Data Management Skills Research Articles

Factors influencing the data sharing behavior of researchers in sociology and political science

PurposeOpen data and data sharing should improve transparency of research. The purpose of this paper is to investigate how different institutional and individual factors affect the data sharing behavior of authors of research articles in sociology and political science.Design/methodology/approachDesktop research analyzed attributes of sociology and political science journals (n=262) from their websites. A second data set of articles (n=1,011; published 2012-2014) was derived from ten of the main journals (five from each discipline) and stated data sharing was examined. A survey of the authors used the Theory of Planned Behavior to examine motivations, behavioral control, and perceived norms for sharing data. Statistical tests (Spearman’s ρ, χ2) examined correlations and associations.FindingsAlthough many journals have a data policy for their authors (78 percent in sociology, 44 percent in political science), only around half of the empirical articles stated that the data were available, and for only 37 percent of the articles could the data be accessed. Journals with higher impact factors, those with a stated data policy, and younger journals were more likely to offer data availability. Of the authors surveyed, 446 responded (44 percent). Statistical analysis indicated that authors’ attitudes, reported past behavior, social norms, and perceived behavioral control affected their intentions to share data.Research limitations/implicationsLess than 50 percent of the authors contacted provided responses to the survey. Results indicate that data sharing would improve if journals had explicit data sharing policies but authors also need support from other institutions (their universities, funding councils, and professional associations) to improve data management skills and infrastructures.Originality/valueThis paper builds on previous similar research in sociology and political science and explains some of the barriers to data sharing in social sciences by combining journal policies, published articles, and authors’ responses to a survey.

Effectively Engaging Industry Partners Within the Classroom

Student projects with an industry partner provide a meaningful way for students to translate abstract knowledge into practice, and to develop the data management and communication skills desired in industry. This paper provides suggestions for engaging an industry partner in a classroom and improving the classroom project experience. Set up as a competition, a single industry partner works with a multitude of student teams on the same problem. The project design aims to develop students’ project framing, data management, and communication skills. The paper covers general considerations for field-based course projects, and provides suggestions on how to address these issues. In general, students have given exceptionally good feedback ratings for the project. Learnings from the student, faculty, and industry partner perspectives are discussed. Although based primarily on experiences in a simulation class, instructions and practitioners can apply many of these observations more widely in an operations research or analytics curriculum.

Combining hospital and general practice data to predict the risk of hospitalisation in the Australian context

Introduction : Predictive analytics have been used to identify people who are at high risk of a health event, and then offer a preventive intervention to avoid or delay that event. Health care systems in the UK, US, Europe facilitate the linkage of health data to allow predictive algorithms to be developed and applied. Data linkage in Australia can be challenging, with state funded public hospitals, private hospitals and federally funded independent general practices. Problem statement – Is predictive modelling using hospital and general practice data feasible in the Australian setting? What is the utility of these predictive models? Methods : Two general practices were approached to take part in this quality improvement project. Principal GPs were interested and willing to participate, and the practices used the same clinical practice software. Central Coast hospital admission data for people from these practices were extracted from the hospital information systems and provided to the two practices. An extract of each practice’s clinical system was taken for all active patients. These two datasets were combined at the general practice site, de-identified, and then provided to the researchers for analyses and predictive modelling. This project was approved by the health service research office as a quality improvement activity Results : Data extraction and linkage were achieved at both sites for over 3000 people. Predictive models were developed for each site, with mostly the same variables, and minor differences in the estimates of effect. Models developed had a c-statistic of 0.74 to 0.77, and when applied to a practice population, were found to have a positive predictive value of about 25%. Discussion : The usefulness of these models when applied to the practice population raises important questions. What is the intervention at the practice level? Do model results need to be incorporated into a staged selection process that leads to a more specific intervention? How will this operate in a treatment oriented fee for service primary health care system? Conclusions : Predictive modelling using hospital and general practice data was feasible. There are challenges in the transferability and sustainability of modelling, and how results are used to improve outcomes for people. Lessons learned : It required a multiple stage process to address privacy and ethics concerns. It is complex – there is variability in general practice clinical software, versions of software, versions of operating systems, individual use of clinical structures within software, and expertise of practice staff and IT support staff. Predictive analytics are the first step. Perhaps more importantly, how will each person’s risk estimate be used to improve their care, and prevent or minimise adverse health outcomes? Limitations : This was a resource intensive process, requiring high level data management skills to address IT variability. Practice and practitioner approach to the use of clinical software – how hospital, specialist and other services were recorded limited the scope for using this information. Suggestions for future research : What are the likely quantifiable benefits of these approaches? (in Australia) Are there other opportunities for this type of work in existing linkage projects?

College Students’ Data Management Skills in a Private University in the Philippines

College Students’ Data Management Skills in a Private University in the Philippines

A Pilot Competency Matrix for Data Management Skills: A Step toward the Development of Systematic Data Information Literacy Programs

Initial work in identifying data management or data information literacy skills generally went as far as identifying a list of proposed competencies without further differentiation between those competencies, whether by discipline, complexity, or use case. This article describes a significant innovation upon existing competencies by identifying a scaffolding (built upon existing competencies) that moves students progressively from undergraduate training through post graduate coursework and research to post-doctoral work and into the early years of data stewardship. The scaffolding ties together existing research that has been completed in research data management skills and data information literacy with research into the outcomes that are desirable for individuals to present in data management at each of the levels of education. Competencies are aligned according to application (personal, team, research enterprise) in such a way that the skills attained at the undergraduate level give students moving on to graduate work greater familiarity with data management and therefore greater likelihood of success at the graduate and then post graduate and data steward levels.

Team-based data management instruction at small liberal arts colleges

This paper describes a collaborative approach taken by librarians at five small, regional liberal arts colleges to developing/enhancing research data management services on their campuses. The five colleges collectively belong to a consortium known as the Northwest Five Consortium. Over 10 months, librarians from the five schools collaborated to plan a data management and curation workshop with the goals of developing relationships with researchers working with data, developing their own research data management skills and services, and building a model for future training and outreach around institutional research data management services. This workshop brought together research teams including faculty, students, and librarians, and incorporated active learning modules as well as in-depth pre-workshop discussion. This article will discuss the context and background for this workshop, the model itself, and the outcomes and possibilities for future developments.

Advancing Research Data Management in the Social Sciences: Implementing Instruction for Education Graduate Students Into a Doctoral Curriculum

ABSTRACTResearch data management (RDM) skills are vital yet often untaught in graduate programs, especially in the social sciences. In this article, the authors present a case study of how a research data librarian and an education librarian partnered to provide targeted RDM instruction for a previously unconsidered student group: education doctoral students. They discuss the design, development, and implementation of this focused RDM support. Assessment data from a workshop and in-class sessions are presented and contextualized. From this information, the authors offer practical suggestions that other social science librarians can use to create similar workshops at their institutions.

An Analysis of Selected Data Practices: A Case Study of the Purdue College of Agriculture.

This paper describes a survey of data practices given to the Purdue College of Agriculture. Data practices are a concern for many researchers with new governmental funding mandates that require data management plans, and for the institution providing resources to comply with these mandates. The survey attempted to answer these questions: What are the characteristics of the data held by respondents? What tools do the respondents use in managing, analyzing, or manipulating their data? Where do students primarily learn research data management skills? The survey documents that there is a statistically significant difference in data holding sizes between faculty and graduate students, and that MS-Excel is still the analysis tool of choice. Results also showed that many researchers in the College were not thinking of the Libraries as a resource for data management practices, preservation, or data literacy instruction for graduate students. The survey results may inform the Libraries in developing new data services and instruction, while also highlighting the need for additional research into data practices for specific disciplinary areas or types of researchers. [ABSTRACT FROM AUTHOR]

Improving Quality and Safety Through Use of Secondary Data: Methods Case Study.

Researchers need to evaluate the strengths and weaknesses of data sets to choose a secondary data set to use for a health care study. This research method review informs the reader of the major issues necessary for investigators to consider while incorporating secondary data into their repertoire of potential research designs and shows the range of approaches the investigators may take to answer nursing research questions in a variety of context areas. The researcher requires expertise in locating and judging data sets and in the development of complex data management skills for managing large numbers of records. There are important considerations such as firm knowledge of the research question supported by the conceptual framework and the selection of appropriate databases, which guide the researcher in delineating the unit of analysis. Other more complex issues for researchers to consider when conducting secondary data research methods include data access, management and security, and complex variable construction.

The Electronic Health Record Objective Structured Clinical Examination: Assessing Student Competency in Patient Interactions While Using the Electronic Health Record.

Because many medical students do not have access to electronic health records (EHRs) in the clinical environment, simulated EHR training is necessary. Explicitly training medical students to use EHRs appropriately during patient encounters equips them to engage patients while also attending to the accuracy of the record and contributing to a culture of information safety. Faculty developed and successfully implemented an EHR objective structured clinical examination (EHR-OSCE) for clerkship students at two institutions. The EHR-OSCE objectives include assessing EHR-related communication and data management skills. The authors collected performance data for students (n = 71) at the first institution during academic years 2011-2013 and for students (n = 211) at the second institution during academic year 2013-2014. EHR-OSCE assessment checklist scores showed that students performed well in EHR-related communication tasks, such as maintaining eye contact and stopping all computer work when the patient expresses worry. Findings indicated student EHR skill deficiencies in the areas of EHR data management including medical history review, medication reconciliation, and allergy reconciliation. Most students' EHR skills failed to improve as the year progressed, suggesting that they did not gain the EHR training and experience they need in clinics and hospitals. Cross-institutional data comparisons will help determine whether differences in curricula affect students' EHR skills. National and institutional policies and faculty development are needed to ensure that students receive adequate EHR education, including hands-on experience in the clinic as well as simulated EHR practice.

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

Advancing research data publishing practices for the social sciences: from archive activity to empowering researchers

Sharing and publishing social science research data have a long history in the UK, through long-standing agreements with government agencies for sharing survey data and the data policy, infrastructure, and data services supported by the Economic and Social Research Council. The UK Data Service and its predecessors developed data management, documentation, and publishing procedures and protocols that stand today as robust templates for data publishing. As the ESRC research data policy requires grant holders to submit their research data to the UK Data Service after a grant ends, setting standards and promoting them has been essential in raising the quality of the resulting research data being published. In the past, received data were all processed, documented, and published for reuse in-house. Recent investments have focused on guiding and training researchers in good data management practices and skills for creating shareable data, as well as a self-publishing repository system, ReShare. ReShare also receives data sets described in published data papers and achieves scientific quality assurance through peer review of submitted data sets before publication. Social science data are reused for research, to inform policy, in teaching and for methods learning. Over a 10 years period, responsive developments in system workflows, access control options, persistent identifiers, templates, and checks, together with targeted guidance for researchers, have helped raise the standard of self-publishing social science data. Lessons learned and developments in shifting publishing social science data from an archivist responsibility to a researcher process are showcased, as inspiration for institutions setting up a data repository.

Integration of water, sanitation and hygiene for the control of neglected tropical diseases: a review of progress and the way forward.

A WHO roadmap to control, eliminate and eradicate neglected tropical diseases (NTDs) proposes a public health approach integrating diverse prevention and treatment interventions. Water, sanitation and hygiene (WASH) has long been a recognized, yet under-prioritized intervention of global disease control efforts. Through collaboration with the WASH sector, efforts have been made to integrate WASH in NTD control. This article reviews progress made in recent years, explores mechanisms supporting advances, and identifies priorities and next steps for accelerating WASH integration. This paper reveals advances in collaboration between WASH and NTD sectors, resulting in progress made across areas of programming; research; advocacy and policy; training and capacity building; and mapping, data collection and monitoring. Face to face meetings between WASH and NTD sector experts with a clear purpose of informing wider sector discussions, and the development of actionable joint workplans, have been particularly critical in supporting progress. Priority next steps include building capacity for WASH programming among NTD control teams, coordination at the country level, and strengthening the epidemiological evidence and operational learning for joint WASH and NTD interventions. In order to accelerate WASH integration in NTD control through strong collaborations with the WASH sector, the NTD sector could make use of strong data management skills and advocacy opportunities.

Electronic Resources for Developing Data Management Skills and Data Management Plans

ABSTRACTDeveloping a data management plan is an important part of planning any research endeavor, and increasingly, grant institutions require these kinds of formal, written documents. Medical and academic libraries have begun reaching out and offering help to researchers developing plans for managing research data. Several tools are available to aid researchers and librarians alike in both building the skills needed to make well-informed choices when it comes to managing research data and to walk users step-by-step through the process of creating a formal data management plan. Data Research MANTRA is an online course for those who manage, or plan to manage, research data. DMPTool and DMPonline are step-by-step guides to creating data management plan documents that adhere to DMP best practices, as well as to the requirements of specific grantors and institutions. These tools can help librarians and the researchers with whom they work to both meet institutional requirements and to make informed decisions about handling research data that will help ensure its availability and usefulness in the years to come.

Academic libraries’ role in Research Data Management Services: a South African perspective

Research Data Management (RDM) services are being implemented by academic and research libraries globally in support of university research activities. In South Africa, some libraries are beginning to provide frameworks for these services with some degree of success as policies are being formulated, infrastructure set up, library staff trained, and awareness and advocacy campaigns held with academic staff and researchers. Challenges being faced include availability of resources and infrastructures and limited data management skills among library staff. This paper reports on how the Library at the Cape Peninsula University of Technology is developing and integrating RDM services into institutional research workflows. The paper includes issues that are driving e-research at the institution and how requirements of researchers in the field of biomedical research have been used in a pilot e-research project. The report also details how the university library is using these user requirements to develop tools such as data management plans, electronic laboratory journals and systems for integration with institutional research workflows. The paper further outlines how an international collaborative approach has assisted the Library to participate in the development of an open source platform for the management of the full research lifecycle in support of RDM. It concludes with how further skills development within the Library is being undertaken to support data services and some of the likely challenges for further development of the services.

Data management by using R: big data clinical research series.

Electronic medical record (EMR) system has been widely used in clinical practice. Instead of traditional record system by hand writing and recording, the EMR makes big data clinical research feasible. The most important feature of big data research is its real-world setting. Furthermore, big data research can provide all aspects of information related to healthcare. However, big data research requires some skills on data management, which however, is always lacking in the curriculum of medical education. This greatly hinders doctors from testing their clinical hypothesis by using EMR. To make ends meet, a series of articles introducing data management techniques are put forward to guide clinicians to big data clinical research. The present educational article firstly introduces some basic knowledge on R language, followed by some data management skills on creating new variables, recoding variables and renaming variables. These are very basic skills and may be used in every project of big data research.

Learning to manage and share data: jump-starting the research methods curriculum

Researchers’ responsibilities towards their research data are changing across all domains of social scientific endeavour. Government, funders and publishers expect greater transparency of, open access to, and re-use of research data, and fears over data loss call for more robust information security practices. Researchers must develop, enhance and professionalise their research data management skills to meet these challenges and to deal with a rapidly changing data sharing environment. This paper sets out how we have contributed to jump-starting the research methods training curriculum in this field by translating high-level needs into practical guidance and training activities. Our pedagogical approach involves applicable, easy-to-digest, modules based on best practice guidance for managing and sharing research data. In line with recent findings on successful practices in methods teaching, we work on the principle of embedding grounded learning activities within existing narratives of research design and implementation.

Taking a Chance in the Classroom: Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics

Many have argued that statistics students need additional facility to express statistical computations. By introducing students to commonplace tools for data management, visualization, and reproducible analysis in data science and applying these to real-world scenarios, we prepare them to think statistically. In an era of increasingly big data, it is imperative that students develop data-related capacities, beginning with the introductory course. We believe that the integration of these precursors to data science into our curricula-early and often-will help statisticians be part of the dialogue regarding "Big Data" and "Big Questions".

Genomics data curation roles, skills and perception of data quality

Compared to a decade ago, genomics scientists, driven by technical changes and availability of massive genomics data, are performing a wider plurality of curation roles, including end user, curator, and dual-role user. Scientists with different curation roles (including that of end user) may focus on different data quality aspects and skill requirements in a community curation environment. This study examines how genomics scientists' perceived priorities for data quality and data quality skills differ when assuming different roles played in genomics data curation work. The analysis of survey data collected from 147 genomics scientists found that curators of genomics data valued quality criteria that can be assessed through direct examination of the data more highly, while end users placed a high value on the quality criteria that can be assessed indirectly, such as believability. With regard to data quality skills, curators appeared to care more about understanding user's requirements and specific data management skills than end users, while end users valued the skills needed to deal with information overload more highly — those needed to identify useful, relevant information from large amounts of data. Scientists with different curation roles, given common curation tasks with the same skill requirements, prioritized different data quality criteria. The data quality, skill priorities, and tradeoffs identified by this study can inform the development of effective data curation mandates and policies, data quality assurance planning and training, and the design of curation role specific tool dashboards and visualization interfaces for genomics data.

Required Data Management Training for Graduate Students in an Earth and Environmental Sciences Department

The increasing importance of data management in the sciences has led the Department of Earth & Environmental Sciences at a research intensive university to work closely with the Physical Sciences Librarian and Data Services Librarian on campus to provide mandatory training to its graduate students. Although integrating data management training into the graduate program curriculum may not be possible, there are still opportunities to ensure students learn such skills prior to graduating. This article describes the four approaches taken thus far — a seminar about basic data management during the department’s weekly seminar series, creation of a Data Profile form that students were asked to complete, an interactive workshop during the department’s annual retreat, and assistance with writing data management plans. Buy-in for requiring data management training was essential from both faculty and students and was possible because both groups understood the value of research data management skills. Also vital to the success of these approaches was how the subject specialist and data librarians leveraged their respective areas of expertise in a complementary fashion to address disciplinary as well as broader data-related concerns.