Open Scientific Data

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.

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?

The notes and transcript from the first meeting of the Working Group on Open Data in Science (online, June 2, 2009) are now available.

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.

Between 2-4% of researchers admit to have falsified or fabricated their data. The prevalence of such unethical behavior can be as high as 10% in some disciplines or countries. Data falsification is an extreme form of questionable research practices that are both less problematic and much more widespread: surveys on different disciplines have shown that more than half of researchers make some form of selective reporting or add new data until they obtain significant results. Unethical practices harm the global quality of research. Provided they have been validated by peer review, fabricated, distorted or selected data are featured in literature review or meta-analysis. They can in turn influence directions for future research or, even, policy decisions with wide range implications in health, economics or politics. Negative incentives have attracted significant attention: publishers, institutions and research evaluators tend to favor unprecedented research that does not simply confirm common hypotheses. The lack of proper tools, standards and workflows to deal efficiently with data is also a fundamental issue. In most disciplines, data collection is not well organized nor maintained: it has been estimated that as much as half of the datasets created from the 1990s in life science are already lost. Questionable research practices partly stem from common deficiencies in scientific data management. Open science and data sharing have recently emerged as a common framework to solve issues of research integrity. While initially focused on access to publications, the open science movement is more broadly concerned with transparency at all the stages of the research lifecycle. The diffusion of datasets in open repositories and infrastructures has already largely solved major issues of long term preservation. It also ensures that potential errors or adjustments to statistical indicators can be subsequently corrected, as posterior analysis and replications have access to the original data source. Open science is now increasingly integrated into ethical standards, following community-led initiatives like the TOP Guidelines (2014). The European Code of Conduct for Research Integrity from 2017 includes full requirements for open access, open data and reproducible workflow: “Researchers, research institutions and organizations ensure access to data is as open as possible, as closed as necessary”. The Hong Kong principles for assessing researchers (2020) acknowledge open science as one of the five pillars of scientific integrity. Open science is changing the nature of the debate over research integrity which had remained until now largely detached from the public space. 60%-90% of the audience of open scientific platforms comes from non-academic professionals and private citizens. This increased diffusion creates new responsibilities but also new opportunities to involve non-academic stakeholders in the spirit of citizen science.

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.

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

Scientific data management has recently become a critical research issue due to computerization and automation of scientific research procedures and the resulting explosion of electronic data. For proper management of scientific data, adequate data types must be provided for representing the semantics of scientific data directly. Issues such as large data volume and data evolution are common among scientific applications, so traditional database support, such as storage management, are also beneficial to them. Current approaches such as standardized data formats and adoption of traditional databases do not accommodate the data support requirements of existing scientific applications well. Object-oriented databases, on the other hand, seem to hold promise, by combining flexible data models with traditional database support. In this dissertation, we constructed an experimental platform for exploring the design space for a scientific data management system. With this experimental approach, we could dynamically examine possible architectures for data support against actual instances of scientific data and typical operations executed on them. As many of the dynamic features of existing scientific applications, such as data access patterns, are yet to be discovered, the platform also provides an opportunity to explore such features. Based on the observed potential of object-oriented databases for scientific data management, the GemStone object-oriented database management system was chosen for the baseline data management architecture of the platform. Among scientific applications where better data support is desired, a scientific persistent language and data analysis environment called NewS and applications using it were selected as a target for the study. Connecting NewS with GemStone provided a cost-effective experimental platform where we could investigate a variety of scientific applications with single implementation. We incorporated GemStone into the NewS environment in such a manner that we could use existing NewS applications without modifications for experiments. We describe the design and implementation of our platform, GemStone-based NewS, and experiments performed on the platform. At the end, we describe primary contributions of the work, and assess whether or not our approach was a productive initial step toward improved scientific data management.

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

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.