Research Data Management and Sharing

Start Date: 02/23/2020

Course Type: Common Course

Course Link:

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About Course

This course will provide learners with an introduction to research data management and sharing. After completing this course, learners will understand the diversity of data and their management needs across the research data lifecycle, be able to identify the components of good data management plans, and be familiar with best practices for working with data including the organization, documentation, and storage and security of data. Learners will also understand the impetus and importance of archiving and sharing data as well as how to assess the trustworthiness of repositories. Today, an increasing number of funding agencies, journals, and other stakeholders are requiring data producers to share, archive, and plan for the management of their data. In order to respond to these requirements, researchers and information professionals will need the data management and curation knowledge and skills that support the long-term preservation, access, and reuse of data. Effectively managing data can also help optimize research outputs, increase the impact of research, and support open scientific inquiry. After completing this course, learners will be better equipped to manage data throughout the entire research data lifecycle from project planning to the end of the project when data ideally are shared and made available within a trustworthy repository. This course was developed by the Curating Research Assets and Data Using Lifecycle Education (CRADLE) Project in collaboration with EDINA at the University of Edinburgh. This course was made possible in part by the Institute of Museum and Library Services under award #RE-06-13-0052-13. The views, findings, conclusions or recommendations expressed in this Research Data Management and Sharing MOOC do not necessarily represent those of the Institute of Museum and Library Services. Hashtag: #RDMSmooc

Course Syllabus

This week introduces multiple types of research data in an array of contexts as well as important data management concepts including metadata and the research data lifecycle. We will also define the concept of data management, identify the roles and responsibilities of key stakeholders, and examine various data management tasks throughout the research data lifecycle.

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Course Introduction

Research Data Management and Sharing The capstone course for the Research Data Management and Sharing Specialization is designed to help you apply the knowledge you’ve gained about data sources and the types of shared data, and to give you the opportunity to apply those skills to a real world project. The project will be two to three months long, and will require the use of data sources that you or your organization uses. You will need to purchase data warehouse for your project, and for that you will get additional resources. You may also use open data access. The research data management and sharing Specialization is a great course for anyone working in data science, providing them with a solid foundation to get started in more advanced data science. Specialization track certificates are issued by Carnegie-Mellon University for continuing professional study in data science, and for those who complete all tracks, specializations are also issued. It is important that you have completed the Specialization Track in order to be eligible to take the Capstone and be able to apply for a certificate. Track 1: Capstone Course Overview & Data Sources In this track you will have a chance to sit down and learn about a real world project. We'll use the two data warehouses that we have discussed in the Specialization track as an example. You will be asked to choose one of these two and will then use it as the study group for the project. Your choice of data warehouse will have an impact on the project, and you will need to

Course Tag

Data Management Plan Research Data Archiving Metadata Data Management

Related Wiki Topic

Article Example
Data sharing The NIH Final Statement of Sharing of Research Data says:
Data Sharing for Demographic Research Data Sharing for Demographic Research (DSDR) is a research data-sharing project funded by the Population Dynamics Branch (PDB) of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) of the National Institutes of Health (NIH)"." DSDR disseminates, archives, and preserves data for population studies.
Data management plan ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world.
Data sharing The Data Observation Network for Earth (DataONE) and Data Conservancy are projects supported by the National Science Foundation to encourage and facilitate data sharing among research scientists and better support meta-analysis. In environmental sciences, the research community is recognizing that major scientific advances involving integration of knowledge in and across fields will require that researchers overcome not only the technological barriers to data sharing but also the historically entrenched institutional and sociological barriers. Dr. Richard J. Hodes, director of the National Institute on Aging has stated, "the old model in which researchers jealously guarded their data is no longer applicable".
Data sharing A number of funding agencies and science journals require authors of peer-reviewed papers to share any supplemental information (raw data, statistical methods or source code) necessary to understand, develop or reproduce published research. A great deal of scientific research is not subject to data sharing requirements, and many of these policies have liberal exceptions. In the absence of any binding requirement, data sharing is at the discretion of the scientists themselves. In addition, in certain situations agencies and institutions prohibit or severely limit data sharing to protect proprietary interests, national security, and subject/patient/victim confidentiality. Data sharing may also be restricted to protect institutions and scientists from use of data for political purposes.
Data sharing Data sharing is the practice of making data used for scholarly research available to other investigators. Replication has a long history in science. The motto of The Royal Society is 'Nullius in verba', translated "Take no man's word for it." Many funding agencies, institutions, and publication venues have policies regarding data sharing because transparency and openness are considered by many to be part of the scientific method.
Data sharing Funding agencies such as the NIH and NSF tend to require greater sharing of data, but even these requirements tend to acknowledge the concerns of patient confidentiality, costs incurred in sharing data, and the legitimacy of the request. Private interests and public agencies with national security interests (defense and law enforcement) often discourage sharing of data and methods through non-disclosure agreements.
UK Data Service The UK Data Service encourages data sharing and reuse as a means to extend the inherent value in primary data for replicating research results as well as for additional analysis and teaching use. To this end, it supports the ESRC's Research Data Policy, which requires researchers funded by the research council to commit to a structured data management plan to enable data produced in the course of research to be deposited and archived for future sharing and reuse. To support researchers in developing robust data management plans, the UK Data Service makes a toolkit of resources available in formats designed for researchers as well as those responsible for teaching data management skills.
Data sharing Data sharing poses specific challenges in participatory monitoring initiatives, for example where forest communities collect data on local social and environmental conditions. In this case, a rights-based approach to the development of data-sharing protocols can be based on principles of free, prior and informed consent, and prioritise the protection of the rights of those who generated the data, and/or those potentially affected by data-sharing.
Master data management Master data management (MDM) is a comprehensive method of enabling an enterprise to link all of its critical data to one file, called a master file, that provides a common point of reference. When properly done, master data management streamlines data sharing among personnel and departments. In addition, master data management can facilitate computing in multiple system architectures, platforms and applications.
Data sharing Requirements for data sharing are more commonly imposed by institutions, funding agencies, and publication venues in the medical and biological sciences than in the physical sciences. Requirements vary widely regarding whether data must be shared at all, with whom the data must be shared, and who must bear the expense of data sharing.
Genomics data sharing Sharing of human genomics data has challenges similar to the issues related to sharing of health data and the handling, managing and sharing of the data must be in accordance with patient consent.
Technical data management system Technical data refers to both scientific and technical information recorded and presented in any form or manner (excluding financial and management information). A Technical Data Management System is created within an organisation for archiving and sharing information such as technical specifications, datasheets and drawings. Similar to other types of data management system, a Technical Data Management System consists of the 4 crucial constituents mentioned below.
Data management plan Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high quality data management.
Clinical data management Clinical data management (CDM) is a critical phase in clinical research, which leads to generation of high-quality, reliable, and statistically sound data from clinical trials. Clinical data management assures collection, integration and availability of data at appropriate quality and cost. It also supports the conduct, management and analysis of studies across the spectrum of clinical research as defined by the National Institutes of Health (NIH). The ultimate goal of CDM is to assure that data support conclusions drawn from research. Achieving this goal protects public health and confidence in marketed therapeutics.
Data sharing Data and methods may be requested from an author years after publication. In order to encourage data sharing and prevent the loss or corruption of data, a number of funding agencies and journals established policies on data archiving. Access to publicly archived data is a recent development in the history of science made possible by technological advances in communications and information technology.
Data sharing Some research organizations feel particularly strongly about data sharing. Stanford University's WaveLab has a philosophy about reproducible research and disclosing all algorithms and source code necessary to reproduce the research. In a paper titled "WaveLab and Reproducible Research," the authors describe some of the problems they encountered in trying to reproduce their own research after a period of time. In many cases, it was so difficult they gave up the effort. These experiences are what convinced them of the importance of disclosing source code. The philosophy is described:
Business and management research Business and management research is a systematic inquiry that helps to solve business problems and contributes to management knowledge. It Is an applied research.
Technical data management system Data plans (long-term or short-term) are constructed as the first essential step of a proper and complete TDMS. It is created to ultimately help with the 3 other constituents, Data Acquisition, Data Management and Data sharing. A proper data plan should not exceed 2 pages and should address the following basics:
Data management plan A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this ensures that data are well-managed in the present, and prepared for preservation in the future.