cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25345/c53t9d97h
title: MassIVE Reanalysis RMSV000000362.74 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000081607 Confetti, LysC HCD [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5028ph45
title: MassIVE Reanalysis RMSV000000584.69 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000079514/PXD000561 [5 out of 41], CD8 Tcells [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5v97zv95
title: MassIVE Reanalysis RMSV000000470.3 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080694/PXD003562 [4 out of 4] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.6373423
title: Image dataset to train a deep learning model to decode Leetspeak obfuscated characters

description: The dataset contains an image database (18,981 images) that could be used to train a deep learning model to accurately detect characters. We have successfully used it to create a model that identifies characters encoded using LeetSpeak. The original dataset can be found in the Mondragon Unibertsitatea Repository -- https://gitlab.danz.eus/datasharing/ski4spam The training dataset consists of: - Alphabetic letters (a-z) written using different fonts and styles (regular, cursive, bold, cursive+bold) - Handwritten letters: English handwriting from the Chars74k dataset [2] which is available at http://www.ee.surrey.ac.uk/CVSSP/demos/chars74k/.
DOI: 10.6084/m9.figshare.19391583
title: Fat mass and obesity associated (FTO)-mediated N6-methyladenosine modification of Krüppel-like factor 3 (KLF3) promotes osteosarcoma progression

description: N6-methyladenosine (m6A) methylation is the most common and abundant methylation modification of eukaryotic mRNAs, which is involved in tumor initiation and progression. The study aims to explore the potential role and the regulatory mechanism of fat mass and obesity associated (FTO) in osteosarcoma (OS) progression. In this study, we detected the expressions of Krüppel-like factor 3 (KLF3) in OS cells and tissues and found that the mRNA and protein levels of KLF3 were increased in OS cells and tissues and significantly related to tumor size, metastasis, and TNM stage and poor prognosis of OS patients. FTO promoted the proliferation and invasion and suppressed apoptosis of OS cells through cell experiments in vitro. Further mechanism dissection revealed that FTO and YTHDF2 enforced the decay of KLF3 mRNA and decreased its expression. FTO-mediated mRNA demethylation inhibited KLF3 expression in the YTHDF2-dependent manner. Moreover, KLF3 overexpression abrogated FTO-induced oncogenic effects on the proliferation and invasion of OS cells. Overall, our findings showed that FTO-mediated m6A modification of KLF3 promoted OS progression, which may provide a therapeutic target for OS.
DOI: 10.6084/m9.figshare.19391583.v1
title: Fat mass and obesity associated (FTO)-mediated N6-methyladenosine modification of Krüppel-like factor 3 (KLF3) promotes osteosarcoma progression

description: N6-methyladenosine (m6A) methylation is the most common and abundant methylation modification of eukaryotic mRNAs, which is involved in tumor initiation and progression. The study aims to explore the potential role and the regulatory mechanism of fat mass and obesity associated (FTO) in osteosarcoma (OS) progression. In this study, we detected the expressions of Krüppel-like factor 3 (KLF3) in OS cells and tissues and found that the mRNA and protein levels of KLF3 were increased in OS cells and tissues and significantly related to tumor size, metastasis, and TNM stage and poor prognosis of OS patients. FTO promoted the proliferation and invasion and suppressed apoptosis of OS cells through cell experiments in vitro. Further mechanism dissection revealed that FTO and YTHDF2 enforced the decay of KLF3 mRNA and decreased its expression. FTO-mediated mRNA demethylation inhibited KLF3 expression in the YTHDF2-dependent manner. Moreover, KLF3 overexpression abrogated FTO-induced oncogenic effects on the proliferation and invasion of OS cells. Overall, our findings showed that FTO-mediated m6A modification of KLF3 promoted OS progression, which may provide a therapeutic target for OS.
DOI: 10.6084/m9.figshare.17868478.v2
title: The design of a matrix linking work situations to chemical health risk at the workplace

description: In France, laws require each company to draw up an inventory of the risks that may threaten employees’ health in order to prioritize the preventive actions to be implemented. Focusing on chemical risk, databases on hazards or exposures are widely available but they lack information regarding chemical risks resulting from combining the hazards of chemicals with their conditions of use, thus generating exposures. Our objective is to build a matrix of French work situations associated with their chemical risk. Eighty-eight work situations were collected from reports written by professionals from the French public health insurance service. Each work situation is defined by descriptive parameters of the task, the exposure, and the hazard. According to an expert elicitation method (Delphi, n = 21 experts), each work situation was assessed and a chemical risk score defined, taking into account all the descriptive exposure and hazard parameters. Chemical risk scores were expressed as a range of values from 0 to 100, with the size of the range chosen by the experts themselves according to their uncertainty. The experts' assessments were merged to assign one risk score for each work situation, variability, and confidence. The results showed that 50% of the work situations had a risk score between 40 and 60. The average variability and confidence were around 15% and 82%, respectively. This work situation matrix constructed from French data can be used by occupational safety and health managers that have similar work situations in their company (Western European industrial sector). In this context, it may be useful to easily determine the level of risks for similar tasks and prioritize those that are most risky. Moreover, it could be used to compare and define the differences between a risk assessment performed by "expertise" and another defined by a software.
DOI: 10.6084/m9.figshare.17868478
title: The design of a matrix linking work situations to chemical health risk at the workplace

description: In France, laws require each company to draw up an inventory of the risks that may threaten employees’ health in order to prioritize the preventive actions to be implemented. Focusing on chemical risk, databases on hazards or exposures are widely available but they lack information regarding chemical risks resulting from combining the hazards of chemicals with their conditions of use, thus generating exposures. Our objective is to build a matrix of French work situations associated with their chemical risk. Eighty-eight work situations were collected from reports written by professionals from the French public health insurance service. Each work situation is defined by descriptive parameters of the task, the exposure, and the hazard. According to an expert elicitation method (Delphi, n = 21 experts), each work situation was assessed and a chemical risk score defined, taking into account all the descriptive exposure and hazard parameters. Chemical risk scores were expressed as a range of values from 0 to 100, with the size of the range chosen by the experts themselves according to their uncertainty. The experts' assessments were merged to assign one risk score for each work situation, variability, and confidence. The results showed that 50% of the work situations had a risk score between 40 and 60. The average variability and confidence were around 15% and 82%, respectively. This work situation matrix constructed from French data can be used by occupational safety and health managers that have similar work situations in their company (Western European industrial sector). In this context, it may be useful to easily determine the level of risks for similar tasks and prioritize those that are most risky. Moreover, it could be used to compare and define the differences between a risk assessment performed by "expertise" and another defined by a software.
DOI: 10.6084/m9.figshare.19322257
title: Endoscopic drainage versus percutaneous drainage for the management of infected walled-off necrosis: a comparative analysis

description: Comparative data on percutaneous catheter drainage (PCD) vs EUS-guided drainage (EUS-D) for management of symptomatic walled-off-necrosis (WON), specially infected WON with/without organ failure(OF) is limited. Patients with symptomatic WON were divided into two groups of PCD and EUS-D, depending on the modality of drainage. Resolution of OF, adverse events, and other outcome measures were recorded. The two modalities were compared among infected WON sub-cohort and also degree of solid component (SC). 218 patients (175 males; 80.3%) were included who underwent either PCD (n = 102) or EUS-D (n = 116). Clinical success was significantly higher in the EUS-D group (92.1% vs 64.6%; p 40% SC had the worst clinical success/OF resolution rates, while EUS-D with &lt;40% SC had the best outcomes. EUS-D should be preferred over PCD in the management of WON, infected or otherwise, for higher clinical success, and higher/faster resolution of OF. PCD should be avoided in WON with&gt;40% SC.
DOI: 10.6084/m9.figshare.19322257.v1
title: Endoscopic drainage versus percutaneous drainage for the management of infected walled-off necrosis: a comparative analysis

description: Comparative data on percutaneous catheter drainage (PCD) vs EUS-guided drainage (EUS-D) for management of symptomatic walled-off-necrosis (WON), specially infected WON with/without organ failure(OF) is limited. Patients with symptomatic WON were divided into two groups of PCD and EUS-D, depending on the modality of drainage. Resolution of OF, adverse events, and other outcome measures were recorded. The two modalities were compared among infected WON sub-cohort and also degree of solid component (SC). 218 patients (175 males; 80.3%) were included who underwent either PCD (n = 102) or EUS-D (n = 116). Clinical success was significantly higher in the EUS-D group (92.1% vs 64.6%; p 40% SC had the worst clinical success/OF resolution rates, while EUS-D with &lt;40% SC had the best outcomes. EUS-D should be preferred over PCD in the management of WON, infected or otherwise, for higher clinical success, and higher/faster resolution of OF. PCD should be avoided in WON with&gt;40% SC.
DOI: 10.25345/c5qj7823v
title: MassIVE Reanalysis RMSV000000366.42 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000086387 A Quantitative Proteome Map of the Human Body, TMT10 MS3 [Mixture: 36] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5ks6j78m
title: MassIVE Reanalysis RMSV000000470.4 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080694/PXD003562 [2 out of 4] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5g15tf58
title: MassIVE Reanalysis RMSV000000535.2 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080783/PXD003186 [1 out of 3] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5bc3t15b
title: MassIVE Reanalysis RMSV000000409.7 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080267/PXD002854 [9 out of 11] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c56m33722
title: MassIVE Reanalysis RMSV000000369.17 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080757/PXD002619 [7 out of 24] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.6373435
title: Digital health monitoring and digital endpoints in oncology

description: Raw data set from GLOBOCAN and www.clinicaltrials.gov used for thesis work of "<strong>Digital health monitoring and digital endpoints in oncology</strong>"
DOI: 10.5281/zenodo.6373434
title: Digital health monitoring and digital endpoints in oncology

description: Raw data set from GLOBOCAN and www.clinicaltrials.gov used for thesis work of "<strong>Digital health monitoring and digital endpoints in oncology</strong>"
DOI: 10.25345/c52v2cd89
title: MassIVE Reanalysis RMSV000000497.2 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080787/PXD002686 [2 out of 3] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5z31ns37
title: MassIVE Reanalysis RMSV000000409.8 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080267/PXD002854 [4 out of 11] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.5281/zenodo.6373440
title: Animal welfare: a survey of the opinion of farmers and consumers in Sardinia

description: Datasets and survey texts regarding the opinion on animal welfare of farmers and consumers in Sardinia
DOI: 10.25345/c5tb0xz80
title: MassIVE MSV000089087 - 03162022-Johns-Hopkins-human-serum-project
title: Human serum samples from Johns Hopkins University. LCMS run on 03-16-2022. Polar C18 column.
DOI: 10.25345/c5pk07535
title: MassIVE Reanalysis RMSV000000398.14 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000086899 SubCellBarcode Scalable proteome-wide mapping of protein localization and relocalization [BioReplicate: Subcell_MVYB_H322_ctrl_HF1_20160616_TMT_IPG37-49] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5js9hc0z
title: MassIVE Reanalysis RMSV000000473.16 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000079830/PXD000815 [6 out of 17] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5f47gx9t
title: MassIVE Reanalysis RMSV000000592.30 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000083508 29 tissues, fat_adipose_tissue Trypsin_HCD_QExactiveplus [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c55m62b2t
title: MassIVE Reanalysis RMSV000000362.75 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000081607 Confetti, AspN-Elastase CID [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c59c6s46k
title: MassIVE Reanalysis RMSV000000377.8 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080851/PXD001736 [6 out of 8] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c51v5bh7k
title: MassIVE Reanalysis RMSV000000390.3 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080274/PXD002389 [4 out of 4] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.
DOI: 10.25345/c5x34mw11
title: MassIVE Reanalysis RMSV000000396.15 - CCMS MassIVE Knowledge Base (MassIVE-KB) MS-GF+ reanalysis of MSV000080848/PXD002214 [10 out of 21] [launched by cfca6e4ba8934e65aa11617b56091339]
title: Center for Computational Mass Spectrometry (CCMS) systematic reanalysis of public proteomics mass spectrometry (MS) datasets provides a public, large-scale and comparable set of dataset reanalyses results including detailed provenance records. To further facilitate the broad reusability of public proteomics MS data, these reanalyses were also used to build the MassIVE Knowledge Base (MassIVE-KB) spectral library, a community-wide resource derived from hundreds of millions of identified spectra from tens of terabytes of mass spectrometry data.