cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14812021
title: Dataset (2025) for article "Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters"

description: This dataset was generated and used in the publication "Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters." The dataset is organized into three stages: raw data, preprocessed data, and processed data.

Each workload execution includes log files, application code, executables, and launching scripts. Execution times are extracted from log files, specifically from "slurm-dmr_*.out" and "slurm-dmr_*.info", where "*" represents a number corresponding to a specific job execution.

Dataset Structure:

1. raw_data

Contains the output files from executing workloads on the MarenostrumV HPC cluster. This section is divided into three subsections, each corresponding to a different workload type:



Static_Workload: Data for the static workload, which does not use malleability.

Sync_Workload: Data for the synchronous dynamic workload, including results for both baseline and merge configurations (5 executions each).

Async_Workload: Data for the asynchronous dynamic workload, including results for both baseline and merge configurations (5 executions each).


2. preprocessed_data

This section contains the collected raw data in .pkl files, following the same structure as the raw_data folder. For each workload execution, four .pkl files are generated. The variable name can take values from [baseline, merge, static], while X represents a workload execution number:



If X = J, the file contains a compilation of all workloads with the same configuration.

If A appears before X, it refers to an asynchronous execution.


The four types of .pkl files are:



nameX_data.pkl: Contains application runtime data. A description is available in nameX_data_description.txt.

nameX_data_resize.pkl: Contains application resize data. A description is available in nameX_data_resize_description.txt.

nameAX_iter_data.pkl: Contains iteration time data for asynchronous (A) workloads. A description is available in nameAX_iter_data_description.txt.

nameX_workload.pkl: Contains Slurm workload metrics. A description is available in nameX_workload_description.txt.


3. processed_data

Includes the analyzed results from the preprocessed_data folder. This section contains .xlsx files and images used in the Experimental Setup section of the paper. The Excel files are categorized as follows:



Exec_dataX.xlsx: Contains application execution results.

Mall_dataX.xlsx: Contains resize time results for dynamic executions.


4. Codes

This folder contains the scripts used to convert raw_data into preprocessed_data, along with a Jupyter Notebook used for data analysis and visualization. To understand or use these codes, please contact the dataset creators.
DOI: 10.5281/zenodo.14812022
title: Dataset (2025) for article "Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters"

description: This dataset was generated and used in the publication "Resource Optimization with MPI Process Malleability for Dynamic Workloads in HPC Clusters." The dataset is organized into three stages: raw data, preprocessed data, and processed data.

Each workload execution includes log files, application code, executables, and launching scripts. Execution times are extracted from log files, specifically from "slurm-dmr_*.out" and "slurm-dmr_*.info", where "*" represents a number corresponding to a specific job execution.

Dataset Structure:

1. raw_data

Contains the output files from executing workloads on the MarenostrumV HPC cluster. This section is divided into three subsections, each corresponding to a different workload type:



Static_Workload: Data for the static workload, which does not use malleability.

Sync_Workload: Data for the synchronous dynamic workload, including results for both baseline and merge configurations (5 executions each).

Async_Workload: Data for the asynchronous dynamic workload, including results for both baseline and merge configurations (5 executions each).


2. preprocessed_data

This section contains the collected raw data in .pkl files, following the same structure as the raw_data folder. For each workload execution, four .pkl files are generated. The variable name can take values from [baseline, merge, static], while X represents a workload execution number:



If X = J, the file contains a compilation of all workloads with the same configuration.

If A appears before X, it refers to an asynchronous execution.


The four types of .pkl files are:



nameX_data.pkl: Contains application runtime data. A description is available in nameX_data_description.txt.

nameX_data_resize.pkl: Contains application resize data. A description is available in nameX_data_resize_description.txt.

nameAX_iter_data.pkl: Contains iteration time data for asynchronous (A) workloads. A description is available in nameAX_iter_data_description.txt.

nameX_workload.pkl: Contains Slurm workload metrics. A description is available in nameX_workload_description.txt.


3. processed_data

Includes the analyzed results from the preprocessed_data folder. This section contains .xlsx files and images used in the Experimental Setup section of the paper. The Excel files are categorized as follows:



Exec_dataX.xlsx: Contains application execution results.

Mall_dataX.xlsx: Contains resize time results for dynamic executions.


4. Codes

This folder contains the scripts used to convert raw_data into preprocessed_data, along with a Jupyter Notebook used for data analysis and visualization. To understand or use these codes, please contact the dataset creators.
DOI: 10.15468/dl.hkdsa8
title: Occurrence Download

description: A dataset containing 6348 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hyla femoralis Bosc, 1800"
 ]
}

 The dataset includes 6348 records from 61 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018364-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14826518
title: Data Set of Regulatory Provisions for Hydrogen
DOI: 10.5281/zenodo.14826519
title: Data Set of Regulatory Provisions for Hydrogen
DOI: 10.5517/ccdc.csd.cc2jcq2q
title: CCDC 2334953: Experimental Crystal Structure Determination

description: Related Article: Arne Merschel, Shkelqim Heda, Yury V. Vishnevskiy, Beate Neumann, Hans-Georg Stammler, Rajendra S. Ghadwal|2025|Chemical Science|16|2222|doi:10.1039/D4SC06782G
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jnk70
title: CCDC 2343452: Experimental Crystal Structure Determination

description: Related Article: Pooja Sah, Manmohan Kapur|2025|Chem.Commun.|61|1874|doi:10.1039/D4CC05607H
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jnwcg
title: CCDC 2343766: Experimental Crystal Structure Determination

description: Related Article: C. E. Shields, T. Fellowes, A. G. Slater, A. I. Cooper, K. G. Andrews, F. T. Szczypiński|2024|Chem.Commun.|60|6023|doi:10.1039/D4CC01407C
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jqt9d
title: CCDC 2345624: Experimental Crystal Structure Determination

description: Related Article: Xinyu Xu, Kai Hua, Ming-Tian Zhang|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2jqtbf
title: CCDC 2345625: Experimental Crystal Structure Determination

description: Related Article: Xinyu Xu, Kai Hua, Ming-Tian Zhang|2024|CSD Communication|||
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.15468/dl.8z3pgh
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Archaea"
 ]
}

 The dataset includes 76 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018366-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14826504
title: Wyniki badań B+R (surowe dane badawcze) do projektu INNOMOTO "Nysa. Ultralekki modularny pojazd ciężarowy zasilany energią elektryczną" - POIR.01.02.00-00-0301/16

description: Niniejsze pliki zawierają surowe dane badawcze do projektu B+R INNOMOTO "Nysa. Ultralekki modularny pojazd ciężarowy zasilany energią elektryczną" - POIR.01.02.00-00-0301/16
DOI: 10.5281/zenodo.14826505
title: Wyniki badań B+R (surowe dane badawcze) do projektu INNOMOTO "Nysa. Ultralekki modularny pojazd ciężarowy zasilany energią elektryczną" - POIR.01.02.00-00-0301/16

description: Niniejsze pliki zawierają surowe dane badawcze do projektu B+R INNOMOTO "Nysa. Ultralekki modularny pojazd ciężarowy zasilany energią elektryczną" - POIR.01.02.00-00-0301/16
DOI: 10.7924/r4474kt5f
title: Data and scripts from: Deep compressed multi-channel adaptive optics scanning light ophthalmoscope

description: <strong>Data embargoed until publication of related article, or up to no more than 1 year from data upload</strong>


Adaptive optics scanning light ophthalmoscopy (AOSLO) reveals individual retinal cells and their function, microvasculature, and micropathologies in vivo. As compared to the single-channel offset pinhole and two-channel split detector non-confocal AOSLO designs, by providing multi-directional imaging capabilities, a new generation of multi-detector and (multi-)offset aperture AOSLO modalities have been demonstrated to provide critical information about retinal microstructures. However, increasing detection channels requires expensive optical components and/or critically increases imaging time. To address this issue, we introduce a novel combination of machine learning and optics as an integrated technology to compressively capture twelve non-confocal channel AOSLO images simultaneously. Imaging of healthy and diseased subjects using the proposed Deep Compressed Multi-Channel AOSLO (DCAOSLO) showed enhanced visualization of rods, cones, and mural cells with over an order of magnitude improvement in imaging speed as compared to conventional offset aperture imaging.
DOI: 10.5281/zenodo.14824026
title: Operando x-ray tomography scans of a granular zeolite compressed in uniaxial (oedometric) compression

description: This dataset (ZSZKOD06) is a series of operando ("in-situ scanning") x-ray tomography scans performed during quasi-static uniaxial loading of zeolite partices until high stresses, meaning that by the last scan there is very significant particle breakage (see animated GIF as a middle-vertical-slice through all the volumes).

The data was collected in the context of Zeynep Karatza's PhD Thesis on the x-ray tomography scanner in Laboratoire 3SR.

The data and experimental process are described in this paper where it is called "OCS1".

In short data were collected during a compression test performed inside an x-ray scanner. The x-ray source is polychromatic running at 100kV and emits a cone beam, the detector is a flat panel.

Reconstruction with FPB, voxel size in the reconstruction 12.25µm.

Radiographies available on request.
DOI: 10.5281/zenodo.14824027
title: Operando x-ray tomography scans of a granular zeolite compressed in uniaxial (oedometric) compression

description: This dataset (ZSZKOD06) is a series of operando ("in-situ scanning") x-ray tomography scans performed during quasi-static uniaxial loading of zeolite partices until high stresses, meaning that by the last scan there is very significant particle breakage (see animated GIF as a middle-vertical-slice through all the volumes).

The data was collected in the context of Zeynep Karatza's PhD Thesis on the x-ray tomography scanner in Laboratoire 3SR.

The data and experimental process are described in this paper where it is called "OCS1".

In short data were collected during a compression test performed inside an x-ray scanner. The x-ray source is polychromatic running at 100kV and emits a cone beam, the detector is a flat panel.

Reconstruction with FPB, voxel size in the reconstruction 12.25µm.

Radiographies available on request.
DOI: 10.5066/p942j2bc
title: Salinas Valley Hydrologic System: Regional Climate Data (ver 2.0, February 2025)

description: This digital dataset contains the climate data used for the Salinas Valley Hydrologic System, including the Salinas Valley Watershed Model (SVWM) and the Lower Salinas Valley Hydrologic Models (Salinas Valley Integrated Hydrologic Model (SVIHM) and Salinas Valley Operational Model (SVOM)). The climate data include spatially distributed daily precipitation, maximum and minimum air temperature, and potential evapotranspiration for the period from 10/1/1947 to 9/30/2023. This data set includes the following files: raster of the extent used in the Salinas Valley Hydrologic System; shapefiles of the input climate stations and daily gridded climate data for daily precipitation (PPT), maximum and minimum air temperature (TMN and TMX respectively), and potential evapotranspiration (PET).
DOI: 10.15468/dl.sf8ytd
title: Occurrence Download

description: A dataset containing 418 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abronia taeniata (Wiegmann, 1828)"
 ]
}

 The dataset includes 418 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018367-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14826561
title: Raw data.Humanising Robots: How Imagery and Narratives Shape Attitudes and Emotional Engagement in Human-Robot Interaction

description: raw data for 

Humanising Robots: How Imagery and Narratives Shape Attitudes and Emotional Engagement in Human-Robot Interaction
DOI: 10.5281/zenodo.14826562
title: Raw data.Humanising Robots: How Imagery and Narratives Shape Attitudes and Emotional Engagement in Human-Robot Interaction

description: raw data for 

Humanising Robots: How Imagery and Narratives Shape Attitudes and Emotional Engagement in Human-Robot Interaction
DOI: 10.15468/dl.rt73gy
title: Occurrence Download

description: A dataset containing 363582 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Monarch Watch"
 ]
}

 The dataset includes 363582 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018348-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14826552
title: Dataset MWE

description: Dataset for the paper MWE
DOI: 10.5281/zenodo.14826553
title: Dataset MWE

description: Dataset for the paper MWE
DOI: 10.5281/zenodo.11164853
title: Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)

description: We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.

TRACE-NTL:

├───ancillary├───data└───metrics    ├───disruption    │   ├───change_segment    │   ├───city_uncertainty    │   ├───daily_change    │   └───daily_qa_flags    └───recovery

https://github.com/srijac/covid-19_Nightlights

Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. Sci Data 12, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x
DOI: 10.5281/zenodo.11206064
title: Global Urban Activity Changes from COVID-19 Physical Distancing Restrictions Dataset: TRacking Anomalous COVID-19 induced changEs in NightTime Lights (TRACE-NTL)

description: We use satellite-derived (NASA Black Marble) nighttime lights to identify, quantify, and map daily changes in human activity that are atypical for each urban area, globally, from the beginning of the pandemic until two years after its onset. The dataset TRACE-NTL consists of global daily urban disruption and recovery metrics as a response to COVID-19.

TRACE-NTL:

├───ancillary├───data└───metrics    ├───disruption    │   ├───change_segment    │   ├───city_uncertainty    │   ├───daily_change    │   └───daily_qa_flags    └───recovery

https://github.com/srijac/covid-19_Nightlights

Accompanying paper: Accompanying paper: Chakraborty, S., Stokes, E.C. & Alexander, O. Global urban activity changes from COVID-19 physical distancing restrictions. Sci Data 12, 98 (2025). https://doi.org/10.1038/s41597-025-04398-x
DOI: 10.15468/dl.hdqadq
title: Occurrence Download

description: A dataset containing 768 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "or" : [
 "Country is Angola",
 "Country is Bahrain",
 "Country is Benin",
 "Country is Cameroon",
 "Country is Comoros",
 "Country is Congo",
 "Country is Congo, Democratic Republic of the",
 "Country is Côte d’Ivoire",
 "Country is Djibouti",
 "Country is Egypt",
 "Country is Eritrea",
 "Country is Equatorial Guinea",
 "Country is Gabon",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Jordan",
 "Country is Kenya",
 "Country is Kuwait",
 "Country is Liberia",
 "Country is Madagascar",
 "Country is Mauritania",
 "Country is Mauritius",
 "Country is Mozambique",
 "Country is Nigeria",
 "Country is Oman",
 "Country is Qatar",
 "Country is Réunion",
 "Country is Saudi Arabia",
 "Country is Senegal",
 "Country is Seychelles",
 "Country is Sierra Leone",
 "Country is Somalia",
 "Country is South Africa",
 "Country is South Sudan",
 "Country is Sudan",
 "Country is Tanzania, United Republic of",
 "Country is Togo",
 "Country is United Arab Emirates",
 "Country is Yemen",
 "Country is Zambia",
 "Country is Zimbabwe"
 ]
 },
 "TaxonKey is Acrostichum aureum L.",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (VAGRANT)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "DegreeOfEstablishment" : [
 "is one of (CULTIVATED)"
 ]
 }
 },
 "DegreeOfEstablishment is null"
 ]
 }
 ]
}

 The dataset includes 768 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018372-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.25676718
title: The importance of assessing wall deposits in samplers for airborne particulates

description: The importance of assessing wall deposits in samplers for airborne particulates
DOI: 10.6084/m9.figshare.25676718.v1
title: The importance of assessing wall deposits in samplers for airborne particulates

description: The importance of assessing wall deposits in samplers for airborne particulates
DOI: 10.15468/dl.vhk4rf
title: Occurrence Download

description: A dataset containing 1631 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Acrostichum speciosum Willd."
 ]
}

 The dataset includes 1631 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018374-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ctv878
title: Occurrence Download

description: A dataset containing 594 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 {
 "or" : [
 "Country is Angola",
 "Country is Bahrain",
 "Country is Benin",
 "Country is Cameroon",
 "Country is Comoros",
 "Country is Congo",
 "Country is Congo, Democratic Republic of the",
 "Country is Côte d’Ivoire",
 "Country is Djibouti",
 "Country is Egypt",
 "Country is Eritrea",
 "Country is Equatorial Guinea",
 "Country is Gabon",
 "Country is Gambia",
 "Country is Ghana",
 "Country is Guinea",
 "Country is Guinea-Bissau",
 "Country is Iran (Islamic Republic of)",
 "Country is Iraq",
 "Country is Jordan",
 "Country is Kenya",
 "Country is Kuwait",
 "Country is Liberia",
 "Country is Madagascar",
 "Country is Mauritania",
 "Country is Mauritius",
 "Country is Mozambique",
 "Country is Nigeria",
 "Country is Oman",
 "Country is Qatar",
 "Country is Réunion",
 "Country is Saudi Arabia",
 "Country is Senegal",
 "Country is Seychelles",
 "Country is Sierra Leone",
 "Country is Somalia",
 "Country is South Africa",
 "Country is South Sudan",
 "Country is Sudan",
 "Country is Tanzania, United Republic of",
 "Country is Togo",
 "Country is United Arab Emirates",
 "Country is Yemen",
 "Country is Zambia",
 "Country is Zimbabwe"
 ]
 },
 "TaxonKey is Bruguiera gymnorhiza (L.) Lam.",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (VAGRANT)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 {
 "not" : {
 "DegreeOfEstablishment" : [
 "is one of (CULTIVATED)"
 ]
 }
 },
 "DegreeOfEstablishment is null"
 ]
 }
 ]
}

 The dataset includes 594 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0018375-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.28255499
title: camelidos5productionHapMap20220103.vcf

description: Proyect Title: GBS-Based Genetic Insights into Argentine Llama Populations and Breeding ImpactsAnlaysis: TASSEL 5.0v2 pipeline was used for extracting and calling SNPs (Bradbury et al., 2007; Glaubitz et al., 2014). Reference genome Camelus ferus (BCGSAC_Cfer_1.0), unplaced sequences and the mitochondrial genome were excluded from the alignment.Sample: all samples correspond to Lama glama from Argentina