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The Advancing Data Justice Research and Practice project aims to broaden understanding of the social, historical, cultural, political, and economic forces that contribute to discrimination and inequity in contemporary ecologies of data collection, governance, and use. This is the consultation draft of a guide for impacted communities. It provides actionable information for communities who wish to implement the principles and priorities of data justice. In the first section, we introduce the nascent field of data justice, from its early discussions to more recent proposals to relocate understandings of what data justice means. This section includes an account of the outreach we conducted with stakeholders throughout the world in developing a nuanced and pluralistic conception of data justice and concludes with a description of the six pillars of data justice around which this guidance revolves. Depending on their contexts, potential impacts, and scale, data activities may be carried out in a way that involves stakeholder engagement. To facilitate this process, the next section provides an explainer of the Stakeholder Engagement Process and the steps it includes—preliminary horizon scanning, policy scoping and stakeholder analysis, positionality reflection, and establishing stakeholder engagement objectives and methods. This section sets out considerations relating to internal community engagement (i.e. engagement within your community) as well as approaches to engaging external stakeholders (i.e. to inform or influence external activities). Additionally, it sets out considerations to be addressed when participating in externally-led engagement processes (e.g. where communities are invited to participate in stakeholder engagement initiated by developers or policy-makers). Finally, the last section presents the guiding questions that will help communities to address issues of data, digital infrastructures, and affected areas of civic, public, and private life, in relation to past, present and future dimensions of ...

The term "data journalism" arrived to newsrooms some timeago, and now every news media wants be part of it. In asmall country like Uruguay —despite the isolated effortsof some companies, NGOs and government agencies— ithas still not found its place in the media. This research triesto clarify the concept of data journalism in order to breakdown the state of the art in Uruguay, through interviewswith journalists, members of NGOs and members of the academicstaff of the universities where the future journalistsare being formed. Thus being able to point out the impulsesand obstacles data journalism has encountered in order toforesee its possible future.Keywords: ; El término periodismo de datos es un término nuevo que llegó a las redacciones hace algunos años, pero ahora ningún medio quiere quedar afuera. En un país pequeño como Uruguay —a pesar de que en todas las redacciones están al tanto de su existencia y ha habido algunas iniciativas— es una práctica que en el año 2015 todavía no ha encontrado su lugar en los medios. Este trabajo busca esclarecer de qué se habla cuando se habla de periodismo de datos para luego, a través de información recabada de medios, organizaciones no gubernamentales y centros educativos, averiguar cuál es su situación actual en el país, los impulsos que ha tenido, los obstáculos que ha encontrado y el futuro que le espera.

Urban Informatics: Using Big Data to Understand and Serve Communities introduces the reader to the tools of data management, analysis, and manipulation using R statistical software. Designed for undergraduate and above level courses, this book is an ideal onramp for the study of urban informatics and how to translate novel data sets into new insights and practical tools. The book follows a unique pedagogical approach developed by the author to enable students to build skills by pursuing projects that inspire and motivate them.Each chapter has an Exploratory Data Assignment that prompts readers to practice their new skills on a data set of their choice. These assignments guide readers through the process of becoming familiar with the contents of a novel data set and communicating meaningful insights from the data to others. Key Features: The technical curriculum consists of both data management and analytics, including both as needed to become acquainted with and reveal the content of a new data set. Content that is contextualized in real-world applications relevant to community concerns. Unit-level assignments that educators might use as midterms or otherwise. These include Community Experience assignments that prompt students to evaluate the assumptions they have made about their data against real world information. All data sets are publicly available through the Boston Data Portal.

SowiDataNet ist eine im Aufbau befindliche Forschungsdateninfrastruktur für die Sozial- und Wirtschaftswissenschaften. Zentraler Baustein ist die Entwicklung eines web-basierten Forschungsdatenrepositoriums, welches es Forscherinnen erlaubt, ihre Daten sicher und dauerhaft zu dokumentieren, zu publizieren und mit anderen Forschern zu teilen. Die Entwicklung dieses neuen Services orientiert sich am konkreten Bedarf der Fachcommunity, was sich u.a. in der Durchführung einer detaillierten Anforderungsanalyse widerspiegelt. Ein spezieller Fokus liegt zudem auf der flexiblen Einbindung des Repositoriums in die praktischen Workflows des institutionellen Forschungsdatenmanagements.

Inflation expectations are extracted from the Consumer Survey, which is conducted by the European Commission for the European Union. Using the probability method to quantify the qualitative answers different distribution functions and scaling parameters are assumed to cope with the properties of the data properly. The forecasting ability of respective series is assessed also compared to the balance statistic. Furthermore the time horizon of the survey participants while answering the questionnaire is analyzed, because the empirical results show that it might not coincide with the time horizon implied by the formulation of the question.

Effective Research Data Management (RDM) is a key component of research integrity and reproducible research, and its importance is increasingly emphasised by funding bodies, governments, and research institutions around the world. However, many researchers are unfamiliar with RDM best practices, and research support staff are faced with the difficult task of delivering support to researchers across different disciplines and career stages. What strategies can institutions use to solve these problems? Engaging Researchers with Data Management is an invaluable collection of 24 case studies, drawn from institutions across the globe, that demonstrate clearly and practically how to engage the research community with RDM. These case studies together illustrate the variety of innovative strategies research institutions have developed to engage with their researchers about managing research data. Each study is presented concisely and clearly, highlighting the essential ingredients that led to its success and challenges encountered along the way. By interviewing key staff about their experiences and the organisational context, the authors of this book have created an essential resource for organisations looking to increase engagement with their research communities. This handbook is a collaboration by research institutions, for research institutions. It aims not only to inspire and engage, but also to help drive cultural change towards better data management. It has been written for anyone interested in RDM, or simply, good research practice.

International audience ; Background Contemporary bioscience sometimes demands vast sample sizes and there is often then no choice but to synthesise data across several studies and to undertake an appropriate pooled analysis. This same need is also faced in health-services and socio-economic research. When a pooled analysis is required, analytic efficiency and flexibility are often best served by combining the individual-level data from all sources and analysing them as a single large data set. But ethico-legal constraints, including the wording of consent forms and privacy legislation, often prohibit or discourage the sharing of individual-level data, particularly across national or other jurisdictional boundaries. This leads to a fundamental conflict in competing public goods: individual-level analysis is desirable from a scientific perspective, but is prevented by ethico-legal considerations that are entirely valid. Methods DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonized Individual-levEL Databases), provides a simple approach to analysing pooled data that circumvents this conflict. This is achieved via parallelized analysis and modern distributed computing and, in one key setting, takes advantage of the properties of the updating algorithm for generalized linear models (GLMs). Results The conceptual use of DataSHIELD is illustrated in two different settings. Conclusions As the study of the aetiological architecture of chronic diseases advances to encompass more complex causal pathways - e.g. to include the joint effects of genes, life-style and environment - sample size requirements will increase further and the analysis of pooled individual-level data will become ever more important. An aim of this conceptual paper is to encourage others to address the challenges and opportunities that DataSHIELD presents, and to explore potential extensions, for example to its use when different data sources hold different data on the same individuals.

Digital convergence has reshaped a gamut of industry demographics in the 21C, comprised of cloud computing, healthcare, pharmaceutical, consumer supplies, government, defense, manufacturing, entertainment, and even on-line education, among many others. At the center of the web-convergence lies the problem of network bandwidth limitation. Traditionally, research has focused on two major resolution strategies in parallel: data compression to minimize the network traffic (i.e. algorithmic; software) and alternative conduit for data transfer such as wireless (i.e. infrastructural; hardware).Building a network infrastructure is extremely costly. In addition, maintenance and upgrade costs may be prohibitive, given the U.S. consumer demographics. To this end, the emphasis is placed on seeking existing infrastructure which connects business and residential entities.The objective of this research, therefore, is to seek the possibility and feasibility of (digital) data transfers via the extensive water and/or sewer network(s), while minimizing structural modifications to the existing infrastructure. To date, sonar has been proven to be effective. If successful, the value of this intellectual property may be immeasurable.

The views expressed in this report are those of the author and do not reflect the official policy or position of the Department of Defense or the United States Government. ; An algorithm to fuse redundant observations due to multiple sensor coverage, in order to reduce duplicate track information provided to Vessel Traffic Services (VTS) operator displays, is reported. The algorithm receives inputs from multiple sensors as long as the basic decision criteria elements are provided. The algorithm is tested with real data collected from the VTS system at Puget Sound in September 1996. The results indicate that the algorithm correctly fuses redundant sensor observations on the same vessel resulting in a significant reduction in the amount of unnecessary information presented to the VTS operator. ; U.S. Coast Guard Command Control Engineering Center, Portsmouth, VA. ; U.S. Coast Guard Command Control Engineering Center ; N6600197WR00229 ; Approved for public release; distribution is unlimited.

Although Big Data generates many benefits for individuals, organizations and society, significant ethical issues are forcing governments to review their regulations so that citizens' rights are protected. Given these ethical issues and a gradual increase of awareness about them, individuals are in need of new technical solutions to engage with organizations that extract value from Big Data. Currently, available solutions do not adequately accommodate the conflicting interests of individuals and organizations. In this paper, we propose a conceptual design for an artifact that will raise awareness amongst individuals about Big Data ethical issues and help to restore the power balance between individuals and organizations. Furthermore, we set forward a design agenda outlining future activities towards building and evaluating our proposed artifact. Our work is grounded in discourse ethics and stakeholder theory and intertwined with the European General Data Protection Regulation (GDPR)

"The measurement infrastructure for the production of economic statistics in the United States largely was established in the middle part of the 20th century. As has been noted by a number of commentators, the data landscape has changed in fundamental ways since this infrastructure was developed. Obtaining survey responses has become increasingly difficult, leading to increased data collection costs and raising concerns about the quality of the resulting data. At the same time, the economy has become more complex and users are demanding ever more timely and granular data. In this new environment, there is increasing interest in alternative sources of data that might allow the economic statistics agencies to better address users' demands for information. Recent years have seen a proliferation of natively digital data that have enormous potential for improving economic statistics. These include item-level transactional data on price and quantity from retail scanners or companies' internal systems, credit card records, bank account records, payroll records and insurance records compiled for private business purposes; data automatically recorded by sensors or mobile devices; and a growing variety of data that can be obtained from websites and social media platforms. Staggering volumes of digital information relevant to measuring and understanding the economy are generated each second by an increasing array of devices that monitor transactions and business processes as well as track the activities of workers and consumers. Incorporating these non-designed Big Data sources into the economic measurement infrastructure holds the promise of allowing the statistical agencies to produce more accurate, more timely and more disaggregated statistics, with lower burden for data providers and perhaps even at lower cost for the statistical agencies. The agencies already have begun to make use of novel data to augment traditional data sources. Modern data science methods for using Big Data have advanced sufficiently to make the more systematic incorporation of these data into official statistics feasible. Indeed, the availability of new sources of data offers the opportunity to redesign the underlying architecture of official statistics. Considering the threats to the current measurement model arising from falling survey response rates, increased survey costs and the growing difficulties of keeping pace with a rapidly changing economy, fundamental changes in the architecture of the statistical system will be necessary to maintain the quality and utility of official statistics. This volume presents cutting edge research on the deployment of big data to solve both existing and novel challenges in economic measurement. The papers in this volume show that it is practical to incorporate big data into the production of economic statistics in real time and at scale. They report on the application of machine learning methods to extract usable new information from large volumes of data. They also lay out the challenges-both technical and operational-to using Big Data effectively in the production of economic statistics and suggest means of overcoming those challenges. Despite these challenges and the significant agenda for research and development they imply, the papers in the volume point strongly toward more systematic and comprehensive incorporation of Big Data to improve official economic statistics in the coming years"--

The idea of "data justice" is of recent academic vintage. It has arisen over the past decade in Anglo-European research institutions as an attempt to bring together a critique of the power dynamics that underlie accelerating trends of datafication with a normative commitment to the principles of social justice—a commitment to the achievement of a society that is equitable, fair, and capable of confronting the root causes of injustice.However, despite the seeming novelty of such a data justice pedigree, this joining up of the critique of the power imbalances that have shaped the digital and "big data" revolutions with a commitment to social equity and constructive societal transformation has a deeper historical, and more geographically diverse, provenance. As the stories of the data justice initiatives, activism, and advocacy contained in this volume well evidence, practices of data justice across the globe have, in fact, largely preceded the elaboration and crystallisation of the idea of data justice in contemporary academic discourse. We have organised the stories contained in this repository into two groups. The first group, 'Challenges to Data Justice: Stories of Data Discrimination and Inequity", poses the question: What are the sorts of problems and challenges to which data justice practitioners are responding? This section is intended to orient the reader to the range of empirical problems faced by data justice researchers and practitioners the world over. We have provided examples of data practices that have been criticised as posing risks of moral injury and that have been identified as leading to inequitable or discriminatory outcomes. Case studies include a national ID card that serves as a government payment system in Venezuela, a courier service/digital technology company in Colombia, and a digital registry of 'rights, tenancy, and crops' in India. The second group, 'Transformational Stories of Data Justice: Initiatives, Activism, and Advocacy', poses the questions: What do responses to the range of ...