cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28764987.v1
title: Genomic islands and molecular mechanisms relating to drug-resistance in <i>Clostridioides</i> (<i>Clostridium</i>) <i>difficile</i> PCR ribotype 176

description: To analyse characteristics of <i>Clostridioides difficile</i> PCR ribotype 176 clinical isolates from Poland, the Czech Republic and Slovakia with regard to the differences in its epidemiology. Antimicrobial susceptibility testing and whole genome sequencing were performed on a selected group of 22 clonally related isolates as determined by multilocus variable-number tandem repeat analysis (n = 509). Heterologous expression and functional analysis of the newly identified methyltransferase were performed. Core genome multilocus sequence typing found 10-37 allele differences. All isolates were resistant to fluoroquinolones (<i>gyr</i>A_p. T82I), aminoglycosides with <i>aac</i>(6’)-<i>Ie-aph</i>(2'’)-<i>Ia</i> in six isolates. Erythromycin resistance was detected in 21/22 isolates and 15 were also resistant to clindamycin with <i>erm</i>B. Fourteen isolates were resistant to rifampicin with <i>rpo</i>B_p. R505 K or p. R505 K/H502N, and five to imipenem with <i>pbp</i>1_p. P491L and <i>pbp</i>3_p. N537 K. P<i>nim</i>B<sup>G</sup> together with <i>nim</i>B_p. L155I were detected in all isolates but only five were resistant to metronidazole on chocolate agar. The <i>cfr</i>E, <i>van</i>Z1 and <i>cat</i>-like genes were not associated with linezolid, teicoplanin and chloramphenicol resistance, respectively. The genome comparison identified six transposons carrying antimicrobial resistance genes. The <i>erm</i>B was carried by new Tn<i>7808</i>, Tn<i>6189</i> and Tn<i>6218</i>-like. The <i>aac</i>(6’)-<i>Ie-aph</i>(2'’)-<i>Ia</i> were carried by Tn<i>6218</i>-like and new Tn<i>7806</i> together with <i>cfr</i>E. New Tn<i>7807</i> carried a <i>cat</i>-like gene. Tn<i>6110</i> and new Tn<i>7806</i> contained an RlmN-type 23S rRNA methyltransferase, designated MrmA, associated with high-level macrolide resistance in isolates without <i>erm</i>B. Multidrug-resistant <i>C. difficile</i> PCR ribotype 176 isolates carry already described and unique transposons. A novel mechanism for erythromycin resistance in <i>C. difficile</i> was identified.
DOI: 10.17632/g9r987v9j4.1
title: Mouse oocyte maturation with/without translation inhibition

description: Translation inhibition leads to oocyte maturation disorders.
DOI: 10.6084/m9.figshare.28935875.v1
title: A Global Perspective on the Interconnectedness of Humanitarian Crises.pdf

description: Humanitarian crises are becoming increasingly complex and interconnected, driven by global factors such as climate change, conflict, economic disparity, and health emergencies. These crises often reinforce and exacerbate one another, creating a vicious cycle that affects vulnerable populations disproportionately. This paper explores the interconnectedness of humanitarian crises, examining their underlying drivers, manifestations, and cascading impacts.<br>Drawing on real-world examples and interdisciplinary research, the article highlights the urgent need for holistic and cooperative approaches to crisis response and mitigation.
DOI: 10.17632/d2jr97dk73.1
title: Transcriptome sequencing data

description: Transcriptome sequencing data
DOI: 10.6084/m9.figshare.28935875
title: A Global Perspective on the Interconnectedness of Humanitarian Crises.pdf

description: Humanitarian crises are becoming increasingly complex and interconnected, driven by global factors such as climate change, conflict, economic disparity, and health emergencies. These crises often reinforce and exacerbate one another, creating a vicious cycle that affects vulnerable populations disproportionately. This paper explores the interconnectedness of humanitarian crises, examining their underlying drivers, manifestations, and cascading impacts.<br>Drawing on real-world examples and interdisciplinary research, the article highlights the urgent need for holistic and cooperative approaches to crisis response and mitigation.
DOI: 10.17632/8gznt7wx8k
title: Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit

description: The dataset contains the research data supporting the manuscript titled "Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit". The data includes the following key components:
Evaluation Indicators Data: This comprises 22 evaluation indicators used to assess the susceptibility of Benggang. These indicators include rainfall erosivity, formation lithology, forest height, vegetation coverage, leaf area index, elevation, slope, modified soil adjusted vegetation index, normalized backscatter coefficients of the VV and VH channels, sand content, soil erosion modulus, clay content, erodibility, slope length and slope factor, topographic humidity index, brightness index, coloring index, slope aspect, plane curvature, profile curvature, and hydrodynamic index. The data is sourced from various remote sensing satellites (e.g., Sentinel-1, Sentinel-2), geological surveys, and other environmental datasets.
Slope Unit Division Data: The study area (Huichang County, Jiangxi Province) was divided into slope units using a multi-scale segmentation algorithm. This dataset includes the spatial distribution and characteristics of the slope units, which serve as the basic spatial units for the susceptibility assessment.
Machine Learning Model Results: The dataset contains the prediction results of three machine learning models (logistic regression, XGBoost, and random forest) applied to assess the susceptibility of Benggang. The results include the AUC values, accuracy statistics, and susceptibility zoning maps under different index contribution rates (100%, 95%, 90%, 85%, 80%, 75%, and 70%).
Key Findings: The data supports the key findings of the study, which indicate that the highest prediction accuracy for Benggang susceptibility is achieved when the index contribution rate is 90%. The random forest model demonstrates superior performance with the highest AUC value of 0.849. The dataset also highlights the spatial distribution of high - susceptibility areas in Huichang County, providing valuable insights for disaster prevention and spatial planning in the region.
DOI: 10.17632/8gznt7wx8k.1
title: Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit

description: The dataset contains the research data supporting the manuscript titled "Influence of index contribution rate and machine learning models on the susceptibility of Benggang under slope unit". The data includes the following key components:
Evaluation Indicators Data: This comprises 22 evaluation indicators used to assess the susceptibility of Benggang. These indicators include rainfall erosivity, formation lithology, forest height, vegetation coverage, leaf area index, elevation, slope, modified soil adjusted vegetation index, normalized backscatter coefficients of the VV and VH channels, sand content, soil erosion modulus, clay content, erodibility, slope length and slope factor, topographic humidity index, brightness index, coloring index, slope aspect, plane curvature, profile curvature, and hydrodynamic index. The data is sourced from various remote sensing satellites (e.g., Sentinel-1, Sentinel-2), geological surveys, and other environmental datasets.
Slope Unit Division Data: The study area (Huichang County, Jiangxi Province) was divided into slope units using a multi-scale segmentation algorithm. This dataset includes the spatial distribution and characteristics of the slope units, which serve as the basic spatial units for the susceptibility assessment.
Machine Learning Model Results: The dataset contains the prediction results of three machine learning models (logistic regression, XGBoost, and random forest) applied to assess the susceptibility of Benggang. The results include the AUC values, accuracy statistics, and susceptibility zoning maps under different index contribution rates (100%, 95%, 90%, 85%, 80%, 75%, and 70%).
Key Findings: The data supports the key findings of the study, which indicate that the highest prediction accuracy for Benggang susceptibility is achieved when the index contribution rate is 90%. The random forest model demonstrates superior performance with the highest AUC value of 0.849. The dataset also highlights the spatial distribution of high - susceptibility areas in Huichang County, providing valuable insights for disaster prevention and spatial planning in the region.
DOI: 10.17632/6nyxx5mgmt.1
title: CBSR

description: This dataset is designed for research in cross-spectral image super-resolution and includes scripts, model training code, and multi-band image data. It supports tasks such as data preprocessing, model evaluation, and spectral enhancement. The dataset is well-structured to facilitate reproducibility and is dynamically updated by the creator to reflect ongoing improvements in algorithms, data quality, and implementation. It is intended for use in remote sensing, hyperspectral image analysis, and machine learning applications related to image super-resolution.
DOI: 10.17632/ptsprmjdb5
title: From Scaffolding to Transfer: The Impact of AI-Powered Narrative Support on Children’s Narrative Skills

description: This dataset contains narrative performance and interaction data from a study examining the impact of AI-powered narrative support on children’s storytelling skills. Below are detailed descriptions of all variables:
1.Participant Information
Child_ID: Unique identifier.
Age_months: Child’s age in months.
Condition: Experimental group (Passive-Active Retelling, Iterative Storytelling with AI Feedback, Autonomous Storytelling with AI Feedback).
Gender: Child’s gender (boy, girl).
Child_AI_Interaction_Duration: Interaction time (minutes).

2. Narrative Scores
Narrative skills were assessed for three stories (Cat, Dog, Baby Bird) across three dimensions: story structure, story complexity, and internal state terms. Scores are labeled by story (Cat, Dog, Baby Bird).
[Story] A1–A20: Story structure subitem scores (e.g., CatA1, DogA1).
[Story] A Total: Total core of story structure.
[Story]B1–B4: Story complexity classification counts (B1=Attempt-Outcome, B2=Goal, B3=Goal-Attempt/Outcome, B4=Goal-Attempt-Outcome).
[Story]B Total: Total score of weighted story complexity (=B1*1 + B2*2 + B3*3 + B4*4).
[Story]C Total: Total score of internal state terms.

3.Subjective Ratings
Enjoyment: Perceived enjoyment.
Interest: Perceived interest.
DOI: 10.17632/ptsprmjdb5.1
title: From Scaffolding to Transfer: The Impact of AI-Powered Narrative Support on Children’s Narrative Skills

description: This dataset contains narrative performance and interaction data from a study examining the impact of AI-powered narrative support on children’s storytelling skills. Below are detailed descriptions of all variables:
1.Participant Information
Child_ID: Unique identifier.
Age_months: Child’s age in months.
Condition: Experimental group (Passive-Active Retelling, Iterative Storytelling with AI Feedback, Autonomous Storytelling with AI Feedback).
Gender: Child’s gender (boy, girl).
Child_AI_Interaction_Duration: Interaction time (minutes).

2. Narrative Scores
Narrative skills were assessed for three stories (Cat, Dog, Baby Bird) across three dimensions: story structure, story complexity, and internal state terms. Scores are labeled by story (Cat, Dog, Baby Bird).
[Story] A1–A20: Story structure subitem scores (e.g., CatA1, DogA1).
[Story] A Total: Total core of story structure.
[Story]B1–B4: Story complexity classification counts (B1=Attempt-Outcome, B2=Goal, B3=Goal-Attempt/Outcome, B4=Goal-Attempt-Outcome).
[Story]B Total: Total score of weighted story complexity (=B1*1 + B2*2 + B3*3 + B4*4).
[Story]C Total: Total score of internal state terms.

3.Subjective Ratings
Enjoyment: Perceived enjoyment.
Interest: Perceived interest.
DOI: 10.17632/6h5z3x6rkm.1
title: Quantitative analysis of macrophage metabolites after RMF stimulation

description: RAW 264.7 cells were polarized to M2 macrophages (Control group). M2 macrophages were incubated with MNMs (MNM group) and treated with rotating magnetic field (RMF) of 1 Hz for 15 min (RMF group). The cells were then collected and the metabolites were determined by liquid chromatography-mass spectrometry after 24 h.
DOI: 10.17632/kxksmyc89v.1
title: Stress granule assembly impairs macrophage efferocytosis

description: Original raw data for 'Stress granule assembly impairs macrophage efferocytosis'
DOI: 10.17632/kxksmyc89v.2
title: Stress granule assembly impairs macrophage efferocytosis

description: Original raw data for 'Stress granule assembly impairs macrophage efferocytosis'
DOI: 10.17632/kxksmyc89v.3
title: Stress granule assembly impairs macrophage efferocytosis

description: Original raw data for 'Stress granule assembly impairs macrophage efferocytosis'
DOI: 10.17632/kxksmyc89v.4
title: Stress granule assembly impairs macrophage efferocytosis

description: Original raw data for 'Stress granule assembly impairs macrophage efferocytosis'
DOI: 10.17632/kxksmyc89v.5
title: Stress granule assembly impairs macrophage efferocytosis

description: Original raw data for 'Stress granule assembly impairs macrophage efferocytosis'
DOI: 10.17632/mzv9t78mwm.1
title: i-CBM-I and deprssion

description: Major Depressive Disorder (MDD) is associated with multiple cognitive biases, including a tendency to interpret ambiguous information negatively and to recall negatively valenced autobiographical memories. Cognitive Bias Modification for Interpretation (CBM-I) is a promising, scalable intervention to alleviate depressive symptoms by targeting these biases, yet little is known about its broader cognitive effects beyond interpretation.. The present study tested the effects of a six-day imagery-based CBM-I (i-CBM-I) intervention on depressive symptoms , interpretation bias, and autobiographical memory valence in individuals with MDD. Fifty-six participants with DSM-5 MDD diagnoses were randomly assigned to either a positive i-CBM-I or a neutral-control group. Using a within-subject design with a one-week no-intervention baseline, we assessed depressive symptoms (using the Beck Depression Inventory-II and the Patient Health Questionnaire-9), interpretation bias (Ambiguous Scenarios Test for Depression), and autobiographical memory valence (Autobiographical Memory Test) at three time points: baseline, pre-intervention, post-intervention. Results revealed that only the positive i-CBM-I group showed significant reductions in depressive symptoms during the intervention week, alongside improvements in both interpretation bias and autobiographical memory valence. Planned contrasts confirmed that these effects were specific to the intervention week and did not occur during the control period. These findings support the Combined Cognitive Biases Hypothesis, suggesting that changing interpretation bias can indirectly affect how individuals recall their personal past. This suggests that self-administered, imagery-based interventions may not only reduce symptoms but also reshape cognitive processes at the core of depressive experiences. 
mem data reprsnt the memory coding (nutral, positive negative) of each particpant in each time point
CBM- reprsnt biasline domgrfices (age, gender) and use of imgery (SUIS, PITN, PITP) and the score in deprssion symptomes (BDI,PHQ) and interpretition bias (ASD) in each time point. 
In addition the R code can shwo you hwo the data was manged.
DOI: 10.17632/mzv9t78mwm
title: i-CBM-I and deprssion

description: Major Depressive Disorder (MDD) is associated with multiple cognitive biases, including a tendency to interpret ambiguous information negatively and to recall negatively valenced autobiographical memories. Cognitive Bias Modification for Interpretation (CBM-I) is a promising, scalable intervention to alleviate depressive symptoms by targeting these biases, yet little is known about its broader cognitive effects beyond interpretation.. The present study tested the effects of a six-day imagery-based CBM-I (i-CBM-I) intervention on depressive symptoms , interpretation bias, and autobiographical memory valence in individuals with MDD. Fifty-six participants with DSM-5 MDD diagnoses were randomly assigned to either a positive i-CBM-I or a neutral-control group. Using a within-subject design with a one-week no-intervention baseline, we assessed depressive symptoms (using the Beck Depression Inventory-II and the Patient Health Questionnaire-9), interpretation bias (Ambiguous Scenarios Test for Depression), and autobiographical memory valence (Autobiographical Memory Test) at three time points: baseline, pre-intervention, post-intervention. Results revealed that only the positive i-CBM-I group showed significant reductions in depressive symptoms during the intervention week, alongside improvements in both interpretation bias and autobiographical memory valence. Planned contrasts confirmed that these effects were specific to the intervention week and did not occur during the control period. These findings support the Combined Cognitive Biases Hypothesis, suggesting that changing interpretation bias can indirectly affect how individuals recall their personal past. This suggests that self-administered, imagery-based interventions may not only reduce symptoms but also reshape cognitive processes at the core of depressive experiences. 
mem data reprsnt the memory coding (nutral, positive negative) of each particpant in each time point
CBM- reprsnt biasline domgrfices (age, gender) and use of imgery (SUIS, PITN, PITP) and the score in deprssion symptomes (BDI,PHQ) and interpretition bias (ASD) in each time point. 
In addition the R code can shwo you hwo the data was manged.
DOI: 10.17632/mzv9t78mwm.2
title: i-CBM-I and deprssion

description: Major Depressive Disorder (MDD) is associated with multiple cognitive biases, including a tendency to interpret ambiguous information negatively and to recall negatively valenced autobiographical memories. Cognitive Bias Modification for Interpretation (CBM-I) is a promising, scalable intervention to alleviate depressive symptoms by targeting these biases, yet little is known about its broader cognitive effects beyond interpretation.. The present study tested the effects of a six-day imagery-based CBM-I (i-CBM-I) intervention on depressive symptoms , interpretation bias, and autobiographical memory valence in individuals with MDD. Fifty-six participants with DSM-5 MDD diagnoses were randomly assigned to either a positive i-CBM-I or a neutral-control group. Using a within-subject design with a one-week no-intervention baseline, we assessed depressive symptoms (using the Beck Depression Inventory-II and the Patient Health Questionnaire-9), interpretation bias (Ambiguous Scenarios Test for Depression), and autobiographical memory valence (Autobiographical Memory Test) at three time points: baseline, pre-intervention, post-intervention. Results revealed that only the positive i-CBM-I group showed significant reductions in depressive symptoms during the intervention week, alongside improvements in both interpretation bias and autobiographical memory valence. Planned contrasts confirmed that these effects were specific to the intervention week and did not occur during the control period. These findings support the Combined Cognitive Biases Hypothesis, suggesting that changing interpretation bias can indirectly affect how individuals recall their personal past. This suggests that self-administered, imagery-based interventions may not only reduce symptoms but also reshape cognitive processes at the core of depressive experiences. 
mem data reprsnt the memory coding (nutral, positive negative) of each particpant in each time point
CBM- reprsnt biasline domgrfices (age, gender) and use of imgery (SUIS, PITN, PITP) and the score in deprssion symptomes (BDI,PHQ) and interpretition bias (ASD) in each time point. 
In addition the R code can shwo you hwo the data was manged.
DOI: 10.17632/ykk6sd27n6.1
title: Good and bad classification of apple

description: Apple Grading Standards

1. Indian Grading Standards (eNAM)

The Electronic National Agriculture Market (eNAM) in India categorizes apples into three primary grades:

Range-I (Superior Quality):

Apples must be of superior quality, characteristic of the variety, and free from defects.

Only very slight superficial defects are permissible, provided they do not affect the general appearance, quality, or presentation.


Range-II (Good Quality):

Apples should be of good quality, characteristic of the variety.

Minor defects in shape, development, coloring, and slight skin or other defects are acceptable, provided they do not affect the overall appearance or quality.


Range-III (Minimum Requirements):

Apples that do not qualify for the higher grades but meet the minimum requirements.

Defects in shape, development, coloring, and skin are permissible, provided the apples retain their essential characteristics regarding quality and presentation.



Minimum Requirements Across All Grades:

Apples must be whole, firm, sound, clean, and free from pests, diseases, and abnormal moisture.

They should have a minimum total soluble solids content of 11° Brix.


Size Classification:

Defect Tolerances:

Russeting outside calyx/stem cavity: up to 1%.

Accumulated blemishes and bruising: not exceeding 1%.

Scab (Venturia inaequalis): not exceeding 0.25 cm² in Class I and 1 cm² in Class II.



---

2. United States Grading Standards (USDA)

The United States Department of Agriculture (USDA) defines the following apple grades:

U.S. Extra Fancy:

Apples must be mature but not overripe, clean, well-formed, and free from decay, internal browning, and other specified defects.

They should have the amount of color specified for the variety.


U.S. Fancy:

Similar to Extra Fancy but allows for slight defects, provided they do not detract from the apple's appearance or quality.


U.S. No. 1:

Apples that meet the requirements of U.S. Fancy, except for color, russeting, and invisible water core.


U.S. Utility:

Apples that are mature but not overripe, not seriously deformed, and free from serious damage.




---

3. European Union Grading Standards

The European Union classifies apples into three main categories:

Class Extra:

Apples of superior quality, characteristic of the variety, and free from defects, with the exception of very slight superficial defects.


Class I:

Good quality apples, allowing for slight defects in shape, development, coloring, and skin, provided they do not affect the overall appearance or quality.


Class II:

Apples that do not qualify for the higher classes but meet the minimum requirements.

Defects in shape, development, coloring, and skin are permissible, provided the apples retain their essential characteristics.Factors Influencing Apple Quality

1. Physical Attributes

Size and Shape:

Larger apples are generally preferred in markets.

Symmetrical and well-shaped apples are favored for their aesthetic appeal.
DOI: 10.17632/g8fmhxw8c6.1
title: Modular multi-enzyme cascades for the synthesis of non-canonical amino acids with C–S, C–Se, and C–N side chains from glycerol

description: Original data for figure 3b, 3c, 3d, 4c, and 5a in the main text and supplementary material.
DOI: 10.15468/dl.t6xuue
title: Occurrence Download

description: A dataset containing 47 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Distribución de la Nutria (Lontra longicaudis) en la cuenca baja del río Sinú en el departamento de Córdoba"
 ]
}

 The dataset includes 47 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017280-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.