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}{% \vskip8pt \textcolor{GJMediumGrey}{\rule{\textwidth}{2pt}} \vskip16pt } \usepackage[absolute,overlay]{textpos} \makeatother \usepackage{lineno} \linenumbers \begin{document} \author[1]{Ajit Singh} \affil[1]{ Patna Womens College} \renewcommand\Authands{ and } \date{\small \em Received: 8 December 2018 Accepted: 5 January 2019 Published: 15 January 2019} \maketitle \begin{abstract} Over the last years, many organizations have been working on infrastructure to facilitate sharing and reuse of research data. This means that researchers now have ways of making their data available, but not necessarily incentives to do so. Several Research Data Alliance (RDA) working groups have been working on ways to start measuring activities around research data to provide input for new Data Level Metrics (DLMs). These DLMs are a critical step towards providing researchers with credit for their work. In this paper, I describe the outcomes of the work of the Scholarly Link Exchange (Scholix) working group and the Data Usage Metrics working group. The Scholix working group developed a framework that allows organizations to expose and discover links between articles and datasets, thereby providing an indication of data citations. The Data Usage Metrics group works on a standard for the measurement and display of Data Usage Metrics. Here I explain how publishers and data repositories can contribute to and benefit from these initiatives. Together, these contributions feed into several hubs that enable data repositories to start displaying DLMs. Once these DLMs are available, researchers are in a better position to make their data count and be rewarded for their work. \end{abstract} \keywords{crossref; research data count; citation; DLM; RDA; scholix; researcher; datasite; DOI, working group.} \begin{textblock*}{18cm}(1cm,1cm) % {block width} (coords) \textcolor{GJBlue}{\LARGE Global Journals \LaTeX\ JournalKaleidoscope\texttrademark} \end{textblock*} \begin{textblock*}{18cm}(1.4cm,1.5cm) % {block width} (coords) \textcolor{GJBlue}{\footnotesize \\ Artificial Intelligence formulated this projection for compatibility purposes from the original article published at Global Journals. However, this technology is currently in beta. \emph{Therefore, kindly ignore odd layouts, missed formulae, text, tables, or figures.}} \end{textblock*} \let\tabcellsep& \section[{Introduction}]{Introduction}\par esearchers who want to build on published research can reuse existing data to arrive at new conclusions. In addition, linking scholarly literature and data leads to increased visibility, discovery and retrieval of both literature and data, facilitating reuse, reproducibility and transparency. In a digital world where data can be more easily shared and documented, scholarly literature and its underpinning data are increasingly seen as inseparable.\par At the same time, while the importance of data sharing is accepted, there are essential questions that still require an answer. For example, why should authors go through the effort of documenting and publishing datasets, if their career depends on the publication of articles and if there is no standard for metadata and basic attribution information around data? Several RDA projects are underway to provide answers to these questions by creating a framework to measure data reuse in a standardized fashion.\par Finding the right way to measure the impact of shared data is crucial if research data is to be included as one of the scholarly outputs used for research evaluation. The current meritocratic system in academia relies heavily on the publication of scientific results in recognized academic journals, supported by an internantional editorial board and peer review system. The most commonly used metric to measure the impact of a publication is counting the number of times it receives a citation from other publications that are also peer reviewed and published in recognized journals.\par The temptation to use the same metrics for data, and measure citations of datasets in articles, is certainly strong. However, the interaction and impact of research data is more complex than that. The very definition of what a citation for data is fuzzier than the equivalent for articles.\par In this paper, I describe how the outputs of two RDA working groups (WGs), the Scholix WG and the Data Usage Metrics WG, can be used to assess data reuse and make data usage statistics and citations available. I will first outline how data repositories and publishers can expose article-data links using Scholix approaches and data usage metrics following the new code of practice for research data. I will then explain how they can consume this information to make DLMs available and help researchers get credit for their work. Within the Scholix framework: Data repositories, journals, and others provide information about the links between literature and data that they hold to community 'hubs' such as OpenAIRE, Crossref and DataCite (with Crossref and DataCite working on a shared infrastructure). This supports and respects existing community-specific practices and the existing means of exchanging this information. \section[{II.}]{II.} \section[{Data Citation}]{Data Citation}\par The community 'hubs' -which are natural places to collect and exchange information about the links between literature and data -commit to a common information model for exchanging the links that they hold and an agreed open exchange method enables this to occur.\par The conceptual model (Figure \hyperref[fig_0]{1}) is about the link between two objects, such as a journal article and the underpinning data. Rather than describing in detail the properties of each of the two objects, the conceptual model focuses on the relationship between the objects. It also enables a record of who asserted the link and who made the link available. \section[{b) Contributing data citations: publishers}]{b) Contributing data citations: publishers}\par As mentioned in the previous section, within the Scholix framework organizations contribute information through community hubs. The majority of scholarly publishers work with non-profit organization Crossref to share metadata about publications. These metadata records include comprehensive information about the items being registered, and increasingly include links to related scholarly artifacts such as data, software, protocols, and reviews.\par As can be seen in Figure \hyperref[fig_1]{2}, Crossref provides two paths to registering data citations: references and relations. Relations are a way to associate related digital objects with each other through metadata. A publisher can register metadata with Crossref explicitly linking a dataset to a journal article. References are formal citations (such as would be provided in a bibliography) and are a type of relation but are provided separately within Crossref metadata. \section[{Crossref members should deposit data citations as references if:}]{Crossref members should deposit data citations as references if:}\par ? The data citation includes a DataCite DOI ? They include data citations in their reference lists (recommended) Crossref members should deposit data citations as relations if: ? They want to capture specific relation types (e.g. is Supplemented By) beyond 'references' ? They are not able to supply data citations as references In 2019 Crossref will be expanding citation support to allow publishers to explicitly identify data citations in line with the data citation roadmap for scientific publishers \hyperref[b6]{(Cousijn et al. 2018}). This will allow for deposition of data citations with all types of persistent identifiers as references. \section[{c) Contributing data citations: data repositories}]{c) Contributing data citations: data repositories}\par Many data repositories actively curate and keep track of which articles are using the datasets they host. This is valuable information that is currently not always available to other organizations in the data community. For data repositories that use DataCite DOIs, the DOIs and accompanying metadata are registered with DataCite. Therefore, information about any journal publications related to a dataset can be included in the metadata records that are sent to DataCite. This additional information should follow the DataCite metadata schema which is aligned with the Scholix metadata schema \hyperref[b3]{(Burton et al. 2017b)}.\par When these elements are added to the metadata that is registered with DataCite, the information about the links will automatically become openly available. \section[{d) Contributing data citations: institutional repositories}]{d) Contributing data citations: institutional repositories}\par For data centers that do not assign DataCite DOIs to datasets, OpenAIRE is currently the best place to deposit article-data links. Institutional repositories can export metadata descriptions of their datasets with links to articles as Dublin Core records or as Scholix records and register with OpenAIRE's Scholexplorer Service \hyperref[b4]{(Burton et al. 2017c}) as a data source. Scholexplorer will bulk collect metadata records from the reposi-tory APIs; Scholexplorer is compatible with the OAI-PMH protocol or REST search APIs that allow collection of all records with a paging system (collecting by means of several calls) and with "last date of indexing" (incremental approach). Scholexplorer will then enrich its graph of article-dataset links with the ones collected from the repository, de-duplicate when necessary, and expose all links as Scholix records via APIs on behalf of the registered repository. All links exported by OpenAIRE carry provenance information about the data sources that provided the links (more than one source may have provided the same link), to ensure visi-bility of the contributing repositories and provide a degree of trust to the consuming services. OpenAIRE asks the database to display the Scholix logo on their website and indicate that it is harvested by Scholexplorer. \section[{III.}]{III.} \section[{Data Usage Metrics a) Standards for data usage metrics}]{Data Usage Metrics a) Standards for data usage metrics}\par Following the Scholix initiative and the related work of the RDA Data Citation WG, it was clear that there are broader metrics for data that the community needs to address. With the Scholix working group focusing on the relationships between articles and datasets and the Data Citation Working Group addressing challenges related to dynamic data citation, there was a need for a working group to define usage for data. The Data Usage Metrics WG started in Ocyober' 2018 and focuses on metrics that reflect usage of research data. The group is working to build a comprehensive list of use cases that covers the spectrum of types of 'usage metrics' that may apply to research data, build a recommendation for community guidance on what types of usage metrics should be applied at the data and repositories levels, and drive adoption of usage metrics across the research landscape. Specifically, the working group is aimed at outlining the barriers to adoption of data-level-metrics standards and current implementations of usage metrics across the data repository landscape. These conversations, surveys, and findings will aid in defining recommendations for types of data and associated metrics that repositories should be considering. The group works closely with the Make Data Count project and leverages the COUNTER code of practice for research data (mentioned below). \section[{b) Contributing data usage metrics}]{b) Contributing data usage metrics}\par This first release of the Code of Practice for Research Data specifically targets research data usage. The recommendations are aligned as much as possible with the COUNTER Code of Practice Release 5 for the major categories of e-resources (journals, databases, books, reference works, and multimedia databases). \section[{Consuming Data Usage Statistics and Citations}]{Consuming Data Usage Statistics and Citations}\par The citations and usage statistics contributed by data repositories and publishers are made openly available to the community via APIs. Crossref and DataCite developed Event Data, a shared underlying infrastructure that holds (among other things) all citations that are contributed as part of article and dataset metadata. Crossref and DataCite each have their own API through which they make these citations available. ? Services such as Scholexplorer retrieve data citations from the Crossref Event Data service using this Scholix API endpoint: http:/api.eventdata. crossref.org/v1/events/scholix. ? Scholexplorer combines this information with the citations that are provided to OpenAIRE. ? Views and downloads processed against the COUNTER Code of Practice are sent to DataCite and any repos-itory or research data service can consume usage statistics for a given dataset DOI from an Event Data Query API provided by DataCite (https://support.datacite.org/docs/eventdata-guide).\par The API combines citations and other events into one API call.\par V. \section[{Conclusions}]{Conclusions}\par Measuring data (re)use and the development of DLMs are crucial if data is to become a first-class research output. Both the Scholix and Data Usage Metrics WGs are making significant contributions in this area by developing clear guidance on how to collect and share data usage statistics and article-data links. Whereas the Scholix WG has reached the end of two very successful 18 month working group terms, the Data Usage Metrics only just started and will continue the work on DLMs and the adoption thereof.\par In this paper, I described how data repositories and publishers can contribute to and participate in these initiatives. The openness of the systems developed offers an infrastructure for collaboration using accepted standards. Community organizations, publishers, data repositories, and service providers can rely on common guidelines and standards to share (re)use information they collect about datasets. The most important next step is for as many organizations as possible to standardize usage counts and contribute usage and citations to the open infrastructure hubs. \section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par Volume XIX Issue I Version I \begin{figure}[htbp] \noindent\textbf{1}\includegraphics[]{image-2.png} \caption{\label{fig_0}Figure 1 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{2}\includegraphics[]{image-3.png} \caption{\label{fig_1}Figure 2 :}\end{figure} \begin{figure}[htbp] \noindent\textbf{3}\includegraphics[]{image-4.png} \caption{\label{fig_2}Figure 3 :}\end{figure} \footnote{© 2019 Global JournalsEnabling Researchers to Make their Data Count} \footnote{( ) C © 2019 Global Journals Enabling Researchers to Make their Data Count} \backmatter \begin{bibitemlist}{1} \bibitem[Cousijn ()]{b6}\label{b6} \textit{A data citation roadmap for scientific publishers. Scientific Data, 5}, H Cousijn . \xref{http://dx.doi.org/10.1038/sdata.2018.259}{10.1038/sdata.2018.259}. \url{https://doi.org/10.1038/sdata.2018.259} 2018. \bibitem[Cantu-Ortiz ()]{b5}\label{b5} F Cantu-Ortiz . \xref{http://dx.doi.org/10.1201/9781315155890}{10.1201/9781315155890}. \url{https://doi.org/10.1201/9781315155890} \textit{Research Analytics: Boosting University Productivity and Competitiveness Through Scientometrics}, (Boca Raton) 2017. CRC Press. \bibitem[Fenner ()]{b7}\label{b7} ‘Code of practice for research data usage metrics release’. M Fenner . \xref{http://dx.doi.org/10.7287/peerj.preprints.26505v1}{10.7287/peerj.preprints.26505v1}. \url{https://doi.org/10.7287/peerj.preprints.26505v1} \textit{PeerJ Preprints} 2018. e26505v1. p. 6. \bibitem[Bierer et al. ()]{b0}\label{b0} ‘Data authorship as an incentive to data sharing’. 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