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Sharing research data to comply with a journal policy: Experience of a 
first-time depositor 
 
Marianne D. Burke PhD, MA, AHIP 
Associate Professor Emerita 
University of Vermont 
Burlington, VT 
 
ABSTRACT 
Background Journals in health sciences increasingly require or recommend that 
authors deposit the data from their research in open repositories. The rationale for 
publicly available data is well understood but many researchers lack the time, 
knowledge, and skills to do it well, if at all. There are few descriptions of the pragmatic 
process a researcher author undertakes to complete the open data deposit in the 
literature. When the author’s manuscript for a mixed methods study was accepted by a 
journal that required shared data as condition of publication, she proceeded to comply 
despite uncertainty with the process. 
 
Purpose The purpose of this study is to describe the experience of an information 
science researcher and first-time data depositor to complete an open data deposit. The 
study illustrates the questions encountered and choices made in the process.  
 
Methods To begin the data deposit process, the author found guidance from the 
accepting journal’s policy and rationale for its shared data requirement. A checklist of 
pragmatic steps from an open repository provide a framework that the author used to 
outline and organize the process. Process steps included organizing data files, 
preparing documentation, determining rights and licensing, and determining sharing and 
permissions. Choices and decisions included which data versions to share, how much 
data to share, repository choice, and file naming. Processes and decisions varied 
between the quantitative and qualitative data prepared.  
 
Results The author deposited data in two datasets and documentation for each in 
Figshare open repository, thus meeting the journal policy requirements to deposit 
sufficient data and documentation to replicate the results reported in the journal article 
and also meeting the publication deadline to include a Data Availability Statement with 
the published article. 
 
Conclusion This experience illustrated some practical data sharing issues faced by a 
librarian author seeking to comply with a journal data sharing policy requirement for 
publication of an accepted manuscript. Both novice data depositors and data librarians 



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may find this individual experience useful for their own work and the advice they give to 
others. 
 
BACKGROUND  

Journals in health sciences increasingly require or recommend that authors 
deposit the data from their research in open repositories. In October 2019, the Journal 
of the Medical Library Association (JMLA) announced its policy to require authors to 
deposit deidentified research data for all original investigations and case report articles 
accepted for publication in October of 2019 [1]. Although many library and information 
science journals recommend data sharing, JMLA was the first and still possibly the only 
one to require it.  

The rationale for publicly available data—that it fosters scientific progress and 
enables replication and reproducibility of research—is well described in the literature 
and in the JMLA editorial justifying its requirement [2]. Data sharing workflows for 
researchers and for repositories are described at a conceptual level by Austin et al [3] 
with little process detail. Educational methods for teaching research data management 
methods to students and researchers are described in a review by Corti and Van den 
Eyden. [4]. They recommend skills training and active learning methods but do not 
cover specific content on how to share data and what data to share. 

While most researchers support data sharing and agree with its value, studies 
show that many researchers have uncertainty and concerns with the pragmatic aspects 
of data sharing [5-7]. A 2018 survey by Stuart et al of 7,000 researchers from various 
disciplines and experience levels found that 76% rated the importance of discoverable 
data highly, while 46% reported organizing data and presenting data as problematic. 
Lack of time was a problem for 35%, and repository choice a problem for 33% [8]. A 
survey of clinical trial investigators, by Tannenbaum et al, found that they spent a 
median of 18 hours (IQR 8 – 40) per data set shared [9]. A meta-synthesis of qualitative 
studies of researcher experience with data sharing found that “researchers lack time, 
resources and skills to effectively share their data in public repositories”[10]. 

I submitted a manuscript to JMLA after its data sharing policy was announced, 
but prior to when it went into effect. Technically I was not required to share the data for 
manuscript. But when my manuscript was accepted for the July 2020 issue, I decided to 
share the data from my research motivated by belief in the value of data sharing and by 
the journal requirement policy. Like other researchers, I was uncertain about how to 
organize and prepare the data, and was apprehensive about the time it would take, but I 
decided to do it.  

The accepted manuscript (now a JMLA article) is a mixed methods assessment 
of a clinical evidence technology based on a survey of 32 primary care providers 
(PCPs) who participated in an earlier randomized trial concerning their use (or non-use) 



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of the technology during that trial, and interviews with 11 PCPs in the intervention arm 
[11]. 

While advantages and obstacles to researcher data sharing are well described, 
few studies describe researchers’ data sharing experiences in detail. The purpose of 
this case study is to describe my experience as an information science researcher and 
first-time data-depositor, and illustrate the practical questions and issues that I 
encountered in the process. 
 
METHODS 

To begin the open repository deposit process, I reviewed the journal’s 
requirements. The JMLA data sharing policy states ”The JMLA requires authors of 
Original Investigation, Case Report, and Special Paper articles to (1) place the de-
identified data associated with the manuscript in a repository and (2) include a Data 
Availability Statement in the manuscript describing where and how the data can be 
accessed” [1]. The JMLA editorial announcing the policy by Akers et al elaborated on 
the requirements stating that “at least minimal data needed to support or replicate 
results”, and “documentation describing the contents of the data files” must be 
deposited [2]. A data availability statement, including a URL or DOI for the data, must 
be provided by the author and included with the published article.  
 The editorial offered guidance for authors depositing data for the first time and 
recommended a selection of resources and references that could be consulted for help, 
including Library-based web-sites and LibGuides, and webpages of open repositories 
and noted that help is available from health science libraries and librarians [2]. I sought 
practical guidance that provided specific data sharing information and advice, and not 
data sharing background or rationale from among the editorial references. Three 
references that provided pragmatic methods for organization and presentation of data 
were Washington University Libraries’ webpage “Preparing data for deposit” [12], the 
2019 Journal of eScience Librarianship Presentations article entitled “Best Practices for 
Data Sharing and Deposit for Librarian Authors”, by Regina Raboin et al [13] , and the 
Inter-University Consortium for Political and Social Research (ICPSR) Guide which 
includes a section entitled “Preparing data for sharing” [14].  
I found clear steps to follow in the Digital Research Materials Repository (DRMR) 
Deposit website created by the Washington University St. Louis Libraries (available at 
https://libguides.wustl.edu/drmr/dataprep) [12]. There, a checklist and a template 
README file form are available as downloadable and printable tools to address the 
organization, presentation, documentation and other tasks. The Checklist outlines four 
major steps and multiple itemized sub-steps.  

The WUSL Data Research Materials Repository (DRMR) checklist tasks are to 
organize your deposit, prepare documentation, determine deposit rights & licensing, and 
determine sharing and permissions. I used the DRMR checklist as a process framework 

https://libguides.wustl.edu/drmr/dataprep


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to move forward with the data deposit but revised it adding “review journal policy and 
requirements” as first step described above and a fifth task, “choose your data 
repository”. The process was recursive in that I worked on several checklist steps at 
once or went back and forth between them rather than proceeding in a consecutive 
workflow. [See Figure 1]  

 
Figure 1: Author's process to deposit research data in an open repository to comply with 
journal policy.  
 
Step 1: Organize Your Deposit  
 After reviewing the journal’s policy and guidelines, the first step, to “Organize 
your deposit” seemed simple, but it was on this step that I encountered the most 
decision points and spent the most time. Sub-items in this category included gathering 
and choosing the dataset files to share, naming files and labelling variables consistently, 
and transforming datasets and files to -non-proprietary file formats.  
The JMLA policy names appropriate data types as “including but not limited to 
spreadsheets, text files, interview recordings or transcripts, images, videos, output from 
statistical software, and computer code or scripts”[2]. I had several of these data types 
and felt uncertainty concerning which versions of similar data files and how much of the 
total available data to prepare. 
 



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Step 1a: Data version  
Owing to the mixed methods design of the research, there were both quantitative 

and qualitative data files, and version was an issue in each type. For the quantitative 
data, potential data files included raw deidentified survey response data output from 
REDCap [15], and the same survey data cleaned of extraneous variables and 
processed for analysis from the Stata statistical software [16]. Raboin et al noted that 
either raw or processed data may be used [13] but recommend REDCap export data 
because the system can automatically remove personal identifying information and 
provides a codebook or data dictionary that describes all variables. On the other hand, 
processed data, i.e. data cleaned of extraneous or administrative variables and possibly 
containing new variables generated and used in analysis, may more completely 
represent the reported results. JMLA policy did not address this difference, but PLoS 
guidelines state, “Authors do not need to submit the raw data collected if the standard in 
the field is to share data that have been processed.”[17]. I did not know of a library 
science standard but, in Clinical and Translational Science, where I was a graduate 
student, the usual practice was to import deidentified raw data into the statistical 
package (Stata) and clean it and sometimes generate new variables. For this reason, I 
chose the processed Stata dataset to deposit. In addition, I edited the Data Dictionary 
exported from REDCap and included it as a deposited file.  

For the qualitative data, there were two versions of the interviews: digital 
recordings and full transcripts. The digital recordings were difficult to edit and de-identify 
given my lack of technical skill and the possibility of voice recognition of the participants. 
I decided to deposit the deidentified transcripts, with the caveat they would need further 
review for deidentification and anonymization (see checklist step 4). I also considered 
preparation of the fully coded interview transcripts as analyzed in NVivo [18] for deposit. 
I found NVivo exported tables in a word processing format (.docx) difficult and time-
consuming to edit and convert into a text (.txt) document. In addition, this information 
would be redundant since many exemplar quotations and associated codes were 
included as supplementary material in the accepted manuscript. Therefore, I decided 
not to deposit the complete coded interviews. 
 
Step 1b: Entire data or portion  

Whether to share all existing data results or just those reported in the accepted 
manuscript was a decision point. According to the JMLA policy “authors are expected to 
deposit at least the minimum amount of data needed to reproduce the results described 
in the manuscript” [1]. A similar policy by PLoS states, “Authors do not need to submit 
their entire data set if only a portion of the data were used in the reported study” [17]. I 
considered depositing data from a baseline survey analyzed and reported in an earlier 
related journal article, but not yet shared [19]. The baseline data were not used or 
analyzed in the results in the current accepted manuscript. Depositing the complete 



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data might be considered a better practice because the data are available sooner for 
review and secondary analyses. However, I decided to deposit only the data needed to 
reproduce the results of the manuscript to reduce the organization and documentation 
effort and save time. I plan to share all data from the earlier study in the near future. 
 Another task at the “Organize your deposit” step was to convert all data files to 
non-proprietary formats. I did this for the chosen files without much difficulty. Tabular 
survey data was exported from Stata as comma separated values (.csv) files, and the 
interview transcripts were converted from word processing documents (.docx) to text 
(.txt) files for the deposit.  
 The last task at this step was to create consistent labels for data headers, files, 
and variables. For me, dataset file naming was an iterative process and was only 
concluded with the final deposit. Variable names and data column headers in the 
quantitative data were consistent as exported from Stata and REDCap as tabular data.  
 
Step 2: Prepare Data Documentation  

This step is the preparation of the metadata, the data descriptions of each data 
file, the names of files deposited and so on. The downloadable WUSL README 
(https://libguides.wustl.edu/ld.php?content_id=29108244) template form described 
earlier presented a complete list of information elements needed to be provided with the 
data. The form kept track of progress on decisions at each of the outlined steps creating 
an inventory of file choices, naming and documentation. Information recorded on this 
form was drawn from every other step in the process and served as a complete source 
of documentation for the deposit.  
 
Step 3: Determine Rights and Licensing 
  This step concerned the rights of others to reuse of the data. Since this would be 
an open data deposit, I knew that a Creative Commons (CC) license was appropriate, 
but there are several types of CC licenses so this needed to be checked. License types 
are defined at https://creativecommons.org/licenses/. I chose the CC BY license which 
lets others distribute remix and adapt your data as long as they credit the author for the 
original creation. I did not seek any copyright permissions for the deposit because my 
co-authors and I were the creators of the data.  
 
Step 4: Determine Sharing and Permissions  

This step involved assuring that authorization and consent to share data 
collected from participants in a study was obtained. For the study reported in the 
accepted manuscript, participants explicitly consented to their data being deidentified, 
aggregated, analyzed, and reported in a research journal in an IRB approved consent 
form. Deidentification of participants in the quantitative data was easily settled with the 

https://libguides.wustl.edu/ld.php?content_id=29108244
https://creativecommons.org/licenses/


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automated removal of personal information step that an investigator utilizes when 
exporting data from REDCap.  

Deidentification in qualitative data is more problematic [14]. Studies have shown 
that even when data is deidentified by the removal of personal information, deductive 
disclosure i.e. logical supposition of who the source is by members of the same 
community is possible, so additional anonymization is needed [20]. I had not explicitly 
stated the likelihood of deposit of full transcripts of interviews in the consent information 
for provider participants, only that their answers to questions would be aggregated with 
those of others. I sent a sample transcript to the University of Vermont Human 
Protections Office for guidance. After review, the IRB administrator ruled that transcripts 
deposit would be allowed because there were no direct identifiers and the “information 
would not be considered sensitive in nature.” i.e. they posed little risk of harm to the 
primary care provider participants [Email communication to Author from L. Crain “Re: 
Research Data Sharing in Repositories”, 31 Jan 2020]. I chose to deposit the transcripts 
after removing additional possibly identifying clues such as clinic site and idiosyncratic 
phraseology.  
 
Step 5: Choose Repository  

For repository choice, I found criteria to consider in the Raboin presentation 
(slide 26) [13], Journal recommendation, visibility through a DOI, usability, and features 
such as embargoes, creative commons licensing, and formats accepted were 
meaningful to me. Several general non-discipline specific open repositories were 
suggested by JMLA. I reviewed a comparison grid of general data repositories at a 
Harvard Medical School website that included four of the journal-recommended 
repositories: Dataverse, Dryad, Figshare and Zenodo [21]. A librarian colleague who 
had deposited data, advised that Figshare [22] was easy to use and provided prompts 
and mini-tutorials. Without having a strong preference, I chose Figshare. When 
exploring the Figshare website, I found simple instructions and few restrictions. I used 
trial and error methods to learn the repository’s features and began uploading the data 
files and documentation. For each dataset entered in Figshare, there are prompts for 
metadata including title, authors categories, links to related keywords, data description, 
any restrictions on use of data, and sharing level. My prepared README form 
contained most of the required information so I cut and paste the authors, titles, 
descriptions, file names, license and permissions into the Figshare metadata boxes. 
Figshare offered additional advice on naming and other metadata conventions to 
increase findability and I did rename files and datasets to increase findability. I entered 
separate documentation metadata for the qualitative and quantitative datasets based on 
my README form. I named dataset authors based on who had participated in design 
and review of each set, and each dataset was described differently [23, 24]. I did 



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change some metadata and file names after publishing. This resulted in the version 
numbers being added to the DOIs.  
 
RESULTS 
 I deposited data in two separate datasets in Figshare, one for the quantitative 
survey dataset, https://doi.org/10.6084/m9.figshare.11893875.v3 and one for the 
qualitative https://doi.org/10.6084/m9.figshare.11893956.v1. The survey dataset 
contains metadata description, tabular processed data with 39 variables and 22 
observations, and a data dictionary file that includes the variable names, corresponding 
survey questions, and response type and possible responses in a comma separated 
file. The qualitative dataset contains metadata description and the interview transcripts 
as a single text (.txt) data file. 

I spent at least 30 hours over three weeks reading polices, referring to links in 
library LibGuides, conferring with my institution’s data librarian and researcher 
colleagues, and performing the tasks in the checklist outline, making decisions, and 
navigating the Figshare repository. I met the journal policy requirements to deposit 
sufficient data and documentation to reproduce the findings of the manuscript, and met 
the publication deadline for including the Data Availability Statement with the published 
article.  
 
DISCUSSION 

My experience supports the finding of Kim and Stanton that authors’ belief in the 
value and benefits of data sharing combined with the open data sharing requirement of 
journals may motivate authors to share data, but the time and effort needed to complete 
the process may negatively impact the researchers’ actual participation in data sharing 
[5].  

As anticipated, my data deposit experience was time-consuming and presented 
myriad decision-points without exact answers even with consulted sources. However, 
this was a first-time experience; I suspect that there will be fewer obstacles next time, 
although different data types and file sizes will certainly present different hurdles. I will 
utilize checklist and pragmatic tools again, and will be less hesitant to make decisions.  

My advice to researcher/authors who are novice data sharers is to use the most 
specific checklists and tools you can find or make your own. Pragmatic advice and tools 
found on the websites supporting open data repositories such as the WSU, ICPSR and 
Figshare repositories may be the most useful. I believe it is better to publish the data 
even if some uncertainties linger. Don’t let hesitation or imperfection prevent completion 
of the deposit. It is possible to revise metadata and upload and deposit revised data 
versions in repositories if that is needed. 

 
 

https://doi.org/10.6084/m9.figshare.11893875.v3
https://doi.org/10.6084/m9.figshare.11893956.v1


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Implications for health science librarians  
Support for the data sharing activities needs of researchers may not be fully 

developed in health sciences libraries. While many academic health science libraries 
provide some level of data services, according to a survey by Ragon of academic health 
science library directors, 25% of those surveyed did not provide data services in their 
libraries and did not plan to do so in the future [25]. A survey of data librarians by 
Federer revealed that most of them spend less than half of their work time on data 
services [26]. A taxonomy of skills needed by data librarians proposed in the same 
article did not include skills or experience in data sharing itself. I appreciated the advice 
and recommendations from the library website and the data librarian at my institution, 
but they answered few of my specific data sharing questions. Library data services 
could emphasize pragmatic data sharing tools and tips at least as much as background 
and advocacy information on their research data management web-sites. Library-
sponsored teaching sessions that utilize group workshop formats and in-class case 
presentations of data sharing by researchers could provide a more practical learning 
experience for researcher authors. Data librarians with experience sharing their own 
data might be better equipped to assist novice depositors (including library and 
information science researchers) with the problems and decision-points that arise in the 
process of data sharing.  
 
Limitations  

This individual case study reflects only one data sharing experience. Other 
researcher authors will find different issues, choices, and take-aways in their experience 
particularly if they are sharing data from randomized trials, other empirical designs, 
large data sets, or complex statistical analyses. Nevertheless, this description of my 
experience may give some insight to the process overall.   
 
CONCLUSIONS 

The burden placed on researcher authors when depositing data is significant. 
Having experienced the process once, I now see where pitfalls and problems make 
open data sharing problematic for researcher authors. As more health science librarians 
gain experience depositing their own research data, they may provide more concrete 
and practical answers when assisting researcher/authors and thereby reduce author 
uncertainty and time spent in the data sharing process.  

This experience illustrated some practical data sharing issues faced by a librarian 
author seeking to comply with a journal data sharing policy requirement for publication 
of an accepted manuscript. Both novice data depositors and data librarians may find 
this individual experience useful for their own work and the advice they give to others. 
 
 



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