Het uitwisselen van onderzoeksgegevens: belangen en barrières

2021 ◽  
Author(s):  
T. DEVRIENDT ◽  
M. SHABANI ◽  
P. BORRY
Keyword(s):  
Data Sharing ◽  
Quality Data ◽  
High Quality Data ◽  
Collective Ownership ◽  
The Common ◽  
Recognition Systems ◽  
Technical Factors ◽  
Legal Restrictions ◽  
Access To Data ◽  
Common Vision

Data sharing: interests, impediments and restrictions. The sharing of data is of increasing importance. Data sharing platforms are currently built, aiming to make data more findable, accessible, interoperable and reusable. These platforms are, however, unable to address non-technical factors that may influence data sharing. Various factors, such as the desire to avoid reputational damage, ensuring a correct interpretation of data, loss of control, short-term grant cycles, the opportunity costs of data sharing, faulty recognition systems that do not reward the production of high-quality data itself, ethical and legal restrictions, can constitute impediments for data sharing. The role that platforms fulfill, will depend on the common vision on the fundamental rules surrounding data sharing and scientific competition. If a system of collective ownership is pursued, including guaranteed access to data under specific circumstances, an appropriate science policy should undergird data sharing platforms.

scholarly journals Individual notions of fair data sharing from the perspectives of Swiss stakeholders

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lester Darryl Geneviève ◽  
Andrea Martani ◽  
Bernice Simone Elger ◽  
Tenzin Wangmo
Keyword(s):  
Qualitative Study ◽  
Distributive Justice ◽  
Data Sharing ◽  
Health Data ◽  
Quality Data ◽  
Snowball Sampling ◽  
Structured Interviews ◽  
High Quality Data ◽  
Negotiation Processes ◽  
Research Domains

Abstract Background The meaningful sharing of health data between different stakeholders is central to the advancement of science and to improve care offered to individual patients. However, it is important that the interests of individual stakeholders involved in this data sharing ecosystem are taken into account to ensure fair data sharing practices. In this regard, this qualitative study investigates such practices from the perspectives of a subset of relevant Swiss expert stakeholders, using a distributive justice lens. Methods Using purposive and snowball sampling methodologies, 48 expert stakeholders from the Swiss healthcare and research domains were recruited for semi-structured interviews. After the experts had consented, the interviews were audio-recorded and transcribed verbatim, but omitting identifying information to ensure confidentiality and anonymity. A thematic analysis using a deductive approach was conducted to identify fair data sharing practices for secondary research purposes. Themes and subthemes were then identified and developed during the analysis. Results Three distributive justice themes were identified in the data sharing negotiation processes, and these are: (i) effort, which was subcategorized into two subthemes (i.e. a claim to data reciprocity and other reciprocal advantages, and a claim to transparency on data re-use), (ii) compensation, which was subcategorized into two subthemes (i.e. a claim to an academic compensation and a claim to a financial compensation), and lastly, (iii) contribution, i.e. the significance of data contributions should be matched with a corresponding reward. Conclusions This qualitative study provides insights, which could inform policy-making on claims and incentives that encourage Swiss expert stakeholders to share their datasets. Importantly, several claims have been identified and justified under the basis of distributive justice principles, whilst some are more debatable and likely insufficient in justifying data sharing activities. Nonetheless, these claims should be taken seriously and discussed more broadly. Indeed, promoting health research while ensuring that healthcare systems guarantee better services, it is paramount to ensure that solutions developed are sustainable, provide fair criteria for academic careers and promote the sharing of high quality data to advance science.

Restoring Faith in HIPAA Privacy Protections while Enabling More Efficient Data Sharing (Preprint)

2020 ◽  
Author(s):  
James Jose ◽  
Rachele Hendricks-Sturrup
Keyword(s):  
Data Sharing ◽  
Control Process ◽  
Data Access ◽  
Quality Data ◽  
Patient Privacy ◽  
Multicenter Studies ◽  
High Quality Data ◽  
Privacy Law ◽  
Efficient Data ◽  
Item Data

UNSTRUCTURED A method of data sharing among healthcare researchers that could provide high levels of efficiency for data access while achieving advanced privacy protections could do much to enable multicenter studies based on data extracted from the electronic health record (EHR). We propose a specific use of a data enclave that fits within the “expert pathway” of the Health Insurance Portability and Accountability Act (HIPAA) rule, which would and that can allow sharing of protected healthcare information (PHI) with substantial reductions in burdens related to data use agreements (DUAs) and IRB reviews. Data is held by an infrastructure custodian acting under a business associate agreement. Researchers access the data while it is retained by the custodian, using remote desktop functionality with analytic tools on the custodian’s site, versus not on personal workstations or laptops. Because researchers cannot review line item data, but rather aggregate results, no IRB or DUA is needed. We anticipate that this data enclave method could be implemented as an effort to improve data access and provide high-quality data insights without weakening patient privacy protections under HIPAA. Lastly, we conclude that a comprehensive federal privacy law or standard is warranted alongside this proposed administrative control process for data enclaves to address important privacy loopholes.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Velocity Analysis Using Separated Diffractions for Lunar Penetrating Radar Obtained by Yutu-2 Rover

2021 ◽  
Vol 13 (7) ◽  
pp. 1387
Author(s):  
Chao Li ◽  
Jinhai Zhang
Keyword(s):  
Dielectric Permittivity ◽  
Lunar Regolith ◽  
Velocity Model ◽  
Velocity Estimation ◽  
Quality Data ◽  
High Quality Data ◽  
Shallow Subsurface ◽  
Subsurface Structures ◽  
Ground Penetrating ◽  
Permittivity Model

The high-frequency channel of lunar penetrating radar (LPR) onboard Yutu-2 rover successfully collected high quality data on the far side of the Moon, which provide a chance for us to detect the shallow subsurface structures and thickness of lunar regolith. However, traditional methods cannot obtain reliable dielectric permittivity model, especially in the presence of high mix between diffractions and reflections, which is essential for understanding and interpreting the composition of lunar subsurface materials. In this paper, we introduce an effective method to construct a reliable velocity model by separating diffractions from reflections and perform focusing analysis using separated diffractions. We first used the plane-wave destruction method to extract weak-energy diffractions interfered by strong reflections, and the LPR data are separated into two parts: diffractions and reflections. Then, we construct a macro-velocity model of lunar subsurface by focusing analysis on separated diffractions. Both the synthetic ground penetrating radar (GPR) and LPR data shows that the migration results of separated reflections have much clearer subsurface structures, compared with the migration results of un-separated data. Our results produce accurate velocity estimation, which is vital for high-precision migration; additionally, the accurate velocity estimation directly provides solid constraints on the dielectric permittivity at different depth.

scholarly journals Redistribution Preferences, Inequality Information, and Partisan Motivated Reasoning in the United States

Societies ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 65
Author(s):  
Clem Brooks ◽  
Elijah Harter
Keyword(s):  
Party Identification ◽  
Motivated Reasoning ◽  
The United States ◽  
Quality Data ◽  
Historical Time ◽  
High Quality Data ◽  
Redistributive Policies ◽  
Election Studies ◽  
New Evidence ◽  
The U.S

In an era of rising inequality, the U.S. public’s relatively modest support for redistributive policies has been a puzzle for scholars. Deepening the paradox is recent evidence that presenting information about inequality increases subjects’ support for redistributive policies by only a small amount. What explains inequality information’s limited effects? We extend partisan motivated reasoning scholarship to investigate whether political party identification confounds individuals’ processing of inequality information. Our study considers a much larger number of redistribution preference measures (12) than past scholarship. We offer a second novelty by bringing the dimension of historical time into hypothesis testing. Analyzing high-quality data from four American National Election Studies surveys, we find new evidence that partisanship confounds the interrelationship of inequality information and redistribution preferences. Further, our analyses find the effects of partisanship on redistribution preferences grew in magnitude from 2004 through 2016. We discuss implications for scholarship on information, motivated reasoning, and attitudes towards redistribution.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

Potential of existing online 24-hour dietary recall tools for national dietary surveys

2021 ◽  
pp. 1-62
Author(s):  
Rozenn Gazan ◽  
Florent Vieux ◽  
Ségolène Mora ◽  
Sabrina Havard ◽  
Carine Dubuisson
Keyword(s):  
Grey Literature ◽  
Quality Data ◽  
High Quality Data ◽  
Require Time ◽  
Dietary Recall ◽  
Dietary Surveys ◽  
Almost All ◽  
The Cost ◽  
Web Platform ◽  
Country Specific

Abstract Objective: To describe existing online 24-hour dietary recall (24hDR) tools in terms of functionalities and ability to tackle challenges encountered during national dietary surveys, such as maximizing response rates and collecting high-quality data from a representative sample of the population, while minimizing the cost and response burden. Design: A search (from 2000 to 2019) was conducted in peer-reviewed and grey literature. For each tool, information on functionalities, validation and user usability studies, and potential adaptability for integration into a new context was collected. Setting: Not country-specific Participants: General population Results: Eighteen online 24hDR tools were identified. Most were developed in Europe, for children ≥10 years old and/or for adults. Eight followed the five multiple-pass steps, but used various methodologies and features. Almost all tools (except three) validated their nutrient intake estimates, but with high heterogeneity in methodologies. User usability was not always assessed, and rarely by applying real-time methods. For researchers, eight tools developed a web platform to manage the survey and five appeared to be easily adaptable to a new context. Conclusions: Among the eighteen online 24hDR tools identified, the best candidates to be used in national dietary surveys should be those that were validated for their intake estimates, had confirmed user and researcher usability, and seemed sufficiently flexible to be adapted to new contexts. Regardless of the tool, adaptation to another context will still require time and funding, and this is probably the most challenging step.

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