Boundary Negotiating Artifacts to Envision the Desired Research Data Infrastructure Toward Reproducibility in Data-Intensive Science

Introduction The recent development of modern and faster computerized infrastructures has enabled researchers to routinely generate research data at an unprecedented level, both quantitatively regarding the amount of data generated from instruments, for example, such as genomic and sensor network data, and qualitatively, producing types of data previously unavailable, such as digital census and health care data. Similarly, an increasing amount of non-born digital research products and source materials are being digitized, such as cultural collections, to facilitate research, management, collaboration and access. These developments have contributed to a ‘data deluge’ (Hey and Trefethen, 2004; e- Science Directors’ Forum Strategy Working Group, 2009) that gives researchers access to a rich and massive amount of data across disciplinary boundaries. At the same time, the provision of high-capacity networks and improvements in research data capture methods have provided researchers with a means to take data sets produced in one geographical location and aggregate them with data produced elsewhere. Some have described this as the evolution of a new kind of science – ‘data-intensive science’ or fourth paradigm (Hey, Tansley and Tolle, 2009) – that is characterized by three principal activities: data capture, curation and analysis. For comparison, the first paradigm was characterized by observational science that has existed from the time of early civilizations, followed by a second paradigm – an empirically based approach, first exemplified by Roger Bacon's descriptions in the 13th century. The third paradigm , which evolved in the latter half of the 20th century, is characterized by increasingly complex research challenges based principally on large-scale computational simulation. This has led to the concept of holistic systems; the evolution from wet labs (hands-on scientific research and experimentation) to virtual labs; and an emphasis on modelling, simulation, projection and prediction. The fourth paradigm (Hey, Tansley and Tolle, 2009) is largely focused on the evolution of large databases and digital data archives, and research methods focused on data analysis and mining and patterns discovery, among others. The age of the fourth paradigm is further aligned with the movement towards ‘open science’, opening up scientific results to the public through the internet, as well as the increased participation of the public (‘citizen science’ (Bonney et al., 2009; Lyon, 2009; Hand, 2010)) as a contributing part to the scientific process, e.g. the global initiative Galaxy Zoo (www.galaxyzoo.org).

Clinical research and health information data sharing are but ripples in a growing wave of reimagined applications of distributed ledger technologies beyond the digital marketplace for which they were originally created. This paper explores the use of distributed ledger technologies to facilitate single institutional ethics review of multi-site, collaborative studies in the dataintensive sciences such as genetics and genomics. Immutable record-keeping, automatable protocol amendments and direct connectivity between stakeholders in the research enterprise (e.g., researchers, research ethics committees, institutions, funders and regulators) comprise several of the conceptual and technological advantages of distributed ledger technologies to research ethics review. This novel-use proposal dovetails recent policy reforms to research ethics review across North America that mandate a single ethics review for any study that takes place across more than one research site. Such reforms in the United States, Canada and Australia replace prior institution-by-institution approval mechanisms that contributed to significant research delays and duplicative procedures for collaborative research worldwide. While this paper centers on the Common Rule revision in the United States, the single ethics review mandate is a noteworthy example of regulation evolving in parallel with advances in the dataintensive sciences it governs. The informational exchange capacities of distributed ledger technologies align well with the procedural goals of streamlining the ethics review system under the new Common Rule ahead of its official implementation on January 19, 2020. The ethical, legal and social implications of applying such technologies to ethics review will be explored in this concept paper. Namely, the paper proposes how administrative data from research ethics committees (REC) could be protected and shared responsibly, as well as interinstitutional cooperation negotiated within a centralized network of research ethics committees using the blockchain.
 Keywords: Blockchain, Data Sharing, Ethics Review, Governance, IRB, Research, Single Mutual Recognition

For over 1,000 years, human knowledge has been recorded in printed formats such as books, journals, and reports archived and stored in libraries or museums. In the past decades, ever since the invention of computers, human knowledge can also be hidden in digital data. Although part of the data has been researched and reported, the pace of the data explosion has been dramatic and exceeds what the printed medium can deal with. This is particularly true for the twenty-first century, enormous data volume of scientific data, including geospatial data (NRC 2003), collected by all kinds of instruments in a variety of disciplines mostly on a 24/7 basis. The lack of related cyberinfrastructure for the data capture, curation and analysis, and for communication and publication alike led Jim Gray to lay out his vision of the fourth research paradigm – data-intensive science (Bell et al. 2009). This fourth paradigm differs fundamentally, in terms of the techniques and technologies involved, from the third paradigm of computational science (ca. 50 years ago) on simulating complex phenomena or processes. Before the computer age, there was only empirical science (ca. 1,000 years ago), and then theoretical science (ca. 500 years ago) like Newton’s Laws of Motion and Maxwell’s Equations. Nowadays, scientists do not look through telescopes but instead mine massive data for research and scientific discoveries. Every discipline x has evolved, over the past decades, into two computer- or information-oriented branches, namely x-informatics and computational x (Gray 2007, cited from Hey et al. 2009). Geography is no exception. Both geoinformatics, which deals with the data collection and analysis of geoinformation, and computational geography or geocomputation (Gahegan 1999), which has to do with simulating geographic phenomena and processes, are concurrent research issues under the banner of geographic information science.

Much attention has been given to the challenges of handling massive data volumes in modern data-intensive science. This paper examines an equally daunting challenge – the diversity of interdisciplinary data, notably research data, and the need to interrelate these data to understand complex systemic problems such as environmental change and its impact. We use the experience of the International Polar Year 2007–8 (IPY) as a case study to examine data management approaches seeking to address issues around complex interdisciplinary science. We find that, while technology is a critical factor in addressing the interdisciplinary dimension of the data intensive science, the technologies developing for exa-scale data volumes differ from those that are needed for extremely distributed and heterogeneous data. Research data will continue to be highly heterogeneous and distributed and will require technologies to be much simpler and more flexible. More importantly, there is a need for both technical and cultural adaptation. We describe a vision of discoverable, open, linked, useful, and safe collections of data, organized and curated using the best principles and practices of information and library science. This vision provides a framework for our discussion and leads us to suggest several short- and long-term strategies to facilitate a socio-technical evolution in the overall science data ecosystem.

With the recent Open Science movement and the rise of data-intensive science, many efforts are in progress to publish research data on the web. To reuse published research data in different fields, they must be made more generalized, interoperable, and machine-readable. Among the various issues related to data publishing, the conditions of use are directly related to their reuse potential. We show herein the types of external constraints and conditions of use in research data publishing in a Japanese context through the analysis of the interview and questionnaire for practitioners. Although the conditions of research data use have been discussed only in terms of their legal constraints, we organize the inclusion of the non-legal constraints and data holders’ actual requirements. Furthermore, we develop practical guideline for examining effective data publishing flow with licensing scenarios. This effort can be positioned to develop an infrastructure for data-intensive science, which will contribute to the realization of Open Science.

The Australian Government has begun an initiative to organise publicly funded national data assets and make them accessible for research through the Research Data Services initiative (RDS), which supports over 40 PBytes of multidisciplinary data at eight nodes around Australia. One of these nodes is at the National Computational Infrastructure (NCI) that provides a national comprehensively integrated high performance computing facility. NCI is a partnership between the ANU, the Australian Bureau of Meteorology, Geoscience Australia (GA) and the Australian Commonwealth Science and Industry Research Organisation (CSIRO) and particularly focuses on Earth system sciences. As part of its activity in RDS, NCI has collocated over 10 PBytes of priority research data collections spanning a wide range of disciplines from geosciences, geophysics, environment, climate, weather, and water resources, through to astronomy, bioinformatics, and the social sciences. To facilitate access, maximise reuse and enable integration across the disciplines, data have been built into a platform that NCI has called, the National Environmental Research Data Interoperability Platform (NERDIP). The platform is co-located with the significant HPC resources: a 1.2 PetaFlop supercomputer (Raijin), and a HPC class 3000 core OpenStack cloud system (Tenjin). Combined, they offer unparalleled opportunities for geosciences researchers to undertake innovative Data-intensive Science at scales and resolutions never before attempted, as well as enabling participation in new collaborations in interdisciplinary science. However, compared with other ‘Big Data’ science disciplines (climate, oceans, weather, astronomy), current geoscience data management practices and data access methods need significant work to be able to scale-up and thus to take advantage of the changes in the global computing landscape. Although the geosciences have many ‘Big Data’ collections that could be incorporated within NERDIP, they typically comprise heterogeneous files that are distributed over multiple sites and sectors, and it is taking considerable time to aggregate these into large High Performance Data (HPD) sets that are structured to facilitate uptake in HPC environments. Once incorporated into NERDIP, the next challenge is to ensure that researchers are ready to both use modern tools, and to update their working practises so as to process these data effectively. This is an issue in part because the geoscience community has been slow to move to peak-class systems for Data-intensive Science and integrate with the rest of the Earth systems community.

IntroductionResearch using linked data sets can lead to new insights and discoveries that positively impact society. However, the use of linked data raises concerns relating to illegitimate use, privacy, and security (e.g., identity theft, marginalization of some groups). It is increasingly recognized that the public needs to be consulted to develop data access systems that consider both the potential benefits and risks of research. Indeed, there are examples of data sharing projects being derailed because of backlash in the absence of adequate consultation. (e.g., care.data in the UK).
 Objectives and methodsThis talk will describe the results of public deliberations held in Vancouver, British Columbia in April 2018 and the fall of 2019. The purpose of these events was to develop informed and civic-minded public advice regarding the use and the sharing of linked data for research in the context of rapidly evolving data availability and researcher aspirations.
 ResultsIn the first deliberation, participants developed and voted on 19 policy-relevant statements. Taken together, these statements provide a broad view of public support and concerns regarding the use of linked data sets for research and offer guidance on measures that can be taken to improve the trustworthiness of policies and process around data sharing and use. The second deliberation will focus on the interplay between public and private sources of data, and role of individual and collective or community consent I the future.
 ConclusionGenerally, participants were supportive of research using linked data because of the value such uses can provide to society. Participants expressed a desire to see the data access request process made more efficient to facilitate more research, as long as there are adequate protections in place around security and privacy of the data. These protections include both physical and process-related safeguards as well as a high degree of transparency.

This paper presents current results and ongoing work to develop effective educational courses on the Big Data (BD) and Data Intensive Science and Technologies (DIST) that is been done at the University of Amsterdam in cooperation with KPMG and by the Laureate Online Education (online partner of the University of Liverpool). The paper introduces the main Big Data concepts: multicomponent Big Data definition and Big Data Architecture Framework that provide the basis for defining the course structure and Common Body of Knowledge for Data Science and Big Data technology domains. The paper presents details on approach, learning model, and course content for two courses at the Laureate Online Education/University of Liverpool and at the University of Amsterdam. The paper also provides background information about existing initiatives and activities related to information exchange and coordination on developing educational materials and programs on Big Data, Data Science, and Research Data Management.

Biomedical research has become a data intensive science in which high throughput experimentation is producing comprehensive data about biological systems at an ever-increasing pace. The Human Vaccines Project is a new public–private partnership, with the goal of accelerating development of improved vaccines and immunotherapies for global infectious diseases and cancers by decoding the human immune system. To achieve its mission, the Project is developing a Bioinformatics Hub as an open-source, multidisciplinary effort with the overarching goal of providing an enabling infrastructure to support the data processing, analysis and knowledge extraction procedures required to translate high throughput, high complexity human immunology research data into biomedical knowledge, to determine the core principles driving specific and durable protective immune responses.

As data-intensive science has emerged, researchers are expected to discover, collect, process, analyze, archive, and share data in their everyday lives and the need for developing proficiency with research data has been recognized. It has been asserted that information professionals in the fields of science and engineering who have supported researchers through a variety of activities are well positioned to assist and meet such needs. This study applied a text mining approach to investigate changes in the requirements for information professionals in science and engineering, with a particular focus on duties and responsibilities to support research data stewardship. Position advertisements posted in the Association of College & Research Libraries’ Science and Technology Section Discussion List from 2010 to 2014 were collected and analyzed.

Data-intensive science is reality in large scientific organizations such as the Max Planck Society, but due to the inefficiency of our data practices when it comes to integrating data from different sources, many projects cannot be carried out and many researchers are excluded. Since about 80% of the time in data-intensive projects is wasted according to surveys we need to conclude that we are not fit for the challenges that will come with the billions of smart devices producing continuous streams of data—our methods do not scale. Therefore experts worldwide are looking for strategies and methods that have a potential for the future. The first steps have been made since there is now a wide agreement from the Research Data Alliance to the FAIR principles that data should be associated with persistent identifiers (PID) and metadata (MD). In fact after 20 years of experience we can claim that there are trustworthy PID systems already in broad use. It is argued, however, that assigning PIDs is just the first step. If we agree to assign PIDs and also use the PID to store important relationships such as pointing to locations where the bit sequences or different metadata can be accessed, we are close to defining Digital Objects (DO) which could indeed indicate a solution to solve some of the basic problems in data management and processing. In addition to standardizing the way we assign PIDs, metadata and other state information we could also define a Digital Object Access Protocol as a universal exchange protocol for DOs stored in repositories using different data models and data organizations. We could also associate a type with each DO and a set of operations allowed working on its content which would facilitate the way to automatic processing which has been identified as the major step for scalability in data science and data industry. A globally connected group of experts is now working on establishing testbeds for a DO-based data infrastructure.

Biodiversity is a data-intensive science and relies on data from a large number of disciplines in order to build up a coherent picture of the extent and trajectory of life on earth (Bowker 2000). The ability to integrate such data from different disciplines, geographic regions and scales is crucial for making better decisions towards sustainable development. As the Biodiversity Information Standards (TDWG) community tackles standards development and adoption beyond its initial emphases on taxonomy and species distributions, expanding its impact and engaging a wider audience becomes increasingly important. Biological interactions data (e.g., predator-prey, host-parasite, plant-pollinator) have been a topic of interest within TDWG for many years and a Biological Interaction Data Interest Group (IG) was established in 2016 to address that issue. The IG has been working on the complexity of representing interactions data and surveying how Darwin Core (DwC, Wieczorek 2012) is being used to represent them (Salim 2022). The importance of cross-disciplinary science and data inspired the recently funded WorldFAIR project—Global cooperation on FAIR data policy and practice—coordinated by the Committee on Data of the International Science Council (CODATA), with the Research Data Alliance (RDA) as a major partner. WorldFAIR will work with a set of case studies to advance implementation of the FAIR data principles (Fig. 1). The FAIR data principles promote good practices in data management, by making data and metadata Findable, Accessible, Interoperable, and Reusable (Wilkinson 2016). Interoperability will be a particular focus to facilitate cross-disciplinary research. A set of recommendations and a framework for FAIR assessment in a set of disciplines will be developed (Molloy 2022). One of WorldFAIR's case studies is related to plant-pollinator interactions data. Its starting point is the model and schema proposed by Salim (2022) based on the DwC standard, which adheres to the diversifying GBIF data model strategy and on the Plant-Pollinator vocabulary described by Salim (2021). The case study on plant-pollinator interactions originated in the TDWG Biological Interaction Data Interest Group (IG) and within the RDA Improving Global Agricultural Data (IGAD) Community of Practice. IGAD is a forum for sharing experiences and providing visibility to research and work in food and agricultural data and has become a space for networking and blending ideas related to data management and interoperability. This topic was chosen because interoperability of plant-pollinator data is needed for better monitoring of pollination services, understanding the impacts of cultivated plants on wild pollinators and quantifying the contribution of wild pollinators to cultivated crops, understanding the impact of domesticated bees on wild ecosystems, and understanding the behaviour of these organisms and how this influences their effectiveness as pollinators. In addition to the ecological importance of these data, pollination is economically important for food production. In Brazil, the economic value of the pollination service was estimated at US$ 12 billion in 2018 (Wolowski 2019). All eleven case studies within the WorldFAIR project are working on FAIR Implementation Profiles (FIPs), which capture comprehensive sets of FAIR principle implementation choices made by communities of practice and which can accelerate convergence and facilitate cross-collaboration between disciplines (Schultes 2020). The FIPs are published through the FIP Wizard, which allows the creation of FAIR Enabling Resources. The FIPs creation will be repeated by the end of the project and capture results obtained from each case study in order to advance data interoperability. In the first FIP, resources from the Global Biodiversity Information Facility (GBIF) and Global Biotic Interactions (GloBI) were catalogued by the Plant-Pollinator Case Study team, and we expect to expand the existing FAIR Enabling Resources by the end of the project and contribute to plant-pollinator data interoperability and reuse. To tackle the challenge of promoting FAIR data for plant-pollinator interactions within the broad scope of the several disciplines and subdisciplines that generate and use them, we will conduct a survey of existing initiatives handling plant-pollinator interactions data and summarise the current status of best practices in the community. Once the survey is concluded, we will choose at least five agriculture-specific plant-pollination initiatives from our partners, to serve as targets for standards adoption. For data to be interoperable and reusable, it is essential that standards and best practices are community-developed to ensure adoption by the tool builders and data scientists across the globe. TDWG plays an important role in this scenario and we expect to engage the IG and other interested parties in that discussion.

The techniques and approaches within geoinformatics and data science rely on the effective coupling of supporting infrastructures and institutions. Without underlying infrastructures for data discovery, analysis, management, distribution, and preservation, new computational techniques wither on the vine for lack of input or remain isolated as niche tools that miss broader potential audiences. Likewise, without supporting institutions that enable governance of policies and finances, coordination of stakeholders, and validation of new knowledge and tools, technological advances become detached from the people and organizations that operate and use them. This paper centers on a case study of work within the National Center for Atmospheric Research (NCAR) and University Corporation for Atmospheric Research (UCAR) to develop effective systems and processes for research data curation, access, discovery, and preservation. By emphasizing iterative alignment of institutional work (policies, intermediaries, governance processes, routines, and financial instruments) and infrastructural work (data storage systems, repositories, tools, and interfaces), balanced progress has been made toward developing solutions to gaps in organizational data services.

Peer Review #1 of "The Modern Research Data Portal: a design pattern for networked, data-intensive science (v0.1)"

We describe best practices for providing convenient, high-speed, secure access to large data via research data portals. We capture these best practices in a new design pattern, the Modern Research Data Portal, that disaggregates the traditional monolithic web-based data portal to achieve orders-of-magnitude increases in data transfer performance, support new deployment architectures that decouple control logic from data storage, and reduce development and operations costs. We introduce the design pattern; explain how it leverages high-performance data enclaves and cloud-based data management services; review representative examples at research laboratories and universities, including both experimental facilities and supercomputer sites; describe how to leverage Python APIs for authentication, authorization, data transfer, and data sharing; and use coding examples to demonstrate how these APIs can be used to implement a range of research data portal capabilities. Sample code at a companion web site, https://docs.globus.org/mrdp, provides application skeletons that readers can adapt to realize their own research data portals.