cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/ry4n-fr48
title: Recognition of common postures in lambs (ICV-TxLamb)

description: Computer vision in animal monitoring has become a research application in stable or confined conditions.Detecting animals from the top view is challenging due to barn conditions.In this dataset called ICV-TxLamb, images are proposed for the monitoring of lamb inside a barn.This set of data is made up of two categories, the first is lamb (classifies the only lamb), the second consists of four states of the posture of lambs, these are: eating, sleeping, lying down, and normal (standing or without activity ).The added tags have been considered by the natural movements that the lamb makes in the observed scene.
DOI: 10.6084/m9.figshare.13271717
title: Epigenetic signals for Enh and TssBiv states

description: H3K27ac signal for Enh states and the signal of 10 marks (H3K4me1, H3K4me2, H3K4me3m H3K9me3, H3K9ac, H3K27me3, H3K27ac, H3K36me3, ATAC, and DNAme) for TssBiv states
DOI: 10.5281/zenodo.4287707
title: Simple Dataset from 1 to 5

description: This is a simple dataset from 1 to 5 This is more description to the first version This is further description
DOI: 10.5281/zenodo.4280621
title: Surveyance of apparent unfolding curves via thiol labelling

description: Dataset contains raw and preprocessed data for fluorescence and proteomic studies respectively. In each case, protein foldedness was probed using thiol reactivity. The raw mass spectrometry proteomics data have also been deposited to the ProteomeXchange Consortium via the PRIDE partner repository, with the dataset identifiers PXD022587 and PXD022640.
DOI: 10.6084/m9.figshare.13077962
title: Distribution of trials per list-length

description: Description of the distribution of trial of the recognition memory in spider monkeys
DOI: 10.6084/m9.figshare.13077962.v2
title: Distribution of trials per list-length

description: Description of the distribution of trial of the recognition memory in spider monkeys
DOI: 10.6084/m9.figshare.13077962.v1
title: Distribution of trials per list-length

description: Description of the distribution of trial of the recognition memory in spider monkeys
DOI: 10.25345/c57v22
title: MassIVE MSV000086513 - GNPS_Synthetic cannabinoids_DDA25_QC1000_0.7
title: Untargeted approach synthetic cannabinoids in oral fluid DDA
DOI: 10.21227/bsbh-sc65
title: Feature datasets on Southern Yellow cattle using three-axis acceleration

description: Vietnam Yellow Cattle plays an important role in beefproduction in developing countries.
DOI: 10.21227/d4qy-7470
title: a
DOI: 10.21227/2ydm-1234
title: Testing

description: Testing
DOI: 10.6084/m9.figshare.13277852
title: raw data

description: Ca<sup>2+</sup>-regulated Exopolysaccharide Biosynthesis in Probiotic <i>Lactobacillus plantarum</i> K25 as Analyzed by Omics Approach
DOI: 10.6084/m9.figshare.13277852.v1
title: raw data

description: Ca<sup>2+</sup>-regulated Exopolysaccharide Biosynthesis in Probiotic <i>Lactobacillus plantarum</i> K25 as Analyzed by Omics Approach
DOI: 10.6084/m9.figshare.12996851
title: Supplementary data for craniometric and genetic analyses of commensal rats in Nusa Tenggara, Indonesia

description: The data presented here comes from a craniometric and genetic study into commensal rodent movements through the Nusa Tenggara island chain, Indonesia. It includes cranial measurements of three rat species - <i>Rattus rattus, Rattus exulans, </i>and <i>Rattus hainaldi,</i> mtDNA haplotypes and nuclear microsatellite genotypes for <i>Rattus rattus, </i>and mtDNA haplotypes for <i>Rattus exulans </i>and <i>Rattus argentiventer </i>from Asia. This dataset is discussed and used in a detailed study published by Louys et al. 2020.
DOI: 10.6084/m9.figshare.12526157
title: Quantification of Cas9 binding and cleavage across diverse guide sequences maps landscapes of target engagement

description: Data accompanying the manuscript Boyle et al 2020.<br>Oligo library information, experimental timepoints and count tables for all high-throughput experiments (either massively parallel filter binding or cleavage assays) are included, plus design files for 3D-printing adaptors for filter-binding and a script for predicting energetic barriers.
DOI: 10.6084/m9.figshare.12526157.v2
title: Quantification of Cas9 binding and cleavage across diverse guide sequences maps landscapes of target engagement

description: Data accompanying the manuscript Boyle et al 2020.<br>Oligo library information, experimental timepoints and count tables for all high-throughput experiments (either massively parallel filter binding or cleavage assays) are included, plus design files for 3D-printing adaptors for filter-binding and a script for predicting energetic barriers.
DOI: 10.6084/m9.figshare.12526157.v1
title: Cas9 activity quantitation by massively parallel filter binding and cleavage assays

description: Data accompanying the manuscript Boyle, et al 2020.<br>Oligo library information, experimental timepoints and count tables for all high-throughput experiments (either massively parallel filter binding or cleavage assays) are included, plus design files for 3D-printing adaptors for filter-binding.
DOI: 10.21227/nebq-6s69
title: Comparison of FOC BLDC control -- SMO vs flux observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC). The performance comparison using Sliding Mode Observer (SMO) and flux observer.All this is implemented on 32-bit Real-Time microcontroller,which has 100-pins,12-bit ADC , 5-pin JTAG. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.
DOI: 10.21227/d7gk-2d30
title: KEH MPC Mass Spring

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Circuits and Systems I".In wearable medicalmass-spring system
DOI: 10.21227/gw8a-nb42
title: Comparison of Fuzzy

description: This dataset is in support of my planned research paper " Performance Optimizer " about to be submitted shortly to "IEEE Transactions on Power Electronics" (https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=63).
DOI: 10.5281/zenodo.4287788
title: Supplemental table S1 for Viengkhou et al.

description: Supplemental table 1 for manuscript by Viengkhou et al., 2020
DOI: 10.5281/zenodo.4014449
title: Supplemental table S1 for Viengkhou et al.

description: Supplemental table 1 for manuscript by Viengkhou et al., 2020
DOI: 10.21227/2fdv-2g02
title: Comparison of FOC BLDC control -- SMO vs flux observer

description: This dataset is in support of my planned research paper shortly to be submitted to "IEEE Transactions on Power Electronics".In this paper and dataset, speed and the position estimation of BLDC is done using the sensorless vector control method i.e., Field Oriented control (FOC). The performance comparison using Sliding Mode Observer (SMO) and flux observer.All this is implemented on 32-bit Real-Time microcontroller,which has 100-pins,12-bit ADC , 5-pin JTAG. The pins usage not mentioned here are used for other General-Purpose-CAN,USB, RS485 etc.
DOI: 10.5281/zenodo.4287794
title: Raw data for reference flow experiments

description: Raw results data for the reference flow study. Reference flow used public sequence data, where is specified in the manuscript, to generate the results. The figures shown in the manuscript were plotted using the provided processed data. Reference flow manuscript: https://www.biorxiv.org/content/10.1101/2020.03.03.975219v4 Reference flow software: https://github.com/langmead-lab/reference_flow Software for reference flow experiments: https://github.com/langmead-lab/reference_flow-experiments An archived version of the software for experiments: https://doi.org/10.5281/zenodo.4287729
DOI: 10.5281/zenodo.4287793
title: Raw data for reference flow experiments

description: Raw results data for the reference flow study. Reference flow used public sequence data, where is specified in the manuscript, to generate the results. The figures shown in the manuscript were plotted using the provided processed data. Reference flow manuscript: https://www.biorxiv.org/content/10.1101/2020.03.03.975219v4 Reference flow software: https://github.com/langmead-lab/reference_flow Software for reference flow experiments: https://github.com/langmead-lab/reference_flow-experiments An archived version of the software for experiments: https://doi.org/10.5281/zenodo.4287729
DOI: 10.5281/zenodo.4181358
title: Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network

description: The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep learning technology, the automated identification of this process can greatly accelerate the contribution to relevant knowledge. Based on the transfer learning technique, we used a convolutional neural network to distinguish the microscopic images of Embryonic stem cells (ESCs) from differentiated trophoblasts -like cells (TBL). To tackle the problem of insufficient training data, the strategies of data augmentation were used. The results showed that the convolutional neural network could successfully recognize trophoblast cells and stem cells automatically, but could not distinguish TBL from the immortalized trophoblast cell lines in vitro (JEG-3 and HTR8-SVneo). We compare the recognition effect of the commonly used convolutional neural network, including DenseNet, VGG16, VGG19, InceptionV3, and Xception. This study extends the deep learning technique to trophoblast cell phenotype classification and paves the way for automatic bright-field microscopic image analysis of trophoblast cells in the future.
description: The cell imaging was collected from the Academy of Medical Sciences of the Zhengzhou University Translational Medicine platform, Zhengzhou University. Random images were taken in an Olympus CKX53 inverted microscope with Integrated Phase Contrast (iPC) System and software focus indicatorVideo. Cells were plated at the indicated cell densities in 12-well plates (Nunc). We used a 10310 x objective with light transmission. Light intensity was set at 40%. Image files were saved in jpg format. The standard output of Olympus CKX53 images is 6000 4000 pixels in three channels (RGB). Each picture was then sliced to get images of 224 224 pixels. Data augmentation including rotations, cropping, and flipping on each image was used to improve accuracy and help to reduce overfitting and resulting in a significant increase in the number of images.
DOI: 10.5281/zenodo.4181357
title: Recognized trophoblast-like cells conversion from human embryonic stem cells by BMP4 based on convolutional neural network

description: The use of models of stem cell differentiation to trophoblastic cells provides an effective perspective for understanding the early molecular events in the establishment and maintenance of human pregnancy. In combination with the newly developed deep learning technology, the automated identification of this process can greatly accelerate the contribution to relevant knowledge. Based on the transfer learning technique, we used a convolutional neural network to distinguish the microscopic images of Embryonic stem cells (ESCs) from differentiated trophoblasts -like cells (TBL). To tackle the problem of insufficient training data, the strategies of data augmentation were used. The results showed that the convolutional neural network could successfully recognize trophoblast cells and stem cells automatically, but could not distinguish TBL from the immortalized trophoblast cell lines in vitro (JEG-3 and HTR8-SVneo). We compare the recognition effect of the commonly used convolutional neural network, including DenseNet, VGG16, VGG19, InceptionV3, and Xception. This study extends the deep learning technique to trophoblast cell phenotype classification and paves the way for automatic bright-field microscopic image analysis of trophoblast cells in the future.
description: The cell imaging was collected from the Academy of Medical Sciences of the Zhengzhou University Translational Medicine platform, Zhengzhou University. Random images were taken in an Olympus CKX53 inverted microscope with Integrated Phase Contrast (iPC) System and software focus indicatorVideo. Cells were plated at the indicated cell densities in 12-well plates (Nunc). We used a 10310 x objective with light transmission. Light intensity was set at 40%. Image files were saved in jpg format. The standard output of Olympus CKX53 images is 6000 4000 pixels in three channels (RGB). Each picture was then sliced to get images of 224 224 pixels. Data augmentation including rotations, cropping, and flipping on each image was used to improve accuracy and help to reduce overfitting and resulting in a significant increase in the number of images.
DOI: 10.5281/zenodo.4285820
title: MTV and COSMOS results

description: Dataset from 3 volunteer subjects, including raw GRE magnitude data (subjects 1-3), as well as processed MTV data (subjects 1-3), COSMOS QSM susceptibility (subjects 1, 3), R2*, T1, PD, as well as derived chi_myelin and chi_iron maps.
DOI: 10.5281/zenodo.4285819
title: MTV and COSMOS results

description: Dataset from 3 volunteer subjects, including raw GRE magnitude data (subjects 1-3), as well as processed MTV data (subjects 1-3), COSMOS QSM susceptibility (subjects 1, 3), R2*, T1, PD, as well as derived chi_myelin and chi_iron maps.
DOI: 10.21227/9re6-gp36
title: MATLAB code to test SPIM approach for SS

description: MATLAB code for test spectrum sensing algorithm based on statistical processing of instantaneous magnitude (SPIM). The associated SCRIPTs allow: Generating different signals to check the method, FHSS, LFM, CW Pulse, etc. Plot the generated signal, the detection threshold and compare it with the ideal detection. Determine the errors for the different hypotheses based on SNR. Calculate errors in the determination of the amplitude and frequency for different SNRs. Evaluate the probability of detection with different threshold control values A and U. Compare the errors in detection for the SPIM method and the energy detector. Plot the values of the TPR FPR rates to calculate the ROC curve. Determines the MAD PDF of the instantaneous phase and the average instantaneous amplitude for comparison.
DOI: 10.21227/r5pd-1496
title: Coronavirus (COVID-19) Geo-tagged Tweets Dataset

description: This dataset contains IDs and sentiment scores of the geo-tagged tweets related to the COVID-19 pandemic. The tweets are captured by an on-going project deployed at https://live.rlamsal.com.np. The model monitors the real-time Twitter feed for coronavirus-related tweets using 90+ different keywords and hashtags that are commonly used while referencing the pandemic. Complying with Twitter's content redistribution policy, only the tweet IDs are shared. You can re-construct the dataset by hydrating these IDs. The tweet IDs in this dataset belong to the tweets tweeted providing an exact location.The paper associated with this dataset is available here: Design and analysis of a large-scale COVID-19 tweets dataset-------------------------------------Related: Coronavirus (COVID-19) Tweets Sentiment Trend (Global), Coronavirus (COVID-19) Tweets Dataset and Tweets Originating from India During COVID-19 Lockdowns 1, 2, 3, 4-------------------------------------Below is the quick overview of this dataset.— Dataset name: GeoCOV19Tweets Dataset— Number of tweets : 267,909 tweets— Coverage : Global— Language : English (EN)— Dataset usage terms : By using this dataset, you agree to (i) use the content of this dataset and the data generated from the content of this dataset for non-commercial research only, (ii) remain in compliance with Twitter's Developer Policy and (iii) cite the following paper:Lamsal, R. Design and analysis of a large-scale COVID-19 tweets dataset. Applied Intelligence (2020). https://doi.org/10.1007/s10489-020-02029-z— Primary dataset : Coronavirus (COVID-19) Tweets Dataset (COV19Tweets Dataset)— Dataset updates : Everyday— Active keywords and hashtags: keywords.tsvPlease visit this page (primary dataset) for details regarding the collection date and time (and other notes) of each CSV file present in this dataset.
DOI: 10.6084/m9.figshare.13278303.v1
title: Additional file 1 of Identification of main malaria vectors and their insecticide resistance profile in internally displaced and indigenous communities in Eastern Democratic Republic of the Congo (DRC)

description: Additional file 1: Excel sheet with details of kdr genotypes from mosquito samples collected via CDC light traps and Excel sheet with details on kdr genotypes from mosquitoes exposed to different insecticides in the WHO insecticide susceptibility bioassay.
DOI: 10.6084/m9.figshare.13278303
title: Additional file 1 of Identification of main malaria vectors and their insecticide resistance profile in internally displaced and indigenous communities in Eastern Democratic Republic of the Congo (DRC)

description: Additional file 1: Excel sheet with details of kdr genotypes from mosquito samples collected via CDC light traps and Excel sheet with details on kdr genotypes from mosquitoes exposed to different insecticides in the WHO insecticide susceptibility bioassay.
DOI: 10.6084/m9.figshare.13278375
title: Additional file 7 of Ancestral morphology of Ecdysozoa constrained by an early Cambrian stem group ecdysozoan

description: Additional file 7. Character matrix used in phylogenetic analyses in NEXUS format.
DOI: 10.6084/m9.figshare.13278375.v1
title: Additional file 7 of Ancestral morphology of Ecdysozoa constrained by an early Cambrian stem group ecdysozoan

description: Additional file 7. Character matrix used in phylogenetic analyses in NEXUS format.
DOI: 10.6084/m9.figshare.13278396
title: Additional file 2 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 2 Supplementary Table 1. Operon structure of the Actinoplanes sp. SE50/110 genome.
DOI: 10.6084/m9.figshare.13278402
title: Additional file 4 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 4 Supplementary Table 3. Transcription dynamics of Actinoplanes sp. SE50/110.
DOI: 10.6084/m9.figshare.13278402.v1
title: Additional file 4 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 4 Supplementary Table 3. Transcription dynamics of Actinoplanes sp. SE50/110.
DOI: 10.6084/m9.figshare.13278399.v1
title: Additional file 3 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 3 Supplementary Table 2. Transcription start sites (TSS) and promoter motifs in the Actinoplanes sp. SE50/110 genome.
DOI: 10.6084/m9.figshare.13278405
title: Additional file 5 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 5 Supplementary Table 4. Proteome dynamics of Actinoplanes sp. SE50/110.
DOI: 10.6084/m9.figshare.13278399
title: Additional file 3 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 3 Supplementary Table 2. Transcription start sites (TSS) and promoter motifs in the Actinoplanes sp. SE50/110 genome.
DOI: 10.6084/m9.figshare.13278396.v1
title: Additional file 2 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 2 Supplementary Table 1. Operon structure of the Actinoplanes sp. SE50/110 genome.
DOI: 10.6084/m9.figshare.13278410.v1
title: Additional file 5 of Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

description: Additional file 5: Table S2. OTUs with minimum VIP values responsible of the separation of samples by diet in the different groups of families (A: fast-growth suprafamily e5e6; B: intermediate-growth family c2c7; C: slow-growth suprafamily c4e4). VIP values represent the variable importance in projection after component 1.
DOI: 10.6084/m9.figshare.13278410
title: Additional file 5 of Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

description: Additional file 5: Table S2. OTUs with minimum VIP values responsible of the separation of samples by diet in the different groups of families (A: fast-growth suprafamily e5e6; B: intermediate-growth family c2c7; C: slow-growth suprafamily c4e4). VIP values represent the variable importance in projection after component 1.
DOI: 10.6084/m9.figshare.13278413
title: Additional file 6 of Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

description: Additional file 6: Table S3. Piphillin inferred KEGG pathways significantly different (Padj &lt; 0.05) between diet groups within each group of families (A: fast-growth suprafamily e5e6; B: intermediate-growth family c2c7; C: slow-growth suprafamily c4e4). log2fc indicates the log2 fold change. SEfc is the standard error of the calculated fold change. Red and green indicate up- and down-regulated pathways in D2 relative to D1, respectively.
DOI: 10.6084/m9.figshare.13278405.v1
title: Additional file 5 of The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase

description: Additional file 5 Supplementary Table 4. Proteome dynamics of Actinoplanes sp. SE50/110.
DOI: 10.6084/m9.figshare.13278413.v1
title: Additional file 6 of Genetic selection for growth drives differences in intestinal microbiota composition and parasite disease resistance in gilthead sea bream

description: Additional file 6: Table S3. Piphillin inferred KEGG pathways significantly different (Padj &lt; 0.05) between diet groups within each group of families (A: fast-growth suprafamily e5e6; B: intermediate-growth family c2c7; C: slow-growth suprafamily c4e4). log2fc indicates the log2 fold change. SEfc is the standard error of the calculated fold change. Red and green indicate up- and down-regulated pathways in D2 relative to D1, respectively.
DOI: 10.6084/m9.figshare.13278500.v1
title: Additional file 3 of RNA sequencing analysis reveals increased expression of interferon signaling genes and dysregulation of bone metabolism affecting pathways in the whole blood of patients with osteogenesis imperfecta

description: Additional file 3. Significantly dysregulated pathways in OI patients compared to healthy controls according to MinePath tool analysis. The table includes KEGG annotation with p-values &lt;0.05.
DOI: 10.6084/m9.figshare.13278494
title: Additional file 1 of RNA sequencing analysis reveals increased expression of interferon signaling genes and dysregulation of bone metabolism affecting pathways in the whole blood of patients with osteogenesis imperfecta

description: Additional file 1. Differently expressed genes with FDR&lt;0.05 in Osteogenesis Imperfecta patients compared to healthy controls.
DOI: 10.6084/m9.figshare.13278494.v1
title: Additional file 1 of RNA sequencing analysis reveals increased expression of interferon signaling genes and dysregulation of bone metabolism affecting pathways in the whole blood of patients with osteogenesis imperfecta

description: Additional file 1. Differently expressed genes with FDR&lt;0.05 in Osteogenesis Imperfecta patients compared to healthy controls.