Facilitating Data Stewardship with Partnerships for Data Innovations

Abstract Sampling the natural world and built environment underpins much of science, yet systems for managing material samples and associated (meta)data are fragmented across institutional catalogs, practices for identification, and discipline-specific (meta)data standards. The Internet of Samples (iSamples) is a standards-based collaboration to uniquely, consistently, and conveniently identify material samples, record core metadata about them, and link them to other samples, data, and research products. iSamples extends existing resources and best practices in data stewardship to render a cross-domain cyberinfrastructure that enables transdisciplinary research, discovery, and reuse of material samples in 21st century natural science.

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Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview

Briefings in Bioinformatics ◽

10.1093/bib/bbaa418 ◽

2021 ◽

Author(s):

Arianna Dagliati ◽

Alberto Malovini ◽

Valentina Tibollo ◽

Riccardo Bellazzi

Keyword(s):

Health Informatics ◽

Data Privacy ◽

Lessons Learned ◽

Data Governance ◽

Data Interoperability ◽

Data Stewardship ◽

Data Infrastructures ◽

Healthcare Processes ◽

Day By Day ◽

Open Issues

Abstract The coronavirus disease 2019 (COVID-19) pandemic has clearly shown that major challenges and threats for humankind need to be addressed with global answers and shared decisions. Data and their analytics are crucial components of such decision-making activities. Rather interestingly, one of the most difficult aspects is reusing and sharing of accurate and detailed clinical data collected by Electronic Health Records (EHR), even if these data have a paramount importance. EHR data, in fact, are not only essential for supporting day-by-day activities, but also they can leverage research and support critical decisions about effectiveness of drugs and therapeutic strategies. In this paper, we will concentrate our attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that COVID-19 had made emerge. Data interoperability, healthcare processes modelling and representation, shared procedures to deal with different data privacy regulations, and data stewardship and governance are seen as the most important aspects to boost collaborative research. Lessons learned from COVID-19 pandemic can be a strong element to improve international research and our future capability of dealing with fast developing emergencies and needs, which are likely to be more frequent in the future in our connected and intertwined world.

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Value in vehicles: economic assessment of automotive data

Digital Policy Regulation and Governance ◽

10.1108/dprg-05-2018-0025 ◽

2018 ◽

Vol 20 (6) ◽

pp. 513-527

Author(s):

Alexander M. Soley ◽

Joshua E. Siegel ◽

Dajiang Suo ◽

Sanjay E. Sarma

Keyword(s):

Model Performance ◽

Economic Assessment ◽

Connected Vehicle ◽

Data Generation ◽

Content Type ◽

Monetary Value ◽

Vehicle Data ◽

Data Market ◽

Data Stewardship ◽

Value Model

Purpose The purpose of this paper is to develop a model to estimate the value of information generated by and stored within vehicles to help people, businesses and researchers. Design/methodology/approach The authors provide a taxonomy for data within connected vehicles, as well as for actors that value such data. The authors create a monetary value model for different data generation scenarios from the perspective of multiple actors. Findings Actors value data differently depending on whether the information is kept within the vehicle or on peripheral devices. The model shows the US connected vehicle data market is worth between US$11.6bn and US$92.6bn. Research limitations/implications This model estimates the value of vehicle data, but a lack of academic references for individual inputs makes finding reliable inputs difficult. The model performance is limited by the accuracy of the authors’ assumptions. Practical implications The proposed model demonstrates that connected vehicle data has higher value than people and companies are aware of, and therefore we must secure these data and establish comprehensive rules pertaining to data ownership and stewardship. Social implications Estimating the value of data of vehicle data will help companies understand the importance of responsible data stewardship, as well as drive individuals to become more responsible digital citizens. Originality/value This is the first paper to propose a model for computing the monetary value of connected vehicle data, as well as the first paper to provide an estimate of this value.

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Data Stewardship – addressing disciplinary data management needs

10.31219/osf.io/5w9pj ◽

2018 ◽

Author(s):

Marta Teperek ◽

Maria J. Cruz ◽

Ellen Verbakel ◽

Jasmin K. Böhmer ◽

Alastair Dunning

Keyword(s):

Data Management ◽

Common Denominator ◽

Management Support ◽

Research Institutions ◽

Multidisciplinary Research ◽

University Of Technology ◽

Address Data ◽

Subject Specific ◽

Data Stewardship ◽

The Common

One of the biggest challenges for multidisciplinary research institutions which provide data management support to researchers is addressing disciplinary differences1. Centralised services need to be general enough to cater for all the different flavours of research conducted in an institution. At the same time, focusing on the common denominator means that subject-specific differences and needs may not be effectively addressed. In 2017, Delft University of Technology (TU Delft) embarked on an ambitious Data Stewardship project, aiming to comprehensively address data management needs across a multi-disciplinary campus. In this practice paper, we describe the principles behind the Data Stewardship project at TU Delft, the progress so far, we identify the key challenges and explain our plans for the future.

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WAVEFORM IDENTIFICATION OF ALTIMETRY SATELLITE DATA OF SHALLOW AND DEEP WATERS IN SOUTHERN JAVA WATERS

Jurnal Ilmu dan Teknologi Kelautan Tropis ◽

10.29244/jitkt.v6i2.9021 ◽

2015 ◽

Vol 6 (2) ◽

Cited By ~ 1

Author(s):

Bisman Nababan ◽

Romdonul Hakim ◽

Danu Adrian ◽

Jonson L Gaol

Keyword(s):

Coastal Waters ◽

Satellite Altimetry ◽

Sea Surface Height ◽

Sea Surface ◽

Height Estimation ◽

Type D ◽

Deep Waters ◽

Data Stewardship ◽

Record Type ◽

Geophysical Data Record

ABSTRACT Waveform patterns of satellite altimetry affect the accuracy of sea surface height estimation from the satellite. The waveform patterns found in the coastal waters are generally not in the ideal form (Brown-waveform), resulting inaccurate in sea surface height estimation. The objec-tives of this research were to identify patterns of the waveform and determine their variability. Satellite altimetry Jason-2 SGDR (Sensor Geophysical Data Record) type D data located in the southern Java island waters of the year of 2013 were used and downloaded from “NOAA's Comprehensive Large Array-data Stewardship System” (www.class.ncdc.noaa.gov) . Waveform identification and analyses were conducted along the satellite pass within the distance of 0-10 km, 10-50 km, and 50-100 km form the coastline. Results showed that the highest number of non-Brown-waveform was found within 0-10 km of the coastline (69%). Meanwhile, within the distance of 10-50 km and 50-100 km from the coastline, the number of non-Brown waveform was 5% and 3%, respectively. Brown waveform patterns could be found generally starting at 7.58 km from the coastline. Factors such as land near coastal waters, the depth and shape of the surface waters, aerosols in the atmosphere, building (example: lighthouse or ship) found in coastal areas suspected to be the cause of the noise in waveforms. Keywords: Borwn and non-Brown waveform, sea level height, altimetry satellite, identification

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Practical Application of a Data Stewardship Maturity Matrix for the NOAA OneStop Project

10.31219/osf.io/fp3js ◽

2018 ◽

Author(s):

Ge Peng ◽

Anna Milan ◽

Nancy A. Ritchey ◽

Robert P. Partee ◽

Sonny Zinn ◽

...

Keyword(s):

North Carolina ◽

Best Practices ◽

Data Quality ◽

User Needs ◽

Data Quality Control ◽

Practical Application ◽

Data Discovery ◽

Data Quality Assessment ◽

Data Stewardship ◽

Do So

Assessing the stewardship maturity of individual datasets is an essential part of ensuring and improving the way datasets are documented, preserved, and disseminated to users. It is a critical step towards meeting U.S. federal regulations, organizational requirements, and user needs. However, it is challenging to do so consistently and quantifiably. The Data Stewardship Maturity Matrix (DSMM), developed jointly by NOAA’s National Centers for Environmental Information (NCEI) and the Cooperative Institute for Climate and Satellites–North Carolina (CICS-NC), provides a uniform framework for consistently rating stewardship maturity of individual datasets in nine key components: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. So far, the DSMM has been applied to over 900 individual datasets that are archived and/or managed by NCEI, in support of the NOAA’s OneStop Data Discovery and Access Framework Project. As a part of the OneStop-ready process, tools, implementation guidance, workflows, and best practices are developed to assist the application of the DSMM and described in this paper. The DSMM ratings are also consistently captured in the ISO standard-based dataset-level quality metadata and citable quality descriptive information documents, which serve as interoperable quality information to both machine and human end-users. These DSMM implementation and integration workflows and best practices could be adopted by other data management and stewardship projects or adapted for applications of other maturity assessment models.

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Planting the S.E.E.D.S of Indigenous Population Health Data Linkage

International Journal for Population Data Science ◽

10.23889/ijpds.v5i5.1626 ◽

2020 ◽

Vol 5 (5) ◽

Author(s):

Robyn K Rowe ◽

Jennifer D Walker

Keyword(s):

Population Health ◽

Indigenous Peoples ◽

Health Care Policy ◽

Indigenous Population ◽

Data Linkage ◽

Indigenous Health ◽

Population Data ◽

Health Data ◽

Indigenous Populations ◽

Data Stewardship

IntroductionThe increasing accessibility of data through digitization and linkage has resulted in Indigenous and allied individuals, scholars, practitioners, and data users recognizing a need to advance ways that assert Indigenous sovereignty and governance within data environments. Advances are being talked about around the world for how Indigenous data is collected, used, stored, shared, linked, and analysed. Objectives and ApproachDuring the International Population Data Linkage Network Conference in September of 2018, two sessions were hosted and led by international collaborators that focused on regional Indigenous health data linkage. Notes, discussions, and artistic contributions gathered from the conference led to collaborative efforts to highlight the common approaches to Indigenous data linkage, as discussed internationally. This presentation will share the braided culmination of these discussions and offer S.E.E.D.S as a set of guiding Indigenous data linkage principles. ResultsS.E.E.D.S emerges as a living and expanding set of guiding principles that: 1) prioritizes Indigenous Peoples’ right to Self-determination; 2) makes space for Indigenous Peoples to Exercise sovereignty; 3) adheres to Ethical protocols; 4) acknowledges and respects Data stewardship and governance, and; 5) works to Support reconciliation between Indigenous Peoples and settler states. S.E.E.D.S aims to centre and advance Indigenous-driven population data linkage and research while weaving together common global approaches to Indigenous data linkage. Conclusion / ImplicationsEach of the five elements of S.E.E.D.S interweave and need to be enacted together to create a positive Indigenous data linkage environment. When implemented together, the primary goals of the S.E.E.D.S Principles is to guide positive Indigenous population health data linkage in an effort to create more meaningful research approaches through improved Indigenous-based research processes. The implementation of these principles can, in turn, lead to better measurements of health progress that are critical to enhancing health care policy and improving health and wellness outcomes for Indigenous populations.

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