Figshare's role in FAIR workflows

Hi, everybody. Welcome to the fixture webinar today: fixtures, row and fair workflows. There's already quite a few people joining. So I'm gonna kick off with just my first housekeeping bits and then I'll pass you over to Andrew from fixture for today's presentation. So everyone is in listen only mode. And I should say now, if you can't hear me, or you can't see me, or you can't see Andrew slides, just pop a note in the Q&A box or the chat function, and we'll try and iron anything out. But you're in listen only mode. If you would like to communicate at any point throughout the webinar, You can use those same question boxes or the Q&A function, as well. We are recording today's session, so you gotta drop off at any point, or you want to send. Send the recording onto.

0:52

A colleague will have that recording out to everyone that registered in the next couple of days. We will have some time for questions at the end, but feel free to ask anything throughout, And, the thing very straightforward. I'll be able to answer via the chat or the Q and A, and if not, we will let Andrew come to it at the end. So, that's about everything from me. So, I'll pass over to Angie for today's presentation.

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Thank you.

1:18

Thanks, Laura. Thank you, all, for joining today.

1:23

I'm starting to play a video on, but I'm actually going to turn the video off. Just seem focused on my slides. But very nice to have all of you here.

1:35

OK, great, so I yeah, pictures, role in fair workflows. Title of the webinar today.

1:42

I'm gonna give a little background on fair and air workflows just super brief.

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I will also give a little background on FIG share and gray the General Repository Ecosystem Initiative.

1:56

Then, I'll dive into fixtures role in air workflows, and I've kinda listed some of the details here. So I'll be talking about creating fair records for all research outputs, using persistent identifiers and providing standard metadata, Actually, then referencing using persistent identifiers. I'm linking to other objects, and then discoverability and accessing files and metadata. And so those are all describe how these all fit into their workflow.

2:29

Excuse me. So super briefly, if you aren't familiar with there, it's an acronym stands for Findable, Accessible, Interoperable, and re-usable. And I've kind of written out some of the super basic, high level concepts that are in each of those.

2:44

So, for findable, it's about using persistent identifiers like DOI, digital, object identifiers, providing metadata, making the objects discoverable. Accessibility is about making these digital objects available without a paywall.

3:01

And being able to just use standard access methods, you know, just using the internet, interoperability is about providing metadata and LinkedIn for the objects. The metadata should be, should be able to translate that metadata into other metadata schemas, should be able to be surfaced in other systems.

3:18

And reusability ultimately is about providing a license that describes how the object can be re-used. The information can be re-used and providing metadata that gives a context.

3:28

And it was all, you know, really published in this 2016 paper, you can see it now as, I'm sure now it has, like, I don't know, well, over 3500 citations are very influential.

3:41

Um, and so this, these principles and the components that are part of them are really aimed at digital objects, like a dataset or no report that's available digitally or other research outputs.

3:58

But fair workflows, what are they? And I want to give a caveat that I am not an expert in fair workflows, and I don't know if anybody has yet. I think in some ways it's a relatively new term.

4:13

Um, so I'm gonna spend most of the webinar today, really, just focusing on how FIG share can support fair workflows. And I'm not going to be talking about fair workflows specifically, but I have a little bit of background here.

4:27

You may have heard of reproducible computational workflows so you know, you do an analysis.

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You have some data.

4:33

Maybe if you give some code that that works with that data, you want to be able to make that available for somebody to reproduce, maybe far into the future. But you want to make sure, you know, that they're using the same software packages, you, the same versions of that software. So you can package all that up and then help someone future, do the exact same analysis that you've done for your research and the various software platforms that can help with that, bind durable tail corrosion and among others.

5:04

So that's really focused on just like one specific maybe analysis or process.

5:12

In there also fair computational workflows, and so I'm kind of side of this paper here, Global at all 2028.

5:22

They're kind of talking about these.

5:25

To make a fair computational workflow, you should be using fair datasets, so datasets that are findable, testable, interoperable, and re-usable.

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And then the workflow itself, the process that you're going through to work with that data, you know, prepared for, you know, get your results, should also be fair. There should be fair criteria in there as well, and it should maybe be its own digital object.

5:50

So, they kind of describe in this paper some of the difficulties there, and barriers, and, and suggest some options, then there are fair workflows.

6:01

And so, the idea here is that you make the entire research process, the entire research life cycle, as, as fair as possible, or following, you know, the fair principles when possible.

6:13

So that means really using persistent identifiers for all the entities whenever possible.

6:19

Using metadata to describe all the different parts of the research project and the outputs.

6:26

Any inputs, and so we're really talking from submitting that grant for funding all the way through publication.

6:36

Now, I want to direct you to this resource.

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This is A, large project, spearheaded by data's sake, but it has a bunch of other partners to be clear.

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Picture is not directly involved with this specific project, but you can use this QR Code and Laura may be able to find that, the right URL that I sent her to put in the chat for this.

7:02

So, this is really exploring the idea of fair workflows, and going through a case study to demonstrate how true, fully fehr workflow could be achieved.

7:13

So there's a lot of information at that URL.

7:16

They've made parts of their workflow available. So I've grabbed this diagram from one of those outputs that's cited down here. And so you can really see, this is just the research data management life cycle, starting with the grant application, going all the way through, publishing that research article.

7:33

And it describes each step, how those steps can be made more fair. But basically, the idea is using persistent identifiers, making things, talk to each other, automating things so that it's, it's reproducible and understandable.

7:52

I wanna kind of, uh, lay out those concepts a little bit more, but then talk about how a repository fits into that.

8:00

So, a fair workflow: projects should be planned as to be as open impossible as possible.

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And that means that various parts of it are findable and accessible.

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Should be using systems as much as possible that inter operate. So you're reducing. You know, the manual work that isn't as reproducible you're you're making it easier for others to understand exactly how your workflow progresses.

8:28

And every part of the workflow should have its own identifier.

8:34

So people, organizations, all the various outputs and inputs, whenever possible, should, should be referenced, and should have an identifier.

8:46

Then the, the digital objects that come out of that research and are part of that research should have, should, should adhere to the fair principles. They should be findable accessible, interoperable, and re-usable. So how does the repository fit into all this?

9:03

Uh, when making things findable and accessible, repositories are generally great at providing discoverability and access, so they get indexed, and various areas are places that are discoverable in major search engines and they provide options to download the files or interact with files.

9:24

Um, repositories can be interoperable by providing open APIs, so application programming interfaces, and so, that's, you know, providing opportunity to reduce that manual work, Um, Repositories provide and provide persistent identifiers like DOI's and then also provide ways to use DOI's to link digital objects together, to reference other objects.

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You know, hopefully the repository records are incorporating the person identifier, usually the ORCID.

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And then, finally, research actually adhere to fair principles.

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Repositories should be really great at making Objects Fair through structured metadata, and providing persistent identifiers, and other things like licenses, and all that stuff.

10:14

So a repository can contribute to many, to lots of the parts of the research data management life cycle, as part of a fair workflow.

10:25

If, if you aren't familiar with FIG share, maybe just you're just here at this webinar on a random Tuesday FIG.

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Share is an established repository platform or storing, accessing and citing research outputs, including papers, data, theses, teaching materials, conference outputs.

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Basically anything that you produce as part of research or academic work can be stored and made available in a fixture repository.

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Things you do buy individuals through ...

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dot com and it's also used by institutions and organizations and other entities to run repositories and it creates this massive global network of research outputs from universities to government entities to research organizations.

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Publishers, Thunders are all using FIG share to make research outputs available. And everything is searchable through fixture dot com.

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All the, you know, institutional portals have their own search interface and then everything is discoverable in search engines and other indexes.

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So, it's a, it's a large, global network of research outputs.

11:42

Fixture is a generalist repository, meaning that, whenever possible, you should be using a disciplinary repository for your data if it exists for your discipline.

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And if that doesn't exist, you should look to your institution.

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Your institution, may provide a repository that you can use, and, uh, then it's, you know, kind of under the stewardship of the institution, which is really great.

12:08

If you don't have any other options, with those two things, though, then you can turn to a generalist repository and share your outputs that way, making them fair that way.

12:18

So fixtures generals repository and along with other generals repository platforms we are part of the great Project Generalist Repository Ecosystem Initiative. It's funded by the NIH.

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And together, we are working in this interesting space called, co-opetition to achieve these objectives, just to make the whole general repository space work better together for the there, for researchers, and administrators, and everyone.

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So, things that are working on implementing best practices for data repositories, supporting the discovery of NIH funded data, bringing consistency to metadata models across the repository's, so that everything talk to each other, better.

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And, you know, we have more interoperability.

13:11

I'm going to touch on some of the things that we're doing a big share to meet these objectives in the coming slides, and so, you'll, you'll hear me probably refer back to gray in some, in some of these ways.

13:27

OK, so, I'm gonna spend the rest of the time talking about the fixture and how it fits into fair workflows.

13:36

So, you remember this little diagram here as part of that fair workflow project, I'm going to just use this more simple version of it.

13:47

So this is the Research Data Management life cycle. Fund and plan research.

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Collect and analyze, data, preserve and store.

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Those, you know, those data and all the other outputs around it, publish and share it all. Hopefully then it's there to be discovered and re-used by the researchers and that whole process can start again.

14:07

So what I'm going to talk about today is a picture fits into this.

14:12

I'll start talking about how you can use FIG share to programmatically deposit records and create records as part of a fair workflow and then also how you can use how you can integrate with tools you may already be using as part of your research workflows and integrate with feature.

14:33

Along with that, I'll mention how you can upload any file type. Any type of file and to FIG share, conserve, even very large files created an institution.

14:42

You potentially have the option to store up to a five terabyte file. You really wanted to do that?

14:47

Um, but, you know, as part of your research, and as part of their workflow, you may need to, you may want to share, you, know, reports coming out of your research.

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You may want to share public communication material, you know, to, to communicate what your research is all about, to the general public.

15:06

I may want us share posters. all these other things that may not fit into the traditional kind of publishing routes. That can be part of the Fair workflow, and fixture can handle that.

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And when you actually publish it, when you make it public, uh, it's going to receive a digital object identifier through fixture, and you'll want to include other identifiers there as well. So part of their workflow is using identifiers for humans and researchers like ORCID.

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Then I'm increasingly adding the research organization Registry ID roar, as well.

15:43

So we'll talk about applying persistent identifiers and publishing with the structured metadata used to receive those identifiers.

15:53

Then I'll talk about discoverability and then the programmatic access for reproducibility and re-use.

15:58

So, you know, making it easy for those who want to reproduce your research or build on your research as part of their workflow, they can just come in and immediately find the, find the data, find that other outputs, and access those through a script. They need to, know, I'm just gonna throw this in here, that another way, picture, you know, kind of links, and can be part of their workflow, is linking to the grant information that's in the dimensions database.

16:27

Um, and along with this, I'll talk more about just linking in general, and how important that is.

16:35

This is kind of our roadmap or today.

16:39

So I want to first talk about creating fair records for all outputs. So I'm gonna talk about programmatic deposit, using tools, uploading all file types.

16:49

So you may have heard Picture, has an open API.

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It's actually had an open API since almost the very beginning, picture back, 10 years ago, well, actually, more than 10 years ago now.

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This image on the left is a screenshot of the documentation for the API.

17:05

Endpoints you can actually just use in the, in this user interface, but it's much more powerful when you use it as part of a script in a help page that gives you some ideas on how to use this, and ways you can try it out in your own workflows. In this case, this is the endpoint for creating a new article. And you can see that, you know, you venture the metadata as structured JSON and fixture will create that that item for you.

17:31

So this is a way you can link, you know, your FIG share account. You can create records in it, programmatically, so maybe you're pulling in data from a machine or a logger. You can have everything, be programmatic, just create the records, kinda while you're while you're working while you're gathering your data. Which can save a lot of time.

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Can make things better documented, um, and can really strengthen that, a fair workflow.

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It also integrates with other tools that you may be using in your research workflows or if you're an administrator or a library and maybe your researchers are already doing this.

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So, every fix your account can integrate with GitHub, Gitlab Bitbucket, and there's some other integrations as well, with other types of tools like lab notebooks, open science framework.

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So, this is really useful to snapshot something from GitHub snapshot to GitHub repository, for example, and read a suitable record and fixture, or, you know, start pulling in information from that lab notebook, pictures integration with ORCID and also create records.

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So if you publish a paper that then goes into, you know, it's harvested into your ORCID profile, you can set up your integration with fixture. So that picture will be notified about that new metadata in your ORCID profile, It can pull that metadata into your fixture account as a draft record. And you can use that to then publish great an open access version of that paper, completer author Manuscript, whatever the publisher allows. So fixture makes it easy, don't have to re type all that metadata. It'll just create that draft record for you.

19:16

So various ways that, um, it's just reducing the manual labor or reducing typos. You can, you know, integrating with these tools, make that much easier. That was mentioned here too.

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There's an FTP Upload option, you know, if you don't want to manually, you know, drag and drop 300 files into your, fix your record, you can use FTP to do that.

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This is an example record, and, uh, Laura may be able to share in the chat a link to this particular record.

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It's an online labor index data.

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You can see at the bottom left version is 2567, This is an example of a record that gets updated through the API.

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In recent months, they think they've been updating it basically every day.

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Um, which is just fantastic, So it's, it's highly updated. You can go back though and see all those previous versions. So I've put a QR code there as well. So this is, I'm gonna come back to this example.

20:17

Actually show how, it's, the data is used programmatically as well, but this is an example of programmatically uploading data as part of a, it's always a fair workflow.

20:30

So, I've mentioned that you may need to upload all sorts of different outputs.

20:34

Picture will accept anything so it'll prove your 100 different file types so in these examples that I have up here there's lidar Data, there's a video, FMRI data, a Reference Collection of Pollen, Images, Confocal Michael Microscope Movies, three-d. model. All of this can support a research project can support research findings. And many of this is many of these. These examples are first class research objects. They can be sited on their own.

21:03

They can either standalone research objects and so it's very important to provide the right metadata and make them discoverable, give them a DOI.

21:13

So I'm gonna move now to persistent identifiers and structured metadata, and this is really a key component of a fair workflow, and a really key way that a repository can help in this area.

21:26

So, uh, in future, every record receives a persistent identifier. If you're using a free accounting fixture dot com, it's going to be a data site DOI, You're at an institution that uses picture. It may be media, Data's idea.

21:41

Why are some things you may receive a handle, um, but the, the ... are great, because they provide persistent access to your resource in total, for the long term. Hopefully they reduce link rot. They reduce the chance that someone in the future is going to click that link to your research, that citation of your data or your paper or something like that, and end up with a 404 error.

22:06

So it's, it's a really important way to, with, within workflow documentation, or within a published paper, always use that DOI provides clear reference to that object.

22:18

And then it also provides another discoverability kind of access point, because with duis, the metadata stored with the DOI provider. And so people can discover your research through that, that route, as well as, maybe doing an Internet search. And on the left here, we have an example of some geospatial data, shared as a KML file, actually, a bunch of other files in this record, as well, and you can see the DOI in the lower left there, along with the formatted citation using that DOI.

22:49

So I'll just put a plug in for another digital science company, pictures or data science repair, has been doing a lot of work on creating trust markers for research papers, looking at, how, how can you, I can evaluate how trustworthy a paper is, one of those though is, is, is the data available?

23:08

And we're looking at data availability statement's, a lot of people say their data's available in GitHub. And git Hub is awesome.

23:16

You know, I think so many people use it as part of their research workflows.

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But, as of now, you know, it doesn't provide a DOI.

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And I automatically, and it doesn't necessarily provide, you know, the metadata in a way that makes it highly discoverable, and the way that a repository, the research repository would.

23:35

So I think it's confusing for a lot of researchers that Git hub, GitHub has repositories and then there are also these other things called repository's.

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And so um, to provide a DOI for a GitHub repository, FIG share, has an integration, as I mentioned earlier and that just creates it, zips up the entire Git Hub repository, as it has a file to the record, and then provide some of the metadata. It just fills it out for you, and links to the GitHub repository from this picture record.

24:08

And now that GitHub repository, that version, that snapshot of that GitHub repository has a DOI so it can be cited, and the re-use can be tracked.

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And so, we can see the usage metrics in this image here, views and downloads these, or make a account compliant.

24:27

So, this is going to, you know, some of that gray work, making everything comparable across repositories.

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But, we also provide a citation, uh, which is comes from just looking for this DOI and publish papers, and an alt metric badge. So, how does this do I mentioned in social media, or policy documents, or news articles?

24:47

And, so, all of this is possible because this is, uh, this GitHub repository with snapshotted and given a DOI.

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So, so important, too, no, use this kind of workflow when citing Git hub in a paper as as part of a fair workflow.

25:07

This is another example of a way that you might want to, another way to, to use a DOI for research that's part of a workflow. So, what we're looking at is a gift of me scrolling through a very long collection.

25:22

Laura, I think there's also a link to put in the chat, or this collection, and, basically, we see all this metadata, it's linked to a published paper. There are a bunch of authors here.

25:33

The description goes through, like, how to use all the items that are associated with this collection, the collection itself has a DOI and a citation there for people to say all of these items that are part of the collection. And each of these items or datasets, software, et cetera, all does also have their own DOI and also have their own metadata.

25:53

So this is a way to, to document a bunch of different objects that are related together and cite those all with one boy as part of a workflow.

26:09

In future Duis Receivable invertible, and this is really useful when you're working through your research project, because you can, you know, maybe you're, you have a machine that's automatically adding data through the API to FIG share, you can reserve a DOI or those records and then use that DOI in other documentation in your manuscript that you're submitting for publication before any of that data is actually public. So, you can reserve that. Do I use that DOI, and then when you publish your data, and publish your paper, everything's gonna be kinda linked up already through that DOI.

26:47

On top of that, you may need to, and, you know, add things to your published objects, whether that's data, or a report, or something like that.

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And fixture will version that DOI for you, so that you can cite a specific example, or a specific version of your research, people can see exactly what your versioning are citing. And then they can also see the most recent version of that.

27:14

An important part of DOI's is the metadata, So to get that DOI, you need to submit some metadata.

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And fixture has, this is our new metadata entry form, It helps you enter all the information you need and the fixture uses that to get you the dois. So, there's title, the eye, the type of item dataset, poster.

27:34

Media can add your authors, categories, and keywords are for discoverability, as is the description, and also for reusability, then there's funding and references, and the All Important License.

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And you can see, we have little help tool tips there to, to give you, to give the researchers and anybody entering the information guidance around that.

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And all of this metadata is then structured, it can be translated to other metadata schemas, it can be read by machines, and you can use that then to document, you know, whatever you've done. You can use it to automate, way to describe your research outputs, whether that's in a paper or in some type of report for your funder.

28:17

It all looks like this for the public, on the public landing page and fixture. So, you know, when somebody finds this dataset, or this presentation through Google, they come here, and you can see the description, the author, the title, or the discoverability, and they can see the license right there.

28:33

But all this metadata, though, is also available in other formats. So on the left side here, this is what the metadata looks like when coming through the API side jaison.

28:43

Formatted. We see the list of authors here with their ID and name, ORCID description. This is just part of the long list that comes out and on the right is a Dublin Core. So on the on every Public fixture page, there's a way to download the metadata in various standard formats data site, Dublin Core, National Library of Medicine and it comes out looking like that sits all labeled properly with the right namespaces.

29:15

So I'm gonna move on to how FIG share encourages linking and using .... So I just talked about how a fixture helps you apply and receive a DOI or persistent identifier and add the right metadata. How does it also help you, you know, link these objects together?

29:33

Because in a fair workflow, it's good to have all the objects kind of referencing each other and being discoverable both on their own but also as part of this workflow.

29:44

So, picture encourages linking and pay to use in several ways. Number one, on the public page for any shared output, there's the DOI along with a formatted citation. So encouraging end users to actually cite you give you credit for that. But it's also useful for you or your research team to grab that DOI and use it in your own research.

30:06

Picture has a references metadata field where you can add in the ... fixture will check and make sure that it's the right format.

30:14

There's also a call out area, so you can point directly to, you know, published paper directly related to that dataset, and I will say A lot of changes in this area are coming soon, so there will be a way to add context to these references, and this is part of that gray English.

30:31

Great project work, so you will say you know this reference here is supported by the dataset in this in this feature item or things like that, So keep your eye out for that big change.

30:48

As I mentioned early earlier, you can link your FIG share records to ORCID are your fix your profile to ORCID.

30:56

So here we see an example public profile and it has the orchid right there, this person perhaps has as link their profiles. Or get it in a way that allows them to harvest information from Oregon to create those draft records, but also push information out for kids so that they had a new dataset that'll show up in their work and account.

31:15

And you can see that the fixture author records include the orchid.

31:21

Whether the account is is you don't have to have your account linked to ORCID to include ORCID.

31:28

With authors when entering them as part of a dataset, You can, as you're adding authors to your, you know, your soon to be public dataset, you can include person's name and also their orbit in there. And that will then be part of the metadata.

31:43

As you can see in this API output, Roars Register, Research Organization, Registry IDs are coming soon. So institutions will be able to make sure that their roar is part of the metadata that's coming out a fixture. And so this is another component.

32:02

Providing the ID for the research organization in the metadata or those items.

32:08

And again, this is also part of the gray, the gray work that fixture is working on.

32:14

I alluded to this earlier as well.

32:16

So, um, providing linking, and information on the funding is very important for lots of different reasons.

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Not only can it help, or perhaps help you or others find new funding opportunities, but it also can give an idea of, like, who's funding this research and, and, using that to interpret the research, perhaps so.

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On the left is a picture screenshot of the funding yield.

32:42

You can add free text funding, but you can also select Thunders from a drop-down list that's sourced from the dimensions Database. And if you do that, you get this on your public record with a find Out, more link.

32:55

When someone clicks that link, they're taking to the dimensions page on the right with all the information about that grant.

33:00

So, you can see there's funding amount, funding period, the program, many publications resulted out of it, and further down, you can see the publications, information about the grant and similar grants. So, a lot of information here directly linked to that published record.

33:18

I thought this probably fit here better than anywhere else, but part of, you know, making these outputs available as part of a workflow, it's making sure people know how to re-use them.

33:30

And to do that you apply a license. Every record fixture requires a license.

33:34

If you're a fixture dot com, you'll only see open licenses, it's our way of encouraging open license use. We have several options for you rather than just just one option. So, depending on what type of object it is, there are various license, open license options here and there, there's a little info tab. There are a tooltip that let that lets you link out to more information about each. If you're at an institution using fixture, you might have more restrictive options here so different. For example, creative Commons licenses are a little more restricted.

34:07

No matter what, the information that you choose here becomes part of the metadata and it's machine readable. So, when someone's harvesting the metadata and the files of your, your record, they receive that license information.

34:21

And there whatever they're using to harvest that Information can can use that information, can understand it in a way that you can link out to a component or information.

34:31

The this is very important to include as part of a workflow because if your research is being re-used as part of a, you know, aggregated Research kind of like maybe a meta, meta study or something like that, it's so important that the the researchers understand the terms around re-use. Generally, they, if you're combining datasets, you're gonna need to use the most whichever dataset has the most restrictive license. You're gonna have to use that for your data, So it's a reason why using the Most Open license, which is CC, zero, is, whenever possible, the best choice. It means that when someone finds your, you know, your fair workflow, and they're, they're going to build on it or use it in some way.

35:19

They won't have to worry as much about, you know, trying to figure out how to navigate different restrictions on the licenses and the data there. That's part of that, that workflow.

35:31

So lastly, I want to talk about discoverability and re-using those files that you've created as part of your research.

35:43

So, I'm referencing back to this Online Labor Index example, where this is being programmatically uploaded and an updated version.

35:53

And you can see on the right, the DOI has the version in there, and I've highlighted the virgen part.

36:01

If you want to reference just this object, and you want people to always be directed to the most recent version, you can just remove the version information, and just use the the base, you know, DOI here.

36:14

And that will always resolve to the most recent version.

36:18

If you want to cite a specific version, then you do this little, you know, dot V version number on the interval.

36:25

Take you to that version.

36:27

I'm going to come back to that, actually, because this is actually, I'll show you how this is re-used programmatically.

36:33

Um, if you, if you wanted to programmatically re-use one or more of these files, there is a There are multiple ways you can do this. But this is one example. This is an API endpoint public file download files public.

36:45

You can put the file ID in here on this page, and it'll download the file for you as part of a script or some type of programmatic workflow.

36:53

It could call this API endpoint and download the file and then do something with it, analyze it, or added into, to some other workflow. So, again, the APIs, at docs that picture dot com, Google, how to use the Feature API?

37:08

You'll find this Help page here.

37:09

So, for that, for this example, the Online Labor Index, there's a link in that metadata item.

37:17

And I've actually, this QR code just takes you to that, the picture item, again, But there's a link in there which will take you to this example.

37:24

This is a visualization using R Shiny App, that lets you interact with the visualization of all that data.

37:32

So, really, cool way that they're, you know, pulling out information programmatically.

37:38

It's providing kind of, like, a public way to interact with it.

37:44

And in some ways, you know, this is a great example.

37:46

Like a fair workflow, I could reproduce this whole thing pretty readily, I think.

37:52

Um, and it's very clear, you know, how to do it, how it's all working.

37:59

There's nothing really hidden there.

38:01

So, I just wanted to point out other ways, too, to access information.

38:07

You know, everybody's needs are going to be different around how they're re-using the public datasets, how they're being incorporated into an analysis, or workflow.

38:19

So, for example, you know, I'm using a DOI here and I'm searching it, I'm using the public article, Search Endpoint in the Fixture API documentation, and when I searched for this DOI, picture returns some basic metadata for me.

38:36

So, you know, this can be very useful when, if you want to automate a documentation of everything that's part of a research project as a workflow, you could automate the harvesting of this metadata, and, and add that into, you know, a report, or your manuscript, or something like that. In this metadata, there's the, the ID for this record. You can use that to get all of the metadata from a different endpoint.

39:04

So, there's, if you have full access to the metadata and files in a programmatic way.

39:12

So, I'm going to start wrapping up here, but discoverability, ultimately. You know, we all want our research to be found and built on, we want to get credit for it, which is where that D Y is so important.

39:24

Big share works really hard to make outputs, shared and fixture discoverable and findable, both on search engines and other indexes.

39:36

And if you have discoverable outputs as part of your, your fair workflow, if it really, you know, it's it's another doorway into your research, and it can provide opportunities for innovation and collaborations. So someone's searching by topic can find your dataset or your open access paper.

39:56

And from there, if everything is, you know, if you've used the DOI's and you aye.

40:02

Describe things well with metadata, they should be able to then find their way to your work, their workflow. However, that's documented wherever that lives or other information about your project. And from there they can, you know, in contact with you, they can collaborate with you, they can build on your research and give you credit for it.

40:21

So this page on the right is just, to help page from FIG share just listing, You know, where pictures indexed just some of the places. But, you know, all of all the Google world. And they say dimensions.

40:33

Permanent link out, Variety of places.

40:37

The last thing I'll mention, is that, you know, research is often collaborative and it's often, involving many people, research team, people, across institutions, Picture, does offer a way for folks to work together.

40:53

Whether that's working together to prepare, you know, outputs in a fair way as part of a workflow.

41:01

Whether you're just using it as a way to provide access to outputs before they're actually public.

41:07

The Fix Share Project tab, and in any picture account.

41:12

Um, you can see this project tab, you can add members, as long as they have a fixture account, you can add them, can add them as viewers or collaborators. You can provide a description of that project here. And then anyone who's a collaborator can add files, whether the file is for the record is public or not. It can add those to the project, and anyone in the project can then access the records and leave comments on that, those records. So, you can see here, someone's added this software to run an analysis record, and this other person has added a, you know, a comment about it. So, this is a way to facilitate collaboration and access to research outputs before and after, you know, things are public.

42:02

Final slide. I just wanted to kind of wrap up and give a summary here fix your role in a fair workflow.

42:08

It can be used in most parts of the, of a workflow or the research data management life cycle.

42:17

You can give all your outputs, whether it's data, code, papers, posers, etcetera. A persistent identifier. Hopefully, a DOI.

42:24

You can provide clear re-use licenses for those outlets.

42:28

You can make all the outputs findable and accessible once they're public.

42:33

Or you can use projects to make them accessible to your research team.

42:38

Then, using those three things that I just mentioned, you can incorporate fixture into the fair workflow. Other fair workflow components. By referencing those persistent identifiers, providing programmatic access to your records through the API. And incorporating tools like git Hub and lab Notebooks to to add all that information as records and reference your fixture records.

43:03

So with that, I will end and thank you again or joining the webinar if you have any questions. And I think we have a little bit of time to answer questions. Also, happy to answer questions via e-mail or get in touch.

43:19

Info fixture dot com should put that on there. But that's our general e-mail as well.

43:26

Thank you, Andrea. I'm just clicking at the questions now, we just have the one in the moments while we're answering this one. If there are any others, please do submit them. But this fast when we have is about Google discoverability. Do you use schema dot org under the hood or other SEO measures?

43:43

Oh, that's a good question.

43:44

And I'm not, I don't know if I can answer that directly.

43:50

We do work with Google to make sure that it's that the pages are marked up in the way that they want them marked up.

43:57

Um.

44:01

How that involves schema dot org?

44:02

I am not sure, but, Laura, maybe we can follow up with that.

44:08

But we do take Yeah, we do pay attention. You know, we do.

44:11

But put a lot of effort, actually, into making sure that we're doing things the way Google needs to be done.

44:17

Well, I will make a note of the e-mail on that one and get back to you afterwards. We have another question which is just, is that a cost to post? And I've seen that's just about the FIG share dot com.

44:29

Yeah, great question. So anyone can post or free unfixed dot com picture started as just a free service just for researchers.

44:37

Because a founder and CEO was a stem cell biologist, needed a place to share videos and images figures, so he created fake share.

44:46

And um, so yes, you can just start an account. Just go to picture dot com and click the sign up.

44:53

There are some limitations. For example, file size, there's some limitations there.

45:00

And you don't have, you can't add, like custom metadata fields. If you're at an institution using FIG share, institutions can add custom metadata.

45:08

They can add custom licenses, and you can upload much larger files. But so you're a researcher.

45:17

If you're just a researcher who wants to use a free account, then yes, it's us. It's, it's free.

45:25

And I guess I should also Laurel just plug that if you have a very large file that your institution can't handle for some reason and there's no disciplinary repository, we also have another service called Picture Plus, where we help you with the really big, you know, like 100 gigabytes, a terabyte data file. There is a cost associated with that.

45:45

Just, you know, it costs money to store huge amounts of data so um, if you have that kind of situation look up FIG share plus and that pricing is on the page.

45:56

Then I've just got a couple of links to fix our Plus and there's a link out to the actual ... repository and then just a page with some more information as well, if anyone would be interested. But, that is all the questions that we have for today, but you can always catch up with us by e-mail, as Andrew mentioned, and we run a lot of webinars, so I'm sure we'll see you all on another one soon. But thank you very much for coming, and thank you, Andrea, for the brilliant presentation, and we'll get back to you with the recording in just a couple of days.

46:25

Thanks, everyone.

46:27

Thanks. Bye.

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