cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.48366/r604181
title: A Catalog EleutherAI's Large Language Models

description: Starting in early 2022, EleutherAI released open-sourced high-performing models built after GPT-2/GPT-3 with an intent to open-source strong LLMs. With its Pythia series, its initiative to open-source LLMs continues, which now extends into releasing the largest collection of 16 total open-source models at different parameter sizes to study the effects of scale on LLMs. This comparison showcases EleutherAI's models released so far in terms of their salient properties.
DOI: 10.17632/47ttsrxnpz.1
title: Enhanced dispersion of graphite fibres in water through surface modification

description: Research data：Enhanced dispersion of graphite fibres in water through surface modification
 Firstly, graphite fibres we studied have smooth, inert surfaces and poor dispersion in water. There are relatively few studies on the surface modification of graphite fibre, which is a challenging process. Secondly, we achieved a high-density and uniform distribution of nanoparticles on the surface of graphite fibre by an electrophoretic deposition process. Thirdly, we chose a new nanomaterial for the modification: Ti3C2Tx MXene. This material has superior hydrophilicity and electrical conductivity and can be electrodeposited at a low voltage within a short time, ensuring little damage to graphite fibre.
DOI: 10.17632/8vtybmb3sv.2
title: Mendeley supplemental table 1. Baseline characteristics and clinical features of 100 men with female pattern hair loss

description: Baseline characteristics and clinical features of 100 men with female pattern hair loss
DOI: 10.17632/8vtybmb3sv.3
title: Supplementary Table 1. Baseline characteristics and clinical features of 100 men with female pattern hair loss

description: Baseline characteristics and clinical features of 100 men with female pattern hair loss
DOI: 10.17632/8vtybmb3sv.1
title: Mendeley supplemental table 1. Baseline characteristics and clinical features of 100 men with female pattern hair loss

description: Baseline characteristics and clinical features of 100 men with female pattern hair loss
DOI: 10.17632/8vtybmb3sv.4
title: Supplementary Table 1. Baseline characteristics and clinical features of 100 men with female pattern hair loss

description: Baseline characteristics and clinical features of 100 men with female pattern hair loss
DOI: 10.17632/b58fs6szbx.1
title: Salesforce Financial-Services-Cloud Questions Solutions - How To Prepare

description: Salesforce Financial-Services-Cloud Certification Preparation Materials and Software Features.
Salesforce Financial-Services-Cloud certification preparation materials that Certstime brings to the table comes in two cases. Salesforce Financial Services Cloud (FSC) Accredited Professional (AP) Exam software structure goes with Salesforce CDP Accredited Professional entire readiness item is straightforward and in state with anybody get to at whatever point wherever. You don't need to worry over your time plan for arrangement, it even takes two or three minutes of your time paying little mind to whether you are on excursion some spot captivating and you can prepare from top learning materials for Salesforce CDP Accredited Professional certification preparation materials by getting to Salesforce CDP Accredited Professional software on your PDA. Salesforce CDP Accredited Professional software underpins all condition's, for example, Windows, Linux, Redhat Linux, Macintosh, Ubuntu, Lubuntu, Macbook and such other working frameworks as it is extraordinarily lively in nature and you don't need to worry over favorable updates since you can by and large and wherever read and learn while Salesforce CDP Accredited Professional Financial-Services-Cloud software manages rest of the stuff at back end like updates thus.

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DOI: 10.5281/zenodo.8214731
title: Data for: Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment

description: Dataset PlantDirect published manuscript: Berauer <em>et al.</em> (2023) - Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment Article DOI: <em><strong>10.1002/pld3.519</strong></em> All data is provided within one excel file. Please, pay attention to the provided ReadMe sheet for information on the dataset.
DOI: 10.5281/zenodo.8214730
title: Data for: Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment

description: Dataset PlantDirect published manuscript: Berauer <em>et al.</em> (2023) - Differences in mucilage properties and stomatal sensitivity of locally adapted Zea mays in relation with precipitation seasonality and vapour pressure deficit regime of their native environment Article DOI: <em><strong>10.1002/pld3.519</strong></em> All data is provided within one excel file. Please, pay attention to the provided ReadMe sheet for information on the dataset.
DOI: 10.17632/cx26mh2wyv.1
title: Solubility parameters of organic liquids obtained from MD simulations

description: Solubility parameter obtained from MD simulations with Gromos-53A6 force field and SPC
DOI: 10.17632/cx26mh2wyv.2
title: Solubility parameters of organic liquids obtained from MD simulations

description: Solubility parameter obtained from MD simulations with Gromos-53A6 force field and SPC
DOI: 10.17632/cx26mh2wyv.3
title: Solubility parameters of organic liquids obtained from MD simulations

description: Solubility parameter obtained from MD simulations with Gromos-53A6 force field and SPC
DOI: 10.17632/cysp6x4n6y.1
title: Analyzing Fitzpatrick Skin Type Distribution of Vitiligo on Instagram

description: Photo selection, exclusion, and analysis process for @VitiligoSociety, @WorldVitiligo, and #Vitiligo posts.
DOI: 10.57745/ri5ja5
title: CS_current_3cl.tif

description: Current conditions: reclassified climate suitability (CS). 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/5tx8dj
title: CS_current.tif

description: Climate suitability according to current conditions. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/ce6vbz
title: CS_consensus_median_2140_ssp245_3cl.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP245: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/mp0b1d
title: CS_consensus_median_4160_ssp126.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP126. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/1zq774
title: CS_consensus_median_4160_ssp585_3cl.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP585: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/nzpjxu
title: CS_consensus_median_4160_ssp370.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP370. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/24oas5
title: CS_consensus_median_2140_ssp585.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP585. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/whck5u
title: CS_consensus_median_4160_ssp585.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP585. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/nsodi4
title: CS_consensus_median_4160_ssp126_3cl.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP126: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/e5lvnj
title: CS_consensus_median_4160_ssp370_3cl.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP370: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/xidxnj
title: CS_consensus_median_2140_ssp585_3cl.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP585: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/acgleo
title: CS_consensus_median_4160_ssp245.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP245. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/xgz62m
title: CS_consensus_median_4160_ssp245_3cl.tif

description: Consensus (median of climate suitability) for the period 2041-2060 and SSP245: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/1fgkb2
title: CS_consensus_median_2140_ssp370_3cl.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP370: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/wftgah
title: CS_consensus_median_2140_ssp126_3cl.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP126: reclassified values. 3 classes: suitable conditions (raster value = 2) / marginal conditions (raster value = 1) / unsuitable conditions (raster value = 0)
DOI: 10.57745/yy7dsb
title: CS_consensus_median_2140_ssp245.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP245. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.57745/ygbzba
title: CS_consensus_median_2140_ssp370.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP370. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.26050/wdcc/ar6.c6spfqfe
title: IPCC DDC: FIO-QLNM FIO-ESM2.0 model output prepared for CMIP6 ScenarioMIP

description: Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.FIO-QLNM.FIO-ESM-2-0' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The FIO-ESM 2.0 climate model, released in 2018, includes the following components: aerosol: Prescribed monthly fields, atmos: CAM4 (0.9x1.25 finite volume grid; 192 x 288 longitude/latitude; 26 levels; top level ~2 hPa), land: CLM4.0 (same grid at atmos), ocean: POP2-W (POP2 coupled with MASNUM surface wave model, Displaced Pole; 320 x 384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4.0 (same grid as ocean). The model was run by the FIO (First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China), QNLM (Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China) (FIO-QLNM) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, ocean: 100 km, seaIce: 100 km.
DOI: 10.57745/jfx0ne
title: CS_consensus_median_2140_ssp126.tif

description: Consensus (median of climate suitability) for the period 2021-2040 and SSP126. The raster values range from 0 (unsuitable) to 1 (suitable)
DOI: 10.3886/e187441v2
title: Collins Holtkamp and Wanamaker Black Americans’ Landholdings and Economic Mobility after Emancipation

description: <b>Abstract:</b> Large and persistent racial disparities in land-based wealth were an important legacy of the Reconstruction era. To assess how these disparities were transmitted intergenerationally, we build a dataset to observe Black households’ landholdings in 1880 alongside a sample of White households. We then link sons from all households to the 1900 census records to observe their economic and human capital outcomes. We show that Black landowners (relative to laborers) transmitted substantial intergenerational advantages to their sons, including an 11 pp advantage in literacy. But such advantages were small relative to the racial gaps in metrics of economic status. <br><br><br><br><br>
DOI: 10.17632/kbt8xt3678.1
title: Ransomware/Benignware System Calls

description: This is a collection of System calls of 270 Ransomware of different family and 270 Benignware of various categories. This data is collected by running the collected samples in Windows 10 within a virtual machine using API Monitor. The  dataset contains the following files :
1-	List of Ransomware.xlsx : List of Ransomware Hash
2-	List of Benign applications.xlsx : List of Benignware
3-	dataset - System calls.zip: The collected System calls
4-	dataset - output.csv : The output vector
5-	Ransomwares Syscall CSV : The collected raw system calls of Ransomware
DOI: 10.17632/kbt8xt3678.2
title: Ransomware/Benignware System Calls

description: This is a collection of System calls of 270 Ransomware of different family and 270 Benignware of various categories. This data is collected by running the collected samples in Windows 10 within a virtual machine using API Monitor. The  dataset contains the following files :
1-	List of Ransomware.xlsx : List of Ransomware Hash
2-	List of Benign applications.xlsx : List of Benignware
3-	dataset - System calls.zip: The collected System calls
4-	dataset - output.csv : The output vector
5-	Ransomwares Syscall CSV : The collected raw system calls of Ransomware
DOI: 10.17632/2gnvwfg2n4.1
title: Supplemental Figure 1. Density map 

description: Supplemental Figure 1. Density map showing patients’ home locations who attended either the Rock Springs (blue) or Moab (red) outreach clinics. Circle size corresponds to number of patients. Yellow dots indicate clinic locations. Map of United States (top left) showing Utah and Wyoming for reference.
DOI: 10.17632/mvgzw9smj4.1
title: ACAPS (Advanced Car Accident Prevention System)

description: The data of the project "ACAPS" also called as CAPS (Car Accident Prevention System) consists of it's complete implementation and testing results during the course of the project. Also, it's been verified by the institute.
DOI: 10.17632/pnb8t5vm87.1
title: Cupo laboral de personas con discapacidad en Neuquén

description: Cupo laboral de personas con discapacidad en la provincia del Neuquén (Argentina), en 2023
DOI: 10.21227/yzs5-1067
title: Drone-based Optical and Thermal Videos of Rotor Blades Taken in Normal Wind Turbine Operation

description: Blade damage inspection without stopping the normal operation of wind turbines has significant economic value. This study proposes an AI-based method AQUADA-Seg to segment the images of blades from complex backgrounds by fusing optical and thermal videos taken from normal operating wind turbines. The method follows an encoder-decoder architecture and uses both optical and thermal videos to overcome the challenges associated with field application. A memory is designed between the encoder and decoder to improve the method’s performance by utilizing time history information in the videos to achieve temporal complementarity. The designed memory shares information between optical and thermal modalities to achieve multimodal complementarity. We collected a large-scale dataset, i.e., 100 video pairs and over 55,000 images, of optical-thermal videos of blades in operational wind turbines to train and test the method. Experimental results show that AQUADA-Seg: i) achieves near real-time thermal-optical blade video segmentation and can analyze videos with complex backgrounds in real-world field applications; ii) achieves 0.996 and 0.981 MIoU on optical and thermal videos, respectively, outperforming state-of-the-art methods, particularly in the videos with complex backgrounds. This study provides an essential step towards automated blade damage detection using computer vision without stopping the normal operation of wind turbines.
DOI: 10.17632/r5ymwgp4tb.1
title: Taxonomic diversity indices for the Atlantic Forest

description: Datasets from 460 tree communities in the Atlantic Forest used to calculate distinctness and taxonomic variation in three vegetation types.
The "Data for taxonomic diversity" spreadsheet contains the taxonomic categories for each species (genus, family, order) and occurrence tables that were used to calculate the indices. 
The "Output Taxonomic diversity" spreadsheet contains the results of the indices for each community/site in the Atlantic Forest and the three vegetation types.
The "Data for Jaccard coefficient" spreadsheet contains the presence/absence tables of each taxonomic level to compare the three vegetation types.
The R script " Taxonomic diversity" used calculated the indices. 
The R scripts " Jaccar index" and " Venn diagram" were used to compare the floristic similarity among the three vegetation types.
The R script "GLMM" GLMM" contains the code to test whether the three vegetation types differ in distinctness and variation.
These data are part of the article "Large-scale patterns of taxonomic distinctness and variation in tropical and subtropical moist forests," which is under peer review.
DOI: 10.17632/92wmxmgxkj.1
title: Mayfly optimization Algorithm - Python code

description: This simplified Python demo code shows how to use the new Mayfly Algorithm to solve global optimization problems.

Researchers are allowed to use this code in their research projects, as long as they cite as:
Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.1016/j.cie.2020.106559 

For more information go to: https://sites.google.com/view/kzervoudakis/research/metaheuristics/mayfly-algorithm
DOI: 10.17632/5w58s8hhz2.1
title: Mayfly optimization Algorithm - Matlab code

description: This simplified Matlab demo code shows how to use the new Mayfly Algorithm to solve global continuous optimization problems. Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.1016/j.cie.2020.106559
DOI: 10.17632/5w58s8hhz2.2
title: Mayfly optimization Algorithm - Matlab code

description: This simplified Matlab demo code shows how to use the new Mayfly Algorithm to solve global continuous optimization problems. Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.1016/j.cie.2020.106559
DOI: 10.17632/5w58s8hhz2.3
title: Mayfly optimization Algorithm - Matlab code

description: This simplified Matlab demo code shows how to use the new Mayfly Algorithm to solve global continuous optimization problems.

Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.1016/j.cie.2020.106559
DOI: 10.17632/5w58s8hhz2.4
title: Mayfly optimization Algorithm - Matlab code

description: This simplified Matlab demo code shows how to use the new Mayfly Algorithm to solve global optimization problems.

Researchers are allowed to use this code in their research projects, as long as they cite as:
Zervoudakis, K., & Tsafarakis, S. (2020). A mayfly optimization algorithm. Computers & Industrial Engineering, 145, 106559. https://doi.org/10.1016/j.cie.2020.106559 

For more information go to: https://sites.google.com/view/kzervoudakis/research/metaheuristics/mayfly-algorithm
DOI: 10.5281/zenodo.8214397
title: Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks

description: This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis. The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.
DOI: 10.5281/zenodo.8214398
title: Supplementary data of article Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks

description: This dataset was generated within the research thesis of Axel Hutomo, under the supervision of Leonardo Alfonso and Ioana Popescu at IHE Delft, and it is published as supplementary data for the article <em>Integrating Data-Driven and Hydraulic Modelling with Acoustic Sensor Information for Improved Leak Location in Water Distribution Networks, </em>currently under review. The Excel sheet provides information about the datasets produced to integrate acoustic sensor data and hydraulic model output data, to be used by the Machine Learning model. The acoustic sensor data were obtained by extracting several features in time and frequency domains from each audio file coming from acoustic sensors, whereas hydraulic model data was obtained by modelling these leaks using a pressure-independent analysis. The Python code shows the building of the ANN for leakage modelling prediction, integrating the two datasets above, for different leak rates.