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Open Science: catch phrase, or a better way of doing research?
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Open Science: catch phrase, or a better way of doing research?
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Language: EN.
Segment:0 .
[MUSIC PLAYING]
OYA RIEGER: Hello. I'm Oya Rieger from Ithaka S+R. It's a great pleasure, today, to be with you during NISO Plus 2022. The speakers in the session will explore what makes Open Science work, and related opportunities, and complex challenges. We have four speakers. First, Shelley Stall from American Geophysical Union will start us with a brief overview of Open Science. Then, Jennifer Gibson from Dryad will share the organization's experience in collaborating with other scientific data repositories to facilitate Open Science.
OYA RIEGER: Our third speaker is Dr. Zhang. She's from the Chinese Academy of Sciences. She will talk about the advantages of smart data frameworks in open scientific publishing, especially in meeting the needs of researchers. And our fourth speaker, Rebecca Grant, from F1000 will conclude our session by talking about the Open Science in practice and collaborations among publishers in support of researchers worldwide.
OYA RIEGER: We will have all these presentations back-to-back. So as you are listening to these presentations, please take notes of your questions, comments. Feel free to share them via the Zoom sessions. And we will make sure to allow time at the end for your questions and comments. And now I'm going to turn it over to Shelley.
SHELLEY STALL: Hello. Welcome to the session. Thank you so much for the opportunity to speak. So Open Science-- catch phrase, or a better way of doing research? So let's explore, in this particular beginning of the session, what Open Science is. So I'm going to walk you through some concepts so you can get a sense for it. I'm Shelley Stall.
SHELLEY STALL: I'm the senior director for data leadership. And what's important here for AGU-- and I have our position statement on data in front of you. The data within the earth and space science community, the disciplines, to us is a world heritage. And what's important is that it be shared, it's preserved, it's documented. And you're certainly welcome to read the entire position statement.
SHELLEY STALL: But we really care about our data, our software, and other digital objects that are part of our community as well as physical samples and things that are related to our research. And it matters to us. It matters that this is available and well-documented. So now that you know where I'm coming from, let me talk a little bit about something that happened. Nature celebrated their 150 years in 2019.
SHELLEY STALL: And they built this amazing connected graph. It's an interactive graph. You can go explore it. I highly recommend it. It's a lot of fun. They made it really interesting. And the earth and space sciences-- they're yellow. Do you see the yellow dots? And these are their publications from the last 150 years.
SHELLEY STALL: And there's a really interesting data paper on how they built this and how they connected everything. And it shows how one paper-- how it relates to others through the citations within the paper. And it's exciting to see some of the pinnacle research and how it impacted our world. And one of the things you very quickly understand is how connected our disciplines are and how important it is for us to be able to understand each other, and have a way of interconnecting and doing cross and interdisciplinary work.
SHELLEY STALL: And they did some additional analysis. And the team that did this is just phenomenal. And one of the analysis shows you that within nature-- and expanding on that to other disciplines, more specific disciplines-- but the single-author paper is essentially disappearing. It's becoming something that doesn't happen anymore. And this makes sense, right? So when we build on our science, we continue to be able to understand more complex things and more complex things.
SHELLEY STALL: So doing it as a single person doesn't make sense anymore. You actually need your collaborators, and your network, and people from other countries. And that's what it's showing here. Blue is single-country papers, domestic papers. And those are decreasing. In orange are multi-country papers. And those are increasing. So this is really important to understand, especially for our early career researchers.
SHELLEY STALL: And so coming out of this and knowing we only have a few minutes to share our concepts, I want to give you a sense for the future of our research. You're going to work in teams. You're not going to be an individual that does research. You're going to collaborate internationally as a researcher. You're going to need really good tools to find the research worldwide.
SHELLEY STALL: It's not just your lab partner, right? You're going to need to be able to take a look at what people are doing all the way around the world. And you're going to need really good documentation to understand other people's data, other people's software to determine if there's something relevant there for you and your work. And keep in mind, we really need to share our data and our software in a way that is easy for other people to consume it.
SHELLEY STALL: It needs to be interoperable. No matter which research team is creating it, it needs to be understandable and usable by others. And additionally, accessibility-- also data should be accessible, also software should be interoperable, but I separated them just to be able to talk about it. But our software should be something that is using current tools, so it's not some arcane system that your discipline no longer uses.
SHELLEY STALL: It really should be something that is commonly used within your discipline. And I only give one example here. And you know there are so many. Jupyter Notebooks is really popular right now for a fantastic way to share software. And then we need to have licensing that supports reuse. And something that's really clear so people know that they can actually use this data again, use the software again in a way that is conducive used to our scientific ecosystem.
SHELLEY STALL: So, OK, now let's do a little bit of fun. So first of all, this is my drawing. You can tell right away I can't draw. So I'm going to walk you through it. And it's meant to be funny. And I hope you enjoy it. So up there on the top left-- here's a researcher. And they want to find some data or software. What else can I use?
SHELLEY STALL: What's out there that I can use for my research? I'm in earth science, any longitudinal study would be great. I don't know who else has been working within the space that I've been working with recently. Let me check this out and see what's out there. So, OK, the little pointy things, the little, black pointy things, those are hands, OK? So, yeah, I explained.
SHELLEY STALL: And the dots-- that's a keyboard. Yes, it is. And they type in a really specific search for the type of data, the location, everything they're looking for. Lo and behold, look at that. They found something really, really useful. Great. They move on with their research-- that's the middle with the lightning bolts.
SHELLEY STALL: They move on. They have some incredible results that come out of it. They're very excited. They're about to publish. They're going to publish. OK, this is compressed time. We all know this can take some time. But the point is, the finding of the data and knowing that it's relevant isn't time consuming.
SHELLEY STALL: They get to spend all of that time on their research. That's really important here. And now it's time for them to submit their paper and get their data into a repository so it can be cited. They're working with a repository that has a data manager. This is fantastic. And the data manager is asking for the metadata to describe their data sets.
SHELLEY STALL: And this researcher can't understand why the data manager wants so much information. And the data manager sits back in their zen-like position. I thought they'd get it, finally. I thought they'd get it. The search-- right? You see the whole like circular conversation here. OK, I'm going to keep going. So let's move on to Open Science.
SHELLEY STALL: So there's a lot of work happening around Open Science and, most recently, the UNESCO recommendation on Open Science, very exciting moment. Just this last November, all of the countries have adopted the recommendation. And one of the value statements that's within the document-- and it's a fantastic document to read-- it's about transparency and trust.
SHELLEY STALL: "Increase openness leads to increased transparency and trust in scientific information." And this is where we're headed. We really need to work collaboratively across our scientific community and know that we can all trust each other. Because we're working towards this same goal-- hard, really hard. Not all cultures are really in this space.
SHELLEY STALL: And this is where we're headed. We want to try to encourage scientific cultures to move in this direction. So Open Science, as defined within UNESCO-- and other countries have done-- US has had an Open Science by design. And it has a slightly different take on it, but it's the same concept. Here, we'll use UNESCO's, where they're talking about knowledge, infrastructures, communication, engagement, and other knowledge systems, which is really important.
SHELLEY STALL: And specifically, perhaps for this conversation, we're talking about publication, the data, the software, and the hardware. And hardware is kind of new to us, so you may not have heard that there is this concept around open hardware. But these are the key pillars and the speakers within this session are going to dig in on these topics. So pulling it back-- within the UNESCO paper, the statement is here.
SHELLEY STALL: From the very beginning of the research process, the researcher both contributes to Open Science and takes advantage of the Open Science practices of other members of the research community. And now I hope you can see why the cartoon was relevant. And we have our relevant data. So what does Open Science look like to a researcher? So this is something that we teach and have a lot of materials at AGU on.
SHELLEY STALL: And we continue to evolve that here in 2022. But you, the researcher-- think of yourself on a journey where there's no beginning and end. You're evolving. And I think sometimes it's good to start with you as the researcher, that you, in fact, can work openly because you want to make sure that your research, all of your research objects-- like data and software-- are discoverable, they're visible.
SHELLEY STALL: So think about persistent identifiers. Think about making sure that the metadata is out there, and you're getting credit for this work, so honing those skill sets and that contribution to research as a researcher yourself. And now expand a little bit. And you're going in the next circle. And it's like, OK, how can my team work more openly? I have a great example of one of my own grant teams, six different countries.
SHELLEY STALL: And we have a really hard time working openly because the cultures across the countries are not the same. And I have countries very willing to work openly, and I have countries that are not. And we have to figure out what is the minimal criteria for actually working together. It's hard. It's very hard. And then you, and your discipline, and your community-- what does it mean that your data can be used no matter where in the world your colleague is?
SHELLEY STALL: And how do we share that? And how do we re-use your data and software beyond just your team? And what does it look like to share openly in that way? And when do you do that? Working within your project, you usually are kind of more close within your team. And then when you publish is when you start to reach out. Where as that's one way, there are other ways.
SHELLEY STALL: And then cross domain-- working across disciplines takes more skill set and more communication. And what does that look like? And what does it mean to be reproducible? This is the coming out and having all of your Open Science skills honed. And not everyone is in the same place. We are all maybe working at every single one of these circles, these spirals, at different levels.
SHELLEY STALL: And that's true. And improvements in one spiral impact improvements in another. So it's a very exciting time right now. So we have resources at AGU. You're welcome to take a look. I won't spend time here, but it talks about a lot of these concepts. And it's open, which is the whole point here, Open Science, right?
SHELLEY STALL: And thank you so very much for the opportunity to speak. And I hope you enjoy the rest of our speakers.
JENNIFER GIBSON: Great. Thank you so much, Shelley. It's really a pleasure to be here, and to represent Dryad, and emphasize the power and importance of collaboration and leveraging tools and systems in concert with one another to advance Open Science. I'm Jennifer Gibson, if we haven't met before. I'm the new executive director for Dryad. I just started in October.
JENNIFER GIBSON: And so by way of kicking off my remarks, I'd just like to say a little bit about how I see Dryad as a new executive director and as a career-long open research advocate. So rather than say Dryad is a data depository or repository, I'd like us to characterize us as an open data publishing platform and a community. And that community is committed to the open availability of all research data and its routine re-use.
JENNIFER GIBSON: So this is the foundation of our conversation, isn't it? Is making sure that the research data has all of its support. And the other research objects have all the support needed to enhance discoverability and connect it with the end user to accelerate the pace of discovery. The way that we feel that Dryad can fit in is by enabling and promoting the re-use of research data on our platform. We help to make the sharing of data easy through our user experience and the experience for authors.
JENNIFER GIBSON: We help to make it powerful through our curation process and the collection of metadata, as well as the integration of tools like frictionless data. And we make the case for re-using the data compelling through the presentation of the data in association with the software, the supplementary information, the research article. So that's where we're coming from. Dryad does publish data for all research domains, so we're a generalist depository.
JENNIFER GIBSON: But, of course, we need to support the subject repositories at the same time. So we need to find a way to help the researchers get the data to the right place, if they don't already know the path. And I'll come back to that in just a moment. We only publish research data now through our collaborations. We send the supplementary information and software elsewhere.
JENNIFER GIBSON: Our process is completely curated. So our colleagues work with the researchers to collect the metadata-- information about the research data-- to enhance its discoverability and accessibility downstream. And Dryad is a not-for-profit organization. Today, the platform represents about 43,000 individual data publications contributed by over 175,000 researchers, associated with 32,000 international institutions, and 1,200 academic journals.
JENNIFER GIBSON: Dryad is fundamentally interconnected. So our strategy for achieving Open Science isn't that we should try and be a one-stop shop, but that we're all more powerful if we work in concert with other services, and that Dryad leverages our strengths in research data and in curation. So today, I'm going to highlight how we connect with researchers, with other research objects, with research articles, and with other repositories.
JENNIFER GIBSON: So starting with researchers-- the all-important audience that Shelley was talking about just a moment ago. One of Dryad's greatest strengths is our connection with the research community. In fact, several disciplines, including ecology and evolutionary biology, built the practice of sharing their data through Dryad from the get go, because they felt it was the important thing to do.
JENNIFER GIBSON: So it was back in 2009-- some journal editors and colleagues did the hard work of convincing their colleagues to share of the data and gain momentum there. So the fact that these disciplines and others come to Dryad as a matter of practice, of their own volition, is a huge thing for us to build on. At the same time, we don't want to detract from the power of sharing data in an appropriate subject repository.
JENNIFER GIBSON: So we're working with CEDAR and with Make Data Count to help us see whether data should be redirected to another repository. So this work-- funded by the US National Science Foundation-- will include the development of domain specific metadata checklists as well as checks for funding that could cue the need to redirect the data to another place. Now this work is very much underway, so there's not much more I can say about it today.
JENNIFER GIBSON: But I think you'll agree that this type of information is very, very important. Dryad has also built a powerful connection between the research data, related software, and other research objects. Our specialism is in research data and its curation. And through our partnership with Zenodo, authors can now easily upload software and supplemental information at the same time that they are loading data for curation at Dryad.
JENNIFER GIBSON: Another powerful aspect of our connection to the wider system for research communication is our collaboration with publishers to capture data as part of the article publishing process. So through our partnership with Editorial Manager and other submission and peer review systems, we make it easy for our authors to load data to Dryad at the same time as they load their manuscript.
JENNIFER GIBSON: So our integrations take the authors very briefly away from the submission and peer review system to Dryad, where our team works with the author to gather the relevant information about the data and files. And then we send them back to the originating system with a DOI that the publisher can tie to peer review and publication. Then, once the data is published, we make sure that the software, the supplementary information, and the research articles are all linked together with the research data in a clear and visible way.
JENNIFER GIBSON: Finally, I didn't want to speak at a NISO meeting without speaking to the importance of our collaboration with the institutions and the academic libraries. Especially since all of our data is available under a CC0 license, we're able to reflect the data or just the metadata to anywhere that's useful. So a number of our institutional member partners have reflected the entire Dryad corpus into their institutional repository or added the metadata into their catalog, so another way in which we can complement one another to make each of these services even more powerful.
JENNIFER GIBSON: So these are just four important ways in which Dryad collaborates with other nodes in the network. And we're always looking for other collaborations to help open research. So if you've got one, please don't hesitate to reach out. But before I close up here, I just wanted to make a couple of comments on inclusion and equity, because it was within the scope of the program that we wanted to put together today.
JENNIFER GIBSON: So, first, I'd just like to emphasize that with collaboration I'm certainly looking to reach around the world and get more people to the table than we have before. There's an important opportunity there. But I'm also trying to move the needle from my own perspective to take whatever action I can, as an individual, to make our environment more inclusive and more equitable. So personally, I'm making it an objective to engage with communities that are not within my immediate reach, to get beyond the network effect and those people that are easily accessible to me.
JENNIFER GIBSON: And when I encounter them, I'm remembering that they are perspectives and needs are going to be different from mine, and not take for granted that they're the same. I think that's an important reminder to me. I will showcase the data through Dryad no matter where it's from. And I would like to help the right people to get credit for having done the work with the data, but also for being the provenance of the data.
JENNIFER GIBSON: And finally, ultimately, my own ambition is to help make data from communities worldwide a first-class citizen in research and in research assessment. So that's it. That's how Dryad works in collaboration with others in research communication and research to enable Open. science. I hope that's useful as part of the discussion.
JENNIFER GIBSON: And I'll look forward to your questions. Thank you.
YONGJUAN ZHANG: Hello, everyone. I'm Yongjuan Zhang. The topic of my report is the Data Standards and FAIR Principles of Open Semantic Dynamic Publishing. I come from Shanghai. No, I'm sorry. I pressed the wrong buttons. Maybe I can start again. You can start again.
YONGJUAN ZHANG: That is no problem. Oh, sorry. Hello, everyone. I'm Yongiuan Zhang. The topic of my report is Data Standards and FAIR Principles of Open Semantic Dynamic Publishing. I come from Shanghai Information Center for Life Sciences, CAS. Our center consists of five sections.
YONGJUAN ZHANG: My report consists of five parts. Firstly, I will briefly introduce the background. The development of open science is very fast, especially under the background of the pandemic. Part of the problem are the data silos still exists. And they do not have a deep connection to the anti relationship and knowledge levels.
YONGJUAN ZHANG: The [INAUDIBLE] of population based on linked data has been greatly developed. However, it can quickly help researchers discover unknown problem and assist in making decision. Linked data-- how to solve the problem of data silos to some extent. That data sets in the LOD are only the association of some real [INAUDIBLE] relationships.
YONGJUAN ZHANG: Therefore, a new standard is needed. And a new publishing model is expected. The concept of smart data is put forward with a vision to support the smart decisions. and the wide range in data. Nevertheless, there is no green definition of smart data, neither the green way of practice. So first, we redefine smart data.
YONGJUAN ZHANG: Thinking that smart data is a data format that is computable, self-explanatory, and actionable in a network environment. Also, we're trying to build a technical system of smart data on the basis of link the data. Smart data is generated by combining knowledge graph and machine learning. In general, the realization of smart data includes six major steps.
YONGJUAN ZHANG: The first and the second step achieve the goal of computable, using machine learning based method to extract [INAUDIBLE] and relationships from big data, [INAUDIBLE] repellent represented in the form of RDF tables and encoded in W3C unified standards such as address and LDA, which makes the tacit and knowledge explicit. Next, assign HTTP URL to each entity and the relationships that organize it with the technology.
YONGJUAN ZHANG: Build a knowledge graph and implementer explicit knowledge of the methods of a machine learning algorithm to find the knowledge of-- to find unknown problems as well as experience in one's sex and relationships from knowledge graphs describes them in RDF and adjusted LD to analog problems explicit. Next, assign HTDBR UAI to unknown problem and implement explicit knowledges, semantics.
YONGJUAN ZHANG: So, you use about six steps, computable, self explanatory, and actionable smart data is generated. To conclude, computable, self explanatory, and actionable are the three characteristics of [INAUDIBLE] large data. Computable includes RDF description, LOD, just an LOD, unified encoding from first big data and the second, knowledge of graph.
YONGJUAN ZHANG: Self explanatory includes the semantic process of explicit knowledge and known conclusions. Actionable includes to applications of using intelligence or machine learning to extract annotated relations from big data and to raise an unknown problem from a knowledge graph.
YONGJUAN ZHANG: Judging from the current technological development, Smart Data is the Open Data standard that best matches the FAIR principle. It is a deeper realization of the FAIR principle. Unknown problems that are discovered based on machine learning algorithms and knowledge graphs and also openly accessible through explaining the techniques and semantics to drive this decision making.
YONGJUAN ZHANG: So based on the Smart Data, we design a new publishing model, called The Open Dynamic [INAUDIBLE] Semantic [INAUDIBLE]. First delay publishing of content and unknown results is open access with acknowledge and unknown problem discovered. Secondly, acknowledge an unknown problem discovered is dynamic growth with the emergency of more and more Smart Data.
YONGJUAN ZHANG: Opened, finally, our results are described in machine understandable semantic form that can identify problems and provide users with a knowledge service that support the researchers to provide smarter solutions and support their decision making. That process involves the authors reveal as editor bot readers, libraries, foundations, and relation individuals and industrial, and outlook, content, and results.
YONGJUAN ZHANG: And the other methods who publish the data. Therefore, the Open Data standards based on smart data are very important. That ecosystem contains three parts. This is our current progress. We built a planned form ecosystem of 122 different kinds of journals and the practice that some of our other research work before.
YONGJUAN ZHANG: In the future, we will continue to explore new functions based on smart data methods and efficient evasion rating based on knowledge graphs, smart data driven legends dynamical reviews systems, smart data driven industrial dynamic self-organization insights and prediction. So I said before, dynamic problem of discovery.
YONGJUAN ZHANG: This is our core [INAUDIBLE] of editors, [INAUDIBLE],, researchers, [INAUDIBLE],, consultants, and more, especially young people. And very good at English. Thank you very much. And questions are welcome.
REBECCA GRANT: Hello, everyone. It's really great to be here and to be part of this session. My name is Rebecca Grant. I'm head of Data and Software Publishing at F1000. And I'll be talking about the workflows of open science, so how do researchers conduct their open science practice and then how does that intersect with publishing workflows. And just thinking about maybe what we could do better.
REBECCA GRANT: If you're not familiar with the F1000 publishing model, it is quite well aligned with some of the principles of open science that Shelley has already alluded to, so authors coming to publish at an F1000 platform submit their article after some technical checks by our editorial team. It is made available nearly immediately alongside any research data that underpins the conclusions of that article.
REBECCA GRANT: And then all of the peer review happens completely openly on the platform. So, peer reviewers provide their peer review reports. They're public. Readers can comment. And then all the revisions are public as well. So every revision of the paper has its own persistent identifier. And it remains on the platform.
REBECCA GRANT: We do have a range of article types on F1000 platforms. So, depending on what stage a researcher is at with their research, they might choose to publish something like methods article, maybe a data note or a genome note, obviously more traditional research articles, and then things like systematic reviews, registered reports, and so on. When I was thinking about putting the slide deck together, I was kind of reminded of two papers I've read quite recently.
REBECCA GRANT: So, both of these were published in January 2022, both looking at how researchers actually work in this open science paradigm. So, the first is developing an open science mindset. In this paper, the author is describing his own experience of starting to work in a more open way, thinking about what it means to be somebody who conducts open science. And he says that these open science practices should be omnipresent in all stages of the research process, from the kernel of an idea to the production of a final research report.
REBECCA GRANT: So, this spirit of openness is really embedded across everything this researcher does. Then I was also reading a second article, which is "Barriers to Full Participation in Open Science Lifecycle Among Early Career Researchers." This was based on a survey that the authors have done of early career researchers, asking them what they understood about how open science practices work.
REBECCA GRANT: And these authors found that among the early career researchers, awareness of these open science practices was particularly low at early stages of the scientific lifecycle. So, at the very start of a research project, these early career researchers weren't really sure what they should be doing, but would make their research practice more open. And the authors also point out that this lack of awareness leads to path dependencies.
REBECCA GRANT: So, if you don't start out with an open mindset at the start of your research project, that can limit what you do in terms of sharing your outputs later on. If we think of open science as being more of a linear workflow, you can see that at the start of a project in the initiation phase, the researcher probably needs to do a bit of planning. For example, if they're intending to publish in a Gold OA journal, maybe they would need to ask their funder for coverage of APCs, potentially creating a data management plan before the research begins, pre-registering studies, if that's something that they're interested in doing.
REBECCA GRANT: As the research progresses, some documentation will be needed. So, for example, maybe documenting methods so you can write them up for a paper later on, creating or capturing that metadata that allows you to describe your research data, again, something Shelley alluded to, which is Lab Notebooks, Jupyter Notebooks, for example, maybe documenting your research practice there. And then when it comes maybe further towards the end of the research project, a researcher might be thinking about sharing those outputs.
REBECCA GRANT: For example, pre-printing a paper, then perhaps thinking of publishing in maybe a more traditional journal, maybe depositing that research data into a data repository. And you can see for some of these options, if you haven't planned for them, then actually you can't really do them at the end of your project. So, for example, if you don't have a data management plan, you might get to the end of your research project and find you just can't share the data because you haven't asked for participants permission.
REBECCA GRANT: If you don't pre-register a study, you can't publish a registered report later on. So, what can we do to build a bit more innovation into the open science publishing workflow? How can we support more innovative practice? This is an example of a project that I'm working on at F1000 on the Wellcome Open Research platform. And it's looking at a more automated publication workflow where information about genomes comes straight from the lab to our publishing platform.
REBECCA GRANT: So, we're working with researchers at the Sanger Institute. They're sequencing the genomes of thousands of species in Great Britain and Ireland. And actually at the point where these genomes are being sequenced, the scientists are also capturing a lot of metadata about the genome sequences. They're then adding some contextual information. So, generally it's a description of the species, how the sample was collected, and so on.
REBECCA GRANT: And then that is packaged together in an XML file and sent to us, the publisher, by an API with the genome sequence itself going to the European Nucleotide Archive. Once we receive that XML package, we continue the publishing process as normal. So, some brief technical checks, the genome note is published. It's made public. And then the peer review happens.
REBECCA GRANT: We've also done a bit of work around innovating peer review. So, on this platform for these genome notes, we're using automated benchmarking, again, coming straight from the lab, to provide a more objective assessment of the genome quality, so, the quality of the genome sequence itself. And we're using these to support peer reviewers when they're making their assessment of the genome note.
REBECCA GRANT: So, they now have a number of metrics that helps them to decide should this pass peer review or not. And all of this information will also be available to future readers of the article. So, everyone who views this will know whether this is a complete, well sequenced genome. Thinking a bit more broadly about innovation and collaboration in publishing, I think it's important to note that not all of the innovation just comes from the publishers.
REBECCA GRANT: So, we're working in a community of stakeholders. And there's a number of really exciting initiatives happening that can impact on publishing methods and the way our authors work with us. So, I just picked out a very small number. But, in industry, so the publishing industry, and then the wider research data sharing communal, things like the Research Data Alliance's policy framework for journals has really helped to support increasing data sharing mandates across journals and publishers.
REBECCA GRANT: Thinking about technical stakeholders, there's initiatives like Scholix, which are connecting bibliographic citations to data and repositories. Obviously, Jen talked a bit about Dryad and what they're working on. And then there's other repositories like figshare also integrating with manuscript submission systems.
REBECCA GRANT: And then also stakeholders like grant funders, so the European Commission or the NIH, if they make changes to their policy requirements that can also impact on what publishers offer to authors, maybe publishing methods, publishing channels as well. And so, just to sum up thinking about open science publishing workflows, I do think there are more opportunities for automation of processes so that authors aren't necessarily going through a manual process of drafting a traditional article and submitting it through a traditional submission system.
REBECCA GRANT: We've also seen there are technical solutions to create less friction. So, where you can integrate a data repository solution into a manuscript submission system, that can make it a lot easier for authors who want to submit their data. As I said, stakeholders include publishers, but also policy makers, researchers, technical providers, also universities, librarians.
REBECCA GRANT: There's those, too. And then just coming back to what I had said at the start around the published article by early career researchers, researcher awareness is really important. So, if we want to, I guess, support researchers in conducting science in an open way, they need to know what's possible, what options are available to them, especially when it comes to sharing those outputs in whatever form makes most sense for them.
REBECCA GRANT: So, thank you very much. I'm really looking forward to the discussion. And I'll leave it there. Thank you.
Segment:0 .
[MUSIC PLAYING]
OYA RIEGER: Hello. I'm Oya Rieger from Ithaka S+R. It's a great pleasure, today, to be with you during NISO Plus 2022. The speakers in the session will explore what makes Open Science work, and related opportunities, and complex challenges. We have four speakers. First, Shelley Stall from American Geophysical Union will start us with a brief overview of Open Science. Then, Jennifer Gibson from Dryad will share the organization's experience in collaborating with other scientific data repositories to facilitate Open Science.
OYA RIEGER: Our third speaker is Dr. Zhang. She's from the Chinese Academy of Sciences. She will talk about the advantages of smart data frameworks in open scientific publishing, especially in meeting the needs of researchers. And our fourth speaker, Rebecca Grant, from F1000 will conclude our session by talking about the Open Science in practice and collaborations among publishers in support of researchers worldwide.
OYA RIEGER: We will have all these presentations back-to-back. So as you are listening to these presentations, please take notes of your questions, comments. Feel free to share them via the Zoom sessions. And we will make sure to allow time at the end for your questions and comments. And now I'm going to turn it over to Shelley.
SHELLEY STALL: Hello. Welcome to the session. Thank you so much for the opportunity to speak. So Open Science-- catch phrase, or a better way of doing research? So let's explore, in this particular beginning of the session, what Open Science is. So I'm going to walk you through some concepts so you can get a sense for it. I'm Shelley Stall.
SHELLEY STALL: I'm the senior director for data leadership. And what's important here for AGU-- and I have our position statement on data in front of you. The data within the earth and space science community, the disciplines, to us is a world heritage. And what's important is that it be shared, it's preserved, it's documented. And you're certainly welcome to read the entire position statement.
SHELLEY STALL: But we really care about our data, our software, and other digital objects that are part of our community as well as physical samples and things that are related to our research. And it matters to us. It matters that this is available and well-documented. So now that you know where I'm coming from, let me talk a little bit about something that happened. Nature celebrated their 150 years in 2019.
SHELLEY STALL: And they built this amazing connected graph. It's an interactive graph. You can go explore it. I highly recommend it. It's a lot of fun. They made it really interesting. And the earth and space sciences-- they're yellow. Do you see the yellow dots? And these are their publications from the last 150 years.
SHELLEY STALL: And there's a really interesting data paper on how they built this and how they connected everything. And it shows how one paper-- how it relates to others through the citations within the paper. And it's exciting to see some of the pinnacle research and how it impacted our world. And one of the things you very quickly understand is how connected our disciplines are and how important it is for us to be able to understand each other, and have a way of interconnecting and doing cross and interdisciplinary work.
SHELLEY STALL: And they did some additional analysis. And the team that did this is just phenomenal. And one of the analysis shows you that within nature-- and expanding on that to other disciplines, more specific disciplines-- but the single-author paper is essentially disappearing. It's becoming something that doesn't happen anymore. And this makes sense, right? So when we build on our science, we continue to be able to understand more complex things and more complex things.
SHELLEY STALL: So doing it as a single person doesn't make sense anymore. You actually need your collaborators, and your network, and people from other countries. And that's what it's showing here. Blue is single-country papers, domestic papers. And those are decreasing. In orange are multi-country papers. And those are increasing. So this is really important to understand, especially for our early career researchers.
SHELLEY STALL: And so coming out of this and knowing we only have a few minutes to share our concepts, I want to give you a sense for the future of our research. You're going to work in teams. You're not going to be an individual that does research. You're going to collaborate internationally as a researcher. You're going to need really good tools to find the research worldwide.
SHELLEY STALL: It's not just your lab partner, right? You're going to need to be able to take a look at what people are doing all the way around the world. And you're going to need really good documentation to understand other people's data, other people's software to determine if there's something relevant there for you and your work. And keep in mind, we really need to share our data and our software in a way that is easy for other people to consume it.
SHELLEY STALL: It needs to be interoperable. No matter which research team is creating it, it needs to be understandable and usable by others. And additionally, accessibility-- also data should be accessible, also software should be interoperable, but I separated them just to be able to talk about it. But our software should be something that is using current tools, so it's not some arcane system that your discipline no longer uses.
SHELLEY STALL: It really should be something that is commonly used within your discipline. And I only give one example here. And you know there are so many. Jupyter Notebooks is really popular right now for a fantastic way to share software. And then we need to have licensing that supports reuse. And something that's really clear so people know that they can actually use this data again, use the software again in a way that is conducive used to our scientific ecosystem.
SHELLEY STALL: So, OK, now let's do a little bit of fun. So first of all, this is my drawing. You can tell right away I can't draw. So I'm going to walk you through it. And it's meant to be funny. And I hope you enjoy it. So up there on the top left-- here's a researcher. And they want to find some data or software. What else can I use?
SHELLEY STALL: What's out there that I can use for my research? I'm in earth science, any longitudinal study would be great. I don't know who else has been working within the space that I've been working with recently. Let me check this out and see what's out there. So, OK, the little pointy things, the little, black pointy things, those are hands, OK? So, yeah, I explained.
SHELLEY STALL: And the dots-- that's a keyboard. Yes, it is. And they type in a really specific search for the type of data, the location, everything they're looking for. Lo and behold, look at that. They found something really, really useful. Great. They move on with their research-- that's the middle with the lightning bolts.
SHELLEY STALL: They move on. They have some incredible results that come out of it. They're very excited. They're about to publish. They're going to publish. OK, this is compressed time. We all know this can take some time. But the point is, the finding of the data and knowing that it's relevant isn't time consuming.
SHELLEY STALL: They get to spend all of that time on their research. That's really important here. And now it's time for them to submit their paper and get their data into a repository so it can be cited. They're working with a repository that has a data manager. This is fantastic. And the data manager is asking for the metadata to describe their data sets.
SHELLEY STALL: And this researcher can't understand why the data manager wants so much information. And the data manager sits back in their zen-like position. I thought they'd get it, finally. I thought they'd get it. The search-- right? You see the whole like circular conversation here. OK, I'm going to keep going. So let's move on to Open Science.
SHELLEY STALL: So there's a lot of work happening around Open Science and, most recently, the UNESCO recommendation on Open Science, very exciting moment. Just this last November, all of the countries have adopted the recommendation. And one of the value statements that's within the document-- and it's a fantastic document to read-- it's about transparency and trust.
SHELLEY STALL: "Increase openness leads to increased transparency and trust in scientific information." And this is where we're headed. We really need to work collaboratively across our scientific community and know that we can all trust each other. Because we're working towards this same goal-- hard, really hard. Not all cultures are really in this space.
SHELLEY STALL: And this is where we're headed. We want to try to encourage scientific cultures to move in this direction. So Open Science, as defined within UNESCO-- and other countries have done-- US has had an Open Science by design. And it has a slightly different take on it, but it's the same concept. Here, we'll use UNESCO's, where they're talking about knowledge, infrastructures, communication, engagement, and other knowledge systems, which is really important.
SHELLEY STALL: And specifically, perhaps for this conversation, we're talking about publication, the data, the software, and the hardware. And hardware is kind of new to us, so you may not have heard that there is this concept around open hardware. But these are the key pillars and the speakers within this session are going to dig in on these topics. So pulling it back-- within the UNESCO paper, the statement is here.
SHELLEY STALL: From the very beginning of the research process, the researcher both contributes to Open Science and takes advantage of the Open Science practices of other members of the research community. And now I hope you can see why the cartoon was relevant. And we have our relevant data. So what does Open Science look like to a researcher? So this is something that we teach and have a lot of materials at AGU on.
SHELLEY STALL: And we continue to evolve that here in 2022. But you, the researcher-- think of yourself on a journey where there's no beginning and end. You're evolving. And I think sometimes it's good to start with you as the researcher, that you, in fact, can work openly because you want to make sure that your research, all of your research objects-- like data and software-- are discoverable, they're visible.
SHELLEY STALL: So think about persistent identifiers. Think about making sure that the metadata is out there, and you're getting credit for this work, so honing those skill sets and that contribution to research as a researcher yourself. And now expand a little bit. And you're going in the next circle. And it's like, OK, how can my team work more openly? I have a great example of one of my own grant teams, six different countries.
SHELLEY STALL: And we have a really hard time working openly because the cultures across the countries are not the same. And I have countries very willing to work openly, and I have countries that are not. And we have to figure out what is the minimal criteria for actually working together. It's hard. It's very hard. And then you, and your discipline, and your community-- what does it mean that your data can be used no matter where in the world your colleague is?
SHELLEY STALL: And how do we share that? And how do we re-use your data and software beyond just your team? And what does it look like to share openly in that way? And when do you do that? Working within your project, you usually are kind of more close within your team. And then when you publish is when you start to reach out. Where as that's one way, there are other ways.
SHELLEY STALL: And then cross domain-- working across disciplines takes more skill set and more communication. And what does that look like? And what does it mean to be reproducible? This is the coming out and having all of your Open Science skills honed. And not everyone is in the same place. We are all maybe working at every single one of these circles, these spirals, at different levels.
SHELLEY STALL: And that's true. And improvements in one spiral impact improvements in another. So it's a very exciting time right now. So we have resources at AGU. You're welcome to take a look. I won't spend time here, but it talks about a lot of these concepts. And it's open, which is the whole point here, Open Science, right?
SHELLEY STALL: And thank you so very much for the opportunity to speak. And I hope you enjoy the rest of our speakers.
JENNIFER GIBSON: Great. Thank you so much, Shelley. It's really a pleasure to be here, and to represent Dryad, and emphasize the power and importance of collaboration and leveraging tools and systems in concert with one another to advance Open Science. I'm Jennifer Gibson, if we haven't met before. I'm the new executive director for Dryad. I just started in October.
JENNIFER GIBSON: And so by way of kicking off my remarks, I'd just like to say a little bit about how I see Dryad as a new executive director and as a career-long open research advocate. So rather than say Dryad is a data depository or repository, I'd like us to characterize us as an open data publishing platform and a community. And that community is committed to the open availability of all research data and its routine re-use.
JENNIFER GIBSON: So this is the foundation of our conversation, isn't it? Is making sure that the research data has all of its support. And the other research objects have all the support needed to enhance discoverability and connect it with the end user to accelerate the pace of discovery. The way that we feel that Dryad can fit in is by enabling and promoting the re-use of research data on our platform. We help to make the sharing of data easy through our user experience and the experience for authors.
JENNIFER GIBSON: We help to make it powerful through our curation process and the collection of metadata, as well as the integration of tools like frictionless data. And we make the case for re-using the data compelling through the presentation of the data in association with the software, the supplementary information, the research article. So that's where we're coming from. Dryad does publish data for all research domains, so we're a generalist depository.
JENNIFER GIBSON: But, of course, we need to support the subject repositories at the same time. So we need to find a way to help the researchers get the data to the right place, if they don't already know the path. And I'll come back to that in just a moment. We only publish research data now through our collaborations. We send the supplementary information and software elsewhere.
JENNIFER GIBSON: Our process is completely curated. So our colleagues work with the researchers to collect the metadata-- information about the research data-- to enhance its discoverability and accessibility downstream. And Dryad is a not-for-profit organization. Today, the platform represents about 43,000 individual data publications contributed by over 175,000 researchers, associated with 32,000 international institutions, and 1,200 academic journals.
JENNIFER GIBSON: Dryad is fundamentally interconnected. So our strategy for achieving Open Science isn't that we should try and be a one-stop shop, but that we're all more powerful if we work in concert with other services, and that Dryad leverages our strengths in research data and in curation. So today, I'm going to highlight how we connect with researchers, with other research objects, with research articles, and with other repositories.
JENNIFER GIBSON: So starting with researchers-- the all-important audience that Shelley was talking about just a moment ago. One of Dryad's greatest strengths is our connection with the research community. In fact, several disciplines, including ecology and evolutionary biology, built the practice of sharing their data through Dryad from the get go, because they felt it was the important thing to do.
JENNIFER GIBSON: So it was back in 2009-- some journal editors and colleagues did the hard work of convincing their colleagues to share of the data and gain momentum there. So the fact that these disciplines and others come to Dryad as a matter of practice, of their own volition, is a huge thing for us to build on. At the same time, we don't want to detract from the power of sharing data in an appropriate subject repository.
JENNIFER GIBSON: So we're working with CEDAR and with Make Data Count to help us see whether data should be redirected to another repository. So this work-- funded by the US National Science Foundation-- will include the development of domain specific metadata checklists as well as checks for funding that could cue the need to redirect the data to another place. Now this work is very much underway, so there's not much more I can say about it today.
JENNIFER GIBSON: But I think you'll agree that this type of information is very, very important. Dryad has also built a powerful connection between the research data, related software, and other research objects. Our specialism is in research data and its curation. And through our partnership with Zenodo, authors can now easily upload software and supplemental information at the same time that they are loading data for curation at Dryad.
JENNIFER GIBSON: Another powerful aspect of our connection to the wider system for research communication is our collaboration with publishers to capture data as part of the article publishing process. So through our partnership with Editorial Manager and other submission and peer review systems, we make it easy for our authors to load data to Dryad at the same time as they load their manuscript.
JENNIFER GIBSON: So our integrations take the authors very briefly away from the submission and peer review system to Dryad, where our team works with the author to gather the relevant information about the data and files. And then we send them back to the originating system with a DOI that the publisher can tie to peer review and publication. Then, once the data is published, we make sure that the software, the supplementary information, and the research articles are all linked together with the research data in a clear and visible way.
JENNIFER GIBSON: Finally, I didn't want to speak at a NISO meeting without speaking to the importance of our collaboration with the institutions and the academic libraries. Especially since all of our data is available under a CC0 license, we're able to reflect the data or just the metadata to anywhere that's useful. So a number of our institutional member partners have reflected the entire Dryad corpus into their institutional repository or added the metadata into their catalog, so another way in which we can complement one another to make each of these services even more powerful.
JENNIFER GIBSON: So these are just four important ways in which Dryad collaborates with other nodes in the network. And we're always looking for other collaborations to help open research. So if you've got one, please don't hesitate to reach out. But before I close up here, I just wanted to make a couple of comments on inclusion and equity, because it was within the scope of the program that we wanted to put together today.
JENNIFER GIBSON: So, first, I'd just like to emphasize that with collaboration I'm certainly looking to reach around the world and get more people to the table than we have before. There's an important opportunity there. But I'm also trying to move the needle from my own perspective to take whatever action I can, as an individual, to make our environment more inclusive and more equitable. So personally, I'm making it an objective to engage with communities that are not within my immediate reach, to get beyond the network effect and those people that are easily accessible to me.
JENNIFER GIBSON: And when I encounter them, I'm remembering that they are perspectives and needs are going to be different from mine, and not take for granted that they're the same. I think that's an important reminder to me. I will showcase the data through Dryad no matter where it's from. And I would like to help the right people to get credit for having done the work with the data, but also for being the provenance of the data.
JENNIFER GIBSON: And finally, ultimately, my own ambition is to help make data from communities worldwide a first-class citizen in research and in research assessment. So that's it. That's how Dryad works in collaboration with others in research communication and research to enable Open. science. I hope that's useful as part of the discussion.
JENNIFER GIBSON: And I'll look forward to your questions. Thank you.
YONGJUAN ZHANG: Hello, everyone. I'm Yongjuan Zhang. The topic of my report is the Data Standards and FAIR Principles of Open Semantic Dynamic Publishing. I come from Shanghai. No, I'm sorry. I pressed the wrong buttons. Maybe I can start again. You can start again.
YONGJUAN ZHANG: That is no problem. Oh, sorry. Hello, everyone. I'm Yongiuan Zhang. The topic of my report is Data Standards and FAIR Principles of Open Semantic Dynamic Publishing. I come from Shanghai Information Center for Life Sciences, CAS. Our center consists of five sections.
YONGJUAN ZHANG: My report consists of five parts. Firstly, I will briefly introduce the background. The development of open science is very fast, especially under the background of the pandemic. Part of the problem are the data silos still exists. And they do not have a deep connection to the anti relationship and knowledge levels.
YONGJUAN ZHANG: The [INAUDIBLE] of population based on linked data has been greatly developed. However, it can quickly help researchers discover unknown problem and assist in making decision. Linked data-- how to solve the problem of data silos to some extent. That data sets in the LOD are only the association of some real [INAUDIBLE] relationships.
YONGJUAN ZHANG: Therefore, a new standard is needed. And a new publishing model is expected. The concept of smart data is put forward with a vision to support the smart decisions. and the wide range in data. Nevertheless, there is no green definition of smart data, neither the green way of practice. So first, we redefine smart data.
YONGJUAN ZHANG: Thinking that smart data is a data format that is computable, self-explanatory, and actionable in a network environment. Also, we're trying to build a technical system of smart data on the basis of link the data. Smart data is generated by combining knowledge graph and machine learning. In general, the realization of smart data includes six major steps.
YONGJUAN ZHANG: The first and the second step achieve the goal of computable, using machine learning based method to extract [INAUDIBLE] and relationships from big data, [INAUDIBLE] repellent represented in the form of RDF tables and encoded in W3C unified standards such as address and LDA, which makes the tacit and knowledge explicit. Next, assign HTTP URL to each entity and the relationships that organize it with the technology.
YONGJUAN ZHANG: Build a knowledge graph and implementer explicit knowledge of the methods of a machine learning algorithm to find the knowledge of-- to find unknown problems as well as experience in one's sex and relationships from knowledge graphs describes them in RDF and adjusted LD to analog problems explicit. Next, assign HTDBR UAI to unknown problem and implement explicit knowledges, semantics.
YONGJUAN ZHANG: So, you use about six steps, computable, self explanatory, and actionable smart data is generated. To conclude, computable, self explanatory, and actionable are the three characteristics of [INAUDIBLE] large data. Computable includes RDF description, LOD, just an LOD, unified encoding from first big data and the second, knowledge of graph.
YONGJUAN ZHANG: Self explanatory includes the semantic process of explicit knowledge and known conclusions. Actionable includes to applications of using intelligence or machine learning to extract annotated relations from big data and to raise an unknown problem from a knowledge graph.
YONGJUAN ZHANG: Judging from the current technological development, Smart Data is the Open Data standard that best matches the FAIR principle. It is a deeper realization of the FAIR principle. Unknown problems that are discovered based on machine learning algorithms and knowledge graphs and also openly accessible through explaining the techniques and semantics to drive this decision making.
YONGJUAN ZHANG: So based on the Smart Data, we design a new publishing model, called The Open Dynamic [INAUDIBLE] Semantic [INAUDIBLE]. First delay publishing of content and unknown results is open access with acknowledge and unknown problem discovered. Secondly, acknowledge an unknown problem discovered is dynamic growth with the emergency of more and more Smart Data.
YONGJUAN ZHANG: Opened, finally, our results are described in machine understandable semantic form that can identify problems and provide users with a knowledge service that support the researchers to provide smarter solutions and support their decision making. That process involves the authors reveal as editor bot readers, libraries, foundations, and relation individuals and industrial, and outlook, content, and results.
YONGJUAN ZHANG: And the other methods who publish the data. Therefore, the Open Data standards based on smart data are very important. That ecosystem contains three parts. This is our current progress. We built a planned form ecosystem of 122 different kinds of journals and the practice that some of our other research work before.
YONGJUAN ZHANG: In the future, we will continue to explore new functions based on smart data methods and efficient evasion rating based on knowledge graphs, smart data driven legends dynamical reviews systems, smart data driven industrial dynamic self-organization insights and prediction. So I said before, dynamic problem of discovery.
YONGJUAN ZHANG: This is our core [INAUDIBLE] of editors, [INAUDIBLE],, researchers, [INAUDIBLE],, consultants, and more, especially young people. And very good at English. Thank you very much. And questions are welcome.
REBECCA GRANT: Hello, everyone. It's really great to be here and to be part of this session. My name is Rebecca Grant. I'm head of Data and Software Publishing at F1000. And I'll be talking about the workflows of open science, so how do researchers conduct their open science practice and then how does that intersect with publishing workflows. And just thinking about maybe what we could do better.
REBECCA GRANT: If you're not familiar with the F1000 publishing model, it is quite well aligned with some of the principles of open science that Shelley has already alluded to, so authors coming to publish at an F1000 platform submit their article after some technical checks by our editorial team. It is made available nearly immediately alongside any research data that underpins the conclusions of that article.
REBECCA GRANT: And then all of the peer review happens completely openly on the platform. So, peer reviewers provide their peer review reports. They're public. Readers can comment. And then all the revisions are public as well. So every revision of the paper has its own persistent identifier. And it remains on the platform.
REBECCA GRANT: We do have a range of article types on F1000 platforms. So, depending on what stage a researcher is at with their research, they might choose to publish something like methods article, maybe a data note or a genome note, obviously more traditional research articles, and then things like systematic reviews, registered reports, and so on. When I was thinking about putting the slide deck together, I was kind of reminded of two papers I've read quite recently.
REBECCA GRANT: So, both of these were published in January 2022, both looking at how researchers actually work in this open science paradigm. So, the first is developing an open science mindset. In this paper, the author is describing his own experience of starting to work in a more open way, thinking about what it means to be somebody who conducts open science. And he says that these open science practices should be omnipresent in all stages of the research process, from the kernel of an idea to the production of a final research report.
REBECCA GRANT: So, this spirit of openness is really embedded across everything this researcher does. Then I was also reading a second article, which is "Barriers to Full Participation in Open Science Lifecycle Among Early Career Researchers." This was based on a survey that the authors have done of early career researchers, asking them what they understood about how open science practices work.
REBECCA GRANT: And these authors found that among the early career researchers, awareness of these open science practices was particularly low at early stages of the scientific lifecycle. So, at the very start of a research project, these early career researchers weren't really sure what they should be doing, but would make their research practice more open. And the authors also point out that this lack of awareness leads to path dependencies.
REBECCA GRANT: So, if you don't start out with an open mindset at the start of your research project, that can limit what you do in terms of sharing your outputs later on. If we think of open science as being more of a linear workflow, you can see that at the start of a project in the initiation phase, the researcher probably needs to do a bit of planning. For example, if they're intending to publish in a Gold OA journal, maybe they would need to ask their funder for coverage of APCs, potentially creating a data management plan before the research begins, pre-registering studies, if that's something that they're interested in doing.
REBECCA GRANT: As the research progresses, some documentation will be needed. So, for example, maybe documenting methods so you can write them up for a paper later on, creating or capturing that metadata that allows you to describe your research data, again, something Shelley alluded to, which is Lab Notebooks, Jupyter Notebooks, for example, maybe documenting your research practice there. And then when it comes maybe further towards the end of the research project, a researcher might be thinking about sharing those outputs.
REBECCA GRANT: For example, pre-printing a paper, then perhaps thinking of publishing in maybe a more traditional journal, maybe depositing that research data into a data repository. And you can see for some of these options, if you haven't planned for them, then actually you can't really do them at the end of your project. So, for example, if you don't have a data management plan, you might get to the end of your research project and find you just can't share the data because you haven't asked for participants permission.
REBECCA GRANT: If you don't pre-register a study, you can't publish a registered report later on. So, what can we do to build a bit more innovation into the open science publishing workflow? How can we support more innovative practice? This is an example of a project that I'm working on at F1000 on the Wellcome Open Research platform. And it's looking at a more automated publication workflow where information about genomes comes straight from the lab to our publishing platform.
REBECCA GRANT: So, we're working with researchers at the Sanger Institute. They're sequencing the genomes of thousands of species in Great Britain and Ireland. And actually at the point where these genomes are being sequenced, the scientists are also capturing a lot of metadata about the genome sequences. They're then adding some contextual information. So, generally it's a description of the species, how the sample was collected, and so on.
REBECCA GRANT: And then that is packaged together in an XML file and sent to us, the publisher, by an API with the genome sequence itself going to the European Nucleotide Archive. Once we receive that XML package, we continue the publishing process as normal. So, some brief technical checks, the genome note is published. It's made public. And then the peer review happens.
REBECCA GRANT: We've also done a bit of work around innovating peer review. So, on this platform for these genome notes, we're using automated benchmarking, again, coming straight from the lab, to provide a more objective assessment of the genome quality, so, the quality of the genome sequence itself. And we're using these to support peer reviewers when they're making their assessment of the genome note.
REBECCA GRANT: So, they now have a number of metrics that helps them to decide should this pass peer review or not. And all of this information will also be available to future readers of the article. So, everyone who views this will know whether this is a complete, well sequenced genome. Thinking a bit more broadly about innovation and collaboration in publishing, I think it's important to note that not all of the innovation just comes from the publishers.
REBECCA GRANT: So, we're working in a community of stakeholders. And there's a number of really exciting initiatives happening that can impact on publishing methods and the way our authors work with us. So, I just picked out a very small number. But, in industry, so the publishing industry, and then the wider research data sharing communal, things like the Research Data Alliance's policy framework for journals has really helped to support increasing data sharing mandates across journals and publishers.
REBECCA GRANT: Thinking about technical stakeholders, there's initiatives like Scholix, which are connecting bibliographic citations to data and repositories. Obviously, Jen talked a bit about Dryad and what they're working on. And then there's other repositories like figshare also integrating with manuscript submission systems.
REBECCA GRANT: And then also stakeholders like grant funders, so the European Commission or the NIH, if they make changes to their policy requirements that can also impact on what publishers offer to authors, maybe publishing methods, publishing channels as well. And so, just to sum up thinking about open science publishing workflows, I do think there are more opportunities for automation of processes so that authors aren't necessarily going through a manual process of drafting a traditional article and submitting it through a traditional submission system.
REBECCA GRANT: We've also seen there are technical solutions to create less friction. So, where you can integrate a data repository solution into a manuscript submission system, that can make it a lot easier for authors who want to submit their data. As I said, stakeholders include publishers, but also policy makers, researchers, technical providers, also universities, librarians.
REBECCA GRANT: There's those, too. And then just coming back to what I had said at the start around the published article by early career researchers, researcher awareness is really important. So, if we want to, I guess, support researchers in conducting science in an open way, they need to know what's possible, what options are available to them, especially when it comes to sharing those outputs in whatever form makes most sense for them.
REBECCA GRANT: So, thank you very much. I'm really looking forward to the discussion. And I'll leave it there. Thank you.
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Form Title Accessibility and Keyboard Shortcuts
The cadmore media player is a professional video and audio player experience designed to deliver media as well as relevant transcript, segmentation, and metadata. On the screen is a traditional html video player with buttons such as play, pause, forward, captioning, language and play speed selection and more. Adjacent to the video player are generally tabs, one of which shows a scrolling transcript of the video and the other shows any segmentation of the media, sometimes referred to as chapters. At the top of the video is a menu bar containing an Explore button and Tools button. The Explore button allows you to dig deeper into the video by opening up an explore dialog which contains tabs for a visual navigation of the video using thumbnails, an about tab which contains rich metadata about the media, a segments tab which contains further information about the video, and a related tab which contains links to other media related to this media. The tools button opens a menu with options for sharing the media on social media, creating bookmarking links, creating embed codes for your website, generating citations, and more.
Keyboard Shortcuts
Keyboard shortcuts only work while you are in forms mode. By entering forms mode, you will be able to quickly access portions of the media player with single key commands.
Spacebar: Play/Pause Toggle
I: Info Menu,
O: Tools Menu
T: Transcript Tab,
S: Segments Tab
F: Forward 15,
R: Rewind 15
L: Languages,
P: Playspeed
M: Mute Toggle,
V: Volume Bar
N: Video Only Toggle,
B: Full Screen Toggle
C: Captions Toggle
Escape: Closes dialogs and menus / Exits Player
Z: Next Transcript Chapter
X: Previous Transcript Chapter
Q: Next Transcript Paragraph
W: Previous Transcript Paragraph
Entire Media
Segment(s)
Custom Clip
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Embed Size
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You Size - Responsive
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We Size - Responsive
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Picture Overlay Popup
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400 x 225
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512 x 288
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400 x 700
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768 x 1024
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1024 x 512
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1280 x 608
Form Title Cite
No citation provided.
Info
Segments
Related
Thumbnails
Exit Clip Mode
You have requested to see a portion of the video outside of the clip.
Click OK to switch to the full video or click if you would like to stay inside the clip. You can always go back to the clip with Tools and Clip Mode