A data management plan or DMP is a structured document outlining how information should be managed both during and after the conclusion of a research project (Data management plan, 2019). As we have, the information about data it will help in saving efforts for a long period of time, there is no need to rearrange, reformat or attempt to recall the previous data information. This is possible as the DMP is prepared well in advance before the actual data has been collected, this guarantees that the information is in the correct format, well-structured and properly annotated.
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It is important to have a Data Management Plan as it gives an idea about the project to the target audiences. (Jones, 2011) says that having a plan will help you save time and effort and promote the research process. Understanding what information is going to be created and how, helps in determining the data attributes. DMP also allows to make responsible decisions, taking into account the broader context and consequences of various options. DMP makes it easier to share and use the data, to understand a product or make a financial investment plan for the project. All these is done without obviating any policies or standards. When a researcher wants to view some information of a project, he has a look at the DMP for the project and gain insights for the project. DMP is also helpful incase when a new employee is hired for the project or a different research team is going conduct further research on a project. In that case the researchers can directly go through the DMP, as DMP follows all international standards it will be more or less the same world-wide. This helps in reducing the readers efforts and time as he will already have an idea about the structure of DMP.
The case study for the assigned DMP was about "Viral Evolution". The initiative "Viral Evolution" seeks to develop the ability to forecast the infectious potential of major disease threats through analyzing influenza strains through the WHO network from the earliest point of their discovery and risk assessment by health officials. ComTelDat and AusUni (fictional) telecommunications companies want to use ComTelDat's phone data to produce population movement information for the project. Globally, such information can be international movement, such as arrival and departure at airports, and local movement of people within (or between) places that can be monitored through their mobile phones. The project will develop the information infrastructure and identify the telecommunications data that can be obtained legally, ethically and morally from the operating systems of ComTelDat so that it can be used in conjunction with health data from other sources.
In this DMP (Data Management Plan), we focus on data recovery and management of influenza virus data provided by government and global health institutions and telecommunications data generated by ComTelDat and other related data, such as demographic data, to provide a basis for research on the evolution of global influenza virus. Data curation is required in this viral evolution project to organize and maintain data storage and archiving, define the level of access provided to the research participants, while data management is crucial to the security of research data for internal use and public dissemination. The leading member of the project is AusUni, who will collaborate with the project partners to determine the data needed for this project, compile it again for the need to analysis and conserve it for potential beneficial projects. This DMP would impact curation, storage, access, use and reuse, relocation and disposal in the data curation lifecycle.
The very first section of the DMP explains all the data files that will be produced by the projects, the third-party data file and other project related file. These files would be generated will the help of data provided by the Stakeholders, files are present in different format. This section also gives an idea to the targeted audience about which all files will be used to publish the information. The second section gives information about the ownership, copyright, intellectual property (IP) of the project. It identifies any agreements or contracts that applies to this DMP, states who will own the copyright and Intellectual Property of any data that will be created using this DMP.
The DMP provides access using Copyright Protection, Ownership, Intellectual Property. All project data files, third party data, project related data files and publications are protected by copyright, as copyright protection automatically occurs when a work is created. Most of the recorded, produced or published information will be in Australia, meaning that Australian copyright will applied on the DMP, and as we collect and create data files for research purposes, the Australian Copyright Act allows people (and ourselves) to use copyright material for research or study purposes without infringing copyright, providing that the use is appropriate (Research or Study Information Sheet G053v09,2014). As far as shared ownership is concerned, there must collaboration with ComTelDat to collect telecommunications data to help study population movement, a copy of the agreement will be provided. AusUni Researcher will use information from other groups, such as WHO, TGA and OIE organizations. They have already done the study, or will do so, and we will ask for their permission to use the data. Description of the Data Use Agreement will be given when completed. Since all the data files that we plan to obtain from third parties are subject to copyright, we will contact either the owner or the organization to obtain permission to use their data. We will only use the information for the sake of this research at AusUni and not for commercial purposes; users who will have access to the material will only be those involved in the research: researchers and investors (although some of the data files are already available and open to the public); no changes will be made to the original data and full credit and recognition will be granted. The licenses will be given with references and copies.
The further part is about the data file format and would explain if the file formats are widely accepted and do, they follow the standards. Most of the data collected for the stakeholder will be spectral in nature. Hence there will be some need to reformat the data, it would also provide the targeted audience with the hardware and software requirements. The DMP provides a brief overview about the Storage and backup options taken by the project. The data will be stored in a digital format in AusUni FigShare Repository. However, the data which is present in non-digital format would be converted into digital format and then use for research. The required volume of the data for the project is also descripted in this section.
The preceding section than explains the how the metadata would be handled, and the standards and formats the project follows also define the data dictionaries, data definition and schema files. The retention and disposal part of the DMP identifies the likely retention period for the DMP data and ensures that the requirements for the retention and disposal of the DMP are met. This is divided into two parts minimum retention and Long-term or permanent retention. Minimum retention identifies the period as early as possible and long-term or permanent retention can be completed by the researchers in the form of draft which starts at the start of the project and is reviewed regularly. The later section states that the data is stored in AusUni data repository. These sections give an idea to the researcher how the data will be preserved. Data will be shared with people working in AusUni on this project. Students can be recruited as interns to assist the group. Students will only be granted access to the data if they are interns. Researchers who do not work on the project will not have access to the data unless and otherwise they request access. Data will be shared publicly with the project's stakeholders. Data can be accessed through an AusUni Network Drive. For security purposes, login credentials will be given to all users. There will be restricted access to sensitive data, such as data on population movement. Sensitive data will only be accessible to the core research team. User login and password would be required to access the sensitive data. AusUni offers a secure data transfer method, including secure protocol, port and authentication information (username and password). It is prohibited to exchange information using physical media as specified by the AusUni ISO 27001 (Information Security Management) standard. An inventory will store the data generated by AusUni. It will include name, content, update frequency, user license, owner / maintainer, privacy considerations, data source metadata standards about public data collected by stakeholders and will be maintained. Stakeholders will be provided with the same copy. It decreases processing of information and reliability of data. Data registers and inventories will use Title, Definition, Resource manager, Reference e.g. web address and id, Classification i.e. is the resource critical, important or minor, Classification component, General remarks as metadata. Protocol for directory and file naming in all repositories is used to ensure consistency and ease of indexing and searching for large data. Data structure guidelines will be given for this representation and this standard will be used for all documentation and reporting. All data identified in this project and generated by the research will be provided as part of the data organization with their data dictionary explaining the metadata of their database schema. We can use Infosphere Metadata Workbench to connect the metadata. It is used by enterprise and data analysts to discover and evaluate information resources and metadata repository relationships. The required retention of information for this project refers to AusUni retention for recommendations for research data. The normal disposal period is five years after the research operation has been completed. Minors will be involved in the research, as they are also potential victims of influenza. It therefore requires different retention periods. Research may involve clinical testing for influenza vaccine, so it needs to be specifically treated. There is no community-based work in the study. Research incorporates health and telecommunications data, which is why it is considered a groundbreaking technique and will be of enduring interest to researchers in this field (health and telecommunications). If the algorithm for integrating telecommunications and virus information and visualizing the evolution of viruses requires enormous and complex effort and is subject to patent protection, then it would be supported for permanent retention. The data describing this project's research method and its data sets will be stored in the AusUni Research data repository. Data relating to broad influenza virus photos can be stored in MyTardis Repository. Using proper attribution, data collected in this research can be used to teach at AusUni. Access to the data is given by the AusUni Research Committee's permission. It is necessary to destroy data that no longer have a longterm value. This will give more room for future research data and also reassure the ethical standard in the treatment of sensitive data.
The sections were divided amongst the team equally. The first section of the DMP was considered by everyone in team however the remaining sections were divided in two sections per person. Initially the complexity of the task was not identified by all the team member. The task was not seen equally difficult as well. Mutual aid of the team was at risk due to the difference in perception. It was also difficult to formulate the DMP at one glance. We conducted meeting every week and helped each other overcome doubts. Our team achieved a successful outcome later. This was only possible because of regular team meetings and discussing the progress done in the DMP.
The section I handled were Data in This Data Management Plan, Durable Formats, Storage and Backup and Integration and review of DMP. The Data in data management plan section provides the viewer with the understanding of the files that will be generated by the project and the files the projects needs to work on. Identifying and analyzing the type of files that would be generated is a tedious task. As preventive measures need to be taken when formulating the data in data management plan.
The interpreted data will be available in different data formats, such as some files in PDF format and others in CSV or TXT format. A shape file will also be used to determine the local and regional borders in order to track population movement of the humans inflected by the virus. As the shape file is not that persistent the file will be converted into json format. This helps the developer to efficiently code the program. The data formats meet all the international standards that are widely acceptable and best in practice. Computer code of the project will be available on authorize request. However, the copyright and the reports generated will be in a PDF format uploaded to the AUSUNI website. There is no special requirement of Hardware for the project. However, the researcher will need a platform supported by an analytical programming language such as R or Python to perform exploratory data analysis to the datasets. As during the research, it was found that many datasets also explain the data using some visual aid tools, this helps the viewer to gain a close insight of the project. Data will be stored in AusUni Allocated (Network drives), which will be handled by AusUni. This would prevent information silos. However, if any of the members of AusUni wishes to access the drive, the data will be provided to them on application. (Borgman,2010) says that for information to be available for selection and curation, those who obtained or created it must share it. Data are objects that are complex, messy and take many forms. As a result, they need to handle with care and should not to renounced to an unauthorize person. Automatic Nightly Backup will take place at two AusUni physical locations these are AusUni allocated storage and AusUni managed storage. We do this because so that we don't lose on any piece of information or that some changes to the documents do not affect the entire database. This enables to maintain the data and use it for advance research.
DMP applies in each and every sector, as a result it can be said that data curation process is closely associated with Data management plan. Studying Data Curation schemas, it can be inferred that the
DMP follows "Capability Maturity Model" of Data Curation. This model follows 4 key processes such as Data acquisition, processing and quality assurance, Data description and representation, Data dissemination and Repository services/preservation. It is possible to design a Data Curation schema will the help of DMP. For example, the designing of ISO 2146 Registry Services Standard schema using a DMP.
The most important thing while designing a DMP is to keep in mind that all the attributes of the DMP needs to address accurately, proper justification must be provided when specifying an attribute. Any section of the DMP should not violate the legal and ethical policies. (Michener, 2015) explains the 10 simple rules when designing a DMP. It is said that the DMP should be well planned, analyzed, should ensure the quality of data. The DMP should also be also to demonstrate the use of data, preservation and access to the data. A data management plan should provide everyone with an easy-to-follow road map to direct and explain how information is handled throughout the project life and after completion of the project. In the end, it can be said that designing a DMP is challenging task. However, it is essential to have a DMP before the initialization of any project. As DMP guides the project in right direct before and after completion of the project.
Borgman, C. L. (2010). Research Data: Who will share what, with whom, when, and why?
Data management plan. (2019). Retrieved 25 October 2019, from https://en.wikipedia.org/wiki/Data_management_plan
Jones, S. (2011). How to develop a data management and sharing plan.
Michener, W. K. (2015). Ten simple rules for creating a good data management plan. PLoS computational biology, 11(10), e1004525.
Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015). Ethical challenges of big data in public health.
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