cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7589131
title: M100 dataset 2: from 21-01 to 21-06

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 21-01, 21-06. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7589319
title: M100 dataset 3: from 21-07 to 21-09

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 21-07, 21-09. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7589320
title: M100 dataset 3: from 21-07 to 21-09

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 21-07, 21-09. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7589629
title: M100 dataset 4: from 21-10 to 21-12

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 21-10, 21-12. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7589630
title: M100 dataset 4: from 21-10 to 21-12

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 21-10, 21-12. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.23728/b2share.bf21401f84914a44a82fa2006a61b93c
title: Gesäuse-Johnsbachtal - Austria, Soil pH

description: Soil pH data of the Gesäuse-Johnsbachtal site
DOI: 10.5281/zenodo.7589941
title: M100 dataset 5: from 22-01 to 22-02

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-01, 22-02. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7589942
title: M100 dataset 5: from 22-01 to 22-02

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-01, 22-02. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7590060
title: M100 dataset 6: 22-03

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-03. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7590061
title: M100 dataset 6: 22-03

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-03. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7590307
title: M100 dataset 7: 22-04

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-04. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7590308
title: M100 dataset 7: 22-04

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-04. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.17632/s2tg7pwfv5.1
title: Value and proportion of SUS billing for hospitalizations (SIH) and outpatient care (SIA) between the years 2018 and 2022.

description: Dataset with the value and proportion of billing from the Hospital Information System [Sistema de Informação Hospitalar (SIH)] and Ambulatory Information System [Sistema de Informação Ambulatorial (SIA)] of the Brazilian Unified Health System (SUS) between the years 2018 and 2022.
DOI: 10.5281/zenodo.7590546
title: M100 dataset 8: 22-05

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-05. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.
DOI: 10.5281/zenodo.7590547
title: M100 dataset 8: 22-05

description: This entry is a part of a larger data set collected from the most recent Tier-0 supercomputer hosted at CINECA (Marconi100, https://www.hpc.cineca.it/hardware/marconi100). The data covers the entirety of the system, ranging from the computing nodes (980+ computing nodes) internal information such as core loads, temperatures, frequencies, memory write/read operations, CPU power consumption, fan speed, GPU usage details, etc., to the system-wide information, including the liquid cooling infrastructure, the air conditioning system, the power supply units, workload manager statistics, and job-related information, system status alerts, and weather forecast.<br> It comprises hundreds of metrics measured on each computing node, in addition to hundreds of other metrics gathered from sensors monitored along all system components.<br> The whole data set is stored as a collection of Zenodo entries; this particular entry corresponds to the period: 22-05. The dataset is stored as a partitioned Parquet dataset, with this partitioning hierarchy: year_month ("YY-MM"), plugin, metric. The data is distributed as tarball files, each corresponding to one month of data (first-level partitioning, year_month).<br> The collected data is generated by a monitoring infrastructure working on unstructured data (to improve efficiency and scalability); however, this data has been organized in a structured manner to facilitate its fruition. The simplest way to understand how the access the data is to refer to the companion software modules released together with the dataset itself, which can be found at: https://gitlab.com/ecs-lab/exadata.