City Open Data Policies

The capture and analysis of data is transforming the 21st Century. As society becomes more data driven, data has the ability to drive the bottom line for private companies and help the public sector to define where and how services can best be delivered. In City Open Data Policies: Learning by Doing, the National League of Cities identifies how cities can take advantage of the opportunities presented by open data initiatives.SUMMARY OF RECOMMENDATIONSLeadership: Political support stands out as one of the key requirements to implementing a successful open data project.Appropriate Legislation: Enacting legislation or formal policies is a crucial step toward ensuring the growth and sustainability of open data portals. Funding: Open data initiatives do not require high levels of funding. It is, however, important that the programs have their own budget line items where resources are specifically allocated. Technical Approach: Leading U.S. cities rely on commercial platforms that facilitate the implementation of open data initiatives, provide technical expertise, and ensure 24/7 customer support, often at a lower cost than providing these services in-house. Stakeholder Involvement: Open data is a two-way process. It is, therefore, essential to encourage participation and engagement among multiple stakeholders including: community members; non-profits; universities; the press; businesses; city departments; and other levels of government. Many cities adopt a flexible, and usually informal, approach to interact with the stakeholders. Measuring Success: Developing evaluation tools should be an integral part of any future open data policies

Propagating Data Policies: a User Study

When publishing data, data licences are used to specify the actions that are permitted or prohibited, and the duties that target data consumers must comply with. However, in complex environments such as a smart city data portal, multiple data sources are constantly being combined, processed and redistributed. In such a scenario, deciding which policies apply to the output of a process based on the licences attached to its input data is a difficult, knowledge- intensive task. In this paper, we evaluate how automatic reasoning upon semantic representations of policies and of data flows could support decision making on policy propagation. We report on the results of a user study designed to assess both the accuracy and the utility of such a policy-propagation tool, in comparison to a manual approach

Institutional, Funder, and Journal Data Policies

Data curation exists within a larger framework of laws and policies covering topics like copyright and data retention. These obligations must be considered in order to properly care for data as it is being created and preserved. While laws may transition slowly, the policies applying to research data by funding bodies, institutions, and journals have seen significant change since the turn of the century. These policies have directly impacted the practices of researchers and prompted the creation of data curation services by many libraries in partnership with their larger institutions. This chapter examines three important categories of policies, primarily covered from the US perspective, that affect data curation practices in libraries: funding agency policies, institutional data policies, and journal data policies. This chapter was first published in Curating Research Data, Volume One: Practical Strategies for Your Digital Repository published by ACRL

Evaluating and promoting open data practices in open access journals

The last decade has seen a dramatic increase in attention from the scholarly communications and research community to open access (OA) and open data practices. These are potentially related because journal publication policies and practices both signal disciplinary norms and provide direct incentives for data sharing and citation. However, there is little research evaluating the data policies of OA journals. In this study we analyse the state of data policies for OA journals by employing random sampling of the Directory of Open Access Journals and Open Journal Systems journal directories and applying a coding framework that integrates both previous studies and emerging taxonomies of data sharing and citation. This study, for the first time, reveals both the low prevalence of datasharing policies and practices in OA journals, which differs from the previous studies of commercial journals in specific disciplines

Journal Data Policies: Are Croatian Journals Following Trends?

The aim of this work is to give an overview of recent developments of journal data sharing policies, with summary and examples of standardised guidelines for journal publishers. In addition, it examines the prevalence of journal data policies in Croatian journals and explores the content of these policies. To give an overview of the current state of data sharing policies, published articles that review existing journal data policies and develop model data policies or guidelines for journals were identified and examined. For the analysis of Croatian journals, data was collected from the Hrčak portal, using a software script for harvesting journal metadata and attached files from the portal. Searching for content related to data archiving through downloaded files was done using the following keywords: 'data', 'deposit', 'archiving', 'supplement', including Croatian variants and different grammar forms. The search process was facilitated by using software tools that extracted lines of text from source documents containing defined keywords, together with two lines of text above and below the position of keywords in the text as context. The script parses through documents and creates one file containing file name of the identified document and snippets of extracted text from that document. Created file is than manually examined to identify journals that have any content related to research data and eliminate content that is related to data in another context. A dataset is created which contains journal metadata and coded information about the content of the policy. Coding framework for the analysis of content related to research data in journal editorial documents was developed based on previous research (1, 2) and adapted for this analysis. Results and Discussion Recent studies (1, 2, 3) show lack of clear data sharing and transparency policies in the majority of journals. Where the policies were present, wide variety in quality of existing policies was found. This is an obstacle in the practice of data sharing, especially for the authors who need clear guidelines on how to deposit and make their data transparent and available for others to re-use. Standardisation of data policies could help journal editors and research funders to formulate clear mandates and recommendations that can influence the development of research transparency culture. Several attempts to develop a model data policies are identified in recent years, and the most prominent existing implementation guidelines for journals, publishers and funders are: Research data for journal editors by the Australian National Data Service(4), Transparency and Openness Promotion (TOP) Guidelines by the Center for Open Science(5), Research Data Policy Framework for all journals and publishers by Data policy standardisation and implementation Interest Group (IG) of the Research Data Alliance (RDA)(6), Journal Research Data Policy Model Framework by The Journal Research Data (JoRD) Project, funded by JISC (Joint Information Systems Committee)(2). These guidelines identify the key elements of a good data policy such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data. Although all of them attempt to establish standard features, they provide flexibility for adoption depending on disciplinary variation. In order to find out if Croatian journals are implementing and promoting data sharing policies, the analysis of data policies of Croatian journals was conducted. Preliminary results, based on a sample of editorial documents, show that around 15% of journals mention research data in their policy and guidance documents, whether the statement expresses only the general principle of research transparency or the statement is a requirement. Journals that explicitly mention data access and retention are usually referring to the outdated ALPSP-STM Statement on data and databases. Conclusion Data sharing is encouraged in contemporary research environment. Journals in the stage of planning to implement research data policy might find it very useful to consult existing guidelines and follow examples of good practice presented in this work. Some Croatian journals started to adopt basic data sharing policies and transparency principles, but often it is not clear how and if the existing policies are enforced

How policy tools utilized in open data policies in China

The aim of this poster is to investigate which policy tools are used in open data policies in China. 98 Chinese national open data policy documents were investigated in this research. We found that Chinese open data policies em- phasized on the use of environmental and supply-oriented policy tools, with par- ticular attention to information support, regulations and standards. However, the use of demand-side policy tools was insufficient, and the combination of policy tools remained to be optimized. This poster provides suggestions for the improve- ment of open data policies in the future

Do funding agency data policies conflict with text mining license terms?

Objective: As text/data mining (TDM) becomes more prevalent, researchers seek to mine library resources for their projects. Some vendors are including language in their TDM licenses that aims to protect their investments by limiting dissemination and/or retention of TDM data. At the same time, researchers are increasingly being called upon by funding agencies to share and retain data from their projects. This work investigated whether vendor restrictions on TDM data sets from research projects might conflict with funder policies on data sharing and retention. Methods: Language from existing TDM licenses was compared with guidance from several grant-funding agencies to identify potential conflicts with sharing or retaining data generated in the course of TDM research projects. Results: Potential incompatibilities between TDM licensing language and funding agency data policies were identified. Vendor limitations on the length of TDM output could conflict with data sharing policies. Data retention is an area of particular concern, as in some cases, funder policies on data retention periods are at odds with TDM licensing terms that require data to be destroyed upon conclusion of the work. Conclusions: In some cases, language in library vendor TDM licenses is at odds with funding agency policies on data sharing and retention. As support for TDM research continues to evolve, librarians who assist researchers with data management plans should be aware of potential conflicts between vendor TDM licenses and funder data policies on data sharing and preservation