Research Data Infrastructure Research Articles

Research Data in Chemistry – Results of the first NFDI4Chem Community Survey

Good research data management (RDM) requires a constant endeavour of the researchers but – first and foremost – user‐friendly infrastructure and well accepted standards need to be developed for the researchers. The national research data infrastructure initiative for chemistry in Germany NFDI4Chem sets out to embrace the challenge of creating a general research data management portal and hereby connecting already existing infrastructure as well as to foster the cultural change in chemistry towards digitalization by developing general minimum information standards for all methods used and teaching RDM principles to the community. In order to serve the needs of the chemical community at its best, NFDI4Chem accomplishes regular community surveys. The first survey has been performed in 2019 and the results were condensed into the NFDI4Chem proposal. This article summarizes the design of the study and its results. With regard to the project development, this first national survey serves as zero‐point of all upcoming efforts in research data management.

Academic library's leadership and stakeholder involvement in research data services

AbstractIn the last decade, academic libraries have made progress in establishing themselves as hubs and leaders for research data services on campus. The importance of collaborating with a range of institutional stakeholders, such as the research or information technology office, as well as external partners in developing and delivering research data policy, services, and infrastructure, has been well‐documented. However, there is less evidence as to how libraries play a crucial role in leadership, whether other stakeholders’ involvement actually makes a difference and if so, how they make a difference. As such, the goal of this study is to explore the academic library's leadership role in research data services and how collaboration and partnership with stakeholders and interested parties might impact the maturity of the research data services the library provides. The secondary analysis of the existing survey data found that libraries offer more mature services when they take a primary responsibility in developing the services. It also found internal stakeholders’ and external partners’ involvement leads to more mature services in the selected activities.

Data curation as collective action during COVID‐19

AbstractIn this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public‐facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.

Research Data Management Status of Science and Technology Research Institutes in Korea

Recent advances in digital technology and the data-driven science paradigm has led to a proliferation of research data, which are becoming more important in scholarly communications. The sharing and reuse of research data can play a key role in enhancing the reusability and reproducibility of research, and data from publicly funded projects are assumed to be public goods. This is seen as a movement of open science and, more specifically, open research data. Many countries, such as the USA, UK, and Australia, are pushing ahead with implementing policies and infrastructure for open research data. In this paper, we present survey results pertaining to the creation, management, and utilization of data for researchers from government-funded research institutes of science and technology in Korea. We then introduce recent regulations stipulating a mandated data management plan for national R&D projects and on-going efforts to realize open research data in Korea.

FAIR, safe and high-quality data: The data infrastructure and accessibility of the YOUth cohort study

The YOUth cohort study aims to be a trailblazer for open science. Being a large-scale, longitudinal cohort following children in their development from gestation until early adulthood, YOUth collects a vast amount of data through a variety of research techniques. Data are collected through multiple platforms, including facilities managed by Utrecht University and the University Medical Center Utrecht. In order to facilitate appropriate use of its data by research organizations and researchers, YOUth aims to produce high-quality, FAIR data while safeguarding the privacy of participants. This requires an extensive data infrastructure, set up by collaborative efforts of researchers, data managers, IT departments, and the Utrecht University Library. In the spirit of open science, YOUth will share its experience and expertise in setting up a high-quality research data infrastructure for sensitive cohort data. This paper describes the technical aspects of our data and data infrastructure, and the steps taken throughout the study to produce and safely store FAIR and high-quality data. Finally, we will reflect on the organizational aspects that are conducive to the success of setting up such an enterprise, and we consider the financial challenges posed by individual studies investing in sustainable science.

Assessing the readiness of digital data infrastructure for opioid use disorder research

BackgroundGaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs.MethodsWe conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as “exact”, “partial”, or “none.”ResultsThe literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches.ConclusionsDespite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.

The CARL Portage Partnership Story

Portage is the research data management initiative of the Canadian Association of Research Libraries and its story has been very much about establishing partnerships in a complex environment to advance research data management services and infrastructure in Canada. Many jurisdictions make up the space in which research data management takes place. A variety of legal, political, cultural, economic, technological, and scientific factors are at play and how they fit together depends on the connections between a number of stakeholders. The levels at which these stakeholders operate and the transient nature of research data itself made the development of partnerships a complex undertaking for Portage. This article describes the building of partnerships in a multi-jurisdictional environment, discusses challenges in operating in Canada's digital research ecosystem, and highlights the importance of working with Canada's regional academic library associations in laying the foundations for digital research infrastructure to support data management.

NFDI4Chem - Towards a National Research Data Infrastructure for Chemistry in Germany

The vision of NFDI4Chem is the digitalisation of all key steps in chemical research to support scientists in their efforts to collect, store, process, analyse, disclose and re-use research data. Measures to promote Open Science and Research Data Management (RDM) in agreement with the FAIR data principles are fundamental aims of NFDI4Chem to serve the chemistry community with a holistic concept for access to research data. To this end, the overarching objective is the development and maintenance of a national research data infrastructure for the research domain of chemistry in Germany, and to enable innovative and easy to use services and novel scientific approaches based on re-use of research data. NFDI4Chem intends to represent all disciplines of chemistry in academia. We aim to collaborate closely with thematically related consortia. In the initial phase, NFDI4Chem focuses on data related to molecules and reactions including data for their experimental and theoretical characterisation. This overarching goal is achieved by working towards a number of key objectives: Key Objective 1: Establish a virtual environment of federated repositories for storing, disclosing, searching and re-using research data across distributed data sources. Connect existing data repositories and, based on a requirements analysis, establish domain-specific research data repositories for the national research community, and link them to international repositories. Key Objective 2: Initiate international community processes to establish minimum information (MI) standards for data and machine-readable metadata as well as open data standards in key areas of chemistry. Identify and recommend open data standards in key areas of chemistry, in order to support the FAIR principles for research data. Finally, develop standards, if there is a lack. Key Objective 3: Foster cultural and digital change towards Smart Laboratory Environments by promoting the use of digital tools in all stages of research and promote subsequent Research Data Management (RDM) at all levels of academia, beginning in undergraduate studies curricula. Key Objective 4: Engage with the chemistry community in Germany through a wide range of measures to create awareness for and foster the adoption of FAIR data management. Initiate processes to integrate RDM and data science into curricula. Offer a wide range of training opportunities for researchers. Key Objective 5: Explore synergies with other consortia and promote cross-cutting development within the NFDI. Key Objective 6: Provide a legally reliable framework of policies and guidelines for FAIR and open RDM.

Providing Digital Infrastructure for Audio-Visual Linguistic Research Data with Diverse Usage Scenarios: Lessons Learnt

This article describes the development of the digital infrastructure at a research data centre for audio-visual linguistic research data, the Hamburg Centre for Language Corpora (HZSK) at the University of Hamburg in Germany, over the past ten years. The typical resource hosted in the HZSK Repository, the core component of the infrastructure, is a collection of recordings with time-aligned transcripts and additional contextual data, a spoken language corpus. Since the centre has a thematic focus on multilingualism and linguistic diversity and provides its service to researchers within linguistics and other disciplines, the development of the infrastructure was driven by diverse usage scenarios and user needs on the one hand, and by the common technical requirements for certified service centres of the CLARIN infrastructure on the other. Beyond the technical details, the article also aims to be a contribution to the discussion on responsibilities and services within emerging digital research data infrastructures and the fundamental issues in sustainability of research software engineering, concluding that in order to truly cater to user needs across the research data lifecycle, we still need to bridge the gap between discipline-specific research methods in the process of digitalisation and generic digital research data management approaches.

Healthcare Research and Analytics Data Infrastructure Solution: A Data Warehouse for Health Services Research.

BackgroundHealth services researchers spend a substantial amount of time performing integration, cleansing, interpretation, and aggregation of raw data from multiple public or private data sources. Often, each researcher (or someone in their team) duplicates this effort for their own project, facing the same challenges and experiencing the same pitfalls discovered by those before them.ObjectiveThis paper described a design process for creating a data warehouse that includes the most frequently used databases in health services research.MethodsThe design is based on a conceptual iterative process model framework that utilizes the sociotechnical systems theory approach and includes the capacity for subsequent updates of the existing data sources and the addition of new ones. We introduce the theory and the framework and then explain how they are used to inform the methodology of this study.ResultsThe application of the iterative process model to the design research process of problem identification and solution design for the Healthcare Research and Analytics Data Infrastructure Solution (HRADIS) is described. Each phase of the iterative model produced end products to inform the implementation of HRADIS. The analysis phase produced the problem statement and requirements documents. The projection phase produced a list of tasks and goals for the ideal system. Finally, the synthesis phase provided the process for a plan to implement HRADIS. HRADIS structures and integrates data dictionaries provided by the data sources, allowing the creation of dimensions and measures for a multidimensional business intelligence system. We discuss how HRADIS is complemented with a set of data mining, analytics, and visualization tools to enable researchers to more efficiently apply multiple methods to a given research project. HRADIS also includes a built-in security and account management framework for data governance purposes to ensure customized authorization depending on user roles and parts of the data the roles are authorized to access.ConclusionsTo address existing inefficiencies during the obtaining, extracting, preprocessing, cleansing, and filtering stages of data processing in health services research, we envision HRADIS as a full-service data warehouse integrating frequently used data sources, processes, and methods along with a variety of data analytics and visualization tools. This paper presents the application of the iterative process model to build such a solution. It also includes a discussion on several prominent issues, lessons learned, reflections and recommendations, and future considerations, as this model was applied.

Open research data in African academic and research libraries: a literature analysis

PurposeThe paper presents a literature review on research data management services in African academic and research libraries on the backdrop of the advancing open science and open research data infrastructures. It provides areas of focus for library to support open research data.Design/methodology/approachThe literature analysis and future role of African libraries in research data management services were based on three areas as follows:open science, research infrastructures and open data infrastructures. Focussed literature searches were conducted across several electronic databases and discovery platforms, and a qualitative content analysis approach was used to explore the themes based on a coded list.FindingsThe review reports of an environment where open science in Africa is still at developmental stages. Research infrastructures face funding and technical challenges. Data management services are in formative stages with progress reported in a few countries where open science and research data management policies have emerged, cyber and data infrastructures are being developed and limited data librarianship courses are being taught.Originality/valueThe role of the academic and research libraries in Africa remains important in higher education and the national systems of research and innovation. Libraries should continue to align with institutional and national trends in response to the provision of data management services and as partners in the development of research infrastructures.

One Archaeology: A Manifesto for the Systematic and Effective Use of Mapped Data from Archaeological Fieldwork and Research

The Infrastructure for Spatial Information in Europe (INSPIRE) Directive (2007) requires public organisations across Europe to share environmentally-related spatial datasets to support decision making and management of the environment. Despite the environmental focus of INSPIRE, it offers limited guidance for archaeological datasets. Most primary data is created outside, but ultimately curated within, the public sector. As spatial evidence from fieldwork activities is not considered by the Directive, it overlooks a range of barriers to sharing data, such as project-based fieldwork, a lack of data standards, and formatting and licencing variations. This paper submits that these challenges are best addressed through the formalised management of primary research data through an archaeological Spatial Data Infrastructure (SDI). SDIs deliver more efficient data management and release economic value by saving time and money. Better stewardship of archaeological data will also lead to more informed research and stewardship of the historic environment. ARIADNE already provides a digital infrastructure for research data, but the landscape and spatial component has been largely overlooked. However, rather than developing a separate solution, the full potential of spatial data from archaeological research can and should be realised through ARIADNE.

Practices, Challenges, and Prospects of Big Data Curation: a Case Study in Geoscience

Open and persistent access to past, present, and future scientific data is fundamental for transparent and reproducible data-driven research. The scientific community is now facing both challenges and opportunities caused by the growingly complex disciplinary data systems. Concerted efforts from domain experts, information professionals, and Internet technology experts are essential to ensure the accessibility and interoperability of the big data. Here we review current practices in building and managing big data within the context of large data infrastructure, using geoscience cyberinfrastructure such as Interdisciplinary Earth Data Alliance (IEDA) and EarthCube as a case study. Geoscience is a data-rich discipline with a rapid expansion of sophisticated and diverse digital data sets. Having started to embrace the digital age, the community have applied big data and data mining tools into the new type of research. We also identified current challenges, key elements, and prospects to construct a more robust and future-proof big data infrastructure for research and publication for the future, as well as the roles, qualifications, and opportunities for librarians/information professionals in the data era.

Prospects for anationwide mortality registry: lessons learned from the Bremen Mortality Index

The time of death and the diseases leading to death provide important information for health research and disease registries. They facilitate conclusions about the state of health of apopulation and contribute to the assessment of health promotion measures as well as disease prevention and care. In many countries, these data are provided through mortality registries.In Germany, these data are available on death certificates, which must be filled out by physicians with all information available for each death. However, use of these data for research is very difficult as there is no central registry. Instead, regional health authorities archive the respective death certificates locally. Furthermore, the data provided on death certificates are partly uncoded or only available in paper form. Bremen is the only federal state that has more than 20years of expertise with aregional mortality registry that demonstrates how health research can benefit from an efficient processing and provision cause of death (CoD) data and how this may also improve the quality of cancer registration.Currently, automatic coding of the CoD by the statistical offices is being implemented on anational level. As aconsequence, the free-text information from death certificates will have to be recorded electronically. This requires organizational restructuring that offers aunique opportunity to make all the necessary provisions that are needed for the establishment of mortality registries at the federal state level. This would be an important step towards the establishment of along overdue national mortality registry - aresearch data infrastructure guaranteeing international competitiveness.

THE QUT DIGITAL OBSERVATORY PROJECT: BUILDING A TRUSTED DATA INFRASTRUCTURE FOR SOCIAL MEDIA RESEARCH

Trust is fragile. The 2018 Facebook and Cambridge Analytica debacles highlighted how data harvested from social media platforms can be used not only for commercial purposes but also for political manipulation. This incident and the widespread discussion around it further demonstrated the following issues: unethical data collection enabled by a platform; unethical use of data for corporate and political interest; and unethical data sharing by an academic.&#x0D; Research needs to be credible to maintain social license. Data is the lifeblood of research. For research to remain credible, research needs to remain fundamentally ethical and research methods comprising data collection and data analysis need to be robust, transparent, repeatable, and auditable. Such methods alone cannot create credibility, but research data infrastructure design and implementation can provide a foundation for credibility by addressing these fundamental processes.&#x0D; Social science research has traditionally relied on data collection methods such as surveys, interviews, and ethnographic observations. However, an increasing proportion of human life is being mediated by online platforms, with approximately 2.3 billion active users on Facebook and 326 million active users on Twitter (Statista 2019). Social media data collection and analysis have become imperative for researchers interested in various phenomena playing out in these new media. This paper discusses the current state and issues of social media data collection and describes the Digital Observatory’s approach to establishing a credible and trusted research data infrastructure.&#x0D;

Implementation of a REDCap-Based Research Data Collection System in Cameroon.

Implementing electronic data collection for health research can be challenging in resource-limited settings, where electricity, Internet access, and study staff with computer training may be limited. Our team has established a successful research data infrastructure using the REDCap software at three HIV clinics and one coordinating center in Cameroon. We describe our recommended network architecture and guidance for study data teams working in similar settings.

NFDI4BioDiversity: Biodiversity, ecology and environmental data

Background: The NFDI process in Germany The digital revolution is fundamentally transforming research data and methods. Mastering this transformation poses major challenges for stakeholders in the domains of science and policy. The process of digitalisation creates immense opportunities, but it must be structured proactively. To this end, the establishment of effective governance mechanisms for research data management (RDM) is of fundamental importance and will be one key driver for successful research and innovation in the future. In 2016 the German Council for Information Infrastructures (RfII) recommended the establishment of a “Nationale Forschungsdateninfrastruktur” (National Research Data Infrastructure, or NFDI), which will serve as the backbone for research data management in Germany. The NFDI should be implemented as a dynamic national collaborative network that grows over time and is composed of various specialised nodes (consortia). The talk will provide a short overview of the status and objectives of the NFDI. It will commence with a description of the goals of the NFDI4BioDiversity consortium which was established for the targeted support of the biodiversity community with data management. The NFDI4BioDiversity Consortium: Biodiversity, Ecology &amp;amp; Environmental Data Biodiversity is more than just the diversity of living species. It includes genetic diversity, functional diversity, interactions and the diversity of whole ecosystems. Mankind continuous to dramatically impact the earth’s ecosystem: species dying-out genetic diversity as well as whole ecosystems are endangered or already lost. Next to the loss of charismatic species and conspicuous change in ecosystems, we are experiencing a quiet loss of common species which together has captured high level policy attention. This has impacts on vital ecosystem services that provide the foundation of human well-being. A general understanding of the status, trends and drivers of the biodiversity on earth is urgently needed to devise conservation responses. Besides the fact that data are often scattered across repositories or not accessible at all, the main challenge for integrative studies is the heterogeneity of measurements and observation types, combined with a substantial lack of documentation. This leads to inconsistencies and incompatibilities in data structures, interfaces and semantics and thus hinders the re-usability of data to answer scientifically and socially relevant questions. Synthesis as well as hypothesis generation will only proceed when data are compliant with the FAIR (Findable, Accessible, Interoperable and Re-usable) data principles. Over the last five years these key challenges have been addressed by the DFG funded German Federation for Biological Data (GFBio) project. GFBio encompasses technical, organizational, financial, and community aspects to raise awareness for research data management in biodiversity research and environmental sciences. To foster sustainability across this federated infrastructure the not-for-profit association “Gesellschaft für biologische Daten e.V. (GFBio e.V.)” has been set up in 2016 as an independent legal entity. NFDI4BioDiversity builds on the experience and established user community of GFBio and takes advantage of GFBio e.V. GFBio already comprises data centers for nucleotide and environmental data as well as the seven well-established data centers of Germany´s largest natural science research facilities, museums and world’s most diverse microbiological resource collection. The network is now extended to include the network of botanical gardens and the largest collections of crop plants and their wild relatives. All collections together host more than 75% of all museum objects (150 millions) in Germany and &amp;gt;80% of all described microbial species. They represent the biggest and internationally-relevant data repositories. NFDI4BioDiversity will extend its community engagement at the science-society-policy interface by including farm animal biology, crop sciences, biodiversity monitoring and citizen science, as well as systems biology encompassing world-leading tools and collections for FAIR data management. Partners of the German Network for Bioinformatics Infrastructure (de.NBI) provide large scale data analysis and storage capacities in the cloud, as well as extensive continuous training and education experiences. Dedicated personnel will be responsible for the mutual exchange of data and experiences with NFDI4Life-Umbrella,NFDI4Earth, NFDI4Chem, NFDI4Health and beyond. As digitalization and liberation of data proceeds, NFDI4BioDiversity will foster community standards, quality management and documentation as well as the harmonization and synthesis of heterogeneous data. It will pro-actively engage the user community to build a coordinated data management platform for all types of biodiversity data as a dedicated added value service for all users of NFDI.

Building Open Access to Research (OAR) Data Infrastructure at NIST.

As a National Metrology Institute (NMI), the USA National Institute of Standards and Technology (NIST) scientists, engineers and technology experts conduct research across a full spectrum of physical science domains. NIST is a non-regulatory agency within the U.S. Department of Commerce with a mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life. NIST research results in the production and distribution of standard reference materials, calibration services, and datasets. These are generated from a wide range of complex laboratory instrumentation, expert analyses, and calibration processes. In response to a government open data policy, and in collaboration with the broader research community, NIST has developed a federated Open Access to Research (OAR) scientific data infrastructure aligned with FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Through the OAR initiatives, NIST’s Material Measurement Laboratory Office of Data and Informatics (ODI) recently released a new scientific data discovery portal and public data repository. These science-oriented applications provide dissemination and public access for data from across the broad spectrum of NIST research disciplines, including chemistry, biology, materials science (such as crystallography, nanomaterials, etc.), physics, disaster resilience, cyberinfrastructure, communications, forensics, and others. NIST’s public data consist of carefully curated Standard Reference Data, legacy high valued data, and new research data publications. The repository is thus evolving both in content and features as the nature of research progresses. Implementation of the OAR infrastructure is key to NIST’s role in sharing high integrity reproducible research for measurement science in a rapidly changing world.

Biological &amp;amp; Geological Collections as a Research Infrastructure: A Dutch case

Within the Netherlands, large scale digitization efforts of natural science collections have taken place in recent years. This has led to a wealth of digital information on natural science collections. Still, large quantities of collection data remain untapped and undigitized. The usage of all these digital collections data as driver for science and society remains underexplored. Especially important, is the opportunity for such data to be combined and/or enriched with other data types with the aim to empower different user groups. A consortium of Dutch partners has committed themselves in working together to make biological and geological collections into a joint research infrastructure, underpinning other research infrastructures and scientific uses also beyond the biodiversity research domain. This consortium combines the Dutch contribution to the Distributed Systems of Scientific Collections (DiSSCo), LifeWatch, the Catalogue of Life and the Global Biodiversity Information facility, under the coordination of the Netherlands Biodiversity Information Facility. As part of a preparatory project for DiSSCo, funded by the Dutch science council, we connected the different users groups of collection managers (data providers), scientists (end-users), IT-specialists and policymakers. With collection managers we explored how to move towards an overview of all natural science collections in the Netherlands. In addition, we studied to what extent collection holdings of different musea could be combined, managed, and shared into one research infrastructure. Using a research data management cycle perspective, we surveyed and interviewed the Dutch research community for the barriers and opportunities in using natural science collections and related data. The outcomes of the project should lead to the next steps in creating a more comprehensive and inclusive biodiversity research data infrastructure in the Netherlands that interacts seamlessly with existing international research infrastructures, including DiSSCo.

SwissCollNet – A National Initiative for Natural History Collections in Switzerland

More than 60 million specimens are housed in geological and biological collections in numerous museums and botanical gardens located all over Switzerland. They are of national and international origin. Taken together they form an entity with a high scientific value and international recognition for their contribution to scientific research. Due to the federalistic organisation of Switzerland, natural history collections are located and curated in numerous institutions. So far, no common strategy for digitisation, documentation and long-term data archiving has been developed. This shortcoming has been widely identified by concerned parties. Under the lead of the Swiss Academy of Sciences, several organisations have assembled information about Swiss natural history collections. They identified measures to be taken to promote the scientific and educational potential of natural history collections in Switzerland (Beer et al. 2019). With a national initiative, the Swiss Natural History Collections Network (SwissCollNet) aims to unite Swiss natural history collections under a common vision and with a common strategy. The goal is to promote the collections themselves and to harness the scientific and educational potential of these collections for research and training. SwissCollNet consists of representatives of research, teaching, museums and botanical gardens, the data centers for information on the national fauna and flora, the Swiss Systematics Society and the Swiss node of GBIF, the Global Biodiversity Information Facility. The initiative aims to foster research on natural history collections. It will provide a single decentralised data infrastructure framework for Swiss research related to natural history. It will help to harmonise nationwide collection data management, digitisation and long-term data archiving. It will facilitate identification of specimens and revision of taxonomic groups. New research techniques, fast-evolving computer technologies and internet connectivity, create new opportunities for deciphering and using the wealth of information housed in Swiss and international collections. The development of an agreed strategy and research priorities on a national scale will allow fluent, fluid and permanent collaboration across all Swiss natural history collections by promoting interoperability and unified access to collections as well as creating opportunities for scientific collaboration and innovation. This national approach will create an internationally compatible research data infrastructure, while respecting and integrating regional and decentralized conditions and requirements. Thus, it will maximize the impact for science, policy and society.