cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.26030/sec3-y188
title: Gene expression profiling of PBL in response to ionizing radiation and modeled microgravity

description: BACKGROUND: Ionizing radiation (IR) can be extremely harmful for human cells since an improper DNA-damage response (DDR) to IR can contribute to carcinogenesis initiation. Perturbations in DDR pathway can originate from alteration in the functionality of the microRNA-mediated gene regulation, being microRNAs (miRNAs) small noncoding RNA that act as post-transcriptional regulators of gene expression. In this study we gained insight into the role of miRNAs in the regulation of DDR to IR under microgravity, a condition of weightlessness experienced by astronauts during space missions, which could have a synergistic action on cells, increasing the risk of radiation exposure. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed miRNA expression profile of human peripheral blood lymphocytes (PBL) incubated for 4 and 24 h in normal gravity (1 g) and in modeled microgravity (MMG) during the repair time after irradiation with 0.2 and 2Gy of gamma-rays. Our results show that MMG alters miRNA expression signature of irradiated PBL by decreasing the number of radio-responsive miRNAs. Moreover, let-7i*, miR-7, miR-7-1*, miR-27a, miR-144, miR-200a, miR-598, miR-650 are deregulated by the combined action of radiation and MMG. Integrated analyses of miRNA and mRNA expression profiles, carried out on PBL of the same donors, identified significant miRNA-mRNA anti-correlations of DDR pathway. Gene Ontology analysis reports that the biological category of Response to DNA damage is enriched when PBL are incubated in 1 g but not in MMG. Moreover, some anti-correlated genes of p53-pathway show a different expression level between 1 g and MMG. Functional validation assays using luciferase reporter constructs confirmed miRNA-mRNA interactions derived from target prediction analyses. CONCLUSIONS/SIGNIFICANCE: On the whole, by integrating the transcriptome and microRNome, we provide evidence that modeled microgravity can affects the DNA-damage response to IR in human PBL. Overall Design: Gene expression signature was defined in PBL irradiated with gamma-rays (2.0 Gy) and incubated in modeled microgravity (mmg) and in parallel ground conditions (1g) for 24h. Five independent experiments were performed for each donor to address which mRNAs were regulated on IR stress. The level of each transcript was represented as Log2.
DOI: 10.34740/kaggle/dsv/10815791
title: Antutu Benchmark
DOI: 10.6084/m9.figshare.28458767
title: Python dictionary of segmental duplications

description: Python dictionary containing segmental duplications in chm13 v2.0 (human T2T genome)<br>Dictionary is in format as follows:<br>key = "chr_dup1:pos_dup1"value = "chr_dup2:pos_dup2|nuc_dup1:nuc_dup2|strand_dup1:strand_dup2|kimura_whole_sd|perc_identity_whole_sd"
DOI: 10.21227/epxf-hr31
title: LeafNet: A large-scale dataset for training image-text models in leaf disease identification

description: The PlantVillage dataset, with over 54,000 images spanning 14 plant species and 26 disease types, has been widely used for leaf disease classification. However, it is limited in both scale and diversity. To address these limitations, we developed LeafNet, a large-scale dataset designed to support foundation models for leaf disease diagnosis. LeafNet comprises over 186,000 images from 22 crop species, covering 43 fungal diseases, 8 bacterial diseases, 2 mould (oomycete) diseases, 6 viral diseases, and 3 mite-induced diseases, categorized into 97 classes. The dataset was meticulously collected and processed to minimize intra-class variations while ensuring clarity by maintaining a consistent imaging distance. The disease symptom descriptions were curated from reputable sources, including UME, NIH, and published studies, providing high-quality annotations to support AI-driven plant pathology research.
DOI: 10.3886/e149062
title: Concealed Carry Weapons License Database

description: The Concealed Carry Weapons License Database (CCWLD) is a longitudinal collection of state and county-level data on concealed carry weapons licenses (CCWs). <br><br>Data were collected from a series of internet searches and freedom of information requests sent to state governments during the fall of 2019 and winter of 2020. Data cleaning was conducted by research assistants and by Trent Steidley in the winter of 2021.<br>Documentation memos for each state are provided in the archived files. Along with raw data files, Stata syntax for cleaning, and the final cleaned database.<br><br>This database was supported with funding from the Center on American Politics at the University of Denver and a Professional Research Opportunity for Faculty (PROF) grant from the University of Denver. <br>If you use these data in your research please cite them appropriately.<br>
DOI: 10.3886/e149062v1-168681
title: Concealed Carry Weapons License Database

description: The Concealed Carry Weapons License Database (CCWLD) is a longitudinal collection of state and county-level data on concealed carry weapons licenses (CCWs). <br><br>Data were collected from a series of internet searches and freedom of information requests sent to state governments during the fall of 2019 and winter of 2020. Data cleaning was conducted by research assistants and by Trent Steidley in the winter of 2021.<br>Documentation memos for each state are provided in the archived files. Along with raw data files, Stata syntax for cleaning, and the final cleaned database.<br><br>This database was supported with funding from the Center on American Politics at the University of Denver and a Professional Research Opportunity for Faculty (PROF) grant from the University of Denver. <br>If you use these data in your research please cite them appropriately.<br>
DOI: 10.5281/zenodo.12549394
title: IFC Submarine Interconnection Projects Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.
DOI: 10.5281/zenodo.14866380
title: IFC Submarine Interconnection Projects Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.
DOI: 10.5281/zenodo.14907080
title: IFC Submarine Interconnection Projects Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.
DOI: 10.5281/zenodo.8277680
title: IFC Submarine Interconnection Projects Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram and data model tables.
DOI: 10.5281/zenodo.8277681
title: O&G Subsea Flexible Interconnections IFC Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram, data model tables and IFC sample files in JSON and STEP formats.


Obs. All components of the 3D model are in the origin (coordinate 0,0,0).
DOI: 10.5281/zenodo.8339157
title: IFC Submarine Interconnection Projects Data Model

description: Technical specification of the O&G Subsea Flexible Interconnections IFC Data Model, containing class relationships diagram, data model tables and IFC sample files in JSON and STEP formats.


Obs. All components of the 3D model are in the origin (coordinate 0,0,0).
DOI: 10.5281/zenodo.12705216
title: AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

description: This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.

In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.
description: AIMS: Artificial Intelligence to Monitor our Seas is funded by the European Union - NextGeneration EU within the PRIN 2022 PNRR program (D.D.1409 del 14/09/2022 Ministero dell’Università e della Ricerca). This document reflects only the authors’ view, and the Commission and Ministry cannot be considered responsible for any use that may be made of the information it contains.
DOI: 10.5281/zenodo.12705217
title: AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

description: This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.

In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.
description: AIMS: Artificial Intelligence to Monitor our Seas is funded by the European Union - NextGeneration EU within the PRIN 2022 PNRR program (D.D.1409 del 14/09/2022 Ministero dell’Università e della Ricerca). This document reflects only the authors’ view, and the Commission and Ministry cannot be considered responsible for any use that may be made of the information it contains.
DOI: 10.5281/zenodo.13981053
title: AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

description: This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.

In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.
description: AIMS: Artificial Intelligence to Monitor our Seas is funded by the European Union - NextGeneration EU within the PRIN 2022 PNRR program (D.D.1409 del 14/09/2022 Ministero dell’Università e della Ricerca). This document reflects only the authors’ view, and the Commission and Ministry cannot be considered responsible for any use that may be made of the information it contains.
DOI: 10.5281/zenodo.14907103
title: AIMS - Earth Observation Satellite Data of Wave and Wind in the Tyrrhenian Sea

description: This dataset is part of the AIMS project (Artificial Intelligence to Monitor our Seas), which has the vision to develop and validate novel Artificial Intelligence (AI) algorithms to unlock the true potential of remote monitoring and enable a faster transition to a climate neutral society and economy: the AI algorithms will leverage the advantages of usual monitoring methodologies of the features of waves and offshore wind, and eventually overcome their intrinsic limitations. The value and resolution of sparse measurements of satellites and unevenly-distributed in-situ instruments will be increased, hence leading to a significant reduction of the cost and execution time of data collection, ultimately making knowledge wider and more accessible.

In particular, this dataset aggregates earth observation satellite data from 10 different satellites, measureing the significant wave height and the wind speed at 10 meters above sea leavel in the Tyrrhenian Sea, from January 2021 to May 2024.
description: AIMS: Artificial Intelligence to Monitor our Seas is funded by the European Union - NextGeneration EU within the PRIN 2022 PNRR program (D.D.1409 del 14/09/2022 Ministero dell’Università e della Ricerca). This document reflects only the authors’ view, and the Commission and Ministry cannot be considered responsible for any use that may be made of the information it contains.
DOI: 10.5281/zenodo.14907100
title: Data and code for: Microscale Variation in Temperature and Oxygen: Key Niche Axes for Aquatic Life Across Time and Space.
DOI: 10.5281/zenodo.14905645
title: Ferroptosis spreads to neighboring cells via plasma membrane contacts

description: The uploaded lipidomics dataset contains Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) data associated to the publication of Bernhard F. Roeck et al. from the laboratory of Ana J. Garcia-Saez. 

The title of the publication is:

Ferroptosis spreads to neighboring cells via plasma membrane contacts

This article is to be published in Nature Communications.

Article Abstract

Ferroptosis is a lytic, iron-dependent form of regulated cell death characterized by excessive lipid peroxidation and associated with necrosis spread in diseased tissues through unknown mechanisms. Using a novel optogenetic system for light-driven ferroptosis induction via degradation of the anti-ferroptotic protein GPX4, we show that lipid peroxidation and ferroptotic death can spread to neighboring cells through their closely adjacent plasma membranes. Ferroptosis propagation was dependent on cell distance and completely abolished by disruption of a-catenin-dependent intercellular contacts or by chelation of extracellular iron. Remarkably, bridging cells with a lipid bilayer or increasing contacts between neighboring cells enhanced ferroptosis spread. Reconstitution of iron-dependent spread of lipid peroxidation between pure lipid, contacting liposomes provides evidence for the physicochemical mechanism involved. Our findings support a model in which iron-dependent lipid peroxidation propagates across proximal plasma membranes of neighboring cells, thereby promoting the transmission of ferroptotic cell death with consequences for pathological tissue necrosis spread.
DOI: 10.5281/zenodo.14905646
title: Ferroptosis spreads to neighboring cells via plasma membrane contacts

description: The uploaded lipidomics dataset contains Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) data associated to the publication of Bernhard F. Roeck et al. from the laboratory of Ana J. Garcia-Saez. 

The title of the publication is:

Ferroptosis spreads to neighboring cells via plasma membrane contacts

This article is to be published in Nature Communications.

Article Abstract

Ferroptosis is a lytic, iron-dependent form of regulated cell death characterized by excessive lipid peroxidation and associated with necrosis spread in diseased tissues through unknown mechanisms. Using a novel optogenetic system for light-driven ferroptosis induction via degradation of the anti-ferroptotic protein GPX4, we show that lipid peroxidation and ferroptotic death can spread to neighboring cells through their closely adjacent plasma membranes. Ferroptosis propagation was dependent on cell distance and completely abolished by disruption of a-catenin-dependent intercellular contacts or by chelation of extracellular iron. Remarkably, bridging cells with a lipid bilayer or increasing contacts between neighboring cells enhanced ferroptosis spread. Reconstitution of iron-dependent spread of lipid peroxidation between pure lipid, contacting liposomes provides evidence for the physicochemical mechanism involved. Our findings support a model in which iron-dependent lipid peroxidation propagates across proximal plasma membranes of neighboring cells, thereby promoting the transmission of ferroptotic cell death with consequences for pathological tissue necrosis spread.
DOI: 10.5281/zenodo.14261072
title: Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

description: In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.

In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.
DOI: 10.5281/zenodo.14261073
title: Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

description: In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.

In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.
DOI: 10.5281/zenodo.14294639
title: Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

description: In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.

In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.
DOI: 10.5281/zenodo.14888024
title: Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub

description: In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.

In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.
DOI: 10.5281/zenodo.14907117
title: GP17_FTICR
DOI: 10.5281/zenodo.14907118
title: GP17_FTICR
DOI: 10.60635/c3v59t
title: SST_Isla Coibita

description: Sea Surface temperature monitoring near Isla Coibita, Gulf of ChiriquiLocation: 7.63525 -81.75878Depths: 1.2mStatus: Inactive
DOI: 10.25318/1610008901-eng
title: Inventories held and owned, estimated value in all manufacturing industries

description: This table contains 4 series, with data for years 1949 - 1984 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada); Principal statistics (2 items: Ratio of finished products to shipments; Ratio of inventories owned to shipments); Seasonal adjustment (2 items: Unadjusted; Seasonally adjusted).
DOI: 10.21430/m3otd0z7jj
title: T cell responses to H1N1v and a longitudinal study of seasonal influenza vaccination SLVP015 2016

description: Systems biology approach to examine effects of seasonal flu vaccination in adults of different ages on gene expression, cytokine stimulation and serum cytokines with parameters such as immune senescence to uncover new markers and mechanisms behind failure of immune function in many older people.
DOI: 10.21430/m3nguzlchs
title: T cell responses to H1N1v and a longitudinal study of seasonal influenza vaccination SLVP015 2015

description: Systems biology approach to examine effects of seasonal flu vaccination in adults of different ages on gene expression, cytokine stimulation and serum cytokines with parameters such as immune senescence to uncover new markers and mechanisms behind failure of immune function in many older people.
DOI: 10.21430/m3m02erg3v
title: Tumor-specific CD8+ Tc9 cells activate host CD4+ T cells to control antigen-lost tumors

description: Host effector CD4+ T cells are emerging as an important contributor for tumor regression but whether they can be activated by adoptively transferred CD8+ T cells remains unknown. We reported that adoptive transfer of murine tumor-specific CD8+ Tc9 but not Tc1-CTL cells achieved long-term control of tumor growth in vivo. Here, we demonstrate that murine tumor-specific Tc9 cells not only kill antigen expressing primary tumors but also control the outgrowth of antigen-lost relapsed tumors by recruiting and activating host effector CD4+ T cells that recognize relapsed tumors. Tc9 cells secreted interleukin (IL) 24 and recruited CCR7-expressing conventional type-2 dendritic cells (cDC2) into tumor-draining lymph nodes to prime host CD4+ T cell response against relapsed tumors. Depleting host CD4+ T cells or deficiency in cDC2 impaired Tc9 cells ability to control the outgrowth of relapsed tumor. We also observed that intratumoral IL24 expression was positively correlated with gene signatures of cDC2 and effector CD4+ T cells in human cancers and expression of IL24, cDC2 and CD4+ T cell gene signatures were positively associated with patients overall survival. This study thus uncovers a novel mechanism underlying the activation of tumor-specific CD4+ T cells in vivo.