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Data |
Broadening
Participation in Biological Monitoring: |
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Data handling, storage, and backup—Acquiring high quality data is a major step in the monitoring process, but is of little use unless the data are stored, handled, summarized, analyzed, interpreted and reported properly. Handling involves such activities as transferring raw data to formatted databases, compiling new and old data, doing checks for reasonableness, and preparing data for analysis. Storing data involves categorizing types of data, labeling files with names that are easy to understand, and organizing files in logical folders. It also entails collecting information about the type of information in each file (see metadata below). All of this is a great deal of work and if data are not likely to be used, this work is wasted effort. For instance, one common form of over-commitment is the natural inclination to collect as much information as possible while in the field. If the effort expended to collect miscellaneous or nonessential data is not commensurate with the effort needed to store, manage, analyze and interpret this additional information, then such data becomes superfluous and participants could become discouraged by not seeing the results of their labor. Such unfocused or misguided endeavors can also detract from more important activities. The effort put into routines to regularly back-up data should be tailored to the risk of losing data and the consequences of squandering the effort to collect it. All computer hard drives eventually fail and new hardware technologies constantly replace older ones. Other catastrophes such as fire, floods, hurricanes, earthquakes, theft or vandalism can destroy data too. Reducing the risk of losing data entails not only keeping multiple copies and updating each on a regularly scheduled basis, but in keeping copies in separate locations in the event of unforeseen physical destruction in any one location. Organizations such as schools, universities, businesses, or government agencies virtually always have such mechanisms and policies for data protection already implemented; hence they are obvious choices as data repositories. Another effective method of backing up data is to routinely distribute copies to all the participants in the project. Sharing data, however, brings up the issue of who owns it and controls its use. Data ownership and use—Although participants in a collaborative monitoring project would naturally expect to have a right to share the data collected, some information could be sensitive or proprietary. For instance, if a rare or endangered species is being monitored, it might be in the best interest of all concerned not to release information about the location of threatened populations. Similarly, private landowners or timber companies concerned about restrictive regulations that could be imposed on their property if data are misinterpreted by advocacy groups might wish to ensure that the collaboratively collected information is first analyzed and interpreted in an objective and peer-reviewed manner. Tribes also might be concerned that the data include culturally sensitive information or they might wish to be recognized for contributing traditional knowledge to the project. Prior discussions about the ownership and use of local participants’ ecological knowledge (especially if they consider it specialized or valuable) can be important for acknowledging their contributions or establishing intellectual property rights. Issues of ownership could also arise in the case of bioprospecting when, for example, pharmaceutical companies wish to capitalize on discoveries of organisms with unique and useful medicinal properties. Even if data are not particularly sensitive, some participants might wish to recuperate expenses by selling it. Regardless of the specific circumstances, all these concerns are best addressed when the participants are delineating and examining their motivations and concerns for joining a participatory monitoring project (Communication and Incentives modules). At that stage, mutual agreements such as policy accords or contractual stipulations can be arranged in advance for determining how data will be shared, used, or sold (Organization module). If these issues arise as an afterthought, then involving all participants in resolving the issue as quickly as possible will prevent subsequent conflicts and controversies and allow the data to be applied to the original goals of the project. If data are sensitive or proprietary, forethought given to methods for safeguarding data against unauthorized access will contribute to data security. Passwords and locked rooms are simple strategies. Data distribution—If the data are not controversial or proprietary, and if summaries or simple analyses can be arranged in advance, then prompt distribution of results can be very rewarding for both participants and other users of the data. Routinely updated web sites are an excellent means of distributing such information, although some participants or stakeholders might not have internet access. More organized networks or clearinghouses for information can provide the service of combining current data from multiple projects or sources so managers or policy makers have the most recent facts for informed decisions. Data syntheses—Meta-analysis refers to combining disparate data from numerous sources to reach conclusions that data or analyses from individual sources would not be sufficient to justify. Typically such analyses are conducted at larger geographical scales than are feasible to address by any one project. The process of drawing valid conclusions from different kinds and sources of data is complex, but similarity in data formats, measurement methods, or sampling protocols facilitate the process. Therefore, examining other similar projects and anticipating this potential use of the data is a useful consideration during the stage of sampling design and sampling protocol development (see the Sampling module for further discussion). Incorporating common data elements from other similar projects also is useful. Likely the best example is ensuring that data is spatially explicit. With the advent of inexpensive geographical information system (GIS) recorders, this process is now easy and cheap. Interpretation of biodiversity information is invariably linked to location and habitat, so recording coordinates of sample locations provides a common ground for many types of metadata analysis. Metadata—Meta-analyses are greatly facilitated by the collecting of meta-data. Metadata is literally data about data and refers to information that describes attributes of data such as information content and format, data quality, the history of the project, contact information, database condition, and more. The National Biological Information Infrastructure web site provides authoritative information about standardized types of metadata. In their words, “Metadata records preserve the usefulness of data over time by detailing methods for data collection and data set creation….Metadata makes it possible for data users to search, retrieve, and evaluate data set information”. Perusal of this web site will give users a detailed understanding of the types of information that are useful to record. Although recording meta-data might seem like yet another time-consuming documentation task, this is an effort that can truly guarantee the data remain useful for a long time. Doing so also provides additional credence to claims of data credibility. Most institutions that expend the resources to maintain multiple databases also require the collection of meta-data, so it behooves the project coordinators to become familiar with the particular requirements of the organization that might act as a repository of their project’s data.
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