DOI: 10.3897/rio.12.e195997

Authors: Dragan Ivanovic, Grischa Fraumann, Jennifer Dusdal, Kim Holmberg, Vladimir Trajkovik, Houcemeddine Turki, Colin Layfield, Stevo Popovic, Haris Memisevic, Lidija Ivanovic, Tim Engels, Georgia Kapitsaki, Cristina Huidiu, Đilda Pečarić, Romain David, Rossana Morriello

Abstract: The open science paradigm, digitalisation, interdisciplinarity and internationalisation have significantly changed the research process, collaboration, dissemination and impact of scholarly work in the 21st century. Research impact assessment should include new metrics based on Web 2.0 channels suffering from the following issues: data quality (i.e. accuracy, coverage, comprehensiveness), heterogeneity of data sources and APIs and potential manipulation (i.e. metrics gaming). Although the Findable, Accessible, Interoperable and Reusable (FAIR) principles were designed for research data, they can also be applied to research impact metrics to increase their discoverability and reusability. The main aim of this European Cooperation in Science and Technology (COST) Action is to remove barriers for the wider adoption and reusability of metrics, based on Web 2.0 technologies, which are a significant and vital part of research ecosystems. These metrics can serve as the basis for enhanced research impact assessment and, thus, improve recognition of excellence and foster the further development of science and society. Although Scientometrics, based on Web 2.0, is a paradigm that is over 10 years old, it has not yet been widely adopted. Therefore, a plan or roadmap for transition to Scientometrics 2.0 is needed. This should include recommendations for overcoming the challenges associated with new research impact metrics, as well as frameworks for the evaluation of new metrics and data sources. These challenges include the heterogeneity and comprehensiveness of metrics data sources, the varying quality of metrics data, metrics data gaming etc. Due to the multifaceted nature of these challenges, the Action proposes to create synergies between all interested actors: researchers, research software engineers, librarians, representatives of metrics data providers and policy-makers.This article presents an edited version of the original funding proposal submitted to the COST Open Call 2021.

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DOI: 10.3897/arphapreprints.e197166

Authors: Martin Jung, Maximilian Wolschlager, Louise O'Connor, Matea Osti, Carla Freund, Kyle J Brumm, Piero Visconti

Abstract: Ambitious commitments under the European Biodiversity Strategy for 2030, including protecting at least 30% of land area and restoring 20% of ecosystems, are an opportunity to halt and reverse biodiversity loss. Achieving these objectives would benefit from coordinated, integrated and biodiversity-inclusive spatial planning approaches to identify where conservation and restoration actions will be most effective and resilient. Systematic conservation planning (SCP) provides such a framework, but its outputs are often complex and need to be translated into actionable and interpretable information for decision makers. Here in the context of the NaturaConnect project, we developed stand-alone policy support tools designed to bridge this gap between science, policy and practice, specifically tailored to the implementation of the EU Biodiversity Strategy in the terrestrial realm. Specifically, we developed two interactive platforms, described in this deliverable: NaturaConnector and PriorityCheck. Both tools are web-based and enable to visualise spatially explicit prioritisation outputs generated using the prioritizr R-package. We produced multiple spatial scenarios reflecting different objectives and planning assumptions, allowing exploration of trade-offs and synergies across different scenarios. NaturaConnector provides a web-based interface that enables users to explore our prioritisation outputs interactively and to better understand the implications of alternative planning strategies. It allows users to compare different scenarios, adjust planning criteria, and visualise how priorities shift under different objectives, assumptions, and implications in terms of performance across a range of ecological, geographic and socio-economic indicators. To facilitate uptake and dissemination, the platform also includes a link to downloadable infosheets for 39 countries and 10 biogeographic regions. The infosheets showcase consensus prioritization outputs as well as an assessment of the performance of the spatial planning solutions with some key takeaways specific to each country or geographic region. PriorityCheck is an online tool that enables users (stakeholders, practitioners, and experts) to engage directly with the prioritisation outputs, including querying the species and habitats composition at each site, and provide spatially explicit feedback to the research team on selected priority areas, regarding their implementation challenges, feasibility, and local relevance and value for conservation or restoration. Based on this spatially-explicit feedback entered by stakeholders and regional experts on PriorityCheck, we then further refined and improved the spatial prioritisation outputs.

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DOI: 10.3897/rio.12.e188665

Authors: Nathaniel Narra, Grischa Fraumann, Raul Palma, Marcin Płóciennik, Michał Błaszczak, Yuansong Qiao

Abstract: The main goal of 'CAgriLab – Consolidated virtual living lab platform for knowledge sharing and adaption in regenerative agriculture' is to establish a consolidated virtual living lab to enable real-time data sharing and collaboration amongst isolated living labs, lighthouses and farmers for exchanging regenerative agriculture knowledge and practices across diverse environments, leveraging Digital Twin (DT), dataspace, blockchain and artificial intelligence (AI) technologies.The CAgriLab consortium is composed of four organisations from three different EU Member States (Ireland, Poland and Finland). It consists of one university of technology, one university of applied sciences, one research centre and one industrial partner. CAgriLab will leverage existing partner projects to pilot and evaluate a decentralised dataspace platform across three countries, covering different use cases i.e. living labs (Ireland), lighthouses (Finland) and farms (Poland). Three sets of DTs (three countries) will be created and connected by the platform.This article contains an edited version of the original funding proposal. The proposal was evaluated by an International Evaluation Committee and selected for funding by the Call Steering Committee.

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DOI: 10.3897/rio.12.e176590

Authors: Beatrice Bock

Abstract: Fungi exhibit diverse trophic strategies, ranging from obligate symbiosis to saprotrophy, with some taxa capable of occupying multiple ecological roles. Manually identifying trophic versatility from literature is time-consuming and difficult to scale. Here, we present a pilot workflow that automates the classification of fungal trophic modes using transformer-based language models. A curated dataset of 56 fungal ecology abstracts was manually labelled as dual (occupying multiple trophic modes) or solo (restricted to one mode) and used to fine-tune four models: BioBERT, BERT-base-cased, BERT-base-uncased and BiodivBERT. Stratified 5-fold cross-validation revealed that BioBERT and BERT-base-cased performed equally well (~ 89% accuracy, balanced precision and recall), highlighting the importance of case sensitivity in taxonomic text. BiodivBERT and uncased BERT models underperformed, indicating that domain adaptation alone is not sufficient. This pilot study emphasises reproducibility, transparency and open data integration, offering a generalisable proof-of-concept for linking literature-derived ecological information to existing fungal trait databases such as FUNGuild and FungalTraits. All code and data are openly available to support reuse and scaling to larger datasets.

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DOI: 10.3897/rio.12.e183261

Authors: Enriko Kroon, Ricardo Borges, Rômulo de Jesus, Stefan Kuhn

Abstract: It has been shown that it is possible to train a (simple) neural network to classify nuclear magnetic resonance spectra by a substructures either being part of the chemical structure measured or not. We now explore the interpretability of such models using techniques from explainable AI, specifically Grad-CAM. We show that those techniques do not give ideal results in the context of NMR, which would be able to identify individual peaks. On the other hand, they enable a better interpretation of the results than those metrics just based on "right or wrong". We can also confirm the result from our previous work, that the trained network performs well for pure compounds, but its generalisability to mixtures is questionable, a limitation that could only be assumed in the original study.

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DOI: 10.3897/rio.11.e160367

Authors: Rajapreethi Rajendran, Claus Weiland, Jonas Grieb, Soulaine Theocharides, Sam Leeflang, Wouter Addink, Sharif Islam

Abstract: The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labelling and categorisation of NSC's digital assets.To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine-learning models for complex tasks, such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine-learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles.This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine-learning models, such as Mask R-CNN and YOLO11, are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. The determination of quantitative characteristics of plant specimens, such as measuring leaf area or the timestamp of the floral transition, opens up herbarium data for reuse in the large prognosis platforms currently developed in the framework of the Common European Data Spaces. In this way, plant trait data mobilised from natural science collections can improve the predictive capability of the vegetation model components of climate-related data spaces.

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DOI: 10.3897/rio.11.e179253

Authors: Ronny Gey, Daniel Mietchen, Oliver Karras, Tim Wittenborg, Moritz Schubotz, Jan Bumberger

Abstract: Across essentially all fields of research, many aspects of the respective research processes – whether experimental, theoretical, empirical or outright computational – are closely related to software. Yet the process of finding software that is directly suitable or at least a good starting point for a given research task is cumbersome.This project aims to develop a community-driven system that provides potential users of research software with a diversity of pathways towards actually finding software that closely matches their research needs if such software exists. Conversely, it will provide software developers with mechanisms to make their software findable for research-related tasks and it will highlight mismatches between software supply and demand for specific tasks.To this end, we will document how various stakeholders of the research landscape have been searching for – or stumbling upon – research software so far, identify variables associated with successful search outcomes and build workflows that assist in describing software and associated concepts in a standardised fashion. These descriptions will then be aligned across various sources of relevant information and integrated into Wikidata, the knowledge graph that anyone can edit and that already contains considerable breadth and depth of information related to research, software and their interactions.While keeping an eye on similar approaches to software discovery that might work in parts of the research ecosystem, existing Wikidata content and workflows will be reviewed and built upon. Additional documentation, tooling and workflows will be developed to enrich, expand, curate, query and explore this content, both for specific use cases and with ongoing engagement of the communities involved in research software, open data or collaborative curation. Within its three years, the project seeks to establish a dedicated community overseeing a well-documented and smoothly running infrastructure for software discovery and to devise a plan for how this can be sustained for the longer term.

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DOI: 10.3897/arphapreprints.e180864

Authors: Sara Si-Moussi, Marianne Tzivanopoulos, Gabrielle Deschamps, Maxime Hoareau, Julien Renaud, Rémi LEMAIRE-PATIN, Wilfried Thuiller

Abstract: This deliverable outlines the creation of high-resolution (1km²) distribution maps for species and habitats across Europe, crucial for biodiversity conservation, policy compliance, and ecosystem management. Employing advanced Species Distribution Models (SDMs) and Habitat Distribution Models (HDMs), the task addressed plants, vertebrates, invertebrates, and all EUNIS Level 3 habitats.Species distribution modeling involved machine learning algorithms, carefully selected environmental variables, and spatially comprehensive occurrence datasets from GBIF, EVA, and other databases. Ensemble modeling techniques, spatial block cross-validation, and pseudo-absence generation ensured robust, reliable predictions, validated with metrics like True Skill Statistic (TSS).Habitat modeling similarly utilized environmental predictors (climate, topography, hydrography, geology, soil properties) alongside vegetation plot data from EVA and additional regional databases. Multi-class classification and ensemble forecasting methods provided high-quality predictive habitat maps validated externally and through cross-validation.Current and future scenarios (2050) were developed under varying climate and land-use trajectories (SSP1-RCP2.6, SSP3-RCP7.0), incorporating model uncertainty and expert-informed constraints. These maps support targeted conservation planning, monitoring programs, and decision-making, guiding efforts to enhance Europe's protected area network and biodiversity management.

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DOI: 10.3897/rio.11.e177037

Authors: Christoph Steinbeck, Nicole Jung, Felix Bach, Steffen Neumann, Sonja Herres-Pawlis, Johannes Liermann, Oliver Koepler, Christoph Bannwarth, Theo Bender, Thomas Bocklitz, Franziska Boehm, Christian Bonatto Minella, Frank Biedermann, Werner Brack, Ricardo Cunha, Paul Czodrowski, Franziska Eberl, Thomas Engel, Albert Engstfeld, Tillmann G. Fischer, Pascal Friedrich, Frank Glorious, Benjamin Golub, Christoph Grathwol, Rainer Haag, Johannes Hunold, Christoph Jacob, Jochen Johannsen, John Jollife, Stefan Kast, Carsten Kettner, Stefan Kuhn, Giacomo Lanza, Jan Lisec, Georg Manolikakes, Ricardo Mata, Jens Meiler, Matthias Müller, Ralph Müller-Pfefferkorn, Jochen Ortmeyer, Wendy Patterson, Jürgen Pleiss, Annalisa Riedel, Jens Riedel, Ulrich Schatzschneider, Leonie Schuster, Peter Seeberger, Johann-Nikolaus Seibert, Peter Stadler, Philip Strömert, Robert Strötgen, Patrick Théato, Nicolas Tielker, Pierre Tremouilhac, Hans-Georg Weinig, Wolfgang Wenzel, Kirsten Zeitler

Abstract: The first funding period of NFDI4Chem established a robust foundation for research data management (RDM) in chemistry by promoting FAIR data principles and creating a cohesive infrastructure to capture well-annotated data early in the lab through electronic lab notebooks (ELNs) and making this data available in public repositories. Key achievements include standardised data formats and metadata, a federated repository environment, and improved data visibility and accessibility. Training programs and outreach have significantly increased awareness and adoption of best RDM practices. In the second funding period, the consortium aims to advance these achievements by consolidating this infrastructure, developing a model for its sustainable maintenance and operation, and fostering cultural change for its widespread adoption. Goals include ensuring seamless data workflows from laboratories to open repositories, enhancing interoperability, and supporting innovative research through AI-ready data. The work plan is organised into six task areas (TAs). TA1 (Management) provides leadership and supports all other TAs in achieving their objectives. TA2 (Smart Lab) aims to develop a fully digital research environment, including an ELN as a modular platform. This environment will support data collection, management, storage, analysis, and sharing. Integrating devices and external resources will enable seamless data transfer to repositories. TA3 (Repositories) will consolidate the repository ecosystem. The goal is to integrate repositories into a federated system for better accessibility and interoperability, ensuring long-term data availability and sustainability. TA4 (Metadata, Data Standards, and Publication Standards) focuses on developing and promoting new data and metadata standards in an international community process. This includes applying ontologies to create a semantic foundation for linking research data, making it machine-readable and enabling knowledge graphs. TA5 (Community and Training) is dedicated to fostering a cultural shift towards digital chemistry through continuous engagement, collecting requirements, and providing extensive training and support through workshops and open education resources. It will promote FAIR-compliant machine learning applications, embedding RDM into academic curricula to ensure future scientists are well-versed in these practices. TA6 (Synergies and Cross-Cutting Topics) aims to enhance collaboration across NFDI consortia and beyond. This includes developing ontologies, terminology services, the search service, and other cross-cutting solutions, integrating these developments into existing infrastructure, enabling interdisciplinary data harmonisation and fostering machine learning applications.

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DOI: 10.3897/rio.11.e176476

Authors: Tina Heger, Stella Berger, Jonathan Jeschke, Chris Kittel, Peter Kraker, A. Makower, Daniel Mietchen, Jens Nejstgaard, Maxi Schramm

Abstract: Water is vital for life on Earth, but aquatic environments worldwide are facing critical challenges that cause severe problems for biodiversity and human well-being. These challenges include, for example, water pollution, habitat degradation, escalating water and air temperatures, salinisation of freshwaters, ocean acidification and invasive species. Since these stressors interact in complex ways, developing predictions and mitigation measures is difficult. Mesocosm experiments, offering controlled, yet realistic settings, are crucial for understanding and mitigating the impact of various stressors and their combinations on aquatic ecosystems. Mesocom facilities are key Research Infrastructures (RI), as they bridge the gap between laboratory experiments and natural systems allowing studies of highly complex environments comparable to natural ecosystems, while still offering controlled and replicated settings not available in natural systems.The AQUACOSM-RI consortium, comprising over 60 individual state-of-the-art mesocosm facilities at 28 host institutions across Europe, has therefore been instrumental in advancing aquatic environmental research across climate zones including marine, brackish and freshwater ecosystems. In addition, the EU H2020-INFRAIA projects AQUACOSM (CORDIS No. 731065) and AQUACOSM-plus (CORDIS No. 871081, www.aquacosm.eu) have developed a virtual network beyond Europe of presently > 85 host institutions with > 120 aquatic mesocosm facilities around the world, www.mesocosm.org. However, the rich, yet disconnected resources in aquatic mesocosm-based experimental research and mitigation approaches need to be combined in a modern, visible and accessible way.The project AQUANAVI (Navigating Grand Challenges and their Mitigation using Aquatic Experimental RIs) aims to enhance existing efforts by creating an interactive atlas of aquatic mesocosm facilities and related mesocosm-based experimental research. Integrating data, publications, reports and information on mesocosm facility capacities generated by the AQUACOSM consortium and other mesocosm facilities in Europe and beyond, AQUANAVI will facilitate fast discovery of resources and unused potentials of available mesocosm facilities in a modern, visible and accessible way that is presently not available. Such a multidimensional tool is expected to enable novel collaborations and a much faster setup and execution of connected and/or distributed experiments and efficient development of environmental mitigation strategies. Built upon the AQUACOSM-RIs and their encompassing data and information repository as well as scientific and technical competence, while also leveraging related infrastructures like AnaEE, EMBRC, JERICO-RI and eLTER, AQUANAVI will provide a comprehensive resource platform to more effectively explore available resources for aquatic experimental research.AQUANAVI will bridge this wealth of scientific data, expertise and mesocosm facility information through Hi Knowledge, an innovative analysis and visualisation platform that merges Wikidata, Open Knowledge Maps,and Scholia. Hi Knowledge harnesses the semantic capabilities of Wikidata to rapidly construct a FAIR and open corpus for a domain, based on a sophisticated conceptual classification system. Subsequently, Hi Knowledge incorporates visualisation components from Open Knowledge Maps and Scholia, allowing researchers to smoothly navigate information using cutting-edge visualisation techniques, artificial intelligence and knowledge synthesis methods.Open and collaborative by design, AQUANAVI’s architecture will engage a broad range of research communities. By consolidating data and information from diverse RIs, the platform will leverage and enhance the AQUACOSM and related research infrastructures, securing the reusability and interoperability of existing data collections and better exploration of existing RIs in the future. Compliant with FAIR principles and EOSC requirements, AQUANAVI will ensure the long-term sustainability and openness of its resources, enriching both the ENVRI services portfolio and the broader scientific community. In summary, AQUANAVI will empower researchers and stakeholders to implement measures to mitigate the effects of climate change and other Grand Challenges facing aquatic environments, serving as a key resource within and beyond the European research area.

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DOI: 10.3897/rio.11.e171656

Authors: Daniel Mietchen, Sheeba Samuel

Abstract: Computational notebooks like Jupyter have transformed scientific and educational workflows in computational fields by combining code, text, and visualizations. They have also become a popular mechanism to share computational workflows. However, ensuring their reproducibility remains a persistent challenge due to often insufficiently documented direct and indirect dependencies, missing data, and inconsistencies in execution environments. Existing datasets lack the multimodal, fine-grained structure needed for AI applications. FAIRJupyter4AI aims to address this gap by creating a large-scale, AI-ready corpus of Jupyter notebooks enriched with executable code, markdown, outputs, and structured annotations. The project integrates these into a hybrid knowledge graph (KG) that incorporates symbolic, statistical, and execution-based representations. Key objectives include: curating diverse notebooks (initially Python, later R, with provisions for additional languages); automating reproducibility testing; building a KG for cross-notebook queries; training AI models for tasks like error repair and notebook generation; and fostering community use via APIs and integration with community platforms like NFDI or Hugging Face.The project will be implemented using the infrastructure established by the NFDI Basic Service Jupyter4NFDI, in the upcoming Integration Phase of which (October 2025-September 2027) the applicants are actively involved. Its central JupyterHub provides cross-consortial and cross-institutional access to scalable computing and data resources and associated software stacks for both research and training purposes.The FAIRJupyter4AI work programme is structured around five interlinked work packages: (1) Data Collection & Curation, (2) Reproducibility Assessment, (3) Knowledge Graph Development (4) AI Model Training, and (5) Communication, Community & Sustainability. Key innovations include continuous updates and enrichment pipelines (avoiding static snapshots), unifying multimodal content for AI, and bridging reproducibility with AI. Building on prior work involving 27,000+ notebooks and the FAIR Jupyter Knowledge Graph, FAIRJupyter4AI will curate, annotate and release over 20,000 notebooks that are research-related and openly licensed. In addition, we will share a metadata corpus for 50,000 research-related notebooks, along with open-source tools, models, and associated documentation. By making Jupyter notebooks metadata FAIR, reusable, and machine-understandable, this project will set a new standard for reproducible and AI-enhanced computational science, and it will open up new opportunities for learning and teaching about computational reproducibility across multiple domains of research.

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DOI: 10.3897/rio.11.e163817

Authors: Laurel Haak, Katherine Skinner, Kristen Ratan

Abstract: Research activities utilise and depend on interlocking infrastructures – tools, standards, protocols, and other systems and structures at local, national, and international scales that enable researchers to collaborate, analyze and share data and software, and discuss their research findings. Despite growing policy momentum towards open science, a significant challenge persists: a substantial portion of research infrastructure remains inherently closed or restrictive. This lack of openness undermines transparency, integrity, limits reproducibility, and constrains researchers’ ability to fully engage with each other. In this paper, we examine how research infrastructures can be designed to embed open principles throughout their development and operation, borrowing elements from Manufacturing Principles, Systemic Service Design, and Open Science frameworks. We propose an open-by-design reference framework for infrastructure builders and guidance for enabling, making visible, and incentivizing specific elements of “openness” within research infrastructures that are prerequisites for a thriving research and knowledge ecosystem.

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DOI: 10.3897/rio.11.e163815

Authors: Gonzalo Bravo, Gregorio Bigatti, Mariana Lozada, Luke Thompson, Juan Livore, María Mendez, Lorena Arribas, Lino Bigatti, Tyler Christian, Erasmo Macaya, Edgardo Londoño-Cruz, Nicolas Moity, Juan Cruz-Motta, Augusto Flores, Gabriela Vélez-Rubio, Maria Palomo, Cesar Cordeiro, Franciane Pellizzari, Maritza Cárdenas-Calle, La Daana Kanhai, Ivonne Vivar Linares, Patricia Gil-Kodaka, Linsey Martinez, Pablo Sugliano, Agostina Trigo, Juan Zottola, Dulce Blanco, Matias Tricase, Nadia Bravo, Mariana Degrati, Camila Tavano Formigo, Frank Muller-Karger, Enrique Montes

Abstract: The Marine Biodiversity Observation Network Pole to Pole of the Americas (MBON Pole to Pole) brought together 30 participants from 10 countries in Patagonia, Argentina, to strengthen observing capacity of coastal biodiversity across the Americas. The network held a five-day workshop focused on three core components: standardized rocky intertidal photo-quadrat surveys, low-cost environmental DNA (eDNA) sampling, and affordable plankton imaging tools. Participants included researchers, park rangers, and conservation practitioners fostering a collaborative and inclusive environment. Key outcomes included field validation of protocols, identification of context-specific methodological adaptations (e.g., for low tidal amplitude areas), adoption of novel tools for monitoring marine life, and strategies for broader participation and data harmonization. The workshop highlighted the potential of simple, replicable methods to support long-term monitoring, and emphasized the value of shared protocols, tools, and open data for building a more connected and resilient regional observation network.

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DOI: 10.3897/rio.11.e154845

Authors: Abigail Goben, Kristin Briney

Abstract: While funders and publishers continue to expand requirements for data management planning and sharing, few books have been written for academic researchers and research trainees to help them understand both introductory or discipline-specific concepts and practices. In this annotated bibliography, we review currently available English-language data management books and identify the limitations and opportunities for future publications.

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DOI: 10.3897/arphapreprints.e157339

Authors: Soulaine Theocharides, Sam Leeflang, Wouter Addink, Sharif Islam

Abstract: Biodiversity research relies on physical specimens stored in natural science collections, which serve as enduring reservoirs of data about organisms and their environments. However, these reservoirs remain siloed. The concept of Digital Specimen addresses the challenges posed by the vast amount of disconnected digital biodiversity data available today. The existing approach involves converting analogue records into digital replicas stored in local databases, leading to isolated and fragmented datasets that are difficult to integrate and utilise efficiently. The Digital Specimen aims to overcome this by establishing an interconnected network of digital objects on the Internet. Digital Specimens are FAIR Digital Objects (FDOs), structured digital entities that adhere to the FAIR principles: Findable, Accessible, Interoperable, and Reusable. FDOs have the potential to enhance the accessibility and interoperability of data from natural science collections by providing unique identifiers, descriptive metadata, and defined operations. DiSSCo utilises the FDO framework to enhance the accessibility and interoperability of biodiversity research data from natural science collections. FDOs facilitate seamless data exchange by providing structured digital objects with unique identifiers, descriptive metadata, and defined operations. As part of making Digital Specimens FDOs, DiSSCO implemented FDO records, metadata records associated with a Persistent Identifier, which further enable machine actionability. A Digital Object repository was developed for the purposes of storing and acting upon digital specimens. Three technological pillars compose the repository: a relational database stores the latest version of the digital specimen and is used for retrieving specimens by their identifier; an indexing solution provides full search capabilities on digital specimens; and a document store holds previous versions of a digital specimen for provenance purposes. There are three ways a user may interact with the digital object repository: a REST API; a user-friendly web portal; and a DOIP server.To ingest data from multiple source systems, a harmonised data model was developed, called OpenDS. Built upon existing international standards like DarwinCore and ABCD, OpenDs accommodates complex structures necessary to capture information about multiple taxonomic identifications, events, agents, and relationships to other data sources. DiSSCo has decided to adapt the GBIF Unified Model (UM) for specimen data, ensuring interoperability and avoiding the development of potentially competing standards. By aligning with the GBIF UM, DiSSCo enhances interoperability with GBIF and promotes the establishment of a unified data modelling standard within the biodiversity community, facilitating seamless data exchange and integration with data aggregators like GBIF.

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DOI: 10.3897/rio.11.e156185

Authors: Denis Michez, Michel Bocquet, Philippe Bulet, Marie-Pierre Chauzat, Pilar De la Rúa, Reet Karise, Tomasz Kiljanek, Alexandra Klein, Marion Laurent, Elli Leadbeater, Marika Mänd, Anne-Claire Martel, Teodor Metodiev, Marija Miličić, Julia Osterman, Robert Paxton, Simon Potts, Sara Reverte, Marie-Pierre Rivière, Oliver Schweiger, Deepa Senapathi, Olga Tcheremenskaia, Simone Tosi, Ante Vujic, Dimitry Wintermantel, Mark Brown

Abstract: Wild fauna and flora are facing variable and challenging environmental disturbances. One of the animal groups that is most impacted by this, concerns pollinators. Pollinators face multiple threats, but the spread of anthropogenic chemicals (i.e. pesticides) form a major potential driver of these threats. WildPosh is a multi-actor, transdisciplinary project whose overarching mission and ambition are to significantly improve the evaluation of risk to pesticide exposure of wild pollinators, and enhance the sustainable health of pollinators and pollination services in Europe. As chemical exposure varies geographically, across cropping systems, inside the crop system and among pollinators, we will characterise exposure by doing fieldwork in 4 countries representing the four main climatic European regions, Mediterranean, Atlantic, Continental and Boreal climate in Germany, England, Estonia and Spain. We will also develop experiments in controlled conditions on different species of bees, syrphid flies, moths and butterflies, and collect in silico data on their traits and on toxicity of pesticides. With WildPosh, we aim to achieve the following objectives:1. Determining the real-world agrochemical exposure profile of wild pollinators at landscape level, within and among sites;2. Using integrated and controlled laboratory and semi-field experiments to characterise causal relationships between pesticides and pollinator health;3. Building an open database on pollinator traits/distribution and chemicals to define exposure and toxicity scenarios by developing databases on ecological traits and the spatial distribution of pollinators in relation to their potential exposure to pesticide;4. Proposing integrated systems-based risk assessment tools for risk assessment for wild pollinators; and5. Driving policy and practice through interactive innovation, meeting the need for monitoring tools, novel and innovative screening protocols for practice and policymaker use.

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DOI: 10.3897/rio.11.e155393

Authors: Elizabeth Adams, Natalie Raia, Saebyul Choe, Isaac Wink, Doug Curl

Abstract: State geological surveys create and steward valuable long-term earth and environmental science datasets and often serve as physical archives for material samples. Often funded directly through state legislatures, these agencies face varying degrees of support, nuanced regulations and public-serving missions that direct their research and day-to-day operations. Scientists at state geological surveys produce a range of outputs: datasets that may be stored internally, through an institutional repository or disseminated to broader community repositories and publications that may include both grey and peer-reviewed literature. This paper discusses a workshop held at the Kentucky Geological Survey to introduce researchers to data management, sharing and stewardship practices and to better understand obstacles to implementing such practices.

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DOI: 10.3897/rio.10.e134825

Authors: Tom August, Mario Balzan, Paul Bodesheim, Gunnar Brehm, Lisette Cantú-Salazar, Sílvia Castro, Joseph Chipperfield, Guillaume Ghisbain, Alba Gomez-Segura, Jérémie Goulnik, Quentin Groom, Laurens Hogeweg, Chantal Huijbers, Andreas Kamilaris, Karolis Kazlauskis, Wouter Koch, Dimitri Korsch, João Loureiro, Youri Martin, Angeliki Martinou, Kent McFarland, Xavier Mestdagh, Denis Michez, Charlie Outhwaite, Luca Pegoraro, Nadja Pernat, Lars Pettersson, Pavel Pipek, Cristina Preda, David Rolnick, Tobias Roth, David Roy, Helen Roy, Veljo Runnel, Martina Sasic, Dmitry Schigel, Julie Sheard, Cecilie Svenningsen, Heliana Teixeira, Nicolas Titeux, Thomas Tscheulin, Elli Tzirkalli, Marijn van der Velde, Roel van Klink, Nicolas Vereecken, Sarah Vray, Toke Thomas Høye

Abstract: The InsectAI COST action will support insect monitoring and conservation at the national and continental scale in order to understand and counteract widespread insect declines. The Action will bring together a critical mass of researchers and stakeholders in image-based insect AI technologies to direct and drive the research agenda, build research capacity across Europe and support innovation and application.There is mounting evidence that populations of insects around the world are in sharp decline. Understanding trends in species and their drivers is key to knowing the size of the challenge, its causes and how to address it. To identify solutions that lead to sustainable biodiversity alongside economic prosperity, insect monitoring should be efficient and provide standardised and frequently updated status indicators to guide conservation actions.The EU Biodiversity Strategy 2030 identifies the critical challenge of delivering standardised information about the state of nature and image-based insect AI can contribute to this. Specifically, the EU Nature Restoration Law will likely set binding targets for the high resolution data that cameras can provide. Thus, outputs of the Action will contribute directly to EU policies implementation, where biodiversity monitoring is considered a key component.The InsectAI COST Action will organise workshops, conferences, short-term scientific missions, hackathons, design-sprints and much more, across four Working Groups. These groups will address how image-based insect AI technologies can best address Societal Needs, support innovation in Image Collection hardware, create standardised approaches for Image Processing and develop novel Data Analysis and Integration methods for turning data into actionable insights.

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DOI: 10.3897/rio.10.e138833

Authors: Lorna Mugambi, Gabriel Kiarie, Jason Kabi, Ciira wa Maina, Suvodeep Mazumdar

Abstract: The DSAIL-GeJuSTA Data Science Education Workshop was a joint initiative by the Centre for Data Science and Artificial Intelligence (DSAIL) and Gender Justice in STEM Research in Africa (GeJUSTA). GeJUSTA is a programme funded by the International Development Research Centre (IDRC) that is working towards increasing the representation of women in STEM. The workshop was held on 9 November 2023, during the 7th DeKUT International Conference on Science, Technology, Innovation and Entrepreneurship (STI&E) at Dedan Kimathi University of Technology (DeKUT). The conference ran from 8-10 November 2023. The event successfully convened 31 participants. The composition of the attendees was diverse, ranging from data-science educators, industry participants using data science, researchers who use data science and students in a myriad of courses, including engineering and pharmacy. The primary focus of the workshop was to have a discussion with the attendees and share practices around designing data-science curriculum, strategies for achieving gender equity in data-science education, addressing new technological challenges in education and fostering multidisciplinary approaches to data-science education. This report encapsulates the collective vision of the workshop participants, whose contributions have set the stage for progressive strides in data-science education.

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DOI: 10.3897/rio.10.e129578

Authors: Zoe Ryan, Emily Clark, Beatrice Cundiff, Joslyn Nichols, Maya Mahoney, Nkosi Evans, Thomas Campbell, Danny Kreider, Matt von Konrat

Abstract: Over the last decade, access to global data has become increasingly critical for research, allowing insights into diverse biological, environmental and societal questions at a macro scale. Digitisation has greatly enhanced the use of herbarium data in the analysis of species distributions and ecological niche modelling. Yet, sources on modelling and mapping methodology using open-source software is greatly lacking for beginners. We have created a replicable and thorough tutorial to visualise species occurrence data and exploratory analysis that was developed by undergraduates with broad backgrounds and levels of experience. This tutorial integrates the open-source programmes QGIS, MaxEnt and R to develop distribution maps, using bryophytes as a case study, to promote the accessibility of open-source software and remote access learning. This tutorial has already set the foundation for further research into distribution modelling of rare Illinois bryophytes to better understand the potential impact of climate change.

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DOI: 10.3897/arphapreprints.e138026

Authors: Andrea Tarallo, Giuseppe Turrisi, Davide Raho, Ilaria Rosati

Abstract: This paper presents the collaborative efforts of Working Group 1 (WG1 - Occurrence and detection of zoosporic parasites) and Working Group 2 (WG2 - Drivers underlying the dynamic of zoosporic diseases) within the ParAqua COST Action, a research initiative focused on understanding zoosporic parasites and their interactions with algae. Initially conceived as an interactive web page, one of the deliverables of WG1 has evolved into a centralised database for data gathered by the scientific community of ParAqua. In this paper we present a summary of our work, carried out from July 2022 to October 2023. After gathering and analysing community needs, we have harmonised data collection, designed and implemented the database structure. The upcoming steps involve the data upload and integration process into the database and creating a Graphical User Interface to query the database. The paper focuses on the methodology and discusses the challenges faced during the activity. As part of our commitment to promoting the practices of open science, we wrote this paper to document the entire process of the database development. Our aim is to provide a clear pathway for others to expand, challenge, and refine our database designing process, encouraging a dynamic exchange of ideas that propels the field forward. In a broad sense, this research contributes to the understanding of algae-parasite interactions, by providing a unique experience of data mobilisation and integration in the field of algae parasites research.

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DOI: 10.3897/rio.10.e134840

Authors: Talia Karim, Erica Krimmel, Holly Little, Lindsay Walker

Abstract: A stakeholder engagement workshop was held in May 2024 as part of the "Community-driven enhancement of information ecosystems for the discovery and use of paleontological specimen data" project, which is funded under the United States National Science Foundation (NSF) Geosciences Open Science Ecosystem (GEO OSE) program. This report describes the activites and outcomes of the workshop.

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DOI: 10.3897/rio.10.e125086

Authors: Bekir Afsar, Kyle Eyvindson, Tuomas Rossi, Martijn Versluijs, Otso Ovaskainen

Abstract: Forests are crucial in supporting biodiversity and providing ecosystem services. Understanding forest biodiversity dynamics under different management strategies and climate change scenarios is essential for effective conservation and management. This paper introduces the Forest Biodiversity Dynamics Prototype Digital Twin (pDT), integrating forest and biodiversity models to predict the effects of management options on forest ecosystems. The primary objective is to identify optimal management strategies that promote biodiversity, focusing on conservation and adaptation to different climate conditions. We start with the case of Finnish forests and bird species and plan to expand to include more European countries and a variety of species as the pDT is further developed.

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DOI: 10.3897/rio.10.e124579

Authors: Taimur Khan, Ahmed El-Gabbas, Marina Golivets, Allan Souza, Julian Gordillo, Dylan Kierans, Ingolf Kühn

Abstract: Invasive alien species (IAS) threaten biodiversity and human well-being. These threats may increase in the future, necessitating accurate projections of potential locations and the extent of invasions. The main aim of the IAS prototype Digital Twin (IAS pDT) is to dynamically project the level of plant invasion at habitat level across Europe under current and future climates using joint species distribution models. The pDT detects updates in data sources and versions of the datasets and model outputs, implementing the FAIR principles. The pDT’s outputs will be available via an interactive dashboard. All input and output data will be freely accessible.

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DOI: 10.3897/rio.10.e125192

Authors: Desalegn Chala, Erik Kusch, Claus Weiland, Carrie Andrew, Jonas Grieb, Tuomas Rossi, Tomas Martinovic, Dag Endresen

Abstract: Amidst population growth and climate-driven crop stresses such as drought, extreme weather, fungal and insect pests, as well as various crop diseases, ensuring food security demands innovative strategies. Crop wild relatives (CWR), wild plants in the same genus as the crop as well as wild populations belonging to the same species as the crop, offer novel genetic resources crucial for enhancing crop resilience against these stress factors. Here, we introduce a prototype digital twin (pDT) to aid in searching and utilising CWR genetic resources. Using the MoDGP (Modelling the Germplasm of Interest) tool, the pDT enables mapping geographic areas where stress-tolerant CWR populations can be found. With its graphical user interface, it offers flexibility in selecting genetic resources from CWR tailored to enhance resilience of various crops against diverse stress factors.

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DOI: 10.3897/arphapreprints.e124640

Authors: Julian Lopez Gordillo, Patrik Lauha, Ari Lehtiö, Ossi Nokelainen, Anis Rahman, Allan Souza, Jussi Talaskivi, Gleb Tikhonov, Aurélie Vancraeyenest, Otso Ovaskainen

Abstract: Bird populations respond rapidly to environmental change making them excellent ecological indicators. Climate shifts advance migration, causing mismatches in breeding and resources. Understanding these changes is crucial to monitor the state of environment. Citizen science offers vast potential to collect biodiversity data. We outline a project that combines citizen science with AI-based bird sound classification. The mobile app records bird vocalizations that are classified by AI and stored for re-analysis. Also, it shows a shared observation board that visualizes collective classifications. By merging long-term monitoring and modern citizen science, this project harnesses both approaches’ strengths for comprehensive bird population monitoring.

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DOI: 10.3897/rio.10.e112227

Authors: Octavio Mesa-Varona, Carolina Plaza-Rodríguez, Lars Valentin, Matthias Filter

Abstract: Epidemiological outbreak investigations often prove to be lengthy and inconclusive due to the time-consuming nature of the currently-used approaches. An alternative approach to address these challenges could involve the application of algorithms to support authorities and food business operators by providing timely, relevant and reliable information. Algorithms, such as gravity models, could be applied as commodity trade models, but they require a large amount of reliable and consistent data on trade for generating projections at international, national or even regional level. Several trade databases, such as UN COMTRADE, EUROSTAT, BACI, CHELEM and GTAP, provide information on trace, albeit with variations in the provided information and in the structure. However, it is worth noting that not all of these databases are freely accessible and data management can pose challenges, hampering the access to the trade data. WarenstromInfo (WI) was created as a software solution that allows easy trade data extraction and visualisation for application in different areas, such as outbreak investigations.WarenstromInfo (WI) is an application tool that automatically extracts, decodes and visually displays trade flow data from EUROSTAT "EU trade since 2002 by statistical procedure, by HS2-4-6 and CN8 (DS-059322)" (hereinafter referred to EUROSTAT) and the BACI databases, based on user input.WI was developed by using the open-source desktop software KNIME Analytics Platform. WI offers the flexibility to be executed either as a web service on a KNIME Web Server infrastructure or as a local resource.To integrate the BACI database into WI, the database is annually downloaded as csv files, rebuilt as a SQLite database and hosted locally into the KNIME Web Server Infrastructure. In order to optimise storage space on the KNIME server, this SQLite database specifically includes only agrifood data, reflecting the tool´s focus. However, if new objectives are established, this database can be expanded. Further, data of the SQLite database can be customised by executing the WI workflow locally, enabling the user to expand the database at any time.In contrast to BACI, trade data extraction from the EUROSTAT database is performed via the EUROSTAT’s API (Application Programming Interface) applying GET requests and XML data management.WI displays four User-friendly Interfaces (UIs) designed with interactive KNIME nodes that facilitate the input of variables. The extracted trade flow data are shown through interactive tables directly within the UIs. This feature enables users to easily explore the data in a structured and user-friendly manner. Additionally, WI incorporates the extracted trade flow data into maps. These maps provide a visual representation of the data, allowing users to gain insights and identify patterns and trends geographically. Both, the data table and the maps, can be downloaded as a single Excel file (containing multiple preformatted tables) and as png files, respectively.

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DOI: 10.3897/arphapreprints.e116672

Authors: Reagon Karki, Andrea Zaliani, Philip Gribbon, Carolina Simon, Jose Ramon Macias, Bugra Özdemir, Aastha Mathur

Abstract: This Science Project (SP) brings together three different domains of life sciences with the aim to create reproducible workflows, tools and web-services for data visualization. This SP focuses in building resources for handling data from bioimaging, structural and bio-chemical studies. The Euro-Bioimaging will implement a community standard cloud compatible open image data format and data submission workflow for high-throughput screening data. Whereas, Instruct-ERIC will develop a user-friendly web-service to access to multi-dimensional structural and imaging data. Lastly, EU-OpenScreen/Fraunhofer ITMP will create reproducible workflow for generating Knowledge Graphs that represent phenotype-chemotype of diseases. While these resources are being developed, the collaborators will also simultaneously harmonize the resources right from the beginning to enable FAIR data principles. This SP uses COVID-19 as a demonstrator, however the resources will be generalized for any disease of interest.

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DOI: 10.3897/arphapreprints.e116669

Authors: Bugra Özdemir, Aastha Mathur, Johanna Bischof, John E. Eriksson, Jean-Karim Hériché, Josh Moore, Christian Tischer, Antje Keppler

Abstract: File formats incompatibility has become a major obstacle in biological imaging, complicating downstream processes such as image processing and analysis. One way to address this challenge is to convert the acquired image data into standard image file formats. Here we introduce BatchConvert, a command line tool for parallelised conversion of image collections into OME-TIFF or OME-Zarr using the workflow management system Nextflow. BatchConvert offers functionalities such as remote input-output support, optional execution on Slurm clusters and pattern-based filtering of input files. Conversion can be coupled to image concatenation, allowing selected images to be merged along specified dimensions. Support for remote locations includes an option to submit the output data to S3-compatible object stores or public archives such as BioImage Archive. Overall, BatchConvert is a flexible tool for researchers who are routinely managing and analysing large multidimensional image data that is either locally or remotely stored.

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DOI: 10.3897/arphapreprints.e116230

Authors: Marco Visani

Abstract: This study is centered on Natural Products (NPs) - specific chemicals synthesized by living organisms. These NPs hold significant importance in various domains, notably medicine, agriculture, and ecology. A primary resource for our research is the LOTUS database, which catalogues a vast array of NPs and their occurrence. Yet, a gap exists: there are no existing model to predict the occurrence of these NPs across different species.In our initial strategy, the occurrence of natural products was viewed as a collection of observations and their associated variables. Although simple, this strategy immediately showed its limits when dealing with the complex nature of NPs. We switched to an advanced graph-based method after seeing the necessity for a more thorough strategy to accurately represent the intricate interactions governing NPs expression. When considering species in a phylogeny or molecular pathways, the graph-based method perceives data as a network of connected entities, offering a far more logical and natural way of thinking. By employing this better methodology, we have developed a more effective approach for investigating the intricate world of Natural Products. We hope that this strategy will open up new research directions and possibly result in ground-breaking NP-related findings.

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DOI: 10.3897/arphapreprints.e115047

Authors: Arofan Gregory, Joachim Wackerow, Hilde Orten

Abstract: The Climate Neutral and Smart Cities project is part of the EOSC Future WP 6.3, exploring the best approaches for sharing data within cross-domain research projects. This paper looks at the implications for metadata exchange in a cross-domain research project, as explored in the project prototype. Cross-domain standard metadata is meeded to support collaborative research teams combining a mix of expertise, particularly around data lineage. 

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DOI: 10.3897/arphapreprints.e114920

Authors: Mathias Dillen, Andreas Plank

Abstract: In this task, the aim is to develop a workflow that should facilitate the linking process of collector name strings to PIDs for those collectors. Such a workflow should help scale up the number of links being made, make the process more efficient and should take advantage as much as possible of existing work and infrastructures, so as not to reinvent the wheel. As such, the work can be roughly split into a few subtasks:- Make existing linking workflows more easily implementable in other contexts and by other infrastructures. This includes finding ways for such workflows to produce links that can easily be published, i.e. in a standardised format compatible with existing infrastructure. The suitability of different infrastructures for making established links available should also be assessed.- Establish, document and improve the comprehensiveness, findability and interoperability of the content in PID-minting resources, in particular Wikidata as it can be edited openly.- Refine the decision making process of establishing links, by implementing and improving the methods that can be used to validate potential links.In this document, the focus lies on linking people. We will propose a workflow to 'roundtrip' links established through the Bionomia platform back to the collections holding the attributed specimens, as well as making them available for use by other BiCIKL infrastructures. We will also refine existing automated linking workflows and pilot the new functionalities on the (botanical) collections of the task partners. These refinements will be influenced by an assessment of the current state of Wikidata, investigated through shape expressions constructed from commonly used queries and from Wikidata records which have been linked in previous efforts such as the Botany Pilot, Bionomia and published specimen data to GBIF.

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DOI: 10.3897/arphapreprints.e114048

Authors: Maëlle Wannier

Abstract: The Digital Botanical Gardens Initiative (DBGI) embarks on an innovative journey to curate, manage, and disseminate digital data from living botanical collections, with an emphasis on mass spectrometric evaluations of chemodiversity. Using semantic web technology, this data is linked with relevant metadata, propelling ecosystem research and guiding biodiversity conservation efforts. Central to the success of DBGI is the creation of an interactive platform for both humans and machines to assimilate this knowledge. This report outlines our efforts to design the prototype of a data visualization portal intended to evolve into the DBGI dashboard. Starting with a Plotly Dash application, the project transitioned to a Node.js application leveraging Javascript, HTML, and CSS for enhanced customization. This provides a basis for future improvements, some of which are proposed in the report.

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DOI: 10.3897/arphapreprints.e107873

Authors: Christian Ohmann, Steve Canham, Kurt Majcen, Petr Holub, Gary Saunders, Jing Tang, Tanushree Tunstall, Philip Gribbon, Reagon Karki, Mari Kleemola, Katja Moilanen, Walter Daelemans, Pieter Fivez, Daan Broeder, Franciska de Jong, Maria Panagiotopoulou

Abstract: This publication details the workplan of the Science Project (SP) “COVID-19 metadata findability and interoperability in EOSC” (short: META-COVID) that is part of the Horizon Europe funded project EOSC Future. The COVID-19 pandemic has generated a huge variety of research activities, studies, and policies across both the life sciences (LS) and the social sciences and humanities (SSH). Useful insights from combining the data and conclusions from these different forms of research are, however, hampered by the lack of a common metadata framework with which to describe them. This is because different scientific disciplines have different ways of organising research activities. For example, the type of the research (e.g., hypothesis testing versus hypothesis generating) and the methodology chosen (e.g., experimental, survey, cohort, case study) are key elements in understanding the data generated and in supporting its secondary use. Another issue to be tackled is the integration of various sources of metadata related to parliamentary and social media metadata. In META-COVID, scientists from the LS and SSH domains gathered to discuss ways in which metadata could go beyond the description of the data itself to include the basic elements of the research process (“contextual metadata”) within the frame of the European Open Science Cloud (EOSC). The main outcomes of the SP will be: i) An inventory of metadata schemas applied across infrastructures and domains; ii) The development of a framework for a metadata model characterising the research approach and workflow across research infrastructures; iii) The application of the framework to selected COVID-19 use cases; iv) The development of an ontology of COVID-19 related topics from parliamentary data and social media.

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DOI: 10.3897/arphapreprints.e107169

Authors: Wouter Addink, Niki Kyriakopoulou, Lyubomir Penev, David Fichtmueller, Ben Norton, David Shorthouse

Abstract: United and coordinated efforts of biodiversity data infrastructures are needed to bring together various data forms from many different scientific areas. Biodiversity data are considered of great importance and use when they form a network of knowledge that can be seamlessly integrated and presented to various audiences, promoting both research and education. The Biodiversity Community Integrated Knowledge Library (BiCIKL) project seeks to maximise the potential of integrated data sources by striving to connect fragmented data derived from biological, paleontological, and geological specimens and collections, as well as all derived information such as literature in the form of taxonomic treatments, research papers etc., taxonomic information and molecular sequences provided by these infrastructures, under the umbrella of common digital practices and policies in curation, data sharing and open data access over different scientific fields. One of the main goals of BiCIKL is to create bi-directional links between various data types, a process enabled by: a) the adoption of globally unique and persistent identifiers upon agreement among all stakeholders, that link to digital specimen objects, collections, taxonomic treatments, people, sequence data and taxa, and b) implementation of the best practices for the generation, management and curation of interlinked data by the host infrastructures. At the same time, infrastructures should be readily discoverable and accessible by end users, providing data that enable re-usability. In this manual we give an overview of the best practices and their associated recommendations for infrastructures on making the most out of their services and data, for establishing a network of knowledge with other infrastructures, for servicing researchers, data providers and other end users. These guidelines have been developed in collaboration with the infrastructures through Technical RI Forum meetings organised in the context of the BiCIKL project. Practices and recommendations were divided into six categories: 1) modalities of access, 2) building communities and trust, 3) technology and standards, 4) versioning of APIs and their data, 5) bi-directional linking between infrastructures and 6) API design patterns and naming conventions. A second division into three user groups (Infrastructures, Data providers, Users e.g. Researchers, Developers and Citizen scientists) is presented in Appendix I.

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DOI: 10.3897/arphapreprints.e107168

Authors: Wouter Addink, Sharif Islam, Mathias Dillen, Anton Güntsch, Soulaine Theocharides

Abstract: Persistent Identifier (PID) systems are the foundation for achieving the FAIR Guiding Principles (“findable, accessible, interoperable and reusable”). As FAIR data and connecting different data classes (i.e. specimens, genomics, observations, taxonomy and publications) are essential aspects of the BiCIKL project, we need a PID system at least at the European level to create and maintain identifiers for the digital representation of specimens and samples, called Digital Specimens (DS) (Hardisty et al. 2022). The PID system provides the mechanism to ensure that identifiers are globally unique, persistent and resolvable. This system should also manage associated metadata, facilitate provenance, enable discovery, manage states and the life cycle of the PID, link to other derived data and digital content, and allow content providers to enforce metadata constraints. For the successful provision of a PID system, this design document has been created to guide us during the implementation and operation phases. The document is based on an earlier milestone (MS28) that was used for discussion and evaluation with potential end-users.

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DOI: 10.3897/arphapreprints.e107167

Authors: Kessy Abarenkov, Allan Zirk, Guy Cochrane, Vishnukumar Kadhirvelu, Joana Pauperio, Olaf Bánki, Jerry Lanfear, Filipp Ivanov, Timo Piirmann, Raivo Pöhönen, Urmas Kõljalg

Abstract: This deliverable report includes description of the work steps towards building a web interface for the reporting of errors and gaps in sequenced material source annotations as part of the Task 8.3 of BiCIKL. Beta version of the web interface has been published and is available for the registered users of PlutoF platform.

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DOI: 10.3897/arphapreprints.e107166

Authors: Soulaine Theocharides, Niki Kyriakopoulou

Abstract: Work package 11 of the BiCIKL project involves developing software tools to support a FAIR experience for members of the biodiversity research community. The package overall focuses on Findability, by providing tools to search and answer questions, and Accessibility, through developing links across various biodiversity data sources and research tools. Task 11.2 specifically involves prediction of new links using machine learning. We chose to demonstrate the functionality of machine learning link prediction with plant-pollinator interactions. This type of interaction was chosen due to the wealth of data available, particularly on the Global Biotic Interactions (GloBI) database, as well as this kind of interaction’s ecological and economic significance. The result was a RESTful API capable of predicting plant-pollinator interactions among a predefined set of species. Predictions are made on-the-fly, at the time of the request. The GitHub repository for the API can be found here: https://github.com/DiSSCo/BiCIKL_Linkages_APIThe API takes either a plant or a pollinator as inputs, and outputs potential matches based on a user-defined confidence score. The API’s prediction is powered by a random forest classifier stored on disk. The classifier was trained on the taxonomic hierarchy of observed plant-pollinator pairs obtained from the GloBI database. When evaluating the likelihood of an interaction, the trained classifier looks at the taxonomic hierarchy of both the plant and pollinator and outputs a confidence score. What pairs are returned is determined by the minimum confidence score set by the user.

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DOI: 10.3897/arphapreprints.e106369

Authors: Christos Arvanitidis, Ron Dekker, Andreas Petzold, Niklas Blomberg, Giovanni Lamanna, Rudolf Dimper, Cristina Isabel Huertas Olivares, Ana Mellado, Matthew Viljoen, Sally Chambers, Montserrat González, Sophie Viscido

Abstract: The Special Collection of articles on the Science Projects of the EOSC Future project, funded by the European Commission, refers to one of the essential components of the project. This editorial article explains how the Science Projects fit to the EOSC Future, the way their concept has been developed and evolved during the preparation and the implementation of the project and it also makes an introduction to the templates developed by the Science Projects as a plan to carry out their activities.

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DOI: 10.3897/rio.9.e101006

Authors: Anton Andreev, Grégoire Cattan, Sylvain Chevallier, Quentin Barthélemy

Abstract: Quantum computing is a promising technology for machine learning, in terms of computational costs and outcomes. In this work, we intend to provide a framework that facilitates the use of quantum machine learning in the domain of brain-computer interfaces – where biomedical signals, such as brain waves, are processed.To this end, we integrated Qiskit, a well-known quantum library, with pyRiemann, a framework for the analysis of biomedical signals using Riemannian Geometry. In this paper, we describe our approach, the main elements of our implementation and our research directions. A key result is the creation of a standardised pipeline (QuantumClassifierWithDefaultRiemannianPipeline) for the binary classification of brain waves. The git repository reported in this paper also contains a complete test suite and examples to guide practitioners. We believe that this software will enable further research on the joint field of brain-computer interfaces and quantum computing.

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DOI: 10.3897/arphapreprints.e102613

Authors: Slavena Peneva, Kristina Hristova, Anna Sapundzhieva, Boris Barov, Pavel Stoev, Margarita Grudova, Iva Kostadinova

Abstract: This document presents BiCIKL’s recognizable visual identity, including the project logo, visual identity guide, brochure, poster, document, presentation templates and website design and functionality developed in the first three months. These materials will ensure that BiCIKL is communicated effectively and professionally with the aim to raise awareness and build a community from the start of the project.The modern and user-friendly public website (bicikl-project.eu) provides an easy-to-navigate, continuously updated platform allowing fast access to general information about BiCIKL and its activities, operating on several levels. It also prominently features the participating project partners and Research Infrastructures and their extensive service portfolio.

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DOI: 10.3897/arphapreprints.e102612

Authors: Lyubomir Penev, Mariya Dimitrova, Georgi Zhelezov, Teodor Georgiev

Abstract: To the best of our knowledge, OpenBiodiv is the first production-stage  semantic  system running on top  of  a  reasonably-sized biodiversity knowledge graph. It stores biodiversity data in  a semantic interlinked format and offers facilities for working with it (Senderov et Penev 2016, Senderov et al.   2018, Penev et al. 2019, Dimitrova et al. 2021). It is a dynamic system that continuously updates its database as new biodiversity information becomes available by several international biodiversity publishers. It also allows its users to ask complex queries via SPARQL (a query language for semantic graph databases) and a simplified semantic search interface.OpenBiodiv was created during two EU-funded Marie Sklodowska-Curie PhD projects: BIG4 (Grant Agreement No 642241) and IGNITE (Grant Agreement No 764840). During those projects, the backend Ontology-0, the first versions of RDF converters and the basic website functionalities have been created (see Dimitrova et al. 2021 for overview).After the start of the BiCIKL project, the entire workflow for processing and RDF conversion of full-text articles in XML and Plazi’s treatments in XML has been re-built using up-to-date technological solutions (such as Apache Kaka  and  Elasticsearch)  to  fully  automatise  and speed up the conversion process and to make it trackable and efficient. As a result, the entire graph content has been re-processed and indexed. New user applications described  in Milestone MS27 App specifications have been discussed and implemented.The present deliverable describes the newly built workflow and tools for data extraction, conversion and indexing and the user applications, created in the BiCIKL project.

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DOI: 10.3897/arphapreprints.e102609

Authors: Lyubomir Penev, Teodor Georgiev, Boris Barov, Pavel Stoev, Kristina Hristova

Abstract: The  main goal of the BiCIKL project is to improve, for the first time, seamless access, linking and usage tracking of data within a network of Research Infrastructures managing different data classes (literature, specimens, samples, occurrences, sequences, taxon names and Operational Taxonomic Units (OTU)), ultimately represented also in a biodiversity knowledge graph. To achieve this, the consortium members will operate with huge amount of data during and after the end of the project.As a Horizon 2020 project, BiCIKL conforms to the Open Research Data Pilot (ORDP)1 and   Article 29.3 of the H2020 Model Grant Agreement by default, hence the consortium aims to improve and maximise access, sharing, linking and reuse of FAIR Open Research Data (ORD), generated or managed by the project. A detailed Data Management Plan is a critical part of   the ORDP. The DMP described in the present document is developed in BiCIKL within the first  six months of the project and it will evolve as a “living document” during the lifetime of the project and beyond in order to present the status of the project's reflections on data management.The BiCIKL DMP outlines the handling of research data and provides the basis of the project consortium’s data management life cycle for the data collected, generated and processed by the participants in the project. The DMP also covers the methodologies and standards previously developed for data sharing and open access, curation  and  preservation.  The subject of the DMP is the management of research data. Personal data management  is covered by deliverable D9.1 Protection of Personal Data.The BiCIKL DMP was developed in close collaboration with all project partners and involved Research Infrastructures (RI) who provided information on their data management practices and policies in a questionnaire and planned generation, collection, and processing of data for the purposes of building a resilient data management strategy of the project which meets all criteria for open research.This DMP aims to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) data management criteria of Horizon 20202.

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DOI: 10.3897/arphapreprints.e101543

Authors: Oliver Barić

Abstract: Since the creation of ParAqua social media pages, the number of page likes and follows has been tracked. After reaching a sufficient number of followers, an analysis was performed to begin the tracking of meaningful metrics. For Facebook and Instagram, an integrated analytics tool was used, and for Twitter and LinkedIn the metrics are analyzed in less detail since no integrated analytics tools were available. The role of each social media platform was discussed depending on the platform`s characteristics, dominant groups among the followers, and strategies applied by other organizations.A list was made containing conferences and workshops in the near future. The scientific interests of the Action members have been taken into consideration while creating the list, so it contains a somewhat broad specter of events, but is still related to the Action`s mission. Only those events whose deadline for submission hasn’t passed were selected. The conferences are divided into two tables depending on whether they are taking place in or outside of Europe and arranged in chronological order. 

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DOI: 10.3897/rio.7.e70183

Authors: Nazareno Scaccia, Taras Günther, Estibaliz Lopez de Abechuco, Matthias Filter

Abstract: In many interdisciplinary research domains, the creation of a shared understanding of relevant terms is considered the foundation for efficient cross-sector communication and interpretation of data and information. This is also true for the domain of One Health (OH) where many One Health Surveillance (OHS) documents rarely contain glossaries with a list of terms for which their specific meaning in the context of the given document is defined (Cornelia et al. 2018, Buschhardt et al. 2021). The absence of glossaries within these documents may lead to misinterpretation of surveillance results due to the wrong interpretation of terminology specifically when term definitions differ across OH sectors. Under the One Health EJP project ORION, the OHEJP Glossary was recently created. The OHEJP Glossary is a tool to improve communication and collaboration amongst OH sectors by providing an easy-to-use online resource that lists relevant OH terms and sector-specific definitions. To improve the accessibility of content from the OHEJP Glossary and support the creation of integrative glossaries in future OHS-related documents, the OHEJP Glossaryfication Web Service was created. This service can support the practical use of the OHEJP Glossary and other relevant online glossaries by OH professionals.The Glossaryfication Web Service (GWS) is an application that automatically identifies terms in any uploaded text-based document and creates a document-specific list of matching definitions in selected online glossaries. This auto-generated document-specific glossary can easily be adjusted by the user, for example, by selecting the desired definition in case multiple definitions were found for a specific term. The document-specific glossary could then be downloaded, manually adjusted and finally included into the original document where it supports the correct interpretation of terminology used. Especially in sector-specific reports, such as from animal health or public health authorities, this can be beneficial to ensure the correct interpretation by other OH sectors in the future. The GWS was developed with the open-source desktop software KNIME Analytics Platform and runs as a web service on a KNIME Web Server infrastructure. The core data processing functionality in the GWS is based on KNIME’s Text Processing extension. KNIME's JavaScript nodes provided the basis for an interactive user interface where users can easily upload their files and select between different reference glossaries, such as the OHEJP Glossary, the CDC Glossary, the WHO Glossary or the EFSA Glossary. After retrieval of the user input settings, the GWS tags words within the provided document and maps these tagged words with matching entries in the selected glossaries. As the main output, the user receives a downloadable list of matching terms with their corresponding definitions, sectorial assignments and references, which can then be added by the user to the original document. The GWS is freely accessible via this link as well as the underlying KNIME workflow.

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DOI: 10.3897/rio.7.e68121

Authors: Daniel Mietchen, Lane Rasberry, Thais Morata, John Sadowski, Jeanette Novakovich, James Heilman

Abstract: In this era of information overload and misinformation, it is a challenge to rapidly translate evidence-based health information to the public. Wikipedia is a prominent global source of health information with high traffic, multilingual coverage, and acceptable quality control practices. Viewership data following the Ebola crisis and during the COVID-19 pandemic reveals that a significant number of web users located health guidance through Wikipedia and related projects, including its media repository Wikimedia Commons and structured data complement, Wikidata.The basic idea discussed in this paper is to increase and expedite health institutions' global reach to the general public, by developing a specific strategy to maximize the availability of focused content into Wikimedia’s public digital knowledge archives. It was conceptualized from the experiences of leading health organizations such as Cochrane, the World Health Organization (WHO) and other United Nations Organizations, Cancer Research UK, National Network of Libraries of Medicine, and Centers for Disease Control and Prevention (CDC)'s National Institute for Occupational Safety and Health (NIOSH). Each has customized strategies to integrate content in Wikipedia and evaluate responses.We propose the development of an interactive guide on the Wikipedia and Wikidata platforms to support health agencies, health professionals and communicators in quickly distributing key messages during crisis situations. The guide aims to cover basic features of Wikipedia, including adding key health messages to Wikipedia articles, citing expert sources to facilitate fact-checking, staging text for translation into multiple languages; automating metrics reporting; sharing non-text media; anticipating offline reuse of Wikipedia content in apps or virtual assistants; structuring data for querying and reuse through Wikidata, and profiling other flagship projects from major health organizations.In the first phase, we propose the development of a curriculum for the guide using information from prior case studies. In the second phase, the guide would be tested on select health-related topics as new case studies. In its third phase, the guide would be finalized and disseminated.

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DOI: 10.3897/rio.7.e67887

Authors: Felicitas Löffler, Andreas Schuldt, Birgitta König-Ries, Helge Bruelheide, Friederike Klan

Abstract: Searching for scientific datasets is a prominent task in scholars' daily research practice. A variety of data publishers, archives and data portals offer search applications that allow the discovery of datasets. The evaluation of such dataset retrieval systems requires proper test collections, including questions that reflect real world information needs of scholars, a set of datasets and human judgements assessing the relevance of the datasets to the questions in the benchmark corpus. Unfortunately, only very few test collections exist for a dataset search. In this paper, we introduce the BEF-China test collection, the very first test collection for dataset retrieval in biodiversity research, a research field with an increasing demand in data discovery services. The test collection consists of 14 questions, a corpus of 372 datasets from the BEF-China project and binary relevance judgements provided by a biodiversity expert.

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DOI: 10.3897/rio.7.e63748

Authors: Martin Palma, Alessandro Zandonai, Luca Cattani, Johannes Klotz, Giulio Genova, Christian Brida, Norbert Andreatta, Georg Niedrist, Stefano Della Chiesa

Abstract: Easily accessible data is an essential requirement for scientific data analysis. The Data Browser Matsch | Mazia was designed to provide a fast and comprehensible solution to access, visualize and download the microclimatic measurements of the IT 25 LT(S)ER Match | Mazia research site in South Tyrol, Northern Italy, with the overall aim to provide straightforward data accessibility and enhance dissemination.Data Browser Matsch | Mazia is a user-friendly web-based application to visualize and download micrometeorological and biophysical time series of the Long-Term Socio-Ecological Research site Matsch | Mazia in South Tyrol, Italy. It is designed both for the general public and researchers. The Data Browser Matsch | Mazia drop-down menus allow the user to query the InfluxDB database in the backend by selecting the measurements, time range, land use and elevation. Interactive Grafana dashboards show dynamic graphs of the time series.

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DOI: 10.3897/rio.5.e30250

Authors: Raniere Gaia Costa da Silva, Shoaib Sufi, Selina Aragon Camarasa

Abstract: The Collaborations Workshop 2018 (CW18) took place at The School of Mathematics, Cardiff University from the 26th to 28th March 2018. 90 people attended the event to discuss the themes of the workshop: culture change, productivity and sustainability. With a mix of lightning talks, keynotes, Q&A panels, discussions with speed blogging, collaborative ideas sessions, mini-workshops & demos, a social programme and a Hack Day in the agenda, CW18 was a feature-packed and immersive event.

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DOI: 10.3897/rio.4.e32449

Authors: Katja Seltmann, Sara Lafia, Deborah Paul, Shelley James, David Bloom, Nelson Rios, Shari Ellis, Una Farrell, Jessica Utrup, Michael Yost, Edward Davis, Rob Emery, Gary Motz, Julien Kimmig, Vaughn Shirey, Emily Sandall, Daniel Park, Christopher Tyrrell, R. Sean Thackurdeen, Matthew Collins, Vincent O'Leary, Heather Prestridge, Christopher Evelyn, Ben Nyberg

Abstract: Georeferencing is the process of aligning a text description of a geographic location with a spatial location based on a geographic coordinate system. Training aids are commonly created around the georeferencing process to disseminate community standards and ideas, guide accurate georeferencing, inform users about new tools, and help users evaluate existing geospatial data. The Georeferencing for Research Use (GRU) workshop was implemented as a training aid that focused on the creation and research use of geospatial coordinates, and included both data researchers and data providers, to facilitate communication between the groups. The workshop included 23 participants with a wide background of expertise ranging from students (undergraduate and graduate), professors, researchers and educators, scientific data managers, natural history collections personnel, and spatial analyst specialists. The conversations and survey results from this workshop demonstrate that it is important to provide opportunities for biocollections data providers to interact directly with the researchers using the data they produce and vice versa.

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DOI: 10.3897/rio.4.e31656

Authors: Christian Hauschke, Simone Cartellieri, Lambert Heller

Abstract: Within the project "Reference implementation for Open Scientometric Indicators" (ROSI), new assessments and visualizations of conventional and alternative metrics (altmetrics) will be developed and their effect on researchers will be investigated. For this purpose, a reference implementation based on the open source research information system VIVO will be developed in which various metrics are combined with data from different openly licensed sources. In order to develop the requirements of the target groups, surveys are going to be conducted to investigate the effect of scientometric indicators on scientist's and their expectations regarding those indicators. The objectives of the project are firstly to evaluate the scientometric needs and concerns of the target groups, and secondly to implement a usable reference implementation of a toolset that reflects the results of the study and that enables transparent, license-free, flexibly adaptable analysis of the output of researchers, contributors and organisations.

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DOI: 10.3897/rio.4.e29773

Authors: Daisuke Tsugama

Abstract:

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DOI: 10.3897/rio.4.e28163

Authors: Dasapta Irawan, Cut Rachmi

Abstract: Every researcher needs data in their working ecosystem, but despite of the resources (funding, time, and energy), that they have spent to get the data, only a few are putting more real attention to data management. This paper is mainly describing our recommendation of RDMP document at university level. This paper would be a form of our initiative to be developed at university or national level, which also in-line with current development in scientific practices mandating data sharing and data re-use. Researchers can use this article as an assessment form to describe the setting of their research and data management. Researcher can also develop more detail RDMP to cater specific project's environment. In this Research Data Management Plan (RDMP), we propose three levels of storage: offline working storage, offline backup storage and online-cloud backup storage, located on a shared-repository. We also propose two kinds of cloud repository: a dynamic repository to store live data and a static repository to keep a copy of final data. Hopefully, this RDMP could solve problems on data sharing and preservation, and additionally could increase researchers' awareness about data management to increase the value and impact of their researches.

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DOI: 10.3897/rio.4.e26439

Authors: John Borghi, Stephen Abrams, Daniella Lowenberg, Stephanie Simms, John Chodacki

Abstract: Researchers are faced with rapidly evolving expectations about how they should manage and share their data, code, and other research materials. To help them meet these expectations and generally manage and share their data more effectively, we are developing a suite of tools which we are currently referring to as "Support Your Data". These tools, which include a rubric designed to enable researchers to self-assess their current data management practices and a series of short guides which provide actionable information about how to advance practices as necessary or desired, are intended to be easily customizable to meet the needs of a researchers working in a variety of institutional and disciplinary contexts.

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DOI: 10.3897/rio.4.e25742

Authors: Lilliana Sancho-Chavarria, Fabian Beck, Daniel Weiskopf, Erick Mata-Montero

Abstract: Maintenance and curation of large-sized biological taxonomies are complex and laborious activities. Information visualization systems use interactive visual interfaces to facilitate analytical reasoning on complex information. Several approaches such as treemaps, indented lists, cone trees, radial trees, and many others have been used to visualize and analyze a single taxonomy. In addition, methods such as edge drawing, animation, and matrix representations have been used for comparing trees. Visualizing similarities and differences between two or more large taxonomies is harder than the visualization of a single taxonomy. On one hand, less space is available on the screen to display each tree; on the other hand, differences should be highlighted. The comparison of two alternative taxonomies and the analysis of a taxonomy as it evolves over time provide fundamental information to taxonomists and global initiatives that promote standardization and integration of taxonomic databases to better document biodiversity and support its conservation. In this work we assess how ten user visualization tasks for the curation of biological taxonomies are supported by several visualization tools. Tasks include the identification of conditions such as congruent taxa, splits, merges, and new species added to a taxonomy. We consider tools that have gone beyond the prototype stage, that have been described in peer-reviewed publications, or are in current use. We conclude with the identification of challenges for future development of taxonomy comparison tools.

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DOI: 10.3897/rio.4.e25485

Authors: Álvaro Briz-Redón, Ángel Serrano-Aroca

Abstract: Programming a computer is an activity that can be very beneficial to undergraduate students in terms of improving their mental capabilities, collaborative attitudes and levels of engagement in learning. Despite the initial difficulties that typically arise when learning to program, there are several well-known strategies to overcome them, providing a very high benefit-cost ratio to most of the students. Moreover, the use of a programming language usually raises the interest of students to learn any specific concept, which has caused that many teachers around the world employ a programming language as a learning environment to treat almost every possible topic. Particularly, mathematics can be taught and learnt while using a suitable programming language. The R programming language is endowed with a wide range of capabilities that allow its use to learn different kind of concepts while programming. Therefore, complex subjects such as mathematics could be learnt with the help of this powerful programming language. In addition, since the R language provides numerous graphical functions, it could be very useful to acquire simultaneously basic plane geometry and programming knowledge at the undergraduate level. This paper describes the LearnGeom R package, a novel pedagogical tool, which contains multiple functions to learn geometry in R at different levels of difficulty, from the most basic geometric objects to high-complexity geometric constructions, while developing numerous programming skills.

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DOI: 10.3897/rio.4.e24224

Authors: Annalisa Minelli, Alessandro Oggioni, Alessandra Pugnetti, Alessandro Sarretta, Mauro Bastianini, Caterina Bergami, Fabrizio Bernardi Aubry, Elisa Camatti, Tiziano Scovacricchi, Giorgio Socal

Abstract:

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DOI: 10.3897/rio.4.e23394

Authors: Ulrike Sturm, Sven Schade, Luigi Ceccaroni, Margaret Gold, Christopher Kyba, Bernat Claramunt, Muki Haklay, Dick Kasperowski, Alexandra Albert, Jaume Piera, Jonathan Brier, Christopher Kullenberg, Soledad Luna

Abstract: Apps for mobile devices and web-based platforms are increasingly used in citizen science projects. While extensive research has been done in multiple areas of studies, from Human-Computer Interaction to public engagement in science, we are not aware of a collection of recommendations specific for citizen science that provides support and advice for planning, design and data management of mobile apps and platforms that will assist learning from best practice and successful implementations. In two workshops, citizen science practitioners with experience in mobile application and web-platform development and implementation came together to analyse, discuss and define recommendations for the initiators of technology based citizen science projects. Many of the recommendations produced during the two workshops are applicable to citizen science project that do not use mobile devices to collect data. Therefore, we propose to closely connect the results presented here with ECSA’s Ten Principles of Citizen Science.

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DOI: 10.3897/rio.3.e21074

Authors: David Rose, Prue Addison, Malcolm Ausden, Leon Bennun, Craig Mills, Stephanie O’Donnell, Caroline Parker, Melanie Ryan, Lauren Weatherdon, Katherine Despot-Belmonte, William Sutherland, Rebecca Robertson

Abstract: A workshop held at the University of Cambridge in May 2017 brought developers, researchers, knowledge brokers, and users together to discuss user-centred design of decision support tools. Decision support tools are designed to take users through logical decision steps towards an evidence-informed final decision. Although they may exist in different forms, including on paper, decision support tools are generally considered to be computer- (online, software) or app-based. Studies have illustrated the potential value of decision support tools for conservation, and there are several papers describing the design of individual tools. Rather less attention, however, has been placed on the desirable characteristics for use, and even less on whether tools are actually being used in practice. This is concerning because if tools are not used by their intended end user, for example a policy-maker or practitioner, then its design will have wasted resources. Based on an analysis of papers on tool use in conservation, there is a lack of social science research on improving design, and relatively few examples where users have been incorporated into the design process. Evidence from other disciplines, particularly human-computer interaction research, illustrates that involving users throughout the design of decision support tools increases the relevance, usability, and impact of systems. User-centred design of tools is, however, seldom mentioned in the conservation literature. The workshop started the necessary process of bringing together developers and users to share knowledge about how to conduct good user-centred design of decision support tools. This will help to ensure that tools are usable and make an impact in conservation policy and practice.

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DOI: 10.3897/rio.3.e14837

Authors: Lynn Woolfrey

Abstract: The purpose of this project is to demonstrate that tobacco-related data from selected Africa countries can be collected and distributed from an Open Data platform. The platform and data will improve the capacity for tobacco control research in key sub-Saharan African countries, and help develop a continent-wide research approach to tobacco control. 

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DOI: 10.3897/rio.3.e14944

Authors: W. Daniel Kissling

Abstract: Seed dispersal by fruit-eating animals is a pivotal ecosystem function in tropical forests, but the role that frugivores have played in the biogeography and macroevolution of species-rich tropical plant families remains largely unexplored. This project investigates how frugivory-relevant plant traits (e.g. fruit size, fruit color, fruit shape etc.) are distributed within the angiosperm family of palms (Arecaceae), how this relates to diversification rates, and whether and how it coincides with the global biogeographic distribution of vertebrate frugivores (birds, bats, primates, other frugivorous mammals) and their ecological traits (e.g. diet specialization, body size, flight ability, color vision etc.). Palms are particularly suitable because they are well studied, species-rich, characteristic of tropical rainforests, and dispersed by all groups of vertebrate seed dispersers. Using newly compiled data on species distributions and ecological traits in combination with phylogenies we will test (1) how fruit trait variability relates to palm phylogeny and other aspects of plant morphology (e.g. leaf size, plant height, growth form), (2) whether geographic variability in fruit traits correlates with geographic distributions of animal consumers and their traits, and (3) to what extent interaction-relevant plant traits are related to palm diversification rates. This combined macroecological and macroevolutionary approach allows novel insights into the global ecology and the evolution of a tropical keystone plant family. This is important for the conservation and sustainable management of tropical rainforests because palms are often key components of subsistence economies, ecosystem dynamics and carbon storage and therefore help to enhance nature’s goods, benefits and services to humanity.

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DOI: 10.3897/rio.3.e14013

Authors: Katherine Despot-Belmonte, Michel Doudin, Quentin Groom, Florian Wetzel, Donat Agosti, Kim Jacobsen, Larissa Smirnova, Lauren V. Weatherdon, Tim Robertson, Lyubomir Penev, Eugenie Regan, Anke Hoffmann, Brian MacSharry, Yara Shennan-Farpon, Corinne S. Martin

Abstract: The EU BON (“Building the European Biodiversity Observation Network”) project has made important contributions towards the achievement of global conservation targets. This infographic illustrates EU BON's contributions towards the achievement of Aichi Biodiversity Target 19 "By 2020, knowledge, the science base and technologies relating to biodiversity, its values, functioning, status and trends, and the consequences of its loss, are improved, widely shared and transferred, and applied.”

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DOI: 10.3897/rio.3.e13820

Authors: Timm Schoening, Jennifer Durden, Inken Preuss, Alexandra Branzan Albu, Autun Purser, Bart De Smet, Carlos Dominguez-Carrió, Chris Yesson, Daniëlle de Jonge, Dhugal Lindsay, Jan Schulz, Klas Ove Möller, Kolja Beisiegel, Linda Kuhnz, Maia Hoeberechts, Nils Piechaud, Stephanie Sharuga, Tali Treibitz

Abstract: Marine optical imaging has become a major assessment tool in science, policy and public understanding of our seas and oceans. Methodology in this field is developing rapidly, including hardware, software and the ways of their application. The aim of the Marine Imaging Workshop (MIW) is to bring together academics, research scientists and engineers, as well as industrial partners to discuss these developments, along with applications, challenges and future directions. The first MIW was held in Southampton, UK in April 2014. The second MIW, held in Kiel, Germany, in 2017 involved more than 100 attendees, who shared the latest developments in marine imaging through a combination of traditional oral and poster presentations, interactive sessions and focused discussion sessions. This article summarises the topics addressed during the workshop, particularly the outcomes of these discussion sessions for future reference and to make the workshop results available to the open public.

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DOI: 10.3897/rio.3.e13593

Authors: Leo Lahti, Filipe da Silva, Markus Laine, Viivi Lähteenoja, Mikko Tolonen

Abstract: This paper gives the reader a chance to experience, or revisit, PHOS16: a conference on the History and Philosophy of Open Science. In the winter of 2016, we invited a varied international group to engage with these topics at the University of Helsinki, Finland. Our aim was a critical assessment of the defining features, underlying narratives, and overall objectives of the contemporary open science movement. The event brought together contemporary open science scholars, publishers, and advocates to discuss the philosophical foundations and historical roots of openness in academic research. The eight sessions combined historical views with more contemporary perspectives on topics such as transparency, reproducibility, collaboration, publishing, peer review, research ethics, as well as societal impact and engagement. We gathered together expert panelists and 15 invited speakers who have published extensively on these topics, which allowed us to engage in a thorough and multifaceted discussion. Together with our involved audience we charted the role and foundations of openness of research in our time, considered the accumulation and dissemination of scientific knowledge, and debated the various technical, legal, and ethical challenges of the past and present. In this article, we provide an overview of the topics covered at the conference as well as individual video interviews with each speaker. In addition to this, all the talks were recorded and they are offered here as an openly licensed community resource in both video and audio form.

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DOI: 10.3897/rio.3.e13236

Authors: Caroline Jay, Robert Haines, Markel Vigo, Nicolas Matentzoglu, Robert Stevens, Jonathan Boyle, Alan Davies, Chiara Del Vescovo, Nicolas Gruel, Anja Le Blanc, David Mawdsley, Dale Mellor, Eleni Mikroyannidi, Richard Rollins, Andrew Rowley, Julio Vega

Abstract: The Code/Theory workshop explored the process of translating between theory and code, from the perspective of those who do this work on a day to day basis. This report contains individual contributions from participants reflecting on their own experiences, along with summaries of their lightning talks and outputs from the discussion sessions. We conclude that translating between theory and code successfully requires a diversity of roles, all of which are central to the process of research.

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DOI: 10.3897/rio.3.e13088

Authors: Lyubomir Penev, Teodor Georgiev, Peter Geshev, Seyhan Demirov, Viktor Senderov, Iliyana Kuzmova, Iva Kostadinova, Slavena Peneva, Pavel Stoev

Abstract: The ARPHA-BioDiv Тoolbox for Scholarly Publishing and Dissemination of Biodiversity Data is a set of standards, guidelines, recommendations, tools, workflows, journals and services, based on the ARPHA Publishing Platform of Pensoft, designed to ease scholarly publishing of biodiversity and biodiversity-related data that are of primary interest to EU BON and GEO BON networks. ARPHA-BioDiv is based on the infrastructure, knowledge and exeprience gathered in the years-long research, development and publishing activities of Pensoft, upgraded with novel tools and workflows that resulted from the FP7 project EU BON.

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DOI: 10.3897/rio.3.e13086

Authors: Stephanie Simms, Sarah Jones, Daniel Mietchen, Tomasz Miksa

Abstract: This report presents outputs of the International Digital Curation Conference 2017 workshop on machine-actionable data management plans. It contains community-generated use cases covering eight broad topics that reflect the needs of various stakeholders. It also articulates a consensus about the need for a common standard for machine-actionable data management plans to enable future work in this area.

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DOI: 10.3897/rio.3.e12733

Authors: Swati Rane, Eshin Jolly, Anne Park, Hojin Jang, Cameron Craddock

Abstract: We designed a modular machine learning program that uses functional magnetic resonance imaging (fMRI) data in order to distinguish individuals with autism spectrum disorders from neurodevelopmentally normal individuals. Data was selected from the Autism Brain Imaging Dataset Exchange (ABIDE) I Preprocessed Dataset.

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DOI: 10.3897/rio.3.e12394

Authors: Kesshi Jordan, Anisha Keshavan, Maria Luisa Mandelli, Roland Henry

Abstract: Cluster-viz is a web application that provides a platform for cluster-based interactive quality-control of tractography algorithm outputs. This tool facilitates the creation of white matter fascicle models by employing a cluster-based approach to allow the user to select streamline bundles for inclusion/exclusion in the final fascicle model. This project was started at the 2016 Neurohackweek and BrainHack events and is still under development. We welcome contributions to the Cluster-viz github repository (https://github.com/kesshijordan/Cluster-viz).

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DOI: 10.3897/rio.3.e12342

Authors: Julia Huntenburg, Alexandre Abraham, João Loula, Franziskus Liem, Kamalaker Dadi, Gaël Varoquaux

Abstract: Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.

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DOI: 10.3897/rio.3.e12346

Authors: Julia Huntenburg, Konrad Wagstyl, Christopher Steele, Thomas Funck, Richard Bethlehem, Ophélie Foubet, Benoit Larrat, Victor Borrell, Pierre-Louis Bazin

Abstract: Increasingly available high-resolution brain imaging data require specialized processing tools that can leverage their anatomical detail and handle their size. Here, we present user-friendly Python tools for cortical depth resolved analysis in such data. Our implementation is based on the CBS High-Res Brain Processing framework, and aims to make high-resolution data processing tools available to the broader community.

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DOI: 10.3897/rio.3.e12276

Authors: Anisha Keshavan, Christopher Madan, Esha Datta, Ian McDonough

Abstract: Mindcontrol is an open-source web-based dashboard to quality control and curate neuroimaging data. At Neurohackweek 2016, a group assembled to add new features to the Mindcontrol interface. Contributors used Python, Javascript, and Git to configure Mindcontrol for the ABIDE and CoRR open datasets, and add new types of plots to the interface. All contributions are freely available online, and the code is being actively maintained at http://www.github.com/akeshavan/mindcontrol.

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DOI: 10.3897/rio.3.e11825

Authors: John Chodacki, Thomas Lemberger, Jennifer Lin, Maryann Martone, Daniel Mietchen, Jessica Polka, Richard Sever, Carly Strasser

Abstract:

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DOI: 10.3897/rio.3.e11731

Authors: Steren Chabert, Tomás Mardones, Rodrigo Riveros, Maximiliano Godoy, Alejandro Veloz, Rodrigo Salas, Pablo Cox

Abstract: Cerebral aneurysm is a cerebrovascular disorder characterized by a bulging in a weak area in the wall of an artery that supplies blood to the brain. It is relevant to understand the mechanisms leading to the apparition of aneurysms, their growth and, more important, leading to their rupture. The purpose of this study is to study the impact on aneurysm rupture of the combination of different parameters, instead of focusing on only one factor at a time as is frequently found in the literature, using machine learning and feature extraction techniques. This discussion takes relevance in the context of the complex decision that the physicians have to take to decide which therapy to apply, as each intervention bares its own risks, and implies to use a complex ensemble of resources (human resources, OR, etc.) in hospitals always under very high work load. This project has been raised in our actual working team, composed of interventional neuroradiologist, radiologic technologist, informatics engineers and biomedical engineers, from Valparaiso public Hospital, Hospital Carlos van Buren, and from Universidad de Valparaíso – Facultad de Ingeniería and Facultad de Medicina. This team has been working together in the last few years, and is now participating in the implementation of an “interdisciplinary platform for innovation in health”, as part of a bigger project leaded by Universidad de Valparaiso (PMI UVA1402). It is relevant to emphasize that this project is made feasible by the existence of this network between physicians and engineers, and by the existence of data already registered in an orderly manner, structured and recorded in digital format. The present proposal arises from the description in nowadays literature that the actual indicators, whether based on morphological description of the aneurysm, or based on characterization of biomechanical factor or others, these indicators were shown not to provide sufficient information in order to predict by themselves the risk of rupture. Therefore, our hypothesis is that the risk of rupture lies on the combination of multiple actors. These actors together would play different roles that could be: weakening of the artery wall, increasing biomechanical stresses on the wall induced by blood flow, in addition to personal sensitivity due to family history, or personal history of comorbidity, or even seasonal variations that could gate different inflammation mechanisms. The main goal of this project is to identify relevant variables that may help in the process of predicting the risk of intracranial aneurysm rupture using machine learning and image processing techniques based on structured and non-structured data from multiple sources. We believe that the identification and the combined use of relevant variables extracted from clinical, demographical, environmental and medical imaging data sources will improve the estimation of the aneurysm rupture risk, with respect to the actual practiced method based essentially on the aneurysm size. The methodology of this work consist of four phases: (1) Data collection and storage, (2) feature extraction from multiple sources in particular from angiographic images, (3) development of the model that could describe the risk of aneurysm rupture based on the fusion and combination of the features, and (4) Identification of relevant variables related to the aneurysm rupture process. This study corresponds to an analytic transversal study with prospective and retrospective characteristics. This work will be based on publicly available health statistics data, data of weather conditions, together with clinical and demographic data of patients diagnosed with intracranial aneurysm in the Hospital Carlos van Buren. As main results of this project we are expecting to identify relevant variables extracted from images and other sources that could play a role in the risk of aneurysm rupture. The proposed model will be presented to the physicians of the Hospital Carlos van Buren, to be further implemented in this Institution according to the demonstrated impact of our results. The main results will be published in indexed journals and presented at national and international conferences.

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DOI: 10.3897/rio.2.e11563

Authors: Veljo Runnel, Florian Wetzel, Quentin Groom, Wouter Koch, Israel Pe’er, Nils Valland, Emmanouela Panteri, Urmas Kõljalg

Abstract: Citizen science is an approach of public participation in scientific research which has gained significant momentum in recent years. This is particularly evident in biology and environmental sciences where input from citizen scientists has greatly increased the number of publicly available observation data. However, there are still challenges in effective networking, data sharing and securing data quality. EU BON project has analyzed the citizen science landscape in Europe with regards to biodiversity research and proposes several policy recommendations. One of the recommendations is a Pan-European citizen science gateway for biodiversity data with dedicated tools for data collection and management. The prototypes of the gateway components are part of the EU BON biodiversity portal and described in current report.

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DOI: 10.3897/rio.2.e10708

Authors: Ethan White

Abstract:

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DOI: 10.3897/rio.2.e10445

Authors: Sarah Faulwetter, Evangelos Pafilis, Lucia Fanini, Nicolas Bailly, Donat Agosti, Christos Arvanitidis, Laura Boicenco, Terry Catapano, Simon Claus, Stefanie Dekeyzer, Teodor Georgiev, Aglaia Legaki, Dimitra Mavraki, Anastasis Oulas, Gabriella Papastefanou, Lyubomir Penev, Guido Sautter, Dmitry Schigel, Viktor Senderov, Adrian Teaca, Marilena Tsompanou

Abstract: The objective of Workpackage 4 of the European Marine Observation and Data network (EMODnet) is to fill spatial and temporal gaps in European marine species occurrence data availability by carrying out data archaeology and rescue activities. To this end, a workshop was organised in the Hellenic Center for Marine Research Crete (HCMR), Heraklion Crete, (8–9 June 2015) to assess possible mechanisms and guidelines to mobilise legacy biodiversity data. Workshop participants were data managers who actually implement data archaeology and rescue activities, as well as external experts in data mobilisation and data publication. In particular, current problems associated with manual extraction of occurrence data from legacy literature were reviewed, tools and mechanisms which could support a semi-automated process of data extraction were explored and the re-publication of the data, including incentives for data curators and scientists were reflected upon.

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DOI: 10.3897/rio.2.e9933

Authors: Dimitrios Koureas, Christos Arvanitidis, Lee Belbin, Walter Berendsohn, Christian Damgaard, Quentin Groom, Anton Güntsch, Gregor Hagedorn, Alex Hardisty, Donald Hobern, Arnald Marcer, Daniel Mietchen, David Morse, Matthias Obst, Lyubomir Penev, Lars Pettersson, Soraya Sierra, Vincent Smith, Rutger Vos

Abstract: Europe is building its Open Science Cloud; a set of robust and interoperable e-infrastructures with the capacity to provide data and computational solutions through cloud-based services. The development and sustainable operation of such e-infrastructures are at the forefront of European funding priorities. The research community, however, is still reluctant to engage at the scale required to signal a Europe-wide change in the mode of operation of scientific practices. The striking differences in uptake rates between researchers from different scientific domains indicate that communities do not equally share the benefits of the above European investments. We highlight the need to support research communities in organically engaging with the European Open Science Cloud through the development of trustworthy and interoperable Virtual Research Environments. These domain-specific solutions can support communities in gradually bridging technical and socio-cultural gaps between traditional and open digital science practice, better diffusing the benefits of European e-infrastructures.

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DOI: 10.3897/rio.2.e9651

Authors: Joakim Philipson

Abstract:

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DOI: 10.3897/rio.2.e9232

Authors: Pekka Neittaanmäki, Timo Huttula, Juha Karvanen, Tom Frisk, Jouni Tuomisto, Antti Simola, Tero Tuovinen, Janne Ropponen

Abstract: Open data and models are becoming increasingly available, but there are not yet good methods and platforms to turn those into systematic evidence-based decision support. Unicorn will produce such an enviro­­nment based on existing theoretical and practical knowledge about decision support and models. This con­sortium possesses the necessary models, data, and skills to set up an environment and demonstrate its func­tionality and usefulness with several case studies related to the environmental issues, human health, and economy. The Unicorn environment will be built in a generic and systematic way so that it could even be­come an international standard for evidence-based decision support. Developing a technical environment or standard is not enough. Using the Unicorn environment is a large cul­­tural change for both researchers and decision makers, as the current decision support practices do not re­flect the principles of openness, criticism, or reuse. Therefore, this cultural change must be promoted by train­ing to use the environment, by informing the society about its possibilities, and solving a number of practi­cal and technical problems related to current practices in research institutes, ministries, and municipalities. We acknowledge these problems and offer solutions to them with an extensive interaction plan.

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DOI: 10.3897/rio.2.e9113

Authors: Katja Heuer, Satrajit Ghosh, Amy Robinson Sterling, Roberto Toro

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DOI: 10.3897/rio.2.e8848

Authors: Arno Klein

Abstract: Mobile phones provide a new way of collecting behavioral medical research data at a scale never before possible – Sage Bionetworks’ mPower Parkinson research app, launched at Apple’s March 9, 2015 ResearchKit announcement, is currently collecting data related to Parkinson symptoms, such as voice recordings, from thousands of registered study participants. Before making such voice data available to any qualified researcher in the world, they need to undergo quality control and editing, which is currently something only a human can do well. To achieve this goal and the required scale, we will crowdsource these tasks through Amazon's Mechanical Turk.

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DOI: 10.3897/rio.2.e8835

Authors: Arno Klein, Satrajit Ghosh

Abstract: The proposed research develops new computational tools to identify, diagnose, and predict treatment outcome for different mental illnesses. The research will be applied first to major depressive disorder, which affects millions of Americans, but is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain structure, activity, or connectivity between any two populations.

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DOI: 10.3897/rio.2.e8817

Authors: Arno Klein

Abstract: We will analyze variations in brain anatomy and create the first integrated software environment to extract patterns from brains and target differences related to inter-individual variability, pathology, development, or degeneration. We will evaluate how well these differences can help diagnose and predict treatment outcome for major depressive disorder, which affects millions of Americans, but our work is intended to be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain anatomy between any two populations.

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DOI: 10.3897/rio.2.e8816

Authors: Arno Klein

Abstract: The BigBrain, a high-resolution 3-D model of a human brain at nearly cellular resolution, is the best brain imaging data set in the world to establish a canonical space at both microscopic and macroscopic resolutions. However, for the cell-stained microstructural data to be truly useful, it needs to be segmented into cytoarchitectonic regions, a challenge no single lab could undertake. The principal aim of this proposal is to crowdsource the segmentation of cytoarchitectonic regions by means of a computer game, to transform an arduous, isolated task performed by experts into an engaging, collective activity of non-experts.

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DOI: 10.3897/rio.2.e8827

Authors: Jennifer McWhorter, Darren Wright, Julie Thomas

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DOI: 10.3897/rio.2.e8813

Authors: Arno Klein

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DOI: 10.3897/rio.2.e8814

Authors: Arno Klein

Abstract: Visual communication is ubiquitous, commanding our attention and commandeering our inattention. The presentation of information can take myriad visual forms, such as bar charts, scatter plots, network diagrams, and tables. These information graphics are attempts to map potentially large amounts of complex data to easily navigable visual form for rapid and accurate knowledge transfer. However, there is not yet a satisfactory formal methodology for selecting the most appropriate visualization method for a given set of data. A data taxonomy and novel visual taxonomy will be used to select visual stimuli from a database of acquired and newly generated information graphics. Oculomotor responses (eye tracking data) and task-based responses (mouse clicks or keyboard input) are recorded; performance on the latter is used to establish an expert subgroup. These results will be used to satisfy the three primary objectives of the proposed research, determining: how the choice of data visualization impacts oculomotor behavior and task performance, if this behavior is discriminable between experts and novices, and an empirically-based taxonomy of visualization based on the results of 1 and 2. Intellectual merit of the proposed activity The proposed research will create a novel taxonomy for and database of acquired and generated information graphics as well as an associated web application to search, organize, and compare entries in the database. Part of this research program is intended to establish the most comprehensive, manually annotated (and taxonomically classified) information graphics database in the world, for use by the public via a web interface. These images will be important for procuring stimuli for other kinds of perceptual and cognitive psychology experiments. The eye tracking and task performance results should help lead to a better understanding of how humans look at data, respond to the relationship between data structures and visual composition, and respond differentially to visualizations of different types. With respect to qualifications, the PI has a background in brain imaging research, image processing, and programming applications for generating graphs. Through his collaborator Dr. Ferrera of Columbia University, he has access to facilities and faculty specialized in eye tracking and psychophysics research. Collaborator Dr. Michelle Zhou, a research manager at IBM T. J. Watson Research Center, has years of experience in the areas of data and visual taxonomies, image databases, and automated generation of information graphics [Zhou and Feiner 1998, Zhou et al. 2002b, Zhou et al. 2002a]. Broader impacts of the proposed activity In addition to contributions the image taxonomy, database, and web application are intended to make to research, they will serve as a rich resource for teaching about the history and scope of visualization methods and design within and across disciplines, and for the general public with an interest in information graphics. The research will be conducted on subjects of varied background and race and will be broadly disseminated via websites in addition to publications. Additionally, defining a visual taxonomy will inform design choices made in information visualization. One implication of this research is a determination of how effective different visualization methods are at conveying information; this understanding will be of profound help to anyone interested in conveying information effectively in a graphical form.

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DOI: 10.3897/rio.2.e8815

Authors: Arno Klein, Steven Ellis

Abstract: The proposed research develops new computational tools to identify, diagnose, and predict treatment response for different mental illnesses. The research will first be applied to publicly available resting state fMRI BOLD data from patients with attentiondeficit hyperactivity disorder and autism. It will also be applied to existing clinical and biological data concerning suicidality in the context of major depressive disorder. These disorders affect millions of Americans, but these tools can be applied to any mental illness, such as Alzheimer’s disease, bipolar disorder, schizophrenia – indeed to analyze differences in brain, clinical, and biological data between any two populations.

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DOI: 10.3897/rio.2.e8760

Authors: Stephen Abrams, Andrea Goethals, Martin Klein, Rosalie Lack

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DOI: 10.3897/rio.2.e8767

Authors: Roderic Page

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DOI: 10.3897/rio.2.e8649

Authors: Stephanie Simms, Sarah Jones, Kevin Ashley, Marta Ribeiro, John Chodacki, Stephen Abrams, Marisa Strong

Abstract: The DMPTool and DMPonline were developed to meet an emerging need arising from the advent of open data policies and each is now well established as the resource for researchers seeking guidance in creating data management plans (DMPs) in the US and UK respectively. Both services, and their sponsoring organizations, the California Digital Library (CDL) and the Digital Curation Centre (DCC), have succeeded in enabling researchers to comply with funder requirements in producing DMPs. However, this is just one step along the road to advancing open science. We see an opportunity to further leverage DMPs to support open science by integrating them into the broader ecosystem of data management infrastructure. In order to achieve this goal, we must redefine success to include not just adoption of our services by institutions but also widespread adoption by individual researchers, disciplinary communities, and funders. Working together with all stakeholders to make DMPs an essential, open part of the research lifecycle, and not just a matter of compliance, is the next step toward effectively managing and sharing research data. We propose to join forces and build a new, global data management advisory platform that links DMPs to other components of the research lifecycle. The biomedical research community provides an opportunity to adapt the infrastructure and associated educational resources to one specific disciplinary community and plug into new initiatives. We will reposition DMPs as living documents useful for structuring the course of biomedical research activities and integrating with related data management systems to lower the barriers for implementation and promote culture change. Consolidating around a single platform for DMPs extends our reach, keeps costs down, and moves best practices forward, allowing us to participate in a truly global open science ecosystem.

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DOI: 10.3897/rio.2.e8462

Authors: Marcin Wojnarski, Debra Hanken Kurtz

Abstract:

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