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Governments and funding bodies enact policies to set up Research Infrastructures (RIs) to integrate data from different environmental monitoring research sites. The goal of such policies is to publicly open access to research data to support research and innovation and to develop policies for sustaining RIs and the environment at large. However, little is known about the data curation practices of environmental monitoring research scientists at the origins of data. Informed by practice theories on the constitutive entanglement of the social and the material in everyday organisational work, the study zooms-in on the data curationpractices utilised by environmental monitoring research scientists to create data that is of good quality and reliable for sharing. Early findings are based on observations and semi-structured interviews of participants in environmental research sites, who collaborate on taking samples of animal and plant species in marine and terrestrial environments. We find that data curation at the origins of data are rife with challenges and opportunities in: data competence, data management and data quality practices. This poster submission concludes with implications of such practices for data governance of large-scale RIs.

BackgroundWith funding from HDR UK, Research Data Scotland (RDS) commissioned award-winning production company Media Co-op to create three short graphic animations targeted at both researchers and members of the public, building on our existing text-based content by introducing educational resources in a new format. These videos highlight three topics that have generated substantial interest among RDS users: Trusted Research Environments, data linkage and synthetic data.
IntroductionFounded in 2021, RDS's brand values centre on providing clarity, encouraging simplicity and building public trust. These values closely align with the aims of the HDR UK Transparency Standards, particularly around transparent application processes and criteria (Standard 2) and consideration for target audiences (Standard 4).
MethodsRDS worked with creative agency Media Co-op to develop the animations with input from both subject matter experts and members of the public. Data experts were consulted throughout the project to ensure information accuracy. Two rounds of feedback from the Scotland Talks Data public panel, run jointly by RDS and the Scottish Centre for Administrative Data Research (SCADR), were also incorporated with the aim of ensuring appropriate and accessible language.
ResultsThe resulting animations explain complex topics in layperson terms to aid comprehension. Each video also has a corresponding British Sign Language (BSL) version to aid wider accessibility. We anticipate that these videos will ensure both researchers and the public can glean a clearer understanding of these intricate concepts.
ConclusionsBy applying a Creative Commons Non-Commercial Share Alike 4.0 license to all three videos, we intend for all partner organisations across the UK to be able to reuse or share these videos with their respective audiences. The more organisations share these videos, the more we will build a collective understanding of these complex topics.
The final animations can be viewed on the RDS YouTube channel:

How is data kept secure?
What is data linkage?
What is synthetic data?

To honour our commitment to creating accessible content, BSL versions of all three animations are also available:

How is data kept secure? (BSL)
What is data linkage? (BSL)
What is synthetic data? (BSL)

One of the main challenges in cloud computing is to build a healthy and efficient storage for securely managing and preserving data. This means a cloud service provider needs to make sure that its clients' outsourced data are stored securely and, data queries and retrievals are executed correctly and privately. On the other hand, it may also mean businesses are willing to outsource their data to a third party only if they trust their data are not accessible and visible to the service provider and other non-authorized parties. However, one of the major obstacles faced here for ensuring data reliability and security is Byzantine faults. While Byzantine fault tolerance (BFT) has received growing attention from the academic research community, the research done is generally from the distributed computing point of view, and hence finds little practical use in cloud computing. To that end, the focus of this paper is to discuss how these faults can be tolerated with the authors' proposed conceptualization of Byzantine data faults and fault-tolerant architecture in cloud data management.

In the business environment, the goal of data analytics can be characterized as improving decision making and its links to big data and other data-driven technologies. In UK higher education, degree apprenticeships are business-led and government-supported nationally recognized qualifications, where delivery is tailored to partner employer requirements. This paper focuses on the development of the data analytics specialism of the BSc Digital and Technology Solutions degree apprenticeship at the University of Winchester Business School informed by current research and practice. A data-driven analytical framework is first proposed to provide an overarching methodology for extracting knowledge and insights from (big) data. It covers key components of the analytics lifecycle from data management, data preprocessing, and integration through data modeling and business intelligence to insight management. Software tools related to collecting, cleansing, processing, analyzing, and visualizing data have been systematically discussed to provide the technological dimension. The methodology is then applied to the development of the specialist modules in data analytics, which represent the core thematic structure of the degree apprenticeship pathway. The culmination of the paper is an evaluation of the educational innovation of this digital technologies curriculum, highlighting teaching, learning, and assessment from the perspective of data-analytic thinking.

Article ; Lack of translation of data obtained in preclinical trials to clinic has kindled researchers to develop new methodologies to increase the power and reproducibility of preclinical studies. One approach relates to harmonization of data collection and analysis, and has been used for a long time in clinical studies testing anti-seizure drugs. EPITARGET is a European Union FP7-funded research consortium composed of 18 partners from 9 countries. Its main research objective is to identify biomarkers and develop treatments for epileptogenesis. As the first step of harmonization of procedures between laboratories, EPITARGET established working groups for designing project-tailored common data elements (CDEs) and case report forms (CRFs) to be used in data collection and analysis. Eight major modules of CRFs were developed, presenting >1000 data points for each animal. EPITARGET presents the first single-project effort for harmonization of preclinical data collection and analysis in epilepsy research. EPITARGET is also anticipating the future challenges and requirements in a larger-scale preclinical harmonization of epilepsy studies, including training, data management expertise, cost, location, data safety and continuity of data repositories during and after funding period, and incentives motivating for the use of CDEs. ; final draft ; http://purl.org/eprint/status/PeerReviewed

This article explores the implicit philosophical framework that underpins, and provides the moral and political justification for, the move towards treating data as a so-called common resource. It begins by tracing the emergence of the idea of viewing data as an open access common resource. It then outlines the regulatory, policy and legislative mechanisms that have been instituted to encourage and ensure that researchers comply with data sharing requirements, and that are institutionalising new ownership regimes away from research data being treated de facto as private property towards it becoming public property. It also spells out the case being made for treating data as a public good, including scientific, moral, economic and political arguments. The article then moves on to suggest that positioning data as a common resource is dependent on a Cartesian and representational understanding of data, their production, and their use in the making of knowledge, drawing in particular on the work of Karen Barad. Barad's critique of classical Cartesian and Newtonian metaphysical assumptions helps to reveal the positionality of the assumed universalism of treating research data as a given and a priori common resource. The final section of the article considers what treating data as a common resource and public good, and the exclusion of the labour and relations of data producers that it depends on, does ontologically, epistemologically, morally and politically. In particular, it suggests that emerging regulatory, policy, legislative and discursive practices reinforce, institutionalise and legitimise power differentials and inequalities precisely along the lines that feminist scholars have been contesting for over four decades.

The accuracy, equity, and jurisprudence of criminal risk assessment /Sharad Goel, Ravi Shroff, Jennifer Skeem and Christopher Slobogin --The many faces of facial recognition /Stephen Caines --Artificially intelligent government : a review and agenda /David Freeman Engstrom and Daniel E. Ho --Big data and copyright law /Daniel Seng --Big data analytics, online terms of service and privacy policies /Przemysław Pałka and Marco Lippi --Data analytics and tax law /Benjamin Alarie, Anthony Niblett and Albert Yoon --Experience of big data anti-corruption in China /Ran Wang --Machine learning and law : an overview /Harry Surden --SCOTUS outcome prediction : a new machine learning approach /Ashkon Farhangi and Ajay Sohmshetty --Legal information retrieval /Ashraf Bah Rabiou --LexNLP : natural language processing and information extraction for legal and regulatory texts /Michael J. Bommarito II, Daniel Martin Katz and Eric M. Detterman --Quantitative legal research in Germany /Dirk Hartung --Big data analytics for e-discovery /Johannes C. Scholtes and Hendrik Jacob van den Herik --Generalizability : machine learning and humans-in-the-loop /John Nay and Katherine J. Strandburg --The VICTOR Project : applying artificial intelligence to Brazil's Supreme Federal Court /Ricardo Vieira de Carvalho Fernandes, Danilo Barros Mendes, Gustavo Henrique T.A. Carvalho and Hugo Honda Ferreira --Explainable artificial intelligence /Mary-Anne Williams --Explainability and transparency of machine learning in ADM systems /Bernhard Waltl --Certifying artificial intelligence systems /Florian Möslein and Roberto V. Zicari --Rules, cases and arguments in artificial intelligence and law /Heng Zheng and Bart Verheij --Artificial intelligence and the zealous litigator /James Yoon --Evaluating legal services : the need for a quality movement and standard measures of quality and value /Daniel W. Linna Jr. --Machine learning and EU data-sharing practices : legal aspects of machine learning training datasets for AI systems /Mauritz Kop --AI-driven contract review : a product development journey /Shlomit Labin and Uri Segal --Practical guide to artificial intelligence and contract review /Andrew Antos and Nischal Nadhamuni --Legal marketplaces using machine learning techniques /Verónica Sorin and Martí Manent.

Insgesamt kann festgehalten werden, dass sich bei dem Thema Forschungsdaten-Repositorien in Deutschland in den letzten Jahren viel bewegt hat, auf fachwissenschaftlicher, technologischer und politischer Ebene. Dabei hat sich auch gezeigt, dass die Technologie nur eine Seite der Herausforderungen darstellt. Die andere Seite besteht darin, sowohl die politischen als auch strukturellen Rahmenbedingungen für den Aufbau und den dauerhaften Betrieb von fachwissenschaftlichen Forschungsdaten-Repositorien zu schaffen, als auch die Fachwissenschaftler sowie die weiteren Akteure (Infrastruktureinrichtungen wie Rechenzentren und Bibliotheken) in einem organisatorischen Gesamtkonzept sinnvoll einzubeziehen. Es bleibt abzuwarten, wie die Öffentlichkeit und die Politik auf den Gesamtbericht der KII reagieren und welche konkreten Maßnahmen in Deutschland ergriffen und umgesetzt werden.

AbstractHydrology is the prime control in wetland ecosystems, but little formal hydrological data are gathered in wetlands by hydrometric authorities. Normally, most of the hydrological data for wetlands can be obtained from informal sources, non‐hydrological organizations, the local population and archival information ‐ especially photographs. Some short‐term field monitoring can be useful in augmenting the large hydrological data sets which can be gathered ‐ even when initial enquiries yield minimum information. A case study from the North Kent Marshes is used to illustrate this fact.