cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.11028933
title: Generic and ML Workloads in an HPC Datacenter

description: Updated Version of the previous upload, adjusts node timestamps lacking behind at the beginning of the data collection.

This archive contains hardware and workload traces from SURF Lisa, a Dutch datacenter consisting of 338 nodes, used by universities and researchers for various jobs. Around 85% of the nodes are equipped only with CPUs, handling generic compute-heavy workloads, the other 15% come with additional GPUs, serving as accelerators for Machine Learning (ML) jobs. Individual node hardware configurations are listed in `node_hardware_info.parquet`.

Jobs within Lisa are submitted over the SLURM scheduler, where we logged job start and end time, resource allocation, and exit state for roughly 10 months (December 2021 to November 2022). This data saved in `slurm_table_cleaned.parquet`.

Addidionally, we provide detailed Prometheus monitoring logs from all nodes over a timespan of 5 months (June 2022 to November 2022) in `prom_table_cleaned.parquet`. These logs contain over 90 attributes, including CPU/GPU power and temperatures, network I/O, memory and storage usage, and many more. These metrics are sampled at 30s intervals, resulting in a total of almost 130 million records across all nodes.

Finally, job and node data are provided as a joined dataset in `prom_slurm_joined.parquet` for their 4 months of overlapping timespan. This combined data can provide more insights into the resource consumption and performance patterns of jobs.

We conducted detailed analysis of this data where we specifically looked at the different characteristics of generic vs. ML workloads in a heterogeneous HPC environment. The pre-print of our analysis work can be found on arXiv. Our code used for evaluation can be found on GitHub.




Dataset Name
Explanation


slurm_table_cleaned.parquet
Job data collected by SLURM


prom_table_cleaned.parquet
Node data collected by Prometheus


prom_slurm_joined.parquet
Joined Job and Node dataset


node_hardware_info.parquet
Hardware configurations of each node
DOI: 10.5281/zenodo.11028934
title: Generic and ML Workloads in an HPC Datacenter

description: This archive contains hardware and workload traces from SURF Lisa, a Dutch datacenter consisting of 338 nodes, used by universities and researchers for various jobs. Around 85% of the nodes are equipped only with CPUs, handling generic compute-heavy workloads, the other 15% come with additional GPUs, serving as accelerators for Machine Learning (ML) jobs. Individual node hardware configurations are listed in `node_hardware_info.parquet`.

Jobs within Lisa are submitted over the SLURM scheduler, where we logged job start and end time, resource allocation, and exit state for roughly 10 months (December 2021 to November 2022). This data saved in `slurm_table_cleaned.parquet`.

Addidionally, we provide detailed Prometheus monitoring logs from all nodes over a timespan of 5 months (June 2022 to November 2022) in `prom_table_cleaned.parquet`. These logs contain over 90 attributes, including CPU/GPU power and temperatures, network I/O, memory and storage usage, and many more. These metrics are sampled at 30s intervals, resulting in a total of almost 130 million records across all nodes.

Finally, job and node data are provided as a joined dataset in `prom_slurm_joined.parquet` for their 4 months of overlapping timespan. This combined data can provide more insights into the resource consumption and performance patterns of jobs.

We conducted detailed analysis of this data where we specifically looked at the different characteristics of generic vs. ML workloads in a heterogeneous HPC environment. Our code used for evaluation can be found on GitHub.




Dataset Name
Explanation


slurm_table_cleaned.parquet
Job data collected by SLURM


prom_table_cleaned.parquet
Node data collected by Prometheus


prom_slurm_joined.parquet
Joined Job and Node dataset


node_hardware_info.parquet
Hardware configurations of each node
DOI: 10.5281/zenodo.13625495
title: Generic and ML Workloads in an HPC Datacenter

description: Updated Version of the previous upload, fixes the timestamps of the records being in inconsistent format, now they are all "yyyy-MM-dd HH:mm:ss" format in CEST time.

This archive contains hardware and workload traces from SURF Lisa, a Dutch datacenter consisting of 338 nodes, used by universities and researchers for various jobs. Around 85% of the nodes are equipped only with CPUs, handling generic compute-heavy workloads, the other 15% come with additional GPUs, serving as accelerators for Machine Learning (ML) jobs. Individual node hardware configurations are listed in `node_hardware_info.parquet`.

Jobs within Lisa are submitted over the SLURM scheduler, where we logged job start and end time, resource allocation, and exit state for roughly 10 months (December 2021 to November 2022). This data saved in `slurm_table_cleaned.parquet`.

Addidionally, we provide detailed Prometheus monitoring logs from all nodes over a timespan of 5 months (June 2022 to November 2022) in `prom_table_cleaned.parquet`. These logs contain over 90 attributes, including CPU/GPU power and temperatures, network I/O, memory and storage usage, and many more. These metrics are sampled at 30s intervals, resulting in a total of almost 130 million records across all nodes.

Finally, job and node data are provided as a joined dataset in `prom_slurm_joined.parquet` for their 4 months of overlapping timespan. This combined data can provide more insights into the resource consumption and performance patterns of jobs.

We conducted detailed analysis of this data where we specifically looked at the different characteristics of generic vs. ML workloads in a heterogeneous HPC environment. Our code used for evaluation can be found on GitHub.




Dataset Name
Explanation


slurm_table_cleaned.parquet
Job data collected by SLURM


prom_table_cleaned.parquet
Node data collected by Prometheus


prom_slurm_joined.parquet
Joined Job and Node dataset


node_hardware_info.parquet
Hardware configurations of each node
DOI: 10.5281/zenodo.13685426
title: Generic and ML Workloads in an HPC Datacenter

description: Updated Version of the previous upload, adjusts node timestamps lacking behind at the beginning of the data collection.

This archive contains hardware and workload traces from SURF Lisa, a Dutch datacenter consisting of 338 nodes, used by universities and researchers for various jobs. Around 85% of the nodes are equipped only with CPUs, handling generic compute-heavy workloads, the other 15% come with additional GPUs, serving as accelerators for Machine Learning (ML) jobs. Individual node hardware configurations are listed in `node_hardware_info.parquet`.

Jobs within Lisa are submitted over the SLURM scheduler, where we logged job start and end time, resource allocation, and exit state for roughly 10 months (December 2021 to November 2022). This data saved in `slurm_table_cleaned.parquet`.

Addidionally, we provide detailed Prometheus monitoring logs from all nodes over a timespan of 5 months (June 2022 to November 2022) in `prom_table_cleaned.parquet`. These logs contain over 90 attributes, including CPU/GPU power and temperatures, network I/O, memory and storage usage, and many more. These metrics are sampled at 30s intervals, resulting in a total of almost 130 million records across all nodes.

Finally, job and node data are provided as a joined dataset in `prom_slurm_joined.parquet` for their 4 months of overlapping timespan. This combined data can provide more insights into the resource consumption and performance patterns of jobs.

We conducted detailed analysis of this data where we specifically looked at the different characteristics of generic vs. ML workloads in a heterogeneous HPC environment. The pre-print of our analysis work can be found on arXiv. Our code used for evaluation can be found on GitHub.




Dataset Name
Explanation


slurm_table_cleaned.parquet
Job data collected by SLURM


prom_table_cleaned.parquet
Node data collected by Prometheus


prom_slurm_joined.parquet
Joined Job and Node dataset


node_hardware_info.parquet
Hardware configurations of each node
DOI: 10.18150/wffi5c
title: On Morphology of Aluminum–Gallium Nitride Layers Grown by Halide Vapor Phase Epitaxy: The Role of Total Reactants’ Pressure and Ammonia Flow Rate

description: This dataset contains the data used for the publication titled "On Morphology of Aluminum–Gallium Nitride Layers Grown by Halide Vapor Phase Epitaxy: The Role of Total Reactants’ Pressure and Ammonia Flow Rate". The following paragraphs contain: list of equipment used for data acquisition, abbreviations used in the article and the publication's abstract.1) List of Equipment:Custom-built Halide Vapor Phase Epitaxy reactorZeiss Ultra Plus scanning electron microscope (SEM) equipped with a Bruker Quantax400 Energy Dispersive Spectroscopy (EDS) module.Nikon Eclipse LV100ND Differential Interference Contrast (DIC) optical microscope (OM) with Nomarski contrast and both visible (VIS) and ultraviolet (UV) light. 2) Abbreviations:GaN - Gallium nitrideAlN - Aluminum nitrideAlxGa1-xN - Aluminum–gallium nitride ternary alloyHVPE - Halide Vapor Phase EpitaxyPVT - Physical Vapor TransportOM - Optical MicroscopeDIC - Differential Interference ContrastSEM - Scanning Electron MicroscopeUV - UltravioletN2 - nitrogenH2 - hydrogenAl - aluminumGa - galliumAlCl3 - aluminum trichlorideGaCl - gallium chlorideHCl - hydrogen chlorideCl2 - chlorineNH3 - ammoniaμm - micrometersmbar - milibars°C - degrees Celsius3) Abstract:AbstractThe focus of this study was the investigation of how the total pressure of reactants and ammonia flow rate influence the growth morphology of aluminum–gallium nitride layers crystallized by Halide Vapor Phase Epitaxy. It was established how these two critical parameters change the supersaturation levels of gallium and aluminum in the growth zone, and subsequently the morphology of the produced layers. A halide vapor phase epitaxy reactor built in-house was used, allowing for precise control over the growth conditions. Results demonstrate that both total pressure and ammonia flow rate significantly affect the nucleation and crystal growth processes which have an impact on the alloy composition, surface morphology and structural quality of aluminum–gallium nitride layers. Reducing the total pressure and adjusting the ammonia flow rate led to a notable enhancement in the homogeneity and crystallographic quality of the grown layers, along with increased aluminum incorporation. This research contributes to a deeper understanding of the growth mechanisms involved in the halide vapor phase epitaxy of aluminum–gallium nitride, and furthermore it suggests a trajectory for the optimization of growth parameters so as to obtain high-quality materials for advanced optoelectronic and electronic applications.
DOI: 10.5281/zenodo.13864975
title: Dataset for: Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal Cells

description: Data used in the publication "Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal CellsBy: Kristina Manzhula, Alexander Rebl, Kai Budde-Sagert, Sascha Spors, Henrike Rebl".

Abbreviations:A = A431 cellsH = HaCaT cells

1. The GraphPad PRISM file "paper.pzfx" contains all final data used for the analyses of vitality, ROS, the antioxidant capacity of the cells, and hydrogen peroxide in the medium. Statistical analyses, as well as graphics, are included.

2. The file "imagedata.zip" contains the microscopy images.

3. The file "Rscript.zip" contains a directory with the analysis results. (This data can be recreated when changing the parameter 'reanalyze_images == TRUE'.) The zip container also contains the R script for further analyzing and plotting the image analysis results.
DOI: 10.5281/zenodo.13864976
title: Dataset for: Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal Cells

description: Data used in the publication "Interplay of Cellular Nrf2/NFκB Signalling after Plasma Stimulation of Malignant vs. Non-Malignant Dermal CellsBy: Kristina Manzhula, Alexander Rebl, Kai Budde-Sagert, Sascha Spors, Henrike Rebl".

Abbreviations:A = A431 cellsH = HaCaT cells

1. The GraphPad PRISM file "paper.pzfx" contains all final data used for the analyses of vitality, ROS, the antioxidant capacity of the cells, and hydrogen peroxide in the medium. Statistical analyses, as well as graphics, are included.

2. The file "imagedata.zip" contains the microscopy images.

3. The file "Rscript.zip" contains a directory with the analysis results. (This data can be recreated when changing the parameter 'reanalyze_images == TRUE'.) The zip container also contains the R script for further analyzing and plotting the image analysis results.
DOI: 10.5281/zenodo.10390823
title: Instances for the coil slitting process in the steel industry

description: The dataset presented is part of the one used in the paper "A heuristic algorithm to improving the coil slitting process in the steel industry" by Óscar Soto-Sánchez, María Sierra-Paradinas, Micael Gallego, Antonio Alonso-Ayuso, Francisco Gortázar submitted for publication (2024).

 

This paper, which is currently under review, proposes a heuristic algorithm for a cutting stock problem in the steel industry. This problem occurs in a Spanish company and the proposed algorithm is currently in production after being validated on real company data and compared with an exact mixed integer linear optimization model (MILO).

 

The dataset presented here includes twenty instances used in the paper (the rest cannot be presented for confidentiality reasons). For each instance, the order and coil characteristics are provided, as well as the compatibility between these two. Finally, for each instance, the solution of the model, the heuristic algorithm, and the hybrid algorithm are provided.

 

The content of the zenodo repository consists of a Dataset.zip file and a README.pdf file (this file explains the entire content of the dataset). The Dataset.zip file contains the instances, the solutions and the README.md and README.pdf files. Therefore, it is only necessary to download the Dataset.zip file.
DOI: 10.18150/6zsj6k
title: Europium diffusion in ammonothermal gallium nitride - Raw Data

description: This dataset contains the raw data used for the publication titled "Europium diffusion in ammonothermal gallium nitride". The following paragraphs contain: list of equipment used for data acquisition, abbreviations used in the article and the publication's abstract.1) List of Equipment:CAMECA IMS6F microanalyzerDiode Pulse Solid State Laser (Lasos)Nanoscope Dimension 3100 in a semi-contact (tapping) mode with a standard silicon tip.Horiba Jobin Yvon T6400 spectrometerEmpyrean X-ray diffractometer operating at the Cu Kα1 wavelength. The diffractometer was equipped with a hybrid 2 bounce monochromator and a threefold Ge (2 2 0) analyzer.Custom-built Ultra-high Pressure reactor2) Abbreviations:O - OxygenEr - ErbiumEu - EuropiumGaN - Gallium nitrideUHPA - Ultra-high pressure annealingHVPE - Halide Vapor Phase EpitaxySIMS - Secondary Ion Mass SpectrometryXRD - X-ray diffraction/diffractometryAFM - Atomic Force MicroscopyPL - PhotoluminescenceSRIM - Stopping and Range of Ions in Matter (software)3) Abstract:Europium doping of gallium nitride using a novel ultra-high pressure annealing method was investigated. Ammonothermal gallium nitride substrates (n-type) were used as europium ion implantation targets using beam energy of 490 keV and ion fluences ranging from 1 × 1015 to 1 × 1016 Eu/cm2. The implanted samples were annealed at temperatures between 1473 K and 1753 K in high nitrogen pressure. Europium concentration profiles were analyzed along the [0001] crystallographic direction. The morphology and structural quality of implanted and annealed samples were examined. The “out-diffusion” phenomenon was noted. The presence of optically active EuGa-X defect complexes was suggested based on the analysis of photoluminescence spectra measured at low temperature (20 K). Limitations of diffusion-based europium doping of ammonothermal gallium nitride are discussed.
DOI: 10.57703/ce-gwqcv
title: A27_100X

description: “Sample A27”
The surface-topography challenge. 
Raw measurement of the 3D topography of the surface using a KEYENCE VK-100 series microscope, at X100 magnification.
DOI: 10.18150/nlpbiw
title: Ultra-high pressure annealing of Mn-implanted HVPE-GaN - raw data

description: This dataset contains the raw data used for the publication titled "Ultra-high pressure annealing of Mn-implanted HVPE-GaN". The following paragraphs contain: list of equipment used for data acquisition, abbreviations used in the article and the publication's abstract.1) List of Equipment:CAMECA IMS6F microanalyzerPhilips X′pert Pro diffractometer with an Empyrean Cu LFF HR (9430 033 7310x) DK33 source and a Xe proportional detector.Custom-built Ultra-high Pressure reactor2) Abbreviations:Mn - ManganeseSi - SiliconO - OxygenEr - ErbiumEu - EuropiumGaN - Gallium nitrideSiC - silicon carbideHEMT - High Electron Mobility TransistorSI - semi-insulating (i.e. exhibiting high resistivity)UHPA - Ultra-high pressure annealingHVPE - Halide Vapor Phase EpitaxySIMS - Secondary Ion Mass SpectrometryXRD - X-ray diffraction/diffractometrySRIM - Stopping and Range of Ions in Matter (software)3) Abstract:Manganese diffusion in ion implanted gallium nitride crystals was investigated. The ions were implanted into a sample composed of a GaN layer grown using halide vapor phase epitaxy on an ammonothermal gallium nitride substrate. The sample was polished into an atomically flat state prior to the implantation. Manganese ions were implanted into the surface of a (0001) plane (also known as the c-plane) using the beam energy of 230 keV and ion fluence of 5E15 cm 2. The beam was tilted relative to the sample’s normal to prevent channeling. After the implantation, ultra-high pressure annealing method was used to both, remove the post-implantation damage and induce the diffusion of implanted Mn ions. Manganese profiles were analyzed along the c-direction using sec- ondary ion mass spectrometry after 5 and 30 h of annealing in temperatures ranging from 1473 K to 1753 K. Two diffusion mechanisms were observed. The diffusion parameters, activation energy and temperature-independent diffusion coefficients, are presented for each mechanism. Additionally, the structural quality of the samples is assessed after 5 and 30-hour annealing in the aforementioned temperature range through the means of X-ray diffractometry.
DOI: 10.15468/dl.w2e4mx
title: Occurrence Download

description: A dataset containing 154 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Physalaemus maculiventris (Lutz, 1925)"
 ]
}

 The dataset includes 154 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007752-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57703/ce-vfcs2
title: Q50_100X

description: “Sample Q50” The surface-topography challenge. Raw measurement of the 3D topography of the surface using a KEYENCE VK-100 series microscope, at X100 magnification.
DOI: 10.15468/dl.vy7bcy
title: Occurrence Download

description: A dataset containing 778682 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Calypte anna (R.Lesson, 1829)"
 ]
}

 The dataset includes 778682 records from 100 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007136-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dx5k75
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Physalaemus maximus Feio, Pombal & Caramaschi, 1999"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007754-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57703/ce-7medy
title: Q57_100X

description: “Sample Q57” The surface-topography challenge. Raw measurement of the 3D topography of the surface using a KEYENCE VK-100 series microscope, at X100 magnification.
DOI: 10.18150/zyx2fd
title: Research data for "Influence of GaN substrate miscut on the XRD quantification of plastic relaxation in InGaN" published in Acta Materialia vol 276, 120082 (2024)

description: The data contains original cathodoluminescence and transmission electron microscopy images used to construct the figures in this article. The data also contains original X-ray diffraction data used to construct X-ray maps.
DOI: 10.15468/dl.h8jwcf
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Physalaemus moreirae (Miranda-Ribeiro, 1937)"
 ]
}

 The dataset includes 91 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007755-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bdv2ma
title: Occurrence Download

description: A dataset containing 249 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Physalaemus nanus (Boulenger, 1888)"
 ]
}

 The dataset includes 249 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007756-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13918681
title: The Sanger sequencing data of HCAPV-1
DOI: 10.5281/zenodo.13918682
title: The Sanger sequencing data of HCAPV-1
DOI: 10.5281/zenodo.13918688
title: THE ROVING MACHINE EFFECT IN THE RING-SPINNING PROCESS
DOI: 10.5281/zenodo.13918689
title: THE ROVING MACHINE EFFECT IN THE RING-SPINNING PROCESS
DOI: 10.15468/dl.v8u8bs
title: Occurrence Download

description: A dataset containing 533 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Papilio polyctor Boisduval, 1836"
 ]
}

 The dataset includes 533 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007759-241007104925546/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f5e34s
title: Occurrence Download

description: A dataset containing 134 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cantinoa racemulosa (Mart. ex Benth.) Harley & J.F.B.Pastore"
 ]
}

 The dataset includes 134 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0146182-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wsnzzn
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyanea asplenifolia (H.Mann) Hillebr."
 ]
}

 The dataset includes 38 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0192009-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.