cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6641278
title: The conventional versus a constructionist Scratch programming and first-year students' achievements in higher education classes: experimental data.

description: Globally, learning or teaching the first programming (popularly called CS1) remains a significant educational challenge. Indicators such as CS1 students' engagement, failure and attrition rates, and lack of diversity, continue to show the need for innovating the learning or teaching of novice computer science students. To ease initiating novices to programming, Scratch, a visual programming language, has become a staple of K-12 CS1 classes. As outcomes of a research project aiming to explore a constructionist Scratch pedagogy with novice CS students in higher education, we present these datasets. In the research lasting two successive academic sessions, we conducted two quasi-experimental studies involving four intact CS1 classes in selected public polytechnic in the north central Nigeria. In each study, we randomly assigned the classes to the experimental and control groups, constituting the constructionist Scratch and the conventional CS1 classes, respectively. Instruments for collecting data include a student profile questionnaire, a pretest, and posttest. Sequel to ethical clearance and permission from the selected schools, we conducted each study during the first semester of each academic session, in the first seven to eight weeks. During the first to second week, we administered students who consented to take part with the questionnaire and the pretest. Learning or teaching in the two classes lasted six weeks. Then both classes took the posttest. An independent CS educator who is not part of this research marked all the achievement tests, following a rubric prepared by the first author. To strengthen the research design and the possibility of arriving at valid causal evidence, we employed a Coarsened Exact Matching (CEM) algorithm to generate matched samples of experimental and control data, which we used in the analysis. Data presented here includes the raw, unmatched and matched experimental datasets from both studies. A researcher can make use of the data: To explore if some background variables not addressed in the original research may moderate CS1 students' achievements. For instance, their prior achievements in mathematics, physics, or English. To uncover some interesting patterns using machine learning algorithms. To validate the outcome of the original experiment by using the unmatched, matched or newly generated matched samples. The authors welcome further research collaborations in using the data or the accompanying research instruments. Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentence4Edit in Ginger×
DOI: 10.6084/m9.figshare.14775126
title: Table S04 Lipodomic dataset

description: <strong>A set of gene knockouts as a resource for global lipidomic changes</strong> <em>Aleksandra Spiegel, Chris Lauber, Mandy Bachmann, Anne-Kristin Heninger, Christian Klose, Kai Simons, Mihail Sarov, Mathias J. Gerl</em> https://doi.org/10.1038/s41598-022-14690-0<br> <br> <strong>Lipidomic measurements as molar fraction (mol%)</strong>. Also containing: the gene knocked out (gene), the respective clone, the growth state (20% or 80% confluency), the method of knockout (gene trap or deletion), the genotype, and the total amount of lipid found in the measurement (total pmol). Lipid measurements were included in the dataset, if there were at least 2 valid measurements in any cell line. The fatty acid profile (in species percent) is also provided for each TAG species.
DOI: 10.6084/m9.figshare.14775126.v1
title: Table S04 Lipodomic dataset

description: <strong>A set of gene knockouts as a resource for global lipidomic changes</strong> <em>Aleksandra Spiegel, Chris Lauber, Mandy Bachmann, Anne-Kristin Heninger, Christian Klose, Kai Simons, Mihail Sarov, Mathias J. Gerl</em> https://doi.org/10.1038/s41598-022-14690-0<br> <br> <strong>Lipidomic measurements as molar fraction (mol%)</strong>. Also containing: the gene knocked out (gene), the respective clone, the growth state (20% or 80% confluency), the method of knockout (gene trap or deletion), the genotype, and the total amount of lipid found in the measurement (total pmol). Lipid measurements were included in the dataset, if there were at least 2 valid measurements in any cell line. The fatty acid profile (in species percent) is also provided for each TAG species.
DOI: 10.6073/pasta/44683470fbb40014f370bc5e5e7e9d44
title: Zooplankton energy densities from literature

description: A data set of the dry energy density and wet to dry mass ratios of zooplankotn. these data were found searching web of science and google scholar.
DOI: 10.6084/m9.figshare.20066432
title: agriculture_inputs_outputs.xlsx

description: Input-output data for United States Agriculture.
DOI: 10.6084/m9.figshare.20066432.v1
title: agriculture_inputs_outputs.xlsx

description: Input-output data for United States Agriculture.
DOI: 10.22008/fk2/0afgqt/g9dlvp
title: summer_WTD.tif

description: :unav
DOI: 10.6084/m9.figshare.16644928
title: Table S05 Results of statistical test

description: <strong>A set of gene knockouts as a resource for global lipidomic changes</strong> <em>Aleksandra Spiegel, Chris Lauber, Mandy Bachmann, Anne-Kristin Heninger, Christian Klose, Kai Simons, Mihail Sarov, Mathias J. Gerl</em> https://doi.org/10.1038/s41598-022-14690-0 <br> <br> <strong>Table S05 Results of statistical tests</strong>. <strong>Tab “Gene names”</strong>: Uniprot IDs and Protein names of the genes knocked out in this study. <strong>Tab “Overview comparisons”</strong>: Overview of all comparisons displayed in the file, including the tab names, IDs used, number of replicates, and number of lipids compared. <strong>Tabs on statistical tests</strong> contain 4 sets of comparisons: lipid class, fatty acid (FA), fatty acids with a lipid class (class FA), and (sub-)species, with mean value, standard deviation, number of replicates of the feature (n), fold change of means, p-value and the p-value corrected for multiple testing based on the set (BH).
DOI: 10.6084/m9.figshare.16644928.v1
title: Table S05 Results of statistical test

description: <strong>A set of gene knockouts as a resource for global lipidomic changes</strong> <em>Aleksandra Spiegel, Chris Lauber, Mandy Bachmann, Anne-Kristin Heninger, Christian Klose, Kai Simons, Mihail Sarov, Mathias J. Gerl</em> https://doi.org/10.1038/s41598-022-14690-0 <br> <br> <strong>Table S05 Results of statistical tests</strong>. <strong>Tab “Gene names”</strong>: Uniprot IDs and Protein names of the genes knocked out in this study. <strong>Tab “Overview comparisons”</strong>: Overview of all comparisons displayed in the file, including the tab names, IDs used, number of replicates, and number of lipids compared. <strong>Tabs on statistical tests</strong> contain 4 sets of comparisons: lipid class, fatty acid (FA), fatty acids with a lipid class (class FA), and (sub-)species, with mean value, standard deviation, number of replicates of the feature (n), fold change of means, p-value and the p-value corrected for multiple testing based on the set (BH).
DOI: 10.5281/zenodo.6641831
title: Observations on the interaction between plant growth-promoting bacteria and the root knot nematode Meloidogyne javanica

description: This is the data set associated with the paper currently under peer review, on the biocontrol potential of plant growth promoting rhizobacteria against root knot nematodes.
DOI: 10.5281/zenodo.5948678
title: Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site)

description: This package contains the datasets and supplementary materials used in the IPIN 2021 Competition. <strong>Contents:</strong> IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition 01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors. 02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results. <strong>Please, cite the following works when using the datasets included in this package:</strong> Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678
description: We would like to thank GISS Research team at University of Extremadura for their support in colllecting the datasets and the ISTI-CNR for managing the competition and find sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable committment in organizing and promoting the IPIN competition and conference. This work has been done within the framework of the IPIN 2021 Conference in Lloret de Mar, organised by A. Pérez-Navarro, R. Montoliu and J. Torres-Sospedra. Parts of this work were carried out with the financial support received from projects and grants: - A-WEAR (H2020-MSCA-ITN-2018, Grant Agreement 813278) - INSIGNIA (PTQ2018-009981) - REPNIN+ network (TEC2017-90808-REDT) - LORIS (TIN2012-38080-C04-04) - SmartLoc(CSIC-PIE Ref.201450E011) - TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER) - MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)
DOI: 10.5281/zenodo.5948677
title: Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site)

description: This package contains the datasets and supplementary materials used in the IPIN 2021 Competition. <strong>Contents:</strong> IPIN2021_Track03_TechnicalAnnex_V1-02.pdf: Technical annex describing the competition 01-Logfiles: This folder contains a subfolder with the 105 training logfiles, 80 of them single floor indoors, 10 in outdoor areas, 10 of them in the indoor auditorium with floor-trasitio and 5 of them in floor-transition zones, a subfolder with the 20 validation logfiles, and a subfolder with the 3 blind evaluation logfile as provided to competitors. 02-Supplementary_Materials: This folder contains the matlab/octave parser, the raster maps, the files for the matlab tools and the trajectory visualization. 03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 82 evaluation points. It requires the Matlab Mapping Toolbox. The ground truth is also provided as 3 csv files. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT files include the closest timestamp matching the timing provided by competitors for the 3 evaluation logfiles. It contains samples of reported estimations and the corresponding results. <strong>Please, cite the following works when using the datasets included in this package:</strong> Torres-Sospedra, J.; et al. Datasets and Supporting Materials for the IPIN 2021 Competition Track 3 (Smartphone-based, off-site). http://dx.doi.org/10.5281/zenodo.5948678
description: We would like to thank GISS Research team at University of Extremadura for their support in colllecting the datasets and the ISTI-CNR for managing the competition and find sponsors for the winner's award. We are also grateful to Francesco Potortì, Sangjoon Park and the ISTI-CNR team for their invaluable committment in organizing and promoting the IPIN competition and conference. This work has been done within the framework of the IPIN 2021 Conference in Lloret de Mar, organised by A. Pérez-Navarro, R. Montoliu and J. Torres-Sospedra. Parts of this work were carried out with the financial support received from projects and grants: - A-WEAR (H2020-MSCA-ITN-2018, Grant Agreement 813278) - INSIGNIA (PTQ2018-009981) - REPNIN+ network (TEC2017-90808-REDT) - LORIS (TIN2012-38080-C04-04) - SmartLoc(CSIC-PIE Ref.201450E011) - TARSIUS (TIN2015-71564-C4-2-R, MINECO/FEDER) - MICROCEBUS (MICINN, ref. RTI2018-095168-B-C55, MCIU/AEI/FEDER UE)
DOI: 10.6084/m9.figshare.20047313
title: Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification

description: Applications of convolutional neural network (CNN)-based object detectors in agriculture have been a popular research topic in recent years. However, complicated agricultural environments bring many difficulties for ground truth annotation as well as potential uncertainties for image data quality. Using YOLOv4 as a representation of state-of-the-art object detectors, this study quantified YOLOv4’s sensitivity against artificial image distortions including white noise, motion blur, hue shift, saturation change, and intensity change, and examined the importance of various training dataset attributes based on model classification accuracies, including dataset size, label quality, negative sample presence, image sequence, and image distortion levels. The YOLOv4 model trained and validated on the original datasets failed at 31.91% white noise, 22.05-pixel motion blur, 77.38° hue clockwise shift, 64.81° hue counterclockwise shift, 89.98% saturation decrease, 895.35% saturation increase, 79.80% intensity decrease, and 162.71% intensity increase with 30% mean average precisions (mAPs) for four apple flower bud growth stages. The performance of YOLOv4 decreased with both declining training dataset size and training image label quality. Negative samples and training image sequence did not make a substantial difference in model performance. Incorporating distorted images during training improved the classification accuracies of YOLOv4 models on noisy test datasets by 13 to 390%. In the context of apple flower bud growth-stage classification, except for motion blur, YOLOv4 is sufficiently robust for potential image distortions by white noise, hue shift, saturation change, and intensity change in real life. Training image label quality and training instance number are more important factors than training dataset size. Exposing models to test-image-alike training images is crucial for optimal model classification accuracies. The study enhances understanding of implementing object detectors in agricultural research.
DOI: 10.6084/m9.figshare.20047313.v2
title: Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification

description: Applications of convolutional neural network (CNN)-based object detectors in agriculture have been a popular research topic in recent years. However, complicated agricultural environments bring many difficulties for ground truth annotation as well as potential uncertainties for image data quality. Using YOLOv4 as a representation of state-of-the-art object detectors, this study quantified YOLOv4’s sensitivity against artificial image distortions including white noise, motion blur, hue shift, saturation change, and intensity change, and examined the importance of various training dataset attributes based on model classification accuracies, including dataset size, label quality, negative sample presence, image sequence, and image distortion levels. The YOLOv4 model trained and validated on the original datasets failed at 31.91% white noise, 22.05-pixel motion blur, 77.38° hue clockwise shift, 64.81° hue counterclockwise shift, 89.98% saturation decrease, 895.35% saturation increase, 79.80% intensity decrease, and 162.71% intensity increase with 30% mean average precisions (mAPs) for four apple flower bud growth stages. The performance of YOLOv4 decreased with both declining training dataset size and training image label quality. Negative samples and training image sequence did not make a substantial difference in model performance. Incorporating distorted images during training improved the classification accuracies of YOLOv4 models on noisy test datasets by 13 to 390%. In the context of apple flower bud growth-stage classification, except for motion blur, YOLOv4 is sufficiently robust for potential image distortions by white noise, hue shift, saturation change, and intensity change in real life. Training image label quality and training instance number are more important factors than training dataset size. Exposing models to test-image-alike training images is crucial for optimal model classification accuracies. The study enhances understanding of implementing object detectors in agricultural research.
DOI: 10.6084/m9.figshare.20066429.v1
title: Supplementary Material for: Suitability of Different Diagnostic Platforms for Virological Testing of Blood Samples from Cornea Donors

description: <b><i>Background:</i></b> To minimize the risk of disease transmission in cornea transplantation, donor screening for blood-derived viral infections is mandatory. Ideally, pre-mortem blood samples are used, but based on availability, cadaveric blood samples of cornea donors may also be used. However, serological and nucleic acid amplification tests (NATs) need to be validated for the use of cadaveric specimens. <b><i>Methods:</i></b> Hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), human T-lymphotropic virus (HTLV) 1/2, and <i>Treponema pallidum</i> (syphilis)-specific serological and/or NAT assays were validated on different platforms (Abbott Alinity i, Alinity m, Roche Cobas 6800, and Roche Cobas AmpliPrep/Cobas TaqMan (CAP/CTM)) using (un)spiked paired pre- and post-mortem cornea donor blood samples from the same individual (up to 23.83 h after death) of 28 individuals in accordance with the specifications of the German Federal Institute for Vaccines and Biomedicines (Paul-Ehrlich-Institut [PEI]). In addition, routinely HBV-, HCV- and HIV-PCR-negative tested post-mortem blood samples of 24 individuals were used to assess NAT specificity. <b><i>Results:</i></b> For the majority of serological parameters on the Abbott Alinity i (HBsAg, anti-HBc, anti-HBs, anti-HCV, anti-HIV, anti-HTLV 1/2, and anti-<i>Treponema pallidum</i>), ratios of generated test results of (un)spiked paired pre- and post-mortem blood samples differed ≤25%, with an agreement of qualitative pre- and post-mortem test results ranging from 91.2 to 100%. For NAT parameters (HBV, HCV, and HIV) on the Cobas 6800, Alinity m, and CAP/CTM, no significant deviation in virus concentrations (factor &gt;5) of spiked pre- and post-mortem blood samples could be observed. Ct-values of corresponding internal controls did also not differ significantly (&gt;1.5 Ct-values). In addition, no false-positive test results were generated when specificity was assessed. <b><i>Conclusion:</i></b> Overall, fluctuations of test results for serological and NAT parameters in pre- and post-mortem blood samples examined in this study, were only limited and within the range of what is also observed when routinely testing fresh patient specimens. We conclude that all examined assays are eligible for the screening of blood samples taken up to about 24 h after the occurrence of death.
DOI: 10.6084/m9.figshare.20066501.v1
title: Supplementary Material for: Delving into the Genetic Causes of Language Impairment in a Case of Partial Deletion of <b><i>NRXN1</i></b>

description: <b><i>Introduction:</i></b> Copy-number variations (CNVs) impacting on small DNA stretches and associated with language deficits provide a unique window to the role played by specific genes in language function<i>.</i> <b><i>Methods:</i></b> We report in detail on the cognitive, language, and genetic features of a girl bearing a small deletion (0.186 Mb) in the 2p16.3 region, arr[hg19] 2p16.3(50761778_50947729)×1, affecting exons 3–7 of <i>NRXN1</i>, a neurexin-coding gene previously related to schizophrenia, autism (ASD), attention deficit hyperactivity disorder (ADHD), mood disorder, and intellectual disability (ID). <b><i>Results:</i></b> The proband exhibits many of the features commonly found in subjects with deletions of <i>NRXN1</i>, like ASD-like traits (including ritualized behaviors, disordered sensory aspects, social disturbances, and impaired theory of mind), ADHD symptoms, moderate ID, and impaired speech and language. Regarding this latter aspect, we observed altered speech production, underdeveloped phonological awareness, minimal syntax, serious shortage of active vocabulary, impaired receptive language, and inappropriate pragmatic behavior (including lack of metapragmatic awareness and communicative use of gaze). Microarray analyses point to the dysregulation of several genes important for language function in the girl compared to her healthy parents. <b><i>Discussion:</i></b> Although some basic cognitive deficit – such as the impairment of executive function – might contribute to the language problems exhibited by the proband, molecular evidence suggests that they might result, to a great extent, from the abnormal expression of genes directly related to language.
DOI: 10.5281/zenodo.6641895
title: Erythroxylum savannarum SPAdes preassembly

description: .
DOI: 10.5281/zenodo.6641894
title: Erythroxylum savannarum SPAdes preassembly

description: .
DOI: 10.6084/m9.figshare.16885261
title: Human Gut Microbiome Reference Set

description: Zipped fasta file of the 3594 species representatives. Zipped csv file of genetic annotations of that fasta (too large for excel). Csv & xlsx files of antibiotics resistances of the fasta. Csv & xlsx of metadata of all assemblies used. Csv & xlsx of relative abundances of 2 cohorts of validation samples. Csv & xlsx of mapping percentages, for 3 reference sets, of 5 cohorts of validation samples.
DOI: 10.5281/zenodo.6048723
title: Dataset (I) related to publication: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist

description: MD simulation data related to the publication Rashidian et al.: Discrepancy in interactions and conformational dynamics of pregnane X receptor (PXR) bound to an agonist and a novel competitive antagonist. https://doi.org/10.1016/j.csbj.2022.06.020 Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files) dataset I: systems SRL+Co, C-100 and BAY-1797 dataset I: each file contains all branched replicas and the main replica. dataset1: C_100_Replica1 contains four branched replicas and the main replica. The two of four branched replicas which stem from the middle of the main replica named: b_c_D1_r1_2285 (corresponding name in the SI data is R1_a) and b_c_D1_2285_r1_2 (corresponding name in the SI data is R1_b). dataset1: C_100_Replica2–5 , each file contains one main replica and the two branches. dataset I: system SRL+Co ;each file contains one main replica dataset I: system BAY-1797; each file contains one main replica dataset II:system SRL ; each file contains one main replica.