Data Management at the University of Iowa: A University Libraries Report on Campus Research Data Needs

2020 ◽  
Author(s):  
Shawn Averkamp ◽  
Xiaomei Gu ◽  
Ben Rogers
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Data Storage ◽  
Analysis Data ◽  
Data Publishing ◽  
Second Phase ◽  
Sensitive Data ◽  
University Of Iowa ◽  
Challenges And Opportunities ◽  
The University

<p>This data management report was commissioned by the University of Iowa Libraries with the intention of performing a survey of the campus landscape and identifying gaps in data management services. The first stage of data collection consisted of a survey conducted during summer 2012 to which 784 responses were received. The second phase of data collection consisted of approximately 40 in-depth interviews with individuals from the campus and were completed during summer 2013. Findings are presented as challenges and opportunities within five broad areas of data management: data management planning, data storage, data organization and analysis, data publishing and dissemination and sensitive data and compliance, with additional findings reported in the areas of research culture and funding models.</p>

Coastal monitoring and data management for restoration in Louisiana

Shore & Beach ◽  
2020 ◽  
pp. 92-101
Author(s):  
Richard Raynie ◽  
Syed Khalil ◽  
Charles Villarrubia ◽  
Ed Haywood
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Management System ◽  
Monitoring Program ◽  
Document Retrieval ◽  
Information Management System ◽  
System Level ◽  
Coastal Protection ◽  
Second Phase ◽  
Coastal Louisiana

The Coastal Protection and Restoration Authority (CPRA) of Louisiana was created after the devastating hurricanes of 2005 (Katrina and Rita) and is responsible for planning and implementing projects that will either reduce storm-induced losses (protection) or restore coastal ecosystems that have been lost or are in danger of being lost (restoration). The first task of the CPRA board was to develop Louisiana’s first Coastal Master Plan (CPRA 2007), which formally integrates and guides the protection and restoration of Louisiana’s coast. The System-Wide Assessment and Monitoring Program (SWAMP) was subsequently developed as a long-term monitoring program to ensure that a comprehensive network of coastal data collection activities is in place to support the planning, development, implementation, and adaptive management of the protection and restoration program and projects within coastal Louisiana. SWAMP includes both natural-system and human-system components and also incorporates the previously-developed Coastwide Reference Monitoring System (CRMS), the Barrier Island Comprehensive Monitoring (BICM) program, and fisheries data collected by the Louisiana Department of Wildlife and Fisheries (LDWF) in addition to other aspects of system dynamics, including offshore and inland water-body boundary conditions, water quality, risk status, and protection performance, which have historically not been the subject of CPRA-coordinated monitoring. This program further facilitates the integration of project-specific data needs into a larger, system-level design framework. Monitoring and operation of restoration and protection projects will be nested within a larger hydrologic basin-wide and coast-wide SWAMP framework and will allow informed decisions to be made with an understanding of system conditions and dynamics at multiple scales. This paper also provides an update on the implementation of various components of SWAMP in Coastal Louisiana, which began as a Barataria Basin pilot implementation program in 2015. During 2017, the second phase of SWAMP was initiated in the areas east of the Mississippi River. In 2019, development of SWAMP design was completed for the remaining basins in coastal Louisiana west of Bayou Lafourche (Figure 1). Data collection is important to inform decisions, however if the data are not properly managed or are not discoverable, they are of limited use. CPRA is committed to ensuring that information is organized and publicly available to help all coastal stakeholders make informed, science-based decisions. As a part of this effort, CPRA has re-engineered its data management system to include spatial viewers, tabular download web pages, and a library/document retrieval system along with a suite of public-facing web services providing programmatic access. This system is collectively called the Coastal Information Management System (CIMS). CPRA and U.S. Geological Survey (USGS) are also developing a proposal to create an interface for CIMS data to be exported to a neutral template that could then be ingested into NOAA’s Data Integration Visualization, Exploration and Reporting (DIVER) repository, and vice versa. DIVER is the repository that the Natural Resource Damage Assessment (NRDA) program is using to manage NRDA-funded project data throughout the Gulf of Mexico. Linking CIMS and DIVER will make it easier to aggregate data across Gulf states and look at larger, ecosystem-level changes.

scholarly journals LAYING THE GROUNDWORK FOR BECOMING AN AGE-FRIENDLY UNIVERSITY: A MULTI-PHASE STUDY

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S78-S79
Author(s):  
Melissa L Cannon ◽  
Renata Kerwood ◽  
Mandie Kondash ◽  
Samuel Rowley ◽  
Hannah Wehr
Keyword(s):  
Older Adults ◽  
Data Collection ◽  
Lifelong Learning ◽  
Second Phase ◽  
Senior Center ◽  
University Campus ◽  
Research Participants ◽  
Phase Study ◽  
Multi Phase ◽  
The University

Abstract As evidenced by the growing Age-Friendly University (AFU) global network, institutions of higher education are increasingly seeking ways to enhance the experiences of older adults who use them for reasons such as lifelong learning, career development, and intergenerational engagement. This multi-phase study explored the barriers and facilitators for older adults accessing a public university in a small Oregon town. The first phase of the study involved survey data collection from 46 members of the local senior center adjacent to the university campus. For the second phase, researchers recruited a sample from the survey respondents and used snowball sampling to conduct 12 interviews with senior center members, past and current senior center directors, and key contacts among university staff. The third phase of data collection paired student researchers with older adult research participants (N=14) in participatory action research to capture the unique perspectives of the research participants visiting the university campus using photovoice and a follow-up focus group. Quantitative data were analyzed using SPSS while qualitative data were analyzed using team coding and intensive group discussion to develop categories and themes. Findings revealed various opportunities to increase age-friendliness according to the principles outlined by the AFU initiative, such as developing a lifelong learning center on campus, strengthening the university-senior center partnership, and removing accessibility barriers in order to make the university campus friendlier for people of all ages and abilities. These findings are being used in a proposal to join the AFU network and to shape the university’s AFU vision.

Comparison Study of Different NoSQL and Cloud Paradigm for Better Data Storage Technology

2018 ◽  
pp. 312-343
Author(s):  
Pankaj Lathar ◽  
K. G. Srinivasa ◽  
Abhishek Kumar ◽  
Nabeel Siddiqui
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Data Storage ◽  
High Performance ◽  
Large Scale ◽  
Web Based ◽  
Storage Technology ◽  
Data Store ◽  
Challenges And Opportunities ◽  
User Data

Advancements in web-based technology and the proliferation of sensors and mobile devices interacting with the internet have resulted in immense data management requirements. These data management activities include storage, processing, demand of high-performance read-write operations of big data. Large-scale and high-concurrency applications like SNS and search engines have appeared to be facing challenges in using the relational database to store and query dynamic user data. NoSQL and cloud computing has emerged as a paradigm that could meet these requirements. The available diversity of existing NoSQL and cloud computing solutions make it difficult to comprehend the domain and choose an appropriate solution for a specific business task. Therefore, this chapter reviews NoSQL and cloud-system-based solutions with the goal of providing a perspective in the field of data storage technology/algorithms, leveraging guidance to researchers and practitioners to select the best-fit data store, and identifying challenges and opportunities of the paradigm.

scholarly journals Research Data Management Education for Future Curators

2013 ◽  
Vol 8 (1) ◽  
pp. 288-294
Author(s):  
Mark Scott ◽  
Richard Boardman ◽  
Philippa Reed ◽  
Simon Cox
Keyword(s):  
Data Management ◽  
Case Studies ◽  
Large Scale ◽  
Management Practices ◽  
Future Generations ◽  
Research Data Management ◽  
Scholarly Communications ◽  
Challenges And Opportunities ◽  
Data Lifecycle ◽  
The University

Science has progressed by “standing on the shoulders of giants” and for centuries research and knowledge have been shared through the publication and dissemination of books, papers and scholarly communications. Moving forward, much of our understanding builds on (large scale) datasets, which have been collected or generated as part of the scientific process of discovery. How will this be made available for future generations? How will we ensure that, once collected or generated, others can stand on the shoulders of the data we produce?Educating students about the challenges and opportunities of data management is a key part of the solution and helps the researchers of the future to start to think about the problems early on in their careers. We have compiled a set of case studies to show the similarities and differences in data between disciplines, and produced a booklet for students containing the case studies and an introduction to the data lifecycle and other data management practices. This has already been used at the University of Southampton within the Faculty of Engineering and is now being adopted centrally for use in other faculties. In this paper, we will provide an overview of the case studies and the guide, and reflect on the reception the guide has had to date.

scholarly journals Data management and sharing in neuroimaging: Practices and perceptions of MRI researchers

2018 ◽  
Author(s):  
John A. Borghi ◽  
Ana E. Van Gulick
Keyword(s):  
Data Collection ◽  
Data Management ◽  
Data Storage ◽  
Online Survey ◽  
Ad Hoc ◽  
Open Access Publishing ◽  
Complex Data ◽  
Data Collection And Analysis ◽  
Neuroimaging Data ◽  
Magnetic Resonance Imaging Mri

ABSTRACTNeuroimaging methods such as magnetic resonance imaging (MRI) involve complex data collection and analysis protocols, which necessitate the establishment of good research data management (RDM). Despite efforts within the field to address issues related to rigor and reproducibility, information about the RDM-related practices and perceptions of neuroimaging researchers remains largely anecdotal. To inform such efforts, we conducted an online survey of active MRI researchers that covered a range of RDM-related topics. Survey questions addressed the type(s) of data collected, tools used for data storage, organization, and analysis, and the degree to which practices are defined and standardized within a research group. Our results demonstrate that neuroimaging data is acquired in multifarious forms, transformed and analyzed using a wide variety of software tools, and that RDM practices and perceptions vary considerably both within and between research groups, with trainees reporting less consistency than faculty. Ratings of the maturity of RDM practices from ad-hoc to refined were relatively high during the data collection and analysis phases of a project and significantly lower during the data sharing phase. Perceptions of emerging practices including open access publishing and preregistration were largely positive, but demonstrated little adoption into current practice.

BIG DATA PROCESSING: BIG CHALLENGES AND OPPORTUNITIES

2012 ◽  
Vol 13 (03n04) ◽  
pp. 1250009 ◽  
Author(s):  
CHANGQING JI ◽  
YU LI ◽  
WENMING QIU ◽  
YINGWEI JIN ◽  
YUJIE XU ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Data Management ◽  
Data Storage ◽  
Large Scale ◽  
Distributed Applications ◽  
Big Data Processing ◽  
Cloud Data ◽  
Management Platform ◽  
Challenges And Opportunities

With the rapid growth of emerging applications like social network, semantic web, sensor networks and LBS (Location Based Service) applications, a variety of data to be processed continues to witness a quick increase. Effective management and processing of large-scale data poses an interesting but critical challenge. Recently, big data has attracted a lot of attention from academia, industry as well as government. This paper introduces several big data processing techniques from system and application aspects. First, from the view of cloud data management and big data processing mechanisms, we present the key issues of big data processing, including definition of big data, big data management platform, big data service models, distributed file system, data storage, data virtualization platform and distributed applications. Following the MapReduce parallel processing framework, we introduce some MapReduce optimization strategies reported in the literature. Finally, we discuss the open issues and challenges, and deeply explore the research directions in the future on big data processing in cloud computing environments.

scholarly journals Analisis sitiran skripsi Program Studi Ilmu Perpustakaan Fakultas Ilmu Administrasi Universitas Brawijaya tahun 2015-2017

2019 ◽  
Vol 15 (2) ◽  
pp. 124
Author(s):  
Dwi Eliana ◽  
Muhammad Rosyihan Hendrawan
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Half Life ◽  
Analysis Data ◽  
Library Science ◽  
Quantitative Approach ◽  
Collection Method ◽  
Form Type ◽  
The University ◽  
Administrative Science

Introduction. This paper investigate the research behaviour in Undergraduate Program of Library Science Faculty of Administrative Science Universitas Brawijaya.Data Collection Method. This research uses descriptive methods with quantitative approach through documentation and observation for data collection, a number of ninety-four undergraduate theses are explained in this study.Data Analysis. Data analysis used in this study is descriptive statistical analysis.Results and Discussion. This study has several result: 1) The characteristics of literature based on  form type is book which most often used with 6.074 times (66,8%), after that, literature in print is most frequently cited literature with the 8.236 times (90,6%), then literature in Indonesian probably cited with the 8.116 times (88,25%), the amount of 6.617 times (73,3%) use literature from 0 to 10 years.2) Author that probably cited was Sugiyono with 542 times (6,91%). 3) half life of literature is 10 years.Conclusions. Based of the findings, the research behaviour was defined, so the provided suggestion for collection developmet and weeding policies in the the university library, also for administrators of education in the faculty to provide more control over policies in terms of citation.

Research data management at a public university in Malawi: the role of “three hands”

2020 ◽  
Vol 41 (6/7) ◽  
pp. 467-485
Author(s):  
Winner Dominic Chawinga ◽  
Sandy Zinn
Keyword(s):  
Data Management ◽  
Data Storage ◽  
Management Practices ◽  
Public University ◽  
Research Data ◽  
Raw Material ◽  
Content Type ◽  
Research Data Management ◽  
Research Activities ◽  
The University

PurposeConsidering that research data is increasingly hailed as an important raw material for current and future science discoveries, many research stakeholders have joined forces to create mechanisms for preserving it. However, regardless of generating rich research data, Africa lags behind in research data management thereby potentially losing most of this valuable data. Therefore, this study was undertaken to investigate the research data management practices at a Malawian public university with the aim to recommend appropriate data management strategies.Design/methodology/approachThe study is inspired by the pragmatic school of thought thereby adopting quantitative and qualitative research approaches. Quantitative data was collected using a questionnaire from 150 researchers and 25 librarians while qualitative data was collected by conducting an interview with the Director of Research.FindingsResearchers are actively involved in research activities thereby generating large quantities of research data. Although researchers are willing to share their data, only a handful follow through. Data preservation is poor because the university uses high risk data storage facilities, namely personal computers, flash disks, emails and external hard drives. Researchers and librarians lacked core research data-management competencies because of the lack of formal and information training opportunities. Challenges that frustrate research data-management efforts are many but the key ones include absence of research data management policies, lack of incentives, lack of skills and unavailability of data infrastructure.Research limitations/implicationsThe study's findings are based on one out of four public universities in the country; hence, the findings may not adequately address the status of research data management practices in the other universities.Practical implicationsConsidering that the university under study and its counterparts in Malawi and Africa in general operate somewhat in a similar economic and technological environment, these findings could be used as a reference point for other universities intending to introduce research data management initiatives.Originality/valueWith seemingly limited studies about research data management in Africa and particularly in Malawi, the study sets the tone for research data management debates and initiatives in the country and other African countries.

The consolidation of microscopy technology into a university research support facility: Reflections on the 1980's and projections for the 1990's at the university of iowa

1993 ◽  
Vol 51 ◽  
pp. 476-477
Author(s):  
Kenneth C. Moore
Keyword(s):  
Laser Scanning ◽  
Freeze Fracture ◽  
Freeze Substitution ◽  
Research Support ◽  
University Of Iowa ◽  
Staff Members ◽  
The Past ◽  
Research Grade ◽  
The University ◽  
Support Facility

The University of Iowa Central Electron Microscopy Research Facility(CEMRF) was established in 1981 to support all faculty, staff and students needing this technology. Initially the CEMRF was operated with one TEM, one SEM, three staff members and supported about 30 projects a year. During the past twelve years, the facility has replaced all instrumentation pre-dating 1981, and now includes 2 TEM's, 2 SEM's, 2 EDS systems, cryo-transfer specimen holders for both TEM and SEM, 2 parafin microtomes, 4 ultamicrotomes including cryoultramicrotomy, a Laser Scanning Confocal microscope, a research grade light microscope, an Ion Mill, film and print processing equipment, a rapid cryo-freezer, freeze substitution apparatus, a freeze-fracture/etching system, vacuum evaporators, sputter coaters, a plasma asher, and is currently evaluating scanning probe microscopes for acquisition. The facility presently consists of 10 staff members and supports over 150 projects annually from 44 departments in 5 Colleges and 10 industrial laboratories. One of the unique strengths of the CEMRF is that both Biomedical and Physical scientists use the facility.

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