Data Papers are Witness to Trusted Resources in Grey Literature: Driving Access to Data thru Public Awareness
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Lecture/Conference
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Transcript: English(auto-generated)
00:01
Thank you very much. Interesting, the first two speakers exposing great literature to wider audiences, and one through means of training and education, and another with automation, and I have another way of pushing great literature to wider audiences, hopefully.
00:23
A few years ago, I heard the term data papers. And as with all developments in the field of information, I began to think how are data papers related to great literature, and how does GreenNet, as an organization,
00:41
incorporate them in and among its own resources. What sparked my initiative, or initial initiative, was that data papers were a new document type of great literature, and that they had a place within GreenNet's ongoing enhanced publications project, which was dated back to 2011.
01:05
So in early April of this year, I had the opportunity to attend a meeting in Barcelona where Peter Dorn, where's Peter, there he is, Director of the Dans Data Archive in the Netherlands, presentation on fair data principles.
01:21
I hadn't heard of the term before. But there was a moment of inspiration for myself. I made a hypothesis that data papers were one of the most tangible means of learning the fair data principles.
01:41
And with that, I then returned quickly back to Amsterdam, very excited about the prospects, and I phoned Plato Smith, and Plato is a data management librarian at the University of Florida, who was at last year's conference, GL18 in New York.
02:02
I knew I could always talk on Jerry, there he is, for the technical support needed, as well as the ongoing support by the Dans Data Archive, where GreenNet's data collection has been housed, and is housed. However, the insight and expertise of a data management librarian,
02:24
such as Plato, was imperative in this project, stands or falls. Within a week, and just under the wire of closing the GL19 call for papers, held to that myself, our proposal was written up and submitted
02:42
to the GL19 program committee. My presentation on behalf of our project team is quite straightforward. In other words, a long introduction, but the remainder of the presentation will be straightforward. It reflects on the work over the past six months,
03:05
or should I say, the first six months of GreenNet's data papers project. The presentation briefly looks at a definition of data papers, the background of the project,
03:21
developments of GreenNet's data collection, the instrument of a data paper template, the population of this project in terms of data sets, the review process of data papers, the publication of GreenNet's data papers,
03:42
some early project results, and a spin-off, a little extra, in New Orleans they call it Lagniappe, something extra was from the initial data papers project, something unexpected. The final will be, what is the follow-up
04:04
to this project? A definition that I pulled out was from Wikipedia. I'm a fan of Wikipedia. Scholarly publications of a searchable metadata document
04:22
describing a particular online accessible data set or group of data sets published in accordance to standard academic practices. As such, data papers represent a scholarly communication approach to sharing data,
04:41
or data sharing. The background of this particular, as I mentioned in the introduction, was that we had a enhanced publications project going. It's ongoing. So this project of data papers is added
05:01
or comes within the enhanced publications project. And that project, that is, I think the definition described by the driver, is that a publication that is enhanced with three categories of information, extra materials and post publication data.
05:23
Further, it combines textual resources, that is documents intended to be read by human beings, which contain an interpretation or analysis of primary data. And enhanced publication inherently contribute to the review process of great literature,
05:42
as well as the replication of research and improve visibility of research results in the scholarly communication chain. So it was, for me, the data paper project fit as a glove within the enhanced publications project.
06:03
In 2011, again with Don's data archive, we had a survey. The survey was a number of questions using SurveyMonkey. We asked, we had a population of our authors,
06:23
researchers within the great net community, going back to 1993. Of that, there were 95 of the 300 or more authors that were selected for this population. The 50 of the 95 answered the questionnaire.
06:42
And it was basically, are you prepared? What are your attitudes? Are you prepared to share your research data? We're talking about 2011. So I mean, that was pretty good. The response was positive. At least 50% of those who were surveyed
07:02
were willing to share their data. Whether they were willing to share it within the Don's data archive or within one of their own national data archives, that was something to be further discussed. But what we did immediately the next year in 2012, we said, okay, let's move on to the data acquisition,
07:24
the publication of the datasets in the Don's data archive, and incorporate this into a workflow, great net's workflow. And that is the workflow actually of the GL conference series. The results being also that we wanted
07:42
to link the data to existing records that were not only in the Don's data archive, but were also in the open, great archive, which you're very familiar with. We are all of great net's content is made accessible.
08:00
From 2012 on, it's in the workflow and we have now accomplished by 2017, 30 datasets, 30 or sets of data that are in and published in that archive. It doesn't sound like a lot,
08:20
but it certainly was for us representative. And it was the inspiration for this leg of the enhanced publications project, the data papers project. The next step, or should I say one of the first steps
08:44
was a tool, an instrument in which the data papers could be, we didn't wanna, we didn't want to develop the wheel for data papers because it's been around for a number of years, but we wanted to streamline it
09:02
for the great literature community and for great net's community. So we went out, found three templates that would, you could find many more, but we took three, the research data journal from which is in the Netherlands,
09:20
the journal of open humanities data and the journal of open psychology data. And we looked at those templates and then we took from them much, but we did important that when you were asking a researcher or an author,
09:44
certainly one who has had done a data set, created a data set five years prior, and you're asking them retrospectively to create a data paper, which you need to do is to make sure that the note fields that are in the template
10:01
in this instrument, that they are sufficient and clear and they also give examples that can be used so that it facilitates the implementation of and the creation of the data template. The data template is not the end of the template, it's actually the model that is used
10:23
in order to create the final data paper. Now we have this, as I mentioned, we had a population of 30 sets of data.
10:40
And from this, we were able to constitute that 15 authors or researchers were first authors or sole authors to the data that was in the Downs, in Graynet's collection in Downs. So what we did, we sent out then the data template,
11:07
we sent out the hour, that is our team, that data papers team, our example of what we did with a data paper and we asked them their willingness,
11:22
these 15 authors, researchers, their willingness to write a data paper. Unexpectedly, 24 hours after the first request, we got Daniela Aloia and Alton from New York Academy of Medicine
11:40
immediately came back and said, yes, we're gonna submit a data paper. And then we came and said, so actually what happened was in a very short period of time, we had, and I think, oh, no, no, no, no, no, no, I'm wrong.
12:02
You're right, you're right, you're right. Okay, okay, and I said it was gonna be straightforward, but I'm not, I just lied. Okay, so what we did was we were getting, we have now five data papers who are on the way
12:21
and we, I do want to mention that there is a review process, all great literature, whether it's data or whether it's full text or whether it's bibliographic meta metadata has to be in some way reviewed and we have a review process. There will be a paper on this explanation. A data paper that is submitted and published
12:46
first in the Donsdata archive is something which you can call a preprint and it is scheduled, once it is accepted by our data management library, it is then eligible for a publication as a data article in the
13:05
Great Journal. Promotion, the Great Journal, two data articles in the autumn issue of the Great Journal and we're really happy that this could happen prior to this conference today. The results of that paper project, the template has been
13:25
compiled, edited, the six authors have indicated interest. I already told you that there was eight. Five have been published. The data at papers is now a term within as a document type within the gray source index and you'd say
13:42
well the gray source index was that it's a web page on GrayNet's website and it gives a hundred document types of great literature, but it is the most trafficked webpage that GrayNet has. It's unbelievable and it's the simplest one but it's the most trafficked one. And of course data papers is one of the
14:06
15 points of the PISA declaration. It supports the PISA declaration on policy for great literature. Spin-off, I think I have a minute.
14:22
A spin-off is that you have the Great Journal and you have the GL conference series and the GL conference series, this was the population for the data sets, but there are other articles that are coming into the Great Journal that were never in the GL series. So we went to those that were not in GL and we
14:44
asked are they willing and already to have acknowledged that they are starting to work on a data paper. So it's for us a great. And then the final spin-off is that seeing if we're going to go further with this and certainly within
15:02
the workflow, we have to have perhaps a workshop, a training on the creation of data papers and we are going to have a test, a pilot of this workshop at the University of Florida. They will be hosting this and we follow up to the project. We're just getting started, it's only six months. It's got to be implemented
15:24
in the workflow not only of the conference series but also of the Journal, the Great Journal. We have to get user statistics, we have to see how data citations examined and references to the data, to itself, and
15:42
author feedback. Grazie.