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 Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies (Preprint)

2019 ◽  
Author(s):  
Xiaochen Zheng ◽  
Shengjing Sun ◽  
Raghava Rao Mukkamala ◽  
Ravi Vatrapu ◽  
Joaquín Ordieres-Meré
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Data Sharing ◽  
Data Access ◽  
Health Data ◽  
Security And Privacy ◽  
Access Management ◽  
Related Data ◽  
Distributed Ledger ◽  
Health Related

BACKGROUND Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

scholarly journals A descriptive analysis of the data availability statements accompanying medRxiv preprints and a comparison with their published counterparts

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250887
Author(s):  
Luke A. McGuinness ◽  
Athena L. Sheppard
Keyword(s):  
Data Sharing ◽  
Descriptive Analysis ◽  
Open Data ◽  
System Change ◽  
Research Data ◽  
Data Availability ◽  
Published Data ◽  
Editorial Policies ◽  
Journal Editors ◽  
Closed Data

Objective To determine whether medRxiv data availability statements describe open or closed data—that is, whether the data used in the study is openly available without restriction—and to examine if this changes on publication based on journal data-sharing policy. Additionally, to examine whether data availability statements are sufficient to capture code availability declarations. Design Observational study, following a pre-registered protocol, of preprints posted on the medRxiv repository between 25th June 2019 and 1st May 2020 and their published counterparts. Main outcome measures Distribution of preprinted data availability statements across nine categories, determined by a prespecified classification system. Change in the percentage of data availability statements describing open data between the preprinted and published versions of the same record, stratified by journal sharing policy. Number of code availability declarations reported in the full-text preprint which were not captured in the corresponding data availability statement. Results 3938 medRxiv preprints with an applicable data availability statement were included in our sample, of which 911 (23.1%) were categorized as describing open data. 379 (9.6%) preprints were subsequently published, and of these published articles, only 155 contained an applicable data availability statement. Similar to the preprint stage, a minority (59 (38.1%)) of these published data availability statements described open data. Of the 151 records eligible for the comparison between preprinted and published stages, 57 (37.7%) were published in journals which mandated open data sharing. Data availability statements more frequently described open data on publication when the journal mandated data sharing (open at preprint: 33.3%, open at publication: 61.4%) compared to when the journal did not mandate data sharing (open at preprint: 20.2%, open at publication: 22.3%). Conclusion Requiring that authors submit a data availability statement is a good first step, but is insufficient to ensure data availability. Strict editorial policies that mandate data sharing (where appropriate) as a condition of publication appear to be effective in making research data available. We would strongly encourage all journal editors to examine whether their data availability policies are sufficiently stringent and consistently enforced.

scholarly journals Paving the Way to Open Data

2019 ◽  
Vol 1 (4) ◽  
pp. 368-380 ◽  
Author(s):  
Yan Wu ◽  
Elizabeth Moylan ◽  
Hope Inman ◽  
Chris Graf
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Data Availability ◽  
Peer Reviews ◽  
Open Research ◽  
Research Communities ◽  
The Way

It is easy to argue that open data are critical to enabling faster and more effective research discovery. In this article, we describe the approach we have taken at Wiley to support open data and to start enabling more data to be FAIR data (Findable, Accessible, Interoperable and Reusable) with the implementation of four data policies: “Encourages”, “Expects”, “Mandates” and “Mandates and Peer Reviews Data”. We describe the rationale for these policies and levels of adoption so far. In the coming months we plan to measure and monitor the implementation of these policies via the publication of data availability statements and data citations. With this information, we'll be able to celebrate adoption of data-sharing practices by the research communities we work with and serve, and we hope to showcase researchers from those communities leading in open research.

Initiating FAIR geothermal data in Indonesia

2021 ◽  
Author(s):  
Dasapta Erwin Irawan
Keyword(s):  
Data Sharing ◽  
Data Exchange ◽  
Open Data ◽  
Data Reuse ◽  
Data Availability ◽  
Scientific Development ◽  
For Profit ◽  
Deep Well ◽  
Data Schema ◽  
Corporate Social

<p>One of the main keys to scientific development is data availability. Not only the data is easily discovered and downloaded, there's also needs for the data to be easily reused. Geothermal researchers, research institutions and industries are the three main stakeholders to foster data sharing and data reuse. Very expensive deep well datasets as well as advanced logging datasets are very important not only for exploitation purposes but also for the community involved eg: for regional planning or common environmental analyses. In data sharing, we have four principles of F.A.I.R data. Principle 1 Findable: data uploaded to open repository with proper data documentations and data schema, Principle 2 Accessible: removed access restrictions such as user id and password for easy downloads. In case of data from commercial entities, embargoed data is permitted with a clear embargo duration and data request procedure, Principle 3 Interoperable: all data must be prepared in a manner for straightforward data exchange between platforms, Principle 4 Reusable: all data must be submitted using common conventional file format, preferably text-based file (eg `csv` or `txt`) therefore it can be analyzed using various software and hardware. The fact that geothermal industries are packed with for-profit motivations and capital intensive would give even more reasons to embrace data sharing. It would be a good way for them to share their role in supporting society. The contributions from multiple stakeholders are the most essential part in science development. In the context of the commercial industry, data sharing is a form of corporate social responsibility (CSR). It shouldn't be defined only as giving out funding to support local communities.</p><p><strong>Keywords</strong>: open data, FAIR data, data sharing </p><p> </p>

scholarly journals Data availability, reusability, and analytic reproducibility: Evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Author(s):  
Tom Elis Hardwicke ◽  
Maya B Mathur ◽  
Kyle Earl MacDonald ◽  
Gustav Nilsonne ◽  
George Christopher Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive, and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (“analytic reproducibility”). To investigate, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition. Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification, and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals Data availability, reusability, and analytic reproducibility: evaluating the impact of a mandatory open data policy at the journal Cognition

2018 ◽  
Vol 5 (8) ◽  
pp. 180448 ◽  
Author(s):  
Tom E. Hardwicke ◽  
Maya B. Mathur ◽  
Kyle MacDonald ◽  
Gustav Nilsonne ◽  
George C. Banks ◽  
...  
Keyword(s):  
Data Sharing ◽  
Open Data ◽  
Interrupted Time Series ◽  
Data Availability ◽  
Quality Of Data ◽  
Critical Feature ◽  
Target Values ◽  
Data Policy ◽  
Access To Data ◽  
The Impact

Access to data is a critical feature of an efficient, progressive and ultimately self-correcting scientific ecosystem. But the extent to which in-principle benefits of data sharing are realized in practice is unclear. Crucially, it is largely unknown whether published findings can be reproduced by repeating reported analyses upon shared data (‘analytic reproducibility’). To investigate this, we conducted an observational evaluation of a mandatory open data policy introduced at the journal Cognition . Interrupted time-series analyses indicated a substantial post-policy increase in data available statements (104/417, 25% pre-policy to 136/174, 78% post-policy), although not all data appeared reusable (23/104, 22% pre-policy to 85/136, 62%, post-policy). For 35 of the articles determined to have reusable data, we attempted to reproduce 1324 target values. Ultimately, 64 values could not be reproduced within a 10% margin of error. For 22 articles all target values were reproduced, but 11 of these required author assistance. For 13 articles at least one value could not be reproduced despite author assistance. Importantly, there were no clear indications that original conclusions were seriously impacted. Mandatory open data policies can increase the frequency and quality of data sharing. However, suboptimal data curation, unclear analysis specification and reporting errors can impede analytic reproducibility, undermining the utility of data sharing and the credibility of scientific findings.

scholarly journals A descriptive analysis of the data availability statements accompanying medRxiv preprints and a comparison with their published counterparts

2020 ◽  
Author(s):  
Luke A McGuinness ◽  
Athena Louise Sheppard
Keyword(s):  
Data Sharing ◽  
Descriptive Analysis ◽  
Open Data ◽  
Research Data ◽  
Data Availability ◽  
Published Data ◽  
Editorial Policies ◽  
Journal Editors ◽  
Closed Data ◽  
Require Data

ObjectiveTo determine whether medRxiv data availability statements describe open or closed data - that is, whether the data used in the study is openly available without restriction - and to examine if this changes on publication based on journal data sharing policy. Additionally, to examine whether data availability statements are sufficient to capture code availability declarations.DesignObservational study, following a pre-registered protocol, of preprints posted on the medRxiv repository between 25th June 2019 and 1st May 2020 and their published counterparts.Main outcome measuresDistribution of preprinted data availability statements across eight categories, determined by a prespecified classification system.Change in the percentage of data availability statements describing open data between the preprinted and published versions of the same record, stratified by journal sharing policy.Number of code availability declarations reported in the full-text preprint which were not captured in the corresponding data availability statement.Results4101 medRxiv preprints were included in our sample, of which 911 (22.2%) were categorized as describing open data, 3027 (73.8%) as describing closed data, 163 (4.0%) as not applicable (e.g. editorial, protocol). 379 (9.2%) preprints were subsequently published, and of these published articles, only 159 (42.0%) contained a data availability statement. Similar to the preprint stage, most published data availability statements described closed data (59 (37.1%) open, 96 (60.4%) closed, 4 (2.5%) not applicable).Of the 151 records eligible for the comparison between preprinted and published stages, 57 (37.7%) were published in journals which mandated open data sharing. Data availability statements more frequently described open data on publication when the journal mandated data sharing (open at preprint: 33.3%, open at publication: 61.4%) compared to when the journal did not mandate data sharing (open at preprint: 20.2%, open at publication: 22.3%).ConclusionRequiring that authors submit a data availability statement is a good first step, but is insufficient to ensure data availability. Strict editorial policies that require data sharing (where appropriate) as a condition of publication appear to be effective in making research data available. We would strongly encourage all journal editors to examine whether their data availability policies are sufficiently stringent and consistently enforced.

scholarly journals Sharing research data to comply with a journal policy: Experience of a first-time depositor

Hypothesis ◽  
2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Marianne D Burke
Keyword(s):  
Data Sharing ◽  
Information Science ◽  
Journal Article ◽  
Open Data ◽  
Data Availability ◽  
Time Data ◽  
Shared Data ◽  
Data Files ◽  
First Time ◽  
Made In

Background Journals in health sciences increasingly require or recommend that authors deposit the data from their research in open repositories. The rationale for publicly available data is well understood, but many researchers lack the time, knowledge, and skills to do it well, if at all. There are few descriptions of the pragmatic process a researcher author undertakes to complete the open data deposit in the literature. When my manuscript for a mixed methods study was accepted by a journal that required shared data as condition of publication, I proceeded to comply despite uncertainty with the process. Purpose The purpose of this work is to describe the experience of an information science researcher and first-time data depositor to complete an open data deposit. The narrative illustrates the questions encountered and choices made in the process. Process Methods To begin the data deposit process, I found guidance from the accepting journal’s policy and rationale for its shared data requirement. A checklist of pragmatic steps from an open repository provided a framework used to outline and organize the process. Process steps included organizing data files, preparing documentation, determining rights and licensing, and determining sharing and permissions. Choices and decisions included which data versions to share, how much data to share, repository choice, and file naming. Processes and decisions varied between the quantitative and qualitative data prepared.   Results  Two datasets and documentation for each were deposited in the Figshare open repository, thus meeting the journal policy requirements to deposit sufficient data and documentation to replicate the results reported in the journal article, and also meeting the deadline to include a Data Availability Statement with the published article. Conclusion This experience illustrated some practical data sharing issues faced by a librarian author seeking to comply with a journal data sharing policy requirement for publication of an accepted manuscript. Both novice data depositors and data librarians may find this individual experience useful for their own work and the advice they give to others.

Logistics Costs Behavior and Management in the Auto Industry

2015 ◽  
Vol 31 (4) ◽  
pp. 389-408 ◽  
Author(s):  
Marcela M. Porporato
Keyword(s):  
Real Life ◽  
Systems Design ◽  
Management Control ◽  
Data Availability ◽  
Manufacturing Companies ◽  
Bottom Line ◽  
Related Data ◽  
Profitability Analysis ◽  
Confidentiality Agreement ◽  
Cost Behavior

ABSTRACT This case, based on a real-life situation of how logistics costs function in daily operations, aims to provide students with the opportunity to understand how logistics costs are calculated and how the inter-organizational nature of these costs affects the profitability of two companies. The case hinges on understanding cost behavior (fixed and variable) and on management control systems design. Although logistics costs represent a small fraction of total costs in manufacturing companies, they can negatively affect the bottom line if left unattended. Students are presented with data relating to a three-year project in the automotive industry that shows that the project has been experiencing a sustained increase in costs that has eroded its profit margin. While it appears that logistics costs are the problem, it cannot be verified until the contracts are studied. In addition, the financial- and contract-related data provided are sufficient to extend the profitability analysis to the provider of logistics services. This case is suitable for management accounting courses at the master's or advanced undergraduate level; it has been tested and well received by students who want to gain a greater understanding of logistics costs—their nature, behavior, possible containment strategies, and inter-organizational effects. Data Availability: Some of the data are from public sources, but the logistics contracts and cost schedules are private; the confidentiality agreement with the two companies requires masking certain details and modifying the numeric data.

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