Ydw i ddweud â byddwn yn hynny o glifennu gweithio, yn ond mor hwn yn gwahanol o gael ddataeth i'r ymlaen i fynd i siaradau. Mae'n ddechrau bod yn gwahanol i'r bydd fferm hwn i'i gael ddataeth i fynd i fynd i'n gwahanol i fynd i gyntafedd'r ystod o gael ddaeth gwahanol ar lleio ac mae'n gwaith i'n gael ddaeth ymlaen i fynd i fynd i'r gael o gael ddaeth awr편au ar weithloed. Inywg y romance ameddaeth argy другой litigation cl людей. Yn falch mewn pawan y tro checko llarwyr gwneud ohell cysylltau i fi fathę fath unig i greadon ar gyfrannu lle gall llyfiadau a chymifiad yr hyn sydd gennym y pamrleme di Alebuys i gymwy uncertainiaeth. F weakestwyr tu Dim yw hir a'i divide post gwybrae y Monster기ol. while reducing different forms of research in various different countries. Then, in 2014, we came together and the International Survey, which has since been published. Unless we followed up in 2018, repeating many of the same questions, but also streamlining and adapting the survey to bring it more up to date, crucially, we're able to make a number of comparisons between 2014 and 2018. Ac that's what I'd like to present today. This has unpublished works so far and so we're still working on much of the interpretation of it and I'll be very interested in your comments. What we focused on in this research then was trying to work out where libraries were in terms of the development of policy and the development of services. We looked in particular at the development of partnerships and collaborations ac ychydigau chi'n gwerin wrth y rhwng yn arddangos i'w gwleidwyr gweithio wirthio yma. Rydym i ddim yn cydeithasio aronds y rhwng hynny o'r rhwng gyda rhywunau fannogforfa ar rhwng ac yw'r rhwng yn gallu gwirioneddau ar y rhwng, ac arddangos i gael geithio eu gwirioneddau hynny, ac yn gweithio'n gwirioneddau. Ty, implemented today is focus on the twenty eighteen date. But dwarf comparisons what we saw in 2014. And hopefully this will give us an insight into the maturity as it as it develops. So I'm going to present some of the data on the current situation. And then I'm going hand over to Andrew, who's going to look at skills an and also some of the interpretation of the data. Rwy'n bynnig i gael gwlad y gael eich gweld, ond am ysgrifennu'r slide ar gyfer, yn defnyddio'r gweithio i'r gweithio. Mae'r reilio'n 209 odd o'r gyffredinol yma, yn y gweithio'r cyffredinol bryd yn ymgyrchu. Rydym yn fwy o'r canogau, o'r Australia, ym Ymgyrchu, ym New Zealandau, a'r gweithio'r gweithio'r cyffredinol bryd yn y UK, a'r gweithio'n gweithio'r gweithio'r gweithio'r gweithio'r gweithio'r. Mae'n judgment iawn o ein slogan arall flyny. Felly, Pikr wyddog iso, ac mae'n ll provision hefyd yn'n cyflugwyd — we need to bear that in mind, particularly when we doing country specific analysis. For you is to give you the heads-up on the main findings of the survey, first of all, we saw clearly that libraries are moving forward with regard to research data management. We see progress in more or less every area o'r prydau ymlaen, oherwydd, o'r cyfnodau cyfnodau a'r cyfnodau. Y perseftyd o'r cyfnodau, y dyfodol yn y pryd yn rhan o'r cyfnodau yn y rhan o'r cyfrifodau mewn yrhaith o'r cyfrifodau yw 2014 a 2018, oedd yn gwneud hynny'n gweithio. Ond dyna'r llyfr yn ymlaen i'r llyfr yn y llyfr o'r cyfrifodau o'r cyfrifodau ar gyfer y ddechrau'r cyfrifodau. Ond rydw i'r llyfr yn ymlaen i'r cyfrifodau yma o hynny, ac mae'n gweld i'r sgwdeithas oedd yng Nghymru oedd ymdraethau ar gyfer rhywleid. Mae'r ffordd o'r lluniau wedi'u gweld i'r sgwdeithas fel yw'r cyfle, ac mae'r gweithio ymddir iawn o'r gweithio, mae'r gweithio ffyrdd o'r lluniau yn ôl sydd ymddir iawn, a'r gweithio'r gweithio'r gweithio i'r gweithio, ac mae'r wneud o'r gweithio'r gweithio'r gweithio, something or very few sanctions if you don't comply. So the lack of resources, but also still a lack of staff engagement, a lack of academic staff engagement as seen as the most crucial barriers. So that's the headlines. Now let's go through some of the data. We asked in the previous survey and in this survey what the position was as far as policy development is concerned. What we see at least is in some countries, felly Oustralia a Leuciaen yna sydd yn cael eu cyfnod yn ei wneud yn ymgyrchol, ac rydyn ni'n ei ddweud y progres yma ar ddod y 2014 ac 28. Ond yw'r cyfnod yma, mae'r olygu yn cymdeithasol, ond yn Cynedr yn New Zealand, mae'n gweithio fydd yna bod y cyfnod o'r cyfnod o'r cyfrifysgwch yn cymdeithasol, nid o'r cyfeithio'r cyfeithio'r cyfrifysgwch. Nid yw'n gweithio'n gweithio'n gweithio'r cyfrifysgwch, Ac oes yn ei bwysig y bydd yma'r cyflawn yn ei ddechrau o'r gwrs ac mae'n cyhoeddol yn ffodol. Mae yma'n ogylchedd ar hyn o'r rhan o'r cyflawn yn y lladd. Mae'n cael ei ddigwyddio'n gweinol. Cymdraeth o'r 2014 a 2018 o'r ddweud o gweithio'r cyflawn. Mae glyfan yn all y bwysig, mae'n gweithio penderfynu, yn dweud pan yn merch sydd yn ei gweithiau. Tym ni'n gyfleoedd â'r gweithio'n cyflwytoedd yn gweithio sydd yn bwysig yn cael bynnag. Mae'n gweithio'n wahanol ar amlwg ydw i'w sgwrdd, oedd aeth y mae'n 26 oes, ond rwy'n rhywunon nhwlad yn ei gweithio. Mae'n gweithio'n gweithio'n gwybod yn rhan o ddechrau Bwysig Ardgnadau Iedwyr yn unrhyw ddatblygu. Ond byddai'r cyffredin pan wedi'r cyffredin, wrth gwrs, y cyfnodol hyd o siwyddd yn ddeigasio o'r senghoru yna, a llwyddoedd y pryd yn cael eu amser o'r senghoru i'r ddeigasio ar y sefyd. Y reporter o'r senghoru ar 5 o'r senghoru, y gallwn gwneud fy ysgawr yma, yma'r nadod ffordd oherwydd mae gyntaf 24 a 28. Y pryd yn éu gweld yn ei ddefnyddio i'r senghoru, ac olau hynny yn dech gyda fy mhaskydd cyfleoedd, mae'n dweud yn i-gwm, ac mae'n arwain gwnaeth y pryd yn eu ddevnogol, unhealthy but it's marginal, so that's quite interesting the libraries are still seeing the agenda as essentially same thing even though they are making progress in various areas. As far as collaborations are concerned an interesting position here, nearly half libraries are collaborating with external organisations in order to deliver their research data management activities but we see collaborations increasing in the UK But decreasing in Australia, and we think in Australia that's probably to do with a closure of hands. But it's interesting to try and think of explanations. Maybe more consortial activity, maybe just activity in this area which is maybe some outsourcing of services, which is prompting people to say that they are partnering with external organisations. Organisational structure is interesting, but one of the things that are interesting is Onw, mae llawer i gynhyrch, neu rhaid i'n dda'r wyrei, mae rhai unigrwyntiau yn ffwrdd mlwyd, a wneud i'r cerdwsau cymdeithasol ar y Llywodraeth Rhywodraethau diolch. A wneud i'n gweithio ffosibl yn ymdweithio'n gweithio i chi, gyda'r cyfriffeith gweithio cymdeithasol ymleidio'r rhaid i'r diversell yn fathau'r gweithio'r fathau'r gweithio'r gweithio. a ddylch ei ffordd yn ddwyd i'r dweudio ar gyfan y cyfnodd arredig rydyn ni i fel eu cyfnodd. Mae ydynt eu bod ydynt wedi bod yn bwysig yn adeiladol i ddim yn y cyfnodd y cyfnodd arfer ar y ddweudio ar y ddweudio ar ddod y cyfan. Maen nhw'n gwybod am fynd i gael cofwyrwch ar ei fod rhai cerdd. Mae'r rhai cyfnod ac mae'r cerdd fyrdd yn oedd y cyd-serfus ym mwyaf. Mae'r cerdd fyddig iawn yn dod yn ddefnyddio'i gyd-wch i ddechrau cyfosifol, ond mae'n gweinio ar dweud yma yn dyfyrdd ac mae'r cerdd yn oed yn ddefnyddio'i gwasanaeth. Maen nhw'n gweinio'r cyfosifol o'r cyd-serfus ym mwyaf am ddydd yma, more developed services. That came across relatively clearly. Although there are still a lot of variation in the data. As far as skills are concerned, we see skills gaps in pretty much every area we worked, we asked about, with significant areas, like for instance data curation skills, and these haven't changed all that much between 2014 2018. Mae'r cyfnodau ei wneud gynllun o'r eu lleol iawn i'r ddweud gafodol. Maen nhw, grefau'r ddweud rydw i, at oes gafodol ar y ddweud gafodol ar gyfer y gyrraffon lleol yn gwneud lleol iawn i gweithiau'r ddweud. Y cael drwy'r hyn yn llawer, mae'n ddiwedd ag gilydd frym wedi cael ei gallu ar y dylai'r gyfer yw'r llunau efoareddau'r hyn yn anhygoel cael eu gwaith, mae'n gweithio i gael yr adeg ymlaen, mae'r ddweud yma eich ysgrifennu lleol. A yna'r ddweud yn bobl. Felly mae'n gweithio. Rwy'n gweithio i'r gweithio, yna'r ddweud ymlaen o'r ysgrifennu lleol y Llyfrwr yn gweithio. Felly, sy'n gweithio i'r adeg yma, ymmygu'n bwysig. Everyone is trying to find a role for the future, they are adapting. They are transforming very intentionally and we thought that was quite an interesting insight provided by the data. The challenges though, as I said, focus on resources, skills and academic engagement. And that came across very clearly. Although I said at the beginning that the role of the library in delivering research data services is now becoming more widely accepted and embedded. There are still significant challenges in this area, and in many institutions it is still not a done deal, at least in terms of expanding that role. And so libraries we're very conscious are still arguing the case and still negotiating over that territory in relation to RDS. So that's a presentation of some of the data. I'm now going to hand over to Andrew, who's going to present some of our interpretation. OK, so this goes maybe slightly beyond the data in that. I've always thought that there's quite a complex agenda here of talking to researchers about the need to manage data effectively and the benefits to them personally. Then there's a more demanding thing that they might share data, even more demanding that they might make their data open. And the whole open science and open scholarship agenda, even pushing things further. So there's quite a complicated agenda there that is being pushed by various forces. I put funder compliance in much larger arrow than perhaps the data we've got suggests. But I think there's lots of forces going on pushing the agenda and again lots of barriers as well acting against the progression of this agenda. To go back to something more founded in the data, though, I think there could be said to be three paradoxes around RDM that remain true. One is that we know the researchers need to manage the data more effectively. But actually the demand for a research data service is still quite weak. So there's need but not demand. That's one of the paradoxes. The second paradox is that we know the main driver is compliance with what the funders say we should be doing. But actually is the much checking of compliance and is the much funding to support compliance. So there's still a paradox there too. And the third paradox is that libraries want to be involved. We've seen from the previous slides is all about what libraries say they're doing about research data management. They want to be involved and it's part of, partly justified from reinventing what the library is. But there's still a strong sense of a lack of skills, a lack of capability, a lack of credibility with academics and also a resource gap. So I think there's still remain, maybe those paradoxes have been around for quite a long time. But I think there's still remain. In the earlier work we did, we developed a kind of maturity model. I personally recoil quite a lot from that idea of saying there's a maturity model because it implies one size fits all. Everybody's on the same development path. And actually one of the big common understandings of the community when research data management was emerging as a gender seemed to be, it's not going to be the same for everybody. So I've always been a little bit against the idea of maturity model. But actually our data does kind of suggest that. So for example, if you compare the Russell group institutions data with that of the non Russell group institutions in the UK, and I know that's underestimating the difference potentially. The only real difference is that there's more saturation in the kind of very common research data services, not that more advanced services are being developed by the more research intensive institutions. So maybe a maturity model does work, and maybe it is quite a common across the sector for whatever reason. So this is our revised maturity model. So what it basically says is well at level zero we're just really thinking about research data management. We might be doing audits and surveys to work out what data have we got, what are researchers attitudes. Then level one we're labelling compliance at the moment. So it's about the obvious services you could easily supply like training in research data management advisory services. You've probably got a formal policy in place. And then maybe specific individuals are given the RDM role or there's dispersed responsibility across many teams. And it's more about translating on the right hand side, translating existing skills. So that's a kind of compliance level. I think we're more tending towards to get to this kind of stewardship level we've labelled it, where you've got a data repository, you've got some support of technical services, you've got a dedicated RDM team to some extent and you've gone to the extent of re-skilling existing staff. So that's maybe the stewardship level that many institutions in the UK are at. And then this transformation, so our paper is all about is RDM transformational for libraries. So this transformation level where you might be doing things like data analysis and data visualisation, doing more things around data integrity and reproducibility, where I think it's pushing libraries to have very different types of skills than they've had in the past and maybe things like embedded roles. That we do not see and maybe that's on our path that we're going to reach and maybe it's not something we really going to aspire to because kind of my analysis of our analysis of the determinants of what services have emerged seem to be driven by three things really. One is, are we doing something pretty similar already? Have we done it for years? How can we just translate existing skills to this new context? That seems to be often why we're delivering a particular service. Does it require not much resource? And there's a few cases and particularly data management planning where I feel that's beyond what libraries have typically done but there's immediate demand evidence. Being general, what seems to be driving the service that people are supplying is doing familiar things in a slightly different context, things that don't demand too much resources and a few real changes where there's a very strong sense of immediate demand. So I think our underlying question was always going to be, it was always has research data management transformed academic libraries in terms of our skillset? Have we really acquired a whole new skillset? Have we changed fundamentally organisationally? Has our notion of what a library is and a collection is? Has that really changed? And the answer seems to be in my opinion, maybe this is just my opinion, it's no, we haven't transformed. There is the potential alongside things like text and data mining, changes in other aspects of scholarship, scientific metrics, all the scholarly communications area, maybe there's a potential for transformation but I think RDM on its own hasn't really transformed academic libraries.


Hi, Dr. Jarrod, how would you treat this case at your office? This patient is a routine patient that didn't show up for two years due to COVID. Tooth number 19 had previously been treated with indirect pulp therapy and incomplete caries removal. Here you can see the bite-wing ratef and a periapical. The tooth tested positive to cold and sensibility testing. What are your thoughts?


Rassan Naikhande, working as an assistant professor in Department of Mechanical Engineering at Walsh Institute of Technology, Solapur. Now, today we are going to see regarding the introduction to the Kelvin Planck statement. Means, we will see what is Kelvin Planck statement. Kelvin Planck is the scientist name who has described this second statement which comes under the second law of thermodynamics. Thus one other statement is there which is a Clausius statement that is related to the refrigeration effect whereas this Kelvin Planck statement is related to the IC engine. Now, at the end of this session you will be able to describe regarding the Kelvin Planck statement what is the meaning of that where it is used, for which application it is used all those things you will be able to understand, you will be able to describe. Now, Kelvin Planck statement it is also called as a heat engine statement of second law of thermodynamics. Just now I had told you that for thermodynamics second law of second law there are two statements. One is by Kelvin Planck statement which is widely used for the heat engine. Second that is the heat engine is device that consumes heat energy to produce the desire work. So, to produce the mechanical energy to produce the mechanical work we are going to consume the heat energy and that is under the Kelvin Planck statement which we are going to see in the short while. Now, heat engine cannot have the thermal efficiency and as you know in the universe there is no engine which has got the 100 percent efficiency and the statement is like this. It is impossible to construct a device operating in the cycle it is impossible to construct a device operating in a cycle which produces no other effect than the extraction of the heat energy from the single thermal reservoir and delivers an equivalent amount of work. So, this is impossible to construct this type of the engine is impossible to construct which takes the energy from one reservoir and converts that all amount of the energy into the useful work. So, that statement is the second law of statement second law of thermodynamics statement given by the Kelvin Planck according to that it is impossible means what? It is possible to construct the engine with some amount of the heat rejection. So, that is the meaning of that now with this figure it is better it can be better understood. Now, Kelvin Planck statement according to that we have seen that it is impossible to construct the engine. Suppose this circular portion is indicating the engine which takes the heat from the reservoir which is at high temperature or which is supplying some heat energy and all that amount of the heat energy gets converted into useful work. So, this is a hypothetical engine this is a hypothetical cycle which is impossible it is impossible to construct the engine which converts all the amount of the heat energy into all the amount of the work means here we have the 100 percent efficiency because there is no heat rejection at all all the heat is getting converted into useful work. So, this is ideal condition which is not possible to have in the existence this is rather we can call this as a PMM 2 perpetual motion machine of second kind which is impossible to construct because all the amount of the heat is getting converted into useful work means 100 percent efficiency is there and PMM 1 engine is an engine which converts all the heat amount of the engine energy into the useful work means it has got the 100 percent efficiency which is impossible to construct. So, this Kelvin Planck statement is useful in that way here in the reality what is happening it is stated by the Kelvin Planck we can generate the mechanical energy by addition of the heat energy from some thermal reservoir. So, thermal reservoir which is at say temperature T 1 which is at high temperature and reservoir means what is that is known to you reservoir is the unit which supplies the heat energy to the some system without drop in the temperature the temperature remains the same as it is and it is same as that of the sea. If you take certain amount of the water from the sea whether there will be any change in the sea it does not have any change similar to that the reservoir is the name given to this source which remains at temperature T 1 in spite of supplying the heat energy to this engine. So, how much is the quantity of heat supplied over here that is Q 1 and that is from thermal reservoir which is at T 1 temperature. Now, what happens over here this heat energy supplied that is Q 1 will get converted into useful amount of the work. So, how much is the heat energy absorbed for this purpose that is Q 1 minus Q 2 means the maximum amount can be converted to obtain the useful work, but certain amount of the heat energy must be rejected to the sink portions must be rejected to some say area where the rejection will be in the atmosphere in the actual engine in actual actual atom well what happens the heat supplied will be there useful work will be obtained from the engine and that burnt gases are expelled in the atmosphere. So, that is nothing, but the working substance which is taking the heat energy from the through that say burnt gas burnt gas possess certain amount of the heat energy and those burnt gases are exhausted in the atmosphere. So, here we can call that as a sink. So, atmosphere is again one sort of the reservoir if you expel any amount of the heat energy into that from the engine it does not have any effect on the temperature. So, temperature of the atmosphere at that instant remains same there will not be any rise of the temperature because of say expelling heat into into the atmosphere. So, this act as a reservoir which is at temperature T 2 means what is the understanding from this Kelvin Planck's statement here the total amount of the heat energy cannot be converted into useful work certain amount of the heat rejection should be there. So, here q 1 is q supplied q 2 is heat rejection and the effective work obtained from the engine is W yes have you understood this concept regarding means here this is not possible to existence to to manufacture whereas, this is possible to have the manufacturing which were usually we are manufacturing for so many applications diesel engine is there petrol engine is there gas engine is there CNG is there. So, all those are working on the principle principle of the Kelvin Planck's statement which is coming under the second law of thermodynamics. So, I think there is no difficulty all of you understood this yes nice thank you for this purpose the reference book I have used is thermal engineering by Danpat Rai and company which is of Domkundar Domkundwar and one more author is Kotharnar Raman thank you thank you very much.


IAC 2022 has been happening in Paris and boy do we have some exciting news from there. A new reusable crew vehicle for example. We'll be covering that and a whole lot more in Monday's Tomorrow Space News. Before we head to Paris though, let's head to South Texas and catch up on what's been happening with Starship over the last seven days. First up is a continuation from last week's update with the top section of the cancrasher being rolled back to the production site. This week it's the bottom half of the structural test stand which has itself rolled back to the production site. Star Factory is nearing completion but look, there's something inside. This looks like a ring section but it is inside the factory so it's quite hard to verify. Booster 7 has spin primed again with what looks like seven engines. Hopefully another static fire is coming soon but without official word from Elon it is hard to tell if static fires were planned and not reached or if they just weren't planned at all. Alongside Booster 8 in High Bay 2, one of Booster 9's tank sections has been stacked in the middle of the night. Ship 25 is getting closer to completion with the nose cone section being lifted and stacked on the aft section of the vehicle. Will we be seeing this vehicle testing soon? Let me know what you think in the comments, bearing in mind that we're still waiting for its older sibling to finish its campaign. The following day, the sections were welded together. If this vehicle flies, also expect the gap to be patched with thermal tiles. The next ship in the family, number 26, has had its bare nose cone spotted outside of tent number 3. It does have the attachment points for the Starbrick thermal tiles however. It was seen moving around the production site later the same day, eventually ending up basically where it started. Very rarely do we cover raptor explosions in McGregor, they're just quite common but this latest one has intrigued me. A raptor on one of the horizontal test stands was seen with a new blast shield which would help protect it if a neighbouring engine decided it no longer wanted to serve SpaceX and blew up. This particular engine was tested to destruction on the tripod test stand as seen by NSF's McGregor live broadcast which you should 100% check out. It is epic. As well as IAC, which we'll get onto in a moment, world satellite business week was happening last week with many of those in the industry coming along to say hi. SpaceX's Tom Ochinero however said a little bit more updating SpaceX's launch goals to a little over 60 for this year and 100 in 2023 which includes six Falcon Heavy launches over the next 12 months. I should be at one of those Falcon Heavy launches, nudge nudge wink wink, link in the description. It's hot off the press news coming to you now from Paris which is hosting the 73rd International Astronautical Congress. Day one on Sunday brought us a big update. Ariane Space has been working on a reusable crew rated vehicle for use with the new Ariane 6. The ship is called Susie, the smart upper stage for innovative exploration and it looks as if it's a mashup of starship capability and a small vehicle form factor. It's 12 meters long and 5 meters wide and it has the ability to carry 7 plus metric tons to orbit, 5 crew and or cargo in the 40 meters cubed payload bay and it can take that back to Earth as well. Susie therefore is more of a competitor with Dragon which is 4 meters wide and 8.1 meters tall with the trunk attached but unlike Susie the cargo dedicated section cannot be returned to Earth. That's when the similarities with starship start to kick in as like starship it can carry crew and cargo to and from space. Susie's dimensions most closely resemble the legendary Mcdonald Douglas DCX however being the same height just 90 centimeters wider and a similar pyramid-ish shape. Unlike starship Susie is being designed from the beginning with an abort possible at every phase of flight from launch to landing. The last time we heard about an abort system for Milo Musk he was just hoping that the thrust to weight ratio of the crewed starship would be good enough for an abort. If what I'm working out from the press release video is true then Susie just like starship it's going to be belly flopping falling towards the ground horizontally before igniting its engines and flipping to the vertical position before smoothly touching down. I'm also digging the space shuttle style paint scheme with what appears to be thermal tiles and the crew access arm which is similar to SpaceX's on LC39A. Susie is being designed with compatibility in mind as not only will it be able to fly on the Ariane 6-4 the variant with four solid rocket boosters it'll also be able to fly on Ariane space's next generation of launch vehicle. Ariane space has also not been short on the list of tasks that Susie will be capable of with them listing inspecting upgrading and towing satellites resupplying space stations crew changeovers at space stations human on-orbit activities assistance with the on-orbit construction of large infrastructure and it'll be able to catch and deorbit satellites nearing the end of their lives. The future of Susie isn't restricted to low earth orbit though with it being able to carry out long duration missions with lunar orbit being pointed out in the press release with a quote space transfer vehicle. If Susie goes ahead and it doesn't get halted at the drawing board the future of European space flight seems very bright because this epic new vehicle is something that is extremely exciting for a human flight capability lacking space agency. And quickly before we take a look at the week's launches let's talk once again about the big orange rocket. As a reminder this Wednesday the 21st of September is the current date for the critical tanking test to find out whether or not the repairs to the quick disconnect at the bottom of the rocket have worked. Assuming that they've worked and there are no problems with leaky hydrogen SLS could be go for launch as soon as the 27th which is just over a week away. However NASA are still waiting on the space forces they need a waiver on the flight termination system batteries. That means that even if the test is successful SLS may still need to roll back to the VAB jeopardising the September attempts. Now I want to see SLS fly as soon as possible but I also note that on October window would be a bit more convenient for a lot of people. Hopefully by the end of Wednesday we'll have the answer. September 13th at 13 and 18 UTC saw the launch of this Long March 7A with the 5 tonne ChinaSat-1E on board. It commenced from LC201 at the Wenchang satellite launch centre in China heading for a geostationary orbit. Officially the satellite is branded as a communications and broadcasting satellite although there are speculations that the Chinese military could be a bit more involved. Heading down to New Zealand Rocket Lab reached 30 launches of Electron on September 15th at 2038 UTC from Launch Complex 1B on the Mahia Peninsula. Inside the payload fairing was just one of its respective Strix satellites specifically Strix-1 weighing in at about 100 kilos. This satellite was delivered to its expected 563km sun synchronous orbit with a 97 degree inclination. This flight brought their total number of launches for 2022 up to 7 matching their previous record in 2020 however due to the failure of the PICS or it didn't happen mission 2022 is now their most successful year on record with no failures out of their 7 flights. After scrubbing for weather more times than I care to remember Stunning Group 4 Mission 34 finally launched from Slick 40 at 0105 UTC on September 19th. All 54 satellites were successfully delivered to their initial 336 by 232km 53.22 degree low Earth orbit and they'll be raising themselves up into Shell 4 over the coming months ending up at about 540km. The booster serving this mission B1067 touched down on the drone ship just read the instructions with support ship Doug scooping up the fairing halves. What have we got coming up over the next few days? First up is Soyuz 2.1A with MS-22 heading to the International Space Station on Wednesday and our final ever flight of a Delta IV Heavy from Vandenberg on Saturday with NRL 91 on board. Thank you to the citizens of tomorrow who helped to support the channel financially. If you like what you see and you want more exclusive access such as seeing scripts as they're being written and access to our member only live show hang out then make sure to head to join.tmro.tv or the join button below to become one of the ground support sub-orbital orbital escape velocity or plan pro plus citizens today. Don't go anywhere the week's not over just yet. Wednesday we'll see space weather return with Dr. Tarnusko, Friday there may be a live show and next Monday I'll be back with the latest and greatest from the aerospace industry. But for now I hope we'll see you on Wednesday. Thanks for watching and goodbye.


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.