cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21227/nfxa-sx32
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Please cite the related articles as:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, 2019. DOI: 10.1016/j.dib.2019.104514[2] H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. DOI: 10.1109/ACCESS.2019.2936536 Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.25345/c5396t
title: MassIVE MSV000084491 - Adhikari et al Proteomics data supporting "Development of a Covalent Inhibitor of Gut Bacterial Bile Salt Hydrolases"
title: Adhikari et al Proteomics data supporting "Development of a Covalent Inhibitor of Gut Bacterial Bile Salt Hydrolases"
DOI: 10.7910/dvn/bdx8zu/iyp5a7
title: Research data - F1000 final.tab

description: :unav
DOI: 10.21227/p51y-q886
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Please cite the related articles as:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, 2019. DOI: 10.1016/j.dib.2019.104514[2] H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. DOI: 10.1109/ACCESS.2019.2936536 Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.5281/zenodo.3517032
title: Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting

description: Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods. Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina). The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.
DOI: 10.5281/zenodo.3517033
title: Raw data supporting Identifying invertebrates from pitfall, flight interception traps and hand collecting

description: Raw data supporting identifying invertebrates from pitfall, flight interception traps and hand collecting: comparing metabarcoding with traditional methods. Two step PCRs were performed on each sample replicate using modified primers mICOIintF and jgHCO2198 followed by the Nextera XT index kit v2 Set A (Illumina). The pool was loaded onto an illumina MiSeq using a MiSeq Reagent Kit v2 500 cycle kit (Illumina), with 10% Phi-X to generate 250-bp paired-end reads.
DOI: 10.21227/ha0w-q745
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Please cite the related articles as:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, 2019. DOI: 10.1016/j.dib.2019.104514[2] H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. DOI: 10.1109/ACCESS.2019.2936536 Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.5281/zenodo.3516981
title: Matlab code for pH estimation

description: Matlab code of the ANN model including generate and execute codes and 11 cruises data building the model.
DOI: 10.5281/zenodo.3516982
title: Matlab code for pH estimation

description: Matlab code of the ANN model including generate and execute codes and 11 cruises data building the model.
DOI: 10.21227/bftr-k339
title: Date Fruit Dataset for Automated Harvesting and Visual Yield Estimation

description: The date fruit dataset was created to address the requirements of many applications in the pre-harvesting and harvesting stages. The two most important applications are automatic harvesting and visual yield estimation. The dataset is divided into two subsets and each of them is oriented into one of these two applications. The first dataset consists of 8079 images of more than 350 date bunches captured from 29 date palms. The date bunches belong to five date types: Naboot Saif, Khalas, Barhi, Meneifi, and Sullaj. The pictures of date bunches were captured using a color camera in six imaging sessions. The imaging sessions covered all date maturity stages: immature, Khalal, Rutab, and Tamar. The dataset is provided with a large degree of variations to reflect the challenges in natural environments and date fruit orchards. These variations in images include different angels and scales, different daylight conditions having poor illumination images, and date bunches covered by bags. The dataset is fully labeled according to type, maturity, and harvesting decision. We can use this dataset in many applications including fruit detection, segmentation, classification, maturity analysis, and automatic harvesting. The second dataset contains images, videos, and weight measurements to help in many applications such as yield estimation. In this dataset, we marked date bunches for selected palms, recorded 360° video for each palm, and measured their data (height, trunk circumference, total yield, number of bunches, and weight of bunches). We also captured images of each bunch from different angles before harvesting and on a graph paper after harvesting. Both datasets have been arranged with a coding scheme to simplify referring, linking, and facilitating future extensions. Please cite the related articles as:[1] H. Altaheri, M. Alsulaiman, G. Muhammad, S. U. Amin, M. Bencherif, and M. Mekhtiche, “Date Fruit Dataset for Intelligent Harvesting", Data in Brief, 2019.[2] H. Altaheri, M. Alsulaiman and G. Muhammad, "Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning", in IEEE Access, vol. 7, pp. 117115-117133, 2019. Update: Dataset-2 was uploaded. Date: 16/05/2019. The PDF files of dataset documentation and experiment papers were attached. Date: 23/10/2019.
DOI: 10.5281/zenodo.3516707
title: COSMO-CLM outputs Central Asia Russo et al. 2019

description: Monthly values of 2-meter temperature (T2M), total precipitation (TOT_PREC) and diurnal temperature range (DTR) derived from the simulations performed with the Regional Climate Model COSMO-CLM (Roeckel et al. 2008) for the CORDEX Central Asia Domain, presented in Russo et al. 2019 ( https://doi.org/10.5194/gmd-2019-22, 2019). The data have a spatial resolution of ~25 km. The data cover the period 1996-2005. For some realization, a file covering the period 2005-2015 is also present. All simulations are initialized on the 01.01.1991. The only exception are 4 simulations used to investigate the model internal variability, whose initial time has been shifted of +1,-1,+3 and -3 with respect to the default initial date.
DOI: 10.5281/zenodo.3516708
title: COSMO-CLM outputs Central Asia Russo et al. 2019

description: Monthly values of 2-meter temperature (T2M), total precipitation (TOT_PREC) and diurnal temperature range (DTR) derived from the simulations performed with the Regional Climate Model COSMO-CLM (Roeckel et al. 2008) for the CORDEX Central Asia Domain, presented in Russo et al. 2019 ( https://doi.org/10.5194/gmd-2019-22, 2019). The data have a spatial resolution of ~25 km. The data cover the period 1996-2005. For some realization, a file covering the period 2005-2015 is also present. All simulations are initialized on the 01.01.1991. The only exception are 4 simulations used to investigate the model internal variability, whose initial time has been shifted of +1,-1,+3 and -3 with respect to the default initial date.
DOI: 10.25386/genetics.10029161
title: Supplemental Material for Keener, Connelly and Greider 2019

description: File S1-cloning describes the cloning strategies used. TableS1-strains has the genotypes of all strains used. TableS2-plasmids contains a list of all plasmids used and a brief description of what it was used for. TableS3-primers contains a list of all oligonucleotides used in this study and a brief description of what it was used for. TableS4-reagents is the reagents table for this study. MRX.figures.2019-10-18-main.figs.pdf has the supplemental figures and their corresponding legends. Supplemental.References contains all literature cited in the supplement.<br>
DOI: 10.25386/genetics.10029161.v1
title: Supplemental Material for Laffafian and Tepass

description: Supplemental File S1, Reagents table, Supplemental Figures and Tables. <br>
DOI: 10.5281/zenodo.3332359
title: Anexo Catálogo Tareas de Evaluación.

description: [Plan TL/General/Base de Datos/Campañas de Evauación] Catálogo Campañas de Evaluación.
description: Funded by Plan de impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3332360
title: Anexo Catálogo Tareas de Evaluación.

description: [Plan TL/General/Base de Datos/Campañas de Evauación] Catálogo Campañas de Evaluación.
description: Funded by Plan de impulso de las Tecnologías del Lenguaje (Plan TL).
DOI: 10.5281/zenodo.3497353
title: Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis

description: This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241 1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort. Header for each summary file: Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score 2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples). Header for each summary file: chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)
DOI: 10.5281/zenodo.3497354
title: Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis

description: This dataset contains the results of 2 related analyses, described in "Bayesian network analysis of plasma microRNA sequencing data in patients with venous thrombosis" (European Heart Journal Supplements, OUP). Link to the article: https://www.hal.inserm.fr/inserm-02310241 1) In the directory "miRNAs_MARTHA_GWAS" : GWAS summary statistics for 162 circulating miRNAs in 344 VTE patients from the MARTHA cohort. Header for each summary file: Trait: miRNA id<br> chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> freq_A1: Frequency of reference allele<br> rsqr: Imputation quality defined by MACH<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)<br> z.score: Z-score 2) In the directory "meta_analysis": Random effect meta-analysis combining the results of our GWAS on the MARTHA cohort, and the results from a similar analysis conducted by Nikpay et al. (doi: 10.1093/cvr/cvz030). Summary statistics of 142 microRNAs, common to both datasets, were processed (and combine 1054 samples). Header for each summary file: chr: Chromosome<br> pos.hg19: Position of the variant in hg19/GRCh37 coordinates<br> SNP: rsid<br> A1: Reference allele on the forward strand<br> A2: Alternate allele on the forward strand<br> N: Sample size<br> Q: Cochran's heterogeneity statistic<br> Q.p: p-value of Cochran's Q<br> beta_A1: Estimated effect size (beta regression coefficient) of reference allele<br> se_A1: Estimated standard error of beta<br> p: p-value (significance of estimated beta)
DOI: 10.5281/zenodo.3516960
title: The Ubiquitin Ligase WWP1 Contributes to Shifts in Matrix Proteolytic Profiles and a Myocardial Ageing Phenotype with Diastolic Heart Failure

description: <strong><em>Aims</em></strong>. Ubiquitylation is a key event that regulates protein turnover, and induction of the ubiquitin ligase E3 WWP1 has been associated with age. Left ventricular hypertrophy (LVH) commonly occurs as a function of age and can cause heart failure with a preserved ejection fraction (EF; HFpEF). We hypothesized that overexpression (O/E) of WWP1 in the heart would cause LVH as well as functional and structural changes consistent with the ageing HFpEF phenotype. <strong><em>Methods and Results.</em></strong> Global WWP1 O/E was achieved in mice (n=11) and echocardiography (40 MHz) performed to measure LV mass, EF, Doppler velocities (early-E, late/atrial-A), myocardial relaxation (E’), and isovolumetric relaxation time (IVRT) at 4, 6, and 8 weeks. Age matched wild type animals (n=15) were included as referent controls. LV EF was identical (60+1% vs 60+1%, p&gt;0.90) with no difference in LV mass (67+3 vs 75+5, p&gt;0.25) at 4 weeks. LVH and diastolic dysfunction occurred with WWP1 O/E wherein LV mass increased by over two-fold, E/A fell (impaired passive filling), and E/E’ was lower and IVRT prolonged (impaired LV relaxation). Collagen percent area increased by over two-fold with WWP1 O/E and increased expression of determinants of fibrosis and growth (qPCR) were also concomitantly increased. <strong><em>Conclusion.</em></strong> Inducing WWP1 expression caused LVH with significant diastolic dysfunction, consistent with the HFpEF phenotype. Thus, targeting the WWP1 pathway may be a novel therapeutic target for this intractable form of HF associated with ageing
DOI: 10.5281/zenodo.3516959
title: The Ubiquitin Ligase WWP1 Contributes to Shifts in Matrix Proteolytic Profiles and a Myocardial Ageing Phenotype with Diastolic Heart Failure

description: <strong><em>Aims</em></strong>. Ubiquitylation is a key event that regulates protein turnover, and induction of the ubiquitin ligase E3 WWP1 has been associated with age. Left ventricular hypertrophy (LVH) commonly occurs as a function of age and can cause heart failure with a preserved ejection fraction (EF; HFpEF). We hypothesized that overexpression (O/E) of WWP1 in the heart would cause LVH as well as functional and structural changes consistent with the ageing HFpEF phenotype. <strong><em>Methods and Results.</em></strong> Global WWP1 O/E was achieved in mice (n=11) and echocardiography (40 MHz) performed to measure LV mass, EF, Doppler velocities (early-E, late/atrial-A), myocardial relaxation (E’), and isovolumetric relaxation time (IVRT) at 4, 6, and 8 weeks. Age matched wild type animals (n=15) were included as referent controls. LV EF was identical (60+1% vs 60+1%, p&gt;0.90) with no difference in LV mass (67+3 vs 75+5, p&gt;0.25) at 4 weeks. LVH and diastolic dysfunction occurred with WWP1 O/E wherein LV mass increased by over two-fold, E/A fell (impaired passive filling), and E/E’ was lower and IVRT prolonged (impaired LV relaxation). Collagen percent area increased by over two-fold with WWP1 O/E and increased expression of determinants of fibrosis and growth (qPCR) were also concomitantly increased. <strong><em>Conclusion.</em></strong> Inducing WWP1 expression caused LVH with significant diastolic dysfunction, consistent with the HFpEF phenotype. Thus, targeting the WWP1 pathway may be a novel therapeutic target for this intractable form of HF associated with ageing
DOI: 10.5281/zenodo.3516991
title: Performance Indicators of Photovoltaic Heat Pumps

description: Qualitative and quantitaive results included in the review 'Performance Indicators of Photovoltaic Heat Pumps' (DOI:10.1016/j.heliyon.2019.e02691) are given, together with the original data and the corresponding calculations. The files with the simulation info for the SISIFO simulation tool are also available.
DOI: 10.5281/zenodo.3516992
title: Performance Indicators of Photovoltaic Heat Pumps

description: Qualitative and quantitaive results included in the review 'Performance Indicators of Photovoltaic Heat Pumps' (DOI:10.1016/j.heliyon.2019.e02691) are given, together with the original data and the corresponding calculations. The files with the simulation info for the SISIFO simulation tool are also available.
DOI: 10.5281/zenodo.3516722
title: Characterization of p53 family homologs in evolutionary remote branches of Holozoa

description: Supplementary materials to article: Characterization of p53 family homologs in evolutionary remote branches of Holozoa The p53 family of transcription factors plays key roles in development, genome stability, senescence and tumor development, and p53 is the most important tumor suppressor protein in humans. Although intensively investigated for many years, its initial evolutionary history is not yet fully elucidated. Using bioinformatic and structure prediction methods on current databases containing newly-sequenced genomes and transcriptomes, we present a detailed characterization of p53 family homologs in remote members of the Holozoa group, in the unicellular clades Filasterea, Ichthyosporea and Corallochytrea. Moreover, we show that these newly characterized homologous sequences contain domains that can form structures with high similarity to the human p53 family DNA-binding domain, and some also show similarities to the oligomerization and SAM domains. The presence of these remote homologs demonstrates an ancient origin of the p53 protein family.