cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7482145
title: Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"

description: This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks. The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset. The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points. The annotations are given in two types of files: "TRL.csv" files: contain the image names and corresponding TRL values (mm). "pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.
description: This research was supported by the Israeli Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the Root of the Matter—The Root Zone Knowledge Center for Leveraging Modern Agriculture (grant number 16-38-0044) of the Israeli Ministry of Agriculture and Rural Development, and the European Union's Horizon 2020 Research and Innovation Program (grant agreement no. 777222) (ATTRACT project "NextMR-IAA"). Partial support was provided by the Ben-Gurion University of the Negev W. Gunther Plaut Chair of Manufacturing Engineering.
DOI: 10.5281/zenodo.7482146
title: Dataset for "Root Length Estimation: Automated Minirhizotron Image Analysis with Convolutional Networks without Segmentation"

description: This data contains 4015 root images, splitted into 4 datasets, acquired using two minirhizotron (MR) system types - manual (Dataset 1 & Dataset 4) and automated (Dataset 2 & Dataset 3). It includes four crop species (corn, pepper, melon, and tomato) grown under various abiotic stresses. The data was acquired by researchers from Ben-Gurion University of the Negev, Beer Sheva, Israel, and used for research of automated TRL estimation with Convolutional Neural Networks. The annotations were conducted manually using the Rootfly software (Wells and Birchfield, Clemson University, South Carolina, USA), and data were transformed as CSV formats. In this software, the annotator must draw a root by marking points along the selected root. These points usually correspond to the coordinates at the start and the end of the root, and curving points along the root. These points are then connected in a line, the length of which reflects the real length of the selected root. The annotations has been done for all roots within an image, and for all images in the provided dataset. The provided annotations include the total root length (TRL) per image (mm) and the coordinates of annotated points. The annotations are given in two types of files: "TRL.csv" files: contain the image names and corresponding TRL values (mm). "pointsOutput.csv" files: contain the annotated image names and the coordinates of the points of the roots in the image (if the image contains roots) in the form of x1, y1, x2, y2, x3, y3, etc. It the image doesn't have roots, the file contains only its name.
description: This research was supported by the Israeli Ministry of Agriculture and Rural Development (Eugene Kandel Knowledge Centers) as part of the Root of the Matter—The Root Zone Knowledge Center for Leveraging Modern Agriculture (grant number 16-38-0044) of the Israeli Ministry of Agriculture and Rural Development, and the European Union's Horizon 2020 Research and Innovation Program (grant agreement no. 777222) (ATTRACT project "NextMR-IAA"). Partial support was provided by the Ben-Gurion University of the Negev W. Gunther Plaut Chair of Manufacturing Engineering.
DOI: 10.6084/m9.figshare.16826463
title: Quantum mechanical, virtual screening, molecular docking, molecular dynamics, ADME and antimicrobial activity studies of some new indole-hydrazone derivatives as potent agents against <i>E. faecalis</i>

description: In this study, a new series of indole-5-carbaldehyde hydrazone derivative compounds were designed, synthesized, and their antimicrobial activities were determined by the microdilution method, and the <i>in vitro</i> cytotoxic effects on Beas-2b cell lines were investigated by MTT assay. When the activity results were examined, <b>5i12</b> showed promising activity against <i>E. faecalis</i> with MIC: 2 µg/mL compared to ampicillin, gentamicin, and vancomycin, although the antimicrobial activities of the indole derivatives were generally weaker than those of the standard drugs. Compounds showed no cytotoxic activity on the A549, MCF-7, and Beas-2b cell lines. Molecular docking studies were performed on 15 different proteins to understand the mechanism of <b>5i12</b>'s good antimicrobial action against <i>E. faecalis</i>, and it was concluded that the compounds interacted with FabH, not enough other protein structures. Molecular dynamics simulations were performed to investigate the protein–ligand stability of the most active compound against <i>E. faecalis</i>. The RMSD value of <b>5i12</b> varied between 0.02 and 0.16 nm during the MD simulation. The apoprotein peaked at 0.55 nm at the beginning of the simulation and stabilized below 0.5 nm. The theoretical ADME profiles of all compounds were calculated and found to comply with Lipinski and other limiting rules. In addition, some theoretical quantum parameters (HOMO-LUMO) of compounds, and both MEP analysis and geometric optimization analysis for <b>5i12</b> were calculated using the 6-311 G (d,p) base set and DFT/B3LYP theory, and the results were visualized. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.14333066
title: Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT

description: Reverse transcriptase is the most therapeutic target for the discovery of novel, potent, and non-toxic new anti-retroviral drugs. In the present work, various docking software such as Sybyl Surflex-Dock, OpenEye FRED, and Hermes GOLD were evaluated for their efficiency to reproduce known cognate inhibitors' conformations. Three metrics were used and compared to assess the performance of the applied scoring functions, i.e. enrichment factor, receiver operating characteristic (ROC) curves, and Bedroc analysis. Twelve different scoring functions of three softwares were used to assess their ability to rank the cognate ligand within the active site of its proteins. The extensive virtual screening task was performed on eight crystal structures, and the performance of docking and scoring was assessed by their ability to efficiently detect known active compounds enriched in the top-ranked of the list among a randomly selected dataset of the ten thousand compounds of the NCI database. The effectiveness of post-docking relaxation in Surflex was also evaluated. The top 20, 50, and 100 compounds were selected based on consensus scoring functions from all 48 proteins with different ligand complexes. Further, the shortlisted leads were subjected to ADMET <i>via</i> using Discovery Studio. The results further implicate the importance of various statistical tools that should be followed before large-scale virtual screening for the drug discovery process. <i>In silico</i> results demonstrating the experiment was successful. The study of the research covers the combinatorial <i>in silico</i> techniques such as benchmarking of the softwares and scoring functions, statistical tools applied for screening and different conformations of HIV-RT crystal structures for virtual screening with rigid and flexible molecular docking and molecular dynamics simulation approach. This study reveals a clear roadmap to identify novel scaffolds against HIV-RT for antiretroviral therapy, thus providing the remedial solutions of HIV related infections and other diseases caused by malfunctioning of the target protein. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.14333066.v1
title: Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT

description: Reverse transcriptase is the most therapeutic target for the discovery of novel, potent, and non-toxic new anti-retroviral drugs. In the present work, various docking software such as Sybyl Surflex-Dock, OpenEye FRED, and Hermes GOLD were evaluated for their efficiency to reproduce known cognate inhibitors' conformations. Three metrics were used and compared to assess the performance of the applied scoring functions, i.e. enrichment factor, receiver operating characteristic (ROC) curves, and Bedroc analysis. Twelve different scoring functions of three softwares were used to assess their ability to rank the cognate ligand within the active site of its proteins. The extensive virtual screening task was performed on eight crystal structures, and the performance of docking and scoring was assessed by their ability to efficiently detect known active compounds enriched in the top-ranked of the list among a randomly selected dataset of the ten thousand compounds of the NCI database. The effectiveness of post-docking relaxation in Surflex was also evaluated. The top 20, 50, and 100 compounds were selected based on consensus scoring functions from all 48 proteins with different ligand complexes. Further, the shortlisted leads were subjected to ADMET <i>via</i> using Discovery Studio. The results further implicate the importance of various statistical tools that should be followed before large-scale virtual screening for the drug discovery process. <i>In silico</i> results demonstrating the experiment was successful. The study of the research covers the combinatorial <i>in silico</i> techniques such as benchmarking of the softwares and scoring functions, statistical tools applied for screening and different conformations of HIV-RT crystal structures for virtual screening with rigid and flexible molecular docking and molecular dynamics simulation approach. This study reveals a clear roadmap to identify novel scaffolds against HIV-RT for antiretroviral therapy, thus providing the remedial solutions of HIV related infections and other diseases caused by malfunctioning of the target protein. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.14368806
title: <i>In</i><i>silico</i> inquest reveals the efficacy of Cannabis in the treatment of post-Covid-19 related neurodegeneration

description: Coronavirus (SARS-CoV-2), the causative agent of the Covid-19 pandemic has proved itself as the deadliest pathogen. A major portion of the population has become susceptible to this strain. Scientists are pushing their limits to formulate a vaccine against Covid-19 with the least side effects. Although the recent discoveries of vaccines have shown some relief from the covid infection rate, however, physical fatigue, mental abnormalities, inflammation and other multiple organ damages are arising as post-Covid symptoms. The long-term effects of these symptoms are massive. Patients with such symptoms are known as long-haulers and treatment strategy against this condition is still unknown. In this study, we tried to explore a strategy to deal with the post-Covid symptoms. We targeted three human proteins namely ACE2, Interleukin-6, Transmembrane serine protease and NRP1 which are already reported to be damaged via Covid-19 proteins and upregulated in the post-Covid stage. Our target plant in this study is <i>Cannabis</i> (popularly known as ‘Ganja’ in India). The molecular docking and simulation studies revealed that Cannabidiol (CBD) and Cannabivarin (CVN) obtained from <i>Cannabis</i> can bind to post-Covid symptoms related central nervous system (CNS) proteins and downregulate them which can be beneficial in post-covid symptoms treatment strategy. Thus we propose <i>Cannabis</i> as an important therapeutic plant against post-Covid symptoms. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.14368806.v2
title: <i>In</i><i>silico</i> inquest reveals the efficacy of Cannabis in the treatment of post-Covid-19 related neurodegeneration

description: Coronavirus (SARS-CoV-2), the causative agent of the Covid-19 pandemic has proved itself as the deadliest pathogen. A major portion of the population has become susceptible to this strain. Scientists are pushing their limits to formulate a vaccine against Covid-19 with the least side effects. Although the recent discoveries of vaccines have shown some relief from the covid infection rate, however, physical fatigue, mental abnormalities, inflammation and other multiple organ damages are arising as post-Covid symptoms. The long-term effects of these symptoms are massive. Patients with such symptoms are known as long-haulers and treatment strategy against this condition is still unknown. In this study, we tried to explore a strategy to deal with the post-Covid symptoms. We targeted three human proteins namely ACE2, Interleukin-6, Transmembrane serine protease and NRP1 which are already reported to be damaged via Covid-19 proteins and upregulated in the post-Covid stage. Our target plant in this study is <i>Cannabis</i> (popularly known as ‘Ganja’ in India). The molecular docking and simulation studies revealed that Cannabidiol (CBD) and Cannabivarin (CVN) obtained from <i>Cannabis</i> can bind to post-Covid symptoms related central nervous system (CNS) proteins and downregulate them which can be beneficial in post-covid symptoms treatment strategy. Thus we propose <i>Cannabis</i> as an important therapeutic plant against post-Covid symptoms. Communicated by Ramaswamy H. Sarma
DOI: 10.6084/m9.figshare.14368806.v1
title: <i>In</i><i>silico</i> inquest reveals the efficacy of Cannabis in the treatment of post-Covid-19 related neurodegeneration

description: Coronavirus (SARS-CoV-2), the causative agent of the Covid-19 pandemic has proved itself as the deadliest pathogen. A major portion of the population has become susceptible to this strain. Scientists are pushing their limits to formulate a vaccine against Covid-19 with the least side effects. Although the recent discoveries of vaccines have shown some relief from the covid infection rate, however, physical fatigue, mental abnormalities, inflammation and other multiple organ damages are arising as post-Covid symptoms. The long-term effects of these symptoms are massive. Patients with such symptoms are known as long-haulers and treatment strategy against this condition is still unknown. In this study, we tried to explore a strategy to deal with the post-Covid symptoms. We targeted three human proteins namely ACE2, Interleukin-6, Transmembrane serine protease and NRP1 which are already reported to be damaged via Covid-19 proteins and upregulated in the post-Covid stage. Our target plant in this study is <i>Cannabis</i> (popularly known as ‘Ganja’ in India). The molecular docking and simulation studies revealed that Cannabidiol (CBD) and Cannabivarin (CVN) obtained from <i>Cannabis</i> can bind to post-Covid symptoms related central nervous system (CNS) proteins and downregulate them which can be beneficial in post-covid symptoms treatment strategy. Thus we propose <i>Cannabis</i> as an important therapeutic plant against post-Covid symptoms. Communicated by Ramaswamy H. Sarma
DOI: 10.21227/5bcz-rb09
title: Extractive Ground-truth Summary dataset (PORTRAIT)

description: This dataset is a disaster tweet summarization dataset which contains 5 different disaster tweets along with their ground truth summaries.
DOI: 10.6084/m9.figshare.21780583.v1
title: Supplementary data from Temperature modifies trait-mediated infection outcomes in a <i>Daphnia</i>–fungal parasite system

description: Supplementary data from Temperature modifies trait-mediated infection outcomes in a Daphnia-fungal parasite system
DOI: 10.6084/m9.figshare.21780583
title: Supplementary data from Temperature modifies trait-mediated infection outcomes in a <i>Daphnia</i>–fungal parasite system

description: Supplementary data from Temperature modifies trait-mediated infection outcomes in a Daphnia-fungal parasite system
DOI: 10.6084/m9.figshare.21762584
title: Count matrix and meta-data table [Hypertension T cells, icell8]

description: Adaptive immune response mediated by T cells plays a vital role in the initiation and maintenance of blood pressure (BP). Memory T cells, antigen-specific T cells, could response specific towards repeated hypertensive stimuli. Though the roles of memory T cells in animal models are well studied, the maintainance and functions of them in hypertensive patients are poorly understood. Here, we focused on the circulating memory T cells of hypertensive patients. By using single cell RNA sequencing technology, subsets of memory T cells were identified. DEGs and functional pathways were explored for the related biological function in each memory T populations. Our data suggest CD8 TEMs as well as the marker genes could be preventive targets for patients with hypertensive cardiovascular disease. <br> Dataset description: This dataset covers five hypertension samples (H1-H5) and five normal samples (N1-N5). It contains two files: 1. scmatrix.tsv.gz: count matrix of all cells. 2. scmetadata10.txt.gz: Meta-data annotation of each cell. <br>
DOI: 10.6084/m9.figshare.19811854.v3
title: Global_1981-2021_4km_Improved_VHI_Index_and_Best_Contribution_Parameter

description: A global high-spatial-resolution annual improved VHI raster dataset for 1981-2021 were developed by combining meteorological, vegetation, and soil moisture information, , which significantly improved vegetation drought detection efficiency. It has a spatial resolution of 4km, a temporal resolution of 1-year. All data are in Geotiff format.Our drought datasets serve as a unique resource providing solutions to a variety of research communities including meteorology, geography, natural hazards and agricultural production study. The developed annual VHIopt dataset is free, open and persistent publicly available from this study.
DOI: 10.6084/m9.figshare.17000071.v4
title: Bankuptcy

description: This dataset includes stata sintax: Sintaxis Bankrupty luck July 2022.docx stata data: Bankrupty luck July 202.dta
DOI: 10.6084/m9.figshare.17000071.v5
title: Bankuptcy

description: This dataset includes stata sintax: Sintax of November 22 version.docx stata data: FILING BBDD2.dta
DOI: 10.21227/jwy8-wg06
title: Dataset for Evaluating Glyph Design Sampled from Quantum Spins Simulations

description: This dataport will be useful to those interested in visual design for complex physics phenomena. We have included two quantum physics data sample datasets and our empirical study results. (1) evaluation results from two experiments to empirically validate that separable bivariate pairs of large-magnitude-range vector magnitude representations are more efficient than integral pairs. (2) quantum physics simulation data used in the first experiment with twenty participants. The simulation data contains more than 10K vector spins, and the samples contain about 445-550 spins. In the study, participants performed three local tasks requiring reading no more than two glyphs. These quantum spins are large in magnitude range and thus were shown to participants (physicists) as a series of separable bivariate pairs. These spin vector studies supported that separable-pair visualization led to the most accurate answers and the shortest task execution time, while integral ones were among the least accurate;   unless a redundant categorical color feature was added.} (3) quantum simulation data from the second study when we scale up the search space and when participants must look at the entire scene of hundreds of vectors to get an answer.We measured if more separable bivariate pairs can help viewers obtain scene structures first. Eighteen participants used three separable pairs in three global tasks: find a specific target in $20$ seconds, locate the maximum magnitude in 20 seconds, and estimate the total number of regions of interests (here vector exponents) within 2 seconds. Our results revealed that the higher the separability, the higher the accuracy. We believe that the reason was that the separable glyph pairs introduced emergent global (and preattentive) scene features for viewers to manage complexity.Associated source code, training documents, participants' results collected, statistical methods, and results at https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580}{$https://osf.io/4xcf5/?view_only=94123139df9c4ac984a1e0df811cd580$ for reproducible research.
DOI: 10.34740/kaggle/dsv/4773136
title: YouTube Daily
DOI: 10.34740/kaggle/dsv/4773210
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.21780656
title: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document

description: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document
DOI: 10.6084/m9.figshare.21780656.v1
title: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document

description: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document
DOI: 10.6084/m9.figshare.21780641
title: Fire and water: fire impacts on physicochemical properties of freshwater ecosystems

description: Raw data for "Fire and water: fire impacts on physicochemical properties of freshwater ecosystems"
DOI: 10.6084/m9.figshare.21780641.v1
title: Fire and water: fire impacts on physicochemical properties of freshwater ecosystems

description: Raw data for "Fire and water: fire impacts on physicochemical properties of freshwater ecosystems"
DOI: 10.6084/m9.figshare.21780671
title: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study: Vignette Suite and Screenshots

description: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study: Vignette Suite and Screenshots
DOI: 10.6084/m9.figshare.21221006.v2
title: Evaluating the Accuracy of a Novel Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study : results document

description: Evaluating the Accuracy of a Novel Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document
DOI: 10.34740/kaggle/dsv/4773339
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.21221006.v3
title: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study : results document

description: Evaluating the Accuracy of a New Artificial Intelligence Based Symptom Checker: A Clinical Vignettes Study - results document