 Hi, everyone. My name is Hannah and I work within the research data management team. We are based at UK Data Archive University of Essex and I mainly oversee the training and guidance related to ethical and legal issues in data sharing. So just a quick introduction to your background in the chat box just to give a couple of minutes if anyone else can join us. So you can write it in the chat box in a word or two about your field. For example, if you're a post-credit researcher or a professional services staff, if you want to share, then that's fine. So practice aid for research at the Curd Quality Commission. That's fantastic. Graphic design researcher, professional services, PhD student, MFIL students, UKDA staff, someone from UKDA. Researcher, librarian, staff, early career researcher, research data coordinator, postdoc, research facilitation, PhD student. So fellow copyright and licensing librarian, data manager, PhD student. So that's great that we have a mix of people. So I will try to cover the content that is useful for today in today's session. Hopefully. Thank you for your responses. So another question. If you can, please go to menti.com using this code. What type of data are you working with? You can use menti meter on your mobile phones as well. Qualitative, mix, quantitative. So majority of you are working with mix data. Qualitative, qualitative as well. So that's great. Again, we have it and it's working on different data types, which is great. And I will try to cover everything in today's session. I hope that you will find it useful. So majority of you working with mix method data. So thank you very much for your responses. So as you may be aware that research data is of two types, primary data and secondary data. Primary data is the data that is collected by a researcher directly from the original source through experiments, surveys, interviews, observations, focus groups. And on the other hand, secondary data is an existing data gathered from studies, surveys, or experiments that have been run by other people or for other research, such as existing data sets at archives, essays, reviews, information available on social media, etc. So the overall aim of this session is to show you how you can share research data from human participants within ethical and legal boundaries. The first section focuses on the key considerations when collecting primary data and you plan to share that data for future and the second part of the session will focus on the key considerations if you are using secondary data and plan to use that data for future use by others. And I have also added some of the useful resources on the last slide and I will respond to the questions in the end. So in the first section, which is related to primary data, I will briefly discuss the key principles of research ethics. I will also talk you through to some of the ethical considerations and best practices in data sharing. That includes research ethics, self-assessment tool by UK statistics authority and duty of confidentiality, data protection considerations and a role of consent in research and data sharing. Again, time for Mentimeter. What do you think are your ethical obligations as a researcher, especially when you plan to share your data for future reuse? Any thoughts on that? Privacy and consent, that's right. Anonymity, that's right. That you have consent, confidentiality, considering GTPR degree of anonymization in line with participant consent, transparency, confidentiality. Yeah, informed consent. Ensuring participants know why their data is being collected, how it will be stored and why it will be reused. That's right. Meet your obligations, that's correct. Protect confidentiality, transparency, no harm to participants, confidentiality, privacy. Being transparent, so many responses, that's brilliant. I must say a very well-informed audience today that you try to make any identifying characteristics, meeting obligations, relevance of reuse, transparency, integrity, pseudonymization, GDPR, data agreement, ensure accessibility for future researchers, that's right. Brilliant, brilliant answers, very well-informed audience, data is relevant and useful, that's right. We will be covering all of these briefly in today's session. Thank you very much for your responses. Well done. So, ethic issues are most likely to arise around privacy, equality, diversity, health and safety as all of you have mentioned in your responses. So, research ethics go on the standards of conduct for scientific researchers and it is important to adhere to ethical principles in order to protect the dignity, rights, welfare of the research participants. So, ESRC has set out key principles for ethical research and these principles are researchers should ensure that their research is beneficial to participants, individuals, science and society and there should be realistic about the benefits that it is likely to deliver. So, research should be designed and conducted in a way that respects the rights, interests, values, dignity and autonomy and if possible of participants including individuals, groups and communities. So, researchers should inform participants that they have a right to refuse to participate free of consequences and can withdraw from the research for any reason and integrity demands that there is a clear fit between what researchers say they will do and how they will conduct their research and transparency means being clear about the nature of the research and communicating this to the involved. Researchers must exercise self-critical responsibility in the planning and conduct of their research. Research ethics committees and research organizations have a responsibility to guide and support researchers especially when the research involves difficult ethical decisions and most of all researchers should maintain the independence of their research and their conflict of interest cannot be avoided they should be made explicit and independence of research is founded on academic credentials, professional standards, equity and experience and it should be free from personal organizational and political bias. This honesty and consideration of gain should be safeguarded at all times. So, it is important that researchers do not just consider what can be done with the data methods, expertise and technology available to them. It is equally important that researchers consider what should be done. So, national statisticians data ethics advisory committee provide a framework to help all researchers to think about the ethics of their research at an early stage and give them confidence that their plans address ethical principles and practices. So, they have designed a very useful ethics self-assessment tool to review the ethics of the project to be undertaken. Here's a link of that tool for you to have a look at in your own time. This tool enables to identify and mitigate any ethical issues and it is based on six main principles which are public good confidentiality and data security methods and quality, legal compliance, public union engagement and transparency. And you are asked to assess your project against 22 items grouped against these six ethical principles on a scale and it is beyond the scope of this session to go into further details of this tool but please go and have a look into it as it is really useful tool. So, some of the best practices are that ethical obligations should be considered throughout the research lifecycle from planning and research design stage all the way to the data collection stage to the future use including publications, archiving, sharing and linking of data. So, it is essential to have a knowledge about the standards and requirement of the relevant research organizations and it's good that you always comply with the relevant laws. You need to avoid social and personal harm and you can always check with data centers such as us as they facilitate ethical and legal reuse of research data and protection of what is claimed since they are guarding of personal data. In the UK there is a duty of confidentiality that is based in common law and that occurs where confidential information comes to the knowledge of a person in circumstances where it would be unfair if it were then to be disclosed to others. So, however, there are some exceptions when you can disclose information. For example, if participant consents to onboard sharing of their personal data then sharing does not reach due to your confidentiality and sometimes public interest can override due to your confidentiality. So, occasionally there are instances when you may need to give up data such as code order. So, the best practice is to avoid very specific promises in consent forms. So, what do you think constitutes as personal data? If you can write in a chat box or you can go to the Mentimeter again, I think Mentimeter would be okay as you may have it open. So, what do you think constitutes as personal information or personal data? Any thoughts on that? Any identifier, medical condition, date of birth, social insurance number, ethnicity, gender, date of birth, name, postcode address, that's right. All the answers so far they are right. Sexuality, demographic data, sexual orientation type of ailment, contact details, phone number, gender, exactly. Demographic data, medical records, any identifying information, that's right. Explicit IDs, that's right. Health conditions, special category variable, that's right. Addresses, yeah, attributable identifiers, that's right. Financial details, exactly. Thank you very much for your responses. So, personal data is any information relating to an identified or identifiable natural person as you all have mentioned in the Mentimeter. People can be identified directly or indirectly. Examples of direct identifiers as you have mentioned are name, address, postcode, telephone number, voice and picture as well. And some of the examples of indirect identifiers are occupation, geography, unique or exceptional values. For example, some outliers in the data. So, personal data also includes special category data that needs more protection because it is sensitive. The UK GDPR defines special category data as personal data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs and trade union memberships, genetic data, biometric data and data concerning health, sex life and sexual orientation. So, all of your responses were right. So, as researchers, we must adhere to data protection requirements when managing or sharing personal data. So, if personal information about people is collected or used in research, then the data protection regulation applies. Data protection legislations that are most widely applicable to the research are Data Protection Act, EU GDPR, and which is now known as the UK GDPR in the UK. And EU GDPR is the EUY Data Protection Regulation that was introduced in 2018 and replaced UK Data Protection Act 2018 that was used until that time. However, since January last year when the transition period of Brexit ended, it is now called the UK GDPR. Currently, the UK GDPR and the EU GDPR both are aligned. They places the same legal obligations on researchers but in the future, the two bases of legislation may diverge as the UK has now left the EU. So, it will therefore be important for researchers to ensure that they gain local support from their university or data protection officer when their research project will span across the EU. So, if the researcher based in the UK collect personal data about people anywhere in the world or a researcher outside the UK collect personal data on UK citizens, then Data Protection Act and UK GDPR applies. And however, if the researchers are undertaking research projects which span across the EU, then the EU GDPR will also need to be considered and adhered to. UK GDPR specifies the rights a data subject has when their personal data are processed. Individuals have the rights to be informed about the collection and use of their personal data. They have a right to access the information you hold and they can request any change in the information they can ask for a racial object or ask to restrict processing. They can also object in terms of data portability or transfers and any sort of decision making. Which of these rights will be relevant to processing personal data for your research project will depend on the nature of the project. The chosen processing ground in which country the research is taking place. So, the Data Protection Act and the UK GDPR define six principles that need to be complied with when processing personal data. All personal data must be processed lawfully, fairly and transparently and it should be kept to its original purpose. Personal data should only be collected if it is necessary and removed if it is not necessary. So, accuracy should always be upheld and researchers should ensure confidentiality and integrity. So, under the UK GDPR there are six possible grounds for processing personal data and one of these must be present. These are consent, public interest, legitimate interest, protection of vital interest, legal obligation and performance of a contract. But in the context of research the first three consent, public task or legitimate interest are the most applicable grounds for the processing of personal data. However, consent and public tasks are likely to be the most widely used ground for processing personal data. There is a misconception that data protection laws such as GDPR prohibits data sharing. However, it does not prevent data sharing as long as you approach it in a sensible and proportionate way. GDPR is useful for research as it legalizes much of the current good practices in research and it places people in the center. GDPR offers enhanced rights to individuals whose data is being processed. So, in the context of research, GDPR has the potential to further benefit research and archiving. Helping to improve trust and confidence between the public and universities and between researchers and their participants. Much research data, even sensitive data, can be shared ethically and legally if researchers employ certain strategies. For example, offering protection of identities through anonymization, de-identification, using processing ground for personal data or regulating access where needed. All or part of data by group, by use, time and putting safeguards and security. So, first of all, you need to keep the identity of the research participants confidential. Then the risk of disclosure need to be considered before, during and after the data are collected. So, assessing disclosure rates is done by evaluating key characteristics for variables in data files that are most risky for leading to participant identification in a specific project. So, there can be direct identifiers. For example, a person's name, national insurance number, picture or detailed geographic location or there may be indirect identifiers that can provide enough information to identify someone in combination with other. For example, large household-sized specialized profession, unusual health condition. So, once disclosure assessment has been completed, relevant strategies such as anonymization or de-identification, use of consent for accessing, regulating access to the data can be employed for processing the personal data for future use. So, I'll go through each of these briefly to show how these enable you to share your research data. Researchers need to consider how to protect identity or confidentiality of the participants when sharing data. In both quantitative and qualitative research, anonymization and de-identification are the two that allows data to be shared while preserving privacy. As stated earlier, identity can be disclosed from direct identifiers such as name, post post, or indirect identifiers which when linked with other available information could identify someone. For example, salary occupation and other things. So, to anonymize data, the most widely used techniques are de-identification and anonymization. De-identification is removing direct identifiers in personal data while anonymization is ensuring that the risk of somebody being identified is negligible. And anonymization should be planned early in research. So, some techniques used for de-identification of quantitative data may include removing direct identifiers such as removing names or replacing them with codes, aggregating categories to reduce precision, for example, birth year versus date of birth, restricting upper or lower ranges of variables to hide outliers in the data. And generalizing the meaning of detailed text, for example, detailed areas of medical expertise could identify a doctor. So, expertise variable could be replaced by a general text such as one area of medical expertise. You can have more information. I have added a link at the bottom of the slide. So, you can have a detailed information on anonymizing quantitative data on a website. So, if there are instances when anonymization is impossible, specifically in qualitative research, sometimes researchers are faced with the situation that if they choose to anonymize data, they may lose valuable information. For example, if you anonymize the post codes in the UK, you could lose critical information that can be very informative. And in some instances, anonymization is not possible. So, either consider obtaining consent for sharing, non-anonymized data, or as a last resort, regulate or restrict user execs. So, next, I will go through each of these briefly. I'm sure you all are familiar with what informed consent is. However, when it comes to data sharing, then consent is used for two purposes. We are all familiar with the consent that is used for research participation and is considered as one of the founding principles of research ethics where it is sought before participation in any research activity. And for all participants, consent usually involves providing information regarding study purpose, risk, benefits, voluntary participation. However, as stated earlier, consent can be used as one of the legal basis of processing personal data under the UK GDPR. If a researcher collects, manages and shares personal data, then consent of the data subject can be used as a legal base to process this personal information. So, in a way, it can be used as a compliance with the data protection regulation. The consent can be gained in written on oral form. The format of the consent depends on the kind of research. However, it is important that whatever format is being used, written or verbal, it should be documented. You need to document how it has been gained, what information has been provided to the participants and what they have agreed to. So, consent form plays a vital role in data sharing. And it is very important that you design the consent form, keeping in mind these three important sections in the form if you plan to share data. The first section should be about taking part in this study that includes some basics such as participants have read and understood information about the project. They have been given the opportunity to ask questions. They understand that they can withdraw all those basic things that addresses taking part in this study. The second section is all about how the information that is being collected will be used. For example, how the data will be stored for how long, how the confidentiality will be maintained. And final section should be around providing information about future uses of the data such as publications, archiving data and so on. So, this final section in the consent form is really important if you are to share the data for future reviews by other researchers. Yeah, again, time for Mentimeter. I have added a link to our consent form on the last slide, the sample consent form, which you can have a look into your free time. So, any thoughts if you have encountered any challenges in obtaining consent? If you have not yet, could you research any challenges? Any thoughts on that? When using data already collected as part of the service, that's right. Yeah, for social media data, that's right. It's always impossible. Being too specific about future uses could close the door on uses which are valid here. You can have a look at our model consent form on the website. It has addressed all these issues in a very specific, clear statements. Yeah, use of data for purposes unknown at the time of consent. That's true sometimes in a research project where the purpose of the research evolves and we always advise either to go for a retrospective consent, which is not very feasible option, but I think it's a good idea to add a sentence in the consent form that the purpose of the research may evolve something. This sort of statement might be a team of participants who had a brain injury. Yeah, that's tricky. Yeah, that's another aspect that participants, they are not bothered reading the form or clans, sign it, saying they're happy and won't read. Doctors especially do this, they can be dismissive of consent. Yeah, so it's always a best practice to discuss with them verbally what they are signing up to, miscommunication, not being clear in your intentions of research. Yeah, that's right. So lots of issues related to consent challenges. I must say, yeah, it is challenging in the research, especially when it comes to explaining the purposes of research, not really understanding what they are signing. That's right. Yeah, individuals could be very tissue data if information required is too much and not concise. Yeah, I think a prior discussion is very helpful if you discuss verbally with the participants. If you have a chance to do so, it's not always possible to discuss with the participants. But whenever there is a chance that you can verbally discuss with them, explain to them, then it is useful as some researchers have mentioned earlier. Yeah, using lay language is very important. You should not use jargon of any sort. I will be running a detailed workshop next week on 24th of November on consent where we can discuss all of these issues. If you want to register for that, that would be useful for you. I will be discussing all these issues in detail next week on 24th. I have added a link on a last slide for that workshop. Thank you very much for your responses. So yeah, some of the challenges that researcher face to obtain consent may include participants' perception as well. Or if the sample comprises of children and vulnerable people, patients' poor awareness of their rights, failure to provide adequate information, absence of consideration of participants. As you have all mentioned, these are the things that a researcher can face in terms of consent, obtaining informed consent, time constraints, unclear language. So in terms of data sharing, if not communicated clearly, participants are skeptical as you say of confidentiality issues. So always try to think carefully and be open to discussions. That's the only way I think forward. As I mentioned earlier, the third option is to restrict access to the data that is available for future reuse. You can share just to remind you that we are discussing the strategies that we can implement to share data and this is the third one regulating access. So this could be used if it is not possible to anonymize data or to obtain consent to process personal information. In that case, you can restrict user access. For example, here at UK Data Service, we've facilitated three levels of access for data. Open access for data that contains no personal information and safeguarded access for data that contains no personal information, but the data owner considers a risk of disclosure resulting from linkage to other data. And it is available under end user license and users need to register to access their data. And users also need to agree to certain conditions such as not to disclose any identifying information. And controlled access is for the data that may be disclosive. And control data are only available to users who have been trained and accredited in their data users. Data use has been approved by the relevant data access committee. And access is through a virtual or physical secure environment. And access varies according to user type location, data access conditions and project type. So when you start a research project that involves collecting information from people, for example, through a survey interview or focus groups or through video recordings, then these questions can help you to comply with data protection legislation in practice. The first consideration should be whether the project needs to collect information that would be defined as personal data. If not, then do not collect it. If the research does not collect personal data, then the data protection legislation will not apply. If personal data are being collected, the researcher needs to identify who will be the data controller for the collection storage and handling of the data. And this is unlikely to be the researcher themselves and most instances will be the researchers university or institute. So if the research involves collaboration of different partners, it will be important to clarify whether they will be joint controllers of the data or data processors. And it will be crucial to ensure data sharing agreements are in place. And where necessary, a processor or controller agreement should be in place as well. So the data controller is the person who determines the purposes for which and the way in which personal data is processed by contrast data processor is anyone who processes personal data on behalf of the data controller. For example, if you deposit data with us, you are the controller and we are the processor. And an assessment will be made about the most appropriate processing ground to use for each research project. There are three grounds that appear most applicable for research. As I mentioned earlier, consent public task or estimate interest. So if you are using consent as the processing ground, it is crucial that this is distinguished from consent for other ethical and legal purposes. And that participants can withdraw their consent for processing personal data. Keep in mind that this is different from the right to withdraw from the research. If public task is used as the processing ground, you must ensure that your university or institute is classified as a public authority and that the research will be in the public interest. If you are using legitimate interest as a processing ground, legitimate interest assessment should be undertaken. And this will need to identify the legitimate interest being pursued, demonstrating that personal data processing is necessary due to this and this is being balanced with the rights and freedoms of the participants. So the information that needs communicating will be influenced by which processing ground is chosen. Broadly, participants should be informed about how any personal data collected about them will be used, stored, processed, transferred, who the data controller is, the legal ground and the purpose of the processing, any recipients of the personal data, the period of retention and their rights, so on. So now the second section is about what needs to be considered if you are planning to use secondary data sources. Most important issues are the rights inherent in secondary data. Two most relevant types of rights in the research context are copyright and database rights. You can have detailed information on our website on different rights inherent in research data in different scenarios. I have added a link at the bottom of the slides. I will be focusing on just the copyright due to the time constraints. So in the context of primary data, if you plan to share it for future reuse, you need to consider how you want your data to be reused by other researchers or students. So you can specify this by licensing the data to match the intended basis and various types of licenses for sharing data have been developed by data archives. As I showed you earlier, the licenses we at UK Data Service offers that were open safeguarded in control. So coming back to copyright, which is the most relevant issue that needs to be considered when you plan to share secondary data, copyright or IP right are assigned automatically to the creator or the researcher who owns the data. So when data are shared or archived, the original owner retains the right. Data archive cannot archive data unless all right holders are identified and give permission for their data to be shared. The in the UK copyright arises automatically once the work is created. So to enjoy copyright protection, the work must be original. That is to say it must be your own work, not copied from someone else. There is no copyright in ideas or facts, only in the way those ideas are expressed such as diagrams, tables. So I would like you to read this scenario and then go to the menti.com again using the same code to answer a question. So there is a scenario. How health issues around obesity are reported in the media in the last years. So can the researcher question is can the researcher use public data without preaching copyright? So majority of you have said yes, some no, not sure. Any more responses? Yeah, I would say that it there is no true or false answer. It could be yes. It could be no. It could be it depends or not sure because even the articles obtained are in the public domain. They are freely available newspapers and websites. They may be still under copyright because they there is a catch always a catch when using secondary information and secondary data that it is available for the personal use of a researcher. They can download it. They can use it for their own research. But when it comes to data sharing, you need to archive your data. You need to check with them whether you are allowed to share it with a third party or third organization. You are allowed to share it for future reuse. So sometimes the terms and conditions on the website of these different sources, they do say that you are free to modify. You are free to share it for with others. So then that's fine. You can share it. But sometimes in the terms and conditions sections, they have mentioned that you are not allowed to share it. You are allowed to use it for your personal use, but not allowed to share it with others. So you need to check that. So if you are using secondary sources, then best practice is to assess who the copyright holder of the data set is. Are you allowed to use them and in what way? Are you allowed to archive and publish them in our data repository? These are very important questions when it comes to data sharing. So most of the time we encounter problems here at UK Data Service when researchers come with the data and they are allowed to use their data as the data is under open license or they use, they register to use it. But what they do not realize is that it is accessible to them for their use, but they need permission from the data owner for sharing it or archiving it. So you may need to seek for further permission to distribute material you do not own. Because if permission is not granted, you need to remove copyright variables before publishing or sharing it. So I have just added another scenario from a real research that researcher has come to us to deposit the data. He has used secondary data sources for a research project and he plans to share this data. The sources he used were World Bank Open Data and Microsoft Academics. So I have taken a screenshot of the terms and condition of the World Bank Open Data to show you that here it is mentioned that you can see that you may, in the second line, you may extract, download, make copies of the data contained in the dataset and you may share that data with third parties according to these terms. So that's fine. You can share the data he has used from World Bank Open Data for share with us, archive the data with us for future reuse. However, when we check the terms and conditions of the Microsoft Academics they mentioned that you may not modify, copy, distribute, transmit, display, reproduce, publish, license, create derivative works from the data obtained from Microsoft Academics. So you see that you need to always check the terms and conditions before you plan to when use the data. So I have added a link on this slide to a variable information log template for secondary data users, which is very useful. It is available on our website and you can see that you can use this log to put all the information related to the secondary data you have used. It will be useful for you to find out the license information and all these columns and it is useful for future researchers as well if they use your data. So I think the last question is would you like to share what do you usually struggle with in the context of research ethics and legal compliance? If you would like to go to Mentimeter, that's the last I think question. There are a few more quick questions as a quiz, but the detailed question, this is the last one. Legal, understanding compliance in different jurisdictions. That's right. It is complicated. Complexity and scope. Social media data, it's always I think the hardest one. Data sharing for commercial purpose. Our students use the University OneDrive to store data. OneDrive always downloads wine onto the hard drive when opening a pipe. Is this a problem? I think it depends on the institutional policies. Lack of licensing information. Understanding geographic restrictions of access to data. This is complicated. There are local laws everywhere. Understanding weakness and guidance. Time it takes to get through epic committees. That's right. Yeah, anonymous versus pseudo-anonymous data. Anonymization, yeah. Sharing data with a translator. That's right. That's another issue. Cloud-based storage, yeah. So many I think challenges that researchers have to struggle with in the context of research ethics and legal compliance. That's right. I think the best way is to just to check with your ethics boards, data protection officers at your organizations. They are the best to advise research in the two European countries. My university is in the UK. So for you, I think you need to consider EU GDPR as well as the Data Protection Act and the UK GDPR. So three of them for you to take into account free data protection legislation. Yeah, thank you. So you see, there are so, so many issues in this context and it's not one to go through. So it's not easy, but at least to be legally compliant, I would say always investigate early which laws applies to your data, including cross-country collaborative working. Do not collect or keep personal or sensitive data if not essential to your research. I think that's the best advice. Plan early on. Seek advice from your research office. Ensure that you check participants know how this data will be used and remember not all research data are personal. For example, anonymous data is not personal. So always be open to discussions. So here I have added some of the very useful resources for you to have a look in your own time and the slides of the sessions are available on our past event page and on our website where you can check these links. These are some of the upcoming training events. As I said, there is a link to the consent workshop I'm running next week. Then there are past training events link where you can have, I think I did run a detailed workshop on copyright issues in publishing and copyright issues in secondary data used. So you can have those slides in here and the recording of those events. I have added a YouTube link. There is an event run by my colleague on anonymization. So it's a very useful resource. So just before the Q&A, some quick questions on the Mentimeter, if you would like to answer just very quick questions, just a recap of the content that we have gone through. I know it's a lot to take in. Very complicated. You can always email me with any specific questions, any project related specific questions. Feel free to email me. So the first question is ethical obligations should be considered throughout the research. Is it true? I think that's obvious with so much knowledgeable audience. That's right. Should be considered throughout the research all the way from planning the research stage until to the future uses of the data. So if the researcher based in the UK collects data outside the UK, UK GDPR does not apply. That's right. Yeah, I think it's false because if you are a UK based researcher, UK GDPR does apply to you. However, if you are not collecting personal data, then it doesn't matter. So it's something similar. Yeah, so that's again, I think true. If we are speaking in terms of personal data that UK based researchers, if they collect data from EU, then both the UK GDPR and the GDPR, they both apply. So is it true or false? Data protection legislation still applies if there is no personal information in the data. As I mentioned earlier that personal information or personal data is the data that discloses someone's identity through direct or indirect identifiers. So if personal information is not there, data protection legislation will not apply. Is it true or false? I think I should add it depends as well here. Yeah, I think so. It can be shared as I said that if you employ certain strategies such as anonymization or obtain consent or regulate access, it can be shared. But I think there should be a third bar as well. It should be it depends. So yeah, it can be shared by employing relevant strategies. So information in the public domain means that researchers can freely use that data. Is it true or false? Yeah, it could be true that it depends on the purpose. If you want to use that data for your own personal use and it is available, you can use it for your own personal use. But when it comes to data sharing, then you do need to check the terms and conditions, what they allow and what they don't. So there isn't any true or false. So this is related to the, these statements are usually put in the consent form. So it is in the context of consent form. Is it appropriate? Any information I will, I give will be used for research only and will not be used for any other purpose. Yeah, so such statements, if you plan to share your research data, then this sort of statements preclude data sharing. So you all, you need to avoid such statements if you are planning to share your data for future use. Otherwise, if it is just for your own research project and you do not plan to share it, then that's fine. So somewhat and no can be okay. That's another statement that we every now and then we see in the consent forms. Yeah, it's the same as previous. You can say somewhat if you do not plan to share personal data, then in that case it is fine. So always be specific that sometimes researchers, they just separate the personal data from the other data. They keep it with them. So you always need to be specific that this statement is for personal data or the old data, overall data. So be specific. I think yeah, that's it. Thank you. Thank you very much for your responses. That's my email address. You can send me any specific project related queries that you may have.