EarthCODE 

Human functional neuroimaging has evolved dramatically in recent years, driven by increased technical complexity and emerging evidence that functional neuroimaging findings are not generally reproducible. In response to these trends, neuroimaging scientists have developed principles, practices, and tools to both manage this complexity as well as to enhance the rigor and reproducibility of neuroimaging science. We group these best practices under four categories: experiment pre-registration, FAIR data principles, reproducible neuroimaging analyses, and open science. While there is growing recognition of the need to implement these best practices there exists little practical guidance of how to accomplish this goal. In this work, we describe lessons learned from efforts to adopt these best practices within the Brain Imaging Research Center at the University of Arkansas for Medical Sciences over 4 years (July 2018–May 2022). We provide a brief summary of the four categories of best practices. We then describe our center's scientific workflow (from hypothesis formulation to result reporting) and detail how each element of this workflow maps onto these four categories. We also provide specific examples of practices or tools that support this mapping process. Finally, we offer a roadmap for the stepwise adoption of these practices, providing recommendations of why and what to do as well as a summary of cost-benefit tradeoffs for each step of the transition.

The Open Science and Innovation Vision included in ESA’s EO Science Strategy (2024) addresses 8 key elements: 1) openness of research data, 2) open-source scientific code, 3) open access papers with data and code; 4) standards-based publication and discovery of scientific experiments, 5) scientific workflows reproducible on various infrastructures, 6) access to education on open science, 7) community practice of open science; and 8) EO business models built on open-source. EarthCODE (https://earthcode.esa.int) is a strategic ESA EO initiative to support the implementation of this vision. EarthCODE (Earth Science Collaborative Open Development Environment) will form part of the next generation of cloud-based geospatial services, aiming towards an integrated, cloud-based, user-centric development environment for European Space Agency’s (ESA) Earth science activities. EarthCODE looks to maximise long-term visibility, reuse and reproducibility of the research outputs of such projects, by leveraging FAIR and open science principles and enabling, thus fostering a sustainable scientific process. EarthCODE proposes a flexible and scalable architecture developed with interoperable open-source blocks, with a long-term vision evolving by incrementally integrating industrially provided services from a portfolio of the Network of Resources.  Additionally, EarthCODE is a utilisation domain of EOEPCA+, contributing to the development and evolution of Open Standards and protocols, enabling internationally interoperable solutions.  EarthCODE will provide an Integrated Development Platform, giving developers tools needed to develop high quality workflows, allowing experiments to be executed in the cloud and be end-to-end reproduced by other scientists. EarthCODE is built around existing open-source solutions, building blocks and platforms, such as the Open Science Catalogue, EOxHub and EOEPCA. It has additionally begun to integrate platform services from DeepESDL, Euro Data Cube, Polar TEP and the openEO federation on CDSE platforms, with more being added annually through ESA best practices. With it’s adopted federated approach, EarthCODE will facilitate processing on other platforms, i.e. DeepESDL, ESA EURO Data Cube, Open EO Cloud/Open EO Platform and AIOPEN/AI4DTE.   The roadmap for the portal includes the initial portal release by end of 2024, followed by the capability to publish experiments in Q1 2025 (including development, publishing, finding and related community engagement), and by mid-2025 to have a further release with reproducibility capabilities around accessibility and execute functionalities.  Collaboration and Federation are at the heart of EarthCODE. As EarthCODE evolves we expect providing solutions allowing federation of data and processing. EarthCODE has ambition to deliver a model for a Collaborative Open Development Environment for Earth system science, where researchers can leverage the power of the wide range of EO platform services available to conduct their science, while also making use of FAIR Open Science tools to manage data, code and documentation, create end-to-end reproducible workflows on platforms, and have the opportunity to discover, use, reuse, modify and build upon the research of others in a fair and safe way. Overall, EarthCODE aims to enable elements for EO Open Science and Innovation vision, including open data, open-source code, linked data/code, open-access documentation, end-to-end reproducible workflows, open-science resources, open-science tools, and a healthy community applying all the elements in their practice.

Poland is characterised by a relatively high variety of living organisms attributed to terrestrial and water environments. Currently, close to 57.000 species of living organisms are described that occur in Poland (Symonides 2008), including lowland and mountain species, those attributed to oceanic and continental areas, as well as species from forested and open habitats. Poland comprehensively represents biodiversity of living organisms on a continental scale and thus, is considered to have an important role for biodiversity maintenance. The Mammal Research Institute of Polish Academy of Sciences (MRI PAS), located in Białowieża Forest, a UNESCO Heritage Site, has been collecting biodiversity data for 90 years. However, a great amount of data gathered over the years, especially old data, is gradually being forgotten and hard to access. Old catalogues and databases have never been digitalized or publicly shared, and not many Polish scientists are aware of the existence of such resources, not to mention the rest of the scientific world. Recognizing the need for an online, interoperable platform, following FAIR data principles (findable, accessible, interoperable, reusable), where biodiversity and scientific data can be shared, MRI PAS took a lead in creation of an Open Forest Data (OFD) repository. OpenForestData.pl (Fig. 1) is a newly created (2020) digital repository, designed to provide access to natural sciences data and provide scientists with an infrastructure for storing, sharing and archiving their research outcomes. Creating such a platform is a part of an ongoing development of life sciences in Poland, aiming for an open, modern science, where data are published as free-access. OFD also allows for the consolidation of natural science data, enabling the use and processing of shared data, including API (Application Programming Interface) tools. OFD is indexed by the Directory of Open Repositories (OpenDOAR) and Registry of Research Data Repositories (re3data). The OFD platform is based entirely on reliable, globally recognized open source software: DATAVERSE, an interactive database app which supports sharing, storing, exploration, citation and analysis of scientific data; GEONODE, a content management geospatial system used for storing, publicly sharing and visualising vector and raster layers, GRAFANA, a system meant for storing and analysis of metrics and large scale measurement data, as well as visualisation of historical graphs at any time range and analysis for trends; and external tools for database storage (Orthanc) and data visualisation (Orthanc plugin Osimis Web Viewer and Online 3D Viewer (https://3dviewer.net/), which were integrated with the system mechanism of Dataverse. Furthermore, according to the need for specimen description, Darwin Core (Wieczorek et al. 2012) metadata schema was decided to be the most suitable for specimen and collections description and mapped into a Dataverse additional metadata block. The use of Darwin Core is based on the same file format, the Darwin Core Archive (DwC-A) which allows for sharing data using common terminology and provides the possibility for easy evaluation and comparison of biodiversity datasets. It allows the contributors to OFD to optionally choose Darwin Core for object descriptions making it possible to share biodiversity datasets in a standardized way for users to download, analyse and compare. Currently, OFD stores more than 10.000 datasets and objects from the collections of Mammal Research Institute of Polish Academy of Sciences and Forest Science Institute of Białystok University of Technology. The objects from natural collections were digitalized, described, catalogued and made public in free-access. OFD manages seven types of collection materials: 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. 3D and 2D scans of specimen in Herbarium, Fungarium, Insect and Mammal Collections, images from microscopes (including stereoscopic and scanning electron microscopes), morphometric measurements, computed tomography and microtomography scans in Mammal Collection, mammal telemetry data, satellite imagery, geospatial climatic and environmental data, georeferenced historical maps. In the OFD repository, researchers have the possibility to share data in standardized way, which nowadays is often a requirement during the publishing process of a scientific article. Beside scientists, OFD is designed to be open and free for students and specialists in nature protection, but also for officials, foresters and nature enthusiasts. Creation of the OFD repository supports the development of citizen science in Poland, increases visibility and access to published data, improves scientific collaboration, exchange and reuse of data within and across borders.

Collaborative Open Science is essential to addressing complex challenges whose solutions prioritize integrity and require cross-domain integrations. Today, building workflows, processes, and data flows across domains and sectors remains technically difficult and practically resource intensive, creating barriers to whole-systems change. While organizations increasingly aim to demonstrate accountability, they often lack the tools to take action effectively. By making it simple to connect data and platforms together in transparent, reusable and reproducible workflows, the OGC Open Science Persistent Demonstrator (OSPD) aims to enable responsible innovation through collaborative open science. The OSPD focuses specifically on using geospatial and earth observation (EO) data to enable and demonstrate solutions that create capacity for novel research and accelerate the practical implementation of this research.Collaborative Open Science and FAIR (Findable, Accessible, Interoperable, Reusable) data are widely recognized as critical tools for taking advantage of the opportunities created through addressing complex social and environmental challenges. To date, many millions have been invested in hundreds of initiatives to enable access to analytical tools, provide data management, data integration and exchange, translate research results, and support reproduction and testing of workflows for new applications. These investments have resulted in a plethora of new data, protocols, tools and workflows, but these resources frequently remain siloed, difficult to use, and poorly understood, and as a result they are falling short of their full potential for wider impact and their long term value is limited.This presentation will illustrate how the OGC OSPD Initiative, through its design, development and testing activities, provides answers to leading questions such as:How can we design Open Science workflows that enable integration across platforms designed for diverse applications used in different domains to increase their value? How can we lower barriers for end users (decision makers, managers in industry, scientists, community groups) who need to create Open Science workflows, processes, and data flows across domains and sectors remains technically difficult and practically resource intensive, creating? How can Open Science workflows and platforms enable collaboration between stakeholders in different domains and sectors? How can we empower organizations to demonstrate accountability in their analytical workflows, data, and representations of information through Open Science? What Open Science tools do organizations need to take action effectively? How can Open Science and FAIR data standards practically support accountability? How can we make it simple to connect data and platforms together in transparent, reusable and reproducible (FAIR) workflows? What are the specific challenges of using geospatial, earth observation (EO), and complementary data in this context?

The modern open science makes an ecosystem comprising open scientific knowledge, science infrastructure, dialogue with other systems of knowledge and engaged social actors. The latter is channelized through the concept of wide social involvement in scientific research – the civil science concept. Despite the libraries are mostly oriented toward the former two components, which is due to their information functions, they are also able to build themselves in the ecosystem through collaboration with civil science project. However, such projects have not become popular, which significantly narrows down the potentialities of open science. The purpose of the article is to analyze the possibilities for libraries; entering the open science ecosystem through supporting is to open engagement of social players in science. The author discusses the obstacles on the way of the citizen science as a part open science ecosystem, and libraries involvement. Thus, underdevelopment of civil science in Russia may be partially due to insufficient dissemination of scientific thinking in the non-scientific spheres, the closeness of scientific community and its low interest in communicating with the wider audience. The analysis of 431 websites of Russian research institutes proves that just about 42 % of them popularize scientific activities. Besides, many research institute are not so eager to disseminate instructions for developing the sphere of open and citizen science. Consequently, the libraries cannot jump to the collaboration with citizen science. As the first step, the author suggests to contribute to popularization of science and scientific system and in particular, the open science, and within its context, of the citizen science oriented on the community and researchers.

Society is moving towards Open Science. Open Science enhances reproducibility and transparency both of which are important features of research integrity. Scientometrics, the field which analyses scientific publications, has been criticised because of the use of traditional selective closed databases in its analyses. The present project wants to create a more comprehensive method to demonstrate the potential of Scientometrics to be able to visualise the contributions to research beyond subscribed databases in a more global, precise and complete way. Some studies have demonstrated there may be bias towards English-speaking countries in research analyses. Additionally, it seems that publications from some areas i.e. Humanities and Social Sciences research areas may be underestimated. The research community relies so much on publications included in the subscribed citation databases, they are the main sources of university rankings, research institutions, and the main source where researchers look for where to publish. This study aims to visualise publications beyond traditional and subscribed databases, Scopus and Web of Science. OpenAlex will be used as a complement to the analysis. OpenAlex is an index of hundreds of millions of interconnected entities across the global research system is a free and open source which uses data from Microsoft Academics Graph and Crossref. The database offers by far the most comprehensive coverage of the scientific literature than traditional databases. It also shows that offers a greater coverage of publications that are not in English. To calculate indicators, the study will use OpenCitations an independent not-for-profit infrastructure organisation for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. It complies with the FAIR data principles that data should be findable, accessible, interoperable and re-usable. The publication window will be 10 years from 2013 to 2022. The study will show the influence of COVID 19 in the publication sample. How would research outcomes be impacted by the utilisation of new open data sources? Do publications that are not part of subscribed citation databases have an impact? Are there publications that contribute to knowledge even though they are not part of mainstream databases? Would subscription databases be able to expand their publication coverage in order to become more inclusive? By creating a fairer system for analysing and evaluating research this will ultimately benefit society as a whole. This project would be aligned with the recent developments for responsible research assessment i.e. DORA, CoARA. The originality of this project is it will be the first one done by an experienced scientometrician and this project will be the first one has been done using open databases to assess country’s research productivity.

This chapter describes how the new emerging digital environment challenges historians’ existing training and practice of source criticism. In an environment with increasing amounts of digitized data and digital methods there are new requirements for historians to develop new skills as well as new more extensive provenance data. Historians are faced with new challenges regarding new increasing demands for transparency and open scholarship that has comes with the growth of digital humanities in general and with digital history in particular. The old demands that historical research has to be well documented and reproducible has to be adapted to the promises and pitfalls of the new digital environment which especially means developing and adapting new standards and practices for what counts as good data management. The study discusses how the FAIR data principles can offer valuable guidance, but also how they cannot be implemented without supporting services that take into account different types of data and the data lifecycle.

Compound heat waves and drought events draw our particular attention as they become more frequent. Co-occurring extreme events often exacerbate impacts on ecosystems and can induce a cascade of detrimental consequences. However, the research to understand these events is still in its infancy. DeepExtremes is a project funded by the European Space Agency (https://rsc4earth.de/project/deepextremes/) aiming at using deep learning to gain insight into Earth surface under extreme climate conditions. Specifically, the goal is to forecast and explain extreme, multi-hazard, and compound events. To this end, the project leverages the existing Earth observation archive to help us better understand and represent different types of hazards and their effects on society and vegetation. The project implementation involves a multi-stage process consisting of 1) global event detection; 2) intelligent subsampling and creation of mini-data-cubes; 3) forecasting methods development, interpretation, and testing; and 4) cloud deployment and upscaling. The data products will be made available to the community following the reproducibility and FAIR data principles. By effectively combining Earth system science with explainable AI, the project contributes knowledge to advancing the sustainable management of consequences of extreme events. This presentation will show the progress made so far and specifically introduce how to participate in the challenges about spatio-temporal extreme event prediction in DeepExtremes.

This study explores the factors affecting communication scholars’ data-sharing intentions, a critical component of reproducibility and replicability in open science. We replicate Harper and Kim’s (2018) study, which employs the theory of planned behavior to demonstrate the impacts of attitudinal, normative, and resource factors. Specifically, their original research examines data-sharing practices among psychologists, and our replication aims to reinforce their findings within the communication field. Data from a survey of Chinese communication scholars (<em>N</em> = 351) are analyzed using structural equation modeling. The findings indicate that perceived benefit and perceived risk significantly influence the attitudes of communication scholars towards sharing their data, positively and negatively, respectively. Additionally, attitudes, subjective norms, journal pressure, and the conditions facilitating data sharing have a significant positive impact on communication scholars’ behavioral intentions. Perceived effort inversely affects attitudes toward data sharing but does not impact behavioral intentions. This study provides a theoretical framework for understanding data-sharing intentions and behaviors in the open science movement. The role of this research as a replication study serves as a compelling demonstration of scientific inquiry. Practical suggestions, such as fostering open dialog, institutional incentives, and cooperation between different actors to increase communication scholars’ data-sharing intentions, and recommendations for carrying out replication and reproduction studies, are discussed. Finally, we judiciously reflect on the methodological limitations of our research and highlight directions for future research on open science.

<p>Citizen science has globally been recognized as a vital part of open science and as a way of doing research that enables new levels of science education and science communication. Due to its high levels of public participation, citizen science can be of great value in bringing society and science closer together. Universities across the world have acknowledged this value and aim to incorporate citizen science in their policies and daily practices as part of their open science practices.</p><p>The Delft University of Technology has set the goal to develop an open science program that includes citizen science. However, implementing and incorporating citizen science in an open science program is not a straightforward task and demands knowledge, understanding, and experience of the field as well as the practical implications. What should a university do to support the goals of various citizen science initiatives, within an open science context, and to assist and facilitate researchers to perform effective participatory science? To gain a deeper understanding of what a citizen science project entails within the context of a university, we performed a case-study implementing citizen science methods for hydrological research. The project, called Delft Measures Rain, was developed in collaboration with external partners and several internal departments and their staff, some already having experience with developing and coordinating citizen science projects. Citizens of Delft were encouraged to participate and work together with scientists from the Water Management department to investigate rainfall patterns within the city. In total, 95 citizens collaborated for two months to collect over 1900 individual rainfall measurements spread over the city and taken with home-made rain gauges.  We developed tailored recruitment strategies, data collection and validation tools, data visuals, and communication strategies. Overall, the project has delivered valuable results, including reliable rainfall data, involvement and enthusiasm of citizens, and valuable feedback from participants. Additionally, this project has led to more cooperation of relevant institutions and civil society organizations (CSO) across the city and between different departments within the university itself.</p><p>This case-study has showcased how various stakeholders (researchers, citizens, civil servants, CSO’s, etc.) can benefit from co-developed participatory research implementing citizen science and open science principles. With this case study, we were able to identify the benefits, drawbacks, and opportunities for all stakeholders involved. Furthermore, we identified key tools and facilitation needs to assist researchers within the university to perform effective participatory science. During the session, we would like to share our methods, successes, challenges, and lessons learned. This project shows that, with the right knowledge and tools, citizen science can deliver what it promises and be of great value to universities and open science in general. </p>

Open science is a powerful way to discover and disseminate new knowledge. But the very norms that animate open science can sometimes inhibit its subsequent commercialization. There exists a Valley of Death between the culmination of a scientific project, and the later commercial use of that science. While a fundamental knowledge of how a technology works may have been achieved, there is far too little known for businesses to invest in developing that knowledge further. And the best use of a new discovery often is unknown to those who first discover it. This is where open science must start to give way to Open Innovation. IMEC and CERN’s ATTRACT project provide two examples wherein a strong culture of open science co-exists with a thriving Open Innovation culture as well to traverse the Valley of Death

The reuse and integration of data give big opportunities, supported by the FAIR data principles. Seamless data integration from heterogenous sources has been an interest of the geospatial community for a long time. However, 3D city models, building information models, and information supporting smart cities present higher semantic and geometrical complexity, which pose new challenges never tackled in a comprehensive methodology. Building on previous theories and studies, this article proposes an overarching workflow and framework for multisource (geo)spatial data integration. It starts from the definition of use case‐based requirements for the integrated data, guides the analysis of integrability of the involved datasets, suggesting actions to harmonize them, until data merging and validation. It is finally tested and exemplified in a case study. This approach allows the development of consistent, well‐documented, and inclusive data integration workflows, for the sake of use case automation in various geospatial domains and the production of interoperable and reusable data.

Developments in space activities in Poland

Construction of learning structures for Bayesian Networks is considered in this work when data is securely maintained by different parties, not willing to reveal their individual private data to each other. We propose a privacy-preserving protocol for Bayesian Network from data which ishomogeneously partitioned among two or more parties by usingK2 algorithm, a heuristic algorithm typically used to construct Bayesian Network. Three secure building blocks are also presented to use inside the main protocol; Secure Exponentiation, Secure Multi-party Factorial and Secure Product Comparison. We have also modified two existing building blocks which are used in this paper, Secure Multi-Party Addition and Multiplication, to improve their resistance against colluding attack. These protocols have the added advantage that they can even be used over public channels. That is, channels over which any party is able to see any messages exchanged between any two or more parties.

Open Life Science (OLS) offers Open Seeds, a 16-week training and mentoring programs to enable researchers and their teams to lead open research projects in their respective domains and become open science ambassadors for their communities. With the combination of practical training and 1:1 support from our mentors, we guide participants to reflect on and apply open practices in the context of the socio-cultural environment where they conduct their research. Participants join the program with an open science project. They attend cohort calls to learn about open science practices and frameworks to apply hands-on open science skills including FAIR research principles and equitable and inclusive community practices. In mentoring calls every alternating week, they reflect on their progress and identify the next steps. To strengthen their skills, program graduates can re-join subsequent cohorts as mentors, call facilitators, and expert speakers. We offer micro-grants, live transcription, and other resources to make the program inclusive and accessible. Since 2020, we have run 7 cohorts and trained >300 participants from 6 continents, across 50+ low- and middle- (LMICs), and high-income countries (HICs), with the help of 120+ mentors and 150+ experts. This poster will describe the various aspects of the program and highlight all materials (including speaker deck, recorded videos, assignment materials, and organisational processes) that are shared openly utilising FAIR practices.