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720 results for “Identification data”
Supporting data for "Quality control for the target decoy approach for peptide identification"
<p>Supporting data for the manuscript "Quality control for the target decoy approach for peptide identification". Input files are raw mass spectrometry runs, to be downloaded from the PRIDE Archive (https://www.ebi.ac.uk/pride/), which can be processed with the parameter files, Nextflow workflow, and Python scripts in "workflow-scripts-parameters.zip". The resulting output files that were used for the manuscript are provided in search-results.zip.</p>
Identification, Demographic and Sociometric Data of the Students and Classroom Data / Datos de Identificación, Demográficos y Sociométricos de los Alumnos y Datos del Aula // SUJ-1_Data_CC-CS_T1toT6
<p>This is a longitudinal file that provides information about the students and the classroom in all grades, from first to sixth. It is an essential file that researchers will necessarily have to merge with the other files containing the variables of interest. The file provides information to identify and locate each student in each grade (e.g., identification number, cohort and sample to which he/she belongs, classroom in which he/she is enrolled in each grade, year of entry or exit from the sample,...), and demographic information (sex, date of birth, age in each grade,...), inclusion information (ethnicity, time in Spain, Spanish language proficiency, degree of absenteeism,...), his/her sociometric status at each moment and whether he/she meets criteria to receive intervention. Finally, it also includes data on the classroom in which the participant is enrolled each year and the tutor teacher (e.g., classroom ratio, whether the composition of the classroom is changing on a scheduled basis, sex of the teacher and whether the tutor has changed with respect to the previous year). Most of the information was provided by the teacher and is part of the STUDENT-INFORM and CLASSROOM-INFORM files, but it also includes information provided by researchers in the SUJ-2 files and information provided by peers in the SOCIOMET-P files. The data were collected in public schools in Castellón and Seville (Spain) mostly in the fall of each school year, but many variables were also measured in the spring, from first to sixth grade of primary education, from school year 2009-10 to school year 2014-15. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a></p> <p> </p> <p>Se trata de un fichero longitudinal que aporta información sobre los estudiantes y el aula en todos los cursos, de primero a sexto. Es un fichero esencial que necesariamente los investigadores tendrán que fusionar con los otros ficheros que contengan las variables de su interés. El fichero proporciona información para identificar y localizar a cada estudiante en cada curso (p.e, número de identificación, cohorte y muestra a la que pertenece, aula en la que está escolarizado en cada curso, curso de incorporación o de salida de la muestra,…), e información demográfica (sexo, fecha de nacimiento, edad en cada curso,..), de inclusión (etnia, tiempo en España, dominio de la lengua española, grado de absentismo,…), su estatus sociométrico en cada momento y si reúne criterios para recibir intervención. Por último, también incluye datos sobre el aula en la que el participante está escolarizado cada curso y del profesor tutor (p.e., ratio del aula, si la composición del aula cambia de forma programada en determinados cursos, el sexo del profesor y si la clase ha cambiado de tutor respecto del curso anterior). La mayor parte de la información ha sido proporcionada por el profesor y forma parte de los ficheros STUDENT-INFORM y CLASSROOM-INFORM, pero también incluye información aportada por los investigadores en los ficheros SUJ-2 e información aportada por los iguales en los ficheros SOCIOMET-P. Los datos fueron recogidos en centros públicos de Castellón y Sevilla (España) mayoritariamente en otoño de cada curso, pero muchas variables también fueron medidas en primavera, desde primero a sexto curso de educación primaria, desde el curso escolar 2009-10 al curso escolar 2014-15. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a></p>
Identification, Demographic and Sociometric Data of the Students and Classroom Data / Datos de Identificación, Demográficos y Sociométricos de los Alumnos y Datos del Aula // SUJ-1_Data_IC_T1toT6
<p>This is a longitudinal file that provides information about the students and the classroom in all grades, from first to sixth. It is an essential file that researchers will necessarily have to merge with the other files containing the variables of interest. The file provides information to identify and locate each student in each grade (e.g., identification number, cohort and sample to which he/she belongs, classroom in which he/she is enrolled in each grade, year of entry or exit from the sample,...), and demographic information (sex, date of birth, age in each grade,...), inclusion information (ethnicity, time in Spain, Spanish language proficiency, degree of absenteeism,...), his/her sociometric status at each moment and whether he/she meets criteria to receive intervention. Finally, it also includes data on the classroom in which the participant is enrolled each year and the tutor teacher (e.g., classroom ratio, whether the composition of the classroom is changing on a scheduled basis, sex of the teacher and whether the tutor has changed with respect to the previous year). Most of the information was provided by the teacher and is part of the STUDENT-INFORM and CLASSROOM-INFORM files, but it also includes information provided by researchers in the SUJ-2 files and information provided by peers in the SOCIOMET-P files. The data were collected in public schools in Castellón (Spain) mostly in the fall of each school year, but many variables were also measured in the spring, from first to sixth grade of primary education, from school year 2010-11 to school year 2015-16. To learn more about our research and access to other datasets of this or other measures, follow the link <a href="https://www.uji.es/departaments/psi/base/opengrei/">GREI Longitudinal Project</a></p> <p> </p> <p>Se trata de un fichero longitudinal que aporta información sobre los estudiantes y el aula en todos los cursos, de primero a sexto. Se trata de un fichero esencial que necesariamente los investigadores tendrán que fusionar con los otros ficheros que contengan las variables de su interés. El fichero proporciona información para identificar y localizar a cada estudiante en cada curso (p.e, número de identificación, cohorte y muestra a la que pertenece, aula en la que está escolarizado en cada curso, curso de incorporación o de salida de la muestra,…), e información demográfica (sexo, fecha de nacimiento, edad en cada curso,..), de inclusión (etnia, tiempo en España, dominio d la lengua española, grado de absentismo,…), su estatus sociométrico en cada momento y si reúne criterios para recibir intervención. Por último, también incluye datos sobre el aula en la que el participante está escolarizado cada curso y del profesor tutor (p.e., ratio del aula, si la composición del aula cambia de forma programada en determinados cursos, el sexo del profesor y si la clase ha cambiado de tutor respecto del curso anterior). La mayor parte de la información ha sido proporcionada por el profesor y forma parte de los ficheros STUDENT-INFORM y CLASSROOM-INFORM, pero también incluye información aportada por los investigadores en los ficheros SUJ-2 e información aportada por los iguales en los ficheros SOCIOMET-P. Los datos fueron recogidos en centros públicos de Castellón (España) mayoritariamente en otoño de cada curso, pero muchas variables también fueron medidas en primavera, desde primero a sexto curso de educación primaria, desde el (curso escolar 2010-11 al curso escolar 2015-16. Para saber más sobre nuestra investigación y acceder a otros ficheros de datos de esta medida o de otras, siga el enlace <a href="https://www.uji.es/departaments/psi/base/opengrei/">Proyecto Longitudinal GREI</a></p>
Data and scripts for: Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification
<p>Data derivates and analysis scripts (in R) used for the paper "Quantifying annual spatial consistency in chick-rearing seabirds to inform important site identification", published in Biological Conservation, on analyzing annual spatial overlap of 25 seabird populations across 23 species to assess variability and inform global efforts to improve spatial conservation measures.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.</p>
CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1
<p>This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.</p>
Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
<p>Selecting a suitable nest site is critical to the survival and reproduction of birds. Prospecting allows individuals to gather information on the local quality of potential future breeding sites, which may help them make the best nest site selection decision. However, few studies have focused on the direct links between the prospecting activity of breeders and subsequent nest site selection. In this study, we investigated the prospecting pattern of Japanese tits (<em>Parus minor</em>) during the pre-breeding period of the first breeding attempt and whether nest site characteristics influence their nest box visiting behaviour and occupied nest site. We used radio frequency identification (RFID) to track the movements of Japanese tits visiting nest boxes and compared nest site characteristics between visited and unvisited (control) nest boxes, as well as between visited and occupied nest boxes. We found that Japanese tits started visiting nest boxes approximately 20 days before breeding, visited an average of 6 nest boxes and eventually chose the most visited nest box for breeding activities. Japanese tits were more likely to visit nest boxes that had less canopy cover and lower shrub density but a greater total number of surrounding trees and ultimately chose breeding nest boxes with a smaller entrance inclination, in nesting trees with a larger diameter at breast height (DBH) which were surrounded by trees with a larger DBH. Our results suggest that Japanese tits visit several potential breeding sites before choosing breeding nest boxes and that nest site characteristics can influence their prospecting activity and nest site selection.</p>
Data Files of "A Multi-Year Photopolarimetric Study of the Semi-Regular Variable V CVn and Identification of Analogue Sources"
<p>The semi-regular variable star V Canum Venaticorum (V CVn) is well-known for its unusual linear polarization position angle (PA).<br> Decades of observing V CVn reveal a nearly constant PA spanning hundreds of pulsation cycles. This phenomenon has persisted<br> through variability that has ranged by 2 magnitudes in optical brightness and through variability in the polarization amplitude over<br> 0.3% and 6.9%. Additionally, the polarization fraction of V CVn varies inversely with brightness.<br> This paper presents polarization measurements obtained over three pulsation cycles. We find that the polarization maximum does<br> not always occur precisely at the same time as the brightness minimum. Instead, we observe a small lead or lag in relation to the<br> brightness minimum, spanning a period of a few days up to three weeks. Furthermore, the PA sometimes exhibits a non-negligible<br> rotation, especially at lower polarization levels.<br> To elucidate the unusual optical behavior of V CVn, we present a list of literature sources that also exhibit polarization variability<br> with a roughly fixed PA.We find this correlation occurs in stars with high tangential space velocities, i.e., “runaway” stars, suggesting<br> that the long-term constant PA is related to how the circumstellar gas is shaped by the star’s high-speed motion through the interstellar<br> medium.</p>
Supplementary Data: Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS
<p>Supplementary data for the SCINPAS analysis as part of the manuscript "Identification of experimentally-supported poly(A) sites in single-cell RNA-seq data with SCINPAS".</p> <p>It includes the output of SCINPAS, available here: <a href="https://github.com/zavolanlab/SCINPAS">https://github.com/zavolanlab/SCINPAS</a>, used for the analyses. Additionally, the scripts and data for the comparison with other tools is provided. Please read the README.md for more information.</p>
Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms - Datasets
<p>Datasets for reproducibility of the results found in Automatic Identification of Kidney Cell Types in scRNA-seq and snRNA-seq Data Using Machine Learning Algorithms. This study utilized data from the following 4 journals:</p> <p>Lake, B.B. et al. A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys. Nat Commun 10, 2832 (2019).</p> <p>Liao, J., Yu, Z., Chen, Y. et al. Single-cell RNA sequencing of human kidney. Sci Data 7, 4 (2020).</p> <p>Menon, R. et al. Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker. JCI Insight 5, e133267 (2020).</p> <p>Wu, H. et al. Single-cell transcriptomics of a human kidney allograft biopsy specimen defines a diverse inflammatory response. J Am Soc Nephrol 29: 2069–2080 (2018).</p> <p>Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).</p>
Identification of the regulatory circuit governing corneal epithelial fate determination and disease trackhub and additional data
<p>The transparent corneal epithelium in the eye is maintained through the homeostasis regulated by limbal stem cells, while the non-transparent epidermis relies on epidermal keratinocytes for renewal. Despite their cellular similarities, the precise cell fates of these two types of epithelial stem cells, which give rise to functionally distinct epithelia, remain unknown. We performed a multi-omics analysis of human limbal stem cells from the cornea and keratinocytes from the epidermis, and characterized their molecular signatures, highlighting their similarities and differences. Through gene regulatory network analyses, we identified shared and cell type-specific transcription factors that define specific cell fates, and established their regulatory hierarchy. Single-cell RNA-seq analyses of the cornea and the epidermis confirmed these shared and cell type-specific transcription factors. Notably, the shared and limbal stem cell-specific transcription factors can cooperatively target genes associated with corneal opacity. Importantly, we discovered that <em>FOSL2</em>, a direct PAX6 target gene, is a novel candidate associated with corneal opacity, and it regulates genes implicated in corneal diseases. By characterizing molecular signatures, our study unveils the regulatory circuitry governing the limbal stem cell fate and its association with corneal opacity.</p> <p> </p> <p>Files present are within the UCSC trackhub format containing bigwig and bignarrowpeak files associated with the publication:</p> <p>Multi-omics analyses identify transcription factor interplay in corneal epithelial fate determination and disease</p> <p>doi: https://doi.org/10.1101/2022.07.13.499857</p> <p> </p>
Data and codes: Who is calling? Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers
<p>Data and codes that accompany the article titled "Who is calling? Optimising source identification from marmoset vocalisations with hierarchical machine learning classifiers".</p>
Data from: Data fusion for integrative species identification using deep learning
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Data for: Using radio frequency identification (RFID) technology to characterize nest site selection in wild Japanese tits (Parus minor)
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Data from: Robust mosquito species identification from diverse body and wing images using deep learning
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Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations
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Data, code, and supplementary information for Hyracoid locus identification
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Data from: harnessing the power of regional baselines for broad-scale genetic stock identification: a multistage, integrated, and cost-effective approach
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Data from: Exploring the potential of Angiosperms353 markers for species identification of Eastern Mediterranean orchids
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Data from: Identification of a minority population of LMO2+ breast cancer cells that integrate into the vasculature and initiate metastasis.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.