In an effort to increase transparency regarding the evidence on which scientific conclusions are based, the ASHA journals are implementing policies outlined in the Transparency and Openness Promotion (TOP) guidelines that encourage the adoption of open science practices.
Increasing transparency in the research process and the reporting of studies improves the reproducibility of research by enabling others to gather enough information to allow for replication and validation, building trust in science. In addition, data sharing supports the creation of new science that is built on previous findings, making the research process more efficient.
About the Data Standards
At ASHA Journals, we expect—but do not require—data sharing. Authors are encouraged to share or make available any research data and materials supporting the results or analyses presented in their paper and to provide data availability statements.
Please note that authors can only share data that they are legally permitted to share or publish. Data should only be shared when it does not violate the protection of human subjects, or other valid ethical, privacy, or security concerns. The decision to publish will not be affected by whether or not authors share their research data.
Depositing Data
The preferred mechanism for sharing research data is via data repositories. Authors are encouraged to deposit data in a recognized data repository that can provide a persistent identifier, such as a digital object identifier (DOI), and supports a long-term preservation plan. We encourage researchers to consider the FAIR Data Principles when depositing data, and we recommend use of FAIRsharing and re3data.org to search for a suitable repository.
Datasets that are assigned digital object identifiers (DOIs) by a data repository may be cited in the reference list. Data citations should include the minimum information recommended by DataCite: authors, title, publisher (repository name), identifier.
Data Availability Statement (DAS)
All ASHA journals will—effective January 1, 2022—require authors to provide a data availability statement (DAS), detailing where data supporting the results reported in the article can be found, including, where applicable, hyperlinks to publicly archived datasets analyzed or generated during the study. The DAS should be submitted within the article manuscript, before the ‘References’ section. Data availability statements can also indicate whether data are available on request from the authors and where no data are available, if appropriate. Several templates for the DAS are shown below. Additional examples are available in the Quick Resources sidebar.
Availability of Data
Template for Data Availability Statement
Data openly available in a public repository
The datasets generated during and/or analyzed during the current study are available in the [NAME] repository, [PERSISTENT WEB LINK TO DATASETS]
Data not available due to [ethical/legal/commercial] restrictions
The datasets generated during and/or analyzed during the current study are not publicly available due to [REASON WHY DATA ARE NOT PUBLIC] but are available from the corresponding author on reasonable request.
Data available on request from the authors
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
Data sharing not applicable – no new data generated
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
Data available within the article or its supplementary materials
All data generated or analyzed during this study are included in this published article (and/or its supplemental material files, if applicable).
Adapted from Hrynaszkiewicz, I, Simons, N, Hussain, A, Grant, R., and Goudie, S. (2020). Developing a research data policy framework for all journals and publishers. Data Science Journal, 19, 5. DOI: http://doi.org/10.5334/dsj-2020-005
Data Citation
All data, program code, and other methods referenced in articles published by ASHA Journals should be appropriately cited in the text and listed in the References section. Such materials should be recognized as original intellectual contributions, afforded recognition through citation, and accessible.
References for datasets and program code should include a persistent identifier, such as a Digital Object Identifier (DOI). In general, you should always include the following elements in data citations:
- Author – the individual(s) responsible for the creation of the data
- Material Designator – the tag “[dataset]”
- Electronic Retrieval Location – a persistent identifier (e.g., DOI) when available or applicable
- Publisher Location – this is often the repository where the author has deposited the data set
According to the 7th edition of the Publication Manual of the American Psychological Association, a data set citation would be formatted as follows:
Author, A. A., & Author, B. B. (2020). Title of data set (Version 1.2) [Data set]. Publisher Name. https://doi.org/xxxxx
For example:
Campbell, A., & Kahn, R. L. (2015). ANES 1948 Time Series Study (ICPSR 7218) [Data set]. Inter-university Consortium for Political and Social Research. https://doi.org/10.3886/ICPSR07218.v4
This will help the reader identify and find the data set, and ensures you give credit to the individual or group who created the data. You should also include relevant information about your data (such as the DOI and publisher location) in your data availability statement.
View Quick Resources box for further examples and materials on data citation.
Data Practices to Avoid
There are several data practices that may be problematic. They include:
- Data Aggregation, in which previously published data is republished with some new additional data. This is problematic when the reader is not informed that a portion of the data being presented has been previously published.
- Data Disaggregation, in which the same data is published minus same data and without informing the reader. Anytime an author is republishing any data they should always give proper and clear credit to the previously published study and seek permission when necessary.
- Data Segmentation (sometimes called “Salami Publication” or the “Least Publishable Unit”), in which the author breaks a large data set into the maximum number of possible publications. While there are some cases where a single study may need to be broken up into multiple publications, such as a large-scale longitudinal study, authors should take care to publish their research in as few units as possible. The practice of unnecessary data segmentation can mislead the reader to believe the data presented from each “salami slice” are independently derived from a different data collection effort or subject sample. Dividing into smaller segments must always be done with full transparency, showing exactly how the data being reported in the later publication are related to the earlier publication.
If, in the editor’s judgment, the author has engaged in these practices, the manuscript will be subject to immediate rejection.
Open Science Badges
Articles submitted for publication in ASHA Journals starting in 2022 are eligible to earn badges that recognize open scientific practices: publicly available data, material, or preregistered research plans. Please read more about the badges on our Open Science Badges page, and you can also find information in the Open Science Framework (https://osf.io/tvyxz/wiki/home/).
Interested authors apply for badges on a self-disclosure basis. We ask authors to verify that criteria have been fulfilled in a signed badge disclosure form (PDF), submitted on revision. Once an article is accepted, the editor checks the form to confirm that criteria are met, and the form is published with the article as supplemental material.
For all badges, items must be made available on an open-access repository with a persistent identifier in a format that is time-stamped, immutable, and permanent. Data and materials must be made available under an open license allowing others to copy, share, and use the data, with attribution and copyright as applicable.
The decision to publish will not be affected by whether authors adhered to open science practices and/or choose to submit a disclosure form. Authors who indicate via the disclosure form that they have adhered to any of these open science practices will be eligible for the badge associated with each practice used, and will be asked if they wish to have the badge or badges included with their published article. The badges can be viewed on the Center for Open Science website – https://www.cos.io/initiatives/badges.
Author Checklist
About this checklist
This checklist is intended for use by authors who are submitting to the ASHA Journals, which is implementing Level 1 of the TOP Guidelines.
Citation
All data sets and program code used in a publication are cited in the text and listed in the References section.
References for data sets and program code include a persistent identifier, such as a Digital Object Identifier (DOI).
Data, Analytical Methods (or code), and Research Materials Transparency
In a data availability statement before the references, it is indicated whether data, analytic methods (or code), and study materials will be made available to other researchers.
If data, code, or materials are available, specify where and how that material will be made available:
See example disclosure statements[link].
Design and Analysis Transparency
The process by which you follow standards for disclosing key aspects of the research design and data analysis has been reported. For example, you are encouraged to review the reporting standards available for many types of research designs from http://www.equator-network.org/ and use those that are relevant for your work. More guidance on how ASHA Journals encourages use of reporting guidelines can be found on the ASHA Journals Academy under Guidelines for Reporting Your Research.
Preregistration of Analysis Plan
Y / N
It is indicated in the acknowledgments or the first footnote if you did or did not preregister the research with or without an analysis plan in an independent, institutional registry.
If you did preregister the research with an analysis plan, then check to confirm the items below:
It is confirmed in the text that the study was registered prior to conducting the research with links to the time-stamped preregistrations at the institutional registry, and that the preregistration adheres to the disclosure requirements of the institutional registry or those required for the preregistered badge with analysis plans maintained by the Center for Open Science.
All of the pre-registered analyses are reported in the text, or, if there were changes in the analysis plan following preregistration, those changes are disclosed and explained.
Text analyses that were preregistered are clearly distinguished from those that were not, such as having separate sections in the results for confirmatory and exploratory analyses.
Replication
Y / N
This study is a replication of a previously published study, or it includes a direct replication of previously published work in part.
