cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10100531
title: Morphological parameters of microglia in the CDS-based microglia depletion model

description: The data set includes morphological parameters (cell soma size, process length, branching points, and Sholl analysis) of microglial cells after treatment with clodronate disodium salt (CDS), phosphate buffered saline (PBS) or sham in mice.
DOI: 10.5281/zenodo.10100532
title: Morphological parameters of microglia in the CDS-based microglia depletion model

description: The data set includes morphological parameters (cell soma size, process length, branching points, and Sholl analysis) of microglial cells after treatment with clodronate disodium salt (CDS), phosphate buffered saline (PBS) or sham in mice.
DOI: 10.6084/m9.figshare.24541246
title: ERR5021550.vcf

description: /////
DOI: 10.6084/m9.figshare.24541246.v1
title: ERR5021550.vcf

description: /////
DOI: 10.6084/m9.figshare.24541243
title: A 10-m resolution impervious surface area map for subtropical China via a terrain-guided gated fusion network

description: Large-scale, high-resolution and multi-temporal impervious surface maps from remote sensing are essential for socioeconomic and environmental studies. However, in complex regions like subtropical China, climate and terrain severely contaminate optical and synthetic aperture radar (SAR) imagery, resulting in relatively poor accuracy here. Therefore, this paper presents a novel network for accurately extracting impervious surfaces, in which a terrain-guided gated fusion module is proposed to adaptively fuse Sentinel-2 optical and Sentinel-1 SAR imagery. To eliminate the tedious manual annotation over vast and mixed regions, an automatic samples generation and weighting strategy is further introduced. Then, the framework monitors impervious surfaces in subtropical China from 2016 to 2021 with the support of Google Earth Engine. The final impervious surface map scores an overall accuracy and Kappa of 93.19-94.04% and 0.843-0.859, respectively. Moreover, its consistent performance in various scenarios and superior visual details than most products evidence the feasibility and reliability of the proposed framework. This map provides precise dynamics of impervious surfaces that will help to inform urban studies.
DOI: 10.6084/m9.figshare.24541252
title: Source data

description: <b>Crop rotation and native microbiome inoculation restore soil capacity to suppress a root disease</b>
DOI: 10.6084/m9.figshare.24541252.v1
title: Source data

description: <b>Crop rotation and native microbiome inoculation restore soil capacity to suppress a root disease</b>
DOI: 10.4121/5e6b4b53-6188-4254-a729-47991bbb8832
title: Chemical and population data for Nasonia vitripennnis females collected from the Hoge Veluwe

description: <strong>Abstract</strong>Ecotypes, strains locally adapted to divergent ecological conditions, are often considered to be the first steps in sympatric speciation. It has been suggested that two distinguishable ecotypes hint at incipient sympatric speciation in <em>Nasonia vitripennis</em>, the prominent model organism for parasitoid wasps, with one ecotype parasitizing fly pupae in bird nests, and the other parasitizing fly pupae on carrion. In the present study, we investigated the differentiation into these two distinct ecotypes on a population genetic and on a phenotypic level in a wild <em>N. vitripennis</em> population in the Netherlands. Isofemale lines were obtained from bird nest boxes and from deer carrion, respectively, representing both microhabitats. Using a panel of 14 microsatellites, we determined the population genetic structure and tested for genetic differentiation between the foundresses obtained from both microhabitats. To test for phenotypic differentiation, we determined the cuticular hydrocarbon (CHC) profiles from wasps of both microhabitats. Both the genetic and the phenotypic datasets show no evidence for any kind of separation based on the postulated two ecotypes, but rather suggest free interbreeding with no gene flow interruption between the two distinct host patches. This challenges previous assumptions on distinguishable ecotypes in <em>N. vitripennis</em>, and invites a re-evaluation of potential ecological speciation mechanisms in parasitoid wasps.<br>This dataset contains:1) microsatellite data for 14 loci 25 N. vitripennis females collected in 2018 in the Hoge Veluwe in The Netherlands (HV2018_MicrosatelliteDataset.csv)2) Raw data of GC-MS profiles of cuticular hydrocarbon (CHC) of N.vitripennis males and females from iso-female lines collected in 2018 in the Hoge Veluwe in The Netherlands (HV2018_CHC_RawData.tar) and an overview (HV2018_GCMS_Data.csv).
DOI: 10.4121/5e6b4b53-6188-4254-a729-47991bbb8832.v1
title: Chemical and population data for Nasonia vitripennnis females collected from the Hoge Veluwe

description: <strong>Abstract</strong>Ecotypes, strains locally adapted to divergent ecological conditions, are often considered to be the first steps in sympatric speciation. It has been suggested that two distinguishable ecotypes hint at incipient sympatric speciation in <em>Nasonia vitripennis</em>, the prominent model organism for parasitoid wasps, with one ecotype parasitizing fly pupae in bird nests, and the other parasitizing fly pupae on carrion. In the present study, we investigated the differentiation into these two distinct ecotypes on a population genetic and on a phenotypic level in a wild <em>N. vitripennis</em> population in the Netherlands. Isofemale lines were obtained from bird nest boxes and from deer carrion, respectively, representing both microhabitats. Using a panel of 14 microsatellites, we determined the population genetic structure and tested for genetic differentiation between the foundresses obtained from both microhabitats. To test for phenotypic differentiation, we determined the cuticular hydrocarbon (CHC) profiles from wasps of both microhabitats. Both the genetic and the phenotypic datasets show no evidence for any kind of separation based on the postulated two ecotypes, but rather suggest free interbreeding with no gene flow interruption between the two distinct host patches. This challenges previous assumptions on distinguishable ecotypes in <em>N. vitripennis</em>, and invites a re-evaluation of potential ecological speciation mechanisms in parasitoid wasps.<br>This dataset contains:1) microsatellite data for 14 loci 25 N. vitripennis females collected in 2018 in the Hoge Veluwe in The Netherlands (HV2018_MicrosatelliteDataset.csv)2) Raw data of GC-MS profiles of cuticular hydrocarbon (CHC) of N.vitripennis males and females from iso-female lines collected in 2018 in the Hoge Veluwe in The Netherlands (HV2018_CHC_RawData.tar) and an overview (HV2018_GCMS_Data.csv).
DOI: 10.5281/zenodo.10100590
title: Prognostic Significance of Iron Metabolism and Immune-Related Genes as Risk Markers in Hepatocellular Carcinoma
DOI: 10.5281/zenodo.10100589
title: Prognostic Significance of Iron Metabolism and Immune-Related Genes as Risk Markers in Hepatocellular Carcinoma
DOI: 10.6084/m9.figshare.21836196
title: A 10-m resolution impervious surface area map for the greater Mekong subregion from multisource remote sensing images

description: Here, the 2016-2022 impervious surface area map exclusively for the greater Mekong subregion is prepared. We present a novel machine-learning framework implemented on the Google Earth Engine platform that merges Sentinel-1 Synthetic Aperture Radar images and Sentinel-2 Multispectral images to extract impervious surface area. Furthermore, we also introduce a training sample migration strategy that eliminates the need for collecting additional training samples and automates multi-temporal impervious surface area mapping. Finally, we perform a quantitative assessment with validation samples interpreted from Google Earth. Results show that the overall accuracy and kappa coefficient of the final impervious surface area maps range from 93.67% to 93.75% and 0.872 to 0.873, respectively. This dataset provides comprehensive measurements of impervious surface coverage and configuration that will help to inform urban ecological studies.
DOI: 10.6084/m9.figshare.24541270
title: Err5021550.vcf.gz

description: ////<br>
DOI: 10.6084/m9.figshare.24541270.v1
title: Err5021550.vcf.gz

description: ////<br>
DOI: 10.5281/zenodo.10056219
title: Dataset of the A2S2 laboratory experiment results

description: Dataset of the A2S2 laboratory experiment results
DOI: 10.5281/zenodo.10056220
title: Dataset of the A2S2 laboratory experiment results

description: Dataset of the A2S2 laboratory experiment results
DOI: 10.57841/casdc.0003107
title: https://www.tribology.com.cn/cn/article/doi/10.16078/j.tribology.2018178
DOI: 10.5281/zenodo.10100619
title: Behavioral Data Cluster Analysis

description: The data set includes the main behavioral readouts (mean % prepulse inhibition, social preference index, % alternation in the Y-maze, and total distance moved in the open field) used for cluster analyses in the poly(I:C)-based mouse model of maternal immune activation.
DOI: 10.5281/zenodo.10100620
title: Behavioral Data Cluster Analysis

description: The data set includes the main behavioral readouts (mean % prepulse inhibition, social preference index, % alternation in the Y-maze, and total distance moved in the open field) used for cluster analyses in the poly(I:C)-based mouse model of maternal immune activation.
DOI: 10.57841/casdc.0003108
title: 考虑空化效应的螺旋槽机械密封液膜动力学特性研究
DOI: 10.5281/zenodo.10100834
title: Cytokine Data MIA Model

description: The data set includes maternal and fetal cytokines, as measured by Meso Scale Discovery Electrochemiluminescence (MSD) in a poly(I:C)-based mouse model of maternal immune activation.
DOI: 10.5281/zenodo.10100835
title: Cytokine Data MIA Model

description: The data set includes maternal and fetal cytokines, as measured by Meso Scale Discovery Electrochemiluminescence (MSD) in a poly(I:C)-based mouse model of maternal immune activation.
DOI: 10.5281/zenodo.10045017
title: Clasificación de los mejores libros de todos los tiempos según Goodreads

description: El conjunto de datos incluye información detallada sobre los 5000 mejores libros de todos los tiempos de acuerdo a una lista creada en el sitio web Goodreads. Cada libro en el conjunto de datos está representado por los siguientes campos:

ranking: El ranking del libro en la lista de Goodreads.
book_name: El nombre del libro.
author_name: El nombre del autor del libro.
avg_rating: La calificación promedio del libro.
num_ratings: El número de calificaciones que el libro ha recibido.
score: La puntuación del libro.
n_people_voted: El número de personas que han votado por el libro.
book_url: La URL del libro en Goodreads.
author_url: La URL del autor en Goodreads.
Además de estos campos, el conjunto de datos también incluye información adicional sobre cada libro obtenida de la página de detalles del libro en Goodreads, como:

original_title: El título original del libro.
series: La serie a la que pertenece el libro.
author_role_list: La lista de autores, traductores, ilustradores, editores, etc. del libro y sus roles.
pages_format: El numero de páginas del libro en su primera edición.
publication_info: La información sobre la primera publicación del libro, como la fecha y en algunos casos el editor.
genres: Los géneros del libro.
setting: El lugar donde ocurre la historia del libro.
this_ed_pag: El número de páginas para esta edición del libro.
date_this_ed: La fecha de publicación de esta edición del libro.
publisher: El editor del libro.
language: El idioma de esta edición del libro.
ISBN13: El ISBN-13 del libro.
ISBN10: El ISBN-10 del libro.
awards_list: La lista de premios que ha recibido el libro.
people_reading: El número de personas que están leyendo el libro.
people_want_read: El número de personas que quieren leer el libro.
reviews_count_text: El número de reseñas que tiene el libro.
star5, star4, star3, star2, star1: El número de calificaciones de 5 estrellas, 4 estrellas, 3 estrellas, 2 estrellas y 1 estrella que ha recibido el libro, respectivamente.
titles_related: La lista de títulos relacionados con el libro.
De manera que, el conjunto de datos proporciona una gran cantidad de información detallada sobre una amplia gama de libros, incluyendo su clasificación, detalles del libro, estadísticas de calificación y otros. Este conjunto de datos podría ser útil para una variedad de análisis y aplicaciones, como la construcción de un sistema de recomendación de libros, el análisis de tendencias en la lectura de libros, el estudio de las estadísticas de las calificaciones de los libros, entre otros.
DOI: 10.5281/zenodo.10045018
title: Clasificación de los mejores libros de todos los tiempos según Goodreads

description: El conjunto de datos incluye información detallada sobre los 5000 mejores libros de todos los tiempos de acuerdo a una lista creada en el sitio web Goodreads. Cada libro en el conjunto de datos está representado por los siguientes campos:

ranking: El ranking del libro en la lista de Goodreads.
book_name: El nombre del libro.
author_name: El nombre del autor del libro.
avg_rating: La calificación promedio del libro.
num_ratings: El número de calificaciones que el libro ha recibido.
score: La puntuación del libro.
n_people_voted: El número de personas que han votado por el libro.
book_url: La URL del libro en Goodreads.
author_url: La URL del autor en Goodreads.
Además de estos campos, el conjunto de datos también incluye información adicional sobre cada libro obtenida de la página de detalles del libro en Goodreads, como:

original_title: El título original del libro.
series: La serie a la que pertenece el libro.
author_role_list: La lista de autores, traductores, ilustradores, editores, etc. del libro y sus roles.
pages_format: El numero de páginas del libro en su primera edición.
publication_info: La información sobre la primera publicación del libro, como la fecha y en algunos casos el editor.
genres: Los géneros del libro.
setting: El lugar donde ocurre la historia del libro.
this_ed_pag: El número de páginas para esta edición del libro.
date_this_ed: La fecha de publicación de esta edición del libro.
publisher: El editor del libro.
language: El idioma de esta edición del libro.
ISBN13: El ISBN-13 del libro.
ISBN10: El ISBN-10 del libro.
awards_list: La lista de premios que ha recibido el libro.
people_reading: El número de personas que están leyendo el libro.
people_want_read: El número de personas que quieren leer el libro.
reviews_count_text: El número de reseñas que tiene el libro.
star5, star4, star3, star2, star1: El número de calificaciones de 5 estrellas, 4 estrellas, 3 estrellas, 2 estrellas y 1 estrella que ha recibido el libro, respectivamente.
titles_related: La lista de títulos relacionados con el libro.
De manera que, el conjunto de datos proporciona una gran cantidad de información detallada sobre una amplia gama de libros, incluyendo su clasificación, detalles del libro, estadísticas de calificación y otros. Este conjunto de datos podría ser útil para una variedad de análisis y aplicaciones, como la construcción de un sistema de recomendación de libros, el análisis de tendencias en la lectura de libros, el estudio de las estadísticas de las calificaciones de los libros, entre otros.
DOI: 10.57841/casdc.0003109
title: 织构深度对不锈钢表面油润滑条件下摩擦学性能影响的试验和仿真研究
DOI: 10.6084/m9.figshare.24541285
title: data.zip

description: Raw data and data processing, including calculation conditions and some data processing, including some Chinese names
DOI: 10.6084/m9.figshare.24541285.v1
title: data.zip

description: Raw data and data processing, including calculation conditions and some data processing, including some Chinese names
DOI: 10.5281/zenodo.10083567
title: Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)

description: Background
The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research
 
Description of the dataset
This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.
The following data is included

individual experimental data and results in the folders

210812 (10 µm, coarse sand, low-flow)
220727 (1 µm, coarse sand, low flow)
220803 (3 µm, coarse sand, low-flow)
220818 (1 µm, fine sand, low-flow)
220901 (1 µm, coarse sand, high-flow)
Comparison (comparing individual results of the experiments)
Scripts (contains the individual matlab scripts)
labbook.xlsx (contains metadata on the experiments, which are read out in the matlab scripts)
 
Description of the code
The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.

Main scripts to analyze experimental data

Experiment_Main.m Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV
Experiment_Main_Compare.m Comparison of individual experiment results
FIS-dataset

FIS_Cal_Individual.m: Realizes individual calibrations of one experiment
FIS_Cal_Result.m: Merges individual calibrations of one experiment
Experiment_FIS.m: Load data of one experiment, detect interfaces. Followed by

Experiment_FIS_1pix: Individual particle detection (for 10 µm experiment, no binning)
Experiment_FIS_10pix: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)
Experiment_FIS_10pix_compare.m: Compare results of particle cloud analysis for all experiments.
Fluo-data

Fluo_Cal.m Realizes calibration for Fluorometer devices
PIV-dataset

PIV_individual.m Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)
PIV_merge.m Merge 9 individual results of PIV for a result for one experiment
Profiler-dataset

Profiler.m  Analyses data from bedform profiling (merging individual measurements after the experiment)
Profiler_Compare.m Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)
Profiler_Time.m Analyses temporal change of bedform elevation during the experiment
 
Disclaimer
The data and code are provided as is without any warranty.
 
Funding
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.
DOI: 10.5281/zenodo.10083568
title: Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 1(2)

description: Background
The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research
 
Description of the dataset
This dataset is the main dataset used for the analysis. There is a twin archive connected to this one, which contains the dataset which was used for the individual particle detection routines (10.5281/zenodo.10081788). The files have to be downloaded and merged into the folder structure.
The following data is included

individual experimental data and results in the folders

210812 (10 µm, coarse sand, low-flow)
220727 (1 µm, coarse sand, low flow)
220803 (3 µm, coarse sand, low-flow)
220818 (1 µm, fine sand, low-flow)
220901 (1 µm, coarse sand, high-flow)
Comparison (comparing individual results of the experiments)
Scripts (contains the individual matlab scripts)
labbook.xlsx (contains metadata on the experiments, which are read out in the matlab scripts)
 
Description of the code
The matlab scripts *.m contain the code to read and analyse all experimental data. The scripts are divided for the different input file types.

Main scripts to analyze experimental data

Experiment_Main.m Main routine for individual experiments, reading and analysing Fluorometer, Levelogger, Flowmeter, Ultrasonics PIV
Experiment_Main_Compare.m Comparison of individual experiment results
FIS-dataset

FIS_Cal_Individual.m: Realizes individual calibrations of one experiment
FIS_Cal_Result.m: Merges individual calibrations of one experiment
Experiment_FIS.m: Load data of one experiment, detect interfaces. Followed by

Experiment_FIS_1pix: Individual particle detection (for 10 µm experiment, no binning)
Experiment_FIS_10pix: Particle cloud analysis (all experiments, binning 10 Pix * 10 Pix)
Experiment_FIS_10pix_compare.m: Compare results of particle cloud analysis for all experiments.
Fluo-data

Fluo_Cal.m Realizes calibration for Fluorometer devices
PIV-dataset

PIV_individual.m Individual analysis of Particle Image Velocimetry (in total 9 different subdatasets, from 3 camera positions, and each 3 different illumination positions)
PIV_merge.m Merge 9 individual results of PIV for a result for one experiment
Profiler-dataset

Profiler.m  Analyses data from bedform profiling (merging individual measurements after the experiment)
Profiler_Compare.m Compares bedform elevations and metrics between the 5 experiments (acquired after the experiment)
Profiler_Time.m Analyses temporal change of bedform elevation during the experiment
 
Disclaimer
The data and code are provided as is without any warranty.
 
Funding
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.
DOI: 10.5281/zenodo.10081787
title: Data and code for the publication "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments" - Part 2(2)

description: Background
The dataset contains data on Microplastic transport experiments run in an experimental flume of the University of Bayreuth. It was analysed in the paper by J.P. Boos, F. Dichgans, J.H. Fleckenstein, B.S. Gilfedder and S. Frei, "Assessing the Behavior of Microplastics in Fluvial Systems: Infiltration and Retention Dynamics in Streambed Sediments", currently under review in Water Resources Research
 
Description of the dataset
This dataset contains data used for individual particle detection, and is a companion of the main dataset (10.5281/zenodo.10083568). The files need to be downloaded and merged into the given folder structure. Put the folder "1Pix" along with the folder "10Pix" to the folder in "210812/Data-FIS/matlab/2_Experiment/exp/".
 
Disclaimer
The data and code are provided as is without any warranty.
 
Funding
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -– Project Number 391977956 –- SFB 1357.