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Professional interventions in landscapes of indigenous origin have a cultural complexity rarely considered by designers and planners. Today, on the verge of various environmental crises, such as the water crisis, attention should be paid to sustainable landscape knowledge when it is part of the worldview of its inhabitants. In this article, we compare two towns in the municipality of Texcoco, Mexico which have had similar behaviors regarding their environment in the 16th century. They have distanced themselves in such ways that the comparison can work as an example for other cases of countries with indigenous communities. While San Luis Huexotla seems to have lost its sacred linkage with the Tlaloc volcano that stands out on the horizon and with the pre-Hispanic buildings of the town, San Miguel Coatlinchan continues to carry out ritual practices in places of archaeological importance, which sustain the care and maintenance of its natural and cultural heritage. Here we demonstrate that following a historical-geographical model that explains the relationship between inhabitants and their environment, it is possible to understand the differences that culturally separate these two towns; furthermore, to design an architectural landscape project in Huexotla to evidence the rupture of the community with its landscape and try to improve it.

This dataset contains transcripts from eight User-Centered Design Focus Groups conducted in the Fall of 2020. The COVID Pandemic necessitated that our focus groups be conducted virtually using the Zoom Platform. To keep the virtual focus groups manageable and maintain a connection amongst participants, we hosted two focus groups for each of our four themes: Addressing the Knowledge Barrier (n=8), Understanding the Risk Perception Barrier (n=10), Understanding the Sefl-efficacy Barrier (n=9), and is Tailoring a reasonable strategy for addressing barriers (n=11). Each focus group head between 3 and 6 attendees, with a total of 38 unique participants across all themes.

This dataset was used to test the difference between spatially aggregating optimization error using the mean performance and the median performance across all gauges in the calibration of hydrologic model. Two comparative and parallel model optimization tests are included: one that optimizes mean error from all gauges, and a second that optimizes median error. The watershed model used for this research is a CHARM model of the Athabasca River basin, which is included in the dataset. Model calibrations were performed using the OSTRICH program (version 17.12.19), values for 27 parameters covering all simulated hydrological storages were calibrated using the DDS algorithm. One set of calibrations was run with the objective to maximize the mean Kling-Gupta Efficiency (KGE) for 22 gauges, and a second set was run with the objective to maximize the median KGE for the same 22 gauges. Five random seeds and initial solutions were generated and used as starting points for both sets of calibrations. In total, 10 calibrations (5 paired calibration trials) were run with 5000 iterations per calibration. Mean-calibrated models perform significantly better than median-calibrated models. This dataset includes: 1) The complete hydrologic model used in the calibration experiment, including the calibration software and statistics calculation script 2) The calibration trial input and output files (ostIn, OstOutput) with the summary data files combining results for plotting

Raw read counts and source dataset associated with the manuscript on "Rationally Designed Pooled CRISPRi-Seq Uncovers an Inhibitor of Bacterial Peptidyl-tRNA Hydrolase". All the source codes are available here: https://github.com/zisanurrahman/CRISPRi_EGML_Main

The Data was recollected by CIAT Group. This Data is for the article Rice (Oryza Sativa L.) Bioeconomy: A DEA approach (VRS, CRS & Bootstrapping). The present dataset contains the original data from 612 rice farms in the five provinces of Ecuador. The dataset includes adjusted data for application in R for statistical analysis and the DEA methodology with BCC and CCR models adjusted with Bootstrap. Data were collected from 612 rice-producing farms in Ecuador during the 2019-2020 year or cycle. Details were gathered on Total Income [ti], Total Cost [tc], Total CO2 Emissions (kg CO2 eq/cycle) [te], Urea Used (kg/ha) [u], Farmer Age [age], Years of Study [Study_year], Years of Experience [experience], Land Area (ha) [area_ha], and Yield in Tons per Hectare [rend_ton_ha]. The provinces in Ecuador where the data were collected are: Guayas, El Oro, Manabi, Loja, and Los Rios.

This article creates fruit (banana and guava) image datasets for non-destructive quality classifications. All images were shot with a Redmi Note 10-Pro mobile camera in natural sunlight. All the images were captured at different angles and saved in JPG format. A total of 1738 original images were collected. The images were augmented to total 8740 images through data enhancement methods (image flipping horizontally, enhancing the image contrast and brightness, boosting the color of the images, and image rotation at 30 degrees). This dataset allows researchers to study different algorithms of machine learning or deep learning for quality classification of fruits.

Multiobjective Mixed Integer Fractional Programming (MOMIFP): Data from test instances. This data set includes integer, mixed integer, and problems with binary variables only - Multiobjective Multidimensional Knapsack (MMK) and Multiobjective General Assignment (MGA) problems. The instances have 2 to 4 fractional objective functions, 5 or 10 constraints, and 20 to 50 variables.

Data from 98 Irish dairy farms. Data set includes the performance of several different interoperations of the Wisconsin clinical score derived from clinical exams undertaken on calves on these farms and thoracic ultrasound. Associated rjags code is also included. Header interpretation key: T= Positive test result F= Negative test result TUS= Thoracic ultrasound ca= California clinical score (Love et al. (2014)) ws= Wisconsin clinical score (McGuirk and Peek (2014)) bin = Wisconsin clinical score (Binversie et al. (2020)) amor = Wisconsin clinical score (Calderón-Amor and Gallo (2020)) Lago = Wisconsin clinical score (Lago et al. (2006)) medrano = Wisconsin clinical score (Medrano-Galarza et al. (2018)) population = total number of calves tested on a given farm Example: TUSFwsT = number of calves thoracic ultrasound negative and Wisconsin clinical score (McGuirk and Peek (2014)) positive on a given farm. Each line in the table represents a different farm.

Supplementary data for the JAAD Research Letter entitled "Time to First Skin Cancer Diagnosis Following Kidney Transplant".

Protein of unknown function - conserved; Leishmania donovani (HU3 strain)