cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.12556220
title: Dataset: Enlight Renewable Energy Ltd (ENLT) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556221
title: Dataset: Enlight Renewable Energy Ltd (ENLT) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556222
title: Dataset: Enlivex Therapeutics Ltd. (ENLV) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556223
title: Dataset: Enlivex Therapeutics Ltd. (ENLV) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556224
title: Dataset: Enphase Energy, Inc. (ENPH) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556225
title: Dataset: Enphase Energy, Inc. (ENPH) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556226
title: Dataset: Ensysce Biosciences, Inc. (ENSC) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556227
title: Dataset: Ensysce Biosciences, Inc. (ENSC) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556228
title: Dataset: The Ensign Group, Inc. (ENSG) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556229
title: Dataset: The Ensign Group, Inc. (ENSG) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556230
title: Dataset: Enanta Pharmaceuticals, Inc. (ENTA) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556231
title: Dataset: Enanta Pharmaceuticals, Inc. (ENTA) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556234
title: Dataset: Entegris, Inc. (ENTG) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556235
title: Dataset: Entegris, Inc. (ENTG) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556236
title: Dataset: Entero Therapeutics, Inc. (ENTO) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556237
title: Dataset: Entero Therapeutics, Inc. (ENTO) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556238
title: Dataset: ERShares Entrepreneur ETF (ENTR) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556239
title: Dataset: ERShares Entrepreneur ETF (ENTR) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556240
title: Dataset: Entera Bio Ltd. (ENTX) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556241
title: Dataset: Entera Bio Ltd. (ENTX) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556242
title: Dataset: Enveric Biosciences, Inc. (ENVB) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.5281/zenodo.12556243
title: Dataset: Enveric Biosciences, Inc. (ENVB) Stock Performance

description: This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
DOI: 10.15468/dl.w3zx3j
title: Occurrence Download

description: A dataset containing 11924068 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 11924068 records from 2210 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000846-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ue7wt5
title: Occurrence Download

description: A dataset containing 89 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-82.98023 -16.79058,-65.43143 -16.79058,-65.43143 2.38195,-82.98023 2.38195,-82.98023 -16.79058))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "InstitutionCode is one of (fmnh, rom, cas, mcz, ummz, uf, ku, usnm)",
 "TaxonKey is Crenicichla proteus Cope, 1872"
 ]
}

 The dataset includes 89 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061006-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dd3swt
title: Occurrence Download

description: A dataset containing 65761308 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is iNaturalist Research-grade Observations"
 ]
}

 The dataset includes 65761308 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0061395-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jngnaz
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ceratomantis saussurii Wood-Mason, 1876"
 ]
}

 The dataset includes 42 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0088228-230530130749713/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2tajq
title: Occurrence Download

description: A dataset containing 472444286 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is EOD – eBird Observation Dataset",
 "PublishingCountry is United States of America"
 ]
}

 The dataset includes 472444286 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0056506-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zgstte
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is (Table 1) Proportion of Toxoplasma gondii seropositive arctic foxes (Alopex lagopus) by age group and geographic area"
 ]
}

 The dataset includes 8 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024577-180508205500799/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tv9fsj
title: Occurrence Download

description: A dataset containing 625106754 species occurrences available in GBIF matching the query:
{ }

 The dataset includes 625106754 records from 13909 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018424-160910150852091/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.24080729.v1
title: Transfer learning-based fault detection in wind turbine blades using radar plots and deep learning models

description: Faults in wind turbine blades are considered a critical issue that can affect the safety and performance of wind turbines. The proposed research aimed to monitor wind turbine blades and identify fault conditions using a transfer learning approach. The study utilized one good and four faulty blade conditions: bend, hub-blade loose connection, erosion, and pitch angle twist. Vibration signals for each blade condition were collected and converted as radar plots that were fed and analyzed using pre-trained deep learning models including ResNet-50, AlexNet, VGG-16, and GoogleNet. Hyperparameters including optimizer, train-test split ratio, batch size, epochs, and learning rate were examined to determine the optimal configuration for each network. The study’s core findings indicate that ResNet-50 outperformed all other models, achieving an impressive accuracy rate of 99.00%. The other models achieved lower accuracy rates, with AlexNet achieving 96.70%, GoogleNet achieving 97.00%, and VGG-16 achieving 95.00%. These findings highlight the potential of using deep learning models for wind turbine monitoring and fault detection, which could significantly improve the efficiency and reliability of wind turbines.
DOI: 10.6084/m9.figshare.28861823.v1
title: Análise de arquivos científicos em Arquivologia

description: Este trabalho apresenta uma análise detalhada de três artigos científicos na área de Arquivologia, realizada como parte da disciplina de Tecnologia da Informação I, no curso de Arquivologia, na Universidade Federal da Paraíba. A análise foi estruturada em cinco componentes principais: mapa conceitual, análise lexicográfica, identificação de metadados descritivos e estruturais, e levantamento dos principais conceitos e referências. Os artigos analisados abordam temas complementares dentro da Arquivologia: o primeiro discute a prática docente em disciplinas comuns aos cursos de Arquivologia e Biblioteconomia; o segundo analisa a construção da política nacional de arquivos no Brasil; e o terceiro explora padrões de metadados para preservação digital de websites.
DOI: 10.6084/m9.figshare.14616627.v1
title: Supplementary Material for: Bacterial Predation on Cyanobacteria

description: Predatory bacteria gained interest in the last 20 years. Nevertheless, only a few species are well characterized. The endobiotic predator <i>Bdellovibrio bacteriovorus</i> invades its prey to consume it from the inside, whereas <i>Myxococcus xanthus</i> hunts as a whole group to overcome its prey. Both species were described to prey on cyanobacteria as well. This minireview summarizes the findings of the last 20 years of predatory bacteria of cyanobacteria and is supplemented by new findings from a screening experiment for bacterial predators of the model organism <i>Anabaena variabilis</i> PCC 7937. Known predatory bacteria of cyanobacteria belong to the phyla Proteobacteria, Bacteroidetes, and Firmicutes and follow different hunting strategies. The underlying mechanisms are in most cases not known in much detail. Isolates from the screening experiment were clustered after predation behaviour and analyzed with respect to their size. The effect of predation in high nitrate levels and the occurrence of nitrogen-fixing cells, called heterocysts, are addressed.
DOI: 10.6084/m9.figshare.24981225.v1
title: Supplementary Material for: A novel method using gadolinium-ethoxybenzyl diethylenetriamine pentaacetate acid-enhanced magnetic resonance imaging for predicting post-hepatectomy liver failure in hepatocellular carcinoma patients with a major portal vein tumor thrombus

description: Introduction: The usefulness of gadolinium-ethoxybenzyl diethylenetriamine pentaacetate acid-enhanced magnetic resonance imaging (EOB-MRI) in assessing the functional future remnant liver volume (fFRLV) to predict post-hepatectomy liver failure (PHLF) has been previously reported. Herein, we evaluated the efficacy of this technique in patients with hepatocellular carcinoma (HCC) with a major portal vein tumor thrombus (PVTT).
Methods: This study included 21 patients with PVTT in the ipsilateral first-order branch (Vp3) and 30 patients with PVTT in the main trunk/contralateral branch (Vp4). To evaluate fFRLV, the signal intensity (SI) of the remnant liver was determined on T1-weighted images, using both conventional and newly developed methods. The fFRLV was calculated using the SI of the remnant liver and muscle, remnant liver volume, and body surface area. Preoperative factors predicting PHLF (≥grade B) in HCC patients with Vp3/4 PVTT were evaluated.
Results: In the Vp3 group, we found fFRLV AUCs above 0.70 (AUC = 0.875, 0.750) using EOB-MRI results calculated using either the plot or whole method. None of the parameters in the Vp4 group had an AUC greater than 0.70.
Conclusion: The fFRLV calculated by EOB-MRI using the whole method can be as useful as the conventional method in predicting PHLF (≥grade B) for HCC patients with Vp3 PVTT.
DOI: 10.6084/m9.figshare.25411588.v1
title: Experimental Data for Article.xlsx

description: Data for an article on problem-solving as concurrent processing
DOI: 10.6084/m9.figshare.28751420.v2
title: TWITTER UKR-PL DATABASE

description: The dataset contains posts from the X/Tweeter platform. Extraction of unstructured data from X/Twitter has been performed using R scripts through the Application Programming Interface (API) v2 for Academic Research, which enabled researchers to retrieve posts from the entire X/Twitter archive. At the time the data was collected, access to the Twitter API for Academic Research was still possible, but was restricted after the company changed its policy in February 2023. The post selection criteria were (i) posts published in the Polish language, (ii) posts containing the keywords “Ukraińcy” (“Ukrainians”), “w Polsce” (“in Poland”), and (iii) posts that were published between 22 February 2022 (12:00 a.m. CET) and 31 December 2022 (11:59 p.m. CET). The time frame selected for this study is related to the date when the Russian Federation invaded Ukraine and the closing date of the first calendar year of the conflict. The X/Twitter users included in the data analysis were those who sent posts with the above-mentioned characteristics during the pre-defined period. Unverified users were also included, as one of the objectives of the study was to analyse message dissemination. A total of 55,035 posts (original content), reposts (forwarded content), and replies (discussions among users) were collected. These were then extracted, and imported into NodeXL software, which is a professional tool for analysing social media, used in many research projects.<br>Rows are posts. Columns are variables, described as:<br>Vertex1: Author of the postVertex2: Target of the interaction (the user whose tweet is being retweeted or replied to)Relationship: Type of interaction (Tweet, Retweet, Reply)Relationship Date: Time of post publicationTweet: Content of the postRetweet Count: Number of repostsFavorite Count: Number of likesReply Count: Number of replies to the postQuote Count: Number of times the post was quotedHashtags in Tweet: Hashtags included in the post (if any)URLs in Tweet: URLs included in the post (if any)Domains in Tweet: Referenced domains (if any)Mentions in Tweet: Mentions in the post (if any)Media in Tweet: Referenced media (if any)Media Type: Type of referenced media (if applicable)Twitter Page for Tweet: Link to the webpage with the source tweetTweet Date (UTC): Date of original tweet publicationTweet Image File: Avatar of the post's authorImported ID: ID of the post after importConversation ID: Thread ID to which the post is assignedIn Reply To Tweet ID: ID of the post to which this post is a reply (if applicable)Quoted Status ID: ID of the post that was quoted (if applicable)Retweet ID: ID of the repostAuthor ID: ID of the post's authorVertex1 Group: User group to which the post's author was assigned through clusteringVertex2 Group: User group to which the recipient of the post was assigned through clustering
DOI: 10.6084/m9.figshare.7609772.v1
title: TA porphyrin 2 in THF solution

description: ultrafast transient absorption spectroscopy of poprhyrin 2 in THF solution
DOI: 10.17632/chxvtg2fnn
title: Sustained release of a GLP-1 and FGF21 dual agonist from an injectable depot protects mice from obesity and hyperglycemia

description: All raw data associated with the article entitled "Sustained release of a GLP-1 and FGF21 dual agonist from an injectable depot protects mice from obesity and hyperglycemia." Separated into individual spreadsheets.
DOI: 10.6084/m9.figshare.24579109.v2
title: Blastocystis genomes

description: Blastocystis tortoise isolate (Blastoise) genome, Blastocystis ST1 JDR genome, Blastocystis ST1 NandII genome, Blastocystis ST3 DL genome, Blastocystis ST3 NMH genome, Blastocystis ST4 BT1 genome
DOI: 10.6084/m9.figshare.20212751.v1
title: Survey_of_Moz_HEis_Students_clean.csv

description: The dataset has the following fields: <br> Indicators for university service quality: D1 - D4: Indicator for dimension Teaching QA1 - QA5 - Quality of administration AF1 - Af4 - Physical environment AS1 - AS2 - Social environment QC1 - Q3 - Quality of Curricula <br> Indicators for student satisfaction: S1 - S4 <br> Indicator for student loyalty: FE1 - FE5 <br> Indicadores for University reputation: CA1 - CA11 - Academic Competence ATS1 - AST6 - Social atractiveness GR1 - GR 3 - Responsible management <br>
DOI: 10.6084/m9.figshare.28784762.v1
title: HADS MAGs from Long-read and Short-read Metagenomes

description: FASTA files for metagenome-assembled genomes (MAGs) reconstructed from short-read and long-read sequencing of the HADS metagenomes. They are the outputs of quite a preliminary effort, thus secondary attempts to recover genomes from the co-assemblies are most welcome (and very much encouraged). Please see the following URL for more information:https://merenlab.org/data/hads
DOI: 10.6084/m9.figshare.11889018.v3
title: Data File 4_Candidatus Afipia apatlaquensis-Description and Features.pdf

description: <i>Description of </i>“<i>Candidatus </i>Afipia apatlaquensis”<i> </i>sp. nov.,<i> and Genomic features</i><br><i></i><i></i>