cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.24046092.v1
title: Additional file 1 of Mycobacterium abscessus pulmonary disease presenting with spontaneous pneumomediastinum and subcutaneous emphysema in childhood acute lymphoblastic leukemia: a case report and literature review

description: Additional file 1.
DOI: 10.17630/81a3a656-4624-4dc1-80c2-7b4b5ae4744d
title: Investigation of solid base catalysts for biodiesel production from fish oil (dataset)
DOI: 10.6084/m9.figshare.24574399
title: qPCR and kit data.pzfx

description: Ulcerative colitis (UC) is characterized by chronic inflammation and ulceration of the intestinal inner lining, resulting in various symptoms. Sea buckthorn berries contain a bioactive compound known as sea buckthorn polysaccharide (SBP). However, the precise mechanisms underlying the impact of SBP on UC remain unclear. In this study, we investigated the effects of pretreatment with SBP on colitis induced by DSS. Our findings demonstrate that SBP pretreatment effectively reduces inflammation, oxidative stress, and intestinal barrier damage associated with colitis. To further elucidate the role of SBP-modulated gut microbiota in UC, we performed fecal microbiota transplantation (FMT) on DSS-treated mice. The microbiota from SBP-treated mice exhibits notable anti-inflammatory and antioxidant effects, improves colonic barrier integrity, and increases the abundance of beneficial bacteria, as well as enhancing SCFA production. Collectively, these results strongly indicate that SBP-mediated amelioration of colitis is attributed to its impact on the gut microbiota, particularly through the promotion of SCFA-producing bacteria and subsequent elevation of SCFA levels. This study provides compelling evidence supporting the efficacy of pre-emptive SBP supplementation in alleviating colitis symptoms by modulating the gut microbiota, thereby offering novel insights into the potential of SBP as a regulator of the gut microbiota for colitis relief
DOI: 10.6084/m9.figshare.24574399.v1
title: qPCR and kit data.pzfx

description: Ulcerative colitis (UC) is characterized by chronic inflammation and ulceration of the intestinal inner lining, resulting in various symptoms. Sea buckthorn berries contain a bioactive compound known as sea buckthorn polysaccharide (SBP). However, the precise mechanisms underlying the impact of SBP on UC remain unclear. In this study, we investigated the effects of pretreatment with SBP on colitis induced by DSS. Our findings demonstrate that SBP pretreatment effectively reduces inflammation, oxidative stress, and intestinal barrier damage associated with colitis. To further elucidate the role of SBP-modulated gut microbiota in UC, we performed fecal microbiota transplantation (FMT) on DSS-treated mice. The microbiota from SBP-treated mice exhibits notable anti-inflammatory and antioxidant effects, improves colonic barrier integrity, and increases the abundance of beneficial bacteria, as well as enhancing SCFA production. Collectively, these results strongly indicate that SBP-mediated amelioration of colitis is attributed to its impact on the gut microbiota, particularly through the promotion of SCFA-producing bacteria and subsequent elevation of SCFA levels. This study provides compelling evidence supporting the efficacy of pre-emptive SBP supplementation in alleviating colitis symptoms by modulating the gut microbiota, thereby offering novel insights into the potential of SBP as a regulator of the gut microbiota for colitis relief
DOI: 10.25405/data.ncl.25226303.v1
title: Extended Data Figure 2 source data

description: This dataset is part of the 'Source data and representative raw data for "Peptidoglycan synthesis drives a single population of septal cell wall synthases during division in Bacillus subtilis"' collection. For a link to the related paper, study-level information and guide to the data, as well as viewing all other related research data, please visit the collection.
220303_slide2_3_MMStack_Default_561_denoise.ome_ring18 and 220303_slide2_3_MMStack_Default_561_denoise.ome_ring18_kymo correspond to the top kymograph in ED Fig 2.
220322_slide2_1_MMStack_Default_561_denoise.ome_ring36 and 220322_slide2_1_MMStack_Default_561_denoise.ome_ring36_kymo correspond to the second kymograph in ED Fig 2.
220316_4_MMStack_Default_561_denoise.ome_ring6 and 220316_4_MMStack_Default_561_denoise.ome_ring6_kymo correspond to the third kymograph in ED Fig 2.
220322_slide2_5_MMStack_Default_561_denoise.ome_ring1 and 220322_slide2_5_MMStack_Default_561_denoise.ome_ring1_kymo correspond to the bottom kymograph in ED Fig 2.
DOI: 10.25405/data.ncl.25226303
title: Extended Data Figure 2 source data

description: This dataset is part of the 'Source data and representative raw data for "Peptidoglycan synthesis drives a single population of septal cell wall synthases during division in Bacillus subtilis"' collection. For a link to the related paper, study-level information and guide to the data, as well as viewing all other related research data, please visit the collection.
220303_slide2_3_MMStack_Default_561_denoise.ome_ring18 and 220303_slide2_3_MMStack_Default_561_denoise.ome_ring18_kymo correspond to the top kymograph in ED Fig 2.
220322_slide2_1_MMStack_Default_561_denoise.ome_ring36 and 220322_slide2_1_MMStack_Default_561_denoise.ome_ring36_kymo correspond to the second kymograph in ED Fig 2.
220316_4_MMStack_Default_561_denoise.ome_ring6 and 220316_4_MMStack_Default_561_denoise.ome_ring6_kymo correspond to the third kymograph in ED Fig 2.
220322_slide2_5_MMStack_Default_561_denoise.ome_ring1 and 220322_slide2_5_MMStack_Default_561_denoise.ome_ring1_kymo correspond to the bottom kymograph in ED Fig 2.
DOI: 10.17630/00aff760-0732-438f-a9d1-30c7cf3a87a0
title: Data underpinning "Synthesis of Fused Indoline-Cyclobutanone Derivatives via an Intramolecular [2+2] Cycloaddition"
DOI: 10.6084/m9.figshare.25305244.v1
title: <b>Untitled Item</b><b>Spatial_DataSet_for_Global public </b><b>risk</b><b> perception of artificial intelligence</b>

description: This study conducted a thorough examination of public perceptions of artificial intelligence (AI) risks and their spatiotemporal development patterns. Employing a combination of Gensim, Perspective, and other natural language processing tools, alongside manual review methods, the data underwent systematic processing and categorization. Building on this foundation, the multidimensional mathematical convex hull approach was utilized to extrapolate public geospatial data, offering an in-depth analysis of the spatiotemporal distribution characteristics, regional heterogeneity, and potential influencing factors of global public perceptions towards AI risks. To mitigate the uncertainties inherent in methodological, data, and mathematical assumptions, the study integrated various machine learning models with the game theory-based Shap model to attribute and dissect the public's risk perceptions across different nations and regions, uncovering the impact of economic, political, and religious domains on public AI perception levels. Furthermore, the study elucidates the evolution of public risk perceptions towards AI over the past few decades. Ultimately, the empirical findings provided both theoretical and empirical bases for managers and policymakers to devise effective intervention strategies and social policies, aiming to foster a smooth and orderly development of AI technology, while advocating for the necessity of enhancing public risk awareness.
DOI: 10.6084/m9.figshare.25305244
title: <b>Spatial_DataSet_for_Global public </b><b>risk</b><b> perception of artificial intelligence</b>

description: This study conducted a thorough examination of public perceptions of artificial intelligence (AI) risks and their spatiotemporal development patterns. Employing a combination of Gensim, Perspective, and other natural language processing tools, alongside manual review methods, the data underwent systematic processing and categorization. Building on this foundation, the multidimensional mathematical convex hull approach was utilized to extrapolate public geospatial data, offering an in-depth analysis of the spatiotemporal distribution characteristics, regional heterogeneity, and potential influencing factors of global public perceptions towards AI risks. To mitigate the uncertainties inherent in methodological, data, and mathematical assumptions, the study integrated various machine learning models with the game theory-based Shap model to attribute and dissect the public's risk perceptions across different nations and regions, uncovering the impact of economic, political, and religious domains on public AI perception levels. Furthermore, the study elucidates the evolution of public risk perceptions towards AI over the past few decades. Ultimately, the empirical findings provided both theoretical and empirical bases for managers and policymakers to devise effective intervention strategies and social policies, aiming to foster a smooth and orderly development of AI technology, while advocating for the necessity of enhancing public risk awareness.
DOI: 10.6084/m9.figshare.25303591
title: Twitter Datasets.zip

description: The third-party datasets from "Adapting BERT for Target-Oriented Multimodal Sentiment Classification" (DOI: https://doi.org/10.24963/ijcai.2019/751) are available at Github: https://github.com/jefferyYu/TomBERT.
DOI: 10.6084/m9.figshare.24046146
title: Additional file 1 of WGCNA and transcriptome profiling reveal hub genes for key development stage seed size/oil content between wild and cultivated soybean

description: Additional file 1: Table S1. Statistics of traits associated with soybean seed oil and seed weight for RNA-seq sample. SL: seed length; SW: seed width; SH: seed height; HGW: hundred-grain weight ; OC: oil content; OA: oleic acid; LNA: linolenic acid. The values associated with seed size and oil were continuously measured from 2015 to 2017. The data is presented in the form of mean ± standard deviation (SD). Table S2. Information of all primer sequence in qRT-PCR. Table S3. The candidate genes involved in lipid metabolism. Table S4. The candidate genes involved in regulation of seed size. Table S5. Candidate hub genes in six modules according to kME value. Fig. S1. qRT-PCR validation of candidate genes in G1 group. (A) The fold change (FC) of gene expression in G1_1; (B) The FC of gene expression in G1_2; (C) Th FC of gene expression in G1_3; (D) The FC of gene expression in G1_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes. Fig. S2. qRT-PCR validation of candidate genes in G2 group. (A) The fold change (FC) of gene expression in G2_1; (B) The FC of gene expression in G2_2; (C) Th FC of gene expression in G2_3; (D) The FC of gene expression in G2_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes. Fig. S3. qRT-PCR validation of candidate genes in G3 group. (A) The fold change (FC) of gene expression in G3_1; (B) The FC of gene expression in G3_2; (C) Th FC of gene expression in G3_3; (D) The FC of gene expression in G3_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes.
DOI: 10.6084/m9.figshare.24046146.v1
title: Additional file 1 of WGCNA and transcriptome profiling reveal hub genes for key development stage seed size/oil content between wild and cultivated soybean

description: Additional file 1: Table S1. Statistics of traits associated with soybean seed oil and seed weight for RNA-seq sample. SL: seed length; SW: seed width; SH: seed height; HGW: hundred-grain weight ; OC: oil content; OA: oleic acid; LNA: linolenic acid. The values associated with seed size and oil were continuously measured from 2015 to 2017. The data is presented in the form of mean ± standard deviation (SD). Table S2. Information of all primer sequence in qRT-PCR. Table S3. The candidate genes involved in lipid metabolism. Table S4. The candidate genes involved in regulation of seed size. Table S5. Candidate hub genes in six modules according to kME value. Fig. S1. qRT-PCR validation of candidate genes in G1 group. (A) The fold change (FC) of gene expression in G1_1; (B) The FC of gene expression in G1_2; (C) Th FC of gene expression in G1_3; (D) The FC of gene expression in G1_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes. Fig. S2. qRT-PCR validation of candidate genes in G2 group. (A) The fold change (FC) of gene expression in G2_1; (B) The FC of gene expression in G2_2; (C) Th FC of gene expression in G2_3; (D) The FC of gene expression in G2_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes. Fig. S3. qRT-PCR validation of candidate genes in G3 group. (A) The fold change (FC) of gene expression in G3_1; (B) The FC of gene expression in G3_2; (C) Th FC of gene expression in G3_3; (D) The FC of gene expression in G3_4. The x-axis represents the name of sixteen candidate genes, y-axis shows the FC increase/decrease in expression level of the genes.
DOI: 10.6084/m9.figshare.6220361
title: Factor and Idiosyncratic Empirical Processes

description: The distributions of the common and idiosyncratic components for an individual variable are important in forecasting and applications. However, they are not identified with low-dimensional observations. Using the recently developed theory for large dimensional approximate factor model for large panel data, the common and idiosyncratic components can be estimated consistently. Based on the estimated common and idiosyncratic components, we construct the empirical processes for estimation of the distribution functions of the common and idiosyncratic components. We prove that the two empirical processes are oracle efficient when <i>T</i> = <i>o</i>(<i>p</i>) where <i>p</i> and <i>T</i> are the dimension and sample size, respectively. This demonstrates that the factor and idiosyncratic empirical processes behave as well as the empirical processes pretending that the common and idiosyncratic components for an individual variable are directly observable. Based on this oracle property, we construct simultaneous confidence bands (SCBs) for the distributions of the common and idiosyncratic components. For the first-order consistency of the estimated distribution functions, T=o(p) suffices. Extensive simulation studies check that the estimated bands have good coverage frequencies. Our real data analysis shows that the common-component distribution has a structural change during the crisis in 2008, while the idiosyncratic-component distribution does not change much. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.24721035
title: ITC-Net-Audio-5: An Audio Streaming Dataset for Application Identification in Network Traffic Classification

description: This dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 PCAP files (i.e., 100 files per application) captured by Wireshark and PCAPdroid tools during voice calls and online music playback.Each application's data is stored in a dedicated ZIP file with the application's name, and they are named as [application name]_[file number].pcap. Alongside the dataset, there is a PDF document containing tables and figures that provide a brief description of the dataset specifications, a visual representation of the methodology, and versions of the tools used.For more details, please refer to our paper published in the BMC journal. link: https://doi.org/10.1186/s13104-024-06718-7If you use this dataset in academic publications, please cite our BMC journal paper.<br>
DOI: 10.6084/m9.figshare.24721035.v1
title: ITC-Net-Audio-5: An Audio Streaming Dataset for Application Identification in Network Traffic Classification

description: This dataset contains network traffic captured during audio streaming communications on five trending applications: Google Meet, Skype, Telegram, WhatsApp, and SoundCloud. It includes 500 PCAP files (i.e., 100 files per application) captured by Wireshark and PCAPdroid tools during voice calls and online music playback.Each application's data is stored in a dedicated ZIP file with the application's name, and they are named as [application name]_[file number].pcap. Alongside the dataset, there is a PDF document containing tables and figures that provide a brief description of the dataset specifications, a visual representation of the methodology, and versions of the tools used.For more details, please refer to our paper submitted to the BMC journal. link:
DOI: 10.6084/m9.figshare.14608975.v1
title: Additional file 1 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 1: Table S1. Propensity score weighted univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608978
title: Additional file 2 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 2: Table S2. Univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608981
title: Additional file 3 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 3: Table S3. Propensity score-matched univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608975
title: Additional file 1 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 1: Table S1. Propensity score weighted univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608981.v1
title: Additional file 3 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 3: Table S3. Propensity score-matched univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608984
title: Additional file 4 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 4: Table S4. Results of propensity score weighted univariable and multivariable ordinal logistic model using three levels of FLI as response
DOI: 10.6084/m9.figshare.14608978.v1
title: Additional file 2 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 2: Table S2. Univariable and multivariable analysis of associations between meat subtypes consumptions and NAFLD
DOI: 10.6084/m9.figshare.14608984.v1
title: Additional file 4 of Association of meat consumption with NAFLD risk and liver-related biochemical indexes in older Chinese: a cross-sectional study

description: Additional file 4: Table S4. Results of propensity score weighted univariable and multivariable ordinal logistic model using three levels of FLI as response
DOI: 10.22002/mxcj0-k3j35
title: purified BamA from E.coli

description: Raw data files of purified BamA from E.coli
description: <strong>Tilt Series Date:</strong> 2015-05-08</p>
<strong>Data Taken By:</strong> Poorna Subramanian</p>
<strong>Species / Specimen:</strong> Escherichia coli</p>
<strong>Strain:</strong> </p>
<strong>Tilt Series Settings:</strong>  Single Axis, tilt range: (-65.0°, 65.0°), step: 1.0°, constant angular increment, dosage: 95.0 eV/Å²,  defocus: -5.0 μm,  magnification: 34000x. </p>
<strong>Microscope:</strong> Caltech Polara</p>
<strong>Acquisition Software:</strong> UCSFTomo</p>
<strong>Upload Method:</strong> pipeline</p>
<strong>Processing Software Used:</strong> Batchruntomo</p>
<strong>Collaborators and Roles:</strong> Chris Hayes and David Low UCSB</p>
DOI: 10.6084/m9.figshare.23291708
title: magetTemplateBrainMask.nii.gz

description: template
DOI: 10.6084/m9.figshare.23291708.v1
title: magetTemplateBrainMask.nii.gz

description: template
DOI: 10.6084/m9.figshare.23291711
title: magetTemplate.nii.gz

description: template <br>
DOI: 10.6084/m9.figshare.23291711.v1
title: magetTemplate.nii.gz

description: template <br>
DOI: 10.6084/m9.figshare.23291714
title: magetCerebellumTemplatePriors.nii.gz

description: template
DOI: 10.6084/m9.figshare.23291714.v1
title: magetCerebellumTemplatePriors.nii.gz

description: template
DOI: 10.6084/m9.figshare.23291717
title: magetCerebellumTemplate.nii.gz

description: template
DOI: 10.6084/m9.figshare.23291717.v1
title: magetCerebellumTemplate.nii.gz

description: template
DOI: 10.5281/zenodo.8002077
title: forpus_xanthopterygius_decontaminated_gx.fasta.gz

description: .
DOI: 10.5281/zenodo.8002076
title: forpus_xanthopterygius_decontaminated_gx.fasta.gz

description: .
DOI: 10.25625/0guomc/320jem
title: event_classification.zip

description: :unav
DOI: 10.5281/zenodo.8001474
title: Regra de três: teorias da conspiração sobre Covid-19 no YouTube

description: Material suplementar do artigo "Regra de três: teorias da conspiração sobre Covid-19 no YouTube"
DOI: 10.5281/zenodo.8001473
title: Regra de três: teorias da conspiração sobre Covid-19 no YouTube

description: Material suplementar do artigo "Regra de três: teorias da conspiração sobre Covid-19 no YouTube"
DOI: 10.6084/m9.figshare.23291765
title: Replication Package of "Automated Versioning for Software Releases: A Retrospective Study and A New Lightweight Approach"

description: Replication Package of Automatic Versioning
DOI: 10.6084/m9.figshare.23291765.v2
title: Replication Package of "Automated Versioning for Software Releases: A Retrospective Study and A New Lightweight Approach"

description: Replication Package of Automatic Versioning
DOI: 10.6084/m9.figshare.23291765.v1
title: Replication Package of "Automated Versioning for Software Releases: A Retrospective Study and A New Lightweight Approach"

description: Replication Package of Automatic Versioning
DOI: 10.6084/m9.figshare.23291762
title: Raw scRNA-seq data for Uhlitz et al.

description: Raw, unnormalized scRNA-seq data from Uhlitz et al. colorectal cancer patient cohort. format: h5ad saved under scanpy version 1.9.1, anndata version 0.8.0 gzip compressed internally (may be slower to load) counts were soupx-corrected for ambient mRNA contains clinical metadata annotations in obs and gene annotations in var dataframes. Originally published in: Uhlitz, F., Bischoff, P., Peidli, S., Sieber, A., Trinks, A., Lüthen, M., ... & Morkel, M. (2021). Mitogen‐activated protein kinase activity drives cell trajectories in colorectal cancer. <em>EMBO molecular medicine</em>, <em>13</em>(10), e14123. <br>
DOI: 10.15468/dl.am9yyy
title: Occurrence Download

description: A dataset containing 10695 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euphorbia hypericifolia L."
 ]
}

 The dataset includes 10695 records from 336 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0183080-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.