cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7590555
title: M100 dataset 9: 22-06

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: 22-06. 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.7590554
title: M100 dataset 9: 22-06

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: 22-06. 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.7590564
title: M100 dataset 10: 22-07

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: 22-07. 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.7590565
title: M100 dataset 10: 22-07

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: 22-07. 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: 10.17632/46z6k8kr4v.1
title: Spatial Covid-19 assessment in Mashhad city

description: Spatial Covid-19 assessment in Mashhad city
DOI: 10.5281/zenodo.7590582
title: M100 dataset 12: 22-09

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: 22-09. 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.7590583
title: M100 dataset 12: 22-09

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: 22-09. 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.7590573
title: M100 dataset 11: 22-08

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: 22-08. 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.7590574
title: M100 dataset 11: 22-08

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: 22-08. 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/fjtsjrrvst.1
title: Conceptual Challenges to the Recognition and Enforcement of the Right to Clean, Safe and Healthy Environment

description: The right to clean or healthy environment, or what may be called environmental right, is one of the most controversial emerging rights since the agitation for the recognition of the link between human rights and the environment started gaining momentum at international law forums. This is happening partly because, at the global level, no treaty attempts to delimit the scope of this right explicitly; an endeavour which would have served as a form of guide to national jurisdictions. Given that the UN General Assembly recently officially resolved that a clean, healthy and sustainable environment is a universal human right, and considering the implication this may have on national jurisdictions, it has become more imperative to redefine this right for the ease of enforcement. This paper seeks to examine the conceptual and the theoretical conundrum as well as the criticisms of the right to clean, safe and healthy environment that have largely played a prominent role against the enforcement of the rights in general. The paper also examines constitutional challenges associated with the recognition of the rights in Nigeria and the judicial effort in the case of Gbemre v SPDC in attempting to expound the constitutional right to life to include the right to the environment. The paper finds that the right to the environment has been described and qualified diversely from one jurisdiction to another rendering the same susceptible to the challenges of interpretation. The paper, however, suggests that given the importance of the right, same should be interpreted, no matter how it is qualified, to mean a right to an environment fit for human living, the courts being sufficiently able to draw the line between what environment is fit and what is not for human habitation.
DOI: 10.17632/zdmpdkb2wv.1
title: Table and figure

description: The supplementary tables and figures in this document facilitate the interpretation of the results obtained. The growth kinetics of Limosilactobacillus fermentum UCO-979C in modified MRS broth were determined. The figures and tables contain the results obtained for the mathematical modeling and growth curves of the strain under the study culture conditions.
DOI: 10.34740/kaggle/dsv/5741253
title: AutoXGB
DOI: 10.17632/fbjjgzgv7f.1
title: (First ever) Tunisian Sign Language Dataset

description: This is a dataset of hand gestures that represent tunisian sign language. It is the first ever dataset of TunSL, it was collected in collaboration with Tunisian Association of Sign Language Interpreters (ATILS) as one of the tasks for the developement of a mobile app for the recognition of TunSL.

The dataset consists of 4423 images of TunSL common words performed by 7 people for 57 standard Tunisian signs. The number of images per class differs from one class to another with each image having the size of 224 × 224 pixels. These images were taken in different environments using laptop cameras with different backgrounds, clothes, hand angles, brightness level, hand accessories... The folders and images have been named in Tunisian dialect.

↬ GitHub link: https://github.com/W7CH/First-ever-Tunisian-Sign-Language-Dataset
DOI: 10.17632/jph6c962tg.1
title: Healthcare Resources and Immigrant Integration

description: Healthcare Resources and Immigrant Integration:
DOI: 10.17632/v3yh6tfm76.1
title: The data of SlMPK1- and SlMPK2- mediated SlBBX17 phosphorylation positively regulates CBF-dependent cold tolerance in tomato 

description: The verification and replication of the results in the paper "SlMPK1- and SlMPK2- mediated SlBBX17 phosphorylation positively regulates CBF-dependent cold tolerance in tomato ".
DOI: 10.17632/49kc5g6z25.1
title: Graphical and statistical analysis

description: Graphical analysis of the toxicity testing and the potency of millet extracts in reversing the tachycardic and bradycardic conditions.  The results show significant changes and it is effectively supported by the statistical data (correlation analysis) performed using the basic functions of Microsoft Excel.
DOI: 10.17632/7h9mdf5fvt.1
title: Data for the project management, life cycle inventory, costings and energy production of a ground-mounted photovoltaic farm in Greece

description: The data collected refer to the construction, costings, operation and environmental impacts of a photovoltaic farm and are provided in four separate Excel files with respect to their type and usage. Each file can be used separately for studying an individual topic or in combination with certain of the others to facilitate the study of more complex concepts. 
These four Excel files refer to the following dataset types:  
1.	Project Management Data
2.	Life Cycle Ιnventory (LCI)
3.	Electricity Generation Data
4.	Operational Cost Data
DOI: 10.17632/3c3j29t4r5.1
title: Dateset for  Subsurface Structure Regulates Water Storage in the Alpine Critical Zone on the Qinghai-Tibet Plateau

description: This dataset was used to generate figures and tables for the paper entitled "Subsurface Structure Regulates Water Storage in the Alpine Critical Zone on the Qinghai-Tibet Plateau".
DOI: 10.17632/mdhdp8mdb6.1
title: Supplementary Data for: Geometric morphometric analysis and meristic variations of Philippine populations of the crescent perch Terapon jarbua (Centrarchiformes: Terapontidae)

description: Supplementary Data File 1 (.docx). Representative specimens from the nine Philippine sampling sites.
Supplementary Data File 1 (folder): All photographs of Terapon jarbua specimens from the nine Philippine sampling sites.
Supplementary Data File 2 (.xlsx). Measurements, meristic counts and metadata information of all Terapon jarbua specimens in the study.
DOI: 10.17632/878dcmxs87.1
title: ESG and the perception of JS

description: This is a repository of data and runs in smartPLS for the development of the model mentioned in the study titled: "An assessment of ESG practices on the perception of job satisfaction in Mexican companies: a PLS-SEM analysis."

Abstract
This paper defines the ESG factors that determine the perception of job satisfaction in Mexican employees. We used a custom instrument in a way of survey and collected data from 102 full-time and part-time employees across a wide range of hierarchical levels, tenure, and organizations in Mexico. After list-wise deletion of responses with missing values, we concluded in a usable sample of 97 employees. The majority of respondents were male (66.7%) in the 31 to 40 age range. Additionally, we used demographic values as control variables for gender, age, marital status, and hierarchical position within the organization. We proposed 21 hypotheses divided into 4 types: direct relationship, mediation, moderation, and multigroup analysis. Demographic variables were used in the last two types of hypotheses. Significance tests are shown for the path coefficients of the model, for total effects using bootstrap analysis, for moderation analysis, and multigroup analysis. Additionally, reliability and validity tests of our model are presented, along with fit parameters, which are highly satisfactory compared to the reference thresholds. The findings suggest that there is a direct and positive relationship between ESG factors and the perception of job satisfaction. The environmental and social dimensions mediate the relationship between other factors and job satisfaction. The marital status of the employee moderates the relationship between the social dimension and job satisfaction. Furthermore, age, marital status, and hierarchical position of the employee influence the strength of the relationship between certain ESG dimensions and their perception of job satisfaction.

Sample.
To test our hypotheses, we conducted a field study. We collected data from 102 full-time and part-time employees across a wide range of hierarchical levels, tenure, and organizations in Mexico. Due to any potential sensitivity of the data collected, participants responded to the questionnaire anonymously online. After list-wise deletion of responses with missing values, we concluded in a usable sample of 97 employees. PLS-SEM offers solutions with small sample sizes when models comprise many constructs and a large number of items (Fornell & Bookstein, 1982; Willaby et al., 2015; (Hair et al., 2017). The  majority  of  respondents  were  male  (66.7%) in the 31 to 40 age range.  A database with 97 observations was used for the empirical analysis of PLS-SEM.
DOI: 10.17632/fvcgpdmhz4.1
title: HbA1c and NAFLD show a positive but not causal association in non-diabetic populations: Results from NHANES and MR study-supplementary files

description: The supplementary files of HbA1c and NAFLD show a positive but not causal association in non-diabetic populations: Results from NHANES and MR study.
DOI: 10.17632/fvcgpdmhz4.2
title: HbA1c and NAFLD show a positive but not causal association in non-diabetic populations: Results from NHANES and MR study-supplementary files

description: The supplementary files of Association between HbA1c and NAFLD: Insights from NHANES 2017- March 2020 and Mendelian Randomization Study.
DOI: 10.17632/hfntj4vhpb.1
title: BBQ_wgs_DatainBrief2023

description: Genome assemblies and microsatellite mining data of Turnix suscitator.
DOI: 10.17632/hfntj4vhpb.2
title: BBQ_wgs_DatainBrief2023

description: Genome assemblies and microsatellite mining data of Turnix suscitator.
DOI: 10.17632/5vf2f4hks2.1
title: Supplementary data for Mwelwa et al. (2023), Biotransfer of heavy metals along the soil-plant-edible insect-human food chain in Africa

description: This file includes supplementary data for the concentration of eight heavy metals in  soil, host plants and associated edible insects.