Most of data sharing policies share some common principles such as protecting the cumulative data outputs, recognizing data as a public good and data sharing as strong value chains of innovation for subsequent scientific exploitation. A improved data access and sharing not only helps to maximize the research potential but it also reinforces open scientific research which encourages diversity of analysis and opinion. In a latest article published in October 9 issue of journal Science, Dawn Field, John Wilbanks and others highlight several issues related to convergence and confluence of data sharing efforts-
1. Data managing versus data sharing
Funding agencies are more focused on data managing efforts such creating institutional infrastructure and repositories rather than data sharing. Authors suggest that although such centralization efforts provides economy of scale, institutional memory, and reusable capability, in long term it also finds a substantial direct cost that may compete with research funding.
2. Heterogeneity and seamless interoperability
Despite the fact that most funding agencies are committed to data sharing efforts they can hardly avoid the heterogeneity and overlap of these kind of efforts due to different types of communities served by each funder and the data types they generate. Seamless interoperability should be prime focus in this kind of matters.

3. Challenges due to proliferation of standardization efforts
There is an exponential increase in number of community-driven efforts to create minimal information guidelines, ontologies and file-format projects. On one side it seems a positive sign of community engagement, but on the other side this proliferation brings new challenges because most of these projects are focused on a particular biological domain and due to their domain specific virtue most of these projects are fragmented and not suitable for the interoperability. Also on several occasions one can observe unnecessary overlaps and duplication of these efforts.
4. Intellectual and ethical issues
Not all but few researchers’ are concerned about intellectual ownership and the commercial aspects of their research work. Ethical issues such as whether or not patient specific data should be open especially to avoid the potential misuses (For instance a recent UK government scheme to establish nationality through DNA testing using recent development of large SNP databases has sparked a intense debate)
5. Lack of funding
Last but not least lack of funding may be detrimental for these substantial and long term undertakings.

Recommendations
Further authors recommend that there should be a single detailed multi-level data sharing guideline which can serve as a template for policy documents at the various levels such as funder, community, and project level. For enforcement of policy they suggest that funding agency should bring a clear mandate for the inclusion of data-sharing plans in grant applications including the compulsory deposition of data generated due to funding of research in appropriate databases within a specified time line adhering with best standards.
They also emphasize the requirement of a centralize hub which can build essential linkages between various topdown(guided by public and private funding agencies) and buttom-up (grassroots developments such as Science Commons, MIBBI, OBO, BioPortal, FUGE etc) data sharing initiatives to serve a “one-stop shop” for those who are in pursuit of data sharing policies and information.