This paper discusses many of the issues associated with formally publishing data in academia, focusing primarily on the structures that need to be put in place for peer review and formal citation of datasets. Data publication is becoming increasingly important to the scientific community, as it will provide a mechanism for those who create data to receive academic credit for their work and will allow the conclusions arising from an analysis to be more readily verifiable, thus promoting transparency in the scientific process. Peer review of data will also provide a mechanism for ensuring the quality of datasets, and we provide suggestions on the types of activities one expects to see in the peer review of data. A simple taxonomy of data publication methodologies is presented and evaluated, and the paper concludes with a discussion of dataset granularity, transience and semantics, along with a recommended human-readable citation syntax.

The NERC Science Information Strategy Data Citation and Publication project aims to develop and formalise a method for formally citing and publishing the datasets stored in its environmental data centres. It is believed that this will act as an incentive for scientists, who often invest a great deal of effort in creating datasets, to submit their data to a suitable data repository where it can properly be archived and curated. Data citation and publication will also provide a mechanism for data producers to receive credit for their work, thereby encouraging them to share their data more freely.

[1] Long-term statistics of tropospheric attenuation were derived from almost 4 years of measurements made in the south of England using the ITALSAT F1 beacon signals at 49.5, 39.6, and 18.7 GHz; coincident rainfall rate measurements were made at the site of the receiving ground station. A method to remove the nonatmospheric changes of the beacon signals and to establish the reference levels from which to measure the excess and total attenuation has been presented in detail. The accuracy of fade level retrieval is estimated to be $±0.5 dB. A new method for predicting the annual total attenuation statistics has been proposed and validated against our data and data collected in Italy at 18.7, 39.6, and 49.5 GHz. For both locations, the new proposed method gives much better predictions compared with the established International Telecommunication Union recommendation method. A significant monthly and seasonal variation was observed in the attenuation and rainfall statistics and should be taken into consideration when planning the design and use of future slant path systems. We have seen that the attenuation statistics are subject to diurnal variations; however, for the period analyzed, this variation does not seem to follow a particular pattern.Citation: Ventouras, S., and C. L. Wrench (2006), Long-term statistics of tropospheric attenuation from the Ka/U band ITALSAT satellite experiment in the United Kingdom, Radio Sci., 41, RS2007,

Peer review holds a central place within the scientific communication system. Traditionally, research quality has been assessed by peer review of journal articles, conference proceedings, and books. There is strong support for the peer review process within the academic community, with scholars contributing peer reviews with little formal reward. Reviewing is seen as a contribution to the community as well as an opportunity to polish and refine understanding of the cutting edge of research. This paper discusses the applicability of the peer review process for assessing and ensuring the quality of datasets. Establishing the quality of datasets is a multifaceted task that encompasses many automated and manual processes. Adding research data into the publication and peer review queues will increase the stress on the scientific publishing system, but if done with forethought will also increase the trustworthiness and value of individual datasets, strengthen the findings based on cited datasets, and increase the transparency and traceability of data and publications. This paper discusses issues related to data peer review—in particular, the peer review processes, needs, and challenges related to the following scenarios: 1) data analyzed in traditional scientific articles, 2) data articles published in traditional scientific journals, 3) data submitted to open access data repositories, and 4) datasets published via articles in data journals.