Data is currently being used, and reused, in ecological research at unprecedented rates. To ensure appropriate reuse however, we need to ask the question: “Are aggregated databases currently providing the right information to enable effective and unbiased reuse?” We investigate this question, with a focus on designs that purposefully bias the selection of sampling locations (upweighting the probability of selection of some locations). These designs are common and examples are those that have unequal inclusion probabilities or are stratified. We perform a simulation experiment by creating datasets with progressively more bias, and examine the resulting statistical estimates. The effect of ignoring the survey design can be profound, with biases of up to 250% when naive analytical methods are used. The bias is not reduced by adding more data. Fortunately, the bias can be mitigated by using an appropriate estimator or an appropriate model. These are only applicable however, when essential information about the survey design is available: the randomisation structure (e.g. inclusion probabilities or stratification), and/or covariates used in the randomisation process. The results suggest that such information must be stored and served with the data to support inference and reuse. <b>Citation: </b>S.D. Foster, J. Vanhatalo, V.M. Trenkel, T. Schulz, E. Lawrence, R. Przeslawski, and G.R. Hosack. 2021. Effects of ignoring survey design information for data reuse. Ecological Applications 31(6): e02360. 10.1002/eap.2360

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.

This web service delivers data from an aggregation of sources, including several Geoscience Australia databases (provinces (PROVS), mineral resources (OZMIN), energy systems (AERA, ENERGY_SYSTEMS) and water (HYDROGEOLOGY). Information is grouped based on a modified version of the Australian Bureau of Statistics (ABS) 2021 Indigenous Regions (IREG). Data covers population centres, top industries, a regional summary, groundwater resources and uses, energy production and potential across six sources and two energy storage options. Mineral production and potential covers 36 commodities that are grouped into 13 groups.