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Driven by changes to policies of governments and funding agencies, Open Access to content and data is quickly becoming the prevailing model in academic publishing. Open Access benefits scientists with greater dissemination and citation of their work, and provides society as a whole with access to the latest research. Open Access is, however, only one facet of scholarly communication. Core scientific statements or assertions are intertwined and hidden in the scholarly narratives, and the data underlying these statements are often obscured to the point that replication of results is impossible (Nature Editorial 2012). This is in part a result of the way scientific papers are written as narratives, rather than sources of data.  An often cited reason for the lack of published data is the absence of a reward mechanism for the individuals involved in creating and managing information (Smith 2009, Costello 2009, Vision 2010, McDade et al. 2011, Duke and Porter 2013). Preparing data for publication is a time consuming activity that few scholars will undertake without recognition from their peers. Data papers are a potential solution to this problem (Chavan and Penev 2011, Chavan and Penev 2013). They allow authors to publish data and receive reward through the traditional citation process. Coupling tools to rapidly and simply generate publications will incentivise this behaviour and create a culture of data curation and sharing within the biodiversity science community.  If we are going to incentivise the mass publication of data, we also need mechanisms to ensure quality. Traditional peer review is one of the bottlenecks in standard publication practice (Hauser and Fehr 2007, Fox and Petchey 2010). A common criticism of peer review is the lack of transparency and accountability on the part of the reviewers. To cope with the additional volume of papers created by data publication and to move to a more transparent system, we need to rethink peer review. We need both new methods of reviewing and new tools to automate as much of the review process as possible. This requires a new publishing platform, not just a new journal.  An abundance of small isolated datasets does not, however, allow us to address the fundamental problems within the biodiversity science community. These islands of data are only of value if connected and interlinked. The task of interlinking is performed by biodiversity data aggregators like the Global Biodiversity Information Facility (GBIF) and Encylopedia of Life (EOL) which form the backbone of data-driven biodiversity research. By automating the submission of data to these aggregators, we can increase their value to more than the sum of their parts, making small data big. A renewed appreciation of the value of small data will help to reduce the vast amount of research data that exists only on laptops and memory sticks - data that is often lost when people change roles or retire.  Works of potentially very limited length can hold intrinsic value to the community, but are almost impossible to publish in traditional journals chasing impact factors. Examples include single species descriptions, local checklists and software descriptions, or ecological surveys and plot data. An infrastructure that allows datasets of any size to be important means we can publish them at any time. There is no need to wait for datasets to reach a critical mass suitable for publication in a traditional journal.  Today, we are pleased to announce the official release of the first series of papers published in Biodiversity Data Journal (BDJ). After years of hard work in analyzing, planning and programming the Pensoft Writing Tool (PWT), we now have a publishing platform that addresses the key concerns raised above. This provides the first workflow to support the full life cycle of a manuscript - from writing through submission, community peer-review, publication and dissemination, all within a single online collaborative environment.  Shortening distance between “data” and “narrative” publishing Most journals nowadays clearly separate data from narrative (text). Moreover, data publishing through data centres and repositories has almost become a separate sector within the scholarly publishing landscape. BDJ is not a conventional journal, nor is it a conventional “data journal”. It aims to integrate data and text in a single publication by converting several kinds of biodiversity data (e.g., species occurrences, checklists, or data tables) into the text for human-readable use, while simultaneously making data units from the same article harvestable and downloadable. The text itself is marked up and presented in a highly structured and machine readable form.  BDJ aims to integrate small data into the text whenever possible. Supplementary data files that underpin graphs, hypotheses and results can also be uploaded on the journal’s website and published with the article.  Nonetheless, this is usually not possible for large or complex data, for which we recommend deposition in an established open international repository (for details, see Penev et al. 2011):   Large primary biodiversity data sets (e.g., institutional collections of species-occurrence records) should be published with the GBIF Integrated Publishing Toolkit (IPT); small data sets of this kind are imported into the article text through an Excel template, available in PWT.   Genomic data should be deposited with INSDC (GenBank/EMBL/DDBJ), either directly or via a partnering repository, e.g. Barcode of Life Data Systems (BOLD). Transcriptomics data should be deposited in Gene Expression Omnibus (GEO) or ArrayExpress.   Phylogenetic data should be deposited at TreeBASE, either directly or through the Dryad Data Repository.   Biodiversity-related geoscience and environmental data should be deposited in PANGAEA.   Morphological images other than those presented in the article should be deposited at Morphbank. Images of a specific kind should be deposited in appropriate repositories if these exist (e.g., Morphosource for MicroCT data).   Videos should be uploaded to video sharing sites like YouTube, Vimeo or SciVee and linked back to the article text. Similarly, audio files should go to platforms like FreeSound or SoundCloud, and presentations to Slideshare. In addition, multimedia files can also be uploaded as supplementary files on the journal’s website. 3D and other interactive models can be embedded in the article’s HTML and PDF.   Any other large data sets (e.g., ecological observations, environmental data, morphological and other data types) should be deposited in the Dryad Data Repository, either prior to or upon acceptance of the manuscript. Other specialised data repositories can be used if these offer unique identifiers and long-term preservation.    All external data used in a BDJ paper must be cited in the reference list, and links to these data (as deposited in external repositories) must be included in a separate data resources section of the article.  All datasets, images or multimedia are freely downloadable from the text under the Open Data Commons Attribution License or a Creative Commons CC-Zero waiver / Public Domain Dedication. The article text is available under a Creative Commons (CC-BY) 3.0 license. Primary biodiversity data within an article can be exported in Darwin Core Archive format, which makes them interoperable with biodiversity tools based on the Darwin Core standard.  By facilitating open access to the data that underlie every publication, BDJ is setting a new standard in transparency and repeatability in biodiversity science. Perpetual and universal access to primary data stimulates scientific progress by helping authors build upon existing datasets. BDJ’s commitment to supporting automated data aggregation and interlinking is happening alongside multiple advances in biodiversity informatics infrastructure that herald the dawning of an era of collaborative, big-data biodiversity science (Page 2008, Patterson et al. 2010, Thessen and Patterson 2011, Parr et al. 2012).

Beyond dead trees: integrating the scientific process in the Biodiversity Data Journal