cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.19149482.v1
title: data.description.TXT from Precipitation during two weeks in spring influences reproductive success of first-year females in the long-lived Natterer's bat

description: Bats are characterized by low reproductive rates in contrast with most of other small mammals. This makes their populations vulnerable when inclement environmental conditions such as cold and rainy weather impair the reproductive success of females. The fine-scale effect of weather on bats, however, remains largely unknown. Using a sliding window analysis approach on an 18-year individualized dataset on six Natterer's bat (<i>Myotis nattereri</i>) colonies, we investigated the effect of fine-scale weather conditions on age-specific reproductive success. We found that increased precipitation during a short time window in spring strongly reduced the probability of successful reproduction of first-year (FY) females. Our data suggest that this time window is concomitant with implantation or early pregnancy, before substantial investment into embryo development. In addition, larger FY had higher reproductive success, suggesting that reproduction may be condition dependent in young females. Reproductive success of older females was not affected by either weather or individual parameters. Our results show that changes in precipitation pattern may compromise the reproductive success of FY females. Further studies are needed to better understand the impact of weather conditions on reproductive success in long-lived bats under climate change scenarios.
DOI: 10.3886/e162061v2
title: Mexican medical research database.

description: This database results from retrieving bibliometric information from the Web of Science database; therefore, we used a query line to extract medical-related documents published by Mexican Authors. We cleaned the database and enriched it with different techniques (NLP, Cluster Analysis, temporal analysis, and manual curation). <br>The specific query is as follows: CU=Mexico AND (SU=ANATOMY OR SU=MEDICAL ETHICS OR SU=BIOLOGY OR SU=GENETICS OR SU=MICROBIOLOGY OR SU=BIOPHYSICS OR SU=NEUROLOGY OR SU=DENTISTRY OR SU=ONCOLOGY OR SU=DERMATOVENEROLOGY OR SU=OPHTHALMOLOGY OR SU=EPIDEMIOLOGY OR SU=OTOLARYNGOLOGY OR SU=FORENSIC MEDICINE OR SU=PATHOLOGY OR SU=GYNECOLOGY OR SU=OBSTETRICS OR SU=PEDIATRICS OR SU=HISTOLOGY OR SU=EMBRYOLOGY OR SU=PHARMACOLOGY OR SU=HYGIENE OR SU=PHYSIOLOGY OR SU=PATOPHYSIOLOGY OR SU=BIOCHEMISTRY OR SU=PSYCHOLOGY OR SU=PSYCHIATRICS OR SU=IMMUNOLOGY OR SU=RADIOLOGY OR SU=INFECTIOUS DISEASES OR SU=SOCIAL MEDICINE OR SU=TELEMEDICINE OR SU=SPORTS MEDICINE OR SU=INTERNAL MEDICINE OR SU=SURGERY)<br><br>Data is stored in .csv; codified in UTF-8; tab separated; UNIX (LF) linebreak; String delimiter "Multiple values are separated using "***"<br><br>
DOI: 10.25346/s6/o1zazh/s9syd7
title: kp_data.mat

description: ydata is the kp value for the corresponding dates in xdata. These dates are in serial date number format.
DOI: 10.25346/s6/o1zazh/iguon3
title: flux_results_data.mat

description: x_obs and x_model are timestamps for the corresponding y-values. They are in serial date number format. y_obs are the values of observed flux; y_model are the values of flux predicted by our NARX model.
DOI: 10.25346/s6/o1zazh/acoker
title: data_processed_25-Oct-2019.mat

description: Processed data used in all aspects of model creation (training, validation, tests). The flux values are in flux_cell as a MATLAB cell. The input data is in both MATLAB cell format and MATLAB arrays labeled X_cell and x, respectively. The data are normalized MLT, Kp, SymH, AE, and VSW in that order.
DOI: 10.25346/s6/o1zazh/lz8sww
title: data_processed_w_both04-May-2021.mat

description: Processed data used in all aspects of model creation (training, validation, tests). The flux values are in flux_cell as a MATLAB cell. The input data is in both MATLAB cell format and MATLAB arrays labeled X_cell and x, respectively. The data are normalized MLT, Kp, SymH, AE, VSW, Solar Wind Density, and Solar Wind Pressure in that order.
DOI: 10.25346/s6/o1zazh/2k5nwl
title: expanded_input_net_and_results.mat

description: These are the results of the model once it has been improved by adding solar wind density and pressure. The matlab network structure is also included.
DOI: 10.25346/s6/o1zazh/khwars
title: time_arr.mat

description: The time array for the input
DOI: 10.25346/s6/o1zazh/tn6axq
title: verb_results_kp_bf.mat

description: Comparison of the CRESS observations and VERB code simulations. bf and kp are MATLAB structures. time, L* and log10(flux) of CRESS observations (CRESS), VERB code simulations with constant upper boundary conditions (VERB_const) and VERB code simulations with NARX Bf driven boundary conditions (VERB_narx) are in corresponded MALTAB structures.
DOI: 10.25346/s6/o1zazh/f5oih8
title: narx_bf_tests.mat

description: Input delay (input_delay), feedback delay (feedback_delay) and corresponded cc (cc) and standard deviation (cc_std) of NARX Bf model tests. The test results of hidden neurons is in hidden_neurons MALTAB structure.
DOI: 10.25346/s6/o1zazh/q7xiww
title: narx_bf_input_net.mat

description: Processed data used in all aspects of NARX Bf model creation (training, validation, tests). The Bf values are in flux_cell as a MATLAB cell. The input data is in X_cell (Kp) as a MATLAB cell. Time is in time as a MATLAB array. The network is a MATLAB object, netc_best. log10 of Bf must be used as an input of the network.
DOI: 10.25346/s6/o1zazh/vxwm1v
title: narx_flux_tests.mat

description: Input delay (input_delay), feedback delay (feedback_delay) and hidden neurons (hidden_neurons) test results for NARX flux model are in corresponded MALTAB structures.
DOI: 10.25346/s6/o1zazh/go4kg0
title: narx_flux_input_net.mat

description: Processed data used in all aspects of NARX flux model creation (training, validation, tests). The flux values are in flux_cell as a MATLAB cell. The input data is in X_cell as a MATLAB cell. Time is in time as a MATLAB array. The network is a MATLAB object, netc_best. The input data (X_cell) are normalized MLT, Kp, Dst, AE, Solar Wind Speed, and Solar Wind Pressure in that order. The normalization coefficients are in coeff MATLAB structure. The input flux must be normalized before using the network.
DOI: 10.25346/s6/o1zazh/krgpgl
title: narx_flux_input_net.mat

description: Processed data used in all aspects of NARX flux model creation (training, validation, tests). The flux values are in flux_cell as a MATLAB cell. The input data is in X_cell as a MATLAB cell. Time is in time as a MATLAB array. The network is a MATLAB object, netc_best. The input data (X_cell) are normalized MLT, Kp, Sym-H, AE, Solar Wind Speed, and Solar Wind Pressure in that order. The normalization coefficients are in coeff MATLAB structure. The input flux must be normalized before using the network.
DOI: 10.25346/s6/o1zazh/oq7en7
title: narx_flux_tests.mat

description: Input delay (input_delay), feedback delay (feedback_delay) and hidden neurons (hidden_neurons) test results for NARX flux model are in corresponded MALTAB structures.
DOI: 10.6084/m9.figshare.19149498
title: Supplementary Table of AIC model selection from Age, breed, sex and diet influence serum metabolite profiles of 2000 pet dogs

description: Supplementary Tables in excel-format describing the AIC model selection process for each measurand in GLM analyses.
DOI: 10.6084/m9.figshare.19149498.v1
title: Supplementary Table of AIC model selection from Age, breed, sex and diet influence serum metabolite profiles of 2000 pet dogs

description: Supplementary Tables in excel-format describing the AIC model selection process for each measurand in GLM analyses.
DOI: 10.6084/m9.figshare.19149504.v1
title: Supplementary Table of GLM results from Age, breed, sex and diet influence serum metabolite profiles of 2000 pet dogs

description: Supplementary Tables in excel-format describing the detailed results of GLM models for each measurand.
DOI: 10.6084/m9.figshare.19149504
title: Supplementary Table of GLM results from Age, breed, sex and diet influence serum metabolite profiles of 2000 pet dogs

description: Supplementary Tables in excel-format describing the detailed results of GLM models for each measurand.
DOI: 10.17632/wvztspxbbd.1
title: Chaperones directly and efficiently disperse stress-triggered biomolecular condensates

description: This dataset contains all raw and processed data used to generate figures in the paper "Chaperones directly and efficiently disperse stress-triggered biomolecular condensates" by Yoo et al., Molecular Cell (2022). The dataset also contains custom R (version 3.5.2) scripts for data processing, analysis, and figure generation.
DOI: 10.5281/zenodo.6029384
title: players_stats

description: Dataset NBA players
DOI: 10.5281/zenodo.6029383
title: players_stats

description: Dataset NBA players
DOI: 10.5517/ccdc.csd.cc2844l5
title: CCDC 2089356: Experimental Crystal Structure Determination

description: Related Article: Shao-Chi Lee, Hsuan-Hung Liao, Shinje Miñoza, Yu-Ling Hsu, Yong-Ting Tsao, Cheng-chau Chiu, Li-Yun Li, Hsin Kao, Che-Ming Hsu, Zong-Nan Tsai|2021|CSD Communication|||
DOI: 10.25919/c3fk-p489
title: Parkes observations for project P595 semester 2021OCTS_11

description: We propose to continue the PULSE@Parkes project in which high school students from Australia and around the World use the Parkes radio telescope in a remote observing model to observe and analyse pulsar data. The data from some PULSE@Parkes observations are used to support the space missions, other observations supplement the P456 Pulsar Timing Array project and the remainder were chosen in order to make a detailed analysis of pulsar timing irregularities and intermittency.
DOI: 10.25919/czv8-2e25
title: Parkes observations for project P971 semester 2021OCTS_04

description: We propose further timing observations of the relativistic pulsar - white-dwarf binary, PSR J1141$-$6545. This unique system offers access to a wide range of physics, from relativistic phenomena through pulsar emission cone studies, to the structure and distribution of interstellar plasma along our line-of-sight. The analysis of existing data has already revealed the first detection of Lense-Thirring precession in a binary star system, and hinted at complex emission beam structure probed via geodetic precession. Further timing of this system for the next few years is needed for studies of these effects at higher precision. From this semester, we are converting this project into a large project proposal. We request the TAC to award us pre-graded status for the next semester.
DOI: 10.5281/zenodo.6029158
title: The artifact for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.

description: This artifact is for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks. More detail and update please refer to https://github.com/ZHYfeng/Dependency.
DOI: 10.5281/zenodo.6029157
title: The artifact for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.

description: This artifact is for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks. More detail and update please refer to https://github.com/ZHYfeng/Dependency.
DOI: 10.5281/zenodo.6029530
title: Colon mucosa single-cell IMC dataset

description: Here we provide the colon mucosa single-cell IMC dataset which was used in our paper "Rarity: Discovering rare cell populations from single-cell imaging data". The file `colon_mucosa_dataset.csv` contains marker intensities for 40364 cells across 28 markers. Additionally, `rarity_results.csv` contains the results from our Rarity analysis, including cluster indices and which cells were identified as double-negative T cells.
DOI: 10.5281/zenodo.6029529
title: Colon mucosa single-cell IMC dataset

description: Here we provide the colon mucosa single-cell IMC dataset which was used in our paper "Rarity: Discovering rare cell populations from single-cell imaging data". The file `colon_mucosa_dataset.csv` contains marker intensities for 40364 cells across 28 markers. Additionally, `rarity_results.csv` contains the results from our Rarity analysis, including cluster indices and which cells were identified as double-negative T cells.
DOI: 10.5281/zenodo.5441138
title: Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.

description: Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks. 2.1 results of static analysis.zip<br> - Metadata collected when fuzzing (protobuf format)<br> - the related protobuf files are in https://github.com/ZHYfeng/Dependency/05-proto<br> - Information to support manual analysis (generated by metadata and static analysis)<br> - with name "0xffffffffaddress.txt", including every information about this uncovered address. 2.2 results of manual analysis.zip<br> - Information of non-UD and UD for each module<br> - Overall statistic data, for example, coverage, prevalent of UD<br> - Sample cases and accuracy checking of sampled UD and its WS<br> - Root causes of sampled UDs 2.3 example case.zip<br> - example cases of different root causes
DOI: 10.5281/zenodo.5440093
title: Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks.

description: Evaluation Data for the paper "Demystifying the Dependency Challenge in Kernel Fuzzing" in ICSE 2022 Technical Tracks. 2.1 results of static analysis.zip<br> - Metadata collected when fuzzing (protobuf format)<br> - the related protobuf files are in https://github.com/ZHYfeng/Dependency/05-proto<br> - Information to support manual analysis (generated by metadata and static analysis)<br> - with name "0xffffffffaddress.txt", including every information about this uncovered address. 2.2 results of manual analysis.zip<br> - Information of non-UD and UD for each module<br> - Overall statistic data, for example, coverage, prevalent of UD<br> - Sample cases and accuracy checking of sampled UD and its WS<br> - Root causes of sampled UDs 2.3 example case.zip<br> - example cases of different root causes
DOI: 10.6084/m9.figshare.19134866
title: Additional file 4

description: <b>Microarray data in mouse blastocysts.</b>
DOI: 10.6084/m9.figshare.19134866.v1
title: Additional file 4

description: <b>Microarray data in mouse blastocysts.</b>
DOI: 10.6084/m9.figshare.19134914
title: Additional file 8

description: <b>RNA-seq data in mouse blastocysts.</b>
DOI: 10.6084/m9.figshare.19134914.v1
title: Additional file 8

description: <b>RNA-seq data in mouse blastocysts.</b>
DOI: 10.6084/m9.figshare.19134920
title: Additional file 9

description: <b>Riken number converted to gene symbol.</b>
DOI: 10.6084/m9.figshare.19134920.v1
title: Additional file 9

description: <b>Riken number converted to gene symbol.</b>
DOI: 10.6084/m9.figshare.19136207
title: Additional file 13

description: <b>Antibody information.</b>
DOI: 10.6084/m9.figshare.19136207.v1
title: Additional file 13

description: <b>Antibody information.</b>
DOI: 10.6084/m9.figshare.19136393
title: Additional file 14

description: <b>Set value information of ratio analysis for cell counting.</b>
DOI: 10.6084/m9.figshare.19136393.v1
title: Additional file 14

description: <b>Set value information of ratio analysis for cell counting.</b>
DOI: 10.6084/m9.figshare.19136894
title: Additional file 15

description: <b>Numbers of markers-expressing cells and DAPI-positive cells in Figures 2O, 3S, 5D, 5Y, 6D, 7F, S1D, S2G, S4N, S8F, S8G, S8J, S8M, </b><b><b>S9J, </b>and S9K.</b>
DOI: 10.6084/m9.figshare.19136894.v1
title: Additional file 15

description: <b>Numbers of markers-expressing cells and DAPI-positive cells in Figures 2O, 3S, 5D, 5Y, 6D, 7F, S1D, S2G, S4N, S8F, S8G, S8J, S8M, </b><b><b>S9J, </b>and S9K.</b>
DOI: 10.6084/m9.figshare.19136897
title: Additional file 16

description: <b>Intensity value of pSTAT3 in Figure S4G.</b><b></b>
DOI: 10.6084/m9.figshare.19136897.v1
title: Additional file 16

description: <b>Intensity value of pSTAT3 in Figure S4G.</b><b></b>
DOI: 10.6084/m9.figshare.19136903
title: Additional file 17

description: <b>PCR primer sets and their amplified lengths used for generation of <i>in situ</i> hybridization RNA probes.</b>
DOI: 10.6084/m9.figshare.19136903.v1
title: Additional file 17

description: <b>PCR primer sets and their amplified lengths used for generation of <i>in situ</i> hybridization RNA probes.</b>
DOI: 10.6084/m9.figshare.19136909
title: Additional file 18

description: <b>Genomic regions for RNA-FISH probes and their lengths.</b>
DOI: 10.6084/m9.figshare.19136912
title: Additional file 19

description: <b>The number of samples for experiments.</b><b></b>
DOI: 10.6084/m9.figshare.19136912.v1
title: Additional file 19

description: <b>The number of samples for experiments.</b><b></b>
DOI: 10.5281/zenodo.5969041
title: Sistema automatizado para el análisis de la situación de salud.

description: Artículo Publicado en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21252011000300004 RESUMEN<br> INTRODUCCIÓN: El análisis de la situación de salud es una práctica necesaria en la atención primaria de salud. Entre las principales dificultades para realizarlo se destaca la falta de habilidades por parte de los miembros del equipo básico de salud para el procesamiento de la información y por ende para la construcción de los indicadores necesarios, lo que resta calidad a los informes.<br> OBJETIVOS: Proponer un sistema automatizado para la consolidación de la información estadística relacionada con el estado de salud de la población necesaria para la realización del análisis de la situación de salud.<br> MÉTODOS: Se realizó una investigación de innovación tecnológica, que consistió en el diseño de un sistema automatizado para el procesamiento de la información estadística. Para esto se realizó una revisión de las guías existentes además de consultas a expertos. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2007.<br> RESULTADOS: Se propone el presente sistema cuya característica principal es la simplicidad de su uso. Este contribuye a organizar y facilitar el procesamiento de la información estadística tanto a nivel individual en el consultorio como a nivel del área de salud; permite ahorrar tiempo y recursos.<br> CONCLUSIONES: Contribuye de manera efectiva a facilitar la tarea de nuestros profesionales en la tabulación y cálculo de los indicadores necesarios para la realización del análisis de la situación de salud del consultorio y del policlínico.
description: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21252011000300004
DOI: 10.5281/zenodo.5969040
title: Sistema automatizado para el análisis de la situación de salud.

description: Artículo Publicado en: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21252011000300004 RESUMEN<br> INTRODUCCIÓN: El análisis de la situación de salud es una práctica necesaria en la atención primaria de salud. Entre las principales dificultades para realizarlo se destaca la falta de habilidades por parte de los miembros del equipo básico de salud para el procesamiento de la información y por ende para la construcción de los indicadores necesarios, lo que resta calidad a los informes.<br> OBJETIVOS: Proponer un sistema automatizado para la consolidación de la información estadística relacionada con el estado de salud de la población necesaria para la realización del análisis de la situación de salud.<br> MÉTODOS: Se realizó una investigación de innovación tecnológica, que consistió en el diseño de un sistema automatizado para el procesamiento de la información estadística. Para esto se realizó una revisión de las guías existentes además de consultas a expertos. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2007.<br> RESULTADOS: Se propone el presente sistema cuya característica principal es la simplicidad de su uso. Este contribuye a organizar y facilitar el procesamiento de la información estadística tanto a nivel individual en el consultorio como a nivel del área de salud; permite ahorrar tiempo y recursos.<br> CONCLUSIONES: Contribuye de manera efectiva a facilitar la tarea de nuestros profesionales en la tabulación y cálculo de los indicadores necesarios para la realización del análisis de la situación de salud del consultorio y del policlínico.
description: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S0864-21252011000300004
DOI: 10.5281/zenodo.6029630
title: Sistema automatizado para determinar el Riesgo Cardiovascular Global.

description: <strong>Artículo Publicado en: </strong> http://www.revinformatica.sld.cu/index.php/rcim/article/view/406 <strong>RESUMEN </strong> <strong>Introducción: </strong>El riesgo cardiovascular global es la probabilidad que tiene un individuo de contraer una enfermedad cardiovascular en un periodo de tiempo, basado en el número de factores de riesgo presentes en el mismo individuo o teniendo en cuenta la magnitud de cada uno de ellos. Las técnicas automatizadas en el procesamiento de la información logran una mayor eficacia en el trabajo. <strong>Objetivos: </strong>Diseñar un sistema automatizado para determinar el riesgo cardiovascular global<strong>. </strong> <strong>Métodos: </strong>Se realizó una investigación de innovación tecnológica, consistente en el diseño de un sistema automatizado para determinar el Riesgo Cardiovascular Global<strong>.</strong> Se realizó además un análisis de las herramientas computacionales disponibles para la realización del sistema y determinar cuál o cuáles eran las más apropiadas de acuerdo al objetivo del sistema y a las características del personal encargado de su implementación y uso. Se utilizaron las herramientas para tabulación de información y fórmulas que ofrece el programa Excel del paquete Microsoft Office 2013. Para el diseño se utilizó una interface muy simple, de fácil manejo por el posible usuario. <strong>Resultados:</strong> Se propone el presente sistema cuya característica principal es la simplicidad de su uso. El mismo determina el Riesgo Cardiovascular Global de forma automatizada<strong>.</strong> Según las tablas de riesgo de Framingham por categorías (Wilson), en su versión de 1998 y las tablas de predicción del riesgo cardiovascular global de la OMS. Para la región AMR A del 2008. <strong>Conclusiones: </strong>Se realizó el diseño del sistema automatizado para facilitar la determinación del Riesgo Cardiovascular Global.
description: http://www.revinformatica.sld.cu/index.php/rcim/article/view/406
DOI: 10.15139/s3/fbjacf
title: Sam Nunn Constituent Casework Correspondence

description: This collection consists of coded data from casework correspondence received by the office of Senator Sam Nunn from 1994-1996. The correspondence was coded to include information about the geographic locations of the constituents and the topics covered by the casework. The data was received as part of the Sam Nunn papers (MSS800); archivists discarded and shredded the casework files that were included in the collection. The data are in both Stata and .csv format. We have also provided data and documentation from the CD-ROM on which Emory University received the data in 1996. See the README file for additional information about the origins and nature of the data