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Preserving Scientific Data on Our Physical Universe A New Strategy for Archiving the Nation's Scientific Information Resources (1995) / Chapter Skim
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3 Retention Criteria and the Appraisal Process
Pages 33-41

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From page 33...
... At the same time, it is difficult to predict the full value of the data to researchers and other users decades or centuries from now, although past experience has shown that scientific data collected many years ago provide unique contributions to new understanding of our physical universe. RETENTION CRITERIA The criteria that follow are to be used during the appraisal process to determine retention of physical science data.
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From page 34...
... Data sets in the laboratory sciences that are candidates for long-term preservation can be classified into three generic types: (1) massive records and data from an original experiment, particularly a costly "mega-experiment," that there is no realistic chance of replicating (e.g., data obtained from expensive facilities such as plasma fusion devices, or data of interest in physics and chemistry derived from special events such as nuclear tests)
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From page 35...
... As has already been noted, one difference arises from the fact that transient natural phenomena are not reproducible; the fact that the resulting observational data are "snapshots in time" sometimes means that the data have historical or evidential value in addition to their informational value. Observational data sets that provide a continuous time-series record of the physical universe, or of human impact upon it, are important to future generations for comparison and the identification of trends.
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From page 36...
... The U.S. Historical Climate Network data are a high priority for archiving because they represent a long time series of high-quality data, with excellent metadata; this combination of attributes of data of a common type makes the overall data set exceptionally valuable.
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From page 37...
... A critical evaluation of especially important quantities (such as the fundamental physical constants or key thermodynamic values) may end up with only a few hundred data points, but include massive documentation and citations to a hundred years of literature.
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From page 38...
... The content realm identifies, defines, and describes primary data items including units, acceptable values, and so forth. The representation realm specifies the physical representation of each value domain, often technology dependent, and the physical storage structure of aggregated data items, often arbitrary.
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From page 39...
... An example of a generalized metadata framework in the observational sciences is presented in the working paper of the Ocean Sciences Data Panel (NRC, 1995~. OTHER ELEMENTS OF THE APPRAISAL PROCESS A data management plan should be created for any new research project or mission plan, consistent with the requirements of OMB (1994)
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From page 40...
... RECOMMENDATIONS The committee makes the following recommendations regarding the retention criteria and appraisal process for physical science data: As a general rule, all observational data that are nonredundant, useful, and documented well enough for most primary uses should be permanently maintained. Laboratory data sets are candidates for long-term preservation if there is no realistic chance of repeating the experiment, or if the cost and intellectual effort required to collect and validate the data were so great that the long-term retention is clearly justified.
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From page 41...
... Classified data must be evaluated according to the same retention criteria as unclassified data in anticipation of their long-term value when eventually declassified. Evaluation of the utility of classified data for unclassified uses needs to be done by stakeholders with the requisite clearances to access such data.
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Key Terms

  • appraisal process
  • observational data
  • physical universe
  • classified data
  • laboratory sciences

  • data items
  • physical science
  • individual data
  • science data
  • management plan

  • transient natural
  • overarching decisions
  • realistic chance
  • special events
  • evaluated compilations

  • information management
  • critical evaluation
  • primary data
  • natural phenomena
  • major user

  • intellectual effort
  • management professionals
  • observational sciences
  • time series
  • data volumes

  • management activities
  • research managers
  • metadata requirements
  • critically evaluated
  • representative subsets


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