scholarly journals Flipping the lab: Using consumer electronics for high-quality data collection

2020 ◽  
Author(s):  
Frederick J. Gallun
Keyword(s):  
Data Collection ◽  
Consumer Electronics ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

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 Information Explanatory Applications in Street Activity Security – A Survey

2020 ◽  
pp. 81-83
Author(s):  
Samsudeen S ◽  
Salomi M
Keyword(s):  
Data Collection ◽  
Path Selection ◽  
Data Sources ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Start Up ◽  
Improvement Strategies ◽  
Paper Survey

The paper survey helps to diminish the start-up complex of knowledge assortment and clear analytics for factual modeling & course improvement for probability connected by engine vehicles. We tend to seem that the writing is isolated into 2 totally different inquire concerning areas: (a) discerning/illustrative methods which endeavor in order to urge it and assess clatter hazard supported distinctive powerful conditions, and (b) improvement strategies which center by minimizing clatter probability by route, path-selection and break design. Interpretation based on inquire concerning results of the 2 streams are restricted to beat the problem that tends to show freely accessible high-quality data sources (diverse take into account plans, result factors, and indicator factors) and communicative instructive strategies (information summarization, visualization, and measuring decrease) which are used for understanding safer-routing and provides code to encourage data collection/exploration by practitioners/res

A fibre-based crystal mounting technique for protein cryocrystallography

2012 ◽  
Vol 45 (2) ◽  
pp. 362-366 ◽  
Author(s):  
Michihiro Sugahara
Keyword(s):  
Data Collection ◽  
Success Rate ◽  
Single Crystals ◽  
Conventional Technique ◽  
Synthetic Zeolite ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
X Ray ◽  
Loop Technique

The CryoFibre, a crystal mounting tool, has been developed for protein cryocrystallography. The technique attaches single crystals to the tips of polyester fibres, allowing removal of excess liquid around each crystal. Single-wavelength anomalous dispersion phasing using a Cu Kα X-ray source (Cu SAD) was applied to crystals from five proteins without any derivatization, demonstrating a clear improvement in the success rate of Cu SAD compared with the conventional loop technique. In addition, a xylanase crystal on the surface of a synthetic zeolite as a hetero-epitaxic nucleant was directly mounted on the CryoFibre without separation treatment of the crystal from the zeolite. The crystal had a lower mosaicity than that observed using the conventional technique, indicating that the fibre technique is suitable for high-quality data collection from zeolite-mediated crystals.

The CART Principles in More Detail

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Theory Of Change ◽  
Quality Data ◽  
Opportunity Costs ◽  
High Quality ◽  
High Quality Data

Chapter 4 discusses the CART principles in more detail, showing how they can help organizations make difficult tradeoffs about the data they should collect. To ensure credibility, organizations should collect high-quality data and analyze them accurately. This means that all data collected must be valid, reliable, and appropriately used. Actionability requires that organizations only collect data they can commit to use. This chapter explains how the actionable principle, combined with a well-articulated theory of change, guides organizations to only collect data that will have a specific use. It then explains that, for credible data collection, organizations must ensure that the benefits of data collection outweigh the costs. All data have opportunity costs—the money and time spent collecting data could also be spent implementing programs. Finally, it explains how organizations can collect transportable data that can generate knowledge for other programs.

scholarly journals Low-cost double pendulum for high-quality data collection with open-source video tracking and analysis

HardwareX ◽  
2020 ◽  
Vol 8 ◽  
pp. e00138
Author(s):  
Audun D. Myers ◽  
Joshua R. Tempelman ◽  
David Petrushenko ◽  
Firas A. Khasawneh
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Low Cost ◽  
Video Tracking ◽  
Quality Data ◽  
Double Pendulum ◽  
High Quality ◽  
High Quality Data

scholarly journals Personalized analytics and wearable biosensor platform for early detection of COVID-19 decompensation (DeCODe: Detection of COVID-19 Decompensation): protocol for development of COVID-19 Decompensation Index (Preprint)

2021 ◽  
Author(s):  
Karen Larimer
Keyword(s):  
Data Collection ◽  
Early Detection ◽  
Digital Health ◽  
World Health ◽  
Quality Data ◽  
Ordinal Scale ◽  
High Quality ◽  
High Quality Data ◽  
Digital Platform ◽  
Health Technologies

BACKGROUND During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of the SARS CO-V2 disease, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders in this space to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets. OBJECTIVE To this end, we seek to develop a COVID-19 biomarker that could provide early detection of a patient’s physiologic decompensation. As a contributing spoke in this model, we propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ’s end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed. METHODS This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study. RESULTS Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between subjects decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 – β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored. CONCLUSIONS Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19. CLINICALTRIAL Trial Registration: ClinicalTrials.gov NCT NCT04575532

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