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Trustworthy scientific evidence is essential if school psychologists are to use evidence-based practices to solve the big problems students, teachers, and schools face. Open science practices promote transparency, accessibility, and robustness of research findings, which increases the trustworthiness of scientific claims. Simply, when researchers, trainers, and practitioners can ‘look under the hood’ of a study, (a) the researchers who conducted the study are likely to be more cautious, (b) reviewers are better able to engage the self-correcting mechanisms of science, and (c) readers have more reason to trust the research findings. We discuss questionable research practices that reduce the trustworthiness of evidence; specific open science practices; applications specific to researchers, trainers, and practitioners in school psychology; and next steps in moving the field toward openness and transparency.

New Zealand’s goal to double agricultural exports as well as meet Kyoto protocol commitments is a challenge. New Zealand sheep and beef farmers have been advised that applying more nitrogen fertiliser is the cheapest way to boost productivity. However, an increase in nitrogen fertiliser application results in greenhouse gas emissions increasing. This study analysed the implications of increased nitrogen fertiliser application on productivity and greenhouse gas emissions for New Zealand sheep and beef farms. Three scenarios were modelled to use the additional pasture production achieved from the different rates of nitrogen fertiliser and compared with a base model sheep and beef farm. These scenarios were (1) better feeding livestock to increase end live weight, (2) increasing stocking rate and (3) better feeding livestock to reduce the number of grazing days. The model farm was based on weighted average of all sheep and beef farms in New Zealand (Class 9). Simulations were then run with the different rates of nitrogen fertiliser ranging from 20 to 100kgN/ha/yr through each scenario. Consistent with the hypotheses, the efficiency of utilisation of extra grass production is an important determinant of the ratio of product output to GHG emissions. For scenario 1 and 2, productivity increased with the ratio of profit to kg of GHG emissions increasing. In scenario 1, the profit per kg of GHG emissions increased 27% in simulation 1 from the base model farm. This occurred when nitrogen fertiliser was increased to 20 kg/N/ha/yr from 5.6 kg/N/ha/yr. The ratio increased 0.6% for scenario 2 for the same change in nitrogen fertiliser. In scenario 3, the ratio of profit to kg of GHG emissions decreased 9% for the same change in nitrogen fertiliser. In all scenarios, GHG emissions increased. When N fertiliser is increased, productivity increased, greenhouse gas emissions could not be reduced and the proposed emissions trading scheme will have little impact on profitability. Strategic use of N could improve hill-country resilience. With an increase in strategic N fertiliser application, livestock can be better fed; thus, increased live weight and reducing the number of grazing days.

from Scythe : Proceedings and Bulletin of the International Data Farming Community, Issue 1 Workshop 13 The concept of system-level testing of defense acquisition programs has changed. While system level testing remains necessary, it is no longer sufficient for making meaningful acquisition decisions. DoD planning guidance directs that test and evaluation activities include tests in joint environments. This will require acquisition program managers to conduct testing in environments where their new system is a participant in an overarching, joint system of systems. Such a test environment may include live assets along with virtual and constructive simulations. One can immediately recognize the difficulties of such testing. Planning and designing system of system tests is complex due to the need to replicate an entire mission environment, and the number of potential factors that could affect results or outcomes. Agent based simulation and Data Farming are methods that can help testers determine which factors significantly affect mission outcomes and which do not. This, in turn, allows testers to focus scarce resources on the most important test conditions.