Common use of Data Variables Clause in Contracts

Data Variables. The main focus of the workshop was on how data users ranked each variable in terms of priority for data collection, and how many observer programs collect each variable. Bar graphs were presented to the workshop participants summarizing the results of the surveys for each data variable, grouped by category (Figures 3-23)2. Bar graphs included the following: • Data variables along the x axis • Percentage of data users that ranked each variable along the y axis, expressed in terms of cumulative percent for each ranking category • Percentage of observer programs that collect each variable along the top of the bar graph. On each chart, a line was drawn horizontally at y=50% to provide a reference point to determine whether there was a majority of data users that indicated the variable was a priority for data collection (i.e., ranked as critical or preferred by data users). Using this simple measure, a majority is considered any number of respondents greater than 50%. Each category of variables and set of bar graphs is accompanied by a brief summary identifying the variables included in that category and highlighting those variables in which >50% of data 2 The bar graphs presented in this summary were updated from the ones presented at the workshop, after incorporating survey responses received shortly after the workshop. users responded were either critical or preferred. For priority data variables, the narrative also indicates how observer programs rated each variable in terms of how feasible or easy it was to collect. Appendix C provides the full list of variables included in the surveys, grouped by category, as well as the number of responses received by data users for each category of response (critical, preferred, optimal, not important, and not applicable/blank), by variable. Appendix C also provides the actual number of observer program respondents that indicated they collect each variable. Appendix D provides observer program responses to feasibility of collecting each variable. Nine temporal variables were included in the pre-workshop surveys: date of gear deployment, date of gear retrieval, time gear deployment began, time gear deployment ended, time gear retrieval began, time gear retrieval ended, time of capture (of the bycatch species)3, time zone, and time-of-day4 (Figure 3). Data users indicated that gear deployment and gear retrieval dates had the highest priority for data collection among the temporal variables. Seventy two percent of data user respondents ranked date of gear deployment as a critical or preferred variable, and 70% ranked it as critical or preferred. Gear deployment start and end times were also a high priority for data collection, with 65% of respondents ranking these variables as critical or preferred. Gear retrieval start and end times were rated as critical or preferred by <50% of data users. These results indicate that the ability to calculate total effort, as indicated by total fishing time (i.e., soak time), is important for data users. The majority (>92%) of observer programs collect these variables, and most (>80%) rated these variables as easy to collect. Time of capture was rated by data user respondents as critical (28%), and preferred (19%), and time zone5 was rated as critical (30%) and preferred (12%) (Figure 3). Time zone can be inferred given accurate position information. Time-of-day was rated by 33% of data users as critical and 17% as preferred; however, two respondents noted that if you have the deployment time, calculating time-of-day as a function of date, time and position can be done using a mathematical algorithm. Only 13% of observer program respondents reported they collect time- of-day. Where collected, this variable may be difficult to interpret due to variations or subjectivities in defining twilight6.