# Open data: the benefits and challenges of sharing a precious resource

An episode of [Physics World Weekly Podcast](https://invisiblenetwork.ai/podcast/1400686733), 28 May 2026, 26 min.

Audio: https://media.blubrry.com/physicsworldweeklypodcast/content.blubrry.com/physicsworldweeklypodcast/PWWeekly28May2026OpenData.mp3

Laura Feetham-Walker of IOP Publishing is our podcast guest

## Transcript

[0:07] - Hello, and welcome to the Physics World weekly
[0:10] - podcast. I'm Hamish Johnston.
[0:13] - Data are at the core of science,
[0:16] - but traditional journal articles
[0:18] - normally deliver a distillation
[0:21] - of the raw data
[0:22] - gathered by authors.
[0:24] - While the movement towards open access to data
[0:28] - is widely supported by researchers and funding agencies,
[0:32] - a 2024
[0:34] - study by IOP Publishing
[0:36] - revealed that many scientists
[0:39] - still encounter a wide range of practical, ethical,
[0:43] - and technical
[0:44] - barriers when it comes to sharing their data.
[0:47] - As a result, I OPP has launched a
[0:50] - free online course that aims to give early
[0:54] - career researchers
[0:55] - the practical skills and confidence
[0:58] - that they need to share and manage research
[1:01] - data effectively.
[1:03] - To talk about the course and IOPP's
[1:06] - open data policy,
[1:08] - I'm joined by Laura Fedom Walker, who is
[1:12] - head of publishing strategy and performance
[1:15] - at IOPP.
[1:17] - And in the interest of openness,
[1:19] - IOPP
[1:20] - publishes Physics World, and therefore, Laura is a
[1:24] - colleague of mine.
[1:33] - Hi, Laura. Welcome to the podcast.
[1:35] - Hi, Hamish. Thanks very much for having me
[1:37] - on to talk about open data. I think
[1:40] - this is a much more interesting topic than
[1:42] - maybe initially sounds, and I'd like to try
[1:44] - and prove that today.
[1:46] - Well, I I definitely agree,
[1:48] - Laura. I I mean, I think that this
[1:49] - is something that means a lot to many
[1:52] - researchers,
[1:53] - and there's lots of different opinions on it.
[1:55] - So yeah. Definitely. So
[1:57] - can we get started,
[1:59] - with a definition
[2:01] - of open data in the context of
[2:05] - scholarly scientific publishing?
[2:07] - Sure.
[2:08] - I can definitely give a definition, and I
[2:10] - will give a definition.
[2:11] - But actually, I just wanna start by talking
[2:13] - about the kind of broader context and the
[2:15] - history of open data a little bit.
[2:18] - So
[2:19] - essentially, in the predigital era, scientists around the
[2:22] - world worked very hard on collecting data and
[2:25] - doing their experiments.
[2:27] - And the output of that research would often
[2:29] - be reams and reams of handwritten
[2:31] - or or typed up notes.
[2:34] - And through sheer necessity, if they were going
[2:36] - to publish and disseminate and communicate this research,
[2:40] - you know, journal editors would say, you have
[2:42] - eight pages, and that that's
[2:45] - the absolute limit.
[2:46] - And so
[2:48] - researchers would have to really carefully cherry pick
[2:52] - which of that data they disseminated
[2:55] - and presented.
[2:56] - And through that,
[2:59] - the research article was born, which is still
[3:01] - the same, you know, kind of research output
[3:03] - that we see today. I mean, the general
[3:06] - structure is
[3:07] - an abstract background, methods, results, discussion, and some
[3:11] - references.
[3:14] - Even in the predigital era, there were some
[3:17] - voices who were kind of talking about the
[3:18] - importance of being more transparent and sharing more
[3:21] - of the data from experiments.
[3:24] - One prominent example was Robert King Merton, who
[3:27] - back in 1942
[3:29] - was calling for what we now call open
[3:31] - data.
[3:33] - But,
[3:34] - obviously, in the nineteen nineties, the Internet came
[3:36] - along, and suddenly there was this capacity here
[3:39] - for scientists to share everything if they wanted
[3:41] - to. And still, it was quite slow in
[3:45] - getting started. There wasn't some immediate change where
[3:47] - scientists started sharing all of their data.
[3:50] - And that's because a culture had built up
[3:52] - around
[3:53] - cherry picking data, maybe
[3:55] - being quite proprietary around datasets
[3:57] - and
[3:58] - trying to get multiple
[4:00] - publications out of each dataset,
[4:03] - and generally people being quite protective.
[4:06] - And so,
[4:08] - I don't know if you've ever heard the
[4:09] - the idea that,
[4:11] - if alcohol was invented today, it would be
[4:14] - a class a drug. Well, in the same
[4:16] - kind of logic,
[4:17] - I think if scientific communication was invented today,
[4:21] - open data complete open data would probably be
[4:25] - standard. It would be standard to share
[4:27] - all of the data. And cherry picking data
[4:30] - really has only come from from the sheer
[4:32] - necessity and and the kind of the space
[4:34] - available in physical journals.
[4:38] - So
[4:39] - the definition of open data, there's lots of
[4:42] - definitions that kind of most straightforward simple one
[4:44] - from the Open Knowledge Foundation is data that
[4:47] - can be freely used, reused, and redistributed by
[4:50] - anyone. So that's the main open data definition
[4:52] - that we use. But another,
[4:55] - way to talk about open data is, of
[4:56] - course, the FAIR principles, which stand stand for
[4:59] - findable, accessible, interoperable, and reusable.
[5:02] - And data that is made open with the
[5:05] - FAIR and FAIR principles is kind of the
[5:07] - gold standard.
[5:09] - And the overall goal really is to kind
[5:11] - of break down boundaries,
[5:13] - reduce waste in research,
[5:16] - and to supercharge
[5:18] - scientific discovery.
[5:20] - I I just wanted to ask you about
[5:21] - a term you use, cherry picking. I mean,
[5:23] - I'm guessing that doesn't mean
[5:26] - selecting data that all that only supports your
[5:29] - hypothesis.
[5:30] - It's select it's using the best data that
[5:34] - you have. Yeah. It's there's nothing sinister there
[5:37] - about,
[5:38] - well, they can be, actually. You know, we
[5:41] - we do know that sometimes
[5:44] - scientists
[5:45] - do choose data that best supports their hypothesis,
[5:48] - and there is,
[5:50] - a temptation to do so often. However, that
[5:53] - is research misconduct. And I suppose what I
[5:55] - meant was,
[5:57] - in general, researchers just have to be very
[6:00] - selective about the data that they they choose
[6:02] - to present,
[6:04] - because they don't have the space to share
[6:06] - it all. Except nowadays, they do, and we're
[6:08] - trying to encourage them to do that more.
[6:11] - And I suppose the idea would be that,
[6:15] - lurking somewhere in that bad data
[6:17] - or data that appears to be difficult to
[6:20] - deal with. There could
[6:21] - be a new discovery or or a gem
[6:24] - in there,
[6:25] - and that's why it's important for sort of
[6:27] - all data to be shared rather than just,
[6:30] - you know, your best experimental run.
[6:32] - Yeah. And just making it available to other
[6:35] - people that people can have insights
[6:37] - that the original researcher might not have. You
[6:40] - can use datasets
[6:42] - that have been used to answer one research
[6:43] - question, and it can be applied to completely
[6:46] - different areas often and kind of solve problems
[6:49] - somewhere else in the scientific
[6:51] - world.
[6:53] - And, also, there's there's kind of a a
[6:55] - crowdsourcing element as well. So when making data
[6:58] - fully open, and I've got a good example
[6:59] - of this that we can discuss later on.
[7:02] - You know, when you get lots of great
[7:04] - minds around it, some really exciting discoveries can
[7:07] - come out of it that wouldn't necessarily have
[7:08] - happened if just one group of researchers are
[7:10] - looking at it in one way.
[7:12] - But also it really does reduce waste, and
[7:14] - it it reduce it reduces duplication. There's there's
[7:17] - a lot of duplication that happens,
[7:20] - in science that would be
[7:23] - stopped if people shared their open more date
[7:26] - Shared their data more openly. Mhmm. Yeah.
[7:29] - Definitely.
[7:30] - And in 2024,
[7:32] - IOP publishing,
[7:34] - did a study.
[7:36] - And the study revealed that most researchers support
[7:40] - open data.
[7:41] - But those researchers face challenges when it comes
[7:44] - to implementing it. What are some of these
[7:47] - challenges?
[7:49] - Yeah. So, obviously, we have the capacity now
[7:52] - to share a lot of data openly and
[7:54] - fairly,
[7:56] - and not all researchers do that all the
[7:58] - time. And so we really wanted to better
[8:00] - understand the kind of barriers that exist.
[8:04] - Like a lot of publishers,
[8:06] - we ask authors to include a data availability
[8:09] - statement in their
[8:10] - manuscript when it goes under peer review.
[8:13] - A really common data availability statement is data
[8:16] - available on request. That's probably kind of the
[8:18] - most common thing. So people are
[8:21] - sharing their data, but there's it's not fair.
[8:24] - It's not findable, accessible,
[8:27] - interoperable, and reusable because people are gonna have
[8:29] - to jump through hoops, and they might not
[8:30] - be given the whole dataset and things like
[8:32] - that.
[8:33] - So we wanted to better understand this, and
[8:35] - what we did is we took 30,000 data
[8:38] - availability statements from across different,
[8:41] - disciplines,
[8:42] - and we analyzed them.
[8:45] - And what we found was there was real
[8:47] - variation in data sharing between fields.
[8:51] - So the environmental sciences
[8:53] - are fantastic,
[8:55] - comparatively
[8:56] - at sharing their data openly and fairly. About
[8:58] - 60% of environmental science papers,
[9:02] - shared their data in a fair way. But
[9:04] - compare that against
[9:06] - materials and engineering,
[9:08] - that was only 5% of papers that shared
[9:11] - their data in an open and fair way.
[9:13] - And so when we we looked a bit
[9:15] - deeper into kind of of the reasons for
[9:16] - this,
[9:18] - in materials and engineering,
[9:20] - authors were much more likely to say, oh,
[9:23] - we're not sharing our data because it's commercially
[9:25] - sensitive or confidential.
[9:27] - Now those are both kind of legitimate reasons
[9:30] - not to share data.
[9:32] - Lots of material scientists
[9:34] - interestingly said,
[9:36] - oh, they didn't
[9:38] - know of a suitable repository for material science
[9:41] - papers.
[9:42] - And
[9:43] - the most common barrier that physicists
[9:46] - cited was they were worried that their their
[9:48] - data were in a really unusual format, and,
[9:50] - basically, people wouldn't be able to to access
[9:52] - it and use it.
[9:54] - When we see such
[9:56] - huge variations in data sharing between fields, there's
[9:59] - there's obviously some,
[10:01] - practical and technical reasons for that, but there
[10:03] - is a cultural element here.
[10:06] - Some fields are just culturally,
[10:09] - more open to sharing data.
[10:12] - So, yeah, that was an interesting study, and
[10:14] - it's it's made us assess,
[10:16] - the way we approach open data as an
[10:18] - organization as well. I mean, did do some
[10:21] - people think, well, maybe no one's gonna be
[10:24] - interested in this. I mean, I can imagine,
[10:26] - you know, a condensed matter physicist working in
[10:28] - a small lab somewhere
[10:30] - doing a very specialized
[10:32] - experiment. You know, they're measuring voltage versus
[10:35] - magnetic fields, and that's their data. And they,
[10:38] - you know, they might be thinking, is it
[10:40] - is it really worthwhile for me to to,
[10:43] - you know, to make a big effort to
[10:45] - make this available because
[10:47] - nobody's gonna be interested in it anyway. I
[10:50] - mean, is that sort of a barrier, or
[10:53] - can that be a barrier?
[10:54] - It might be, but the effort of writing
[10:57] - a research paper is much more than the
[10:59] - effort of of putting data into a suitable
[11:02] - repository.
[11:03] - There are things around making your data open,
[11:05] - things like licensing
[11:07] - and and kind of there's a bit of
[11:09] - admin around it, but there's a lot more
[11:11] - admin
[11:12] - around kind of writing a paper and putting
[11:14] - it through peer review.
[11:15] - So I suppose
[11:17] - what I'd say to that point is if
[11:19] - your findings are interesting enough to publish a
[11:21] - paper
[11:22] - and go through peer review and you want
[11:24] - people to read that, then certainly your data
[11:26] - are interesting enough to make open as well.
[11:29] - And so IOP, I mean, I suppose as
[11:31] - a result of that study,
[11:34] - in
[11:35] - IOP publishing has launched a free training course
[11:39] - that's called Open Data Excellence,
[11:42] - and it's aimed at early career researchers.
[11:45] - How does this course help researchers
[11:48] - overcome some of the problems or challenges
[11:51] - that we've been talking about with regards to
[11:53] - open data?
[11:54] - Yeah. We've just launched our open data course,
[11:57] - and it's online at www.iopexcellence.org.
[12:02] - It is absolutely free and available to anyone
[12:04] - in the world. If anyone listening wants to
[12:06] - go to that URL, you just click create
[12:08] - an account and and you're off.
[12:10] - And it sits alongside our peer review excellence
[12:13] - course, which teach teaches people how to do
[12:15] - excellent peer review, which has been really popular.
[12:18] - The open data excellence course, it takes about
[12:21] - sort of one to two hours to complete.
[12:24] - There are assessments to unlock the next stage
[12:27] - of learning because we like to keep people
[12:28] - on their toes.
[12:30] - It's very interactive, so there aren't just kind
[12:32] - of walls of text. We try to highlight
[12:34] - interesting case studies and,
[12:37] - and anyone who completes the course gets a
[12:39] - certificate.
[12:40] - And, really, the goal
[12:42] - for us was to equip people with the
[12:44] - kind of practical skills to make their data
[12:48] - open. I think particularly for early career researchers,
[12:51] - this process can be quite daunting.
[12:54] - So we cover we cover everything. We cover
[12:56] - things like, what does your funder want? What
[12:58] - are their expectations?
[13:00] - How do data management plans work? Where do
[13:02] - you keep those? We look at we take
[13:05] - a deep dive into licensing as well, which
[13:06] - is kind of a big scary thing for
[13:08] - for some people, but we try and break
[13:09] - it down in a really clear and straightforward
[13:12] - way.
[13:14] - And we also
[13:15] - really look at the ethics of open data
[13:18] - because, of course, there are
[13:20] - some times when you shouldn't make your data
[13:22] - open. For example, if if it might be
[13:25] - a threat to national security
[13:27] - or if it might mean that some people,
[13:29] - can be identified.
[13:31] - And we look at things like the mosaic
[13:33] - effect. So, the mosaic effect is when,
[13:38] - some researchers might,
[13:40] - make some data open, and it's fully anonymized
[13:42] - so that, you know, individuals cannot be identified
[13:45] - from that dataset.
[13:46] - But when it's combined with other datasets that
[13:49] - are out there in the wild as well,
[13:51] - the datasets can be brought together, and individuals
[13:54] - can be identified. So kind of data being
[13:57] - put together like a mosaic to form a
[13:59] - picture of an individual.
[14:01] - And that's a risk that any researcher
[14:03] - needs to be aware of when they're making
[14:05] - their data open.
[14:07] - So, yeah, we've created this online course. We've
[14:10] - got some great case studies in there. One
[14:13] - that I particularly
[14:14] - I'm a big fan of is the Vesuvius
[14:16] - challenge and the Herculaneum scrolls, which is just
[14:19] - a great example of
[14:21] - open data really solving
[14:24] - a problem and an interesting problem.
[14:27] - So,
[14:29] - Herculaneum was the town kind of next to
[14:31] - Pompeii.
[14:32] - And so in seventy nine AD, Mount Vesuvius
[14:35] - erupted, and both Pompeii and Herculaneum were covered
[14:38] - in ash and a pyroclastic flow.
[14:41] - And in Herculaneum, there was this huge villa
[14:43] - which had a library in it, and the
[14:45] - library was full of scrolls. And these scrolls
[14:47] - got
[14:48] - completely enveloped in this pyroclastic flow, and they
[14:51] - were kind of charred.
[14:53] - And in 1752,
[14:54] - some builders discovered these scrolls, and they wanted
[14:57] - to know what was in them. But over
[14:58] - the years, it proved completely impossible. Whenever people
[15:01] - tried to kind of unroll them, they just
[15:03] - crumbled apart.
[15:05] - And then in 2015,
[15:07] - some researchers used X-ray tomography to kind of
[15:10] - virtually unroll these scrolls,
[15:13] - where they looked at kind of the tiny
[15:16] - bumps on the surface of these skulls where
[15:18] - carbon scrolls where carbon based ink was sat
[15:21] - on the fibers.
[15:22] - And the output of that,
[15:25] - study was kind of quite,
[15:28] - complex
[15:29] - computational
[15:30] - kind of data.
[15:32] - And so in 2023,
[15:35] - two entrepreneurs,
[15:36] - and a researcher launched the Vesuvius Challenge, and
[15:38] - they basically said, we're gonna make $1,000,000
[15:41] - available in prizes.
[15:43] - And anyone in the world who wants to
[15:45] - look at this data, which is completely open,
[15:47] - they made all of the data open, and
[15:49] - they said,
[15:50] - please
[15:51] - help us find out what's on these scrolls.
[15:53] - And it's absolutely fascinating. So teams of people
[15:56] - from all around the world were competing to
[15:58] - try and figure out what was on these
[16:00] - scrolls. And the first prize was won because
[16:03] - a small team of really quite young
[16:06] - computer scientists and researchers,
[16:09] - three people,
[16:10] - got together and they deciphered
[16:12] - 5%
[16:13] - of one scroll. So that's as far as
[16:15] - they got.
[16:16] - And they found that it was the work
[16:18] - of an ancient Greek philosopher called Philodemus.
[16:21] - So I just think and and that's ongoing,
[16:23] - and that that will be years of kind
[16:24] - of thousands of people around the world trying
[16:27] - to to sort of solve this problem with
[16:29] - with open data.
[16:31] - So in our online course, we include examples
[16:34] - like that.
[16:36] - And yeah. Basically, anyone listening, please go and
[16:39] - check it out. It's at www.iopexcellence.org.
[16:43] - I mean, I was just thinking,
[16:45] - you know, here at IOP publishing, we I
[16:47] - mean, we publish a broad range of journals.
[16:51] - You know, it's it's it's not just physics.
[16:55] - So is it, I mean, is it diff
[16:57] - I mean, is it difficult to make the
[16:59] - course
[17:00] - relevant to you know, on on one hand,
[17:03] - you've got the, you know, small team working
[17:05] - in a lab somewhere
[17:06] - measuring voltage versus magnetic field.
[17:11] - We we publish medical physics journals where, you
[17:14] - know, you're probably
[17:15] - talking about
[17:16] - studies that are done
[17:18] - with real people.
[17:20] - And, you know, the I suppose the challenges
[17:23] - with open data
[17:24] - are are so different,
[17:26] - at, you know,
[17:28] - either end of that
[17:30] - spectrum.
[17:31] - Is it was it a challenge to sort
[17:33] - of make it relevant to everybody, or are
[17:36] - there sort of similarities?
[17:38] - Or do you just have to, I suppose,
[17:41] - live with the fact that people have very
[17:43] - different data and very different challenges?
[17:46] - Yeah. Well, similar to the peer review excellence
[17:48] - course, there are kind of fundamentals that apply
[17:52] - no matter what the subject area is. Right?
[17:53] - So, similarly, if you're peer reviewing a paper,
[17:56] - there are certain things that you're gonna have
[17:57] - to look at, whether it's,
[17:59] - astrophysics or or medical physics.
[18:01] - And it's the same for open data. Right?
[18:04] - So, sure, different funders
[18:06] - might want slightly different things
[18:08] - depending on the field of research, but, ultimately,
[18:11] - understanding what your funder wants, where to find
[18:14] - that information, how to translate that into something
[18:16] - that's usable and fair,
[18:18] - That that applies across all fields of physics.
[18:23] - The course,
[18:24] - we didn't want it to be too in-depth
[18:26] - for that exact reason. You know, we we
[18:28] - want to give people an overview. We want
[18:30] - them to be able to complete it in
[18:31] - two hours and then feel like they really
[18:33] - have the confidence to go out and do
[18:35] - this. They will probably still have to go
[18:37] - out and talk to their supervisor, talk to
[18:39] - their wider research group,
[18:41] - to get it exactly right and in line
[18:43] - with what works for their research project.
[18:46] - But we really think this is gonna start
[18:48] - people off on the right foot and
[18:50] - and feel like
[18:51] - this is the right thing to do. This
[18:53] - is a good thing to do, and it
[18:54] - and it's absolutely achievable for most researchers as
[18:57] - well.
[18:58] - I see. And so so in addition to
[19:01] - offering this training, what else is Institute of
[19:03] - Physics Publishing doing
[19:05] - to encourage authors to adopt open data practices?
[19:10] - Sure. Well, I just talked about the fact
[19:12] - that we saw quite a big discrepancy
[19:14] - between
[19:15] - different fields in terms of their data sharing
[19:17] - capacity. And and we we did see that,
[19:19] - you know, the environmental sciences
[19:22] - have a really good capacity and a really
[19:24] - good culture for data sharing. So we want
[19:26] - to encourage more data sharing
[19:28] - among our authors.
[19:30] - And so as well as kind of highlighting
[19:32] - subject specific repositories on our website so that
[19:35] - all of our authors know there's a place
[19:37] - for their dataset.
[19:39] - We also did a trial recently on two
[19:40] - of our environmental journals,
[19:43] - environmental
[19:44] - research climate and environmental research food systems.
[19:48] - And in that trial, we created a new
[19:50] - policy, which was that authors
[19:53] - had to share their data openly.
[19:55] - So we're still asking for data availability statements,
[19:58] - which we do on all of our proprietary
[20:00] - journals,
[20:01] - but we discouraged authors from putting on the
[20:04] - data availability statement data available on request. Right?
[20:07] - They had to include a link to a
[20:09] - repository,
[20:10] - a link
[20:11] - to some kind of fairly shared
[20:14] - data.
[20:15] - And the results of that trial were really
[20:17] - positive. So we got really good feedback from
[20:20] - the environmental science community. A lot of people
[20:22] - who said they really support this, they try
[20:24] - and make their data open,
[20:26] - as much as they can.
[20:27] - And in fact, that was so successful that
[20:30] - we've recently decided to,
[20:32] - expand it out across
[20:34] - more journals,
[20:35] - and that is now our permanent policy on
[20:38] - those two journals.
[20:39] - So it's
[20:41] - all of this, I think the general culture
[20:43] - around data sharing is a collaborative exercise between
[20:46] - publishers
[20:48] - and researchers
[20:49] - and funders and institutions and repositories.
[20:52] - And
[20:53] - often it feels like baby steps, but I
[20:55] - think, actually, we've come quite far in the
[20:57] - last ten to twenty years in terms of,
[21:00] - sharing more data openly.
[21:03] - I see. And
[21:05] - peer review obviously is is core to academic
[21:09] - publishing.
[21:11] - And
[21:12] - so, you know, the papers will be peer
[21:14] - reviewed. But what what about the quality
[21:16] - of the data?
[21:18] - I mean, does anybody
[21:20] - cast an eye over data that's uploaded to
[21:23] - a repository?
[21:24] - Or
[21:25] - it is it is it just up there
[21:26] - and it's sort of buyer beware
[21:29] - in terms of people who use it?
[21:31] - So we don't ask our peer reviewers to
[21:33] - review
[21:34] - the data specifically.
[21:38] - You know, that that is
[21:40] - a
[21:41] - peer reviewers already spend a lot of time
[21:43] - analyzing the manuscript, and,
[21:45] - so we don't make that demand of our
[21:47] - peer reviewers. However,
[21:49] - of course, being incredibly diligent people who are
[21:52] - really interested in the field, some reviewers do
[21:54] - go and look at that information and kind
[21:55] - of dig deeper into it.
[21:58] - But, really,
[22:00] - what the open data is there for, it
[22:02] - it's it's less about being there during the
[22:04] - peer review process and more about being a
[22:07] - permanent record after the paper is published
[22:09] - for other people to access it,
[22:11] - do different analyses,
[22:13] - check the results. So a really important
[22:16] - and useful outcome of making data open is
[22:18] - it it addresses the reproducibility
[22:20] - crisis.
[22:21] - So often what we see is a couple
[22:23] - of years down the line, someone can go
[22:25] - in, take that data, try and reproduce the
[22:27] - same findings.
[22:29] - So in answer to your question,
[22:31] - we don't look at it in peer review
[22:33] - specifically,
[22:35] - but it's important that it's there for people
[22:37] - to access. And and do you think that
[22:39] - having
[22:40] - open data encourages trust
[22:43] - in the sense that, okay, this paper has
[22:45] - been peer reviewed. There's a certain level of
[22:48] - trust
[22:49] - in that. But then, oh, yes, the the
[22:51] - authors have made the data public.
[22:54] - Well, now I trust it even more because,
[22:56] - you know, they've gone
[22:58] - the extra mile to make that information available
[23:01] - so that really anybody
[23:03] - can peer review it? I mean, do do
[23:05] - you think it's important,
[23:07] - in, you know, in terms of trust as
[23:09] - well as,
[23:10] - you know, the dissemination
[23:11] - of of information?
[23:13] - Yeah. It's a good question. I'd love to
[23:15] - see some real research on that question. But
[23:18] - my personal opinion is that if authors are
[23:21] - willing to kind of stand by their data
[23:24] - and they proudly, you know, making it accessible
[23:26] - to anyone, it shows that they really believe
[23:29] - in it, and they believe in the the
[23:30] - quality of their research and the quality of
[23:32] - their data collection.
[23:34] - So absolutely, I think it makes that,
[23:36] - research more trustworthy.
[23:38] - Yeah.
[23:39] - Well, that's great, Laura. Thanks for coming on
[23:41] - the podcast. Can you can you just remind
[23:43] - listeners where they can where where's the best
[23:45] - place to to go and find out about
[23:47] - the course and and sign up?
[23:49] - So listeners can go directly to the course
[23:52] - at www.iopexcellence.org
[23:55] - and click on create an account
[23:57] - and do the course. Yeah. Whenever they want,
[23:59] - it's accessible. It's mobile responsive. It's accessible day
[24:02] - and night around the world. Good stuff. And
[24:04] - I'll put a link in the podcast notes
[24:06] - to that URL.
[24:08] - Thanks for coming on the podcast. Thanks so
[24:10] - much for having me, Hamish.
[24:19] - I'm afraid that's all the time we have
[24:21] - for this week's podcast.
[24:23] - Thanks to Laura for a fascinating
[24:25] - discussion
[24:26] - about open data.
[24:27] - I will put a link to IOPP's
[24:30] - course in the notes for this podcast.
[24:33] - I'm Hamish Johnston, and our producer is Fred
[24:37] - Iles.
[24:38] - The original music for this podcast is called
[24:41] - one three seven,
[24:43] - and it was composed and performed by the
[24:46] - physicist
[24:47] - Philip Moriarty.
[24:49] - We'll be back again next week.

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