Windows-Based Guided Data Capture Software for Mass-Scale Thermophysical and Thermochemical Property Data Collection

2002 ◽  
Vol 43 (1) ◽  
pp. 15-24 ◽  
Author(s):  
Vladimir V. Diky ◽  
Robert D. Chirico ◽  
Randolph C. Wilhoit ◽  
Qian Dong ◽  
Michael Frenkel
Keyword(s):  
Data Collection ◽  
Mass Scale ◽  
Data Capture ◽  
Thermochemical Property ◽  
Property Data

scholarly journals XML-based IUPAC standard for experimental, predicted, and critically evaluated thermodynamic property data storage and capture (ThermoML) (IUPAC Recommendations 2006)

2006 ◽  
Vol 78 (3) ◽  
pp. 541-612 ◽  
Author(s):  
Michael Frenkel ◽  
Robert D. Chiroco ◽  
Vladimir Diky ◽  
Qian Dong ◽  
Kenneth N. Marsh ◽  
...  
Keyword(s):  
Data Storage ◽  
Chemical Reactions ◽  
Data Communication ◽  
Thermochemical Property ◽  
Uncertainty In Measurement ◽  
Communication Processes ◽  
Property Data ◽  
Extensible Markup ◽  
Pure Compounds ◽  
Expression Of Uncertainty

ThermoML is an Extensible Markup Language (XML)-based new IUPAC standard for storage and exchange of experimental, predicted, and critically evaluated thermophysical and thermochemical property data. The basic principles, scope, and description of all structural elements of ThermoML are discussed. ThermoML covers essentially all thermodynamic and transport property data (more than 120 properties) for pure compounds, multicomponent mixtures, and chemical reactions (including change-of-state and equilibrium reactions). Representations of all quantities related to the expression of uncertainty in ThermoML conform to the Guide to the Expression of Uncertainty in Measurement (GUM). The ThermoMLEquation schema for representation of fitted equations with ThermoML is also described and provided as supporting information together with specific formulations for several equations commonly used in the representation of thermodynamic and thermophysical properties. The role of ThermoML in global data communication processes is discussed. The text of a variety of data files (use cases) illustrating the ThermoML format for pure compounds, mixtures, and chemical reactions, as well as the complete ThermoML schema text, are provided as supporting information.

Use of secondary data

2018 ◽  
pp. 90-98
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Data Collection ◽  
Direct Contact ◽  
Secondary Data ◽  
Data Capture ◽  
Primary Data ◽  
Electronic Data Capture ◽  
Alternative Source ◽  
Individual Subject ◽  
Robust Analysis ◽  
Source Of Information

Primary data collection is challenging and with increasing electronic data capture in routine healthcare and other aspects of life, it is possible to address several epidemiological questions by robust analysis of such ‘secondary data’. There are considerable advantages in terms of scope, size, and speed of study to be balanced against the quality and depth of using primary data. Even when such direct contact is not required, there is often the need to extract necessary information from individual subject records such as medical files. There is often no alternative source of information, although the greater digitization of information is changing that scenario with the potential that the availability of such information might preclude the need for primary data.

Data capture for injury prevention

2020 ◽  
Vol 26 (4) ◽  
pp. 391-394 ◽  
Author(s):  
Yvette Holder
Keyword(s):  
Missing Data ◽  
Data Collection ◽  
Data Capture ◽  
Data Sources ◽  
Surveillance Systems ◽  
New Developments ◽  
Coding Systems ◽  
Wide Range ◽  
Data Elements

More than a half-century of developments have expanded the demand for data for the prevention of injuries. This article follows the progress as data collection becomes more comprehensive, encompassing all types of injuries, in a wide range of economic and cultural environments. It describes the challenges of new developments and the responses to deal with them, challenges of poor coordination of data sources, sector ownership, non-uniformity and missing data elements that are critical for prevention. The tools and approaches that may be employed are outlined, from observatories to surveillance systems, from standardised injury coding systems such as the International Classification of External Cause of Injuries to manuals and guidelines for collecting injury data through surveillance and surveys. More and better data encourages greater utilisation which in turn identifies new issues to be addressed, a most exciting situation for any injury practitioner.

ThermoMLAn XML-Based Approach for Storage and Exchange of Experimental and Critically Evaluated Thermophysical and Thermochemical Property Data. 2. Uncertainties

2003 ◽  
Vol 48 (5) ◽  
pp. 1344-1359 ◽  
Author(s):  
Robert D. Chirico ◽  
Michael Frenkel ◽  
Vladimir V. Diky ◽  
Kenneth N. Marsh ◽  
Randolph C. Wilhoit
Keyword(s):  
Thermochemical Property ◽  
Property Data

A multidisciplinary-guided digital solution to data capture in early-phase clinical trials.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e18063-e18063
Author(s):  
Donna M. Graham ◽  
Joanna Clarke ◽  
Gemma Wickert ◽  
Leanna Goodwin ◽  
Carla Timmins ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Focus Group ◽  
Data Collection ◽  
Early Phase ◽  
Patient Data ◽  
Simulated Patient ◽  
Data Capture ◽  
Health Staff ◽  
Case Report Form ◽  
Acceptance Test

e18063 Background: Data capture in early phase cancer clinical trials (EPCCT) is usually via paper records with manual transcription to the sponsor’s case report form. Capturing real time trial data directly to computer (eSource) may reduce errors and increase completeness and timeliness of data entry. A simulated system pilot took place between Oct 2018 and Jan 2019 at an EPCCT facility to appraise Foundry Health’s eSource system “ClinSpark”. Aims were to assess consistency and effectiveness of creating electronic templates for source data capture and live data collection compliance. Methods: A multidisciplinary focus group (2 research nurses, 1 doctor, 3 data managers) was created to collaborate with Foundry Health staff. The focus group agreed on a 52 item user acceptance test listing ideal features for a data collection tool, classifying items as high, medium or low priority. Specialised features of the eSource system were adapted to handle the complex needs of EPCCT. The pilot incorporated a 5 day boot camp for familiarisation to the digital platform; a conference room test using simulated patient data; construction of a trial template including contingency planning; and a clinic floor test with live simulated patient data collection using digital tablets. Results: During the 3 month pilot, templates for 2 EPCCT were planned and created. Using eSource, 43 items (83%) of the acceptance test were passed compared with 27 items (52%) for the current (paper-based) system. The paper system did not pass any of the 9 items for which eSource failed. For the 30 high priority items, eSource passed 30 (100%) compared with 22 for the paper system (73%). Time saving and potential error reduction were noted as additional benefits. Conclusions: This process demonstrates that a multidisciplinary approach can be used to successfully integrate a customised eSource system working with previously untrained staff. Improved performance across pre-specified domains and potential additional benefits were noted. As FDA encourages the use of digital solutions in clinical trials, using eSource provides a potential solution for compliant and efficient capture of data from protocol assessments at investigator sites and rapid data transfer to sponsors.

scholarly journals Using Android and Open Data Kit Technology in Data Management for Research in Resource-Limited Settings in the Niger Delta Region of Nigeria: Cross-Sectional Household Survey (Preprint)

2017 ◽  
Author(s):  
Omosivie Maduka ◽  
Godwin Akpan ◽  
Sylvester Maleghemi
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Mobile Devices ◽  
Niger Delta ◽  
Open Data ◽  
Household Survey ◽  
Data Capture ◽  
Cross Sectional ◽  
Niger Delta Region ◽  
Open Data Kit

BACKGROUND Data collection in Sub-Saharan Africa has traditionally been paper-based. However, the popularization of Android mobile devices and data capture software has brought paperless data management within reach. We used Open Data Kit (ODK) technology on Android mobile devices during a household survey in the Niger Delta region of Nigeria. OBJECTIVE The aim of this study was to describe the pros and cons of deploying ODK for data management. METHODS A descriptive cross-sectional household survey was carried out by 6 data collectors between April and May 2016. Data were obtained from 1706 persons in 601 households across 6 communities in 3 states in the Niger Delta. The use of Android mobile devices and ODK technology involved form building, testing, collection, aggregation, and download for data analysis. The median duration for data collection per household and per individual was 25.7 and 9.3 min, respectively. RESULTS Data entries per device ranged from 33 (33/1706, 1.93%) to 482 (482/1706, 28.25%) individuals between 9 (9/601, 1.5%) and 122 (122/601, 20.3%) households. The most entries (470) were made by data collector 5. Only 2 respondents had data entry errors (2/1706, 0.12%). However, 73 (73/601, 12.1%) households had inaccurate date and time entries for when data collection started and ended. The cost of deploying ODK was estimated at US $206.7 in comparison with the estimated cost of US $466.7 for paper-based data management. CONCLUSIONS We found the use of mobile data capture technology to be efficient and cost-effective. As Internet services improve in Africa, we advocate their use as effective tools for health information management.

scholarly journals Use of research electronic data capture (REDCap) in a COVID-19 randomized controlled trial: a practical example

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sina Kianersi ◽  
Maya Luetke ◽  
Christina Ludema ◽  
Alexander Valenzuela ◽  
Molly Rosenberg
Keyword(s):  
Data Collection ◽  
Web Application ◽  
Controlled Trial ◽  
Antibody Test ◽  
Data Capture ◽  
Electronic Data Capture ◽  
Protective Behavior ◽  
Electronic Data ◽  
Randomized Controlled ◽  
Longitudinal Data Collection

Abstract Background Randomized controlled trials (RCT) are considered the ideal design for evaluating the efficacy of interventions. However, conducting a successful RCT has technological and logistical challenges. Defects in randomization processes (e.g., allocation sequence concealment) and flawed masking could bias an RCT’s findings. Moreover, investigators need to address other logistics common to all study designs, such as study invitations, eligibility screening, consenting procedure, and data confidentiality protocols. Research Electronic Data Capture (REDCap) is a secure, browser-based web application widely used by researchers for survey data collection. REDCap offers unique features that can be used to conduct rigorous RCTs. Methods In September and November 2020, we conducted a parallel group RCT among Indiana University Bloomington (IUB) undergraduate students to understand if receiving the results of a SARS-CoV-2 antibody test changed the students’ self-reported protective behavior against coronavirus disease 2019 (COVID-19). In the current report, we discuss how we used REDCap to conduct the different components of this RCT. We further share our REDCap project XML file and instructional videos that investigators can use when designing and conducting their RCTs. Results We reported on the different features that REDCap offers to complete various parts of a large RCT, including sending study invitations and recruitment, eligibility screening, consenting procedures, lab visit appointment and reminders, data collection and confidentiality, randomization, blinding of treatment arm assignment, returning test results, and follow-up surveys. Conclusions REDCap offers powerful tools for longitudinal data collection and conduct of rigorous and successful RCTs. Investigators can make use of this electronic data capturing system to successfully complete their RCTs. Trial registration The RCT was prospectively (before completing data collection) registered at ClinicalTrials.gov; registration number: NCT04620798, date of registration: November 9, 2020.

Collecting information

2018 ◽  
pp. 101-107
Author(s):  
Alan J. Silman ◽  
Gary J. Macfarlane ◽  
Tatiana Macfarlane
Keyword(s):  
Data Collection ◽  
Epidemiological Study ◽  
Data Capture ◽  
Data Sources ◽  
Primary Data ◽  
Similar Data ◽  
Pilot Testing ◽  
New Information ◽  
Comprehensive Information ◽  
Epidemiological Information

Collecting accurate and comprehensive information both direct from the participants, or indirectly from records or other data is one of the major challenges to a successful epidemiological study. Epidemiological information comes from a variety of sources. These may be conveniently divided into those that are available from previously documented data and those that require the gathering of new information. Examples of the former include extracting information about individuals from their medical records, occupational records, and similar data sources. Design and choice of delivery of patient data capture forms by direct interview or telephone, by post, email, or other electronic means all require considerable thought and pilot testing. Attention to the specific wording of certain questions is crucial. This chapter therefore focuses on the issues surrounding the collection of information that otherwise would not be available: primary data collection.

ThermoML†—An XML-Based Approach for Storage and Exchange of Experimental and Critically Evaluated Thermophysical and Thermochemical Property Data. 4. Biomaterials

2010 ◽  
Vol 55 (4) ◽  
pp. 1564-1572 ◽  
Author(s):  
Robert D. Chirico ◽  
Michael Frenkel ◽  
Vladimir Diky ◽  
Robert N. Goldberg ◽  
Heiko Heerklotz ◽  
...  
Keyword(s):  
Thermochemical Property ◽  
Property Data

Challenges of Biomechanical Data Capture in an Anthropometrically Diverse Geriatric Population at Risk for Falls

2013 ◽  
Vol 2 (1) ◽  
pp. 143-146
Author(s):  
Alexa Doig ◽  
Andrew Merryweather ◽  
Janice Morse ◽  
Donald Bloswick
Keyword(s):  
Older Adults ◽  
At Risk ◽  
Data Collection ◽  
Motion Capture ◽  
Biomechanical Study ◽  
Data Capture ◽  
Geriatric Population ◽  
Hospital Bed ◽  
Fall Arrest ◽  
Gait Impairments

A range of methodological challenges were encountered during a biomechanical study of 56 older adults ranging from ages 50-95 years of age. The sample included individuals with strength and gait impairments who were at risk for falls and fall-related injury. Methodological and participant-related issues encountered during data collection were 1) preventing falls and fall-related injuries, 2) difficulty with osteological landmark palpation and retroreflective marker placement due to obesity, 3) retroreflective marker obstruction by body fat and clothing, as well as the safety harness, hospital bed and other structures in the motion capture space, 4) marker loss during in-bed movements, 5) participant fatigue and instability necessitating trial modification. The development of a customized fall arrest system and other solutions will be discussed.

Sign in / Sign up

Export Citation Format

Share Document