The Application of Archival Concepts to a Data-Intensive Environment: Working with Scientists to Understand Data Management and Preservation Needs

The collection, organization, and long-term preservation of resources are the raison d’être of archives and archivists. The archival community, however, has largely neglected science data, assuming they were outside the bounds of their professional concerns. Scientists, on the other hand, increasingly recognize that they lack the skills and expertise needed to meet the demands being placed on them with regard to data curation and are seeking the help of “data archivists” and “data curators.” This represents a significant opportunity for archivists and archival scholars but one that can only be realized if they better understand the scientific context.National Science Foundation under Grant No. 0724300Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/86738/1/Akmonetal2011.pd

The record of aerosol deposited species in ice cores, and problems of interpretation

Ice cores have now become established as one of the primary archives of paleoclimatic information, covering timescales from seasonal up to 100,000 years or more. In the polar ice sheets, where there is little or no melting, snow layers build up year by year. Included in them are samples of the atmosphere: trace gases in air bubbles, particles and adsorbed gases, and the water molecules themselves. By drilling into the ice at suitable places, we can collect cores that give profiles of chemical content and physical properties of the ice. These are then used to infer the state of the atmosphere in the past

Big data from small data: data-sharing in the 'long tail' of neuroscience

The launch of the US BRAIN and European Human Brain Projects coincides with growing international efforts toward transparency and increased access to publicly funded research in the neurosciences. The need for data-sharing standards and neuroinformatics infrastructure is more pressing than ever. However, ‘big science’ efforts are not the only drivers of data-sharing needs, as neuroscientists across the full spectrum of research grapple with the overwhelming volume of data being generated daily and a scientific environment that is increasingly focused on collaboration. In this commentary, we consider the issue of sharing of the richly diverse and heterogeneous small data sets produced by individual neuroscientists, so-called long-tail data. We consider the utility of these data, the diversity of repositories and options available for sharing such data, and emerging best practices. We provide use cases in which aggregating and mining diverse long-tail data convert numerous small data sources into big data for improved knowledge about neuroscience-related disorders

If We Share Data, Will Anyone Use Them? Data Sharing and Reuse in the Long Tail of Science and Technology

Research on practices to share and reuse data will inform the design of infrastructure to support data collection, management, and discovery in the long tail of science and technology. These are research domains in which data tend to be local in character, minimally structured, and minimally documented. We report on a ten-year study of the Center for Embedded Network Sensing (CENS), a National Science Foundation Science and Technology Center. We found that CENS researchers are willing to share their data, but few are asked to do so, and in only a few domain areas do their funders or journals require them to deposit data. Few repositories exist to accept data in CENS research areas.. Data sharing tends to occur only through interpersonal exchanges. CENS researchers obtain data from repositories, and occasionally from registries and individuals, to provide context, calibration, or other forms of background for their studies. Neither CENS researchers nor those who request access to CENS data appear to use external data for primary research questions or for replication of studies. CENS researchers are willing to share data if they receive credit and retain first rights to publish their results. Practices of releasing, sharing, and reusing of data in CENS reaffirm the gift culture of scholarship, in which goods are bartered between trusted colleagues rather than treated as commodities