Data Management Policy
* All users of BNZ data are obligated to adhere to the BNZ Data Management Policy.
Updated: June 2019
1.0 Policy Governance.
1.1 This policy governs all data collected under the auspices of the National Science Foundation Bonanza Creek Long-Term Ecological Research project. This policy has been set by the principal investigator(s) in consensus with other investigators associated with the BNZ LTER. Additionally, this policy is designed to be in accordance with the Long Term Ecological Research Network Data Access Policy
1.2 The BNZ LTER data manager, as directed by the principal investigator, is responsible for data management policy implementation.
2.0 Use of Data
2.1 Investigators will have a reasonable opportunity to have first use of data that they have collected.
2.2 Investigators are encouraged to make data sets available to the scientific community in a timely fashion with a community accepted data repository with as few restrictions as possible, on a nondiscriminatory basis. Continuation of investigator support from the LTER program will be contingent upon the investigator contributing well documented data to the LTER database.
2.3 Researchers should receive adequate acknowledgment for the use of their data by others and should be provided with copies of publications using their data. Users of data from the data base must be aware that data is not to be sold or redistributed.
3.0 Citing BNZ Datasets.
3.1 It is considered a matter of professional ethics to acknowledge the work of other scientists. Thus, the Data User will properly cite the Data Set in any publications or in the metadata of any derived data products that were produced using the Data Set.
3.2 Bonanza Creek dataset citation policy is as follows:
Creator(s), Year of Data Publication, Title of Dataset, Publisher, Dataset identifier, Dataset URL, Dataset DOI.
Van Cleve, Keith; Chapin, F. Stuart; Ruess, Roger W. 2016. Bonanza Creek Experimental Forest: Hourly Temperature (sample, min, max) at 50 cm and 150 cm from 1988 to Present, Bonanza Creek LTER - University of Alaska Fairbanks. BNZ:1, http://www.lter.uaf.edu/data/data-detail/id/1. doi:10.6073/pasta/725db90d86686be13e6d6b2da5d61217.
4.0 Management of LTER data sets.
4.1 Data sets should be carefully documented throughout the process of data collection, correction, aggregation, analysis etc. to provide for the use of data by investigators not originally involved in this process.
4.2 Standard methods of quality assurance and quality control should be used to generate data included in the LTER database. These methods should be clearly documented.
5.0 BNZ LTER Database and Data Catalog.
5.1 A site-wide database will be maintained by data management personnel. Long-term archival storage will be assured for relevant data sets.
5.2 Investigators should document studies for the data catalog as early as practical in the process of collecting data.
5.3 Data will not be accepted for inclusion in the BNZ LTER database unless it is adequately documented.
6.0 Access to Data
6.1 All metadata will be freely available to those requesting it when, or before, the dataset itself is released and available regardless of any restrictions on access to the data.
6.2 All metadata will follow LTER recommended standards and will minimally contain adequate information on proper citation, access, contact information, and discovery. Complete information including methods, structure, semantics, and quality control/assurance is expected for most datasets and is strongly encouraged.
6.3 Data collected with LTER funding should be remain available even though an investigator is no longer associated with the project through transfer, retirement, disassociation, death etc.
7.0 Data Types
Type I – Data are to be released to the general public according to the terms of the general data use agreement within 2 years from collection and no later than the publication of the main findings from the dataset.
Type II – Data are to be released to restricted audiences according to terms specified by the owners of the data. Type II data are considered to be exceptional and should be rare in occurrence. The justification for exceptions must be well documented and approved by the lead PI and Site Data Manager. Some examples of Type II data restrictions may include: locations of rare or endangered species, data that are covered under prior licensing or copyright (e.g., SPOT satellite data), or covered by the Human Subjects Act. Researchers that make use of Type II Data may be subject to additional restrictions to protect any applicable commercial or confidentiality interests.