Governments and policy-setting bodies in North America, Europe and Australasia, as well as in other global nations, have set a clear agenda for open science and open data, which is supported by research funding agency requirements for Data Management or Data Sharing Plans as a component of submitted research proposals e.g. NSF and NIH, UK Research Councils and the European Commission. Research transparency is an identified concept, principle or value articulated within many of these policy statements. The thirty countries of the OECD identified transparency as one of the principles in the OECD Guidelines for access to research data from public funding (OECD, 2007). These guidelines highlight four factors to consider in ensuring transparency: “documentation on available datasets and conditions of use should be easy to find”, “research agencies should actively disseminate information on research data policies”, “members of the various research communities should assist in establishing agreements on standards for cataloguing data” and “Information on data management and access conditions should be communicated among data archives and data producing institutions”. In the United States, the Obama Administration released a memorandum on Transparency and Open Government (Holdren et al., 2009) which set out three specific actions for departments and government agencies: Transparency, Participation and Collaboration. Transparency as a value has been discussed in ideological terms by Etzioni (2010), who describes the strong variant relating to regulatory contexts and disclosure. The Royal Society Report (2012) “Science as an Open Enterprise” makes reference to “transparent policies for custodianship, data quality and access” in outlining a set of principles of stewardship which should be shared by custodians of scientific work. In the UK, the Research Councils have published a set of Common Principles on Data Policy (originally published in 2011 and revised: RCUK, 2015) which state: “Making research data available to users is a core part of the Research Councils’ remit and is undertaken in a variety of ways. We are committed to transparency and to a coherent approach across the research base”. The G8 countries Open Data Charter (Gov.UK, 2013) states that “Open data can increase transparency about what government and business are doing” and Principle 4 states that “We will be transparent about our own data collection, standards, and publishing processes, by documenting all of these related processes online”. Furthermore, transparency was an integral element of the European Commission Horizon 2020 program calls, which highlighted transparency elements within innovation pilots for open government. In 2015, the OECD published a substantive report on open science, which identifies “increasing transparency and quality in the research validation process” as a rationale for open science and open data (OECD, 2015); also in 2015, four major global science organizations (the International Council for Science, the InterAcademy Partnership, the World Academy of Sciences and the International Social Science Council) published an international accord, which includes the assertion that “Openness and transparency have formed the bedrock on which the progress of science in the modern era has been based” (ICSU, 2015).

Moving from the policy perspective to look more widely at the literature describing the practice of research, there has been a gradual increase in attention given to transparency concepts and reproducible science protocols, processes and products. At the disciplinary level, there is considerable diversity in open practices with certain domains having well-established norms for data release (e.g. astronomy and genomics), whilst in other disciplines, notably across the humanities and social sciences, open data practices are less common. Peng (2011), for example, speaks to these diversity differences. Addressing reproducibility in computational science, Peng observes disciplinary differences in the ‘culture of replication’ and suggests that replication may be hindered by the size of datasets and the amount of computing power, time, and money necessary to reproduce the study. Gezelter (n.d.) expands on the importance of verifiability in good science and in particular notes that we need “verifiability in practice as well as verifiability in principle”. Collins and Tabak (2014) call for enhanced reproducibility in research funded by the US National Institutes of Health, and describe positive steps associated with publishing practice and scholarly communications.

Examples of uncertain outcomes and flawed research due to a lack of transparency are highlighted in two articles in The Economist (2010, 2013), a New York Times (Carey, 2011) exposé on fraud in psychological research, and a discussion of reproducibility issues in the biomedical/clinical trials domain (Ince, 2011). The related controversy around peer review failures and the current “retraction epidemic” has been described by Fang, Steen, and Casadevall (2013), where many retractions (43% of the sample) were due to fabrication or falsification. Many of these retractions have led to significant adverse reputational impacts on both institutions and individuals.

In parallel with these high-profile cases of research malpractice, a number of practical approaches towards achieving greater research transparency have emerged. These include institutional data policies that recommend data deposit and data sharing (e.g. University of Bath, 2014), open data repositories such as Dryad, open software tools for sharing workflows such as Taverna, and electronic laboratory notebooks like RSpace. The Reproducibility Initiative and the associated Reproducibility Project in Cancer Biology, led by the Science Exchange1 and its independent validation service, have taken transparency and trust goals a stage further, by seeking to reproduce the results in fifty high-impact cancer articles published in 2010–2012 (Errington et al., 2014). A similar initiative was taken in psychology, where a collaborative group of researchers have attempted to replicate studies published in three psychological journals in 2008 (Nosek, 2012; Open Science Collaboration, 2015). This group has introduced the publication format of Registered Replication Reports (Nosek & Lakens, 2014), where the research methodology undergoes peer-review before the experimental procedures are executed, data collected and results published.

There is also ongoing work to explore different approaches to open data peer review processes (Mayernik, Callaghan, Leigh, Tedds, & Worley, 2014), and efforts to incentivize peer review exemplified by the GigaScience partnership with Publons.2 Scholarly publishers and professional societies have an influential role in the research landscape, with organizations articulating their open data policies and expectations from researchers (American Meteorological Society, 2013). However the perceived lack of transparency in the scholarly publishing process has led to a proposal for a new Transparency Index which could contain details of editorial boards, data requirements and procedures for dealing with retractions (Marcus & Oransky, 2012). Furthermore, whilst the different stakeholders in the scholarly communications arena are exploring ways to increase the rigor of the research process, libraries are considering the conceptual framing and scope of open science, and delivering innovative research data services.

Open research has been characterized by a commitment and adherence to accessibility, sharing, transparency, and inclusivity (Borgman, 2015). Previously, a Continuum of Openness was described by Lyon (2009) which had two orthogonal axes: ‘Access’ and ‘Participation’ or inclusivity. The ‘Access’ dimension addressed the ability to (freely) locate and retrieve articles, ranging from ‘closed’ sources, such as a peer-reviewed journal positioned behind a subscription paywall, to an open access institutional repository such as D-Scholarship (university name removed for review). The ‘Participation’ dimension encompassed the degree of collaboration in research ranging from a single lone scholar, through collaborative professional research teams (team science) to citizen science, where members of the public are partners in the design, implementation and publication of research, such as the Zooniverse projects. Thus a ‘continuum’ of openness can be described, with organisations, projects and infrastructure platforms positioned on the axes, according to their degree of openness. This two-dimensional model was framed as a Continuum of Openness and is shown in Figure 1.

The first of these two dimensions (‘Access’) was also explored in more detail by Corrall and Pinfield (2014), who constructed a typology of “open” (open content, open development, open infrastructure) and examined convergence and coherence amongst initiatives in higher education and research. The second dimension (‘participation’) was examined in the context of libraries by Lyon and Beaton (2015), who reviewed citizen science initiatives, education and skills development and opportunities for academic libraries, public libraries and Library Schools/iSchools, recognizing a need for greater awareness and education of librarians to the opportunities in this field.

The concept of transparency and the associated term ‘reproducibility’, have become increasingly important in the current interdisciplinary research environment which is exemplified by greater data volumes, burgeoning numbers of research publications and a critical requirement for accountability for the expenditure of and impact derived from research supported by public funds, particularly in these times of economic constraint. The Transparency Principle has been presented as “Information on research data and data-producing organization, documentation on the data and conditions attached to the use of the data should be internationally available in a transparent way, ideally through the Internet” (OECD, 2007). Further interpretations are “full transparency in reporting experimental details so that others may reproduce and extend the findings” (NIH, 2016) and “the reporting of experimental materials and methods in a manner that provides enough information for others to independently assess and/or reproduce experimental findings” (FASEB, 2016).

Whilst definitions of reproducibility and repeatability were published over twenty years ago by Taylor and Kuyatt (1994) in a NIST Technical Note, reproducibility concepts have been examined again from both computational and legal perspectives by Stodden (2009). Useful definitions of open science and reproducible research were proposed: Open or reproducible research is auditable research made openly available. Furthermore: Auditable research is where sufficient records (including data and software) have been archived so that the research can be defended later if necessary or differences between independent confirmations resolved. The archive might be private, as with traditional laboratory notebooks. (Stodden et al., 2013).

The use of the terms ‘archiving’ and ‘records’ in this definition, serve to emphasise the potentially significant role of libraries and information services in supporting open research in the long term. Three distinct categories of reproducibility: computational (access to code, data and other implementation details), empirical (non-computational empirical scientific experiments) and statistical (analyses and assessments) were described by Stodden, Leisch, and Peng (2014). Easterbrook (2014) used a Venn Diagram to illustrate the relationship between repeatability and reproducibility based on open source code, which contributes towards the goal of verifying the computational research which has been undertaken and subsequently published. In this environment of accountability, the original 2D-model (or continuum) of open science has been extended to include a third dimension: “Transparency”, which is shown in Figure 2. This third dimension projects the efforts of the Retraction Watch (a blog which tracks retractions), towards the Reproducibility Initiatives in Cancer Biology (an initiative to replicate the results of high-profile cancer studies) and the eLife journal, which has published the outcomes of these reproducibility studies.

Many academic institutions have a Research Policy or Research Code of Practice which states the principles, ethical foundations and expectations of researcher behaviour within that institution. These types of document may cover aspects of Open Science which correspond to the dimensions of the 3D Model. For example, a Research Policy may describe scholarly publication channels and include commentary on Open Access (OA) journals and institutional OA funds. The Policy may have some narrative regarding participation, inclusivity and academic inter-relationships with the public; it may explicitly support citizen science collaborations. Furthermore, the Research Policy may have clauses relating to Research Quality and Research Integrity in broad terms. Building on this point, LIS senior managers can highlight transparency and reproducibility issues and ensure that institutional policy developments in OA and research data management, reflect the third dimension of open science, through requirements for transparent science processes, methodologies and peer review. In this way, LIS can lead on the inclusion of transparency principles as part of institutional policy.

The increasing data volumes generated from high-throughput devices such as sequencers, computational analysis, large-scale simulations and expanding collections of observational and environmental sensor data have led to the emergence of a new field of Data Science. There are a range of new roles associated with this field which encompass data analysis, stewardship, software engineering, journalism, managing data archives and data librarianship (Lyon & Brenner, 2015). Some academic libraries are extending their existing Research Support Services to include Research Data Management as a key component with advocacy and advisory services (Cox & Pinfield, 2014). Librarians can act as a transparency advocate with faculty by advising on open (transparent) scholarship, reproducible methods and validation approaches. Raising the awareness of new-entrant researchers, providing transparency and reproducibility information, tools and training, are opportunities for libraries to further demonstrate their value and reach.

National and academic libraries are making significant progress towards sustainable digital stewardship. This goal involves supporting all of the stages of the research lifecycle: designing research protocols and project planning, developing data management or sharing plans; creating, collecting or locating data including metadata descriptions and the use of logs/records and electronic laboratory notebooks; processing data, including cleaning and integrity checks; analysing data, including statistical analysis and visualization; storing and publishing data including through deposit in a repository for long-term preservation; provenance and version control; data peer review and linking to journal articles; managing access to the data with licenses and rights documentation; re-using data via persistent identifiers; and data citation and data attribution metrics. A range of data tools are appearing that address particular aspects of the research data lifecycle such as the DMPTool5 for data management planning and ImpactStory6 for collecting impact evidence and metrics. The Open Science Center has launched a tool called the Open Science Framework (OSF), which is positioned as an open and collaborative project management tool. OSF aims to integrate with other data workflow and research infrastructure components (e.g. data repositories such as figshare7), and thereby increase transparency in the practice of science. Other examples of research infrastructures which support transparency are open source code hubs, open workflow tools, open repositories for data and textual publications, open lab notebooks and open discussions spaces and forums. Libraries can adopt infrastructure which supports open protocols and processes, create new library services around this open infrastructure and ensure that library curation workflows support transparency.

The need to re-engineer LIS education to deliver a curriculum suited to the new data science roles has been noted (Lyon & Brenner, 2015). iSchool programs and courses require real-world relevance in order to produce work-ready graduates who can assume one of these new data science positions. An analysis of the educational requirements, skills, knowledge, and competencies from recent job descriptions for data librarians, data archivists and data stewards has identified the range of themes and topics in scope (Lyon, Mattern, Acker, & Langmead, n.d.). Many iSchools now have data curation or digital stewardship courses, but are transparency and reproducibility concepts embedded in the curriculum? The School of Information Sciences (iSchool) University of Pittsburgh MLIS Program is adopting an innovative translational data science approach (‘the transition of data skills, software tools and research intelligence from the iSchool to the marketplace’ defined in Lyon and Brenner, 2015), which mirrors the established terminology of translational medicine. New Masters courses in Research Data Management and Research Data Infrastructures address transparency, reproducibility and validation concepts. The aim is to produce transparency-savvy LIS graduates and to upskill current LIS staff for these new data science roles.