A dataset on Nelore Cattle supplementation in the midwest region of brazil for integrated analysis of supplementation, satellite remote sensing, and weather variables.

Monitoring the herd during supplementation is essential to understanding animals' ingestive activities, making decisions when choosing the supplement parameters, and correctly managing the livestock and agriculture processes. Programmable Automatic Feeders (PAFs) are important tools that support stakeholders in the treatment process, decreasing the time and cost compared to traditional supplementation methods. This paper presents a dataset that consists of data acquired from a supplementation experiment using a PAF with a Nelore herd in a paddock of 16 ha with brachiaria Decumbens forage. The experiment was performed in the Midwest region of Brazil (20°26'37.7″S 54°50'58.5″W) and according to the Köppen climate classification system, the region has a tropical wet and dry climate (Aw). This climate type is typically associated with high temperatures throughout the year. The herd had free access to water and the forage. The PAF supplemented the herd three times a day and collected data such as the frequency and time spent in the feeder of each animal. The experiment data started in December 2022 up to October 2023. The animals were weighed every 13-28 days, and the animals' Average Daily Gain (ADG) was registered. In addition, spectral and weather parameters were acquired via geoprocessing and meteorological (Application Programming Interfaces - APIs). This dataset can support stakeholders in understanding the bovines' collective behaviour at the supplementation process and designing and developing machine-learning models to estimate the quantity of supplementation ingested by the herd.

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator.

In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.

Una mirada actual a la caracterización de datos y ficheros de neurociencias / A Current Look at Characterization of Neuroscience Data and Files

Para los neurocientíficos constituye un desafío realizar un seguimiento de los datos y metadatos generados en cada investigación y extraer con precisión toda la información relevante, hecho crucial para interpretar resultados y requisito mínimo para que los investigadores construyan sus investigaciones sobre los hallazgos anteriores. Se debe mantener tanta información como sea posible desde el inicio, incluso si esta pudiera parece ser irrelevante, además de registrar y almacenar los datos con sus metadatos de forma clara y concisa. Un análisis preliminar sobre la literatura especializada arrojó ausencia de una investigación detallada sobre cómo incorporar la gestión de datos y metadatos en las investigaciones clínicas del cerebro, en términos de organizar datos y metadatos completamente en repositorios digitales, recopilar e ingresar estos teniendo en cuenta su completitud, y sacar provecho de dicha recopilación en el proceso de análisis de los datos. Esta investigación tiene como objetivo caracterizar conceptual y técnicamente los datos y metadatos de neurociencias para facilitar el desarrollo de soluciones informáticas para su gestión y procesamiento. Se consultaron diferentes fuentes bibliográficas, así como bases de datos y repositorios tales como: Pubmed, Scielo, Nature, Researchgate, entre otros. El análisis sobre la recopilación, organización, procesamiento y almacenamiento de los datos y metadatos de neurociencias para cada técnica de adquisición de datos (EEG, iEEG, MEG, PET), así como su vínculo a la estructura de datos de imágenes cerebrales (BIDS) permitió obtener una caracterización general de cómo gestionar y procesar la información contenida en los mismos(AU)

For neuroscientists, it is a challenge to keep track of the data and metadata generated in each investigation and accurately extract all the relevant information, a crucial fact to interpret results and a minimum requirement for researchers to build their investigations on previous findings. Keep as much information as possible from the start, even if it may seem irrelevant and record and store the data with its metadata clearly and concisely. A preliminary analysis of the specialized literature revealed an absence of detailed research on how to incorporate data and metadata management in clinical brain research, in terms of organizing data and metadata completely in digital repositories, collecting and inputting them taking into account their completeness. , and take advantage of such collection in the process of data analysis. This research aims to conceptually and technically characterize neuroscience data and metadata to facilitate the development of computer solutions for its management and processing. Different bibliographic sources were consulted, as well as databases and repositories such as: Pubmed, Scielo, Nature, Researchgate, among others. The analysis on the collection, organization, processing and storage of neuroscience data and metadata for each data acquisition technique (EEG, iEEG, MEG, PET), as well as its link to the brain imaging data structure (BIDS) allowed to obtain a general characterization of how to manage and process the information contained in them(AU)

Linking research of biomedical datasets.

Biomedical data preprocessing and efficient computing can be as important as the statistical methods used to fit the data; data processing needs to consider application scenarios, data acquisition and individual rights and interests. We review common principles, knowledge and methods of integrated research according to the whole-pipeline processing mechanism diverse, coherent, sharing, auditable and ecological. First, neuromorphic and native algorithms integrate diverse datasets, providing linear scalability and high visualization. Second, the choice mechanism of different preprocessing, analysis and transaction methods from raw to neuromorphic was summarized on the node and coordinator platforms. Third, combination of node, network, cloud, edge, swarm and graph builds an ecosystem of cohort integrated research and clinical diagnosis and treatment. Looking forward, it is vital to simultaneously combine deep computing, mass data storage and massively parallel communication.

Strengthen the management of external provision of human genetic resource information in clinical trials / 中华医学科研管理杂志

Objective:By strengthening the management of external provision of data generated by clinical trials in medical institutions, to improve the effectiveness of supervision of human genetic resource information, and to promote the legal sharing and effective use of data.Methods:Analyzed problems identified in filing human genetic resource information in clinical trials since July 1, 2019, put forward possible solutions and suggestions.Results:Main problems were identified in external provision of human genetic resource information, including the specification and time limit of external provision of information, the information recipient, the storage location and the final disposal method of information.Conclusions:Hospital and regulatory authority need to carry out more tailored training, optimize management systems and procedures in order to strengthen the management of the external provision of human genetic resources information.

An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content.

BACKGROUND: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. METHODS: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. RESULTS: Ontologies for "food and nutrition" (n = 37), "disease and specific population" (n = 100), "data description" (n = 21), "research description" (n = 35), and "supplementary (meta) data description" (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. CONCLUSION: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

Responding to information about children in adversity: ten years of a differential response model in Western Australia.

This article uses a comprehensive database about children in adversity collected over the 16-year period from 1990 to 2005 in the state of Western Australia. The focus of this interrogation is the effect of major changes in responses to information about children brought to the attention of the Western Australian statutory authority in a 10-year period during this 16 years. The initiative for these changes was termed New Directions, and its associated policy and practice changes were aimed at differentiating information expressing concerns about children and families from allegations of child maltreatment. They emphasized the provision of supportive and empowering services to families experiencing difficulties - a form of differential response to children in adversity. The article covers the period leading up to the policy and practice change and the 10 years during which these changes were implemented. It examines some effects of the new policy and comments on whether the changes resulted in missed opportunities to protect children from harm, which in turn, might have led to higher rates of re-reporting. The authors present an overall picture of the nature of the information accepted by the statutory authority and how the interpretation of that information might have affected subsequent outcomes for children. In doing so, it shows that the policy and consequential practice changes associated with a differential response mechanism had long lasting positive effects that, despite dire warnings, did not compromise the protection of the small group of children identified as requiring protective interventions.