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Based on my experience in data collection/management, which steps in the data management workflow that I have done and have not done? If I have a chance to go back, which steps I should have done to improve my project?
For my research, when I started the project, I have no experience in data management or research methods. However, steps that I have done are
– Protocol discussion: an important part that is necessary and related to the proposal.
– Data design, acquisition, and collection: the decision was based on the data needed for the research and the limitation of the resource at that time. For my project, CRF design and development was one of the most time-consuming processes, and need guidance from my advisor.
– Investigator meeting: Our department has regular meetings for research that ran in the department.
– Data QC: I checked the data regularly to ensure the completeness of the data
– Data QA: I have checked with my protocol that we followed the protocol correctly.Steps that I have not done (or I have done, but have not done appropriately) are
– DMP development: I have not written the plan
– Database access control: when the project start, only I and my advisor are responsible for data handling, so we did not plan and make an agreement for an ongoing process that the research assistants have to be involved in later.
– Database lock and security: I did not encrypt or lock the database.If I have a chance to go back, I would:
– Write a data management plan
– Plan for database access control
– Lock and secure the database for safety and privacy.-
For research projects that are not clinical trials for licensure, DMP may not strictly required. Particularly, a small research that involves only few staff, single sites— a full DMP may not be required.
However, Database access control and data security are tasks that you need to performed for every projects.
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I’ve done data analysis and a bit of data entry and quality control for genotype data.
If I could, I wanted to reduce the number of steps that rely on manual data entry. There was a step where staff needed to input sample IDs into the computer manually, and although we tried to recheck them thoroughly, errors might still happen. The process was also laborious and inconvenient.
Another thing is to keep records, both on paper and computer, more systematically. With several workers, it could be hard to find things, such as documents, as each person organized things differently.
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In my experience, I only involved in data collection as a user, and from my experiences, I think designing an intuitive user interface for application can reduce confusion and frustration.
If I can go back in time, I would suggest some insights with the programmers.
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From my last data collection project that prior told in the fist discussion topic, I involve in many step of data management workflow including…
– protocol discussion
– data design
– data acquisition
– CRF development
– database setup
– data entry screen test
– data validation
– data standard and coding
– external data merging
– data manipulation and analysis
– writing study reportThe project has various limitation such as time, person, budget. Especially for time, the limitation caused the project didn’t well plan. However, the team attemptำก to kept the quality and reliable of the data and the report. The processes that were not implemented in the project and I think it should be done for improve the project are…
– DMP development
– database access control
– data entry screen test (we implemented, but the real end users didn’t involve the test)
– Investigator meeting or CRF completion training
– database lock and security-
Thank you for sharing good experience. What do you think that lack of training for all related project personnel and specific role for each responsibility would be the gaps that needimprovement?
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Thanks for sharing! Sometimes, we also need periodically training over the study period. You should continue monitor data quality during data collection period. If there are some sites or interviewers that show poor data quality, the refresh training should be required.
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Based on my experience (as I have mentioned in the 1st topic discussion), I have been involved in part of protocol developing, data collection design, data entry and monitoring (Lab).
If I have a chance to go back to improve my project, I think that I would like to:
– Data collection design: that might need to add additional questions to gain more useful details for research study. Because, we will know some interesting data after collection. It may be like the new knowledge from the study that we never know before. especially, when we study in the project about discover of new drug, use the new drugs for treatment or clinical trials
– CRF development; if I have a chance to discuss to provide the feed back to the study team or Data manager/ person who design the CRF to revise the CRF because there were some data that entered/ filled into the CFR were duplicated in many section and make it friendly-use for person (study site staff) who enter the data.
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I only have experience on a small research which was not clinical trials and did not involve colleting any biomedical data.
Many steps of data management workflow I have done as the following;
– Protocol discussion
– Data design
– Data Acquisition (Paper based only)
– Data entry and processing
– Data Manipulation and analysis
– Study report
As Ajan Saranath mention, database access control and data security were tasks that we needed to performed and it was a requirement from our ethic committee.
If I have a chance to go back to improve my project, I would like to:
– Develop a data management plan
– Do QC and QA
– database access control
– database lock and security-
Yes! Data security is an important step in data management process. The data should be securely kept even after the study was closed.
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Most paper-based projects will keep the data in Excel or SPSS format. Physical security is usually used to lock the data for example lock files or computer to keep its securely. This step is acceptable? because of the limitation of IT staff in the organization and they cannot handle many projects at the same times.
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Most projects that I have involved or conducted are surveillance or public health project which is not clinic or lab trial project so the certain step of data management work is skipped.
The step that I have done included:
1. Project Initiation
– Protocol Discussion: one of the most crucial steps that need to be done before conducting the project. In this step, the expert and participant were discussed according to the proposal and maybe this takes more than a one-time meeting until finalized.
– Data design: the variables were indicated.
– Data Acquisition: the tool was chosen. Most of the projects were paper-based.
– Database setup
2. During study conduct: I have done both of data entry process and QC. However, consistency and accuracy were the problems sometimes.
3. During study end:
– Data coding
– Data manipulation and analysis
– Data sharingThe step that I have not done included:
– DMP: I did not study in the clinical project so I didn’t write this.
– External data merging: the project is collecting the primary data so no external data were used.
– Database lock: when the project closes, sometimes we still back to edit the data.If I have a chance to go back to improve my project, I would like to:
– Spending more time in the protocol discussion because well-management and well-plan are the most important step to conduct the quality project.
– DPM: after learning this course, I will try to write DMP if I have a chance to conduct a clinical project.
– Data QC: I will enhance and strengthening this step.
– Set up database security and lock -
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I have had experience collecting and entering data in log book and review the data from entering data by other persons but I have no any experiences analyzed the data or used it in other fields.
My experience about data management is very poor and most of things I have learned in this class I never knew before. It’s a whole new my knowledge. -
According to the processes, I found that I missed many processes to ensure the validity, accuracy and reliability of data. At first, I extracted raw data from EMR instead of providing CRF to study site. It greatly affected to data quality also main result. Then, DMP was not develop before study was initiated, it made some confusion during the study period. Database access control was not set up either because I thought only me could access this database and it located in my laptop. One day my laptop broke, and I became furious. However, I have done many ways to ensure data quality, manipulated and analyzed my data to get final result of my research such as EDA, checking validity, read and understand my data over and over again. This experiences and lectures made me clearly understand about data manage process.
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I have only experienced some of the steps in the data management process missing most of the important ones such as protocol discussion, data quality assurance, data coding and data sharing.
If I were to be able to go back and make improvements, I would choose web-based electronic data capture from the beginning to reduce workload. Data management plan would be well written not only with minimum requirements but in the best possible practice. And also, enough time would be given for data entry screen test to reduce entry errors and boost confidence. Apart from data quality control, quality assurance will be performed in accordance with protocols. -
From my experience in data management, I Found that the subject matter was data collection. It seems to be easy but when actually doing it Some of the teams working with us do not do what we originally communicated, such as failing to enter log books, which is where we need to check and reiterate our understanding.
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