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/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.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/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.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/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/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.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.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.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.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.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.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.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.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.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.e9113

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

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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.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.e8462

Authors: Marcin Wojnarski, Debra Hanken Kurtz

Abstract:

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