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Replication Data for: Hennings et al. (2023) STOTEN - Development of complex patterns of anthropogenic uplift and subsidence in the Delaware Basin of West Texas and southeast New Mexico, USA
No full textData sets used to produce all maps for: Hennings et al. (2023) STOTEN Development of complex patterns of anthropogenic uplift and subsidence in the Delaware Basin of West Texas and southeast New Mexico, USA. Data is shared in the form of an ArcGIS LPK file. The LPK file will open in ArcGIS Pro and ArcMap, but will need to be uncompressed in order to be used in non-Esri software like QGI
Intermediate Data Analysis Documents
No full textThese documents were produced during the analysis of project data for publication
Solicitación para limitar o suspender la importación de mercancías extranjeras con respuesta del fiscal, 1723 octubre 21
No full textLos comerciantes solicitan que la corona española limite o prohíba la importación de mercancías estranjeras a la Nueva España. La petición se produjo luego de que arribara al Puerto de San Juan de Ulúa un buque de la Compañía Real de Gran Bretaña. Afirman que los ingleses y franceses no están logrando vender sus mercancías en el puerto y enviar comerciantes tierra adentro para venderlas, detallando las diversas formas en que los comerciantes extranjeros se están instalando tierra adentro y vendiendo. La petición contiene una respuesta del Fiscal. —— Merchants are requesting that the Spanish crown limit or prohibit the importation of foreign goods into New Spain. The petition came after a ship from the Royal Company of Great Britain arrived at the Port of San Juan de Ulúa. They claim that the English and French are not succeeding in selling their goods at the port and sending merchants inland to sell them, detailing the various ways the foreign merchants are settling inland and selling. The petition contains a response from the Fiscal. 10 f. (20 p.
NCIMM NCER Project Summaries
No full textNCIMM project summaries submitted to US EPA for the National Center for Environmental Prediction (NCEP). This report was developed under Cooperative Agreement No. 83595001 awarded by the U.S. Environmental Protection Agency to The University of Texas at Austin. It has not been formally reviewed by EPA . The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication
Carta recordando a los dominicos en Baja California que no aborden las embarcaciones que llegan, 1775 mayo 24
No full textCarta recordando al Presidente de las misiones de California que las órdenes reales sobre la llegada de embarcaciones se aplican a los eclesiásticos. Mora expresa sorpresa al enterarse de que el Presidente y sus frailes abordaron la fragata Nuestra Señora de la Concepción llegada de Filipinas el 18 de diciembre de 1774, durante su visita a la misión de San José del Cabo. Al parecer, se les ofreció refrescos sin pago, a pesar de existir órdenes en contrario. Mora ordena al Presidente y sus frailes que se adhieran de ahora en adelante a estas órdenes y den ejemplo de obediencia y respeto a los habitantes. —— Letter reminding the President of the California missions that royal orders regarding the arrival of vessels apply to ecclesiastics. Mora expresses surprise at learning that the President and his friars boarded the frigate "Nuestra Señora de la Concepción" arriving from the Philippines on December 18, 1774, during their visit to the San José del Cabo mission. They were apparently provided with refreshments without payment, despite existing orders to the contrary. Mora orders the President and his friars adhere henceforth to these orders and set an example of obedience and respect for the inhabitants. 1 f. (2 p.
Replication Data for: "Spatio-temporal object persistence modeling for long-term mapping and navigation planning"
No full textData for pre-published submission "Spatio-temporal object persistence modeling for long-term mapping and navigation planning" including real and simulated map metadata, analysis data, and a complexity study
Building of Texas geographic abbreviations
No full textBuildings of Texas entry code structure spreadsheet created by Katie Pierce Meyer, Head of Architectural Collections, University of Texas Libraries, based on Buildings of Texas original dataset: https://dataverse.tdl.org/dataset.xhtml?persistentId=doi:10.18738/T8/M45YL
Reproduction Data for: Models of Human Preference for Learning Reward Functions
No full textIntroduction
This dataset consists of human preferences over different trajectories in a game that can be framed as a Markov decision process. The game is grid-based, and in it, a car must move to a goal while avoiding obstacles and minimizing costs (e.g., by minimizing gas costs or collecting coins). Trajectories in this game consist of sequences of states and actions. The dataset collects human preferences over segments of these trajectories. For example, do humans prefer that the car drives out of its way to collect a coin, or that it drives directly to the goal?
The data was collected to study how to learn a reward function from human subject preferences for use with reinforcement learning (RL). RL is a powerful tool that allows robots and other software agents to learn new behaviors through trial and error. Recent advancements in RL have significantly improved its effectiveness, making it increasingly applicable to real-world robotics challenges such as quadrupedal locomotion and autonomous driving. To increase the utility and alignment of RL agents, we study how to learn a reward function from human preferences between pairs of trajectory segments using this game. We provide the game code, which we created for this study, in our corresponding codebase. Also included is a data report file, entitled A_data_report.pdf, containing a detailed account of how the dataset was obtained and its content.
Data Collection
Subjects were shown various pairs of behaviors (ie: player trajectories) in this game and asked to label which one they preferred. The game is designed such that the objective of the game is easy to understand, but identifying optimal behavior is difficult for the players. This serves as a non-trivial test bed for various preference learning algorithms, where a good reward function learned from preferences must correctly balance various reward features.
Game Design
We designed a simple grid-world style game to show subjects when eliciting preferences. The game consists of a grid of cells, each of a specific road surface type. The player can move one cell in one of the four cardinal directions, and the player’s goal is to maximize the sum of rewards. The game can terminate either at the destination for +50 reward or in failure at a sheep for −50 reward. Cells contain other items which either result in a positive or negative reward, and the player is penalized -1 for every move they make. The implementation of this game is in our accompanying codebase. We chose one instantiation of this game for gathering our dataset of human preferences. This specific instantiation has a 10 × 10 grid. From every state, the highest return possible involves reaching the goal, rather than hitting a sheep or perpetually avoiding termination. Figure 1 shows this task.
Human Subjects
143 subjects were recruited via Amazon Mechanical Turk. We filtered workers based on task comprehension (see data report for more details) and required that all workers were located in the United States, had an approval rating of at least 99%, and completed at least 100 other MTurk HITs. The resulting dataset comprises data collected from 50 subjects. This filtered data consists of 1812 preferences over 1245 unique segment pairs. This data collection was IRB-approved.
Dataset Organization and Contents
The full dataset is organized in two directories. The directory deliver_mdp contains all collected human preferences, as well as the corresponding game that subjects were shown and all additional data needed to learn a reward function from these preferences. The directory entitled random_mdps contains 200 additional game instantiations, as well as synthetically generated preferences for each of these games. For further information on the specific files and what they contain, refer to the data report located in this repository.
Results Summary
A preference model is a mathematical representation of a person's preferences over different trajectory segments. Preferences are expressed in terms of pairwise comparisons between segments, where the preference model takes in two segments and outputs the probability that a human would prefer one segment to the other. Given a preference model and a dataset of preferences generated by this model, one can then learn a reward function for an RL task. This dataset was used to evaluate two different preference models; the ubiquitously assumed partial return model and our proposed regret model. Our corresponding paper shows that the regret model is a better predictor of human preferences than the partial return model, and that it results in a learned reward function that, when optimized over, induces more performant behavior under the game's true reward function.
Code
We provide scripts for reproducing the experiments which include learning and evaluating reward functions from the provided preference datasets. The code accompanying this dataset can be found here. This dataset contains a script entitled example.py, which provides a bare-bones example of loading a preference dataset and learning a reward function from it. All code is open source.
Data Reuse
This dataset can be used to reproduce the analysis in the paper Models of Human Preference for Learning Reward Functions. See Related Publication referenced in the metadata, as well as to train and test new reward learning algorithms.
Bulk Data Download
A script named download_data.py is provided for bulk data download.
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Supplementary Data for Oldest evidence of significant C4 grasses and habitat heterogeneity in eastern Africa
No full textSupplementary tables for Peppe et al. submitted.
16 Supplementary Tables
RDM Intervention 2
No full textEvaluating the effectiveness of teaching methods for synchronous online instruction is integral to fostering student engagement and maximizing student learning, particularly in one-time workshops or seminars. Using the lens of social constructivism theory, this study investigated the effect of different approaches of synchronous online instruction on the development of graduate students' research data management (RDM) skills during the post-pandemic era. One experimental group received teacher-centered instruction primarily via lecture and the second experimental group received student-centered instruction with active learning activities. A one-way ANCOVA was used to compare the post-test RDM scores between one control group and the two experimental groups, while controlling for the impact of their pre-test RDM scores. Both experimental groups who received online RDM instruction scored higher than participants from the control group who received no instruction. Additionally, our results indicated that learners who were exposed to more engaged and collaborative instruction demonstrated higher learning outcomes than students who received teacher-centered instruction. These findings suggest that interactive teaching that actively engages the audience is essential for successful synchronous online learning. Simply transferring a lecture-based approach to online teaching will not result in optimal student engagement and learning. The interactive online instructional strategies used in this study (e.g., collective note-taking, Google Jamboard activities) can be applied to any instructional content to engage learners and enhance student learning