describes the analyzed holdings, linked to general knowledge bases, as well as to other cultural heritage collections, so careful attention is paid to the high accuracy of metadata and proper links to external sources. The workflow has already been evaluated in two pilots in the DiHeLib project focused on digitizing the so-called "Berlin Collection" and in two workshops with international guests, which allowed for its refinement and confirmation of its correctness and usability for JL. As the proposed workflow does not interfere with existing systems or domain guidelines regarding digitization and basic metadata collection in a given institution (e.g., file type, image quality, use of Dublin Core/MARC-21), but extends them in order to enable rich metadata collection, not previously possible, we believe that it could be of interest to all GLAMs (galleries, libraries, archives, and museums)., Comment: 19 pages; submitted to Multimedia Tools and Applications