Let us be Clear: Why and how to Demonstrate data and Methods Transparency in Nonprofit Research

Abstract

Transparency means being honest and open about one’s practices. Transparency is considered a gold standard in the nonprofit sector and associated with a range of positive outcomes. We propose that transparency should also become a guiding principle and documented practice for nonprofit scholars. In this article, we articulate the context for discussing transparency in social science research and the potential risks of not being transparent. Acknowledging the epistemic and methodological diversity of our field—and therefore, the need for flexibility in how transparency will be practiced by different scholars—we encourage nonprofit researchers to consider how they can enhance their research transparency. To this end, we outline the benefits of transparency and offer concrete suggestions for different ways to demonstrate transparency in nonprofit research.

Introduction

Transparency—being open and honest about one’s practices—is both a guiding principle and a gold standard for the nonprofit sector (National Council of Nonprofits, 2023; Willems, 2021). A growing body of research demonstrates why nonprofits should operate transparently and documents a range of positive outcomes when they do (Dethier et al., 2023). For example, transparent nonprofits benefit from enhanced public trust (Farwell, et al., 2019), increased donations (Blouin et al., 2018), and operate more efficiently (Lu et al., 2020). Transparency brings clarity to the workings of the sector. It is of particular importance when the trustworthiness of organizations is called into question (Chapman et al., 2023a) .

In a similar vein, we propose transparency as a guiding principle and emerging practice for nonprofit scholars. Transparency practices can produce collective benefits such as trust in our research findings, enhance our ability to secure research funding, and over time improve the accuracy and efficiency of our research practices. Transparency is not a binary choice: There are many ways to be transparent in the design, execution, and reporting of research (Miguel et al., 2014). With the goal of encouraging transparency, we outline the context for transparency in social science research and discuss how improvements can be achieved while respecting the epistemic diversity of our field, reflected in the multitude of data, methods, and epistemological stances that scholars use. We then outline some of the key benefits of enhanced transparency in nonprofit research and end with practical suggestions for those wishing to take steps to increase and demonstrate the transparency of their research.

Context

Transparency is an increasing priority and concern across many social science disciplines and methodological practices, including both ‘qualitative’ and ‘quantitative’ approaches. Qualitative researchers, for example, highlight the importance of discussing positionality and reflexivity (Steltenpohl et al., 2023), the rigor of reasoning (Harley & Cornelissen, 2022), and establishing trustworthiness (Pratt et al., 2020). Transparency is an important way to evidence and enhance the validity and rigor of one’s qualitative research (Branney et al., 2023). Transparency does not require making all data public, especially not when doing so would violate privacy laws and expectations of those represented in the research. There has been a long tradition in the social sciences of clearly documenting research methods, which is a transparency practice. Qualitative researchers have also historically adopted transparent research practices (Pownall et al., 2023), such as providing a reflexivity statement with their research, taking field notes and keeping a field journal, making their study materials openly available online (Karhulahti, 2022).

How qualitative researchers appropriately express transparency in their work depends on their epistemological stance (Prosser et al., 2024). Practices that are appropriate for some domains may be wholly inappropriate in others (Huma & Joyce, 2022). Branney et al. (2019) suggest that transparency should be considered within a context-consent framework, where the original research context and participants are fully accounted for. Historically, qualitative researchers have aimed to balance transparency practices with consideration of relevant ethical, philosophical, and practical concerns.

To manage these concerns, many scholars operate using an ‘as open as possible, as closed as necessary’ approach (European Commission, 2024), which is also the basic principle of the European Union’s General Data Protection Regulation in effect since 2018. Respondents’ anonymity and privacy and researchers’ intellectual property rights are two of the limitations of transparency, pointing toward ethical standards required in a more transparent research field.

For quantitative researchers, reproducibility and reliability are major considerations. Many findings which social scientists believed to be reflecting general regularities in human behavior turned out differently when others tried to replicate them. For example, a large-scale coordinated effort to replicate studies published in psychology found significant findings in just 36% of replications (vs. 97% of original studies), with an average standardized effect size of 0.20 (vs. 40 in original studies; Open Science Collaboration, 2015). Replication attempts have also returned disappointing results in accounting (Hail et al., 2020), economics (Chang & Li, 2022), and management (Hensel, 2021). We know very little about the replicability of nonprofit and philanthropy research. Very few quantitative studies in our field are ever replicated (Helmig et al., 2012), which is a risk factor for low reproducibility (Hensel, 2021).

Fields with high reproducibility are often characterized by publicly accessible data. Results of articles in journals that require access to data are more replicable (Hardwicke et al., 2018). In addition, a tolerance for negative or null results and a lower prevalence of questionable research practices such as p-hacking and withholding negative results may also improve replicability (Cova et al., 2021). When reviewers and readers have access to the data, it is easier to check and improve the integrity of research (Munafò et al., 2017). Researchers who share data and materials are more likely to have data fabrication detected by a reviewer or collaborator (Gopalakrishna et al., 2022b). Open data practices enhance research integrity by deterring misconduct, as authors are aware their actions can be scrutinized (Gopalakrishna et al., 2022a). Data accessibility also makes it more likely that unintended errors—which any one of us could make—are detected before publication.

Enhancing standards of data and methodological transparency makes it easier to identify weaknesses in the research and repair them across both qualitative and quantitative methodologies. Only when research is clearly and transparently reported can its quality be evaluated in a fair and independent manner; as Vazire (2019) stated: ‘Transparency plus scrutiny together guarantee that research gets the credibility it deserves.’ Transparency encourages criticism and self-correction in science, ultimately enhancing its reliability and validity. This is important not only for theoretical development, but also for practical applications: Nonprofit managers, fundraisers, and policymakers alike need to trust the findings reported in our field.

Respecting the Epistemic Diversity of our Field

The social sciences have a high degree of epistemic diversity (Leonelli, 2022). In other words, we have diverse ways of producing knowledge and different understandings of truth. This applies especially to our field of nonprofit and philanthropy research (Kim & Raggo, 2022). Our methods are diverse: Researchers collect data through laboratory, field and online experiments, surveys, interviews, focus groups, scraping websites and social media platforms, annual reports and other archived sources, and through participant or hidden observation. Knowledge goals are also diverse. In nomothetic studies, researchers test deductively derived implications of theories and discover patterns in exploratory analysis. In idiographic studies, researchers seek in-depth contextual understanding and analysis of cases or individuals.

The epistemic diversity of our field is reflected in our own positionality as authors of this article. We represent both (neo)positivist stances as well as critical realist and constructivist epistemologies, with both pragmatist and principled approaches. Two authors self-identify as primarily employing qualitative methods, two authors primarily employ quantitative methods, and one has moved from quantitative to mixed methods and conceptual work. Collectively, we conduct both exploratory research, with guiding research questions and open minds about what we may discover, as well as confirmatory research that tests prespecified hypotheses.

As the editors of the special issue on Methods in Voluntas noted in their introductory article, transparency is more important than ever (Kim & Raggo, 2022). If readers and reviewers have detailed knowledge about the data and methods (i.e., how data were collected, what kinds of persons answered what kinds of questions, and how the data were processed and analyzed), they can better evaluate its rigor and understand the context and the potential implications of the research. At the same time, it is important that we adopt a pluralistic approach that respects the epistemic diversity of our field (Kim & Raggo, 2022).

Respect for epistemic diversity implies that transparency policies should be sensitive and specific to research traditions (Clarke et al., 2025; Hensel, 2023; Jacobs et al., 2021; Monroe, 2018; Pratt et al., 2020). Data and methods transparency implies different actions for researchers working with different types of data and different epistemological stances. Transparency policies should not unfairly disadvantage researchers in certain traditions. For researchers working with textual data or with ethnographic methods, for example, anonymization of datasets to be shared publicly requires more effort, while it yields smaller benefits and bears greater challenges and risks (Hensel, 2023; Macedo de Lucas et al., 2025). Stripping away details to anonymize individuals and organizations reduces the epistemic value of the data, is not attuned to emerging practices of co-creation, and may still allow for identifiability through seemingly innocuous details, potentially leading to litigation. The data access and research transparency policies that major journals in political science introduced (Lupia & Elman, 2014) did not initially take sufficient account of epistemic diversity, to the disadvantage of researchers reporting on data from ethnographic field work and interviews (Jacobs et al., 2021; Monroe, 2018). Attempts to introduce one-size-fits all policies in management have also largely failed (Hensel, 2023). In contrast, the discipline of psychology (Levitt et al., 2018) and the field of organization studies (Aguinis et al., 2021) provide positive examples for our field, because they have demonstrated that by co-creating guidelines with scholars in different traditions and using neutral language it is possible to formulate generic guidelines that are respectful of epistemic diversity. Following that lead, we endorse an inclusive and flexible approach to promoting transparency in nonprofit research. In the design and implementation of transparency guidelines, care should be taken to avoid disadvantages to different research approaches.

Collective Benefits of Transparency in Nonprofit Research

Data and methods transparency are core values and a duty for scientists (Merton, 1942). Codes of conduct and the UNESCO recommendation on Open Science (UNESCO, 2022) encourage open science norms of transparent reporting and public access to data and materials whenever possible. We also value knowledge and an approximation of truth, and transparency practices make misleading outcomes less likely.

It is important that our field adopts transparency standards and does not lag behind the general trend in the social sciences. If our journals fail to adopt such standards, we risk attracting studies that rely on questionable research practices deemed unacceptable by other journals. In this scenario, the reputation of journals in our field could suffer, and careers of scholars publishing in nonprofit journals could also be at risk.

Data and method transparency is especially crucial in nonprofit and philanthropy research, given the unique role of civil society in driving social change and funding efforts to create a better world. The societal relevance of research in our field depends on its ability to enhance the impact of nonprofit and voluntary action. Without a reliable knowledge foundation, the societal impact of nonprofit organizations may be hindered. Research on volunteering, fundraising and nonprofit marketing, and finance helps nonprofit organizations secure resources for their missions and maintain financial health. Organizations can deliver programs with greater impact when they build upon reliable evidence about the effectiveness of potential interventions and use strategies that have been demonstrated to yield more resources to fund these programs. Research on regulation, governance, and cross-sector collaboration can help make the activities of nonprofit organizations more impactful. In all these areas, transparency can help avoid selective reporting of best practices, or omission of failed practices, and may therefore improve the validity and replicability of findings.

Data and methods transparency of nonprofit research may help nonprofits and practitioners in three ways. First, when researchers transparently report about their data and methods, reviewers can provide a fair and accurate evaluation of the quality of the study. As a result, the findings are less likely to be false. In addition, when a study providing extensive and clear documentation of data and methods is published, other researchers can easily test the robustness of the findings by altering some of the choices in the data analysis, and check whether the results can be generalized to other samples by repeating the study with new data. As a result, the scientific community and practitioners alike should learn whether the findings are particular to a certain sample of observations, or to particular decisions in the analysis. Researchers can also more easily build on the work of others when data and methods are shared, thus accelerating research on similar topics.

Second, data and methods transparency in nonprofit research facilitates the application of research findings in practice. When practitioners do not know precisely which measures were used in the study, from which target population the study findings were derived, and which interventions were used, for instance, they may struggle putting the findings in practice.

Third, data and methods transparency also help contextualize the findings. When researchers clearly explain the choices in the design of the study, the data collection, and the analysis, other researchers and practitioners can better interpret the results from these choices. Their own contexts may be different, and perhaps the study findings do not apply, or they may not have the same possibilities at their disposal. In these cases, they may not be able to apply the study insights, or they could have different effects.

Ultimately, when nonprofit organizations apply insights from transparently reported research, the predicted outcomes may be more likely to materialize. If they do not, the reasons behind the discrepancy are easier to identify.

Individual Benefits of Open Research and Improved Transparency

Transparency not only yields collective benefits for science as a whole and for our field of research and practice but also offers advantages to individual researchers.

Enhance your impact. Data transparency may improve the impact of your research. In quantitative analyses, if you can use the data and code of your predecessors, your life is much easier. If your successors can use your data and code, their scientific discoveries may be faster. Transparent practices mean your results are more likely to be accurate. Accurate results are more likely to be impactful in the real world. Therefore, transparent quantitative research is effective quantitative research.

Transparency can also enhance the impact of qualitative research: Open sharing of research materials and data in a responsible manner might lead to opportunities for further secondary data analysis (if appropriate) or for building new collaborations with other academics or nonacademic organizations. Research participants in qualitative research on sensitive topics generally support and expect their data to be shared because it enhances the impact of the research (Mozersky et al., 2020). Sharing materials, reflections, findings, and data openly in online repositories with supplementary materials linked in journal articles may also help qualitative researchers to evidence the rigor of their research despite restrictive journal word counts.

Transparency fosters rigor and feels good. Scholars worry sometimes that they have made a mistake or accidentally misrepresented their findings. Poor transparency may hide errors that could later lead to retraction, which could have serious career consequences for authors, even years after publication (Memon, Makovi & AlShebli, 2023). By adopting transparency practices, you could reduce your anxiety about unintended errors. When you open your data and protocols during peer review, you make it more likely that errors will be spotted early and can be fixed before publication.

Get ahead of the curve; you will probably be asked to do it anyway. Journals and scholars in the social sciences are increasingly adopting open science practices (Christensen et al., 2019a, 2019b; Ferguson et al., 2023). In 2020, over 5000 journals had adopted transparency and openness promotion guidelines (Mayo-Wilson et al., 2021). It has long been accepted practice across diverse epistemological approaches to report both the methods employed and analytical techniques adopted with enough clarity that an educated outsider could evaluate the process, and ideally follow it. Recently, it has become more common for researchers to submit detailed materials such as interview protocols, questionnaires, and data (Ferguson et al., 2023). For example, since 2020 Nonprofit and Voluntary Sector Quarterly has adopted a policy mandating submission of data and code for manuscripts reporting results from experiments. Voluntas encourages but does not require data sharing and data citation. When sharing data is not possible, providing a data availability statement creates important opportunities to evidence transparency alongside ethical, legal, and practical concerns (Prosser et al., 2023).

Even without a formal journal policy, reviewers may also request access to research protocols, materials, data, and/or code. As an author, expect to be asked for these resources more often in the future. It is best to be prepared for these requests. Taking small steps today, like those we suggest below, can save you future challenges when you are asked to provide materials during submission or review.

Enhance your scientific career. Getting published and getting cited are essential to a successful academic career; enhanced transparency may help with both. As mentioned above, ever more journals are asking for open science practices as a bar for submission. As transparency standards increase, manuscripts that are not up to these standards may become more difficult to publish. At the same time, studies providing access to data and materials are cited more often (Christensen et al., 2019a, 2019b; Colavizza et al., 2020; Drachen et al., 2016). While these benefits do not appear immediately, they provide researchers with a long-term incentive to make them available.

Consider it an act of altruism. The cost of time to document transparency can also be worthwhile for altruistic reasons, because they provide benefits to research participants, other researchers, and to the field as a whole. Making your materials open simplifies the process for other researchers to replicate your protocols. Making your data ‘as open as possible’ gives opportunities to those with fewer resources, including junior scholars, to access and use your secondhand data when they do not have funding or opportunities to collect their own. As a collective, improving the transparency of nonprofit research also improves its rigor and the confidence we can have in the knowledge we generate. This offers immense service to the global nonprofit sector which may invest precious resources in implementing ideas based on our research.

Transparency practices also have unique benefits for participants in qualitative research. Some participants want their voice to be shared fully. Being transparent in qualitative research (e.g., through sharing transcripts) places the participant front and center of their own story and elevates their voice within the research. Further, transparency can also minimize problems associated with so-called ‘helicopter research’ (Nature, 2022), whereby researchers swoop into a community to extract data and exit just as quickly with little value generated for that community. Making data available for reuse means a community does not have to go through data collection as many times, which is particularly important for topics that might be traumatic. In sum, transparency is good for your fellow scientists, it is good for the community, and it is good for your career.

Concerns Around Transparency

Researchers may have concerns about adopting transparent and open research practices. A recent survey of qualitative organizational scholars showed substantial reticence when researchers considered open practices (Prosser et al., 2024). Some concerns center around the unsuitability of open research practices for interpretive, qualitative methodologies, particularly when these practices overemphasize positivist concerns of ‘reproducibility’ and ‘generalizability’ (e.g., in the case of some preregistrations and open code requests). Scholars who do not adopt positivist perspectives may consider the transparency requirements proposed by positivist epistemology unsuitable for them.

We propose that researchers could consider open research practices as a ‘buffet,’ from which they can pick and choose options most suitable or appropriate (Bergmann, 2023; Castille et al., 2022). Prosser et al.’s (2024) survey showed that many qualitative researchers are supportive of open research practices that facilitate public, policy, and stakeholder engagement. Accordingly, practices that facilitate research engagement, such as open-access publication or preprints, may be good options when beginning to integrate openness into one’s research. Seeing open research practices as a means to evidence qualitative rigor and transparency is also important (Steltenpohl et al., 2023).

Another major concern is the potential additional workload associated with open research practices (Hostler, 2023), especially if that work is not perceived to be rewarded by institutions. For some approaches, open research practices can place a significant additional burden on conducting ethical research, such as when anonymizing sensitive qualitative data for open-access (Campbell et al., 2023). While many organizations are moving toward open practices as a default model (e.g., Hardwicke & Vazire, 2023; UKRI, 2021), transitions in other organizations and disciplines may be slower. Institutions, funders and professional organizations could find ways to better encourage and reward practices among scholars, for example by including openness in promotion criteria, or facilitating training for scholars. Developing communities of practice for open research, and mechanisms for training and support may be helpful in this regard (Korbmacher et al., 2023).

Tips for Improving the Transparency of your Research

How best to get started with transparency? In this section, we offer some practical suggestions for how to practice open science and demonstrate your research transparency. These suggestions include recommended tools, simple practices to add to your research process, and ideas for explaining your methods transparently.

Create an Online Repository for Supplementary Materials

A range of nonprofits exists to facilitate open science, including the Open Science Framework (https://osf.io), Dataverse (https://dataverse.org), and Zenodo (https://zenodo.org/). Use these free platforms to create online repositories for each of your research projects. You can add collaborators, create timestamped (and embargoed) preregistered plans for your studies, store your questionnaires, interview protocols, stimuli, data, and code, and publish supplementary analyses or materials, such as a table of tools—including software relying on artificial intelligence—used for the literature review, production of statistical analyses, graphs, and language editing. Similarly, data journals have emerged, specializing in the comprehensive documentation and publication of deposited datasets while enabling their online exploration (e.g., Research Data Journal for the Humanities and Social Sciences, https://brill.com/view/journals/rdj/rdj-overview.xml).

In addition to transparency, online repositories that the authors manage themselves offer several valuable benefits. First, you can create a persistent identifier (i.e., DOI) so that people can easily cite your data or materials. Second, you can add as many appendices and supplementary materials as you wish, without eating into journal word or page counts. Third, if you choose to make your working projects public (e.g., by uploading a preprint) then other researchers who are scoping new research will be aware of your in-progress work, making it less likely that two teams progress the same research questions simultaneously, with one ‘scooping’ the other because the time stamps demonstrate the chronological order.

Online repositories put you in control of how and when your project is shared. You can make your project public or keep it private until the associated paper is published. If you keep your project repository private, you can create anonymous view-only links for reviewers to access the project for the purposes of peer review without compromising the double-blind review process.

Preregister your Research

Preregistration involves creating a timestamped research plan that articulates the research objectives, hypotheses (where appropriate), study design, key measures or guiding research questions, intended analysis plan, and plans for how the sample size will be determined and conditions under which participant data would be excluded (Nosek et al., 2018). The process of preregistration can be particularly helpful to quantitative researchers as it requires careful forethought about the design and predictions. Preregistration may, in some cases, also be relevant to qualitative research, for which specific templates have been developed (Haven et al., 2020).

Preregistration is also a great way to minimize one common questionable research practice in confirmatory approaches: ‘Hypothesizing After Results are Known’ (i.e., HARKing; Kerr, 1998). When a study is preregistered, reviewers and readers can be confident that the study was indeed designed to examine the relationships as reported in the article. This is important because it reduces the chance that published findings are spurious, due to chance, or the result of p-hacking; for example, by adding or removing covariates or participants until a significant finding is achieved.

The Open Science Framework (https://osf.io) includes many preregistration options, which can be affiliated with the relevant project repository. PROSPERO supports registering systematic reviews (https://www.crd.york.ac.uk/prospero/). An agreement-based template for preregistering qualitative research is also available on the Open Science Framework (Haven et al., 2020). Because these repositories timestamp all uploads, with document control, this can serve as a simple way to preregister your intentions. If you choose this option, however, take care not to update the preregistration document directly, but create a new version, as merely updating it will invalidate the initial preregistration. Wherever you choose to preregister, it is essential to do so before you begin to analyze data and ideally before you collect it.

Preregistration is increasingly common in nomothetic research. When research is confirmatory, specific hypotheses should also be preregistered. If hypotheses are not supported, the preregistration encourages transparent reporting of the original theorizing and tests of those ideas, rather than the questionable research practice of reframing the paper as if the unexpected result was always theorized (i.e., HARKing). Even preregistering broad guiding research questions can be helpful as it demonstrates the intention of the research.

It is likely that researchers will deviate to some extent from their preregistration due to factors that were not considered at the time of preregistration. Sometimes reviewers also ask for additional analyses that were not preregistered. This is fine; a preregistration is not a prison cell. Deviation is normal and expected, and should simply be transparently reported in the manuscript, with appropriate justification. Reflexive engagement with the evolution of a research project is common in qualitative research and is becoming increasingly relevant for quantitative researchers (Jamieson et al., 2023). The objective is to increase transparency about the research intentions and results, rather than to shackle researchers to their initial ideas.

Make your Materials Publicly Available

Publish your research materials alongside your article. If you have already set up an online project repository and given reviewers access to these materials during peer review then this step is very simple: You can make your project repository public and then update the anonymized links in your manuscript to include the direct public URL. You can do this after the paper has been accepted for publication, when the manuscript is sent to production.

Code. For quantitative researchers who have used R, SPSS, Stata, Python, or other form of syntax or code files, you can publish the code that produces the results reported in the manuscript. Annotate the code to clearly explain what each step does. Others should be able to run the code directly on the data file provided to obtain the results reported in the manuscript. Annotated and functional code will benefit readers as well as your future self. Make it a habit to annotate the code while you design the analyses. Then it should not take more than a few extra minutes for authors to check and upload their code at the time of publication. Bekkers (2021) has published a helpful guide for researchers looking for more advice about organizing their code and data. If you have coded data manually, share the coding instructions or instrument, and interrater reliability if data have been double-coded.

For ‘big-Q’ qualitative approaches (Kidder & Fine, 1987), consider making your codebook or a list of extracts associated with themes openly accessible, so that readers can evaluate ‘whether claims are valid and have been adequately substantiated with evidence’ (Cramer, 2018, 11, citing Schwedler et al., 2019, 5). Publishing your codebook, your coding scheme together with some anchor coding, or other materials utilized within your analytic process as supplementary material alongside your article can provide additional detail and evidence of rigor that may not be possible to provide within a strict word count of a manuscript. If using a Qsort procedure, you can publish the images or materials that have been sorted. Depending on the form of research, additional materials (such as a field protocol, or reflective journal) may be relevant for researchers who want to read and build upon your research. Besides, it may be appropriate for some qualitative research methodologies to provide additional quantitative findings, for example, word counts in categories, or demographic averages. The most important thing is to make these materials available alongside the published article. Sharing materials shows your commitment to transparency and builds confidence in your research conclusions.

Instruments. Wording of experimental stimuli, the full text of questionnaires completed by respondents, the questions you asked in interviews, and topic lists used for focus groups allow readers to understand the context of data collection, the researchers’ protocol in engaging with participants, and the structure of the data. Making the research instruments available allows reviewers and readers to check whether additional measures were included in the study but not reported in the article. For qualitative research, demonstrating how the data were collected provides important context for the reported analysis. Sharing study guides such as an interview or focus group schedule, or a selection protocol for media research may help others to evaluate and expand upon your work in the future. Open materials also allow readers to check the exact wording of measures and the design of the study. When running experiments or using special stimuli in your studies, include these materials in the repository: These may be images, videos, audio, or scenario-based stimuli.

Cite secondary data. If your manuscript reports analyses of existing data collected by others, you do not need to publish it again. Instead, identify the data source, ideally with a persistent identifier. Citing the data source gives credit to those who have created it, just as you would like to be cited when others use your data. If the data source itself does not have a DOI, refer to the publication or URL where readers can find instructions for gaining access to the data.

Anonymize and document primary data. Making the data that you have collected available to others can be challenging (Meyer, 2018). Before you make data available, first make sure that you have consent from research participants and your institution’s internal review board or ethics review committee. Informed consent is critical. Incorporate data sharing intentions into the informed consent procedure with participants. If the data are commercial or owned by others, you may not be able to publish it. This issue may be particularly problematic for qualitative research, where anonymization is difficult, and the risks of identification are more pronounced for both participants and researchers. At the same time, in some cases, respondents may wish to be fully identified for their contributions to be recognized (particularly in areas where they are known as an advocate or voice). The risks and benefits of this approach should be clearly examined in line with principles of care, ethical boards, and professional standards. In all research, participants should be consulted about their open data preferences as part of the research design and consent process. Researchers should consider a complex suite of ethical, legal, and political issues before making primary data publicly available (Prosser et al., 2023). If opening the primary data in full is not possible, an option might be to note the option for accessing the data directly from its owners or offer to share the data on request with interested researchers. Research teams may consider different appropriate ‘levels of openness’ alongside their institutional data offices (e.g., restricted data, open upon request, fully open). It may be possible and appropriate to open some parts of the data but not others (e.g., sharing quotes without identifying information).

Except in extraordinary circumstances, any data you plan to make available must be completely anonymized. Before you make data available, double check that they do not contain any personal information about participants. The anonymization process may need to be more extensive for research including minority groups, where identification may be easily possible based on combined demographic characteristics (e.g., it may be very easy to identify someone of a combined minority sexual, religious, and ethnic identity if their geographic location is disclosed). We recommend never sharing raw identifiable data such as video or audio recordings of interviews or focus groups. If the nature of the data means that participants could be identified, then the data should not be shared in that form. The guiding philosophy should be to make our research as open and transparent as is feasible, while safeguarding respondents’ anonymity and privacy.

Normalize Transparency

Humans are inherently social beings, seeking guidance from one another on common behaviors and widely held ideas (e.g., Chapman et al., 2023b). For this reason, one of the best ways to promote transparency is to leverage the power of social norms, or information about what other people do or approve of (Cialdini, 1990). Sharing your commitment to open science and transparency can help others in our community start to see that we are collectively committed to transparent and open research.

Three ways to promote norms of transparency are at conferences, in your articles, and when serving as reviewers. When presenting research at conferences, make sure to tell the audience explicitly which transparency practices you have followed, such as preregistration, open materials, full description of research methods, and open data. When writing research articles, report all open science practices you have engaged in. Finally, when serving as a reviewer you may ask authors to share their materials, protocols, data, details about methods, and code, or to consider preregistering any new studies. You can ask the editor to request this from authors if it has not already been included as part of the peer review process. If the authors are already engaging in transparency practices, even better: You can take the opportunity to highlight and praise their efforts. Doing so can reinforce open science practices and will also communicate to other reviewers that our community values transparency.