scholarly journals Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line (Preprint)

2018 ◽  
Author(s):  
Anthony R Pisani ◽  
Nitya Kanuri ◽  
Bob Filbin ◽  
Carlos Gallo ◽  
Madelyn Gould ◽  
...  
Keyword(s):  
Public Relations ◽  
Data Sharing ◽  
Policy Development ◽  
Open Data ◽  
Scientific Data ◽  
Pilot Program ◽  
Text Line ◽  
Ethical Challenges ◽  
User Privacy ◽  
Academic Researchers

UNSTRUCTURED Data sharing between technology companies and academic health researchers has multiple health care, scientific, social, and business benefits. Many companies remain wary about such sharing because of unaddressed concerns about ethics, data security, logistics, and public relations. Without guidance on these issues, few companies are willing to take on the potential work and risks involved in noncommercial data sharing, and the scientific and societal potential of their data goes unrealized. In this paper, we describe the 18-month long pilot of a data-sharing program led by Crisis Text Line (CTL), a not-for-profit technology company that provides a free 24/7 text line for people in crisis. The primary goal of the data-sharing pilot was to design, develop, and implement a rigorous framework of principles and protocols for the safe and ethical sharing of user data. CTL used a stakeholder-based policy process to develop a feasible and ethical data-sharing program. The process comprised forming a data ethics committee; identifying policy challenges and solutions; announcing the program and generating interest; and revising the policy and launching the program. Once the pilot was complete, CTL examined how well the program ran and compared it with other potential program models before putting in place the program that was most suitable for its organizational needs. By drawing on CTL’s experiences, we have created a 3-step set of guidelines for other organizations that wish to develop their own data-sharing program with academic researchers. The guidelines explain how to (1) determine the value and suitability of the data and organization for creating a data-sharing program; (2) decide on an appropriate data sharing and collaboration model; and (3) develop protocols and technical solutions for safe and ethical data sharing and the best organizational structure for implementing the program. An internal evaluation determined that the pilot satisfied CTL’s goals of sharing scientific data and protecting client confidentiality. The policy development process also yielded key principles and protocols regarding the ethical challenges involved in data sharing that can be applied by other organizations. Finally, CTL’s internal review of the pilot program developed a number of alternative models for sharing data that will suit a range of organizations with different priorities and capabilities. In implementing and studying this pilot program, CTL aimed both to optimize its own future data-sharing programs and to inform similar decisions made by others. Open data programs are both important and feasible to establish. With careful planning and appropriate resources, data sharing between big data companies and academic researchers can advance their shared mission to benefit society and improve lives.

scholarly journals Protecting User Privacy and Rights in Academic Data-Sharing Partnerships: Principles From a Pilot Program at Crisis Text Line

10.2196/11507 ◽  
2019 ◽  
Vol 21 (1) ◽  
pp. e11507 ◽  
Author(s):  
Anthony R Pisani ◽  
Nitya Kanuri ◽  
Bob Filbin ◽  
Carlos Gallo ◽  
Madelyn Gould ◽  
...  
Keyword(s):  
Data Sharing ◽  
Pilot Program ◽  
Text Line ◽  
User Privacy

scholarly journals Open Data in Scientific Communication

2018 ◽  
Vol 60 (3) ◽  
pp. 192-198
Author(s):  
Dorota Grygoruk
Keyword(s):  
Information Technology ◽  
Open Access ◽  
Data Management ◽  
Data Sharing ◽  
Open Data ◽  
Scientific Communication ◽  
Scientific Data ◽  
Scientific Publications ◽  
Management Policies ◽  
Access Model

Abstract The development of information technology makes it possible to collect and analyse more and more data resources. The results of research, regardless of the discipline, constitute one of main sources of data. Currently, the research results are increasingly being published in the Open Access model. The Open Access concept has been accepted and recommended worldwide by many institutions financing and implementing research. Initially, the idea of openness concerned only the results of research and scientific publications; at present, more attention is paid to the problem of sharing scientific data, including raw data. Proceedings towards open data are intricate, as data specificity requires the development of an appropriate legal, technical and organizational model, followed by the implementation of data management policies at both the institutional and national levels. The aim of this publication was to present the development of the open data concept in the context of open access idea and problems related to defining data in the process of data sharing and data management.

scholarly journals Open data repositories: Current risks and opportunities

2020 ◽  
Vol 81 (2) ◽  
pp. 62
Author(s):  
Abigail Goben ◽  
Robert J. Sandusky
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Open Data ◽  
Management Consulting ◽  
Academic Institutions ◽  
Data Repositories ◽  
Professional Societies ◽  
Funding Agencies ◽  
Academic Researchers

As data sharing has become a more familiar obligation for academic researchers, there has been a correlating increase in the roles that librarians play supporting open data repositories and providing data management consulting and services. These repositories are sponsored by governments, funding agencies, academic institutions, professional societies, and scholarly publishers.

scholarly journals When open data closes the door: Problematising a one size fits all approach to open data in journal submission guidelines

2021 ◽  
Author(s):  
Annayah Miranda Beatrice Prosser ◽  
Richard Hamshaw ◽  
Johanna Meyer ◽  
Ralph Bagnall ◽  
Leda Blackwood ◽  
...  
Keyword(s):  
Social Psychology ◽  
Qualitative Research ◽  
Data Sharing ◽  
Open Data ◽  
Ethical Challenges ◽  
Qualitative Approaches ◽  
Replication Crisis ◽  
Journal Policies ◽  
Data Requirements ◽  
Current Journal

Opening data promises to improve research rigour and democratise knowledge production. But it also poses practical, theoretical, and ethical risks for qualitative research. Despite discussion about open data in qualitative social psychology predating the replication crisis, the nuances of this discussion have not been translated into current journal policies. Through a content analysis of 261 journals in the domain of social psychology, we establish the state of current journal policies for open data. We critically discuss how these expectations may not be adequate for establishing qualitative rigour, can introduce ethical challenges, and may place those who wish to use qualitative approaches at a disadvantage in peer review and publication processes. We assert that open data requirements should include clearer guidelines that reflect the nuance of data sharing in qualitative research, and move away from a universal ‘one-size-fits-all’ approach to data sharing.

Earth System Scientific Data Sharing Research and Practice

2010 ◽  
Vol 12 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Yunqiang ZHU ◽  
Jiulin SUN ◽  
Shunbao LIAO ◽  
Yapeng YANG ◽  
Huazhong ZHU ◽  
...  
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Earth System ◽  
Research And Practice

Research on Earth System Scientific Data Sharing Platform Based on SOA

2010 ◽  
Vol 11 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Yunqiang ZHU ◽  
Min FENG ◽  
Jia SONG ◽  
Runda LIU
Keyword(s):  
Data Sharing ◽  
Scientific Data ◽  
Earth System

Why they shared: recovering early arguments for sharing social scientific data

2020 ◽  
Vol 33 (2) ◽  
pp. 101-119
Author(s):  
Emily Hauptmann
Keyword(s):  
Social Science ◽  
Data Sharing ◽  
Financial Stability ◽  
Social Research ◽  
Scientific Data ◽  
Social Scientists ◽  
Ethical Imperative ◽  
Social Scientific ◽  
The University ◽  
University Consortium

ArgumentMost social scientists today think of data sharing as an ethical imperative essential to making social science more transparent, verifiable, and replicable. But what moved the architects of some of the U.S.’s first university-based social scientific research institutions, the University of Michigan’s Institute for Social Research (ISR), and its spin-off, the Inter-university Consortium for Political and Social Research (ICPSR), to share their data? Relying primarily on archived records, unpublished personal papers, and oral histories, I show that Angus Campbell, Warren Miller, Philip Converse, and others understood sharing data not as an ethical imperative intrinsic to social science but as a useful means to the diverse ends of financial stability, scholarly and institutional autonomy, and epistemological reproduction. I conclude that data sharing must be evaluated not only on the basis of the scientific ideals its supporters affirm, but also on the professional objectives it serves.

scholarly journals A Systematic Review of Landsat Data for Change Detection Applications: 50 Years of Monitoring the Earth

2021 ◽  
Vol 13 (15) ◽  
pp. 2869
Author(s):  
MohammadAli Hemati ◽  
Mahdi Hasanlou ◽  
Masoud Mahdianpari ◽  
Fariba Mohammadimanesh
Keyword(s):  
Time Series ◽  
Change Detection ◽  
Policy Development ◽  
Atmospheric Correction ◽  
Open Data ◽  
Detection Methods ◽  
Landsat 8 ◽  
Landsat Images ◽  
Detection Analysis ◽  
The Impact

With uninterrupted space-based data collection since 1972, Landsat plays a key role in systematic monitoring of the Earth’s surface, enabled by an extensive and free, radiometrically consistent, global archive of imagery. Governments and international organizations rely on Landsat time series for monitoring and deriving a systematic understanding of the dynamics of the Earth’s surface at a spatial scale relevant to management, scientific inquiry, and policy development. In this study, we identify trends in Landsat-informed change detection studies by surveying 50 years of published applications, processing, and change detection methods. Specifically, a representative database was created resulting in 490 relevant journal articles derived from the Web of Science and Scopus. From these articles, we provide a review of recent developments, opportunities, and trends in Landsat change detection studies. The impact of the Landsat free and open data policy in 2008 is evident in the literature as a turning point in the number and nature of change detection studies. Based upon the search terms used and articles included, average number of Landsat images used in studies increased from 10 images before 2008 to 100,000 images in 2020. The 2008 opening of the Landsat archive resulted in a marked increase in the number of images used per study, typically providing the basis for the other trends in evidence. These key trends include an increase in automated processing, use of analysis-ready data (especially those with atmospheric correction), and use of cloud computing platforms, all over increasing large areas. The nature of change methods has evolved from representative bi-temporal pairs to time series of images capturing dynamics and trends, capable of revealing both gradual and abrupt changes. The result also revealed a greater use of nonparametric classifiers for Landsat change detection analysis. Landsat-9, to be launched in September 2021, in combination with the continued operation of Landsat-8 and integration with Sentinel-2, enhances opportunities for improved monitoring of change over increasingly larger areas with greater intra- and interannual frequency.

scholarly journals Data Dentistry: How Data Are Changing Clinical Care and Research

2021 ◽  
pp. 002203452110202
Author(s):  
F. Schwendicke ◽  
J. Krois
Keyword(s):  
Health Care ◽  
Data Sharing ◽  
Clinical Care ◽  
Open Data ◽  
User Interaction ◽  
Data Availability ◽  
Related Data ◽  
Data User ◽  
Regulatory Data ◽  
Consumer Data

Data are a key resource for modern societies and expected to improve quality, accessibility, affordability, safety, and equity of health care. Dental care and research are currently transforming into what we term data dentistry, with 3 main applications: 1) medical data analysis uses deep learning, allowing one to master unprecedented amounts of data (language, speech, imagery) and put them to productive use. 2) Data-enriched clinical care integrates data from individual (e.g., demographic, social, clinical and omics data, consumer data), setting (e.g., geospatial, environmental, provider-related data), and systems level (payer or regulatory data to characterize input, throughput, output, and outcomes of health care) to provide a comprehensive and continuous real-time assessment of biologic perturbations, individual behaviors, and context. Such care may contribute to a deeper understanding of health and disease and a more precise, personalized, predictive, and preventive care. 3) Data for research include open research data and data sharing, allowing one to appraise, benchmark, pool, replicate, and reuse data. Concerns and confidence into data-driven applications, stakeholders’ and system’s capabilities, and lack of data standardization and harmonization currently limit the development and implementation of data dentistry. Aspects of bias and data-user interaction require attention. Action items for the dental community circle around increasing data availability, refinement, and usage; demonstrating safety, value, and usefulness of applications; educating the dental workforce and consumers; providing performant and standardized infrastructure and processes; and incentivizing and adopting open data and data sharing.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

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