Notes from open data meeting

Public science is critical to the economy and to society. However, much of the beneficial impact of scientific research only occurs when scientific knowledge is disseminated broadly and is used by others. This thesis examines the emerging policy, law, and practice of facilitating open access to scientific research data. One particular focus is to examine the open data policies recently introduced by research funders and publishers, and the potential in these for driving the practice of open scientific data into the future. This thesis identifies five major stumbling blocks to sustainable open scientific data. Firstly, the prevailing ‘mindset’ that facilitating open access to data is analogous to facilitating open access to publications and, therefore, research data can easily be shared, with research funders and librarians effectively leading the process. Secondly, the unclear meaning of the term ‘data’, which causes confusion among stakeholders. Thirdly, ‘misunderstood incentives’ for data sharing and the additional inputs required from researchers. Fourthly, ‘data privacy’—an issue that only applies to selected research datasets, and yet appears to dominate the discussion about open research data. Finally, there is ‘copyright law’, which poses challenges at different stages of data release and reuse. In this thesis, I argue that the above problems can be addressed using a staged model for open scientific data. I draw specifically on the practice with open scientific data at CERN (the European Organization for Nuclear Research) and the practice of sharing clinical trial data to argue that open data can be shared at various stages of processing and diversification. This model is supplemented by recommendations proposing changes to existing open data mandates and the introduction of a text and data mining exemption into Australian copyright law.

Not opening scientific data is costly. It has been estimated that a significant share of scientific knowledge disappears every year. In a 2014 study less than half of biological datasets from the 1990s have been recovered and when possible the recovery has necessitated significant time and efforts. In comparison, 98% of datasets published on PLOS with unique identifiers (data DOIs) are still available for future research. Open scientific data are fundamental resources for a large variety of scientific activities: meta-analysis, replication of research results or accessibility to primary sources. They also bring a significant economic and social value, as scientific data is commonly used by non-academic professionals as well as public agencies and non-profit organizations. Yet open scientific data is not costless. Ensuring that data is not only downloadable but usable requires significant investment in regards to documentation, data cleaning, licensing and indexation. Not all scientific data can be shared and verifications are frequently necessary to ensure that they do not incorporate copyrighted contents or personal information. To be effective, data sharing has to be anticipated throughout the entire research lifecycle. New principles of scientific data management aims to formalize the preexisting cultures of data in scientific communities and apply common standards. First published in 2016, the FAIR Guiding Principles (findability, accessibility, interoperability, and reusability) is an influential framework for opening scientific data. Policies in support of data sharing have moved from general and broad encouragement to the concrete development of data sharing services. Early initiatives go back to the first computing infrastructures: in 1957 the World Data Center system aimed to make a large range of scientific data readily available. Open data programs were yet severely limited by the lack of technical support and compatibility for data transfer. After 1991, the web created a universal framework for data exchange and entailed a massive expansion of scientific databases. Yet, numerous projects ran into critical issues of long term sustainability. Open science infrastructure have recently become key stakeholders in the diffusion and management of open scientific data. Data repositories ensure the preservation of scientific resources as well as their discoverability. Data hosted on repositories are more frequently used and quoted than data published in a supplementary file.

The Open Knowledge Foundation has launched a Working Group on Open Data in Science.

Purpose of the study. The purpose of this paper is to analyze the current state and prospects for introducing the principles of open access to scientific publications and scientific data in the sphere of science and education in Belarus. Its relevance is related to the need to develop measures to accelerate the process of digital transformation of science and education of the Republic of Belarus.Materials and methods. Information base of the research was made by publications of scientists and specialists on the issues under study, normative documents, final documents of conferences on this topic, data from the Open Data in Belarus portal, national and international aggregators of institutional repositories, open scientific data repositories.Results. The analysis of the state and prospects of introducing the principles of open access to scientific publications and scientific data into the sphere of science and education of Belarus was carried out during the research. It is shown that the digital transformation of science and education is at an early stage. The dissemination of the principles of open science and the introduction of new instruments of scientific communication in the Belarusian academic and university science are uneven, there is a need to develop a strategy in this direction. The principle of open access to publications is being most actively introduced into practice through the development of a network of university repositories. In Belarus, the open data infrastructure is at the very beginning of its formation. In this regard, there is a need to conduct additional research to identify problems associated with the discovery of scientific data. One step in the transition to open science is the unification of all the repositories on a single platform of the national aggregator. The review of national and international aggregators of institutional repositories is presented. The questions on creation of the national system-aggregator of information resources of open access in the Republic of Belarus in the context of the formation of the Republican information and educational environment are considered: the purpose of the system, the platform used, technical solutions for organizing the integration of information systems of higher education institutions and the rules of interaction of system users.Conclusions. The creation of a national system-aggregator will not only provide a single point of access to the institutional repositories of project participants, which will significantly improve the convenience and completeness of the search, but will also solve one of the most important tasks of the project - popularizing the idea of open access to scientific publications. The implementation of the proposed measures to create conditions for the discovery of scientific data in Belarus will contribute to the introduction of the principle of open access to scientific data. The considered approaches will allow accelerating the process of digital transformation of the scientific and educational sphere of Belarus.

The array of problems presented to Extension professionals is broad and growing in number and complexity. As Extension has demonstrated its adaptability in addressing these issues, new ways of working have emerged. Access to an expanding pool of scientific reports and data can potentially provide Extension professionals with the greater tools and knowledge they need to collaboratively engage with their communities. However, a key challenge and possible impediment will be access to rapidly emerging research and data. Open science and open data can broaden the evidence base available to Extension educators, and the emerging field of data science offers new tools to help Extension stakeholders make data-informed decisions. A new data-sharing partnership among Canada, Mexico, and the United States may serve as a model for other countries’ rural advisory services and national Extension systems. Fully implementing this expanded role for Extension will require resources to establish a National Community Learning Network and a national data commons as well as advocacy for open access policies at all levels of government. As abstract as open science and open data may seem to local and regional Extension practitioners, equal access to scientific knowledge and underlying research data is not only imperative for local community engagement but also integral for locally appropriate decision-making. Widening access to research and data directly supports the democratization of science and Extension. Opening scientific research and providing effective access to publicly financed data will become essential platforms for university engagement and Extension. It is critical for Extension professionals to understand the analytic powers and emerging policies that easily-accessed research and data can bring to collaborative community engagement.

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?

Supporters of open data believe that free and complete access to research data is beneficial for science, public policy, and society. In environmental science and policy, open data systems can enable relevant research and inform evidence‐based governmental decisions. This article examines the unlikely case of Brazil's National Institute for Space Research's transition toward an open data model. Considering Brazil's young democracy, incipient practice of government transparency and accountability, and lacking a tradition of science‐policy dialogue, this case is a striking example of how open data can support public debate by making information about forest cover widely available. The case shows the benefits and challenges of developing such open data systems, and highlights the various forms of accessibility involved in making data available to the public.

The use of association networks to calculate metadata similarity for data association and recommendation is a well-established approach. However, challenges persist in the field of open scientific data association within the Earth surface system. Existing methodologies are primarily designed for single data sources, limited dimensions, and specific linguistic domains, limiting their applicability and effectiveness. Enhancing the efficiency and adaptability of association algorithms is crucial for further advancements in this field. In this paper, we propose a multidimensional and multilingual association method for Earth surface system scientific data to support precise data retrieval and intelligent recommendation. The method quantitatively computes one-dimensional and multidimensional similarities using five primary indicators of multilingual data, enabling semantic interconnection and facilitating knowledge discovery within open scientific data related to the Earth surface system. We obtained metadata from several authoritative sources, including NESSDC, NASA, and Data.gov, and performed association calculations, constructed multidimensional association networks, and evaluated them for optimization. The key strengths of this study include its ability to construct one-, two-, and multidimensional association networks effectively, support cross-lingual and cross-standard data interconnections, and assess network node importance. Finally, we conducted a comparative analysis with classical methods, demonstrating that our approach achieves higher accuracy and better scalability.

Decarbonisation and efficiency goals set as a response to global warming issue require appropriate decision-making strategies to promote an effective and timely change in energy systems. Conceptualization of change is a relevant part of energy transitions research today, which aims at enabling radical shifts compatible with societal functions and market mechanisms. In this framework, construction sector can play a relevant role because of its energy and environmental impact. There is, however, the need to move from general instances to specific actions. Open data and open science, digitalization and building data interoperability, together with innovative business models could represent enabling factors to accelerate the process of change. For this reason, built environment research has to address the co-evolution of technologies and human behaviour and the analytical methods used for this purpose should be empirically grounded, transparent, scalable and consistent across different temporal/spatial scales of analysis. These features could potentially enable the emergence of “ecosystems” of applications that, in turn, could translate into projects, products and services for energy transitions in the built environment, proposing innovative business models that can stimulate market competitiveness. For these reasons, in this paper we organize our analysis according to three levels, from general concepts to specific issues. In the first level, we consider the role of building energy modelling at multiple scales. In the second level, we focus on harmonization of methods for energy performance analysis. Finally, in the third level, we consider emerging concepts such as energy flexibility and occupant-centric energy modelling, considering their relation to monitoring systems and automation. The goal of this research is to evaluate the current state of the art and identify key concepts that can encourage further research, addressing both human and technological factors that influence energy performance of buildings.

Earth and Space Science Open Archive Presented WorkOpen AccessYou are viewing the latest version by default [v1]Advancing Open and Reproducible Water Data Science by Integrating Data Analytics with an Online Data RepositoryAuthorsJefferyHorsburghiDScottBlackAnthonyCastronovaSee all authors Jeffery HorsburghiDCorresponding Author• Submitting AuthorUtah State UniversityiDhttps://orcid.org/0000-0002-0768-3196view email addressThe email was not providedcopy email addressScott BlackConsortium of Universities for the Advancement of Hydrologic Sciences, Inc.view email addressThe email was not providedcopy email addressAnthony CastronovaConsortium of Universities for the Advancement of Hydrologic Sciences, Incview email addressThe email was not providedcopy email address

Computational experiments using earth system models, approaching kilometre-scale (k-scale) horizontal resolutions, are becoming increasingly common across modeling centers. Recent advances in high performance computing systems, along with efficient parallel algorithms that are capable of leveraging accelerator hardware, have made k-scale models affordable for specific purposes. Surrogate models developed using machine learning methods also promise to further reduce the computational cost while enhancing model fidelity. The “avalanche of data from k-scale models” (Slingo et al., 2022) has also posed new challenges in processing, managing, and provisioning data to the broader user community. During recent years, a joint effort between the European Center for Medium-Range Weather Forecasts (ECMWF) and the Oak Ridge National Laboratory (ORNL) has succeeded in simulating “a baseline for weather and climate simulations at 1-km resolution,” (Wedi et al., 2020) using the Summit supercomputer at the Oak Ridge Leadership Facility (OLCF). The ECMWF hydrostatic Integrated Forecasting System (IFS), with explicit deep convection on an average grid spacing of 1.4 km, was used to perform a set of experimental nature runs (XNR) spanning two seasons corresponding to a northern hemispheric winter (NDJF), and August - October (ASO) months corresponding the tropical cyclone season in the North Atlantic. We developed a bespoke workflow to process and archive over 2 PB of data from the 1-km XNR simulations (XNR1K). Further, we have also facilitated access to the XNR1K data via an open science data hackathon. The hackathon projects also have access to a data analytics cluster to further process and analyze the data. The OLCF data center supports high speed data sharing via globus data transfer mechanism. External users are using the XNR1K data for a number of ongoing research projects, including observing system simulation experiments, designing satellite instruments for severe storms, developing surrogate models, understanding atmospheric processes, and generating high-fidelity visualizations.During our presentation we will share our challenges, experiences and lessons learned related to the processing, provisioning and management of the large volume of data, and the stakeholder engagement and logistics of the open science data hackathon.Slingo, J., Bates, P., Bauer, P. et al. (2022) Ambitious partnership needed for reliable climate prediction. Nat. Clim. Chang.  https://doi.org/10.1038/s41558-022-01384-8Wedi, N., Polichtchouk, I., et al. (2020) A Baseline for Global Weather and Climate Simulations at 1 km Resolution, JAMES. https://doi.org/10.1029/2020MS002192

The expansion of the research community's ability to collect and store data has grown much more rapidly than its ability to catalog, make accessible, and make use of data. Recent initiatives in Open Science and Open Data have attempted to address the problems of making data discoverable, accessible and reusable at internet scales. The Enhanced Robust Persistent Identification of Data (E-RPID) project's goal is to address these deficiencies and enable options for data interoperability and reusability in the current research data landscape by utilizing Persistent Identifiers (PIDs) and a kernel of state information available with PID resolution. To do this requires integrating a set of preexisting software systems along with a small set of newly developed software solutions. The combination of these software components and the core principles of making data FAIR (findable, accessible, interoperable and reusable) will allow us to use Persistent Identifiers to create an end-to-end fabric capable of realizing the Digital Object Architecture for researchers.This poster will acquaint the audience to the concepts of the Digital Object Architecture, describe the software service architecture necessary to enable this architecture, outline the existing E-RPID testbed that is available for experimental usage from the Jetstream cloud environment, and describe the diverse set of use cases already using E-RPID to enhance their data accessibility, interoperability and reusability. It will focus on how the Digital Object Architecture and E-RPID testbed would interact with XSEDE resources and how E-RPID could assist with interoperability, reusability and reproducibility of HPC workflows.

Research Data in Current Research Information Systems

This introduction presents the two pieces written by Dr. Gasco-Hernandez and Dr. Luna-Reyes, for the fourth issue of the Association for Computing Machinery, Special Interest Groups on Computers and Society (SIGCAS) Newsletter. We introduce the articles through their main topics (open innovation and the data scientist) as linked elements to the OCDE strategies recommended for Digital Government and, specifically, to collaboration and openness as social-enablers.

The need to adapt quickly to online or remote instruction has been a challenge for instructors during the COVID pandemic. A common issue instructors face is finding high-quality curricular materials that can enhance student learning by engaging them in solving complex, real-world problems. The current study evaluates a set of 15 web-based learning modules that promote the use of authentic, high-cognitive demand tasks. The modules were developed collaboratively by a group of instructors during a HydroLearn hackathon-workshop program. The modules cover various topics in hydrology and water resources, including physical hydrology, hydraulics, climate change, groundwater flow and quality, fluid mechanics, open channel flow, remote sensing, frequency analysis, data science, and evapotranspiration. The study evaluates the impact of the modules on students’ learning in terms of two primary aspects: understanding of fundamental concepts and improving technical skills. The study uses a practical instrument to measure students’ perceived changes in concepts and technical skills known as the Student Assessment of Learning Gains (SALG) survey. The survey was used at two-time points in this study: before the students participated in the module (pre) and at the conclusion of the module (post). The surveys were modified to capture the concepts and skills aligned with the learning objectives of each module. We calculated the learning gains by examining differences in students’ self-reported understanding of concepts and skills from pre- to post-implementation on the SALG using paired samplest-tests. The majority of the findings were statistically at the 0.05 level and practically significant. As measured by effect size, practical significance is a means for identifying the strength of the conclusions about a group of differences or the relationship between variables in a study. The average effect size in educational research isd= 0.4. The effect sizes from this study [0.45, 1.54] suggest that the modules play an important role in supporting students’ gains in conceptual understanding and technical skills. The evidence from this study suggests that these learning modules can be a promising way to deliver complex subjects to students in a timely and effective manner.