Enlisted U.S. Marine Education Project: Notice of Data Availability in Qualitative Data Repository

This paper presents the experimental database and corresponding statistical analysis (Part I), which serves as a basis to perform the corresponding parametric analysis and machine learning modelling (Part II) of a comprehensive study on organic soil strength and stiffness, stabilized via the wet soil mixing method. The experimental database includes unconfined compression tests performed under laboratory-controlled conditions to investigate the impact of soil type, the soil’s organic content, the soil’s initial natural water content, binder type, binder quantity, grout to soil ratio, water to binder ratio, curing time, temperature, curing relative humidity and carbon dioxide content on the stabilized organic specimens’ stiffness and strength. A descriptive statistical analysis complements the description of the experimental database, along with a qualitative study on the stabilization hydration process via scanning electron microscopy images. Results confirmed findings on the use of Portland cement alone and a mix of Portland cement with ground granulated blast furnace slag as suitable binders for soil stabilization. Findings on mixes including lime and magnesium oxide cements demonstrated minimal stabilization. Specimen size affected stiffness, but not the strength for mixes of peat and Portland cement. The experimental database, along with all produced data analyses, are available at the Texas Data Repository as indicated in the Data Availability Statement below, to allow for data reproducibility and promote the use of artificial intelligence and machine learning competing modelling techniques as the ones presented in Part II of this paper.

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Are we ready to share qualitative research data? Knowledge and preparedness among qualitative researchers, IRB members, and data repository curators

IASSIST Quarterly ◽

10.29173/iq952 ◽

2020 ◽

Vol 43 (4) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Jessica Mozersky ◽

Heidi Walsh ◽

Meredith Parsons ◽

Tristan McIntosh ◽

Kari Baldwin ◽

...

Keyword(s):

Data Sharing ◽

Qualitative Data ◽

The United States ◽

Open Science ◽

Data Repository ◽

Data Sets ◽

Ethical Challenges ◽

Staff Members ◽

Stakeholder Groups ◽

Secondary Analyses

Data sharing maximizes the value of data, which is time and resource intensive to collect. Major funding bodies in the United States (US), like the National Institutes of Health (NIH), require data sharing and researchers frequently share de-identified quantitative data. In contrast, qualitative data are rarely shared in the US but the increasing trend towards data sharing and open science suggest this may be required in future. Qualitative methods are often used to explore sensitive health topics raising unique ethical challenges regarding protecting confidentiality while maintaining enough contextual detail for secondary analyses. Here, we report findings from semi-structured in-depth interviews with 30 data repository curators, 30 qualitative researchers, and 30 IRB staff members to explore their experience and knowledge of QDS. Our findings indicate that all stakeholder groups lack preparedness for QDS. Researchers are the least knowledgeable and are often unfamiliar with the concept of sharing qualitative data in a repository. Curators are highly supportive of QDS, but not all have experienced curating qualitative data sets and indicated they would like guidance and standards specific to QDS. IRB members lack familiarity with QDS although they support it as long as proper legal and regulatory procedures are followed. IRB members and data curators are not prepared to advise researchers on legal and regulatory matters, potentially leaving researchers who have the least knowledge with no guidance. Ethical and productive QDS will require overcoming barriers, creating standards, and changing long held practices among all stakeholder groups.

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More than 10 years of The International Soil Moisture Network (ISMN) in support of EO science

10.5194/egusphere-egu21-16145 ◽

2021 ◽

Author(s):

Irene Himmelbauer ◽

Daniel Aberer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Wouter A. Dorigo ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

Land Surface ◽

European Space Agency ◽

Data Availability ◽

Data Repository ◽

Source Surface ◽

Web Portal ◽

The Past

The International Soil Moisture Network (ISMN, ) is a unique centralized global and open freely available in-situ soil moisture data hosting facility. Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, WMO, etc.), with continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN is more than ever an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.Following, building and improving standardized measurement protocols and quality techniques, the network evolved into a widely used, reliable and consistent in-situ data source (surface and sub-surface) collected by a myriad off data organizations on a voluntary basis. 66 networks are participating (status January 2021) with more than 2750 stations distributed on a global scale and a steadily increasing number of user community, > 3200 registered users strong. Time series with hourly timestamps from 1952 &#8211; up to near real time are stored in the database and are available through the ISMN web portal for free (), including daily near-real time updates from 6 networks (~ 1000 stations). About 10&#8217;000 datasets are available through the web portal and the number of networks and stations covered by the ISMN is still growing as well as most datasets, that are already contained in the database, are continuously being updated.The ISMN evolved in the past decade into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS), the online validation service Quality Assurance for Soil Moisture (QA4SM) and many more applications, services, products and tools. In general, ISMN data is widely used in a variety of scientific fields with hundreds of studies making use of ISMN data (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.). In this session, we want to inform ISMN users about the evolution of the ISMN over the past decade, including a description of network and dataset updates and new quality control procedures. Besides, we provide a review of existing literature making use of ISMN data in order to identify current limitations in data availability, functionality and challenges in data usage in order to help shape potential future modes in operation of this unique community- based data repository.

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Social science data repositories in data deluge

The Electronic Library ◽

10.1108/el-11-2016-0243 ◽

2017 ◽

Vol 35 (4) ◽

pp. 626-649 ◽

Cited By ~ 3

Author(s):

Wei Jeng ◽

Daqing He ◽

Yu Chi

Keyword(s):

Social Science ◽

Data Sharing ◽

Qualitative Data ◽

Data Curation ◽

Data Repository ◽

Science Data ◽

Data Repositories ◽

Content Type ◽

Social Science Data ◽

Current Practices

Purpose Owing to the recent surge of interest in the age of the data deluge, the importance of researching data infrastructures is increasing. The open archival information system (OAIS) model has been widely adopted as a framework for creating and maintaining digital repositories. Considering that OAIS is a reference model that requires customization for actual practice, this paper aims to examine how the current practices in a data repository map to the OAIS environment and functional components. Design/methodology/approach The authors conducted two focus-group sessions and one individual interview with eight employees at the world’s largest social science data repository, the Interuniversity Consortium for Political and Social Research (ICPSR). By examining their current actions (activities regarding their work responsibilities) and IT practices, they studied the barriers and challenges of archiving and curating qualitative data at ICPSR. Findings The authors observed that the OAIS model is robust and reliable in actual service processes for data curation and data archives. In addition, a data repository’s workflow resembles digital archives or even digital libraries. On the other hand, they find that the cost of preventing disclosure risk and a lack of agreement on the standards of text data files are the most apparent obstacles for data curation professionals to handle qualitative data; the maturation of data metrics seems to be a promising solution to several challenges in social science data sharing. Originality/value The authors evaluated the gap between a research data repository’s current practices and the adoption of the OAIS model. They also identified answers to questions such as how current technological infrastructure in a leading data repository such as ICPSR supports their daily operations, what the ideal technologies in those data repositories would be and the associated challenges that accompany these ideal technologies. Most importantly, they helped to prioritize challenges and barriers from the data curator’s perspective and to contribute implications of data sharing and reuse in social sciences.

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Examining Data Repository Guidelines for Qualitative Data Sharing

Journal of Empirical Research on Human Research Ethics ◽

10.1177/1556264617744121 ◽

2017 ◽

Vol 13 (1) ◽

pp. 61-73 ◽

Cited By ~ 5

Author(s):

Alison L. Antes ◽

Heidi A. Walsh ◽

Michelle Strait ◽

Cynthia R. Hudson-Vitale ◽

James M. DuBois

Keyword(s):

Best Practices ◽

Data Sharing ◽

Qualitative Data ◽

The United States ◽

Future Research ◽

Data Repository ◽

Substantial Variation ◽

Secondary Use ◽

Rich Information ◽

Research Questions

Qualitative data provide rich information on research questions in diverse fields. Recent calls for increased transparency and openness in research emphasize data sharing. However, qualitative data sharing has yet to become the norm internationally and is particularly uncommon in the United States. Guidance for archiving and secondary use of qualitative data is required for progress in this regard. In this study, we review the benefits and concerns associated with qualitative data sharing and then describe the results of a content analysis of guidelines from international repositories that archive qualitative data. A minority of repositories provide qualitative data sharing guidelines. Of the guidelines available, there is substantial variation in whether specific topics are addressed. Some topics, such as removing direct identifiers, are consistently addressed, while others, such as providing an anonymization log, are not. We discuss the implications of our study for education, best practices, and future research.

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Is the quality of hospital EHR data sufficient to evidence its ICHOM outcomes performance in heart failure? A pilot evaluation

10.1101/2021.02.04.21250990 ◽

2021 ◽

Author(s):

Hannelore Aerts ◽

Dipak Kalra ◽

Carlos Saez ◽

Juan Manuel Ramírez-Anguita ◽

Miguel-Angel Mayer ◽

...

Keyword(s):

Heart Failure ◽

Pilot Study ◽

Health Outcomes ◽

Data Quality ◽

Quality Assessment ◽

Temporal Stability ◽

Data Availability ◽

Future Research ◽

Data Repository ◽

The Impact

AbstractThere is increasing recognition that healthcare providers need to focus attention, and be judged against, the impact they have on the health outcomes experienced by patients. The measurement of health outcomes as a routine part of clinical documentation is probably the only scalable way of collecting outcomes evidence, since secondary data collection is expensive and error prone. However, there is uncertainty about whether routinely collected clinical data within EHR systems includes the data most relevant to measuring and comparing outcomes, and if those items are collected to a good enough data quality to be relied upon for outcomes assessment, since several studies have pointed out significant issues regarding EHR data availability and quality.In this paper, we first describe a practical approach to data quality assessment of health outcomes, based on a literature review of existing frameworks for quality assessment of health data and multi-stakeholder consultation. Adopting this approach, we perform a pilot study on a subset of 21 International Consortium for Health Outcomes Measurement (ICHOM) outcomes data items from patients with congestive heart failure. To this end, all available registries compatible with the diagnosis of heart failure within the IMASIS-2 data repository connected to the Hospital del Mar network (142,345 visits of 12,503 patients) were extracted and mapped to the ICHOM format. We focus our pilot assessment on five commonly used data quality dimensions: completeness, correctness, consistency, uniqueness and temporal stability.Overall, this pilot study reveals high scores on the consistency, completeness and uniqueness dimensions. Temporal stability analyses show some changes over time in the reported use of medication to treat heart failure, as well as in the recording of past medical conditions. Finally, investigation of data correctness suggests several issues concerning the proper characterization of missing data values. Many of these issues appear to be introduced while mapping the IMASIS-2 relational database contents to the ICHOM format, as the latter requires a level of detail which is not explicitly available in the coded data of an EHR.To truly examine to what extent hospitals today are able to routinely collect the evidence of their success in achieving good health outcomes, future research would benefit from performing more extensive data quality assessments, including all data items from the ICHOM heart failure standard set, across multiple hospitals.

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Conducting Thematic Analysis with Qualitative Data

The Qualitative Report ◽

10.46743/2160-3715/2021.5008 ◽

2021 ◽

Author(s):

Chad Lochmiller

Keyword(s):

Qualitative Research ◽

Focus Groups ◽

Thematic Analysis ◽

Qualitative Data ◽

Research Community ◽

Data Repository

This article discusses one approach to conducting thematic analysis using structured qualitative data collected from focus groups. Thematic analysis is one of the most used but often poorly defined approaches in the qualitative research community. The method is principally concerned with the identification of patterns which are then reported as researcher-generated themes. In this article, I use data obtained from the Qualitative Data Repository to demonstrate how secondary qualitative data can be analyzed to produce themes. I note the ways in which this process unfolds as well as how it differs from other techniques.

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