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Using data to drive and support strategy: DEI and communication initiatives
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Using data to drive and support strategy: DEI and communication initiatives
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Upload Date:
2022-08-26T00:00:00.0000000
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Language: EN.
Segment:0 .
[MUSIC PLAYING]
HEATHER STAINES: Hi, I'm Heather Staines. I'm really excited to be here with you today to present this session-- using data to drive and support strategy, DEI and communications initiatives. I'm senior consultant and director of community engagement at Delta Think, where I also run the OA data and analytics tool. So I've gotten extremely interested in data and all aspects of data use to drive strategy within organizations.
HEATHER STAINES: So this session is one that's very close to my heart. I'm going to introduce our presenters today. I'll introduce all of them and then I will hand over to them. First, we're going to hear from Jessie Slater. Jessie is the executive assistant to the editor-in-chief for the science journals published by the American Association for the Advancement of Science. Prior to joining science in 2017, Jessie completed a PhD in archeology and prehistory at the University of Sheffield.
HEATHER STAINES: She also served as an editor for the department's postgraduate journal. She's previously worked at multiple US government agencies, including NIMH, USDA, and the NSF, where she was science education analyst in the division of undergraduate education. Next, we'll hear from Doctor Nicola Nugent. She is publishing manager, quality and ethics at the Royal Society of Chemistry, where she's the strategic lead for quality and impact across journals and books.
HEATHER STAINES: She has responsibility for the journals peer-review strategy as well as publishing ethics and inclusion and diversity. She brings together over four-- sorry. Nicole leads the joint commitment for action on inclusion and diversity and publishing, where she brings together over 40 publishing organizations to accelerate progress on IND and scholarly publishing. She's got over 15 years experience in STM publishing in a variety of operational and strategic roles with an editorial focus.
HEATHER STAINES: And she has a PhD in chemistry from the University of Bristol. And rounding out our panel today will be Sherri Young. In her role at IEEE, where she is IEEE global products and marketing product manager, Sherri oversees the institutional open access agreement workflow. She manages the abstracting and indexing partnerships for IEEE along with IEEE's data repository, IEEE DataPort. She's been with IEEE since 2009.
HEATHER STAINES: She was previously with Pearson Education as an archivist in the custom publishing division. Sherri holds a BS in photography from Drexel University and an MS in communication from Purdue University. She's also a member of the ALPSP Copyright Committee and holds a PMP certification. Thank you very much. And with that, I will hand over to our first speaker.
JESSIE SLATER: Hi, I'm Jessie Slater, and I'm speaking on fostering, diversity, equity, and inclusion in science. So I think many of us can agree that science has a diversity and inclusion problem. We know this overall, but what can we do to improve? There is much talk about fixing the STEM leaky pipeline and bringing more gender and ethnic diversity to academia and industry. It's scientific publishing sits at the very end of a very long pipeline.
JESSIE SLATER: Some are comfortable saying if we treat all authors and reviewers that come to us equally, we are doing our part. Seems like a bit of a cop out. Showing equal respect to everyone is the bare minimum that we must do. But as we're all becoming more and more aware, equality does not make equity. It is our duty to be more inclusive and make science more equitable in all that we do.
JESSIE SLATER: Scientific publishing plays a unique role at the interface between scientists and the wider world, whether it be other researchers in a niche field or the general public. As curators of scientific content, that has of course been given an imprimatur via peer review, our choices signal what and also who is important and seemingly worthy of attention. The clearest way to measure if you're making any improvement is to start with a baseline assessment.
JESSIE SLATER: This could be looking at a single journal or perhaps your entire organization. Is a lack of diversity and representation systemic for a particular field? Is the balance of representation reflected at your journal or is it better or worse? In 2021, AAAS produced its first DEI report detailing the gender and racial/ethnic makeup of its employees as well as those it serves through its programs and the science journals.
JESSIE SLATER: Just this past week, we've released the second annual report. This is not an example of an organization putting out PR to get praise for our diverse workforce, but rather an objective look at who we are, who we serve, and where we can and should do better. We pride ourselves on publishing the best research, but we also want to be representative of all the members of the existing and future scientific community.
JESSIE SLATER: Diversity is only a tool as we strive for equity and inclusion. Improving the diversity of authors, reviewers, advisors, and editors cannot be approached as an activity or goal but must instead underlie all our actions and efforts. I simply do not believe that editorial and publishing professionals can encourage diversity and inclusion without being transparent. We must start by acknowledging our shortcomings.
JESSIE SLATER: For instance, the preponderance of white male reviewers and corresponding authors, the dominance of Western institutions in published research. Research into implicit bias informs us that despite all the best intentions, many individuals have deeply held biases against women and researchers from developing nations, both of which can be easily discerned from authors' lists.
JESSIE SLATER: Ignoring these issues to date has accomplished little. But by acknowledging these biases, I am hopeful that we can actively work to correct them. In 2018, at the science journals, we began to collect demographics from authors, reviewers, advisors, and editors that interact with our content tracking system, also known as CTS. We now prompt all new users to respond to our questions at registration, always giving the option of no response as a choice to the various questions.
JESSIE SLATER: And existing users are able to provide demographics through their account settings. Prior to 2018, we relied on algorithmic identification of authors' gender to examine trends in authorship, a method that we know is inherently biased against those with non-Western names. Now with self-reported data from first authors, from 2018 to 2021, we find that for those individuals who have self-identified, we have equal acceptance rates for female and male first authors as well as corresponding authors, as you can see on the left-hand side of the screen.
JESSIE SLATER: We're hopeful that if users elect to make their demographics visible to editors, we also gain the ability to discern the gender of individuals with names that are unfamiliar to us. And we will ultimately be able to search our own system to readily identify female referees to something we are also lacking. Our recent acceptance rates by gender are encouraging, but women are still significantly underrepresented on manuscripts-- on submitted manuscripts, with the percentage of female first authors recently reaching 28% and the percentage of female corresponding authors rising from 28 to 29%.
JESSIE SLATER: Both are well below the representation of females in academia and scientific industry. Clearly, we've still got work to do here. The ratios of female first and corresponding authors on submitted manuscripts are also not in line with what we hope to see amongst our advisors and our board of reviewing editors. Self-reported data are the ideal, but they are not infallible.
JESSIE SLATER: In 2021, 63% of all authors and 78% of first authors provided data on their gender, but only 39% of all authors and 54% of first authors self-identified their race/ethnicity. Is there anything we can do to improve upon this result? And for instance, are there particular reasons underlying why we are seeing greater reporting of gender than race/ethnicity?
JESSIE SLATER: From these snapshots of our demographics process and the data we've collected, we can see we're improving in some areas but still coming up short in others. You cannot start out with the perfect system for collecting demographics. Only through individuals interactions with your prompts and their candid responses can you begin to evolve a scheme that is tailored to your community's needs.
JESSIE SLATER: As we endeavor towards equity and inclusion, we want to know where our authors and reviewers feel they fit in, rather than just another exercise in labeling and categorizing people. The questions you ask and the options you provide signal to your community what identities you believe belong in science. Do your options only fit a Western worldview? Are they gender inclusive?
JESSIE SLATER: Do they ask unnecessary questions, for example, marital status for women alone? First, as mentioned earlier, our reliance on the US census designations in our categories for race and ethnicity created much confusion for residents of other countries who frequently responded with one of the racial categories and their national identification, for example, Black British or Chinese Australian.
JESSIE SLATER: While I believe international data interoperability is what we should strive for, if you're an organization based in the same country that most of your users and our customers come from, then I would definitely recommend sticking with your national standards. As imperfect as those options might be, they are generally well understood and are likely a good means of measuring racial bias within your nation.
JESSIE SLATER: One of the most common responses given in the free-form text when people selected other as an ethnicity was Han ethnicity or Han Chinese. While in the US, we automatically consider this to be Asian/Pacific Islander, it is illustrative of the distinction between race and ethnicity in other countries. We might consider two groups such as Han and Uyghur as both Asian ethnicities.
JESSIE SLATER: But to those from China, these are not the same race and have a profound majority-minority relationship. In internal discussions, we're considering adding an additional question where individuals can indicate if they self-identify as a minority race/ethnicity within their country of residence and/or origin. Also, following the US census, we have lumped Hispanic or Latino into our general race ethnicity category.
JESSIE SLATER: We rely on radio buttons rather than checkboxes, making it difficult for someone to identify as white Hispanic or Black African Hispanic without selecting multiracial or other. The categorization of Hispanic or Latino also presents considerable confusion for individuals from Central and South America as well as those from the Iberian Peninsula in Europe. While we can argue about the distinction between race and ethnicity, I believe it is important to allow individuals the opportunity to select all options that would make up their identity rather than be forced to choose one over the other.
JESSIE SLATER: By expanding our net internationally, you really do begin just to understand how imperfect and socially circumscribed terms like race and ethnicity are. Again, the US census would have individuals with Middle Eastern or North African descent identify as white. But it's hard to see how this racial designation matches the lived experience of many of these people who are frequently profiled by Western police and security forces.
JESSIE SLATER: Plus, we've had some-- we've received some unexpectedly semi-positive comments from individuals who were pleased that we were taking the step to collect these data, but they hoped we could do more. Why weren't we considering gathering data on sexual orientation or disability status? These are also key facets of individual identity and a frequent source of discrimination in and out of science.
JESSIE SLATER: We are now actively considering adding these in our next rollout of categories to our demographics. So here are some quick lessons learned. I would definitely say radio buttons aren't helpful except for data analysts. But people rarely agree to fit neatly into distinct categories. I would recommend over explaining. Do not assume that the categories you've spent months deciding upon will make sense to anyone outside of your internal focus group.
JESSIE SLATER: Whilst working on this presentation and mulling over these data, I've spent a not insignificant amount of time considering the merits of asking about race, ethnicity, or both. We did receive a few responses that indicated individuals were human or that told us the question was racist or even that it was illegal to ask about race in their country. Through reading quite a few books on the misuse of race and science, I'm coming round to favoring continuing to query on race.
JESSIE SLATER: Like we mentioned early on, we're striving not for equality, but equity. And we cannot ignore the systemic racism that it was shown to certain groups through this country and others histories. By documenting the gender and race of our authors, reviewers, advisors, and editors, we hope we are doing our part to truly give previously excluded individuals a seat at the table.
JESSIE SLATER: Thank you for listening.
HEATHER STAINES: Thank you so much, Jessie, for kicking us off today. And we will hand over next to Nicola.
NICOLA NUGENT: Thank you. I'll just share my screen. OK, so my name is Nicola Nugent, and I'm publishing manager, quality and ethics at Royal Society of Chemistry. And I'm going to be talking today about a Royal Society of Chemistry LED initiative called the joint commitment for action on inclusion and diversity in publishing and specifically some work from that group, following on from what Jessie was saying about the classification and schemas for collection of diversity data.
NICOLA NUGENT: So just to kick off, what is the joint commitment for action on inclusion and diversity in publishing? Well, it's a collaboration between 49 publishing organizations, although that number does tend to go up every now and then as more organizations join. And together, we own over 15,000 scholarly journals. And the initiative was launched in June of 2020, following a workshop convened by the Royal Society of Chemistry where we shared our framework for action document.
NICOLA NUGENT: I don't have time today to go into the detail of that document. But essentially, it's a framework that maps out the steps we're taking to eliminate bias across our journals. And so we convened a workshop with other publishers to share that with them as we felt it would be useful. And in the same workshop, we also facilitated a conversation between the publishers present to talk about the areas in which publishers could work together and collaborate to accelerate progress on inclusion and diversity in publishing.
NICOLA NUGENT: And the output of that workshop was essentially the joint commitment initiative. You can see the full joint commitment statement if you follow the link on the top right-hand side of the slide. But what I've done is pull out the sort of four key areas that we identified and articulated as the areas that we felt publishers could work together on.
NICOLA NUGENT: And these form the commitments that we've made. So first of all, we've committed to understanding our research community. And that means collaborating to enable diversity data to be self-reported by members of our communities. We've committed to reflecting the diversity of our community. So this is about using diversity data to set or uncover diversity baselines and potentially to set targets to achieve improved and appropriate and inclusive representation amongst authors, reviewers, and editorial decision-makers.
NICOLA NUGENT: We've also committed to sharing success to achieve impact and this is really just about sharing our learning and knowledge, working together on developing resources, pooling our resources, transparently sharing policies, language and standards to move inclusion and diversity and publishing forward together. And finally, we committed to setting minimum standards on which to build. And as you'll see on a later slide, we launched those minimum standards in November of last year.
NICOLA NUGENT: And what's been really important from the start of the initiative and throughout is that, as I said, it's the diversity-- sorry. The joint commitment is a statement that's available on our website, but it's more than just words. It's very clearly about action. And so in order to make sure that we are taking the action we need to take in order to deliver on the commitments that we've made, we formed a working group.
NICOLA NUGENT: So each initiative-- sorry. Each organization who has signed up to the joint commitment initiative and is a member has at least one representative on a working group that meets three times per year to discuss progress and to set direction. And that's-- as I said, we currently have 49 member organizations. So it is a large group and we've been able to put in place smaller subgroups.
NICOLA NUGENT: So these are subgroups taking forward specific areas of action under each of the four priority areas that we identified. So just for example and perhaps most relevant for today, under the first priority of understanding our research community, we have two smaller subgroups looking at diversity data collection systems and one looking at diversity data collection questions. And as you can see, there are other subgroups taking forward different areas of action under the other commitments.
NICOLA NUGENT: And this timeline shows some of the achievements, milestones, and general work of the joint commitment group since its first launch back in June of 2020 at that workshop. And for the purposes of today, I'm going to focus on the diversity data collection aspect. So back in February of 2021, the group put forward a recommendation for a schema for the collection of gender data and has also been working on a schema for a collection of race and ethnicity.
NICOLA NUGENT: And I'll be able to share a draft of that with you in a moment. And throughout, we've also been working with and facilitating conversations between major peer-review systems providers [INAUDIBLE] manager and ScholarOne on the development of the sort of infrastructure and the system side of things to enable publishers to collect this data within their peer-review systems in a way that's compliant with various privacy regulations and so on.
NICOLA NUGENT: So in the next slides, I'm going to show those schemas that I talked about for collection of gender data and for the collection of race and ethnicity. But I just first want to be clear that what I'll be presenting is the work of data questions subgroup of the joint commitment, which is currently very ably and enthusiastically led by Holly Falk-Krzesinski from Elsevier. And the group has membership that represents at least 20 different publishers.
NICOLA NUGENT: Royal Society of Chemistry is one, but there are many others, including Springer Nature, WileyPLUS, ACS, and many more. So I want to make sure that they're given due credit for the work that they've done. So here is the gender identity question that's been endorsed by the joint commitment group. And it was-- as I said, it was agreed on in about February of last year and a number of publishers are starting to implement it.
NICOLA NUGENT: At first glance, it might seem relatively straightforward, but a great deal of thought and care and research went into the exact wording and the various options that are there. So first of all, we have the specific terminology, gender identity, rather than the more amorphous term, gender. We ask a very specific question and respondents can provide one answer. We make sure-- sorry-- the options that are available are terms that refer to gender identity and not biological sex and there's no distinction made between cis and transgender.
NICOLA NUGENT: And the options are not limited to binary options and there's also an-- on number six, where there's an option to prefer-- where you can prefer not to disclose your gender identity information in line with data protection regulations. And lastly, importantly in this-- this is an Elsevier example. There's a statement at the end here explaining the rationale for the collection of this data, how it will be used, and a link to the publisher's privacy policy.
NICOLA NUGENT: So as I say, this question schema has been endorsed by the joint commitment working group. But of course, we'll continue to review and update that as and when that becomes necessary. Next, I'll move on to talk about the race and ethnicity schema, which is vastly more complicated as Jessie touched on when she spoke about this. So our aim was to develop a global universal race and ethnicity schema, which, as Jessie mentioned, simply doesn't exist.
NICOLA NUGENT: There isn't one that you can just pick off the shelf and use. So we set up about developing one. And that was an iterative process that's through that joint commitment subgroup, where all members of the group contributed and shared their knowledge and insights and the types of diversity questions that they may have already been using. We had input from a number of different publishers.
NICOLA NUGENT: There's some examples here from an ACS inclusion and diversity survey from the Elsevier external Inclusion and Diversity Board. Springer Nature shared information about their staff inclusion and diversity survey. And there was also input from the sort of published literature and research and knowledge from there was taken into consideration. And importantly, Elsevier engaged an external subject matter expert, Professor Ann Morning, at New York University.
NICOLA NUGENT: Professor Morning's an associate professor of sociology and her research interests include race and ethnicity and especially racial classification and related topics. So all of that information and expertise was fed into drafting a schema for collecting race and ethnicity data that was then put forward for a testing. So the testing was done by a survey led by Elsevier's customer and market insights team.
NICOLA NUGENT: They sent the survey to over 100,000 global researchers. And the survey closed when they reached over 1% response rate, so about 11,000 total responses. And the respondents were publisher agnostic. The data was, I believe, from the Scopus database. They were not just Elsevier authors. In the survey, respondents were asked about their race and ethnicity using the draft schema and they were also asked about how they perceived the representativeness of the options that were provided and there were opportunities for free-text feedback.
NICOLA NUGENT: Respondents were also asked about how comfortable they would be with sharing their race and ethnicity information in the context of serving as an editorial board member reviewer or author. And again, there were free text options available to provide feedback. So all of that stakeholder feedback as well as more input from our subject matter expert went into revising the question to its current form, which I'll show you now.
NICOLA NUGENT: And just to set out, from the beginning, the intention is not to devise a single objective or a prescriptive truth about a researcher's race and ethnicity. Rather, the objective is to develop a set of options that resonate with communities that we serve from around the globe so that they're willing to self-report their racial and ethnic identity. And the level of aggregation and the number of options that are offered to respondents has to parallel the scale of diversity data we can practically accommodate and make use of.
NICOLA NUGENT: And this approach ties in really with general survey best practice but also importantly with the legitimate interest requirement of legislation such as GDPR and others where it's stated that we should not capture data that we do not intend to actively use. So as you can see, this is not a single combined ethnoracial question. It's split into two questions as a two-question format. The first part asks for ethnic origins or ancestry and the options are geographic areas.
NICOLA NUGENT: So respondents are asked to select all the geographic areas from which their families ancestors originated. And as you can see, the responses are geographic areas. And so the goal here is in the context of a worldwide survey, you need to have a set of responses that seem sort of reasonably familiar and pertinent to people from around the globe. And geographic location and ancestry have that salience across the world.
NICOLA NUGENT: Only a small minority of nations employ race and their official data collection, making racial self-identification a much less common experience in most areas of the world than it is in the United States or the United Kingdom. So in an international survey, a question on geographic origin or ancestry is likely to furnish more complete data and a higher response rate than racial categories alone will do.
NICOLA NUGENT: Having said that, there is a second question there, as you can see, which does use race labels. And so when we think about the different locations where a person's ancestors may have resided, there's potentially likely to be one with which a respondent is usually identified or usually identifies themselves, especially if their physical appearance is associated with that geographical area. And so that strand of ancestry is generally what is captured by these race labels.
NICOLA NUGENT: And these labels are likely to be particularly influential for individuals opportunities and life outcomes. And so they are particularly informative in the context of inclusion and diversity work. So despite the limitations of racial classifications already noted, especially in the context of a global survey, asking about race can in some cases provide additional information that is useful in the context of inclusion and diversity work.
NICOLA NUGENT: Lastly, just-- this is the text of a sort of proposed introductory statement to go with this question. I won't go through it in detail, but it will be available through sharing of the slides. But this was crafted at taking into account feedback from researchers about their level of comfort and trust in terms of sharing this information.
NICOLA NUGENT: And it's important to be explicit about how the data will be used, how it will not be used, why it's being collected, who will access it, how it will be stored, and so on. So finally, just some next steps. The race and ethnicity question that I showed is still sort of formally a draft. It will go to the joint commitment working group for endorsement in early March.
NICOLA NUGENT: There's work ongoing, as I mentioned, to develop the architecture and the systems to collect this within major peer-review systems, Editorial Manager, and ScholarOne. Elsevier will be integrating both the gender identity and the race and ethnicity schemas into their Editorial Manager implementations. We know we need to develop a robust communication plan, both in terms of getting those schema out there to publishers, but also and importantly to researchers to build trust and help them understand why we're collecting this data and what we're doing with it.
NICOLA NUGENT: It's potential to publish a scholarly, peer-reviewed article with Professor Morning. And we also hope to work with NISO and others to expand the use of these schemas across publishers. I'll finish there and say thank you very much and hand over to Sherri.
HEATHER STAINES: Yeah, thanks, Nicola. And while Sherri sets up, I just want to say we're shifting gears slightly to talk about some other types of data projects, but I think you'll see that there's a lot of commonalities that cross these different efforts. Take it away, Sherri.
SHERRI YOUNG: OK, great. Thank you. So hi, everyone. I'm Sherri Young, IEEE product manager. And I'm going to provide a few publisher use cases, the center around data, but a bit different than the two other presentations you've heard so far. So I'm responsible for two major products at IEEE OA and abstract and indexing and both of those touch on data quite a bit.
SHERRI YOUNG: For our open access program, I oversee the institutional OA agreements, as Heather mentioned earlier. And I'm going to focus on two use cases around that workflow. And then with abstract and indexing, it's all about data. So with that will come the third use case I'll share today. So just as an agenda in trying to provide more information on what I'm going to go over, the first use case speaks to the process of OA article matching.
SHERRI YOUNG: And what that means-- it may vary greatly depending on your role in the scholarly publishing industry. So the maturity of the systems and the data that one encounters really varies. Secondly, I'm going to speak to a recent project that we took on that many publishers are likely looking to undertake in 2022 around informing authors about available funding as part of an OA agreement with a publisher.
SHERRI YOUNG: And then finally, I'll put on my A&I hat and share about how data sharing is evolving and that includes outward reporting around OA as well as protecting Oracle data, and user data, author data, protecting all the data around derivative products. So going into the first use case, I mentioned on OA article matching on data this may be a familiar process to many of you.
SHERRI YOUNG: But I thought it important to highlight for those not in the OA trenches that data drives the entire submission and publication process for the funded APC author. So who is the funded author? They may not know that they're the funded author and that's what this is about. So funding is typically available to them and they may be informed about halfway through the publication lifecycle.
SHERRI YOUNG: So that's why the accuracy of the data entered in the initial step is so important. So I'm showing on screen here a-- we use ScholarOne. So this is a ScholarOne interface where an author is first entering their metadata. When that happens, assuming the article is accepted sometime later after peer review, the data comes back into play for a payment of the article processing charge.
SHERRI YOUNG: Several API calls are made from ScholarOne to Ringgold to internal systems. For IEEE, that could mean for CCC systems and back again in order to service available funding for that author. So when data is entered incorrectly, as I-- I quite quickly went over all those different APIs that are talking to each other.
SHERRI YOUNG: And when that data is entered incorrectly, the matching criteria on the institution's profile could be incomplete and the data chain breaks down. So that author institution will reach out for help. If the author isn't familiar with this type of workflow-- maybe they haven't published before at all. Maybe they haven't published OA before. They may simply pay that APC out of pocket if the data hasn't connected two and two together to make the match to available funding.
SHERRI YOUNG: Or they could turn around and say, you know what? I'm not going to publish at all. So as a publisher, our role is often making sure that the data is clean, the systems are functioning as they should be and that we're making that match 100% of the time when funding exists. Trust me, no one wants to enter manually refunding the author when a trip is done at the end of the year and an APC needs to be used, but now an author's credit card is due for a refund.
SHERRI YOUNG: So when the data is right from the beginning between the author, the institution, and the publisher, it's a better workflow and more efficient publishing lifecycle for all involved. In order to reach an end goal of clean, usable data, we have to start publishing the workflow with a clear set of expectations and that includes the role of the author. So the more effort we put behind educating the author, providing clean and consistent user interfaces with functioning APIs, the more likely we are to get to 100% identification of author funding availability.
SHERRI YOUNG: So that first use case ties into what I'm going to share here and piggyback on informing that author, getting the word out, and making sure that that data that's being shared through multiple systems is accurate. So in 2021, IEEE conducted an author awareness campaign to make eligible authors aware of existing funding for their article processing charge.
SHERRI YOUNG: Ensuring data accuracy was a priority of the campaign. Much like a bank account, institutional OA profiles are a living, constantly changing data point. And so we take the role of that communication seriously. So imagine with me for a minute how this could go without a firm hand on data accuracy. On Monday, the publisher emails you and says, hey, you've been identified as a potential author for this journal and congratulations.
SHERRI YOUNG: Your institution has an OA agreement with us and that may cover your charges. Tuesday comes and your colleague or someone maybe you don't even know, another author from your institution goes crazy and publishes five articles, using up all the APCs on that profile. So now Wednesday comes. You're ready to submit your article that you were thinking about on Monday and now all the tokens or APCs have been used.
SHERRI YOUNG: But wait, the publisher just told you you had funding available. So this is a very easy scenario that can come about if we're not monitoring that data closely, we're not careful about the communication that we're putting outwards to authors and to institutions to inform them that funding is available. So in that workflow, where does the data come from?
SHERRI YOUNG: So for us, IEEE-- the data-- we have a data warehouse and we're using Tableau. And we're having even internal systems talking to one another to make sure that we're servicing real-time data that's accurate, that we're matching the author to the institution using criteria that's standardized and that we're also observing opt-out users through prior campaigns.
SHERRI YOUNG: So there's a great deal of collaboration in that workflow. It could be between myself as a product manager. We have a product marketing team. We have a great deal of publication staff that monitors that data and is inputting quite a bit of that data through manuscripts, through membership data. And then there's sales, who ultimately owns a relationship with the institution.
SHERRI YOUNG: So to ensure the campaign's success in informing authors of eligible funding with accurate information, data is pulled from a number of sources as I'm showing on screen here. We're careful to observe anything regarding GDPR and the campaign is relying on those data points to send outward communication. So did we do a good job?
SHERRI YOUNG: The awareness campaign was successful in reaching the goal to increase awareness among eligible authors and secondly to engage our partner institutions with communicating to their authors. I mean, we are very much a team with our institutions trying to make sure that that communication to the author is clear, timely, and being absorbed. So our unsubscribe rate was quite low at just 0.42%. And we did receive author feedback, like the one I've included here in the blue box.
SHERRI YOUNG: So going forward, we're going to be repeating the campaign in 2022 with an increase in data needs as the complexity in volume of OA increases and the program matures. So finally, I'm going to share with you that A&I data reporting and sharing. And so data reporting in an OA world has evolved and is continuing to evolve quickly than I think some of us can keep track of.
SHERRI YOUNG: So the final use case is around that. So for OA, most license agreements today include specific terms around required data and reporting on a schedule to the institution. The goal of this reporting is to allow the institution to track APC usage and catching those articles that may slip through the cracks, as I mentioned in the first use case. So reporting not only on OA articles, but those published traditionally will provide a complete data set that can be analyzed by the institution and ultimately our customer.
SHERRI YOUNG: So reporting needs are growing. And this isn't only from the institution, but also in the way of funder mandates, repository depositing, and A&I partners that are downstream indexers. So all this put together can mean some risk in sharing too much information while still meeting those supporting demands. From the publisher perspective, the balance with data reporting really speaks to the importance of data governance, consistent schemas that we have relied on for a number of years evolving to include things like license information, Ringgold or a author affiliation, and finally, the role that the author plays in owning their data and research.
SHERRI YOUNG: So I'd like to end my presentation today with a brief mention of the importance of revisiting agreements through the lens of customers and authors around data. For publishers, that can mean protecting author data from derivative works. In publishers like IEEE that have been around for quite some time, some agreements may have been entered pre-GDPR and pre-OA.
SHERRI YOUNG: And we need to go back and revisit those terms and make sure that we're still comfortable and they still apply in 2022 and going forward. So with the knowledge that we have around data today, it's our responsibility to protect author data with concrete terms and expectations. This includes an active audit of the A&I agreements to ensure our downstream indexes are observing the same level of respect for user data that we've taken on as the publisher.
SHERRI YOUNG: So I'd like to thank you for taking time-- to have me here today, and I look forward to our question and answer session.
HEATHER STAINES: Thanks so much, Sherri. And I'll just ask all the speakers if they may turn their cameras back on at this point. And we're going to kick off with a few questions amongst the panel. And I will say unfortunately, for our live Q&A on the 17th, Nicola is not going to be able to join then. But we can collect questions for her and you can likely reach her in the discourse forum. So we're just going to have some questions today to kick things off.
HEATHER STAINES: So I enjoyed learning about this variety of different initiatives and efforts. And I imagine there are many people in the audience today that are either thinking of kicking off or in the process of working on a project like this. And I'm just wondering short of being able to look out at one year or two year benchmarks, because these are lengthy efforts, how can you meaningfully measure progress toward your goal, whether it's DEI or another data-driven initiative in that interim period?
HEATHER STAINES: And maybe we can start with Jessie.
JESSIE SLATER: Well, I think one of the first things that I think we sort of take for granted whenever you're starting something that has to do with data is that you're going to get data. So I think in the interim, what you can be measuring is your response rate, I think, and that's really important and learning from your lack of response in some areas. So I feel like for us, when we noticed we had lots of other responses to race and ethnicity, it wasn't that people weren't interacting with our categories.
JESSIE SLATER: It's that they just didn't feel they fit them. And so I think it's a learning experience that sort of-- the first few years when you're collecting data, no matter what it is, have you asked the right questions and are you presenting it to people in such a way that they feel like they should participate?
HEATHER STAINES: Right. So definitely not a set it and forget it type opportunity. Nicola, can you pick up on that?
NICOLA NUGENT: Yeah, definitely, just really second everything Jessie's said in terms of collection of inclusion and diversity data. And really, we can't measure progress without data. It's really absolutely fundamental. And I can't underscore enough. If you don't know who makes up your author reviewer or editor community, then you don't know what the challenges are that they're facing and you don't know what are the right interventions to make before you even think about whether those interventions are having the positive effect that you want them to have.
NICOLA NUGENT: So absolutely underscore what Jessie said. Measuring response rates is a good place to be if you're just starting out to collect data. But the thing you have to do is collect the data. That's absolutely important in terms of measuring progress.
HEATHER STAINES: Yeah. And, Sherri, some of the projects that you've mentioned today are a little bit more shorter term and obviously will be revisited in the longer term. What could you add to what we've talked about so far?
SHERRI YOUNG: Yeah, sure. So quite often, we're looking to our users to know have we used data to drive an improvement that has helped you? And so that could be in the form of user feedback. But it's also the feedback that we're not hearing. So we're looking at our customer center or support center and saying, well, what is your inquiry rate for a given year and let's compare that to the year prior where we didn't have these improvements in place.
SHERRI YOUNG: How often were we needing to help that author get through the workflow and how often were we hearing from them that something wasn't working right? And if I'm seeing a decline in those inquiries and those pain points, then we know that the project was worthwhile and that we've done our job. And then we look to the next pain point to keep improving and iterating on.
SHERRI YOUNG:
HEATHER STAINES: Right. And certainly, a lot of times, I feel like the answer that comes out of my mouth is you have to know your authors. You have to know your-- have your data in place around that. But it's also an author behavior issue. And I think that ties into the educational efforts and kind of reassurances that publishers can make. And, Nicola, I remember when we had the planning call, you had talked about trying to kind of gradually phase in some of these efforts.
HEATHER STAINES: What were some of the lessons that you took from that around author behavior and what did you learn?
NICOLA NUGENT: Yeah, I think what's really important is providing that reassurance to researchers when you're talking about diversity data. This came up in Jessie's talk. There is sensitivity around some of this data and some of it has greater sensitivity for people than others. And it's quite common for people to feel comfortable, for example, providing their gender identity data and a lot less comfortable providing information about their race and ethnicity.
NICOLA NUGENT: They're worried about how it's going to be used. Is it going to be used against them in some way? And so with the joint commitment initiative, we want to work together to try and provide that reassurance to the researcher community globally that to help communicate why we want to collect this data, what our intentions are, how we're going to collect it in a way that protects their privacy and their legal rights and what we're going to do with the data and why it's important, because for some people, it's just not obvious as to why you would need to collect this kind of information.
NICOLA NUGENT: So that kind of conversation with the research community-- it needs to happen from-- all publishers need to be starting to have those conversations and starting to speak with the same voice about why we want to collect this data and what we're going to use it for and really try to build that trust with researchers so that they know that we're collecting their data in a way that's safe and secure and protects their privacy.
HEATHER STAINES: Great. And, I mean, I think that that's so important to share that information. And as a person who often online has to fill in information, communicating with folks and trying to get them to actually answer those questions, why it's worth our time to fill up profiles, Jessie, what did you guys kind of find in that regard?
JESSIE SLATER: Well, I think-- I feel like in some respects, as we're getting younger people coming of age and interacting with our systems, they're more experienced in a world that is constantly asking these questions. But I'm also thinking-- and these are gut feelings. These aren't based on data necessarily. But some of the people we consider the most vulnerable are early career researchers. So they may be the ones who are most familiar with the idea of asking demographics.
JESSIE SLATER: But they're also the ones who feel that they might be most at risk if they give up this information about themselves. So I think-- what Nicola said, I can't possibly agree more. But we really need to communicate with this one voice about why are these data important and what do we want to do with them. And I think we do our best by putting statements out and saying this is why we would like to know this and this is how we're going to use these data.
JESSIE SLATER: But I think people are just inherently distrustful and I think that's fair, to be wary. I think that's probably a good position for most people to be in when you're giving up this sort of personal information. But I don't know if it's people writing more editorials, writing more commentary pieces, doing webinars, discussing about what are potential things we could do with this information in a positive way.
JESSIE SLATER: I think is what we need to do from the publishing side so that people feel there is an actual benefit, not just to the publishers knowing this, but to themselves down the road from having this information collected.
HEATHER STAINES: Right. And, Sherri, what have you learned throughout this process around author behavior?
SHERRI YOUNG: Yeah, so I think that inherently, our authors are smart, grateful, educated people and we want to keep that in mind as we look to improve our workflows. So my goal is for them to think less about what they're doing and think more about their research and what they're publishing. And so the more seamless I can make it for them, the better. And the more the data can drive that process, the better. So if the data is entered one time and that author never needs to retype their name, then that's a wonderful thing.
SHERRI YOUNG: I don't think we're there yet. But the easier we can make it for authors, the better it is for publishers and institutions and authors and just making the data flow smoothly throughout.
HEATHER STAINES: Right. And we couldn't have been more thrilled when NISO accepted the session. And one thing we're constantly keeping in mind is what can practical next steps and efforts in-- how can that come out of these conversations that we have? And I'll just put in a plug. If you've never participated in a NISO working group, you should. But Nicola referred to the wider industry collaboration, which I think is fantastic, particularly for smaller publishers or folks who are just getting started.
HEATHER STAINES: What kind of benefits can sharing information about these data projects bring that go beyond just the project itself and help us all kind of piggyback on efforts? And I'm going to-- Nicola, I'm going to end with you. So I'm going to start with Jessie and then move to Sherri and then we'll end with you, Nicola. So, Jessie.
JESSIE SLATER: Well, I feel like Nicola's presentation was sort of like sharing over a year's worth of work in 15 minutes. And, I mean, there's huge benefit in that. I mean, the working groups they have have done an incredible service to all these other publishers who might be looking to implement things on their own that we can look and see they've done all the legwork. And if you get the word out, then you can have people benefit from that.
JESSIE SLATER: And I think that would just be incredible. And the point Nicola made at the end about moving towards ideas of having NISO standards or ORCID would just be incredible, like Sherri saying, put your name in once, put your gender identity in once. If we could have these things associated with ORCID and they could be hidden or visible, you would have the option. But when you're submitting a paper, if you just put in your ORCID ID and all of that information is put in in the same way everywhere, that's a dream I think we could all hope for.
HEATHER STAINES: Yeah, it sounds pretty exciting to me. Sherri.
SHERRI YOUNG: Yeah, I think one way that the broader industry can benefit from the three presentations that we're talking about today is just working towards improvements with different tools. So Jessie mentioned ORCID and I mentioned-- I think Nicola and I both mentioned ScholarOne and these tools. So when-- there are third-party dependencies that our systems are plugging into and then we're all coming to the table and talking to one party and saying we need whatever it may be, gender or whatever.
SHERRI YOUNG: That helps drive the broader goal of improving it for everyone and the broader maturity of these systems trying to adapt to the new demands that we're talking about today.
HEATHER STAINES: Great. And for this question, I'm going to have Nicola have the last word. And then we'll close for today.
NICOLA NUGENT: Thanks. Yeah, I mean, I think for me, just the power of the collaboration has just been just really astonishing and fantastic. And to be able to today share the work of the joint commitment group with contributions from Elsevier, Springer Nature, ACS, all sorts of different publishers having input into this work, it just has made-- it really has achieved our goal of trying to accelerate progress.
NICOLA NUGENT: We really have done that and we've been facilitating conversations with system providers. So area systems are sharing their infrastructure designs with Clarivate to build functionality in ScholarOne. And those conversations have been facilitated because there's a group of four-day-old publishers. Who are their customers? Suddenly, we're all speaking with one voice.
NICOLA NUGENT: And so that's been extremely powerful. So I can't really overstate how important collaboration has been in pushing things forward much more quickly than I think they otherwise would have happened. And then, yeah, just echoing the comments of Jessie and Sherri both that the power of it is we do start to then standardize the language that we're using to talk about different categories or data types and the systems and the processes, making things much more interoperable, making data sets much more comparable, making everything much more seamless, as we've said a few times throughout.
NICOLA NUGENT: So that has-- collaboration has to happen in order for that seamless interaction with our systems and processes to become a reality.
HEATHER STAINES: Well, thank you all three of you so much for your presentations today. I know I've learned a lot, and I'm looking forward to opening this up to wider Q&A with our attendees. Thank you so much. [MUSIC PLAYING]
Segment:0 .
[MUSIC PLAYING]
HEATHER STAINES: Hi, I'm Heather Staines. I'm really excited to be here with you today to present this session-- using data to drive and support strategy, DEI and communications initiatives. I'm senior consultant and director of community engagement at Delta Think, where I also run the OA data and analytics tool. So I've gotten extremely interested in data and all aspects of data use to drive strategy within organizations.
HEATHER STAINES: So this session is one that's very close to my heart. I'm going to introduce our presenters today. I'll introduce all of them and then I will hand over to them. First, we're going to hear from Jessie Slater. Jessie is the executive assistant to the editor-in-chief for the science journals published by the American Association for the Advancement of Science. Prior to joining science in 2017, Jessie completed a PhD in archeology and prehistory at the University of Sheffield.
HEATHER STAINES: She also served as an editor for the department's postgraduate journal. She's previously worked at multiple US government agencies, including NIMH, USDA, and the NSF, where she was science education analyst in the division of undergraduate education. Next, we'll hear from Doctor Nicola Nugent. She is publishing manager, quality and ethics at the Royal Society of Chemistry, where she's the strategic lead for quality and impact across journals and books.
HEATHER STAINES: She has responsibility for the journals peer-review strategy as well as publishing ethics and inclusion and diversity. She brings together over four-- sorry. Nicole leads the joint commitment for action on inclusion and diversity and publishing, where she brings together over 40 publishing organizations to accelerate progress on IND and scholarly publishing. She's got over 15 years experience in STM publishing in a variety of operational and strategic roles with an editorial focus.
HEATHER STAINES: And she has a PhD in chemistry from the University of Bristol. And rounding out our panel today will be Sherri Young. In her role at IEEE, where she is IEEE global products and marketing product manager, Sherri oversees the institutional open access agreement workflow. She manages the abstracting and indexing partnerships for IEEE along with IEEE's data repository, IEEE DataPort. She's been with IEEE since 2009.
HEATHER STAINES: She was previously with Pearson Education as an archivist in the custom publishing division. Sherri holds a BS in photography from Drexel University and an MS in communication from Purdue University. She's also a member of the ALPSP Copyright Committee and holds a PMP certification. Thank you very much. And with that, I will hand over to our first speaker.
JESSIE SLATER: Hi, I'm Jessie Slater, and I'm speaking on fostering, diversity, equity, and inclusion in science. So I think many of us can agree that science has a diversity and inclusion problem. We know this overall, but what can we do to improve? There is much talk about fixing the STEM leaky pipeline and bringing more gender and ethnic diversity to academia and industry. It's scientific publishing sits at the very end of a very long pipeline.
JESSIE SLATER: Some are comfortable saying if we treat all authors and reviewers that come to us equally, we are doing our part. Seems like a bit of a cop out. Showing equal respect to everyone is the bare minimum that we must do. But as we're all becoming more and more aware, equality does not make equity. It is our duty to be more inclusive and make science more equitable in all that we do.
JESSIE SLATER: Scientific publishing plays a unique role at the interface between scientists and the wider world, whether it be other researchers in a niche field or the general public. As curators of scientific content, that has of course been given an imprimatur via peer review, our choices signal what and also who is important and seemingly worthy of attention. The clearest way to measure if you're making any improvement is to start with a baseline assessment.
JESSIE SLATER: This could be looking at a single journal or perhaps your entire organization. Is a lack of diversity and representation systemic for a particular field? Is the balance of representation reflected at your journal or is it better or worse? In 2021, AAAS produced its first DEI report detailing the gender and racial/ethnic makeup of its employees as well as those it serves through its programs and the science journals.
JESSIE SLATER: Just this past week, we've released the second annual report. This is not an example of an organization putting out PR to get praise for our diverse workforce, but rather an objective look at who we are, who we serve, and where we can and should do better. We pride ourselves on publishing the best research, but we also want to be representative of all the members of the existing and future scientific community.
JESSIE SLATER: Diversity is only a tool as we strive for equity and inclusion. Improving the diversity of authors, reviewers, advisors, and editors cannot be approached as an activity or goal but must instead underlie all our actions and efforts. I simply do not believe that editorial and publishing professionals can encourage diversity and inclusion without being transparent. We must start by acknowledging our shortcomings.
JESSIE SLATER: For instance, the preponderance of white male reviewers and corresponding authors, the dominance of Western institutions in published research. Research into implicit bias informs us that despite all the best intentions, many individuals have deeply held biases against women and researchers from developing nations, both of which can be easily discerned from authors' lists.
JESSIE SLATER: Ignoring these issues to date has accomplished little. But by acknowledging these biases, I am hopeful that we can actively work to correct them. In 2018, at the science journals, we began to collect demographics from authors, reviewers, advisors, and editors that interact with our content tracking system, also known as CTS. We now prompt all new users to respond to our questions at registration, always giving the option of no response as a choice to the various questions.
JESSIE SLATER: And existing users are able to provide demographics through their account settings. Prior to 2018, we relied on algorithmic identification of authors' gender to examine trends in authorship, a method that we know is inherently biased against those with non-Western names. Now with self-reported data from first authors, from 2018 to 2021, we find that for those individuals who have self-identified, we have equal acceptance rates for female and male first authors as well as corresponding authors, as you can see on the left-hand side of the screen.
JESSIE SLATER: We're hopeful that if users elect to make their demographics visible to editors, we also gain the ability to discern the gender of individuals with names that are unfamiliar to us. And we will ultimately be able to search our own system to readily identify female referees to something we are also lacking. Our recent acceptance rates by gender are encouraging, but women are still significantly underrepresented on manuscripts-- on submitted manuscripts, with the percentage of female first authors recently reaching 28% and the percentage of female corresponding authors rising from 28 to 29%.
JESSIE SLATER: Both are well below the representation of females in academia and scientific industry. Clearly, we've still got work to do here. The ratios of female first and corresponding authors on submitted manuscripts are also not in line with what we hope to see amongst our advisors and our board of reviewing editors. Self-reported data are the ideal, but they are not infallible.
JESSIE SLATER: In 2021, 63% of all authors and 78% of first authors provided data on their gender, but only 39% of all authors and 54% of first authors self-identified their race/ethnicity. Is there anything we can do to improve upon this result? And for instance, are there particular reasons underlying why we are seeing greater reporting of gender than race/ethnicity?
JESSIE SLATER: From these snapshots of our demographics process and the data we've collected, we can see we're improving in some areas but still coming up short in others. You cannot start out with the perfect system for collecting demographics. Only through individuals interactions with your prompts and their candid responses can you begin to evolve a scheme that is tailored to your community's needs.
JESSIE SLATER: As we endeavor towards equity and inclusion, we want to know where our authors and reviewers feel they fit in, rather than just another exercise in labeling and categorizing people. The questions you ask and the options you provide signal to your community what identities you believe belong in science. Do your options only fit a Western worldview? Are they gender inclusive?
JESSIE SLATER: Do they ask unnecessary questions, for example, marital status for women alone? First, as mentioned earlier, our reliance on the US census designations in our categories for race and ethnicity created much confusion for residents of other countries who frequently responded with one of the racial categories and their national identification, for example, Black British or Chinese Australian.
JESSIE SLATER: While I believe international data interoperability is what we should strive for, if you're an organization based in the same country that most of your users and our customers come from, then I would definitely recommend sticking with your national standards. As imperfect as those options might be, they are generally well understood and are likely a good means of measuring racial bias within your nation.
JESSIE SLATER: One of the most common responses given in the free-form text when people selected other as an ethnicity was Han ethnicity or Han Chinese. While in the US, we automatically consider this to be Asian/Pacific Islander, it is illustrative of the distinction between race and ethnicity in other countries. We might consider two groups such as Han and Uyghur as both Asian ethnicities.
JESSIE SLATER: But to those from China, these are not the same race and have a profound majority-minority relationship. In internal discussions, we're considering adding an additional question where individuals can indicate if they self-identify as a minority race/ethnicity within their country of residence and/or origin. Also, following the US census, we have lumped Hispanic or Latino into our general race ethnicity category.
JESSIE SLATER: We rely on radio buttons rather than checkboxes, making it difficult for someone to identify as white Hispanic or Black African Hispanic without selecting multiracial or other. The categorization of Hispanic or Latino also presents considerable confusion for individuals from Central and South America as well as those from the Iberian Peninsula in Europe. While we can argue about the distinction between race and ethnicity, I believe it is important to allow individuals the opportunity to select all options that would make up their identity rather than be forced to choose one over the other.
JESSIE SLATER: By expanding our net internationally, you really do begin just to understand how imperfect and socially circumscribed terms like race and ethnicity are. Again, the US census would have individuals with Middle Eastern or North African descent identify as white. But it's hard to see how this racial designation matches the lived experience of many of these people who are frequently profiled by Western police and security forces.
JESSIE SLATER: Plus, we've had some-- we've received some unexpectedly semi-positive comments from individuals who were pleased that we were taking the step to collect these data, but they hoped we could do more. Why weren't we considering gathering data on sexual orientation or disability status? These are also key facets of individual identity and a frequent source of discrimination in and out of science.
JESSIE SLATER: We are now actively considering adding these in our next rollout of categories to our demographics. So here are some quick lessons learned. I would definitely say radio buttons aren't helpful except for data analysts. But people rarely agree to fit neatly into distinct categories. I would recommend over explaining. Do not assume that the categories you've spent months deciding upon will make sense to anyone outside of your internal focus group.
JESSIE SLATER: Whilst working on this presentation and mulling over these data, I've spent a not insignificant amount of time considering the merits of asking about race, ethnicity, or both. We did receive a few responses that indicated individuals were human or that told us the question was racist or even that it was illegal to ask about race in their country. Through reading quite a few books on the misuse of race and science, I'm coming round to favoring continuing to query on race.
JESSIE SLATER: Like we mentioned early on, we're striving not for equality, but equity. And we cannot ignore the systemic racism that it was shown to certain groups through this country and others histories. By documenting the gender and race of our authors, reviewers, advisors, and editors, we hope we are doing our part to truly give previously excluded individuals a seat at the table.
JESSIE SLATER: Thank you for listening.
HEATHER STAINES: Thank you so much, Jessie, for kicking us off today. And we will hand over next to Nicola.
NICOLA NUGENT: Thank you. I'll just share my screen. OK, so my name is Nicola Nugent, and I'm publishing manager, quality and ethics at Royal Society of Chemistry. And I'm going to be talking today about a Royal Society of Chemistry LED initiative called the joint commitment for action on inclusion and diversity in publishing and specifically some work from that group, following on from what Jessie was saying about the classification and schemas for collection of diversity data.
NICOLA NUGENT: So just to kick off, what is the joint commitment for action on inclusion and diversity in publishing? Well, it's a collaboration between 49 publishing organizations, although that number does tend to go up every now and then as more organizations join. And together, we own over 15,000 scholarly journals. And the initiative was launched in June of 2020, following a workshop convened by the Royal Society of Chemistry where we shared our framework for action document.
NICOLA NUGENT: I don't have time today to go into the detail of that document. But essentially, it's a framework that maps out the steps we're taking to eliminate bias across our journals. And so we convened a workshop with other publishers to share that with them as we felt it would be useful. And in the same workshop, we also facilitated a conversation between the publishers present to talk about the areas in which publishers could work together and collaborate to accelerate progress on inclusion and diversity in publishing.
NICOLA NUGENT: And the output of that workshop was essentially the joint commitment initiative. You can see the full joint commitment statement if you follow the link on the top right-hand side of the slide. But what I've done is pull out the sort of four key areas that we identified and articulated as the areas that we felt publishers could work together on.
NICOLA NUGENT: And these form the commitments that we've made. So first of all, we've committed to understanding our research community. And that means collaborating to enable diversity data to be self-reported by members of our communities. We've committed to reflecting the diversity of our community. So this is about using diversity data to set or uncover diversity baselines and potentially to set targets to achieve improved and appropriate and inclusive representation amongst authors, reviewers, and editorial decision-makers.
NICOLA NUGENT: We've also committed to sharing success to achieve impact and this is really just about sharing our learning and knowledge, working together on developing resources, pooling our resources, transparently sharing policies, language and standards to move inclusion and diversity and publishing forward together. And finally, we committed to setting minimum standards on which to build. And as you'll see on a later slide, we launched those minimum standards in November of last year.
NICOLA NUGENT: And what's been really important from the start of the initiative and throughout is that, as I said, it's the diversity-- sorry. The joint commitment is a statement that's available on our website, but it's more than just words. It's very clearly about action. And so in order to make sure that we are taking the action we need to take in order to deliver on the commitments that we've made, we formed a working group.
NICOLA NUGENT: So each initiative-- sorry. Each organization who has signed up to the joint commitment initiative and is a member has at least one representative on a working group that meets three times per year to discuss progress and to set direction. And that's-- as I said, we currently have 49 member organizations. So it is a large group and we've been able to put in place smaller subgroups.
NICOLA NUGENT: So these are subgroups taking forward specific areas of action under each of the four priority areas that we identified. So just for example and perhaps most relevant for today, under the first priority of understanding our research community, we have two smaller subgroups looking at diversity data collection systems and one looking at diversity data collection questions. And as you can see, there are other subgroups taking forward different areas of action under the other commitments.
NICOLA NUGENT: And this timeline shows some of the achievements, milestones, and general work of the joint commitment group since its first launch back in June of 2020 at that workshop. And for the purposes of today, I'm going to focus on the diversity data collection aspect. So back in February of 2021, the group put forward a recommendation for a schema for the collection of gender data and has also been working on a schema for a collection of race and ethnicity.
NICOLA NUGENT: And I'll be able to share a draft of that with you in a moment. And throughout, we've also been working with and facilitating conversations between major peer-review systems providers [INAUDIBLE] manager and ScholarOne on the development of the sort of infrastructure and the system side of things to enable publishers to collect this data within their peer-review systems in a way that's compliant with various privacy regulations and so on.
NICOLA NUGENT: So in the next slides, I'm going to show those schemas that I talked about for collection of gender data and for the collection of race and ethnicity. But I just first want to be clear that what I'll be presenting is the work of data questions subgroup of the joint commitment, which is currently very ably and enthusiastically led by Holly Falk-Krzesinski from Elsevier. And the group has membership that represents at least 20 different publishers.
NICOLA NUGENT: Royal Society of Chemistry is one, but there are many others, including Springer Nature, WileyPLUS, ACS, and many more. So I want to make sure that they're given due credit for the work that they've done. So here is the gender identity question that's been endorsed by the joint commitment group. And it was-- as I said, it was agreed on in about February of last year and a number of publishers are starting to implement it.
NICOLA NUGENT: At first glance, it might seem relatively straightforward, but a great deal of thought and care and research went into the exact wording and the various options that are there. So first of all, we have the specific terminology, gender identity, rather than the more amorphous term, gender. We ask a very specific question and respondents can provide one answer. We make sure-- sorry-- the options that are available are terms that refer to gender identity and not biological sex and there's no distinction made between cis and transgender.
NICOLA NUGENT: And the options are not limited to binary options and there's also an-- on number six, where there's an option to prefer-- where you can prefer not to disclose your gender identity information in line with data protection regulations. And lastly, importantly in this-- this is an Elsevier example. There's a statement at the end here explaining the rationale for the collection of this data, how it will be used, and a link to the publisher's privacy policy.
NICOLA NUGENT: So as I say, this question schema has been endorsed by the joint commitment working group. But of course, we'll continue to review and update that as and when that becomes necessary. Next, I'll move on to talk about the race and ethnicity schema, which is vastly more complicated as Jessie touched on when she spoke about this. So our aim was to develop a global universal race and ethnicity schema, which, as Jessie mentioned, simply doesn't exist.
NICOLA NUGENT: There isn't one that you can just pick off the shelf and use. So we set up about developing one. And that was an iterative process that's through that joint commitment subgroup, where all members of the group contributed and shared their knowledge and insights and the types of diversity questions that they may have already been using. We had input from a number of different publishers.
NICOLA NUGENT: There's some examples here from an ACS inclusion and diversity survey from the Elsevier external Inclusion and Diversity Board. Springer Nature shared information about their staff inclusion and diversity survey. And there was also input from the sort of published literature and research and knowledge from there was taken into consideration. And importantly, Elsevier engaged an external subject matter expert, Professor Ann Morning, at New York University.
NICOLA NUGENT: Professor Morning's an associate professor of sociology and her research interests include race and ethnicity and especially racial classification and related topics. So all of that information and expertise was fed into drafting a schema for collecting race and ethnicity data that was then put forward for a testing. So the testing was done by a survey led by Elsevier's customer and market insights team.
NICOLA NUGENT: They sent the survey to over 100,000 global researchers. And the survey closed when they reached over 1% response rate, so about 11,000 total responses. And the respondents were publisher agnostic. The data was, I believe, from the Scopus database. They were not just Elsevier authors. In the survey, respondents were asked about their race and ethnicity using the draft schema and they were also asked about how they perceived the representativeness of the options that were provided and there were opportunities for free-text feedback.
NICOLA NUGENT: Respondents were also asked about how comfortable they would be with sharing their race and ethnicity information in the context of serving as an editorial board member reviewer or author. And again, there were free text options available to provide feedback. So all of that stakeholder feedback as well as more input from our subject matter expert went into revising the question to its current form, which I'll show you now.
NICOLA NUGENT: And just to set out, from the beginning, the intention is not to devise a single objective or a prescriptive truth about a researcher's race and ethnicity. Rather, the objective is to develop a set of options that resonate with communities that we serve from around the globe so that they're willing to self-report their racial and ethnic identity. And the level of aggregation and the number of options that are offered to respondents has to parallel the scale of diversity data we can practically accommodate and make use of.
NICOLA NUGENT: And this approach ties in really with general survey best practice but also importantly with the legitimate interest requirement of legislation such as GDPR and others where it's stated that we should not capture data that we do not intend to actively use. So as you can see, this is not a single combined ethnoracial question. It's split into two questions as a two-question format. The first part asks for ethnic origins or ancestry and the options are geographic areas.
NICOLA NUGENT: So respondents are asked to select all the geographic areas from which their families ancestors originated. And as you can see, the responses are geographic areas. And so the goal here is in the context of a worldwide survey, you need to have a set of responses that seem sort of reasonably familiar and pertinent to people from around the globe. And geographic location and ancestry have that salience across the world.
NICOLA NUGENT: Only a small minority of nations employ race and their official data collection, making racial self-identification a much less common experience in most areas of the world than it is in the United States or the United Kingdom. So in an international survey, a question on geographic origin or ancestry is likely to furnish more complete data and a higher response rate than racial categories alone will do.
NICOLA NUGENT: Having said that, there is a second question there, as you can see, which does use race labels. And so when we think about the different locations where a person's ancestors may have resided, there's potentially likely to be one with which a respondent is usually identified or usually identifies themselves, especially if their physical appearance is associated with that geographical area. And so that strand of ancestry is generally what is captured by these race labels.
NICOLA NUGENT: And these labels are likely to be particularly influential for individuals opportunities and life outcomes. And so they are particularly informative in the context of inclusion and diversity work. So despite the limitations of racial classifications already noted, especially in the context of a global survey, asking about race can in some cases provide additional information that is useful in the context of inclusion and diversity work.
NICOLA NUGENT: Lastly, just-- this is the text of a sort of proposed introductory statement to go with this question. I won't go through it in detail, but it will be available through sharing of the slides. But this was crafted at taking into account feedback from researchers about their level of comfort and trust in terms of sharing this information.
NICOLA NUGENT: And it's important to be explicit about how the data will be used, how it will not be used, why it's being collected, who will access it, how it will be stored, and so on. So finally, just some next steps. The race and ethnicity question that I showed is still sort of formally a draft. It will go to the joint commitment working group for endorsement in early March.
NICOLA NUGENT: There's work ongoing, as I mentioned, to develop the architecture and the systems to collect this within major peer-review systems, Editorial Manager, and ScholarOne. Elsevier will be integrating both the gender identity and the race and ethnicity schemas into their Editorial Manager implementations. We know we need to develop a robust communication plan, both in terms of getting those schema out there to publishers, but also and importantly to researchers to build trust and help them understand why we're collecting this data and what we're doing with it.
NICOLA NUGENT: It's potential to publish a scholarly, peer-reviewed article with Professor Morning. And we also hope to work with NISO and others to expand the use of these schemas across publishers. I'll finish there and say thank you very much and hand over to Sherri.
HEATHER STAINES: Yeah, thanks, Nicola. And while Sherri sets up, I just want to say we're shifting gears slightly to talk about some other types of data projects, but I think you'll see that there's a lot of commonalities that cross these different efforts. Take it away, Sherri.
SHERRI YOUNG: OK, great. Thank you. So hi, everyone. I'm Sherri Young, IEEE product manager. And I'm going to provide a few publisher use cases, the center around data, but a bit different than the two other presentations you've heard so far. So I'm responsible for two major products at IEEE OA and abstract and indexing and both of those touch on data quite a bit.
SHERRI YOUNG: For our open access program, I oversee the institutional OA agreements, as Heather mentioned earlier. And I'm going to focus on two use cases around that workflow. And then with abstract and indexing, it's all about data. So with that will come the third use case I'll share today. So just as an agenda in trying to provide more information on what I'm going to go over, the first use case speaks to the process of OA article matching.
SHERRI YOUNG: And what that means-- it may vary greatly depending on your role in the scholarly publishing industry. So the maturity of the systems and the data that one encounters really varies. Secondly, I'm going to speak to a recent project that we took on that many publishers are likely looking to undertake in 2022 around informing authors about available funding as part of an OA agreement with a publisher.
SHERRI YOUNG: And then finally, I'll put on my A&I hat and share about how data sharing is evolving and that includes outward reporting around OA as well as protecting Oracle data, and user data, author data, protecting all the data around derivative products. So going into the first use case, I mentioned on OA article matching on data this may be a familiar process to many of you.
SHERRI YOUNG: But I thought it important to highlight for those not in the OA trenches that data drives the entire submission and publication process for the funded APC author. So who is the funded author? They may not know that they're the funded author and that's what this is about. So funding is typically available to them and they may be informed about halfway through the publication lifecycle.
SHERRI YOUNG: So that's why the accuracy of the data entered in the initial step is so important. So I'm showing on screen here a-- we use ScholarOne. So this is a ScholarOne interface where an author is first entering their metadata. When that happens, assuming the article is accepted sometime later after peer review, the data comes back into play for a payment of the article processing charge.
SHERRI YOUNG: Several API calls are made from ScholarOne to Ringgold to internal systems. For IEEE, that could mean for CCC systems and back again in order to service available funding for that author. So when data is entered incorrectly, as I-- I quite quickly went over all those different APIs that are talking to each other.
SHERRI YOUNG: And when that data is entered incorrectly, the matching criteria on the institution's profile could be incomplete and the data chain breaks down. So that author institution will reach out for help. If the author isn't familiar with this type of workflow-- maybe they haven't published before at all. Maybe they haven't published OA before. They may simply pay that APC out of pocket if the data hasn't connected two and two together to make the match to available funding.
SHERRI YOUNG: Or they could turn around and say, you know what? I'm not going to publish at all. So as a publisher, our role is often making sure that the data is clean, the systems are functioning as they should be and that we're making that match 100% of the time when funding exists. Trust me, no one wants to enter manually refunding the author when a trip is done at the end of the year and an APC needs to be used, but now an author's credit card is due for a refund.
SHERRI YOUNG: So when the data is right from the beginning between the author, the institution, and the publisher, it's a better workflow and more efficient publishing lifecycle for all involved. In order to reach an end goal of clean, usable data, we have to start publishing the workflow with a clear set of expectations and that includes the role of the author. So the more effort we put behind educating the author, providing clean and consistent user interfaces with functioning APIs, the more likely we are to get to 100% identification of author funding availability.
SHERRI YOUNG: So that first use case ties into what I'm going to share here and piggyback on informing that author, getting the word out, and making sure that that data that's being shared through multiple systems is accurate. So in 2021, IEEE conducted an author awareness campaign to make eligible authors aware of existing funding for their article processing charge.
SHERRI YOUNG: Ensuring data accuracy was a priority of the campaign. Much like a bank account, institutional OA profiles are a living, constantly changing data point. And so we take the role of that communication seriously. So imagine with me for a minute how this could go without a firm hand on data accuracy. On Monday, the publisher emails you and says, hey, you've been identified as a potential author for this journal and congratulations.
SHERRI YOUNG: Your institution has an OA agreement with us and that may cover your charges. Tuesday comes and your colleague or someone maybe you don't even know, another author from your institution goes crazy and publishes five articles, using up all the APCs on that profile. So now Wednesday comes. You're ready to submit your article that you were thinking about on Monday and now all the tokens or APCs have been used.
SHERRI YOUNG: But wait, the publisher just told you you had funding available. So this is a very easy scenario that can come about if we're not monitoring that data closely, we're not careful about the communication that we're putting outwards to authors and to institutions to inform them that funding is available. So in that workflow, where does the data come from?
SHERRI YOUNG: So for us, IEEE-- the data-- we have a data warehouse and we're using Tableau. And we're having even internal systems talking to one another to make sure that we're servicing real-time data that's accurate, that we're matching the author to the institution using criteria that's standardized and that we're also observing opt-out users through prior campaigns.
SHERRI YOUNG: So there's a great deal of collaboration in that workflow. It could be between myself as a product manager. We have a product marketing team. We have a great deal of publication staff that monitors that data and is inputting quite a bit of that data through manuscripts, through membership data. And then there's sales, who ultimately owns a relationship with the institution.
SHERRI YOUNG: So to ensure the campaign's success in informing authors of eligible funding with accurate information, data is pulled from a number of sources as I'm showing on screen here. We're careful to observe anything regarding GDPR and the campaign is relying on those data points to send outward communication. So did we do a good job?
SHERRI YOUNG: The awareness campaign was successful in reaching the goal to increase awareness among eligible authors and secondly to engage our partner institutions with communicating to their authors. I mean, we are very much a team with our institutions trying to make sure that that communication to the author is clear, timely, and being absorbed. So our unsubscribe rate was quite low at just 0.42%. And we did receive author feedback, like the one I've included here in the blue box.
SHERRI YOUNG: So going forward, we're going to be repeating the campaign in 2022 with an increase in data needs as the complexity in volume of OA increases and the program matures. So finally, I'm going to share with you that A&I data reporting and sharing. And so data reporting in an OA world has evolved and is continuing to evolve quickly than I think some of us can keep track of.
SHERRI YOUNG: So the final use case is around that. So for OA, most license agreements today include specific terms around required data and reporting on a schedule to the institution. The goal of this reporting is to allow the institution to track APC usage and catching those articles that may slip through the cracks, as I mentioned in the first use case. So reporting not only on OA articles, but those published traditionally will provide a complete data set that can be analyzed by the institution and ultimately our customer.
SHERRI YOUNG: So reporting needs are growing. And this isn't only from the institution, but also in the way of funder mandates, repository depositing, and A&I partners that are downstream indexers. So all this put together can mean some risk in sharing too much information while still meeting those supporting demands. From the publisher perspective, the balance with data reporting really speaks to the importance of data governance, consistent schemas that we have relied on for a number of years evolving to include things like license information, Ringgold or a author affiliation, and finally, the role that the author plays in owning their data and research.
SHERRI YOUNG: So I'd like to end my presentation today with a brief mention of the importance of revisiting agreements through the lens of customers and authors around data. For publishers, that can mean protecting author data from derivative works. In publishers like IEEE that have been around for quite some time, some agreements may have been entered pre-GDPR and pre-OA.
SHERRI YOUNG: And we need to go back and revisit those terms and make sure that we're still comfortable and they still apply in 2022 and going forward. So with the knowledge that we have around data today, it's our responsibility to protect author data with concrete terms and expectations. This includes an active audit of the A&I agreements to ensure our downstream indexes are observing the same level of respect for user data that we've taken on as the publisher.
SHERRI YOUNG: So I'd like to thank you for taking time-- to have me here today, and I look forward to our question and answer session.
HEATHER STAINES: Thanks so much, Sherri. And I'll just ask all the speakers if they may turn their cameras back on at this point. And we're going to kick off with a few questions amongst the panel. And I will say unfortunately, for our live Q&A on the 17th, Nicola is not going to be able to join then. But we can collect questions for her and you can likely reach her in the discourse forum. So we're just going to have some questions today to kick things off.
HEATHER STAINES: So I enjoyed learning about this variety of different initiatives and efforts. And I imagine there are many people in the audience today that are either thinking of kicking off or in the process of working on a project like this. And I'm just wondering short of being able to look out at one year or two year benchmarks, because these are lengthy efforts, how can you meaningfully measure progress toward your goal, whether it's DEI or another data-driven initiative in that interim period?
HEATHER STAINES: And maybe we can start with Jessie.
JESSIE SLATER: Well, I think one of the first things that I think we sort of take for granted whenever you're starting something that has to do with data is that you're going to get data. So I think in the interim, what you can be measuring is your response rate, I think, and that's really important and learning from your lack of response in some areas. So I feel like for us, when we noticed we had lots of other responses to race and ethnicity, it wasn't that people weren't interacting with our categories.
JESSIE SLATER: It's that they just didn't feel they fit them. And so I think it's a learning experience that sort of-- the first few years when you're collecting data, no matter what it is, have you asked the right questions and are you presenting it to people in such a way that they feel like they should participate?
HEATHER STAINES: Right. So definitely not a set it and forget it type opportunity. Nicola, can you pick up on that?
NICOLA NUGENT: Yeah, definitely, just really second everything Jessie's said in terms of collection of inclusion and diversity data. And really, we can't measure progress without data. It's really absolutely fundamental. And I can't underscore enough. If you don't know who makes up your author reviewer or editor community, then you don't know what the challenges are that they're facing and you don't know what are the right interventions to make before you even think about whether those interventions are having the positive effect that you want them to have.
NICOLA NUGENT: So absolutely underscore what Jessie said. Measuring response rates is a good place to be if you're just starting out to collect data. But the thing you have to do is collect the data. That's absolutely important in terms of measuring progress.
HEATHER STAINES: Yeah. And, Sherri, some of the projects that you've mentioned today are a little bit more shorter term and obviously will be revisited in the longer term. What could you add to what we've talked about so far?
SHERRI YOUNG: Yeah, sure. So quite often, we're looking to our users to know have we used data to drive an improvement that has helped you? And so that could be in the form of user feedback. But it's also the feedback that we're not hearing. So we're looking at our customer center or support center and saying, well, what is your inquiry rate for a given year and let's compare that to the year prior where we didn't have these improvements in place.
SHERRI YOUNG: How often were we needing to help that author get through the workflow and how often were we hearing from them that something wasn't working right? And if I'm seeing a decline in those inquiries and those pain points, then we know that the project was worthwhile and that we've done our job. And then we look to the next pain point to keep improving and iterating on.
SHERRI YOUNG:
HEATHER STAINES: Right. And certainly, a lot of times, I feel like the answer that comes out of my mouth is you have to know your authors. You have to know your-- have your data in place around that. But it's also an author behavior issue. And I think that ties into the educational efforts and kind of reassurances that publishers can make. And, Nicola, I remember when we had the planning call, you had talked about trying to kind of gradually phase in some of these efforts.
HEATHER STAINES: What were some of the lessons that you took from that around author behavior and what did you learn?
NICOLA NUGENT: Yeah, I think what's really important is providing that reassurance to researchers when you're talking about diversity data. This came up in Jessie's talk. There is sensitivity around some of this data and some of it has greater sensitivity for people than others. And it's quite common for people to feel comfortable, for example, providing their gender identity data and a lot less comfortable providing information about their race and ethnicity.
NICOLA NUGENT: They're worried about how it's going to be used. Is it going to be used against them in some way? And so with the joint commitment initiative, we want to work together to try and provide that reassurance to the researcher community globally that to help communicate why we want to collect this data, what our intentions are, how we're going to collect it in a way that protects their privacy and their legal rights and what we're going to do with the data and why it's important, because for some people, it's just not obvious as to why you would need to collect this kind of information.
NICOLA NUGENT: So that kind of conversation with the research community-- it needs to happen from-- all publishers need to be starting to have those conversations and starting to speak with the same voice about why we want to collect this data and what we're going to use it for and really try to build that trust with researchers so that they know that we're collecting their data in a way that's safe and secure and protects their privacy.
HEATHER STAINES: Great. And, I mean, I think that that's so important to share that information. And as a person who often online has to fill in information, communicating with folks and trying to get them to actually answer those questions, why it's worth our time to fill up profiles, Jessie, what did you guys kind of find in that regard?
JESSIE SLATER: Well, I think-- I feel like in some respects, as we're getting younger people coming of age and interacting with our systems, they're more experienced in a world that is constantly asking these questions. But I'm also thinking-- and these are gut feelings. These aren't based on data necessarily. But some of the people we consider the most vulnerable are early career researchers. So they may be the ones who are most familiar with the idea of asking demographics.
JESSIE SLATER: But they're also the ones who feel that they might be most at risk if they give up this information about themselves. So I think-- what Nicola said, I can't possibly agree more. But we really need to communicate with this one voice about why are these data important and what do we want to do with them. And I think we do our best by putting statements out and saying this is why we would like to know this and this is how we're going to use these data.
JESSIE SLATER: But I think people are just inherently distrustful and I think that's fair, to be wary. I think that's probably a good position for most people to be in when you're giving up this sort of personal information. But I don't know if it's people writing more editorials, writing more commentary pieces, doing webinars, discussing about what are potential things we could do with this information in a positive way.
JESSIE SLATER: I think is what we need to do from the publishing side so that people feel there is an actual benefit, not just to the publishers knowing this, but to themselves down the road from having this information collected.
HEATHER STAINES: Right. And, Sherri, what have you learned throughout this process around author behavior?
SHERRI YOUNG: Yeah, so I think that inherently, our authors are smart, grateful, educated people and we want to keep that in mind as we look to improve our workflows. So my goal is for them to think less about what they're doing and think more about their research and what they're publishing. And so the more seamless I can make it for them, the better. And the more the data can drive that process, the better. So if the data is entered one time and that author never needs to retype their name, then that's a wonderful thing.
SHERRI YOUNG: I don't think we're there yet. But the easier we can make it for authors, the better it is for publishers and institutions and authors and just making the data flow smoothly throughout.
HEATHER STAINES: Right. And we couldn't have been more thrilled when NISO accepted the session. And one thing we're constantly keeping in mind is what can practical next steps and efforts in-- how can that come out of these conversations that we have? And I'll just put in a plug. If you've never participated in a NISO working group, you should. But Nicola referred to the wider industry collaboration, which I think is fantastic, particularly for smaller publishers or folks who are just getting started.
HEATHER STAINES: What kind of benefits can sharing information about these data projects bring that go beyond just the project itself and help us all kind of piggyback on efforts? And I'm going to-- Nicola, I'm going to end with you. So I'm going to start with Jessie and then move to Sherri and then we'll end with you, Nicola. So, Jessie.
JESSIE SLATER: Well, I feel like Nicola's presentation was sort of like sharing over a year's worth of work in 15 minutes. And, I mean, there's huge benefit in that. I mean, the working groups they have have done an incredible service to all these other publishers who might be looking to implement things on their own that we can look and see they've done all the legwork. And if you get the word out, then you can have people benefit from that.
JESSIE SLATER: And I think that would just be incredible. And the point Nicola made at the end about moving towards ideas of having NISO standards or ORCID would just be incredible, like Sherri saying, put your name in once, put your gender identity in once. If we could have these things associated with ORCID and they could be hidden or visible, you would have the option. But when you're submitting a paper, if you just put in your ORCID ID and all of that information is put in in the same way everywhere, that's a dream I think we could all hope for.
HEATHER STAINES: Yeah, it sounds pretty exciting to me. Sherri.
SHERRI YOUNG: Yeah, I think one way that the broader industry can benefit from the three presentations that we're talking about today is just working towards improvements with different tools. So Jessie mentioned ORCID and I mentioned-- I think Nicola and I both mentioned ScholarOne and these tools. So when-- there are third-party dependencies that our systems are plugging into and then we're all coming to the table and talking to one party and saying we need whatever it may be, gender or whatever.
SHERRI YOUNG: That helps drive the broader goal of improving it for everyone and the broader maturity of these systems trying to adapt to the new demands that we're talking about today.
HEATHER STAINES: Great. And for this question, I'm going to have Nicola have the last word. And then we'll close for today.
NICOLA NUGENT: Thanks. Yeah, I mean, I think for me, just the power of the collaboration has just been just really astonishing and fantastic. And to be able to today share the work of the joint commitment group with contributions from Elsevier, Springer Nature, ACS, all sorts of different publishers having input into this work, it just has made-- it really has achieved our goal of trying to accelerate progress.
NICOLA NUGENT: We really have done that and we've been facilitating conversations with system providers. So area systems are sharing their infrastructure designs with Clarivate to build functionality in ScholarOne. And those conversations have been facilitated because there's a group of four-day-old publishers. Who are their customers? Suddenly, we're all speaking with one voice.
NICOLA NUGENT: And so that's been extremely powerful. So I can't really overstate how important collaboration has been in pushing things forward much more quickly than I think they otherwise would have happened. And then, yeah, just echoing the comments of Jessie and Sherri both that the power of it is we do start to then standardize the language that we're using to talk about different categories or data types and the systems and the processes, making things much more interoperable, making data sets much more comparable, making everything much more seamless, as we've said a few times throughout.
NICOLA NUGENT: So that has-- collaboration has to happen in order for that seamless interaction with our systems and processes to become a reality.
HEATHER STAINES: Well, thank you all three of you so much for your presentations today. I know I've learned a lot, and I'm looking forward to opening this up to wider Q&A with our attendees. Thank you so much. [MUSIC PLAYING]
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