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Entity resolution (ER) is the process of determining whether two representations refer to the same real-world entity and plays a crucial role in data curation and data cleaning. Recent studies have introduced the KAER framework, aiming to improve pre-trained language models by augmenting external knowledge. However, identifying and documenting the external knowledge that is being augmented and understanding its contribution to the model's predictions have received little to no attention in the research community. This paper addresses this gap by introducing T-KAER, the Transparency-enhanced Knowledge-Augmented Entity Resolution framework. To enhance transparency, three Transparency-related Questions (T-Qs) have been proposed: T-Q(1): What is the experimental process for matching results based on data inputs? T-Q(2): Which semantic information does KAER augment in the raw data inputs? T-Q(3): Which semantic information of the augmented data inputs influences the predictions? To address the T-Qs, T-KAER is designed to improve transparency by documenting the entity resolution processes in log files. In experiments, a citation dataset is used to demonstrate the transparency components of T-KAER. This demonstration showcases how T-KAER facilitates error analysis from both quantitative and qualitative perspectives, providing evidence on `"what" semantic information is augmented and ``"why" the augmented knowledge influences predictions differently.

Missing and conflicting data values create problems when integrating datasets from multiple collections. Moreover, when the collections to be integrated are large and continuously updated, it is not feasible to manually resolve these problems. Instead, disagreements and gaps should be resolved in an automated fashion. To achieve good quality integrated datasets automatically we introduce the Curation-informed Weight Distribution Network (CiWDN), a method that suggests which collection is more reliable in providing a data value in question. CiWDN adapts the PageRank algorithm (PR) to assign and distribute weights across data fields present in the different collections. Weight assignment is rooted in data curation best practices as metrics of a collection's reliability. The metrics include: a) data completeness, b) data coincidence, and c) data consistency over time. Final weights used as collection ranks provide the basis to resolve conflicts between different collections contributing a data value for a given field. CiWDN relies on a data dictionary that normalizes fields across collections, and is implemented on a graph database. We demonstrate CiWDN’s capability using the case of ASTRIAGraph, a knowledge system built to increase transparency of activities in Earth’s orbital environment. CiWDN can assess the reliability of data collections that conflict on space object characteristic data fields, which can be used to resolve the differences. This method for computing collections' reliability can be ported to curate other types of large integrated datasets for use in machine learning and other data-driven applications.

Data metrics are essential to assess the impact of data repositories' holdings and to understand the research practices of the community that they serve. These metrics are useful for reporting to funders, to inform community engagement strategies, and to direct and sustain repository services. In turn, communicating these metrics to the user community conveys transparency and elicits their trust in data sharing. However, because data metrics are time-sensitive and context-dependent, tracking, interpreting, and communicating them is challenging. In this work we introduce data usage analyses including benchmarking and grouping, developed to better assess the impact of the DesignSafe Data Depot, a natural hazards data repository. Make Data Count compliant metrics are analysed in relation to research methods, sub-disciplines, natural hazard types, and time, to learn what data is being used, what influences data usage, and to establish realistic usage expectations. Results are interpreted in relation to the research and publication practices of the community and to natural hazard events. In addition, we introduce strategies to clearly communicate dataset metrics to users.

As datasets have become a more significant aspect of Open Science, attention has turned to the data transformations that drive their creation. Li and Ludäscher have pointed out the importance of identifying data cleaning workflows as a series of modular transformations that can be extracted for reuse. This modular approach aids reproducibility and allows for transparency in data provenance. However, the constantly evolving nature of data science technology means that even once these modules have been identified and implemented, their functionality must be ported to new platforms as old ones become less applicable or less common in a field of study. When these transformations take place, it is important to consider not only practicality and functionality, but also transparency within a data processing team. Clarity of communication within a team is the first step towards providing clear and transparent documentation to the end user. This case study of an updated workflow process for a long-running longitudinal health and well-being study provides practical examples of these principles.

Electronic Health Records (EHRs) offer a rich data source for clinical researchers to assess a wide variety of treatments and outcomes. Researchers can use Honest Data Brokers (HDBs) to gain access to EHR data for research. Unfortunately, HDBs’ data analysts can inadvertently overlook nuances in clinical workflows when generating request code for data capture and cause data extraction errors that negatively impact EHR data quality. This paper presents findings from interviews with clinical researchers who have had experience with requesting EHR data from the Regenstrief Institute Data Core (RDC), an HDB with data analysts who provide authorised access to EHR data of nearly 25 million patients in the state of Indiana. Our participants wanted greater transparency when they had questions about the quality of the datasets they received. Participants wanted their data analysts to double check the data in their system, explain how they extracted the data, let them visit the data in their system, and/or view/edit the code used for data extraction. We offer a set of recommendations for HDBs on how to provide greater transparency to clinical researchers about the processes used to generate the EHR data they receive and discuss future directions for research.

Curation of the growing body of published biomedical research is of great importance to both the synthesis of contemporary science and the archiving of historical biomedical literature. Each of these tasks has become increasingly challenging given the expansion of journal titles, preprint repositories and electronic databases. Added to this challenge is the need for curation of biomedical literature across population groups to better capture study populations for improved understanding of the generalizability of findings. To address this, our study aims to explore the use of generative artificial intelligence (AI) in the form of large language models (LLMs) such as GPT-4 as an AI curation assistant for the task of curating biomedical literature for population groups. We conducted a series of experiments which qualitatively and quantitatively evaluate the performance of OpenAI’s GPT-4 in curating population information from biomedical literature. Using OpenAI’s GPT-4 and curation instructions, executed through prompts, we evaluate the ability of GPT-4 to classify study ‘populations’, ‘continents’ and ‘countries’ from a previously curated dataset of public health COVID-19 studies. Using three different experimental approaches, we examined performance by: A) evaluation of accuracy (concordance with human curation) using both exact and approximate string matches within a single experimental approach; B) evaluation of accuracy across experimental approaches; and C) conducting a qualitative phenomenology analysis to describe and classify the nature of difference between human curation and GPT curation. Our study shows that GPT-4 has the potential to provide assistance in the curation of population groups in biomedical literature. Additionally, phenomenology provided key information for prompt design that further improved the LLM’s performance in these tasks. Future research should aim to improve prompt design, as well as explore other generative AI models to improve curation performance. An increased understanding of the populations included in research studies is critical for the interpretation of findings, and we believe this study provides keen insight on the potential to increase the scalability of population curation in biomedical studies.

This paper describes Infra Finder, a new tool built by Invest in Open Infrastructure to help institutional budget holders and libraries make more informed decisions around adoption of and investment in open infrastructure. Through increased transparency and discoverability, we aim for this tool to foster trust in the decision-making process and to help build connections between services, users, and funders. The design of Infra Finder is intended to contribute to ongoing discussions and developments regarding trust and transparency in open scholarly infrastructure, as well as help level the playing field between organizations with limited resources to conduct extensive due diligence processes and those with their own analyst teams. In this work, we describe the landscape analysis that led to the creation of Infra Finder, the use cases for the tool, and the approach IOI is taking to create and foster use of Infra Finder in the open infrastructure environment. We also address some of the principles of trust in open source and open infrastructure that have informed and impacted the Infra Finder project and our work in creating this tool.

Curation and preservation measures carried out by digital repository staff are an important building block in maintaining the accessibility and usability of digital resources over time. The measures adequate to achieve long-term usability for a given audience strongly depend on scenarios of (re)use, the (intended) users’ needs and skills, the organisational setting (e.g., mission, resources, policies), as well as the characteristics of the digital objects to be preserved. The assessment of curation and preservation measures also forms an important part of existing certification procedures for trustworthy digital repositories (TDRs) as offered, for example, by the CoreTrustSeal foundation, the nestor network, or ISO. The digital curation community is presented with the challenge of finding community-, organisation-, and object-specific approaches to curation and preservation at the same time as defining the minimum level of curation and preservation measures expected from a TDR in sufficiently generic terms to ensure applicability to a wide array of repositories. Against this backdrop, this paper discusses the need for and benefits of community-agreed levels of curation and preservation to address this challenge, and considers the tiered model proposed by the CoreTrustSeal Board as an example. The proposed model is then applied in an analysis of successful CoreTrustSeal applications from 2018–2022 in an effort to better understand the capacity of the curation and preservation levels to capture the respective practices of repositories and to identify potential gaps.

This paper explores an enhanced curation lifecycle being developed at the UK Data Service (UKDS), with our Data Product Builder. Through a Graphical User Interface, we aim to provide the researcher with a tailored digital resource. We detail the threefold motivation behind this initiative: data dissemination scalability, researcher satisfaction and the reduction of nationwide duplication of research effort. Subsequent sections detail the technical components and challenges involved. In addition to more standard data subsetting, filtering and linking components, this data dissemination platform offers dynamic disclosure assessments – identifying combinations of variables that present a potential disclosure risk. All components are underpinned by the Data Documentation Initiative’s new Cross-Domain Integration standard (DDI-CDI), designed to handle the many structures in which data may be organised. Ever conscious of the scale of the task we are embarking on, we remain motivated by the need for such advances in data dissemination and optimistic of the feasibility of such a system to meet the needs of the researcher while balancing the data disclosivity concerns of the data depositor.

Research about trust and transparency within the realm of research data management and sharing typically centres on accreditation and compliance. Missing from many of these conversations are the social systems and enabling structures that are built on interpersonal connections. As members of the Data Curation Network (DCN), a consortium of United States-based institutional and non-profit data repositories, we have experienced first-hand the effort required to develop and sustain interpersonal trust and the benefits it provides to curation. In this paper, we reflect on the well-documented realities of curator and labour invisibility; the importance of fostering active communities (such as the DCN); and how trust, vulnerability and connectivity among colleagues leads to better curation practices. Through an investigation into data curators in the DCN, we found that, while curation can be isolating and invisible work, having a network of trusted peers helps alleviate these burdens and makes us better curators. We conclude with practical suggestions for implementing trust and transparency in relationships with colleagues and researchers.

While small labs produce much of the fundamental experimental research in Material Science and Engineering (MSE), little is known about their data management and sharing practices and the extent to which they promote trust in, and transparency of, the published research. In this research, we conduct a case study of a leading MSE research lab to characterize the limits of current data management and sharing practices concerning reproducibility and attribution. We systematically reconstruct the workflows, underpinning four research projects by combining interviews, document review, and digital forensics. We then apply information graph analysis and computer-assisted retrospective auditing to identify where critical research information is unavailable or at risk. We find that while data management and sharing practices in this leading lab protect against computer and disk failure, they are insufficient to ensure reproducibility or correct attribution of work — especially when a group member withdraws before project completion. We conclude with recommendations for adjustments to MSE data management and sharing practices to promote trustworthiness and transparency by adding lightweight automated file-level auditing and automated data transfer processes.

Trusted Research Environments (TREs) enable the analysis of sensitive data under strict security assertions that protect the data with technical, organizational, and legal measures from (accidentally) being leaked outside the facility. While many TREs exist in Europe, little information is available publicly on the architecture and descriptions of their building blocks and their slight technical variations. To highlight on these problems, an overview of the existing, publicly described TREs and a bibliography linking to the system description are provided. Their technical characteristics, especially in commonalities and variations, are analysed, and insight is provided into their data type characteristics and availability. The literature study shows that 47 TREs worldwide provide access to sensitive data, of which two-thirds provide data predominantly via secure remote access. Statistical offices (SOs) make the majority of sensitive data records included in this study available.

Emulation and migration are still our main tools for digital curation and preservation practice. Both strategies have been discussed extensively and have been demonstrated to be effective and applicable in various scenarios. Discussions have primarily centered on technical feasibility, workflow integration, and usability. However, there remains one important aspect when discussing these two techniques: managing and preserving operational knowledge. Both approaches require specialized knowledge but especially emulation requires future users to also have a great variety of knowledge about past software and computer systems for successful operation. We investigate how this knowledge can be stored and utilized, and to what extent it can be rendered machine-actionable, using modern large language models. We demonstrate a proof-of-concept implementation that operates an emulated software environment through natural language.

Data Management Plans (DMPs) are vital components of effective research data management (RDM). They serve not only as organisational tools but also as a structured framework dictating the collection, processing, sharing/publishing, and management of data throughout the research data life cycle. This can include existing data curation standards, the establishment of data handling protocols, and the creation, when necessary, of community curation policies. Therefore, DMPs present a unique opportunity to harmonise project management efforts for optimising the formulation and execution of project objectives. To harness the full potential of DMPs as project management tools, the SMART approach (i.e., Specific, Measurable, Achievable, Relevant, and Time-bound) emerges as a compelling methodology. During the initial stage of the project proposal, drafted SMART metrics can offer a systematic approach to map work packages (WPs) and deliverables to the overarching project objectives. Then, the Principal Investigators (PIs) can ensure the consortia that all the project potential intellectual assets (i.e., expected research results) were considered properly, as well as their necessary timelines, resources, and execution. It becomes imperative for data stewards (DSs) and governance policymakers to educate and provide guidelines to researchers on the advantages of developing well-curated DMPs that align results with SMART metrics. This alignment ensures that every intellectual asset intended as a research result (e.g., intellectual properties, publications, datasets, and software) within the project is subject to rigorous drafted planning, execution, and accountability. Consequently, the risk of unforeseen setbacks and/or deviations from the original objectives is minimised, increasing the traceability and transparency of the research data life cycle. In addition, the integration of Technology Readiness Levels (TRLs) into this proposed enhanced DMP provides a systematic method to evaluate the maturity and readiness of technologies across scientific disciplines. Regular TRL assessments will allow PIs: (1) to monitor the WP progress, (2) to adapt research strategies if required, and (3) to ensure the projects remain in line with the drafted SMART metrics in the enhanced DMP before the project started. The TRLs can also help PIs maintain their focus on project milestones and specific tasks aligned with the original objectives, contributing to the overall success of their endeavours, while improving the transparency for the reporting and divulgation of the research results. The paper presents the overall framework for enhancing DMPs as project management tools for any intellectual assets using SMART metrics and TRLs, as well as introducing suggested support services for data stewardship teams to assist PIs when implementing this novel framework effectively.

Trust is an essential pre-condition for the acceptance of digital infrastructures and services. Transparency has been identified as one mechanism for increasing trustworthiness. Yet, it is difficult to assess to which extent and how exactly different aspects of transparency contribute to trust, or potentially impede it in cases of overwhelming complexity of the information provided. To address these issues, we performed two initial studies to help determining the factors that influence or have impact on trust, focusing on transparency across a range of elements associated with data, data infrastructures and virtual research environments. On one hand, we performed a survey among IT experts in the field of data science focusing on quality aspects in the context of re-using and sharing open source software, assessing issues such as the need for documentation, test cases, and accountability. On the other hand, we complemented this with a set of semi-structured interviews with senior researchers to address specific issues of the degree of transparency achievable with different approaches. They include, for example, the amount of transparency we can achieve with approaches from explainable AI, or the usefulness and limitations of data provenance in determining the suitability of data for reuse and others. Specifically, we consider mechanisms on three levels, i.e. technical, process-oriented as well as social mechanisms. Starting from attributes of trust in the “analogue world”, we aim to understand which of these can be applied in the digital world, how they differ, and what additional mechanisms need to be established, in order to support trust in complex socio-technological processes and their emergent results when the traditional approaches cannot be applied anymore.

This paper aims to better understand early career researchers’ (ECRs’) research data management (RDM) competencies by assessing the contents and quality of data management plans (DMPs) developed during a multi-stakeholder RDM course. We also aim to identify differences between DMPs in relation to several background variables (e.g., discipline, course track). The Basics of Research Data Management (BRDM) course has been held in two multi-faculty, research-intensive universities in Finland since 2020. In this study, 223 ECRs’ DMPs created in the BRDM of 2020 - 2022 were assessed, using the recommendations and criteria of the Finnish DMP Evaluation Guide + General Finnish DMP Guidance (FDEG). The median quality of DMPs appeared to be satisfactory. The differences in rating according to FDEG’s three-point performance criteria were statistically insignificant between DMPs developed in separate years, course tracks or disciplines. However, using content analysis, differences were found between disciplines or course tracks regarding DMP’s key characteristics such as sharing, storing, and preserving data. DMPs that contained a data table (DtDMPs) also differed highly significantly from prose DMPs. DtDMPs better acknowledged the data handling needs of different data types and improved the overall quality of a DMP. The results illustrated that the ECRs had learned the basic RDM competencies and grasped their significance to the integrity, reliability, and reusability of data. However, more focused, further training to reach the advanced competency is needed, especially in areas of handling and sharing personal data, legal issues, long-term preserving, and funders’ data policies. Equally important to the cultural change when RDM is an organic part of the research practices is to merge research support services, processes, and infrastructure into the research projects’ processes. Additionally, incentives are needed for sharing and reusing data.

This article addresses the problem of formulating efficient and reliable operational preservation policies that ensure bit-level information integrity over long periods, and in the presence of a diverse range of real-world technical, legal, organizational, and economic threats. We develop a systematic, quantitative prediction framework that combines formal modelling, discrete-event-based simulation, hierarchical modelling, and then use empirically calibrated sensitivity analysis to identify effective strategies. Specifically, the framework formally defines an objective function for preservation that maps a set of preservation policies and a risk profile to a set of preservation costs, and an expected collection loss distribution. In this framework, a curator’s objective is to select optimal policies that minimize expected loss subject to budget constraints. To estimate preservation loss under different policy conditions optimal policies, we develop a statistical hierarchical risk model that includes four sources of risk: the storage hardware; the physical environment; the curating institution; and the global environment. We then employ a general discrete event-based simulation framework to evaluate the expected loss and the cost of employing varying preservation strategies under specific parameterization of risks. Source code is available at:https://github.com/MIT-Informatics/PreservationSimulation The framework offers flexibility for the modeling of a wide range of preservation policies and threats. Since this framework is open source and easily deployed in a cloud computing environment, it can be used to produce analysis based on independent estimates of scenario-specific costs, reliability, and risk. We present results summarizing hundreds of thousands of simulations using this framework. This exploratory analysis points to a number of robust and broadly applicable preservation strategies, provides novel insights into specific preservation tactics, and provides evidence that challenges received wisdom. An earlier version of this paper was published previously in IJDC 15(1) 2020

Science, Technology, Engineering, and Mathematics (STEM) and Research Data Librarians collaborated with an international research team of conservation geneticists to create an instructional and practical guide combining genetic biodiversity initiatives and data curation. Over the course of two months, the academic librarians held multiple community-based Curate-A-Thons where an international group of students, researchers, librarians, and faculty researchers participated in tracking down publications and metadata for genomic sequence data, thus crowd-sourcing this effort of metadata enhancement. This article details the successful Curate-a-Thon design and implementation process; the openly available instructional materials created and used to host the Curate-a-Thons; and the challenges and successes of these community-based events.

Antique books, old and rare documents are fragile and vulnerable to different hazards. Preserving them for an extended period is a real challenge. From ancient times people started expressing their knowledge by writing and keeping records and subsequently started collecting and storing these at later ages as antique materials. These can be seen in different museums, libraries, archives, individual households, and other places all over the world. Preserving and conserving these antique, old and rare books, documents etc. in good condition is a challenge for librarians, conservators, preservation administrators or persons associated with storing these. In this paper, details of the digital preservation of such a collection available in the Directorate of Historical and Antiquarian Studies (DHAS), Guwahati, Assam, India, are discussed. DHAS is a Government of Assam wing and is mainly mandated to collect, preserve and research historical and antiquarian resources. The collection of DHAS is one of the oldest collections and has been serving as a study and research centre in Assam since 1928. A special drive has been taken for the digital preservation of an identified part of the collection, with grant support from the National Archive of India. This paper discusses the entire project process starting from the project proposal formulation to the structuring of the digital collection. The paper sequentially discusses the different steps of the entire work of digitization of a collection of 241 old and rare books from the main collection of DHAS.

This paper provides a systematic analysis of publications that discuss data curation in interdisciplinary and highly collaborative research (IHCR). Using content analysis methodology, it examined 159 publications and identified patterns in definitions of interdisciplinarity, projects’ participants and methodologies, and approaches to data curation. The findings suggest that data is a prominent component in interdisciplinarity. In addition to crossing disciplinary and other boundaries, IHCR is defined as curating and integrating heterogeneous data and creating new forms of knowledge from it. Using personal experiences and descriptive approaches, the publications discussed challenges that data curation in IHCR faces, including an increased overhead in coordination and management, lack of consistent metadata practices, and custom infrastructure that makes interoperability across projects, domains, and repositories difficult. The paper concludes with suggestions for future research.

In this article I describe the process and results of tracking a citation from a data repository through the article publication process and trying to add a citation event to one of our DOIs. I also discuss some other confusing aspects related to citation counts as indicated in various systems, including reference managers, the publisher’s perspective, aggregators, and DOI minters. I discovered numerous problems with citations. Addressing these problems is important as citations can be key to determining both the original use and reuse of a dataset, especially for repositories that do not track usage by requiring people to login or provide an email to download a dataset. The lack of transparency in some data citation systems and processes obscures how and where data is being used.

Federal funding agencies in the United States (U.S.) continue to work towards implementing their plans to increase public access to funded research and comply with the 2013 Office of Science and Technology memo Increasing Access to the Results of Federally Funded Scientific Research. In this article we report on an analysis of research data sharing policy documents from 17 U.S. federal funding agencies as of February 2021. Our analysis is guided by two questions: 1.) What do the findings suggest about the current state of and trends in U.S. federal funding agency data sharing requirements? 2.) In what ways are universities, institutions, associations, and researchers affected by and responding to these policies? Over the past five years, policy updates were common among these agencies and several themes have been thoroughly developed in that time; however, uncertainty remains around how funded researchers are expected to satisfy these policy requirements.

This brief report outlines some initial findings and challenges identified by the Community-Led Open Publication Infrastructures for Monographs (COPIM) project when looking to archive and preserve open access books produced by small, scholar-led presses. This paper is based on the research conducted by Work Package 7 in COPIM, which has a focus on the preservation and archiving of open access monographs in all their complexity, along with any accompanying materials.

Research data are often released upon journal publication to enable result verification and reproducibility. For that reason, research dissemination infrastructures typically support diverse datasets coming from numerous disciplines, from tabular data and program code to audio-visual files. Metadata, or data about data, is critical to making research outputs adequately documented and FAIR. Aiming to contribute to the discussions on the development of metadata for research outputs, I conducted an exploratory analysis to determine how research datasets cluster based on what researchers organically deposit together. I use the content of over 40,000 datasets from the Harvard Dataverse research data repository as my sample for the cluster analysis. I find that the majority of the clusters are formed by single-type datasets, while in the rest of the sample, no meaningful clusters can be identified. For the result interpretation, I use the metadata standard employed by DataCite, a leading organization for documenting a scholarly record, and map existing resource types to my results. About 65% of the sample can be described with a single-type metadata (such as Dataset, Software orReport), while the rest would require aggregate metadata types. Though DataCite supports an aggregate type such as a Collection, I argue that a significant number of datasets, in particular those containing both data and code files (about 20% of the sample), would be more accurately described as a Replication resource metadata type. Such resource type would be particularly useful in facilitating research reproducibility.

As a contribution to the general effort in research to generalize and improve the practices of Open Research Data (ORD), we developed a model conceptualizing the degrees of maturity of a research community in terms of ORD. This model may be used to assess the ORD capacity or maturity level of a specific research community, to strengthen the use of standards with respect to ORD within this community, and to increase its ORD maturity level. We present the background and our motivations for developing such an instrument as well as the reasoning leading to its design. We present its elements in detail and discuss possible applications.

This paper looks at the question of how and why to bring about greater reusability of Research Platforms (variously called Virtual Laboratories, Virtual Research Environments, or Science Gateways). It begins with some context for the Australian Research Data Commons, where the authors are based. It then examines the infrastructure concerns that are driving the need for platforms to be created and remain sustainable, and the connection from this to reusability. The paper then proceeds to discuss the ways in which FAIR is being extended to a range of research objects and infrastructure elements, before reviewing the work of the FAIR4VREs WG. The core of the paper is an examination, with examples or case studies, of four different paradigms for platform reusability: accessing, adopting, adapting, and abstracting. The paper concludes by examining actions undertaken by the ARDC to increase the likelihood of reusability.

Big social research repurposes existing data from online sources such as social media, blogs, or online forums, with a goal of advancing knowledge of human behavior and social phenomena. Big social research also presents an array of challenges that can prevent data sharing and reuse. This brief report presents an overview of a larger study that aims to understand the data curation implications of big social research to support use and reuse of big social data. The study, which is based in the United States, identifies six key issues relating to big social research and big social data curation through a review of the literature. It then further investigates perceptions and practices relating to these six key issues through semi-structured interviews with big social researchers and data curators. This report concludes with implications for data curation practice: metadata and documentation, connecting with researchers throughout the research process, data repository services, and advocating for community standards. Supporting responsible practices for using big social data can help scale up social science research, thus enhancing our understanding of human behavior and social phenomena.

As an experiment, the Research Data Journal for the Humanities and Social Sciences (RDJ) has temporarily extended the usual format of the online journal with so-called ‘showcases’, separate web pages containing a quick introduction to a dataset, embedded multimedia, interactive components, and facilities to directly preview and explore the dataset described. The aim was to create a coherent hyper document with content communicated via different media (multimodality) and provide space for new forms of scientific publication such as executable papers (e.g. Jupyter notebooks). This paper discusses the objectives, technical implementations, and the need for innovation in data publishing considering the advanced possibilities of today's digital modes of communication. The data showcases experiment proved to be a useful starting point for an exploration of related developments within and outside the humanities and social sciences. It turns out that small-scale experiments are relatively easy to perform thanks to the easy availability of digital technology. However, real innovation in publishing affects organization and infrastructure and requires the joint effort of publishers, editors, data repositories, and authors. It implies a thorough update of the concept of publication and adaptation of the production process. This paper also pays attention to these obstacles to taking new paths.

This paper presents original research about the behaviours, histories, demographics, and motivations of scholars who code, specifically how they interact with version control systems locally and on the Web. By understanding patrons through multiple lenses – daily productivity habits, motivations, and scholarly needs – librarians and archivists can tailor services for software management, curation, and long-term reuse, raising the possibility for long-term reproducibility of a multitude of scholarship.

A bit-level object storage system is a foundational building block of long-term digital preservation (LTDP). To achieve the purposes of LTDP, the system must be able to: preserve the authenticity and integrity of the original digital objects; scale up with dramatically increasing demands for preservation storage; mitigate the impact of hardware obsolescence and software ephemerality; replicate digital objects among distributed data centers at different geographical locations; and to constantly audit and automatically recover from compromised states. A realistic and daunting challenge to satisfy these requirements is not only to overcome technological difficulties but also to maintain economic sustainability by implementing and continuously operating such systems in a cost-effective way. In this paper, we present OpenStack Swift, an open-source, mature and widely accepted cloud platform, as a practical and proven solution with a case study at the University of Alberta Library. We emphasize the implementation, application, cost analysis and maintenance of the system, with the purpose of contributing to the community with an exceedingly robust, highly scalable, self-healing and comparatively cost-effective bit-level object storage system for long-term digital preservation.

ARCHIVER and Pre-Commercial Procurement funding has enabled small to medium enterprises (SMEs) to innovate and deliver new services for EOSC. Within the framework of the ARCHIVER pre-commercial procurement tender, between December 2020 and August 2021, three commercial consortia competed to deliver innovative, prototype solutions for long-term data preservation. Two of them were selected to continue with the pilot phase and deliver research-ready solutions for long-term data preservation of research data, therefore filling a gap in the current European Open Science panorama. Digital preservation relies on technological infrastructure (information and communication technology, ICT) that can have environmental impacts. While altering technology usage can reduce the impact of digital preservation practices, this alone is not a strategy for sustainable practice. Moving toward environmentally sustainable digital preservation requires critically examining the motivations and assumptions that shape current practice. The use of scalable cloud infrastructures can reduce the environmental impacts of long-term data preservation solutions.

Data driven applications often require using data integrated from different, large, and continuously updated collections. Each of these collections may present gaps, overlapping data, have conflicting information, or complement each other. Thus, a curation need is to continuously assess if data from multiple collections are fit for integration and reuse. To assess different large data collections at the same time, we present the Synchronic Curation (SC) framework. SC involves processing steps to map the different collections to a unifying data model that represents research problems in a scientific area. The data model, which includes the collections' provenance and a data dictionary, is implemented in a graph database where collections are continuously ingested and can be queried. SC has a collection analysis and comparison module to track updates, and to identify gaps, changes, and irregularities within and across collections. Assessment results can be accessed interactively through a web-based interactive graph. In this paper we introduce SC as an interdisciplinary enterprise, and illustrate its capabilities through its implementation in ASTRIAGraph, a space sustainability knowledge system.

Digital curation of materials available in large online repositories is required to enable the reuse of Cultural Heritage resources in specific activities like education or scientific research. The digitization of such valuable objects is an important task for making them accessible through digital platforms such as Europeana, therefore ensuring the success of transcription campaigns via the Transcribathon platform is highly important for this goal. Based on impact assessment results, people are more engaged in the transcription process if the content is more oriented to specific themes, such as First World War. Currently, efforts to group related documents into thematic collections are in general hand-crafted and due to the large ingestion of new material they are difficult to maintain and update. The current solutions based on text retrieval are not able to support the discovery of related content since the existing collections are multi-lingual and contain heterogeneous items like postcards, letters, journals, photographs etc. Technological advances in natural language understanding and in data management have led to the automation of document categorization and via automatic topic detection. To use existing topic detection technologies on Europeana collections there are several challenges to be addressed: (1) ensure representative and qualitative training data, (2) ensure the quality of the learned topics, and (3) efficient and scalable solutions for searching related content based on the automatically detected topics, and for suggesting the most relevant topics on new items. This paper describes in more details each such challenge and the proposed solutions thus offering a novel perspective on how digital curation practices can be enhanced with the help of machine learning technologies.

As policies, good practices and mandates on research data management evolve, more emphasis has been put on the licencing of data, which allows potential re-users to quickly identify what they can do with the data in question. In this paper I analyse a pre-existing collection of 840 Horizon 2020 public data management plans (DMPs) to determine which ones mention creative commons licences and among those who do, which licences are being used. I find that 36% of DMPs mention creative commons and among those a number of different approaches towards licencing exist (overall policy per project, licencing decisions per dataset, licencing decisions per partner, licensing decision per data format, licensing decision per perceived stakeholder interest), often clad in rather vague language with CC licences being “recommended” or “suggested”. Some DMPs also “kick the can further down the road” by mentioning that “a” CC licence will be used, but not which one. However, among those DMPs that do mention specific CC licences, a clear favourite emerges: the CC-BY licence, which accounts for half of the total mentioning of a specific licence. The fact that 64% of DMPs did not mention creative commons at all is an indication for the need for further training and awareness raising on data management in general and licencing in particular in Horizon Europe. For those DMPs that do mention specific licences, 60% would be compliant with Horizon Europe requirements (CC-BY or CC0). However, it should be carefully monitored whether content similar to the 40% that is currently licenced with non- Horizon Europe compliant licences will in the future move to CC-BY or CC0 or whether such content will simply be kept fully closed by projects (by invoking the “as open as possible, as close as necessary” principle), which would be an unintended and potentially damaging consequence of the policy.

While stakeholders including funding agencies and academic publishers implement more stringent data sharing policies, challenges remain for researchers in the humanities who are increasingly prompted to share their research data. This paper outlines some key challenges of research data sharing in the humanities, and identifies existing work which has been undertaken to explore these challenges. It describes the current landscape regarding publishers’ research data sharing policies, and the impact which strong data policies can have, regardless of discipline. Using Routledge Open Research as a case study, the development of a set of humanities-inclusive Open Data publisher data guidelines is then described. These include practical guidance in relation to data sharing for humanities authors, and a close alignment with the FAIR Data Principles.

In order to promote sound management of research data the European Commission, under the Horizon 2020 framework program, is promoting the adoption of a Data Management Plan (DMP) in research projects. Despite the value of a DMP to make data findable, accessible, interoperable and reusable (FAIR) through time, the development and implementation of DMPs is not yet a common practice in health research. Raising the awareness of researchers in small projects to the benefits of early adoption of a DMP is, therefore, a motivator for others to follow suit. In this paper we describe an approach to engage researchers in the writing of a DMP, in an ongoing project, FrailSurvey, in which researchers are collecting data through a mobile application for self-assessment of fragility. The case study is supported by interviews, a metadata creation session, as well as the validation of recommendations by researchers. With the outline of our process we also outline tools and services that supported the development of the DMP in this small project, particularly since there were no institutional services available to researchers

While data management planning for grant applications has become commonplace alongside articles providing guidance for such plans, examples of data plans as they have been created, implemented, and used for specific projects are only beginning to appear in the scholarly record. This article describes data management planning for an eight-institution, multi-year research project. The project leveraged four data management plans (DMP) in total, one for the funding application and one for each of the three distinct project phases. By understanding researcher roles, development and content of each DMP, team internal and external challenges, and the overall benefits of creating and using the plans, these DMPs provide a demonstration of the utility of this project management tool.

Data curation is a complex, multi-faceted task. While dedicated data stewards are starting to take care of these activities in close collaboration with researchers for many types of (usually file-based) data in many institutions, this is rarely yet the case for data held in relational databases. Beyond large-scale infrastructures hosting e.g. climate or genome data, researchers usually have to create, build and maintain their database, care about security patches, and feed data into it in order to use it in their research. Data curation, if at all, usually happens after a project is finished, when data may be exported for digital preservation into file repository systems. We present DBRepo, a semantic digital repository for relational databases in a private cloud setting designed to (1) host research data stored in relational databases right from the beginning of a research project, (2) provide separation of concerns, allowing the researchers to focus on the domain aspects of the data and their work while bringing in experts to handle classic data management tasks, (3) improve findability, accessibility and reusability by offering semantic mapping of metadata attributes, and (4) focus on reproducibility in dynamically evolving data by supporting versioning and precise identification/cite-ability for arbitrary subsets of data.

Preserving software and providing access to obsolete software is necessary and will become even more important for work with any kind of born-digital artifacts. While usability and availability of emulation in digital curation and preservation workflow has improved significantly, productive (re)use of preserved obsolete software is a growing concern, due to a lack of (future) operational knowledge. In this article we describe solutions to automate and document software usage in a way, such that the result is not only instructive but also productive.

In the past twenty-five years, cross-institutional communities have come together in the creation and use of open source software and open data standards to build digital collections (Madden, 2012). These librarians, developers, archivists, artists, and researchers recognize that the custom-built architectures and bespoke data structures of earlier digital collections development are unsustainable. Their collaborations have produced now-standard technologies such as Samvera, Fedora, GeoBlacklight, Islandora 8, as well as RDF, and JSON-LD among other open schemas. A core principle animating these efforts is reusability: data, schemas, and technologies in the open era must be coherent and flexible enough to be reused across multiple digital contexts. The authors of this paper show how reuse guided the migration of the Hopkins Digital Library from an outdated isolated system to a sustainable interconnected environment in GeoBlacklight, Islandora, with metadata based in Linked Open Data. Three areas of reuse focus this paper: the creation of robust interoperable metadata; the expansion of IIIF functionality to integrate the needs of the Hopkins Geoportal’s users; the development of a broadly re/usable data migration module focused on expanding a diverse community of invested users. In focusing on reusability as an organising principle of digital collections development, this case study shows how one digital curation team produced a platform that meets the changing and specific needs of an individual institution, on the one hand, and participated in and furthered the creative coherence of the open communities supporting the team’s work, on the other.

Vast amounts of scientific, cultural, social, business and government, and other, information is being created every day. There are billions of objects, in a multitude of formats, semantics and associated software. Much, perhaps the majority, of this information is transitory but there is still an immense amount which should be preserved for the medium and long term – perhaps even indefinitely. Preservation requires that the information continues to be usable, not simply to be printed or displayed. Of course, the digital objects (the bits) must be preserved, as must the “metadata” which enables the bits to the understood which includes the software. Before LABDRIVE no system could adequately preserve such information, especially in such gigantic volume and variety. In this paper we describe the development of LABDRIVE and its ability to preserve tens or hundreds of petabytes in a way which is conformant to the OAIS Reference Model and capable of being ISO 16363 certified.

The goal of data cleaning is to make data fit for purpose, i.e., to improve data quality, through updates and data transformations, such that downstream analyses can be conducted and lead to trustworthy results. A transparent and reusable data cleaning workflow can save time and effort through automation, and make subsequent data cleaning on new data less errorprone. However, reusability of data cleaning workflows has received little to no attention in the research community. We identify some challenges and opportunities for reusing data cleaning workflows. We present a high-level conceptual model to clarify what we mean by reusability and propose ways to improve reusability along different dimensions. We use the opportunity of presenting at IDCC to invite the community to share their uses cases, experiences, and desiderata for the reuse of data cleaning workflows and recipes in order to foster new collaborations and guide future work.

OpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations collaborates with projects that are part of the Open Science ecosystem and complies with the UNESCO founding principles of Open Science, the I4OC recommendations, and the FAIR data principles that data should be Findable, Accessible, Interoperable and Reusable. Since its data satisfies all the Reuse guidelines provided by FAIR in terms of richness, provenance, usage licenses and domain-relevant community standards, OpenCitations provides an example of a successful open e-infrastructure in which the reusability of data is integral to its mission.

Accessibility of research data to disabled users has received scant attention in literature and practice. In this paper we briefly survey the current state of accessibility for research data and suggest some first steps that repositories should take to make their holdings more accessible. We then describe in depth how those steps were implemented at the Qualitative Data Repository (QDR), a domain repository for qualitative social-science data. The paper discusses accessibility testing and improvements on the repository and its underlying software, changes to the curation process to improve accessibility, as well as efforts to retroactively improve the accessibility of existing collections. We conclude by describing key lessons learned during this process as well as next steps.

Modern life sciences studies depend on the collection, management and analysis of comprehensive datasets in what has become data-intensive research. Life science research is also characterised by having relatively small groups of researchers. This combination of data-intensive research performed by a few people has led to an increasing bottleneck in research data management (RDM). Parallel to this, there has been an urgent call by initiatives like FAIR and Open Science to openly publish research data which has put additional pressure on improving the quality of RDM. Here, we reflect on the lessons learnt by DataHub Maastricht, a RDM support group of the Maastricht University Medical Centre (MUMC+) in Maastricht, the Netherlands, in providing first-line RDM support for life sciences. DataHub Maastricht operates with a small core team, and is complemented with disciplinary data stewards, many of whom have joint positions with DataHub and a research group. This organisational model helps creating shared knowledge between DataHub and the data stewards, including insights how to focus support on the most reusable datasets. This model has shown to be very beneficial given limited time and personnel. We found that co-hosting tailored platforms for specific domains, reducing storage costs by implementing tiered storage and promoting cross-institutional collaboration through federated authentication were all effective features to stimulate researchers to initiate RDM. Overall, utilising the expertise and communication channel of the embedded data stewards was also instrumental in our RDM success. Looking into the future, we foresee the need to further embed the role of data stewards into the lifeblood of the research organisation, along with policies on how to finance long-term storage of research data. The latter, to remain feasible, needs to be combined with a further formalising of appraisal and reappraisal of archived research data.

The management of research data in academic institutions is increasing across most disciplines. In Namibia, the requirement to manage research data, making it available for the purposes of sharing, preservation and to support research findings, has not yet been mandated. At the University of Namibia (UNAM) there is no institutional research data management (RDM) culture, yet RDM may nevertheless be practiced among its researchers. The extent to which these practices have been adopted is, however, not known. This study investigated the extent of RDM adoption by researchers at UNAM. It identifies current or potential challenges in managing research data, and proposes solutions to some of these challenges that could aid the university as it attempts to encourage the adoption of RDM practices. The investigation used Rogers’ Diffusion of Innovations (DOI) theory, with a focus on the innovation-decision process, as a means to establish where UNAM researchers are in the process of adopting RDM practices. The population under study were the UNAM faculty members who conduct research as part of their academic duties. Questionnaires were used to gather quantitative data. The study found that some researchers practice RDM to some extent out of their own free will, but there are many challenges that hinder these practices. Overall, though, there is a lack of interest in RDM as the knowledge of the concept among researchers is relatively low. The study found that most researchers were at the knowledge stage of the innovation-decision process and recommended, among other things, that the university puts effort into creating RDM awareness and encouraging data sharing, and that it moves forward with infrastructure and policy development so that RDM can be fully adopted by the researchers of the institution.

Open science data benefit society by facilitating convergence across domains that are examining the same scientific problem. While cross-disciplinary data sharing and reuse is essential to the research done by convergent communities, so far little is known about the role data play in how these communities interact. An understanding of the role of data in these collaborations can help us identify and meet the needs of emerging research communities which may predict the next challenges faced by science. This paper represents an exploratory study of one emerging community, the environmental health community, examining how environmental health research groups form, collaborate, and share data. Five key insights about the role of data in emerging research communities are identified and suggestions are made for further research.

Trustworthy data repositories ensure the security of their collections. We argue they should also ensure the privacy of researcher and research subject data. We demonstrate the use of a privacy impact assessment (PIA) to evaluate potential privacy risks to researchers using the ICPSR’s Researcher Passport as a case study. We present our workflow and discuss potential privacy risks and mitigations for those risks. [A previous version of this article is available as an IDCC2020 Conference Paper]

We have created tools that automate one of the most burdensome aspects of documenting the provenance of research data: describing data transformations performed by statistical software. Researchers in many fields use statistical software (SPSS, Stata, SAS, R, Python) for data transformation and data management as well as analysis. The C2Metadata ("Continuous Capture of Metadata for Statistical Data") Project creates a metadata workflow paralleling the data management process by deriving provenance information from scripts used to manage and transform data. C2Metadata differs from most previous data provenance initiatives by documenting transformations at the variable level rather than describing a sequence of opaque programs. Command scripts for statistical software are translated into an independent Structured Data Transformation Language (SDTL), which serves as an intermediate language for describing data transformations. SDTL can be used to add variable-level provenance to data catalogues and codebooks and to create "variable lineages" for auditing software operations. Better data documentation makes research more transparent and expands the discovery and re-use of research data.

Before data from multiple sources can be analyzed, data cleaning workflows (“recipes”) usually need to be employed to improve data quality. We identify a number of technical problems that make application of FAIR principles to data cleaning recipes challenging. We then demonstrate how transparency and reusability of recipes can be improved by analyzing dataflow dependencies within recipes. In particular column-level dependencies can be used to automatically detect independent subworkflows, which then can be reused individually as data cleaning modules. We have prototypically implemented this approach as part of an ongoing project to develop open-source companion tools for OpenRefine. Keywords: Data Cleaning, Provenance, Workflow Analysis