Ensuring and Improving Information Quality for Earth Science Data and Products: Role of the ESIP Information Quality Cluster

Quality of products is always of concern to users regardless of the type of products. The focus of this paper is on the quality of Earth science data products. There are four different aspects of quality - scientific, product, stewardship and service. All these aspects taken together constitute Information Quality. With increasing requirement on ensuring and improving information quality, there has been considerable work related to information quality during the last several years. Given this rich background of prior work, the Information Quality Cluster (IQC), established within the Federation of Earth Science Information Partners (ESIP) has been active with membership from multiple organizations. Its objectives and activities, aimed at ensuring and improving information quality for Earth science data and products, are discussed briefly

The Work of Art in the Age of Digital Fragility

Impermanence and fragility have become the defining conditions of the digital age. Technologies that were ubiquitous barely a decade ago, like floppy disks, now look like archaeological relics. It takes only a few years, if not months, before software environments are replaced by newer versions, often with limited backward compatibility. At the same time, digital technologies rely on hardware that has short life expectancy. The radical obsolescence of this new digital register raises a number of important questions. How are we going to prevent the fragile memories of contemporary digital cultures from receding into oblivion? This essay answers this question by looking at one of the institutions in which the problems associated with digital fragility are most especially felt, the Museum of Modern Art (MoMA), and by exploring the ontological displacements that digital objects are operating at the heart of the museum

Formalizing an Attribution Framework for Scientific Data/Software Products and Collections

As scientific research and development become more collaborative, the diversity of skills and expertise involved in producing scientific data are expanding as well. Since recognition of contribution has significant academic and professional impact for participants in scientific projects, it is important to integrate attribution and acknowledgement of scientific contributions into the research and data lifecycle. However, defining and clarifying contributions and the relationship of specific individuals and organizations can be challenging, especially when balancing the needs and interests of diverse partners. Designing an implementation method for attributing scientific contributions within complex projects that can allow ease of use and integration with existing documentation formats is another crucial consideration. To provide a versatile mechanism for organizing, documenting, and storing contributions to different types of scientific projects and their related products, an attribution and acknowledgement matrix and XML schema have been created as part of the Attribution and Acknowledgement Content Framework (AACF). Leveraging the taxonomies of contribution roles and types that have been developed and published previously, the authors consolidated 16 contribution types that could be considered and used when accrediting team member’s contributions. Using these contribution types, specific information regarding the contributing organizations and individuals can be documented using the AACF. This paper provides the background and motivations for creating the current version of the AACF Matrix and Schema, followed by demonstrations of the process and the results of using the Matrix and the Schema to record the contribution information of different sample datasets. The paper concludes by highlighting the key feedback and features to be examined in order to improve the next revisions of the Matrix and the Schema.

Sharing, and reusing quality information of individual digital datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity.The development and baseline of the community FAIR-DQI guidelines document would not have been possible without the voluntary and dedicated effort of the domain experts of the International FAIR-DQI Community Guidelines Working Group. We would like to thank all members of the working group for their interest, participation, and contribution.Peer Reviewed"Article signat per 11 autors/es: Ge Peng , Carlo Lacagnina, Robert R. Downs, Anette Ganske, Hampapuram K. Ramapriyan, Ivana Ivánová, Lesley Wyborn, Dave Jones, Lucy Bastin, Chung-lin Shie, David F. Moroni"Postprint (published version

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re)use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity

What Do We Know About The Stewardship Gap?

In the 21st century, digital data drive innovation and decision-making in nearly every field. However, little is known about the total size, characteristics, and sustainability of these data. In the scholarly sphere, it is widely suspected that there is a gap between the amount of valuable digital data that is produced and the amount that is effectively stewarded and made accessible. The Stewardship Gap Project (http://bit.ly/stewardshipgap) seeks to investigate characteristics of and measure the stewardship gap for sponsored scholarly activity in the United States. This paper presents a preliminary definition of the stewardship gap based on a review of relevant literature and investigates areas of the stewardship gap for which metrics have been developed and measurements made, and where work to measure the stewardship gap is yet to be done. The main findings presented are 1) there is not one stewardship gap but rather multiple “gaps” that contribute to whether data is responsibly stewarded; 2) there are relationships between the gaps that can be used to guide strategies for addressing the stewardship gap; and 3) there are imbalances in the types and depths of studies that have been conducted to measure the stewardship gap.Alfred P. Sloan Foundationhttp://deepblue.lib.umich.edu/bitstream/2027.42/122726/1/StewardshipGap_Final.pdfDescription of StewardshipGap_Final.pdf : Main articl

Global Community Guidelines for Documenting, Sharing, and Reusing Quality Information of Individual Digital Datasets

Open-source science builds on open and free resources that include data, metadata, software, and workflows. Informed decisions on whether and how to (re)use digital datasets are dependent on an understanding about the quality of the underpinning data and relevant information. However, quality information, being difficult to curate and often context specific, is currently not readily available for sharing within and across disciplines. To help address this challenge and promote the creation and (re) use of freely and openly shared information about the quality of individual datasets, members of several groups around the world have undertaken an effort to develop international community guidelines with practical recommendations for the Earth science community, collaborating with international domain experts. The guidelines were inspired by the guiding principles of being findable, accessible, interoperable, and reusable (FAIR). Use of the FAIR dataset quality information guidelines is intended to help stakeholders, such as scientific data centers, digital data repositories, and producers, publishers, stewards and managers of data, to: i) capture, describe, and represent quality information of their datasets in a manner that is consistent with the FAIR Guiding Principles; ii) allow for the maximum discovery, trust, sharing, and reuse of their datasets; and iii) enable international access to and integration of dataset quality information. This article describes the processes that developed the guidelines that are aligned with the FAIR principles, presents a generic quality assessment workflow, describes the guidelines for preparing and disseminating dataset quality information, and outlines a path forward to improve their disciplinary diversity

Maturity Models for Research Data Management – IT process optimisation in science operations

Kontinuierliche Veränderungen des institutionellen Forschungsdatenmanagements stellen dienstleistende Einrichtungen an Hochschulen vor die Herausforderung, ihre Services zu professionalisieren. In einer vergleichenden Analyse wird herausgearbeitet, welche Reifegradmodelle in welchem Maße dafür geeignet sind. Für diesen Vergleich werden Analysekriterien entwickelt, die gleichermaßen das Forschungsdatenmanagement sowie das IT-Service Management in den Betrachtungsmittelpunkt stellen. Abschließend werden herausgearbeitete Vorteile und entdeckte Interferenzen der Modelle diskutiert.Constant changes in institutional research data management present service-providing institutions at universities with the challenge of professionalising their services. In a comparative analysis, we will work out which maturity models are suitable for this and to what extent. For this comparison, analysis criteria will be developed that focus equally on research data management and IT service management. Finally, the advantages identified and the gaps found in the models will be discussed.Les changements constants dans la gestion institutionnelle des données de recherche mettent les établissements d’enseignement supérieur prestataires de services au défi de professionnaliser leurs services. Une analyse comparative permettra de déterminer quels modèles de maturité sont adaptés à cet effet et dans quelle mesure. Pour cette comparaison, des critères d’analyse sont développés, qui mettent l’accent à la fois sur la gestion des données de recherche et sur la gestion des services informatiques. Enfin, les avantages mis en évidence et les interférences découvertes entre les modèles sont discutés.Peer Reviewe

Open Data, Grey Data, and Stewardship: Universities at the Privacy Frontier

As universities recognize the inherent value in the data they collect and hold, they encounter unforeseen challenges in stewarding those data in ways that balance accountability, transparency, and protection of privacy, academic freedom, and intellectual property. Two parallel developments in academic data collection are converging: (1) open access requirements, whereby researchers must provide access to their data as a condition of obtaining grant funding or publishing results in journals; and (2) the vast accumulation of 'grey data' about individuals in their daily activities of research, teaching, learning, services, and administration. The boundaries between research and grey data are blurring, making it more difficult to assess the risks and responsibilities associated with any data collection. Many sets of data, both research and grey, fall outside privacy regulations such as HIPAA, FERPA, and PII. Universities are exploiting these data for research, learning analytics, faculty evaluation, strategic decisions, and other sensitive matters. Commercial entities are besieging universities with requests for access to data or for partnerships to mine them. The privacy frontier facing research universities spans open access practices, uses and misuses of data, public records requests, cyber risk, and curating data for privacy protection. This paper explores the competing values inherent in data stewardship and makes recommendations for practice, drawing on the pioneering work of the University of California in privacy and information security, data governance, and cyber risk.Comment: Final published version, Sept 30, 201

Will Today’s Data Be Here Tomorrow? Measuring The Stewardship Gap

Stakeholders in scholarly research are paying increased attention to stewardship of digital research data¹ for the purposes of advancing scientific discovery, driving innovation, and promoting trust in science and scholarship. However, little is known about the total amounts, characteristics, and sustainability of data that could be used for these purposes. The Stewardship Gap is an 18-month project funded by the Alfred P. Sloan Foundation to understand issues in defining metrics for and measuring the stewardship gap: the potential gap between the amount of valuable data produced through sponsored projects in the United States and the amount that is effectively stewarded and made accessible. This paper reports on the first phase of the project, which sought to develop an instrument to gather information about research data sustainability from a broad variety of researchers and research disciplines and make progress toward the ultimate goals of 1) shedding light on the size, characteristics, and sustainability of valuable sponsored research data and creative work in the United States, and 2) recommending actions stakeholders can take to address the stewardship gap if one is found to exist