Repositório de Dados de Pesquisa da Unicamp (Universidade Estadual de Campinas)
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HealDB - an open portuguese language database for health information systems
No full textHealDB is an open, extensible, relational database whose core structure, in Portuguese, was derived from open data provided by primary Brazilian health regulatory sources. It was constructed to serve as a basis for health information systems that require standardized data sources for researchers and practitioners in Brazil.
It notably includes curated structured information on medications, and drug leaflets in Portuguese released by the Brazilian ANVISA.
Textual sections from these leaflets were analyzed to identify the diseases treated by each medication, which were then standardized using ICD-10 codes.
Each medication is composed of one or more active ingredients. These active ingredients were used to identify hundreds of thousands
of drug-drug and drug-food interactions through an extensive cross-checking process with DrugBank, a public database containing information on drugs, their mechanisms of action, interactions, and targets.
Additionally, HealDB includes external identifiers linked to each active ingredient, enabling interoperability with major health-related
data sources such as ChEBI, RxNorm, PubChem, IUCN Red List, ATC, ClinicalTrials.gov, among others
Genetic study of cortical malformations - Exomes
No full textThis dataset comprises genomic information obtained through Whole Exome Sequencing (WES) from 96 patients diagnosed with malformations of cortical development (MCDs), previously evaluated by clinical and neuroimaging criteria. WES focuses on the protein-coding regions of the genome, where most disease-related variants are found. Samples were processed with the Agilent SureSelect V6 kit and sequenced on the Illumina NovaSeq platform using a paired-end 2 × 150 bp run. The raw data underwent quality control, discarding low-quality reads and retaining only sequences with sufficient base quality. Reads were aligned to the human reference genome (GRCh38) using BWA v0.7.17, followed by processing with Picard 2.23.8 and Samtools 1.14. Variant calling was performed with GATK 4.1.8.0, and detected variants were annotated with population allele frequency and pathogenicity predictors. Variants were then prioritized according to ACMG guidelines and filtered by patient phenotype, with additional validation when required. The resulting dataset supports the identification of pathogenic variants, genotype–phenotype correlations, and a deeper understanding of the genetic basis of MCD, conditions frequently associated with epilepsy, developmental delay, and intellectual disability.
The raw FASTQ files are not currently available as they have not yet been published. Upon publication of the article, links to the raw FASTQ files will be provided, and an updated table including the corresponding accession numbers and repositories will be made available
Coffee bean defect image dataset
No full textThis dataset contains 3672 individual images of coffee beans measuring 384x384 pixels. A balanced dataset with manually annotated images, following official standards, comprising nine total classes of coffee beans used for classification and quality analysis. Among them, two correspond to healthy beans and seven to beans with different types of defects. The bean images were obtained during the doctoral research of student Juliana Cardoso do Prado, carried out between 2017 and 2021, in her thesis entitled “Uso de técnicas tradicionais e computacionais para caracterização da qualidade de grãos de café” (DOI: https://doi.org/10.47749/T/UNICAMP.2021.1490251 ).
The identified grain classes (labels 1 to 9 in the ‘grain_metadata.csv’ file) are:
(1) Sound Bean, (2) Peaberry, (3) Black, (4) Immature, (5) Sour, (6) Shell, (7) Shell Center, (8) Broken, and (9) Insect-Damaged.
To create this database, the raw coffee beans were arranged on a cut sheet of paper measuring 9 x 8 cm, totaling an area of 72 cm², forming a rectangle divided into 6 smaller rectangles measuring 4 x 3 cm = 12 cm². This sheet with the grains was placed against a dark background inside a box. The framing ensured the capture of the entire sheet against a dark background to allow for later correction. The grains were arranged in each small rectangle, thus obtaining images of 6 grains at a time. To obtain the images, a total of 1836 grains were used, that is, 204 grains from each of the 9 classes, with 7 classes having defects and 2 classes without defects. The images were acquired using a camera (Brand/model: Samsung/EK-GC200). The camera was placed on a tripod and the self-timer was used to eliminate camera shake during the capture process. The lighting was indirect natural light from a window, supplemented with a 6500 K LED lamp, and to reduce variations in lighting, all photos were taken between 2 pm and 4 pm. To avoid harsh shadows, the camera's flash was not used. The digital camera was set to manual focus, ISO 100, 3960 x 2640 pixel resolution, f/4 aperture, 1/4s exposure time, and was positioned 23 cm above the surface of the grains. To reduce systematic biases that could be improperly used by the classification model, the capture was done in an alternating fashion for the grain classes (example: 1 photo of 6 grains from class 1, 1 photo of 6 grains from class 2, and so on). The data preprocessing included identifying the trapezoid formed by the sheet of paper and rectifying it into a rectangle with the expected proportions (9x8). The second operation consisted of identifying the grains in each of the six cells and cutting them into 3672 individual images of 384x384 pixels. The third operation consisted of identifying the background pixels (excluding the grains) of the sheet of paper and using them to normalize the image, thus reducing variations in lighting and capture between images. The preprocessing was implemented in Python, with the aid of the OpenCV and Scikit-Image libraries. Thus, the data are annotated, with the faces of the grain indicated as: ventral face (face 1) and dorsal face (face 2) of the coffee beans (in the ‘grain_metadata.csv’ file)
Análise fitoquímica da oleorresina de Copaifera reticulata Ducke
No full textEste conjunto de arquivos contém dados sobre as análises fitoquímicas da oleorresina de Copaifera reticulata Ducke. Contém as massas das frações obtidas por hidrodestilação (destilação simples e por aparelho Clevenger), as massas das sub-frações obtidas por cromatografia em coluna das frações, bem como os constituintes identificados nas frações voláteis por Cromatografia Gasosa Acoplada por Espectrometria de Massas(CG-EM) e seus espectros de massas
NMR and HRMS data of 1H-indole-2-carboxamide derivatives
No full textThis dataset contains all NMR and HRMS data for 1H-indole-2-carboxamide derivatives. It includes both raw and processed data for intermediates and final compounds
Dados estimativas efeitos variáveis no pré-tratamento de bagaço de cana-de-açúcar com solvente Gama-Valerolactona
No full textArquivo .sta utilizado para estimar o efeito de variáveis avaliadas em Planejamento Experimental Fracionário do pré-tratamento de bagaço de cana-de-açúcar. As variáveis estudadas são temperatura, tempo, concentração de GVL no solvente e concentração de catalisador ácido
Dados relacionados ao desempenho do salto vertical e nível de força no agachamento
No full textOs dados são referentes a pesquisa realizada com o tema melhoria de desempenho pós-ativação (PAPE), na qual foi analisado o efeito da manipulação do volume da atividade condicionante no desempenho do salto vertical
Um estudo interdisciplinar sobre tecnologias assistivas e estética musical de pessoas surdas
No full textO conjunto de dados tem como propósito apoiar investigações interdisciplinares sobre a estética musical da comunidade surda, com ênfase na utilização de tecnologias assistivas hápticas e visuais. De natureza teórico bibliográfica, os dados foram organizados a partir de uma revisão crítica de textos científicos provenientes das áreas de musicologia, cognição, educação musical e estudos culturais surdos. A seleção dos documentos baseou-se em critérios de relevância, atualidade e pertinência temática.
O escopo do conjunto abrange informações sobre processos sensoriais e cognitivos distintos, práticas musicais específicas da comunidade surda, uso da Língua de Sinais como expressão rítmica e estética, bem como a aplicação de tecnologias assistivas — como dispositivos hápticos — na mediação da experiência musical. Os dados foram organizados em eixos temáticos que revelam convergências e divergências teóricas, permitindo análises aprofundadas sobre a sinergia entre música, cognição e acessibilidade tecnológica.
Este conjunto de dados contribui para a valorização da musicalidade surda como forma de expressão estética própria, mediada por múltiplos sentidos e tecnologias, e oferece suporte a pesquisadores interessados em arte sonora, inclusão e acessibilidade cultural
Digital estressors scale: tradução, adaptação transcultural e validade de pequena escala
No full textProcesso de Adaptação de tradução e adaptação transcultural do instrumento de avaliação de estresse digital chamado Digital stressors scale
Dataset relacionado a tese "Mecanismos moleculares e funcionais envolvidos nos efeitos centrais do FGF19 frente a desregulação hipotalâmica"
No full textEste dataset contém dados de RNAseq de célula única (scRNAseq) processados com o algoritmo de aprendizado de máquina não supervisionado diffusion-based Manifold Approximation and Projection (dbMAP), no formato h5ad. O dbMAP gera representações bidimensionais e tridimensionais que preservam a estrutura transcricional dos dados, posicionando células transcricionalmente semelhantes próximas e tipos celulares distintos mais afastados. A expressão gênica foi visualizada utilizando as funções scanpy.plot.embedding() e scanpy.plot.violin_plot(). Todo o código utilizado para a análise está disponível em https://github.com/OCRC/Zangerolamo-et-al-202