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Data management plan for the grant, "Non-Genetic Inheritance of Hypoxia Tolerance in Fishes: Dynamics and Mechanisms." Research quantifying the inheritance of tolerance to low oxygen in a model fish and then determine the tolerance mechanisms, at organismal to molecular levels, that are passed on from parents to their offspring. The investigators will not only focus on conventional, well-studied genetic mechanisms for inheritance, but will explore so-called “epigenetic” forms of inheritance that may transfer parental characteristics for only a generation or two. Such “temporary inheritance” might actually require less energy and be more beneficial to a species than the more permanent form of genetic inheritance. This project will quantify non-genetic inheritance of hypoxia tolerance in zebrafish as a model organism and then identify underlying mechanisms, at organismal to molecular levels, in parents and in their progeny. Specifically, this project will quantify non-genetically inherited traits that allow hypoxia tolerance, determine “wash-in” and “wash-out” (i.e., the dynamics) of hypoxia-tolerant phenotypes across multiple generations, and establish epigenetic mechanism(s) of non-genetic inheritance in subsequent generations. The information provided by this project will allow biologists to better predict, and perhaps even mitigate, the negative consequences of future episodes of low oxygen in rivers and lakes.

[This dataset contains all data used for Studies 2 (qualitative), 3 (quantitative survey) and 4 (longitudinal) in my PhD research.]<br>Thesis abstract:This thesis explores the potential positive impact of artificial intelligence (AI) technology on sustainability in and outside of the tourism industry through four studies. Study 1 introduced the AI4GoodTourism framework, emphasising the need for sustainability inclusion</em> and tourist involvement</em> to achieve a successful sustainability transition. Five themes were identified through a systematic review: intelligent automation to enhance tourist experience, preserve heritage, promote quality of life, measure tourist experience, and preserve the environment. The latter theme was the least explored scholarly topic. Study 2 conceptualised a conversational AI chatbot to promote pro-environmental behaviour spillover among tourists visiting the Gili Islands, Indonesia. A theoretical model was proposed, highlighting factors influencing chatbot usage and spillover effects. Study 3 identified relationships between factors from Study 2, revealing that factors such as performance expectancy, timing, </em>and credibility</em> significantly influenced people’s intention to use the proposed chatbot technology. A significant relationship was established between people’s intentions to use the chatbot and environmentally friendly transport. Scenario-based experiments showed that using the chatbot with educational information on sustainability was sufficient to trigger behaviour change. Study 4 explored the underlying mechanism of pro-environmental behaviour spillover through human-chatbot interactions using flashback nudging. A longitudinal experiment involving the Gili tourists demonstrated that flashback nudging delivered through chatbot technology strengthened their environmental self-identity, leading to significant differences in self-reported pro-environmental behaviour between treatment and control groups. In conclusion, the thesis demonstrates that AI technology, designed with high sustainability inclusion, can positively impact sustainability through tourists’ marginal contributions. The proposed AI4GoodTourism framework and the conceptualised chatbot technology, especially with flashback nudging, show potential for facilitating pro-environmental behaviour spillovers among tourists. All four studies in this thesis highlight the importance of prioritising sustainability in AI innovations for the tourism industry, offering insights for future AI development and adoption to support the global sustainability agenda.

# Data from: Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States This dataset was used to generate 3 figures and 5 tables in the publication, "Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States". All data was collected in Florence, South Carolina at the Clemson University Pee Dee Research and Education Center during 2021 and 2022. \#Description of dataset and file structure Data is presented in an Microsoft Excel Spreadsheet, with separate tabs for datasets related to each of the 3 figures/subfigures and 5 tables in the published manuscript. For all data the following treatment abbreviations are used: Fallow = weedy, unplanted treatment ARG = annual ryegrass RC+WC = 50/50 mixture of red and white clover ARG+RC+WC = 50% annual ryegrass, 25% red clover, and 25% white clover **Data for Figure 1** These data were used to generate Figure 1, featuring mean weather data for the study years, 2021 & 2022, along with 30-year mean weather data for the nearest NOAA weather station (Florence, SC Regional Airport). Units are provided in the column headers. **Data for Figure 2a & 2b** These data were used to generate Figures 2a and 2b, featuring the amount of spring forage mass accumulation preceding and in between cotton intercropping. Forage mass in the RC+WC and ARG+RC+WC treatments consited of a mix of weeds and clovers, while ARG and fallow treatments are entirely comprised of weedy biomass **Data for Figure 3a & 3b** These data were used to generate Figures 3a and 3b, featuring the red and white clover populations in each treatment for each year of the study. **Data for Figure 3c** These data were used to generate figure 3c, featuring the weedy species population changes from spring to fall before, between, and after two seasons of cotton intercropping in 2021 and 2022.Final data in the published figure was Weeds per square meter. **Data for Tables 1 and 2** These data were used to generate Tables 1 & 2, featuring height data for individual clover, annual ryegrass, and weedy species observed among different treatments. **Data for Table 3** These data were used to generate part of table 3, featuring the forage nutritive value data (crude protien, CP; acid detergent fiber, ADF; neutral detergent fiber, NDF; non-fibrous carbohydrates, NFC; total digestible nutrients, TDN; net energy of lactation, NEL; net energy of maintenance, NEM; net energy of gain, NEG) **Data for Tables 3, 4, and 5** These data were used to generate part of Table 3, and Tables 4 and 5, featuring nutrient compositions of the forage plant tissues collected during the trial. All data are in g/kg dry matter. ## Sharing Access Information These data were originated from the published manuscript: [https://doi.org/10.1002/agj2.21625](https://doi.org/10.1002/agj2.21625). This is digital research data corresponding to a published manuscript, Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive value in the southeastern United States, in Agronomy Journal. Integrated forage–row cropping systems provide important agronomic and economic benefits to producers. However, little attention has been given to incorporating forages into row crop systems unique to the southeastern United States. This study assessed the viability of intercropping cotton (Gossypium hirsutum L.) on perennial, cool-season legumes during the summer months in the Southeast Coastal Plain over two production years. Treatments included a weedy fallow, annual ryegrass (ARG; Lolium multiflorum Lam.) monoculture, a red clover (RC; Trifolium pratense L.) and white clover (WC; Trifolium repens L.) mixture, and a three-species mixture of ARG, RC, and WC. Plots were established in fall 2020 with forage grown until May 2021 and 2022, when plots were strip-tilled and planted with cotton. Cotton was managed with minimal herbicide use to preserve perennial clovers. Data was collected over two years (October 2020 - October 2022) at the Clemson Pee Dee Research and Education Center near Florence, SC. Data was collected by field measurements of plant height, biomass accumulation, and species persistence and diversity, with laboratory assays conducted to collect plant nutritional composition. Forage nutrtitive value parameters and fiber content were conducted by a third-party laboratory (Dairy One LLC, Ithaca, NY).

Climate models encode our collective knowledge about the climate system and are among the best tools available for estimating past and future climate change. However, in response to greenhouse gas forcing, climate models exhibit a large intermodel spread in various aspects of the climate system, adding considerable uncertainty to future climate projections. This dissertation introduces a series of conceptual models and frameworks to understand the behavior of climate models under greenhouse gas forcing and, consequently, Earth's changing climate. A simple statistical model is used to explain and constrain the intermodel spread in Arctic sea ice projections across climate models. The probability of encountering seasonally ice-free conditions in the twenty-first century is also explored by systematically constraining components of the statistical model with observations. A conceptual framework is introduced to understand controls on the strength and structure of the Atlantic meridional overturning circulation (AMOC) in climate models. This framework is used to explain why climate models suggest the present-day and future AMOC strength are related. This framework, in conjunction with observations, implies modest twenty-first-century AMOC weakening. A simple energy budget framework is used to examine precipitation over a wide range of climates simulated by climate models. It is shown that in extremely hot climates, global-mean precipitation decreases despite increasing surface temperatures because of increased atmospheric shortwave absorption from water vapor, which limits energy available for surface evaporation. These results have large implications for understanding weathering rates in past climates as well as Earth's climate during the Hadean and Archaean eons. Finally, a framework is introduced to reconcile two different approaches for quantifying the effect of climate feedbacks on surface temperature change. The framework is used to examine the influence of clouds on Arctic amplification in a climate model and an energy balance model. This work introduces an important non-local mechanism for Arctic amplification and shows that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic warming. This dissertation advances our understanding of various aspects of Earth's changing climate and provides a series of conceptual frameworks that can be used to further constrain the behaviour of climate models in response to external forcing.