cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28792304.v1
title: Bacterial membrane nanovesicles encapsulating prodrug assemblies combine chemical and immunological therapies for chronic bacterial infection management

description: Addressing the challenges of drug delivery to infection sites and modulating the immunosuppressive microenvironment are critical hurdles in treating chronic bacterial infections, characterized by intracellular bacteria and resilient biofilms. To overcome these barriers, we report a multifunctional nanomedicine (CpE@BMV). The prodrug conjugate (CpE), composed of two phenylboronic acid-modified ciprofloxacin (Cip-pba) molecules and ellagic acid (Ea), self-assembles due to its hydrophobic nature and π-π stacking. Bacterial membrane vesicles (BMVs) derived from Escherichia coli aid in CpE assembly and structural stabilization. Upon administration, pathogen-associated molecular patterns on CpE@BMV engage toll-like receptors on macrophages, activating these cells and enhancing their phagocytic response. Once internalized, CpE responds to elevated intracellular H₂O₂ levels, releasing Cip to eliminate intracellular bacteria. Additionally, Ea scavenges excess reactive oxygen species in inflamed macrophages and modulates the expression of inflammatory factors, preventing an exaggerated inflammatory response. The CpE@BMV formulation also penetrates biofilms, eliminating bacteria and releasing antigens. These antigens are transported to draining lymph nodes, where they induce dendritic cell maturation and trigger a robust T and B cell-mediated immune response, helping restore immune balance andcombat pathogens effectively. Therefore, our CpE@BMV provide an efficient strategy combining chemical and immunological therapies for chronic bacterial infection management.
DOI: 10.17632/bjyvny8g4x
title: Microbiome data

description: Count data from 16S metagenomic analysis.
DOI: 10.17632/jp96vj3frz
title: Nano-PCM Intelligence Classification Dataset for Energy Storage and Heat Transfer Analysis using Deep Neural Networks

description: Big data images for free convection flow and heat transfer of nano-encapsulated phase change (NePCM) suspensions. 
The dataset contains 3290 images of temperature fields for natural convection of nano-encapsulated phase change material suspensions in an enclosure. The provides codes try to classify the model parameters using the available images of temperature field. The codes have been provided in Phyton and Matlab languages. 

A sample of the dataset has one normalized temperature image between 0 and 1 as input and four output parameters consisting of Fi, TTF, Ra, and Ste. It has 3290 samples of data. This dataset is derived from the following dataset and article:
Edalatifar, Mohammad; Ghalambaz, Mohammad; Tavakoli, Mohammad Bagher; Setoudeh, Farbod (2023), “Deep Learning Approach to Natural Convection Heat Transfer in a Cavity: A Simulation Dataset for Nano-Encapsulated Phase Change Material Suspensions”, Mendeley Data, V1, doi: 10.17632/j5f6r56jnb.1

M. Edalatifar, M. B. Tavakoli and F. Setoudeh, "A Deep Learning Approach to Predict the Flow Field and Thermal ‎Patterns of Nonencapsulated Phase Change Materials ‎Suspensions in an Enclosure‎", Journal of Applied and Computational Mechanics 2022, Vol. 8, Issue 4, Pages 1270-1278, DOI: 10.22055/jacm.2021.37805.3092.
https://jacm.scu.ac.ir/article_16903.html

The related paper of this dataset has been published as:
Mohammad Ghalambaz, Mohammad Edalatifar, Sara Moradi Maryamnegari, and Mikhail Sheremet, An intelligence parameter classification approach for energy storage and natural convection and heat transfer of nano-encapsulated phase change material: Deep neural networks, Neural Computing and Applications, https://doi.org/10.1007/s00521-023-08708-5
DOI: 10.17632/n47kwdgx2v
title: MOLECULAR-CELL-D-24-01788
DOI: 10.6084/m9.figshare.104919.v2
title: Tranmission electron microscopy of a swa2 mutant in yeast

description: Electron micrographs of <em>swa2</em> mutant cells.
DOI: 10.6084/m9.figshare.28642319.v1
title: dataset for CKD prevalence in type 2 diabetes.xlsx

description: The study aimed to find the prevalence of chronic kidney disease in patients with type 2 diabetes mellitus in Basrah, Southern Iraq. It was a cross-sectional study that included 1779 patients with type 2 diabetes mellitus. Screening for CKD was done using the calculated glomerular filtration rate and urine albumin creatinine ratio.
DOI: 10.6084/m9.figshare.28821782.v1
title: IPD Maturity Model

description: Data and supporting documents for developing IPD Capability Maturity Model
DOI: 10.17632/5j2pgxgg73
title: Impact of ATF6 deletion on the embryonic brain development

description: Although the unfolded protein response (UPR) is involved in brain development, its roles remain unclear. Here, we report that deletion of ATF6, a major branch of the UPR, consisting of ATF6α and ATF6β, in the developing brain caused microcephaly and neonatal death in mice. Analysis of Atf6a/Atf6b double conditional knockout (dcKO) brains revealed diverse neuronal phenotypes such as reduced neurogenesis, increased cell death, impaired cortical layer formation, and axon projection defects. Furthermore, hypervasculature, glial defects, and neuroinflammation were observed in dcKO brains. Notably, hypervasculature was detected at E14.5, when endoplasmic reticulum (ER) stress was morphologically unclear, but the UPR was activated to a greater extent in dcKO brains. Expression profiles revealed reduced levels of molecular chaperones in the ER and enhanced levels of PERK- and IRE1-downstream molecules, including VEGFa, in dcKO brains. Administration of a chemical chaperone 4-phenylbutyric acid partly rescued phenotypes of dkKO mice, suggesting roles of ATF6 for improving proteostasis and for coordinating the UPR activation.
DOI: 10.6084/m9.figshare.7627304.v2
title: Zymo-Oxygen

description: Two-hour timecourse growth curve
DOI: 10.17632/kp5pyzk4b5
title: Turmeric Prices Data

description: The time series daily data on Turmeric Prices traded at National Commodities Derivatives Exchange India for the period 01 January 2005 to 31 December 2015. The data  were used to identify the relationship between spot and futures prices. The spot prices data set were classified into near month futures, mid month futures and far month futures based on the maturity date. The spot prices data set were classified into near month futures, mid month futures and far month futures based on the maturity date. The findings indicate no relationship between spot and near month, mid month, far month futures of turmeric during the study period. The first sheet gives the raw data on turmeric prices, the second sheet provides the data after removing duplication of same day prices along with logarithmic returns, the  third, fourth and the fifth sheet gives the data on near month futures , mid month futures and far month turmeric futures along with logarithmic returns. The findings indicate no relationship between spot and near month, mid month, far month futures of turmeric during the study period.
DOI: 10.25452/figshare.plus.22840730.v1
title: Phenotyping Data for Saccharomycotina Yeasts

description:   
<strong>Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Paradigms proposed to explain this variation either invoke trade-offs between performance efficiency and breadth or underlying intrinsic or extrinsic factors. We assembled genomic (1,154 yeast strains from 1,090 species), metabolic (quantitative measures of growth of 843 species in 24 conditions), and ecological (environmental ontology of 1,088 species) data from nearly all known species of the ancient fungal subphylum Saccharomycotina to examine niche breadth evolution. We found large interspecific differences in carbon breadth stem from intrinsic differences in genes encoding specific metabolic pathways but no evidence of trade-offs and a limited role of extrinsic ecological factors. These comprehensive data argue that intrinsic factors driving microbial niche breadth variation</strong>. 
<br>
Phenotypic data for 853 yeast strains in 24 conditions. These data include the raw optical density (OD) reads from the plate reader and the average growth rates quantified from those raw reads. These data were used throughout the manuscript.
<br>
The data is further described in the<strong> README_PhenotypingData.txt</strong>
DOI: 10.6084/m9.figshare.28934987.v1
title: Systematic functional screening of switchable aptamer probes - data sets

description: data sets of the manuscript "Systematic functional screening of switchable aptamer probes"
DOI: 10.6084/m9.figshare.7506896.v1
title: DEVELOPMENT OF PIGEON PEA INOCULATED WITH RHIZOBIUM ISOLATED FROM COWPEA TRAP HOST PLANTS

description: ABSTRACT Pigeon pea is an important protein source grown in several tropical and sub-tropical countries, and is considered a multi-purpose plant that is resistant to the conditions of the Brazilian Cerrado. Among the possible uses for cowpea, its use as a green manure, increasing soil nitrogen content through the association with diazotrophic bacteria, generically known as rhizobia, is noteworthy. The present work aimed to evaluate the efficiency of Rhizobium strains isolated from cowpea plants in the development of pigeon peas cultured in Red Latosol. The experiment was conducted in a greenhouse, using a completely randomized design with seven treatments and four replications. Treatments consisted of inoculation with four Rhizobium strains (MT8, MT15, MT16, and MT23) and one commercial inoculant comprising Bradyrhizobium spp. strains BR 2801 and BR 2003. There were two controls, one absolute (without inoculation or nitrogen fertilization) and the other with nitrogen fertilization. Each experimental plot consisted of an 8-dm3 vase containing three plants. Analyzed variables included plant height, SPAD index, number and dry weight of nodules, and shoot and root dry masses. Pigeon peas responded significantly to inoculation treatment, since all the plants inoculated with Rhizobium strains isolated from cowpea strains showed results similar to plants in the nitrogen control and commercial inoculant treatments. This demonstrates a favorable plant-bacteria interaction, which can be utilized as an alternative nitrogen source for pigeon peas.
DOI: 10.6084/m9.figshare.8248760.v1
title: Regan et al_2019_summary-data.xlsx

description: Data from Regan et al (2019) "Impacts of groundwater drainage on peatland subsidence and its ecological implications on an Atlantic raised bog"
DOI: 10.17632/39nrzzn8v6
title: Inclusive Total Factor Productivity based on Biennial Malmquist–Luenberger Productivity Index

description: We conceptualise and define inclusive economic development and propose a productivity perspective for evaluating inclusive economic development and construct the inclusive total factor productivity index on the most relevant components of inclusive economic development based on data envelopment analysis.
DOI: 10.17632/3n74zj2vzm
title: Identification of the viral and cellular microRNA interactomes during SARS-CoV-2 infection. Fossat et al

description: Supplemental Tables S1, S2, S3, S4, S5, S6, S7, S8 & S10
DOI: 10.17632/b9cfr6skh7
title: Dynamic_scheduling_dataset

description: It contains environmental factors, power and energy consumption, and charging and discharging state of VESS.
DOI: 10.17632/f63nhynzhx
title: AI used to diagnose and treat genetic diseases.

description: Step 2: Data Collection & Preprocessing

We will need genetic datasets such as:

• 1000 Genomes Project (for genetic variants)
• ClinVar (for pathogenic mutations)
• GTEx (for gene expression)
Python Code for Data Loading and Preprocessing
Generate a Synthetic Genetic Dataset

This dataset will include:
. Gene Mutations (Encoded as numerical values)
Expression Levels (Simulating gene expression data)
Mutation Type (Categorical: Missense, Nonsense, Frameshift)
Disease Labels (Binary classification: 0 = No Disease, 1 = Genetic Disease)
import pandas as pd
import numpy as np
# Set random seed for reproducibility
np.random.seed(42)






































# Generate
data
num_samples = 1000
gene_mutations = np.random.randint(0, 10, num_samples) # 10 different mutation types
expression_levels = np.random.uniform(0.1, 10.0, num_samples) # Simulated expression levels
mutation_types = np.random.choice(["Missense", "Nonsense", "Frameshift"], num_samples)
disease_labels = np.random.choice([0, 1], num_samples) # 0 = No Disease, 1 = Disease

# Create DataFrame
df = pd.DataFrame({
 "Gene_Mutation": gene_mutations,
 "Expression_Level": expression_levels,
 "Mutation_Type": mutation_types,
 "Disease_Label": disease_labels
})

# Save to CSV
df.to_csv("genetic_data.csv", index=False)

print("Synthetic genetic dataset saved as 'genetic_data.csv'.")

 Gene_Mutation Expression_Level Mutation_Type Disease_Label
0 6 2.634554 Missense 0
1 3 7.288346 Missense 1
2 7 5.970333 Frameshift 0
3 4 1.111905 Frameshift 1
4 6 9.195630 Missense 0

RangeIndex: 1000 entries, 0 to 999
Data columns (total 4 columns):
 # Column Non-Null Count Dtype 
--- ------ -------------- ----- 
 0 Gene_Mutation 1000 non-null int64 
 1 Expression_Level 1000 non-null float64
 2 Mutation_Type 1000 non-null object 
 3 Disease_Label 1000 non-null int64 
dtypes: float64(1), int64(2), object(1)
memory usage: 31.4+ KB
None
 Gene_Mutation Expression_Level Mutation_Type Disease_Label
0 6 2.634554 1 0
1 3 7.288346 1 1
2 7 5.970333 0 0
3 4 1.111905 0 1
4 6 9.195630 1 0

RangeIndex: 1000 entries, 0 to 999
Data columns (total 4 columns):
 # Column Non-Null Count Dtype 
--- ------ -------------- ----- 
 0 Gene_Mutation 1000 non-null int64 
 1 Expression_Level 1000 non-null float64
 2 Mutation_Type 1000 non-null int32 
 3 Disease_Label 1000 non-null int64
DOI: 10.6084/m9.figshare.13102670.v4
title: 12sDBAll_Sequences

description: 12S Gene part from MARE-MAGE database<br>https://mare-mage.weebly.com/
DOI: 10.17632/9nrm29bb3s
title: ENECO-PROCEEDİNGS OF ENERGY ECONOMİC RESEARCH CENTER

description: Periodical journal
DOI: 10.17632/myxg4rnsp9
title: olive oil production

description: these files contain actual measurement field data for olive oil production.
DOI: 10.4121/21648152.v1
title: Drone images over the Saigon river - Hyacinth & Plastic patches

description: The dataset is comprised of a total of 3,688 UAV images. All UAV images were taken over the Saigon river, Vietnam. UAV images were taken across the river channel, with a frequency of one to four flights per measurement day. Each flight consisted of two overpasses across the Saigon river, with a range of 41 to 65 images taken per flight. UAV surveys were carried at a constant elevation of approximately 10 m above the water level. The dataset is organized in subfolders by date and flight.
DOI: 10.6084/m9.figshare.22260316.v3
title: Table A-3 Summary of Nutrient Functions in HSD and EDS

description: An anti-inflammatory Mediterranean-style diet may provide some support for the chronic pain that is associated with HSD and EDS. Table A-3 introduces some of the nutrients that are found in an anti-inflammatory Mediterranean-style diet that may have importance for mitigating symptoms associated with the conditions.
DOI: 10.6084/m9.figshare.27960216.v1
title: Suplementary data.docx

description: The strategy presented in the current work is a proof-of-concept for future vaccine candidate design bypassing the need for an external toxoid coupling and for growing large amounts of pathogens to isolate lipopolysaccharides. If the candidate is confirmed to offer protection in an animal model of periodontosis, the design of future vaccines could be performed faster and more safely for many other pathogens, Select Agents included. Preparation for animal verification of the proposed periodontosis vaccine candidate is in progress.
DOI: 10.6084/m9.figshare.28539962.v1
title: Ellipsoid Simulation Dataset for 2D-to-3D Reconstruction

description: We share a part of the synthetic ellipsoid dataset used in the article "ONIX: An X-ray deep-learning tool for 3D reconstructions from sparse views" . The file ellipsoid_5to10.npy contains the projection data used for 3D reconstruction. It includes 1000 3D objects, each with 10 projections uniformly spaced between 0° and 180°. The 3D ground truth is available for the first 10 objects, which are used for validation.For more details about the dataset, please refer to our article. The full dataset is available from the corresponding author upon reasonable request.
DOI: 10.17632/yfxkc9nwgs
title: Data for: Seismic data of a rockslide: evaluation of noise levels, site effects, frequency content and identification of seismic phases

description: These dataset are raw SAC files from EPOB (from Instituto Geográfico Nacional, IGN) and POBL (from Laboratori d'Estudis Geofísics Eduard Fontserè, LEGEF-IEC) seismic stations containing the seismic signal of a rock slide recorded approximately 10km away.
DOI: 10.17632/zrddng9xpr
title: Thermal Image Dataset of Okra

description: The Okra, a fruit widely appreciated globally for its substantial seeds, holds significant importance at the international level. Its value transcends mere culinary appeal, positioning it as a valuable contributor to nutrition, agriculture, and environmental sustainability. The classification of harvested okra into over-matured and sufficiently matured pods post-harvesting brings forth numerous advantages, influencing both quality and culinary aspects. The field of quality inspection for edible items is rapidly evolving, and utilizing thermal images to assess okra maturity offers a non-invasive method for farmers and consumers to efficiently categorize it for various purposes. The FLIR E75 thermal imaging device was employed to capture thermal images of okra, with both the cameras and okra positioned at a distance greater than 0.5 meters. The image acquisition process maintained a controlled environment with a consistent room temperature. The data collection involved systematically selecting representative okra specimens, considering their maturity levels. Visual examination of the selected okra specimens was conducted, and samples were chosen for imaging to ensure accurate labeling and categorization. The collected dataset comprises 501 images, categorized into two main groups: over-matured okras and adequately matured okras. Since thermal images capture temperature distribution over the surface, FLIR-supplied software such as FLIR Thermal Studio and FLIR Research Studio can be utilized to determine the temperature of individual okra specimens. Various color palettes for representing thermal images are available, and the rainbow color palette is employed in this dataset. The FLIR software facilitates temperature measurement at specific points or selected regions, enabling temperature analysis. Moreover, computer vision techniques based on thermal imaging can be applied to these images, ensuring thorough scrutiny for accurate analysis, detection, and classification.
DOI: 10.6084/m9.figshare.25754823.v1
title: The stability of plant richness, composition, and cover responds nonlinearly to warming in a decade-long experiment

description: The raw data of all indicators under different warming treatments are given in the dataset, and the data provided can be directly used for mapping and analysis of variance.In every sheet, C represents control treatment; W1, W2, W3, W4 represents different level warming. The unit of species richness is 50cm×50cm. Mean indicates mean value, se means standard error.
DOI: 10.17632/79jjcbkxzt
title: A study of "One-carbon metabolism supports S-Adenosylmethionine and histone methylation to drive inflammatory macrophages". Weiwei Yu, Zhen Wang, Kailian Zhang et al.

description: Activated macrophages adapt their metabolic pathways to drive the pro-inflammatory phenotype, but less is known about the mechanistic basis for this process. Here we find that lipopolysaccharide (LPS) activates glycolysis offshoots pentose phosphate pathway (PPP), serine synthesis pathway (SSP), and one-carbon metabolism, whose synergism drives epigenetic reprogramming for the expression of interleukin-1β (IL-1β) but not tumor-necrosis factor-α in pro-inflammatory macrophages. In contrast to predominantly supporting nucleotide synthesis for rapidly dividing cells, one-carbon flux synergistically supported by glucose and serine metabolism integrate into methionine cycle through de novo ATP synthesis, and fuels the generation of S-adenosylmethionine (SAM) in pro-inflammatory macrophages. Impairments of these metabolic pathways or depletions of exogenous amino acids that feed SAM generation lead to anti-inflammatory outcomes, implicating SAM as a key immunometaboite during LPS-induced inflammation. Mechanistically, SAM generation and its controlled SAM/SAH ratio coordinately support histone H3 lysine 36 trimethylation (H3K36me3) to promote IL-1β production in response to LPS. We therefore identify a synergistic effect of glucose and amino acids metabolism on orchestrating SAM availability that is intimately link to the chromatin state for LPS-induced inflammation. The datasets mainly includes all the Western Blotting images used in the article. The ".jpg" files are named after figure identifier plus name of bloting protein, for example, "1A Actin.jpg" stands for the protein Actin of Figure 1A. The ".pptx" files consist of the labeled figure panel and some small descriptions. Every figure has a independent zip file, which involved all the ".jpg" and ".pptx" files associated with the corresponding figure in the article.