 The topic we are going to discuss further is managing research data. Once the data has been collected, we have to devise different things on the basis of data. Data are the most important outputs of research process. These can be used to either accept or reject a hypothesis or even to frame a new hypothesis. Data management therefore is very crucial even after the completion of the research. We understand that the hypothesis could be something which has provided us the basis to accept something. And at times we think that if this hypothesis is not being accepted, we have to custom tailor it and we have to devise a new hypothesis. The data management in this process is a very important role contributing. Data management can include the following aspects. Data ownership. It implies ownership of the legal rights of the research data during and after the research project. The important stakeholders include funders, research institutions, principal investigators and even data sources. When our data is completed and we have it, ownership of it is very important. Because ownership of it remains with you even after research and research. For example, we say that the government of Pakistan conducted a national health survey. Now that national health survey is the property of that department by the government of Pakistan who have conducted that research. So now the data is not only important in that year when the data is gathered. The important thing in the coming years is that if we want to compare the 2007 national health survey then we can do it with the 2012 results. Like if the health status was different in 2007 and 2012 then the data of 2007 will always be important for us. We can never undermine its importance. Similarly, the census data is collected in different epidemics. For example, the wave of COVID, the health disaster caused by COVID, the statistics of the wave 1 to wave 5 is important because we have to see in which wave the health problems people faced and what were the causes of the death of the cohort. So basically, data is always important and it is never important after the completion of research. Then data collection. It implies consistent and quality controlled collection of data. Few important aspects including obtaining required authorization using appropriate method and applying attention to details. Now after the data ownership it is also very important the way we are going to collect the data. We should always use some authorized methods or methods of data collection and the latest and easiest tools we have we should use them for data collection. Then data storage. It implies protection of data from damage, theft or loss. We have a lot of devices for data storage in which we can store our data which is in e-sources or physical sources. So the best available source we should protect our data. Data storage is important to recheck the findings and to prioritize the search activities, tasks and to be reanalyzed by others. I just rightly mentioned that if a data collection happened in 2007 and after 100 years we need that data to reanalyze by someone else to see what are the major changes we can see during this century so if the data is not stored we cannot use that data to do any kind of analysis further. Then data sharing. It implies deciding what to share and with to home. General public or with other researchers whether it is a government property or a legal authority property to share the preliminary data or final result. Whether we are giving the original source to someone or giving the final statistics like an SPSS file example which will be well understood for students or a complete data file of SPSS we can share it with someone or we can share the total of a scale with them. So if we share the total of one cannot have any authorization to do any kind of duplication in data but if we give it a complete file in which all the entries will be included then we can analyze that data in different ways and produce the results we like. Data withholding is also an important aspect. Researchers have the responsibility to maintain the integrity of research data. When we talk about research we talk about integrity because if the data is falsified or we get some results or some meaning from their manipulation which are not justified or righteous then it is a very big crime. The group members involved in handling of the data should maintain privacy and confidentiality of the data while recording of hard copy and electronic evidence. It is not that you have worked on a system and worked in a library and did not save your data and you went from there. Because that data could be used by any other person and they can just probably change the name of the variables and they can use that data for any other purposes. Then labs in the management of research data can give rise to many ethical issues as I discussed earlier and these issues are more prominent in studies involving the human subjects. For instance, there is a data on one place where there are no variables and some ethical researchers change those variables and put their own variables on it. Some of them change the entries and delete some of them and duplicate some of them and present a new research analysis and output. This is something very ethical on the part of the human subjects because if that research is published then no authenticity of that research will be there but some people will cite that research and discuss it and this is all considered into an unethical practice and we should be very careful in storing and sharing of our data and we should keep our data very confidential.